Chapter 5: Farmer-Oriented Assessment of Soil Quality using Field, Laboratory, and VNIR Spectroscopy Methods

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Chapter 5: Farmer-Oriented Assessment of Soil Quality using Field, Laboratory,

and VNIR Spectroscopy Methods

, Abawi . S . G , Moebius . N . B , Gugino . K . B , Schindelbeck . R . R , Idowu . J . O , Ball . I . J , van Es . M . H

D.W. Wolfe, and J.E. Thies ed . cornell @ 1 hmv : email 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 therefore regard soil health

as the integration of the physical, biological and chemical aspects of soils. This paper describes a

process for the selection of soil quality/health indicators that are being offered as part of the new

Cornell Soil Health Test. Over 1500 samples were collected from controlled research and

experiments and commercial farms and analyzed for 41 potential soil quality indicators. Physical

and biological indicators were evaluated based on sensitivity to management, relevance to

functional soil processes, ease and cost of sampling, and cost of analysis, and four indicators were

selected for each. Eleven chemical indicators were selected as they constitute the standard soil

test. Soil health test reports were developed to allow for 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 tool

for rapid soil health assessment, presumably at lower cost. The methodology appears to show

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was developed for integrative assessment of the physical, biological, and chemical aspects of soils,

thereby facilitating better soil management.

Introduction

Soil Quality and Health

Soil quality 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 (NRCS, 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

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 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 easy 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 health” is 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

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introduction of inorganic fertilizers soil fertility management mostly involved a two-stage process

where organic nutrient sources were added to the soil, followed by mineralization through soil

biological processes, and inorganic nutrients were subsequently available to the crops. Magdoff

and van Es (2000) argue that with inorganic fertilizers the nutrition of plants has been short-cut and

the limited organic sources do not adequately sustain important biological processes and the

associated physical benefits (e.g., aggregation). A new emphasis on soil health through the

linkages between the chemical, biological and physical processes therefore provides a useful

framework to discuss soil management in the age of diverse cropping systems with tools such as

organic and inorganic fertilizers, reduced tillage, cover cropping, new rotations, etc.

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 the 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). 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 (Brejda et al., 2000a; Doran and Parkin, 1996; Larson and Pierce,

1991).

Sojka and Upchurch (1999) argued that the optimization of processes may require different

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 to the single function of crop

production. Additionally, we place emphasis on the valuable information that is acquired from

measurement of the soil quality indicators, rather than an interpretation within a narrowly-defined

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and consultants in that it identifies soil constraints and aids the selection of management solutions

(Idowu et al., 2007). The interpretation of the test results thus requires professional 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 interpretation 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; van Leeuwen et al., 2004). In this respect,

soil health testing must be similar to human health assessment where the results of tests (blood,

NRI, etc.) 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 development of a new three-faceted soil

health test for the following reasons:

• Education: Farm-specific soil quality information facilitates discussion.

• Targeting management practices: Identified soil constraints can be addressed, while no

investments are needed in unsubstantiated problems.

• Quantifying soil degradation or aggradation from management: Farmers, consultants, and

applied researchers can evaluate the soil quality benefits of new management practices (e.g.,

conversion to no-tillage). Governments can link green payments to soil quality improvements.

• Soil inventory assessment: Evaluation of the dynamic soil quality in addition to the traditional

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• Land valuation: Effective quantification of soil quality allows for better assessment of the

monetary 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

streamlining of methodologies (Fig. 2). Forty-one potential soil health indicators were evaluated

(Table 1). The suitability of the soil properties as quality indicators was evaluated through samples

from (i) long-term research experiments related to tillage, rotation and cover cropping studies that

allowed for assessment of soil response under controlled conditions, and (ii) commercial farms that

provided real-world perspective under the 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 1500 samples were included in the evaluation, although not all 41

properties were measured on all. For 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 66-mm height, 72 66-mm ID, 1.5 66-mm wall thickness). Disturbed

samples were collected from the 5 to 150 mm depth using trowels. All samples were stored at 2oC

until analysis.

A detailed description of the specific methodologies used for all 41 potential indicators is beyond

the scope of this paper. Analysis of the chemical indicators was based on the standard soil fertility

test offered by the Cornell Nutrient Analysis Laboratory. The physical tests were based on standard

methodology (Moebius, et al., 2007), 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., 2007). The biological test also mostly involved established methods. The decomposition

rate was based on loss of filter paper volume after 3-week soil incubation. The active Carbon test

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assessment involved a bioassay method where snap bean seed were planted in the sampled soil

material and root damage was rated based on root morphological features (Gugino et al., 2007).

Indicator selection

The 11 soil chemical indicators were automatically adopted in the comprehensive soil health test as

it involves a well-established set of procedures that are widely offered at reasonable cost. The

general criteria used for physical and biological indicator selection into the test included (as

described by Moebius et al., 2007):

Sensitivity to management, i.e., frequency of significant treatment effects in the controlled

experiments and directional consistency of these effects.

Precision of measurement method, i.e, CV values associated with residual errors.

Relevance to functional soil processes such as aeration, water infiltration/transmission, water

retention, root proliferation, nitrogen mineralization, development of root diseases, etc.

Ease and cost of sampling

Cost of analysis.

Qualitative ratings for sensitivity to sampling error and ability to represent four soil functional

processes were assigned using relationships established in the literature (Andrews et al., 2004;

Larson and Pierce, 1991; Luxmoore, 1981) and experience from this study. Quantitative data were

obtained from data analyses (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. Many soil biological indicators were rejected due to the high cost of analysis, often

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Selected Test Indicators

Fig. 3 shows the selected physical, biological and chemical indicators for the soil health test. The

test requires penetrometer data as the only measurements made in the field. The remaining

indicators are based on a composited disturbed sample, which we recommend are obtained from 2

locations nested within five sites in a field or section. Although it is widely regarded as essential, we

did not select bulk density as an indicator, because we found it imprecise and correlated with other

physical indicators (thereby redundant). Moreover, the use of ring samplers proved to be a serious

obstacle with practitioners, and additionally added to unreliable results.

The properties can be considered as indicators of different soil processes (Fig. 3). The soil

health test thereby evaluates the soil’s ability to accommodate most of the relevant processes

relevant to crop production and soil hydrology. Soil texture is an integrative property and is also

used to interpret test results. Root health assessment is an integrative biological measurement

related to overall pressure from soil-borne disease organisms. The minor elements of the chemical

analysis were grouped to prevent an overemphasis on chemical quality characteristics.

The Cornell Soil Health Test is being offered as a for-fee service starting in 2007 at a price

of US $45 per sample (including the standard chemical test), and at subsidized rates ($20) for New

York farmers. Samples will only be received during the early spring, prior to tillage (15 April to 1

June), because some indicators were shown to have significant within-season variability (Moebius

et al., 2007), and the tillage practice is a confounding influence for soil physical and biological

indicators. Also, sampling is then facilitated by soil water conditions near field capacity, and

biological assessments benefit from the more uniform conditions following overwintering.

Data Interpretation and Scoring Curves

Effective use of soil health test results requires the development of an interpretive framework for the

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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”).

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.

4), “more is better”, “less is better”, and “optimum”. The scoring curves for aggregate stability (Fig.

5) is an example 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 soils, respectively. Respective aggregate stability

values of greater than 40, 45 and 60% are scored as 10, and intermediate values are linearly

interpreted. 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. 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 less than the 25th percentile were

given scores of 1, and greater than the 75th percentile were given scores of 10. This approach was

evaluated relative to literature reports and in some cases minor modifications were made. Scoring

curves for all indicators are discussed in Gugino et al. (2007).

Soil Health Test Report

The soil health test report has been designed to facilitate integrative assessment on the one

hand, and targeted identification of soil constraints on the other. This is accomplished through the

combined use of quantitative results and color coding (Fig. 6). 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

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overview of the test report. If results are coded red, the associated soil constraints are additionally

listed (Fig. 6). Finally, the percentile rating is shown for each indicator, based on the sample’s

ranking in the data base. An overall soil health score is provided at the bottom of the report, which

is standardized to 100. It is noted that the interpretation of the test results are generalized for most

dryland field, vegetable and fruit crop production systems, but may require alternative interpretation

in some cases. Hence, we recommend that the reports are interpreted by professional consultants

and include consideration of farm and crop-specific information.

Soil management recommendations were developed 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 assessment protocols), the reporting 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.cals.cornell.edu.

Use of Reflectance Spectroscopy for Rapid Assessment

Although the cost of the Cornell Soil Health Test is quite reasonable, a more inexpensive

assessment approach is desirable. We evaluated the use of Visible- Near Infrared Reflectance

Spectroscopy (VNIRRS) for more rapid and less expensive assessment of the soil quality

indicators.

Potential of Methodology

In VNIRRS, soil reflectance characteristics are determined over the entire visible

(350-700nm) and near infrared (700-2500nm) region with the use of a spectroradiometer. In these

wavelength regions overtones of unique absorption features can be measured due to stretching and

bending vibrations in molecular bonds such as C-C, C-H, N-H and O-H (Dalal and Henry, 1986).

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measured soil properties. More than thirty soil variables were predicted simultaneously with

variable level of success by Chang et al.(2001), and they reported successful predictions (r2> 0.80)

for total organic carbon and nitrogen (g kg-1), gravimetric soil moisture content, 1.5 Mpa soil water at

wilting point (kg kg-1), exchangeable calcium, CEC (cmolc kg-1), silt and sand (%). Brown et

al.(2005) used over 4100 surface and subsurface soils across the United States, Africa and Asia to

evaluate the accuracy of VNIR empirical models for global soil characterization 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.

Methodology

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 hyperspectral sensor (Analytical

Spectral Devices, Inc., Boulder, Colorado) and the absolute reflectance of samples was recorded

from 350 to 2500 nm at 1-nm resolution, yielding a total 2150 data points per spectrum. Air dried

soil samples were put into optical quality petri dishes with 4 cm diameter and illuminated with a

tungsten quartz halogen lamp. Reflectance was recorded through the glass bottom of each dish

with a constant angle and 4 cm distance from the light source. Five consecutive readings were

averaged, then the sample was rotated 90o and five additional readings were collected to avoid

possible spectral differences originating from particle size variations within soil samples. The unit

was regularly calibrated using standards (Spectralon, Soil and Kaolinite).

Reflectance data were translated from binary to ASCII using ViewspecPro (Analytical

Spectral Devices, Inc., Boulder, CO, 80301) and readings were averaged. Five types of spectral

data were used in the analysis: (i) raw reflectance(untransformed), (ii) first-derivative

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and (iv) moving averages of 5 and 11 reflectance observations, and (v) absorbance transformation

(1/reflectance) using Unscrambler v 8.05 software (CAMO Process, Oslo, Norway).

Calibrations between VNIRRS data and soil parameters were performed using both Partial

Least Square regression (PLS) and Multivariate Adaptive Regression Splines (MARS) analysis (the

latter not reported here). PLS regression 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 Unscrambler software.

The measured indicators are listed in Table 2 and include a wider range of properties than

those eventually selected for the soil health test. The data sets for each indicator were separated

into two-third and one-third of the data. The former was used for calibration and the latter for

independent validation. The independent validation approach generally provides a more realistic

estimate of the predictability of the regression procedure, but also results in lower correlation

statistics.

VNIRRS results

The use of raw reflectance data generally provided the best validated prediction accuracy

(Table 2 for the case of active C). First-derivative processing apparently is not needed, presumably

because a consistent light source is used. The use of moving averages, 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 3). Organic matter and active C 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, presumably due to limited involvement or effects of the molecular

bonds. Several other properties showed reasonable predictability, but in some cases presumably

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exchangeable acidity is also well predicted, which may be expected based on its relation to organic

matter content and mineralogy.

Conclusion

Soil health management requires an integrated approach that recognizes the physical, biological

and chemical aspects and processes in soils. The development of an integrated soil health test was

seen as a research priority to allow farmers to make better management decision, especially those

other than basic fertilizer management. From a total of 41 potential indicators, a set of

measurements were selected to represent an integrative assessment of soil health, which is now

being offered on a for-fee basis. It is anticipated that some of the indicators may in the future be

assessed through VNIRRS, but it is unlikely that this methodology will completely replace laboratory

and field measurements.

Acknowledgements

We acknowledge support from the USDA Northeast Sustainable Agriculture Research and

Education Program (USDA 2003-3860-12985), the Northern New York Agricultural Development

Program, and USDA-Hatch funds .

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80 TABLES AND FIGURES

Table 1. Forty-one soil health indicators evaluated for the Cornell Soil Health Test.

Physical Indicators Biological Indicators Chemical Indicators

Bulk density Macro-porosity

Meso-porosity Micro-porosity

Available water capacity Residual porosity

Penetration resistance at 10 kPa Saturated hydraulic conductivity Dry aggregate size (<0.25 mm) Dry aggregate size (0.25 - 2 mm) Dry aggregate size (2 - 8 mm) Wet aggregate stability (0.25 -2 mm)

Wet aggregate stability (2 - 8 mm)

Surface hardness (penetrometer) Subsurface hardness

(penetrometer) Field infiltrability

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Table 2. Comparison of the five VNIRRS data processing methods for the assessment of active carbon.

Table 3. Independent validation results (r-values) of VNIRRS assessment of physical, biological and chemical soil quality indicators.

Physical Biological Chemical

Aggr Stab 0.72 Org Matter 0.89 pH 0.84

Avail Water Cap 0.69 Active C 0.91 Exch. Acidity 0.87

Surface Hardness 0.46 Pot Min N 0.48 P 0.63

Subsurf Hardness 0.48 Root Health 0.75 K 0.77

Bulk Density 0.72 Fe 0.68

Zn 0.52

Transformation Calibration R Prediction R RMSEP

Raw Reflectance 0.937688 0.909083 97.1329

Moving Average (11) 0.913118 0.895234 98.97735

Moving Average (5) 0.90656 0.887433 102.3762

Absorbance 0.876416 0.834978 122.5278

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Fig. 1: Conceptual view of soil health, connoting the integration of chemical, biological and physical processes.

Chemical

Physical

Biological

Chemical

Physical

Biological

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Fig. 2. Approach to the refinement and streamlining of soil quality indicators

POTENTIAL INDICATORS

(field and wet laboratory)

BEST INDICATORS

(field and laboratory)

BEST INDICATORS

(field, lab, and VNIR spectroscopy)

POTENTIAL INDICATORS

(field and wet laboratory)

BEST INDICATORS

(field and laboratory)

BEST INDICATORS

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Fig. 3. Soil quality indicators included in the Cornell Soil Health Test, and associated processes.

Fig. 4. Models of scoring curves used for the interpretation of measured values of soil quality indicators.

1 10

1 10

1 10

Optimum

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Fig. 5. Scoring curves used for interpretation of aggregate stability data for sand, silt, and clay soils.

1 2 3 4 5 6 7 8 9 10

10.0 20.0 30.0 40.0 50.0 60.0 70.0

Aggregate Stability (%)

In

d

ic

a

to

r

S

co

re

Sand Silt

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