Abstract
The definition of the distance between sampling grid points directly impacts the development of fertility maps because it affects the spatial dependence of geostatistics and the estimates for locations not sampled in the interpolation. Based on geostatistical concepts, it is common to recommend one or more soil samples per hectare. However, there is a need to understand how a cost-effective grid-sampling scheme can be developed to produce accurate digital maps. This study aimed to assess how + 5% and + 10% additional random points in the original sample grids affect the development of soil fertility maps under Brazilian Cerrado conditions. Four agricultural areas located in different states of Brazil were analyzed by applying the various sampling techniques. In total, 625 points were sampled, and ten sub-samples within 5 m of the central point were collected. Additional sampling points (+ 5% and + 10%) were randomly placed in the four quadrants of the field boundary following the original grid scheme. The soil attributes evaluated were pH, cation exchange capacity, base saturation, and Ca, Mg, K, and P contents. Geostatistical variograms were modeled for each field, attribute, and statistical sampling treatment. The best variogram model was selected based on the minimal difference between the root mean square error and average standard error of the cross-validation procedure, along with an evaluation of the root mean square standardized error value. The maps were compared using the relative deviation coefficients. The inclusion of additional 5% and 10% sampling points in the original grid was found to be effective in generating soil fertility maps, resulting in improved root mean square and average standard error values.
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Data Availability
The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.
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Acknowledgements
The authors would like to thank the Universidade Federal de Mato Grosso do Sul (UFMS), Universidade do Estado do Mato Grosso (UNEMAT), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) – Grant numbers 303767/2020-0, 309250/2021-8 and 306022/2021-4, and Fundação de Apoio ao Desenvolvimento do Ensino, Ciência e Tecnologia do Estado de Mato Grosso do Sul (FUNDECT) TO numbers 88/2021, and 07/2022, and SIAFEM numbers 30478 and 31333. This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brazil (CAPES) – Financial Code 001.
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Baio, F.H.R., Alixame, D., Neves, D.C. et al. Adding random points to sampling grids to improve the quality of soil fertility maps. Precision Agric 24, 2081–2097 (2023). https://doi.org/10.1007/s11119-023-10031-x
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DOI: https://doi.org/10.1007/s11119-023-10031-x