<b>Agrometeorological models for groundnut crop yield forecasting in the Jaboticabal, São Paulo State region, Brazil

Abstract

ABSTRACT. Forecast is the act of estimating a future event based on current data. Ten-day period (TDP) meteorological data were used for modeling: mean air temperature, precipitation and water balance components (water deficit (DEF) and surplus (EXC) and soil water storage (SWS)). Meteorological and yield data from 1990-2004 were used for calibration, and 2005-2010 were used for testing. First step was the selection of variables via correlation analysis to determine which TDP and climatic variables have more influence on the crop yield. The selected variables were used to construct models by multiple linear regression, using a stepwise backwards process. Among all analyzed models, the following was notable: Palavras-chaves: modelo de cultivo, balanço hídrico, predição, produção.

Estimation of Air Temperature Using Climate Factors in Brazilian Sugarcane Regions

“…These regions also showed higher accuracy, with an average MAPE of 3.92% ± 2.48 for the North and 2.94% ± 1.37 for the Southeast. A MAPE value below 5% is considered low, as described by Aparecido et al (2020); Moreto and Rolim, (2015).…”

“…Climate is defined as the grouping of atmospheric dispositions, providing the characterization of a region (WMO345w, 2008;Werndl, 2016), influencing several economic, social, and environmental activities (Palinkas and Wong, 2020;Tol, 2018), especially the agribusiness. All stages of agricultural development suffer from climate interference (Schauberger et al, 2017;Syed et al, 2022), providing higher or lower crop yields (De Moraes et al, 2020;Yu et al, 2020).…”

“…Air temperature is one of the main weather elements related to plant development, interfering with its metabolic activities (Dos Santos et al, 2022;Zhao et al, 2017). The most affected processes are the maintenance respiration, transpiration, vegetative resting, phenological stage duration, flowering induction, and germination rate (Clemente, 2019;Pedro Júnior et al, 2004). Air temperature affects both the development and the quality of the final product (Figueirredo et al, 2008;Marcari;De Souza Rolim and De Oliveira Aparecido, 2015;Tabtiang et al, 2022).…”

Previsão Da Produtividade Do Café Com Base Em Dados Agroclimáticos E Aprendizagem De Máquina / Forecasting Coffee Yield Based on Agroclimatic Data and Machine Learning

“…O rendimento médio do estado na safra de 2019 foi de 25,83 sc/ha (CONAB, 2019; HAMEED; HUSSAIN; SULERIA, 2020) e conhecer esse valor antecipadamente por meio de previsões é útil para o planejamento agrícola (BARBOSA et al, 2020). No Brasil para realizar a previsão da produtividade agrícola é utilizado um sistema baseado em opiniões de técnicos e economistas das áreas, o que acaba tornando-se um método subjetivo, tendo em vista que não permite uma análise quantitativa dos erros envolvidos (MORETO; ROLIM, 2015); (ROSA et al, 2010). Uma alternativa é a utilização de modelos de regressões para estimar o desenvolvimento e produtividade das culturas agrícolas (JAYAKUMAR; RAJAVEL; SURENDRAN, 2016;MERLE et al, 2020).…”