Combined diffuse reflectance spectroscopy and digital soil mapping for soil assessment in smallholder farms

Purushothaman, N K and Garg, K K and Budama, N and Akuraju, V R and Anantha, K H and Singh, R and Jat, M L and Das, B S (2026) Combined diffuse reflectance spectroscopy and digital soil mapping for soil assessment in smallholder farms. Geoderma, 467. pp. 1-17. ISSN 0016-7061

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Abstract

Diffuse reflectance spectroscopy (DRS) and digital soil mapping (DSM) offer opportunities to rapidly assess soil in large areas. Specifically, the combined DRS-DSM modelling pipeline may be used to create soil test recommendations for every smallholder farm in a given region although comprehensive testing of such a pipeline is rarely attempted. With multi-year and multi-site soil spectral data from the smallholder farms of the Bundelkhand region, we evaluated the DRS-DSM pipeline for estimating soil properties and making nutrient recommendation for every smallholder farm both within and outside the DRS calibration zones. Specifically, we compared both measured and DRS-estimated soil properties as inputs in DSM approaches using 1112, 607, and 407 soil samples collected during 2018 (T2018: calibration zone), 2021 (T2021: within the calibration zone), and 2022 (T2022: outside the calibration zone), respectively, for estimating 17 soil parameters and their soil test crop response (STCR) ratings. For T2022 samples, DRS models calibrated within the calibration zone accurately predicted 7 out of 17 soil properties with Lin’s concordance correlation coefficients (LCCC) exceeding 0.6. Spiking these datasets with T2022 data further improved predictions to 10 properties and reduced errors by 3–29%. In T2021 dataset, both measured property- and DRS-based DSM approaches achieved comparable accuracy. Estimated STCR rating accuracies for the DRS-DSM pipeline exceeded 70% for 9 out of 13 properties suggesting that these two emerging technologies may be combined to make nutrient recommendations across smallholder farms within a given region.

Item Type:	Article
Divisions:	Global Research Program - Resilient Farm and Food Systems
CRP:	UNSPECIFIED
Uncontrolled Keywords:	Chemometric modelling, Cubist, Environmental covariates, Feature selection, Quantile regression forest
Subjects:	Others > Soil Others > Smallholder Agriculture Others > Soil Science
Depositing User:	Mr Nagaraju T
Date Deposited:	27 Feb 2026 06:26
Last Modified:	27 Feb 2026 06:26
URI:	http://oar.icrisat.org/id/eprint/13504
Official URL:	https://www.sciencedirect.com/science/article/pii/...
Projects:	UNSPECIFIED
Funders:	UNSPECIFIED
Acknowledgement:	The first author acknowledges the Prime Minister’s Research Fellowship (PMRF), Round 10 (lateral entry) for his PhD research. We thank the Government of Uttar Pradesh for providing financial assistance to implement a range of drought mitigation and productivity enhancement interventions in the Tahrauli cluster under RKVY (Rashtriya Krishi Vikas Yojana) during 2022–2025. We also acknowledge the CGIAR Mega Program (NEXUS Gains and Multifunctional Landscapes) for partially supporting the time of ICRISAT scientists.
Links:	Google Scholar

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