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PUTTING SENSORS TO WORK:TARGETED APPLICATION OF NUTRIENTS AND PESTICIDESLITERATURE REVIEW AND RESULTS FROM A SURVEY
PUTTING SENSORS TO WORK: TARGETED APPLICATION OF NUTRIENTS AND PESTICIDES (TARG APP)LITERATURE REVIEW AND RESULTS FROM A SURVEY Task 1.1 Desk-study
are published by
SEGESLandbrug & Fødevarer F.m.b.A.Agro Food Park 15, SkejbyDK 8200 Aarhus N
ContactAnna Marie Thierry, SEGESD +45 2974 2783
Editing Anna Marie Thierry, SEGES Stine Styrup Bang, SEGESMichael Nørremark, AU Jens Erik Jensen, SEGES Kathrine Hauge Madsen, SEGESKjell Gustafsson, AgroVästThomas Börjesson, AgroVästPertti Rajala, EkesisBo Stenberg, SLU
Photo: Jens Tønnesen
November 2017
This publication is funded by
This publication must be copied in agreement with SEGES.
2 / 33
Contents
ABOUT THIS REPORT ................................................................................................................................................. 2
INTRODUCTION TO SENSOR-CONTROLLED VARIABLE RATE APPLICATION (VRA) ...................................................... 3
TOOLS FOR VRA ........................................................................................................................................................ 4
SATELLITE IMAGES .............................................................................................................................................................. 5
CROPSAT ......................................................................................................................................................................... 6
APPS FOR PREDICTION OF N-STATUS ...................................................................................................................................... 7
VEGETATION INDEXES ......................................................................................................................................................... 7
VRA OF FERTILISER - REVIEW .................................................................................................................................... 9
SCIENTIFIC LITERATURE ON VRA OF NITROGEN....................................................................................................................... 10
EXPERIENCE OF VRA OF PESTICIDES PRESENTED IN FARMER JOURNALS ........................................................................................ 10
SCIENTIFIC LITERATURE ON VRA PLANT PROTECTION ............................................................................................................... 11
RESULTS FROM SURVEYS ON PRECISION AGRICULTURE IN SWEDEN, DENMARK AND FINLAND ............................. 12
CONCLUSIONS REGARDING BARRIERS FOR USE OF CROP SENSORS ........................................................................ 14
CAN WE IDENTIFY BEST PRACTICE OF CROP SENSOR USAGE? ................................................................................. 14
REFERENCES ............................................................................................................................................................ 15
APPENDIX 1. DANISH SURVEY ON CURRENT USE OF CROP SENSORS AND BARRIERS FOR USE ................................ 18
ABSTRACT ...................................................................................................................................................................... 18
INTRODUCTION ................................................................................................................................................................ 18
METHOD ........................................................................................................................................................................ 18
RESULTS ......................................................................................................................................................................... 18
DISCUSSION .................................................................................................................................................................... 32
About this report
This report is a deliverable in the ICT-AGRI funded project ‘Targ-App’. It addresses the part of the
project which aims to identify ‘best practice’ and barriers to implementation for crop sensor de-
ployment by farmers and map the practical barriers for use of crop sensors. The first part of the
report includes a review of agricultural newsletters and magazines from Denmark, Sweden and
Finland, scientific literature, and websites. The review on Finnish literature revealed limited infor-
mation regarding farmers’ experience with crop sensors. The second part of the report presents
results from a questionnaire, which was e-mailed, to selected Danish farmers in 2016.
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Introduction to sensor-controlled variable rate application (VRA)
Currently most farmers tend to apply the same treatment to the entire field, as most traditional
machinery is adjusted once for completing the whole field at one level. However, the same farm-
ers will also be aware that typically there are variations in yield within the field, which may be
caused by a range of factors. Sometimes farmers will adjust the treatment in a corner of the field
or at headlands if e.g. the crop is particular dense or there are patches with heavy weed infesta-
tions.
Variable rate application (VRA) aims to apply the optimum nitrogen rate based on the require-
ments observed within the field. The opportunity to measure the nitrogen requirement on crop
level is based on vegetation indexes.
Vegetation indexes like the Normalised Difference Vegetation Index (NDVI) correlates with a
range of plant parameters, e.g. biomass, leaf area index (LAI), content of chlorophyll as indicator
of nitrogen content in plants (Pettorelli, 2013).
Some tractor mounted crop sensors can apply variable rate of fertiliser and pesticides on the go
using integrated algorithms. The on-the-go function is unique for the tractor mounted crop sen-
sors. But for collecting vegetation index maps tractor mounted crop sensors are one out of sever-
al tools. Drones equipped with multispectral cameras also provide the possibility to collect infor-
mation on variations within fields. And recently the free access to satellite data has made VRA
assessable to a larger group of farmers.
The incentive to adopt crop sensor technology is the prospect of either increase in yield, better
utilisation of fertiliser, and/or reductions in the expenses regarding pesticides. The field variation
determines whether a site-specific approach is cost-effective or not. However, the field experi-
ments comparing uniform rate applications (URA) and VRA show inconsistent benefits in yield
and profit (as cited by Jørgensen and Jørgensen, 2007) and few farmers have yet adopted preci-
sion farming technologies as VRA from crop sensor readings.
Most farmers and advisers that have been interviewed for farmer journals regarding their experi-
ences with crop sensors are propelled by a common interest in technology (Østerlund, 2016, Kel-
strup, 2016, Petersen and Andersen, 2013, Hattesen, 2012a and Hattesen, 2012b). Some have
adopted GPS, yield monitoring and crop sensors as early as 1992 (Kelstrup, 2016). They are ac-
tively using the collected data e.g. by comparing yield maps from different years and thereby de-
tecting year to year changes in biomass variations within the field (Kelstrup, 2016). However they
constitute a small group of farmers, approx. 5 % in Denmark (Thierry et al., 2016).
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Tools for VRA
Table 1 lists technologies for crop sensing/generating vegetation index maps available in Denmark, Sweden and Finland.
Name
Yara N-sensor (YARA) Spectral reflectance crop sensor measuring in a
radius of 3 meters around the tractor, installed on
tractor roof.
Crop Sensor/ISARIA
(Claas/ Fritzmeier)
Spectral reflectance sensors installed on a boom
in front of a tractor.
Trimble GreenSeeker
(Geoteam)
Spectral reflectance crop sensor, optional number
of sensors on a tractor (disc spreader installed on
a boom in front of tractor) or on a sprayer boom
(liquid fertiliser use)
Trimble UX5 (Ge-
oteam).
Spectral reflectance crop sensor attached to a
fixed wing drone/UAV.
Satellites, CropSAT CropSAT is an online service delivering remote sensing of spectral reflectance based on data from the Sentinel satellites. NDVI maps and application maps are free to download.
CropSpec (TopCon) Spectral reflectance crop sensor measuring on
both sides of a tractor, two sensors installed on
tractor roof.
OptRx (AgLeader) Spectral reflectance crop sensor, optional number
of sensors installed on a boom in front of a tractor
(disc spreader use) or on a sprayer boom (liquid
fertiliser use).
Airinov Spectral reflectance crop sensor attached to a
fixed wing drone/UAV.
5 / 33
Since 2010 Danish farmers have been able to apply for subsidy when investing in certain envi-
ronmental technologies e.g. crop sensors. The subsidy scheme is part of the rural development
programme and in the period from 2010 - 2012 it was possible to apply for 40 % investment sub-
sidy for new advanced sprayers with crop sensors and other tools, for reduction of nutrient losses
and greenhouse gas emissions (Christensen, 2015). From 2013 the subsidy scheme was rede-
signed to provide financial support for the sensor equipment only, and not the whole sprayer sys-
tem (Elbæk, 2015). Around 213 farmers have applied for funding under the subsidy scheme (Na-
turErhvervstyrelsen, unpublished). It is unknown, to which extend the sensors are used when ap-
plying fertiliser and plant protection.
In Sweden the most preferred crop sensor is the Yara N-Sensors and 180 farmers have invested
in this sensor (Nissen, 2016).
In Finland approximately 10-15 farmers have invested in sensor equipment. In Finland as well as
in Sweden, Yara N-sensor is the market leader and there are about ten contractors who use it.
Geotrim is another company active in fertiliser sensors (GreenSeeker). Mr Ylikleemola (farmer
and Yara N-sensor contractor) has informed us that VRA is done in connection to nitrogen-
fertilisation and to some degree when spraying for grain leaf diseases (eye inspection). Less fun-
gicide is applied to areas which appear as if the yield will be low (P. Rajala, personal communica-
tion, June 2016).
Since satellite data has become freely available and services like CropSat has been developed
more farmers have access to NDVI data for their fields and thus they are not dependent on tractor
mounted sensors for VRA. About 3% of all Danish farms have used vegetation indices originated
from satellites (85%) or drones (18%) in the period from May 2016 to May 2017. The satel-
lite/drone was mainly used for variable fertiliser rate mapping (44%). Less common was the map-
ping for variable rate of plant protection (16%) and seed (6%). The majority of users (64%) indi-
cates that they use satellite/drones for other management purposes, e.g. monitoring, drainage,
liming or to support the making of cultivation plans (Statistics Denmark, 2017). The survey did not
provide any statistics on the use of machine mounted crop sensors in specific.
Satellite images
After the European Space Agency (ESA) launched the satellite Sentinel 2, remote sensing of
plant growth from satellites has been re-introduced as freely available web applications to provide
fertilisation prescription maps. The satellite Sentinel 2 was launched in June 2015 and its main
task is to measure reflections from the surface, including reflection from the crops. Reflectance
measurements can be used as input for the various vegetation indices. The Sentinel 2 satellite
program is identical to the known Landsat, and has the following capabilities;
i) carries an innovative wide-swath, high-resolution, multispectral imager (MSI) measur-
ing reflectance on 13 wave bands in the VIS (whereof three bands in the red-edge)
and NIR (whereof two bands in the short wave infra-red) of the spectrum,
ii) systematic global coverage of land surfaces from 56° S to 84° N latitude,
iii) revisiting every 5 days, and
6 / 33
iv) Delivers spatial resolution down to 10 m. The reflectance measurements provided by
the Sentinel 2 satellite is free to use in its raw data format. Atmospheric corrections
have to be done in order to make the spectral reflectance measurements usable.
Mainly due to the high data volumes, the atmospheric correction of raw data into multi-
spectral imaging will not be performed by the ESA operated ground segments, neither
the generator of vegetation indices image representation. Both will rely on software
packages to be run by the users on the raw data sets. It has become a business case
to manage the complexity of ortho-rectification down to field boundary level, provide
vegetation reflectance indices, handling imagery archive and make the archive availa-
ble for end-users via easy-to-integrate web services. Corrections also include the or-
tho-rectification and spatial registration on a global reference system (combined UTM
projection and WGS84 ellipsoid) with sub-pixel accuracy.
CropSAT
Dataväxt in Sweden has developed the web application CropSat (cropsat.se) which processes
multispectral data from satellites. In 2016 CropSat has been made available to Danish farmers as
well (). In 2017 4100, 7300 and 1500 new users visited CropSat in Sweden, Denmark and Nor-
way, respectively.
Users of CropSat can prepare a fertiliser application map both at field level and subfield level. The
fertiliser prescription map can be exported to a range of spreader terminals and task controllers
that support the CropSat prescription map file format. The practical experience is that cloud free
vegetation index data for fields in Denmark and in south Sweden was obtained 1 to 2 times per
month beginning in April until August (source www.cropsat.dk and www.cropsat.se).
Figure 1: Screen print from CropSAT.se. Files for variable rate application can be generated and downloaded
from the web based program CropSAT. Farmers in Sweden, Denmark and Norway can use CropSat.
7 / 33
The vegetation indices derived from satellite remote sensed foliage reflections will not necessarily
match completely the data output from the above described ground vehicle mounted sensors.
However, satellite sensed reflection data from crop foliage is valuable for making decisions within
and between fields. The resolution is 10 x 10 m and data is free. This open data policy of the EU
Sentinel programme opens for innovative solutions like CropSat, AgriSat and other programmes
which are promising, low cost and practical adaptable (i.e. uncomplicated and at hand) tools for
crop management.
Recently published vegetation indices like the Normalized Area Over reflectance Curve (NAOC)
and the Double-peak Canopy Nitrogen Index (DCNI), combined with biomass indices show prom-
ising results on assessment of crop nitrogen status (Delegido et al., 2010; Chen et al., 2010).
Apps for prediction of N-status
Further the remote sensing of crop nutrient status has recently become of interest in relation to
App’s for smart phones, and other smart devices equipped with high resolution digital cameras.
The digital cameras of standard devices can only store the reflection in the visual spectrum. The
user is required to walk around the field to locate patches with divergent foliage mass and/or col-
our. The app captures images and through on-line communication a vegetation index based on
the visual spectrum is instantly computed. An example is the Dark Green Color Index (DGCI) of
plant leaves that utilise the image colour space (hue, saturation and brightness) (Karcher & Rich-
ardson, 2003; Rorie et al., 2011). This visual spectrum based vegetation index has been correlat-
ed to a database of direct chlorophyll meter readings (SPAD) of different crop species at different
growth stages and at different nitrogen uptake (database is not complete yet for all crop species).
The App outputs the nitrogen uptake and/or nitrogen contents of the crop. The information can be
used to decide on optimum fertiliser rates in average of the field. There are no current compre-
hensive agronomic evaluation of the available App’s from YARA ImageIT© and FieldScout©. Dif-
ferences in light conditions, camera quality, and available camera settings could affect DGCI-
readings and limit their utility in diagnosing nitrogen deficiencies. Similar to other remote sensing
technologies, factors such as disease, water status and deficiencies in other nutrients than nitro-
gen may affect the DGCI-readings.
Vegetation indexes
NDVI-data based on sensor readings from satellite, drone or a vehicle mounted sensor provides
information about the reflectance from different wave-length bands only. In order to practice VRA
the biomass images is processed into application maps based on vegetation indexes.
Vegetation indexes describe plant chlorophyll content, which correlates with plant nitrogen con-
tent. Nitrogen availability is crucial for crop development and quality. Nitrogen availability in soil
differs not only between different soil types. Climatic variations also cause differences in nitrogen
availability. Precipitation and temperature affect the mineralisation of nitrogen and soil water con-
tent affects the biological activity and thereby also nitrification and denitrification. The previous
crop also has a significant influence on nutrient availability. A vegetation index like NDVI give an
indication of photosynthetic activity which correlates with the amount of nitrogen in the plant. This
correlation has been extensively studied and verified in many crop studies (Pettorelli, 2013).
8 / 33
The vegetation-index values express reflectance (ρ) (where ρ is reflected wavelengths in nm ())
of R (red light) and NIR (near infrared light) (Söderstrøm et al., 2016). No crop sensor measures
nitrogen content or nitrogen demand directly, however, correlations with sensor readings are of-
ten good.
NDVI (equation 1) was initially designed for satellites to measure the reflection from vegetation on
the surface of the Earth. The index is calculated by sensors measuring both the R and NIR reflec-
tance. Chlorophyll absorbs R whereas NIR is reflected by a dense fertile crop.
𝑁𝐷𝑉𝐼 =(𝜌𝑁𝐼𝑅−𝜌𝑅)
(𝜌𝑁𝐼𝑅+𝜌𝑅) (equation 1)
Pixels values range from -1 and 1 in NDVI. Thus, areas covered by vegetation vary between
close to zero and close to one. Based on the NDVI scale the photosynthesis activity can be esti-
mated (Weier and Herring, 2000).
Figure 2: Differences in NDVI-value is an indicator for biomass density. The picture is derived from the freely available Danish programme CropSAT.dk, which is based on the Swedish CropSAT-programme, which was originally developed within the framework of Precision Farming Sweden (POS).
It is well known that the standard NDVI loses sensitivity when the leaf area index (LAI) exceeds
about 2.0–2.5 (Heege et al., 2008, Vinã et al., 2011). A well-developed cereal crop can have a
LAI of up to 8.0. Therefore the NDVI may miss important information about the nitrogen supply for
a dense cereal crop at e.g. the 2nd fertiliser application (Heege et al., 2008). Nitrogen deficiency
within normally developed foliage may be compensated by increased fertilisation, whereas it cer-
tainly is not reasonable to apply high amounts of nitrogen to larger patches of bare soil. For ex-
9 / 33
ample, the ratio of near infrared - to visible red light reflectance can change from 20 to 3 on winter
killed patches, where the crop sensor then should be able to differentiate between nutrient defi-
ciency and winter killed patches. In many situations, the crop sensor application is mostly limited
to the second and/or later fertiliser applications. Some crop sensor brands also offer the output of
the red edge inflection point (REIP) calculated by the approximating generic formula:
𝑅𝐸𝐼𝑃 = 700 + 40 ((𝜌670−𝜌780) 2⁄ −𝜌700
𝜌730−𝜌700) (Equation 2)
Where are reflected wavelengths (number added to is wavelength in nm) in percentage.
This provides the best relationship to nitrogen supply to crop plants (reviewed in e.g. Heege et al.,
2008). When chlorophyll in plant leaves degrades, less absorption in the red light spectrum leads
to a shift towards the blue spectrum. The above index for REIP (Dawson & Curran, 1998) is af-
fected by biochemical and biophysical parameters and has been used as a mean to estimate foli-
ar chlorophyll or nitrogen content (the higher the number, the greater the amount) (Vinã et al.,
2011). Distortion of crop sensor measurements derived from reflections from other plant species
like weeds, drought stress, diseases, and other nutrient deficits than nitrogen are common. Most
current sensor units have an active illumination source which reduces influence from changing
ambient light conditions and may extend the operational hours. It furthermore eliminates the noise
associated with the varying irradiance of natural light and shade effects caused by the machinery
or surroundings. Technically, the artificial light needs to be modulated in order to separate the
artificial light from the natural sunlight.
VRA of fertiliser - review
Experience on VRA fertilisation presented in farmer journals
The experiences of farmers who have adopted sensor technology has been presented in farmer journals.
VRA of nitrogen fertiliser was tested on 300 ha winter wheat. The resulting increase in yield
equalled 1 hkg/ha (Elbæk, 2011, Knudsen and Hørfarter, 2009, Andersen, 2003, Pedersen (red.)
2002, Pedersen (red.) 2001). The effect on yield was assessed to be influenced by the Danish
nitrogen-norm policy at the time of the experiment. Allocating nitrogen from one part of the field to
another has a strong impact on the nitrogen-yield response curve when nitrogen input is already
at least 10% below optimum (Thalbitzer, 2005). The increase in nitrogen-input to Danish crops up
to economic optimum is expected to improve the effects from VRA. The VRA based on biomass
recordings can, furthermore, potentially reduce leaching of nutrients by optimal allocation of ferti-
liser (Knudsen and Hørfarter, 2011 and Delin et al., 2015).
A Swedish farmer used the sensor to monitor the effect of the autumn fertilisation with manure in
oilseed rape in order to save nitrogen and distribute the nitrogen to the parts of the field where it
was needed. This saved approx. 23 % of the nitrogen dosage to the fields. The farmer also ob-
served that oilseed rape had a more even flowering. This farmer had overcome the challenge of
unprecise sensor readings due to wilted leaves in oilseed rape by monitoring the field late autumn
and using these data for the spring VRA (Gunnarson, 2010).
10 / 33
Scientific literature on VRA of nitrogen
Recent studies on application of nitrogen according to sensor-data at the field level do not show
statistically significant results on harvested yield (Jørgensen and Jørgensen, 2007; Boyer et al.,
2011; Bragagnolo et al., 2016).
However above ground crop nitrogen was found to vary around 100 kg N/ha within fields in unfer-
tilised cereal crops showing that variations within fields can be as large as between fields (Delin
et al., 2015).
Delin et al. (2015) quote a study where good correlation was found between the Yara N-sensor
value (SN value) and nitrogen in harvested grain from unfertilised plots using the handheld Yara
N-sensor and concludes that SN values from unfertilised plots are useful in predicting the opti-
mum nitrogen application when the potential yield is known.
Mayfield and Trengove (2009) made a detailed analysis where they compared grain yield re-
sponses in different biomass areas for wheat with uniform and variable nitrogen applications by
using an N-sensor. For each 20 % interval of biomass the difference in yield were compared.
They found the greatest yield increase (+2.8 hkg/ha) on areas with the lowest 20 % crop biomass
and a negative difference in yield for higher biomass intervals.
Besides yield increase, studies show higher nitrogen use efficiency (NUE) when nitrogen fertiliser
was based on sensor-data. NUE increases with 10% and 15-35 % for corn and winter wheat, re-
spectively (Raun et al., 2002; Li et al., 2008; Bragagnolo et al., 2016). Delin et al. (2015) reviewed
reduction in leaching of 0.5-3.8 kg N/ha and 0.2-1.6 kg N/ha on clay and sandy soils respectively
compared to uniform application and dependent on within field variation. These benefits indicate
that VRA of fertiliser according to the plant nutritional state can reduce the risk of fertiliser losses.
The review by Delin et al., 2015 found studies showing a reduction in leaching of 3.2-6.8 kg N/ha
(sandy soil) and 1.4-3.0 kg N/ha (clay soil) when going from fertilisation rates 10 kg/ha above av-
erage optimum to optimum fertilisation rates.
Experience of VRA of pesticides presented in farmer journals
Agricultural newsletters and magazines contain some articles about farmers and advisers, exper-
imenting with the use of crop sensors. Examples of sensor-use are shown below:
- For VRA of PGRs (Plant Growth Regulators) and fungicides in winter wheat (Bröker, 2011)
- For identification of areas with high vegetation-index values shortly before harvest in order
to desiccate weed and unripe cereal thereby obtaining a more homogeneous crop for har-
vest (Hattesen, 2012a).
- For registration of biomass in autumn. Using registrations for VRA of herbicides in early
spring (Hattesen, 2012b).
- Using maps generated by crop sensors to identify areas of special attention. Here vegeta-
tion maps are integrated as part of the overall planning of nitrogen-allocation (Østerlund,
2016).
- For PGR application
11 / 33
A field trial which tested VRA for plant protection showed increases in yield of up to 1 hkg/ha for
winter cereals. In the trial, areas with dense biomass received up to 20 % higher dose and vice
versa. The field trial was carried out by LandboNord (Pedersen (red.), 2009).
In the northern part of Denmark, approx. 25 farmers participated in a learning group to learn how
to use sensor guided sprayers at their farms. After the installations of a sensor and test-runs,
about 20-25 % of the farmers used VRA as an integrated part of their management system.
Farmers found the new sprayers technically challenging to use. In one case a farmer had learned
to use the sensor guided application system, but when the son took over the field management,
he experienced a technical problem and stopped using the VRA function. Another farmer who
participated in the learning group had experienced the same problem and solved the obstacle
within a few minutes (Elbæk, personal communication, July 2016). This experience illustrates the
wide range of challenges that arise when implementing sensor technology in the crop production
and the value of timely support.
One farmer who used VRA for fertilisation and plant protection stated, that he saved 5-8 % of the
pre-harvest glyphosate application by using sensor based VRA, but for fungicides the overall
dosage was the same although the applied dose varied up to 40 % (Kelstrup, 2014).
The activities listed below show VRA of plant protection operations that are profitable due to ei-
ther reduced pesticide use, increases in yield, or both (Petersen, 2013).
Table 2: Operations where VRA of pesticides are profitable (Petersen, 2013).
Operation Potential reduction in pesticide use Potential yield increase
Glyphosate prior to harvest 10-20 % 0
Fungal attack 0 % 0.5-1 %
Growth regulation 5-10 % 0.5-1 %
Controlling weed in winter cereals 5-10 % 0-?
Desiccation in potatoes 20-30 % 0
Scientific literature on VRA plant protection
A brief review of the scientific literature supports the findings in Tab. 2 by indicating that there is
considerable pesticide saving to be gained from sensor-based VRA on heterogeneous crops. For
fungicides, growth regulators and desiccants, the low-biomass areas are sprayed with reduced
dosages of pesticides because a smaller plant area needs to be covered compared to high-
biomass areas. Therefore, in heterogeneous fields with large patches of low-biomass areas, the
savings can be considerable. A one-year study by Tackenberg et al. (2016) and another by
Dammer et al. (2009) showed 10-20 % savings of fungicides in winter wheat by using sensor-
based data. Disease infestation level was assessed after a fungicide application in winter wheat,
the experiment showed that VRA had the lowest disease infestation level with flag leaves: 6 %
12 / 33
infestation (uniform 12 % infestation), and the first leaves: 32 % infestation (uniform 42 % infesta-
tion) (Tackenberg et al., 2016). A five-year study show similar results, however higher fungicide
savings, on average 22 %, were achieved when using variable rate spraying in winter wheat
(Dammer & Ehlers, 2006). Dammer and Wartenberg (2007) combined a standard patch sprayer
with similar technology as the GreenSeeker for weed sensing in tramlines of growing cereals and
peas fields, and achieved an average herbicide saving of 24.6 % and no yield reduction com-
pared to conventional application (dominant weed species in this study were couch-grass (Ag-
ropyron repens), common lambs quarter (Chenopodium album), dead-nettle (Lamium spp.),
bindweed (Polygonum convolvulus) and knotgrass (Polygonum aviculare)).
For herbicides, similar savings can be found in the literature. Herbicides savings of 20-48 % are
documented for corn and 23-28 % for wheat and peas, respectively (Williams & Mortensen, 2000;
Carrara et al., 2004; Dammer & Wartenberg, 2007). Carrara et al. (2004) found a more even grain
yield across the field when using variable-rate application of herbicides compared to uniform ap-
plication, demonstrating that variable-rate application was effective to produce an even crop.
Several field trials have applied site-specific weed management in fields with weed infested
patches (reviewed in Miller, 2003; Christensen et al., 2009). In some studies, the effect of the size
of treated area (treatment resolution) has been investigated; typically the minimum treatment unit
is 3 m x 3 m (Barosso et al., 2004) or 1 m x 1 m (Paice et al., 1998). Wallinga (1998) found that
potential herbicide saving increased with increasing spatial resolution to smaller areas of weed
control.
In two of three years, in field trials with winter wheat increased doses of fungicides, an interaction
of crop biomass and fungicide dose rate had a significant influence on disease severity indicating
a biomass dependent dose response. The interaction occurred in the two years with high yield
potential in combination with severe disease attack (Jensen & Jørgensen, 2016).
None of the scientific studies showed an effect on crop yield; however the demonstrated pesticide
savings ranged between approximately 20-40%. This indicates that VRA of pesticides can reduce
pesticide use and reduce the costs without affecting crop yields.
A recent study with VRA of PGS’s showed a yield benefit on three of four fields compared to the
farmers best uniform practice (Griffin & Hollis, 2017).
Results from surveys on precision agriculture in Sweden, Denmark and Finland
A Swedish study (Söderström et al., 2013) on farmers’ experiences with Yara N-sensors showed
that at least 250 ha were minimal for return of investing in the sensor equipment for fertilisation. In
this study sensors were available to farmers through contractors who did the scanning and fertili-
sation. A survey showed that almost 70 % of the farmers experienced that the VRA resulted in
increased yields and more uniform protein content, and approx. 80 % saw less lodging in the N-
sensor fields.
A Norwegian master thesis (Helmen, 2012) included a questionnaire sent to 9 Swedish farmers
and 1 Norwegian farmer who had invested in a Yara N-sensor. The study concluded that the re-
duced risk of lodging, high uniform protein content and higher yields were stated as the main
13 / 33
benefits of using an N-sensor for nitrogen fertilisation. The system was also expected to increase
the harvest capacity by producing a more uniform crop. Three of ten farmers used the same ferti-
lisation amount as before they switched to VRA, five had reduced the fertiliser amount by 4-7 %,
and two had reduced the fertiliser amount by 10 %. The thesis also investigated technical prob-
lems and on average the 10 farmers experienced 1.2 stops per season because of failures in the
N-sensor system. Regarding user support, the farmers seemed quite happy with the technical
support provided by the supplier.
A survey in Germany, Finland and Denmark from 2012 (Bligaard, 2013) revealed that only 18 %
of the farmers were willing to spend more than 3 minutes extra per field for e.g. typing in infor-
mation. However, at least two third of the respondents also found that VRA of pesticides and ferti-
lisers were important.
In spring 2016 a survey was sent to Danish farmers who had applied for subsidy to invest in a
sensor guided sprayer or had previously bought a Yara N-sensor (see appendix 1). The study
indicates that farmers who had invested in a crop sensor have a high level of knowledge on how
the sensor adjusts the application rate for plant protection. 33 % did not know whether or not to
apply more or less fertiliser where plant coverage is low. However, most algorithms actually re-
duce the dosage when plant coverage is below a certain value, whereas other algorithms have an
inverse linear relationship between NDVI and application rates, therefore the result may reflect
the different algorithms rather than lack of knowledge. Of the responding 23 farmers, 78 % had
invested in a Yara N-sensor and 55 % had invested in the crop sensor with subsidy from the envi-
ronmental technology scheme. Despite this investment, 43 % did not use the sensor for VRA of
nitrogen and 38 % did not use it for spraying. This low usage may be explained by several fac-
tors: 52 % of the responding farmers had not experienced a reduction in the use of fertiliser and
pesticides; to the statement “my sensor is simple to use” only 34 % agreed, implying that the
complexity may be a barrier; furthermore, 52 % had experienced technical problems.
The Danish survey (appendix 1) may also provide some guidance as to where farmers get advi-
sory support as 50 % used the support provided by the sensor manufacturer or dealer, whereas
only 25 % had used their local crop management consultant. Only 5 % of the farmers perceived
the support to be highly adequate.
Recent interviews with 11 Danish stakeholders (farmers, industry, and advisory system) in preci-
sion agriculture reveal that the majority of the interviewed stakeholders experience high interest
for PA technologies among farmers, and they all expected that PA technologies will be more inte-
grated in future crop production as a result of the general digitisation of society. The interviewed
stakeholders mainly expect savings in pesticides and fuel consumption and on large farms they
think there may be considerable savings on other resources such as fertiliser use. However, there
seems to be a general agreement among the interviewed stakeholders, that the technologies are
used too little, even when the technology is present at the farm. The interviewed farmers used
auto-steering, sensor, yield sensor, controlled traffic farming and software products for planning
and registration. The group of Danish farmers working actively with PA technologies are estimat-
ed to be 2-10 % of the total number of Danish farmers; however, area-wise they constitute a larg-
er part of the Danish farmland (Thierry et al., 2016).
14 / 33
Among the possible barriers for the use of technology, approx. half of the interviewed mentions
the economic situation for agriculture, whereas others perceive investments in technology as a
necessity in order to produce a profit. The interviewed advisors experienced lack of user friendli-
ness and compatibility between different brands of equipment as a barrier, whereas some of the
companies pointed towards more education and a better data infrastructure on the farms as the
way forward to mainstream PA technologies (Thierry et al, 2016).
Conclusions regarding barriers for use of crop sensors
The sensor technology is adopted primarily by farmers with a general interest in technology,
and these farmers will typically have adopted GPS, yield monitoring several years ago and
use e.g. biomass/yield maps actively in the management.
Yield increases from VRA compared to uniform application of fertiliser or pesticides are small
Farmers often experience lack of user-friendliness in new technology
Farmer experience technical problems and lack of compatibility in technology between brands
Farmers are stressed for time, and are unwilling to spend more than 3 minutes extra per field
task to get e.g. new technology started
Farmers often feel that they receive inadequate technical and agronomic support
Farmers do not use their local advisor for support within this area
Can we identify best practice of crop sensor usage?
One of the farmer journals described an example where a farmer, who had successfully adopted
the technology for both fertilisation and spraying, estimated that the investment had paid for itself
within 4 years. This indicates that documentation of usefulness and benefits for the farmer,
which includes the added value of multiple uses at the farm level, is essential. Secondly the farm-
ers who are not as interested in technology would benefit from learning how to get started,
computer settings etc. from other more technology interested farmers. To narrow the ac-
ceptable timespan for solving technical problems, a high level of advisory support and/or quick
fix easily comprehensible guidelines may enable the farmer to keep using the technology. Fi-
nally it seems that the less technology interested farmers should be targeted with a more “plug-
and-play” approach.
15 / 33
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Appendix 1. Danish survey on current use of crop sensors and barriers for use
Abstract
In connection to the project Putting Sensors to Work – Targeted application of nutrients and pes-ticides (Targ_App) a questionnaire was designed to supplement literature reviews and shed light on the key issues that needs to be addressed in order to mainstream sensor based management. The aim was to investigate the current use as well as barriers for effective implementation of crop sensors in agriculture. Farmers who applied for funding under the Danish subsidy scheme for green technologies were asked about their use of crop sensors, about their perception of crop sensor effectiveness in regard to fertiliser, plant protection and yield and their expectations in re-gard to advisory services. 213 farmers applied for funding and around 35 were contacted, 19 farmers replied and additionally four farmers replied partly. 8 Yara costumers received the ques-tionnaire.
Introduction
One of the critical challenges for the successful and widespread adoption of Precision Farming (PF) in Europe is to mainstream the use of the technologies to ensure it is accessible to all farm-ers and can become an integral part of crop management supported by crop advisors / agrono-mists where decisions are based on cost-benefit analysis. The overall aim of the project Putting sensors to work – Targeted application of nutrients and pesticides (Targ_App) is to achieve ap-propriate research-based validation and comprehensive support to advisors and farmers. This is identified as essential to facilitate the adoption of crop-sensor based management. This question-naire contributes to a clarification of the reality faced by farmers using crop sensors and thereby to shed light on the key issues that needs to be addressed in order to mainstream sensor based management.
Method
A questionnaire was designed with assistance from three specialist advisers as well as one local adviser all engaged with projects and practical advisory services concerning PF. The question-naire was sent to 35 farmers, using the service SurveyXact1, and asking them to share their knowledge on and experience with crop sensors. The respondents were asked 29 questions, be-sides that they were asked for further elaborations. The first question was whether they have crop sensors on their farm or not. Depending on the answer they were redirected to further questions. If the answer was no, the respondent were asked eight more questions. If the answer was yes, the respondent was redirected to 28 further questions. Some gave up personal data e.g. age, name and telephone number for further interview. Most of the farmers receiving the questionnaire were contacted due to their application for funding under the Danish subsidy scheme for technol-ogy. The Subsidy scheme is funding investment in green technologies e.g. crop sensors and the recipients were thus likely to own crop sensors or at least to have considered investing. A few of the farmers who received the questionnaire are Yara customers who have purchased before the subsidy schemes were initiated in 2012.
Results
213 farmers, considering investing in crop sensors, applied for funding under the Danish subsidy scheme. Therefore they were relevant recipients. However, contact information was obtainable for only 35 farmers, 19 replied and 4 replied partly. Thus, the response rate was 66 %.
1 Ramböll, http://www.surveyxact.dk/
19 / 33
The first question separated respondents in two groups, those who already invested in crop sen-sors and those who did not. Only one of the respondents did not own a crop sensor. The remain-ing 22 respondents were redirected to further 27 questions.
Question 1. Do you have crop sensors on you farm? Yes/no.
Question 2. The respondents were asked which manufacturer they purchased their crop sensor
from.
Question 3: How did you finance your crop sensor? Own financial contribu-
tion/subsidy/combination.
20 / 33
Question 4: When did you purchase your crop sensor?
Question 5: To what extent do you agree to the following?-I use crop sensors for nitrogen applica-
tion. Strongly agree/agree/disagree/Strongly disagree/don’t know.
21 / 33
Question 6: To what extent do you agree to the following?-I use crop sensors for application of
plant protection. Strongly agree/agree/disagree/Strongly disagree/don’t know.
Question 7: To what extent do you agree to the following?-The use of crop sensors has lowered
the chemical consumption. Strongly agree/agree/disagree/Strongly disagree/don’t know.
Question 8: To what extent do you agree to the following?-The use of crop sensors has lowered
the fertiliser consumption. Strongly agree/agree/disagree/Strongly disagree/don’t know.
22 / 33
Question 9: To what extent do you agree to the following?-My crop sensor is simple to use.
Strongly agree/agree/disagree/Strongly disagree/don’t know.
Table 1: Further comments to question nine.
Comment Kommentarer
We want data from CropSAT and will use these
data. Manual evaluation of each field based on
our own yield data are taken into account.
Vi går efter data fra CropSAT og vil bruge disse
data. Efter manuel gennemgang af hver enkelt
mark og vor egne høst data tages i betragtning.
As long as we have so little fertiliser and we are
forced to under-fertilise, then it cannot be used
for anything.
Så længe vi har så lidt gødning at gøre med og
vi er tvunget til at undergøde hele vejen rundt,
ja så kan det ikke bruges til noget.
Generally there has been too little information
from the dealer.
Der har generelt været alt for lidt information fra
forhandleren.
I have stopped using it - does not seem to get
enough out of it.
In addition, as it was to be upgraded I needed
to buy me a new one, full price.
Jeg er holdt op med at bruge den - synes ikke
at få nok ud af den.
Derudover da den skulle opgraderes skulle jeg
anskaffe mig en ny til fuld pris
Question 10: To what extent do you recognize the following challenge when using crop sensors?
- missing effect on yields. Strongly agree/agree/disagree/Strongly disagree/don’t know.
23 / 33
Question 11: To what extent do you recognize the following challenge when using crop sensors?
– Technical issues during start-up. Strongly agree/agree/disagree/Strongly disagree/don’t know.
Table 2: Further comments to question 11.
Comments Kommentarer
I haven’t used time making it work, it’s not rele-
vant with the under-fertilisation we have on our
dairy farm.
Har slet ikke brugt tid på at få det til at virke,
ikke relevant med den undergødskning vi har
på vores bedrift med køer.
Too often we lost our signal. Mistede for til signal.
Question 12: Your knowledge on crop sensors – Areas dominated by a dense biomass is given
more nitrogen compared to areas with less dense biomass. Agree/disagree/don’t know.
24 / 33
Question 13: Your knowledge on crop sensors – dose is increased in areas with dense biomass
when targeting fungal attacks. Agree/disagree/don’t know.
Question 14: Your knowledge on crop sensors – dose is increased in areas with dense biomass
when using growth regulators. Agree/disagree/don’t know.
Question 15: Your knowledge on crop sensors – For desiccation, dose is increased in areas with
thin biomass coverage. Agree/disagree/don’t know.
25 / 33
Question 16: Your knowledge on crop sensors – When targeting weed in winter cereals, dose is
increased in areas with thin biomass coverage. Agree/disagree/don’t know.
Table 3: Further comments to question 16.
Comments Kommentarer
That’s what you need to tell us, get going. Det er jo det som i skal fortælle os, kom i gang.
In the first question a growth stage is missing. 1. Spørgsmål mangler et vækst-stadie.
Question 17: To what extent do you agree in the following statements concerning support in re-
gard to the use of crop sensors? – I used advisory services from the dealer during start-up.
Strongly agree/agree/disagree/Strongly disagree/don’t know.
26 / 33
Question 18: To what extent do you agree in the following statements concerning support in re-
gard to the use of crop sensors? I used advisory services from my local advisor during start-up.
Strongly agree/agree/disagree/Strongly disagree/don’t know.
Question 19: To what extent do you agree in the following statements concerning support in re-
gard to the use of crop sensors? The advisory services available were sufficient. Strongly
agree/agree/disagree/Strongly disagree/don’t know.
27 / 33
Question 20-22.
Question 20: What did you need
support for?
Question 21: What kind of
support could you have want-
ed from the dealer?
Question 22: What kind
of support could you
have wanted from the
local adviser?
We need
web-based
software
with a holis-
tic approach
that can
create allo-
cation maps.
(soil quality
cards, yield
maps, soil
analysis,
and not least
the operator
experience)
Vi mangler webba-
seret software, som
ud fra en holistisk
indgang, kan lave
tildelingskort. (boni-
tetskort; udbytte-
kort; jordbundsana-
lyser; og ikke
mindst driftslederer-
faringer)
A product
that works
Et produkt som
virker
Some expe-
rience from
other farm-
ers who had
used similar
equipment
Nogle erfaring ved
planteavler som
havde brugt lignen-
de udstyr
Experience Erfaringer Experience Erfaringer
Start-up,
exchange of
experience
Opstart, erfarings-
udveksling
Better start-
up/settings
Bedre i gang
sætning/indstilling
None Ingen
28 / 33
Things are
moving so
fast that
software are
not devel-
oped before
new hard-
ware are
developed.
We have
seen that
the equip-
ment not be
fulfilled the
expectations
that we had.
Udviklingen går så
hurtigt at software
ikke når at blive
udviklet, inden der
er udviklet nye
hardware. Vi har
oplevet at udstyret
ikke har kunnet ind-
fri de forventninger
som vi havde.
(The need
for support
red.) It was
sparingly. I
asked the
network of
practitioners,
and used
their own
experiences
Det var sparsomt.
Jeg spurgte i net-
værket af praktike-
re, og brugte af eg-
ne erfaringer.
The dealer
must deliver
the chosen
product,
and take
care of the
start-up.
Except that
I mostly
stick to
practical
experience.
Forhandleren
skal levere valgt
produkt, sørge for
igangsætning.
Ellers holder jeg
mig mest til prak-
tiske erfaringer.
Results
from field
practice
and less
results
based on
research in
plots
Tal fra
mark prak-
sis og min-
dre tal fra
forsøgs
parceller
That they
can show
where you
can get re-
sults from
using it,
where you
can earn
money that
is.
At de kan vise hvor
man kan få noget
ud af at bruge det,
altså hvor man kan
tjene noget.
He should
just tell how
it works and
how to in-
stall it.
Han skal bare
fortælle hvordan
det virker og
hvordan det skal
indstilles.
How to
make
money on
it.
Hvor man
tjener pen-
ge på det.
Setting up
the equip-
ment. Info
regarding
functions
etc.
Opsætning af ud-
styr. Info vedr. funk-
tioner etc.
Technical
support
teknisk support Pointing
out rele-
vant tech-
nology to
be used
hvilken
relevant
teknologi
der bør
anvendes
29 / 33
Correct set-
up and use
of equip-
ment.
Rigtig opsætning,
og brugen af udsty-
ret.
Someone
who knew
more about
our needs.
En der vidste
mere om vores
behov.
More sup-
port for
what the
equipment
can be
used for.
Mere hjælp
til hvad
udstyret
kunne bru-
ges til.
To apply for
the subsidy
Søge støtten That their
knowledge
was updat-
ed, and that
they
showed us
what could
be done in
practice.
We experi-
enced that
everything
could be
done until
we bought
the equip-
ment.
At deres viden
var opdateret, og
viste hvad der
kunnet lade sig
gøre i praksis. Vi
har oplevet alt
kunne lade sig
gøre indtil vi køb-
te udstyret.
I have dif-
ficulties
seeing
what they
could con-
tribute
with.
Har svært
at se hvad
de vil kun-
ne byde
ind med.
Overall start-
up
Overordnet opsæt-
ning
Course Kursus Overall
use
Overordnet
brug
Make it work Få det at virke A start-up
package
En opstarts pak-
ke.
How the
variable
rata appli-
cation are
best uti-
lised.
Hvordan
den gradu-
erede tilde-
ling udnyt-
tes bedst.
Start-up Opstart Course Kursus None ingen
Adjusting
the sensor
to make
regulate as
much as
possible
Justering af sensor
for at få den til at
regulere så meget
som muligt
30 / 33
Technical
issues and
the differ-
ence be-
tween the
yield- and
protein ferti-
lisation.
Det tekniske samt
forskellen mellem
udbytte - og prote-
ingødskning.
Everything
was okay
Alt ok When I
started
many
years ago
the specific
knowledge
about the
use of
sensors
was miss-
ing.
Da jeg
startede for
mange år
siden
manglede
der konkret
viden om
brugen af
sensoren.
Start-up Opstart What I got Det jeg fik they do not
know the
technique
of each
sensor
de kender
jo ikke tek-
nikken i
den enkel-
te sensor
Annual up-
grade-
course or-
ganized by
Yara
Der var årlig opgra-
dering kursus af-
holdt af Yara
Running-in Indkøring Support for
start-up
Hjælp i opstart None ingen
Question 23: Which data do you utilise on your farm? Yield maps, weed maps, N-application
maps, lime application maps, auto-steering, variable seeding, field boarders, if others, which?
31 / 33
Table 4: Further comments to question 23.
Comment Kommentarer
Experience Erfaringer
Question 24: Which data do you save from your farm? Yield maps, weed maps, N-application
maps, lime application maps, auto-steering, variable seeding, field boarders, if others, which?
Comments Kommentarer
Stones, wells etc. Sten; brønde etc.
Question 25: To what extent do you agree to the following? – I expect that the use of data on my
farm will increase in the future. Strongly agree/agree/disagree/Strongly disagree/don’t know.
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Question 26: To what extent do you agree to the following? – I expect that I will be collecting
more data from my farm in the future. Strongly agree/agree/disagree/Strongly disagree/don’t
know.
Comments Kommentarer
I am the new manager and it (collecting data
red.) will be amongst my focus areas
I forbindelse med at jeg er ny driftsleder på
stedet vil det være blandt mine fokusområder
If the technique is ok then it will be used more. Hvis teknik ok mere brug.
Discussion
The questionnaire was sent to 35 out of 213 farmers applying for funding via the Danish subsidy scheme for green technologies. 23 from the 35 replied thus giving a response rate equal to 66 %. However, only 11% of the potential owners of crop sensors have replied. The 213 applicants have not necessarily invested in crop sensors and thus the number of 213 farmers does not reflect the actual number of crop sensors purchased in Denmark. The relatively small number of respond-ents should be taken into account when analysing the results. To the question: “do you use a crop sensor when applying fertiliser and pesticide?” only 5 percent answers “strongly agree” in regard to fertiliser and 0 percent when it comes to plant protection. 43 and 38 percent replies “strongly disagree” to the use of crop sensors when applying fertiliser and plant protection, respectively. When asked whether crop sensors have reduced the overall use of plant protection, 52% an-swers “strongly disagree” for both plant protection and fertiliser and 29 and 38 percent replies “disagree”. To the question whether the use of crop sensors have an effect on yield the picture is unclear (see Question 10) with 14, 14, 38, 14 percent replying “strongly agree”, “agree”, “disa-gree” and “strongly disagree”, respectively. This may partly be explained by the relatively small number of respondents, which weakens the strength of the analysis. To the questions “do you find crop sensors easy to use?” and “did you experience technical prob-lems during start-up?” (Question 9 and 11) the answers are relatively evenly distributed across strongly agree/agree and strongly disagree/disagree. This pattern implies a very heterogenous user-group. The heterogeneity of the group of farmers using or owning crop sensors are also ex-posed in question 20 – 22. Despite that the questions shed light on the expectations towards ad-visors, the three questions also reveal that some farmers are very specific in their need for sup-port, some are very unspecific and others have some kind of frustration in relation to bad experi-ences with the technologies. One demands for a specific software solution and uses the term “ho-listic” to underpin the necessity of a wide perspective in decision support tools. Another just want
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it to work not implying an interest in the technology. Finally, one experienced that the dealer promised more than he could deliver when the respondent purchased the equipment. In Question 11 a farmer points out a problem with poor internet connection in the field. This prob-lem has, in other more extensive questionnaires, been identified as a barrier for implementation of PF-technologies in general (Bligaard, 2012). The role of dealers and advisers are addressed in six questions (Question 17-22). Only 5% of the respondents answered “strongly agree” or “agree” to whether they used their local advisor during start-up. Whereas 50% answered “strongly agree” or “agree” to whether they used support from the dealer during start-up. This indicates that the role of the local advisor from a farmer’s point of view is less important when it comes to implementing crop sensors. However, independent sup-port might reduce some of the investments that have resulted in frustration and lost investments. One of the important findings in the EIP-AGRI Focus Group report on precision farming is the need for well-trained advisors in the field of precision agriculture. Further, several of the com-ments in Question 22 implies, crop sensor technology is not an area of expertise farmers connect to advisors.
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