Algorithms for forecasting cotton yield based on climatic parameters in Brazil

References

• Ahamed ATMS, Mahmood NT, Hossain N, Kabir MT, Das K, Rahman F, Rahman RM 2015. Applying data mining techniques to predict annual yield of major crops and recommend planting different crops in different districts in Bangladesh. In: 2015 IEEE/ACIS 16th Int Conf Softw Eng Artif Intell Netw Parallel/Distributed Comput. [place unknown], v.1; p. 1–6.
   Google Scholar
• Akbar A, Kuanar A, Patnaik J, Mishra A, Nayak S. 2018. Application of Artificial Neural Network modeling for optimization and prediction of essential oil yield in turmeric (Curcuma longa L.). Computers and Electronics in Agriculture. 148:160–178. http://www.sciencedirect.com/science/article/pii/S0168169916311371. doi:https://doi.org/10.1016/j.compag.2018.03.002
   Web of Science ®Google Scholar
• Allen RG, PL S, Raes D, Martin S. 1998. Crop evapotranspiration: guidelines for computing crop water requirements. FAO Irrig Drain Pap. 56: 1–15. by Richard G. Allen … [et al.].
   Google Scholar
• Alvares CA, Stape JL, Sentelhas PC, de Moraes JL, Sparovek G. 2013. Köppen’s climate classification map for Brazil. Meteorologische Zeitschrift. 22(6):711–728. doi:https://doi.org/10.1127/0941-2948/2013/0507.
   Web of Science ®Google Scholar
• AS de AJ, Silva FAM, de Lima MG, Amaral AB. 2009. Climatic aptitude zoning for cotton in Piaui State, Brazil. Rev Ciência Agronômica. 40(2):175.
   Web of Science ®Google Scholar
• Assad ED, Martins SC, Beltrão NEDM, Pinto HS. 2013. Impacts of climate change on the agricultural zoning of climate risk for cotton cultivation in Brazil. Pesquisa Agropecuária Brasileira. 48(1):1–8. doi:https://doi.org/10.1590/S0100-204X2013000100001.
   Google Scholar
• Barros MAL, Silva CRCD, Lima LMD, Farias FJC, Ramos GA, Santos RCD. 2020. A Review on Evolution of Cotton in Brazil: GM, White, and Colored Cultivars. Journal of Natural Fibers. 1–13. doi:https://doi.org/10.1080/15440478.2020.1738306.
   Google Scholar
• Batista CH, de Aquino LA, Silva TR, Silva HRF. 2010. Growth and productivity of cotton culture in response to phosphorus application and irrigation methods. Rev Bras Agric Irrig. 4:4.
   Google Scholar
• Bezerra JRC, Luz SE, Pereira MJ, Santana JR, Dias JCF, Santos JM, Santos JW, Silva T. 2003. Effect of soil water deficit on yield and fiber of herbaceous cotton, cultivar BRS 201. Revista Brasileira De Oleaginosas E Fibrosas. 7(2/3):p.727–734. [ Campina Grande, v.7, n.2]. .
   Google Scholar
• Bhojani SH, Bhatt N. 2020. Wheat crop yield prediction using new activation functions in neural network. Neural Comput Appl. 1:1–11.
   Web of Science ®Google Scholar
• Biswas R, Bhattacharyya B. 2019. Rice yield prediction in lower Gangetic Plain of India through multivariate approach and multiple regression analysis. J Agrometeorol. 21(1):101–103.
   Web of Science ®Google Scholar
• Chen X, Qi Z, Gui D, Sima MW, Zeng F, Li L, Li X, Gu Z. 2020. Evaluation of a new irrigation decision support system in improving cotton yield and water productivity in an arid climate. Agricultural Water Management. 234:106139. doi:https://doi.org/10.1016/j.agwat.2020.106139.
   Google Scholar
• Chou J, Xu Y, Dong W, Xian T, Wang Z. 2019. Research on the variation characteristics of climatic elements from April to September in China’s main grain-producing areas. Theoretical and Applied Climatology. 137(3–4):3197–3207. doi:https://doi.org/10.1007/s00704-019-02795-y.
   Google Scholar
• CONAB (Companhia Nacional Do Abastecimento). 2019. [access date 02 Febuary 2020]. www.conab.gov.br
   Google Scholar
• de O ALE, GB T, DZ M, de MKC, da SC de MJR. 2020. Modeling safrinha corn productivity according to climatic conditions in Mato Grosso do Sul. Rev Bras Climatol. 26.
   Google Scholar
• Doorenbos J, Kassam AH. 1979. Yield response to water. Irrig Drain Pap. 33:257.
   Google Scholar
• Draper NR, Smith H. 1980. Applied Regression Analysis, 2nd Edn. Chap. 1, v.3.
   Google Scholar
• Elavarasan D, Vincent DR, Sharma V, Zomaya AY, Srinivasan K. 2018. Forecasting yield by integrating agrarian factors and machine learning models: A survey. Computers and Electronics in Agriculture. October, 155:257–282. doi:https://doi.org/10.1016/j.compag.2018.10.024.
   Web of Science ®Google Scholar
• Everingham Y, Sexton J, Skocaj D, Inman-Bamber G. 2016. Accurate prediction of sugarcane yield using a random forest algorithm. Agronomy for Sustainable Development. 36(2):27. doi:https://doi.org/10.1007/s13593-016-0364-z.
   Web of Science ®Google Scholar
• Feng A, Zhou J, Vories ED, Sudduth KA, Zhang M. 2020. Yield estimation in cotton using UAV-based multi-sensor imagery. Biosystems Engineering. 193:101–114. http://www.sciencedirect.com/science/article/pii/S1537511020300544.doi:https://doi.org/10.1016/j.biosystemseng.2020.02.014
   Web of Science ®Google Scholar
• Gonzalez-Sanchez A, Frausto-Solis J, Ojeda-Bustamante W. 2014. Attribute selection impact on linear and nonlinear regression models for crop yield prediction. Sci World J. 7:2014.
   Google Scholar
• Goyal MK. 2014. Modeling of sediment yield prediction using M5 model tree algorithm and wavelet regression. Water Resources Management. 28(7):1991–2003. doi:https://doi.org/10.1007/s11269-014-0590-6.
   Web of Science ®Google Scholar
• Griddi-Papp IL. 1965. Botany and Genetics. editor. Cotton Culture and Fertilization. São Paulo:Brazilian Institute of Potash;pp.117–157.
   Google Scholar
• Gyamerah SA, Ngare P, Ikpe D. 2020. Probabilistic forecasting of crop yields via quantile random forest and Epanechnikov Kernel function. Agricultural and Forest Meteorology. 280:107808. doi:https://doi.org/10.1016/j.agrformet.2019.107808.
   Google Scholar
• Hansen JW. 2004. Linking dynamic seasonal climate forecasts with crop simulation for maize yield prediction in semi-arid Kenya. . Agricultural and Forest Meteorology. 125(1–2):143–157. doi:https://doi.org/10.1016/j.agrformet.2004.02.006.
   Web of Science ®Google Scholar
• Hussain M, Tariq AF, Nawaz A, Nawaz M, Sattar A, Ul-Allah S, Wakeel A, Farooq S. 2020. Efficacy of fertilizing method for different potash sources in cotton (Gossypium hirsutum L.) nutrition under arid climatic conditions. PLoS One. 15(1):1–9. doi:https://doi.org/10.1371/journal.pone.0228335.
   Google Scholar
• IBGE G. 2020. Sistema IBGE de recuperação automática: SIDRA. Banco De Dados Agregados. http://www.sidra.ibge.gov.br/bda/tabela/protabl.asp.
   Google Scholar
• Iqbal M, Ul-Allah S, Naeem M, Ijaz M, Sattar A, Sher A. 2017. Response of cotton genotypes to water and heat stress: from field to genes. Euphytica. 213(6):1–11. doi:https://doi.org/10.1007/s10681-017-1916-2.
   Google Scholar
• Kaul M, Hill RL, Walthall C. 2005. Artificial neural networks for corn and soybean yield prediction. Agricultural Systems. 85(1):1–18. doi:https://doi.org/10.1016/j.agsy.2004.07.009.
   Web of Science ®Google Scholar
• Krige DG. 1951. A statistical approach to some basic mine valuation problems on the Witwatersrand. J South African Inst Min Metall. 52(6):119–139.
   Google Scholar
• Lasdon LS, Waren AD. 1982. General reduced gradient software for linearly and non-linearly contained problems. Austin: GRG2 users Guid Univ Texas.
   Google Scholar
• Li L, Baker TE, White SR, Burke K. 2016. Pure density functional for strong correlation and the thermodynamic limit from machine learning. Physical Review B. 94(24):245129. others. doi:https://doi.org/10.1103/PhysRevB.94.245129.
   Google Scholar
• Li N, Lin H, Wang T, Li Y, Liu Y, Chen X, Hu X. 2020. Impact of climate change on cotton growth and yields in Xinjiang, China. Field Crops Research. 247:107590. doi:https://doi.org/10.1016/j.fcr.2019.107590.
   Web of Science ®Google Scholar
• Li P. 2012. Robust logitboost and adaptive base class (abc) logitboost. arXiv Prepr arXiv. 8:12033491.
   Google Scholar
• Marcari MA, de S RG, de O. ALE. 2015. Agrometeorological models for forecasting yield and quality of sugarcane. Aust J Crop Sci. 9(11):1049–1056.
   Google Scholar
• Marengo JA, Tomasella J, Nobre CA. 2017. Climate change and water resources. Waters of Brazil: Springer; p. 171–186. [place unknown]
   Google Scholar
• Martins E, de Oliveira Aparecido LE, Santos LPS, JMA DM, de Souza PS. 2015. Weather influence in yild and quality coffee produced in South Minas Gerais region. Coffee Sci. 10(4):499–506.
   Google Scholar
• Marur CJ. 1991. Comparison of rates of liquid photosynthesis, stomatal resistance and yield of two cotton cultivars submitted to water stress. Pesquisa Agropecuaria Brasileira. 26:153–161.
   Web of Science ®Google Scholar
• Mercante E, Lima LEPD, Justina DDD, Uribe-Opazo MA, Lamparelli RAC. 2012. Detection of soybean planted areas through orbital images based on culture spectral dynamics. Engenharia Agrícola. 32(5):920–931. doi:https://doi.org/10.1590/S0100-69162012000500011.
   Web of Science ®Google Scholar
• Brasil.1981. Ministry of Mines and Energy. General secretary. RADAMBRASIL Project. Rio De Janeiro: Survey of Natural Resources, 25, 29, 31.
   Google Scholar
• Moreto VB, de Souza Rolim G, Zacarin BG, Vanin AP, de Souza LM, Latado RR. 2017. Agrometeorological models for forecasting the qualitative attributes of “Valência” oranges. Theor Appl Climatol. 130(3–4):847–864. doi:https://doi.org/10.1007/s00704-016-1920-9.
   Google Scholar
• Nayakekorala H, Taylor HM. 1990. Phosphorus uptake rates of cotton roots at different growth stages from different soil layers. Pant Soil. 122:105–110.
   Web of Science ®Google Scholar
• O ALED, de S RG, SC DMJRD, HG R, GHE L, PS S. 2018. Agroclimatic zoning for urucum crops in the state of Minas Gerais, Brazil. Bragantia. 77(1):193–200.
   Web of Science ®Google Scholar
• Opelt A, Fussenegger M, Pinz A, Auer P. 2004. Weak hypotheses and boosting for generic object detection and recognition. Eur Conf Comput Vis. 8:71–84. [place unknown].
   Google Scholar
• Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V. 2011. Scikit-learn: machine learning in Python. J Mach Learn Res. 12: 2825–2830. others.
   Web of Science ®Google Scholar
• Quinlan JR. 1983. Learning Efficient Classification Procedures and Their Application to Chess End Games. In: Michalski RS, Carbonell JG, Mitchell TM, editors. Mach Learn An Artif Intell Approach [Internet]. Berlin (Heidelberg): Springer Berlin Heidelberg; p. 463–482. doi:https://doi.org/10.1007/978-3-662-12405-5_15
   Google Scholar
• Rahimi J, Khalili A, Butterbach‐Bahl K. 2019. Projected changes in modified Thornthwaite climate zones over Southwest Asia using a CMIP5 multi-model ensemble. . International Journal of Climatology. 39(12):4575–4594. doi:https://doi.org/10.1002/joc.6088.
   Google Scholar
• Rehman TU, Mahmud MS, Chang YK, Jin J, Shin J. 2019. Current and future applications of statistical machine learning algorithms for agricultural machine vision systems. Computers and Electronics in Agriculture. 156:585–605. doi:https://doi.org/10.1016/j.compag.2018.12.006.
   Web of Science ®Google Scholar
• Rosolem CA. 2007. Maximum soybean productivity. Rondonópolis: Foundation MT; p. p.237–244.
   Google Scholar
• Sahoo S, Russo TA, Elliott J, Foster I. 2017. Machine learning algorithms for modeling groundwater level changes in agricultural regions of the U.S. Water Resources Research. 53(5):3878–3895. doi:https://doi.org/10.1002/2016WR019933.
   Web of Science ®Google Scholar
• Schwalbert RA, Amado T, Corassa G, Pott LP, Prasad PVV, Ciampitti IA. 2020. Satellite-based soybean yield forecast: integrating machine learning and weather data for improving crop yield prediction in southern Brazil. Agricultural and Forest Meteorology. 284:107886. doi:https://doi.org/10.1016/j.agrformet.2019.107886.
   Web of Science ®Google Scholar
• Shekoofa A, Emam Y, Shekoufa N, Ebrahimi M, Ebrahimie E, Anisimova M. 2014. Determining the most important physiological and agronomic traits contributing to maize grain yield through machine learning algorithms: a new avenue in intelligent agriculture. PLoS One. 9(5):e97288. doi:https://doi.org/10.1371/journal.pone.0097288.
   PubMedGoogle Scholar
• Silva KA, de Souza Rolim G, Valeriano TTB, da Silva Cabral de Moraes JR. 2020. Influence of El Niño and La Niña on coffee yield in the main coffee-producing regions of Brazil. Theoretical and Applied Climatology. 139(3–4):1019–1029. others. doi:https://doi.org/10.1007/s00704-019-03039-9.
   Google Scholar
• Singh RK. 2008. Artificial neural network methodology for modelling and forecasting maize crop yield. Agric Econ Res Rev. 21((347–2016–16813)):5–10.
   Google Scholar
• Thomas J, Mayr A, Bischl B, Schmid M, Smith A, Hofner B. 2018. Gradient boosting for distributional regression: faster tuning and improved variable selection via noncyclical updates. Statistics and Computing. 28(3):673–687. doi:https://doi.org/10.1007/s11222-017-9754-6.
   Google Scholar
• Thornthwaite CW. 1948. An approach toward a rational classification of climate. Royal Society, London: LWW.
   Google Scholar
• Thornthwaite CW, Mather JR. 1955. Publications in climatology. Water Balanc. 8:1–104.
   Google Scholar
• Tollenaar M, Fridgen J, Tyagi P, Stackhouse PW, Kumudini S. 2017. The Contribution of Solar Brightening to the US Maize Yield Trend. Nature Climate Change. 7:275–278. doi:https://doi.org/10.1038/nclimate3234.
   Google Scholar
• Torkashvand AM, Ahmadi A, Nikravesh NL. 2017. Prediction of kiwifruit firmness using fruit mineral nutrient concentration by artificial neural network (ANN) and multiple linear regressions (MLR). Journal of Integrative Agriculture. 16(7):1634–1644. doi:https://doi.org/10.1016/S2095-3119(16)61546-0.
   Web of Science ®Google Scholar
• Veenadhari S, Mishra B, Singh CD. 2011. Soybean productivity modelling using decision tree algorithms. Int J Comput Appl. 27(7):11–15.
   Google Scholar
• Veenadhari S, Misra B, Singh CD 2014. Machine learning approach for forecasting crop yield based on climatic parameters. In: 2014 Int Conf Comput Commun Informatics. [place unknown], v.4; p. 1–5.
   Google Scholar
• Wrege MS, Caramori PH, Gonçalves SL, Almeida WPD, Marur CJ, Pires JR, R S. Y. 2000. Cotton zoning based on sowing periods of lower risk in Parana state, Brazil. Brazilian Arch Biol Technol. 43(1). doi:https://doi.org/10.1590/S1516-89132000000100010.
   Google Scholar