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Farmer-oriented assessment of soil quality using field, laboratory, and VNIR spectroscopy methods


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Soil quality and health are terms describing similar concepts, but the latter appeals to farmers and crop consultants as part of a holistic approach to soil management. We regard soil health as the integration and optimization of the physical, biological and chemical aspects of soils for improved productivity in an economic and sustainable manner. This paper describes the process used for the selection of soil quality/health indicators that comprise the new Cornell Soil Health Test. Over 1,500 samples collected from controlled research experiments and commercial farms were initially analyzed for 39 potential soil quality indicators. Four physical and four biological indicators were selected based on sensitivity to management, relevance to functional soil processes, ease and cost of sampling, and cost of analysis. Seven chemical indicators were also selected as they are part of the standard soil nutrient test. Soil health test reports were developed to allow for an overall assessment, as well as the identification of specific soil constraints. The new soil health test is being offered on a for-fee basis starting in 2007. In addition, visible near infrared reflectance spectroscopy was evaluated as a possible tool for low-cost soil health assessment. From preliminary analyses, the methodology shows promise for some but not all of the soil quality indicators. In conclusion, an inexpensive soil health test was developed for integrative assessment of the physical, biological, and chemical aspects of soils, thereby facilitating better soil management.
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Farmer-oriented assessment of soil quality using field,
laboratory, and VNIR spectroscopy methods
Omololu J. Idowu &Harold M. van Es &
George S. Abawi &David W. Wolfe &Judith I. Ball &
Beth K. Gugino &Bianca N. Moebius &
Robert R. Schindelbeck &Ali V. Bilgili
Received: 22 April 2007 /Accepted: 6 December 2007 / Published online: 24 January 2008
#Springer Science + Business Media B.V. 2007
Abstract Soil quality and health are terms describing
similar concepts, but the latter appeals to farmers and
crop consultants as part of a holistic approach to soil
management. We regard soil health as the integration
and optimization of the physical, biological and
chemical aspects of soils for improved productivity in
an economic and sustainable manner. This paper
describes the process used for the selection of soil
quality/health indicators that comprise the new Cornell
Soil Health Test. Over 1,500 samples collected from
controlled research experiments and commercial farms
were initially analyzed for 39 potential soil quality
indicators. Four physical and four biological indicators
were selected based on sensitivity to management,
relevance to functional soil processes, ease and cost of
sampling, and cost of analysis. Seven chemical in-
dicators were also selected as they are part of the
standard soil nutrient test. Soil health test reports were
developed to allow for an overall assessment, as well as
the identification of specific soil constraints. The new
soil health test is being offered on a for-fee basis
starting in 2007. In addition, visible near infrared
reflectance spectroscopy was evaluated as a possible
tool for low-cost soil health assessment. From prelimi-
nary analyses, the methodology shows promise for
some but not all of the soil quality indicators. In con-
clusion, an inexpensive soil health test was developed
for integrative assessment of the physical, biological,
and chemical aspects of soils, thereby facilitating better
soil management.
Keywords Soil health .Soil health assessment .
Soil quality .Soil quality indicators .
VNIR spectroscopy
Soil quality and health
Doran and Parkin (1994) defined soil quality as the
capacity of a soil to function, within ecosystem and
land use boundaries, to sustain productivity, maintain
environmental quality, and promote plant and animal
health, a definition that includes an inherent and a
dynamic component (Carter 2002; Larson and Pierce
1991). The former is an expression of the soil forming
factors (Brady and Weil 2002), often documented by
soil surveys (Soil Survey Division Staff 1993).
Dynamic soil quality, on the other hand, generally
refers to the condition of soil that is changeable in a
short period of time by human impact, including
Plant Soil (2008) 307:243253
DOI 10.1007/s11104-007-9521-0
Responsible Editor: Peter M. Neumann.
O. J. Idowu :H. M. van Es (*):G. S. Abawi :
D. W. Wolfe :J. I. Ball :B. K. Gugino :B. N. Moebius :
R. R. Schindelbeck :A. V. Bilgili
Cornell University,
1005 Bradfield Hall,
Ithaca, NY 14853, USA
agricultural management practices (Carter 2002;
Karlen et al. 1997; Magdoff and van Es 2000;
Mausbach and Seybold 1998; Wienhold et al. 2004).
Biological and chemical processes, such as root
growth, organic matter turnover, macrofauna activity,
bacterial and fungal proliferation, and cation charge
density on the exchange complex influence pore size
distribution, density and stability of soil structure
(Amezketa 1999). In turn, the physical structure of a
soil also plays an integral role in controlling chemical
and biological processes (Dexter 2004). A high
proportion of stable aggregates in an agricultural soil
is desirable, especially in fine- and medium-textured
soils, as they sustain a range of pore sizes and
promote aeration, water infiltration, and drainage
(Kemper and Rosenau 1986), as well as better soil
workability, seed-bed quality (Topp et al. 1997), and
easier root penetration (Czyz 2004). Agricultural
management practices such as tillage, traffic patterns,
crop rotation practices, cover crops and organic matter
additions strongly influence the components of soil
quality and thus crop performance (Doran and Parkin
1994; Francis and Kemp 1990).
With farmer audiences, the term soil healthis
often preferred over soil quality as it connotes a
holistic approach to soil management, including the
integration of physical, biological and chemical
processes (Idowu et al. 2007). In the past, an
overemphasis on chemical soil management has
resulted in the deterioration of the biological and
physical processes in soils. Prior to the introduction of
inorganic fertilizers, soil fertility management con-
sisted mostly of adding organic nutrient sources to the
soil. Nutrients held in the organic forms were
mineralized to inorganic forms by the soil microbial
population and then made available for crop uptake.
Magdoff and van Es (2000) argue that with inorganic
fertilizers the nutrition of plants has been short-cut
and the lack of organic additions in many non-
livestock farming systems does not adequately sustain
important biological processes and the associated
physical benefits (e.g., aggregation). A new emphasis
on soil health through the integration of the chemical,
biological and physical processes therefore provides a
useful and holistic framework to discuss soil manage-
ment in the age of diverse cropping systems with
tools such as organic and inorganic fertilizers, reduced
tillage, cover cropping, new rotations and other soil/
crop management practices.
Soil quality assessment
Traditional soil testing, which is equivalent to the
assessment of soil chemical quality for crop nutrition,
has provided farmers and consultants around the
world with relevant information for fertilizer and lime
management. In a more holistic soil health paradigm,
new inexpensive soil tests are needed to provide an
integrative assessment of the triad of soil quality
domains (physical, biological and chemical; Fig. 1).
Such a soil test would need to involve soil quality
indicators that represent soil processes relevant to soil
functions, and also provide information that is useful
for practical soil management. In this context, soil
health is best assessed through soil properties that are
sensitive to changes in management practices
(Andrews and Carroll 2001; Brejda et al. 2000; Doran
and Parkin 1994; Larson and Pierce 1991).
Sojka and Upchurch (1999)arguedthatsoilquality
assessment is hindered by the variable interpretations
of soil quality indicators for the different soil functions.
Our approach gets around that issue by focusing on the
soil processes that are relevant only to the crop
production function. Additionally, we place emphasis
on the value of the information that is acquired from
measurement of soil quality indicators, rather than
attempting a broader interpretation within a narrowly
defined weighting framework. The information on soil
quality (health) as presented through this work is
relevant to land managers and consultants in that it
identifies soil constraints and aids in the selection of
Fig. 1 Conceptual view of soil health, connoting the integra-
tion of chemical, biological and physical processes
244 Plant Soil (2008) 307:243253
management solutions (Idowu et al. 2007). The
interpretation of the test results thus requires profes-
sional judgment and placement into the context of the
cropping system and farm characteristics. For example,
soil health test results from a dairy farm require
different interpretations and management approaches
than for a viticulture operation. The former generally
wants to maximize forage production and enjoys the
availability of organic nutrient sources. The latter farm
often wants to focus on optimizing wine quality, which
requires suboptimal growing conditions and some
nutrient and water stress during the early growing
season (White 2003; Wheeler and Pickering 2003). In
this respect, soil health testing can be regarded as
similar to human health assessment where the results of
tests are interpreted by medical experts within the
broader context of a patients medical history, lifestyle
and financial situation.
Cornell soil health test development
General approach
The Cornell Soil Health Initiative included the devel-
opment of a new three-faceted soil health test to
accomplish the following goals:
Education: Farm-specific soil quality information
facilitates discussion and promotion of soil health
Targeting management practices: Identified soil
constraints may be addressed with high likelihood
for positive results, while no investments are
needed in unsubstantiated soil problems.
Quantifying soil degradation or aggradation from
management: Farmers, consultants, and research-
ers can quantify the soil quality benefits resulting
from changes made in management practices (e.g.,
conversion to no-tillage). Government incentive
programs can link green payments to soil quality
improvements, as documented by the soil health
Soil inventory assessment: The dynamic soil
quality can be assessed in addition to the tradi-
tional genetic (inherent) soil quality as reported in
soil surveys.
Land valuation: Effective quantification of soil
quality allows for better assessment of the mone-
tary value of land for purchasing and rental
transactions, thereby facilitating monetary rewards
for good land management.
The development of the soil health test involved a
triage of potential soil quality indicators and stream-
lining of methodologies. Thirty-nine potential soil
health indicators were evaluated (Table 1). The
suitability of the soil properties as quality indicators
was evaluated through analysis of samples collected
from (1) long-term research experiments related to
tillage, rotation and cover cropping that allowed for
Physical indicators Biological indicators Chemical indicators
Bulk density Root health assessment Nitrate nitrogen
Macro-porosity Organic matter content pH
Meso-porosity Beneficial nematode population Exchangeable acidity
Micro-porosity Parasitic nematode population Phosphorus
Available water capacity Potential mineralizable nitrogen Potassium
Residual porosity Decomposition rate Magnesium
Penetration resistance at 10 kPa Particulate organic matter Calcium
Saturated hydraulic conductivity Active carbon test Iron
Dry aggregate size (<0.25 mm) Weed seed bank Aluminum
Dry aggregate size (0.252 mm) Microbial respiration rate Manganese
Dry aggregate size (28 mm) Glomalin content Zinc
Wet aggregate stability (0.25mm) Copper
Wet aggregate stability (28 mm)
Surface hardness (penetrometer)
Subsurface hardness (penetrometer)
Field infiltrability
Table 1 Thirty-nine soil
health indicators evaluated
for the Cornell Soil Health
Plant Soil (2008) 307:243253 245
assessment of soil response under controlled condi-
tions, and (2) commercial production fields that
reflected a broad range of soil management conditions
in New York State. The latter included samples from
grain, dairy, vegetable, and fruit operations, and a
wide range of soil types. In total, over 1,500 samples
were included in the evaluation, although not all 39
properties were measured on all. For several of the
controlled experiments, soil samples were collected
four times over the course of the 2004 growing season
to evaluate within-season variability.
For all management units, two undisturbed soil
core samples were collected from the 5 to 66-mm
depth using stainless steel rings (61 mm height,
72 mm ID, 1.5 mm wall thickness). Ten to 12
disturbed samples were collected from the 5 to
150 mm depth in a Wor V-shaped transect using
a trowel and mixed together to make a composite
sample. All samples were stored at 2°C until analysis.
A description of the specific methodologies used
for all 39 potential indicators is provided by Gugino
et al. (2007). In short, analysis of the chemical
indicators was based on the standard soil fertility test
offered by the Cornell Nutrient Analysis Laboratory.
The available nutrients are extracted with Morgans
solution, a sodium acetate/acetic acid solution, buff-
ered at pH 4.8. The extraction slurry is filtered and
analyzed for K, Ca, Mg, Fe, Al, Mn, and Zn on an
ICP and plant-available PO
P is measured using an
automated rapid flow analyzer.
The physical tests were based on standard meth-
odology (Moebius 2006), except for wet aggregate
stability which involved the application of simulated
rainfall of known energy (Ogden et al. 1997)to
aggregates on sieves (van Es et al. 2006). The
biological tests also involved established methods.
The decomposition rate was based on the loss of non-
bleached cellulose Whatman no. 42 filter paper
(moistened, 7 cm diameter) which was placed into
the center of a 9 cm diameter plastic Petri dish which
in turn was filled with soil sieved previously through
a 2 mm mesh. The grid line intersection method
(Lindsey 1955) was used to quantify decomposition at
3, 5, and 7 weeks. Linear regression analysis was
used to calculate the decomposition rate (% week
based on the slope of the percent decomposition vs
The active carbon test involved a KMnO
procedure based on the method by Weil et al. (2003).
The root health assessment involved a bioassay
method where snap bean seeds were grown in the
sampled soil and subsequently rated for symptoms
and root damage caused by infection from soil-borne
pathogens such as Rhizoctonia spp., Thieviopsis spp.,
Fusarium spp. and Pythium spp. (Abawi et al. 2004;
Abawi and Widmer 2000).
Indicator selection
The seven soil chemical indicators were easily
integrated into the comprehensive soil health test
since they involve a well-established set of procedures
that are already widely offered at a reasonable cost. In
some cases an additional elemental analysis based on
whole-soil HNO
digestion can be included in the
chemical test for samples where metal contamination
is expected. It is currently handled separately in the
interpretation of the soil quality test as it is an
additional expense that is not needed for the majority
of soils.
The general criteria used for selecting the physical
and biological indicators were previously described
by Moebius (2006) and included:
Sensitivity to management, i.e., frequency of
significant treatment effects in the controlled
experiments and directional consistency of these
Precision of measurement method, i.e., residual
errors from analyses of variance.
Relevance to important functional soil processes
such as aeration, water infiltration/transmission,
water retention, root proliferation, nitrogen min-
eralization, development of root diseases, etc.
Ease and cost of sampling.
Cost of analysis.
Qualitative ratings for sensitivity to sampling error
and ability to represent soil functional processes were
assigned using relationships established in the litera-
ture (Andrews et al. 2004; Larson and Pierce 1991;
Luxmoore 1981) and experience gained in this study.
Quantitative data were obtained from statistical
analyses of replicated experiments (e.g. consistency
of treatment effects and reproducibility) and sample
processing (e.g. cost of labor, equipment and
supplies). Many of the soil physical properties were
rejected as suitable indicators due to the requirement
for undisturbed samples, or due to high variability.
246 Plant Soil (2008) 307:243253
Several soil biological indicators were rejected due to
the high cost of analysis, often associated with labor
Selected test indicators
Tab le 2shows the selected physical, biological and
chemical indicators for the developed soil health test.
The test includes penetrometer measurements as the
only in-field assessment. The remaining indicators are
based on a composited disturbed sample, collected
from 2 locations nested within five sites in a field or a
sampling unit. Although it is widely regarded as
essential, we did not select bulk density as an indicator.
The use of a ring sampler is required for bulk density
determination, and this proved to be a serious obstacle
with field practitioners. Additionally, the reliability of
the results was questionable due to frequent sampling
errors. For this reason, we found it imprecise and since
it also correlated with other indicators such as
aggregate stability and soil organic matter, it was
considered mostly redundant. The selected soil mea-
surements can be considered as indicators of different
soil processes as described in Table 2. The selected
indicators constitute the minimum data set used to
assess the ability of the soil to function for crop
production in New York State. The soil health test
thereby evaluates the soils ability to accommodate
most of the processes relevant to crop production and
soil hydrology. Soil texture is an inherent and
integrative property and provided the basis for results
interpretation. Root health assessment is an integrative
biological measurement related to overall pressure
from soil-borne disease organisms (Abawi and Widmer
2000). The minor elements (magnesium, iron, manga-
nese and zinc) of the chemical analysis were grouped
to prevent a bias of the soil health assessment in favor
of chemical quality.
The Cornell Soil Health Test is being offered as a
for-fee service, but samples will only be received
during the early spring (15 April to 1 June). Some
indicators were shown to have significant within-
season variability (Moebius 2006), and the tillage
practice is a confounding influence for soil physical
and biological indicators. Also, spring sampling is
facilitated by favorable soil water conditions (gener-
ally near field capacity), and biological assessments
benefit from the more uniform conditions following
Data interpretation and scoring curves
Effective use of soil health test results requires the
development of an interpretive framework for the
measured data. The general approach of Andrews et
al. (2004) was applied for this purpose, and scoring
functions were developed for all soil indicators
(except texture) to rate test results. Different scoring
functions were developed for the three main textural
classes, sand, silt, and clay, hence the necessity to
determine soil texture during the testing procedure
(which is done by the rapid and inexpensive feel
method; Brady and Weil 2002).
The scoring functions were defined in the simple
linear-plateau framework, as no justification existed
for curvilinear functions. Three types of scoring
functions were considered (Fig. 2), more is better,
less is better, and optimum range(Karlen and
Stott 1994). The scoring curves for aggregate stability
(Fig. 3) are examples of the more is better
relationship. A low score of 1 is assigned to results
of less than 15, 20 and 30% for sand, silt and clay
Table 2 Soil quality indicators included in the Cornell Soil
Health Test, and associated processes
Soil Indicator Soil Functional process
Soil texture and stone content All
Aggregate stability Aeration, infiltration,
shallow rooting, crusting
Available water capacity Plant-available water
Soil strength (penetrometer) Rooting
Organic matter content Energy/C storage, water and
nutrient retention
Active carbon content Organic material to support
biological functions
Potentially mineralizable
Ability to supply nitrogen
Root health rating Soil-borne pest pressure
pH Toxicity, nutrient availability
Extractable phosphorus Phosphorus availability,
environmental loss potential
Extractable potassium Potassium availability
Minor element contents Micronutrient availability,
elemental imbalances,
Plant Soil (2008) 307:243253 247
soils, respectively. Respective aggregate stability
values of greater than 40, 45 and 60% are scored as
10, and intermediate values are linearly interpolated.
The critical cutoff values for the highest and lowest
curves were developed based on the frequency
distribution of data generated from the indicators
selection process (Fig. 4). The 25th and 75th
percentile of the distribution curve were generally
taken as the extreme values for the linear model
where scores increase from 1 to 10. i.e., test results
with values below the 25th percentile were given
scores of 1, and above the 75th percentile were given
scores of 10. This approach was evaluated relative to
literature reports and in some cases minor modifica-
tions were made. Scoring curves for all indicators are
presented in Gugino et al. (2007).
Soil health test report
The soil health test report was designed for farmer and
consultant audiences, and facilitates both integrative
assessment and targeted identification of soil constraints.
This is accomplished through the combined use of
quantitative results and color coding (Fig. 5). The
physical, biological and chemical indicators are grouped
by blue, green, and yellow colors, respectively. For each
indicator, the measured value is reported as well as the
associated score (using a scoring function). The latter is
interpreted with colors in that scores of less than three
receive a red code, scores greater than 8 a green code,
and those in between are coded yellow. This provides
for an intuitive overview of the test report. If results are
coded red, the associated soil constraints are addition-
ally listed (Fig. 5). Finally, the percentile rating is
shown for each indicator, based on the samples ranking
in the database of accumulated soil indicator measure-
ments (Fig. 5). An overall soil health score is provided
at the bottom of the report, which is standardized on a
scale from 0 to 100 (Fig. 5). It is noted that the
interpretation of the test results are generalized for most
non-irrigated field crop, vegetable and fruit crop
production systems, but may require alternative inter-
Optimum More is better Less is better
Fig. 2 Models of scoring
curves used for the inter-
pretation of measured val-
ues of soil quality indicators
10.0 20.0 30.0 40.0 50.0 60.0 70.0
Aggregate Stability (%)
Indicator Score
Fig. 3 Scoring curves used
for interpretation of aggre-
gate stability data for sand,
silt, and clay soils
248 Plant Soil (2008) 307:243253
pretation in some cases. Hence, we recommend that the
reports are interpreted by professional consultants and
include consideration of farm and crop-specific infor-
mation. Once confidence and understanding is gained, it
is anticipated that growers will be able to interpret the
reports with less reliance on assistance.
Soil management recommendations were develop-
ed to address specific soil management constraints.
Both short-term and long-terms approaches were
identified, but their discussion is beyond the scope of
this paper. A training manual (Gugino et al. 2007) was
developed which discusses soil health concepts, the
basic approaches to soil health assessment (including
sampling methods, and field and laboratory assess-
ment protocols), the report and interpretation of the
results, and the suggested management approaches.
The manual can be accessed and downloaded from
the Cornell Soil Health web site at http://soilhealth.
Use of reflectance spectroscopy for rapid assessment
Although the cost of the Cornell Soil Health Test is
modest ($45 in 2007), a more inexpensive assessment
approach is desirable. We performed preliminary
evaluations of visible-near infrared reflectance spec-
troscopy (VNIRRS) for more rapid and less expensive
assessment of the soil quality indicators.
Potential of methodology
In VNIRRS, soil reflectance characteristics are deter-
mined over the entire visible (350700 nm) and near
infrared (7002,500 nm) region with the use of a
spectroradiometer. In these wavelength regions over-
tones of unique absorption features can be measured due
to stretching and bending vibrations in molecular bonds
such as CC, CH, NHandOH (Dalal and Henry
1986). Hyperspectral soil sensing yields large data sets
that can be used to find correlations with measured soil
properties. More than thirty soil variables were
predicted simultaneously with varying levels of success
by Chang et al. (2001), and they reported successful
predictions (r
>0.80) for total organic carbon and
nitrogen, gravimetric soil moisture content, 1.5 Mpa
soil water at wilting point, exchangeable calcium,
CEC, and silt and sand content. Brown et al. (2005)
used over 4,100 surface and subsurface soils across the
USA, Africa and Asia to evaluate the accuracy of
VNIR empirical models for global soil characterization
0 300 600 900 1200
25th Percentile 75th Percentile
Active Carbon
Fig. 4 Probability distribu-
tion approach to the devel-
opment of scoring functions
Plant Soil (2008) 307:243253 249
and reported strong predictability for kaolinite content,
montmorillonite content, clay content, CEC, SOC,
inorganic C, and extractable Fe. The prediction of soil
constituents that do not absorb within the VNIR range
is often possible through their correlations with
spectrally active constituents (Ben-Dor and Banin
1995), but this can create a false sense of predictability
and should not be extrapolated beyond the data set.
Three hundred eighty-seven soil samples from a wide
range of soils and management practices in New York
State were scanned using a FieldSpec Pro hyper-
spectral sensor (Analytical Spectral Devices, Inc.,
Boulder, Colorado) and the absolute reflectance of
samples was recorded from 350 to 2,500 nm at 1-nm
resolution, yielding a total 2,150 data points per
spectrum. Air dried soil samples were put into
0.04 m-diameter optical quality Petri dishes and
illuminated with a tungsten quartz halogen lamp.
Reflectance was recorded through the glass bottom of
each dish with a constant angle and 0.04 m distance
from the light source. Five consecutive readings were
averaged, then the sample was rotated 90° and five
additional readings were collected to avoid possible
Fig. 5 Example of Cornell
Soil Health Test report
250 Plant Soil (2008) 307:243253
spectral differences originating from particle size
variations within soil samples. The unit was regularly
calibrated using standards (Spectralon®, soil and
Reflectance data were translated from binary to
ASCII using ViewspecPro software (Analytical Spec-
tral Devices, Inc., Boulder, CO, 80301) and readings
were averaged. Five types of spectral data were used
in the analysis: (1) raw reflectance (untransformed),
(2) first-derivative transformation using the Savitsky-
Golay (1964) transformation procedure, (3) moving
averages of five reflectance observations (4) moving
averages of 11 reflectance observations, and (5)
absorbance transformation (1/reflectance) using Un-
scrambler v 8.05 software (CAMO Software Inc., The
Unscrambler version 8.05., 1480 Route 9 North, Suite
209 Woodbridge, NJ, USA).
Calibrations between VNIRRS data and soil
parameters were performed using partial least square
regression (PLS), as it is often applied to spectral
data. Unlike multiple linear regression, it can handle
data with strong co-linearity in independent variables,
which can be more numerous than the observations.
PLS regression was also performed using Unscram-
Prediction ability was evaluated using the corre-
lation coefficients (r) for the calibration and valida-
tion procedures. The data set for each indicator was
separated into two third and one third of the data,
with the former used for calibration and the latter for
validation. The independent validation approach
generally provides a more realistic estimate of the
predictability of the regression procedure, but also
results in lower correlation. The root mean square
integrated assessment of precision and bias, and is
defined as:
predicted variable
measured variable
nnumber of observations
Tabl e 3 Comparison of the prediction accuracy of five
VNIRRS data processing methods for the assessment of three
soil quality indicators
Transformation Calibration rPrediction rRMSEP
Active carbon
Raw reflectance 0.937 0.909 97.13
Moving average (11) 0.913 0.895 98.97
Moving average (5) 0.906 0.887 102.37
Absorbance 0.876 0.834 122.52
First derivative
0.898 0.795 136.21
Aggregate stability
Raw reflectance 0.818 0.724 12.23
Moving average (11) 0.790 0.715 12.38
Moving average (5) 0.771 0.702 12.56
Absorbance 0.704 0.650 13.38
First derivative
0.676 0.558 14.77
Extractable phosphorus
Raw reflectance 0.742 0.634 12.94
Moving average (11) 0.733 0.633 12.07
Moving average (5) 0.703 0.607 12.34
Absorbance 0.729 0.561 13.06
First derivative
0.528 0.390 14.36
Table 4 Independent validation results (rvalues) of VNIRRS
assessment of physical, biological and chemical soil quality
indicators using raw reflectance data
Soil property Validation r
Aggregate stability 0.72
Available water capacity 0.69
Surface hardness 0.46
Subsurface hardness 0.48
Bulk density 0.72
Organic matter 0.89
Active carbon 0.91
Potentially mineralizable nitrogen 0.48
Root health rating 0.75
pH 0.84
Exchange acidity 0.87
Extractable phosphorus 0.63
Extractable potassium 0.77
Extractable iron 0.68
Extractable zinc 0.52
Plant Soil (2008) 307:243253 251
VNIRRS results
Three examples of measured indicators (active C,
aggregate stability and extractable P) are listed in
Table 3. The use of raw reflectance data generally
provided the best validated prediction accuracy for the
case of active carbon, aggregate stability, and extract-
able phosphorus (Table 3). First-derivative processing
apparently is not needed, presumably because a
consistent light source was used. The use of moving
averages, a method often promoted to reduce data
noise, did not improve predictability either.
Preliminary results indicate that some soil indicators
are well predicted, while others are not (Table 4).
Organic matter and active carbon showed high
predictability (r=0.89), which can be expected based
on the fact that VNIRRS directly assesses many of the
molecular bonds that make up soil organic matter.
Some soil physical (hardness) and biological properties
(potentially mineralizable N) had poor prediction
results, possibly due to limited involvement or effects
of the molecular bonds. Several other properties
showed reasonable predictability, but in some cases
presumably due to correlation with better predicted
indicators such as organic matter content. It is notable
that exchangeable acidity is also well predicted, which
may be expected based on its relation to organic matter
content and mineralogy. The use of VNIRRS requires
further exploration, using different data mining meth-
ods, and possibly separation by textural class.
Soil health management requires an integrative
approach that recognizes the physical, biological and
chemical processes in soils. The development of an
integrated soil health test was seen as a research
priority to allow farmers to make better management
decisions, especially those other than basic fertilizer
management. From a total of 39 potential indicators, a
set of indicators were selected to provide an integra-
tive assessment of soil health, which is now being
offered on a for-fee basis. It is anticipated that some
through VNIRRS, but it is unlikely that this method-
ology will completely replace laboratory and field
Acknowledgements We acknowledge support from the USDA
Northeast Sustainable Agriculture Research and Education Pro-
gram (USDA 2003-3860-12985), the Northern New York
Agricultural Development Program, USDA-Hatch funds, and the
Computational Agriculture Initiative at Cornell University.
Abawi GS, Widmer TL (2000) Impact of soil health manage-
ment practices on soilborne pathogens, nematodes and
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... Once the most suitable soil properties are selected, it is necessary to mathematically express their contribution to a composite soil health index, using normalized values. The normalization process is conducted by means of scoring functions (Idowu et al. 2008) that allow one to convert the measured soil property data values (for example, surface penetration resistance [kPa]) into a dimensionless range-usually between 0 and 100, or 0-10 ( Lilburne et al. 2004). The conversion function is chosen based on an expert's interpretation of the effect of the soil property on soil health. ...
... Color coding for each indicator or an aggregated color coding for the three soil traits has been found to be useful in incorporated interpretation of soil health index scores. For example, in the work of Idowu et al. (2008), a three-color code of the Cornell Soil Health Index (CSHI) was used to represent physical, biological, and chemical indicators. This was done in addition to the indicators scores that were provided on a percentile rating, so that the distinction between three levels of scores (high, medium, and low) will be clear. ...
... .1 A list of possible soil properties that can be used as soil health indicators. Assembled from previous studies of soil health indicators in different parts of the world:Idowu et al. (2008),Svoray et al. (2015a),Bünemann et al. (2018),Rinot et al. (2019),Lehmann et al. (2020) ...
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This chapter discusses the formalization of spatial information on soil health, and its implications for the quality of the remaining soil in the wake of erosion processes. This chapter extends the notion of water erosion damage from soil budgets, to comprehensive soil health and the provision of ecosystem services. It describes spatial autocorrelation of soil properties in the Harod catchment, by various methods—Moran’s I, Nugget: Sill ratio, and variogram envelope analysis. These are demonstrated using geoinformatics procedures, to show how GIS layers for the stratified random approach are produced. Spatial interpolation techniques—such as Ordinary Kriging, Universal Kriging and Cokriging—are discussed as tools for predicting spatial variation in soil health. The limited ability to scale up soil health mapping from point measurements to large agricultural areas is a major gap in soil research and is also discussed in this chapter. In this regard, this chapter is of major importance scientifically speaking, as it offers a methodology for studying the effect of water erosion on remaining soil in a spatially explicit fashion. From an applied standpoint, it provides farmers and professionals with a tool for estimating the state and dynamics of their field.
... However currently, few soil health assessment tools have been evaluated in perennial crops in the irrigated region of Western North America [22]. Assessment tools have generally been tested for annual crops and in the rainfed Mid-west, Northeast and Mid-Atlantic [5,17,[23][24][25][26][27] with only a few tools assessed in irrigated annual crops [28]. To our knowledge the only soil health assessment tool for perennial crops in the irrigated Western United States is from Glover, Reganold [29] adapted from the approach of Karlen and Stott [1]. ...
... Soil health indices which focus on environmental processes and response to management [10,17] or median values from regional datasets [18,19,21] also include soil carbon, mineralizable N, BD, and fertility indicators. However, with the exception of Idowu [27] root health indicators were not measured in most of these studies consequently ignoring the potential importance of root disease pressure from soils [4,7,10,18,28,33]. To our knowledge the only other soil health assessment tool for perennial crops in the irrigated west is from Glover, Reganold, and Andrews [29]. ...
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Soil health assessment can be a critical soil testing tool that includes biological and physical indicators of soil function related to crop and environmental health. Soil health indicator minimum data sets should be regional and management goal specific. The objective of this study was to initiate the steps to develop a soil assessment tool for irrigated orchard soils in Central Washington, United States including defining objectives, gathering baseline data and selecting target indicators. This study measured twenty-one biological, physical and chemical properties of soils in irrigated Central Washington apple orchards including indicators of water availability, root health, fertility, and biological activity. Soil factors were related to fruit yield and quality. Principal components and nonlinear Bayesian modeling were used to explore the relationship between soil health indicators and yield. Soil indicators measurements in Washington state orchards varied widely but generally had lower organic matter, available water capacity, wet aggregate stability and higher percent sand than in other regions. Linear mixed effects models for available water capacity and percent sand showed significant effects on yield, and models for root health ratings and Pratylenchus nematodes had moderate effects. The minimum dataset of soil health indicators for Central Washington orchards should include measurements of water availability (available water capacity, percent sand) and of root health (bean root health rating, Pratylenchus nematodes) in addition to standard fertility indicators to meet stakeholder management goals.
... The equations of the score curves were used to calculate the scores of soil indicators. The critical values of the scoring curves were as specified by Shi and Song [24] (Table 2) and the 25th and 75th percentiles, in accordance with following formula [25]. ...
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Tillage management is a direct factor in affecting soil quality, which is a key factor in sustainable agriculture. However soil quality evaluation needs significant manpower, material resources and time. To explore the sensitive indicators of soil quality affected by tillage management, eight soil physical and chemical properties under three tillage managements, including plow tillage, subsoiling tillage and rotary tillage, were determined under a long-term experiment in North China Plain. The results showed that subsoiling tillage management had the highest soil organic carbon and total nitrogen in the 0–20 cm layer and the lowest soil bulk density in the 30–40 cm layer. Rotary tillage management had the highest soil water content in the 0–40 cm layer. Meanwhile, compared to 2002, the soil organic carbon, total nitrogen and soil bulk density had varied greatly in 2012, but there was no significant difference between 2012 and 2018. However, other property concentrations tended to increase in 2002, 2012 and 2018. In addition, there was a significant linear relationship between soil quality index and grain yield. Subsoiling tillage management had the highest soil quality index and gain yield both in 2012 and 2018. The soil quality can be evaluated through the sensitive indicator of soil organic carbon, total nitrogen, soil bulk density, total phosphorus and soil water content, which provides a scientific basis for selecting reasonable tillage management and evaluating soil quality in this agricultural production area or other similar areas.
... Soil quality is the functioning capability of soil within any ecosystem including land use patterns for sustaining productivity, maintaining conservational superiority, and developing floral and faunal health (Idowu et al., 2008;Bünemann et al., 2018). The relations between different biotic and abiotic components with soil fertility determined the potentiality of the production of healthful crops (Gong et al., 2015). ...
Bangladesh is an agriculture based economic country formed by sediment deposition from upstream rivers. This riparian country covered with fertile soil that supports agricultural diversification. The study aimed to compare current soil quality of Jamuna-dhaleshwari (Manikganj) and Surma-kushiyara (Sylhet) floodplain physiographic regions to forecast about agricultural productivity. Soil quality was assessed through physical (soil texture and moisture), and chemical (pH, electrical conductivity (EC), salinity, soil nutrients (N, P, K), and organic matter content) indicators. A total of 36 soil samples in three different depths (0-15cm, 15-50cm, 50-100cm) from 12 sites were collected from Manikganj and Sylhet Districts. The average particle size and moisture content ratios of Manikganj: Sylhet were gravels (7.88:5.8), very coarse sand (6.85:8.53), coarse sand (7.45:13.2), medium sand (7.35:14), fine sand (6.12:16.4), very fine sand (24.3:19.9), silt (39.56:20.57), and clay (29.3:32.81), followed by, pH (7.61:6.31), and EC (0.24:0.18), respectively. The result revealed that the soil was bit alkaline for Manikganj, compared to range from alkaline to acidic in Sylhet and non-saline for both areas that was suitable for agriculture. The average concentration of nitrogen (N), phosphorous (P), potassium (K) nutrients in Manikganj and Sylhet Districts were 0.14%, 3.73 meq/100g, 0.07 µg/g; 0.16%, 3.11 meq/100g, 0.08 µg/g and organic matter were 3.65% and 4.7%, respectively. The results of nutrients in both areas indicated that nutrients were very poor but soil organic matter content was sufficient for agricultural activities. The study concluded that soil texture, pH, salinity and organic matter content in both areas were suitable for agricultural purposes, but a significant declined was found in soil moisture and nutrients quality. Finally, it was recommended that soils of Manikganj were more sustainable for agricultural activities.
... Such an integrative approach may not enable the direct identification of properties or assemblages responsible for the soil quality state, but it can provide a direct assessment of this soil quality, i.e., the soil functioning resultants, which is a strong requisite according to Karlen et al., (1997). Some studies noticed the relevance of this integrative approach (Idowu et al., 2008;Lima et al., 2013;Schimann et al., 2012;Vogel et al., 2018), but reductionist approach still largely prevails in the literature. ...
... Thus, appropriate soil quality evaluation approaches are necessary to monitor and mend the overall soil capacity to support ecosystem services (Doran and Parkin, 1994), especially for soils in dryland regions. Soil quality and health assessment protocols, such as the comprehensive assessment of soil health (CASH; Idowu et al., 2008;Moebius-Clune et al., 2016), focus more on agriculture applications. Alternatively, the Soil Management Assessment Framework (SMAF; Andrews et al., 2002;Viscarra Rossel et al., 2006;Wienhold et al., 2009) is a method that allows flexibility in terms of selecting ad-hoc soil indicators under varying environmental conditions. ...
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Global population growth has resulted in land-use (LU) changes in many natural ecosystems, causing deterioration in the environmental conditions that affect soil quality. The effect of LU on soil quality is acute in water-limited systems that are characterized by insufficient availability of soil organic resources. Thus, the main objective of this study was to assess the effects of human activities (i.e., land-uses as grazing, modern agriculture, and runoff harvesting systems) on soil quality using imaging spectroscopy (IS) in the arid regions of Israel. For this, 12 physical, biological, and chemical soil properties were selected and further integrated into the soil quality index (SQI) as a method to assess the significant effects of LU changes in an arid area in southern Israel. A flight campaign of the AisaFENIX hyperspectral airborne sensor was used to develop an IS prediction model for the SQI on a regional scale. The spectral signatures, extracted from the hyperspectral image itself, were well separable among the four LUs using the partial least squares-discriminant analysis (PLS-DA) classification method (OA = 95.31%, Kc = 0.90). The correlation was performed using multivariate support vector machine-regression (SVM-R) models between the spectral data and the measured soil indicators and the overall SQI. The SVM-R models were significantly correlated for several soil properties, including the overall SQI (R 2 adj Val = 0.87), with the successful prediction of the regional SQI mapping (R 2 adj Pred = 0.78). Seven individual soil properties, including fractional sand and clay, SOM, pH, EC, SAR, and P, were successfully used for developing prediction maps. Applying IS, and statistically integrative methods for comprehensive soil quality assessments enhances the prediction accuracy for monitoring soil health and evaluating degradation processes in arid environments. This study establishes a precise tool for sustainable and efficient land management and could be an example for future potential IS earth-observing space missions for soil quality assessment studies and applications.
... The knowledge about the soil quality parameters may also be useful in assessing the capacity of the soil to support tree development and regeneration, promote nutrient rotation, and organic matter sustenance (Schoenholtz 2000). The pH, accessible nitrogen, phosphate and potassium (N, P, K), are the most significant chemical variables to be evaluated (Idowu 2008). In our study nitrogen, phosphate and conductivity were the most significant influencing parameters for plant development as per CCA. ...
Mining activities cause substantial decimation of environment and biological diversity. Plant populations are disrupted by the operation of coal mining. The present study deals with the ecological assessment of tree species and physicochemical properties of soil in the Dhandardihi coal mining area of Raniganj Coal Field (RCF), West Bengal, India. For this purpose, three coal mine generated wasteland were randomly selected from the Dhandardihi coal mining area. A sum of 23 different tree species belonging to 22 genera and 16 different families were recorded. Most of the tree species showed rare or lower frequency class. Shannon diversity index was varied from 2.224 to 2.572. The findings of the Canonical Correspondence Analysis (CCA) indicated that the available nitrogen and phosphate content of the soil were the factor which heavily impacted the composition of tree species in the study area. The study indicated that Streblus asper and Senna siamea can be recommended for effective eco-restoration of the concerned sites due to cosmopolitan distribution, high regeneration potential with stable population structure. The findings could be used as an impactful means for predicting the suitability of specific species to revegetate mined regions. Even further, the findings suggest adverse effects of the mining operations on the area’s vegetation. Those actions really need to be monitored regularly along with suitable policies should be framed in terms of eco-restoration of mined wastelands based on indigenous vegetation.
... It can be considered as an auxiliary indicator of the presence or absence of land use restrictions resulting from the basic characteristics of the topsoil. The index, as an aggregate indicator of the state of several topsoil properties, can support the objective assessment of the root level of plants (Idowu et al. 2008;Mukherjee and Lal, 2014). In the form presented here it takes into account 6 soil features characterized by stability over time and recognized importance in shaping the cultivation conditions conducive to crop yield. ...
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Soil Vis-NIR spectral response had been widely proposed as an alternative to costly and time-consuming laboratory determination of soil physical and chemical properties. However its use for measuring soil quality index directly has not been well explored. This study compares the effectiveness of different machine learning models on a large spectral library using a database collected by the European Union project “Land Use and Coverage Area frame Survey” (LUCAS). Three approaches to predicting mineral soil features by processing their spectral response for the Vis-NIR range were tested. Prediction models of clay content, pH in CaCl2, organic carbon (SOC), calcium carbonate (CaCO3), nitrogen (N), and cation exchange capacity (CEC) were analyzed. Three types of models were assessed: a Stacked AutoEncoder, a convolutional neural network, and a stack model composed of a set of multilayer perceptron algorithms with two different regression estimation solutions. Modeling with CNN was identified as the optimal solution. Similar, and in some cases, better results can be obtained from ensembles of machine learning algorithms. The estimates of soil characteristics made with the help of the Stacked AutoEncoder showed the greatest errors. The use of soil feature estimates to support soil and land classification was also analyzed. An indicator describing the state of the topsoil is presented, which assists the objective classification of soils. The research showed that the accuracy of the estimation of the proposed Topsoil Quality Index (TQI) estimated directly based on Vis-NIR spectral response and indirectly based on estimated values of selected soil features is practically identical. The research confirms the suitability of Vis-NIR spectroscopy for topsoil assessment.
The improvement of soil quality in agro-ecosystems is one of the major objectives of conservation agriculture (CA) strategies. The objective of this study was to evaluate, quantify and compare the effects of two tillage practices, four crop rotation sequences, two residue management systems and their interactions on the soil quality of a Haplic Plinthosol in South Africa using the Soil Management Assessment Framework (SMAF). The evaluation was done on a CA field trial established in 2012 in the Eastern Cape Province of South Africa. The trial was laid out in a split-split-plot design with tillage: conventional tillage (CT) and no-till (NT) as main plot treatments. Sub-treatments were crop rotations: maize-fallow-maize (MFM); maize-fallow-soybean (MFS); maize-wheat-maize (MWM); maize-wheat-soybean (MWS). Residue management treatments: removal (R-) and retention (R+) were in the sub-sub plots. Soils from the CA trial were sampled at 0 – 5 and 5 – 10 cm depths after five cropping seasons (2012–2015). Thirteen soil quality indicators were determined to assess soil quality. The SMAF soil quality index (SMAF-SQI) was used as an indicator of overall soil quality. The study results demonstrated the dominance of tillage practices in significantly affecting soil biological, chemical and physical properties in the short term than crop rotation sequences and residue management systems. The soil biological indicators viz. SOC, MBC and BG activity were more sensitive to CA strategies, which confirmed their effectiveness as tools for soil quality assessments in the short-term. The study also revealed the short-term significant effects of tillage on the overall SMAF-SQI while crop rotation and residue management had no significant effects. Overall soil quality assessment using the SMAF technique provided a sound basis for distinguishing the short-term impacts of CA strategies on the function of the Haplic Plinthosol in Eastern Cape, South Africa.
Soil health assessment tools are needed to quantify effectiveness of various agricultural practices toward meeting sustainable development goals. Although several soil health tools have been developed and tested through global soil management research, ease of use and site-specific accuracy for farmers and agronomists needs to be optimized. This comprehensive review examines the theories, compares approaches, and examines applications of five soil health assessment methods, and then compares their advantages, disadvantages, application limitations, and feasibility before suggesting potential improvements at various scales. The two predominant soil health assessment tools [Soil Management Assessment Framework (SMAF) and Cornell's Comprehensive Assessment of Soil Health (CASH)] were coupled with six classical mathematical models [Principal Component Analysis, Analytic Hierarchy Process, Iterative Algorithm, Entropy weight method, Euclidean distance and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS)] to create 11 approaches that were compared using field-based soil health indicator measurements. The data were collected from field experiments with cover crops and soil amendments in Mississippi, USA. The Standard Scoring Functions (SSF) associated with the SMAF and the CASH tools were evaluated. Our results, reflecting different data normalization and weighting, created 14 different soil health scores that showed significant differences based on method. Among the three data normalization methods (CASH, SSF, and entropy weighting), soil health scores using SSF were relatively high, while those using entropy weighting were much lower. The latter method, TOPSIS, had the advantage of being able to maximize differences among treatments and thus can help select an optimal management solution. Scores obtained through SSF, SSF + PCA and SSF + AHP had some of the best correlations) with corn (Zea mays L.) and soybean [Glycine max (Merr.) L.] yields, indicating the SSF parameters selected for our study were applicable. CASH provided similar results with a more simplistic approach. Other methods generated soil health scores with poorer fits when compared to the yield data. Overall, we conclude all 11 methods and 14 soil health scores can be useful for soil health evaluation in the study area. The results re-emphasized that soil health assessment is useful for soil researchers, farmers, and any other stakeholder group wanting to determine if specific agricultural practices contribute to sustainable development.
In recent years, viticulture has seen phenomenal growth, particularly in such countries as Australia, New Zealand, the United States, Chile, and South Africa. The surge in production of quality wines in these countries has been built largely on the practice of good enology and investment in high technology in the winery, enabling vintners to produce consistently good, even fine wines. Yet less attention has been paid to the influence of vineyard conditions on wines and their distinctiveness-an influence that is embodied in the French concept of terroir. An essential component of terroir is soil and the interaction between it, local climate, vineyard practices, and grape variety on the quality of grapes and distinctiveness of their flavor. This book considers that component, providing basic information on soil properties and behavior in the context of site selection for new vineyards and on the demands placed on soils for grape growth and production of wines. Soils for Fine Wines will be of interest to professors and upper-level students in enology, viticulture, soils and agronomy as well as wine enthusiasts and professionals in the wine industry.
This report focuses on indicators of soil quality, as related to soil erosion by water. Potential soil quality indicators are identified and individual scores are used to compute an overall soil quality rating based on several physical and chemical indicators. The framework and procedure are demonstrated using data collected from an alternative and conventional farm in central Iowa. The procedure for developing the framework is based on the multi-objective analysis principles of systems engineering. It is concluded that this procedure could be adopted for quantitatively evaluating soil quality for several scales of investigation. -from Authors