Accuracy of Various Sampling Techniques for Precision Agriculture: A Case Study in Brazil

Domingos Sárvio Magalhães Valente (), Gustavo Willam Pereira, Daniel Marçal de Queiroz, Rodrigo Sinaidi Zandonadi, Lucas Rios do Amaral, Eduardo Leonel Bottega, Marcelo Marques Costa, Andre Luiz de Freitas Coelho and Tony Grift
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Domingos Sárvio Magalhães Valente: Department of Agricultural Engineering, Universidade Federal de Viçosa (UFV), Viçosa 36570-900, Brazil
Gustavo Willam Pereira: Instituto Federal Sudeste de Minas Gerais—Campus Muriáe (IF Sudeste MG), Muriaé 36884-036, Brazil
Daniel Marçal de Queiroz: Department of Agricultural Engineering, Universidade Federal de Viçosa (UFV), Viçosa 36570-900, Brazil
Rodrigo Sinaidi Zandonadi: Instute of Agricultural and Environmental Scinces, Universidade Federal do Mato Grosso (UFMT), Sinop 78550-728, Brazil
Lucas Rios do Amaral: School of Agricultural Engineering, University of Campinas (FEAGRI/UNICAMP), Campinas 13083-875, Brazil
Eduardo Leonel Bottega: Academic Coordination, Campus Cachoeira do Sul, Universidade Federal de Santa Maria (UFSM), Santa Maria 96503-205, Brazil
Marcelo Marques Costa: Institute of Agricultural Sciences, Universidade Federal de Jataí (UFJ), Jatai 75801-615, Brazil
Andre Luiz de Freitas Coelho: Department of Agricultural Engineering, Universidade Federal de Viçosa (UFV), Viçosa 36570-900, Brazil
Tony Grift: Department of Agricultural and Biological Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA

Agriculture, 2024, vol. 14, issue 12, 1-17

Abstract: Precision agriculture techniques contribute to optimizing the use of agricultural inputs, as they consider the spatial and temporal variability in the production factors. Prescription maps of limestone and fertilizers at variable rates (VRA) can be generated using various soil sampling techniques, such as point grid sampling, cell sampling, and management zone sampling. However, low-density grid sampling often fails to capture the spatial variability in soil properties, leading to inaccurate fertilizer recommendations. Sampling techniques by cells or management zones can generate maps of better quality and at lower costs than the sampling system by degree of points with low sampling density. Thus, this study aimed to compare the accuracy of different sampling techniques for mapping soil attributes in precision agriculture. For this purpose, the following sampling techniques were used: high-density point grid sampling method, low-density point grid sampling method, cell sampling method, management zone sampling method, and conventional method (considering the mean). Six areas located in the Brazilian states of Bahia, Minas Gerais, Mato Grosso, Goias, Mato Grosso do Sul, and Sao Paulo were used. The Root-Mean-Square-Error ( RMSE ) method was determined for each method using cross-validation. It was concluded that the cell method generated the lowest error, followed by the high-density point grid sampling method. Management zone sampling showed a lower error compared to the low-density point grid sampling method. By comparing different sampling techniques, we demonstrate that management zone and cell grid sampling can reduce soil sampling while maintaining comparable or superior accuracy in soil attribute mapping.

Keywords: soil fertility; variable-rate application; geostatistics; management zones (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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