Dimensioning the Impact of Irrigation on Sugarcane Yield in Brazil
- 180 Downloads
Water deficit is one of the main causes of sugarcane yield limitation around the world. Sugarcane simulation models can be used to evaluate the impact of irrigation on yield where water deficit causes important losses. The aim of this study was to dimension the potential of irrigation on sugarcane yield in different Brazilian regions, by comparing yields simulated by a calibrated FAO-AZM model under rainfed and irrigated conditions, considering different levels of crop water supply. Simulations were run for 12-month plant cane yield planted every month along the year. Three different soil types with low, medium and high soil water holding capacities were considered for 29 locations in Brazil, considering a 30-year weather data series. Irrigation was applied to cover 20, 40, 60, 80 and 100% of the crop water deficit, with the highest level corresponding to full irrigation to obtain potential yield. The results showed that yield increases depend on the interaction between climate type, weather conditions, soil physical properties, planting date and level of irrigation. The soil with low water holding capacity was the one with the largest response to irrigation. On the coast of Northeastern Brazil, sugarcane planted between March and June was those which were more responsive to irrigation. On the other hand, in the producing regions in the interior of the country, the planting dates with higher responses to irrigation were between October and January. This study proved that irrigation is an important strategy to improve sugarcane yield even in the regions where irrigation is currently not recommended.
KeywordsSaccharum spp. Crop simulation models FAO Agro-ecological Zone model Water deficit Deficit irrigation Full irrigation
We are thankful to the São Paulo Research Foundation (FAPESP) for the Master scholarship of first author (Grant#2014/05173-3). The second author is a CNPq research fellow, level 1B.
HBD and PCS were both responsible for designing the study, preparing the dataset, analyzing the data, discussing the results and writing the manuscript.
Compliance with Ethical Standards
Conflict of interest
The authors declare that they have no conflict of interest.
- Allen, R.G., L.S. Pereira, D. Raes, and M. Smith. 1998. Crop evapotranspiration: Guidelines for computing crop water requirements. Irrigation and Drainage Paper No. 56, vol. 300. Rome: FAO.Google Scholar
- BRASIL. 1981. Projeto RADAMBRASIL. Ministério das Minas e Energia, Secretaria Geral, Rio de Janeiro, Brazil.Google Scholar
- Costa, L.G., F.R. Marin, D.S.P. Nassif, H.M.S. Pinto, and M.L.C.R. Lopes-Assad. 2014. Simulação do efeito do manejo da palha e do nitrogênio na produtividade da cana-de-açúcar. Revista Brasileira de Engenharia Agrícola e Ambiental 18: 469–474. https://doi.org/10.1590/S1415-43662014000500001.CrossRefGoogle Scholar
- de Andrade Júnior, A.S., E.A. Bastos, V.Q. Ribeiro, J.A.L. Duarte, D.L. Braga, and D.H. Noleto. 2012. Níveis de água, nitrogênio e potássio por gotejamento subsuperficial em cana-de-açúcar. Pesquisa Agropecuaria Brasileira 47: 76–84. https://doi.org/10.1590/S0100-204X2012000100011.CrossRefGoogle Scholar
- de Oliveira, F.M., I. Aspiazú, and M.K. Kondo. 2011. Crescimento e produção de variedades de cana-de-açúcar influenciadas por diferentes adubações e estresse hídrico. Revista Trópica 5: 56–67.Google Scholar
- Donzelli, J.L., and V.M. Costa. 2010. Workshop sobre o Impacto da produção de etanol nos recursos hídricos nas regiões de expansão. Campinas: Brazilian Bioethanol Science and Technology Laboratory, Brazilian Center for Research in Energy and Materials.Google Scholar
- Doorenbos, J., and A.H. Kassam. 1979. Yield response to water, ed. J. Doorenbos, and A.M. Kassam. Rome: Irrigation and Drainage Paper No. 33, FAO.Google Scholar
- Kassam, A.H. 1977. Net biomass production and yield of crops, ed. A.H. Kassam. Rome: FAO.Google Scholar
- Leal, D.F.P. 2016. Parametrização do modelo CANEGRO (DSSAT) e caracterização biométrica de oito variedades de cana-de-açúcar irrigadas por gotejamento. Universidade de São Paulo, Escola Superior de Agricultura “Luiz de Queiroz”, Piracicaba, Brazil.Google Scholar
- Marin, F.R., P.J. Thorburn, D.S.P. Nassif, and L.G. Costa. 2015. Sugarcane model intercomparison: Structural differences and uncertainties under current and potential future climates. Environmental Modelling and Software 72: 372–386. https://doi.org/10.1016/j.envsoft.2015.02.019.CrossRefGoogle Scholar
- Scarpare, F.V. 2011. Simulação do crescimento da cana-de-açúcar pelo modelo agrohidrológico SWAP/WOFOST. Universidade de São Paulo, Escola Superior de Agricultura “Luiz de Queiroz”, Piracicaba, Brazil.Google Scholar
- Suguitani, C. 2006. Entendendo o crescimento e produção da cana de açúcar: Avaliação do modelo Mosicas. Universidade de São Paulo, Escola Superior de Agricultura “Luiz de Queiroz”, Piracicaba, Brazil.Google Scholar
- Thornthwaite, C.W., and J.R. Mather. 1955. The water balance. In Publications in climatology, vol. 8, ed. C.W. Thornthwaite, and J.R. Mather. Princeton, NJ: Drexel Institute of Technology.Google Scholar