The objective of this work was to locate and quantify, through geotechnologies, areas that are currently used for annual agriculture in the Cerrado biome of Central Brazil, located within the boundaries defined by the homogeneous region of adaptation of wheat cultivars 4 and that present favorable conditions for rainfed wheat (Triticum aestivum) cultivation. The following information layers were crossed: use and coverage of the Cerrado biome, digital elevation model, and water requirement satisfaction index for wheat in the Cerrado biome. In addition, different levels of water stress (low, moderate, and high), risk levels (20, 30, and 40%), available soil water capacities (ASWCs) (35, 55, and 75 mm), cultivar cycles (105, 115, and 125 days), and sowing dates (in February and March) were also considered. A greater favorable area was observed for sowing in early February, and group I of cultivars (105 days) presented the greatest favorable area. Above 800 m altitude, 2.7 million hectares were classified as favorable for the best combination of factors, i.e., sowing on February 5, ASWC of 75 mm, 105-day cycle, 20% risk level, and low and moderate impacts.

-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.

Colletotrichum gossypii var. cephalosporioides, the fungus that causes ramulosis disease of cotton, is widespread in Brazil and can cause severe yield loss. Because weather conditions greatly affect disease development, the objective of this work was to develop weather-based models to assess disease favorability. Latent period, incidence, and severity of ramulosis symptoms were evaluated in controlled environment experiments using factorial combinations of temperature (15, 20, 25, 30, and 35 degrees C) and leaf wetness duration (0, 4, 8, 16, 32, and 64 h after inoculation). Severity was modeled as an exponential function of leaf wetness duration and temperature. At the optimum temperature of disease development, 27 degrees C, average latent period was 10 days. Maximum ramulosis severity occurred from 20 to 30 degrees C, with sharp decreases at lower and higher temperatures. Ramulosis severity increased as wetness periods were increased from 4 to 32 h. In field experiments at Piracicaba, São Paulo State, Brazil, cotton plots were inoculated (10(5) conidia ml(-1)) and ramulosis severity was evaluated weekly. The model obtained from the controlled environment study was used to generate a disease favorability index for comparison with disease progress rate in the field. Hourly measurements of solar radiation, temperature, relative humidity, leaf wetness duration, rainfall, and wind speed were also evaluated as possible explanatory variables. Both the disease favorability model and a model based on rainfall explained ramulosis growth rate well, with R(2) of 0.89 and 0.91, respectively. They are proposed as models of ramulosis development rate on cotton in Brazil, and weather-disease relationships revealed by this work can form the basis of a warning system for ramulosis development.

-The objective of this work was to develop and evaluate a method for estimating corn yield using a minimum number of parameters and limited information about crop management. The proposed method estimates potential and attainable yields based on the technological level of the production systems and on relatively simple agrometeorological models. Corn yield was estimated for the crop seasons from 2000/2001 to 2007/2008, considering several locations and regions in Brazil, and was compared with the actual yield data from official surveys. There was a high correlation between the estimated and observed yield (0.76≤R 2 <0.92; p<0.01), with model efficiency (E1') ranging from 0.45 to 0.73; mean relative error (MRE) between -0.9 and 2.4%; and mean absolute error (MAE) of less than 70 kg ha -1 , depending on the technological level adopted. Based on these results, the proposed yield model can be recommended to forecast yields all over the country, contributing to make this process more precise and accurate.Index terms: Zea mays, large-area crop modeling, paremetrization, risk analysis, technological potential yield, yield forecast. Modelagem da produtividade de milho no Brasil em função das condições meteorológicas e do nível tecnológicoResumo -O objetivo deste trabalho foi desenvolver e avaliar um método para estimar a produtividade de milho com uso de um número mínimo de parâmetros e de informações limitadas sobre o manejo da cultura. O método proposto estima rendimentos potenciais e atingíveis com base no nível tecnológico dos sistemas de produção e em modelos agrometeorológicos relativamente simples. A produtividade de milho foi estimada para as safras de 2000/2001 a 2007/2008, tendo-se considerado vários locais e regiões do Brasil, e comparada aos dados de produção reais de levantamentos oficiais. A produtividade estimada apresentou alta correlação com a observada (0,76≤R 2 <0,92; p<0,01), com eficiência do modelo (E1') entre 0,45 e 0,73; erro médio relativo (MRE) entre -0,9 e 2,4%; e erro médio absoluto (MAE) inferior a 70 kg ha -1 , de acordo com o nível tecnológico considerado. Com base nestes resultados, este modelo pode ser recomendado para estimativas de produtividade em todo o País, e contribuir para tornar este processo mais preciso e exato.Termos para indexação: Zea mays, modelagem de cultura em grandes áreas, parametrização, análise de risco, produtividade potencial tecnológica, previsão de safra.

Uma dificuldade para a elaboração de estudos de zoneamento no Brasil é a inconsistência espacial e temporal das séries de dados meteorológicos no país. O objetivo deste trabalho é delimitar um conjunto de regiões agroclimaticamente homogêneas (RH), baseadas em um processo de agrupamento posteriormente assistido por indicadores espaciais complementares, de forma a permitir zoneamentos espacialmente precisos com menor número de pontos de observação. Uma primeira aproximação das RH foi gerada a partir dos resultados do zoneamento agrícola de risco climático para uma cultura agrícola hipotética, representativa das características médias da maioria dos cultivos anuais do Brasil, gerado a partir de 3500 estações pluviométricas e 1100 estações de temperatura. O agrupamento de elementos para delimitação de regiões foi definido com base na duração e no início dos períodos de plantio. A segunda aproximação consistiu na reavaliação das ZH, aplicando fusão, subdivisão ou reposicionamento de fronteiras das feições iniciais em SIG, utilizando indicadores complementares de altitude, declividade, climatologia de chuvas pelo TRMM, e índice de vegetação otimizado (EVI) e derivados. Foi observado elevado grau de concordância entre as feições da primeira aproximação e os indicadores auxiliares nas regiões com alta densidade de estações. Dessa forma, a metodologia proposta de delimitação assistida se mostra muito promissora para indicar maior detalhamento espacial mesmo em regiões com baixa densidade de estações.