Abstract
This study aimed to evaluate the use of multiple covariates in robust geostatistical modeling of soil chemical properties characterized by the presence of outliers. Different spatial prediction methods were compared using data from two agricultural areas located in Brazil´s Southeast: one with rotational grazing and one cultivated with sugarcane. Considering the variable-rate fertilizer prescription in the context of precision agriculture, the use of multiple covariates for the prediction of four chemical soil properties (phosphorus (P), potassium (K), cation exchange capacity (CEC) and base saturation (V)) was evaluated. The covariates data set was divided into five categories representing soil, vegetation, relief, management of the area and geographic. Five methods were used: inverse distance weighting (IDW), robust multiple linear regression (RMLR), robust ordinary kriging (ROK), robust universal kriging with spatial co-ordinates in the trend (RUKcoord) and robust universal kriging with environmental and management covariates in the trend (RUKcovars). The model based on the mean was used as a null reference. In general, the use of covariates in robust prediction methods improves the accuracy of spatial prediction of soil properties in the presence of outliers. However this effect was not observed in all situations, depending on the dataset characteristics and the spatial variability of the fields. The management practices are important information for modeling the trend in digital soil mapping for fertilizer prescription purposes. RMLR produces prediction results that are, at least, equivalent to that of robust geoestatistics.
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Availability of data and material
The Area 1 data is part of a project that involves other searches that are not yet complete and cannot be made available at this time.
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The code and the data used in analyses for the K property is available at: https://github.com/maiara-pusch/Chapter_1.
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Acknowledgements
This work is part of the PhD thesis of Maiara Pusch at State University of Campinas– Brazil. The authors would like to thank the owner, manager and staff of the Campina Farm (CV Nelore Mocho) for their support and assistance. The Agroindustrial Ipiranga S/A for the availability of Area 2, the employees for assisting in field work and Lucas Pedreschi Tittoto and the plant partner. Thank, Thiago Luis Brasco and Igor Queiroz Morais Valente for soil sampling in Area 1 and Area 2, respectively.
Funding
The Coordination for the Improvement of Higher Education Personnel – Brazil (CAPES) – Financing Code 001, for granting a Ph.D. scholarship to the first author. National Council for Scientific and Technological Development (CNPq) for a PhD scholarship to the third author. This research was funded by FAPESP—São Paulo Research Foundation (Process number 2017/50205–9).
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Pusch, M., Samuel-Rosa, A., Oliveira, A.L.G. et al. Improving soil property maps for precision agriculture in the presence of outliers using covariates. Precision Agric 23, 1575–1603 (2022). https://doi.org/10.1007/s11119-022-09898-z
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DOI: https://doi.org/10.1007/s11119-022-09898-z