Browsing by Author "Dash, Prava Kiran"

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    Exploring the effect of sampling density on spatial prediction with spatial interpolation of multiple soil nutrients at a regional scale
    (MDPI, 2024-10-01) Dash, Prava Kiran; Miller, Bradley A.; Panigrahi, Niranjan; Mishra, Antaryami
    Essential soil nutrients are dynamic in nature and require timely management in farmers’ fields. Accurate prediction of the spatial distribution of soil nutrients using a suitable sampling density is a prerequisite for improving the practical utility of spatial soil fertility maps. However, practical research is required to address the challenge of selecting an optimal sampling density that is both cost-effective and accurate for preparing digital soil nutrient maps across regional extents. This study examines the impact of sampling density on spatial prediction accuracy for a range of soil fertility parameters over a regional extent of 8303 km2 located in eastern India. Surface soil samples were collected from 1024 sample points. The performance of six levels of sampling densities for spatial prediction of 14 soil properties was compared using ordinary kriging. From the sample points, randomization was used to select 224 points for validation and the remaining 800 for calibration. Goodness-of-fit for the semi-variograms was evaluated by R2 of model fit. Lin’s concordance correlation coefficient (CCC) and root mean square error (RMSE) were evaluated through independent validation as spatial prediction accuracy parameters. Results show that the impact of sampling density on prediction accuracy was unique for each soil property. As a common trend, R2 of model fit and CCC scores improved, and RMSE values declined with the increasing sampling density for all soil properties. On the other hand, the rate of gain in the accuracy metrics with each increment in the sampling density gradually decreased and ultimately plateaued. This indicates that there exists a sampling density threshold beyond which the extra effort on additional sampling adds less to the spatial prediction accuracy. The findings of this study provide a valuable reference for optimizing soil nutrient mapping across regional extents.
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    Identifying opportunities to improve digital soil mapping in India: a systematic review
    (Elsevier, 2021-12-27) Dash, Prava Kiran; Panigrahi, Niranjan; Mishra, Antaryami
    Soaring food demand, population pressure, land degradation, small size of agricultural land holdings, and diversified soil types in India require advanced digital soil mapping (DSM) for sustainable land management. This paper systematically reviews the common trends of SCORPAN based DSM in India to identify the important research gaps and opportunities to improve in future. A systematic literature search from 2000 to October 2021 has yielded 35 numbers of peer reviewed articles, which have performed DSM in India following the SCORPAN approach. The increased number of published articles from 2017 onwards suggests that there is a growing interest for DSM in India over the past few years. However, only two articles have prepared digital soil maps at the national extent. Moreover, the local and regional extent DSM are being limited to only a few parts of the country. There still remains 50% of the states and Union Territories of the country where no DSM studies have been performed so far except the national and global level interventions. Among the target variables, soil carbon related attributes have been predicted most frequently, whereas soil classes have been rarely predicted. Environmental covariates representing organism (O) and relief (R) have been widely included for DSM, whereas the use of other covariates has been limited. Among different machine learning (ML) algorithms, regression kriging has been adopted most frequently followed by random forest and quantile regression forest. Most articles have adopted data splitting (76%) as the model and map evaluation approach, whereas independent validation has been limited to only 5% of the articles. Only 34% of the articles have presented the uncertainty maps. Major research gaps identified by this review include lack of standardized digital soil databases, poor sampling density, coarse resolution, limited use of environmental covariates, insufficient comparative studies among ML algorithms, inadequate independent validation, and undersupply of uncertainty maps. Key evidences from this review will be helpful for improving future DSM activities by scientists and practitioners involved with DSM in India and abroad.