Rice yield estimation based on weather conditions and on technological level of production systems in Brazil

Abstract

-The objective of this work was to evaluate an estimation system for rice yield in Brazil, based on simple agrometeorological models and on the technological level of production systems. This estimation system incorporates the conceptual basis proposed by Doorenbos & Kassam for potential and attainable yields with empirical adjusts for maximum yield and crop sensitivity to water deficit, considering five categories of rice yield. Rice yield was estimated from 2000/2001 to 2007/2008, and compared to IBGE yield data. Regression analyses between model estimates and data from IBGE surveys resulted in significant coefficients of determination, with less dispersion in the South than in the North and Northeast regions of the country. Index of model efficiency (E 1 ') ranged from 0.01 in the lower yield classes to 0.45 in higher ones, and mean absolute error ranged from 58 to 250 kg ha -1 , respectively.

Modeling of corn yield in Brazil as a function of meteorological conditions and technological level

“…If the corn crop of an entire area of a given municipality is irrigated, dty tends to 1, whereas ky* tends to zero. Such procedure was used by Monteiro et al (2013), for distinguishing lowland and upland rice yields in Brazil, and it was decisive for improving yield estimation performance for this crop.…”

“…Simpler yield models, as those based mostly on water availability throughout the crop cycle, have also been used to estimate potential and actual yields (Assad et al, 2007;Monteiro et al, 2013), as well as to characterize genotype sensitivity to water deficits (Andrioli & Sentelhas, 2009;Battisti & Sentelhas, 2015). Among these, the FAO method (Doorenbos & Kassam, 1979), which uses a physiologicalmathematical approach, has proved to be robust, since it is based on a conservative relationship between biomass and water use (Hsiao, 1993;Morison et al, 2008).…”

Modelagem numérica para previsão de safra de arroz irrigado no Rio Grande do Sul

“…Cabe salientar que o método para a previsão de safra foi desenvolvido para o Estado do Rio Grande do Sul, uma vez que o modelo SimulArroz foi calibrado para cultivares e condições ambientais de produção de arroz deste Estado (Rosa et al, 2015) e a previsão climática sazonal foi gerada para uma área abrangente aos municípios de Itaqui e Restinga Seca. Assim, a previsão proposta pode ser utilizada como um modelo regional, inserida em um modelo nacional para previsão de safra de arroz, como o modelo proposto por Monteiro et al (2013).O método proposto no presente trabalho pode ser utilizado para prever safra de arroz para o RS, pois atende com maior precisão as caracteristicas específicas da produção de arroz desse Estado, gerando uma previsão mais segura e confiável.…”

“…Atualmente, o monitoramento e a previsão de safra de arroz no Brasil são realizados por meio de entrevistas com agricultores, feitas por Técnicos Agrícolas e Engenheiros Agrônomos, sobre a produtividade esperada (Monteiro et al, 2013). Em outros países, além desse método das entrevistas, modelos mecanísticos são utilizados como ferramentas complementares para a previsão de safra, pois são capazes de descrever as Pesq.…”

AgMIP Regional Activities in a Global Framework: The Brazil Experience

“…This methodology was adapted from the agroecological zones methodology developed by Food and Agriculture Organization. It has been used for studies of bioenergy products and rice in Brazil (Monteiro et al, 2013). The data are processed to calculate potential evapotranspiration (PET) and crop water requirements (ISNA, indice de satisfação de necessidade deágua), which are defined as:…”