Sistema de previsão da safra de soja para o Brasil

Abstract

Resumo -Este trabalho teve por objetivo avaliar um sistema de previsão de safra de soja para o Brasil, baseado em modelos empíricos regionalizados para estimativa da produtividade, a partir de um banco de dados de área cultivada em escala municipal, e de um sistema de monitoramento agrometeorológico de abrangência nacional. Forecast system of soybean crop yield for BrazilAbstract -The aim of this work was to evaluate a system to forecast soybean crop yield for Brazil, based on regional empirical models to assess crop yield, with data from a national database of soybean cropped area in municipal scale, and from an agrometeorological monitoring system covering all Brazilian States. The models incorporated the conceptual bases proposed by Doorenbos & Kassam, with empirical adjustments for each region of Brazil, considering the diverse potential yield of most used varieties and the regional differences in the soybean production systems. Cultivated area database from each State was structured with data from IBGE and Conab. Soybean yield was estimated for harvests from 2000/2001 to 2005/2006 and compared to Conab surveys. Statistical analysis using Test t point out that there is no significant difference between estimates and official data. Good fittings were gotten for region grouped yield data (R 2 ≥0.87), with higher deviations for harvest assessment of Rio Grande do Sul, Santa Catarina, Mato Grosso do Sul, Maranhão, Piauí and Bahia. In national scale, the highest observed deviation was 5.81% for the 2000/2001 harvest, and the smaller one was 0.62% for the 2005/2006 yield.

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

“…Data from the municipality of Pelotas in 2005 is a possible example (Figure 3). Similar results were reported in Assad et al (2007), when soybean showed high-yield losses in 2005 in this region.…”

“…One of the changes in this model, if compared to the one cited above, is that yield calculation is updated daily, whenever the system receives new weather data, at the end of each day, similarly to the system developed for soybean by Assad et al (2007) in Brazil. Another difference is the introduced empirical parameter, called adjusted yield response factor (ky*), bellow explained, which results in higher or lower response to water deficit, according to technological specificities related to different production systems, in each yield class.…”

“…Water deficit, even if moderate, can cause high-yield losses, especially when occurring during flowering and early grain filling . Therefore, upland rice crops are considered under high climate risk (Silva & Assad, 2001) Many models have been developed for crop yield estimation considering different parameters and complexity levels, most of them are based on agrometeorological variables (Doorenbos & Kassam, 1979;Assad et al, 2007;). There is a group of simulation models for rice yield estimation and forecasting, among them is Infocrop (Aggarwal et al, 2006), Warm (Confalonieri et al, 2009) and Oryza (Bouman et al, 2006).…”

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

“…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).…”

Spatial Correlation of Soybean Productivity, Enhanced Vegetation Index (Evi) and Agrometeorological Variables

“…To estimate the production it is necessary to know the cultivated areas with the species of interest and its productivity in different regions (ASSAD et al, 2007). The crops productivity can also be estimated from agrometeorological models (FONTANA et al, 2001).…”