Assessment of Soybeans Crop Management Strategies Using Crop Growth Models for Central Brazil

  • Rafael BattistiEmail author
  • Derblai Casaroli
  • Jéssica Sousa Paixão
  • José Alves Júnior
  • Adão Wagner Pêgo Evangelista
  • Marcio Mesquita
Part of the Innovations in Landscape Research book series (ILR)


The assessment of crop management can help to improve yield across different climate and soil conditions. Soybean is the main crop in Central Brazil, where sowing date, maturity group, and irrigation management are an important decision need to be taken by farmers to get higher yields. This way, the aim of this study was to assess the total production in the region in function of crop management (sowing date, maturity group and irrigation), considering gridded weather data (0.5 × 0.5°), local total plant-available soil water capacity and current production intensity of soybean by county. The yield was simulated using three crop models considering four sowing dates, two maturity groups under rainfed and irrigated conditions. The total production in the region was obtained combining yield for each management simulated to the local soil and the production intensity by county. The higher uncertainty was observed for growing seasons (coefficient of variation, mean CV = 7.13%) under rainfed, and for maturity group (mean CV = 4.97%) under irrigation. The use of irrigation reduced considerably the CV for management of sowing dates and soil types (mean CV < 1.55%). The use of irrigation resulted in a yield gain higher than 1200 kg ha−1 with irrigation requirement in most of the area above 51 and lower than 200 mm cycle−1. The total production in the region can be increased around 12 million tons by using supplemental irrigation. Maturity groups (MG) 7.2 and 8.4 had a higher production occurring for sowing date on 20 Oct under the rainfed condition, totalizing 65.12 and 68.74 million tons, respectively. For irrigation, MG 7.2 had 76.82 million tons occurring for sowing date on 20 Oct, while for MG 8.4, the total production was most stable across sowing date, ranging from 81.14 to 82.34 million tons. The uses of local soil and weather, current production intensity and different crop management based on multiple crop models to simulate soybean yield help to identify the best management to obtained higher total production in the region, indicating the best strategies to put efforts to promote these best management through agricultural extension and public policy.


Landscape decision Production intensity Total soybean production Sowing date Irrigation Maturity group Cerrado biome 


  1. Almeida V, Alves J Jr, Mesquita M, Evangelista AWP, Casaroli D, Battisti R (2018) Comparison of the economic viability of agriculture irrigated by central pivot in conventional and no-tillage systems with soybean, maize and industrial tomato crops. Glob Sci Technol 11:256–273Google Scholar
  2. Alvares CA, Stape JL, Sentelhas PC, Gonçalves JLM, Sparovek G (2013) Köppen’s climate classification map for Brazil. Meteorologische Zeitschirift 22:711–728. Scholar
  3. Bassu S, Brisson N, Durand J-L, Boote KJ, Lizaso J, Jones JW, Rosenzweig C, Ruane AC, Adam M, Baron C, Basso B, Biernath C, Boogaard H, Conijn S, Corbeels M, Deryng D, Sanctis GS, Gayler S, Grassini P, Hatfield J, Hoek S, Izaurralde C, Jongschaap R, Kemanian AR, Kersebaum KC, Kim S-H, Kumar MS, Makowski D, Müller C, Nendel C, Priesack E, Pravia MV, Sau F, Shcherbak I, Tao F, Teixeira E, Timlin D, Waha K (2014) How do various maize crop models vary in their responses to climate change factors? Glob Change Biol 20:2301–2320. Scholar
  4. Battisti R (2016) Calibration, uncertainties and use of soybean crop simulation models for evaluating strategies to mitigate the effects of climate change in Southern Brazil. Thesis (Phd in Agricultural Engineering Systems)—ESALQ, University of São Paulo, Piracicaba, SP, Brazil, p 188Google Scholar
  5. Battisti R, Sentelhas PC (2014) new agroclimatic approach for soybean dates recommendation: a case study. Revista Brasileira de Engenharia Agrícola e Ambiental 18:1149–1156. Scholar
  6. Battisti R, Sentelhas PC (2017) Improvement of soybean resilience to drought through deep root system in Brazil. Agron J 109:1612–1622. Scholar
  7. Battisti R, Parker PS, Sentelhas PC, Nendel C (2017a) Gauging the sources of uncertainty in soybean yield simulations using the MONICA model. Agric Syst 155:9–18. Scholar
  8. Battisti R, Sentelhas PC, Boote KJ (2017b) Inter-comparison of performance of soybean crop simulations models and their ensemble in southern Brazil. Field Crop Research 200:28–37. Scholar
  9. Battisti R, Sentelhas PC, Boote KJ, Câmara GMS, Farias JRB, Basso CJ (2017c) Assessment of soybean yield with altered water-related genetic improvement traits under climate change in Southern Brazil. Eur J Agron 83:1–14. Scholar
  10. Battisti R, Sentelhas, PC, Pascoalino, JAL, Sako, H, Dantas, JPS, Moraes MF (2018a) Soybean yield gap in the areas of yield contest in Brazil. Int J Plant Prod 1–10.
  11. Battisti R, Sentelhas PC, Parker PS, Nendel C, Câmara GMS, Farias JRB, Basso CJ (2018b) Assessment of crop-management strategies to improve soybean resilience to climate change in Southern Brazil. Crop Pasture Sci 69:154–162.
  12. Battisti R, Bender, FD, Sentelhas PC (2018c) Assessment of different gridded weather data for soybean yield simulations in Brazil. Theor Appl Climatol 1–11.
  13. Battisti R, Sentelhas PC (2019) Characterizing Brazilian soybean-growing regions by water deficit patterns. Field Crop Res (under review)Google Scholar
  14. Boote KJ, Jones JW, Batchelor WD, Nafziger ED, Myers O (2003) Genetic coefficients in the CROPGRO-Soybean model: Link to field performance and genomics. Agron J 95:32–51. Scholar
  15. Chauhan YS, Solomon KF, Rodriguez D (2013) Characterization of north-eastern Australian environments using APSIM for increasing rainfed maize production. Field Crops Research 144:245–255. Scholar
  16. CONAB (2018) Survey of crop season: soybean. Accessed 15 Nov 2018
  17. Confalonieri R, Orlando F, Paleari L, Stella T, Gilardelli C, Movedi E, Pagani V, Cappelli G, Vertemara A, Alberti L, Alberti P, Atanassiu S, Bonaiti M, Cappelletti G, Ceruti M, Confalonieri A, Corgatelli G, Corti P, Dell’Oro M, Ghidoni A, Lamarta A, Maghini A, Mambretti M, Manchia A, Massoni G, Mutti P, Pariani S, Pasini D, Pesenti A, Pizzamiglio G, Ravasio A, Rea A, Santorsola D, Serafini G, Slavazza M, Acutis M (2016) Uncertainty in crop model predictions: what is the role of users? Environ Model Softw 81:165–173. Scholar
  18. Doorenbos J, Kassam AM (1979) Yield response to water. Irrigation and drainage paper, 33. FAO, Rome, p 300Google Scholar
  19. EMBRAPA (2015) Soybean in numbers. Accessed 10 July 2015
  20. Ewert F, Rötter RP, Bindi M, Webber H, Trnka M, Kersebaum KC, Olesen JE, van Ittersum MK, Janssen S, Rivington M, Semenov MA, Wallach D, Porter JR, Stewart D, Verhagen J, Gaiser T, Palosuo T, Tao F, Nendel C, Roggero PP, Bartošová L, Asseng S (2015) Crop modelling for integrated assessment of risk to food production from climate change. Environ Model Softw 72:287–303. Scholar
  21. FAO (2018) FAOSTAT: FAO statistical databases. Accessed 10 Dec 2018
  22. Heinemann AB, Ramirez-Villegas J, Souza TLPO, Didonet AD, Di Stefano JG, Boote KJ, Jarvis A (2016) Drought impact on rainfed common bean production areas in Brazil. Agric For Meteorol 225:57–74. Scholar
  23. IBGE (2018) Agricultural production. In Portuguese. Accessed 1 Aug 2018
  24. Jones JW, Hoogenboom G, Porter CH, Boote KJ, Batchelor WD, Hunt LA, Wilkens PW, Singh U, Gijsman AJ, Ritchie JT (2003) The DSSAT cropping system model. Eur J Agron 18:234–265. Scholar
  25. Kassam AH (1977) Net biomass production and yield of crops. FAO, Rome, 29 pGoogle Scholar
  26. Lopes-Assad ML, Sans LMA, Assad ED, Zullo J Jr (2001) Relações entre água retida e conteúdo de areia total em solos brasileiros. Revista Brasileira de Agrometeorologia, Passo Fundo 9:588–596Google Scholar
  27. Maharjan GR, Hoffmann H, Webber H, Srivastava AK, Weihermüller L, Villa A, Coucheney E, Lewan E, Trombi G, Moriondo M, Bindi M, Grosz B, Dechow R, Kuhnert M, Doro L, Kersebaum K-C, Stella T, Specka X, Nendel C, Constantin J, Raynal H, Ewert F, Gaiser T (2019) Effects of input data aggregation on simulated crop yields in temperate and Mediterranean climates. Eur J Agron 103:32–46. Scholar
  28. Martre P, Wallach D, Asseng S, Ewert F, Jones JW, Rötter RP, Boote KJ, Ruane AC, Thorburn PJ, Cammarano D, Hatfield JL, Rosenzweig C, Aggarwal PK, Angulo C, Basso B, Bertuzzi P, Biernath C, Brisson N, Challinor AJ, Doltra J, Gayler S, Goldberg R, Grant RF, Heng L, Hooker J, Hunt LA, Ingwersen J, Izaurralde RC, Kersebaum KC, Müller C, Kumar SN, Nendel C, O’Leary G, Olesen JE, Osborne TM, Palosuo T, Priesack E, Ripoche D, Semenov MA, Shcherbak I, Steduto P, Stöckle CO, Stratonovitch P, Streck T, Supit I, Tao F, Travasso M, Waha K, White JW, Wolf J (2015) Multimodel ensembles of wheat growth: many models are better than one. Glob Change Biol 21:911–925. Scholar
  29. Nendel C, Berg M, Kersebaum KC, Mirschel W, Specka X, Wegehenkel M, Wenkel KO, Wieland R (2011) The MONICA model: testing predictability for crop growth, soil moisture and nitrogen dynamics. Ecol Model 222:1614–1625CrossRefGoogle Scholar
  30. OECD (2017) Oilseeds and oilseed products. In: OECD-FAO agricultural outlook 2017–2026. Organization for economic co-operation and development (OECD) Publishing, Paris.
  31. QGIS Development Team (2018) QGIS geographic information system. Version 3.0.1. 2018. Accessed 10 Jan
  32. RADAMBRASIL (1974) Levantamento de recursos naturais. Rio de Janeiro, 4 (in Portuguese)Google Scholar
  33. Rao NH, Sarma PBS, Chander S (1988) a simple dated water-production function for use in irrigated agriculture. Agric Water Manag 13:25–32CrossRefGoogle Scholar
  34. Sentelhas PC, Battisti R, Câmara GMS, Farias JRB, Hampf A, Nendel C (2015) The soybean yield gap in Brazil—magnitude, causes and possible solution. J Agric Sci 158:1394–1411. Scholar
  35. Teixeira EI, Zhao G, Ruiter JD, Brown H, Ausseil A-G, Meenken E, Ewert F (2017) The interactions between genotype, management and environment in regional crop modelling. Eur J Agron 88:106–115. Scholar
  36. Teixeira WWR, Battisti R, Sentelhas PC, de Moraes MF, de Oliveira Jr A (2019) Uncertainties assessment of soybean yield gaps using DSSAT-CSM-CROPGRO calibrated by cultivar maturity groups. J Agron Crop Sci (under review)Google Scholar
  37. Wegerer R, Popp M, Hu X, Purcell L (2015) Soybean maturity group selection: irrigation and nitrogen fixation effects on returns. Field Crop Res 180:1–9. Scholar
  38. Xavier AC, King CW, Scanlon BR (2015) Daily gridded meteorological variables in Brazil (1980–2013). Int J Climatol 36:2644–2659. Scholar
  39. Zanon AJ, Streck NA, Grassini P (2016) Climate and management factors influence soybean yield potential in a subtropical environment. Agron J 108:1447–1454. Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Rafael Battisti
    • 1
    Email author
  • Derblai Casaroli
    • 1
  • Jéssica Sousa Paixão
    • 1
  • José Alves Júnior
    • 1
  • Adão Wagner Pêgo Evangelista
    • 1
  • Marcio Mesquita
    • 1
  1. 1.Research Group on Climate and Water Resources of the Cerrado BiomeCollege of Agronomy, Federal University of GoiásGoiâniaBrazil

Personalised recommendations