The planning of planting/harvest operations improves yield and economic returns of sugarcane production systems. This study aims to define homogenous regions and optimum planting dates for sugarcane using simulated water-limited yield in the state of Goiás, Brazil. Yw was simulated using crop model and 24 planting dates across the year, including gridded weather data and soil water availability to the crop over the state in a grid cell size of 0.5 × 0.5°. The crop model was evaluated comparing simulated and measured yield tendency across planting dates. Homogeneous regions were obtained based on Yw, using the Ward’s method and Euclidean distance. The crop model was able to replicate yield tendency across planting dates. The clustering divided the state into four homogenous regions, where optimum planting period had different intervals due to the interaction with climate and soil. The optimum planting window had four dates for the region with lower Yw (105 t ha−1). The region with higher Yw (131 t ha−1) had the longer optimum window, with seven dates, but with the higher yield reduction (− 6%) than other regions (− 3%) when planting date was changed from 1st to 2nd better dates. This way, the results and the approach used in this study defines yield level and optimal planting dates, which can be apply to define harvest period and the area required to supply the sugarcane mill demands, leading to better machinery and labor work management, helping to elaborate mills and state-level strategies to increase sugarcane production.
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Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (1998). Crop evapotranspiration-guidelines for computing crop water requirements. Irrig. Drain. Pap. No. 56. FAO, Rome, Italy.
Alvares, C. A., Stape, J. L., Sentelhas, P. C., Moraes, G., Leonardo, J., & Sparovek, G. (2013). Köppen’s climate classification map for Brazil. Meteorologische Zeitschrift, 22, 711–728. https://doi.org/10.1127/0941-2948/2013/0507.
Araújo, R., Alves Junior, J., Casaroli, D., & Evangelista, A. W. P. (2016). Variation in the sugar yield in response to drying-off of sugarcane before harvest and the occurrence of low air temperatures. Bragantia., 75, 118–127. https://doi.org/10.1590/1678-4499.170.
Basnayake, J., Jackson, P. A., Inman-Bamber, N. G., & Lakshmanan, P. (2012). Sugarcane for water-limited environments. Genetic variation in cane yield and sugar content in response to water stress. Journal of Experimental Botany, 63, 6023–6033. https://doi.org/10.1093/jxb/ers251.
Battisti, R., Bender, F. D., & Sentelhas, P. C. (2019). Assessment of different gridded weather data for soybean yield simulations in Brazil. Theoretical and Applied Climatology, 135, 237–247. https://doi.org/10.1007/s00704-018-2383-y.
Battisti, R., Ferreira, M. D. P., Tavares, É. B., Knapp, F. M., Bender, F. D., Casaroli, D. A., & Júnior, J. (2020). Rules for grown soybean-maize cropping system in Midwestern Brazil: Food production and economic profits. Agricultural Systems, 182, 102850. https://doi.org/10.1016/j.agsy.2020.102850.
Battisti, R., & Sentelhas, P. C. (2014). New agroclimatic approach for soybean sowing dates recommendation: A case study. Revista Brasileira de Engenharia Agrícola e Ambiental, 18, 1149–1156. https://doi.org/10.1590/1807-1929/agriambi.v18n11p1149-1156.
Battisti, R., Sentelhas, P. C., Pascoalino, J. A. L., Sako, H., De Sá, J. P. D., & Moraes, M. F. (2018). Soybean yield gap in the areas of yield contest in Brazil. International Journal of Plant Production, 12, 159–168. https://doi.org/10.1007/s42106-018-0016-0.
Bordonal, R. D. O., Carvalho, J. L. N., Lal, R., Figueiredo, E. B., Oliveira, B. G., & La Scala Jr., L. S. N. (2018). Sustainability of sugarcane production in Brazil. A review. Agronomy for Sustainable Development, 38, 13. https://doi.org/10.1007/s13593-018-0490-x.
Caetano, J. M., & Casaroli, D. (2017). Sugarcane yield estimation for climatic conditions in the center of state of Goiás. Revista Ceres, 64, 298–306. https://doi.org/10.1590/0034-737x201764030011.
Cardozo, N. P., & Sentelhas, P. C. (2013). Climatic effects on sugarcane ripening under the influence of cultivars and crop age. Science in Agriculture, 70, 449–456. https://doi.org/10.1590/S0103-90162013000600011.
Cardozo, N. P., Sentelhas, P. C., Panosso, A. R., & Ferraudo, A. S. (2014). Multivariate analysis of the temporal variability of sugarcane ripening in south-eastern Brazil. Crop and Pasture Sci., 65, 300–310. https://doi.org/10.1071/cp13160.
Cardozo, N. P., Sentelhas, P. C., Panosso, A. R., Palhares, A. L., & Ide, B. Y. (2015). Modeling sugarcane ripening as a function of accumulated rainfall in Southern Brazil. International Journal of Biometeorology, 59, 1913–1925. https://doi.org/10.1007/s00484-015-0998-6.
CONAB. (2019). Crops Historical Data. https://www.conab.gov.br/info-agro/safras/. Accesse: 12 Apr 2019 (in Portuguese).
Dias, H. B., & Inman-Bamber, N. G. (2020). Sugarcane: contribution of process-based models for understanding and mitigating impacts of climate variability and change on production. In M. Ahmed (Ed.), Systems modeling. Singapore: Springer. https://doi.org/10.1007/978-981-15-4728-7.
Dias, H. B., & Sentelhas, P. C. (2017). Evaluation of three sugarcane simulation models and their ensemble for yield estimation in commercially managed fields. Field Crops Research, 213, 174–185. https://doi.org/10.1016/j.fcr.2017.07.022.
Dias, H. B., & Sentelhas, P. C. (2018a). Dimensioning the impact of irrigation on sugarcane yield in Brazil. Sugar Technology, 21, 29–37. https://doi.org/10.1007/s12355-018-0619-x.
Dias, H. B., & Sentelhas, P. C. (2018b). Sugarcane yield gap analysis in Brazil–A multi-model approach for determining magnitudes and causes. Science of the Total Environment, 637, 1127–1136. https://doi.org/10.1016/j.scitotenv.2018.05.017.
Dias, H. B., & Sentelhas, P. C. (2018c). Drying-off periods for irrigated sugarcane to maximize sucrose yields under brazilian conditions. Irrigation and Drainage, 67, 527–537. https://doi.org/10.1002/ird.2263.
Dias, H. B., & Sentelhas, P. C. (2019). Dimensioning the Impact of Irrigation on Sugarcane Yield in Brazil. Sugar Technology, 21, 29–37. https://doi.org/10.1002/ird.2263.
Dias, H. B., Inman-Bamber, G., Bermejo, R., Sentelhas, P. C., & Christodoulou, D. (2019). New APSIM-Sugar features and parameters required to account for high sugarcane yields in tropical environments. Field Crops Research, 235, 38–53. https://doi.org/10.1016/j.fcr.2019.02.002.
Dias, H. B., Inman-Bamber, G., Everingham, Y., Sentelhas, P. C., Bermejo, R., & Christodoulou, D. (2020). Traits for canopy development and light interception by twenty-seven Brazilian sugarcane varieties. Field Crops Research. https://doi.org/10.1016/j.fcr.2020.107716.
Donaldson, R. A., & Bezuidenhout, C. N. (2000). Determining the maximum drying-off periods for sugarcane grown in different regions of the South African Industry. Proceedings of The South African Sugar Technologists’ Association, 74, 162–166.
Doorenbos J., & Kassam, A. M. (1979). Yield response to water. Irrig. Drain. Pap. No. 33. FAO, Rome, Italy.
Duarte, Y. C. N., & Sentelhas, P. C. (2020). NASA/POWER and DailyGridded weather datasets-how good they are for estimating maize yields in Brazil? International Journal of Biometeorology, 64, 319–329. https://doi.org/10.1007/s00484-019-01810-1.
Estatcamp. (2014). Software Action. Estatcamp- Consultoria em estatística e qualidade, São Carlos - SP, Brasil. URL http://www.portalaction.combr/. Retrieved 1 Mar 2020.
Hoffman, N., Singels, A., Patton, A., & Ramburan, S. (2018). Predicting genotypic differences in irrigated sugarcane yield using the Canegro model and independent trait parameter estimates. European Journal of Agronomy, 96, 13–21. https://doi.org/10.1016/j.eja.2018.01.005.
Hoogenboom, G., Porter, C. H., Boote, K. J., Shelia, V., Wilkens, P. W., Singh, U., et al. (2019). The DSSAT crop modeling ecosystem. In K. J. Boote (Ed.), Advances in crop modelling for a sustainable agriculture. Cambridge: Burleigh Dodds Science Publishing. https://doi.org/10.19103/AS.2019.0061.10.
IBGE, 2020. Agricultural Production. In Portuguese. http://www.sidra.ibge.gov.br/bda/pesquisas/pam. Accessed 12 Apr 2020 (in Portuguese).
Inman-Bamber, N. G. (2004). Sugarcane water stress criteria for irrigation and drying off. Field Crops Reserach, 89, 107–122. https://doi.org/10.1016/j.fcr.2004.01.018.
Inman-Bamber, N. G. (2014). Sugarcane yields and yield-limiting processes. In P. H. Moore & F. C. Botha (Eds.), Sugarcane: Physiology, biochemistry, and functional biology (pp. 579–600). Chichester: Wiley. https://doi.org/10.1002/9781118771280.ch21.
Inman-Bamber, N. G., Jackson, P. A., Stokes, C. J., Verrall, S., Lakshmanan, P., & Basnayake, J. (2016). Sugarcane for water-limited environments: Enhanced capability of the APSIM sugarcane model for assessing traits for transpiration efficiency and root water supply. Field Crops Research, 196, 112–123. https://doi.org/10.1016/j.fcr.2016.06.013.
Inman-Bamber, N. G., & Smith, D. M. (2005). Water relations in sugarcane and response to water deficits. Field Crops Research, 92, 185–202. https://doi.org/10.1016/j.fcr.2005.01.023.
Kassam, A. H. (1977). Net biomass production and yield of crops (p. 29p). Rome: FAO.
Keating, B. A., Robertson, M. J., Muchow, R. C., & Huth, N. I. (1999). Modelling sugarcane production systems I. Development and performance of the sugarcane module. Field Crops Research, 61, 253–271. https://doi.org/10.1016/S0378-4290(98)00167-1.
Laclau, P. B., & Laclau, J. P. (2009). Growth of the whole root system for a plant crop of sugarcane under rainfed and irrigated environments in Brazil. Field Crops Research, 114, 351–360. https://doi.org/10.1016/j.fcr.2009.09.004.
Machado, R. S., Ribeiro, R. V., Marchiori, P. E. R., Machado, D. F. S. P., Machado, E. C., & Landell, M. G. A. (2009). Biometric and physiological responses to water deficit in sugarcane at different phenological stages. Pesquisa Agropecuária Brasileira, 44, 1575–1582. https://doi.org/10.1590/S0100-204X2009001200003.
Manly, B. F., & Alberto, J. A. N. (2016). Multivariate statistical methods: a primer. London: Chapman and Hall/CRC.
MAPA-Ministry of Agriculture, Livestock and Supply Climate Risk Zoning. (2020). http://www.agricultura.gov.br/politica-agricola/zoneamento-agricola. Accessed April 2020 (in Portuguese).
Marin, F. R., & Carvalho, G. L. D. (2012). Spatio-temporal variability of sugarcane yield efficiency in the state of São Paulo, Brazil. Pesquisa Agropecuária Brasileira, 47(2), 49–156. https://doi.org/10.1590/S0100-204X2012000200001.
Marin, F. R., Thorburn, P. J., Nassif, D. S. P., & Costa, L. G. (2015). Sugarcane model intercomparison: structural differences and uncertainties under current and potential future climates. Environmental Modelling & Software, 72, 372–386. https://doi.org/10.1016/j.envsoft.2015.02.019.
Mehareb, E. M., & Abazied, S. R. (2017). Genetic variability of some promising sugarcane varieties (Saccharum Spp) under harvesting ages for juice quality traits, cane and sugar yield. Open Access Journal of Agricultural Research, 2, 1–14. https://doi.org/10.23880/OAJAR-16000127.
Monteiro, L. A., & Sentelhas, P. C. (2014). Potential and actual sugarcane yields in southern Brazil as a function of climate conditions and crop management. Sugar Technology, 16, 264–276. https://doi.org/10.1007/s12355-013-0275-0.
Monteiro, L. A., & Sentelhas, P. C. (2017). Sugarcane yield gap: Can it be determined at national level with a simple agrometeorological model? Crop and Pasture Science, 68, 272–284. https://doi.org/10.1071/CP16334.
Monteiro, L. A., Sentelhas, P. C., & Pedra, G. U. (2018). Assessment of NASA/POWER satellite-based weather system for Brazilian conditions and its impact on sugarcane yield simulation. International Journal of Climatology, 38, 1–11. https://doi.org/10.1002/joc.5282.
Muchow, R. C., Robertson, M. J., & Keating, B. A. (1997). Limits to Australian sugar industry: Climate and biological factors. In B. A. Keating & J. Wilson (Eds.), Intensive sugarcane production: Meeting the Challenges beyond 2000 (pp. 37–54). WallingfordK: CAB International.
Nendel, C., Kersebaum, K. C., Mirschel, W., & Wenkel, K. O. (2014). Testing farm management options as climate change adaptation strategies using the MONICA model. European Journal of Agronomy, 52, 47–56. https://doi.org/10.1016/j.eja.2012.09.005.
Oliveira, A. P. P., Thorburn, P. J., Biggs, J. S., Lima, E., Santos, L. H. C., Pereira, M. G., & Zanotti, N. E. (2016). The response of sugarcane to trash retention and nitrogen in the Brazilian coastal tablelands: a simulation study. Experimental Agriculture, 52, 69–86. https://doi.org/10.1017/S0014479714000568.
Oliveira, M. P. G., & Rodrigues, L. H. A. (2020). How good are the models available for estimating sugar content in sugarcane? European Journal of Agronomy, 113, 125992. https://doi.org/10.1016/j.eja.2019.125992.
Otto, R., Silva, A. P., Franco, H. C. J., Oliveira, E. C. A., & Trivelin, P. C. O. (2011). High soil penetration resistance reduces sugarcane root system development. Soil Tillage Reserach, 117, 201–210. https://doi.org/10.1016/j.still.2011.10.005.
Paixão, J. S., Casaroli, D., Battisti, R., Evangelista, A. W. P., Alves, J. J., & Mesquita, M. (2020). Characterizing sugarcane production areas using actual yield and edaphoclimatic condition data for the State of Goiás, Brazil. International Journal of Plant Production. https://doi.org/10.1007/s42106-020-00101-9.
Pinto, H. M. S., Vianna, M. S., Costa, L. G., & Marin, F. R. (2018). Sugarcane crop yield predictions under climate change based on a multi-model approach for São Paulo state. Agrometeoros., 26, 11–24. https://doi.org/10.31062/agrom.v26i1.26300.
Robertson, M. J., Inman-Bamber, N. G., Muchow, R. C., & Wood, A. W. (1999). Physiology and productivity of sugarcane with early and mid-season water deficit. Field Crops Research, 64, 211–227. https://doi.org/10.1016/S0378-4290(99)00042-8.
Scarpare, F. V., Hernandes, T. A. D., Ruiz-Corrêa, S. T., Picoli, M. C. A., Scanlon, B. R., Chagas, M. F., et al. (2016). Sugarcane land use and water resources assessment in the expansion area in Brazil. Journal of Cleaner Production, 133, 1318–1327. https://doi.org/10.1016/j.jclepro.2016.06.074.
Singels, A., Jones, M., & van den Berg, M. (2008). DSSAT v4.5 Canegro sugarcane plant module: scientific documentation. South African Sugarcane Res. Inst. (Mount Edgecombe).
Tagliapietra, E. L., Streck, N. A., Rocha, T. S. M., Richter, G. L., Silva, M. R., Cera, J. C., et al. (2018). Optimum leaf area index to reach soybean yield potential in subtropical environment. Journal of Agronomy., 110, 932–938. https://doi.org/10.2134/agronj2017.09.0523.
Thorburn, P. J., Biggs, J. S., Palmer, J., Meier, E. A., Verburg, K., & Skocaj, D. M. (2017). Prioritizing crop management to increase nitrogen use efficiency in Australian sugarcane crops. Frontiers in Plant Science, 8, 1–16. https://doi.org/10.3389/fpls.2017.01504.
Thornthwaite, C. W., & Mather, J. R. (1955). The water balance. Centerton: Drexel Institute of Technology, Laboratory of Climatology. Publications in Climatology., 8(1), 104.
van den Wall Bake, J. D., Junginger, M., Faaij, A., Poot, T., & Walter, A. (2009). Explaining the experience curve: Cost reductions of Brazilian ethanol from sugarcane. Biomass and Bioenergy, 33, 644–658. https://doi.org/10.1016/j.biombioe.2008.10.006.
van Heerden, P. D. R., Eggleston, G., & Donaldson, R. A. (2014). Ripening and postharvest deterioration. In P. H. Moore & F. C. Botha (Eds.), Sugarcane: Physiology, biochemistry, and functional biology (pp. 55–84). Chichester: Wiley. https://doi.org/10.1002/9781118771280.ch4.
van Ittersum, M. K., Cassman, K. G., 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. https://doi.org/10.1016/j.fcr.2012.09.009.
Vianna, M. S., & Sentelhas, P. C. (2014). Simulation of the water deficit risk in sugarcane-crop expansion regions in Brazil. Pesquisa Agropecuária Brasileira, 49, 237–246. https://doi.org/10.1590/S0100-204X2014000400001.
Vianna, M. S., & Sentelhas, P. C. (2016). Performance of DSSAT CSM-CANEGRO under operational conditions and its use in determining the “saving irrigation” impact on sugarcane crop. Sugar Tech, 18, 75–86. https://doi.org/10.1007/s12355-015-0367-0.
Ward, J. H. (1963). Hierarchical grouping to optimize an objective function. Journal of American Statistical Association. https://doi.org/10.1080/01621459.1963.10500845.
Xavier, A. C., King, C. W., & Scanlon, B. R. (2015). Daily gridded meteorological variables in Brazil (1980–2013). International Journal of Climatology, 36, 2644–2659. https://doi.org/10.1002/joc.4518.
Zanon, A. J., Streck, N. A., & Grassini, P. (2016). Climate and management factors influence soybean yield potential in a subtropical environment. Journal of Agronomy, 108, 1447–1454. https://doi.org/10.2134/agronj2015.0535.
Zu, Q., Mi, C., Liu, D. L., He, L., Kuang, Z., Fang, Q., et al. (2018). Spatio-temporal distribution of sugarcane potential yields and yield gaps in Southern China. European Journal of Agronomy, 92, 72–83. https://doi.org/10.1016/j.eja.2017.10.005.
The authors would like to thank the Coordination for the Improvement of Higher Education Personnel (CAPES) for the research fellowship for the first author; the National Council for Scientific and Technological Development (CNPq) for the research fellowship for the second, fourth a fifth authors; and the Foundation for Research Support of the State of Goiás for the financial support through process no. 201610267001488.
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Paixão, J.S., Casaroli, D., dos Anjos, J.C.R. et al. Optimizing Sugarcane Planting Windows Using a Crop Simulation Model at the State Level. Int. J. Plant Prod. 15, 303–315 (2021). https://doi.org/10.1007/s42106-021-00134-8
- Saccharum spp.
- Sugarcane harvest
- Water-limited yield
- FAO—agroecological zone model
- Yield gap