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For the simulations, rooting depth was set at 2 m (sugarcane), 0.8 m (soybean and maize), and 0.4 m (upland rice) to reflect the limitation to root growth in deep horizons due to low pH and differences among crop species in rooting patterns and/or tolerance to low pH (Pivetta et al., 2011, Battisti et al., 2017; Franchini et al., 2017). Calibrated pedo-transference functions for tropical soils were used to derive soil water limits (Tomasella et al., 2000). Field capacity was set at -10 kPa following the observations for tropical soils by Reichardt (1998) and Tomasella and Hodnett (2004). Soil properties were not considered for simulation of yield potential for irrigated rice. 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 it exhibits strong seasonality in the rest of the producing regions, with wet summers and dry winters. Pivetta, L.A., G. Castoldi, G. Santos, and C.A. Rosolem. 2011. Soybean root growth and activity as affected by the production system. Pesquisa Agropecu. Bras. 46, 1547–1554. Table 1. Average (2015-2019) total production, harvested area, and average yield of soybean, maize, sugarcane and rice in Brazil. Source: CONAB. Most typical maize and soybean 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 in October and matures in January. Maize is planted after soybean harvest. The rainy season ends before maize maturity, leading to terminal drought in most years. For maize, we simulated both safra and safrinha when both accounted for >30% of maize area within each buffer; if not, only the most dominant maize system was simulated in each buffer.

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 117, 227-239. For each crop-RWS combination, each crop sequence 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 (or Yp in the case of irrigated rice). Simulations assumed no limitations to crop growth by nutrients and no incidence of biotic stresses such as weeds, insect pests, and pathogens.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. Battisti, R.B., Sentelhas, P.C., 2017. Improvement of soybean resilience to drought through deep root systems in Brazil. Agron. J. 109, 1612–1622. Data from the Atlas is available for use under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Heinemann, A. B., Ramirez-Villegas, J., Rebolledo, M. C., Neto, G. M. F. C., & Castro, A. P., 2019. Upland rice breeding led to increased drought sensitivity in Brazil. Field Crops Research 231, 57-67. 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. 209-210, 45-58.

Marin, F.R.; Thorburn, P.; Nassif, D.S.P.; Costa, L.G. 2015. Sugarcane model intercomparison: Structural differences and uncertainties under current and potential future climates. Environmental Modelling & Software, 72, 372-386. Figure 1. Comparison of simulated and observed phenology (left) and grain yields (right) for rice (top), soybean (middle), and maize (bottom).The solid red line represents y = x and the dashed red lines represents ± 20% deviation from the y -x line.RMSE = mean square root of error.The phenological stages of rice, soybean and maize were based on the scales ofCounce et al.(2000), Fehr and Caviness (1977), and Ritchie et al. (1993), respectively . Duarte, Y.C.N., Sentelhas, P.C., 2019. NASA / POWER and DailyGridded weather datasets — how good they are for estimating maize yields in Brazil ? Int. J. Biometeorol. doi: 10.1007/s00484-019-01810-1 A weighted average yield was calculated based on the average 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. Reported Yw (or Yp for irrigated rice) in the Atlas are long-term averages. Yield gap (Yg) was calculated as the difference between long-term average Yw (rainfed crops) or Yp (irrigated crops) and average (2012-2017) farmer yield. Including more years before 2012 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. In the case of buffers where both safra and safrinha were common maize, average maize yield was estimated by averaging their respective average yields, weighting by the proportion of maize area under each crop system. Franchini, J.C., Antonio, A., Junior, B., Debiasi, H., Nepomuceno, A.L., 2017. Root growth of soybean cultivars under different water availability conditions Crescimento radicular de cultivares de soja em campo em diferentes disponibilidades hídricas. Ciências Agrárias, Londrina, 38, 715–724.Aramburu Merlos, F., Monzon, J.P., Mercau, J.L., Taboada, M., Andrade, F., Hall, A.J., Jobbagy, E., Cassman, K.G., Grassini, P. 2015. Potential for crop production increase in Argentina through closure of existing yield gaps. Field Crops Research 184, 145-154. 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. Long-term(20 years) daily weather data were retrieved from Brazilian Institute of Meteorology (INMET) and include maximum and minimum temperature and precipitation for the period between years 1999 and 2018. Relative humidity, dew temperature, and ETo were estimated following Allen et al., (1998). Quality control and filling/correction of the weather data were performed based on the propagation technique developed by van Wart et al. (2014). In all cases, solar radiation was retrieved from NASA-POWER, which has shown good correlation with measured solar radiation (Bender and Sentelhas, 2018; Monteiro et al., 2018; Duarte et al., 2019). Measured weather data were not available in 20% of the buffers); hence, we used weather data (including all variables) from NASA-POWER. There are two dominant rice systems: irrigated lowland rice (southern brazil) and rainfed upland rice (north-central and western Brazil). Rice is grown as a single crop per year; in southern Brazil rice is sown from late September to early December and with the onset of rainfall (typically between early November and early December) in north-central Brazil rice is planted. In all cases, rice is direct seeded. Bender, F.D., Sentelhas, P.C., 2018. Solar Radiation Models and Gridded Databases to Fill Gaps in Weather Series and to Project Climate Change in Brazil. Advances in Meteorology, 2018, 1-15.

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. Annual crop production area in Brazil occupies 69 million ha. Major crops are soybean, maize, sugarcane, and rice which account for 90% of total crop area, and (except for rice) the country is one of the largest producers and exporters of these crops. Most sugarcane, soybean, and maize is produced in rainfed conditions (>90%); rice is produced in irrigated (80%) and rainfed (20%) conditions in the southern and north-central regions, respectively. Tomasella, J, Hodnett, 2004. Pedotransfer functions for tropical soils. In: Developments in Soil Science, pp. 415-429. Management practices for each RWS buffer zone were retrieved from local EMBRAPA agronomists and other experts. Requested information include: dominant crop rotations and proportion of each of them to the total harvested area, sowing window, dominant cultivar name and maturity, and optimal plant population density (CONAB, 2019). The provided data were subsequently corroborated by other local and national experts.Bouman, B.A.M.; Kropff, M.J.; Tuong, T.P.; Wopereis, M.C.S.; Ten Berge, H.F.M.; Laar van, H.H, 2004. Van. Oryza 2000: modeling lowland rice. Manila, Philippines: International Rice Research Institute (IRRI). 245 pp. The 1-3 dominant soil series were identified for each RWS buffer based on data from the Radambrasil project (see Cooper et al., 2005). In each buffer, dominant soils were selected to cover at least 30%. Each selected soil had at least 10% of the area. Selected soils were verified by local experts and modified as needed to ensure that simulated soils represented the most common agricultural soils. Allen, R.G., Luis, S.P., RAES, D., Smith, M., 1998. FAO Irrigation and Drainage Paper No.56. Crop Evapotranspiration, Rome, Italy To portray most dominant practices in sugarcane farms, 3 main cycles of ratoon crops of 12-month duration each were simulated at each location: early (April-15), mid (Aug-15), and late planting (Nov 15). Monteiro, L.A., Sentelhas, C., Pedra, G.U., 2018. Assessment of NASA / POWER satellite-based weather system for Brazilian conditions and its impact on sugarcane yield simulation. Int. J. Climatol. 38, 1571–1581.

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