Estimation of rice yield using multivariate analysis techniques based on meteorological parameters
Abstract This study aims to develop predictive models for rice yield by applying multivariate techniques. It utilizes stepwise multiple regression, discriminant function analysis and logistic regression techniques to forecast crop yield in specific districts of Haryana. The time series data on rice...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-06-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-63596-6 |
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| author | Ajay Sharma Joginder Kumar Mandeep Redhu Parveen Kumar Mohit Godara Pushpa Ghiyal Pingping Fu Mehdi Rahimi |
| author_facet | Ajay Sharma Joginder Kumar Mandeep Redhu Parveen Kumar Mohit Godara Pushpa Ghiyal Pingping Fu Mehdi Rahimi |
| author_sort | Ajay Sharma |
| collection | DOAJ |
| description | Abstract This study aims to develop predictive models for rice yield by applying multivariate techniques. It utilizes stepwise multiple regression, discriminant function analysis and logistic regression techniques to forecast crop yield in specific districts of Haryana. The time series data on rice crop have been divided into two and three classes based on crop yield. The yearly time series data of rice yield from 1980–81 to 2020–21 have been taken from various issues of Statistical Abstracts of Haryana. The study also utilized fortnightly meteorological data sourced from the Agrometeorology Department of CCS HAU, India. For comparing various predictive models' performance, evaluation of measures like Root Mean Square Error, Predicted Error Sum of Squares, Mean Absolute Deviation and Mean Absolute Percentage Error have been used. Results of the study indicated that discriminant function analysis emerged as the most effective to predict the rice yield accurately as compared to logistic regression. Importantly, the research highlighted that the optimum time for forecasting the rice yield is 1 month prior to the crops harvesting, offering valuable insight for agricultural planning and decision-making. This approach demonstrates the fusion of weather data and advanced statistical techniques, showcasing the potential for more precise and informed agricultural practices. |
| format | Article |
| id | doaj-art-d4f1193c626b43d889ac4f7984c753c9 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-d4f1193c626b43d889ac4f7984c753c92025-01-19T12:24:42ZengNature PortfolioScientific Reports2045-23222024-06-011411810.1038/s41598-024-63596-6Estimation of rice yield using multivariate analysis techniques based on meteorological parametersAjay Sharma0Joginder Kumar1Mandeep Redhu2Parveen Kumar3Mohit Godara4Pushpa Ghiyal5Pingping Fu6Mehdi Rahimi7Department of Mathematics and Statistics, CCS, Haryana Agricultural UniversityDepartment of Mathematics and Statistics, CCS, Haryana Agricultural UniversityDepaprtment of Plant, Soil and Agricultural System, Southern Illinois UniversityDepartment of Agronomy, CCS, Haryana Agricultural UniversityDepartment of Agricultural Meteorology, CCS, Haryana Agricultural UniversityDepartment of Mathematics and Statistics, CCS, Haryana Agricultural UniversitySchool of Education, Southern Illinois UniversityDepartment of Biotechnology, Institute of Science and High Technology and Environmental Sciences, Graduate University of Advanced TechnologyAbstract This study aims to develop predictive models for rice yield by applying multivariate techniques. It utilizes stepwise multiple regression, discriminant function analysis and logistic regression techniques to forecast crop yield in specific districts of Haryana. The time series data on rice crop have been divided into two and three classes based on crop yield. The yearly time series data of rice yield from 1980–81 to 2020–21 have been taken from various issues of Statistical Abstracts of Haryana. The study also utilized fortnightly meteorological data sourced from the Agrometeorology Department of CCS HAU, India. For comparing various predictive models' performance, evaluation of measures like Root Mean Square Error, Predicted Error Sum of Squares, Mean Absolute Deviation and Mean Absolute Percentage Error have been used. Results of the study indicated that discriminant function analysis emerged as the most effective to predict the rice yield accurately as compared to logistic regression. Importantly, the research highlighted that the optimum time for forecasting the rice yield is 1 month prior to the crops harvesting, offering valuable insight for agricultural planning and decision-making. This approach demonstrates the fusion of weather data and advanced statistical techniques, showcasing the potential for more precise and informed agricultural practices.https://doi.org/10.1038/s41598-024-63596-6Rice yieldEstimationWeather parametersMultivariate analysisRMSE and MAPE |
| spellingShingle | Ajay Sharma Joginder Kumar Mandeep Redhu Parveen Kumar Mohit Godara Pushpa Ghiyal Pingping Fu Mehdi Rahimi Estimation of rice yield using multivariate analysis techniques based on meteorological parameters Scientific Reports Rice yield Estimation Weather parameters Multivariate analysis RMSE and MAPE |
| title | Estimation of rice yield using multivariate analysis techniques based on meteorological parameters |
| title_full | Estimation of rice yield using multivariate analysis techniques based on meteorological parameters |
| title_fullStr | Estimation of rice yield using multivariate analysis techniques based on meteorological parameters |
| title_full_unstemmed | Estimation of rice yield using multivariate analysis techniques based on meteorological parameters |
| title_short | Estimation of rice yield using multivariate analysis techniques based on meteorological parameters |
| title_sort | estimation of rice yield using multivariate analysis techniques based on meteorological parameters |
| topic | Rice yield Estimation Weather parameters Multivariate analysis RMSE and MAPE |
| url | https://doi.org/10.1038/s41598-024-63596-6 |
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