Space-time dynamics of the expansion of irrigated areas and agricultural diversification in Casa Branca [state of Sao Paulo] Brazil

• Autor(a) principal: Parreiras, Taya Cristo
• Data de Publicação: 2024
• Outros Autores: Bolfe, Édson Luis, Sano, Edson Eyji, Silva, Lucas Agusto Pereira da
• Tipo de documento: Artigo
• Idioma: por
• Título da fonte: Labor & Engenho (Online)
• Texto Completo: https://periodicos.sbu.unicamp.br/ojs/index.php/labore/article/view/8674282
• topic: Irrigação; Sensoriamento remoto; Cerrado; Monitoramento agr´ícola; Irrigation; Remote sensing; Cerrado; Agricultural monitoring; Irrigação; Sensoriamento remoto; Cerrado; Monitoramento agrícola

This work aimed to evaluate the expansion of center pivot irrigation systems (SIPCs) in the São Paulo municipality of Casa Branca, between 1985 and 2019, through the analysis of the space-time dynamics of agricultural production, in addition to the possible impacts on environments and the role of remote sensing in their monitoring. During this period, Casa Branca became the second municipality in Brazil with the largest relative area occupied by SIPCs, consolidating itself as the most relevant player from the National Pole of Irrigation Hillside of Rio Pardo and Mogi-Guaçu (PRPMG). The expansion of the SIPCs, over 13,000 hectares, took place over areas previously occupied by rainfed crops and pastures without removing native vegetation. There was a significant increase in production and portfolio of temporary crops, deconcentration of land with smaller rural establishments, and the consolidation of Casa Branca as a highly diversified agricultural municipality. However, it is also the largest user of water resources in the Pardo River Basin. Considering the environmental impacts associated with intensive irrigation, with direct implications for climate change, monitoring its agricultural dynamics is crucial for environmental and economic sustainability. In this sense, studies have shown that remote sensing by combining satellite images of medium spatial and temporal resolution with machine learning or deep learning techniques, is an adequate tool for detecting and mapping the dynamics of irrigated production systems, benefiting strategies of monitoring agricultural practices aimed at greater efficiency and sustainability in agriculture such as Measurement, Reporting/Reporting and Verification (MRV).