Register      Login
Crop and Pasture Science Crop and Pasture Science Society
Plant sciences, sustainable farming systems and food quality
RESEARCH ARTICLE

Yield gap of the double-crop system of main-season soybean with off-season maize in Brazil

Rogério de Souza Nóia Júnior https://orcid.org/0000-0002-4096-7588 A B and Paulo Cesar Sentelhas https://orcid.org/0000-0002-9277-6871 A B
+ Author Affiliations
- Author Affiliations

A Department of Biosystems Engineering, College of Agriculture Luiz de Queiroz, University of São Paulo,13418-900, Piracicaba, São Paulo, Brazil.

B Corresponding authors. Emails: rogeriosouzanoia@gmail.com; pcsentel.esalq@usp.br.

Crop and Pasture Science 71(5) 445-458 https://doi.org/10.1071/CP19372
Submitted: 12 September 2019  Accepted: 6 April 2020   Published: 18 May 2020

Abstract

The succession of main-season soybean (Glycine max (L.) Merr.) with off-season maize (Zea mays L.) is an important Brazilian agricultural system contributing to increased grain production without the need for crop land expansion. Yield-gap studies that identify the main factors threatening these crops are pivotal to increasing food security in Brazil and globally. Therefore, the aim of the present study was to determine, for the soybean–off-season-maize succession, the magnitude of the grain and revenue yield gap (YG) caused by water deficit (YGW) and suboptimal crop management (YGM), and to propose strategies for closing these gaps in different Brazilian regions. The ensemble of three previously calibrated and validated models (FAO-AZM, DSSAT and APSIM) was used to estimate yields of soybean and off-season maize for 28 locations in 12 states for a period of 34 years (1980–2013). Water deficit is the biggest problem for soybean and off-season maize crops in the regions of Cocos (state of Bahia), Buritis (Minas Gerais) and Formosa (Goiás), where the YGW accounted for ~70% of total YG. The YGM revealed that locations in the central region of Brazil, mainly in the state of Mato Grosso, presented an opportunity to increase yields of soybean and off-season maize, on average, by 927.5 and 909.6 5 kg ha–1, respectively. For soybean, YGM was the main cause of total YG in Brazil, accounting for 51.8%, whereas for maize, YGW corresponded to 53.8% of the total YG. Our results also showed that the choice of the best sowing date can contribute to reducing soybean YGW by 34–54% and off-season maize YGW by 66–89%.

Additional keywords: actual yield, attainable yield, double-cropping, crop simulation models, multi-model approach, potential yield.


References

Alvares CA, Stape JL, Sentelhas PC, de Moraes Gonçalves JL, Sparovek G (2013) Köppen’s climate classification map for Brazil. Meteorologische Zeitschrift 22, 711–728.
Köppen’s climate classification map for Brazil.Crossref | GoogleScholarGoogle Scholar |

Andrea MCS, Boote KJ, Sentelhas PC, Romanelli TL (2018) Variability and limitations of maize production in Brazil: potential yield, water-limited yield and yield gaps. Agricultural Systems 165, 264–273.
Variability and limitations of maize production in Brazil: potential yield, water-limited yield and yield gaps.Crossref | GoogleScholarGoogle Scholar |

Anjum SA, Ashraf U, Tanveer M, Khan I, Hussain S, Shahzad B, Zohaib A, Abbas F, Saleem MF, Ali I, Wang LC (2017) Drought induced changes in growth, osmolyte accumulation and antioxidant metabolism of three maize hybrids. Frontiers in Plant Science 08, 69
Drought induced changes in growth, osmolyte accumulation and antioxidant metabolism of three maize hybrids.Crossref | GoogleScholarGoogle Scholar |

Asseng S, Ewert F, Rosenzweig C, Jones JW, Hatfield JL, Ruane AC, Boote KJ, Thorburn PJ, Rötter RP, Cammarano D, Brisson N, Basso B, Martre P, Aggarwal PK, Angulo C, Bertuzzi P, Biernath C, Challinor AJ, Doltra J, Gayler S, Goldberg R, Grant R, Heng L, Hooker J, Hunt LA, Ingwersen J, Izaurralde RC, Kersebaum KC, Müller C, Naresh Kumar S, Nendel C, O’Leary G, Olesen JE, Osborne TM, Palosuo T, Priesack E, Ripoche D, Semenov MA, Shcherbak I, Steduto P, Stöckle C, Stratonovitch P, Streck T, Supit I, Tao F, Travasso M, Waha K, Wallach D, White JW, Williams JR, Wolf J (2013) Uncertainty in simulating wheat yields under climate change. Nature Climate Change 3, 827–832.
Uncertainty in simulating wheat yields under climate change.Crossref | GoogleScholarGoogle Scholar |

Battisti R, Sentelhas PC (2017) Improvement of soybean resilience to drought through deep root system in Brazil. Agronomy Journal 109, 1612–1622.
Improvement of soybean resilience to drought through deep root system in Brazil.Crossref | GoogleScholarGoogle Scholar |

Battisti R, Sentelhas PC (2019) Characterizing Brazilian soybean-growing regions by water deficit patterns. Field Crops Research 240, 95–105.
Characterizing Brazilian soybean-growing regions by water deficit patterns.Crossref | GoogleScholarGoogle Scholar |

Battisti R, Parker PS, Sentelhas PC, Nendel C (2017a) 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 |

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, deS Câmara GM, Farias JRB, Basso CJ (2017c) 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, Bender FD, Sentelhas PC (2018a) Assessment of different gridded weather data for soybean yield simulations in Brazil. Theoretical and Applied Climatology 135, 237–247.
Assessment of different gridded weather data for soybean yield simulations in Brazil.Crossref | GoogleScholarGoogle Scholar |

Battisti R, Sentelhas PC, Pascoalino JAL, Sako H, de Sá Dantas JP, Moraes MF (2018b) Soybean yield gap in the areas of yield contest in Brazil. International Journal of Plant Production 12, 159–168.
Soybean yield gap in the areas of yield contest in Brazil.Crossref | GoogleScholarGoogle Scholar |

Ben LHB, Peiter MX, Robaina AD, Parizi ARC, Silva Gu DA (2016) Influence of irrigation levels and plant density on ‘second season’ maize. Revista Caatinga 29, 665–676.
Influence of irrigation levels and plant density on ‘second season’ maize.Crossref | GoogleScholarGoogle Scholar |

Bender FD (2017) Mudanças climáticas e seus impactos na produtividade da cultura de milho e estratégias de manejo para minimização de perdas em diferentes regiões brasileiras. PhD Thesis, São Paulo University, Brazil. http://www.teses.usp.br/teses/disponiveis/11/11152/tde-20102017-084031/pt-br.php

Bender FD, Sentelhas PC (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.
Solar radiation models and gridded databases to fill gaps in weather series and to project climate change in Brazil.Crossref | GoogleScholarGoogle Scholar |

Bergamaschi H, Dalmago GA, Comiran F, Bergonci JI, Müller AG, França S, Santos AO, Radin B, Bianchi CAM, Pereira PG (2006) Deficit hídrico e produtividade na cultura do milho. Pesquisa Agropecuária Brasileira 41, 243–249.
Deficit hídrico e produtividade na cultura do milho.Crossref | GoogleScholarGoogle Scholar |

Boote KJ, Jones WJ, Batchelor DW, Nafziger DE, Myers O (2003) Genetic coefficients in the CROPGRO-Soybean model. Agronomy Journal 95, 32–51.
Genetic coefficients in the CROPGRO-Soybean model.Crossref | GoogleScholarGoogle Scholar |

Braccini A de L, Stülp M, Albrecht LP, Ávila MR, Scapim CA, Ricci TT (2010) Desempenho agronômico e produtividade na sucessão soja–milho safrinha. Acta Scientiarum. Agronomy 32, 651–661.
Desempenho agronômico e produtividade na sucessão soja–milho safrinha.Crossref | GoogleScholarGoogle Scholar |

Brazilian Ministry of National Integration (2014) Territorial analysis for the development of irrigated agriculture in Brazil. Available at: https://www5.usp.br/103324/brasil-tem-potencial-para-expandir-agricultura-irrigada/ (accessed 29 April 2020).

Catuchi TA, Guidorizzi FVC, Guidorizi KA, Barbosa A de M, Souza GM (2012) Physiological responses of soybean cultivars to potassium fertilization under different water regimes. Pesquisa Agropecuária Brasileira 47, 519–527.
Physiological responses of soybean cultivars to potassium fertilization under different water regimes.Crossref | GoogleScholarGoogle Scholar |

Ceccon G, Raga A, Duarte AP, Siloto RC (2004) Efeito de inseticidas na semeadura sobre pragas iniciais e produtividade de milho safrinha em plantio direto. Bragantia 63, 227–237.
Efeito de inseticidas na semeadura sobre pragas iniciais e produtividade de milho safrinha em plantio direto.Crossref | GoogleScholarGoogle Scholar |

CEPEA (2018) Soybean and maize prices. Centre for Advance Studies on Applied Economics, University of São Paulo, SP. Available at: https://www.cepea.esalq.usp.br/en (accessed 21 January 2019).

Conab (2019) Grains—historical series. Agricultural Information Portal. Companhia Nacional de Abastecimento, Brasilia. Available at: https://portaldeinformacoes.conab.gov.br/index.php/safras/safra-serie-historica (accessed 21 January 2019).

Dias HB, Sentelhas PC (2017) Evaluation of three sugarcane simulation models and their ensemble for yield estimation in commercially managed fields. Field Crops Research 213, 174–185.
Evaluation of three sugarcane simulation models and their ensemble for yield estimation in commercially managed fields.Crossref | GoogleScholarGoogle Scholar |

Dias HB, Sentelhas PC (2018) Sugarcane yield gap analysis in Brazil—a multi-model approach for determining magnitudes and causes. The Science of the Total Environment 637–638, 1127–1136.
Sugarcane yield gap analysis in Brazil—a multi-model approach for determining magnitudes and causes.Crossref | GoogleScholarGoogle Scholar | 29801206PubMed |

Duarte YCN (2018) Maize simulation models—use to determine yield gaps and yield forecasting in Brazil. Masters Thesis, University of São Paulo, SP, Brazil.

Duarte AP, Silva AC, Deuber R (2007) Plantas infestantes em lavouras de milho safrinha, sob diferentes manejos, no Médio Paranapanema. Planta Daninha 25, 285–291.
Plantas infestantes em lavouras de milho safrinha, sob diferentes manejos, no Médio Paranapanema.Crossref | GoogleScholarGoogle Scholar |

Ehrlich PR, Harte J (2015) Opinion: to feed the world in 2050 will require a global revolution. Proceedings of the National Academy of Sciences of the United States of America 112, 14743–14744.
Opinion: to feed the world in 2050 will require a global revolution.Crossref | GoogleScholarGoogle Scholar | 26627228PubMed |

FAO (2017) Sustainable irrigated agriculture in Brazil: identification of priority areas. Food and Agriculture Organization of the United Nations, Rome, Italy.

FAO (2019) FAOSTAT: food and agriculture data. Food and Agriculture Organization of the United Nations, Rome. http://www.fao.org/faostat/en/#home (accessed 21 January 2019).

Fischer RA (2009) Farming systems of Australia. In ‘Crop physiology’. pp. 22–54. (Elsevier: Amsterdam) https://doi.org/10.1016/B978-0-12-374431-9.00002-5

Foley JA (2005) Global consequences of land use. Science 309, 570–574.
Global consequences of land use.Crossref | GoogleScholarGoogle Scholar | 16040698PubMed |

Foley JA, Ramankutty N, Brauman KA, Cassidy ES, Gerber JS, Johnston M, Mueller ND, O’Connell C, Ray DK, West PC, Balzer C, Bennett EM, Carpenter SR, Hill J, Monfreda C, Polasky S, Rockström J, Sheehan J, Siebert S, Tilman D, Zaks DPM (2011) Solutions for a cultivated planet. Nature 478, 337–342.
Solutions for a cultivated planet.Crossref | GoogleScholarGoogle Scholar | 21993620PubMed |

Foloni JSS, Calonego JC, Catuchi TA, Belleggia NA, Tiritan CS, Barbosa AM (2014) Cultivares de milho em diferentes populações de plantas com espaçamento reduzido na safrinha. Revista Brasileira de Milho e Sorgo 13, 312–325.
Cultivares de milho em diferentes populações de plantas com espaçamento reduzido na safrinha.Crossref | GoogleScholarGoogle Scholar |

Garcia RA, Ceccon G, Sutier GA da S, dos Santos ALF (2018) Soybean–corn succession according to seeding date. Pesquisa Agropecuária Brasileira 53, 22–29.
Soybean–corn succession according to seeding date.Crossref | GoogleScholarGoogle Scholar |

Godfray HCJ, Beddington JR, Crute IR, Haddad L, Lawrence D, Muir JF, Pretty J, Robinson S, Thomas SM, Toulmin C (2010) Food security: the challenge of feeding 9 billion people. Science 327, 812–818.
Food security: the challenge of feeding 9 billion people.Crossref | GoogleScholarGoogle Scholar |

Goldemberg J, Mello FFC, Cerri CEP, Davies CA, Cerri CC (2014) Meeting the global demand for biofuels in 2021 through sustainable land use change policy. Energy Policy 69, 14–18.
Meeting the global demand for biofuels in 2021 through sustainable land use change policy.Crossref | GoogleScholarGoogle Scholar |

Gomes JM, Rodrigues FA, Oliveira MCN, Farias JRB, Neumaier N, Abdelnoor RV, Marcelino-Guimarães FC, Nepomuceno AL (2013) Expression patterns of GmAP2/EREB-like transcription factors involved in soybean responses to water deficit. PLoS One 8, e62294
Expression patterns of GmAP2/EREB-like transcription factors involved in soybean responses to water deficit.Crossref | GoogleScholarGoogle Scholar |

Guan K, Sultan B, Biasutti M, Baron C, Lobell DB (2017) Assessing climate adaptation options and uncertainties for cereal systems in West Africa. Agricultural and Forest Meteorology 232, 291–305.
Assessing climate adaptation options and uncertainties for cereal systems in West Africa.Crossref | GoogleScholarGoogle Scholar |

Guilpart N, Grassini P, Sadras VO, Timsina J, Cassman KG (2017) Estimating yield gaps at the cropping system level. Field Crops Research 206, 21–32.
Estimating yield gaps at the cropping system level.Crossref | GoogleScholarGoogle Scholar | 28515571PubMed |

Hampf AC, Stella T, Berg-Mohnicke M, Kawohl T, Kilian M, Nendel C (2020) Future yields of double-cropping systems in the Southern Amazon, Brazil, under climate change and technological development. Agricultural Systems 177, 102707
Future yields of double-cropping systems in the Southern Amazon, Brazil, under climate change and technological development.Crossref | GoogleScholarGoogle Scholar |

Heinemann AB, Sentelhas PC (2011) Environmental group identification for upland rice production in central Brazil. Scientia Agrícola 68, 540–547.
Environmental group identification for upland rice production in central Brazil.Crossref | GoogleScholarGoogle Scholar |

Hertel TW, Lobell DB (2014) Agricultural adaptation to climate change in rich and poor countries: current modeling practice and potential for empirical contributions. Energy Economics 46, 562–575.
Agricultural adaptation to climate change in rich and poor countries: current modeling practice and potential for empirical contributions.Crossref | GoogleScholarGoogle Scholar |

Holzworth DP, Huth NI, DeVoil PG, Zurcher EJ, Herrmann 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 GL, Robertson MJ, 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) Mapas interativos: solos. Instituto Brasileiro de Geografia e Estatística, Rio de Janeiro, Brazil. https://mapas.ibge.gov.br/tematicos/solos (accessed 21 January 2019).

IBGE (2019) Municipal Agricultural Research. Instituto Brasileiro de Geografia e Estatística, Rio de Janeiro, Brazil. https://sidra.ibge.gov.br/home/ipca/brasil (accessed 21 January 2019).

Jones J, Hoogenboom G, Porter C, Boote K, Batchelor W, Hunt L, Wilkens P, Singh U, Gijsman A, Ritchie J (2003) The DSSAT cropping system model. European Journal of Agronomy 18, 235–265.
The DSSAT cropping system model.Crossref | GoogleScholarGoogle Scholar |

Kassam AH (1977) ‘Net biomass production and yields of crops.’ (Soil Resources, Management and Conservation Service, Land and Water Development Division, FAO: Rome, Italy)

Keating BA, Carberry PS, Hammer GL, Probert ME, Robertson MJ, Holzworth D, Huth NI, Hargreaves JNG, Meinke H, Hochman Z, McLean G, Verburg K, Snow V, Dimes JP, Silburn M, Wang E, Brown S, Bristow KL, Asseng S, Chapman S, McCown RL, Freebairn DM, Smith CJ (2003) An overview of APSIM, a model designed for farming systems simulation. European Journal of Agronomy 18, 267–288.
An overview of APSIM, a model designed for farming systems simulation.Crossref | GoogleScholarGoogle Scholar |

Lobell DB, Cassman KG, Field CB (2009) Crop yield gaps: their importance, magnitudes, and causes. Annual Review of Environment and Resources 34, 179–204.
Crop yield gaps: their importance, magnitudes, and causes.Crossref | GoogleScholarGoogle Scholar |

López-Ovejero RF, Soares DJ, Oliveira NC, Kawaguchi IT, Berger GU, de Carvalho SJP, Christoffoleti PJ (2016) Interferência e controle de milho voluntário tolerante ao glifosato na cultura da soja. Pesquisa Agropecuária Brasileira 51, 340–347.
Interferência e controle de milho voluntário tolerante ao glifosato na cultura da soja.Crossref | GoogleScholarGoogle Scholar |

Mar GD, Marchetti ME, de Souza LCF, Gonçalves MC, Novelino JO (2003) Produção do milho safrinha em função de doses e épocas de aplicação de nitrogênio. Bragantia 62, 267–274.
Produção do milho safrinha em função de doses e épocas de aplicação de nitrogênio.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 | 25330243PubMed |

Michelotto MD, Duarte AP, de Freitas RS, Miguel FB, Crosariol Netto J (2017) Fall armyworm control in transgenic maize in late-season in São Paulo State, Brazil: ten years of use. Nucleus 67–74.
Fall armyworm control in transgenic maize in late-season in São Paulo State, Brazil: ten years of use.Crossref | GoogleScholarGoogle Scholar |

Mussadiq Z, Hetta M, Swensson C, Gustavsson A-M (2011) Plant development, agronomic performance and nutritive value of forage maize depending on hybrid and marginal site conditions at high latitudes. Acta Agriculturæ Scandinavica. Section B, Soil and Plant Science 62, 420–430.
Plant development, agronomic performance and nutritive value of forage maize depending on hybrid and marginal site conditions at high latitudes.Crossref | GoogleScholarGoogle Scholar |

Nóia Júnior RS, Sentelhas PC (2019a) Soybean-maize succession in Brazil: impacts of sowing dates on climate variability, yields and economic profitability. European Journal of Agronomy 103, 140–151.
Soybean-maize succession in Brazil: impacts of sowing dates on climate variability, yields and economic profitability.Crossref | GoogleScholarGoogle Scholar |

Nóia Júnior RdeS, Sentelhas PC (2019b) Soybean–maize off-season double crop system in Brazil as affected by El Niño Southern Oscillation phases. Agricultural Systems 173, 254–267.
Soybean–maize off-season double crop system in Brazil as affected by El Niño Southern Oscillation phases.Crossref | GoogleScholarGoogle Scholar |

Nora DD, Amado TJC, Nicoloso R da S, Gruhn EM (2017) Modern high-yielding maize, wheat and soybean cultivars in response to gypsum and lime application on no-till Oxisol. Revista Brasileira de Ciência do Solo 41, e0160504
Modern high-yielding maize, wheat and soybean cultivars in response to gypsum and lime application on no-till Oxisol.Crossref | GoogleScholarGoogle Scholar |

Pacheco LP, Monteiro MM de S, Silva RF, da , Soares L dos S, Fonseca WL, Nóbrega JCA, Petter FA, Alcântara Neto F de, Osajima JA (2013) Produção de fitomassa e acúmulo de nutrientes por plantas de cobertura no cerrado piauiense. Bragantia 72, 237–246.
Produção de fitomassa e acúmulo de nutrientes por plantas de cobertura no cerrado piauiense.Crossref | GoogleScholarGoogle Scholar |

Pegorare AB, Fedatto E, Pereira SB, Souza LCF, Fietz CR (2009) Irrigação suplementar no ciclo do milho ‘safrinha’ sob plantio direto. Revista Brasileira de Engenharia Agrícola e Ambiental 13, 262–271.
Irrigação suplementar no ciclo do milho ‘safrinha’ sob plantio direto.Crossref | GoogleScholarGoogle Scholar |

RADAMBRASIL (1974) Levantamento de Recursos Naturais. Departamento Nacional da Produção Mineral, Rio de Janeiro, Brazil.

Rao NH, Sarma PBS, Chander S (1988) A simple dated water-production function for use in irrigated agriculture. Agricultural Water Management 13, 25–32.
A simple dated water-production function for use in irrigated agriculture.Crossref | GoogleScholarGoogle Scholar |

Reichert JM, Albuquerque JA, Kaiser DR, Reinert DJ, Urach FL, Carlesso R (2009) Estimation of water retention and availability in soils of Rio Grande do Sul. Revista Brasileira de Ciência do Solo 33, 1547–1560.
Estimation of water retention and availability in soils of Rio Grande do Sul.Crossref | GoogleScholarGoogle Scholar |

Sentelhas PC, Battisti R, Câmara GMS, Farias JRB, Hampf AC, Nendel C (2015) The soybean yield gap in Brazil—magnitude, causes and possible solutions for sustainable production. The Journal of Agricultural Science 153, 1394–1411.
The soybean yield gap in Brazil—magnitude, causes and possible solutions for sustainable production.Crossref | GoogleScholarGoogle Scholar |

Shiferaw B, Prasanna BM, Hellin J, Bänziger M (2011) Crops that feed the world 6. Past successes and future challenges to the role played by maize in global food security. Food Security 3, 307–327.
Crops that feed the world 6. Past successes and future challenges to the role played by maize in global food security.Crossref | GoogleScholarGoogle Scholar |

Soler CMT, Hoogenboom G, Sentelhas PC, Duarte AP (2007) Impact of water stress on maize grown off-season in a subtropical environment. Journal of Agronomy & Crop Science 193, 247–261.
Impact of water stress on maize grown off-season in a subtropical environment.Crossref | GoogleScholarGoogle Scholar |

Soratto RP, Pereira M, Costa TAM, da , Lampert V do N (2010) Fontes alternativas e doses de nitrogênio no milho safrinha em sucessão à soja. Revista Ciência Agronômica 41, 511–518.
Fontes alternativas e doses de nitrogênio no milho safrinha em sucessão à soja.Crossref | GoogleScholarGoogle Scholar |

Tilman D, Balzer C, Hill J, Befort BL (2011) Global food demand and the sustainable intensification of agriculture. Proceedings of the National Academy of Sciences of the United States of America 108, 20260–20264.
Global food demand and the sustainable intensification of agriculture.Crossref | GoogleScholarGoogle Scholar | 22106295PubMed |

Tůmová L, Tarkowská D, Řehořová K, Marková H, Kočová M, Rothová O, Čečetka P, Holá D (2018) Drought-tolerant and drought-sensitive genotypes of maize (Zea mays L.) differ in contents of endogenous brassinosteroids and their drought-induced changes. PLoS ONE 13, e0197870
Drought-tolerant and drought-sensitive genotypes of maize (Zea mays L.) differ in contents of endogenous brassinosteroids and their drought-induced changes.Crossref | GoogleScholarGoogle Scholar | 29795656PubMed |

van Bussel LGJ, Grassini P, Van Wart J, Wolf J, Claessens L, Yang H, Boogaard H, de Groot H, Saito K, Cassman KG, van Ittersum MK (2015) From field to atlas: upscaling of location-specific yield gap estimates. Field Crops Research 177, 98–108.
From field to atlas: upscaling of location-specific yield gap estimates.Crossref | GoogleScholarGoogle Scholar |

van Ittersum MK, Cassman KG, Grassini P, Wolf J, Tittonell P, Hochman Z (2013) Yield gap analysis with local to global relevance—a review. Field Crops Research 143, 4–17.
Yield gap analysis with local to global relevance—a review.Crossref | GoogleScholarGoogle Scholar |

Xavier AC, King CW, Scanlon BR (2015) Daily gridded meteorological variables in Brazil (1980–2013). International Journal of Climatology 2659, 2644–2659.
Daily gridded meteorological variables in Brazil (1980–2013).Crossref | GoogleScholarGoogle Scholar |