How to cite this paper
APA: Pinto, A., Achcar, J., Hermosilla, J., Siqueira, L & Nagano, M. (2026). Forecasting total recoverable sugar index and sugarcane production by using multiple regression models. Management Science Letters, 16(2), 131-146.
Chicago/Turabian: Pinto, A., Achcar, J., Hermosilla, J., Siqueira, L & Nagano, M. 2026. "Forecasting total recoverable sugar index and sugarcane production by using multiple regression models." Management Science Letters 16, no. 2 (2026): 131-146.
AMA: Pinto, A., Achcar, J., Hermosilla, J., Siqueira, L & Nagano, M. Forecasting total recoverable sugar index and sugarcane production by using multiple regression models. Management Science Letters. 2026;16(2):131-146.
References
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Bordonal, R. D. O., Carvalho, J. L. N., Lal, R., De Figueiredo, E. B., De Oliveira, B. G., & La Scala, N. (2018). Sustaina-bility of sugarcane production in Brazil. A review. Agronomy for Sustainable Development, 38, 1–23. https://doi.org/10.1007/s13593-018-0490-x
Box, G. E. P., & Cox, D. R. (1964). An analysis of transformations. Journal of the Royal Statistical Society: Series B (Methodological), 26(2), 211–252. https://doi.org/10.1111/j.2517-6161.1964.tb00553.x
Breiman, L. (2001). Statistical modeling: The two cultures. Statistical Science, 16(3), 199–231. http://dx.doi.org/10.1214/ss/1009213726
Canata, T. F., Júnior, M. R. B., de Oliveira, R. P., Furlani, C. E. A., & da Silva, R. P. (2024). AI-driven prediction of sugarcane quality attributes using satellite imagery. Sugar Tech, 26(3), 741–751. https://doi.org/10.1007/s12355-024-01399-9
Cezarino, L. O., & Liboni, L. (2012). Social and environmental impacts of the sugarcane industry. Future Studies Re-search Journal: Trends and Strategies, 4(1), 202–230. https://doi.org/10.24023/FutureJournal/2175-5825/2012.v4i1.101
Charoen-Ung, P., & Mittrapiyanuruk, P. (2018). Sugarcane yield grade prediction using random forest and gradient boosting tree techniques. In 2018 15th International Joint Conference on Computer Science and Software Engineer-ing (JCSSE) (pp. 1–5). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/JCSSE.2018.8457391
Charoen-Ung, P., & Mittrapiyanuruk, P. (2019). Sugarcane yield grade prediction using random forest with forward fea-ture selection and hyper-parameter tuning. In 14th International Conference on Computing and Information Tech-nology (pp. 1–5). Springer Verlag. https://doi.org/10.1007/978-3-319-93692-5_4
Coelho, A. P., Bettiol, J. V. T., Dalri, A. B., Fischer Filho, J. A., Faria, R. T., & Palaretti, L. F. (2019). Application of ar-tificial neural networks in the prediction of sugarcane juice Pol. Revista Brasileira de Engenharia Agrícola e Ambi-ental, 23(1), 9–15. https://doi.org/10.1590/1807-1929/agriambi.v23n1p9-15
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Corrêdo, L. P., Wei, M. C., Ferraz, M. N., & Molin, J. P. (2021). Near-infrared spectroscopy as a tool for monitoring the spatial variability of sugarcane quality in the fields. Biosystems Engineering, 206, 150–161. https://doi.org/10.1016/j.biosystemseng.2021.04.001
De Matos, M., Santos, F., & Eichler, P. (2020). Sugarcane world scenario. In Sugarcane biorefinery, technology and perspectives (pp. 1–19). Academic Press. https://doi.org/10.1016/B978-0-12-814236-3.00001-9
Denham, T. (2024). Forgetting cane grasses: Switching temporal focus to reveal mosaics of Saccharum diversi-ty. Plants, People, Planet, 1–13. Advance online publication. https://doi.org/10.1002/ppp3.10489
De Oliveira, M. P. G., Bocca, F. F., & Rodrigues, L. H. A. (2017). From spreadsheets to sugar content modeling: A data mining approach. Computers and Electronics in Agriculture, 132, 14–20. https://doi.org/10.1016/j.compag.2016.11.012
Dias Neto, A. F., Albiero, D., Rossetto, R., & Biagi, J. D. (2022). Modeling of mechanized sugarcane harvesting to sup-port decision-making on asset management. Sugar Tech, 24(3), 798–812. https://doi.org/10.1007/s12355-022-01126-2
Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Marclim, W. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070
Draper, N. R., & Smith, H. (1981). Applied regression analysis. Wiley.
Everingham, Y. L., Inman-Bamber, N. G., Thorburn, P. J., & McNeill, T. J. (2007). A Bayesian modelling approach for long lead sugarcane yield forecasts for the Australian sugar industry. Australian Journal of Agricultural Research, 58(2), 87–94. http://dx.doi.org/10.1071/AR05443
Everingham, Y. L., Smyth, C. W., & Inman-Bamber, N. G. (2009). Ensemble data mining approaches to forecast region-al sugarcane crop production. Agricultural and Forest Meteorology, 149(3–4), 689–696. https://doi.org/10.1016/j.agrformet.2008.10.018
Fisch, C., & Block, J. (2018). Six tips for your (systematic) literature review in business and management re-search. Management Review Quarterly, 68(1), 103–106. https://doi.org/10.1007/s11301-018-0142-x
Florentino, H. D. O., Irawan, C., Aliano, A. F., Jones, D. F., Cantane, D. R., & Nervis, J. J. (2018). A multiple objective methodology for sugarcane harvest management with varying maturation periods. Annals of Operations Research, 267, 153–177. https://doi.org/10.1007/s10479-017-2568-2
Florentino, H. D. O., & Pato, M. V. (2014). A bi-objective genetic approach for the selection of sugarcane varieties to comply with environmental and economic requirements. Journal of the Operational Research Society, 65(6), 842–854. https://doi.org/10.1057/jors.2013.21
Florentino, H. D. O., Pato, M. V., Jones, D., & Cantane, D. R. (2015). Production and management of sugarcane biomass - Process optimization. In Biomass production and uses. IntechOpen. http://dx.doi.org/10.5772/61533
Haegeman, K., Marinelli, E., Scapolo, F., Ricci, A., & Sokolov, A. (2013). Quantitative and qualitative approaches in fu-ture-oriented technology analysis (FTA): From combination to integration? Technological Forecasting and Social Change, 80(3), 386–397. https://doi.org/10.1016/j.techfore.2012.10.002
Julien, M. H. R., Irvine, J. E., & Benda, G. T. A. (1988). Sugarcane anatomy, morphology and physiology. In Diseases of sugarcane—Major diseases (pp. 1–19). Elsevier. https://doi.org/10.1016/B978-0-444-42797-7.50005-3
Kutner, M. H., Nachtsheim, C. J., Neter, J., & Li, W. (2005). Applied linear statistical models (5th ed.). McGraw-Hill Irwin.
Lim, W. M., Kumar, S., & Ali, F. (2022). Advancing knowledge through literature reviews: ‘What’, ‘why’, and ‘how to contribute’. The Service Industries Journal, 42(7–8), 481–513. https://doi.org/10.1080/02642069.2022.2047941
Luciano, A. C. S., Picoli, M. C. A., Duft, D. G., Rocha, J. V., Leal, M. R. L., & Maire, G. (2021). Empirical model for forecasting sugarcane yield on a local scale in Brazil using Landsat imagery and random forest algo-rithm. Computers and Electronics in Agriculture, 184, Article 106063. https://doi.org/10.1016/j.compag.2021.106063
Madeiro, S. S., da Silva Oliveira, F. R., Alexandre, F. B. A., & de Lima Neto, F. B. (2006). Intelligent modeling of sug-arcane maturation. In *Computers in Agriculture and Natural Resources, 23-25 July 2006, Orlando Florida* (p. 642). American Society of Agricultural and Biological Engineers. https://doi.org/10.13031/2013.21950
Matović, N., & Ovesni, K. (2023). Interaction of quantitative and qualitative methodology in mixed methods research: Integration and/or combination. International Journal of Social Research Methodology, 26(1), 51–65. https://doi.org/10.1080/13645579.2021.1964857
Medar, R., & Rajpurohit, V. S. (2019). Supervised machine learning techniques for predicting sugarcane yield. International Journal of Recent Technology and Engineering, 8(2), 5662–5668. https://doi.org/10.35940/ijrte.B2612.078219
Montgomery, D. C., & Runger, G. C. (2011). Applied statistics and probability for engineers (5th ed.). Wiley.
Murali, P., Revathy, R., Balamurali, S., & Tayade, A. S. (2020). Integration of RNN with GARCH refined by whale op-timization algorithm for yield forecasting: A hybrid machine learning approach. Journal of Ambient Intelligence and Humanized Computing, 1–13. Advance online publication. https://doi.org/10.1007/s12652-020-01922-2
Nihar, A., Patel, N. R., & Danodia, A. (2022). Machine-learning-based regional yield forecasting for sugarcane crop in Uttar Pradesh, India. Journal of the Indian Society of Remote Sensing, 50(8), 1519–1530. https://doi.org/10.1007/s12524-022-01549-0
Oliveira, M. P. G., Bocca, F. F., & Rodrigues, L. H. A. (2017). From spreadsheets to sugar content modeling: A data mining approach. Computers and Electronics in Agriculture, 132, 14–20. https://doi.org/10.1016/j.compag.2016.11.012
Phuphaphud, A., Saengprachatanarug, K., Posom, J., Maraphum, K., & Taira, E. (2019). Prediction of the fibre content of sugarcane stalk by direct scanning using visible-shortwave near infrared spectroscopy. Vibrational Spectroscopy, 101, 71–80. https://doi.org/10.1016/j.vibspec.2019.02.005
Pignède, E., Roudier, P., Diedhiou, A., N'Guessan Bi, V. H., Kobea, A. T., Konaté, D., & Péné, C. B. (2021). Sugarcane yield forecast in Ivory Coast (West Africa) based on weather and vegetation index data. Atmosphere, 12(11), Article 1459. https://doi.org/10.3390/atmos12111459
Pravallika, K., Karuna, G., Anuradha, K., & Srilakshmi, V. (2021). Deep neural network model for proficient crop yield prediction. In 3rd International Conference on Design and Manufacturing Aspects for Sustainable Energy (ICMED 2021). EDP Sciences. https://doi.org/10.1051/e3sconf/202130901031
Rahman, M. M., & Robson, A. (2020). Integrating landsat-8 and sentinel-2 time series data for yield prediction of sug-arcane crops at the block level. Remote Sensing, 12(8), Article 1313. https://doi.org/10.3390/RS12081313
Scarpari, M. S., & Beauclair, E. G. F. D. (2004). Sugarcane maturity estimation through edaphic-climatic parame-ters. Scientia Agricola, 61, 486–491. https://doi.org/10.1590/S0103-90162004000500004
Seber, G. A. F., & Lee, A. J. (2003). Linear regression analysis (2nd ed.). Wiley.
Sexton, J., Inman-Bamber, N. G., Everingham, Y., Basnayake, J., Lakshmanan, P., & Jackson, P. (2014, April). Detailed trait characterisation is needed for simulation of cultivar responses to water stress. In Proceedings of the Australian Society of Sugar Cane Technologists (Vol. 36, pp. 82–92). Australian Society of Sugar Cane Technologists.
Shanthi, R. M., Alarmelu, S., Mahadeva Swamy, H. K., & Lakshmi Pathy, T. (2023). Impact of climate change on su-crose synthesis in sugarcane varieties. In Agro-industrial perspectives on sugarcane production under environmental stress (pp. 13–38). Springer Nature Singapore. https://doi.org/10.1007/978-981-19-3955-6_2
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Bertrand, J. W. M., & Fransoo, J. C. (2002). Operations management research methodologies using quantitative model-ling. International Journal of Operations and Production Management, 22(2), 241–264. https://doi.org/10.1108/01443570210414338
Bordonal, R. D. O., Carvalho, J. L. N., Lal, R., De Figueiredo, E. B., De Oliveira, B. G., & La Scala, N. (2018). Sustaina-bility of sugarcane production in Brazil. A review. Agronomy for Sustainable Development, 38, 1–23. https://doi.org/10.1007/s13593-018-0490-x
Box, G. E. P., & Cox, D. R. (1964). An analysis of transformations. Journal of the Royal Statistical Society: Series B (Methodological), 26(2), 211–252. https://doi.org/10.1111/j.2517-6161.1964.tb00553.x
Breiman, L. (2001). Statistical modeling: The two cultures. Statistical Science, 16(3), 199–231. http://dx.doi.org/10.1214/ss/1009213726
Canata, T. F., Júnior, M. R. B., de Oliveira, R. P., Furlani, C. E. A., & da Silva, R. P. (2024). AI-driven prediction of sugarcane quality attributes using satellite imagery. Sugar Tech, 26(3), 741–751. https://doi.org/10.1007/s12355-024-01399-9
Cezarino, L. O., & Liboni, L. (2012). Social and environmental impacts of the sugarcane industry. Future Studies Re-search Journal: Trends and Strategies, 4(1), 202–230. https://doi.org/10.24023/FutureJournal/2175-5825/2012.v4i1.101
Charoen-Ung, P., & Mittrapiyanuruk, P. (2018). Sugarcane yield grade prediction using random forest and gradient boosting tree techniques. In 2018 15th International Joint Conference on Computer Science and Software Engineer-ing (JCSSE) (pp. 1–5). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/JCSSE.2018.8457391
Charoen-Ung, P., & Mittrapiyanuruk, P. (2019). Sugarcane yield grade prediction using random forest with forward fea-ture selection and hyper-parameter tuning. In 14th International Conference on Computing and Information Tech-nology (pp. 1–5). Springer Verlag. https://doi.org/10.1007/978-3-319-93692-5_4
Coelho, A. P., Bettiol, J. V. T., Dalri, A. B., Fischer Filho, J. A., Faria, R. T., & Palaretti, L. F. (2019). Application of ar-tificial neural networks in the prediction of sugarcane juice Pol. Revista Brasileira de Engenharia Agrícola e Ambi-ental, 23(1), 9–15. https://doi.org/10.1590/1807-1929/agriambi.v23n1p9-15
Companhia Nacional de Abastecimento (CONAB). (2024). Boletim - Cana-de-açúcar - 1º levantamento - 2024/25. *Tabela de dados - Produção de cana-de-açúcar e subprodutos, 12*(1). https://www.conab.gov.br/info-agro/safras/cana
Confederação da Agricultura e Pecuária do Brasil (CNA). (2022). *Sumário Executivo I OCDE - FAO Perspectivas Agrícolas 2023-2032*. Brasília. https://www.cnabrasil.org.br/publicacoes/sumario-executivo-i-ocde-fao-perspectivas-agricolas-2023-2032
Conselho dos Produtores de Cana-de-Açúcar, Açúcar e Etanol do Estado de São Paulo (CONSECANA-SP). (2023). Regulamento do CONSECANA. https://www.consecana.com.br/regulamento.asp
Corrêdo, L. P., Wei, M. C., Ferraz, M. N., & Molin, J. P. (2021). Near-infrared spectroscopy as a tool for monitoring the spatial variability of sugarcane quality in the fields. Biosystems Engineering, 206, 150–161. https://doi.org/10.1016/j.biosystemseng.2021.04.001
De Matos, M., Santos, F., & Eichler, P. (2020). Sugarcane world scenario. In Sugarcane biorefinery, technology and perspectives (pp. 1–19). Academic Press. https://doi.org/10.1016/B978-0-12-814236-3.00001-9
Denham, T. (2024). Forgetting cane grasses: Switching temporal focus to reveal mosaics of Saccharum diversi-ty. Plants, People, Planet, 1–13. Advance online publication. https://doi.org/10.1002/ppp3.10489
De Oliveira, M. P. G., Bocca, F. F., & Rodrigues, L. H. A. (2017). From spreadsheets to sugar content modeling: A data mining approach. Computers and Electronics in Agriculture, 132, 14–20. https://doi.org/10.1016/j.compag.2016.11.012
Dias Neto, A. F., Albiero, D., Rossetto, R., & Biagi, J. D. (2022). Modeling of mechanized sugarcane harvesting to sup-port decision-making on asset management. Sugar Tech, 24(3), 798–812. https://doi.org/10.1007/s12355-022-01126-2
Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Marclim, W. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070
Draper, N. R., & Smith, H. (1981). Applied regression analysis. Wiley.
Everingham, Y. L., Inman-Bamber, N. G., Thorburn, P. J., & McNeill, T. J. (2007). A Bayesian modelling approach for long lead sugarcane yield forecasts for the Australian sugar industry. Australian Journal of Agricultural Research, 58(2), 87–94. http://dx.doi.org/10.1071/AR05443
Everingham, Y. L., Smyth, C. W., & Inman-Bamber, N. G. (2009). Ensemble data mining approaches to forecast region-al sugarcane crop production. Agricultural and Forest Meteorology, 149(3–4), 689–696. https://doi.org/10.1016/j.agrformet.2008.10.018
Fisch, C., & Block, J. (2018). Six tips for your (systematic) literature review in business and management re-search. Management Review Quarterly, 68(1), 103–106. https://doi.org/10.1007/s11301-018-0142-x
Florentino, H. D. O., Irawan, C., Aliano, A. F., Jones, D. F., Cantane, D. R., & Nervis, J. J. (2018). A multiple objective methodology for sugarcane harvest management with varying maturation periods. Annals of Operations Research, 267, 153–177. https://doi.org/10.1007/s10479-017-2568-2
Florentino, H. D. O., & Pato, M. V. (2014). A bi-objective genetic approach for the selection of sugarcane varieties to comply with environmental and economic requirements. Journal of the Operational Research Society, 65(6), 842–854. https://doi.org/10.1057/jors.2013.21
Florentino, H. D. O., Pato, M. V., Jones, D., & Cantane, D. R. (2015). Production and management of sugarcane biomass - Process optimization. In Biomass production and uses. IntechOpen. http://dx.doi.org/10.5772/61533
Haegeman, K., Marinelli, E., Scapolo, F., Ricci, A., & Sokolov, A. (2013). Quantitative and qualitative approaches in fu-ture-oriented technology analysis (FTA): From combination to integration? Technological Forecasting and Social Change, 80(3), 386–397. https://doi.org/10.1016/j.techfore.2012.10.002
Julien, M. H. R., Irvine, J. E., & Benda, G. T. A. (1988). Sugarcane anatomy, morphology and physiology. In Diseases of sugarcane—Major diseases (pp. 1–19). Elsevier. https://doi.org/10.1016/B978-0-444-42797-7.50005-3
Kutner, M. H., Nachtsheim, C. J., Neter, J., & Li, W. (2005). Applied linear statistical models (5th ed.). McGraw-Hill Irwin.
Lim, W. M., Kumar, S., & Ali, F. (2022). Advancing knowledge through literature reviews: ‘What’, ‘why’, and ‘how to contribute’. The Service Industries Journal, 42(7–8), 481–513. https://doi.org/10.1080/02642069.2022.2047941
Luciano, A. C. S., Picoli, M. C. A., Duft, D. G., Rocha, J. V., Leal, M. R. L., & Maire, G. (2021). Empirical model for forecasting sugarcane yield on a local scale in Brazil using Landsat imagery and random forest algo-rithm. Computers and Electronics in Agriculture, 184, Article 106063. https://doi.org/10.1016/j.compag.2021.106063
Madeiro, S. S., da Silva Oliveira, F. R., Alexandre, F. B. A., & de Lima Neto, F. B. (2006). Intelligent modeling of sug-arcane maturation. In *Computers in Agriculture and Natural Resources, 23-25 July 2006, Orlando Florida* (p. 642). American Society of Agricultural and Biological Engineers. https://doi.org/10.13031/2013.21950
Matović, N., & Ovesni, K. (2023). Interaction of quantitative and qualitative methodology in mixed methods research: Integration and/or combination. International Journal of Social Research Methodology, 26(1), 51–65. https://doi.org/10.1080/13645579.2021.1964857
Medar, R., & Rajpurohit, V. S. (2019). Supervised machine learning techniques for predicting sugarcane yield. International Journal of Recent Technology and Engineering, 8(2), 5662–5668. https://doi.org/10.35940/ijrte.B2612.078219
Montgomery, D. C., & Runger, G. C. (2011). Applied statistics and probability for engineers (5th ed.). Wiley.
Murali, P., Revathy, R., Balamurali, S., & Tayade, A. S. (2020). Integration of RNN with GARCH refined by whale op-timization algorithm for yield forecasting: A hybrid machine learning approach. Journal of Ambient Intelligence and Humanized Computing, 1–13. Advance online publication. https://doi.org/10.1007/s12652-020-01922-2
Nihar, A., Patel, N. R., & Danodia, A. (2022). Machine-learning-based regional yield forecasting for sugarcane crop in Uttar Pradesh, India. Journal of the Indian Society of Remote Sensing, 50(8), 1519–1530. https://doi.org/10.1007/s12524-022-01549-0
Oliveira, M. P. G., Bocca, F. F., & Rodrigues, L. H. A. (2017). From spreadsheets to sugar content modeling: A data mining approach. Computers and Electronics in Agriculture, 132, 14–20. https://doi.org/10.1016/j.compag.2016.11.012
Phuphaphud, A., Saengprachatanarug, K., Posom, J., Maraphum, K., & Taira, E. (2019). Prediction of the fibre content of sugarcane stalk by direct scanning using visible-shortwave near infrared spectroscopy. Vibrational Spectroscopy, 101, 71–80. https://doi.org/10.1016/j.vibspec.2019.02.005
Pignède, E., Roudier, P., Diedhiou, A., N'Guessan Bi, V. H., Kobea, A. T., Konaté, D., & Péné, C. B. (2021). Sugarcane yield forecast in Ivory Coast (West Africa) based on weather and vegetation index data. Atmosphere, 12(11), Article 1459. https://doi.org/10.3390/atmos12111459
Pravallika, K., Karuna, G., Anuradha, K., & Srilakshmi, V. (2021). Deep neural network model for proficient crop yield prediction. In 3rd International Conference on Design and Manufacturing Aspects for Sustainable Energy (ICMED 2021). EDP Sciences. https://doi.org/10.1051/e3sconf/202130901031
Rahman, M. M., & Robson, A. (2020). Integrating landsat-8 and sentinel-2 time series data for yield prediction of sug-arcane crops at the block level. Remote Sensing, 12(8), Article 1313. https://doi.org/10.3390/RS12081313
Scarpari, M. S., & Beauclair, E. G. F. D. (2004). Sugarcane maturity estimation through edaphic-climatic parame-ters. Scientia Agricola, 61, 486–491. https://doi.org/10.1590/S0103-90162004000500004
Seber, G. A. F., & Lee, A. J. (2003). Linear regression analysis (2nd ed.). Wiley.
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