Delineation of management zones in integrated crop-livestock systems

Autor(es)

Rodriguez Miranda, Diana Alexandra ^[1] ; Alari, Fernando de Oliveira ^[2] ; Oldoni, Henrique ^[3] ; Bazzi, Claudio Leones ^[4] ; do Amaral, Lucas Rios ^[5] ; Graziano Magalhaes, Paulo Sergio ^[5]

Afiliação do(s) autor(es)

^[1] Univ Campinas UNICAMP, Sch Agr Engn, Av Candido Rondon 501, BR-13083875 Campinas, SP - Brazil

^[2] Sao Paulo State Univ, Fac Agr & Vet Sci, Rua Quirino Andrade 215, BR-01049010 Jaboticabal, SP - Brazil

^[3] Univ Estadual Campinas, Interdisciplinary Ctr Energy Planning, Rua Cora Coralina 330, BR-13083896 Campinas, SP - Brazil

^[4] Univ Tecnol Fed Parana, Dept Comp Sci, Av Brasil 4232, BR-85884000 Medianeira, PR - Brazil

^[5] Univ Estadual Campinas, Sch Agr Engn, Av Candido Rondon 501, BR-13083875 Campinas, SP - Brazil

• Tipo de documento: Artigo Científico
• Fonte: AGRONOMY JOURNAL; v. 113, n. 6 NOV 2021.
• Citações Web of Science: 0

Resumo

The lack of studies on spatial variability in integrated crop-livestock systems (ICLS) hinders understanding how to increase their efficiency by implementing precision agriculture (PA) practices. As such, little is known about how grain and forage crops interact and how to improve the decision-making process on fertilization and forage management. One technique that can help manage such systems is the delineation of management zones (MZs), regions with similar yield potential and soil and topography characteristics. Thus, this paper assesses the spatial correlation between yield and potential factors affecting it, and identifies whether it is possible to establish MZs for field management of grain and forage crops in succession in ICLS. Bivariate Moran's index was used to identify the attributes most spatially correlated with the yields. Elevation, soil apparent electrical conductivity, and clay content were the most spatially correlated variables with soybean {[}Glycine max (L.) Merr.] yield, while soil organic matter content and elevation were the most spatially correlated with the forage yield. Spatial principal components analysis and fuzzy c-means clustering algorithm were combined to delineate MZs for each crop. The MZs created for soybean were statistically different in grain yield, available phosphorus (P) in the 0-to-0.40-m layer and pH in the 0-to-0.20-m layer. The forage MZs showed significant differences in terms of available P in the 0-to-0.40-m layer. We conclude that MZs for ICLS tends to be crop specific, demanding different MZs to characterize soybean and forage spatial variability. (AU)

• Processo FAPESP: 17/50205-9 - Monitoramento de sistemas integrados lavoura-pecuária por meio de sensoriamento remoto e agricultura de precisão para uma produção mais sustentável - rumo à agricultura de baixo carbono
• Beneficiário: Paulo Sergio Graziano Magalhães
• Modalidade de apoio: Auxílio à Pesquisa - Temático
• Processo FAPESP: 20/02223-0 - Agricultura de precisão em sistemas intensificados lavoura-pasto
• Beneficiário: Henrique Oldoni
• Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado