Applicability of calibrated diffuse reflectance spectroscopy models across spatial and temporal boundaries

Purushothaman, N K and Garg, K K and Venkataradha, A and Anantha, K H and Singh, R and Jat, M L and Das, B S (2024) Applicability of calibrated diffuse reflectance spectroscopy models across spatial and temporal boundaries. Geoderma, 449. pp. 1-21. ISSN 0016-7061

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Abstract

Diffuse reflectance spectroscopy (DRS) is an emerging soil testing approach. Although several studies have validated the DRS approach, limited efforts are made to assess the applicability of calibrated DRS models on new samples collected at different locations and/or time. To test such spatio-temporal applicability of calibrated DRS models, we collected surface soil samples from 1,112 smallholder farms during 2018 (T2018) and 607 farms during 2021 (T2021) covering seven districts of the Bundelkhand region of central India. The T2018 samples covered 7 development blocks; the T2021 samples were also collected from these blocks but from different sampling locations. Additionally, a new sampling site (Jhansi-Bamour block) was added during 2021 to create an independent test dataset. Collected samples were analysed for 17 soil parameters (basic soil properties, macronutrients, and micronutrients) and spectral reflectance over the visible to near-infrared region. Corresponding soil test crop response (STCR) ratings were also estimated. The Cubist model was calibrated in the T2018 dataset and tested against the T2021 dataset using the coefficient of determination (R2), root-mean-squared error (RMSE), and percentage relative error deviation (PRED) at 30% error threshold as performance statistics. Model applicability was assessed at each block level (site-specific), by dividing the study site into their two geology-specific regions, and by treating the entire dataset as a regional-scale spectral library. Results showed that DRS models calibrated on a finer scale (site-specific) are less efficient in estimating soil parameters in broader scale (geology-specific and regional-scale) test T2021 samples although their STCR ratings may safely be estimated at local scales. When site-specific data were aggregated to broader scales and T2018 dataset was spiked with 20% samples from the T2021 dataset, model performance improved for critical soil parameters such as soil organic carbon (SOC) contents and several plant nutrients and their ratings; application of such large-scale models also improved the estimation accuracy when applied to site-specific datasets. Exchangeable Ca and Mg, clay and SOC contents were frequently well-estimated with R2 values ranging from 0.54 to 0.93. Fine sand was the next best estimated soil property with R2 values in the range of 0.40–0.75. The STCR ratings estimated in the DRS approach matched the wet chemistry-based STCR ratings to the tune of 43 to 100%. Overall, as many as 60% of all new samples could be estimated with more than 70% accuracy for 8 out of 17 parameters. With the DRS approach tested on both spatially- and temporally-independent test datasets and, specifically, with high estimation accuracy of STCR ratings, our results suggest that the DRS approach may safely be used as a viable alternative to conventional soil testing in smallholder farms.

Item Type:	Article
Divisions:	Global Research Program - Resilient Farm and Food Systems
CRP:	UNSPECIFIED
Uncontrolled Keywords:	Chemometric models, Smallholder farms, Soil test crop response rating, Spiking, Localizing
Subjects:	Others > Smallholder Farmers Others > Soil
Depositing User:	Mr Nagaraju T
Date Deposited:	11 Sep 2024 10:53
Last Modified:	11 Sep 2024 10:53
URI:	http://oar.icrisat.org/id/eprint/12810
Official URL:	https://www.sciencedirect.com/science/article/pii/...
Projects:	UNSPECIFIED
Funders:	UNSPECIFIED
Acknowledgement:	We thankfully acknowledge the funding support received from Rastriya Krishi Vikas Yojana (RKVY) through Department of Agriculture, Government of Uttar Pradesh for undertaking Doubling Farmers’ Income (DFI) initiative in Uttar Pradesh Bundelkhand region, Central India. Support in spectral data collection by Dr. Israr Majeed is fully acknowledged. The first author acknowledges the Prime Minister’s Research Fellowship (PMRF), Round 10 (lateral entry) for his PhD research.
Links:	Google Scholar

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