Brazil Brazil

Description of cropping systems, climate, and soils

Annual crop production area in Brazil occupies 67 million ha. Major crops are soybean, maize, and sugarcane which account for 70% of total crop area, and the country is one of the largest producers and exporters of these crops.

Table 1. Average (2008-2014) total production, harvested area, and average yield of soybean, maize and  sugarcane in Brazil. Source: CONAB.


Total production


Harvested area


Average yield















Most part of Brazil has a favorable climate for rainfed crop production, with total annual rainfall that ranges, across the major producing regions, from 700 mm (northeast region) to 2100 mm (south, southeast and west region). Precipitation is well distributed during the year in the south (Rio Grande do Sul, Santa Catarina, and Parana), while precipitation exhibits strong seasonality in the rest of the producing regions, with wet summers and dry winters. Irrigated area is less than 9% of total planted area. Dominant soils correspond to the Latosols, Neosols and Argisols orders, which covered approximately 70% of the arable land of the country.

Remarkable features of current Brazil agriculture are:

  1. only 3% of the crop area is rented,
  2. average farm size ranges from 10 ha (north-east) to 1000+ ha (west),
  3. 50% of the cropland is under no-till,
  4. soybean is the main crop occupying 42% of the total cropped area,
  5. 90% of soybean is transgenic glyphosate resistant while 82% of maize is Bt,
  6. Dominant soils have low pH and require continuous lime application.
  7. While only one crop per year is grown in the eastern part of the country, most producers grow two crops (1-year soybean-maize sequence called ‘safrinha') in the western region (Mato Grosso, Mato Grosso do Sul, Tocantins, Goiás, and Parana).


Data Sources and Assumptions (following GYGA protocols: put weblink to Methods tab here)

Harvested area and actual yields

District-level data on crop harvested area and average yields for each crop was retrieved from the IBGE – Institute Brazilian of Geography and Statistic). Statistics from the most recent 5 cropping seasons (2006-2010) were used to calculate crop area and average yields.

Weather data and reference weather stations

Long-term (15+ years) daily weather data were retrieved from Brazilian Institute of Meteorology (INMET) and include daily maximum and minimum temperature and precipitation. Quality control and filling/correction of the weather data was performed based on the propagation technique developed by van Wart et al. (2014). Solar radiation was estimated using the Bristow and Campbell (1984) method, with locally-calibrated coefficients (Marin et al., 2012). Based on crop harvested area distribution and the agro-climatic zones defined for Argentina (Van Wart et al., 2013a), a total of 25 (maize) and 19 (sugarcane) reference weather stations (RWS) were selected. RWS buffer zones accounted for 28 and 47% of total harvested for maize and sugarcane, while the agro-climatic zones where these locations were located accounted for 70 and 88% of national area for these crops.

Soil data

The 2-3 dominant soil series were identified for each RWS buffer based on data from the Radambrasil project (see Cooper et al., 2005). Rooting depth was set at 2.0 m (sugarcane) and 1.0 m (maize) to reflect the limitation to root growth in deep horizons due to low pH and the different sensitivity of crop species to this factor. Calibrated pedo-transference functions for tropical soils were used to derive soil water limits (Tomasella et al., 2000).

Crop system and management information for crop simulations

Management practices for each RWS buffer zone were retrieved from local EMBRAPA agronomists. Requested information include: dominant crop rotations and proportion of each of them to the total harvested area, average planting dates, dominant cultivar name and maturity, and actual and optimal plant population density. The provided data were subsequently corroborated by other local and national experts.

Simulations of widespread maize and sugarcane and varieties for each region were performed using Hybrid-Maize model (Yang et al., 2004) and CANEGRO (Inman-Bamber, 1991) embedded in DSSAT v 4.5 (Jones et al., 2003). Genetic coefficients for CANEGRO were derived data from well-managed rainfed and irrigated experiments (Marin et al., 2011, 2012). In order to account for differences in initial soil water at planting time among years, the entire crop sequence (including fallow) was simulated (sugarcane) or simulations were started by harvest time of the previous crop assuming 50% of plant available soil water (maize). For sugarcane, 3 main cycles of ratoon crops were simulated at each location: early (March-15), mid (Aug-15), and late planting (Nov 15). Most typical maize crop systems were: 2-y soybean-maize (with one crop per year) and 1-y soybean-maize (‘safrinha'). In the latter, soybean is planted with the onset of rains and matures in Feb or early March. Maize is planted after soybean harvest. The rainy season ends well before maize maturity, which experiences terminal drought. Separate simulations were performed for potential (Yp) and water-limited conditions (Yw). Simulations assumed no limitations to crop growth by nutrients.

For each crop-RWS combination, each crop rotation x soil type combination was simulated, and then weighted by their relative proportion to retrieve an average Yw at the level of the RWS buffer zone. A weighted average yield was calculated based on the actual yield reported for the municipalities located within the buffer zone and the relative contribution of each department to the total crop harvested area in the buffer zone. There were, at least, 5 departments within each RWS buffer. Reported Yw and Yp in the Atlas are long-term averages. Yield gap (Yg) was calculated as the difference between long-term average Yw or Yp and average (2006-2010) actual farmers' yields. Including more years before 2006 in the calculation of average actual yield would have led to a biased estimate of average actual yield due to a strong technology trend in Brazil.



Tomasella, J, Hodnett, MG, Rossato, L, 2000. Pedotransfer functions for the estimation of soil water retention in Brazilian soils. Soil Sci Soc Am J 69, 649-652.

Radambrasil Project. 1973–1986. Levantamento de recursos naturais. Vol. 1–34. Inst. Brasileiro Geogr. Estatistica, Rio de Janeiro, Brazil.

Marin, FR, Jones, JW, Singles, A., Royce, F., Assad, E.D., Pellegrino, G.Q., Justino, F., 2012. Climate change impacts on sugarcane attainable yield in southern Brazil. Climatic Change DOI 10.1007/s10584-012-0561-y

Marin, F. R. Jones, J. W. Royce, F. 2011. Parameterization and Evaluation of Predictions of DSSAT/CANEGRO for Brazilian Sugarcane. Agron. J. 103, 297-303.

Yang, H.S., Dobermann, A., Lindquist, J.L., Walters, D.T., Arkebauer, T.J., Cassman, K.G., 2004. Hybrid-Maize: a maize simulation model that combines two crop modeling approaches. Field Crops Res. 87, 131–154.

Van Wart, J., Van Bussel, L.G.J., Wolf, J., Licker, R., Grassini, P., Nelson, A., Boogaard, H., Gerber, J., Mueller, N.D., Claessens, L., Cassman, K.G., Van Ittersum, M.K., 2013b. Reviewing the use of agro-climatic zones to upscale simulated crop yield potential. Field Crops Res. 143, 44-45.

Van Wart, J., Grassini, P., Yang, H.S., Claessens, L., Jarvis, A., Cassman, K.G., 2015. Creating long-term weather data from the thin air for crop simulation modelling. Agric. For. Meteoro. 208, 49-58.

Cooper, M., Mendes, L.M.S., Silva, W.L.C., Sparovek, G., 2005. A national soil profile database for Brazil available to international scientists. Soil Sci. Soc. Am. J. 69, 649-652.

Jones, J.W., Hoogenboom, G., Porter, C.H., Boote, K.J., Batchelor, W.D., Hunt, L.A., Wilkens, P.W., Singh, U., Gijsman, A.J., Ritchie, J.T., 2003. The DSSAT Cropping System Model. Eur. J. Agron. 18, 235–265.

Inman-Bamber, N.G., 1991. A growth model for sugarcane based on a simple carbon balance and the CERES-Maize water balance. S. Afr. J. Plant Soil 8, 93–99.

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Country agronomists Country agronomists


Geraldo Martha Jr

Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA)

Fabio Marin

Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA)

Alencar Zanon

Federal University of Santa Maria

Nereu Streck

Federal University of Santa Maria

Giovana Ribas

Federal University of Santa Maria

Alexandre Heinemann  

Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA)