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8/9/2019 Nesbitt - Differences in Soil Quality Indicators Between Organic and Sustainably Managed Potato Fields in Canada
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Ecological Indicators 37 (2014) 119130
Contents lists available at ScienceDirect
Ecological Indicators
journal homepage: www.elsevier .com/ locate /ecol ind
Differences in soil quality indicators between organic and sustainably
managed potato fields in Eastern Canada
Johanna E. Nesbitt a, Sina M. Adl a,b,
a Department of Biology, Dalhousie University, Halifax, NS,Canada, B3H4J1b Department of Soil Science, University of Saskatchewan, Saskatoon, SK,Canada, S7N5A8
a r t i c l e i n f o
Article history:
Received 2 April 2013Received in revised form 13 August 2013
Accepted 1 October 2013
Keywords:
Agro-ecosystem
Bio-indicators
Farming systems
Organic agriculture
Soil ecology
Soil quality
a b s t r a c t
The aim ofthis study was to determine iforganic management offields promoted soil quality indicators
compared to sustainably managed fields following best-management practice guidelines. Using a soil
quality minimum data set, conventionally and organically managed commercial potato fields in eastern
Canada were compared. Microbial biomass, testate amoebae, nematodes, and microarthropods served as
bioindicators, while soil pH, C:N ratio, light fraction, bulk density, and soil moisture served as the chemical
and physical indicators. We also investigated whether differences in site location (different soil texture
and local climate) were more or less important than field management (organic or conventional). When
site location and seasonal factors were considered, the soil quality indicators were better at differentiating
organic and conventional potato fields. There was no single indicator that could clearly differentiate, on
its own, between the two field managements due to variability with site location or month ofsampling.
Microbial biomass, testate amoebae, microarthropod and soil moisture varied significantly through the
growing season. The mean soil pH, C:N ratio, and moisture were significantly different between sites.
However, the indicators were affected to different degrees, and differed to some extent to both site
location and time ofsampling. The results ofthis study also provide a baseline for similar soil quality
evaluations in management of eastern Canada potato fields. We recommend that several indicators,
including bioindicators should be used together, and that several sites should be sampled. In addition,
one-time field sampling ofan indicator, as it has been often practiced by growers, is likely to give falseresults as it does not account for variability through the growing season.
2013 Elsevier Ltd. All rights reserved.
1. Introduction
Intensification of agriculture was accompanied with increased
use of synthetic fertilizers, pesticidesand herbicides that has raised
concerns regarding their side-effect on the environment. In reac-
tion to this, a variety of sustainable agriculture practices have
gainedpopularity,as well as organic agriculture as an alternative to
the intensive inputs in conventional agriculture. These sustainable
practices include adding organic matter to the soil, covering soil
with cover crops or crop residues,reducingtillage intensityor prac-
ticing conservation tillage, using legumes within a crop rotation,
implementing strip cropping, improving drainage, and avoiding
compaction (Madgoff, 2001; Kennedy and Papendick, 1995). Best
management practices were proposed to reduce the amount of
synthetic chemicals used in conventional agriculture while main-
taining acceptable levels of economic return (Hilliard and Reedyk,
Corresponding author at: Department of Soil Science, University of
Saskatchewan,Saskatoon, SK, Canada, S7N 5A8. Tel.: +1 306966 6866.
E-mail address: [email protected](S.M. Adl).
2000; Korol, 2004). Organic agriculture claims to be environmen-
tally sustainable, socially just, and economically sound production
practices but prohibits using most synthetic fertilizers, herbicides,
and pesticides, as well as other restrictions (Lotter, 2003; El-Hage
Scialabba and Hattam, 2002; Biao et al., 2003). Increasing soil
biological activity in order to maintain long term soil fertility
through decomposition of the organic matter are the first priori-
ties of organic agricultural management practices (IFOAM, 2011;
Fliessbach and Mader, 2000; Biao et al., 2003). In this study we
compared conventional fields under best management practise to
fields under organic agriculture.
Soil quality is a key element in evaluating the sustainability of
agriculture practices (Carter, 2002). By combining Brookes (1993)
criteria, Doran and Parkins (1994) criteria, and Doran and Safleys
(1997) criteria, Stenberg (1999) produced a list of five essential
criteria used in determining proper soil quality indicators. Because
soil functions are difficult to measure, soil properties that are sen-
sitive to change in a specific ecosystem are often used as indicators
of soil quality (Stenberg, 1999; Acton and Padbury, 1993). A min-
imum data set is a group of soil quality indicators that are chosen
based on a definition of soil quality and soil quality indicator
1470-160X/$ see front matter 2013 Elsevier Ltd. All rights reserved.
http://dx.doi.org/10.1016/j.ecolind.2013.10.002
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120 J.E. Nesbitt, S.M. Adl / Ecological Indicators37 (2014) 119130
criteria (Larson and Pierce, 1991; Doran and Parkin, 1994; Harris
et al., 1996). Modern studies have argued that physical, chemi-
cal, and biological indicators must be evaluated together in order
to provide a correct assessment of soil quality (Stenberg, 1999;
Wander and Bollero, 1999; Stenberg et al., 1998; Gomez et al.,
2004; Schloter et al., 2003a). However, a comprehensive analysis
of soil will not accurately describe soil quality unless the indi-
cators are chosen with a specific soil function in mind, within a
defined ecosystem (Janzen et al., 1992; Stenberg, 1999; Acton and
Padbury, 1993). In this study, we focused on potato production
within the eastern Canada Maritimes and indicators were selected
accordingly.
Potatoes (Solanum tuberosum L.) are the third most important
food crop in the world and play an important role in feeding the
worlds population (International Potato Center, 2011). Potato pro-
ductionineasternCanadacontinuestoincreaseandproducesabout
1,772,000 tonnes annually, and it is the main crop from this region
(Agriculture Canada, 2011). However, potato nutrient demand on
soil is high, andtuberquality requires both high organic matter and
nitrogen availability. The intense use of synthetic chemicals, as fer-
tilizer and pathogen control, in conventional potato production has
also caused concern for the adjacent waterways and the surround-
ing environment (Patriquin et al., 1991). Sustained conventional
potato monoculture as practised is leading to decreased outputper
hectare without substantially increasingchemical inputs, thus rais-
ing costs (Saini andGrant, 1980; Porter andMcBurnie, 1996; Carter
et al., 1998, 2003). To sustain soil fertility and production levels,
more sustainable forms of potato production have been proposed
(i.e.rotations and spring tillage) thatwould reduce production costs
(Porter and McBurnie, 1996; Patriquin et al., 1991; Carter et al.,
1998).
In this study we tested the hypothesis that fields under each
management practice (conventional-best management or organic-
management) would not be different based on indicators of soil
quality. The two management practices were chosen because they
are claimed to be sustainable. Potato fields were chosen because
of the significance of this food crop in the region and globally.
Although other soil quality studies have been conducted on potatofields in Prince Edward Island (Canada) and Maine (U.S.A.) (Carter,
2002; Porter and McBurnie, 1996; Carter et al., 2003), this is the
first study to include bioindicators and to compare fields across the
region. It is also the first study to compare two types of sustainable
practices in potato production in the region.
2. Materials and methods
2.1. Soil sampling
Three conventional and three organic potatofields were chosen
within a 150 km radius for a total of six field sites. All six sites werecommercial farms, not experimental fields. Each organic field was
located within a few kilometres of a conventional field. Fields were
sampled in each of May, July, andSeptember withinone day of each
other. For each field, soil samples were collected at three locations
on a randomly selected diagonal transect across the field. Samples
were taken inthe middleof thepotatohill,approximately1520 cm
from the stem, to a depth of 10 c m. At each sampling location,
separate samples were collected for nematode, testate amoebae,
soil pH, C:N ratio, and soil moisture using a 2.5cm soil corer, as
described below. Similarly, separate samples for microarthropod,
light fraction and bulk density were taken using a 5 cm soil corer,
and microbial biomass was measured from a 1kg composite soil
sample. Soil samples were transported to the lab in a cooler where
they were processed within 24h of sampling.
2.2. Field sites
The first organic management field (O1) is an easilydrained fine
sandy loam Charlottetown series soil, but has a small percentage
of easily drained fine sandy loam Alberry soil as well. The farm has
been under cultivation since the mid 1900s, but was converted to
organic agriculture over a seven year period (19932000) and was
certified in 2000. Parasol 50% (copper hydroxide, fungicide) was
applied twice, Bluestone (copper sulphate, fungicide) was applied
twice, and Entrust (spinosad, insecticide) was applied once during
the 2004 growing season. Potato tops were physically cut off at the
end of the season.
The first conventional management field (C1) is an easily
drained fine sandy loam of the Charlottetown series. The land has
been under potato cultivation since 1942. The potato rows were
seededalongside a bandfertilizer of NPK-Mg. Lorox (linuron, herbi-
cide)was applied once,Manzate(mancozeb, fungicide)was applied
six times, Ridol-Bravo was applied once, Bravo (chlorothalonil,
fungicide) was applied three times, and mineral oil was applied
five times throughout the 2004 growing season. Reglone (diquat
dibromide (37.3%), desiccant/herbicide) was applied twice at the
end of the season as a top kill.
The second organic management field (O2) is a poorly draining
clay loam Washburn series soil. The farm has been in cultivationsince 1980 and was certified organic in 1987. The rotation used in
this field is manure, clover, potatoes, and then mixed vegetables.
Parasol wasapplied three times andEntrust wasapplied twice dur-
ing the 2004 growing season. A propane flamer wasused atthe end
of theseason tocleanup late blight, as a final ColoradoPotato Beetle
control and as a top-kill.
The second conventional management field (C2) is on a poorly
drained silty loam of the Interval series. The land has been under
cultivation since the early 1800s. The rotation includes corn, bras-
sica, and potatoes. Chemical applications to the field did occur but
were not recorded.
Thethirdorganicmanagementfield(O3) is found onlightbrown
sandy loam of the Torbrook series with good to excessive drainage.
The farm has been under organic cultivation since 1988. The rota-tion consists of potatoes, two years of mixed vegetables, followed
by a green manure of oats and peas which are left in through the
winter and harrowed under in the spring. Floating row covers are
used to speed the early stages of growth, and to avoid pests and
disease.
Thethirdconventional management field (C3) is found on sandy
loam Truro series soil with good to fair drainage. Admire (imidac-
loprid, insecticide) and an NPK fertilizer were banded in furrow
when the crop was seeded. An N fertilizer was also broadcast
on the crop mid-growing season. Sencor (herbicide) was applied
once, Bravo was applied three times, and Tatoo C (fungicide) and
Cymbush (cypermethrin, insecticide/miticide) were applied once
throughout the growing season. Reglone was applied once as a top
kill.
2.3. Soil quality indicator measurements
Microbial biomass C was measured using chloroform
fumigation-extraction according to standard procedures (Paul
et al. , 1999). On each sampling occasion, a composite sample of
approximately 1 kg of soil was taken from each field. The soil
was sieved using a 2.83cm diameter sieve and organic particles
larger than 3 m m were removed by hand. The fumigated and
unfumigated extracted filtrates were stored in 50mLfalcon tubes
at 20 C until the chloroform labile C analysis was analysed with
a LECO CNS auto-analyser.
Testate amoebae were stained and enumerated using standard
procedures (Adl et al., 2006a) from three 2.5 cm10cm deep soil
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J.E. Nesbitt, S.M. Adl / Ecological Indicators37 (2014) 119130 121
cores taken at each sampling site of every field (3 samples per
field). One soil composite was obtained from each sampling site
and three subsamples of 1g placed into each of three test tubes.
This resulted in three 1 g subsamples per soil sample for a total of
nine test tubes per field. The slides were dried on a slide dryer and
examined under one drop of glycerine with a cover slide. Three
4cm long transects were observed at 400 on a Zeiss compound
microscope. The number of stained testate amoebae were enumer-
ated along each transect to obtain abundances from line transects
(Krebs, 1998).
Nematodes were extracted and enumerated according
to standard procedures (Coleman et al., 1999) from three
2.5cm10 c m soil cores per sampling location (3 sampling
sites per field) and combined into one composite soil core for
each sampling site. Three subsamples of known weight (48g)
were extracted per sampling location and stored in 5% formalin.
Nematodes were enumerated with a Nikon inverted microscope
at 100 and functional groups were identified at 400.
Microarthropods were extracted and enumerated according to
standard procedure (Coleman et al., 1999). Two 5cm10cm deep
soil cores were taken per field sampling site (3 samples per field),
weighed and placed into an extraction cup. The microarthropods
were collectedin 95% ethanol andenumerated in a Petri dish with a
Nikon dissectingmicroscope. The samples were enumerated at 30
andidentified at 80 to sub-order(mites) or to family (collembola).
Soil pH, light fraction (LF), bulk density (BD) and gravimet-
ric water content were obtained according to Robertson et al.
(1999). The soil C:N ratio was measured according to Elliott et al.
(1999) fromsix compositefield samples that were airdried, ground
to
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122 J.E. Nesbitt, S.M. Adl / Ecological Indicators37 (2014) 119130
May July September
-1000
0
1000
2000
3000
4000
Micro
arthropodAbundance
g-1drysoil
0
2000
4000
6000
Tes
tateAmoebae
g-1drysoil
0
20000
40000
60000
80000
100000
Field and Month
ChloroformLabileC
ugg-1drysoil
0
100
200
300
400
500
May
July
September
Nem
atodeAbundance
g-1drysoil
0
10
20
30
50
100
150
200
250
300
350
20000
30000
40000
50000
60000
70000
Organic
Conventional
Month
0
5
10
15
20
25
O1 C1 O2 C2 O3 C3
A B
C D
E F
G H
Fig. 1. Bioindicators measuredin organically managed fields(O1, O2,O3) and in conventional fields (C1,C2, C3).Pooled dataof all organic (filled circle) or conventional(clear
circle) fields foreach month.
biomass, as implied from chloroform labile carbon, increased over
the growing season in every field (Fig. 1a). Fields under organic
management had the highest chloroform labile C means in the
soil, throughout the growing season, compared to conventional
fields (Fig. 1b). Mean testate amoebae and nematode abundance
was more variable between field sites through the growing season
(Fig. 1c and d). When combining all organic and all conventional
field sites, mean abundance for both decreased over the growing
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J.E. Nesbitt, S.M. Adl / Ecological Indicators37 (2014) 119130 123
Table 2
Nematode functional groupabundance means (with standard error)with respect to theinteraction effects of therandomized block designgeneral linearmodel.
Nematodes(g1) Bacterivores Fungivores Root feeders Omnivores Predators
Management practice
Organic 12 (3) 0 (0) 2 (1) 0 (0) 0 (0)
Conventional 5 (2) 0 (0) 3 (1) 0 (0) 0 (0)
Month
May 10 (3) 0(0) 1 (1) 0 (0) 0 (0)
July 9 (3) 0 (0) 4 (1) 0 (0) 0 (0)
September 6 (3) 2 (1) 2 (1) 0 (0) 0 (0)
Management practicemonth
Organic
May 17 (4) 0 (0) 1 (2) 0 (0) 0 (0)
July 12 (4) 1 (0.2) 2 (2) 0 (0) 0 (0)
September 8 (5) 0 (0) 2 (2) 0 (0) 0 (0)
Conventional
May 4 (4) 0 (0) 2 (2) 0 (0) 0 (0)
July 6 (4) 0 (0) 5 (2) 0 (0) 0 (0)
September 4 (4) 1 (0.3) 3 (2) 0 (0) 0 (0)
Location
A 7 (3) 0 (0) 4 (1) 0 (0) 0 (0)
B 8 (3) 0 (0) 1 (1) 0 (0) 0 (0)
C 11 (3) 1 (0.2) 2 (1) 0 (0) 0 (0)
Means were estimated with Fishers least significant difference withP= 0.05, type III sum of squares.
season (Fig. 1e and f). There was no significant difference in tes-
tateamoebae mean abundances between organic and conventional
fields (Fig. 1d). Mean nematode abundance in the organic fields
was higher than in conventional fields in every month sampled,
but this was not statistically significant. Among the nematode
functional groups, bacterivore, fungivore, root feeder, predator,
and omnivore. Means and standard errors were analysed by ran-
domized block design (Table 2). Nematode functional groups did
not significantly differ between organic and conventional fields,
samplingdate, the interaction between managementand datesam-
pled, or between locations (Tables 2 and 3). Mean microarthropod
abundance was higher in September in most field sites sampled
except in C3 (Fig. 1g), and this was significantly higher for organic
fields (Fig. 1h, Tables 1 and 4). Mean abundance of mite subor-
ders and Collembola families did not significantly differ between
organic and conventional fields, months sampled, interaction
between management practice and month, or between locations
(Table 5).
3.2. Physical and chemical indicators
When organic fields were combined, soil pH was fairly stable
over the growing season, but in the conventional fields the mean
soil pH decreased slightly through the growing season (Fig. 2a and
b, Table 1). The mean C:N ratio was consistently higher in the con-
ventional fields than in the organic fields, but increased over the
growing season in the organic fields (Fig. 2c and d). Light fraction
was measured at the beginning and at the end of the growing sea-
son, in May and September. Mean light fraction weight remained
significantly higher in the organic fields compared to conventional
Table 3
Effect of management practice, month sampled, their interaction, and location on nematode functional groups.
Effect Statistic Value F Error d.f. p-Value Significance
Management practice
Pillais trace 0.754 3.05 5 0.123 NS
Wilks lambda 0.25 3.05 5 0.123 NS
Hotellings trace 3.05 3.05 5 0.123 NS
Roys largest root 3.05 3.05 5 0.123 NS
Month
Pillais trace 1.201 1.78 12 0.170 NS
Wilks lambda 0.06 2.97 10 0.050 *
Hotellings trace 10.69 4.28 8 0.025 NS
Roys largest root 10.30 12.36 6 0.004 **
Management practicemonth
Pillais trace 1.192 1.75 12 0.1777 NS
Wilks lambda 0.09 2.42 10 0.089 NS
Hotellings trace 7.53 3.01 8 0.066 NS
Roys largest root 7.08 8.49 6 0.011 *
Location
Pillais trace 0.903 0.99 12 0.498 NS
Wilks lambda 0.26 0.97 10 0.518 NS
Hotellings trace 2.26 0.90 8 0.569 NS
Roys largest root 1.93 2.32 6 0.168 NS
Abbreviations and symbols:d.f. (degreesof freedom), typeIII sumof squares wereused, NS (notsignificant), ***p0.001, 1, 2, 3, 4orderof theeffectscontributionto the overall
model.* p0.05.
**
p0.01.
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124 J.E. Nesbitt, S.M. Adl / Ecological Indicators37 (2014) 119130
Field and Month
0.000
0.001
0.002
0.003
0.004
0
2
4
6
8
0
2
4
6
8
10
12
14
16
May
July
September
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
SoilpH
5.0
5.2
5.4
5.6
5.8
6.0
6.2
6.4
C:N
Ratio
7
8
9
10
11
12
Organic
Conventional
Month
May July September
BulkDensity
gcm-3
0.8
0.9
1.0
1.1
LightFraction
g-1drysoil
0.0000
0.0005
0.0010
0.0015
0.0020
0.0025
A B
C D
E F
G H
O1 C1 O2 C2 O3 C3
Fig.2. Physicaland chemicalindicatorsin organicallymanagedfields(O1,O2, O3)and in conventionalfields(C1,C2, C3).Pooleddataof allorganic(filledcircle)or conventional
(clear circle) fields foreach month.
fields, with a slight butnot-significant difference, over the growing
season (Fig. 2e and f). Over the growing season, mean bulk density
decreased from May to July to September in all fields except C2
(Fig. 2g). Conventional sites had a consistently higher mean bulk
density than theorganic fields (Fig.2h). Soil moisture at each samp-
ling time showed the organic fields retained significantly higher
mean soil moisture content than the conventional fields at the time
of sampling (Table 1).
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126 J.E. Nesbitt, S.M. Adl / Ecological Indicators37 (2014) 119130
Table 5
Effect of management practice, month sampled, their interaction, and location on mite and collembolan abundances.
Effect Value F Error d.f.a p-Valueb
Management practice
Pillais trace 0.983 5.27 1 0.327 NS
Wilks lambda 0.02 5.27 1 0.327 NS
Hotellings trace 47.42 5.27 1 0.327 NS
Roys largest root 47.42 5.27 1 NS
Month 0.572
Pillais trace 1.632 0.99 4 0.361 NS
Wilks lambda 0.00 2.18 2 NS
Hotellings trace 154.22 0.00 0 0.029 NS
Roys largest root 152.45 33.88 2 NS
Management practicemonth
Pillais trace 1.711 1.32 4 0.433 NS
Wilks lambda 0.00 1.65 2 0.443 NS
Hotellings trace 70.67 0.00 0 NS
Roys largest root 68.02 15.11 2 0.064
Location
Pillais trace 0.284 18.00 4 0.975 NS
Wilks lambda 0.21 18.00 2 0.979 NS
Hotellings trace 0.00 18.00 0 NS
Roys largest root 1.10 9.00 2 0.562 NS
NS, not-significant;1,2,3,4 order of effects contribution to theoverall model;type IIIsum of squares.a
Degrees of freedom.b Significance.
Table 6
Meansand standard errorsof the soil quality indicators with respect to management practice, month, and location,usingthe randomized block generalized linearmodel.
Indicators Microbial biomass
(mg g1)
Testate amoebae
(# g1)
Nematodes
(# g1)
Micro-arthropods
(# g1)
Soil pH C:N ratio Bulk density
(gcm3)
Soil moisture
(%)
Management practice
Organic 242.64 (13.2) 50,100 (5200) 15 (3) 1450 (250) 6.06 (0.1) 9.99 (0.5) 0.905 (0.02) 22.38 (0.8)
Conventional 136.12 (14.7) 47,305 (4700) 9 (3) 660 (230) 5.53 (0.1) 11.46 (0.4) 1.107 (0.01) 15.89 (0.7)
Month
May 135.74 (14.7)a 66,000 (5800) a 12 (4) a 300 (280) a 5.93 (0.1) a 10.17 (0.5) a 1.096 (0.02) a 19.57 (0.9) a
July 202.63 (14.7)b 45,000 (5800)ab 13 (4) a 354 (280) a 5.74 (0.1) a 10.76 (0.5) a 1.066 (0.01) a 16.77 (0.9) b
September 229.77 (16.8) b 35,100 (6600) b 9 (4) a 2510 (320) b 5.72 (0.1) a 11.26 (0.6) a 1.160 (0.02) a 21.07 (1.0) ab
Location
A 171.40 (14.7) a 48,900 (5800) a 11 (4) a 1020 (280) a 5.16 (0.1) a 10.19 (0.5) a 0.984 (0.03) a 15.70 (0.9) a
B 202.00 (14.7) a 44,200 (5800) a 10 (4) a 1200 (280) a 6.40 (0.1) b 12.83 (0.5) b 1.022 (0.03) a 21.52 (0.9) b
C 194.74 (16.8) a 53,000 (6600) a 14 (4) a 950 (320) a 5.82 (0.1) a 9.16 (0.6) a 1.042 (0.05) a 20.19 (1.0) ab
Means estimated using Fishers least significant difference with p= 0.05. Values within the same column labelled with the same letter are not significantly different from
each other, according to Tukeys honestly significant difference calculated atp= 0.05.
Fig. 3. Canonicalcorrespondence analysisbiplot of soil quality indicators, manage-
ment practices and month sampled.
Fig. 4. Canonical correspondence analysis biplot of management practices and
month sampled.
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J.E. Nesbitt, S.M. Adl / Ecological Indicators37 (2014) 119130 127
Table 7
Effect of management practice,monthsampled, their interaction,and location on soil quality,with respect to theinteractioneffects of therandomized block designgeneral
linear model.
Effect Statistic Value F Error d.f. p-Value Significance
Management practice
Pillais trace 0.994 21.01 2 0.046 *
Wilks lambda 0.01 21.01 2 0.046 *
Hotellings trace 84.04 21.01 2 0.046 *
Roys largest root 84.04 21.01 2 0.046 *
Month
Pillais trace 1.961 20.64 6 0.01 **
Wilks lambda 0.00 15.28 4 0.009 **
Hotellings trace 135.66 8.48 2 0.110 NS
Roys largest root 97.49 36.56 3 0.007 **
Management practicemonth
Pillais trace 1.893 6.44 6 0.015 *
Wilks lambda 0.00 5.33 4 0.059 NS
Hotellings trace 52.78 3.30 2 0.257 NS
Roys largest root 42.29 15.86 3 0.022 *
Location
Pillais trace 1.922 8.93 6 0.006 **
Wilks lambda 0.00 6.45 4 0.042 *
Hotellings trace 55.86 3.49 2 0.245 NS
Roys largest root 38.85 14.57 3 0.025 *
Abbreviations and symbols: d.f. (degrees of freedom), type IIIsum of squares were used, NS (not significant),1,2,3,4
order of theeffects contribution to theoverall model.* p0.05.** p0.01.
Table 8
Effect of management practice, month sampled, their interaction,and location on thesoil quality indicatorlight fraction.
Effect Light fraction (g LF g1 dry soil) F d.f. p-Value Significance
Management practice
Organic 0.00182 (0.0003) 5.77 1 0.061 NS
Conventional 0.00080 (0.0003)
Month
May 0.00137 (0.0003) 0.09 1 0.776 NS
September 0.00124 (0.0003)
Management practicemonth
Organic May 0.00194 (0.0004) 0.08 1 0.787 NS
September 0.00169 (0.0005)
Conventional May 0.00080 (0.0004)
September 0.00079 (0.0004)
Location
A 0.00246 (0.0003) 7.83 2 0.029 *
B 0.00087 (0.0003)
C 0.0060 (0.004)
Abbreviations and symbols: d.f. (degrees of freedom), type IIIsum of squares were used, NS (not significant).* p0.05.
the smaller the deviation from the grand mean of all the envi-
ronmental variables (Jongman et al., 1995). When the organic and
conventional fields, and May, July and September scores were sep-
arated, the organic, conventional, andJuly scores were shown to be
multicolinear (Table 9 and Fig. 3). When organic and conventionalvariables were combined into a single variable called management
practice, and May, July, and September were combined into a sin-
gle variable called month in CCA biplot B, all multicolinearity was
eliminated(Table9 and Fig.4). The eigenvalues, percentages, cumu-
lative percentages and speciesenvironment correlations of both
biplots are shown in Table 9. Both CCA biplots illustrate the same
correlations and relationships between variables.
Theoccurrenceof testate amoebae close to thecentre of theplotindicates they were not sufficiently affected by management prac-
tise or by any of the other variables to be useful indicators in this
study. The microarthropods indicate they are strongly responsive
Table 9
Canonical correspondence analysis of soil quality indicators, filed site management practices and month sampled.
CCA Biplot, Fig. 3 CCA Biplot, Fig. 4
Axis one Axis two Axis one Axis two
Eigenvalues 0.044 0 0.035 0
Percentage 52.715 0.398 41.833 0.212
Cumulative percentage 52.715 53.113 41.833 42.045
Indicator-environment correlations 0.732 0.505 0.654 0.329
Multicollinearity detected Organic, conventional, July
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128 J.E. Nesbitt, S.M. Adl / Ecological Indicators37 (2014) 119130
Table 10
Intraset correlations between environmental variables and constrained site scores ofFigs. 3 and 4.
CCA Fig.3 CCA Fig. 4
Scores Envi. axis 1 Envi. axis 2 Scores Envi. axis 1 Envi. axis 2
Organic 0.076 0.379 Mngt pract 0.011 0.242
Conventional 0.076 0.379
May 0.436 0.725 Month 0.887 0.231
July 0.226 0.825
September 0.792 0.057
% moisture 0.229 0.193 % moisture 0.155 0.569
Bulk density 0.581 0.159 Bulk density 0.583 0.320
pH 0.195 0.194 pH 0.134 0.502
C:N ratio 0.379 0.263 C:N ratio 0.212 0.305
to the local climate, season and month of sampling, as well as soil
pH, moisture and C:N ratio, but negatively affected by higher bulk
density. The seasonal effect dominates their abundance dynamics
and they are therefore not useful to distinguish between the two
management practices in this study. The microbial biomass esti-
mated from chloroform labile C also responded in the same way as
the microarthropods, but to a lesser extent, and higher biomass
correlated more strongly with organic management. The nema-
todeabundancewas strongly affected by managementpractiseandhigher abundance correlated more with organic management. The
light fraction weightwas higherin organic plots (Table 6 and Fig.2).
The environmental indicators alsorespondedto month of sampling,
showing higher moisture, pH and C:N ratio towards September,
but lower bulk density in September in part caused by potato
tuber mounding. Overall the organic fields correlated with higher
pH, moisture, nematode abundance, micro-arthropod abundance,
microbial biomass, and lower bulk density and C:N ratio. The con-
ventional fields under best management practise correlated with
higher bulk density, higher C:N ratio, lower pH, and lower abun-
dance of bioindicators.
4. Discussion
4.1. Soil management effect on soil quality indicators
Soil quality indicators were meant to be practicaland fast meas-
ures to determine health or fertility levels of soils. When chosen
well, indicators can be used to monitor field sites and to make
management decisions. One needs to exercise caution as there are
studies that exerted huge effortonlyto conclude thewrongbioindi-
cator was selected (Geissen and Kampichler, 2004). The indicators
selected for this study were chosen because they had been pro-
posed as useful indicators in the literature. However, as this study
shows,not allindicatorsare usefulfor thesamepurpose. Some indi-
catorswere betterused forcertain comparisons, butnot others. For
example, some indicators may discriminate between large differ-ences between sites (such as a forest and an agriculture field), but
not smaller differences between similar fields under agriculture. In
addition, the level of taxonomic resolution used in bio-indicators
may be more significant than abundance measures typically used.
Furthermore, our results show that, predictably, indicators fluc-
tuate with month of sampling through the growing season and
with site location. Therefore, for soil quality indicators a one-time
measurement does not suffice, reducing the practicality of such
measures. Using an aggregate of indicators, with sufficient samp-
ling through the growing season, as well as multivariate statistical
analysis of the data provide better indicators of field management
effects on soil. Our results show that our minimum data set of
indicators, when used together, could differentiate between
organic and conventional best-practice potato fields.
When used on their own, the indicators provided mixed results,
some showing no significant difference while others detecting
differences (Table 1): individually, six of the nine soil quality indi-
cators included in the minimum data set differed between organic
and conventional fields. Some indicators responded more to site
location or month than fieldmanagement. For nematode functional
groups, mite suborders, or collembolan family, the abundances
were not significantly different, using a variety of statistical meas-
ures (Tables 25). However, we note not all statistical techniqueswere consistent, so that comparing several is useful. Better dis-
crimination between organic and conventional best-practice fields
was obtained when the fields were averaged by management
(Tables 6 and 7). The indicators detected significant overall differ-
ences between organic and best-practice management, and were
less affected by month of sampling or site location. The multivari-
ate CCA results provided a better visualization of correspondences
among the indicators (Table 10 and Figs. 3 and 4).
4.2. Interactions among indicators andwith management
Overall, the general linear model and the canonical correspon-
dence analysis showed fields under organic management had
higherpH,soilmoisture,litterlightfraction,andlowerC:Nratioandbulk density than the conventional fields. Given the emphasis on
the role of the soil food web in decomposition and mineralization
in organic agriculture, it was expected that organic management
would promote soil conditions that were favourable for biological
activity (Neher, 1999a,b; Fliessbach and Mader, 2000). However,
conventional fields under best management practice were not sig-
nificantly different from the organic fields for testate amoebae
and nematode abundances. The organic fields supported higher
microarthropod abundance but the significant microarthropod
effect is due to the elevated abundance in the organic fields in
September. Only microbial biomass was significantly higher in the
organic fields, and this was probably affected by the lower bulk
density and lower C:N ratio in these fields. The organic field mean
C:N ratio (= 9 .99) and the conventional field mean C:N ratio(= 1 1.46) were below 20:1 (Table 6), which may indicate the
predominance of the bacterial energy channel (Ferris and Matute,
2003). Dendooven et al. (2000) also reported significant differ-
ence between their low conventional and organic soil C:N values
(CON= 8 and ORG= 5). Their study suggests that the low C:N ratio
could be attributed to differences in C availability, to differences in
microbialbiomassC:N ratios, todifferencesin N dynamics,or todif-
ferences that cannot be reflected in the measurements of organic
and conventional practices (Dendooven et al., 2000). Lower bulk
density suggests that the soil in the organic fields was favourable
to biological activity (Harris et al., 1996). When used as a physical
soil quality indicator in comparisons of organic and conventional
soil bulk density, results in other studies have differed. In a field
experiment by Bulluck et al. (2002) organic and synthetic fertility
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J.E. Nesbitt, S.M. Adl / Ecological Indicators37 (2014) 119130 129
amendments were compared. The organic fertilizer, comprised of
cotton-gin trash, compostedyard waste and cattle manure showed
a decrease in bulk density, while the synthetic fertilizer did not
(Bulluck et al., 2002). In a study of three groups of organic and
conventional fields, organic field bulk density was shown to be
significantly higher in the first group, not significantly different in
the second group, and significantly lower in the third group com-
pared to the conventional counterpart (Schjnning et al., 2002).
Schjnning et al. (2002) suggest that their results indicate that
soil compaction due to extensive traffic may reach the same lev-
els in organic fields as is currently experienced in conventional
fields.
Higher microbial biomass and microarthropod abundance in
organic fields may indicate a difference in the quality (C:N ratio)
or availability of plant litter and compost amendments, since both
are strongly related to resource quality and availability (Wardle
and Lavelle, 1997; Wardle et al., 1999). The lower soil C:N ratio
(higher quality)in organic fieldswould support an increased micro-
bial biomass, however microarthropods generally thrive on lower
quality organic matter (higher C:N ratio) (Wardle and Lavelle, 1997;
Georgieva et al., 2005). Microarthropod abundance is known to be
heavily influenced by resource quality andavailability, buthas also
varied with management practice (Behan-Pelletier, 1999, 2003;
Mebes and Filser, 1998; Wardle et al., 1999). In other comparisons
of organic and conventional fields, as in this study, both testate
amoebae and nematode abundances have been shown not to dif-
fer significantly (Foissner, 1997; Neher, 1999b). Testate amoebae
abundance has been cited as a poor indicator of agriculture man-
agement practices differences, while nematode abundances were
expected to differentiate between differing managementpractices,
months and provinces (Georgieva et al., 2005). Our study agrees
with the literature, that the majority of the nematodes found in
organic soils are bacteriovores, and the remaining population is
made upof rootfeeders (Neher, 1999b). Bacteriovores (both nema-
todes and protozoa) have been suggested as better indicators of
bacterial activity, substrate quality, and nutrient release in the
soil than direct measurement of bacterial populations (Georgieva
et al., 2005). If decomposition and nutrient mineralization hadbeen more efficient in the organic systems, indicated by higher
microbial biomass and microarthropod abundance, the abundance
of bacterivorous nematodes and testate amoebae should have
been significantly higher as well. Neher (1999b) suggests that the
nematode communities in organic and conventional fields are too
similar, supporting the view that even with the lack of inorganic
inputs, the organic soil food web composition is not as different
from a conventional soil food web as was once thought. Although
testate amoebae, nematode, and microarthropod abundance are
standard bioindicators that have traditionally been enumerated
(Behan-Pelletier, 1999; Foissner, 1999; Neher, 2001), species rich-
ness and diversity may be more useful indicators of soil quality
and soil health (Naeem, 2002; Adl et al., 2006b). However, it is
most likely that the fields under best-management practice arenot all that different biologically from fields near-by under organic
management.
After discussions with the growers, it became evident that the
organic fields were managed with more frequent tillage than the
fields under best-management practice. We therefore sought a no-
tillage potato field under organic management in the area. We
compared our data to the same quality indicators in the no-till
organic potatoplot forthe monthof September. Theresults suggest
thatwithouttillage,the plots underorganicmanagement haveboth
more functional diversity and higher abundance of indicator orga-
nisms, and are significantly different from the best-management
practice fields (Nesbitt, unpublished). This is consistent with previ-
ous observations on the effect of tillage on soil diversity (Adl et al.,
2006b).
5. Conclusion
Using a combination of indicators that included physical, chem-
ical and biological parameters provided a better evaluation of the
field sites, than if a single or fewer indicators are selected. Indica-
tors varied through the growing season so that multiple sampling
times are necessary to infer any conclusions on field manage-
ment. Bioindicators under best-management-practice were not
that different from the organic management fields. We suggest the
increased tillage frequency in the organic fields is responsible for
preventing a recovery of the biodiversityand organism abundance.
The differences in physical parameters between both management
practices are most likely due to the increased organic matter in the
fields under organic practice. This increased abundance of some
bioindicators but not to the point of being consistently significant.
Acknowledgements
This research was supported by a NSERC grant to S.M.A. We
thank the Dalhousie University Faculty of Graduate Studies, the
School for Resource and Environmental Studies, and the Soil Ecol-
ogySocietyfor their financialsupport andtravel awardsto J.E.N.We
thank the potato growers that co-operated with this study, pro-vided access to fields and information about field management:
Raymond Loo, Lori Robinson, Karen Davidge, Gordon Harvey, Nor-
bert Kungl, Kris Pruski.
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