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DROUGHT TOLERANCE OF BRAZILIAN SOYBEAN CULTIVARS SIMULATED BY A SIMPLE AGROMETEOROLOGICAL YIELD MODEL

Published online by Cambridge University Press:  10 September 2014

R. BATTISTI  and
P. C. SENTELHAS
R. BATTISTI
Affiliation:
Department of Biosystems Engineering, University of São Paulo – ESALQ, Piracicaba, São Paulo, Brazil
P. C. SENTELHAS*
Affiliation:
Department of Biosystems Engineering, University of São Paulo – ESALQ, Piracicaba, São Paulo, Brazil
*
†Corresponding author. Email: pcsentel.esalq@usp.br
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Summary

The objective of this study was to calibrate and evaluate a simple crop yield model for 101 Brazilian soybean cultivars, and through the calibrated water deficit sensitivity index (Ky) to classify groups of cultivars in relation to drought tolerance. The cultivars’ actual yield was obtained from field experiments conducted by Pro-Seeds Foundation in 17 locations in southern Brazil from 2008 to 2011. Daily weather data were obtained from the government weather networks and rainfall was recorded at each experimental location. The crop yield model FAO–Agroecological zone was used to estimate potential yield (Yp), while the water deficit yield depletion model was used to estimate actual yield (Ya) and to determine Ky. Calibrated Ky values were used in a cluster analysis to determine groups of soybean cultivars with the same degree of drought tolerance. The crop yield model performed very well with lower values of mean absolute error (284 kg ha−1) and mean error (7 kg ha−1). The Ky values of 0.97, 0.90, 0.88 and 0.78 were obtained for the most sensitive soybean phenological phase to water deficit (flowering/yield formation), and were used to identify the groups of low, medium-low, medium-high and high drought tolerance respectively. In spite of Ky differences in cultivar groups, harvest index (CH) also varied, ranging from 0.31 to 0.35 for the group of high to low drought tolerance. The crop yield model proved to be an efficient tool for identifying drought tolerance of Brazilian soybean cultivars and for choosing the best cultivar for a given environment.

Type
Research Article
Information
Experimental Agriculture , Volume 51 , Issue 2 , April 2015 , pp. 285 - 298
Copyright
Copyright © Cambridge University Press 2014 

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References

REFERENCES

Allen, G. R., Pereira, L. S., Raes, D. and Smith, M. (1998). Crop evapotranspiration – guidelines for computing crop water requirements. Irrigation and Drainage Paper 56, FAO, Rome, Italy.Google Scholar
Alvares, C. A., Stape, J. L., Sentelhas, P. C., Gonçalves, J. L. de M. and Sparovek, G. (2013). Köppen's climate classification map for Brazil. Meteorologische Zeitschirift 22:711–728.Google Scholar
Andrioli, K. G. and Sentelhas, P. C. (2009). Brazilian maize genotypes sensitivity to water deficit estimated through a simple crop yield model. Pesquisa Agropecuária Brasileira 44:653–660.CrossRefGoogle Scholar
Araújo, P., Féres, J. and Reis, E. (2011). Assessing the impacts of ENSO-related weather effects on the Brazilian agriculture. Proceedings of the Conference on Climate Change and Development Policy 2011.Google Scholar
Battisti, R., Sentelhs, P. C. and Pilau, F. G. (2012). Eficiência agrícola da produção de soja, milho e trigo no estado do Rio Grande do Sul entre 1980 e 2008. Ciência Rural 42:24–30.Google Scholar
Battisti, R., Sentelhs, P. C., Pilau, F. G. and Wollmann, C. A. (2013). Eficiência climática para as culturas da soja e do trigo no estado do Rio Grande do Sul em diferentes datas de semeadura. Ciência Rural 43:390–396.Google Scholar
Boote, K. J., Jones, J. W., Batchelor, W. D., Nafziger, E. D. and Myers, O. (2003). Genetic coefficients in the CROPGRO-Soybean model: links to field performance and genomics. Agronomy Journal 95:32–51.Google Scholar
Camargo, A. P. and Sentelhas, P. C. (1997). Avaliação do desempenho de diferentes métodos de estimativa da evapotranspiração potencial no estado de São Paulo, Brasil. Revista Brasileira de Agrometeorologia 5:89–97.Google Scholar
Carbone, G. J., Mearns, L. O., Mavromatis, T., Sadler, E. J. and Stooksburry, D. (2003). Evaluating CROPGRO-soybean performance for use in climate impact studies. Agronomy Journal 95:537–544.Google Scholar
Catuchi, T. A., Vitolo, H. F., Bertolli, S. C. and Souza, G. M. (2011). Tolerance to water deficiency between two soybean cultivars: transgenic versus conventional. Ciêncial Rural 41:373–378.Google Scholar
Confalone, A. E., Bernardes, M. S., Costa, L. C., Righi, C. A., Dourado-Neto, D., Martin, T. N., Manfron, P. A. and Pereira, C. R. (2010). Expolinear model on soybean growth in Argentina and Brazil. Ciência Rural 40:1009–1016.Google Scholar
Dogan, E., Kirnak, H. and Copur, O. (2007). Deficit irrigations during soybean reproductive stages and CROPGRO-soybean simulations under semi-arid climatic conditions. Field Crops Research 103:154–159.CrossRefGoogle Scholar
Doorenbos, J. and Kassam, A. M. (1979). Yield response to water. Irrigation and Drainage Paper 33, FAO, Rome, Italy.Google Scholar
FAO (2014). FAOSTAT: production. Available at: http://faostat.fao.org/site/339/default.aspx (accessed July 2014).Google Scholar
Farias, J. R. B., Assad, E. D., Almeida, I. R., Evangelista, B. A., Lazzarotto, C., Neumaier, N. and Nepomuceno, A. L. (2001). Caracterização de risco de déficit hídrico nas regiões produtoras de soja no Brasil. Revista Brasileira de Agrometeorologia 9:415–421.Google Scholar
Fehr, W. R. and Caviness, C. E. (1977). Stages of Soybean Development. Special Report, 80. Ames, IO: Iowa State University, 11 p.Google Scholar
Husfstetler, E. V., Boerma, H. R., Carter, T. E. and Earl, H. J. (2007). Genotypic variation for three physiological traits affecting drought tolerance in soybean. Crop Science 47:25–35.Google Scholar
Kaboosi, K. and Kaveh, F. (2010). Sensitivity analysis of Doorenbos and Kassam (1979) crop water production function. African Journal of Agricultural Research 5:2399–2417.Google Scholar
Karam, F., Massad, R. and Sfeir, T. (2005). Evapotranspiration and seed yield of field grown soybean under deficit irrigation conditions. Agricultural Water Management 75:226–244.Google Scholar
Kassam, A. H. (1977). Net biomass production and yield of crops. Present and Potential Land Use by Agro-Ecological Zones Project, FAO, Rome, Italy.Google Scholar
Liu, X., Jin, J., Herbert, S. J., Zhang, Q. and Wang, G. (2005). Yield components, dry matter, LAI and LAD of soybeans in Northeast China. Field Crop Research 93:85–93.CrossRefGoogle Scholar
Lopes-Assad, M. L., Sans, L. M. A., Assad, E. D. and Zullo, J. JR. (2001). Relações entre água retida e conteúdo de areia total em solos brasileiros. Revista Brasileira de Agrometeorologia 9:588–596.Google Scholar
Monteiro, L. A. and Sentelhas, P. C. (2014). Calibration and testing of an agrometeorological model for the estimation of soybean yields in different Brazilian regions. Acta Scientiarum, Agronomy 36:265–272.Google Scholar
Moraes, A. V. C., Camargo, M. B. P., Mascarenhas, H. A. A., Miranda, M. A. C. and Pereira, J. C. V. N. A. (1998). Teste e análise de modelos agrometeorológicos de estimativa de produtividade para a cultura da soja na região de Ribeirão Preto. Bragantia 57:393–406.Google Scholar
Neyshabouri, M. R. and Hatfield, J. L. (1986). Soil water deficit effects on semi-determinate and indeterminate soybean growth and yield. Field Crops Research 15:73–84.Google Scholar
Oya, T., Nepomuceno, A. L., Neumaier, N., Farias, J. R. B., Tobita, S. and Ito, S. (2004). Drought tolerance characteristics of Brazilian soybean cultivars – evaluation and characterization of drought tolerance of various Brazilian soybean cultivars in the field. Plant Production Science 7:129–137.Google Scholar
Pereira, A. R., Angelocci, L. R. and Sentelhas, P. C. (2002). Agrometeorologia: fundamentos e aplicações práticas. Guaíba: Agropecuária 1:413–431.Google Scholar
Priestley, C. H. B. and Taylor, R. J. (1972). On the assessment of surface heat flux and evaporation using large-scale parameters. Monthly Weather Review 100:81–92.2.3.CO;2>CrossRefGoogle Scholar
Rao, N. H., Sarma, P. B. S. and Chander, S. (1988). A simple dated water-production function for use in irrigated agriculture. Agricultural Water Management 13:25–32.Google Scholar
Reichert, J. M., Albuquerque, J. A., Kaiser, D. R., Reinert, D. J., Urach, F. L. and 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.Google Scholar
Rodrigues, O., Teixeira, M. C., Didonet, A., Lambhy, J. C. B., Bertagnolli, P. F. and Luz, J. A. (2006). Efeito do Fotoperíodo e da Temperatura do ar no Desenvolvimento da Área Foliar em Soja (Glycine max (L.) Merril). Passo Fundo, Brazil: Embrapa Trigo, 27 p. (Boletim de Pesquisa e Desenvolvimento Online, 33).Google Scholar
Rolim, G. S., Sentelhas, P. C. and Ungaro, M. R. G. (2001). Análise do risco climático para a cultura do girassol, em algunas localidade de São Paulo e do Paraná, usando os modelos DSSAT/OILCROP-SUN e FAO. Revista Brasileira de Agrometeorologia 9:91–102.Google Scholar
Sadras, V. O. and Calviño, P. A. (2001). Quantification of grains yield response to soil depth in soybean, maize, sunflower, and wheat. Agronomy Journal 93:577–583.Google Scholar
Sinclair, T. R., Messina, C. D., Beatty, A. and Samples, M. (2010). Assessment across the United States of the benefits of altered soybean drought traits. Agronomy Journal 102:475–482.Google Scholar
Statsoft. (2008). Statistica: data analysis software systems. Version 8.0. Tulsa. StatSoft.Google Scholar
Thornthwaite, C. W. and Mather, J. R. (1955). The Water Balance. Publications in Climatology 1. Centerton, NJ: Drexel Institute of Technology.Google Scholar