2014 Annual Project Report
165
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Published as an industry service by the Members of the California Tomato Research Institute, Inc.
2014 Research Project ReportsProjects are categorized by project type and listed, in order of starting date
Use the bookmarks feature to easily navigate to each project.
Agronomic/Water/Nutrient Mgmt. $33,880
MitchellJeffEvaluation of Precision Commercial Tomato ProductionSystems - continuation
$5,000
AegerterBrennaEvaluation of irrigation practices on water use, soilsalinity, and tomato productivity in the Delta
$6,880
BurgerMartinDeveloping a sampling protocol to estimate pre-plantnitrate availability in subsurface drip irrigated tomato
$6,000
MiyaoGeneInvestigation of Sustaining Tomato Plant Health andYield with Composted Manure
$16,000
Breeding/Genetics/Varieties $37,590
ChetelatRogerTomato Genetics Resource Center $15,000
St. ClairDinaFruit yields with less water: beneficial genes from wildtomato
$12,990
St. ClairDinaMarker Genotyping of Chromosome 9 From Water-stress Resistant Wild Tomato to Generate BreedingLines
$9,600
Insect & Invertibrate Mgmt. $28,127
TuriniThomasEvaluation of Tactics for Improvement of Stink BugControl
$28,127
Pathogen and Nematode Mgmt. $270,185
GilbertsonRobert L.Tomato Spotted Wilt Virus (TSWV) Analysis andManagement
$31,000
NuñezJoeEvaluation of Fungicides, & Others for the Control ofSouthern Blight
$7,000
BeckerJ. OleEvaluation of New Nematicides Against Root-knotNematodes inProcessing Tomato Production
$35,862
ScowKateEffect of Cover Crops, Compost and Gypsum SoilAdditions on Processing Tomatoes
$39,318
CoakerGittaGenome sequencing of the bacterial canker pathogen, todevelop robust detection and disease control strategies.
$46,259
StergiopoulosIoannisDetection of Aazole and Strobilurin Fungicide ResistantStrains of Tomato Powdery Mildew in California
$17,003
GilbertsonRobert L.Curly Top Research: Objectives Relevant to Tomatoes $42,000
MiyaoGeneEvaluation of Chemical Control of Bacterial Speck $7,500
LeveauJohanSynergy-based Biocontrol of Fusarium Wilt of Tomato $39,993
MiyaoGeneEvaluation of Root Knot Nematode Control withResistant Wheat and Various Cover Crops
$4,250
Weed Control and Mgmt. $50,016
SosnoskieLynnField Bindweed Management in Processing Tomatoes $15,836
HembreeKurtEvaluating Herbicide Carryover in Sub-surface DripIrrigated Tomatoes
$34,180
$419,798
Project Title: Evaluation of Precision Commercial Tomato Production Systems for Increased Competitiveness, Resource Use Efficiency and Soil Quality Using Study Sites in Five Points and Los Banos
Project Leader: Jeff Mitchell Department of Plant Sciences, University of California, Davis, 9240 S. Riverbend Avenue,
Parlier, CA 93648, Telephone (559) 646-6565, Fax (559) 646-6593, Mobile (559) 303-9689, [email protected]
Project Collaborators: Rad Schmidt Department of Soils and Biogeochemistry, University of California, Davis, Kate Scow Department of Soils and Biogeochemistry, University of California, Davis Karen Klonsky Department of Agricultural and Natural Resource Economics, University of California, Davis,
Telephone (530) 752-3563, Fax (530) 752-5614, [email protected] Dan Munk Fresno County Cooperative Extension, 1720 S. Maple Avenue, Fresno, CA 93720, Telephone
(559) 456-7561, Fax (559) 456- 7575, [email protected] Anil Shrestha Department of Plant Science and Mechanized Agriculture, California State University, Fresno,
2415 E. San Ramon Avenue M/S AS 72, Fresno, CA 93740-8033 Telephone (559) 278-5784 [email protected]
Kurt Hembree Fresno County Cooperative Extension, 1720 S. Maple Avenue, Fresno, CA 93720, Telephone (559) 456-7285, Fax (559) 456-7575, [email protected];
Tom Turini Fresno County Cooperative Extension, 1720 S. Maple Avenue, Fresno, CA 93720, Telephone (559) 456-7285, Fax (559) 456-7575
John Diener Red Rock Ranch, P.O. Box 97, Five Points, CA 93624 (559) 288-8540 [email protected] Scott Schmidt Farming ‘D’, P.O. Box 248, Five Points, CA 93624 (559) 285-9201
[email protected] Ron Harben California Association of Resource Conservation Districts, 4974 E. Clinton Way, Suite 214,
Fresno, CA 93727, Telephone (559) 252-2192, Fax (559) 252-5483, [email protected]
Brook Gale USDA Natural Resources Conservation Service, Fresno Service Center, 4625 W. Jennifer Avenue, Suite 125, Fresno, CA 93722, Telephone (559) 276-7494, Ext. 121, Fax (559) 276-1791, [email protected]
Monte Bottens Bottens Ag Solutions, Inc. 4746 W. Jennifer Avenue, Suite 104, Fresno, CA 93722 Telephone (559) 694-1582, [email protected]
Collaborators: In this project, we also worked with a processing tomato farmer in Los Banos, CA in his ongoing efforts to evaluate potential benefits and trade-offs associated with cover crops in tomato production systems. This work was conducted in a quarter-section field south of Hwy 152 and will be reported on in our 2015 annual summary report to CTRI. Objectives: The goals of this proposed project are to:
1. To evaluate the sustained productive capacity of reduced-tillage, cover-cropped, drip irrigated tomato production as a means of reducing production costs and risk and for also improving the soil resource,
2. To ‘scale up’ this evaluation in 2014 with a commercial California tomato producer who will be evaluating complete reduced-till tomato cover-cropped production,
3. To provide a public field day showcasing findings from this farm evaluation To determine depletions in soil water content that occur under winter fallow and winter cover cropped conditions,
4. To evaluate cover crop mixtures suitable for tomato production systems as a means for improving the sustainability of tomato cropping, and
5. To provide information developed in these studies widely to Central Valley tomato producers Procedures: In 2014, we conducted characterizations of a range of soil biology attributes and now have data that will be shared with CTRI Board Members in the 2014 December annual meeting. We provide a summary of these data here as evidence that this work has been conducted, however we caution that it is all very much still in the process of undergoing analysis and further interpretation. We thus caution against making conclusions based on these quite preliminary findings which are, to our knowledge, the first time that such determinations have been made on central San Joaquin Valley soils and on the type of long-term tillage and cover crop comparison study that we have in Five Points. These findings are not only being shared at the CTRI meeting in December, but they will also, upon quality control review, be discussed at a number of public meetings also in December. Background and Objectives:
• Illumina sequencing allows the analysis of whole soil bacterial communities by identifying bacteria to their respective species (sometimes only genus) level based on their 16S rRNA gene sequences.
• The primary aim of this study was to establish a baseline comparison of the depth-dependent and treatment-dependent microbial population composition differences of the four treatments (CCCT, CCST, NOCT and NOST) at the Five Points conservation tillage test plots.
• For this baseline analysis, we were interested in community composition differences that are associated with the different treatments regardless of their position within the test field. The field block scheme was not used to differentite sample source plots (for technical reasons using the block scheme would have also been both more difficult and considerably more costly to implement).
This short report summarizes initial analysis data; more detailed analysis results will follow Methods
• Soil source – plots 1-16 – sampling date 11/22/13.
• Sampling intensity – 0-5, 5-15, 15-30 cm. – 6 samples per depth per plot. – the 6 samples from each plot/depth were homogenized before further analysis.
• Next generation sequencing – DNA extracted in triplicate – 12 subsamples per treatment and depth (4 plots, triplicate extractions) amplified individualy, but with
identical barcodes. – 144 total barcoded PCR products purified, pooled and submitted to UCD genome center for sequencing. – Data analyzed using the QIIME data analysis pipeline.
Table 1. Treatments and plots sampled (November 2013)
Treatment Plot numbers
CCCT 3 8 9 15
CCST 1 7 12 13
NOCT 4 5 10 16
NOST 2 6 11 14
Table 2. Sample numbers associated with the 12 composite samples sequenced.
Treatment
Depth (cm) CCCT CCST NOCT NOST
0-5 CIG0001 CIG0004 CIG0007 CIG0010
5-15 CIG0002 CIG0005 CIG0008 CIG0011
15-30 CIG0003 CIG0006 CIG0009 CIG0012
4
Firmicutes -‐Bacillales(mostly Bacillus and other Bacillaceae)
Proteobacteria
γδαβ
Actinobacteria
AcidobacteriaArchaea
Bacteroidetes
Figure 1. Taxonomic composition of CCCT, CCST, NOCT and NOST treatments at three depths 0-‐5, 5-‐15, 15-‐30 cm. November 2013 sampling. Some of the most important groups are shown next to their respective color bands to the right of the graph.
Community Taxonomic Composition• NOST at all depths and NOCT 0-‐5 cm show much higher proportion of Firmicutes (mainly Bacillus and other Bacillaceae)
(28.1±5.5%) than all other soils (8.3±3.5%).• Higher Fimicute numbers are offset primarily by lower Proteobacteria in the high Firmicute soils in comparison to other soils
(19.9±3.5% vs 27.4±3.9% respectively).• Some information on Archaea is available, though the primers used may not provide a highly accurate representation of the
Archaeaal community.
Initial Conclusions:
Some of the cover crop treatment soils show highest species richness, while some of the no cover crop soils show least richness. The standard till no cover crop treatments and the conservation tillage no cover crop treatment at the 0 – 5 cm show similar trends in community composition and also cluster together in beta diversity analysis. The sequencing results are consistent with other data from Five Points and show that:
• Cover crops exert a strong influence on microbial community composition as well as soil properties, and • The standard tillage no cover crop treatment is distinct from the other three treatments.
Over the fifteen years that we have been evaluating the use of reduced tillage and cover crops in Five Points, a total of 25.6 t ha-1 of aboveground cover crop biomass was produced with a total precipitation of 209 cm and 20 cm of supplemental irrigation. Cover crop biomass varied from 39 kg ha-1 in the low precipitation period of the 2006 – 2007 winter to 9,346 kg ha-1 in the first winter (2000 – 2001). We also determined changes in soil water storage under three cover crop mixtures compared to fallowed plots during two
(2013 and 2014) winter periods in a separate study to investigate tradeoffs associated with water use by cover crops in this region. Soil water storage in the sampled depth for the fallow and each of the cover crop mixtures was compared each year from January to March. Soil water storage increased during this period for the fallow system by 4.8 cm in 2013 and 0.43 cm in 2014, but in the cover crop mixture plots, there was no additional water storage. Instead, water was used by the cover crops resulting in a negative water balance of an average of 0.47 cm in 2013 and 0.26 cm in 2014 for the cover crop mixes. Thus, compared to the fallow system, cover crops depleted 5.3 cm and 0.67 cm more water in from the 0 – 90 cm profile in 2013 and 2014, respectively. While the greater water depletion of cover crops may present concerns associated with integrating cover crops into cash cropping systems in water-limiting environments, these greater water depletions can be recovered or even be turned into water surpluses by implementing cover crops in crop rotations along with conservation tillage practices that maintain crop residues on the soil surface thereby reducing evaporative losses. This study illustrates the critical value of long-term systems research in providing clear, robust implications of crop management options that may not be apparent in shorter duration investigations due to not being able to account for the impacts of inter-annual variability of climatic conditions on research findings. Our data suggest that while vigorous growth of winter cover crops in this area of the Central Valley may not be possible in all years due to low and erratic precipitation patterns, in most years there may be benefits of cover cropping in terms of providing greater crop cover, residue, and photosynthetic energy capture. We are now working with two SJV tomato farmers to further evaluate tradeoffs between the use of cover crops and soil water depletions under local conditions.
Project Title: Evaluation of irrigation practices on water use, soil salinity, and tomato productivity in the Delta
Project Leaders: Brenna Aegerter and Michelle Leinfelder-‐Miles, Farm Advisors, University of California Cooperative Extension, San Joaquin County, 2101 E. Earhart Ave. Ste. 200, Stockton, CA 95206, phone 209-‐953-‐6100, FAX 209-‐953-‐6128, email: [email protected] and [email protected]
Objective and Justification:
We are evaluating how conversion to drip irrigation affects water use, soil salinity, tomato yields, and
fruit quality in the Delta. Results from this study will give growers knowledge on whether drip irrigation
improves tomato yield and/or quality in the unique Delta growing environment, which is challenged by
salinity.
Procedures:
Our study was conducted in a second-‐year drip irrigated field and a furrow irrigated field on Roberts
Island in the Sacramento-‐San Joaquin River Delta region. The fields were selected for proximity, similar
soil characteristics, and similar water sourcing. The soil type at both fields was an Egbert silty clay loam.
The Egbert series occupies approximately 21,882 acres in the San Joaquin County Delta. Water was
sourced from Middle River. The furrow field was transplanted April 28th -‐ 29th with the variety H5608;
the drip field was transplanted May 23rd with the variety UG 19406. This was a demonstration study
with the purpose of gaining a better understanding of the plant-‐soil-‐water relations of using drip
irrigation in the Delta.
Pit sampling. Our project commenced in May when we collected soil samples from both the drip and
furrow irrigated fields. We sampled from the drip irrigated field 10 days prior to transplanting and from
the furrow irrigated field 21 days after transplanting, otherwise following the same procedures. We
collected 80 soil samples from each soil pit, and we sampled from two pits in the furrow field and two
pits in the drip field. The samples were collected at 4-‐inch wide by 4-‐inch deep spacing, which began at
the soil surface and went to a depth of 40 inches and ran from the middle of the row to the middle of
the furrow (approximately 32 inches of the 60 inch beds). From the bottom of the pits, we augured a
hole to the depth of the water table and collected a water sample.
When sampling the furrow field, we dug one pit near the top of the field, where irrigation water
enters, and one pit near the bottom of the field, where irrigation water exits, to observe any differences
as a result of differences in water distribution uniformity. In the drip field, we dug the soil pits in
locations that would correspond to treatments for the grower’s irrigation program and our own
irrigation schedule of full irrigation followed by a mid-‐season deficit irrigation strategy.
In September (furrow irrigated field) and October (drip irrigated field), just prior to harvest, we
followed the same soil sampling procedures as we used in the spring. We offset the fall pits from the
spring pits so as not to sample from previously-‐disturbed soil.
Survey. We had proposed using the non-‐invasive electromagnetic probe (EM38) to indirectly
characterize salinity in these fields. The EM38 explores a large volume of soil, and we were interested in
its relevance in cultivated agricultural settings. Nevertheless, after evaluating data from last year and
attending a salinity short course at the USDA Salinity Laboratory in Riverside, CA, we learned that the
salinity values in these fields are too low for the EM38 to accurately characterize. The EM38 is robust at
determining salinity when electrical conductivity (EC) measures 2.0 dS/m or greater, but our 2013 and
2014 results from the drip-‐irrigated field show that all soil samples had an EC lower than 2.0 dS/m. For
this reason, we did not use the EM38 in 2014 and focused our attention on intensive soil sampling from
the soil pits.
Soil and water testing. Soil salinity was measured as ECe and chloride (Cle) ion concentration of the
saturated paste extract, where higher measures of ECe and Cle indicate higher levels of dissolved salts in
the soil. Following procedures of the Soil Science Society of America (Sparks et al., 1996), soil samples
are brought to saturation, rather than adding a uniform amount of water across all samples. This
standardizes the soils and allows for the comparison of soil salinities even when soil textures differ. The
saturation percentage (SP) is the weight of water added to an air dry soil sample to achieve saturation
divided by the weight of the soil sample. To conduct these procedures, a paste was made by saturating a
soil sample with deionized water until all pores were filled but before water pooled on the surface.
When saturation was achieved, the liquid and dissolved salts were extracted from the sample under
partial vacuum. We measured the EC of the saturated paste extracts (ECe), and of the irrigation (ECw)
and groundwater (ECgw), in the laboratory of UC Cooperative Extension in San Joaquin County using a
conductivity meter (YSI 3200 Conductivity Instrument). Chloride in the saturated paste extracts and
water was measured at the UC Davis Analytical Laboratory by flow injection analysis colorimetry
(http://anlab.ucdavis.edu/analyses/soil/227). Salinity of groundwater was tested pre-‐plant and post-‐
harvest, and salinity of the irrigation water was tested from eight to ten irrigations throughout the
season.
Evapotranspiration (ET) and deficit irrigation. Within the drip field, in 2013 we installed in-‐line valves on
three rows in order to institute more severe irrigation cutbacks than the grower’s strategy, allowing us
to compare soil salinity under differing degrees of deficit irrigation. The number of hours that the
system was on was determined from the grower’s log book and was monitored with a timer attached to
a pressure switch, which logged the number of hours that the system exceeded a pressure of 4 psi. The
pressure switches with timers were installed on both sides of our in-‐line valves so that we could monitor
run time of the grower’s system as well as our three isolated rows. The grower’s flow meter was
monitored during two irrigations to determine the amount of water the drip system put out per hour.
The acreage being irrigated was measured with GPS, and thus, we were able to determine how many
inches of water were being applied via the drip system. At each location where we dug pits for soil
samples, we also augered a hole to take volumetric soil samples to determine bulk density and soil
moisture. Additional sampling for soil moisture occurred at the onset of our irrigation cutbacks on
August 14thand at harvest. From those, we were able to determine the stored soil moisture pre-‐plant, 6
weeks prior to harvest, and at harvest. Crop evapotranspiration was estimated assuming full ET for the
late-‐season, once canopy cover was full. We used 2014 values for reference ET from the CIMIS weather
station in Manteca, which is located about 10 miles SE of the field. Crop coefficients were calculated
based on the method of Hanson and May (2006).
Yield and fruit quality. In the drip irrigated field, yield and fruit quality data were collected for the two
irrigation strategies. Machine-‐harvested fruit weight was collected on September 29th and 30th from two
600-‐foot sections of the drip field; one each in the grower-‐irrigated and experimental-‐irrigated rows. In
the furrow field we were not able to measure yield in the study row, but the grower reported to us his
average yield from the 20-‐acre section of field around our soil sampling areas. Soluble solids, pH and
color of raw fruit were determined from 5-‐lb samples analyzed by a commercial grading station
managed by the Processing Tomato Advisory Board. Because this is not a replicated experiment,
statistical analyses were not performed.
Results and Discussion:
Pit sampling and soil and water testing. In 2014 we processed 629 soil samples from the two fields. The
saturation percentage (SP) of the drip field soil samples are presented in Figure 1. The figure illustrates
that in the deficit irrigation row, the water-‐to-‐soil ratio to achieve saturation was highest between 32”
and 40”; whereas, in the grower irrigation row, the SP was generally highest from 24” to 36”. This
corresponds with the ECe values being higher at these respective depths (Figure 2) and provides an
explanation for the irrigation water moving across the bed, rather than down the soil profile. The higher
SPs at these depths suggest a finer texture soil with reduced saturated hydraulic conductivity.
Additionally there is more organic matter at these depths, particularly fine, highly decomposed organic
matter. The fine particles clog pore space, further reducing the ability for water to move through and for
salts to leach downward in the field. While we did not measure organic matter in this study, we
observed textural differences in the soil pits and a different consistency of soil saturated pastes in the
laboratory.
Despite the fact that water was not applied in excess of estimated crop evapotranspiration, the
upper part of the root zone was kept fairly clean of salts, as illustrated by ECe level and Cle concentration
(Figures 2 and 3). Leaching occurred mostly horizontally rather than vertically (below the tape). Salts did
accumulate above the drip tape and also in the third foot down, likely due to soil textural differences
which may have impeded downward leaching as described above. In the grower row, the horizontal
band of cleaner soil extended all the way across to the furrow. In the experimental program row, the
cleaner zone did not extend quite as far out from the drip tape. This zone surrounding the tape is where
the majority of roots would be found. In both cases, the overall average salinity in the root zone (down
to 40”) was 1.18 dS/m (grower row) and 1.36 dS/m (experimental row). Based on previous work (Ayers
and Westcot, 1985), impacts to tomato growth or yield are not expected until salinity levels reach 2.5
dS/m. The average salinity increased only slightly from the first year to the second year, in part
attributable to the good quality irrigation water (average ECw of 0.6 over the course of the irrigation
season). Had the quality of the irrigation water been poorer, then it would be recommended to increase
applied water to levels exceeding ET to leach salts out of the root zone.
Groundwater depth varied between spring and fall; at spring sampling time points in both years
it was at 54 inches below the top of the bed, while at the fall sampling time points it ranged between 67
to 73 inches below the top of the bed. In all cases, the ECgw ranged from 0.98 to 1.24 dS/m. Fluctuating
groundwater depth and quality may be a factor contributing to the lower root zone salinity in these
profiles.
For the furrow-‐irrigated field, salinity results are presented in figures 4 and 5. Average ECe values
increased over the course of the season by 0.5 dS/m towards the bottom end of the field, but did not
change over the season in the pit towards the top end of the field. At the fall sampling time point, the
pit towards the bottom of the field had many samples exceeding the 2.5 dS/m salinity threshold, but
most of these samples were from the lower depths where there were fewer roots. The average of the
top foot was 1.36 dS/m, the second foot down averaged 1.96 dS/m, and the third foot down averaged
2.66 dS/m. The highest increases in ECe (1 dS/m increase from spring to fall) were at the 24” to 28”
depth in the pit towards the bottom end. Average chloride concentration (Figure 5) increased by 132
ppm in the pit towards the bottom end and by 44 ppm in the pit towards the top end. But in no cases
did the chloride concentration exceed the threshold of 875 ppm (Tanji, 1990). Again, the highest
increases were in the third foot depth, although the second foot depth also had significant increases.
This field was irrigated weekly using alternate furrows for each irrigation. This appears to have been
effective at preventing a buildup of salinity at the center of the bed. Water quality of the irrigation
water was, on average, 0.6 dS/m and 108 ppm chloride. Groundwater salinity ranged from 2.07 to 2.96
dS/m depending on sampling time and location. Depth to groundwater ranged from 47 to 58” (spring)
and 61 to 83” (fall).
The lower ECe and Cle values of the top pit compared to the bottom pit suggest that there is a
longer opportunity time for irrigation water to infiltrate into the top end of the field. The result is better
leaching at the top end of the field compared to the bottom end of the field. Irrigating over a longer run
time may provide for better leaching at the bottom end of the field. However, longer run times will also
increase runoff from the field and could result in problems with standing water on clay soils.
Evapotranspiration and deficit irrigation; drip field. Consumptive water use (stored water used plus
irrigation water applied) was below estimated full ET by one inch (grower) or two inches (experimental
rows) (Table 2). Therefore, the irrigation programs neither leached salts with deep drainage of excess
irrigation water, nor resulted in a severe deficit for the plant. Our deficit irrigation cutback was not that
much more severe than the grower’s cutback at the end of the season, and amounted to 2.3 inches less
water applied via the drip system. However, in the experimental program rows, the crop used 1.6 inches
more of the stored soil moisture than in the sampled grower row (4.3” stored moisture used versus
2.7”). Therefore, the consumptive water use in the experimental rows was only one inch less than that
in the grower rows. In the grower program rows, applied water met or exceeded estimated crop ET
during the period from July 9th to August 26th, and during this time some leaching of salts out of the zone
surrounding the drip tape may have occurred. After August 26th, amounts of applied water were below
estimated crop ET. Soil moisture measurements made on August 14th indicated, however, that there was
significant stored soil moisture at that time, and soil moisture actually increased between Aug 14th and
harvest time in the grower row and decreased only slightly in the experimental row. In retrospect, both
the grower and we could have safely implemented an earlier and more severe irrigation cutback due to
the soil moisture accumulated during an earlier period of irrigation in excess of ET. However, more
severe cutbacks could reduce the degree of leaching of the salts out of the zone around the drip tape.
Yield and fruit quality. In the furrow irrigated field, the grower averaged 46.7 tons per acre in the
section of the field where our soil sampling was conducted. In the drip field, machine harvested yields
were above average and were similar between the two irrigation strategies. Fruit quality was also similar
between treatments (Table 3).
References cited
Ayers, R. S. and D. W. Westcot. 1985. Water Quality for Agriculture. FAO Irrigation and Drainage Paper
29 Rev. 1. FAO, United Nations, Rome. 174 p.
Hanson, B. R. and May, D. M. 2006. New crop coefficients developed for high-‐yield processing tomatoes.
California Agriculture 60:95-‐99.
Sparks, D. L., A. L. Page, P. A. Helmke, R. H. Loeppert, P. N. Soltanpour, M. A. Tabatabai, C. T. Johnston,
and M. E. Sumner (ed.). 1996. Methods of soil analysis, part 3, chemical methods. Soil Science Society of
America, Inc. and American Society of Agronomy, Inc. Madison, WI.
Tanji, K.K. 1990. (ed). Agricultural salinity assessment and management. ASCE manuals and reports on engineering practice No. 71. Am. Soc. Civil Eng., New York
Figure 1. Saturation percentages (SP) of soil samples collected from drip-‐irrigated tomato beds. Higher SPs are correlated with greater water holding capacity, but likely also represent higher levels of organic matter, lower hydraulic conductivity, and less easily leached soil strata. Soil depths are relative to the top of the bed, thus the blank areas near the furrow.
BED CENTER BED CENTER BED CENTER BED CENTER
depth
0 -‐ 4" 57.6 57.5 54.2 56.6 49.5 56.5 58.5 49.4 51.5 52.5 52.1 51.2 52.5 51.7 54.8 53.8 54.5 55.0 53.8 52.2
4 -‐ 8" 55.0 55.3 53.4 54.5 61.7 55.7 57.6 51.6 53.6 53.4 52.8 52.6 52.6 54.4 53.9 51.2 56.0 54.4 56.3 55.8 55.3 54.2 52.1 53.4
8 -‐ 12" 55.2 57.5 59.0 57.8 61.5 54.9 54.3 60.1 53.6 55.9 55.3 54.8 54.9 54.9 55.2 52.5 57.2 55.9 55.4 56.3 57.4 56.4 57.2 55.4
12 -‐ 16" 64.3 54.9 55.1 54.3 55.0 53.8 55.0 60.4 54.5 54.4 54.5 54.8 63.9 54.8 56.4 56.6 56.5 56.3 57.9 58.9 58.2 58.4 60.0 58.6
16 -‐ 20" 59.5 56.5 60.6 58.3 55.0 57.9 56.9 56.6 57.5 58.2 59.8 58.9 58.6 57.4 57.3 58.3 57.2 58.2 57.9 56.8 55.6 56.7 56.1 56.4
20 -‐ 24" 54.8 61.4 60.4 61.0 60.1 57.0 59.6 63.9 61.7 60.9 60.7 62.9 61.9 62.0 61.5 62.7 55.1 57.0 57.4 57.3 57.8 57.1 59.3 61.0
24 -‐ 28" 64.0 63.7 65.6 65.3 64.3 62.3 67.4 71.8 65.5 65.6 66.1 66.4 66.2 65.4 66.2 66.7 59.7 59.0 62.6 61.3 60.7 59.6 59.4 60.0
28 -‐ 32" 70.6 74.4 70.2 69.4 67.8 66.6 76.8 68.2 74.5 75.2 75.1 72.4 72.6 72.1 73.4 76.6 63.3 65.2 63.4 65.8 64.1 64.9 64.5 65.0
32 -‐ 36" 87.4 95.0 81.0 88.0 83.6 76.2 75.6 86.2 82.6 82.5 81.2 82.4 79.4 84.6 81.9 81.1 68.2 71.4 67.8 71.4 70.0 68.1 68.8 67.7
36 -‐ 40" 85.8 92.9 88.9 86.9 84.3 83.6 94.2 94.7 91.1 90.0 92.3 89.7 91.7 95.9 94.7 97.8 82.3 81.8 84.3 82.5 79.4 82.2 80.8 84.4
BED CENTER BED CENTER BED CENTER BED CENTER
depth
0 -‐ 4" 56.3 57.5 57.6 53.0 53.6 52.3 50.7 55.6 54.3 52.9 50.8 51.4 50.3 51.1 56.5 55.4 52.6 53.2 56.7 48.3
4 -‐ 8" 51.3 54.2 62.0 56.9 58.3 52.5 53.4 54.4 53.2 52.7 52.3 53.2 53.4 53.0 53.7 52.8 56.6 55.4 53.9 53.9 56.1 56.2 54.9 55.4
8 -‐ 12" 52.5 54.7 52.0 52.6 56.7 55.8 53.3 54.2 55.2 53.3 55.0 54.8 55.4 55.1 55.3 55.7 56.1 56.1 55.3 54.8 55.9 56.0 54.1 56.0
12 -‐ 16" 54.4 55.4 55.3 55.7 56.5 57.8 56.5 55.2 55.1 54.9 54.1 53.4 55.8 55.1 55.6 60.2 57.1 54.6 54.1 54.4 54.8 58.3 55.9 56.6
16 -‐ 20" 55.4 62.3 62.7 60.1 58.2 58.8 59.0 57.2 55.3 55.1 57.4 59.7 56.9 56.0 58.7 59.0 60.1 59.2 51.7 56.7 58.2 57.3 55.4 57.8
20 -‐ 24" 58.9 64.8 62.8 61.6 67.8 65.0 66.5 64.5 62.8 66.1 66.5 65.2 61.5 57.1 63.2 64.9 65.2 65.4 62.3 62.6 63.5 60.0 58.1 62.2
24 -‐ 28" 74.1 77.9 77.3 92.1 81.1 83.6 77.2 72.7 79.0 81.3 81.1 79.9 76.2 77.9 74.4 78.2 74.5 72.7 79.3 74.3 74.7 70.3 66.0 69.6
28 -‐ 32" 82.0 86.5 89.1 88.9 85.8 91.4 89.7 84.2 78.9 77.4 80.0 81.7 79.3 81.1 84.3 87.8 89.9 86.4 91.1 86.9 89.1 87.3 82.6 84.9
32 -‐ 36" 69.4 73.0 84.4 79.5 83.6 85.8 86.0 91.1 58.9 57.8 58.6 63.0 67.9 69.7 71.0 75.3 89.1 91.3 95.3 92.5 100.6 93.1 94.9 92.9
36 -‐ 40" 55.2 58.3 57.5 56.4 61.6 65.8 72.4 62.9 46.0 47.1 47.0 46.8 49.0 65.7 67.5 55.2 77.6 76.6 80.5 88.2 90.1 90.6 90.1 92.3
Grower irrig
ation row
Deficit irrigation row
Saturation Percentage (%)
FURROW FURROW FURROWFURROW
Pre-‐transplant May 13, 2013 Post-‐harvest Oct. 3, 2013 Pre-‐transplant May 13, 2014 Harvest Sept 29, 2014
53.0 51.4 55.5 53.4
53.7
57.3 54.6 52.9 53.8
52.3 53.9 49.6
53.2 62.4 51.4 51.2
53.7 53.5 52.9 51.3
55.1 54.6 54.7
60.6 59.4 58.8 59.8
62.0 63.3 62.2 63.2
55.2 53.1 51.7
70.8 63.6 72.8 71.1
78.2 77.7 86.2 74.5
FURROW FURROW FURROW FURROW
54.1 53.5 55.7 53.8
56.5 54.7 56.0 57.1
60.6 58.6 63.5 55.4
59.3 57.6 59.7 59.5
70.3 61.1 61.6 61.1
70.2 68.0 65.0 71.7
74.8 75.7 80.2 87.5
83.7 84.0 83.5 85.0
83.8 86.6 83.4 86.1
Figure 2. Soil salinity of drip-‐irrigated tomato beds in four sampling time points over two seasons, measured as electrical conductivity of the saturated paste extract (ECe). The blue oval at 12” depth at bed center represents the location of the drip tape. Soil depths are relative to the top of the bed, thus the blank areas near the furrow.
BED CENTER BED CENTER BED CENTER BED CENTER BED CENTER
depth
0 -‐ 4" 1.19 1.16 0.85 1.14 1.85 2.28 2.68 2.98 1.75 1.62 1.33 1.60 1.65 1.86 2.80 3.73 2.41 2.24 2.81 5.12 1.05 2.11 1.08 0.65 1.16 3.26
4 -‐ 8" 0.86 0.70 0.95 0.85 0.64 0.83 1.49 1.32 1.43 1.16 1.03 1.20 1.57 1.47 1.39 1.07 1.74 1.42 1.39 1.29 1.2 1.54 5.09 3.69 0.31 0.26 0.37 0.09 -‐0.36 0.07 3.70 2.62
8 -‐ 12" 0.77 0.71 0.57 0.68 0.56 0.83 0.69 0.60 1.44 1.12 1.42 1.25 1.20 1.32 1.43 1.05 1.12 0.96 0.94 0.89 1.18 1.06 1.13 1.19 -‐0.32 -‐0.16 -‐0.49 -‐0.37 -‐0.02 -‐0.26 -‐0.30 0.15
12 -‐ 16" 0.55 0.65 0.64 0.83 0.63 0.48 0.77 0.43 1.3 1.32 1.33 0.98 1.00 1.08 0.94 0.96 0.82 0.74 0.79 0.87 1.03 1.06 0.93 0.84 -‐0.48 -‐0.59 -‐0.54 -‐0.10 0.03 -‐0.02 -‐0.01 -‐0.12
16 -‐ 20" 0.52 0.83 0.91 0.69 0.71 0.59 0.58 0.40 1.27 1.14 0.94 0.98 1.19 0.98 1.03 0.96 0.61 0.85 0.73 0.78 0.82 0.89 1.50 0.92 -‐0.66 -‐0.29 -‐0.21 -‐0.20 -‐0.37 -‐0.09 0.47 -‐0.04
20 -‐ 24" 0.49 1.00 1.06 1.04 0.89 0.91 0.57 0.53 1.26 1.08 1.14 1.14 1.01 1.24 1.02 0.93 0.91 0.85 1.14 0.80 0.80 1.03 1.45 0.86 -‐0.35 -‐0.23 0.00 -‐0.34 -‐0.21 -‐0.21 0.43 -‐0.06
24 -‐ 28" 0.59 0.74 1.18 1.06 0.93 1.19 0.69 0.62 0.92 1.26 1.29 1.19 1.48 1.05 1.33 0.98 0.72 0.94 0.95 0.90 0.95 1.52 0.92 1.18 -‐0.20 -‐0.32 -‐0.34 -‐0.30 -‐0.53 0.47 -‐0.41 0.20
28 -‐ 32" 0.69 1.04 1.39 1.23 1.14 1.00 0.82 0.95 1.55 1.23 1.32 1.35 1.53 1.18 1.39 1.14 0.97 1.13 1.14 1.20 1.33 1.14 1.04 1.03 -‐0.58 -‐0.11 -‐0.19 -‐0.16 -‐0.20 -‐0.05 -‐0.35 -‐0.11
32 -‐ 36" 0.66 0.95 1.26 0.94 1.17 1.57 1.03 1.00 1.13 1.17 1.13 1.17 1.10 1.05 1.05 1.25 0.94 1.14 1.36 1.75 1.58 1.41 1.41 1.46 -‐0.19 -‐0.03 0.24 0.59 0.48 0.37 0.36 0.21
36 -‐ 40" 0.67 0.64 1.18 0.92 0.97 1.01 0.83 0.86 1.13 1.27 1.28 1.14 1.16 1.30 1.17 1.14 1.13 1.26 1.70 1.46 1.26 1.22 1.54 1.24 0.00 -‐0.01 0.41 0.32 0.11 -‐0.08 0.37 0.10
BED CENTER BED CENTER BED CENTER BED CENTER BED CENTER
depth
0 -‐ 4" 1.38 1.09 0.81 0.70 1.17 3.07 1.33 3.01 2.01 1.90 1.77 1.66 1.56 1.55 2.69 2.58 1.58 1.10 1.44 3.95 0.68 0.67 -‐0.20 -‐0.56 -‐0.12 2.40
4 -‐ 8" 0.58 0.57 0.53 0.52 0.51 0.78 0.68 1.20 1.86 1.63 1.64 1.58 1.56 1.41 1.58 1.40 1.35 0.98 0.8 0.74 1.13 1.10 1.66 2.46 -‐0.51 -‐0.65 -‐0.84 -‐0.84 -‐0.43 -‐0.31 0.09 1.06
8 -‐ 12" 0.87 0.49 0.73 0.52 0.52 0.59 0.56 0.77 1.46 1.20 1.45 1.51 1.56 1.30 1.14 1.22 0.98 0.86 1.08 0.85 0.91 0.95 0.87 0.74 -‐0.48 -‐0.34 -‐0.38 -‐0.66 -‐0.65 -‐0.36 -‐0.28 -‐0.48
12 -‐ 16" 0.43 0.48 0.49 0.52 0.51 0.48 0.91 0.39 1.31 1.14 1.08 1.53 1.31 1.13 1.07 1.07 0.90 1.09 1.00 0.98 0.80 0.71 0.70 0.61 -‐0.41 -‐0.05 -‐0.08 -‐0.55 -‐0.51 -‐0.42 -‐0.37 -‐0.46
16 -‐ 20" 0.97 0.63 0.59 0.73 0.59 0.59 1.34 0.50 1.15 1.01 1.27 1.07 1.12 0.88 0.95 1.07 0.87 0.86 0.87 0.79 0.85 1.10 1.03 0.77 -‐0.29 -‐0.14 -‐0.40 -‐0.28 -‐0.27 0.22 0.09 -‐0.31
20 -‐ 24" 0.60 0.95 0.82 0.77 0.75 0.82 0.72 1.26 1.17 1.01 1.05 1.05 1.22 1.01 1.32 1.39 1.14 1.11 1.14 1.33 1.04 1.08 0.86 0.82 -‐0.03 0.10 0.09 0.28 -‐0.17 0.07 -‐0.46 -‐0.57
24 -‐ 28" 0.54 0.70 0.81 0.85 0.88 1.06 0.86 0.84 1.12 1.02 1.13 1.33 1.20 1.15 1.00 1.02 1.34 1.41 1.48 1.30 1.55 1.22 1.20 1.44 0.22 0.39 0.36 -‐0.03 0.35 0.06 0.21 0.41
28 -‐ 32" 0.55 0.65 0.87 0.94 0.88 0.85 0.78 0.77 1.09 1.14 1.05 1.26 1.33 1.17 1.27 1.14 1.19 1.67 1.32 1.41 1.65 1.68 1.65 1.27 0.09 0.53 0.27 0.15 0.32 0.52 0.38 0.12
32 -‐ 36" 0.46 0.52 0.73 0.71 0.66 0.76 0.66 0.75 1.10 1.17 0.95 1.12 1.19 1.12 1.15 1.12 1.05 1.20 1.25 1.18 1.30 1.26 1.03 0.91 -‐0.05 0.02 0.30 0.06 0.11 0.14 -‐0.11 -‐0.21
36 -‐ 40" 0.51 0.6 0.67 0.68 0.58 0.55 0.52 0.67 0.83 0.9 0.81 0.94 1.15 0.92 0.94 0.91 0.89 0.94 1.08 0.95 1.05 0.94 0.92 0.78 0.06 0.04 0.27 0.01 -‐0.10 0.02 -‐0.02 -‐0.13
0.79
FURROW
Grow
er irrig
ation row
Deficit irrigation row
Electrical Conductivity (dS/m)
0.80 0.78 0.66
FURROW
Pre-‐transplant May 13, 2014
FURROW
0.77 0.79 0.71
0.88
0.73
0.71
0.64 0.76 0.74 0.79
0.89
0.84 0.74 1.24
1.27
0.81
0.83 0.77 0.74
0.94 0.84 0.73
0.68 0.79 0.75
0.67 0.92 0.74
0.82
0.72
0.76
0.81 0.84 0.92 0.74
0.93 0.84 0.75
FURROW
0.75 0.85 0.79 0.77
1.10 0.94 1.06
FURROW
0.92 1.11 0.89 0.93
1.09 1.06
0.72 0.77 0.70 0.81
0.87 1.10 0.81 0.76
0.93 0.97 1.11 0.89
0.95 1.18 1.25 0.84
FURROW
Harvest Sept 29, 2014 Change in EC from spring to fall 2014
0.91 1.01 0.97 0.75
0.85 0.820.90
FURROWFURROW
Pre-‐transplant May 13, 2013 Post-‐harvest Oct. 3, 2013FURROWFURROW
Figure 3. . Soil salinity of drip-‐irrigated tomato beds in four sampling time points over two seasons, measured as chloride concentration. The blue oval at 12” depth at bed center represents the location of the drip tape. Soil depths are relative to the top of the bed, thus the blank areas near the furrow.
BED CENTER BED CENTER BED CENTER BED CENTER BED CENTER
depth
0 -‐ 4" 160.3 258.0 114.8 78.8 154.0 261.8 388.9 462.0 137 133 134 174 182 201 372 544 271 245 304 576 236 411 137 71 123 375
4 -‐ 8" 131.3 107.5 65.1 74.6 50.4 60.6 97.7 101.5 141 135 146 135 175 146 117 69.3 243 203 192 162 131 144 340 517 102 68 46 27 -‐43 -‐3 223 448
8 -‐ 12" 128.8 85.8 67.2 79.8 55.0 92.1 51.5 42.4 168 144 191 152 136 147 102 77.4 147 119 104 103 134 97.7 91.4 92.8 -‐21 -‐25 -‐87 -‐49 -‐2 -‐50 -‐11 15
12 -‐ 16" 106.8 93.1 104.0 118.0 100.6 63.4 55.7 37.5 162 191 202 129 117 127 99.1 94.9 120 111 107 111 128 130 109 83 -‐42 -‐81 -‐95 -‐18 12 2 10 -‐12
16 -‐ 20" 99.4 170.5 186.6 103.3 106.8 76.0 74.4 43.8 194 180 151 151 171 130 124 114 102 155 120 133 128 140 215 123 -‐92 -‐25 -‐30 -‐18 -‐43 10 91 9
20 -‐ 24" 85.4 146.7 218.4 147.7 120.4 206.2 67.9 67.9 221 201 201 266 159 182 130 116 177 195 236 155 152 202 260 147 -‐44 -‐6 36 -‐111 -‐7 20 131 32
24 -‐ 28" 89.8 140.7 184.5 193.9 128.8 110.6 84.0 90.0 202 237 242 222 258 173 197 132 152 191 191 180 187 317 179 236 -‐50 -‐46 -‐50 -‐42 -‐71 144 -‐18 104
28 -‐ 32" 98.0 147.4 178.9 177.1 184.3 133.0 116.2 157.5 304 246 232 226 257 179 203 159 222 238 217 220 244 207 193 198 -‐83 -‐7 -‐15 -‐6 -‐13 27 -‐10 40
32 -‐ 36" 82.6 129.9 212.5 141.8 141.8 157.9 167.8 160.7 203 208 202 193 177 161 156 186 208 236 264 329 284 255 258 273 5 28 62 136 107 94 103 88
36 -‐ 40" 93.5 83.7 108.2 156.1 148.1 143.9 129.9 136.2 203 229 225 191 195 208 183 179 249 270 341 279 230 220 292 230 46 41 116 88 35 12 109 51
BED CENTER BED CENTER BED CENTER BED CENTER BED CENTER
depth
0 -‐ 4" 273.7 166.6 80.9 70.7 107.5 415.5 128.1 537.3 166.6 151.9 155.4 145.3 130.6 131.6 378.4 429.1 209.7 113.8 171.9 468.0 212 277 54 -‐32 41 336
4 -‐ 8" 73.7 78.8 54.6 59.2 59.2 70.7 54.6 134.4 175.7 164.5 175.0 166.6 149.8 131.6 131.3 96.6 173.6 115.9 91.0 90.3 138.6 109.6 146.3 286.3 -‐2 -‐49 -‐84 -‐76 -‐11 -‐22 15 190
8 -‐ 12" 144.6 78.8 94.5 73.5 70.4 69.3 57.8 82.3 169.8 136.9 172.9 175.0 159.3 131.6 96.3 87.5 109.2 107.1 129.5 99.4 99.4 87.9 71.1 69.3 -‐61 -‐30 -‐43 -‐76 -‐60 -‐44 -‐25 -‐18
12 -‐ 16" 66.5 85.8 80.2 92.1 80.3 69.1 227.9 54.3 161.4 139.3 134.4 186.2 152.3 125.0 107.5 137.2 138.6 180.6 142.8 139.7 112.0 106.8 94.2 84.4 -‐23 41 8 -‐47 -‐40 -‐18 -‐13 -‐53
16 -‐ 20" 178.2 136.2 121.8 137.6 109.6 108.5 231.4 72.8 183.8 144.2 142.1 113.1 104.3 155.4 115.2 108.2 183.1 177.5 171.2 155.1 175.0 224.4 202.3 147.7 -‐1 33 29 42 71 69 87 40
20 -‐ 24" 125.7 211.4 181.7 167.3 158.6 171.5 129.2 238.0 181.3 152.3 167.7 164.2 188.3 147.0 184.1 181.3 258.7 391.0 246.4 291.6 231.7 244.7 183.1 188.7 77 239 79 127 43 98 -‐1 7
24 -‐ 28" 103.4 150.2 279.3 167.3 76.3 209.7 173.3 168.4 178.9 161.7 188.3 241.2 203.0 191.5 149.1 140.7 290.2 298.6 317.8 279.0 353.9 261.5 272.3 326.2 111 137 130 38 151 70 123 186
28 -‐ 32" 220.5 119.7 160.5 175.0 157.5 145.3 141.4 148.1 183.8 192.5 186.2 226.5 231.7 195.7 197.8 169.8 240.1 331.8 265.0 295.1 353.5 376.6 361.9 260.8 56 139 79 69 122 181 164 91
32 -‐ 36" 74.9 86.5 128.1 131.3 109.7 99.4 106.8 146.3 180.6 202.3 155.8 197.4 204.8 186.6 184.5 161.4 205.8 226.5 253.4 241.5 274.1 273.7 216.7 177.8 25 24 98 44 69 87 32 16
36 -‐ 40" 85.8 95.9 108.5 117.6 94.9 81.9 85.9 132.3 133.0 144.2 125.0 147.0 184.1 139.3 143.9 134.8 171.2 177.1 216.3 193.2 218.4 198.5 195.0 155.1 38 33 91 46 34 59 51 20
FURROW FURROW FURROW FURROW FURROW
Chloride ion concentration (ppm)Change in chloride concentration from spring to
fall 2014Pre-‐transplant May 13, 2013 Post-‐harvest Oct. 3, 2013 Pre-‐transplant May 13, 2014 Harvest Sept 29, 2014
73.9 77.4 78.1
73.2
42.7 67.2 95.9 104.7
Deficit irrigation row
50.4 58.8 74.6
59.2 57.1 75.3
135.1 121.8 100.1
74.9
107.1 123.6 91.7 105.4
68.3 81.6 93.8 65.8
71.1 99.1 64.4 57.8
66.7 72.5 62.7 66.5
FURROW FURROW FURROW FURROW
95.9
169.1 141.1 121.8 133.0
FURROW
Grow
er irrig
ation row
71.8 61.3 108.2
82.3 56.4 57.1 53.9
63.7 63.7 106.8 52.2
78.1 67.9
58.8 73.2 72.8 73.2
79.8 83.7 85.8 73.9
61.3 55.3
55.7 78.1 63.4 64.1
104.7 101.5 94.2 84.7
99.1 90.3 89.3 82.6
107.5 91.0 88.9 90.0
Figure 4. Soil salinity of furrow-‐irrigated tomato beds in two sampling time points in 2014, measured as electrical conductivity of the saturated paste extract (ECe). Soil depths are relative to the top of the bed, thus the blank areas near the furrow. Top row of profiles are from pits one-‐third in from the field bottom end, while the bottom row of profiles are from pits one-‐third in from the top of the field.
tail pitsBED CENTER BED CENTER BED CENTER
depth
0 -‐ 4" 3.87 2.10 0.97 0.73 0.72 0.95 1.10 1.55 3.95 3.50 1.63 1.54 1.53 1.16 0.09 1.41 0.66 0.82 0.81 0.21
4 -‐ 8" 1.74 1.31 1.06 0.99 0.89 0.94 0.84 1.11 1.88 1.27 0.85 0.86 0.92 0.70 0.74 1.68 0.14 -‐0.04 -‐0.21 -‐0.13 0.03 -‐0.24 -‐0.10 0.57
8 -‐ 12" 1.28 1.40 1.40 1.11 1.03 1.02 1.18 1.22 1.26 1.13 0.91 0.99 0.99 0.84 0.80 0.80 -‐0.02 -‐0.27 -‐0.49 -‐0.12 -‐0.04 -‐0.19 -‐0.38 -‐0.42
12 -‐ 16" 1.15 1.31 1.35 1.43 1.23 1.22 1.58 1.28 1.74 2.14 1.53 1.24 1.15 0.90 1.32 1.39 0.59 0.83 0.18 -‐0.20 -‐0.07 -‐0.32 -‐0.26 0.12
16 -‐ 20" 1.52 1.49 1.48 1.50 1.34 1.48 1.46 1.43 2.30 2.48 1.50 1.59 1.85 1.98 2.17 2.44 0.78 0.99 0.02 0.09 0.51 0.49 0.70 1.01
20 -‐ 24" 1.62 1.74 1.53 1.54 1.38 1.43 1.54 1.54 1.96 2.63 2.23 2.24 2.29 2.53 2.56 2.85 0.33 0.89 0.70 0.70 0.91 1.10 1.02 1.31
24 -‐ 28" 1.75 1.67 1.48 1.52 1.43 1.43 1.68 1.72 2.48 2.52 2.47 2.45 2.67 2.66 2.60 2.61 0.72 0.85 0.99 0.94 1.24 1.23 0.92 0.88
28 -‐ 32" 2.04 1.78 1.77 1.61 1.77 1.68 1.72 1.91 2.60 2.81 2.76 2.54 2.53 2.82 2.65 2.54 0.56 1.03 0.99 0.93 0.76 1.14 0.94 0.63
32 -‐ 36" 1.93 1.96 1.98 1.72 1.80 1.81 1.80 2.25 2.65 2.79 2.88 2.96 3.11 2.84 2.76 2.14 0.73 0.83 0.90 1.24 1.31 1.03 0.96 -‐0.11
36 -‐ 40" 2.26 2.36 2.28 1.90 1.95 1.87 2.11 2.40 1.99 2.2 2.41 2.58 2.82 2.84 2.46 2.57 -‐0.27 -‐0.18 0.13 0.69 0.87 0.97 0.35 0.17
head pitsBED CENTER BED CENTER BED CENTER
depth
0 -‐ 4" 2.94 1.43 0.91 0.90 1.20 1.19 3.41 1.60 1.27 1.59 1.77 1.80 0.46 0.16 0.37 0.69 0.57 0.61
4 -‐ 8" 1.12 0.91 0.9 0.83 0.85 0.73 0.72 0.58 0.94 0.91 0.71 0.88 0.50 0.52 1.24 -‐0.18 0.00 -‐0.19 0.05 -‐0.36 -‐0.21 0.51
8 -‐ 12" 0.98 1.09 1.12 0.96 1.05 1.00 0.81 0.88 0.94 0.76 0.71 0.48 0.85 0.65 0.71 0.81 -‐0.04 -‐0.33 -‐0.41 -‐0.48 -‐0.20 -‐0.36 -‐0.10 -‐0.07
12 -‐ 16" 1.11 1.13 0.98 0.90 1.12 1.18 0.93 0.85 0.86 0.82 0.97 0.76 0.68 0.83 0.79 0.97 -‐0.26 -‐0.30 -‐0.01 -‐0.14 -‐0.44 -‐0.36 -‐0.14 0.13
16 -‐ 20" 1.35 1.17 0.99 1.01 1.04 1.12 1.04 1.02 0.86 1.02 0.81 0.74 0.84 0.85 0.84 0.80 -‐0.50 -‐0.15 -‐0.18 -‐0.27 -‐0.20 -‐0.28 -‐0.20 -‐0.21
20 -‐ 24" 1.15 1.14 1.06 1.09 1.15 1.11 1.20 1.10 1.01 1.01 1.05 0.82 0.88 0.92 0.98 0.86 -‐0.14 -‐0.13 -‐0.01 -‐0.27 -‐0.27 -‐0.19 -‐0.22 -‐0.24
24 -‐ 28" 1.29 1.15 1.17 1.16 1.22 1.16 1.14 1.47 1.19 1.43 1.54 1.31 1.21 1.06 1.08 1.10 -‐0.10 0.28 0.37 0.15 -‐0.01 -‐0.10 -‐0.06 -‐0.37
28 -‐ 32" 1.24 1.24 1.21 1.20 1.22 1.27 1.17 1.19 1.24 1.49 1.73 1.80 1.40 1.56 1.35 1.22 -‐0.01 0.25 0.52 0.60 0.18 0.29 0.17 0.03
32 -‐ 36" 1.27 1.38 1.38 1.33 1.35 1.42 1.39 1.30 1.40 1.59 1.77 1.73 1.28 1.25 1.53 1.36 0.13 0.20 0.39 0.40 -‐0.07 -‐0.17 0.15 0.07
36 -‐ 40" 1.40 1.33 1.32 1.33 1.34 1.23 1.41 1.50 1.28 1.25 1.30 1.44 1.29 1.17 1.38 1.15 -‐0.13 -‐0.08 -‐0.02 0.11 -‐0.05 -‐0.06 -‐0.03 -‐0.35
Field top
Field bo
ttom
Electrical Conductivity (dS/m)
FURROW FURROW
Changes in EC from spring to fall 2014FURROW
Changes in EC from spring to fall 2014FURROW
3 wks after transplanting At harvestFURROW FURROW
3 wks after transplanting At harvest
Figure 5. Soil salinity of furrow-‐irrigated tomato beds in two sampling time points in 2014, measured as chloride concentration. Soil depths are relative to the top of the bed, thus the blank areas near the furrow.
tail pitsBED CENTER BED CENTER BED CENTER
depth
0 -‐ 4" 385 168 61 51 57 88 119 164 361 308 135 155 187 137 -‐24 140 75 104 130 48
4 -‐ 8" 166 105 84 78 78 83 78 86 84 75 82 105 124 96 104 297 -‐81 -‐29 -‐2 27 45 13 26 211
8 -‐ 12" 132 135 123 93 99 97 118 113 116 155 130 143 141 124 119 130 -‐15 20 7 50 42 27 1 17
12 -‐ 16" 130 142 132 141 126 127 169 127 269 330 249 184 167 139 216 233 139 187 116 42 41 12 47 106
16 -‐ 20" 166 162 151 152 141 158 152 154 376 418 246 264 294 317 368 414 210 256 95 112 153 159 216 260
20 -‐ 24" 168 182 156 156 142 152 165 165 293 389 352 349 352 387 394 426 125 207 197 193 210 235 229 261
24 -‐ 28" 184 171 152 153 150 153 189 203 310 380 383 374 394 388 393 391 126 209 231 221 244 235 204 188
28 -‐ 32" 238 202 189 173 207 182 205 242 377 418 414 376 375 414 398 393 139 216 224 203 168 232 193 151
32 -‐ 36" 230 233 224 196 201 215 223 306 381 414 426 467 506 462 414 331 151 181 202 271 305 247 191 25
36 -‐ 40" 286 281 288 217 237 202 257 347 281 308 350 383 466 463 386 402 -‐5 27 62 166 229 261 130 55
head pitsBED CENTER BED CENTER BED CENTER
depth
0 -‐ 4" 273 113 64 67 100 131 331 126 143 195 214 249 58 13 78 128 114 118
4 -‐ 8" 91 47 55 53 43 48 55 43 93 91 74 76 58 61 209 3 45 18 22 16 13 154 -‐43
8 -‐ 12" 68 68 71 67 53 62 70 73 115 93 88 113 76 90 108 121 48 25 18 46 23 28 37 49
12 -‐ 16" 84 85 82 76 84 93 78 72 114 111 138 111 104 125 125 165 30 26 56 35 20 33 47 92
16 -‐ 20" 90 109 100 102 99 103 94 97 125 155 113 106 136 130 125 129 35 46 13 4 37 27 31 33
20 -‐ 24" 114 112 109 112 113 108 119 110 149 155 164 126 137 131 152 135 34 43 55 13 24 23 34 26
24 -‐ 28" 148 133 136 131 139 133 121 124 192 232 251 220 187 153 170 165 44 99 116 89 48 19 49 41
28 -‐ 32" 150 161 154 140 152 158 137 138 209 256 299 304 225 246 229 199 60 95 145 163 73 88 92 62
32 -‐ 36" 172 187 177 174 184 191 183 188 219 240 275 272 189 193 242 204 47 53 98 99 5 2 58 16
36 -‐ 40" 180 199 187 193 206 186 214 232 189 183 198 227 198 170 204 175 9 -‐16 11 34 -‐8 -‐15 -‐9 -‐57
Field bo
ttom
Field top
FURROW FURROW
Chloride (ppm)Changes in chloride concentration from spring to
fallFURROW FURROW FURROW
3 wks after transplanting At harvestChanges in chloride concentration from spring to
fall
3 wks after transplanting At harvest
FURROW
Table 1. Moisture content of soil samples, expressed in inches of water per foot of soil. Note that spring samples were after a pre-‐irrigation.
Furrow Drip – Exp. program Drip – grower program
Depth May 20 Aug 29 May 13 Aug 14 Sept 29 May 13 Aug 14 Sept 29
0-‐12" 5.05 3.72 5.38 3.72 3.42 5.06 3.38 3.46
13-‐24" 6.15 5.20 5.32 4.54 4.56 5.64 4.46 5.00
25-‐36" 7.84 5.50 6.58 5.73 5.02 6.88 6.08 6.40
Total inches in top 3 ft
19.0 14.4 17.3 14.0 13.0 17.6 13.9 14.9
Table 2. Amounts of water stored, applied water and water used by the tomato crop in the drip-‐irrigated
field. All values are in inches of water.
Grower irrigation program
Experimental irrigation program
Stored soil moisture (inches in top 3 ft) Soil moisture at transplanting 17.6 17.3
Soil moisture at harvest 14.9 13.0 Stored soil moisture used 2.7 4.3
Irrigation water applied via drip system* 18.3 16.0
Consumptive water use (stored plus applied) 21.0 20.3
Estimated full crop ET** 22.2 22.2
* does not include one pre-‐irrigation, which is captured by the soil moisture measurements.
** Estimate based on 2014 CIMIS data and crop coefficients of Hanson & May (2006).
Table 3. Yield and fruit quality from machine-‐harvest of the drip-‐irrigated field.
Yield
Soluble solids PTAB
(tons/acre) (°Brix) color pH
Grower irrigation program 53.69 5.15 22.5 4.23
Experimental irrigation program 57.99 5.30 23.5 4.23
1
Project Title: Developing a sampling protocol to estimate pre-‐plant nitrate availability in subsurface drip irrigated tomato systems
Principal Investigators: Martin Burger, Dept. of Land, Air and Water Resources. University of California Davis. E-‐mail: [email protected] Phone: 530-‐754-‐6497
Cristina Lazcano, Dept. of Land, Air and Water Resources, University of California Davis. E-‐mail: [email protected]
William R. Horwath, Dept. of Land, Air and Water Resources, University of California Davis. E-‐mail: [email protected] Phone: 530-‐754-‐6496
Collaborators: Brenna Aegerter, Cooperative Extension San Joaquin County, UC ANR. E-‐mail: [email protected] Tom Turini, Cooperative Extension Fresno County, UC ANR. E-‐mail: [email protected] Objectives: 1) Develop a soil sampling protocol to adequately estimate pre-‐plant nitrate (NO3
-‐) availability; 2) Assess the effects of SDI and winter precipitation on the spatial distribution of NO3
-‐, potassium (K) and phosphorous (P) availability in fields of varied precipitation and evapotranspiration regimes; 3) Assess the apparent N use efficiency, defined as the ratio of crop nitrogen (N) uptake to N availability (pre-‐plant NO3
-‐ + N fertilizer applications). Summary Systematic soil sampling was carried out in 16 commercial subsurface drip-‐irrigated tomato production fields in Yolo, San Joaquin, and Fresno Counties to assess the distribution of nitrate, available phosphate, and exchangeable potassium in the top 20 inches of soil at pre-‐plant. At five locations per field, soil samples were taken at 5 inches intervals from the center of the bed to the center of the furrow in 60-‐ and 80-‐inch bed fields. No general pattern of NO3
-‐ with regard to distance from the drip tape was observed, and no depletion of available phosphate and exchangeable potassium concentration was observed in the rooting zone around the drip tape.
2
A soil sampling protocol valid for all the 16 fields was developed that identified the locations (i.e. perpendicular distance from the center line of a bed) and number of cores necessary to derive an estimate of soil NO3
-‐ content in the top 20 inches that did not differ >5% from the field average based on all the sampled locations in a given field. The soil samples collected in this manner could also be used to assess available phosphate and exchangeable potassium although the potential errors would be slightly higher (≤12%) in the case of available phosphate. At harvest, yields, N content of fruit and vines, and post-‐harvest nitrate levels in the top 10 inches of soil were measured. The apparent nitrogen (N) uptake efficiency defined as the ratio of whole plant (fruits and vines) N to available N (pre-‐plant nitrate and fertilizer N inputs) ranged from 0.15 to 0.64, while the crop N removal to fertilizer N input ratio ranged from 0.44 to 1.03. Five of the 16 fields had nitrate levels ≥200 lbs N per acre at pre-‐plant, while post-‐harvest nitrate content in the top 10 inches ranged from 43 to 392 lbs N per acre. These observations of high nitrate levels in some of the fields call for regular monitoring of nitrate concentrations using the protocol developed in this study and adjusting of fertilizer N inputs in order to decrease the risks of nitrate leaching in processing tomato fields. Methods and materials Overview of the sampling sites and procedures
A total of 16 commercial processing tomato production fields were selected for the study. In order to determine the influence of climate conditions on nutrient distribution, fields were located along a transect of decreasing precipitation to evapotranspiration (ETo) ratio (Table 1), with 6 fields in Yolo County, 4 in San Joaquin County and 6 in Fresno County, three of the growing regions with the largest production of processing tomato in the state. The selected fields had been cultivated under SDI for a minimum of 2 and a maximum of 9 years. Fields comprised a range of different management conditions typical for processing tomato production in California including different bed sizes (60 vs. 80 inches), the number of consecutive years with tomatoes back-‐to-‐back, or the use of fall/winter crops in the same fields (Table 2). Pre-‐plant soil sampling was carried out in the 16 fields, from late February to mid-‐May, depending on the planting schedule of each field and before the application of any fertilizers. In each field, five random locations were selected where a systematic sampling was carried out. Briefly, soil samples were taken at regular (5 inches) intervals from the center of the bed to the center of the furrow by using a soil probe. Further, at each distance, samples were taken at two depths (0-‐10 and 10-‐20 in). Two sets of samples were taken at each site and composited later into one single sample per combination of distance and depth. Depending on the size of the beds, between 60 or 80 samples were taken in each field. The exact location of the sampling points was recorded by using GPS latitude-‐longitude coordinates in order to collect plant and soil samples from the same locations at harvest. The soil samples were extracted with 2M KCl solution, and NO3
-‐-‐N concentration in the extracts was assessed colorimetrically (Doane and Horwath, 2003). Available Olsen-‐P was analyzed colorimetrically in the soil samples collected at pre-‐plant after extraction with NaHCO (Kuo, 1996). Exchangeable K was determined by inductively coupled plasma atomic emission
3
spectrometry (ICP-‐AES) in air-‐dried and ground pre-‐plant soil samples after extraction with ammonium acetate (Thomas, 1982). Crop N uptake was determined by hand harvesting of the plants within 1 m of the bed at the pre-‐plant sampling locations. Briefly, we delimitated 1 m along the bed, close to the pre-‐plant sampling points, and all plants within this meter were counted and cut at the soil level. Fruits and vines were subsequently separated, weighed and a subsample of both fractions selected for further determination of dry mass and %N through dry combustion in a C and N analyzer (Costech Analytical Technologies Inc., Valencia, CA) (Dumas, 1848). Vine and fruit biomass and %N per plant were calculated and then extrapolated to the rest of the field by using plant density. Apparent Nitrogen Use Efficiency of the tomato crop (NUEC) was calculated as the ratio between N uptake by the tomato crop, including fruits and vine sampled at each field location, and the available N taking into account both the pre-‐plant nitrate measured in the soil and the fertilizer inputs reported by the growers. Nitrogen outputs were calculated based on the marketable yields reported by the growers and the average N content of the fruits sampled in each field. In the present study, post-‐harvest NO3
-‐ concentrations were measured before the incorporation of vine residue by taking one core 10 inches from the center of the bed to a depth of 20 inches at each of the 5 locations per field. Sampling protocol for pre-‐plant NO3
-‐-‐N With the information obtained regarding the spatial variability in nutrient concentration within the beds at pre-‐plant, we elaborated a sampling protocol in order to accurately estimate the amount of NO3
-‐-‐N in SDI processing tomatoes. The NO3
-‐-‐N sampling protocol was based on a Minimax analysis by selecting the minimum number of samples within the field and locations within the bed (i.e. distances from the drip tape) that best estimated soil NO3
-‐-‐N based on the criterion of the minimum relative error from the field average. Briefly, NO3
-‐-‐N concentration from the two soil depths for each distance from the drip was pooled in all fields. Subsequently, the averages of all the possible combinations of sample locations within the bed or within the field were compared to the overall value obtained for the whole field and the relative errors obtained according to the following formula:
Relative error= (|X//D -‐ X"F|/X$F) Where X'D is the average NO3-‐N concentration for the given combination of 1, 2, 3, 4, or 5 sampling distances within the bed and X&F is the average NO3
-‐-‐N concentration of the field. Average relative error for each combination of sampling distances was calculated for all the fields. Subsequently, the combination of samples with the lower relative error cross all fields (<5% from the field mean) and the lower amount of samples taken was selected as the best sampling procedure to estimate average NO3-‐N. Calculations were made separately for fields with 80’’ and 60’’ beds with SAS v 9.1 statistical software.
4
Collection of pre-‐plant soil samples at 5-‐inch intervals from the center of the bed towards the center of the furrow Results Pre-‐plant NO3
-‐ levels Pre-‐plant NO3
-‐-‐N in the 0-‐20 inches depth interval ranged from 45 – 438 lbs N ac-‐1 among all the fields. The average NO3
-‐-‐N content in this layer was significantly higher in the San Joaquin and Fresno (232 ± 31 and 216 ± 54 lb ac-‐1 respectively) than in the Yolo growing area (70 ± 8 lb ac-‐1) (p<0.001). The growers reported seasonal fertilizer N inputs ranging from 115 -‐ 320 lb ac-‐1, bringing the total of pre-‐plant NO3
-‐ and fertilizer N inputs to range from 209 -‐758 lb N ac-‐1 (Table 4). Concentration of NO3
-‐ at the two sampling depth intervals (0-‐10 and 10-‐20 inches) were generally similar. Significant differences were only observed in the Fresno area (p<0.01, Figure 4). Significant differences between sampling distances from the center of the bed in NO3
-‐ concentration were observed for the majority of the 16 fields. When averaged across growing regions, fields from the Fresno area showed a higher NO3-‐N concentration at 15 and 20 inches from the drip tape than samples taken at 5 inches from the center of the bed, particularly in the upper layer of soil (0-‐10 in.), whereas in the Yolo area concentrations decreased with increasing distance from the drip tape (Figure 4). However, no general pattern in NO3
-‐ distribution around the drip tape was observed among the 16 fields. Pre-‐plant NO3
-‐-‐N concentrations were positively correlated with the number of consecutive years that the fields were cropped with processing tomatoes (p<0.01; Figure 3). Fields in the Yolo growing region had between 0 and 2 years of tomato back-‐to-‐back prior to this study whereas in the San Joaquin and Fresno areas the number of years of tomato back-‐to-‐back was between 2 and 5 (Table 2).
5
Post-‐harvest NO3-‐ levels in the top 10 inches of soil ranged from 43 -‐ 392 lbs NO3
-‐-‐N per acre with an average of 141 lbs NO3
-‐-‐N per acre (Table 4). Sampling protocol to estimate pre-‐plant NO3
-‐, available K+, and PO4+
In fields with 80’’ beds, taking two cores to a depth of 20” at a distance of 15” and 30’’ or 20’’ and 25’’ from the center line, produced an error of <5% relative to the field average NO3
-‐ concentration (Table 5). In fields with 60’’ beds, taking three cores 5”,10” and 20” or 5”, 20” and 25” from the center line resulted in an error of <5% relative to the field average (Table 6). The soil samples taken at each location within a given field could be pooled to reduce the number of analyses. In this study, samples were taken at 5 different locations within each field. The Minimax analysis showed that four different locations within a given field in 80’’ beds, or three locations within a field with 60’’ beds would have been sufficient to produce an error of <5% relative to the field average if the cores at each location were taken at the above distances perpendicular to the center line of the beds. This sampling protocol would also guarantee the collection of representative samples for PO4
-‐ and K+, as shown by the minimax analysis performed for these nutrients. In the case of PO4
-‐, in fields with 80” beds, collecting two soil samples to a depth of 20” at a distance of 15’’ and 30’’ or 20’’ and 25’’ from the center line would result in a sampling error of ≤12% relative to the field average. In fields with 60” beds, collecting three soil samples to a depth of 20” at a distance of 5’’+10’’+20’’ or 5’’+20’’+25’’ from the center of the beds would yield a sampling error of ≤10% relative to the field average (results not shown). In the case of exchangeable K+, sampling error would be significantly lower because of the higher homogeneity of this nutrient across the beds. In fields with 80-‐inch beds we observed a sampling error of 3%, and in 60-‐inch beds of ≤2% relative to the field average (results not shown). Yields and N use efficiency Crop marketable yield reported by the growers in the different fields ranged from 39.9 to 63.1 U.S. tons per acres (Table 4), being higher in the Fresno (57.8 ± 2.9) than in Yolo and San Joaquin growing regions (49.9 ± 2.6 and 51.9 ± 4 U.S. tons per acre, respectively). Similar crop yields have been reported by Hartz and Bottoms (2009) for the Yolo area. However, yields were generally higher than the averages (including yields of furrow-‐irrigated fields) of the five preceding years in each region, i.e: 48.3, 39.4 and 40.2 U.S. tons per acre for Fresno, Yolo and San Joaquin, respectively, as reported by the California Agricultural Statistics Review (CDFA, 2012, 2011, 2010, 2009).
Crop N removal based on marketable yields and average fruit N concentrations in each field ranged from 93 to 174 lb N ac-‐1 (Table 4). The apparent N use efficiency, calculated as the ratio of crop N removal to available N (pre-‐plant soil nitrate and fertilizer inputs) (NUEFruit), was between 0.15 and 0.64 with an average of 0.43 (Table 4), while the ratio of crop N removal to fertilizer inputs was on average 0.74, ranging from 0.44 to 1.03.
6
According to the hand harvest data, average tomato plant N uptake was 274 lbs N per acre, with a range of 149 to 401 lbs N per acre among all the fields (Table 4). Tomato plants allocated on average 166 lbs N per acre (range 82 and 251 lbs N per acre) to the fruits, representing on average 61% (range 50 to 70%) of total plant N. Hartz and Bottoms (2009) reported mean whole plant N uptake of 268 lbs N per acre with 68 to 71% of the N allocated to the fruits among 6 commercial fields. Across the 16 fields of this study, NUEC (ratio of total plant N to total available N) was highly variable, ranging from 1.25 to 0.52 (Table 4), and being higher for the Yolo growing region (0.92 ± 0.11) than for San Joaquin and Fresno (0.78 ± 0.10 and 0.80 ± 0.08 respectively).
NUEC values close to or above 1 show that other sources than fertilizer and pre-‐plant N contributed to plant N uptake. In-‐season soil mineralized N and NO3
-‐ in irrigation water can be substantial sources of N for the tomato plants in addition to fertilizer or pre-‐plant N.
Weighing of the vine (a) and fruit (b) biomass collected at each location within a field.
Krusekopf et al. (2002) estimated soil N mineralization during the growing season based
on soil incubation results at 53 lbs N per acre. Vine biomass, which is returned to the soil after harvest and contributes to the soil mineralizable N pool, was on average 109 lbs N per acre (Table 4). In addition to the N that becomes available during crop growth, irrigation water can also be a substantial source of N. No data was collected in this study regarding the NO3
-‐-‐N content of irrigation water at the different fields. One grower reported that an average of 21 lbs N per acre were supplied to his crop only with irrigation water. If these two N inputs (i.e. soil N mineralization and N in irrigation water) are taken into account, then the actual crop NUE becomes lower than reported here. Pre-‐plant PO4
-‐-‐P levels and distribution within the beds Out of the 16 fields of the study, 12 showed significantly higher PO4
-‐ concentration in the upper (0-‐10 in.) than the deeper layer of soil (10-‐20 in.). Concentrations were, however, quite homogeneous across the beds with only two of the fields showing significant differences between sampling distances. When averaged across growing areas, the highest concentrations
(a) (b)
7
were found in Fresno (13.7± 3.1 ppm), with slightly lower available P concentrations for San Joaquin and Yolo areas (10.6 ± 2.9 and 12 ± 1.7 ppm respectively); nevertheless no significant differences between growing regions were detected (p=0.77). For the three growing regions, Olsen-‐P was significantly higher in the top layer of the soil (0-‐10 in.) than in the deeper one (10-‐20 in.) (Figure 1). Significant differences between sampling distances from the center of the bed in PO4
-‐ concentration were observed only in the Yolo growing region, with higher concentrations closer to the center of the bed (Figure 1). Previous studies evaluating P requirements of processing tomato in California have defined 12-‐20 ppm Olsen-‐P as the threshold for crop yield responses (Hartz et al., 2007). Generally, fields with <15 ppm of available P would respond to a P application, whereas fields with more than 25 ppm are unlikely to require P application according to Hartz (2008). The majority of the fields of this study showed average P concentrations lower than 15 ppm in both layers (Table 3), whereas only one of them had a P concentration higher than 25 ppm. Pre-‐plant K+ levels and distribution within the beds Average field exchangeable K+ concentrations were generally well above 130-‐150 ppm, which has been defined as the threshold for yield responses in processing tomato crops in California (Hartz, 2002; Miyao, 2002). Concentrations of exchangeable K were significantly higher in the Fresno growing area (p<0.01; 472 ± 7.9 ppm K+) followed by San Joaquin (365.5 ± 7.7 ppm K+) and Yolo, which showed the lowest average concentrations of all three growing areas (276.1 ± 8.4 ppm K+). Soil exchangeable K+ content was similar at the different distances and depths sampled within the bed in the Yolo growing region (Figure 2), whereas K+ concentrations were higher in the upper layer of soil in the Fresno and San Joaquin areas (p<0.01 and p<0.05 respectively). In the fields from the San Joaquin area, K+ concentration tended to decrease from the center of the bed towards the furrow. Conclusions This study is a snapshot of current management practices and soil nutrient levels in California’s SDI processing tomato fields. Regular pre-‐plant soil sampling according to the protocol developed in this study would enable growers to adjust fertilizer rates, reduce the occurrence of excessive NO3
-‐ levels, and detect suboptimal nutrient levels in their fields. The excessive levels of NO3
-‐ in some of the tomato production fields increase the risks of nitrate leaching during the growing and rainy seasons. Such situations could be avoided by improved monitoring of NO3
-‐ availability. More back-‐to-‐back tomato production in the Fresno than in Yolo County area may, in part, explain the differences in pre-‐plant NO3
-‐ levels observed among processing tomato growing areas. Another likely reason for the higher pre-‐plant NO3
-‐ levels in the Fresno County may be the lower rainfall amount in this area. Lower precipitation and higher evaporation rates in Fresno may lead to less NO3
-‐ leaching and higher buildup of NO3-‐ closer to the soil surface, whereas in
8
Yolo County, which receives more rainfall, some of the residual NO3-‐ may have been leached
below the 0-‐20 inch layer during the rainy season. At pre-‐plant, no depletion of NO3-‐N, PO4-‐P and exchangeable K+ concentration was observed in the rooting zone around the drip tape for any of the macro nutrients. Our results show that current fertilization practices did not lead to phosphorus and potassium depletion in the root-‐feeding zone. The lack of potassium depletion in the root zone may be because potassium is being supplied as fertigation with the advantage of less potential of fixation before plants take it up due to continuously adequate soil moisture near the drip zone. Accurate estimation of the available soil nutrients at pre-‐plant and the subsequent adjustment of the fertilizer inputs could substantially contribute to tightening N cycling in processing tomato fields and reduce the risks of NO3
-‐ leaching. References Doane TA, Horwath WR. 2003. Spectrophotometric determination of nitrate with a single
reagent. Analytical Letters 36:2713-‐2722. Dumas, J. 1848. Sur les procédés de l’analyse organique. Ann. Chim.: 195–213. Hartz, T. 2002. Potassium Requirements for Processing Tomatoes. Vegetable crop facts. Merced
and Madera County. Special edition: Processing Tomatoes in the San Joaquin Valley. University Of California Cooperative Extension, 6-‐7.
Hartz, T., Bottoms, T. 2009. Nitrogen requirements of drip-‐irrigated processing tomatoes. HortScience, 44(July), 1988–1993.
Hartz, T., 2008. Efficient fertigation management for drip-‐irrigated processing tomatoes. UCCE Vegetable Notes Fresno, Tulare and Kings Counties 4, 2-‐3.
Hartz, T., Miyao, G., Mullen, R. J., Cahn, M. D., Valencia, J., Brittan, K. L. 1999. Potassium requirements for maximum yield and fruit quality of processing tomato. Journal of the American Society of Horticultural Science, 124(2), 199–204.
Hartz, T.K., Miyao, G., Mickler, J., LeStrange, M., Stoddard, S., Nunez, J., Aegerter B., 2008. Processing tomato production in California. University of California Publication 7228.
Krusekopf, H., Mitchell, J., Hartz, T., May, D., Miyao, E., & Cahn, M. (2002). Pre-‐sidedress soil nitrate testing identifies processing tomato fields not requiring sidedress N fertilizer. HortScience, 37(3), 520–524.
Kuo S., 1996. Phosphorus. In: Sparks DL et al (Eds.) Methods of Soil Analysis. Part 3 Chemical methods. SSSA (Madison, WI).
Lecompte, F. 2012. Management of soil nitrate heterogeneity resulting from crop rows in a lettuce–tomato rotation under a greenhouse. Agronomy for Sustainable Development, 32(3), 811–819.
Lecompte, F., Bressoud, F., Pares, L., Bruyne, F. D. E., Paul, D. Saint, Agroparc, S., Blanc, M. 2008. Root and nitrate distribution as related to the critical plant N status of a fertigated tomato crop. Journal of Horticultural Science & Biotechnology, 83(2), 223–231.
9
Miyao, G. Fertilizer Guidelines for Processing Tomatoes. Vegetable crop facts. Merced and Madera County. Special edition: Processing Tomatoes in the San Joaquin Valley. University Of California Cooperative Extension, 5-‐6.
Thomas, G. W. 1982. Exchangeable cations. pp 159-‐165. In: A.L. Page et al. (ed.) Methods of soil analysis: Part 2. Chemical and microbiological properties. ASA Monograph Number 9.
Figure 1. Change in PO4
-‐ content of the soil at different distances from the center of the bed and at two depth intervals (0-‐10, 10-‐20 inches) in Yolo, San Joaquin and Fresno.
10
Figure 2. Exchangeable K+ content of the soil at different distances from the center of the bed and at two depth intervals (0-‐10, 10-‐20 inches) in Fresno, San Joaquin and Yolo.
0
5
10
15
20
25
5 10 15 20 25 30 35 40
PO4-‐ (
ppm)
Distance
0-‐10 in
10-‐20 in
Fresno
0
5
10
15
20
25
5 10 15 20 25 30 35 40
PO4-‐ (
ppm)
0-‐10 in
10-‐20 in
San Joaquin
0
5
10
15
20
25
5 10 15 20 25 30 35 40
PO4-‐ (
ppm)
0-‐10 in
10-‐20 in
Yolo
Depth: p<0.001 Distance: p=0.93 Distance *depth: p=0.18
Depth: p<0.001 Distance: p=0.32 Distance*depth: p=0.15
Depth: p<0.001 Distance: p<0.001 Distance* depth: p=0.70
11
Figure 3. Correlation between soil nitrate concentration at pre-‐plant in the 16 fields of the study and the number of consecutive years with processing tomato crops back to back.
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
5 10 15 20 25 30 35 40
K+ (m
eq 100
g-‐1 )
0-‐10 in
10-‐20 in
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
5 10 15 20 25 30 35 40
K+ (m
eq 100
g-‐1 )
0-‐10 in
10-‐20 in
0 0.2 0.4 0.6 0.8 1
1.2 1.4 1.6 1.8 2
5 10 15 20 25 30 35 40
K+ (m
eq 100
g-‐1 )
Distance (inches)
0-‐10 in
10-‐20 in
Yolo
San Joaquin
Fresno
Depth: p<0.01 Distance: p=0.41 Depth*distance: p=0.26
Depth: p<0.01 Distance: p<0.01 Depth*distance: p=0.23
Depth: p<0.01 Distance: p=0.01 Depth*distance: p=0.03
12
Figure 4. Nitrate content of the soil at different distances from the center of the bed and at two depth intervals (0-‐10, 10-‐20 inches). Average NO3
-‐ content and standard errors by region for each layer and 5-‐inch lateral distance is shown.
0
100
200
300
400
500
0 1 2 3 4 5 6
Pre-‐plan
t NO
3-‐ -‐N lb ac-‐1
ConsecuOve years with tomato back to back
R2=0.67; p<0.01
13
0
10
20
30
40
50
60
5 10 15 20 25 30 35 40
NO
3-‐ -‐N (lb ac
-‐1)
0-‐10 in
Yolo
Depth: p= 0.14 Distance: p=0.01 Depth*distance: p=0.84
0 20 40 60 80 100 120 140 160 180 200
5 10 15 20 25 30 35 40
NO
3-‐ -‐N (lb ac
-‐1)
0-‐10 in
10-‐20 in
San Joaquin
Depth: p= 0.72 Distance: p=0.04 Depth*distance: p=0.93
0
50
100
150
200
250
5 10 15 20 25 30 35 40
NO
3-‐ -‐N (lb ac
-‐1)
Distance (inches)
0-‐10 in
10-‐20 in
Fresno Depth: p= 0.006 Distance: p<0.001 Depth*distance: p=0.18
14
Table 1. Precipitation to evaporation ratio and total precipitation in California’s Central Valley (source: CIMIS)
Area Annual Precipitation/ ETo ratio (2012)
Total precipitation Nov 2012-‐ March 2013 (inches)
Yolo 1.01 11.94 San Joaquin 0.54 6.11 Fresno 0.31 3.79
15
Table 2. Main characteristics and management practices of the 16 fields sampled in this study. Fields Y1 to Y6 were located in the Yolo growing area, SJ1-‐SJ4 in San Joaquin and F1-‐F6 in Fresno growing area.
Soil
texturea Fertilizer inputs
Growing area
Field ID Soil series
% Sand
% Clay Drips/bed
Years under drip
Consecutive years with tomatoesb
Bed size (in)
Drip depth (in)
Cover/winter crop Pre-‐plant In-‐season Fall/winter
Yolo Y1 Yolo silt loam 11.3 21 1 2 1 80 na No 8-‐24-‐5-‐0.5% (Zn) UN-‐32 none
Y2 Sycamore silt loam 11.3 21 1
60 na No 8-‐24-‐5-‐0.5% (Zn) UN-‐32 none
Y3 Marvin silty clay loam 34 23 1 2 1 60 12 Triticale 8-‐24-‐5-‐0.5% (Zn) 28-‐0-‐0-‐5 (S) gypsum
Y4 Tehama loam 34 23 1 4 2 60 12 Triticale 8-‐24-‐5-‐0.5% (Zn) 28-‐0-‐0-‐5 (S) gypsum
Y5 Capay silty clay 5.5 47.5 1 9 0 60 10 Triticale 8-‐24-‐6 UN-‐32, 0-‐0-‐14, HPhos32 manure
Y6 Brentwood silty clay 5.5 47.5 1 2 0 60 10 Triticale 8-‐24-‐6 UN-‐32, 0-‐0-‐14, HPhos32 manure
San Joaquin SJ1 Stockton clay 13.7 50 2 5 0 80 12 No 10-‐34-‐0* UN-‐32, CAN17, KCl none
SJ2 Jacktone clay 22.1 50 2 5 1 80 12 No 10-‐34-‐0* CAN17, KCl none
SJ3 Capay Clay 29.5 39.2 1 4 4 60 na Triticale 8-‐24-‐6-‐3 UN-‐32 none
SJ4 Stomar clay loam 22.1 50 1 3 4 60 na Triticale 8-‐24-‐6-‐3 (Zn) 1-‐3-‐10 none
Fresno F1 Westhaven loam 33 29.5 1 4 2 60 12 Grain crop Transplant supreme 15-‐15-‐0, CAN17 none
F2 Calflax clay loam 27.5 35 1 4 4 60 12 Grain crop Transplant supreme 15-‐15-‐0, CAN17 none
F3 Fresno Fine Sandy Loam 52 21.2 1 5 5 60 12 No 4-‐10-‐10 UN-‐32 none
F4 Fresno Fine Sandy Loam 52 21.2 1 4 4 60 12 No 4-‐10-‐10 UN-‐32 none
F5 Westhaven loam 34 21 1 3 2 80 15 No 6-‐21-‐0 UN-‐32, 22-‐5.9-‐0.6 (S) none
F6 Westhaven loam 34 21 1 3 2 80 15 No 6-‐21-‐0 UN-‐32, 22-‐5.9-‐0.6 (S) none
a: Texture in the 0-‐20 in. interval; b: before the study Table 3. Pre-‐plant concentration of available PO4
-‐-‐P and exchangeable K+ of the 16 fields sampled in this study. Fields Y1 to Y6 were located in the Yolo growing area, SJ1-‐SJ4 in San Joaquin and F1-‐F6 in Fresno growing area.
16
Pre-‐plant PO4-‐-‐P (ppm) Pre-‐plant K+ (meq 100g-‐1)
Growing area Field ID Average 0-‐10'' depth 10-‐20'' depth Average 0-‐10'' depth 10-‐20'' depth Yolo Y1 7.6 11.4 3.8 0.62 0.73 0.5
Y2 13.5 17.2 9.7 0.65 0.67 0.6
Y3 18.1 21.1 15.1 0.79 0.81 0.8
Y4 7.2 9.8 4.7 0.60 0.61 0.6
Y5 14.6 12.0 17.2 1.18 1.03 1.3
Y6 11.2 11.9 10.6 0.70 0.69 0.7
San Joaquin SJ1 4.5 4.6 4.4 1.24 1.25 1.2
SJ2 8.4 8.3 8.5 0.59 0.6 0.6
SJ3 18.6 25.6 11.7 1.12 1.3 0.9
SJ4 10.9 12.5 9.4 0.87 0.89 0.8
Fresno F1 13.9 16.8 10.9 1.10 1.19 1.0
F2 9.5 11.9 7.1 1.40 1.58 1.2
F3 28.0 35.5 20.5 1.48 1.75 1.2
F4 15.3 21.6 9.1 0.66 0.86 0.5
F5 6.7 7.9 5.6 1.17 1.12 1.2
F6 8.7 10.2 7.2 1.42 1.68 1.15 Table 4. Pre-‐plant NO3-‐N contents, fertilizer and total available N, marketable yield, N outputs in harvested fruits, crop N removal, N uptake by plant parts, apparent N uptake efficiency of tomato plants (NUEC) and of the harvested fruits (NUEF), ratio of crop N removal to fertilizer N input, and residual soil N after harvest in the 16 fields of the study.
17
Pre-‐plant NO3-‐-‐N (lb ac-‐1)
Fertilizer N (lb ac-‐1)
Total available N (lb ac-‐1)
Marketable yield (tn ac-‐1)
Crop N (lb ac-‐1)
NUEC
Growing area Field ID
0-‐20'' depth
0-‐10'' depth
10-‐20'' depth
Crop N removal (lb ac-‐1) Vine Fruit
Whole plant NUEF
Crop N removal to N input ratio
Residual soil N (lb ac-‐1)
Yolo Y1 78.2 38.5 39.8 175 253 58.2 148 145 172 317 1.25 0.58 0.84 53
Y2 106.1 71.2 52.6 177 283 58.3 n.d n.d n.d n.d n.d n.d n.d. n.d
Y3 62.0 29.7 25.4 147 209 48.9 134 65 112 177 0.85 0.64 0.91 83
Y4 62.6 21.0 21.9 146 209 49.9 120 66 157 223 1.07 0.57 0.82 107
Y5 45.0 20.6 24.4 213 258 40.7 93 68 82 150 0.58 0.36 0.44 70
Y6 64.7 30.1 34.5 187 252 51.9 123 84 133 217 0.86 0.49 0.66 125
San Joaquin SJ1 199.4 87.1 109.6 115 314 84.6 111 99 180 279 0.89 0.38 1.03 152
SJ2 159.2 73.6 88.3 135 294 92.6 118 110 179 289 0.98 0.38 0.82 123
SJ3 293.0 159.6 133.5 220 513 55.7 156 168 198 366 0.71 0.30 0.71 392
SJ4 275.1 142.7 132.4 220 495 39.9 124 121 136 257 0.52 0.25 0.56 339
Fresno F1 115.7 82.6 70.1 205 321 60.8 172 107 183 290 0.90 0.54 0.84 132
F2 171.7 75.1 104.1 205 377 61.7 174 116 216 332 0.88 0.46 0.85 111
F3 437.7 244.1 193.6 320 758 45.5 110 150 251 401 0.53 0.15 0.34 43
F4 318.0 200.9 117.0 320 638 53.3 n.d n.d n.d n.d n.d n.d n.d. n.d
F5 113.4 55.1 58.3 187 300 57.9 150 139 143 282 0.94 0.50 0.80 89
F6 142.6 76.3 66.3 208 351 63.1 147 82 180 262 0.75 0.42 0.71 151
n.d: not determined
18
Table 5. Average NO3-‐N content of the whole field, and samples taken at different distances across the bed (5, 10, 15, 20, 25, 30, 35 and 40 inches) in the 80-‐inch bed fields. Relative error from the field average of the different sampling distances and best combination of sampling distances according to the ‘minimax’ analysis are shown.
Table 6. Average NO3-‐N content of the whole field, and samples taken at different distances across the bed (5, 10, 15, 20, 25 and 30 inches) in the 60-‐inch bed fields. Relative error from the field average of the different sampling distances and best combination of sampling distances according to the ‘minimax’ analysis are shown.
Average NO3-‐N content (lb ac-‐1) Field ID Field 5'' 10'' 15'' 20'' 25'' 30'' 5''+10''+20' 5''+20''+25'
Average NO3-‐N content (lb ac-‐1) Field ID Field 5'' 10'' 15'' 20'' 25'' 30'' 35'' 40'' 15''+30'' 20''+25'' Y1 78.2 106.9 92.1 93.4 72.3 78.7 72.0 52.0 58.4 82.7 75.5 SJ1 159.2 167.0 162.7 138.3 136.8 186.2 180.0 167.1 135.1 159.1 161.5 SJ2 199.4 206.5 182.0 169.4 237.9 187.8 196.6 210.6 204.0 183.0 212.9 F5 113.4 111.7 175.1 123.7 125.8 102.1 96.3 88.5 84.1 110.0 113.9 F6 148.8 187.9 198.9 150.0 152.5 151.8 132.5 123.4 65.6 141.2 152.2 Relative Error (%)
14.6 23.4 11.5 10.9 7.1 9.7 16.7 24.9 4.4 2.9
19
' ' Y6 64.7 68.0 60.5 60.1 71.1 62.4 66.1 62.2 67.2 Y5 45.0 56.3 47.2 59.0 40.6 34.1 33.1 46.4 43.7 Y4 64.2 40.6 42.6 55.5 56.0 89.1 101.6 66.6 61.9 Y3 63.6 66.2 43.4 52.3 70.8 65.3 83.5 59.7 67.5 Y2 123.8 157.7 159.2 142.8 115.7 90.9 76.4 126.1 121.4 SJ4 275.1 258.2 244.6 256.2 367.5 267.5 256.7 252.5 297.7 SJ3 293.0 277.5 361.0 298.3 349.4 247.0 225.2 294.8 291.3
F2 318.0 153.7 257.2 327.8 445.6 427.8 295.9 293.6 342.4 F4 437.7 226.2 408.2 647.3 619.0 401.6 323.7 459.7 415.6 F3 171.7 142.8 150.8 191.5 191.9 205.7 147.4 163.2 180.2 F1 115.7 161.7 103.7 137.2 110.3 88.1 93.3 111.4 120.0 Relative Error (%)
24.2 17.1 15.9 18.3 18.3 23.0 4.4 4.4
20
Budget summary Personnel: Cristina Lazcano, post-‐doctoral scholar
100%FTE for 7 months $23,042 Benefits, post-‐doctoral scholar (20.7% of FTE) $4,770 Student assistants, 975 hours @8.50 $8,288
Benefits, student assistants (1.3%) $108 Supplies for soil & plant analyses $3,824 Travel 3300 miles @ $0.555 $1,832 TOTAL $41,864
Page 1 of 8 California Tomato Research Institute
CALIFORNIA TOMATO RESEARCH INSTITUTE, INC. 18650 E. Lone Tree Road
Escalon, California 95320-9759
Project Title: INVESTIGATION OF SUSTAINING TOMATO PLANT HEALTH AND YIELD WITH COMPOSTED MANURE
Project Leader(s)
Gene Miyao, UC Cooperative Extension 70 Cottonwood Street, Woodland, CA 95695 (530) 666-8732 [email protected]
Mike Davis, CE Specialist Dept. Plant Pathology, UC Davis 1 Shields Ave, Davis, CA 95616
Ben Leacox, Field Assistant, UCCE.
Summary: Processing tomato yield responses to supplemental applications of 10 tons/acre of composted poultry manure were from 9 to 22%. Yield responses also occurred with supplemental applications of manufactured NPK at similar rates and placement to mimic nutrients in the composted manure treatment. The results indicate yield increases may occur in soils with potassium levels below 200 ppm using an ammonium acetate extraction method and with potassium levels not exceeding 2% on the cation exchange capacity
Objectives: Evaluate the influence of composted manure as a biological and as a supplemental applied nutrient on yield of canning tomato.
Background: Tests using composted poultry manure have provided yield increases as much as 40%, but have also resulted in no yield responses. The continued testing is an attempt to better predict responsive fields.
Methods: Five sites were established in commercial fields to continue to evaluate the benefit of composted poultry manure on tomato yield and vine health. Included was a NPK nutrient ‘equivalent’ to the composted manure to check if the yield response could be achieved with traditional manufactured macronutrient additions as a substitute. Other manipulations included time of application (spring vs fall), placement (trenched into the seed line vs surface incorporated), source (cow vs poultry), and application rate.
Soil potassium level became a focus as a predictor of yield responsiveness to composted manure. Soil K levels ranged widely across the 2014 sites. Two previous, yield-responsive research sites to compost were reselected to check if results would be repeated. Soil samples were collected from each field and submitted to a commercial lab for basic core soil quality determinations with particular interest in ppm K and %K from the cation exchange.
Treatments varied from site to site, but always included composted poultry manure at a minimum of 10 tons per acre mounded on the middle of the bed top of preformed beds.
Page 2 of 8 Composted manure
California Tomato Research Institute
A mimic of the NPK at a 10-ton/acre rate consisted of dry fertilizer sources, which included potassium muriate (62% K20), mono-ammonium phosphate (11-52-0) and ammonium sulfate (21% N). Availability of the NPK from the composted poultry manure was estimated to be 20% of the total N and 80% of the total K and P. The compost was initially weighed in a 5-gallon pail and then
Whole leaf plant tissue samples were gathered at early bloom, full bloom and at 1st ripe growth stages to check NPK levels.
Yields were measured using grower’s machinery or custom mechanical harvesters to capture marketable fruit yields weighed in a portable trailer scale. A 5-gallon volumetric sample of nonsorted fruit from the harvester was collected and sorted for culls on a percent by weight basis. A subsample of nondefect fruit was submitted to a local PTAB inspection station to analyze for color, pH and °Brix.
Results: Yield increases of 9 to 22% were achieved in 3 of the 5 tests in 2014. These results were combined with 10 other tests conducted beginning in 2011. When statistically significant yield increases were graphed against soil K levels in parts per million (ppm), responses to composted manure were mostly from fields with soil K levels below 200 ppm (Table 1). Yield responses also occurred mostly when K levels were less than 2% on the cation exchange capacity (Table 2). Evaluating soil K level using ppm and %K in combination was helpful.
Comparing composted poultry manure versus cow manure did not provide a clear separation (Tables 6 and 7). Performance was not improved with depth of placement by comparing a trenched application to a surface application centered on the bed top (Tables 6 and 7).
Potassium performed well in several sites. In a low K field (at 136 ppm), yields increased linearly from 55.1 to 62.3 tons per acre with potassium muriate from 50 to 800 lbs. of K2O/acre (Table 4).
When comparing composted poultry manure amongst trials with manufactured NPK at rates that mimic those of compost, the composted manure was significantly better performing. Composts were apparently providing a benefit beyond NPK.
Note: In one of the sites, when the NPK was preplant, springtime applied to the surface and shallowly incorporated, the treatment severely stunted transplants causing injury including some stand loss. While plants were replaced when killed at the early seedling stage, yields suffered.
While the composted manure has been a top performer, the addition of K alone has been generally beneficial as well (Table 3 & 4).
----------------------------
We appreciate support of the CTRI and of our cooperating growers: J.H. Meek and Sons, Muller Ranch, Timothy & Viguie Farms, and Payne Brothers Ranches.
Page 3 of 8 Composted manure
California Tomato Research Institute
Table 1. Influence of soil K level (in ppm) on processing tomato yield response to composted poultry manure, Yolo-Solano, 2011-2014.
!5#0#5#10#15#20#25#30#35#40#45#
0# 50# 100# 150# 200# 250# 300#Potassium)level)(ppm)K))
%)YIELD)RESPONSE))
significant#increase#
non!significant#increase#
Table 2. Influence of % K of the cation exchange capacity on processing tomato yield response to composted poultry manure, Yolo-Solano, 2011-2014.
!5#0#5#10#15#20#25#30#35#40#45#
1# 1.5# 2# 2.5# 3#Potassium)Level)(%K)on)the)ca4on)exchange))
%)YIELD)RESPONSE))))
significant#increase#
non!significant#increase#
Page 4 of 8 Composted manure
California Tomato Research Institute
Table 3. Effect of composted manure, K, NPK and biological on processing tomato fruit yield and °Brix, Yolo-Zamora area, 2014.
165 ppm K with 1.8% on the cation exchange
yield full flowertons/A Brix % N % P % K
1 Non treated 63.7 4.3 4.8 0.36 2.32 LH Organics Soil Sytem 1 62.2 4.3 4.7 0.34 2.33 Manure 10 tons 69.5 4.5 4.7 0.36 2.54 manure 5 tons 68.6 4.4 4.8 0.35 2.35 nutrients (compost mimic) 63.4 4.5 4.9 0.42 2.66 Potassium sidedress 200 lbs 66.6 4.4 4.7 0.35 2.77 Potassium fertigate 200 lbs 67.9 4.7 4.8 0.35 2.68 CA Soils H2H 15 fb 10 gpa 63.6 4.1 4.7 0.35 2.39 Agrinos HYT A 65.5 4.3 4.9 0.35 2.4
LSD @ 5% 3.3 0.27% CV 3 4
yield response to manure rates & K applicationsno responses to biologicals or digested food additiveno response to NPK mimic (surface application caused seeding stunting. Adjust to sidedress placement
Page 5 of 8 Composted manure
California Tomato Research Institute
Table 4. Effect of composted manure, K and NPK on processing
tomato fruit yield and °Brix, Woodland area, 2014.
136 ppm K with 1.9% on the cation exchangeYield full flower
treatment tons/A brix % N % P % K1 control 51.3 e 4.35 4.6 0.38 1.72 compost 5 tons 56.8 cd 4.40 4.9 0.43 2.33 compost 10 tons 62.6 a 4.63 4.8 0.40 2.04 NPK compost mimic 61.7 ab 4.53 4.7 0.49 2.35 K20 800 lbs 62.3 a 4.57 4.7 0.36 2.26 K20 400 lbs 59.4 abc 4.55 4.7 0.36 2.27 K20 200 lbs 57.9 bcd 4.53 4.5 0.37 2.08 K20 100 lbs 56.5 cd 4.53 4.5 0.36 1.99 K20 50 lbs sidedress 55.1 de 4.35 4.6 0.39 1.9
LSD @ 5% 3.9 0.2% CV 5 3non-additivity
Class Comparisons:compost rate: linear probability 0.01 0.01 quadratic NS NS
Potassium rate: linear probability 0.00 0.02 quadratic 0.05 0.11
Results: Positive yield response to compost (rates) Positive yield response to sidedressed potassium Positive yield response to NPK compost mimicPotassium is the common theme to the response Brix improved with applications of either compost or with K
Page 6 of 8 Composted manure
California Tomato Research Institute
Table 5. Effect of composted manure, K and NPK on processing tomato fruit yield and °Brix, Knights Landing area, 2014.
180 ppm K w/ 2.2% on cation exchange
Yield full flowerTreatment tons/A Brix % N % P % K
1 control 62.6 cd 4.63 4.9 0.34 2.72 compost 5 tons 67.6 ab 4.50 4.9 0.34 2.73 compost 10 tons 68.1 ab 4.58 4.9 0.35 3.04 compost 20 tons 70.8 a 4.63 5.2 0.36 2.85 mimic NPK 10 tons 64.0 bcd 4.58 4.9 0.36 2.86 K20 400 lbs surface (KCl) 66.3 bc 4.48 4.9 0.34 2.77 K20 400 lbs sidedress 63.9 bcd 4.50 5.1 0.33 2.78 K20 200 lbs fertigate 64.2 bcd 4.55 4.9 0.33 2.79 Sure K fertigate 62.1 d 4.53 4.9 0.35 2.8
LSD @ 5% 4.1 NS NS NS NS% CV 4 2 5 5 8^ non-additivity problem
Class ComparisonsControl vs. 62.6 4.6 potassium only 64.1 4.5Probability NS 0.04
compost rate: linear probability 0.001 NS quadratic NS 0.12
Results: Yield response (linear) to composted poultry manure. Application placement of K not statistically different
Page 7 of 8 Composted manure
California Tomato Research Institute
Table 6. Effect of composted manure, K and NPK on processing tomato fruit yield and °Brix, Dixon area, 2014.
214 ppm K w/ 1.8% on cation exchange
tons/A Yield full flowerTREATMENT DEPTH RATE TIME tons/A Brix % N % P % K
1 0 0 0 fall 33.7 4.38 5.0 0.35 2.62 poultry shallow 5 fall 35.3 4.55 4.9 0.35 2.73 poultry shallow 10 fall 34.7 4.40 5.0 0.36 2.74 poultry shallow 15 fall 36.0 4.58 5.0 0.36 2.75 poultry trench 10 fall 37.7 4.78 5.2 0.41 3.16 cow shallow 10 fall 37.3 4.45 5.0 0.35 2.57 cow trench 10 fall 37.7 4.65 5.1 0.39 2.98 KCl sidedress 400 lb spring 36.6 4.45 5.0 0.35 2.89 NPK surface mimic spring 35.2 4.53 4.9 0.34 2.6
LSD @ 5% NS NS NS 0.03 0.28% CV 7 5 3 5 7significant non-additivity
Class ComparisonsControl vs. 33.7 4.38 5.0 0.35 2.56
composted manure 36.5 4.57 5.0 0.37 2.75Probability 0.04 0.10 NS 0.056 0.09
Materials: poultry vs. 36.2 4.59 5.08 0.383 2.89cow manure 37.5 4.55 5.08 0.369 2.69
Probability NS NS NS 0.168 0.05
Placement: shallow vs. 36.0 4.43 5.0 0.356 2.59trenched 37.7 4.71 5.15 0.40 2.98
Probability NS 0.07 NS 0.00 0.00
compost rate: linear probability NS NS NS NS NS quadratic NS NS NS NS NS
Results:Yield response to composted manureBrix was slight increased with compost (90% confidence level)
Page 8 of 8 Composted manure
California Tomato Research Institute
Table 7. Effect of composted manure, K and NPK on processing tomato fruit yield and °Brix, north Davis area, 2014.
255 ppm K w/ 1.9% on cation exchange
manure(tons/A) Yield full flower
SOURCE DEPTH RATE TIME tons/A Brix % N % P % K1 0 0 0 fall 43.9 4.7 4.6 0.29 2.12 poultry surface 5 fall 43.4 4.6 4.8 0.29 2.13 poultry surface 10 fall 46.2 4.6 4.8 0.30 2.14 poultry surface 15 fall 47.9 4.4 5.0 0.32 2.35 poultry surface 20 fall 48.6 4.6 5.0 0.30 2.26 cow surface 10 fall 44.6 4.6 4.7 0.30 2.17 poultry trench 10 fall 48.9 4.5 5.0 0.31 2.28 400 K20 sidedress 0 spring 45.6 4.5 4.7 0.29 2.19 nutrients NPK mimic spring 46.2 4.6 4.7 0.29 2.0
LSD @ 5% NS NS 0.29 NS 0.17% CV 6 4 4 6 5^ significant non-additivity ^
Class ComparisonsControl vs. 43.9 4.70 4.6 0.29 2.1
composted manure 46.6 4.55 4.9 0.30 2.2Probability 0.10 0.15 0.02 NS NS
compost rate: linear probability 0.01 NS 0.00 0.11 0.01 quadratic NS NS NS NS NS
Results:Linear yield response to composted manure ratesLinear reduction in vine necrosis with manure rates
ANNUAL PROGRESS REPORT
2014
Roger Chetelat, Director/Curator Dept. of Plant Sciences
University of California Davis, CA 95616 [email protected]
http://tgrc.ucdavis.edu
C. M. Rick
T G R C
Tomato Genetics Resource Center
Figure 1. Vines of S. juglandifolium SAL8112 growing near Multitud, Chimborazo province, Ecuador. A recent trip to Ecuador provided an opportunity to observe five of the wild tomato relatives native to this country -- S. pimpinellifolium, S. neorickii, S. habrochaites, S. ochanthum, and S. juglandifolium -- growing in their natural habitats. The photo above shows a plant of S. juglandifolium growing under very wet, cool conditions in dense forest vegetation along a roadside. This rare species is represented by just five active accessions at the TGRC, highlighting the need for additional collections.
2
SUMMARY Acquisitions. The TGRC acquired three new accessions of the wild species S. habrochaites from previously unrepresented geographic regions of Ecuador. We also regenerated more wild species accessions that we formerly considered ‘inactive’ and had never been grown. Obsolete or redundant accessions were dropped. The current total of number of active accessions is 3,829. Maintenance and Evaluation. A total of 2,420 cultures were grown for various purposes, of which 653 were for seed increase (153 of which were of wild species) and 1,008 for germination tests. Progeny tests were performed on 165 stocks of segregating mutants (e.g. male steriles, homozygous lethals, etc) or various lines with unexpected phenotypes. Tests for the presence of transgenes (GMOs) were performed on 149 stocks, all of which were negative. 97 stocks were grown for introgression of the S. sitiens genome. Other stocks were grown for research on interspecific reproductive barriers. Newly regenerated seed lots were split, with one sample stored at ca. 5° C to use for filling seed requests, the other stored in sealed pouches at -18° C to preserve long-term seed viability. As allowed by harvests, backup samples were submitted to the USDA Natl. Center for Genetic Resources Preservation in Colorado, and to the Svalbard Global Seed Vault in Norway. Distribution and Utilization. A total of 5,497 seed samples representing 2,046 unique accessions were distributed in response to 319 requests from 231 researchers and breeders in 33 countries; over 26 purely informational requests were answered. The overall utilization rate (i.e. number of samples distributed relative to the number of active accessions) was 144%, showing that demand for our stocks remains exceptionally high. Information provided by recipients indicates our stocks continue to be used to support a wide variety of research and breeding projects. Our annual literature search uncovered 97 publications mentioning use of our stocks. Documentation. Our website (http://tgrc.ucdavis.edu) was updated to add features and address security issues. Our database was updated with more accurate collection site information for wild species such as S. habrochaites and the two Galapagos species. Our database was modified to improve internal record keeping related to seed requests, plant pedigrees, and seed lots. A dynamically generated ‘reference’ field that pulls key data from different fields, depending on the type of stock, was added. This field provides a short hand summary of the essential features of each accession that can be used on pedigree sheets and seed request packing slips. A revised list of monogenic stocks was published in the Tomato Genetics Cooperative Report (http://tgc.ifas.ufl.edu). Research. The TGRC continued research on the mechanisms of interspecific reproductive barriers and on introgression of the S. sitiens genome. We submitted a paper on the cloning of a pollen factor, ui1.1, one of two major genes that explain why pollen of cultivated tomato is rejected on flowers of most of the wild species. We studied natural variation for pollen compatibility genes in self-compatible biotypes and species, and their role in transitions from outcrossing to inbreeding modes of reproduction. We further advanced a set of breeding lines representing the genome of S. sitiens, a species known for its tolerance to drought and salinity, but which has not been utilized in the past due to strong crossing barriers. The goal of this research is to develop a set of ‘introgression lines’ – prebred stocks containing defined chromosome segments from the donor genome – that will provide a breeder friendly germplasm resource for this wild species. In the first year of this project we focused on filling in gaps, i.e. identifying plants with introgressed segments in ‘missing’ regions of the genome, so that the resource will be as complete as possible.
3
ACQUISITIONS The TGRC acquired three new accessions of S. habrochaites from previously unrepresented regions of Ecuador. The new accessions were generously provided by Maria Jose Diez at the COMAV genebank in Valencia, Spain. Two were collected from the region around Girón, southwest of the city of Cuenca, and are our first accessions of S. habrochaites from this region. The third was collected near San Placido, NW of Manta, in the Province of Manabi, and represents the northernmost site from which S. habrochaites has been reported. Like populations
from nearyby Jipijapa (S of Manta), the San Placido accession has very small, pale flowers, suggesting a predominantly self-pollinated mode of reproduction. Our tests in the greenhouse so far indicate that all three accessions are self-compatible. During a recent trip to Ecuador in April/May 2014 we attempted to recollect these and other S. habrochaites populations, as well as four other wild tomato relatives from known collection sites. We found S. habrochaites still growing near Girón (Fig. 2) and San Placido, in the same general areas where the Spanish group had collected them over 20 years ago. In addition, we regenerated three previously inactive wild species accessions by planting very old seed samples kept in storage for up to 30 years. The rescued accessions include one S. peruvianum (LA1759) and one S. corneliomulleri (LA1756). Both had been collected by Jon Fobes and Eduardo Vallejos in 1976 from the Rio Chillon and Rio Rimac drainages near Lima, Peru. We visited this area in 1996 and 2009, and noted that many of the previously collected wild tomato populations, including those around Lima, have been eliminated or severely impacted by urban
expansion and agricultural intensification. So even though we have other accessions of these species from the Chillon and Rimac drainages, we felt it worthwhile to preserve these new ones as well.
We also rescued an accession of S. pennellii, LA2719, our only population from the Chilca quebrada south of Lima. This accession presented a special challenge because we obtained only one viable plant from the few seeds that were available. This lone plant was self-incompatible (SI), like most accessions of S. pennellii, thus self pollinations produced no seed, as expected. To get around this obstacle we crossed LA2719 with LA0716, a rare self-compatible (SC) strain of S. pennellii, and stored pollen samples of LA2719 in the -80 freezer over desiccant for future crosses (in case the lone plant died, which it did). The F1 and subsequent backcross generations were pollinated with the stored pollen samples. Each backcross generation segregates for SI vs SC, and only the SC plants will set fruit using the stored pollen. Thus we are effectively transferring the self-compatibility trait from LA0716 into the genetic background of
Figure 2. Flowers and plants of S. habrochaites SAL8104 at El Pongo, below Giron, Azuay, Ecuador. We acquired three new accessions of this species from the COMAV genebank in Valencia, Spain.
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LA2719. After five or six backcrosses – we are currently at BC2/BC3 – we should end up with an SC version of LA2719 that can be maintained indefinitely by selfing (or by mass sib pollinations). Such effort to rescue a single plant of one accession is unusual, however LA2719 is the only known S. pennellii from Chilca quebrada. Furthermore, having another SC accession of S. pennellii is potentially useful; we currently have only two SC accessions, both from the same geographic area (southern Peru), one of which, LA0716, is requested more than any other single accession in our collection.
More detailed information on the new accessions can be found on our website at http://tgrc.ucdavis.edu/acq.aspx. Obsolete or redundant accessions were dropped. The current total of number of accessions maintained by the TGRC is 3,829.
Table 1. Number of accessions of each species maintained by the TGRC. The totals include some accessions that are currently unavailable for distribution.
Solanum name Lycopersicon equivalent No. of Accessions S. lycopersicum L. esculentum, including var. cerasiforme 2,671 S. pimpinellifolium L. pimpinellifolium 312 S. cheesmaniae L. cheesmanii 41 S. galapagense L. cheesmanii f. minor 29 S. chmielewskii L. chmielewskii 29 S. neorickii L. parviflorum 52 S. arcanum L. peruvianum, including f. humifusum 46 S. peruvianum L. peruvianum 75 S. huaylasense L. peruvianum 19 S. corneliomulleri L. peruvianum, including f. glandulosum 57 S. chilense L. chilense 118 S. habrochaites L. hirsutum, including f. glabratum 127 S. pennellii L. pennellii, including var. puberulum 51 S. lycopersicoides n/a 24 S. sitiens n/a 13 S. juglandifolium n/a 5 S. ochranthum n/a 9 Interspecific hybrids, RILs n/a 151 Total 3,829
MAINTENANCE Led by Scott Peacock and his crew of undergraduate student assistants, the TGRC managed an unusually large number of field and greenhouse plantings this year. A total of 2,420 families were grown for various purposes; 653 of these were for seed increase, including 153 of wild species accessions, most of which required greenhouse culture. The rest were grown for germination tests, evaluation, introgression of the S. sitiens genome, research on reproductive barriers, or other purposes. Identifying accessions in need of regeneration begins with seed germination testing. Seed lots with a germination rate that fails to meet our threshold of 80% are normally regenerated in the same year. Other factors, such as available space, age of seed and supply on hand, are also taken into account. Newly acquired accessions are typically regenerated in the first year or so after acquisition because seed supplies are limited and of uncertain viability. This year, 1,008 seed lots, representing ca. 25% of our collection, were tested for germination rates. Average germination values continued to be relatively high for most species (Table 2), however
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we again saw relatively low germination in seed samples of cultivated tomato. This year we modified our germination testing procedures based on recommendations from our local Parsons Seed Certification Center. The changes, which involve using Ziploc bags and multiple layers of germination paper, assure a more even moisture level while allowing gas exchange. As a result, overall germination levels were notably improved over last year’s results, particularly for stocks of S. lycopersicum, which averaged 72% this year vs. 44% in 2013. Another change we made was to lower the relative humidity in our seed vault from 30% to 20%. This should reduce seed moisture contents and extend seed longevity.
Table 2. Results of seed germination tests. Values are based on samples of 50-100 seeds per accession, and represent the % germination after 14 days at 25°C. Seed lots with a low germination rate are defined as those with less than 80% germination.
Solanum Species Date of
Tested Lots Avg % Germ.
# Tested
# Low Germ # Growna
S. lycopersicum ‘cerasiforme’ 2004 80.9 38 10 13 S. lycopersicum primitive cultivars 2004 83.1 36 8 11 S. lycopersicum 2003-2004 71.5 619 314 424 S. pimpinellifolium (selfers) 2004 96.7 93 1 30 S. pimpinellifolium (outcrossers) 2004 81.6 18 5 11 S. cheesmaniae, S. galapagense 2001-2004 82.7 15 3 4 S. chmielewskii, S. neorickii 1996-2004 96.2 10 0 1 S. peruvianum clade 1981-2004 91 67 8 12 S. chilense 1997-2004 77.7 13 4 5 S. habrochaites 1982-2004 95.1 54 2 6 S. pennellii 1997-2004 95.5 4 0 1 S. ochranthum 1996-2000 66 2 2 1 a Includes other accessions grown for seed increase in 2014.
We planted a large number of lines in the field this year, for a total of 71 rows, one of our largest field plantings in recent years. The usual sequential plantings were made to spread the workload. Seedlings were transplanted into the field on four separate dates, starting in late April. Early growth and establishment were favorable, however we had a severe outbreak of tomato spotted wilt virus (TSWV), presumably brought on by large numbers of thrips in the greenhouse at the time seedlings were reared. Conditions were otherwise generally favorable for fruit set, with only a few periods of excessive temperatures, during which manual pollinations were suspended.
Most of the wild species, many mutants and certain other genetic stocks require greenhouse culture. For the mutant stocks, we start the weakest lines first, and finish with lines of normal vigor. Most of the introgression lines are reproduced in the greenhouse to assure adequate seed set (some are partially sterile in the field) and to reduce the risk of outcrossing. Our schedule of greenhouse plantings of the wild species are based on photoperiod responses: those with the least sensitivity are planted first, in the early spring; those with intermediate reaction are planted in early summer; the most sensitive (i.e. flower best under short days) are planted in mid summer for fall blooming. Optimal planting dates and other growing recommendations for each species are listed on our website.
Our greenhouse plantings were plagued by the same outbreak of TSWV that affected the field material. Failure to control thrips early in the spring led to many plantings getting infected at the seedling stage. We were forced to discard plants and empty one greenhouse completely so
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it could be disinfected. Weekly applications of insecticides helped control the thrips populations and lowered the incidence of TSWV symptoms.
We had difficulty reproducing S. juglandifolium, a species which grows as a vine at high elevations in dense vegetation under exceedingly wet conditions. Coaxing this species to flower
can take 6 months or longer, and usually only a few plants will bloom at the same time. Pollen fertility is usually so low that crosses succeed at only a low rate. After seeing this species growing in its native environment for the first time in Ecuador (Fig. 1), we experimented with growing them under misting nozzles to mimic the humid conditions. Shade cloth was used to cut light intensities. Plants were allowed to sprawl, without tying or staking, to encourage a more vine-like growth habit. These changes seem to have a somewhat beneficial effect, although the results are not dramatic (Fig. 3).
As in the past, we continue to store samples of all newly regenerated seed lots in our seed vault at 5-7°C; this is our ‘working’ collection, used for filling seed requests. In addition, we package samples of freshly harvested seed in sealed foil pouches for storage at -18°C. Samples of nearly all our wild species accessions have now been stored at -18, which should extend longevity and limit the frequency of regeneration cycles, thereby reducing workload and better preserving diversity. However, we have already filled two freezers with seed and will need to add capacity soon. A more ideal solution would be a walk-in -18 degree seed vault. As in the past, large samples of newly regenerated seed lots were sent to the USDA-NCGRP in Ft. Collins, Colorado, for long-term backup storage. This year, 173 accessions were sent to NCGRP, and 53 to the Svalbard Global Seed Vault in Norway.
EVALUATION All stocks grown for seed increase or other purposes are systematically examined and observations recorded. Older accessions are checked to ensure that they have the correct phenotypes. New accessions are evaluated in greater detail, with the descriptors depending upon type of accession (wild species, cultivar, mutant, chromosomal stocks, etc.). In the case of new wild species accessions, plantings are reviewed at different growth stages to observe foliage, habit, flower morphology, mating system, and fruit morphology. We also record the extent of variation for morphological traits, and in some cases assay genetic variation with markers. Such
observations may reveal traits that were not seen at the time of collection, either because plants were not flowering or were in such poor condition that not all traits were evident, or because certain traits were overlooked by the collector.
Many genetic stocks, including various sterilities, nutritional, and weak mutants, cannot be maintained in true-breeding condition, hence have to be transmitted from heterozygotes. Progeny tests must therefore be made to verify that individual seed lots segregate for the gene in question. We sowed 165 lines for progeny testing of male-steriles or other
Figure 3. S. juglandifolium on a mist bench.
Figure 4. The grn mutant (L, note enlarged trichomes) is maintained via heterozygotes.
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segregating mutants, as well as various other stocks with incorrect phenotypes. This year’s progeny tests included the mutants bul, dgt, div, gh, gh-2, grn (Fig. 4), l2, Lpg, marm, marm2, ms-10, ms-27, mt, pst, ro, Rs, ses, spa, syv, vadec. Other tested stocks included stocks of cv. VF-36, M-82, cv. E-6203, IL 3-4, and Rutgers.
Tests for the presence of transgenes (GMOs) were performed on 149 stocks grown for seed increase, all of which were negative.
DISTRIBUTION AND UTILIZATION The TGRC again filled a very large number of seed requests this year. A total of 5,497 seed samples – more than in any prior year except 2011 – of 2,046 unique accessions were sent in response to 319 seed requests from 231 investigators in 33 countries. In addition, over 26 purely informational requests were answered. Relative to the size of the TGRC collection, the number of seed samples distributed was equivalent to a utilization rate of over 144%, an exceptionally high rate for any genebank. Over half of our active accessions were requested at least once during the year. Considering that we now cap requests at ca. 50 accessions and no longer ship seed to some countries due to phytosanitary restrictions, demand for our stocks is even higher than these numbers suggest.
The various steps involved in filling seed requests – selecting accessions, packaging seeds, entering the information into our database, providing cultural recommendations, obtaining phytosanitary certificates and import permits, etc. – involve a large time commitment. The TGRC crew did an admirable job filling requests promptly and accurately. The online payment system we implemented to recover the costs of phytosanitary certificates and express mail shipping continues to work well, allowing us to keep up with rising costs in these areas. We began shipping through FedEx instead of USPS Priority Mail International because of lower cost and because the USPS customs website is frequently out of service. Many countries are increasing the stringency of their import regulations, and obtaining the necessary phytosanitary certificates and/or import permits is becoming increasingly difficult and time consuming. We spent quite a lot of time trying to meet the various import requirements for Israel and the Philippines. Also, some seed companies placed very large requests just before the Nagoya Protocol went into effect, hoping to avoid this new regulatory regime.
Information provided by recipients regarding intended uses of our stocks is summarized in Table 3. A few trends are apparent in the data. There was again much emphasis on breeding for resistance to various diseases and/or investigations of the molecular biology of host-pathogen interactions as in previous years. The virus diseases, including TSWV and TYLCV, generated the most requests. There continues to be increasing interest in abiotic stress responses, included drought, salinity and low temperatures. Seeds were requested for a wide variety of genetic studies including sequencing, RNAseq, and GBS, adding to the increasing richness of genomics resources being developed for tomato. Other research topics accounting for many requests included studies of interspecific reproductive barriers, flowering and inflorescence development, and responses to wounding. We again received a significant number of requests for instructional uses. As in the past, a large number of requests did not specify an intended use.
There continues to be high demand for introgression lines (ILs) -- stocks containing a defined wild species chromosome segment in the background of cultivated tomato -- as they offer many advantages for breeding and research. A total of 18 requests and 79 seed samples were processed for the S. pennellii ILs, 16 requests and 144 samples for the S. habrochaites ILs, and 14 requests and 349 samples for the S. lycopersicoides ILs. Use of the S. pennellii ILs has
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dropped off compared to previous years, whereas demand for the S. habrochaites and especially the S. lycopersicoides set have increased.
Table 3. Intended uses of TGRC stocks as reported by requestors. Values represent the total number of requests in each category. Requests addressing multiple topics may be counted more than once.
Category # Requests Biotic Stresses Viruses: BCTV 1 PVY 1 TOC 1 ToMV 3 TSWV and other tospoviruses 5 TYLCV and other begomoviruses 5 Viroids 1 Unspecified viruses 3 Bacteria: Bacterial canker 1 Bacterial speck 1 Bacterial spot 1 Bacterial wilt 3 Fungi: Botrytis cinerea 1 FORL 1 Fusarium wilt 3 Late blight 5 Phytophthora capsici 1 Powdery mildew 4 Sclerotinia 1 Verticillium 1 Unspecified fungi 1 Nematodes: Root knot nematode 4 Unspecified diseases 19 Insects: Aphids 1 Thrips 1 Whiteflies 2 Unspecified insects 7 Unspecified biotic stresses 1
Abiotic Stresses
Drought 6 High temperatures 1 Low temperatures 9 Nutrient deficiency 2 Salinity 14 Waterlogging 1 Unspecified abiotic stresses 10
Category # Requests
Fruit Traits �
Anthocyanins 2 Carotenoids 6 Development and ripening 6 Flavor, volatiles, aroma 2 Fruit size 1 Nutrition 1 Postharvest and shelf life 2 Sugars, solids 2 Unspecified fruit traits 1
Miscellaneous Breeding
Cultivar development 1 Doubled haploids 3 Earliness 1 Grafting, rootstocks 3 Heterosis, yield 2 Hybrid seed production 1 Male sterility 2 Marker development 6 Prebreeding 4 Wild species introgressions 2 Unspecified breeding uses 39
Genetic Studies
Association mapping 1 BAC libraries 2 ChIPSeq 1 Complementation tests 1 Diversity studies, natural variation 3 Epigenetics 2 Evolution and domestication 3 Functional genomics 1 Gene cloning 1 Gene expression / transcriptomics 3 Genotyping by sequencing 2 Mapping 3 Mutants 1 Population genetics 2 QTLs 1 Sequencing, RNAseq 5 sRNA 1 Transformation 5 Transient expression 1
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Category # Requests Unspecified genetic, genomic studies 3
Physiology & Development
ABA 2 Abscission 1 Acyl-sugars 5 Brassinosteroids 2 Embryos 1 Ethylene 1 Flower, inflorescence development 14 Flowering time 4 Hormone responses 2 Lateral meristems 1 Leaf shape, development, meristems 4 Metabolites, metabolomics 1
Category # Requests Mycorrhizae, rhizosphere 1 Photomorphogenesis, light responses 3 Plastids 1 Reproductive barriers, mating systems 7 Root biology, architecture, exudates 3 Seed development, physiology 1 Senescence 3 Stomata 2 Trichomes, volatiles, exudates 3 Wounding, defense signaling 8
Miscellaneous
Genebank exchange, backup storage 5 Instructional uses 14 Unspecified uses 49
Our survey of the 2014 literature and unreviewed papers of previous years uncovered 97 journal articles, reports, abstracts, theses, and/or patents that mention use of TGRC stocks (see Bibliography, below). Many additional publications were undoubtedly missed, and cases of utilization by the private sector are generally not publicized. These publications, many in high impact journals, provide examples of how TGRC germplasm is being used for research and breeding.
DOCUMENTATION Our database and website were modified in various ways by Tom Starbuck to address
security issues, improve usability and add content. On our website (http://tgrc.ucdavis.edu) users can now view the top 20 most requested accessions in any specific year. The web pages involved in submitting seed requests were modified with the latest information on phytosanitary certification and costs. Our accession query now allows searches for specific alleles of mutant genes.
Our database was modified in various ways to improve internal record keeping related to seed requests, plant pedigrees, and seed lots. Descriptive data on new accessions were added and records on existing accessions were updated as needed. We entered or revised geographic coordinates for S. habrochaites, S. cheesmaniae and S. galapagense accessions with data obtained by mapping collection sites with GoogleEarth. A dynamically generated ‘reference’ field was added that pulls key data from different fields, depending on the type of stock. This field provides a convenient short hand descriptor for each accession that can be used on pedigree sheets and seed request packing slips. We added a field to categorize seed requests according to the type of institution, i.e. foreign or domestic, commercial or public, etc. Our annual distribution records were provided to the USDA for incorporation into the GRIN database, and we issued a revised stock list, this year covering the monogenic stocks, through the Tomato Genetics Coop. Report (TGC), available at http://tgc.ifas.ufl.edu.
RESEARCH In addition to the core genebank functions described above, the TGRC conducts research synergistic with its overall mission. One research project, funded by the National Science Foundation, focuses on the genetics of interspecific reproductive barriers that restrict crosses between cultivated tomato and its wild relatives. Dr. Wentao Li isolated a second pollen factor,
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ui1.1, involved in interspecific pollen rejection. He discovered that the ui1.1 gene encodes an S-Locus F-box (SLF) protein with homology to proteins implicated in self-incompatibility in other plant systems. The SLF protein interacts in yeast three hybrids with a Cullin1 protein encoded by ui6.1, and an SSK1-like protein. A paper describing this work is currently under review for publication. Dr. Mira Markova is studying natural variation for ui1.1 and ui6.1 among the green-fruited tomato species. Mira has sequenced ui6.1 genes from populations of S. habrochaites that show a loss of function phenotype and is characterizing the mutations in each of them. In another research project, funded by a grant from the USDA-NIFA’s Plant Breeding program, the TGRC is developing a set of introgression lines representing the genome of S. sitiens. This wild tomato relative is known for its tolerance to drought, salinity, and low temperatures, but has not been utilized in the past due to strong crossing barriers. Introgression lines (ILs) are prebred stocks containing defined chromosome segments from the wild species’ genome in the genetic background of cultivated tomato. The goal is to create a complete library of ILs that captures an entire S. sitiens genome in 50-100 tomato lines. Initial DNA marker analysis found roughly 80% of the S. sitiens genome in a sample of families from early backcross generations (BC2-BC3). Dr. Diana Burkart-Waco screened additional BC1 families with CAPs and indel markers and recovered certain missing regions of the genome on chromosomes 1, 2, 3, 9, and 11. Parts of chromosome 3 and 6 have not yet been recovered. To find the indel markers, Diana searched the S. lycopersicum, S. pennellii, and S. tuberosum reference genome sequences (the S. sitiens genome sequence is not available) for indel polymorphisms across these taxa. This method was relatively successful at predicting loci that would also harbor indel polypmorphisms in S. sitiens vs S. lycopersicum.
PUBLICATIONS Chetelat, R. T. (2014) Revised list of monogenic stocks. TGC Report 64. Li W and Chetelat RT (2014) The role of a pollen-expressed Cullin1 protein in gametophytic
self-incompatibility in Solanum. Genetics 196: 439-442. Lin T et al. (2014) Genomic analyses provide insights into the history of tomato breeding.
Nature Genetics 46: 1220-1226.
SERVICE AND OUTREACH Presentations. We gave presentations on the TGRC, our research projects, and related topics to: the Tomato Breeders’ Roundtable held in Asheville, NC; an international symposium on Diversity in Solanum held in Loja, Ecuador; the Plant Breeding Academy taught by the Seed Biotechnology Center at UC-Davis; and the annual California Tomato Conference held in Napa. Press Coverage. We provided interviews or background information to: KQED Radio for a program on crop wild relatives that aired on the California Report; the College of Agricultural and Environmental Sciences for a video spot on the role of the TGRC in tomato breeding; the California Aggie, the student run campus newspaper of UC Davis, for a story on local urban legends; and the National Geographic magazine for an article on food. Visitors. Representatives of the following institutions visited the TGRC: Rijk Zwaan seeds; the USDA / ARS National Plant Germplasm System; Monsanto, the Food and Agricultural Research and Extension Service of Mauritius; the Chinese Ministry of Agriculture and Academy of Agricultural Sciences; and HM Clause seeds; King Abdullah University of Science and Technology; ConAgra Foods.
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PERSONNEL AND FACILITIES Scott Peacock continued to supervise our seed regeneration program, aided by undergraduate students Daniel Short, Christine Nguyen, Adryanna Corral, Matt Valle, Roy Chu and Angela Prada-Baez. Students Kristine Donahue and Marcus Tamura graduated. Former post-doctoral scholar Jennifer Petersen took a position at Monsanto in Woodland, and is replaced by Dr. Mira Markova. Dr. Wentao Li started a new position with NuSeed in Woodland, ending an eight year stint with our group, during which he greatly advanced our understanding of interspecific incompatibility. Post-doctoral scholar Angel Fernandez i Marti was replaced by Dr. Diana Burkart-Waco who leads our effort to synthesize S. sitiens introgression lines. Tom Starbuck continues to maintain our database and website. There were no significant changes in facilities.
FINANCIAL SUPPORT
We thank the following institutions and individuals for their financial support.
California Tomato Research Institute Dr. John Snyder
National Science Foundation Rijk Zwaan Seed Co.
UC-Davis, College of Agricultural and Environmental Sciences UC-Davis, Department of Plant Sciences
USDA – ARS, National Plant Germplasm System USDA – NIFA (National Institute of Food and Agriculture)
TESTIMONIALS “While I have yet to request material directly from the TGRC, my breeding program has
significantly benefited from the diversity maintained in the TGRC collections. Nearly all of the important traits I have to work with, such as disease resistance, novel fruit color, and enhanced nutritional qualities can ultimately be traced back to the TGRC. Without its continued existence, access to and discovery of new & important traits would be severely hampered across the entire seed industry, and the future of farming and food put at risk. I sincerely thank the TGRC for its significant contribution to humankind.” -- Emily Haga, Johnny’s Selected Seeds
Figure 5. Left to right: Roger, Wentao, Mira, and Diana.
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“For the past 25 years my research program has benefited from the ready accessibility of seed stocks from the TGRC to facilitate our work on the molecular basis of disease resistance. In particular, we have relied on the TGRC for seeds of hundreds of wild relatives of tomato which have turned out to be the source of extensive natural variation for plant immune responses. Roger and his crew are always willing to provide advice and their turnaround time is very fast considering they’re likely handling lots of requests along with their other activities associated with maintaining the stocks.” -- Greg Martin, Cornell University
“The TGRC is a vastly important link for both academics but also for those of us in the commercial tomato seed industry. I have and continue to request accessions to both support ongoing disease resistance work but also toward new research with the focus of forging new science that might help us both in the seed side but also toward the release of novel products into the market.” -- Doug Heath, Bejo Seeds
“The project team is very grateful to you and TGRC for sharing these germplasm.” -- Marilyn Belarmino, East West Seed Co.
“Thanks for everything you do for the scientific community, your center is amazing!” -- Charles Goulet, Universite Laval
“We are greatly appreciative to you and others at the TGRC for maintaining/distributing the tomato germplasm.” -- Gregg Howe, Michigan State University
“Acknowledgement for your kindness for sharing seeds! It really helped me a lot for my research.” -- Yujuan Zhong, Guangdong Academy of Agricultural Sciences
“I really appreciate the work that you guys do and I hope we can promote tomato biodiversity through our research.” -- David Johnston-Monje, Symbiota
BIBLIOGRAPHY (publications citing TGRC stocks)
Aflitos, S., E. Schijlen, H. de Jong, D. de Ridder, S. Smit et al. (2014) Exploring genetic variation in the tomato (Solanum section Lycopersicon) clade by whole-genome sequencing. Plant Journal 80: 136-148.
Almeida, P., R. Feron, G.-J. de Boer and A. H. de Boer (2014) Role of Na+, K+, Cl-, proline and sucrose concentrations in determining salinity tolerance and their correlation with the expression of multiple genes in tomato. Aob Plants 6: plu039.
Andolfo, G., F. Jupe, K. Witek, G. J. Etherington, M. R. Ercolano et al. (2014) Defining the full tomato NB-LRR resistance gene repertoire using genomic and cDNA RenSeq. BMC Plant Biology 14: 120.
Antonious, G. F., K. Kamminga and J. C. Snyder (2014) Wild tomato leaf extracts for spider mite and cowpea aphid control. Journal of Environmental Science and Health Part B Pesticides Food Contaminants and Agricultural Wastes 49: 527-531.
Ashrafi, H., et al. (2014) SNP marker discovery for tomato breeding using genotyping-by-sequencing (GBS). Proc.s Tomato Breeders' Round Table (TBRT), Mountain Horticultural Crops Research & Extension Ctr., Mills River, NC. : 12.
Baek, Y. S., P. A. Covey, J. J. Petersen, R. T. Chetelat and P. Bedinger (2014) Testing the ‘SI x SC rule’:
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pollen-pistil interactions in interspecific crosses between members of the tomato clade (Solanum section Lycopersicon, Solanaceae). American Journal of Botany: in press.
Barone, A., et al. (2014) QTL pyramiding and selection of introgression sub-lines represent efficient tools to improve nutritional quality in tomato fruit. Book of Abstracts, 11th Solanaceae Conference SOL 2014: 77.
Batista Silva, W., et al. (2014) Deficiency in auxin signaling impairs physiological parameters in tomato plants. Book of Abstracts, 11th Solanaceae Conference SOL 2014: 149.
Bauchet, G., S. Munos, C. Sauvage, J. Bonnet, L. Grivet et al. (2014) Genes involved in floral meristem in tomato exhibit drastically reduced genetic diversity and signature of selection. BMC Plant Biology 14: 279.
Beyers, T., C. Vos, R. Aerts, K. Heyens, L. Vogels et al. (2014) Resistance against Botrytis cinerea in smooth leaf pruning wounds of tomato does not depend on major disease signalling pathways. Plant Pathology (Oxford) 63: 165-173.
Bhattarai, K., F. J. Louws, J. D. Williamson and D. R. Panthee (2014) Screening tomato (Solanum lycopersicum L.) lines for bacterial spot (Xanthomonas spp.) resistance in North Carolina. Proc.s Tomato Breeders' Round Table (TBRT), Mountain Horticultural Crops Research & Extension Ctr., Mills River, NC. : 3.
Blanca, J., et al. (2014) Tomato variation, origin and domestication. Proc.s XVIIIth EUCARPIA meeting, Avignon, France: 6.
Bolger, A., F. Scossa, M. E. Bolger et al. (2014) The genome of the stress-tolerant wild tomato species Solanum pennellii. Nature Genetics 46: 1034-1036.
Bolger, A., B. Usadel and A. Fernie (2014) The genome of the stress-tolerant wild tomato species Solanum pennellii. Book of Abstracts, 11th Solanaceae Conference SOL 2014: 67.
Bordignon, S. R., A. Asogon, E. da Silva Araujo and N. M. A. Hassimotto (2014) Characterization of fruit carotenoid content in near-isogenic lines of tomato (Solanum lycopersicum L. cv Micro-Tom). Book of Abstracts, 11th Solanaceae Conference SOL 2014: 165.
Carvalho, R. F., et al. (2013) Leaf senescence in tomato mutants as affected by irradiance and phytohormones. Biologia Plantarum 57: 749-757.
Castro, A. P. d., O. Julian and M. J. Diez (2013) Genetic control and mapping of Solanum chilense LA1932, LA1960 and LA1971-derived resistance to tomato yellow leaf curl disease. Euphytica 190: 203-214.
Chen, A.-L., C.-Y. Liu, C.-H. Chen, J.-F. Wang, Y.-C. Liao et al. (2014) Reassessment of QTLs for Late Blight Resistance in the Tomato Accession L3708 Using a Restriction Site Associated DNA (RAD) Linkage Map and Highly Aggressive Isolates of Phytophthora infestans. PLoS One 9: e96417.
Costa, J. H. P. d., G. R. Rodriguez, G. R. Pratta, L. A. Picardi and R. Zorzoli (2014) Pericarp polypeptides and SRAP markers associated with fruit quality traits in an interspecific tomato backcross. Genetics and Molecular Research 13: 2539-2547.
Czosnek, H., et al. (2014) Discovery of gene networks sustaining resistance of tomato to TYLCV: lessons from transcriptome, metabolome and reverse genetic analysis. Proc.s XVIIIth EUCARPIA meeting, Avignon, France: 26.
D.X.Ferro, D., M. E. d. N. Fonseca and L. S. Boiteux (2014) Dosage effect of the genomic region harboring the Ty-1 locus on the phenotypic expression of the resistant reaction to the bipartite species Tomato severe rugose virus (Begomovirus) in tomato. Book of Abstracts, 11th Solanaceae Conference SOL 2014: 184.
D.X.Ferro, D., M. E. d. N. Fonseca and L. S. Boiteux (2014) Development of novel SCAR markers to monitor two introgression events of the locus Ty-1 (begomovirus resistance) from Solanum chilense into cultivated tomato. Book of Abstracts, 11th Solanaceae Conference SOL 2014: 185.
Easlon, H. M., D. A. St. Clair and A. J. Bloom (2014) An introgression from wild tomato (Solanum habrochaites) affects tomato photosynthesis and water relations. Crop Science 54: 779-784.
Ekanayaka, E. A. P., C. Li and A. D. Jones (2014) Sesquiterpenoid glycosides from glandular trichomes
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of the wild tomato relative Solanum habrochaites. Phytochemistry (Amsterdam) 98: 223-231. Fernandes, M. E. d. S., et al. (2014) Selection of tomato hybrids with zingiberene concentration for
breeding programs to pest resistance. J. Agric. Sci. 6: 148-154. Foolad, M. R., M. T. Sullenberger, E. W. Ohlson and B. K. Gugino (2014) Response of accessions within
tomato wild species, Solanum pimpinellifolium to late blight. Plant Breeding 133: 401-411. Francis, D., et al. (2014) Genome assisted selection for tomato fruit nutritional quality, yield components,
and disease resistance emphasizes a need for multi-trait indices. Proc.s XVIIIth EUCARPIA meeting, Avignon, France: 37.
Ghosh, B., T. C. Westbrook and A. D. Jones (2014) Comparative structural profiling of trichome specialized metabolites in tomato (Solanum lycopersicum) and S-habrochaites: acylsugar profiles revealed by UHPLC/MS and NMR. Metabolomics 10: 496-507.
Grandillo, S., et al. (2014) RNAseq transcriptome analysis of fruit pericarp in a set of Solanum habrochaites LA1777 ILs. Proc.s XVIIIth EUCARPIA meeting, Avignon, France: 35.
Grzeskowiak, L., W. Stephan and L. E. Rose (2014) Epistatic selection and coadaptation in the Prf resistance complex of wild tomato. Infection Genetics and Evolution 27: 456-471.
Haak, D. D., B. A. Ballenger and L. C. Moyle (2014) No evidence for phylogenetic constraint on natural defense evolution among wild tomatoes. Ecology 95: 1633-1641.
Haggard, J. E., E. B. Johnson and D. A. St Clair (2013) Linkage Relationships Among Multiple QTL for Horticultural Traits and Late Blight (P. infestans) Resistance on Chromosome 5 Introgressed from Wild Tomato Solanum habrochaites. G3-Genes Genomes Genetics 3: 2131-2146.
Hanson, P., R. Schafleitner, S. Huang, C. Tan, D. Ledesma et al. (2014) Characterization and mapping of a QTL derived from Solanum habrochaites associated with elevated rutin content (quercetin-3-rutinoside) in tomato. Euphytica 200: 441-454.
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Park, Y., J. Hwang, K. Kim, J. Kang, B. Kim et al. (2013) Development of the gene-based SCARs for the Ph-3 locus, which confers late blight resistance in tomato. Scientia Horticulturae 164: 9-16.
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St. Clair, D. A., E. Arms, J. Loundsbery and A. Bloom (2014) Genetic and genomic dissection of traits
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CALIFORNIA TOMATO RESEARCH INSTITUTE, INC. 2014 ANNUAL SUMMARY REPORT
Project Title: Fruit Yields with Less Water: Beneficial Genes from Wild Tomato Project Leader: Dr. Dina St. Clair, University of California-Davis, Department of Plant
Sciences, One Shields Ave., Davis, CA 95616 Tel. (530) 752-1740; [email protected]
Co-investigator: Erin Arms, Graduate Student Researcher, Ph.D. candidate in Genetics, University of California-Davis, Department of Plant Sciences
Objective:
To identify genes from chromosome 9 of wild tomato (S. habrochaites) that confer resistance to water stress and contribute to the maintenance of fruit yields under restricted irrigation.
Introduction: Wild tomato (S. habrochaites) is highly resistant to water stress. Previously, we genetically mapped this resistance to chromosome 9. We used marker-assisted selection to create a set of breeding lines containing different portions of this chromosome 9 region from S. habrochaites. In 2012 and 2013, we conducted replicated field trials at UC-Davis with 18 of these tomato breeding lines under two drip irrigation treatments: full water, equivalent to the evapotranspiration rate (ETo) for tomato; and severely restricted water, 1/3 of ETo for tomato. We measured numerous plant traits, including fruit yields, maturity and plant weight (biomass). Breeding lines containing specific portions of the S. habrochaites chromosome 9 region had the ability to resist water stress and maintain fruit yields under limited water. Identification of the beneficial genes from chromosome 9 of S. habrochaites would provide useful and very specific targets for marker-assisted breeding of water-stress tolerance in processing tomato cultivars. Summary:
We are using mRNA sequencing analysis to identify genes from S. habrochaites involved in resistance to water stress. We conducted replicated water stress experiments with two of our closely related breeding lines (Line 175 with an S. habrochaites segment from chromosome 9 and Line 163 without this segment) to induce expression of genes involved in water stress responses. Root samples from multiple plants of both lines were harvested at several time points in each experiment. RNA from the root samples were used to make mRNA-seq libraries that were sequenced on an Illumina Hi-Seq sequencer. We are analyzing the sequence read data for each sample with specialized statistical and bioinformatics programs. To date our results indicate that 53 differentially expressed transcripts (genes) map specifically to the S. habrochaites introgression contained in Line 175. The differentially expressed genes include amino acid binding proteins, transcription factors, kinases (protein regulatory), receptors and transporters. We are continuing our data analyses. Procedures:
2014 CTRI Annual Summary Report: St.Clair
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To identify genes from S. habrochaites involved in resistance to water stress, we are using a method called “mRNA sequencing analysis” (mRNA-seq analysis). Briefly, this method involves extracting RNA from plant tissue samples, making DNA from the RNA, and sequencing of the DNA products on a sequencer to detect all expressed gene products (i.e., transcripts). The resulting DNA sequence read data is analyzed using statistics and bioinformatics for gene product identification and quantification. These analyses will identify specific genes and gene pathways involved in S. habrochaites resistance to water stress. For this project, we used two of our closely-related tomato breeding lines, one with (Line 175) and one without (Line 163) a chromosome 9 region from S. habrochaites. Line 175 has tolerance to water stress while Line 163 does not. In replicated experiments we conducted earlier this year, plants of both breeding lines were exposed to water stress conditions to induce the expression of genes involved in the stress response. At several time points during exposure to water stress in our experiments, we harvested root tissue samples from multiple plants of the two breeding lines and stored the samples in our -80C freezer. RNA was extracted by grinding each root sample with a mortar and pestle under liquid nitrogen, followed by high-speed mechanical grinding in a liquid buffer solution. With a modified extraction procedure the RNA quality from the fibrous roots was sufficiently good to move forward with mRNA-seq analysis. In August we submitted our extracted root RNA samples of the two breeding lines (Lines 175 and 163) to the UC-Berkeley Sequencing Center. The technical staff prepared barcoded mRNA-seq libraries from our RNA samples. These libraries were sequenced on an Illumina Hi-Seq DNA sequencer which generated “sequence read data” for each root RNA-seq library. When the sequencing was completed in early September, the Illumina sequence read data was transferred to us. We have sequence read data from two independent experiments.
We’re in the process of using statistical and bioinformatics methods on our sequence read data for each root sample to identify genes from S. habrochaites involved in conferring resistance to water stress. Analyses include transcript assembly, mapping transcripts to chromosomes, identification of transcripts (genes), and statistics. Analyses are performed using a combination of different programs available through iPlant Discovery Environment and/or R programs. Results/Findings:
To date, our results from our bioinformatics and statistical analyses of the mRNA-seq data are as follows. We’ve detected a total of 2502 transcripts (genes) that map to chromosome 9. There are 506 transcripts that are differentially expressed between the two breeding lines (175 and 163), and 53 of these transcripts map specifically to the S. habrochaites introgression contained in line 175. The differentially expressed genes include amino acid binding proteins, transcription factors, kinases (regulatory protein), receptors and transporters. Next steps in our analyses include transcript comparisons between the two lines sampled across time points and comparisons across the two experiments. We will also use network analysis to reveal transcriptome patterns of networks and families of genes, and base-by-base sequence comparison to identify potential causative mutations associated with the tolerance phenotype. Our analyses will identify a defined set of transcripts (genes) from S. habrochaites that are candidates for further testing to determine which are involved in water stress tolerance.
CALIFORNIA TOMATO RESEARCH INSTITUTE, INC. 2014 ANNUAL SUMMARY REPORT
Project Title: Marker genotyping of chromosome 9 from water-stress resistant wild tomato
to generate breeding lines
Project Leader: Dr. Dina St. Clair, University of California-Davis, Department of Plant Sciences, One Shields Ave., Davis, CA 95616 Tel. (530) 752-1740; [email protected]
Co-investigator: Erin Arms, Graduate Student Researcher, Ph.D. candidate in Genetics, University of California-Davis, Department of Plant Sciences
Objective: To use marker genotyping to generate a new set of breeding lines for chromosome 9 from wild tomato (S. habrochaites) that is associated with resistance to water stress and other horticultural traits.
Introduction: Wild tomato (S. habrochaites) is highly resistant to water stress. We genetically mapped this resistance to the short arm of chromosome 9. Previously we used marker-assisted selection to create a set of breeding lines containing different portions of this chromosome 9 region from S. habrochaites. In 2012 and 2013, we conducted replicated field trials with 18 of these tomato breeding lines under two drip irrigation water treatments [full water, equivalent to the evapotranspiration rate (ETo) for tomato; and severely restricted water, 1/3 of ETo for tomato]. We measured various traits, including fruit yield, plant weight (biomass), delta C13 (a measure of water use efficiency), and specific leaf area (an indirect measure of leaf thickness, a trait associated with water stress tolerance). We found that all of these traits map to chromosome 9. Our results indicate that this portion of chromosome 9 contains genes controlling important traits, including traits associated with resistance to water stress. Furthermore, most of the traits mapped to one end (nearest the centromere) of the introgressed chromosome 9 region from S. habrochaites, suggesting that the genes controlling these traits are located very close to each other (e.g., closely linked). To unravel multiple traits controlled by closely linked genes, a new set of breeding lines containing chromosome segments that extend into this gene-dense region of chromosome 9 is needed. Summary:
To select new breeding lines with S. habrochaites introgressions that extend towards the centromere of chromosome 9, we created and verified a new set of DNA markers. These markers are being used to genotype an advanced backcross generation segregating for chromosome 9 introgressions from S. habrochaites. Plants that contain unique chromosome 9 segments (i.e., recombinants) are being identified and selected. These are breeding lines that will be used in the future (beyond this project) for mapping traits associated with water stress tolerance on chromosome 9.
2014 CTRI Annual Summary Report: St.Clair
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Procedures: We are using self-pollinated seeds we saved previously from a breeding line that is heterozygous for a large chromosome 9 introgression from S. habrochaites. These seeds form a segregating advanced backcross generation that are being genotyped to identify new breeding lines for this project. For genotyping, we developed new PCR-based genetic markers for the chromosome 9 region from S. habrochaites using the publicly available tomato reference genome sequence v2.5 (S. lycopersicum cv. Heinz 1706). Sets of PCR primers were designed and tested, and each new marker was verified for the ability to detect differences (polymorphisms) between the wild and cultivated tomato alleles extending into the chromosome 9 region towards the centromere. Once a marker was verified as polymorphic, we generated PCR amplification products for each allele and subjected each product to DNA sequencing. Subsequently, the DNA sequences for the wild (habrochaites) and cultivated tomato (lycopersicum) alleles for each marker were aligned to each other and compared to identify single base differences (i.e., single nucleotide polymorphisms or SNPs). We retested these SNPs and identified those that reliably detected differences between the wild and cultivated tomato allele sequences. A subset of these SNPs were selected as markers for use on a high-throughput SNP genotyping platform (e.g., Sequenom MassArray) to genotype the segregating advanced backcross generation and select new recombinant breeding lines. Results/Findings:
We identified 17 PCR-based markers in our target region of chromosome 9. Within these 17 markers, we obtained 27 SNPs that are robust in calling alleles (wild versus cultivated). This set of 27 SNPs is being employed on the Sequenom MassArray genotyping platform to genotype each individual from our segregating advanced backcross generation. We are in the process of extracting DNA from and genotyping ~2800 individual plants with Sequenom. Our goal is to identify at least 15 additional unique recombinants that extend into the centromeric end of our previously mapped introgressed chromosome 9 region from S. habrochaites. These recombinant progeny are breeding lines that each contains a unique small segment of chromosome 9 from S. habrochaites. To date we have genotyped 368 plants and identified 30 recombinants (not all are unique). Each putative recombinant will be re-genotyped to verify. The verified recombinant breeding lines will be transplanted into pots in the greenhouse and self-pollinated seed will be obtained and saved. In the future (not part of this project), we will use these breeding lines to map and refine the chromosomal locations of horticultural traits and traits associated with water-stress resistance. We anticipate obtaining funds from other sources to conduct field experiments with the lines, obtain trait data, and proceed with genetic mapping for each trait.
Research Project Report California Tomato Research Institute
18650 Lone Tree Rd., Escalon, CA 95320 Project Title: Evaluation of tactics for improvement of stink bug control Project Leader: Thomas A. Turini University of California Agriculture and Natural Resources Vegetable Crops Farm Advisor in Fresno County
550 E. Shaw Avenue, Suite 210 Fresno, CA 93710 Tel: (559) 375-3147 Email: [email protected]
Co-Primary Investigators: Frank Zalom Dept. of Entomology and Nematology Univ. of California Davis, CA 95616 Tel: (530) 752-3687 Fax: (530) 752-1537 Email: [email protected]
Peter B. Goodell, PhD Cooperative Extension Advisor, Integrated Pest Management UC Statewide IPM Program 9240 So Riverbend Ave Parlier CA 93654 559/646-6515 office 559/646-6593 - fax [email protected]
SUMMARY Populations of Consperse stink bug were dense in much of Fresno County in 2014 and were present at levels resulting in massive yield reductions in some areas. Attempts to locate overwintering sites in Winter 2013 were unsuccessful, but stink bugs in diapause were detected in Fall 2014 near an infested late-season tomato field. They were detected alone or in small groups of up to 16 in damp leaf litter. The area seems to be somewhat isolated and it does not extend throughout the permanent crop in which it was detected. In early 2014, stink bugs were first captured in pheromone-baited traps in mid-April. They reached detectable levels in a few commercial fields by 28 May. The densities remained high throughout the summer. While the earliest detections of stink bugs were in traps, the traps failed to consistently represent the population densities present in the canopy at later stages of population development. This may suggest that the traps may have utility in detecting early stages of an infestation, but are not useful in quantification of the problem in a tomato field at later stages of an infestation. In insecticide efficacy trials, Leverage applied at early stages of infestation and Thionex consistently provided excellent levels of control, as did Venom, Leverage applied at a standard interval, Danitol, Belay with Warrior, and Endigo CX. However, some of the materials did not deliver any reduction in the levels of stink-bug damaged fruit: Lannate/Asana, dimethoate and Dibrom. Drip applied neonicotinoid insecticides seemed to reduce the level of fruit damage; however, more work is needed to substantiate this unexpected result. OBJECTIVES
• Document sources of stink bug and seasonal population dynamics in Central CA processing tomatoes
• Assess efficacy of insecticides and insecticide rotations against Consperse stink bug PROGRESS
Seasonal Population Monitoring: Overwintering and early season population monitoring 2013-2014: During the 2014 season in the Fresno County production area, population densities were evaluated where damaging levels of the pest were documented in 2013. The three sites of focus were as follows:
• Firebaugh-almond orchard southwest of Russell Ave. and Nees Ave. adjacent to late season tomatoes with high stink bug densities
• Five Points-pistachio orchard northeast of Mt. Whitney Ave. and Lassen Ave. near late season tomatoes with a substantial stink bug infestation.
• Huron-natural reserve area near grassy ground cover and cottonwood trees, which is west of the aqueduct and north of Gale Ave. Extremely high population densities were documented in this area in Oct 2013.
These areas were searched on 16 Dec in Firebaugh, 17 Dec in Huron and Five Points. On 8 Jan additional evaluations were conducted as was repeated to 18 April for overwintering site of focus. No overwintering sites were identified.
Trapping in winter and spring 2014: Pheromone-baited traps were deployed on 4 Mar in the same three locations that were being searched for overwintering sites. No stink bugs were captured until mid-Apr. Three stink bugs were present in the traps at the Huron site, and 1 at the Firebaugh site on 21 Apr. Captures were erratic from late Mar to late May. None were detected in the Five Points area trap (Fig. 1). By the end of May, there was feeding injury detected in the tomatoes in the Huron area and ultimately, very high levels were being detected in the canopy. Correspondingly, tremendous levels of damage were inflicted in these fields. Overwintering site documentation in fall 2014: In Fall 2014, the focus was on the Huron production area in cooperation with managers and consultants. The Huron area was searched on 13, 21, 23 and 28 Oct. Landscapes around structures including groundcover and shrubs with leaf litter were focused on. In situations where the landscape is irrigated, it would seem to be an excellent location for overwintering stink bugs. However none were located at these sites. In trash around oleanders there was cover, but in the absence of irrigation, the area was very dry as were most of the areas near the aqueduct and creek. However, in areas where there was some discharge from the aqueduct, there was substantial groundcover and apparently suitable conditions for stink bug overwintering, but none were detected at these sites either. On 28 Oct 2014, with the help of a manager of a tomato growing operation, stink bugs were found in a citrus grove located adjacent to mid-October harvested tomatoes that had suffered considerable damage due to stink bug feeding. They were detected alone or in groups of up to 16. They were not clustered in very large numbers as are some pests that are in the same order such as leaf footed bug, which can be found in clusters of thousands. On 5 Nov, trash was removed from under the citrus trees with a rake and the ground was inspected for stink bug. Sixteen were found 6 trees from the north end, which is adjacent to the tomato field that was harvested in mid-Oct. Eleven were detected per tree at the 12th tree from the north and 4 were detected per tree at the 18th tree. Around the 24th, 30th and 36th trees from the north end within the same row, no stink bugs located per 4 trees evaluated in each of these three sites. In addition, trees were evaluated in other portions of the orchard and even near the field that was infested with stink bug and similar levels were not detected in other areas even near the harvested tomato field. The management and ownership of the grove is now aware of the presence of the overwintering site and has communicated that the trash will be removed from underneath the trees in the area where the overwintering stink bugs were detected. I have plans to continue monitoring that grove and surrounding areas throughout Fall and Winter. Population monitoring in tomatoes: The trap counts were compared to numbers detected in the canopy with a method that involved using a cafeteria tray placed under the plants, as described by Zalom. Traps placed in the fields at the Huron area and south of Five Points consistently captured stink bug through the season. The results of the 2014 work suggest that the traps may be useful in detection early in the season. However, there is no apparent relationship between the trap captures and the stink bugs detected in the canopy (Fig. 2). It seems that traps may not be of value in estimating quantities in the field during the season.
Insecticide Comparisons: Insecticide efficacy and insecticide program comparisons for control of stink bug were compared at the University of California West Side Research and Extension Center in 2014. Processing tomato transplants, cv. H5608, were planted on 21 May and were irrigated with buried drip through the season. Stink bug population densities in this trial site were sufficient to see differences among treatments but were at lower levels than in some commercial fields. Stink bug were initially detected in a pheromone-baited trap on the northwest corner of the trial field on 15 Jul. On 6 Aug, stink bugs were detected in the canopy at 1 per 3 plants, and the second applications were made on 7 Aug in the programs trial and 8 Aug in the efficacy trial.
Insecticide efficacy: In the 2014 insecticide trials, materials detailed in Table 1 were evaluated. All materials were applied with a backpack CO2-powered sprayer at the equivalent of 50 gal tank mix per acre at 32 psi. The spray boom was equipped three Teejet 8004 EVS double flat fan nozzles spaced 19-in. apart and was used for all applications in the efficacy trial. All materials were applied with 0.25% Activator v/v. In one treatment each, Dibrom and Leverage were applied at the first detection of stink bug in the traps. Those applications were made on 18 Jul. These two plots were retreated when all of the other applications were made on 8 and 29 Aug. In addition, the plot treated with Lannate on 8 and 29 Aug was also treated with Assana on 15 Aug.
Differences among treatments were present. The stink bug counts were extremely variable as were the in-field damage ratings. In spite of this substantial level of variation, the Leverage treatment applied at first detection by trapping on 18 Jul and Thionex treatment consistently were the least affected by stink bug based on evaluations in which the treatments were statistically different at P=0.05 (Table 1). Differences among treatments were most pronounced in the levels of fruit damage at harvest (Table 2). Based on the percentage of stink bug-damaged fruit at harvest, the treatments that had the lowest levels of damage were Leverage (both treatments), Thionex, Venom, Danitol, Belay Warrior, and Endigo CX (Table 2). However, some of the materials did not deliver any reduction in the levels of stink-bug damaged fruit: Lannate/Asana, dimethoate and Dibrom (both treatments) as compared to the untreated control (Table 2).
Insecticide programs: The potential benefit of a drip application of neonicotinoid insecticides were evaluated along with combinations of foliar materials as detailed in Tables 3 and 4. As in the efficacy trial, the variability was very high in terms of in-field population density and feeding damage assessment (Table 3). Although plots receiving drip-applied materials had numerically lower populations and damage based on these in-field evaluations, these differences were only statistically significant in the insect counts taken on 4 Sep (Table 3). The hand sorting of stink bug-damaged fruit appeared to be a more consistent measure of stink bug damage, since there was much lower levels of variation than in the in-field assessments. Stink bug-damaged fruit levels were significantly lower in the plots in which the neonicotinoids were applied. This was unexpected and should be tested again for verification. The foliar materials selected were not very efficacious. Dimethoate was selected based on experience with other insect pests and Dibrom was included because of potential registration if it had promise. However, neither of these materials had impact on stink bug
damage in the efficacy trial this season. Therefore, it is likely that the foliar programs component of this trial did not perform because ineffective insecticides were major components. In conclusion: Given the trend toward increasing levels of damage and the extremely high levels of damage in some areas, research is needed to develop strategies that are easily implemented by the PCA’s with tomato responsibilities in this production area. Based on a single year of data, it seems that the traps are useful in early detection of the stink bug and mid-season evaluations are more accurate with canopy assessments. Based on the insecticide efficacy comparison Thionex remains a very effective material, and other materials demonstrating substantial levels of control were limited to pyrethroids, neonicatinoids, or a combination of the two. A trial comparing drip-applied materials showed some benefit of the application of neonicotinoids, but further work is needed for verification. Additional work is needed to further search for overwintering sites, evaluate population density dynamics during the tomato season and carefully evaluate insecticides and programs for management.
Fig. 1. Stink bugs captured in pheromone-baited traps deployed near potential overwintering
sites.
Fig. 2. The captures of stink bugs in traps against captures of stink bug in canopy shows no
apparent relationship.
0
1
2
3
4
5
6
7
8
9
2-‐Mar 12-‐Mar 22-‐Mar 1-‐Apr 11-‐Apr 21-‐Apr 1-‐May 11-‐May 21-‐May 31-‐May
Firebaugh
Five Points
Huron
y = 0.2382x + 2.7993 R² = 0.00823
0
5
10
15
20
25
0 1 2 3 4 5 6 7 8 9
s"nk
bugs d
etected in can
poy
s"nk bugs captured in pheromone traps
Table 1. Impact of insecticides on stink bug population densities and damage on processing tomatoes at West Side Research and Extension Center, 2014.
Stink bug counts (per 4 ft)z Stink bug damage (0-10)y
Treatmentx 21-Aug 28-Aug 5-Sep 21-Aug 28-Aug 5-Sep Venom 70 SG 4 oz 0.0 2.0 0.8 1.0 3.3 2.0 Leverage 2.7 3.75 oz trapW 0.0 0.0 1.0 1.0 0.5 1.0 Thionex 1 1/3 qts 1.3 0.3 0.5 0.8 1.0 1.0 Leverage 2.7 3.75 oz 0.8 3.8 0.8 1.3 2.0 1.0 Danitol 10.67 oz 0.4 3.2 1.6 0.6 4.0 1.0 Belay 4 oz + Warrior II 1.92 ozv 1.5 0.5 1.3 1.5 1.5 1.0 Endigo CX 4.5 fl oz 0.5 3.0 3.3 1.0 2.0 2.0 Torac 21.0 fl oz 1.0 1.3 1.8 2.3 2.3 2.3 Warrior II 1.92 oz 0.8 1.3 1.0 1.5 1.5 1.0 Lannate SP 1 lb/Assana 9.6 fl ozu 0.5 1.0 2.3 1.3 1.0 2.0 Dibrom 8E 1.0 pts trap 0.3 2.3 2.5 1.5 4.0 2.3 Endigo ZCX 4.5 fl oz 1.3 1.3 0.3 2.8 1.8 0.7 Dibrom 8E 1.0 pts 0.0 0.5 4.3 1.5 1.8 1.7 Dimethoate 1 pt 1.0 3.3 3.0 2.8 3.5 3.7 Untreated 1.3 0.5 4.0 4.5 2.8 3.3 LSD (P=0.05)t NSs 3.05 3.23 1.93 2.24 1.39 CV (%) 149.46 133.74 120.69 80.74 71.74 55.45 z Four feet of canopy on one side of the bed was shaken and pushed over into the other side of the bed and
the soil was examined for stink bugs. y Four feet of canopy was evaluated for stink bug-damaged fruit and rated for the severity and incidence of
the damage based on a scale from 1 to 10 with 0 being unaffected and 10 having every fruit fed on and showing numerous feeding injuries per fruit.
x The experimental area was transplanted on 22 May with cv. H5608 processing tomato plants at UC West Side Research and Extension Center. Foliar applications were made with a backpack sprayer at 50 gpa. Unless otherwise noted, all materials were applied on 8 Aug, which was just after detection of 1 stink bug per 3 plants, and again on 29 Aug.
w Treatments followed by “trap” were applied on 18 Jul, which followed the first stink bug captured in a pheromone-baited trap in that field on 15 Jul. These plots were treated again with the same materials when the other plots were treated.
v Materials separated by a “+” were applied together. u Assana was applied on 15 Aug in addition to the Lannate applications on 8 and 29 Aug. t Means within a column that have differences greater than the Least Significant Difference at probability of
5% (LSD0.05), which appears below are considered significantly different. ts All means appearing above ‘NS’ have no significant differences among them as determined by LSD0.05.
Table 2. Impact of insecticides on yield and quality of processing tomatoes at West Side Research and Extension Center, 2014.
z Twenty-five to 30 pounds of fruit per plot were hand sorted into red, green, sunburn, rot and stink bug injury categories. Each set of fruit were weighed and percentage of the sample represented by each group by weight is presented.
y Fruit chemistry was determined at the Processing Tomato Advisory Board Laboratory located at the Britz processing plant in Helm, Fresno County California.
z The experimental area was transplanted on 22 May with cv. H5608 processing tomato plants at UC West Side Research and Extension Center. Foliar applications were made with a backpack sprayer at 50 gpa. Unless otherwise noted, all materials were applied on 8 Aug, which was just after detection of 1 stink bug per 3 plants, and again on 29 Aug.
w Yields per acre were calculated based on a hand harvest of 20 row feet. v Treatments followed by “trap” were applied on 18 Jul, which followed the first stink bug captured in a pheromone-baited trap in that field on 15 Jul. These
plots were treated again with the same materials when the other plots were treated. u Materials separated by a “+” were applied together. t Assana was applied on 15 Aug in addition to the Lannate applications on 8 and 29 Aug. s Means within a column that have differences greater than the Least Significant Difference at probability of 5% (LSD0.05), which appears below are
considered significantly different. r All means appearing above ‘NS’ have no significant differences among them as determined by LSD0.05.
Fruit quality (%)z PTABy
Treatmentx yield (t/a)x reds greens sunburn rot stink bug color solids (obrix) pH
Venom 70 SG 4 oz 39.24 60.83 12.44 10.01 9.99 6.72 25.25 4.00 4.403 Leverage 2.7 3.75 oz trapv 40.82 73.46 5.31 4.25 9.52 7.47 25.75 3.93 4.455 Thionex 1 1/3 qts 45.80 74.35 6.54 4.34 5.33 9.41 24.75 3.93 4.410 Leverage 2.7 3.75 oz 40.84 55.88 10.09 9.83 13.86 10.34 26.25 3.85 4.422 Danitol 10.67 oz 37.40 66.04 9.84 4.92 8.49 10.71 25.30 3.90 4.410 Belay 4 oz + Warrior II 1.92 ozu 41.80 69.46 5.76 5.36 7.36 12.05 25.50 3.98 4.403 Endigo CX 4.5 fl oz 37.22 59.62 15.77 4.45 7.29 12.87 25.75 3.90 4.412 Torac 21.0 fl oz 41.09 50.05 7.78 13.06 10.66 18.44 25.00 3.80 4.423 Warrior II 1.92 oz 37.00 60.67 8.72 5.73 6.41 18.48 27.00 3.85 4.375 Lannate SP 1 lb Assana 9.6 fl ozt 47.52 58.43 14.55 2.46 6.00 18.56 27.00 3.93 4.405 Dibrom 8E 1.0 pts trap1 45.75 46.33 10.55 11.54 10.69 20.89 26.25 3.88 4.400 Endigo ZCX 4.5 fl oz 41.79 57.33 7.84 4.94 8.47 21.44 26.75 4.00 4.408 Dibrom 8E 1.0 pts 37.70 53.13 8.12 2.79 9.26 26.70 27.00 3.83 4.425 Dimethoate 1 pt 40.84 47.82 6.60 11.83 6.62 27.13 26.25 3.80 4.400 Untreated 38.91 52.84 7.02 7.46 7.30 25.38 26.75 3.85 4.403 LSD (P=0.05)s 8.440 15.935 7.305 8.425 6.346 12.357 NSr NS NS CV (%) 14.33 18.89 56.04 85.95 52.37 52.64 5.99 3.44 1.08
Table 3. Influence of insecticide programs on stink bug population densities and damage on processing tomatoes at West Side Research and Extension Center, 2014. Treatmentz
Injections into drip irrigation system buried to 10 in Stink bug densitiesy Damagex
26 Aug 4 Sep 26 Aug 4 Sep Admire (17 Jul), Venom 6.0 oz (18 Aug) 2.0 2.5 2.92 3.06 Platinum75SG 3.7 oz (17 Jul), Venom 6.0 oz (18 Aug) 4.0 1.8 2.08 2.81 Untreated 4.1 4.3 3.75 4.06 Drip injection, LSD0.05
w NSv 1.41 NS NS Foliar treatments 17 Jul 7 Aug 27 Aug
Dibrom 8E 1.0 pts Leverage 2.7 3.75 oz. 3.2 3.1 2.08 3.50 Leverage 2.7 3.75 oz Dimeth 4EL 1pt. 4.8 1.7 0.67 2.33
Leverage 2.7 3.75 oz 3.5 3.8 2.75 3.58 Untreated 1.9 2.9 1.92 3.83 Foliar application LSD0.05
NS NS NS NS interaction NS NS NS NS CV (%) 90.67 116.02 67.36 44.70
z The experimental area was transplanted on 22 May with cv. H5608 processing tomato plants at UC West Side Research and Extension Center. Injected materials were pumped into a manifold over 30 to 45 min and flushed for 1 hour. Foliar applications were made with a tractor-mounted sprayer at 50 gpa.
y Four feet of canopy on one side of the bed was shaken and pushed over into the other side of the bed and the soil was examined for stink bugs.
x Four feet of canopy was evaluated for stink bug-damaged fruit and rated for the severity and incidence of the damage based on a scale from 1 to 10 with 0 being unaffected and 10 having every fruit fed on and showing numerous feeding injuries per fruit.
w Means within a column that have differences greater than the Least Significant Difference at probability of 5% (LSD0.05), which appears below are considered significantly different.
v All means appearing above ‘NS’ have no significant differences among them as determined by LSD0.05.
Table 4. Impact of insecticides programs on yield and quality of processing tomatoes at West Side Research and Extension Center, 2014.
Treatmentx Injections into drip irrigation system buried to 10 in
Yield (tons/a)w
Fruit characterz PTABy
red grn sun burn rot
stink bug color
solids (obrix) pH
Admire (17 Jul), Venom 6.0 oz (18 Aug) 36.5 49.4 9.44 3.32 4.52 33.33 27.250 3.950 4.433 Platinum75SG 3.7 oz (17 Jul), Venom 6.0 oz (18 Aug) 32.4 57.9 7.22 4.86 5.02 25.04 26.063 3.963 4.434 Untreated 37.8 53.3 9.89 2.41 2.91 36.08 26.750 3.863 4.422 Drip injection, LSD0.05
v NSu NS NS NS NS 1.635 NS NS NS Foliar application
17 Jul 7 Aug 27 Aug Dibrom 8E 1.0 pts Leverage 2.7 3.75 oz. 33.4 48.6 7.44 3.10 4.97 35.84 26.583 3.858 4.432 Leverage 2.7 3.75 oz Dimeth 4EL 1pt. 36.5 54.6 10.42 3.66 3.99 27.37 26.833 3.933 4.427 Leverage 2.7 3.75 oz 37.8 45.3 7.18 4.02 4.00 39.46 25.750 3.983 4.448 Untreated 34.6 49.4 10.35 3.33 3.64 33.26 27.583 3.925 4.412 Foliar application LSD0.05
NS NS NS NS NS NS NS NS NS interaction NS NS NS NS NS NS NS NS NS CV (%) 20.02 27.5 73.86 102.47 76.86 42.43 6.90 4.54 1.12
z Twenty-five to 30 lbs samples were taken from the hand harvest, and hand sorted. Fruit in each category were weighed and percentage by weight was calculated. y Fruit chemistry quality was determined at the Processing Tomato Advisory Board Laboratory located at the Britz processing plant in Helm, Fresno County California. x The experimental area was transplanted on 22 May with cv. H5608 processing tomato plants at UC West Side Research and Extension Center. Injected
materials were pumped into a manifold over 30 to 45 min and flushed for 1 hour. Foliar applications were made with a tractor-mounted sprayer at 50 gpa w Yields per acre were calculated based on a hand harvest of 20 row feet. v Means within a column that have differences greater than the Least Significant Difference at probability of 5% (LSD0.05), which appears below are
considered significantly different. u All means appearing above ‘NS’ have no significant differences among them as determined by LSD0.05.
Project title: Application of a degree-day model and risk index to predict development of thrips and Tomato spotted wilt virus (TSWV) and help implement an IPM program in California processing tomato fields
Principal investigator: Robert L. Gilbertson, Professor of Plant Pathology, Department of Plant Pathology, UC
Davis Cooperating personnel: Ozgur Batuman, Project Scientist, UC Davis; Li-Fang Chen,
Project Scientist, UC Davis; Brenna Aegerter, Farm Advisor, San Joaquin County; Neil McRoberts, Epidemiologist, UC Davis and Diane E. Ullman, Entomologist, UC Davis
Summary The goal of this project is the continued development and implementation of a
predictive thrips phenology (degree-day) model and Tomato spotted wilt virus (TSWV) risk index (TRI), and to complete our ongoing thrips and TSWV monitoring efforts in San Joaquin and Contra Costa Counties (hereafter referred as San Joaquin County or SJC). The long-term goal of this research has been to provide accurate and real-time information to growers about the population dynamics of thrips and development of TSWV infection to facilitate effective disease management with the integrated pest management (IPM) program developed through this project. In 2014, monitoring of tomato fields in San Joaquin County revealed similar thrips population dynamics as in 2013 in which thrips populations started to build-up in early April and rapidly increased to high population levels by early- to mid-May. Populations remained high through the summer, and began to decline in late summer to early fall (late August to early September). However, in 2014, the build-up of thrips populations began earlier than in 2013. TSWV was first detected in a monitored direct-seeded tomato field on 3 April in the Byron area in SJC. TSWV was eventually detected in all monitored fields, but overall incidences were low (<1-4%). Slightly higher incidences (up to 7%) were found in parts of two fields (the direct-seeded field in Byron and another field in the Tracy area) by early June, but economic losses were not experienced. Winter and spring weed surveys revealed very low levels of TSWV infection (~2%). In 2014, the newly identified TSWV weed host, rough-seeded buttercup (Ranunculus muricatus), was again widespread in walnut orchards and some plants were infected with TSWV. Laboratory experiments on the role of the soil-emerging adult thrips as an inoculum source for early season tomatoes revealed that thrips can stay dormant in soil for up to 8 weeks and that many of the emerging adult thrips (62-100%) transmitted TSWV. These results strongly suggest that adult thrips, emerging from soil, can be an early season TSWV inoculum source. During the 2014 growing season, the web site for the thrips phenology (degree-day) model was made available for growers, and was regularly updated to provide thrips population projections for each area. This model predicted the appearance of adult thrips generations with >80% accuracy. Thus, this model can be used as a predictor of when thrips populations begin to increase in the spring, and can help identify when to start implementing thrips management strategies (e.g., early- to mid-April in SJC in 2014). The prototype TSWV risk index (TRI) calculator was also made available on the web as well as on smartphone, tablet and computer-friendly interfaces. Growers were able to
submit required field information interactively to the TRI calculator and received a prompt response with the TRI value for their field (low, moderate or high risk) and recommendations on how to minimize TSWV risk. The TRI for the monitored fields in SJC was moderate, and this was consistent with observed disease levels. The IPM program for thrips and TSWV developed through this project can provide effective disease management, particularly if implemented on a regional level. We will continue to encourage the use of the grower-friendly thrips degree-day model and risk index, and publicize the IPM program for thrips and TSWV management. Objectives
The objectives of this project in 2014 were to 1) conduct surveys of selected processing tomato fields in SJC to gain insight into when and from where thrips and TSWV enter into fields and to use this data to assess the reliability of our degree-day model to predict the appearance of thrips populations in 2014, 2) identify potential TSWV inoculum sources for processing tomatoes, 3) complete experiments assessing the role of soil-emerging adult thrips as an early season inoculum source for TSWV, 4) refine and validate a thrips phenology (degree-day) model and a TSWV risk assessment index, and 5) continue to develop and assess the IPM program for thrips and TSWV in the Central Valley. Information on Materials and Methods can be found in our CTRI proposal for 2014 and are available upon request. RESULTS Field Monitoring: Our monitoring efforts for thrips and TSWV were initiated in selected processing tomato fields beginning 17 March 2014 in SJC. We monitored 5 processing tomato fields. All were established with transplants except one direct-seeded field in the Byron area. Table 1 lists the monitored fields and indicates the final TSWV incidence and calculated TSWV Risk Index (TRI) for each field.
Thrips populations determined on yellow sticky cards: In 2014, the build-up of thrips populations started in mid-March (200-350 thrips/card/two weeks), and populations rapidly increased in early-mid April in most monitored fields (Fig. 1). By early-May, large populations were present in all fields (>1000 thrips/card/two weeks). The build-up of thrips populations started earlier in 2014 (mid-March) than in 2013 (early April; however, a rapid population increase by early- to mid-May was observed in both years (Fig. 2). In 2014, after populations reached high levels in early- to mid-May (>1000 thrips/card/two weeks), populations fluctuated around these levels (900-2500 thrips/card/two week) until late August to early September (Figs. 1 and 2). By mid-October thrips populations had decreased considerably (<500 thrips/card/ two weeks).
In 2013 and 2014, a drop in thrips populations was detected during June-July in all
monitored fields in SJC (Figs. 1, 2 and 3). This was not predicted based on our degree-day model; i.e., there was no evidence of a noticeable delay in thrips generations (Fig. 3). It is possible that the decrease in thrips population in June-July reflected the widespread implementation of thrips management (spraying) in monitored fields in early-June.
Key finding: Thrips population dynamics were similar in 2013 and 2014 in SJC
and the timing of initial thrips sprays was recommended for early- to mid-April in 2014.
TSWV incidence: In 2014, TSWV was first detected in the direct-seeded field in the
Byron area on 3 April (a week earlier than in 2013), but it was not detected until mid- May in the other monitored fields in eastern parts of SJC. As in previous years, TSWV was eventually detected in all monitored fields. The overall incidence of TSWV in the monitored fields was relatively low (<1-4%, Table 1). In two fields, one in Byron and another in Tracy, TSWV was present at higher incidences (7%) in a single corner of each field. Overall, we do not believe that TSWV caused economic losses in any of the monitored fields in 2014.
A number of other viruses were detected in the monitored fields including Beet curly
top virus (see curly top report), Alfalfa mosaic virus (AMV, ~5% incidence), and Tomato necrotic spot virus (sporadic and <1%). High populations of aphids early in the season likely facilitated the movement of AMV out of weeds and alfalfa in to the processing tomato fields.
Key finding: Incidence of TSWV in processing tomatoes fields in SJC was low in
2014 and not economically important. Survey for potential TSWV inoculum sources: We continued our efforts to identify TSWV inoculum sources before, during and after the processing tomato season. We focused our efforts around processing tomato fields that were monitored in SJC, and we collected weeds from these areas in the winter and spring before tomatoes were established. Bridge crops: Fall crops (e.g., lettuce and radicchio) were not grown in the surveyed areas in SJC during fall/winter seasons in 2014. However, in 2014, spring-planted lettuce and radicchio, potential TSWV bridge crops, were grown in Fresno and Kings Counties. Surveys of these crops revealed low TSWV incidences in lettuce (1-3%), but much higher incidences (40%) in a radicchio field that was resprouting following harvest.
Weeds: In 2014, weeds were collected from surveyed areas and tested for TSWV (Table 2). In SJC, weeds were abundant along roadsides and levees, and in fallow fields and some orchards. Most weeds collected before and during the tomato growing season were symptomless and tested negative for TSWV (with immunostrips and/or PCR). A small number of weeds including bindweed, sowthistle and prickly lettuce were infected with TSWV (Table 2). The overall incidence of TSWV infection in weeds was very low (~2% [9 TSWV-positive weeds/395 tested], Table 2). This was similar to results from previous years, and continues to indicate that weeds in the Central Valley of California are not extensively infected with TSWV.
In our 2014 surveys, particular attention was given to the newly identified weed host of TSWV, rough-seeded buttercup. We observed this weed in 13 of 17 walnut orchards that were surveyed. Most of the buttercups in these orchards did not show obvious disease symptoms. However, a relatively small number (~1%) showed virus-like symptoms including some that showed symptoms of TSWV infection (chlorosis, bronzing and necrosis) and others that showed necrotic spots. Because buttercups show symptoms upon infection with TSWV, only plants with symptoms (25 plants from 6 orchards) were tested for TSWV infection with immunostrips and PCR. Of these, only 5 plants tested positive for TSWV, and these came from walnut orchards that had been next to processing tomato fields that had TSWV outbreaks in 2013. Buttercups with necrotic spots were negative for TSWV infection and this symptom was most likely due to chemical damage.
Key finding: Weeds were infected with TSWV at very low incidences (~2%) and
rough-seeded buttercup was again identified as a potentially important TSWV weed host in California.
Assessment of the potential role of the soil-emerging adult thrips as inoculum sources of TSWV for early-planted tomatoes: We compared the emergence rates of viruliferous (virus-carrying) thrips and nonviruliferous thrips by incubating thrips pupae for up to 8 weeks in soil at 4°C. Our results showed that the emergence rate of nonviruliferous thrips declined over time from 50% (1 week), to 10% (5 weeks), to 4% after 8 weeks (Fig. 4). The emergence rate for viruliferous thrips was similar to that of nonviruliferous thrips. Here, the emergence rate declined from 50% (1 week), to 7% (5 weeks), to 3% after 8 weeks (Fig. 4).
Most importantly, we wanted to know whether the emerging viruliferous adult thrips could transmit TSWV to healthy plants. To this end, the viruliferous adult thrips that emerged from soil were tested with a leaf disc assay, and the presence of TSWV in these leaf discs was determined by ELISA. Our results showed that emerging viruliferous adult thrips, regardless of the period of time they were kept at 4°C, efficiently transmitted TSWV as infection was detected in 62-100% of leaf discs.
Thus, thrips pupae survived in soil at a simulated 4°C overwintering condition for
up to 8 weeks, although survival decreased considerably after 4 weeks. The rate of emergence of adult thrips was similar for viruliferous and nonviruliferous thrips and, most importantly, adult thrips emerging from pupae of viruliferous thrips remained viruliferous and efficiently transmitted TSWV.
Next, we wanted to examine the emergence rate of viruliferous adult thrips under
conditions that better approximated overwintering conditions in California. To find out the actual California overwintering temperatures, we looked at weather and soil temperature data from 2001-2010 for Fresno County. Here, we focused on winter temperatures from December to early February, which represents the period of time that thrips pupae would presumably be quiescent in the soil. We found that over this ten year period, the temperature in Fresno soils was consistently ~10°C in these winter months,
and fluctuated little between day and night. Thus, we chose 10°C as our experimental temperature in order to mimic the natural overwintering conditions that thrips pupae would endure in California soil during the winter months. In all nine experimental replicates, our results showed that viruliferous adult thrips emerged from pupae maintained in soil at 10°C, with most of the emergence occurring in the third and fourth weeks of incubation (Fig. 5). The emergence rate of adult thrips from pupae of viruliferous thrips ranged from 35-58% with an average of 45% (Fig. 6). The emerging adult thrips were tested to determine if they were viruliferous and could transmit TSWV. Again, the leaf disc assays was used and TSWV infections were determined by ELISA. Our results showed that adult thrips that emerged from soil were viruliferous and efficiently transmitted TSWV (67-100%). In conclusion, up to 45% of the thrips pupae survived in soil at temperatures approximating overwintering conditions (10°C) in California, and emerging adult thrips were viruliferous and transmitted TSWV at high efficiency.
Key finding: Viruliferous adult thrips emerging from soil efficiently transmit
TSWV and are likely to serve as a source of TSWV inoculum early in the season. This could explain how a single TSWV-infected plant could appear in the middle of a field early in the growing season. Refinement and validation of the degree-day model and the risk index for thrips and tomato spotted wilt disease In 2014, we ran thrips population projections for all 6 locations, i.e., Fresno, Kings, Merced, Yolo/Colusa, and the Western and Eastern SJC. The web site for the phenology (degree-day) model ran through November in 2014 for Fresno and through August for Kings, Merced, Western and Eastern San Joaquin and northern counties. We also regularly updated the web page to provide thrips population projections for each area. In 2014, real-time thrips population dynamics (from yellow sticky card counts) and phenology model projections were compared side by side for the SJC fields. In general, the phenology model continued to be ~80% accurate based upon correlating the projections of new generations with thrips population increases on yellow sticky cards in monitored fields (Fig. 3). In 2014, the model predicted 5 adult thrips generations through August. For example, in Western SJC, the generations were predicted for 12 March, 26 April, 26 May, 21 June and 14 July (Fig. 3). In 2014, the first generation was predicted to occur about three weeks earlier than in 2013 (5 April). The prediction for first generation in 2014 (12 March) coincided with the time that thrips population build-up was detected on yellow sticky cards in monitored fields (17 March), and was fully consistent with our observations that build-up of thrips populations in 2014 was earlier than in 2013 in SJC (Fig. 1, 2 and 3). Additionally, in 2014, we started tracking the usage of phenology model web site by following hits (visitor counts). The substantial increase in number of hits for these pages is evidence that more growers and PCAs are using our phenology model (Fig. 7). In 2014, the TSWV risk index (TRI) calculator was available on the web for growers with smartphone, tablet and computer friendly interfaces. Growers would submit required field information interactively to the TRI calculator and then receive a prompt response
from us with a TRI value for their field (low, moderate and high risk) and recommendations for TSWV management. After harvest of our monitored fields in SJC, we obtained all data needed to calculate the TRI for each field. We then correlated the calculated TRIs for these fields with actual TSWV incidences determined through our survey. All of our monitored fields in SJC in 2014 were given a moderate TRI (Table 1). TSWV incidences in these monitored fields were <1-4%, consistent with the predicted moderate TSWV risk by the TRI. We are now confident that the TRI is reliable and can be used to accurately predict the potential for TSWV in grower fields. We will make the TRI available for growers in the 2015 growing season. Key finding: The thrips phenology (degree-day) model and TSWV TRI are useful tools that growers can use to help implement the IPM program for thrips and TSWV management in the Central Valley of California. ‘Final’ IPM program for thrips and TSWV in processing tomatoes in the Central Valley of California By putting together all the information generated in this project, we have developed the following comprehensive IPM program for TSWV and thrips in processing tomatoes in the Central Valley of California. We believe that implementation of all or parts of this package allows for effective management of TSWV, and that this has been reflected in overall reduction of TSWV to manageable levels over the past 5 years. Clearly, the increasing availability of resistant varieties has provided a key tool for this program, but it is important to remember that this resistance can be overcome by resistance-breaking strains of the virus or high inoculum pressure with non-resistance breaking strains. Thus, growers should implement the complete IPM program to minimize TSWV outbreaks in resistant varieties. Thrips/TSWV IPM Package I- Before planting i) determine the risk index for the field and plan your needs for TSWV management accordingly ii) evaluate planting location/time of planting-this will involve determining proximity to potential inoculum sources during the time of planting (if possible avoid hot spots, planting near fields with bridge crops or late planting dates). iii) use TSWV- and thrips-free transplants iv) plant TSWV resistant varieties (possessing the Sw-5 gene)-these are now widely available, but may not be necessary if other practices are followed. Varieties without the Sw-5 gene can also vary in susceptibility. Resistant cultivars should definitely be planted in hot-spot areas or in late-planted fields that will be established near early-planted fields in which TSWV infections have already been identified. v) implement weed management-maintain weed control in and around tomato fields and especially in fallow fields and orchards, as some weeds are TSWV hosts, such as prickly lettuce, rough-seeded buttercup and sowthistle. If weeds are allowed to grow in
fallow fields, they can amplify thrips and TSWV and serve as inoculum sources for processing tomatoes. II- During the season i) monitor fields for thrips with yellow sticky cards or use the predictive phenology (degree-day) model to estimate when thrips populations begin to increase. ii) manage thrips with insecticides at early stages of crop development when thrips populations begin to increase (typically late March-early-mid-April). iii) rotate insecticides to minimize development of insecticide resistance in thrips. iv) monitor fields for TSWV and remove (rogue) infected plants early in development (up to 30-40 days after transplanting) v) implement weed management-maintain effective weed control in and around tomato fields. III- After harvest i) promptly remove and destroy plants after harvest (typically done during mechanical harvest) ii) avoid planting bridge crops that are thrips/TSWV reservoirs or monitor for and control thrips and TSWV in these crops (e.g., radicchio) iii) control weeds/volunteers in fallow fields, non-cropped or idle land near next years tomato fields
Table 1. List of monitored processing tomato fields in San Joaquin and Contra Costa Counties in 2014: their locations, TSWV incidence and TSWV Risk Index (TRI) values.
Monitored Fields in 2014
Field Location TSWV % TRI TR Processing tomato on W Linne Rd, Tracy 3% moderate
HM* Processing tomato on Hoffman Ln, Byron 4% moderate MP Processing tomato on E Mariposa Rd, Linden 1% moderate BD Processing tomato on S Bird Rd, Tracy 4% moderate BR Processing tomato on Levee Rd, Thorntorn 3% moderate
* Direct-seeded processing tomato field
Fig. 1. Average thrips counts per yellow sticky card in monitored fields in San Joaquin
County in 2014.
TR HM MP BD BR
0 500
1000 1500 2000 2500 3000
Average thrip
s pop
ula<
ons
Fields
Thrips popula<ons in San Joaquin County in 2014
TR HM MP BD BR
Fig. 2. Average thrips counts per yellow sticky card in monitored fields in 2013 and 2014
in San Joaquin County.
Fig. 3. Comparison of degree day model’s projections with actual average thrips counts
that were determined from yellow sticky cards in monitored fields in San Joaquin County in 2013 and 2014. Note that unexpected thrips population decrease ‘drop’ in fields in June-July in both years. Gen1-5= model’s projected adult thrips generations and their dates.
2013 2014
0
500
1000
1500
2000
Average thrip
s pop
ula<
ons
Average thrips counts per yellow s<cky card in 2013 and 2014 in SJC
2013 2014
Table 2. Results of survey of weeds in San Joaquin County for TSWV infection in 2014.
Weed Tested/ number(+)
Weed Tested/ number
(+) Dandelion 5 (0) Bur Clover 20 (0) Prickly Lettuce 25 (1) Sowthistle 45 (2) Bindweed 15 (1) Nettle 5 (0) Vetch 5 (0) Mustard 5 (0) Chickweed 30 (0) Groundsel 5 (0) Knotweed 10 (0) Cutleaf Geranium 5 (0) Swine cress 10 (0) Miner's Lettuce 15 (0) London rocket 15 (0) Henbit 5 (0) Redmaids 15 (0) Fiddleneck 5 (0) Shephard's purse 15 (0) Poison hemlock 15 (0) Malva 25 (0) Pineapple weed 15 (0) Filaree 20 (0) Chinese lantern 10 (0) Rough-seeded Buttercup 25 (5) Pigweed 10 (0) Dandelion 5 (0) Others 20 (0)
Total : 395 (9) (+)= number of plants that tested positive for TSWV by immunostrips and/or RT-PCR
Fig. 4. Percentage of adult thrips emergence from pupae that underwent overwintering
experiments that were conducted at 4°C soil temperature.
0
20
40
60
80
control 1 2 3 4 5 6 7 8
Thrip
s emerging (pe
rcen
t)
Week
Thrips overwintering experiments at 4°C soil temperature
Nonviruliferous Viruliferous
Fig. 5. The dynamics and time course of viruliferous adult thrips emerging from pupae in
overwintering experiments conducted at a 10°C soil-temperature. Rep1-9: individual replicate numbers
Fig. 6. The emergence rate of viruliferous adult thrips from overwintering experiments
that were conducted at 10°C soil-temperature. Rep1-9: individual replicate numbers
0
5
10
15
20
25
1 2 3 4 5 6 7 8 9
Num
ber o
f thrips e
merged
Week
Thrips overwintering experiments at 10°C soil temperature
Rep 1 Rep 2 Rep 3 Rep 4 Rep 5 Rep 6 Rep 7 Rep 8 Rep 9
0% 10% 20% 30% 40% 50% 60% 70%
Rep 1 Rep 2 Rep 3 Rep 4 Rep 5 Rep 6 Rep 7 Rep 8 Rep 9
Emergence rate of viruliferous adult thrips from 10°C soil temperature
Fig. 7. The usage statistics of our web site for the phenology model as was followed by
visitor counts. Note that over 50% of the users were identified as new users.
Title: Evaluation of Fungicides, Bio-Pesticides and Soil Amendments for the Control of Southern Blight in Processing Tomatoes. Project Leader: Joe Nunez, Vegetable/Plant Pathology Advisor, U.C. Cooperative Extension, 1031 So. Mount Vernon Ave., Bakersfield, CA 93307. Office: 661-868-6222, Fax: 661-868-6208, Email: [email protected] Co-Investigators: Mike Davis, Plant Pathology Specialist, Department of Plant Pathology, University of California, at Davis, CA 95616. Office: 530-752-0303, Fax: 530-752-1199, Email: [email protected] In 2014 seven (7) separate trials were conducted in a grower’s field infested with Sclerotium rolfsii (Table 1). The trials included a Metam-sodium fumigation trial, fungicide trial, lime trial, variety trial, biological trial, systemic acquired resistance (SAR) trial and an SAR plus fungicide trial. Unfortunately no data was able to be collected from these trials because nearly every plant succumbed to Southern Blight infection and/or “Curly Top”. This demonstrates just how difficult this pathogen is to control and how potentially devastating it can be to the processing tomato industry. Table 1. List of treatments applied in 2014 for the control of Southern Blight of tomatoes. Metam Trial 1. Control 2. Metam 15 gal/A 3. Metam 30 gal/A 4. Metam 45 gal/A 5. Metam 60 gal/A 6. Metam 75 gal/A
Lime Trial 1. Control 2. Lime 3 tons/A 3. Lime & tebuconazole 4. Lime & Actinovate & Serenade Soil
Biological Trial 1. Control 2. RootSheild & Soilguard & Tenet 3. T-1 4. T-2 5. T-5 6. T-6 7. Actinovate &Actigard &Serenade Soil & Tenet 8. T-1 & T-2 & T-5 & T-6
SAR Trial 1. Control 2. Actigard & Serenade Soil & WECO-Boost 3. Double Nickel & Sil-Matrix 4. Actigard and Companion & WECO-Boost 5. SiTKo & Actigard & Companion
Fungicide Trial 1. Control 2. Quadris & Cannonball & Actigard 3. TebuStar 4. TebuStar 5. Convoy 6. Convoy & TebuStar
Variety Trial 1. SUN 6366 2. H-8504 3. H-5608
4. H-2401 5. DR-1319 6. H-3402
7. H-5508 8. H-1015 9. N-6404
SAR Plus Fungicide Trial 1. Control 2. Actigard & Tebuconazole
Research Project Report 2014 California Tomato Research Institute, Inc.
18650 Lone Tree Rd., Escalon, CA 95320
Project Title: Evaluation of New Nematicides Against Root-Knot Nematodes in Processing Tomato Production
Project Leaders: J. Ole Becker, Department of Nematology, 1463 Boyce Hall, UC Riverside, CA 92521, (951) 827 2185, [email protected] Antoon Ploeg, Department of Nematology, UC Riverside, CA Joe Nunez, UCCE Bakersfield, CA
Summary: Field trials were conducted at the University of California South Coast Research and
Extension Center (SCREC) and at Shafter to evaluate the efficacy of several novel soil nematicides on root-knot nematode population development, tomato root health and yield. The products were applied at different rates and/or times according to the manufacturer's recommendation. Vydate and a non-treated control served as standard checks. Of the products tested, three nematicides showed good to excellent efficacy against the Southern root-knot nematode Meloidogyne incognita. At SCREC under high root-knot nematode disease pressure MCW-2 lowered root galling ratings at mid-season from 4.5 in the control to 0.8 and 1.5 in the high and low rate of MCW-2, respectively. At harvest the gall rating dropped from 9.1 in the non-treated control to 3.4 and 4.2, respectively. The high rate increased tomato yields by 43% over the non-treated check. This product has recently received federal EPA registration for fruiting vegetables. Another development product (Dp1) that was noted for its promising activity in our 2013 trials, again showed excellent efficacy. It reduced mid-season galling from a rating value of 4.5 in the non-treated control to about 1.0 in the Dp1 treatment. At harvest, root galling was limited to a 2.2 rating. This was reflected in increased tomato yields over the non-treated control by approximately 65%. The third development product (Dp3) became available shortly before initiation of the trials; consequently we lack experience in its optimal use. Although gall reduction was slightly less than with the other two products, there was a strong yield response with 40% over the non-treated control.
The Shafter trial suffered from curly top incidence but still confirmed the excellent nematicidal efficacy of MCW-2 and DP1 based on root gall ratings. None of four other tested development products showed significant efficacy against root-knot nematodes. Introduction and objectives Plant-parasitic nematodes are responsible for at least $11 billion in US crop losses (Becker, 2014); more than half of those are attributed to the activities of various species of root-knot nematodes (Meloidogyne spp.). Root-knot nematodes in CA processing tomato production have been responsible for 10-20% yield reductions, despite the wide-spread use of resistant tomato cultivars or nematicides (Koenning et al, 1999). Part of the disease damage is due to secondary microbial colonization of galled roots leading to accelerated tissue senescence. The increasing occurrence of Mi-1 gene resistance-breaking root-knot nematode strains in CA processing tomato production fields (Roberts, 1995, Kaloshian et al., 1996, Williamson and
2
Kumar, 2006) is considered a growing problem not only because of the yield damage potential to the individual farmer but the danger of wider dissemination of these strains. Also, there is concern about the potential introduction of an invasive root-knot nematode species. Meloidogyne enterolobii (syn. M. mayaguensis) is a subtropical root-knot nematode species that was first detected in the US about a decade ago. It is now considered one of the most important nematode species in Florida vegetable production (Brito et al., 2004a, b). It has also been reported around the world including Central America (Rammah, and Hirschmann 1988), China (Yang and Eisenback, 1983), Europe (Castagnone-Sereno, 2012), and South Africa (Onkendi and Moleleki, 2013). The nematode is morphologically indistinguishable from our common root-knot species and prolifically reproduces on Mi-resistant tomato cultivars (Kiewnick et al., 2009). In the past few decades, management of soilborne pathogens in high cash crops has primarily relied on the use of soil fumigants (Noling and Becker, 1994). Although often superior in efficacy compared to contact pesticides, many fumigants have negative attributes, such as potential health hazards, and groundwater or air pollutants. Consequently, several soil fumigants lost registration while others have been restricted in use. The few remaining soil fumigants are generally limited by regulatory restrictions related to their high emission rates (volatile organic compounds), and toxicity. Furthermore, organophosphate and carbamate nematicides have been severely restricted or taken off the market as a consequence of the Food Quality Protection Act. As research and development costs for a new pesticide have increased from approximately $140 million in 1995 to $230 million in 2005 (McDougall, 2012), the agrochemical industry had neglected R&D of nematicides for a number of years in favor of the more profitable markets of other pesticides. More recently, a number of new products for nematode management have been under development. However, little data are available concerning efficacy against root-knot nematodes under California conditions. Materials and Methods For the past few years, several field trials were conducted at the UC South Coast Research and Extension Center (SCREC) and at Shafter to evaluate the efficacy of novel soil nematicides and bionematicides on root-knot nematode population development, tomato root health and yield. After screening out those products that did not perform well under our conditions and nematode populations, we concentrated primarily on two soil nematicides (MCW-2, Dp1) with excellent activity during last year’s trials. In addition we evaluated 2 new development products at SCREC and 3 at Shafter.
A tomato field trial was conducted from 30 May to 27 Aug 2014 at the University of California South Coast Research and Extension Center (SCREC). The SCREC soil at the trial site was a San Emigdio sandy loam with 16% clay, 66% sand and 18% silt, 0.1% OM, pH 7.8 (ANR Analytical Laboratory, University of California, Davis). The test site at SCREC is infested with the Southern root-knot nematode (rkn), M. incognita. For the past five years at least one host crop has been grown to keep the rkn population at a yield-reducing level. During the past winter the field was cropped to root-knot nematode-susceptible wheat (cv. Yecoro Rojo). On June 10, Matrix SG was applied at 2 oz/acre, Prowl H2O at 2 pt/acre, and Treflan HFP at 1.25 pt/acre for weed control. Additional weeding during the season was performed by hand. The trial was designed as a randomized complete block with 5 replications. Each individual plot was 6.1 m long and 0.6 m wide with plant spacing of 0.3 m. At the beginning (Pi) and end (Pf) of the trial, six soil cores were taken to a depth of 0.25 m from each plot, pooled and a subsample was extracted for second-stage juveniles (J2) of rkn. Mean rkn population density at planting was 66
3
J2/100 cm3 (5 days incubation on Baermann funnels at 26˚C; extraction efficacy approx. 35%). Pre-plant treatments with MCW-2 and Dp2 (Tab.1) were suspended in 7.5 L water, applied with a sprinkler can in a 0.5 m band and rototilled into the top 10 cm. An additional 7.5 L water was then sprinkled on top of each plot. Before planting, low volume irrigation tubing (2 L/hr emitter output, 0.3 m spacing) was buried at approximately 10 cm depth. On June 12, the trial was planted with 6-week-old transplants of the root-knot nematode susceptible cultivar Halley 3155 in single rows with 0.3 m spacing. Remaining nematicide treatments were applied immediately after planting through low-volume irrigation tubing (2 L/hr emitter output, 0.3 m spacing) placed on top of the beds. Dp1, Vydate and Dp3 were suspended each in 4 gal water, thoroughly stirred and frequently agitated during application. After a 10-minute water run, the suspensions were injected during a 45 min run, followed by a 15 min flush with water. Additional applications were performed similarly two and four weeks later (Tab.1). Soil temperatures at application were 22.2˚C (May 29), 22.2˚C (June 9), and 22.8˚C (June 12), 22.8˚C (June 26) and 23.9˚C (July 10) at 15 cm depth. Minimum and maximum soil temperature at 15 cm depth were monitored throughout the length of the trial (Tab. 3). Plots with MCW-2 treatments received another 7.5 L of water 10 days before transplanting to accommodate provisional application information (MANA MCW-2 technical bulletin, 2013). To prevent potential virus transmission, Platinum 75G (a.i. thiamethoxam 75.0%) was applied via irrigation system on June 16th at 2.5 oz/acre rate. The trial was fertilized on June 16 with Peter’s soluble 20/20/20 at 20 lb/acre; this was repeated June 26. A month after transplanting (July 10), the plots were rated for vigor (0-10, 10 best). Six weeks after transplanting (July 24) and at harvest (August 27), 5 tomato root systems per replication were evaluated for rkn disease symptoms (galling, 0-10, Zeck, 1971). Plant vigor and disease ratings were arcsine-transformed and nematode population data were log-transformed to normalize variances before statistical analysis. If significant, mean separation was used with Fisher’s Protected LSD (@ P = 0.05). (SuperANOVA, Abacus, Berkeley, CA). At Shafter, each individual plot was 30 ft by 60 inch bed and each treatment was replicated 5 times in a randomized complete block design. All treatments (Tab.2) were applied by use of a hand held watering can with the measured amount of product to be tested along with approximately 1 gallon of water. The mixture was applied as evenly as possible over the top of the 60 inch by 30 foot plot. Immediately after application the beds were roto-tilled/bedshaped and then a quarter inch of water applied by solid set sprinklers.
After the pre-plant applications the trial was planted with nematode susceptible tomato transplants (Halley 3155). Transplants were planted at 18 to 24 inch spacing with a one row transplanter. The planting date was 6/11/14 and the application dates were as follows; pre-plant 6/3/14; at planting 6/11/14 and post-plant 7/9/14 and 8/7/14. On 10/11/14 and 10/12/14 the roots of 5 plants per plot were harvested and evaluated for root galling. The roots were rated on a 1 to 10 scale with 1 being no visible galling present and 10 the roots being completely galled by root-knot nematodes.
Grower field days were held on 9/17/14 and again on 10/14/14 due to considerable grower interest. In addition, several smaller field tours were conducted.
4
Tab. 1 Treatment list of UC SCREC tomato trial # Treatment1 rate application method and timing 1 non-treated control water only @ planting 2 Dp1 (l) drip @ planting, 14 dap, 28 dap 3 Dp1 (h) drip @ planting, 14 dap, 28 dap 4 MCW-2 2.5 L/acre 14 dbp incorporated plus 7.5 L + 7.5 L water 7 dbp 5 MCW-2 3.37 L/acre 14 dbp incorporated plus 7.5 L + 7.5 L water 7 dbp 6 Dp2 (l) 3 dbp incorporated 7 Dp2 (h) 3 dbp incorporated 8 Vydate L 2x 2 qt drip @ planting and 14 dap 9 Dp3 2x drip @ planting and 14 dap Tab. 2 Treatment list of Shafter tomato trial
# Treatment1 rate application method and timing 1 non-treated control 2 Vydate 3 pt/acre 3 MCW-2 3.5 pt/acre pre-plant 4 MCW-2 & Vydate pre-plant, 2x Vydate post-plant 5 CSS H2H 4 gal/acre 6 Nichino 45 fl oz/acre @ planting, 4x in 14 day intervals 7 Nichino 45 fl oz/acre @ planting, 2x in 28 day intervals 8 Nichino 45 fl oz/acre pre-plant, @ planting, 2x in 28 day intervals 9 Dp2 0.75 gal/acre pre-plant 10 Dp2 1.5 gal/acre pre-plant 11 Dp2 3 gal/acre pre-plant 12 Oro Agri 079 2 gal/acre pre-plant, 2x post-plant 13 Dp1 30.7 fl oz @ planting, post-plant 2x half rate 14 Dp1 30.7 fl oz preplant, post-plant 2x half rate
1 Dp1-3: Third party development products Results
The general conditions for the SCREC field trial were excellent. With the exception of the target pathogen M. incognita, no other major pests or diseases were observed. All data are summarized in Tab. 3.
The initial root-knot nematode population density was not different among the treatments. After 4 weeks, plant vigor improved in both the MCW-2 and Dp1 treatments. This was also reflected in the mid-season disease ratings. Both products were equally effective in reducing root galling to about 1-1.5 on a 0-10 scale. At this time the non-treated control was scored at 4.5. Dp3 was in terms of disease expression more similar to the Vydate control with a rating close to 3 (Tab. 2). At harvest the gall rating dropped from 9.1 in the non-treated control to 3.4 and 4.2 in the MCW-2 treatments. The high rate increased tomato yields by 43% over the non-treated check. The other development product Dp1 that was noted for its promising activity in our 2013 trials, limited galling at harvest to a 2.2 rating. This was reflected in increased tomato yields over the non-treated control by approximately 65%. The third development
5
product Dp3 had a higher gall rating than with other two products, but there was nevertheless a strong yield response with 40% over the non-treated control.
The Shafter trial suffered from major curly top incidence that made harvest yield
evaluations meaningless. Under such conditions even gall ratings are likely to be affected by the generally poor growth of the tomatoes. Still, the nematicidal efficacies of MCW-2 and Dp1 treatments at the high rate or repeated applications were documented (Tab. 4). None of the other development compounds showed efficacy against root-knot nematodes. Recently MCW-2 (Nimitz™) received federal EPA registration in fruiting vegetables; California registration is expected in a few months. The active ingredient fluensulfone has a novel mode of action and is said to have favorable toxicological and eco-toxicological profiles. No re-entry interval and little personal protective gear are required. It has only a “Caution” label. There is no word yet on the registration status of Dp1 and Dp3. But the development of three potent new nematicides is good news for growers. However, one must be vigilant with these novel chemistries. Although root-knot nematode resistance to carbamate and organophosphate nematicides has never been observed under field conditions, proper product stewardship needs to be considered to avoid problems similar to those observed with other pesticides.
Literature cited Becker J.O. 2014. Plant Health Management: Crop Protection with Nematicides. In: Neal Van
Alfen, editor-in-chief. Encyclopedia of Agriculture and Food Systems, Vol. 4, San Diego: Elsevier, pp. 400-407.
Becker, J.O., A. Ploeg, and J. Nunez 2012. Evaluation of novel products for root knot nematode management in tomato, 2011. Plant Disease Management Report No. 6:N016.
Becker, J.O., A. Ploeg, and J. Nunez 2013. Efficacy of nematicides for root-knot nematode management in tomato, 2012. Plant Disease Management Report No. 7.
Brito, J., J. Stanley, R. Cetintas, T. Powers, R. Inserra, G. McAvoy, M. Mendes, B. Crow, and D. Dickson 2004a. Meloidogyne mayaguensis a new plant nematode species, poses threat for vegetable production in Florida. 2004 Annual International Research Conference on Methyl Bromide Alternatives and Emissions Reductions. Orlando, FL.
Brito, J., T. O. Powers, P. G. Mullin, R. N. Inserra and D. W. Dickson 2004b. Morphological and molecular characterization of Meloidogyne mayaguensis isolates from Florida. Journal of Nematology 36 (3): 232–240.
Castagnone-Sereno, P. 2012. Meloidogyne enterolobii (= M. mayaguensis): profile of an emerging, highly pathogenic, root-knot nematode species. Nematology 14:133-138.
Kaloshian, I., V. Williamson, G. Miyao, D.A. Lawn, and B.B. Westerdahl 1996. “Resistance-breaking” nematodes identified in California tomatoes. California Agriculture 50(6):18-19.
Kiewnick, S., M. Dessimoz, L. Franck 2009. Effects of the Mi-1 and the N root-knot nematode-resistance gene on infection and reproduction of Meloidogyne enterolobii on tomato and pepper cultivars. Journal of Nematology 41(2): 134–139.
Koenning, S.R., C. Overstreet, J.W. Noling, P.A. Donald, J.O. Becker, and B.A. Fortnum. 1999. Survey of crop losses in response to phytoparasitic nematodes in the United States for 1994. J. Nematology 31:587-618.
McDougal, P. 2012. Presentation for the ABIM (Annual Biocontrol Industry Meeting) Conference, Lucerne, http://www.abim.ch/fileadmin/documentsabim/Presentations_2012.
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Noling, J.W., and J.O. Becker 1994. The challenge of research and extension to define and implement alternatives to methyl bromide. J. Nematology 26:573-586.
Onkendi, E.M., and L.N. Moleleki 2013. Detection of Meloidogyne enterolobii in potatoes in South Africa and phylogenetic analysis based on intergenic region and the mitochondrial DNA sequences. European Journal of Plant Pathology 136:1-5
Rammah A, and H. Hirschmann 1988. Meloidogyne mayaguensis n. sp. (Meloidogynidae), a root-knot nematode from Puerto Rico. Journal of Nematology 20:58–69.
Roberts, P. A. 1995. Conceptual and practical aspects of variability in root knot nematode related host plant resistance. Annual Review Phytopathology 33:199-221.
Williamson, V. M., and A. Kumar 2006. Nematode resistance in plants: the battle underground. Trends Genet. 22:396-403.
Yang B., J.D. Eisenback 1983 Meloidogyne enterolobii n.sp. (Meloidogynidae), a root-knot nematode parasitizing Pacara Earpod Tree in China. Journal of Nematology 15:381–391.
Zeck, W. M. 1971. A rating scheme for field evaluation of root-knot nematode infestations. Pflanzenschutz-Nachrichten, Bayer AG 24:141–144.
Tab. 3 Soil temperature at 15 cm depth at SCREC
20
21
22
23
24
25
26
27
5/29 6/5
6/1
2 6/1
9 6/2
6 7/3
7/10
7/17
7/24
7/31 8/7
8/1
4 8/2
1
soil
tem
pera
ture
at 1
5 cm
dep
th
date
7
Tab. 4 Summary of all data for 2014 tomato trial at SCREC
Tab. 5 Shafter harvest ratings for root-knot nematode disease and Curly Top symptoms
5/30/14 7/10/14 7/24/14 8/27/14pre-season transplant mid-season harvest yield
# Treatment Pi J2/100 cc vigor gall rating kg fruit/plant1 Nt-control 54.4 ± 10.6 5.8 ± 0.8 a 4.5 ± 0.5 d 0.74 ± 0.02 a2 Dp1 3 x (l) 74.8 ± 10.9 7.2 ± 0.7 bc 1.0 ± 0.2 a 1.23 ± 0.04 e3 Dp1 1 x (h), 2 x (l) 64.0 ± 16.0 7.8 ± 0.2 c 0.9 ± 0.2 a 1.20 ± 0.06 de4 MCW-2 (l) 64.4 ± 20.5 7.4 ± 0.4 bc 1.5 ± 0.3 ab 0.91 ± 0.10 abc5 MCW-2 (h) 49.2 ± 6.5 6.4 ± 0.5 ab 0.8 ± 0.2 a 1.06 ± 0.09 cde6 Dp2 (l) 60.8 ± 15.3 6.6 ± 0.5 ab 4.7 ± 0.5 d 0.83 ± 0.06 ab7 Dp2 (h) 61.6 ± 13.9 6.0 ± 0.6 a 3.9 ± 0.7 cd 0.85 ± 0.07 ab8 Vydate L 2 x 59.6 ± 15.0 5.8 ± 0.4 a 3.0 ± 0.4 c 0.95 ± 0.09 bc9 Dp3 2x 110.0 ± 17.4 6.8 ± 0.7 abc 2.6 ± 0.5 bc 1.04 ± 0.05 cd
8/27/14 8/27/14 M. incognitaharvest harvest pop. devel.
# Treatment gall rating Pf J2/100 cc Pf/Pi1 Nt-control 9.1 ± 0.1 e 1414 ± 228 d 33.3 ± 11.02 Dp1 3 x (l) 2.5 ± 0.2 ab 817 ± 196 ab 12.8 ± 4.03 Dp1 1 x (h), 2 x (l) 2.2 ± 0.6 a 528 ± 49 a 11.5 ± 4.04 MCW-2 (l) 4.2 ± 0.5 bc 1094 ± 192 bcd 30.1 ± 13.95 MCW-2 (h) 3.4 ± 0.5 abc 888 ± 93 bc 18.9 ± 2.26 Dp2 (l) 8.1 ± 0.4 de 1212 ± 118 cd 29.7 ± 11.77 Dp2 (h) 7.8 ± 0.7 de 1368 ± 81 d 29.2 ± 8.38 Vydate L 2 x 6.8 ± 0.7 d 1334 ± 85 d 33.0 ± 12.19 Dp3 2x 4.9 ± 0.4 c 1108 ± 231 bcd 12.9 ± 5.5
Root-knot gall Curly Top (CT) Incidence of
Treatments rating (1-10)1 Rating (1-5)2 CT/plot3
1. Non-treated control 9.1 A 4.2 A 7.6 A
2. Vydate 3 pt/acre 6.5 B
3. Nimitiz pre- plant @ 3.5 pts/A 1.1 D 2.2 BC 3.2 B
4. Nimitiz pre-plant & vydate 2 post plant 0.4 D 1.0 C 1.4 B
5. CSS H2H 4 gal /A 9.5 A
6. Nichino 45 fl oz/A, at transplant, 4x 14 day 9.3 A
7. Nichino 45 fl oz/A at transplant, 2x 28 day 9.5 A
8. Nichino 45 fl oz/A Pre-plant, at transplant, 2x 28 day 8.9 A
9. Dp2 0.75 gal/A pre-plant 8.5 A
10. Dp2 1.5 gal/A pre-plant 9.7 A
11. Dp2 3.0 gal/A pre-plant 9.5 A
12. Oro Agri 079 2 gal/A pre-plant, 2x post-plant 9.3 A
13. Dp1 @ planting (30.7 fl oz), 2x post-plant (15.4 fl oz) 4.7 C 2.6 B 3.4 B
14. Dp1 pre-plant (30.7 fl oz), 2x post plant (15.4 fl oz) 4.4 C 2.0 BC 3.0 B
Probability %CV
LSD p=0.05
0.0000 17.96
1.648
0.0087 48.86
1.572
0.0036 56.29
2.808
1 1= no visible galls 2 1=0% CT 3 # plants
10=Roots galled 2=1 to 25 % CT per 30 ft plot
3=26 to 50% CT displaying CT
4=51 to 75% CT symptoms
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The Effect of Cover Crops, Compost, and Gypsum Amendments on Soil Structure and Disease in
Processing Tomatoes Project Leader: Kate Scow, Professor, Land Air and Water Resources; Director Russell Ranch Sustainable Agriculture Facility Address: 3236 Plant and Environmental Sciences Building, One Shields Avenue, Davis, CA 95616 Phone Number: 530-752-4632 Collaborators:
Emma Torbert, Assistant Specialist, Agricultural Sustainability Institute, UC Davis Megan McCaghey, Graduate Student, Agricultural Sustainability Institute, UC Davis Mike Davis, Professor, Plant Pathology, Cooperative Extension Specialist, UC Davis Sharon Dabach, Post Doctoral Scholar, UC Davis Viticulture and Enology, UC Davis Steven Fonte, Researcher, Department of Plant Sciences, UC Davis Gene Miyao, Farm Advisor Vegetable Crops, UC Cooperative Extension, Yolo County Summary of Results:
Amendments such as compost, cover crops, and gypsum have the potential to improve soil structure, improve soil water relations, and to reduce plant damage from pathogens to increase tomato yield. In this year’s study of amendments at Russell Ranch, and in grower Steve Meek’s field, we observed how amendments can modify the soil to improve yield and plant health. In this study, yields at Russell Ranch were highest in compost alone treatments and were lowest in treatments with cover crops. Cover crop treatments also resulted in lower bulk densities. Sodium, phosphorus, and potassium were significantly higher in compost treatments, and SO4-S was significantly higher for gypsum treatments. Foster Farms compost amended soils had significantly higher SO4-S levels with a lower pH, and Jepson Prairie compost had higher levels of calcium in amended soils than control and OLAM amended soils. Saturated hydraulic conductivity was lower in the compost, gypsum, and cover crop treatment than the control and compost with cover crop treatments at Russell Ranch. The compost, gypsum, and cover crop treatment in the grower’s field also has a significantly lower hydraulic conductivity than treatments of cover crop and compost alone. For the second year, beds amended with compost had less disease damage in the August observations before harvest compared to non-compost amended soils. In light of the benefits of these amendments, it will be necessary to compare changes in yield, soil structure, and disease with the cost of amending soils to decide whether or the amendments would be economically beneficial to use. Objectives: Overall: To analyze the impact of cover crops, compost, and gypsum soil additions on yield, soil structure and disease in processing tomatoes Specific: 1) Conduct field trials in a grower’s processing tomato field and at UC Davis Russell Ranch Sustainable Agriculture Facility to measure the effect of soil amendments and cover crops, alone and in combination, on:
● Processing tomato yields ● Soil nutrient content ● Soil structure ● Soil water relationships ● Occurrence and severity of soil-borne diseases
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2) Conduct a cost-benefit analysis of the utilization of soil amendments, both alone and in combination, to suggest financially beneficial options for farmers. 3) Communicate results in field days, workshops and conferences on the effectiveness of adding compost, gypsum, and cover crops, alone and in combination, on soil structure and quality, as well as plant disease incidence Justification:
Intensive farming of processing tomatoes, either in continuous tomatoes or in wheat-tomato rotations, can frequently lead to progressively lower yields and degraded soil properties. Over the long term, many irrigated agricultural soils in California start to suffer from poor physical structure, reduced infiltration rates, low organic matter content, and salt accumulation; these problems with soil often translate into lower crop yields and higher rates of disease. Soils that have been farmed intensively in the Central Valley are sometimes referred to by farmers as “tired” and exhibit decreasing tomato yields over time. The use of cover crops and compost to improve soil structure and water relations, to prevent disease and improve soil health has the potential to increase the productivity of processing tomato fields in California and economically benefit farmers. In the Sacramento Valley region, many soils are low in calcium and high in magnesium which sometimes presents challenges for irrigation. Addition of gypsum can improve soil aggregation in degraded soils and increase the availability of exchangeable calcium. The field sites selected (Steve Meek’s experimental area from 2013 and the Russell Ranch Sustainable Agriculture Facility’s Century Experiment) provide an opportunity to evaluate the application of soil treatments on a commercial scale with readily available amendments. The goal of this project is to evaluate whether the combined treatment of compost, cover crop and gypsum will increase soil organic matter, microbial biomass, and improve soil structure and soil-water properties, and thus tomato yields. Procedures:
Field work was initiated in the fall of 2013 in two field sites: in a grower’s field, (Steve Meek) adjacent to the Russell Ranch Sustainable Agriculture Facility in the same location used for last year’s CTRI funded study, as well as the long term conventional wheat/tomato rotation at the Russell Ranch Sustainable Agricultural Research Facility. The grower’s field is drip irrigated and was planted in tomatoes for the second consecutive year. The Russell Ranch field is furrow irrigated and is in the 20th year of wheat/tomato rotation. At Russell Ranch and in the grower’s field, eight treatments with different combinations of cover crops and soil amendments were applied. These treatments are as follows: 1) Chicken manure compost; 2) Cover crop of vetch and peas; 3) Chicken manure compost and cover crop of vetch and peas; 4) Gypsum; 5) Gypsum with chicken manure compost; 6) Gypsum with cover crop of vetch and peas; 7) Gypsum, cover crop of vetch and peas, and chicken manure compost; 8) No amendment (control) Additionally, in the grower’s field, a separate compost experiment was conducted. The treatments in this location included: 1) Control (no compost)
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2) Chicken manure compost from Foster Farms 3) Green waste from Jepson Prairie Compost 4) Tomato waste compost from OLAM 5) Tomato waste compost from OLAM at a higher application rate, balanced for nitrogen The experiments were conducted in a completely randomized block layout with each treatment replicated 4 times in Steve Meek’s fields and 3 times at Russell Ranch. The plots consisted of 32 beds for the amendment experiment at Steve Meek’s and 20 beds for the compost experiment, each with 750 foot long beds. At Russell Ranch, the wheat tomato rotation had three replicate plots with 8, 210 foot long, 60 inch wide beds which were either planted in a cover crop or fallow. Gypsum and compost applications were made in single rows within the experimental area. Amendments were applied prior to cover crop planting and were trenched to 10 cm. The pea and vetch cover crop was planted in November and was incorporated in March. The composts of the second experiment were incorporated in the spring of 2014, prior to planting. They were
applied in 10 inch bands by a spreader in a shallow trench. After N-P-K testing of composts, it was determined that all composts except OLAM had fairly similar nitrogen levels. For this reason, all composts were applied at rate where dry weights equaled the dry weight of 4 ton/acre field moist application of Foster Farms compost, and a second, higher, application rate was used for OLAM in order to match the N content of Foster Farms compost. Planting occurred on the 28th and 29th of April in both field locations using a mechanical transplanter. The planting density at Russell Ranch is 8,500 plants/acre and the grower planted a density of 8,750 plants/acre. Heinz 8504 variety were planted at Russell Ranch, and Heinz 5508 where planted by Meek.
Saturated hydraulic conductivity was measured using a drip emitter applied to the soil surface with three flow rates as described by Shani et al (1987). This method allows for efficient determination of hydraulic conductivity in field conditions. The saturated hydraulic conductivity is calculated from the radius of the ponded water. These tests were conducted in all fields at two time periods over the growing season, once in May, soon after planting and later in August, after harvest and before incorporation of the tomato organic matter. Values from this data will also be used to calculate water retention of soil and sorptivity of soil.
As an indicator of soil structure, pore size, and the ability of water to move through the soil, undisturbed bulk density was sampled in the month of July from 5 treatments (excluding gypsum alone, gypsum with compost, and gypsum with cover crop) using a core of 381.7 cm3 (Image 2) sampled at a depth of 10cm.
Aggregate size distribution was also sampled as an indicator of soil structure (Image 3). Aggregate sizes influence infiltration and ease of water and root movement in the soil, in addition to soil sealing, erosion, and run off. The procedure involves removing
Image 1: Spreading Compost in Grower's Field
Image 2: Bulk Density Core Image 3: Sample for Aggregate Size Distribution
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minimally disturbed soil samples with a flat shovel and a process of sieving to isolate aggregates of varied size. Sampled in July, analysis of aggregate size distribution will continue this fall. After harvest, soil samples were taken from each plot and analyzed with A & L Western Laboratories for soil macronutrients (N, P, K, S, Ca, Mg), pH, EC and organic matter. Ammonium and Nitrate were also extracted for analysis at three time points throughout the growing season.
Tomato disease severity walks occurred in June, July, and August. Disease was ranked on a scale of 1-5 based on a severity of disease symptoms, with 1 being no disease and 5 being completely necrotic with severely reduced yield. 15 plants were assigned a 1-5 severity ranking from each row, excluding gypsum alone, gypsum with compost, and gypsum with cover crop. Plants were randomly selected by stepping 5 paces between each plant in Steve Meek’s field and 3
paces between each plant at Russell Ranch. Edges were bypassed, as edge effect was apparent. Specimens were also diagnosed for specific diseases. The presence of tomato spotted wilt virus was confirmed with immunostrips. Powdery mildew and nematodes were diagnosed with visual signs and symptoms, and Fusarium and Verticillium were confirmed by growing specimens on acidified potato dextrose agar and selective Verticillium media, microscopy, and genetic sequencing.
Tomatoes were harvested at the end of August in Russell Ranch fields and the beginning of September in the grower’s fields. Yields were measured with a tomato machine harvester and GT cart equipped with a scale in 200 foot long strips within plots.
Results and Discussion
Yield
Yield was significantly higher for the compost only treatment at Russell Ranch (Figure 1). Cover crop treatments had the lowest yields after a single year of cover crop incorporation. Yields at
Image 2: Identifying conidiophore of Verticillium dahliae
Image 3: Nematode Patch in Grower's Field
Figure 1: Graph of yield per treatment at Russell Ranch Figure 2: Russell Ranch Yield per Block
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Russell Ranch also differed by plot, or “block”. 2-5 had a comparatively low yield (Figure 2). In the grower’s field, there were no treatment differences. There was an effect of row location (Figure 3), with the northernmost row having a higher yield in comparison to the southernmost row. This lower yield was at least partially attributable to the presence of a large nematode patch (Image 5). The compost study did not have significant yield differences (Figure 4).
Bulk Density
The amendment study showed a reduced bulk density with cover crop treatments, including the long term tomato, leguminous cover crop corn rotation at Russell Ranch (LTCC) (Figure 5). Rows with cover crops had an average bulk density that was 0.091 g/cm3 less than rows without cover crops. Additionally the compost, gypsum, cover crop treatment and the long term tomato, legume, corn rotation had significantly lower bulk densities than the cover crop alone. This may be an indicator of the volume of amendment needed for observable results and/or soil structural changes that require long term management to observe. There was not a significant interaction between the cover crop and other amendments for bulk density reduction. No significant differences were observed in the grower’s field within the 8 treatments, but bulk density was lower by 0.043g/cm3 when analyzed for the presence of cover crop. Observations matched our hypothesis that the
Figure 2: Grower Yield per Block Figure 1: Compost Yield per Treatment
Figure 3: Russell Ranch Bulk Density by Treatment
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addition of organic matter could facilitate differences in soil structure. Decreased bulk density can facilitate the movement of water, air, and roots within soil. Treatments in the compost study were not significantly different from one another. Nutrients
Nutrient levels, pH, organic matter, and CEC were analyzed with the three amendments. Table 1 displays each nutrient and the estimated increase or decrease in the nutrient given the amendment applied. Significant changes are denoted by an asterisk.
Table 1: Effect of Amendment on Nutrient Content of Soil
Nutrient (lbs/acre) Cover Crop Gypsum Compost
Organic Matter lbs/acre -0.068 -0.018 0.082
Estimated Nitrogen Release lbs/acre
-1.379 -0.55 1.521
Cation Exchange Capacity -0.571* 0.629* 0.314
Sulphur SO4-S (ppm) -8.289 42.046*** 3.346
Calcium -263.5 -23.614 -2.914
Olson Method P2O5 6.259 -4.255 22.885
Magnesium -241.843 -138.186 41.186
Sodium -13.521 -2.121 30.421**
Weak Bray P2O5 -9.052 -8.264 54.296**
Soil pH -0.029 -0.014 -0.021
K2O -34.089 -19.929 58.86*
Estimates of increase of nutrient given presence of amendment
Nutrient levels depended heavily on the field in which they were located. The table below (Table 2) shows, for each nutrient, the difference between Russell Ranch and Steve Meek’s field (a negative value indicates Russell Ranch has decreased nutrients).
Table 2: Effect of Field on Nutrient Content of Soil
Nutrient (lbs/acre) RR increase
Organic Matter -0.616***
Estimated Nitrogen Release -12.194***
Cation Exchange Capacity 0.641*
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Sulphur SO4-S (ppm) -6.214
Calcium -1756.908***
Olson Method P2O5 -110.692***
Magnesium -579.881*
Sodium -14.775
Weak Bray P2O5 -176.779***
Soil pH -0.528***
K20 -194.893***
Estimates of increase of nutrient in Russell Ranch
In the compost study, nutrients were significantly different in the displayed treatments (Figure 7). Phosphorus and sodium were higher in the Foster Farms chicken manure compost amended rows compared to the control and both OLAM application rates of compost. SO4-S content (Figure 8) of Foster Farms amended soils was also higher compared to no addition, Jepson Prairie, and OLAM composts. pH was significantly lower in Foster Farms amended soils versus the control soils (Figure 9). Jepson Prairie compost resulted in soils with higher levels of calcium than control and OLAM amended soils.
Saturated Hydraulic Conductivity
Figure 4: Nutrient Content per Compost Treatment
Figure 5: Compost Treatment Soil pH Figure 6: Compost Treatment SO4S
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Preliminary results from the first measurement point of saturated hydraulic conductivity (Ks) show that Russell Ranch blocks have a significantly lower hydraulic conductivity than the grower’s field. Additionally, the compost, gypsum, cover crop treatment has a lower Ks than the control and compost with cover crop treatments at Russell Ranch. The compost, gypsum, and cover crop treatments in the grower’s field also have a significantly lower hydraulic conductivity than treatments of cover crop and compost alone. Negative Ks values are not likely to occur, and further exploration of the data may be needed to determine the root of such low numbers. A second measurement of Hydraulic conductivity was taken after the tomato harvest using the same method. Hydraulic conductivity was also measured on beds in the long term tomato, legume, and corn rotation. We expect that these plots will reflect long term applications of organic matter and more obvious difference in Ks resulting from modified soil structure. When analyzed, hydraulic conductivity can be compared in both amendment study locations, in the long term cover crop rotation, and in the compost study.
Disease Disease in this experiment was found, as would
be expected, to increase over time, and significant differences were found, in both locations, for reduced disease severity in fields that contained compost, compared to those which did not in the third time period (August 18th). This difference was small however, with only ~4% difference between the two (0.208 greater disease in plants not grown in compost amended soils). There were no significant differences in the first (June 18th) and second (July 2nd) time points. These results are consistent with last year’s project, in which disease was reduced in compost treatments.
Within the compost study, there were no significant differences between treatments or the control. There were also not significant differences between the grower’s field and Russell Ranch in terms of disease severity.
Diseases observed included nearly 100% Verticillium in tomato plants, Fusarium, powdery
mildew, tomato spotted wilt virus and root knot nematodes. Further analyses will explore the prevalence of each disease and the severity of symptoms caused by each disease found in the treatments. Amendment Expenses
Figure 7: Hydraulic Conductivity at Russell Ranch and Grower’s Field
Figure 8: Effect of Compost on Disease Severity over Growing Season
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The market rate for the Foster Farms chicken manure compost is $43/ton at an application rate of 4 tons/acre. OLAM compost is $25/ton, free on board, and Jepson Prairie compost is $20 per yard with a 5 yard minimum. The application cost is $172/acre. Gypsum is applied at a rate of 2 tons/acre and costs $71/ton to spread. A typical vetch and pea cover crop costs $211/acre to implement. Due to these expenses, when considering whether to apply amendments, the cost-benefit of expense versus yield gain must be determined to understand whether the system is worth implementing. In this study, cover crops were shown to reduce bulk density at Russell Ranch, but had the lowest yield. For this reason, it would be of interest to explore the long term implication of cover crop use in altering soil structure as it relates to yield. Compost alone had the highest yield at Russell Ranch, and compost treatments showed decreased disease at the third time period. Therefore benefits of compost to yield, nutrients, and disease reduction versus expense can be determined when considering use of the amendment
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Works Cited Shani, U., et al. "Field method for estimating hydraulic conductivity and matric potential-water content relations." Soil Science Society of America Journal 51.2 (1987): 298-302.
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2014 Annual Progress report Project Title: Genome sequencing of the bacterial canker pathogen, Clavibacter michiganensis subsp. michiganensis, to develop robust detection and disease control strategies.
Project Leader: PI: Gitta Coaker Associate Professor Department of Plant Pathology University of California Davis Phone: 530-752-6541 E-mail: [email protected] Co-PI: Robert Gilbertson Professor Department of Plant Pathology University of California, Davis Phone: 530-752-3163 E-mail: [email protected] Introduction: Bacterial canker of tomato, caused by Clavibacter michiganensis subsp. michiganensis (Cmm), can cause significant losses in greenhouse and field tomato production under favorable environmental conditions (Eichenlaub and Gartemann 2011). Although seed disinfestation with HCl is a highly effective disease control strategy, bacterial canker can still develop if seed treatment is not complete or if other sources of inoculum are present. Detection of the disease in the field is now commonly performed with immunostrips, but these are not very sensitive and are prone to false positives due to the presence of non-pathogenic bacteria that are closely related to Cmm. PCR with Cmm primers is a more sensitive method, but it is technically challenging and the currently available primers may only detect a subset of Cmm strains present in the field (Eichenlaub and Gartemann 2011). Development of a robust PCR detection method for Cmm would enable rapid detection of contaminated seed or seedlings before planting or transplanting, respectively. Furthermore, there are no existing chemical or genetic disease control methods for this pathogen. The goals of the funded research are to sequence the genomes of 14 Clavibacter strains in order to develop a robust PCR detection strategy and test the efficacy of novel control strategies based on genome sequencing information. This is the 2nd year of a proposed three year grant. During the first two years we proposed to: Objective 1: Sequence and assemble 13 California strains of Cmm as well as one saprophytic Clavibacter strain.
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Objective 2: Develop robust PCR detection strategies for Clavibacter michiganensis subsp. michiganensis.
The PI of the project, Gitta Coaker, was recently awarded the “Chancellor’s Fellow for Research Excellence” at UC Davis, which comes with a monetary award. She will use these funds for more detailed analyses of the genome organizations of different Clavibacter strains in the next year and expand our development of primers specific to Cmm beyond what was originally proposed. Therefore, we do not ask for funds in 2015 from CTRI, but will continue to update the knowledge base about Cmm in California.
Progress for Objective 1: Genome sequencing and assembly of Clavibacter strains
During the first year of this project, we proposed to complete the genome sequencing of five Cmm strains (Objective 1). However, we have now completed the genome sequencing, assembly and annotation of 14 strains. Because sequencing costs have declined, we have been able to sequence a total of 13 Cmm strains, nine more genomes than originally proposed. This increase in genome sequencing will enable better insight into how Cmm causes disease in tomato, the genetic diversity among pathogenic Cmm strains, as well as enhance our ability to design specific diagnostic PCR primers. We have completed sequencing of 13 Cmm strains and one saprophytic Clavibacter strain. All the Cmm strains were verified for pathogenicity on tomato and sequenced on the MiSeq/HiSeq platform at UC Davis (Figure 1). For better assembly and downstream analysis, we included 6 strains on the PacBio platform (UC Davis). The sequencing data quality is excellent and we have found promising information about the genome organization, highlighting important areas of the genome that differ between pathogenic and saprophytic Clavibacter strains from California.
Figure 1. Representative bacterial canker disease symptoms. Four-week-old tomato plants were inoculated by piercing the stem with a needle and inoculating 5 µl of 107 CFU/ml culture of Cmm. Disease symptoms were evaluated 2 weeks post-inoculation. Pathogenic strain CASJ001 does not contain plasmid pCM2. Arrows indicate unilateral leaf wilt, a classic symptom of pathogenic Cmm. Disease symptoms were detected after inoculation with pathogenic strains, but never after inoculation with saprophytic strains.
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The genomes of the Cmm strains from California are similar to the reference strains NCPPB382, with high gene homology (similarity) across large segments of the chromosome (Figure 2). The reference strain NCPPB382 was the only pathogenic strain of Cmm sequenced prior to our work and much of the experimental investigation of Cmm pathology has been conducted on this strain. Two plasmids (pCM1 and pCM2) are required for pathogenicity in NCPP382 and deletion of these plasmids significantly decreased the virulence of this strain (Gartemann, Abt et al., 2008; Meletzus, Bermphol et al., 1993). Both of these plasmids differ in their presence and gene content in the California Cmm strains. pCM2 is missing in at least two California Cmm strains and yet the strains are virulent and able to cause disease (Figure 1, pCM2 is missing in CASJ001). Genomic islands (GEIs) are DNA segments of acquired by horizontal gene transfer and contribute to the diversification and adaptation of microorganisms. Thus, these genetic sequences have a significant impact on genome plasticity and evolution as well as the dissemination of antibiotic resistance and virulence genes (Dobrindt, Hacker et al., 2004). Most of the genomic islands present in the reference strain NCPPB382 are missing in the California Cmm strains. Furthermore, Cmm strains from California possess several novel genomic islands. Taken together, these data also indicate different pathogenic Cmm strains use different mechanisms to cause disease on tomato, highlighting the need for investigation of pathogenesis in multiple strains as opposed to a single reference strain.
For the remaining objective, we propose to completely assemble all Clavibacter genomes, and investigate differences at the gene level. Entire genomes and important genes will be compared with the Cmm NCPPB382 reference strain and other strains (isolated from other US states and worldwide). The completion of this objective will provide a large amount of data that can then be analyzed to (1) develop a robust PCR detection strategy for Cmm, (2) understand the pathogenicity of California Cmm strains (3) develop novel disease control strategies and (4) to provide more detailed information about the level of genetic diversity present in Cmm strains in North America.
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Figure 2. Circos plot of genome wide comparison of pathogenic Cmm California strain CASJ001 and saprophyte CASJ009 with reference NCPPB382. The Circos plot illustrates regions of significant similarity between CASJ001 to NCPPB382. The chromosome of CASJ001 and CASJ009 are depicted in arbitrary colors (outer ring). Orange bars depict unique genes whereas blue bars depict genes that share orthologs with the NCPPB382 chromosome. The dark blue bar depicts % identity. The two pathogens are more closely related to each other than to the saprophytic strain. The Clavibacter saprophyte CASJ009 is lacking many genes associated with virulence and has a larger number of unique genes.
Progress for Objective 2: Developing robust PCR detection strategies for Clavibacter michiganensis subsp. michiganensis
Despite the need to develop sets of PCR primers that specifically detect Cmm, the existing diagnostic primers are frequently non-specific or detect only a subset of pathogenic strains. The goal of Objective 2 is to develop a robust PCR detection strategy for pathogenic Cmm. During the first year, we tested the specificity of previously published primers designed based on genes present both on the Cmm chromosome and plasmids (Kleitman, Barash et al. 2008; Cho, Lee et al. 2012). These diagnostic primers were frequently non-specific and gave positive results on saprophytic non-pathogenic Clavibacter or Clavibacter strains pathogenic to potato only (Cho, Lee et al. 2012). We used the genome sequence information in Objective 1 to design 10 different primer pairs targeting both the genome and plasmids and selected two pairs of promising primers. One primer pair amplified a region of the catR gene, a LacI-family transcriptional regulator. The other primer pair amplified a region of the sab gene, a Sugar ABC transporter. These primers were tested for specificity and sensitivity (Table 1, Figure 3). Over the past several years, we have assembled a large collection of Cmm isolates and saprophytic Clavibacter representative of the phylogenetic diversity present worldwide. These Cmm strains, along with a panel of more than 100, related non-pathogenic Clavibacter and other bacterial pathogens of tomato were included
CASJ001
NCPPB382
CASJ009
NCPPB382
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for specificity testing. We also tested the sensitivity of these primers. These primers sets are highly specific and can detect as low as 0.005 ng of bacterial DNA (Table 1, Figure 3).
Table 1. Specificity of newly designed Cmm primers on a panel of Clavibacter strains.
Bacteria Number of strains Primers
catR F/R sab F/R Clavibacter michiganensis subsp. michiganensis 82 + + Saprophytes 6 - - Clavibacter michiganensis subsp. sepedonicus 10 - - Other strains 20 - -
Figure 3. Sensitivity of newly designed Cmm primers using purified DNA. A) Primers were designed to amplify an 800 bp region of the catR gene (LacI-family transcriptional regulator, catR F/R), B) Primers were designed to amply a 850 bp region from the sab gene (Sugar ABC transporter, sab F/R).
For the remaining objective, we propose to determine the robustness of these primers and test them directly on plant materials. We will include inoculated plant materials as well screen potential positives collected in the field. We will also attempt to develop primers to specifically differentiate the REP-PCR types of Cmm, including those that are most prevalent in California. Furthermore, primers that are specific to saprophytes will be designed. All of these primers will be tested for specificity, sensitivity and their robustness. Primer pairs will also be tested for their ability to detect Cmm in seed lots. These more specific PCR primers can be useful for determining which type of Cmm is involved in an outbreak after an initial positive result is obtained. The saprophytic specific primers can be used as negative controls in PCR testing.
1000bp
1000bp 500bp
500bp
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In summary, over the last two years we have significantly updated the knowledge base concerning Cmm in California. The whole genome study has shown that Cmm strains are genetically different from the type Cmm strain, which may reflect adaptation to different climatic or environmental conditions. Genome sequence information was used to design highly specific and sensitive primers for PCR detection of Cmm. The genome sequences will also help us understand the changes undergone at the gene level and how these changes have impacted Cmm virulence and survival in California. This information can also be employed to develop a more robust detection system and a control strategy for bacterial canker of tomato.
References:
Cho, M. S., J. H. Lee, et al. (2012). "A quantitative and direct PCR assay for the subspecies-specific detection of Clavibacter michiganensis subsp. michiganensis based on a ferredoxin reductase gene." J Microbiol 50(3): 496-501.
Dobrindt, U., J. Hacker, et al. (2004). "Genomic islands in pathogenic and environmental microorganisms." Nature Rev Microbiol 2:414–424.
Eichenlaub, R. and K. H. Gartemann (2011). "The Clavibacter michiganensis subspecies: molecular investigation of gram-positive bacterial plant pathogens." Annu Rev Phytopathol 49: 445-464.
Gartemann, K. H., B. Abt, et al. (2008). "The genome sequence of the tomato-pathogenic actinomycete Clavibacter michiganensis subsp. michiganensis NCPPB382 reveals a large island involved in pathogenicity." J Bacteriol 190(6): 2138-2149.
Kleitman, F., I. Barash, et al. (2008). "Characterization of a Clavibacter michiganensis subsp. michiganensis population in Israel." European J Plant Pathol 121(4): 463-475.
Meletzus, D., Bermphol, A., et al. (1993). "Evidence for plasmid-encoded virulence factors in the phytopathogenic bacterium Clavibacter michiganensis subsp. michiganensis NCPPB382". J Bacteriol 175(7):2131-2136.
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Project Title: Detection of azole and strobilurin fungicide resistant strains of tomato powdery mildew in California Project leader: Ioannis Stergiopoulos
Assistant Professor Department of Plant Pathology, University of California Davis 578 Hutchison Hall, One Shields Avenue, Davis, CA 95616-8751 Phone: 530-400-9802, fax: 530-752-1199 email: [email protected]
Cooperating Personnel: Brenna Aegerter,
Cooperative Extension Farm Advisor, San Joaquin County UCCE, San Joaquin County
2101 E. Earhart Ave. Ste. 200, Stockton, CA 95206 Phone 209-953-6100 fax 209-953-6128 email: [email protected] Gene Miyao Cooperative Extension Farm Advisor, Yolo-Solano-Sacramento counties Yolo-Solano-Sacramento counties 70 Cottonwood Ave., Woodland, CA 95695 Phone: 530-666-8732 email: [email protected]
Abstract:
Powdery mildews are obligate biotrophic fungi that cause extensive diseases in crops. In tomatoes, powdery mildew infections are caused by the species Leveillula taurica and Oidium neolycopersici, which are primarily associated with the disease in field and greenhouse grown tomatoes, respectively. The goal in this research was to investigate whether the continuous use of azole and strobilurin fungicides in tomato fields in California has sparked the appearance of resistant strains of L. taurica and O. neolycpersici. Knowledge on the presence and distribution of fungicide resistant strains will help design a strategy for preventing further spread of such strains. Since resistance development is frequently correlated with mutations at the target site of the fungicides, to achieve our goal we followed an approach of first cloning and subsequently screening in L. taurica field populations, for mutations in the CYP51 and cytb genes that encode for the enzymatic targets of azole and strobilurin fungicides, respectively. Analysis of the mitochondrial cytochrome b gene (cytb) showed that strains with mutations conferring resistance to strobilurins, such as the notorious G143A mutation, are already widely present in fields. However, the analysis also indicated that the presence of this mutation in field strains of L. taurica is associated with mitochondrial heteroplasmy for the cytb gene, suggesting that resistance in the fields is not yet complete but rather correlates with the portion of mitochondria in each strain carrying the mutated cytb allele. In contrast, no mutations were identified so far in the CYP51 gene of L. taurica populations collected from the fields in 2014. The characterization of these two genes from L. taurica sets the scene for monitoring shifts in fungicide sensitivity in populations of the fungus.
2
1. Introduction
Powdery mildews are obligate biotrophic fungi that cause extensive diseases in crops1. In tomato, powdery mildew is caused by the species Leveillula taurica2 and Oidium neolycopersici3, which are primarily associated with the disease in field and greenhouse grown tomatoes, respectively4. Powdery mildew epidemics occur almost yearly in vegetable and tomato-growing areas of California and can impact fruit production and quality5. With only a small number of resistant tomato varieties available, current control methods for tomato powdery mildew mostly rely on the use of chemical control agents such as commercial fungicides and sulfur-dusts6. In particular, azole and strobilurin fungicides are the two main fungicide classes that have been used extensively in the field, as an alternative or in addition to sulfur treatments. Rally® (myclobutanil, an azole fungicide), CabrioTM (pyraclostrobin, a strobilurin fungicide), QuadrisTM (azoxystrobin, a strobilurin fungicide), and Quadris TopTM (azoxystrobin and difenoconazole, a mixture of a strobilurin and an azole fungicide), are perhaps the most widely used compounds in California against powdery mildew. The continuous use, however, of these fungicides in the fields increases the danger for fungicide resistance developmen6. Indeed, as with other plant pathogens, fungicide resistant populations of tomato powdery mildew have been reported in California already since 2007, when two cases of resistance to two widely used fungicides were reported. How widespread those cases are or whether there has been a build-up of resistant isolates over time remains unknown. In fungi resistance to fungicides is frequently correlated with point mutations in their target sites such as the CYP51 and cytb genes, which encode for the enzymatic targets of azole and strobilurin fungicides, respectively7,8. The mode of action of strobilurins, in particular, is based on inhibition of mitochondrial respiration by binding to the cytochrome bc1 enzyme complex (complex III) at the QoI site (QoI fungicides or QoIs), which is partially encoded by the mitochondrial cytochrome b (cytb) gene9. So far, at least eleven single or combined mutations have been described in cytb as conferring resistance to QoIs in different phytopathogenic fungi9. Most of these mutations are concentrated in two critical regions of the protein between amino acid positions 127 to 147 (also known as the “cd loop”) and between positions 275 to 296 (also known as the “ef loop”), respectively. Mutations especially within the cd lood of cytb are known to confer resistance to QoIs, and three in particular amino acid substitutions have been detected in fungi as the ones predominately governing resistance to QoIs9. These are
i) the mutation causing a change from phenylalanine to leucine at position 129 (F129L) ii) the mutation causing a change from glycine to arginine at position 137 (G137R) iii) the mutation causing a change from glycine to alanine at position 143 (G143A)
Resistance factors associated with the three mutations are different with the G143A being the most severe one, as strains with these mutation exhibit very high levels of resistance (complete) to QoIs, while the G137R and F129L mutations are known to cause only moderate levels of resistance (partial)9.
In a similar way, the mode of action of azole fungicides is based on inhibition of sterol 14α-demethylase (or CYP51), a cytochrome P450 enzyme that catalyzes the biosynthesis of ergosterol in fungi, which is the main sterol present in fungal cell membranes8. Consequently, mutations in CYP51 are known to confer resistance towards azoles and in this respect, a number of different mutations have been described from an array of plant pathogenic fungi10. In the wheat pathogen Septoria tritici, for example, more than 30 mutations have been reported in the coding sequence of CYP51 that confer various levels of resistance to azole fungicides11. Although, a smaller number of mutations (usually 2-10) have been detected in the CYP51 sequences of other fungi, in general alterations in its coding sequence are shown to have a variable effect on resistance to azoles ranging from moderate effects to almost complete resistance10.
3
2. Methods, Results & Discussion
2.1 Collection of infected material from tomato fields. Samples from tomato plants showing leaf symptoms of powdery mildew were collected from a total of 22 fields that were distributed within the Yolo (13 fields), Sacramento (2 fields), and San Joaquin (7 fields) counties (Table 1). Sampling was performed throughout the months of August and September on an almost biweekly schedule. The date that the samples were collected as well as the tomato cultivar and the fungicide treatments up to the collection point were recorded, when known, for each sample. Sampling in the field was performed by collecting multiple compound leaves from a number of plants, randomly distributed within each field. In the lab, discs of infected leaf tissue were subsequently excised using a sterilized cork borer from individual leaflets of the compound leaves, and each disc was then placed separately in a 2.0 mL microcentifuge tube and immediately frozen in liquid nitrogen for subsequent DNA extractions. The remaining leaflets and/or compound leaves were placed in an envelope and stored at -80°C. A total of 450 leaf discs were collected (hereafter referred to as “samples”) and genomic DNA till the point of preparing this report (11/1/2014) has been extracted using standard kits from approximately 100 of these samples. An additional 150 samples were also obtained using the above method from infected material that was collected from the fungicide trial plots of co-PI Brenna Aegerter. These fungicide trials were not set-up as part of this project but nevertheless presented with an excellent opportunity to compare populations of the fungus from fungicide treated and untreated fields. In more specific terms, samples were collected from plots treated with 1) trifloxystrobin (QoI), 2) cyflufenamid (amide), 3) tetraconazole (azole), 4) azoxystrobin (QoI) + difenoconazole (azole), and 5) non-treated plots. DNA extraction and subsequent analysis of these samples is currently underway. 2.2 Molecular identification of Leveillula taurica as the pathogenic agent of tomato powdery mildew in CA.
As afore mentioned, powdery infections in tomato can be caused by the phylogenetically related species Leveillula taurica2 and Oidium neolycopersici3 which are primarily associated with the disease in field and greenhouse grown tomatoes, respectively4. In California, L. taurica has been reported as the only species to infect tomato plants in outdoor commercial fields. However, in 2013 sporadic infections by O. neolycopersici were observed in fields around the Davis/Sacramento area and San Joaquin valley, particularly towards the end of the growing season (mid-September to mid-October).
Visual inspection under the microscope of the infected leaf samples that were collected during August and September 2014 showed that L. taurica was the causal agent of the powdery mildew infections on tomato. In contrast to the situation in 2013, we did not observe any infections by O. neolycoperscici this year, which implies that this fungus is only rarely present in the fields and thus does not seem to pose a serious threat to outdoors grown tomatoes. It should be noted that due to the climatic conditions in 2014, the severity of powdery mildew infections was particularly high this year, especially during August and September. However, although the conditions in 2014 favored infections by L. taurica, this might not be the case for infections caused by O. neolycoepsirci as the two fungi differ in their requirements in temperature and relative humidity in order to cause disease4.
To further confirm the above visual observations, we amplified using PCR a 600 bp fragment of the ribosomal DNA (rDNA) from a representative set of 25 samples. The amplified fragment spanned the internal transcribed spacer 1 (ITS1), the 5.8S ribosomal DNA gene, and internal transcribed spacer 2
4
(ITS2) sequences (Figure 1). We specifically chose to amplify these sequences, as they are known to provide good phylogenetic resolution at the species level and even sometimes at the isolate level as well. Amplification was performed using the oligonucleotide primers ITS1 (5’- TCC GTA GGT GAA CCT GCG G -3’) and ITS4 (5’- TCC TCC GCT TAT TGA TAT GC -3’) that have been designed on highly conserved regions of fungal rDNA sequences12. These primers are shown to amplify the targeted rDNA regions from a wide spectrum of even phylogenetically distant fungi, including various species of powdery mildews12. As DNA was extracted from diseased leaf material that next to L. taurica might be contaminated by other fungi as well, the use of the fungal conserved ITS1 and ITS4 primers as opposed to Leveillula species-specific primers served the dual purpose of first, confirming that the pathogenic agent was L. taurica and not O. neolycopersici, and second, ensuring that the samples were free of DNA from other fungi in quantities that could obscure our subsequent analyses.
Sequence analysis of the amplified 600 bp fragment confirmed that in all 25 samples examined, L. taurica was the species causing the tomato infections, as the amplified rDNA sequences were all 100% identical to the corresponding rDNA sequences from L. taurica strain WSP71133 that has been deposited in NCBI (GenBank Accession Number: AY912077.1) (Fig. 1). The analysis also confirmed that the samples were free of DNA contamination from other fungi that could obstruct subsequent analyses, as we did not detect foreign trace data within our sequencing files. Given that identical results were obtained for all 25 samples and in order to reduce costs, we did not pursue further the rDNA analysis of the samples but rather the rest of the infections by L. taurica were confirmed visually under the microscope. 2.3 Cloning of the cytb gene from Leveillula taurica and identification of mutations conferring resistance to strobilurin (QoI) fungicides.
Although the cytb gene has been cloned from a number of different fungi, it hasn’t been characterized yet from L. taurica. Cloning of genes from this fungus presents with considerable challenges as this is an obligate biotroph that cannot be cultured in vitro. Nevertheless, to clone the cytb gene from L. taurica, we followed a PCR-based approach with (degenerate) primers that were designed on conserved regions of the cytb gene from different fungal species. Several primer sets were tested on DNA extracted from the different samples, including primers RSCBF1 (5’- TAT TAT GAG AGA TGT AAA TAA TGG -3’) and RSCBR2 (5’- AAC AAT ATC TTG TCC AAT TCA TGG -3’) that have been used successfully in the past for the amplification of the cytb gene from other powdery mildew species13. Indeed, using this primer set a single fragment of ~300 bp in size was amplified that when sequenced showed high similarity to cytb genes from other powdery mildew fungi, including Erysiphe alphitoides (98% identity), Podosphaera fusca (97% identity), Podosphaera leucotricha (97% idemntity), and others. The translated 100 amino acid long product of this fragment showed that it almost perfectly aligned to the cytb protein sequence between amino acid positions 65-165 of other plant pathogenic fungi (fig. 2). This means that the crucial region between amino acid positions 127 to 147 (cd loop) that contains the residues known to confer resistance to QoIs when mutated (i.e F129, G137, and G143), is present in our cloned cytb fragment from L. taurica. Currently, efforts are underway to obtain the full-length cytb gene from L. taurica, using chromosome walking techniques.
The partial 300 bp cytb fragment was subsequently amplified and sequenced from 51 samples that represented all but one fields (1-4 samples per field) from which infected material was collected, in order to determine whether point mutations at sites F129, G137, and G143 or other amino acids were present among the strains (Table 1). Direct sequencing of the PCR product revealed that nucleotide exchanges were present in at least 22 positions that translated into eight amino acid changes in the encoded cytb protein fragment. Among the observed amino acid changes was also the notorious G143A substitution that is known to confer high levels of resistance to QoIs. This mutation was present in 27 of the 51
5
analyzed samples and was broadly distributed in 19 of the fields in Yolo, Sacaramento, and San Joaquin counties (Table 1). Notably, samples that did not have this mutation were mainly from infected material that was collected early in August from the UC vegetable crop field, which had not received any treatments with strobilurins up to that point. Later during the season, however, we did detect this mutation from material that was collected from the UC Davis vegetable crop fields. Despite the prevelant presence of the G143A mutation in the fields, we did not, however, observe any amino acid changes at positions F129 and G137, although a L130T substitution was present in the critical cd loop region of the protein. It is currently unknown whether this mutation has any impact on resistance to QoIs but given the fact that is located in a critical region of cytb and that is also right next to position F129, it can be assumed that it will have an impact on resistance as well. Furthermore, the L130T mutation was also widely present in 45 of the samples that were collected from 20 different fields (Table 1). Two more mutations were detected within the cd loop area of cytb, namely the Y132C and P135T substitutions, but these were present in just one strain each. The effect of these mutations on resistance to QoI is unknown. The remaining four mutations present in the characterized 100 amino acid long fragment of cytb were outside the cd loop region and apart from one of these mutations (i.e. L81T) that was present in 11 of the samples, all others were found in only one sample each (i.e. T85A, R99G, and W76R) or two of the samples (W76R).
Despite the fact that a number of mutations have been identified in cytb, a careful inspection of the sequencing trace data obtained from the direct sequencing of the PCR products revealed the presence of two overlapping peaks at key nucleotide positions that code for amino acids L130, G137, and G143 (fig. 3). In more specific terms, at nucleotide position responsible for the L130T substitution, two overlapping peaks for cytosine (C) and thymine (T) were present, with T leading to the wild-type amino acid (L130) and C to the mutation (T130). The same was true for G137 in which case two peaks for guanine (G) and adenine (A) were present, although both represent the wild-type amino acid G, meaning that the G>A nucleotide substitution is a silent one in this case. Finally, at nucleotide position responsible for the critical G143A substitution again two overlapping peaks were present, namely one for guanine (G) and one for cytosine (C), with G leading to the wild-type amino acid (G143) and C to the mutation (A143).The presence of overlapping peaks at the above nucleotide positions indicates that the pool of PCR products amplified using the total DNA of each sample as a template is an heterogeneous mixture of wild-type and mutated alleles of cytb. This implies that samples collected from each individual leaf represent a mixture of different strains, some of which would bare the wild-type cytb allele while others the mutated alleles for G143A and/or L130T. Although, more than one genotypes (strains) of L. taurica are known to occur on the same infected leaf, it was surprising to see that almost all samples represent mixtures of strains co-existing on the same spot of a leaf. Given the unlikeliness of this scenario, an alternative hypothesis emerges of mitochondrial heteroplasmy for the cytb gene. Heteroplasmy is commonly described as the coexistence of more than one types of mitochondrial genomes (mitotypes) in a single cell and although it regularly occurs in plants, it has only rarely been described in fungi. In L. taurica mitochondrial heteroplasmy for cytb would imply that some mitotypes would code for the wild-type cytb isoform while others for the mutated for G143A and/or L130T isoforms. This situation has also been described in the apple powdery mildew fungus Podosphaera leucotricha where mitochondrial heteroplasmy with respect to the G143A mutation would control the level of QoI resistance to this fungus14. As fungal cells can contain multiple mitochondria in a single cell, the quantity of mitotypes representing the G143A mutation will define in P. leucotricha the level of resistance against QoIs14.
To further confirm the hypothesis of mitochondrial heteroplasmy and to discriminate among the different mitotypes that could be present in a single sample with respect to cytb, the amplified PCR products were cloned into pGEM-T Easy plasmid vector system and individual clones were subsequently sequenced and analyzed. Cloning to pGEM-T allows the insertion of only a single PCR fragment per plasmid, thus enabling the sorting of the different cytb allele fragments that are present in the pool of amplified PCR products from each sample. Indeed, in contrast to sequences obtained from PCR products directly, no
6
overlapping peaks were found in cloned sequences. Although this work is still in progress, sequencing of multiple plasmids for each sample (current target is set to 5 per sample) allowed us to discriminate between the different mitotypes with respect to the cytb (i.e. cytb isoforms) that are present in most of the samples. In this respect, 11 different cytb isoforms were detected so far, including the wild-type cytb isoform that was recovered from almost all samples (fig. 4). The G143A mutation was found in six isoforms as it can be combined with other mutations present in cytb as well. As expected, most samples carried more than one isoforms (usually two to three), one of which is frequently the wild-type one. Currently efforts are directed towards cloning and sequencing multiple PCR fragments from each sample in order to assess the relative frequency of each mitotype in the samples and to detect rare mitotypes as well. This is important because as in other reported cases of mitochondrial heteroplasmy14, the level of resistance towards QoIs would be defined by the quantity of mitotypes with the G143A mutation in cytb as compared to the wild-type one.
Overall, the analysis of the cytb gene carried out so far enables us to infer with certainty that mutations that confer resistance to QoI fungicides, such as the notorious G143A substitution that leads to complete resistance, are already widely present in L. taurica populations in California. The observed mitochondrial hetroplasmy, however, for the G143A and wild-type isoforms of cytb implies that differences in fungicide sensitivity among the fields isolates exist that, as in other fungal systems, most likely correlate with the portion of the mitochondria harboring the G143A mutation. Thus, it is imperative at this point to closely monitor the situation and establish whether the continuous use of QoIs would lead to an increase in the proportion of mitochondria carrying the G143A mutation in cytb. Along the same lines, it is also important to examine how stable the observed heteroplasmy is in the absence of QoI fungicides and whether heteroplasmic isolates for the G143A mutation could be revertd back to wild-type. This might be possible since the presence of both the wild-type and mutated mitochondria in a single strain could imply that there is a fitness penalty associated with the G143A mutation. If such is the case, then extending the fungicide-free cultivation period for a number of fungal generations that can be defined experimentally, could potentially reverse fungicide resistance and reset the situation in the fields.
2.4 Cloning of CYP51 gene from L. taurica and identification of mutations conferring resistance to azole (DMI) fungicides.
In order to clone the CYP51 gene from L. taurica we followed a PCR-based approach with degenerate primers, similar to the one used for the cloning of cytb from this fungus. For this purpose, an amino acid alignment of characterized CYP51 protein sequences from fungi was built and a set of 12 degenerate primers was designed on highly conserved regions of the alignment. By PCR amplification using various combinations of these primers, an approximately 1.3 kb fragment was finally obtained that when translated showed high similarity to the CYP51 sequences from other powdery mildew fungi. In more specific terms, after removal of three suspected introns from the CYP51 sequence of L. taurica, the deduced 386 amino acid long protein sequence showed high similarity to the CYP51 protein sequences from Erysiphae necator (78% ident.), Sclerotinia homoeocarpa (78% ident.), Blumeria graminis f. sp. tritici (76% ident.), Blumeria graminis f. sp. hordei (76% ident.y), Podosphaera fusca (75% ident.), and other fungi (Figure XX). Alignment of these proteins also showed that only ~70 and 80 amino acids are missed from the 5’ and 3’ ends of the L. taurica CYP51 protein, respectively. Currently, efforts are underway to obtain the full-length CYP51, using chromosome walking techniques.
With ¾ of the CYP51 gene already cloned, we subsequently proceeded in amplifying the 1.3kb fragment from a representative set of 25 samples from 14 fields (Table 1). Direct sequinning of the PCR products showed that all amplified fragments were 100% identical to each other, meaning that there were no mutations in the CYP51 coding sequence. Absence of mutations in CYP51 could imply that the
7
population of L. taurica in California is still sensitive to azoles. Alternatively, it might be that mechanisms other than mutations in the target site, such as overexpression of CYP51 or of efflux transporters in the cell could play a more important role in resistance development in this fungus as compared to mutations in CYP51. This is usually the case when resistance development in a fungal population is still at the very early stages, as it can take several generations until mutations appear and become fixed in a population. Finally, absence of mutations could also mean that a dominant CYP51 allele that confers resistance to azoles has already been fixed and dominated within the L. taurica population. This scenario, however, seems rather unlikely as research in other fungi has shown that usually several mutated alleles of CYP51 co-exist in a population in cases of resistance development to azoles10. We are currently screening more samples for the presence of mutations in the CYP51 gene that would provide answers to some of these questions.
3. References
1 Ridout CJ. in The Mycota V: Plant Relationships, 2nd Edition Vol. V (ed H Deising) 51-66 (Springer-Verlag, 2009).
2 Correll JC, Gordon TR & J. EV. Host range, specificity, and biometrical measurements of Leveillula taurica in California. Plant Disease 71, 248-251, (1987).
3 Jones H, Whipps JM & Gurr SJ. The tomato powdery mildew fungus Oidium neolycopersici. Molecular Plant Pathology 2, 303-309, (2001).
4 Corell JC, Gordon TR & Elliott VJ. in Caliornia Agriculture Vol. March-April 8-10 (1988). 5 Jones WB & Thomson SV. Source of Inoculum, Yield, and Quality of Tomato as Affected by Leveillula
taurica. Plant Disease 71, 266-268, (1987).
6 Guzman-Plazola RA. Development of a spray forecast model for tomato powdery mildew (Leveillula taurica (Lev) Arn.). PhD thesis, University of California, Davis, (1997).
7 Bartlett DW, Clough JM, Godwin JR, Hall AA, Hamer M & Parr-‐Dobrzanski B. The strobilurin fungicides. Pest Management Science 58, 649-662, (2002).
8 Lupetti A, Danesi R, Campa M, Tacca MD & Kelly S. Molecular basis of resistance to azole antifungals. Trends in molecular medicine 8, 76-81, (2002).
9 Gisi U, Sierotzki H, Cook A & McCaffery A. Mechanisms influencing the evolution of resistance to Qo inhibitor fungicides. Pest Management Science 58, 859-867, (2002).
10 Cools HJ, Hawkins NJ & Fraaije BA. Constraints on the evolution of azole resistance in plant pathogenic fungi. Plant Pathology 62, 36-42, (2013).
11 Cools HJ & Fraaije BA. Update on mechanisms of azole resistance in Mycosphaerella graminicola and implications for future control. Pest Management Science 69, 150-155, (2013).
12 White TJ, Bruns T, Lee S & Taylor J. Amplification and direct sequencing of fungal ribosomal RNA genes for phylogenetics. PCR protocols: a guide to methods and applications 18, 315-322, (1990).
13 Ishii H, Fraaije BA, Sugiyama T, Noguchi K, Nishimura K, Takeda T, Amano T & Hollomon DW. Occurrence and molecular characterization of strobilurin resistance in cucumber powdery mildew and downy mildew. Phytopathology 91, 1166-1171, (2001).
14 Lesemann SS, Schimpke S, Dunemann F & Deising HB. Mitochondrial heteroplasmy for the cytochrome b gene controls the level of strobilurin resistance in the apple powdery mildew fungus Podosphaera leucotricha (Ell. & Ev.) ES Salmon. Journal of Plant Diseases and Protection 113, 259-266, (2006).
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4. Tables and figures
Table 1. Distribution of Leveillula taurica strains with mutations in the cytb and CYP51 genes in fields around the Yolo, Sacramento, and San Joaquin counties. With respect to cytb, due to observed mitochondrial heteroplasmy in L. taurica, each strain can carry several types of mitochondria that represent the wild-type cytb allele and/or different mutated forms of it. A parenthesis indicates that the specific trait has been detected in direct sequencing of the PCR products but it has not been confirmed yet after cloning and sequencing of the individual PCR fragments from a plasmid vector, suggesting that its frequency might be low in the particular sample. So far, no mutations have been identified in coding sequences of CYP51.
Field Cultivar County Date collected Treatment Wild-
type
Mutations known to confer resistance to QoIs
Mutations with an unknown effect on resistance to QoIs Mutations
in CYP51 F129L G137R G143A1 L81T L130T Other
mutat.
CR27x99 H5508 Yolo Aug. 6th 1) Quadris,
Priaxor YES YES YES YES
P135T W12R
CR99x30, NE H1175 Yolo Aug. 6th (YES) (YES) YES
CR99 x Willow Slough Yolo Aug. 6th (YES) YES YES NO
CR30x99, N H5508 Yolo Aug. 6th (YES) (YES) YES NO
UCD Veg. Crops Sun 6366 Yolo Aug. 6th 2 Sulfur dusts YES NO
Howard Rd. & Tracy Blvd.
San Joaquin Aug. 7th
1) 2 Sulfur dusts 2) Priaxor
YES YES YES
Inland Dr. & Stark Rd. UG19406 San Joaquin Aug. 12th 1) Quadris Top 2) Sulfur dust 3) Priaxor
Runge Rd HyPeel 849 Yolo Aug. 20th YES YES YES Y132C NO
N. Elkhorn, near CR117
Yolo Aug. 22th YES (YES) YES NO
S. Roberts Road & Muller Rd.
San Joaquin Aug. 26th (YES) YES YES NO
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Howard Rd. west of Undine Rd.
H5508 San Joaquin Aug. 26th 1) 2 sulfur dusts 2) Quadris Top
(YES) YES YES NO
Valpico Rd. & Lammers Rd. H5508 San Joaquin Aug. 28th
1) Quadris Top + wettable sulfur, 2) Sulfur dust
(YES) (YES) (YES) NO
CR99x30 NE H1175 Yolo Aug. 29th
Repetitive sample at later sampling date
YES YES YES YES R99G NO
CR27x 99 SE 113 H Yolo Aug. 29th (YES) YES YES YES T85A
Farmington Rd. & Jack Tone Rd.
N6407 San Joaquin Sept. 4th 1) Cabrio 2) Cabrio
(YES) YES YES
Knights Landing 113x13 NW
H5608 Yolo Sept. 8th 1) Cabrio YES YES YES NO
Pedrick x Putah Creek Yolo Sept. 8th (YES) (YES) YES
UCD Veg. Crops Ullman
APT 410 Yolo Sept. 8th None (YES) (YES) YES YES NO
Burnham Rd. & Kaiser Rd.
Valley Cat (fresh market)
San Joaquin Sept. 8th 1) Quadris Top (YES) (YES) YES
Snodgrass Slough S. of Twin Cities Rd.
H5508 Sacramento Sept. 11th
1) Sulfur dust 2) Cabrio 3) Quadris Top 4) Cabrio
(YES) YES YES NO
Grand Island Rd. north of 220
Sacramento Sept. 11th
1) Sulfur dust 2) Cabrio 3) Quadris Top
YES YES YES NO
CR 98x19A, north experimental
Yolo Sept. 12th Sulfur dust within hrs. before sampling
(YES) (YES) YES
1 Most severe mutation known to confer high levels of resistance to QoI (strobilurin) fungicides.
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Figure 1: Example of the nucleotide alignment of the 18S ribosomal DNA gene (partial sequence) amplified from one of our field samples and the Leveillula taurica sequence deposited in NCBI (Acc. Num. AY912077.1). The sequence spans the internal transcribed spacer 1 (ITS1; red), 5.8S ribosomal RNA gene (green), and internal transcribed spacer 2 (ITS2; orange). Stars denote identical nucleotides. The sequences are 100% identical.
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Figure 2. Amino acid alignment of the cytb protein sequences from Leveillula taurica (partial) and other fungal species (full-length). Amino acids known to confer resistance to QoI fungicides when mutated are highlighted in red and their position in the protein is indicated above the alignment. These amino acids are located in the so-called “cd loop” of the protein (highlighted in blue) between amino acids 127 to 147 and changes within this region are alleged to confer resistance to QoIs. Substitutions at amino acids F129, G137, and G143A, in particular (highlighted in red), are shown to be the most common cause for resistance to QoIs. Amino acids that are frequently mutated in L. taurica are also highlighted in green. One of these amino acids (L130) is located within the cd loop.
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Figure 3. Electrophoretic trace data obtained by direct sequencing of the PCR amplified cytb gene from Leveillula taurica. The data show the presence of two overlapping peaks at key nucleotide positions that code for amino acids L130, G137, and G143, indicating that the PCR product is a mixture of different DNA templates. These templates can either be originating from different strains or most likely in this case from different types of mitochondria present in a single strain (mitochondrial heteroplasmy). Note that in all cases a peak for the nucleotide that would represent the wild-type cytb allele is always present. The height of the peaks most likely correlates with the portion of the mitochondria that have the specific nucleotide at that position. For example at position 182, peaks for adenine (A) and thymine (T) are at equal heights, meaning that 50% of the mitochondria will have an A at that position and the reaming 50% a T.
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Figure 4. Isoforms of cytb identified after cloning and sequencing of individual PCR fragments from each sample rather than direct sequencing of the PCR product. Due to the mitochondrial heteroplasmy in Leveillula taurica, multiple mitotypes representing different ctyb isoforms are usually present in single strains.
14
Figure 4. Amino acid alignment of the characterized CYP51 protein sequence (partial) from Leveillula taurica and other fungal species (full-legnth). The alignment shows the L. taurica CYP51 shares a high degree of homology to CYP51 proteins from other powdery mildew fungi. Currently efforts are underway to obtain the full-length CYP51 for L. taurica.
2014 Annual Progress Report Project Title: Curly Top Research in Response to the 2013 Outbreak in Tomato in the Central Valley of California: Objectives Relevant to Tomatoes Project Leader: Robert L. Gilbertson, Professor of plant Pathology Department of Plant Pathology University of California-Davis Davis, CA 95618 Phone: 530-752-3163 E-mail: [email protected] Cooperating personnel: Li-Fang Chen, Ozgur Batuman, Maria Rojas, Tom Turini, and Roger Chetelat Introduction: The 2013 outbreak of curly top disease in processing tomatoes caused losses of ~$100 million, and was a reminder that curly top disease can still cause substantial economic losses to this crop in California. Furthermore, this outbreak occurred despite the ongoing insecticide spray program for the beet leafhopper vector that is carried out by the CDFA curly top virus control board (CTVCB). The factors responsible for the 2013 outbreak are not known, but leafhopper populations early in the year were unusually high and most of these insects carried a high level of curly top virus. Thus, given the severity of the 2013 curly top outbreak, a research project was initiated to further investigate the disease, including aspects of the leafhopper vector and the Beet curly top virus (BCTV). The long-term goal of this research is to develop an effective curly top virus integrated pest management strategy that targets multiple points in the disease cycle rather than only one (the leafhopper vector). Furthermore, the availability of new biotechnological tools for detecting BCTV (PCR and DNA sequencing) and screening plants for resistance to BCTV (agroinoculation) allows for new approaches to disease management.
Because curly top is a disease of crops other than tomato and the proposed research to address the 2013 curly top outbreak involves objectives that specifically involve tomatoes and others that involve all crops, the research tasks and funding for this project were divided between CTRI (objectives related to tomatoes) and the CTVCB (objectives related to all crops). The objectives of the CTRI-funded curly top virus research are to 1) further assess the level and nature of resistance to curly top in a tomato breeding line and other tomato germplasm possessing genes known to confer resistance to whitefly-transmittted geminiviruses, 2) assess the level of curly top in processing tomatoes in 2014 and the associated BCTV strains of the virus, and 3) assess the potential for developing novel strategies for interfering with leafhopper feeding on tomato plants.
1. Assess the level and nature of resistance to curly top in a tomato variety (line 20) and in other germ plasm possessing genes known to confer resistance to whitefly-transmitted viruses
A breeding line referred to as ‘line 20’ was generated by crossing a BCTV-susceptible
cultivar with a breeding line possessing Ty genes, which confer resistance to the whitefly-transmitted geminivirus, Tomato yellow leaf curl virus (TYLCV). Some of the progeny plants of this line, screened for resistance to BCTV-Svr by agroinoculation, showed resistance to curly top. In this objective, we continued screening populations of the breeding line, called ‘line 20’, for resistance to BCTV-Svr using the agroinoculation method. These populations represented plants in the F4-F5 generation. Two F5 selections, 4 and 12, showing the highest levels of resistance (~80% resistant plants), were selected and will be used for further pure-lining and for studies of the genetics of this resistance. To conduct the genetic analysis of the resistance in these line 20 selections, we obtained seeds of two open-pollinated cultivars, M-82 and E-6203, which are highly susceptible to BCTV-Svr. These susceptible cultivars were used as males (pollen donors) in crosses with resistant F5 line 20 selections 4 and 12 plants (mothers). These crosses were successfully made (M-82 X selections 4 and 12 and E-6203 X selections 4 and 12 plants). Controls were resistant selection 4 and 12 plants that were allowed to self-pollinate. Seeds are now being collected from these crosses and the self-pollinated controls. These seeds will be planted (200-400/cross and self-pollinated controls) and seedlings will be agroinoculated with BCTV-Svr. The segregation of the BCTV resistance in these progeny plants will provide insight into the genetics of this resistance. The pure line of line 20 will be used for marker-assisted selection studies to identify the specific Ty genes associated with this resistance, and will eventually be provided to breeders for breeding tomato cultivars with curly top resistance.
To provide further evidence that the Ty genes confer resistance to BCTV and to identify
potential breeding lines for BCTV resistance breeding, we obtained a series of lines with various compliments of Ty genes from a private company (10 lines) and from the World Vegetable Center (previously the Asian Vegetable Research Development Center) in Taiwan (15 lines). To screen these lines for curly top resistance, at least 10 plants/line were agroinoculated twice with BCTV-Svr (at the 2-3 true leaf stage and one week later) and then plants were observed for symptoms over a period of 4 weeks and then rated for resistance on a 0-4 scale with 0=no symptoms to 4 representing severe symptoms. Finally, plants were tested for BCTV infection with the PCR test.
The 10 lines from the private company were heterozygous for a single Ty gene (Ty1, Ty2 or
Ty3) or two Ty genes (Ty2/Ty3). Six of the 10 lines were susceptible to BCTV-Svr, i.e., had disease ratings that were >3, which was similar to the control (cv. Glamour with a rating of 4.0). Three lines showed moderate resistance (two Ty1 lines with 2.5 and 2.8 ratings and a Ty2 line with a 2.8 rating). One of the Ty3 lines had the highest level of resistance (2.1 rating) and 6/23 plants were PCR negative for BCTV infection. Together, these results suggested that the Ty genes confer some level of resistance to BCTV, but higher levels of resistance may require multiple Ty genes or individual genes in the homozygous condition.
More promising results were obtained with the breeding lines from the WVC. Based on
having disease ratings of <2.0 (data shown in bold in Table 1), 8 of the 15 lines showed some
______________________________________________________________________________ Table 1. Results of screening of tomato lines from the World Vegetable Center with different compliments of Ty genes for resistance to whitefly-transmitted begomoviruses
Visual Severity Ratinga PCR Results
Lines Ty Genes Total
Tested
Mean disease rating 0-2 3-4 Negative Positive
11 None 25 3.2 6 (13.5)b 19 (86.5) 0 25 12 Ty1 25 1.9 15 (49) 8 (51) 6 19 13 Ty2 12 2.6 5 (25) 7 (75) 3 9 14 Ty2 and possibly minor Ty1/Ty3 22 2.1 13 (46) 13 (53.5) 11 11 15 Ty3 21 1.2 15 (69.5) 6 (30.5) 9 12 16 Ty3 21 2.2 12 (52.5) 9 (47.5) 10 11 17 Ty5 24 2.5 9 (39.5) 15 (60.5) 2 22 18 Ty2 and Ty5 25 0.8 21 (84.5) 5 (15.5) 18 7 19 Ty3a 23 2.0 16 (70) 8 (30) 17 6 20 Ty3a and Ty2 16 1.2 15 (93.8) 1 (6.2) 13 3 21 Ty3 and Ty2 24 1.6 21 (87.5) 3 (12.5) 10 23 22 Ty3 and Ty2 10 1.6 9 (90) 1 (10) 12 3 23 Ty3 and Ty2 24 1.9 18 (70) 6 (30) 20 14 24 Ty5 and Ty6 23 1.8 15 (67) 8 (33) 26 7 25 Ty5 and Ty6 25 1.3 21 (86.5) 4 (13.5) 12 23
C cv. Glamour None 39 4.0 0 39 (100) 0 39 aRatings based on a scale of 0-4 with 0=no symptoms, indistinguishable from uninoculated controls; 1=mild upcurling or yellowing of leaf margins, no stunted growth; 2=mild upcurling and yellowing of leaves, no obvious stunting; 3=upcurled and distorted leaves, yellowing and stunted growth; and 4=severe stunting and distorted growth, pronounced leaf distortion, yellowing and vein purpling. Ratings of 0, 1 and 2 are indicative of resistance. Lines shown in bold show highest levels of resistance. bNumbers in parenthesis are percentages ____________________________________________________________________________________ level of resistance to BCTV-Svr, which is very encouraging and supports the hypothesis that some of the Ty genes confer will confer resistance to BCTV. The lines that were most promising had the combination of Ty3 and Ty2, which confer resistance to bipartite (Ty3) and monopartite (Ty2) begomoviruses; for example, see lines 20-23. These lines had low disease ratings and numerous plants did not develop symptoms, even 2 months after inoculation, and were PCR negative, suggesting that the virus was not able to systemically infect these plants. This is indicative of a very high level of resistance. Furthermore, evidence that the Ty3/3a gene maybe a particularly important gene in conferring BCTV resistance comes from the finding that lines having only this gene (lines 15, 16, and 19) showed good levels of resistance (ratings of 1.2-2.2), high proportions of resistant plants (all three lines) and many PCR-negative plants (especially line 19). Also showing evidence of BCTV resistance were lines 24 and 25, which possess the Ty5 and Ty6 genes. These two lines had excellent disease ratings (1.3 for line 24 and 1.8 for line
25) and high proportions of resistant plants; these lines differed in the number of PCR-negative plants that were detected.
The results with the WVC breeding lines with Ty genes for whitefly-transmitted
geminiviruses strongly suggest that these genes confer some degree of resistance to BCTV. Indeed, the finding that many plants in the most resistant lines were not infected by BCTV-Svr (Table 1, PCR results) indicates the potential for very high levels of BCTV resistance. This is very encouraging and not totally unexpected, as these viruses are in the same family (family Geminiviridae) and share common characteristics. Therefore, it is not surprising that resistance genes targeting a general geminivirus function (e.g., replication) might confer resistance to whitefly- and leafhopper-transmitted geminiviruses. It is also very encouraging that more than one Ty gene appears to confer resistance and, given that the highest levels of resistance were found in lines with multiple Ty genes, it is likely that pyramiding certain Ty gene combinations will provide the highest levels of resistance to BCTV. It is also clear that the resistance in many of these lines is segregating (see the breakdown of resistant [0-2] and susceptible [3-4] plants in each line, Table 1), and this will complicate breeding efforts. Furthermore, some of these genes are dominant or co-dominant (Ty 1, 2 and 3), whereas others are recessive (Ty5). In conclusion, these results clearly show that these lines possess high levels of resistance to BCTV, and it is likely that it is the Ty genes that are conferring most or all of this resistance. Thus, these lines (with these genes already in ‘tomato’ genetic backgrounds) could be used in efforts to breed BCTV-resistant processing tomato varieties. 2. Assess the genetic diversity of curly top virus species/strains associated with the 2013
outbreak and characterize strains involved in curly top in 2014
One of the big questions for the 2014 processing tomato growing season was what would be the incidence of curly top disease following a year such as 2013 when the incidence of the disease was really high. The two main factors that drive curly top development in tomato are 1) high leafhopper populations and 2) high virus titers (loads) in these leafhoppers. In 2013, both of these factors existed in the Central Valley. In 2014, beet leafhoppers populations in the foothills were low (based on results of CTVCB monitoring), probably reflecting the extremely dry conditions and lack of available vegetation, and these leafhoppers had low levels of BCTV based on results of our PCR tests (Fig. 1). Taken together, these two factors suggested that the incidence of curly top in the 2014 processing tomato crop would be low. Indeed, this turned out to be the case (see below) and curly top disease was not a significant problem. This is consistent with the sporadic nature of the disease and indicates that curly top will not necessarily be a big problem in a year following an outbreak year (like 2013).
Curly top incidence in processing tomatoes in 2014. To assess the incidence of curly top in
the 2014 processing tomato crop, we conducted early season (June) and late season (August) surveys of processing tomato fields in Fresno County and San Joaquin counties. In these surveys, 4-5 fields were selected and the number of plants with symptoms of curly top was assessed in ~100 plants in the four corners of each field. In addition, a number of other fields in each area were surveyed for curly top incidence as part of the collection of samples for typing curly top isolates.
Figure 1. Percent of leafhoppers carrying BCTV and relative virus titer in 2014. Numbers in parenthesis indicate the number of locations from which samples were received.
In Fresno/Kings counties the early season incidence of curly top disease was very low (<1%).
Similar low incidences (<1%) were observed in the other fields in Fresno/Kings counties visited for collection of BCTV isolates. In the late season survey in Fresno/Kings counties, the curly top incidence was slightly higher (~2.5%), but still relatively low. Slightly higher incidences (~5%) were observed in other fields. The slightly higher incidences observed in these fields reflected late season infections, which were due to spread by leafhoppers from generations that developed on the valley floor. Further evidence of this late season beet leafhopper activity came from the observation (and confirmation with our phytoplasma-specific PCR primers) of tomato big bud disease in a number of fields. The phytoplasma that causes this disease is also transmitted by the beet leafhopper. Finally, yellow sticky card monitoring of beet leafhopper populations in and around processing tomato fields in Fresno/Kings counties revealed 0-2 beet leafhoppers per card per two weeks during the growing season; these numbers are relatively low and are consistent with the low incidences of curly top in processing tomatoes in this region in 2014. Leafhopper monitoring will continue through the winter to see if we can capture insects that are migrating from the valley floor to the foothills.
In San Joaquin County, the early season survey also revealed relatively low levels of curly top
disease, ranging from 1-7% incidence (average incidence of 3%). Late season surveys (of these same fields) revealed higher incidences, ranging from 1-12% (average incidence of 5%). A few other fields had much higher incidences of curly top, one in particular with an incidence of 80%, and these high incidences were associated with proximity to fallowed fields or weedy orchards that presumably were the source of the beet leafhoppers. Consistent with this hypothesis, in some of these fields, the distribution of curly top was not uniform, with some sections having much
0%
20%
40%
60%
80%
100%
Weak Medium Strong
higher incidences (20-90%) than others (0-10%). Surveys around these fields for beet leafhoppers revealed 0-3 insects per card per two weeks. In general, the incidence of curly top in San Joaquin County was higher than that observed in Fresno/Kings Counties in 2014. This raises questions about where are the leafhoppers coming from and where are they overwintering.
BCTV strain characterization in 2014. Before presenting these results it is necessary to describe recent changes in the taxonomy of the curtoviruses causing curly top of tomato (Table 2). In 2001, the two main strains associated with curly top of tomato, BCTV-Worland and BCTV-CFH, were renamed as the distinct species Beet mild curly top virus (BMCTV), which is mild in sugar beet but severe in tomato and Beet severe curly top virus (BSCTV), which is severe in both sugar beet and tomato, respectively. In 2014, the taxonomy of curtoviruses was revised again, and these species were returned to strain status: BMCTV was divided into two strains, BCTV-Mild [Mld] and -Worland [Wor]), and BSCTV was re-named BCTV-Svr.
Characterization of the BCTV strains associated with the curly top outbreak of 2013 revealed
that two new BCTV strains, which represent recombinants (mixtures of pieces of previously characterized strains), were the predominant strains involved. One of these strains, BCTV-CO, was more related to the ‘mild strains’ (80% BCTV-Wor and 20% BCTV-Svr), whereas the other strain, LH-71, was more similar to the BCTV-Svr strain ______________________________________________________________________________
Table 2. Recent changes in the taxonomy of curtoviruses causing curly top in tomato. Years Classification Strains/species associated with curly top of tomato Prior to 2001 Strains of Beet curly BCTV-Worland (Wor)* top virus (BCTV) BCTV-CFH* 2001-2014 Distinct curtovirus Beet mild curly top virus (BMCTV=BCTV-Wor)* species Beet severe curly top virus (BSCTV=BCTV-CFH)* Pepper curly top virus (PeCTV) Spinach curly top virus (SpCTV) 2014-present Strains of BCTV BCTV-Mild (Mld) BCTV-Wor* =BMCTV BCTV-Svr (=BSCTV)* BCTV-PeCT (=PeCTV) BCTV-CO** BCTV-LH-71** BCTV-SpCT
*Main strains historically associated with curly top of tomato **Strains associated with the 2013 curly top outbreak _____________________________________________________________________________
To assess the prominent strains of BCTV associated with processing tomatoes in 2014, samples of tomato plants showing symptoms of curly top disease were collected from fields in various counties during the 2014 growing season. These samples were collected during our surveys or by our Farm Advisor cooperators. These plants were tested for BCTV infection by PCR with a general BCTV primer pair. This test detects all known BCTV strains. Subsequently, the BCTV-positive plants were subjected to additional PCR tests to detect ‘mild’ versus ‘severe’ strains and, in some cases, DNA sequence analysis was used to confirm the precise identity of the strain.
The results of the BCTV strain characterization in 2014 is shown in Table 3. First, it can be
seen that only 72% of plants selected as showing curly top symptoms were actually positive for BCTV infection. Our previous results have revealed that this is due to other diseases (e.g., tomato spotted wilt) and factors (e.g., nutrient deficiency) mimicking curly top symptoms, rather than our PCR test not detecting certain BCTV strains. Second, regardless of the county, the predominant BCTV strains associated with curly top of tomato in 2014 were BCTV-CO (predominant mild strain) and BCTV-LH71 (predominant severe strain). Furthermore, these are the same strains that were associated with the 2013 outbreak. In 2014, the most predominant strain overall was BCTV-CO (~80% of total characterized strains). Similar results were obtained for the strains recovered directly from beet leafhoppers and from other crops (peppers and sugar beets); with the exception of sugar beets from the Imperial Valley, where BCTV-Svr (CFH) was predominant (data not shown). Interestingly, the BCTV-CO and –LH-71 strains were also the predominant strains recovered from weeds in the foothills and valley floor, although more of the original strains were detected in weeds (data not shown). Finally, some BCTV isolates were not readily identified with the ‘mild’ and severe’ PCR tests and DNA sequencing revealed that, in some cases, this was due to infection with a new BCTV strain: BCTV-SpCT (previously Spinach curly top virus). This virus was previously only reported from Texas causing curly top in spinach. BCTV-SpCT was shown to infect tomato experimentally, and now we have established that it can infect tomato in the field and that the geographical range of the virus extends beyond Texas. However, only a small number of plants were infected with BCTV-SpCT, so it may not be an important component of curly top disease of tomato in California.
Together, these results indicated that BCTV strains associated with curly top disease of
tomato in 2014 were the same as those associated with the 2013 outbreak. This suggests that there may have been a shift in the BCTV strain composition in the Central Valley. It is interesting to note that BCTV-CO and BCTV-LH71 are highly pathogenic to tomato and relatively mild in sugar beet. This may indicate that the current lack of sugar beet acreage in California may be driving the selection of BCTV strains that are more pathogenic to tomato. This hypothesis is supported by the finding that the BCTV-Svr (CFH) strain was predominant in sugar beet samples with curly top that were received from the Imperial Valley, where sugar beets are still grown. For sure, these new predominant BCTV strains should be incorporated into any resistance breeding program for curly top in tomato.
___________________________________________________________________________ Table 3. Beet curly top virus strains associated with curly top of tomato in 2014 County Total Total Mild strains Severe strains Samples BCTV- positive Total CO Wor Total LH71 Svr Mixed Negative Kern 5 5 4 4* 0 1 0 0 0 0 Fresno 52 36 33 28* 4 3 2* 1 0 16 Imperial 29 11 0 0 0 11 4* 4* 0 18 Sacramento 3 2 2 2* 0 0 0 0 0 1 San Joaquin 221 170 138 132* 4 49 36* 4 17 51 Turlock 3 3 2 2* 0 1 1* 0 0 0 Yolo 12 9 9 8* 1 0 0 0 0 3 Totala 325 236 188 176 9 65 43 9 17 91 (72%) (80%) (94%) (5%) (28%) (66%) (14%) (8%) (28%) *Indicates the predominant mild and severe strains for each county
aBold numbers indicate totals and underlined indicate percentages within mild and severe strains
________________________________________________________________________ 4. Assess the potential for novel strategies for interfering with leafhopper feeding on tomato
plants
Tomatoes are not a preferred host of the beet leafhopper, and the insects will not complete their life cycle on this host. Thus, beet leafhoppers only briefly feed on tomatoes in order to assess if the plants are a preferred host. However, in the course of ‘tasting’ the tomato plants, the leafhoppers can transmit BCTV. Therefore, if there was some way to ‘alert’ the insects that tomatoes were a non-preferred host such that they would not feed and continue to search for a more suitable host, the incidence of curly top could be substantially reduced.
Spraying tomatoes with a feeding deterrent is one approach that could prevent the leafhoppers
from feeding on tomatoes as they migrate out of the foothills. To investigate the possibility of developing a plant-based feeding deterrent, we tested extracts of the tobacco plant, Nicotiana benthamiana. We chose this plant because we previously observed that beet leafhoppers do not like to feed on this plant (or other related tobacco species). In fact, given the chance to feed on N. benthamiana or to die of starvation, leafhoppers will typically chose starvation. Thus, we prepared sap extracts from N. benthamiana and other tobacco plants and sprayed these extracts
onto healthy shepherd’s purse plants, a preferred host of the beet leafhopper. In these experiments, viruliferous beet leafhoppers still fed on the shepherd’s purse plants sprayed with the tobacco plant extracts. Consistent with this observation, all of these plants developed BCTV symptoms 10-14 days after feeding, as did control plants that were sprayed with water only. Later, BCTV infections in these symptomatic plants were also confirmed with PCR tests. Similar experiments were performed with tomato plants and the same results were obtained, i.e., the simple spraying of the extracts on the leaves of tomato plants did not prevent the leafhoppers from feeding or transmitting BCTV.
The finding that spraying of the extract on leaves did not deter the leafhoppers feeding or
transmitting BCTV may have been due to the extract not uniformly covering the leaves. To make sure that the leafhoppers actually fed on the extract and to assess different concentrations of the extracts, we utilized a membrane feeding assay in which various concentrations of the N. benthamiana extract were added to a 10% sucrose solution and then the leafhoppers were allowed to feed for an 18 hour period. After this feeding period, the survival of the leafhoppers was determined. The results of these experiments are presented in Table 4. For the control treatment of buffer+10% sucrose, leafhopper survival was 87%. Interestingly, there was a difference in survival between male and female insects, with higher rates of survival for females (100%) compared with males (78%). There was no difference in survival for leafhoppers fed on treatments with 25% and 50% N. benthamiana extract. However, survival was substantially reduced (42%) when leafhoppers were fed on 100% N. benthamiana extract +10% sucrose. Consistent with the finding of the difference in survival between male and female insects, the survival of the male leafhoppers was dramatically reduced on this treatment (8%) and they essentially refused to even feed and starved to death. In contrast, survival of the females was unaffected (87%) and these insects did feed on this treatment. Our results show that there is the potential to use natural plant extracts to deter beet leafhopper feeding. However, the effective development of such a deterrent will need to address a number of issues including the formulation applied to plants, the concentration and differences between male and female insects. ______________________________________________________________________________ Table 3. Feeding deterrent effect of Nicotiana benthamiana sap on beet leafhoppers determined via an in vitro membrane feeding assay. Survival rates for both male and female beet leafhoppers were determined after an 18 hour feeding period. Feeding solution Total number of leafhoppers Males Females Buffer + 10% sucrose 71/82 (87%) 39/50 (78%) 32/32 (100%) 25% N. benthamiana sap 17/17 (100%) 10/10 (100%) 7/7 (100%) +10% sucrose 50% N. benthamiana sap 53/61 (87%) 27/35 (77%) 26/26 (100%) +10% sucrose 100% N. benthamiana sap 29/69 (42%) 3/39 (8%) 26/30 (87%) +10% sucrose ______________________________________________________________________________
Page 1 of 2 California Tomato Research Institute
CALIFORNIA TOMATO RESEARCH INSTITUTE, INC. 18650 E. Lone Tree Road
Escalon, California 95320-9759
Project Title: EVALUATION OF CHEMICAL CONTROL OF BACTERIAL SPECK
Project Leader(s)
Gene Miyao, UC Cooperative Extension 70 Cottonwood Street, Woodland, CA 95695 (530) 666-8732 [email protected]
Mark Lundy, UC Coop Extension P.O. Box 180 Colusa, CA 95932 [email protected] Janet Caprile, UC Cooperative Extension 75 Santa Barbara Road, 2nd Floor Pleasant Hill, CA 94523-4215 [email protected]
Brenna Aegerter, UC Coop Ext. 2101 E. Earhart Ave, Suite 200 Stockton, CA 95206-3924 [email protected]
Objectives: Evaluate foliar applications of chemicals for bacterial speck control.
Background: Bacterial speck is often a problem when rainy, cool weather conditions persistent in the spring. Control has always been challenging when multiple rain events trigger the onset and development of the disease. Plant resistance that once was effective has been overcome. Most spray control programs are based around the use of copper, which has provided moderate control at best.
Procedures: Two field tests were established: one in eastern Contra Costa County (Brentwood/Byron area) with Simoni & Massoni and in western Yolo County with Muller Farms.
Materials included Kocide, Dithane, Actigard, Oxidate, Regalia, Agriphage and Serenade Max (Table 1). All included copper as a tank mix except the Agriphage treatment. A minimum of 4 applications were made during the season. Sprays were applied with hand-held equipment. A non-ionic surfactant was included in all sprays except for the Agriphage treatment in Brentwood.
Disease development was minimal at the Brentwood site and none was detected at the Yolo site. Several rain events occurred (9 in Brentwood and 4 in Yolo) but weather conditions in general were not conducive for disease development.
The Yolo site in the Esparto area was transplanted on March 28th to Sun 6402 on double lines on beds centered on 5 feet. Sprays were initiated on March 30 and were last applied
Page 2 of 2 Bacterial speck control
California Tomato Research Institute
on May 5th for a total of 4 applications, with 2 additional applications of Agriphage. The field was irrigated only by buried drip tape.
The eastern Contra Costa site in the Brentwood area was direct-seeded on February 3rd to SUN 6366 with double rows per bed and 80” between bed centers. The field was sprinkler-irrigated through May 1st, then irrigated via drip tape placed in the furrow. Rainfall events as measured by the CIMIS station in Brentwood occurred on Feb 26 to Mar 1, Mar 3, Mar 5 to 6, Mar 22, Mar 26 to 27, Mar 29, Mar 31 to Apr 2, Apr 4, and Apr 25. Spray applications were made 4 times on the following dates: Mar 13, Mar 25, Apr 8 and Apr 24. The trial was evaluated for disease development regularly, and a final rating was conducted on May 9th. Ten older leaves were sampled from each plot and evaluated for the percent of the foliage affected by bacterial speck. Disease pressure was low and differences between treatments were minor; results are presented in Table 1. The only statistically significant result was between those treatments that included copper and those that did not include copper (the control and Agriphage treatments). However, this difference was minor as treatments without copper had only 1% more of their leaves affected by speck.
Table 1. Effect of disease control programs on severity of bacterial speck, Brentwood, 2014
Disease severity
TreatmentKocide 3000
rateOther product
rate(% older foliage
affected)
Agriphage alone -‐-‐-‐ 2 pt 1.63
non-‐treated control -‐-‐-‐ -‐-‐-‐ 1.50
Dithane + Kocide 1.75 lbs 2 lb 0.69
Actigard + Kocide 1.75 lbs 0.75 oz 0.69
Kocide 1.75 lbs -‐-‐-‐ 0.56
Serenade Optimum + Kocide 1.75 lbs 1 lb 0.56
Regalia + Kocide 1.75 lbs 3 qt 0.50
Oxidate + Kocide 1.75 lbs 1% vol/vol 0.44
Mean 0.82P-‐ value 0.06
Group contrastsTreatments containing copper 0.57Treatments without copper 1.56
P-‐ value 0.0006
1
FINAL REPORT TO THE CALIFORNIA TOMATO RESEARCH INSTITUTE
Project Title: Synergy-‐based biocontrol of Fusarium wilt of tomato
Project Year: 2014
Project Leader: Johan Leveau, Associate Professor, Department of Plant Pathology, One Shields Ave, University of California, Davis, CA 95616. E-‐mail [email protected]. Telephone (530) 752-‐5046.
Budget: $39,993
Key findings/accomplishments:
• We demonstrated in a field setting the protective effect of Collinade (a mixture of Collimonas bacteria
and Serenade Soil) against Fusarium oxysporum f. sp. lycopersicum (FOL), causative agent of Fusarium
wilt of tomato.
• We showed that this effect (a reduction in FOL-‐induced vascular discoloration and declining shoot
biomass) was unique to the mixture, i.e. Collimonas or Serenade Soil alone did not achieve this effect.
• Collinade also protected approximately 1 ton of fruit per acre from FOL-‐related sunburn damage,
presumably by supporting a thicker canopy.
• We developed a microcosm assay that allows rapid, medium-‐throughput laboratory screening of single
or mixed biological agents (including variations on Collinade) for ability to prevent FOL infection of
tomato seedlings.
• We have presented our Collinade results to various commercial companies, and are actively seeking
their interest in funding follow-‐up research on the practical application and mechanisms of the Collinade
or ‘biocombicontrol’ principle.
This final report covers the period between January 1, 2014 (project start date) and November 7, 2014
(final report due date). We were granted a one-‐year extension on the project, so that the official end
date for the project is now December 31, 2015. This extra time will allow us to perform several
repetitions or variations of experiments described in this report, with the goal to further substantiate
our claims, as explained in the sections below.
Introduction: This project explored the field application and underlying mechanisms of our lab’s
previous discovery that under greenhouse conditions a mixture of the biofungicide Serenade Soil and
2
the antifungal bacterium Collimonas arenae Cal35 (a mixture referred to as Collinade), but not Serenade
Soil alone or Cal35 alone, reduces disease symptoms on tomato plants that are challenged with
Fusarium oxysporum f. sp. lycopersicum (FOL), causative agent of Fusarium wilt (see the 2012 CTRI
Annual Report for details). We argued that this discovery warranted further investigation, not only
because it offers a solution to problems of decreased productivity and yield inconsistency associated
with FOL infestation in commercial field settings, but also because it represents a novel and exploitable
example of combinatorial crop protection.
Objectives: The objectives of this project were formulated in our proposal as follows:
1) Assess the performance of Collinade under field conditions, and
2) Identify potential mechanisms that underlie the antagonistic synergism between Cal35 and Serenade
Soil.
To meet these objectives, we had originally proposed to do two field trials and two greenhouse
experiments. Instead, we performed one field trial and are planning to do another one in 2015 (under
the granted project extension), while the original greenhouse experiments were redesigned in favor of
developing a lab-‐based assay that would yield faster results in our search for the mechanisms that
underlie Collinade activity.
Procedures: 1) We designed and carried out a field trial during the 2014 growing season at the
Armstrong Plant Pathology Field Station at UC Davis. Tomato plants (Heinz 5508, resistant to Fol race 1
and 2 but not race 3, also resistant to Verticillium dahliae race 1 and Tomato Spotted Wilt Virus) were
seeded in 128-‐cell seedling trays with 3.5 x 3.5 x 6.5 cm cells and kept in the greenhouse for three
weeks, at which time trays were dipped for 4 minutes in one of the following four solutions: 1) water, 2)
Cal35 suspension (106 cells per ml), 3) Serenade Soil suspension (1000x dilution of the liquid product in
water), 4) Collinade (1:1 mixture of Cal35 and Serenade suspensions). Dipped trays were placed back in
the greenhouse and after one week, tomato plants were hand-‐transplanted (May 17, 2014) 30 cm apart
into a drip-‐irrigatable plot with a randomized complete block design (five single-‐row 92-‐cm beds with
60-‐cm between-‐row spacing, plus a pair of border rows) featuring 20 blocks with 30 plants each: 8 of
these blocks were planted with water-‐dipped seedlings, 4 with Cal35-‐dipped seedlings, 4 with Serenade
Soil-‐dipped seedlings, and 4 with Collinade-‐dipped seedlings. Immediately after planting, 10 liters of FOL
spore suspension (106 per ml) were delivered by drip to all blocks except for 4 of the blocks planted with
the water-‐dipped seedlings; these blocks represented the no-‐FOL treatment. After one week, 10 liters of
3
water, Cal35 suspension, Serenade Soil suspension, or Collinade were drip-‐delivered into each one of
the corresponding blocks (e.g., Cal35 suspension was delivered into the blocks that were planted with
Cal35-‐dipped seedlings, and water into the blocks with water-‐dipped seedlings). One week later, this
injection was repeated. The field was watered as needed through the drip. Fertilizer UN-‐32 was applied
through the drip once a week until flowering, at 75 ml per block and starting 12 days after transplanting.
Plants also received a one-‐time application of Miracle-‐Gro Quick Start (Scotts Miracle-‐Gro, Marysville,
OH) 5 days after transplanting, at 450 ml per block. Weeding was done manually, no herbicides or
pesticides were used. Fruits were harvested on September 19, 2014 (4 months after planting), at which
time more than 90 percent of the fruit were ripe. Vascular discoloration and shoot dry weight (after
removal of the fruit, see below) were determined for 15 plants in the center of each block, as described
above. Also measured for these 15 plants combined were the weight of red, green, sunburn, and moldy
fruit. Marketable yield was calculated from red fruit weight, and expressed in tons per acre.
2) We developed the following protocol (optimized for Collinade) for rapid microcosm-‐based screening
of single or mixed bacterial strain for activity against FOL on tomato seedlings. Seeds of tomato
(Lycopersicon esculentum) variety Early Pack 7 were soaked in 70% (v/v) ethanol for 3 minutes, then in
1% (v/v) sodium hypochloride for 15 minutes, and finally washed 5 times in autoclaved water and placed
on water agar (0.8%) in Petri dishes. Five days later, the sprouted seeds were dipped in a suspension of
Collinade (prepared as described above), and transplanted into full-‐gas microboxes (Combiness,
Belgium) containing water agar, 8 seedlings per box. Seven days after transplanting, the agar surface
(with the seedling roots) was inoculated with FOL strain D12 (102 conidia per cm2). As a measure for FOL
infection, we recorded as a function of time the number of seedlings with ‘damping-‐off’ symptoms. Data
were analyzed by Fisher’s least significant difference (LSD) test at P = 0.05.
Results: 1) In a controlled trial at the Plant Pathology Field Station at UC Davis which was designed to
replicate our previous greenhouse experiments in a field setting, we observed the same synergistically
Collinade protection from FOL-‐induced symptoms, i.e. vascular discoloration (bar chart C below) and dry
weight loss (bar chart D). Vascular discoloration in the Collinade treatment was significantly reduced
compared to Cal35 alone (p=0.00024) or Serenade (p=0.00067) alone. Similarly, the Collinade treatment
yielded a significantly higher shoot dry weight (after removal of the fruit) than Cal35 alone (p=0.0032) or
Serenade alone (p=0.0084). Marketable fruit yield was highest with Collinade (68.3 tons per acre), but
this value did not differ significantly from other treatments, including the no-‐FOL control, at 56.3 tons
4
per acre (bar chart A). However, we did observe (bar chart B) significantly less (approximately 1 ton per
acre) of sunburn-‐related loss of yield in FOL-‐challenged plots that received Collinade, compared to FOL-‐
challenged plots that received Serenade alone (p=0.046) or no treatment (p=0.043). We suspect that
this is related to our observation (not shown) of a thicker canopy in the Collinade-‐treated blocks which
would better protect the developing fruit from sun exposure. A thicker canopy is consistent with the
observed higher shoot dry weight, which we believe follows from the ability of Collinade to prevent FOL-‐
induced vascular infection and thus wilting.
2) We developed a microcosm assay that allowed us to visualize (see color photographs below) and
quantify (see graph below) the protective properties of Collinade against FOL (isolate D12) on tomato in
as little as 24 days (twice as fast as in greenhouse assays, see 2012 CTRI Annual Report). This assay
features plastic boxes with water agar as the substrate on which tomato seedlings that were previously
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5
dipped in water or Collinade are challenged with FOL. Recorded are the number of seedlings (out of 8
per box) that exhibit damping-‐off symptoms. We could show that Collinade 1) delays the occurrence of
damping-‐off, and 2) reduces the rate at which seedlings succumb to the pathogen (see graph below).
Outlook. We were granted by CTRI a cost-‐free extension for the project (until 31 December 2015) which
allows us to plan the following activities:
1) a repetition of the field experiment in 2015, with special focus on Collinade and assessing
whether seedling drench alone is sufficient to provide protection against FOL;
2) exploitation of the newly developed microcosm assay to address a subset of questions
pertaining to the mode of action of Collinade, in particular the specificity of Cal35-‐Serenade Soil
activity, can this also be achieved with other Collimonas strains or other Bacillus-‐based
biofingicides?
We are planning to expend the remaining funds on the project (approximately $20k) on these activities.
The rollover of funds into 2015 is the main reason we did not apply for new CTRI funding. During the
course of 2014, we have met with and presented the Collinade project to representatives from various
bio-‐ag companies. Response was variable, but sufficiently positive to anticipate the possibility of
securing continued funding for this project from one of these companies.
Page 1 of 3 California Tomato Research Institute
CALIFORNIA TOMATO RESEARCH INSTITUTE, INC. 18650 E. Lone Tree Road
Escalon, California 95320-9759
Project Title: EVALUATION OF ROOT KNOT NEMATODE CONTROL WITH RESISTANT WHEAT CULTIVAR AND VARIOUS COVER CROPS
Project Leader(s)
Gene Miyao, UC Cooperative Extension 70 Cottonwood Street, Woodland, CA 95695 (530) 666-8732 [email protected]
Ben Leacox, field assistant, UCCE, Yolo County Amelia Harlan, Future Farmer of America cooperator
Antoon Ploeg & Ole Becker UCR Nematology Riverside, CA [email protected] [email protected]
Summary: Nematode resistant wheat cultivar Patwin reduced root knot nematode population levels substantially when grown in a heavily infested soil as a cover crop prior to growing tomatoes, but the reduction was not sufficient in this case to elevate tomato yields above the non-treated control. Patwin wheat, in our field test, did not reduce Meloidogyne incognita population levels satisfactorily.
Objective: Evaluate the influence of root knot nematode resistant wheat cultivar on Mi-resistance breaking nematode populations in a field test.
Background: As resistance-breaking populations of Meloidogyne incognita have slowly overcome the root knot nematode resistance conferred by the Mi gene, alternatives to nematode management are sought. Could resistant cultivar Patwin, a hard white spring wheat, serve as a rotational crop ahead of tomatoes to effectively reduce the nematode population?
Methods: A trial was established in a commercial field with a Mi resistance-breaking root knot nematode infestation from a 2013 tomato crop. The treatments included resistant wheat cultivar Patwin, susceptible wheat Anza, Montezuma oats and Trios triticale. The grasses were drilled into residual soil moisture on November 23. Sprinkler irrigations were needed to establish and grow the crop due to lack of sufficient rainfall.
Soil was taken with a sampling tube, 10 cores per plot to a 12-inch depth at the grain planting period. Samples were collected from the center bed of a 3-bed wide plot. Plots length was 73’ replicated 5 times in a Latin square design. Soil sampling was repeated in the spring when all grasses were cut for hay and cropped off. The final sample was taken near the time of harvest.
Page 2 of 3 Nematode resistant wheat
California Tomato Research Institute
Grasses were gathered from a square yard, weighed fresh and then again after drying to measure aboveground plant biomass. Samples were harvested on April 21st. The grower cut the remaining grass on May 15th.
Tomatoes were transplanted on June 11 to Heinz 5508. The late planting allowed the cover crops to nearly reach full maturity. Tomato fruit yields were measured at harvest on October 5th with a portable weigh alongside the grower’s mechanical harvester. Only the center bed of the 3-bed planting was captured. Culls were sorted by weight and calculated on a % basis. A sub sample of fruit was delivered to a local PTAB inspection station to measure pH, color and Brix.
Results: Grass growth started slowly due to low rainfall, but with supplemental irrigation produced from 1.6 to 2.3 ton per acre (Table 1). All grass crops lowered nematode soil levels compared to the fallow control when sampled in the spring prior to the tomato planting (Table 2). Levels in the spring were reduced to 7 to 29% of the population in the fall. The levels were 42 to 124 J2 juveniles compared to 364 per 100 grams of soil in the nontreated control. Results from the soil samples at tomato harvest are pending. Tomato root galling was severe at end of the season average about 8 on a scale of 0 to 10, where 8 is defined as ‘all main roots knotted with few clean roots visible’ from a visual rating scale.
Tomato fruit yields from all the grass treatments were lower than the control, averaging 28.0 vs. 38.8 tons/acre, respectively (Table 2). The resistant wheat Patwin did not improve tomato yield when planted ahead of tomatoes as a cover crop although soil sample indicated levels were reduced compared to the control.
Table 1. Evaluation of Patwin and grass cover crops, establishment
and growth, Harlan Family Farms, Woodland, 2014.
Page 3 of 3 Nematode resistant wheat
California Tomato Research Institute
Table 2. Evaluation of Patwin on nematode level, Harlan Family Farms, Woodland, 2014.
postpre harvest
fall spring % harvest 6-Oct25-Nov 21-Apr of 30-Sep rootRKN J2 RKN J2 initial RKN J2 galling
Treatment /100 g soil /100 g soil level index1
1 Fallow control 472 364 77 7.72 Patwin wheat 422 70 17 in 8.33 Wheat 'Anza' 533 92 17 progress 7.94 Triticale 'Trios' 428 124 29 8.55 Oats 'Montezuma' 640 42 7 8.2
LSD @ 5% NS 202 NS% CV 102 109 9^ non-additivity ^
Class ComparisonsControl vs. 472 364 7.7 any grass 506 82 8.2Probability NS 0.002 0.15
index1 0= none 10= dead using rating chart of Bridge and Page
Table 3. Evaluation of Patwin on H 5508 tomato yield, Harlan Family Farms, Woodland, 2014.
% Yield % sun greeness
Treatment tons/A Brix burn of vine1 Fallow control 38.8 4.32 4 352 Patwin(resistant) wheat 26.8 4.38 8 133 Wheat 'Anza' 31.3 4.52 5 224 Trios' triticale 23.9 4.56 11 65 Oats 'Montezuma' 30.2 4.46 6 18
LSD @ 5% 12.9 NS NS 11% CV 13 6 71 44
Class ComparisonsControl vs. 38.8 4.32 3.6 35 any grass 28.0 4.48 7.8 15Probability 0.01 0.25 0.11 0.00
TITLE: Field Bindweed Management in Processing Tomatoes PROJECT LEADER:
Lynn M. Sosnoskie, Assistant Project Scientist 259 D Robbins Hall, University of California -‐ Davis Department of Plant Sciences, MS-‐4 One Shields Avenue Davis, CA 95616 (229) 326-‐2676 [email protected]
CO-‐PIs:
Bradley D. Hanson, Extension Weed Specialist 276 Robbins Hall, University of California -‐ Davis Department of Plant Sciences, MS-‐4 One Shields Avenue Davis, CA 95616 (530) 752-‐8115 [email protected]
JUSTIFICATION
The agricultural sector has been a significant contributor to California’s economy since
the 1850’s when former miners were forced to seek alternate sources of income following the gold rush (Baker et al. 2013). The subsequent development of a complicated and extensive canal system helped transform California’s Central Valley, which was originally dominated by ranching and dry-‐land crops, into the United States’ most diverse and economically productive farmland (Baker et al. 2013). In 2012, more than 400 different agricultural commodities, worth more than $40 billion, were produced in the state; processing tomatoes, which were ranked as the tenth most important commodity, were valued at $1.2 billion (CDFA 2013).
Processing tomato production has changed, dramatically, over time. Breeding efforts, the switch from seeds to transplants, the commercialization of the mechanical harvester, and the steady adoption of drip irrigation have helped to expand the total area planted to tomatoes (>250,000 acres in 2012) and increase yields (currently, > 45 tons/A) (CDFA 2013; Mitchell et al. 2012; USDA 2013). In order to maintain outputs and meet pre-‐contracted processing needs, growers are in need to effective pest management strategies. Historically, processing tomato production has been heavily dependent on intense, multi-‐pass cultivation programs for weed suppression (Mitchell et al. 2012). The growing adoption of minimum tillage production systems (Mitchell et al. 2012), coupled with the constantly changing costs and availability of hand labor, ensures that herbicides will continue to be an important tool for the control of weedy pests.
Field bindweed (Convolvulus arvensis) is a deep-‐rooted (to depths of 10 feet, or more), drought-‐tolerant perennial that propagates and spreads via asexual (i.e. root and shoot buds developing directly from rhizomes and roots) and sexual (i.e. seed) means. Although bindweed seedlings can be readily managed via chemical and cultural means, perennial plants with extensive root systems are less susceptible to control measures (Dall’Armellina and Zimdahl
1989; Sharma and Singh 2007; Wiese and Lavake 1986; Yerkes and Weller 1996). Cultivation and tillage, if not conducted regularly, may only serve to disperse seeds and root fragments. Herbicides, if applied infrequently and/or at the incorrect time, may do little more than burn off aboveground tissue. Although the use of drip-‐irrigation systems may reduce the impact of many weed species, field bindweed is likely to thrive because of its’ ability to acquire resources from deep in the soil profile.
Research conducted at the University of California – Davis (UCD) campus and the University of California West Side Research and Extension Center (UC WSREC) in previous years has demonstrated that Treflan PPI is one of the most effective soil-‐applied treatments for suppressing established field bindweed in processing tomatoes. Although less effective against bindweed, Matrix, Dual Magnum and Zeus control many other troublesome weed species. The objective of continuing research trials is determine which herbicides, alone and in combination, are the most effective for managing weed species in processing tomato production systems. PROCEDURES
In 2014, research trials were established at the University of California, Davis, campus to evaluate the combined effects of a pre-‐plant (PP) burn-‐down, pre-‐plant incorporated (PPI), preemergence (PRE), postemergence (POST) and shielded-‐spray (SHIELD) herbicide applications for field bindweed management in early and late planted processing tomatoes (Table 1). Table 1. Treatments for field bindweed management in processing tomatoes. Planting date 1. Early (April, 2013 and 2014)
2. Late (June, 2013 and 2014)
PPa 1. glyphosate (Roundup at 56 oz A-‐1) 2. no burn-‐down
PPI/ PRE 1. trifluralin (Treflan at 32 oz A-‐1) 2. trifluralin (Treflan at 32 oz A-‐1) + metolachlor (Dual Magnum at 27 oz A-‐1) 3. trifluralin (Treflan at 32 oz A-‐1) + rimsulfuron (Matrix at 2 oz A-‐1) 4. trifluralin (Treflan at 32 oz A-‐1) + sulfentrazone (Zeus at 3.2 oz A-‐1)
POST/SHIELD 1. rimsulfuron (Matrix at 2 oz A-‐1) 2. carfentrazone (Shark at 2 oz A-‐1)
a A pre-‐plant burndown application of glyphosate was not applied in the early tomato plantings as no field bindweed plants were emerged.
Two planting periods (early and late) were selected to simulate a range of potential planting dates available to growers to meet season-‐long processing needs. The factorial combination of herbicides yielded 16 unique treatments that were evaluated for each planting date; untreated checks were also included for the purpose of comparison. Treatments were arranged as a randomized complete block design within planting date and were replicated three
times. Individual plots consisted of two tomato beds (on 60 inch centers) that were 25 feet in length. Furrow irrigation was used to maximize potential weed pressure (Sutton et al. 2006).
The early and late plantings were established in mid-‐April and early-‐June, both years. Soil at the site is a fine, silty loam (Yolo series, 1.5-‐3% OM, pH 6.7-‐7.0) and is known to be heavily infested with field bindweed. For plots receiving a PP burn-‐down treatment, Roundup (glyphosate) at 56 oz A-‐1 was applied to aboveground vegetation 1-‐2 weeks before bedding. Trifluralin, metolachlor and sulfentrazone were applied to the soil surface prior to final bed shaping and immediately incorporated into the top 2-‐3 inches. Rimsulfuron was surface-‐applied after shaping, but prior to planting. Tomatoes were transplanted into beds within 24 hours of the PPI/PRE herbicide applications and sprinkler set; root plugs were set at a depths ranging from 4-‐6 inches. Post-‐emergence and SHIELD applications of rimsulfuron and carfentrazone, respectively, were made approximately 4-‐6 weeks after the transplants had become established. All materials were applied using a CO2-‐pressurized backpack sprayer equipped with Tee Jet 8002VS nozzles; carrier volumes were adjusted accordingly for each herbicide as per label recommendations. Irrigation, fertilization and insect/disease management schedules were set according to guidelines developed by University of California cooperative extension. One-‐two cultivation events occurred during the growing season; no hand-‐weeding was employed.
Tomato crop injury (e.g. reduction in plant heights, leaf chlorosis and necrosis) and field bindweed control (e.g. estimates of percent cover) and density (e.g. number of plants m-‐2) were assessed, weekly, until canopy closure. Tomatoes were hand harvested when at least 90% of the fruit was mature. Data were subjected to analysis of variance to evaluate the effects of herbicides on crop development and weed control. Because data trends were similar across planting dates, the combined data analyses are presented. RESULTS
Pre-‐plant applications of glyphosate to emerged bindweed in processing tomatoes reduced weed cover, when used in conjunction with PPI/PRE herbicides (Figure 1, Table 2). For example, trifluralin treatments that received a PP application of glyphosate had mean bindweed covers of 8 and 13% at six weeks after transplanting, whereas trifluralin treatments without a burn-‐down had mean percent covers of 45 and 61%. In other words, the use of glyphosate, PP, improved bindweed suppression, in crop, by more than 50%. Similar results were observed for trifluralin plus S-‐metolachlor (12% and 22% versus 31% and 40%), trifluralin plus rimsulfuron (10% and 13% versus 43% and 40%), and trifluralin plus sulfentrazone (7% and 18% versus 38% and 47%) However, growers must be aware that the timing of herbicide applications is crucial; perennial bindweed may be less susceptible to glyphosate in the very early spring (when perennial vines are first emerging) as carbon flow is likely to be moving away from the roots and into the shoots (Stone et al. 2005; Wiese and Lavake 1986; Wiese et al. 1997).
Figure 1. Percent (%) field bindweed cover in response to pre-transplant glyphosate (Roundup PowerMax) applications in processing tomatoes for up to 6 weeks after transplanting.
(Data averaged over all PPI/PRE and POST/SHIELD herbicide treatments).
Table 2. Field bindweed cover (%) in response to PP, PPI/PRE and POST/SHIELD herbicides for up to six
weeks after transplanting.
As has been observed in previous UC trials, PPI/PRE herbicides can be effective at
managing field bindweed (Table 2). Results from 2014 suggest that the use of PPI/PRE herbicides, primarily trifluralin, can suppress field bindweed growth and development for up to 6 weeks after transplanting. Where glyphosate was applied PP, trifluralin alone or applied with S-‐metolachlor, sulfentrazone or rimsulfuron, improved bindweed control (7 to 22% cover) relative to the untreated check (48% cover) at six weeks after transplanting. In the absence of a glyphosate burn-‐down, bindweed control in PPI/PRE treatments began to fail by four weeks
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0 WAT 1 WAT 2 WAT 3 WAT 4 WAT 6 WAT
Percen
t (%) cover
Glyphosate PP
No Glyphosate PP
PP PPI/PRE POST/SHIELD 0 WAT 1 WAT 2 WAT 4 WAT 6 WATglyphosate none none 0.0 5.3 18.3 48.3 48.3glyphosate trifluralin rimsulfuron 0.0 0.7 3.0 6.0 7.5glyphosate trifluralin, metolachlor rimsulfuron 0.0 1.0 3.0 5.3 11.7glyphosate trifluralin, rimsulfuron rimsulfuron 0.0 0.0 2.0 1.0 10.0glyphosate trifluralin, sulfentrazone rimsulfuron 0.0 0.3 2.7 1.3 6.7glyphosate trifluralin carfentrazone 0.0 0.7 4.3 12.3 13.3glyphosate trifluralin, metolachlor carfentrazone 0.0 0.3 2.0 1.7 21.7glyphosate trifluralin, rimsulfuron carfentrazone 0.0 0.0 3.3 1.7 13.3glyphosate trifluralin, sulfentrazone carfentrazone 0.0 0.0 5.0 14.0 18.3none none none 0.0 10.0 18.3 83.3 45.0none trifluralin rimsulfuron 0.0 6.7 10.0 43.3 61.7none trifluralin, metolachlor rimsulfuron 0.0 4.0 5.7 33.3 31.7none trifluralin, rimsulfuron rimsulfuron 0.0 1.5 4.0 12.5 42.5none trifluralin, sulfentrazone rimsulfuron 0.0 2.3 2.3 13.3 38.3none trifluralin carfentrazone 0.0 5.3 10.3 53.3 45.0none trifluralin, metolachlor carfentrazone 0.0 3.0 7.7 43.3 40.0none trifluralin, rimsulfuron carfentrazone 0.0 1.3 3.7 28.3 40.0none trifluralin, sulfentrazone carfentrazone 0.0 4.3 4.7 23.3 46.7
after transplanting (Table 2). At six weeks after transplanting, there were no differences in bindweed cover among any treatments (including the check).
Although the use of S-‐metolachlor, sulfentrazone or rimsulfuron PPI/PRE, did not
appear to improve bindweed suppression, except where glyphosate was used PP (Table 2), these herbicides helped to improve the control of small-‐seeded broadleaf species such as purslane, common lambsquarters and pigweeds (Data not shown) (Adcock et al. 2008; Clewis et al. 2007; Felix and Newberry 2012). Furthermore, all have been shown to be effective against yellow and purple nutsedges (Adcock et al. 2008; Clewis et al. 2007; Felix and Newberry 2012). Where a diversity of weed species are present, and if rotation restrictions permit, the addition of S-‐metolachlor, sulfentrazone or rimsulfuron to trifluralin may be appropriate.
Processing tomato yields increased significantly, relative to the untreated check, when
PPI/PRE and POST/SHIELD products were applied (Table 3). When scaled up and converted to a per acre unit, the maximum yield gains achieved from applying herbicides approached 11,000-‐12,000 lbs per acre. In order to maximize yield, tomatoes must remain relatively weed-‐free for up to two months after planting. Results from this trial are in agreement with previously conducted studies that field bindweed can be suppressed, but not controlled by herbicides.
Table 3. Processing tomato yields in response to PPI/PRE and POST/SHIELD herbicides expressed
as a percent of the untreated check. Increased yields due to the use of PPI/PRE and POST/SHIELD herbicides are attributed to the suppression of bindweed and small-‐seeded broadleaves, collectively.
(Data are averaged over glyphosate burn-‐down applications; although glyphosate significantly affected bindweed control for up to six weeks after tomato transplanting, preliminary data analyses
suggest that its use did not influence crop yields.)
Herbicides Percent (%) of Untreated CheckPPI/PREnone 100.0trifluralin 144.1trifluralin, metolachlor 137.1trifluralin, rimsulfuron 148.3trifluralin, sulfentrazone 139.1
POST/SHIELDnone 100.0carfentrazone 140.2rimsulfuron 144.1
SUMMARY
In order to maximize yield, tomatoes must remain relatively weed-‐free for up to two months after planting. Results from the 2014 CTRI sponsored research trial agree with those achieved in 2013, namely, that the use of a diverse herbicide program can effectively suppress field bindweed and other troublesome weed species common to processing tomatoes (Table 4). The use of a glyphosate burn-‐down treatment one to two weeks prior to bed-‐shaping can improve the management of severe field bindweed infestations. When averaged over all herbicide programs, glyphosate reduced field bindweed cover, in-‐crop by 50% at six weeks after transplanting. Although S-‐metolachlor, sulfentrazone or rimsulfuron are effective against nutsedges and small-‐seeded broadleaved weeds, trifluralin appears to be predominantly responsible for the suppression of field bindweed. The use of rimsulfuron (POST) or carfentrazone (SHIELD) can be effective for managing field bindweed, their utility is most likely to be maximized when additional measures are also undertaken (i.e. burn-‐down and PPI/PRE) applications. Results from this research trial show that successful field bindweed suppression is likely to be achieved when multiple control strategies are employed.
The research trials from 2013 and 2014 are currently being written up as a manuscript
that will be submitted to the peer-‐reviewed journal, Weed Technology. This data will also be developed into a weed science blog (http://wric.ucdavis.edu/) for California processing tomato growers.
Table 2. Field bindweed cover (%) in response to PP, PPI/PRE and POST/SHIELD herbicides for up to eight weeks after transplanting in 2013.
PP PPI/PRE POST/SHIELD 0 WAT 1 WAT 2 WAT 3 WAT 4 WAT 6 WAT 8 WATglyphosate none none 0.0 5.3 14.0 40.0 7.3 28.3 38.3none none none 0.0 10.0 25.7 60.0 7.7 36.7 45.0
glyphosate trifluralin rimsulfuron 0.0 0.7 2.2 5.0 0.8 5.0 5.0glyphosate trifluralin carfentrazone 0.0 0.7 3.7 10.7 2.3 14.0 15.0none trifluralin rimsulfuron 0.0 6.7 13.3 48.3 9.0 31.7 25.0none trifluralin carfentrazone 0.0 5.3 9.0 33.3 9.0 31.7 30.0
glyphosate trifluralin, metolachlor rimsulfuron 0.0 1.0 2.3 9.0 1.7 10.0 7.3glyphosate trifluralin, metolachlor carfentrazone 0.0 0.3 1.5 3.0 1.5 9.3 9.0none trifluralin, metolachlor rimsulfuron 0.0 3.8 7.8 25.5 6.3 27.5 21.3none trifluralin, metolachlor carfentrazone 0.0 3.0 5.7 20.0 3.7 25.0 16.7
glyphosate trifluralin, rimsulfuron rimsulfuron 0.0 0.0 0.5 1.0 1.5 10.7 5.7glyphosate trifluralin, rimsulfuron carfentrazone 0.0 0.0 0.7 1.7 0.7 8.3 9.0none trifluralin, rimsulfuron rimsulfuron 0.0 1.5 2.0 6.0 5.0 25.0 15.0none trifluralin, rimsulfuron carfentrazone 0.0 1.3 3.0 14.0 6.7 35.0 26.7
glyphosate trifluralin, sulfentrazone rimsulfuron 0.0 0.3 2.0 5.3 2.3 10.7 6.7glyphosate trifluralin, sulfentrazone carfentrazone 0.0 0.0 0.7 3.0 1.5 8.3 6.7none trifluralin, sulfentrazone rimsulfuron 0.0 2.3 3.7 20.0 5.7 26.7 15.0none trifluralin, sulfentrazone carfentrazone 0.0 4.3 9.0 33.3 6.3 33.3 18.3
FUTURE RESEARCH
It is the goal of the principal investigators to continue with the development of a weed management research program for processing tomatoes in California. Future studies will be designed to:
1. Describe how the residual activities of pre-‐emergence/pre-‐plant incorporated herbicides applied for bindweed control are affected by irrigation strategy and time of application. The soil-‐applied herbicides registered for use in processing tomato vary significantly with respect to their solubility in water, adsorption and moisture requirements for activation. These factors, in combination with local edaphic/environmental conditions at and following the time of application, affect herbicide performance. For example, while moisture is required for activation, too much water can facilitate leaching or enhance degradation (Gaynor et al. 1993; Grass et al. 1994; Grey and McCullough 2012). In the case of volatile herbicides, increased soil and air temperatures can reduce chemical retention times (Gaynor et al. 1993; Grass et al. 1994; Grey and McCullough 2012). Weed biology can also impact efficacy; the relative activity of herbicides are affected by the size, vigor and developmental stage of the target species (Wiese and Lavake 1986). Deliverables: This research will help to reinforce the results of earlier UC trials and better characterize the abilities and the limitations of currently labeled herbicides in response to irrigation practices, environmental conditions and bindweed vigor.
2. Determine how the growth of field bindweed is affected by rhizome size, burial depth and
pre-‐plant/pre-‐emergence herbicides. Previously published journal articles have reported that trifluralin can control Johnsongrass (Sorghum halapense), a perennial grass that reproduces from rhizomes and seeds, by inhibiting lateral root development (Millhollon 1978; Standifer and Thomas 1965). Results from studies conducted, previously, at UCD also demonstrate that perennial bindweed growth can be suppressed by Treflan and other active ingredients labeled for use in processing tomato production. However, because these herbicides are shallowly incorporated, it is uncertain if more deeply buried rhizomes are significantly impacted by control measures. Deliverables: This study will help determine if/how conventional herbicide applications are affecting the growth and development of more deeply buried bindweed rhizomes.
3. Evaluate the efficacy of sub-‐surface applications of trifluralin (Treflan) for the suppression of
more deeply buried field bindweed rhizomes.
The successful control of deep-‐rooted perennials, such as field bindweed, is dependent upon dependent upon latent root and shoot buds coming into contact with herbicide-‐treated soil. The majority of root/rhizome biomass for field bindweed is located within the top 2 feet of the soil profile, although some vertical roots can reach depths of more than 10 feet. Conversely, Treflan and other residual herbicides registered for use in processing tomatoes, usually are incorporated into the top 2 to 3 inches of the soil profile. Because of their shallow placement, these herbicides are unlikely to suppress bindweed that is emerging from deeply buried
rhizomes. Deliverables: This research will evaluate the efficacy and safety of an unconventional application of Treflan to directly target bindweed rhizomes that are likely escaping traditional control strategies.
4. Evaluate the use of a commercial ethylene inhibitor to reduce transplant shock in tomato and improve early-‐season crop competitiveness relative to field bindweed. One strategy for integrated weed control is to assure that crops remain competitive, especially under stressful production conditions, where weeds may be more likely to grow and develop more quickly than the crop. Therefore, optimizing the health and vigor of the crop should be an important component of a weed control program. Deliverables: Results from this project will determine if mitigating transplant shock with 1-‐MCP can improve tomato canopy development and competitive ability against field bindweed.
5. Develop a set of tomato symptomology photos that can be contributed to an online visual tool being developed to help diagnose herbicide injury in a variety of crops. Crop injury can result from herbicide drift, herbicide volatility, product misapplication, poor tank cleanout, and soil carryover. Deliverables: Results from this project will help growers diagnose instances of herbicide injury. Increased knowledge about injury development will support grower decision-‐making processes regarding crop replant and/or additional management options.
ACKNOWLEDGEMENTS The investigators would like to thank the CTRI for supporting this project. Additionally, we would like to than Jim Jackson, Seth Watkins, Oscar Morales, Marcelo Moretti, Bahar Kutman, Caio Brunharo, Mariano Galla, and Sarah Morran for their assistance with the establishment, maintenance, evaluation and harvest of this trial.
REFERENCES: Adcock, C. W., W. G. Foshee III, G. R. Wehtje, and C. H. Gilliam. 2008. Herbicide combinations in tomato to prevent nutsedge (Cyperus esculentus) punctures in plastic mulch for multi-‐cropping systems. Weed Technol. 22:136-‐141. Baker, G. A. A. N. Sampson and M. J. Harwood. 2013. California water wars: Tough choices at Woolf farming. Int. Food and Agribusiness Manage. Rev. 16:95-‐103. [CDFA] California Department of Food and Agriculture. 2013. www.cdfa.ca.gov. Clewis, S. B., W. J. Everman, D. L. Jordan, J. W. Wilcut. 2007. Weed management in North Carolina peanuts (Arachis hypogaea) with S-‐metolachlor, diclosulam, flumioxazin and sulfentrazone systems. Weed Technol. 21:629-‐635.
Dall’ Armellina, A. A. and R. L. Zimdahl. 1989. Effect of watering frequency, drought and glyphosate on growth of field bindweed (Convolvulus arvensis). Weed Sci. 37:314-‐318. Felix, J. and G. Newberry. 2012. Yellow nutsedge control and reduced tuber production with S-‐metolachlor, halosulfuron plus dicamba, and glyphosate in furrow-‐irrigated corn. Weed Technol. 26:213-‐219. Gaynor, J. D., A. S. Hamill, and D. C. MacTavish. 1993. Efficacy, fruit residues, and soil dissipation of the herbicide metolachlor in processing tomato. J. Amer. Soc. Hort. Sci. 118:68-‐72. Grass, B., B. W. Wenclawiak, and H. Rudel. 1994. Influence of air velocity, air temperature, and air humidity on the volitilisation of trifluralin from soil. Chemosphere. 28:491-‐499. Grey, T. L. and P. E. McCullough. 2012. Sulfonylurea herbicides’ fate in soil: dissipation, mobility, and other processes. Weed Technol. 26:579-‐581. Millhollon, R. W. 1978. Toxicity of soil incorporated trifluralin to Johnsongrass (Sorghum halapense) rhizomes. Weed Sci. 26:171-‐174. Mitchell, J. P., K. A. Klonsky, E. M. Miyao, B. J. Aegerter, A. Shrestha, D. S. Munk, K. Hembree, N. M. Madden and T. A. Turini. 2012. Evolution of conservation tillage systems for processing tomato in California’s Central Valley. Hort Tecnol. 22: 617-‐626. Sharma, S. D. and M. Singh. 2007. Effect of timing and rates of application of glyphosate and carfentrazone herbicides and their mixtures on the control of some broadleaf weeds. Hort. Sci. 42:1221-‐1226 Standifer, L. C. and C. H. Thomas. 1965. Response of Johnsongrass to soil incorporated trifluralin. Weeds. 13:302-‐306. Stone, A. E., T. F. Peeper, and J. P. Kelley. Efficacy and acceptance of herbicides applied for field bindweed (Convolvulus arvensis) control. Weed Technol. 19:148-‐153. Sutton, K. F., W. T. Lanini, J. P. Mitchell, E. M. Miyao and A. Shrestha. 2006. Weed control, yield and quality of processing tomato production under different irrigation, tillage and herbicide systems. Weed Technol. 20:831-‐838. [USDA-‐NASS] United States Department of Agriculture – National Agricultural Statistics Service. 2013. www.nass.gov. Wiese, A. F. and D. E. Lavake. 1986. Control of field bindweed (Convolvulus arvensis) with postemergence herbicides. Weed Sci. 34:77-‐80. Wiese, A. F., M. G. Schoenhals, B. W. Bean, and C. D. Salisbury. 1997. Effect of tillage timing on herbicide toxicity to field bindweed. J. Prod. Agric. 10:459-‐461.
Yerkes, C.N.D and C. Weller. 1996. Diluent volume influences susceptibility of field bindweed (Convolvulous arvensis) biotypes to glyphosate. Weed Technol. 10:565-‐569.
Project Title: Evaluating Herbicide Carryover in Sub-surface Drip-irrigated Tomatoes Project Leader: Kurt Hembree; Farm Advisor, UCCE, Fresno County
550 East Shaw Ave., Suite 210-B, Fresno, CA 93710 Office phone: 559-241-7520; Cell phone: 559-392-6095 Email: [email protected]
Co-investigator: Tom Turini; Farm Advisor, UCCE, Fresno County
550 East Shaw Ave., Suite 210-B, Fresno, CA 93710 Office phone: 559-241-7529; Cell phone: 559-375-3147 Email: [email protected]
Summary: The second season of a three-year field trial was conducted at the UC West Side Research & Extension Center in Five Points, CA to evaluate Treflan (trifluralin) and Prowl H2O (pendimethalin) carryover and potential impact on tomato health and production in a three-year tomato rotation using sub-surface drip irrigation. In spring 2014, residues of trifluralin (up to 0.006 ppm) and pendimethalin (up to 0.031 ppm) were detected up to six inches deep in the soil one year after their application in 2013, with at least 3X more pendimethalin detected than trifluralin. The potential for herbicide carryover, particularly with pendimethalin, seemed to be aided by the dry winter (<2.6″ total rainfall) we had during 2013/14. However, herbicide residue levels detected did not result in reduced tomato plant dry weight or fruit yield. Following re-treatment in 2014, using sprinkler water to wet the bed surface helped reduce the amount of detectable trigluralin by 95% by harvest, compared to 88% for pendimethalin, which was similar to what we saw in 2013. Based on residues detected at the end of 2014, we expect to see an accumulation of pendimethalin in 2015, above 2014 levels, especially if rainfall is again lacking during the winter. If this is the case, we might also expect to see phytotoxic effects from the pendimethalin on tomato growth and yield potential. Objectives (year 2):
1. To determine herbicide residue levels in the upper six inches of the soil profile over time. 2. To determine if using overhead sprinkler irrigation during crop establishment enhances
herbicide degradation. 3. To determine transplant tomato response to herbicide residues.
Procedures: The trial was established in spring 2013 as a split-plot experimental design, with four replications. Irrigation (sprinkler + drip and drip only) were main-plot treatments and pre-plant herbicides (trifluralin, pendimethalin, and no herbicide) were sub-plot treatments. Main-plot treatments were six beds wide and 225′ long and sub-plot treatments were six beds wide and 75′
long. The center two rows within each sub-plot were used for collecting data, leaving two buffer rows on either side. Sub-plots were separated from each other down the rows with a 15′ untreated buffer to prevent cross-plot mixing of herbicides. Following harvest in 2013, tomato plant residues were worked into the beds in November and 60-inch raised beds were rebuilt in April 2014. A pre-plant fertilizer (11-52-0) was applied at 300 lb/acre with sub-surface shanks, and the trial plot area re-staked. The drip system was flushed and leaks in the tape or mainline were repaired. Soil samples were collected from each sub-plot at 0-3″ and 3-6″ depths and analyzed for trifluralin and pendimethalin (applied one year earlier). Treflan and Prowl H2O were again applied at high label rates on April 21 and incorporated 2.5-3″ deep with a 3-row bed shaper. Sub-plots were then re-sampled for herbicide residues. Pre-irrigation water (2.25 acre-inches) was applied to all plots through the drip tape. Tomatoes were then transplanted on April 29 using a 3-row finger planter, with plants placed 10″ apart in a single line per bed, with the tops of the root plugs about 2″ deep. Overhead sprinklers were used to apply 1.5 acre-inch of water over a one-week period to the sprinkler + drip treatment main-plots. All plots then received drip irrigation water for the remainder of the season, with a total of 37 acre-inches of water applied. The trial area was cultivated twice during the season and a hand weeding crew was used to remove weeds. Platinum insecticide was applied through the drip tape to all plots in May for leafhopper control, and fertilizer was injected as needed. Final plant stand and plant shoot/root dry weights were determined at 30 days after transplanting (DAT) and plots were harvested on August 25. To determine crop yield, 20′ of the two data rows were pulled by hand, then tomato fruit was shaken into 30-gal drums and weighed. A 5-gal bucket of fruit was randomly sub-sampled from the drums and sorted by grade (red, green, sunburn, and rot) and weighed. More than 95% of the fruit sorted into the category of “rot” was characterized as having blossom end rot, but was still counted as rot and was not added to the total weight of red fruit harvested. As a result, total red fruit yields referenced in the data tables appear lower than expected. Fifty red, ripe fruit were then collected from each 5-gal sub-sample, weighed, and tested for fruit quality attributes (color, solids, and pH). Data was analyzed using the MSTAT statistical program with a 2-factor procedure and significant means were separated using an LSD test at the p=0.05 level of probability. Results: Both trifluralin and pendimethalin were detected in the soil in April 2014 after final bed preparation and before re-application, suggesting herbicide carryover occurred from the initial application made in spring 2013 (table 1 and figures 1-4). While using sprinklers to add water to the soil surface resulted in less detectable herbicide compared to the drip-only plots, especially at the soil surface (0-3″ depth), it was not significantly different. There was, however, significantly more pendimethalin detected than trifluralin at both sampling depths (2X more at the 0-3″ depth
and 3X more at the 3-6″ depth). Pendimethalin is known to have a longer soil half-life than trifluralin, but little is known on the half-life of Prowl H2O, given its micro-encapsulated, slow-release formulation. Soil herbicide residues detected after the 2014 harvest also seem to indicate increased herbicide breakdown where sprinklers were used to enhance soil surface wetting, as was the case in 2013. However, this slight improvement in herbicide breakdown may be overshadowed by the fact that we were still able to detect much more pendimethalin than trifluralin at the end of the growing season (11% trifluralin remained after treatment and 22% pendimethalin remained after treatment).
Table 1. Soil herbicide residue detection at three soil sampling times in 2014
Irrigation
Herbicide
Sample 11 0-3″ deep (ppm) 2
Sample 11 3-6″ deep (ppm) 2
Sample 21 0-3″ deep (ppm) 2
Sample 21 3-6″ deep (ppm) 2
Sample 31 0-3″ deep (ppm) 2
Sample 31 3-6″ deep (ppm) 2
Drip no herbicide 0.000 0.000 0.000 c 0.000 0.000 d 0.000 Sprinkler, then drip no herbicide 0.000 0.000 0.000 c 0.000 0.000 d 0.000 Drip trifluralin 0.012 0.005 0.587 b 0.040 0.137 b 0.015 Sprinkler, then drip trifluralin 0.000 0.000 1.135 a 0.067 0.075 c 0.000 Drip pendimethalin 0.035 0.020 0.717 b 0.228 0.302 a 0.047 Sprinkler, then drip pendimethalin 0.027 0.022 1.110 a 0.153 0.137 b 0.040 Statistical notation CV (%)
LSD (p=0.05) 49.17 n.s.
77.35 n.s.
25.89 0.222
79.14 n.s.
24.69 0.046
44.39 n.s.
1Sample 1 after bed preparation (2013 treatment); Sample 2 after herbicide incorporation (2014 treatment); Sample 3 after harvest (2014 treatment). 2Herbicide residues analyzed by Dellavalle Laboratory, Inc. Fresno, CA using HPLC.
Using sprinklers to help establish the crop resulted in significantly higher final plant stand counts, but plant stand was not influenced by herbicide type (table 2 and figures 5-8). Regardless
Figure 1. Soil herbicide residue detection at a sampling depth of 0-3" at three timings
00.10.20.30.40.50.60.70.80.9
1
Trifluralin Pendimethalin
Her
bici
de (p
pm)
Bed prep PPI Harvest
Figure 2. Soil herbicide residue detection at a sampling depth of 3-6" at three timings
00.0250.05
0.0750.1
0.1250.15
0.1750.2
Trifluralin Pendimethalin
Her
bici
de (p
pm)
Bed prep PPI Harvest
Figure 3. Soil herbicide residue at a sampling depth of 0-3" at three timings by irrigation
00.10.20.30.40.50.60.70.8
Drip Sprinkler + Drip
Her
bici
de (p
pm)
Bed prep PPI Harvest
Figure 4. Soil herbicide residue at a sampling depth of 3-6" at three timings by irrigation
0
0.025
0.05
0.075
0.1
Drip Sprinkler + Drip
Her
bici
de (p
pm)
Bed prep PPI Harvest
of herbicide used, the levels of residues detected did not appear to be high enough to reduce tomato shoot or root dry weight, although visual observation may have shown otherwise (see photo). It is unclear at this point how much herbicide residue detected in the tomato root plug zone would be required to damage roots and/or shoots of tomato transplants. A greenhouse study started in 2014 at California State University, Fresno seems to indicate that some amount below 1 ppm of pendimethalin and trifluralin are required to damage plant roots of potted transplant tomatoes (data not shown).
Table 2. Final tomato stand count and plant dry weights on 5/27/14 (28 DAT)
Irrigation
Herbicide Number tomato plants per plot1
Tomato shoot dry weight (gm)2
Tomato root dry weight (gm)2
Drip no herbicide 129.8 28.06 4.05 Sprinkler, then drip no herbicide 159.0 27.74 4.22 Drip trifluralin 132.3 27.69 4.90 Sprinkler, then drip trifluralin 154.5 24.95 4.10 Drip pendimethalin 128.3 25.90 4.03 Sprinkler, then drip pendimethalin 168.0 25.99 3.90 Statistical notation CV (%)
LSD (p=0.05) 6.72%
n.s. 25.39 n.s.
19.02 n.s.
1Total healthy plants in center two beds of each sub-plot, 75′ long. 2Two plants per sub-plot were removed, soil washed off roots, shoots and roots separated, oven-dried, and weighed.
Figure 5. Tomato plant stand by irrigation
100
110
120
130
140
150
160
170
Drip Sprinkler + Drip
Pla
nts
in 2
bed
s X
75'
Figure 6. Tomato plant stand by herbicide
100
110
120
130
140
150
160
No herbicide Trifluralin Pendimethalin
Pla
nts
per 2
bed
s X
75'
Figure 7. Tomato dry weight by irrigation
0
5
10
15
20
25
30
Drip Sprinkler + Drip
Dry
wei
ght (
gm)
Shoot Root
Figure 8. Tomato dry weight by herbicide
0
5
10
15
20
25
30
No herbicide Trifluralin Pendimethalin
Dry
wei
ght (
gm)
Shoot Root
Photo. Tomato roots 30 DAT: with drip (top) and no herbicide, trifluralin and pendimethalin (left to right); with sprinkler + drip (bottom) and no herbicide, trifluralin and pendimethalin (left to right). Neither tomato yield or fruit quality were affected by irrigation method or herbicide (tables 3 and 4). Total red fruit yield was low because we did not include weights from tomatoes with blossom end rot, which made up about 95% of the fruit classified as “rot”.
Table 3. Tomato yield on 8/25/14 Irrigation Herbicide Red (%)1 Green (%)1 Sunburn (%)1 Rot (%)1 Red (T/A) 1
Drip no herbicide 84.86 4.40 3.18 7.62 41.28 Sprinkler, then drip no herbicide 86.80 3.11 1.32 8.78 41.80 Drip trifluralin 83.98 3.43 2.79 9.79 36.40 Sprinkler, then drip trifluralin 85.67 3.42 0.81 10.85 41.44 Drip pendimethalin 78.69 3.07 3.43 16.06 36.32 Sprinkler, then drip pendimethalin 83.85 2.32 2.20 11.64 42.48 Statistical notation CV (%)
LSD (p=0.05) 5.96 n.s.
62.91 n.s.
96.23 n.s.
32.59 n.s.
18.17 n.s.
1Fruit yield and ripeness determined by hand pulling all tomato plants in the center two rows (20′ long) shaking fruit into 30-gal drums and weighing it, and then a 5-gal bucket of fruit was sub-sampled, sorted and weighed.
Table 4. Tomato fruit quality on 8/25/14
Irrigation Herbicide 50-count (lb)1 Color1 Solids1 pH1 Drip no herbicide 6.14 22.0 5.1 4.40 Sprinkler, then drip no herbicide 6.45 21.8 5.0 4.40 Drip trifluralin 6.30 22.8 4.9 4.44 Sprinkler, then drip trifluralin 6.09 22.5 4.9 4.40 Drip pendimethalin 6.26 22.5 5.0 4.44 Sprinkler, then drip pendimethalin 6.39 22.0 4.9 4.40 Statistical notation CV (%)
LSD (p=0.05) 7.30 n.s.
2.59 n.s.
3.97 n.s.
0.81 n.s.
150 red, ripe fruit were randomly sampled from harvested plot area then analyzed.
