Assessment of crop-management strategies to improve soybean resilience to climate change in Southern Brazil
Rafael Battisti A G , Paulo C. Sentelhas B , Phillip S. Parker C , Claas Nendel C , Gil M. De S. Câmara D , José R. B. Farias E and Claudir J. Basso F
+ Author Affiliations
- Author Affiliations
A College of Agronomy, Federal University of Goiás, Avenida Esperança, Goiânia, GO 74690-900, Brazil.
B Department of Biosystems Engineering, ESALQ, University of São Paulo, Avenida Pádua Dias, Piracicaba, SP 13418-900, Brazil.
C Leibniz Centre for Agricultural Landscape Research (ZALF), Eberswalder Straße, 15374 Müncheberg, Germany.
D Department of Plant Production, ESALQ, University of São Paulo, Avenida Pádua Dias, Piracicaba, SP 13418-900, Brazil.
E National Soybean Research Center, Embrapa, Caixa Postal 231, Londrina, PR 86001-970, Brazil.
F Department of Agricultural and Environmental Sciences, Federal University of Santa Maria, Linha 7 de Setembro, Frederico Westphalen, RS 98400-000, Brazil.
G Corresponding author. Email: battisti@ufg.br
Crop and Pasture Science 69(2) 154-162 https://doi.org/10.1071/CP17293
Submitted: 28 April 2017 Accepted: 14 November 2017 Published: 31 January 2018
Abstract
Management is the most important handle to improve crop yield and resilience under climate change. The aim of this study was to evaluate how irrigation, sowing date, cultivar maturity group and planting density can contribute for increasing the resilience of soybean (Glycine max (L.) Merr.) under future climate in southern Brazil. Five sites were selected to represent the range of Brazilian production systems typical for soybean cultivation. Yields were obtained from a crop-model ensemble (CROPGRO, APSIM and MONICA). Three climate scenarios were evaluated: baseline (1961–2014), and two future climate scenarios for the mid-century (2041–70) with low (+2.2°C, A1BLs) and high (+3.2°C, A1BHs) deltas for air temperature and with atmospheric [CO2] of 600 ppm. Supplementary irrigation resulted in higher and more stable yields, with gains in relation to a rainfed crop of 543, 719, 758 kg ha–1, respectively, for baseline, A1BLs and A1BHs. For sowing date, the tendencies were similar between climate scenarios, with higher yields when soybean was sown on 15 October for each simulated growing season. Cultivar maturity group 7.8 and a plant density of 50 plants m−2 resulted in higher yields in all climate scenarios. The best crop-management strategies showed similar tendency for all climate scenarios in Southern Brazil.
Additional keywords: adaptation strategies, crop cycle, simulation model, sowing window, soybean yield.
References
Alagarswamy G, Boote KJ, Allen LH, Jones JW (2006) Evaluating the CROPGRO-Soybean model ability to simulate photosynthesis response to carbon dioxide levels. Agronomy Journal 98, 34–42.| Evaluating the CROPGRO-Soybean model ability to simulate photosynthesis response to carbon dioxide levels.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD28XhvVCltbc%3D&md5=75dc4a766196c60c88583a86e72ade03CAS |
Allen GR, Pereira LS, Raes D, Smith M (1998) Crop evapotranspiration—Guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper 56. (FAO: Rome)
Alliprandini LF, Abatti C, Bertagnolli PF, Cavassim JE, Gabe HL, Kurek A, Matsumoto MN, Oliveira MAR, Pitol C, Prado LC, Stecking C (2009) Understanding soybean maturity groups in Brazil: Environment, cultivar classification, and stability. Crop Science 49, 801–808.
| Understanding soybean maturity groups in Brazil: Environment, cultivar classification, and stability.Crossref | GoogleScholarGoogle Scholar |
Bassu S, Brisson N, Durand JL, 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 SH, 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? Global Change Biology 20, 2301–2320.
| How do various maize crop models vary in their responses to climate change factors?Crossref | GoogleScholarGoogle Scholar |
Battisti R (2016) Calibration, uncertainties and use of soybean crop simulation models for evaluation strategies to mitigate the effects of climate change in Southern Brazil. PhD Thesis in Agricultural Systems Engineering, Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, Brazil.
Battisti R, Sentelhas PC (2014) New agroclimatic approach for soybean sowing dates recommendation: A case study. Revista Brasileira de Engenharia Agrícola e Ambiental 18, 1149–1156.
| New agroclimatic approach for soybean sowing dates recommendation: A case study.Crossref | GoogleScholarGoogle Scholar |
Battisti R, Sentelhas PC (2015) Drought tolerance of Brazilian soybean cultivars simulated by a simple agrometeorological yield model. Experimental Agriculture 51, 285–298.
| Drought tolerance of Brazilian soybean cultivars simulated by a simple agrometeorological yield model.Crossref | GoogleScholarGoogle Scholar |
Battisti R, Sentelhas PC, Boote KJ, Câmara GMS, Farias JRF, Basso CJ (2017a) Assessment of soybean yield with altered water-related genetic improvement traits under climate change in Southern Brazil. European Journal of Agronomy 83, 1–14.
| Assessment of soybean yield with altered water-related genetic improvement traits under climate change in Southern Brazil.Crossref | GoogleScholarGoogle Scholar |
Battisti R, Sentelhas PC, Boote KJ (2017b) Inter-comparison of performance of soybean crop simulation models and their ensemble in southern Brazil. Field Crops Research 200, 28–37.
| Inter-comparison of performance of soybean crop simulation models and their ensemble in southern Brazil.Crossref | GoogleScholarGoogle Scholar |
Battisti R, Sentelhas PC, Boote KJ (2017c) Sensitivity and requirement of improvement of four soybean crop growth models to climate change in Southern Brazil. International Journal of Biometeorology 1–10.
| Sensitivity and requirement of improvement of four soybean crop growth models to climate change in Southern Brazil.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC2sXhtVejs7zL&md5=6354c69d58caf580e50deb9642ea00d8CAS |
Battisti R, Parker PS, Sentelhas PC, Nendel C (2017d) Gauging the sources of uncertainty in soybean yield simulations using the MONICA model. Agricultural Systems 155, 9–18.
| Gauging the sources of uncertainty in soybean yield simulations using the MONICA model.Crossref | GoogleScholarGoogle Scholar |
Bernacchi CJ, Kimball BA, Quarles DR, Long SP, Ort DR (2007) Decreases in stomatal conductance of soybean under open-air elevation of [CO2] are closely coupled with decreases in ecosystem evapotranspiration. Plant Physiology 143, 134–144.
| Decreases in stomatal conductance of soybean under open-air elevation of [CO2] are closely coupled with decreases in ecosystem evapotranspiration.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2sXpt1Ogug%3D%3D&md5=ebf9e0d4e46011d8d890bb5802da2473CAS |
Boote KJ, Pickering NB (1994) Modeling photosynthesis of row crop canopies. HortScience 29, 1423–1434.
Boote KJ, Jones JW, Hoogenboom G, Pickering NB (1998) Simulation of crop growth: CROPGRO model. In ‘Agricultural systems modeling and simulation’. Vol. 18. (Eds RM Pearl, RB Curry) pp. 651–692. (Taylor and Francis: New York)
Bunce JA (2016) Responses of soybean and wheat to elevated CO2 in free-air and open top chamber systems. Field Crops Research 186, 78–85.
| Responses of soybean and wheat to elevated CO2 in free-air and open top chamber systems.Crossref | GoogleScholarGoogle Scholar |
Carbone GJ, Mearns LO, Mavromatis T, Sadler EJ, Stooksbury D (2003) Evaluating CROPGRO-Soybean performance for use in climate impact studies. Agronomy Journal 95, 537–544.
| Evaluating CROPGRO-Soybean performance for use in climate impact studies.Crossref | GoogleScholarGoogle Scholar |
Dias HB, Sentelhas PC (2017) Evaluation of three sugarcane simulation models and their ensemble for yield estimation in commercially managed field. Field Crops Research 213, 174–185.
| Evaluation of three sugarcane simulation models and their ensemble for yield estimation in commercially managed field.Crossref | GoogleScholarGoogle Scholar |
do Rio A, Sentelhas PC, Farias JRB, Sibaldelli RNR, Ferreira RC (2016) Alternative sowing dates as a mitigation measure to reduce climate change impacts on soybean yield in southern Brazil. International Journal of Climatology 36, 3664–3672.
| Alternative sowing dates as a mitigation measure to reduce climate change impacts on soybean yield in southern Brazil.Crossref | GoogleScholarGoogle Scholar |
Egli DB, Cornelius PL (2009) A regional analysis of the response of soybean yield to planting date. Agronomy Journal 101, 330–335.
| A regional analysis of the response of soybean yield to planting date.Crossref | GoogleScholarGoogle Scholar |
Embrapa (2013) Production system: Soybean (In Portuguese). Embrapa, Brazil. Available at: http://sistemasdeproducao.cnptia.embrapa.br/#soja/ (accessed 01 June 2015).
FAO (2017) FAOSTAT: Food and agriculture data. Food and Agriculture Organization of the United Nations. Available at: http://faostat3.fao.org/home/E/ (accessed 17 January 2017).
Fleisher DH, Condoril B, Quiroz R, Alva A, Asseng S, Barreda C, Bindi M, Boote KJ, Ferrise R, Franke AC, Govindakrishnan PM, Harahagazwe D, Hoogenboom G, Naresh Kumar S, Merante P, Nendel C, Olesen JE, Parker PS, Raes D, Raymundo R, Ruane AC, Stockle C, Supit I, Vanuytrecht E, Wolf J, Woli P (2017) A potato model inter-comparison across varying climates and productivity levels. Global Change Biology 23, 1258–1281.
| A potato model inter-comparison across varying climates and productivity levels.Crossref | GoogleScholarGoogle Scholar |
Hao X, Gao J, Han X, Ma Z, Merchant A, Ju H, Li P, Yang W, Gao Z, Lin E (2014) Effects of open-air elevated atmospheric CO2 concentration on yield quality of soybean (Glycine max (L) Merr). Agriculture, Ecosystems & Environment 192, 80–84.
| Effects of open-air elevated atmospheric CO2 concentration on yield quality of soybean (Glycine max (L) Merr).Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC2cXms1Gjsb8%3D&md5=c5a8f4f6fa20c7918c380de718c8135eCAS |
Holzworth DP, Huth NI, Devoil PG, Zurcher EJ, Hermann NI, McLean G, Chenu K, Van Oosterom EJ, Snow V, Murphy C, Moore AD, Brown H, Whish JPM, Verrall S,, Fainges J, Bell LW, Peake AS, Poulton PL, Hochman Z, Thorburn PJ, Gaydon DS, Dalgliesh NP, Rodriguez D, Cox H, Chapman S, Doherty A, Teixeira E, Sharp J, Cichota R, Vogeler I, Li FY, Wang E, Hammer G, Robertson M, Dimes JP, Whitbread AM, Hunt J, Van Rees H, McClelland T, Carberry PS, Hargreaves JNG, Macleod N, McDonald C, Harsdorf J, Wedgwood S, Keating BA (2014) APSIM—Evolution towards a new generation of agricultural systems simulation. Environmental Modelling & Software 62, 327–350.
| APSIM—Evolution towards a new generation of agricultural systems simulation.Crossref | GoogleScholarGoogle Scholar |
IBGE (2014) Interactive maps: soils. [In Portuguese]. Instituto Brasileiro de Geografia e Estatística. Available at: http://mapas.ibge.gov.br/ (accessed 18 October 2014).
IBGE (2015) Agricultural production. Instituto Brasileiro de Geografia e Estatística. Available at: www.sidra.ibge.gov.br/bda/pesquisas/pam/default.asp?o=18&i=P (accessed 15 January 2015).
INPE (2015). Projeções de cenários climáticos. Instituto Nacional de Pesquisas Espaciais. Available at: http://pnud.cptec.inpe.br/ (accessed 17 January 2015)
IPCC (2014) Climate change 2014: Synthesis report. In ‘Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change’. (Eds RK Pachauri, LA Meyer) (IPCC: Geneva, Switzerland)
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. European Journal of Agronomy 18, 235–265.
| The DSSAT cropping system model.Crossref | GoogleScholarGoogle Scholar |
Kaster M, Farias JRB (2012) Regionalization of cultivation value and use tests and an indication of soybean cultivars—Third approximation. Documentos 330, Embrapa, Soja, Londrina, Brazil. [In Portuguese]
Kumagai E, Sameshima R (2014) Genotypic differences in soybean yield responses to increasing temperature in a cool climate are related to maturity group. Agricultural and Forest Meteorology 198–199, 265–272.
| Genotypic differences in soybean yield responses to increasing temperature in a cool climate are related to maturity group.Crossref | GoogleScholarGoogle Scholar |
Kuss RCR, Köing O, Dutra LMC, Bellé RA, Roggia S, Sturmer GR (2008) Populações de plantas e estratégias de manejo de irrigação na cultura da soja. Ciência Rural 38, 1133–1137.
| Populações de plantas e estratégias de manejo de irrigação na cultura da soja.Crossref | GoogleScholarGoogle Scholar |
Li T, Hasegawa T, Yin X, Zhu Y, Boote K, Adam M, Bregaglio S, Buis S, Confalonieri B, Fumoto T, Gaydon D, Marcaida M, Nakagawa H, Oriol P, Ruane AC, Ruget F, Balwinder-Singh , Singh U, Tang L, Tao F, Wilkens P, Yoshida H, Zhang Z, Bouman B (2015) Uncertainties in predicting rice yield by current crop models under a wide range of climatic conditions. Global Change Biology 21, 1328–1341.
| Uncertainties in predicting rice yield by current crop models under a wide range of climatic conditions.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC2sXhvVWnsbw%3D&md5=0559e8040121c6405a6d07b7841c85f8CAS |
Marcolin CD, Klein VA (2011) Determination of relative soil density through a pedotransfer function of maximum bulk density. Acta Scientiarum. Agronomy 33, 349–354.
| Determination of relative soil density through a pedotransfer function of maximum bulk density.Crossref | GoogleScholarGoogle Scholar |
Marengo JA, Ambrizzi T, da Rocha RP, Alves LM, Cuadra SV, Valverde MC, Torres RR, Santos DC, Ferraz SET (2010) Future change of climate in South America in the late twenty-first century: intercomparison of scenarios from three regional climate models. Climate Dynamics 35, 1073–1097.
| Future change of climate in South America in the late twenty-first century: intercomparison of scenarios from three regional climate models.Crossref | GoogleScholarGoogle Scholar |
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. Global Change Biology 21, 911–925.
| Multimodel ensembles of wheat growth: many models are better than one.Crossref | GoogleScholarGoogle Scholar |
Mayers JD, Lawn RJ, Byth DE (1991) Agronomic studies on soybean [Glycine max (L.) Merrill] in the dry season of the tropics. II. Interaction of sowing date and sowing density. Australian Journal of Agricultural Research 42, 1093–1107.
| Agronomic studies on soybean [Glycine max (L.) Merrill] in the dry season of the tropics. II. Interaction of sowing date and sowing density.Crossref | GoogleScholarGoogle Scholar |
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. Ecological Modelling 222, 1614–1625.
| The MONICA model: Testing predictability for crop growth, soil moisture and nitrogen dynamics.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3MXksFygtrk%3D&md5=d2c9710f9f24c5726f0fe1ab4bd7a28bCAS |
Nendel C, Kersebaum KC, Mirschel W, Wenkel KO (2014) Testing farm management options as climate change adaptation strategies using the MONICA model. European Journal of Agronomy 52, 47–56.
| Testing farm management options as climate change adaptation strategies using the MONICA model.Crossref | GoogleScholarGoogle Scholar |
NOAA/ESRL (2015) Mauna Loa CO2 monthly mean data. National Oceanic and Atmospheric Administration /Earth System Research Laboratory. Available at: www.esrl.noaa.gov/gmd/ccgg/trends/index.html (accessed 19 November 2015).
RADAMBRASIL (1974) ‘Levantamento de recursos naturais.’ (Departamento Nacional da Produção Mineral (DNPM): Rio de Janeiro)
Reichert JM, Albuquerque JA, Kaiser DR, Reinert DJ, Urach FL, Carlesso R (2009) Estimation of water retention and availability in soil of Rio Grande do Sul. Revista Brasileira de Ciência do Solo 33, 1547–1560.
| Estimation of water retention and availability in soil of Rio Grande do Sul.Crossref | GoogleScholarGoogle Scholar |
Rosenthal DM, Ruiz-Vera UM, Siebers MH, Gray SB, Bernacchi CJ, Ort DR (2014) Biochemical acclimation, stomatal limitation and precipitation patterns underlie decreases in photosynthetic stimulation of soybean (Glycine max) at elevated [CO2] and temperatures under fully open air field conditions. Plant Science 226, 136–146.
| Biochemical acclimation, stomatal limitation and precipitation patterns underlie decreases in photosynthetic stimulation of soybean (Glycine max) at elevated [CO2] and temperatures under fully open air field conditions.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC2cXhtFSgt7bM&md5=fe8c0c9a006db85f732ffefee2bc8d6eCAS |
Ruiz-Vera UM, Siebers M, Gray SB, Drag DW, Rosenthal DM, Kimball BA, Ort DR, Bernacchi CJ (2013) Global warming can negate the expected CO2 stimulation in photosynthesis and productivity for soybean grown in the Midwestern United States. Plant Physiology 162, 410–423.
| Global warming can negate the expected CO2 stimulation in photosynthesis and productivity for soybean grown in the Midwestern United States.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3sXnvVWktro%3D&md5=c2a98097af678f6d443418f7eb990cd1CAS |
Rurinda J, Van Wijk M, Mapfumo P, Descheemaeker K, Supit I, Giller K (2015) Climate change and maize yield in southern Africa: what can farm management do? Global Change Biology 21, 4588–4601.
| Climate change and maize yield in southern Africa: what can farm management do?Crossref | GoogleScholarGoogle Scholar |
Sauer TJ, Singer JW, Prueger JH, Dessuter TM, Hatfield JL (2007) Radiation balance and evaporation partitioning in a narrow-row soybean canopy. Agricultural and Forest Meteorology 145, 206–214.
| Radiation balance and evaporation partitioning in a narrow-row soybean canopy.Crossref | GoogleScholarGoogle Scholar |
Sentelhas PC, Battisti R, Câmara GMS, Farias JRB, Hampf A, Nendel C (2015) The soybean yield gap in Brazil—magnitude, causes and possible solution. The Journal of Agricultural Science 153, 1394–1411.
| The soybean yield gap in Brazil—magnitude, causes and possible solution.Crossref | GoogleScholarGoogle Scholar |
Spehar CR, Francisco ER, Pereira A (2015) Yield stability of soybean cultivars in response to sowing date in the lower latitude Brazilian Savannah Highlands. The Journal of Agricultural Science 153, 1059–1068.
| Yield stability of soybean cultivars in response to sowing date in the lower latitude Brazilian Savannah Highlands.Crossref | GoogleScholarGoogle Scholar |
Xu G, Singh SK, Reddy VR, Barnaby JY, Sicher RC, Li T (2016) Soybean grown under elevated CO2 benefits more under low temperature than high temperature stress: Varying response of photosynthetic limitations, leaf metabolites, growth, and seed yield. Journal of Plant Physiology 205, 20–32.
| Soybean grown under elevated CO2 benefits more under low temperature than high temperature stress: Varying response of photosynthetic limitations, leaf metabolites, growth, and seed yield.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC28Xhtlykt7jI&md5=f8d16d3478b57d8af98b94735f16c3bbCAS |
