Soil condition classification using infrared spectroscopy: A proposition for assessment of soil condition along a tropical forest-cropland chronosequence

• Published in: Geoderma Vol. 143; no. 1; pp. 73 - 84
• Main Authors: Awiti, Alex O., Walsh, Markus G., Shepherd, Keith D., Kinyamario, Jenesio
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 15.01.2008
• Subjects: Case definition; Chronosequence; Infrared spectroscopy; Probabilistic risk-based assessment; Soil condition class; Tropical rainforest; Africa, Sahara Desert; Africa; Infrared spectroscopy; Chronosequence; Soil condition class; Probabilistic risk-based assessment; Case definition; Tropical rainforest
• DOI: 10.1016/j.geoderma.2007.08.021
• ISSN: 0016-7061

Soil fertility depletion in smallholder agricultural systems in sub-Saharan Africa presents a formidable challenge both for food production and environmental sustainability. A critical constraint to managing soils in sub-Saharan Africa is poor targeting of soil management interventions. This is partly due to lack of diagnostic tools for screening soil condition that would lead to a robust and repeatable spatially explicit case definition of poor soil condition. The objectives of this study were to: (i) evaluate the ability of near infrared spectroscopy to detect changes in soil properties across a forest-cropland chronosequence; and (ii) develop a heuristic scheme for the application of infrared spectroscopy as a tool for case definition and diagnostic screening of soil condition for agricultural and environmental management. Soil reflectance was measured for 582 topsoil samples collected from forest-cropland chronosequence age classes namely; forest, recently converted, RC (17 years) and historically converted, HC (ca.70 years). 130 randomly selected samples were used to calibrate soil properties to soil reflectance using partial least-squares regression (PLSR). 64 randomly selected samples were withheld for validation. A proportional odds logistic model was applied to chronosequence age classes and 10 principal components of spectral reflectance to determine three soil condition classes namely; “good”, “average” and “poor” for 194 samples. Discriminant analysis was applied to classify the remaining 388 “unknown” samples into soil condition classes using the 194 samples as a training set. Validation r 2 values were: total C, 0.91; total N, 0.90; effective cation exchange capacity (ECEC), 0.90; exchangeable Ca, 0.85; clay content, 0.77; silt content, 0.77 exchangeable Mg, 0.76; soil pH, 0.72; and K, 0.64. A spectral based definition of “good”, “average” and “poor” soil condition classes provided a basis for an explicitly quantitative case definition of poor or degraded soils. Estimates of probabilities of membership of a sample in a spectral soil condition class presents an approach for probabilistic risk-based assessments of soil condition over large spatial scales. The study concludes that reflectance spectroscopy is rapid and offers the possibility for major efficiency and cost saving, permitting spectral case definition to define poor or degraded soils, leading to better targeting of management interventions.