The gridded database provided by National Aeronautics and Space Administration/Prediction of World Wide Energy Resources (NASA/POWER) presents a global coverage of complete weather data at horizontal resolution of 1° latitude–longitude, becoming a potential source for agrometeorological studies. Once Brazil is a country with continental dimensions and the major sugarcane world producer, and its density of ground weather stations suitable for an efficient agricultural planning is sparse, the objectives of this study were to test how robust is the NASA/POWER database through its comparison with the Brazilian ground weather stations network records (INMET) and to quantify the impacts on potential (Yp) and attainable (Yatt) sugarcane yield simulations when setting NASA/POWER as source of input weather data. The comparisons for weather data records and sugarcane yield simulations were carried out from 1997 to 2016. Statistical indices presented a satisfactory performance for average air temperature (R2 = 0.73; d = 0.91), minimum air temperature (R2 = 0.72; d = 0.91), maximum air temperature (R2 = 0.57; d = 0.84), solar radiation (SR) (R2 = 0.71; d = 0.92), sunshine hours (R2 = 0.68; d = 0.90) and reference evapotranspiration, when calculated through Priestley–Taylor (ETo‐PT) method (R2 = 0.76; d = 0.93). When the weather variables were aggregated and compared with a 10‐day time scale, a strong improvement of statistical indices was obtained. Yp presented root mean square error (RMSE) smaller than 10 t ha−1 while relative mean error (RME) ranged between ±10% for majority of grid cells, with exception for southern Brazil due to low and frost temperatures that satellite cannot capture accurately. Even NASA/POWER offering a relatively coarse grid size database and perhaps some regional data fitting would give better results at higher latitudes and elevation. The results found in this study proved that NASA/POWER products could be used as a source of climatic data for agricultural activities with a reasonable confidence for regional and national spatial scales.

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Brazilian sugarcane yield is below its physiological potential, which has compromised the crop’s profitability. This, together with the expansion of the crop to marginal areas with limiting climatic conditions, requires studies to quantify crop yield gaps (YG) and to identify their main causes (i.e. droughts and/or crop management). One way to determine YG is through crop simulation models, which vary in complexity, mainly in terms of input data requirements. This study evaluated whether a simple agrometeorological crop yield model could be suitable for estimating sugarcane YG at a national level, in order to consider and suggest practices to mitigate yield losses. The model was calibrated and evaluated for different conditions across the country. The calibrated model was used to estimate plant and ratoon sugarcane potential (Yp) and best farmer (Ybf) yields for 259 locations representing all regions of the country where sugarcane is grown. Weather data from 1984 to 2013 and general local soil information were used as inputs. The Yp and Ybf simulations were performed for 30 growing cycles, with the final yields being weighted by the proportion of plant (20%) and ratoon (80%) canes in each area. These data were compared with actual average yields (Yavg), obtained from official surveys. Sugarcane yields varied considerably across the country: Yp range was 68.5–232.7 t ha–1, Ybf 61.7–123.3 t ha–1, and Yavg 11.2–101.1 t ha–1. These yields resulted in an average total YG of 133.2 t ha–1. The main source of YG was water deficit, accounting for 75.6% of total losses, while crop management was responsible for 24.4%. Considering the main sources of YG for sugarcane in Brazil, the use of drought-tolerant cultivars, irrigation, and deep soil preparation seems the best strategy to mitigate the risks, improving yields. Based on these results, the simple agrometeorological crop yield model proved suitable to estimate sugarcane YG at national level.

The need to balance agricultural production and environmental protection shifted the focus of Brazilian land-use policy toward sustainable agriculture. In 2010, Brazil established preferential credit lines to finance investments into low-carbon integrated agricultural systems of crop, livestock and forestry. This article presents a simulation-based empirical assessment of integrated system adoption in the state of Mato Grosso, where highly mechanized soybean-cotton and soybean-maize doublecrop systems currently prevail. We employ bioeconomic modeling to explicitly capture the heterogeneity of farmlevel costs and benefits of adoption. By parameterizing and validating our simulations with both empirical and experimental data, we evaluate the effectiveness of the ABC Integration credit through indicators such as land-use change, adoption rates and budgetary costs of credit provision. Alternative scenarios reveal that specific credit conditions might speed up the diffusion of low-carbon agricultural systems in Mato Grosso.

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To meet rising demands for agricultural products, existing agricultural lands must either produce more or expand in area. Yield gaps (YGs)—the difference between current and potential yield of agricultural systems—indicate the ability to increase output while holding land area constant. Here, we assess YGs in global grazed‐only permanent pasture lands using a climate binning approach. We create a snapshot of circa 2000 empirical yields for meat and milk production from cattle, sheep, and goats by sorting pastures into climate bins defined by total annual precipitation and growing degree‐days. We then estimate YGs from intra‐bin yield comparisons. We evaluate YG patterns across three FAO definitions of grazed livestock agroecosystems (arid, humid, and temperate), and groups of animal production systems that vary in animal types and animal products. For all subcategories of grazed‐only permanent pasture assessed, we find potential to increase productivity several‐fold over current levels. However, because productivity of grazed pasture systems is generally low, even large relative increases in yield translated to small absolute gains in global protein production. In our dataset, milk‐focused production systems were found to be seven times as productive as meat‐focused production systems regardless of animal type, while cattle were four times as productive as sheep and goats regardless of animal output type. Sustainable intensification of pasture is most promising for local development, where large relative increases in production can substantially increase incomes or “spare” large amounts of land for other uses. Our results motivate the need for further studies to target agroecological and economic limitations on productivity to improve YG estimates and identify sustainable pathways toward intensification.

The use of machinery in agriculture is increasing. This leads to soil compaction or degradation when the machinery is operated under inappropriate soil conditions, which in turn results in decreasing crop yields. Therefore, the aim of this study was to identify the periods of the year when it is appropriate to use machinery for soil management, as a function of rainfall and soil moisture, in traditional agricultural regions in southern Brazil. The study used daily rainfall and air temperature data from Piracicaba, SP, Passo Fundo, RS, Londrina, PR and Dourados, MS, Brazil. Favorable conditions were calculated considering daily rainfall lower than 5 mm and soil moisture between 40 and 90% of soil water holding capacity. Daily soil water balance was calculated by the Thornthwaite and Mather (1955) method, for a soil water holding capacity of 100 mm, which represents the main soils of the studied regions. The Markov chain was applied and used to calculate the conditional probabilities which were used for estimating the sequential working days in each ten-day period throughout the year. The working days technique is an important tool for determining the most appropriate periods of the year for soil management. The annual rainfall and soil moisture variability was used to identify the periods of the year with low, medium and high suitability for soil management. For the assessed locations, there was a predominance of 11 to 20 working days per month, except for Londrina where up to 10 working days per month predominated.

ABSTRACT. This study was designed to calibrate and test an agrometeorological model over 18 growing seasons in three soybean production areas in Brazil: Passo Fundo (Rio Grande do Sul State), Londrina (Paraná State), and Dourados (Mato Grosso do Sul State). The soybean potential yield (Yp) was determined by two methods: estimated using the FAO Agroecological Zone Model or based on the maximum yield published by the Brazilian Institute of Geography and Statistics (IBGE), increased by 10, 20 and 30%. The estimate of actual yield (Ya) was calculated by correcting Yp for the relative water deficit at different growth stages. The results showed that the best performance was obtained when Calibração e teste de um modelo agrometeorológico para estimativa da produtividade da soja em diferentes regiões brasileiras RESUMO. Este trabalho teve como objetivo calibrar e testar um modelo agrometeorológico, durante 18 safras para três municípios produtores de soja no Brasil: Passo Fundo (RS), Londrina (PR) e Dourados (MS). A produtividade potencial (Yp) foi estimada de acordo com duas metodologias: a partir do modelo das Zonas Agroecológicas da FAO e por meio da produtividade máxima observada durante a série histórica do IBGE, acrescida de 10, 20 e 30%. A produtividade real (Ya) foi calculada por meio da penalização da Yp pelo déficit hídrico relativo durante as diferentes fases fenológicas. Os resultados mostraram que o melhor desempenho foi obtido quando a Yp foi representada pela produtividade máxima acrescida de certa porcentagem. O modelo apresentou desempenho satisfatório para os três municípios, com R Palavras chave: Glycine max, produtividade potencial, produtividade real, deficiência hídrica, modelos de simulação.