Colaço, André Freitas 
; Bramley, Robert GV; Richetti, Jonathan; Lawes, Roger A
Fonte: Precision Agriculture. Unidade: ESALQ
Assuntos: ANÁLISE ESTATÍSTICA DE DADOS, APRENDIZADO COMPUTACIONAL, CIÊNCIAS AGRÁRIAS, DELINEAMENTO EXPERIMENTAL, DISTRIBUIÇÃO ESPACIAL, MODELOS MATEMÁTICOS, TOMADA DE DECISÃO
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COLAÇO, André Freitas et al. What makes on-farm experimental data suitable for data-driven decision-making?: implications of trial design and spatial distribution of field data for machine learning models. Precision Agriculture, v. 26, p. 1-23, 2025Tradução . . Disponível em: https://doi.org/10.1007/s11119-025-10280-y. Acesso em: 27 nov. 2025.APA
Colaço, A. F., Bramley, R. G. V., Richetti, J., & Lawes, R. A. (2025). What makes on-farm experimental data suitable for data-driven decision-making?: implications of trial design and spatial distribution of field data for machine learning models. Precision Agriculture, 26, 1-23. doi:10.1007/s11119-025-10280-yNLM
Colaço AF, Bramley RGV, Richetti J, Lawes RA. What makes on-farm experimental data suitable for data-driven decision-making?: implications of trial design and spatial distribution of field data for machine learning models [Internet]. Precision Agriculture. 2025 ; 26 1-23.[citado 2025 nov. 27 ] Available from: https://doi.org/10.1007/s11119-025-10280-yVancouver
Colaço AF, Bramley RGV, Richetti J, Lawes RA. What makes on-farm experimental data suitable for data-driven decision-making?: implications of trial design and spatial distribution of field data for machine learning models [Internet]. Precision Agriculture. 2025 ; 26 1-23.[citado 2025 nov. 27 ] Available from: https://doi.org/10.1007/s11119-025-10280-y
