Trivariate Stochastic Weather Model for Predicting Maize Yield
Maize yield prediction in the sub-Saharan region is imperative for mitigation of risks emanating from crop loss due to changes in climate. Temperature, rainfall amount, and reference evapotranspiration are major climatic factors affecting maize yield. They are not only interdependent but also have s...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Journal of Applied Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2022/3633658 |
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| author | Patrick Chidzalo Phillip O. Ngare Joseph K. Mung’atu |
| author_facet | Patrick Chidzalo Phillip O. Ngare Joseph K. Mung’atu |
| author_sort | Patrick Chidzalo |
| collection | DOAJ |
| description | Maize yield prediction in the sub-Saharan region is imperative for mitigation of risks emanating from crop loss due to changes in climate. Temperature, rainfall amount, and reference evapotranspiration are major climatic factors affecting maize yield. They are not only interdependent but also have significantly changed due to climate change, which causes nonlinearity and nonstationarity in weather data. Hence, there exists a need for a stochastic process for predicting maize yield with higher precision. To solve the problem, this paper constructs a joint stochastic process that acquires joints effects of the three weather processes from joint a probability density function (pdf) constructed using copulas that maintain interdependence. Stochastic analyses are applied on the pdf and process to account for nonlinearity and nonstationarity, and also establish a corresponding stochastic differential equation (SDE) for maize yield. The trivariate stochastic process predicts maize yield with R2=0.8389 and MAPE=4.31% under a deep learning framework. Its aggregated values predict maize yield with R2 up to 0.9765 and MAPE=1.94% under common machine learning algorithms. Comparatively, the R2 is 0.8829% and MAPE=4.18%, under the maize yield SDE. Thus, the joint stochastic process can be used to predict maize yield with higher precision. |
| format | Article |
| id | doaj-art-7e3da2850acc45e191f59a96fb19694c |
| institution | Kabale University |
| issn | 1687-0042 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Applied Mathematics |
| spelling | doaj-art-7e3da2850acc45e191f59a96fb19694c2025-02-03T01:24:36ZengWileyJournal of Applied Mathematics1687-00422022-01-01202210.1155/2022/3633658Trivariate Stochastic Weather Model for Predicting Maize YieldPatrick Chidzalo0Phillip O. Ngare1Joseph K. Mung’atu2Pan African University Institute of Basic SciencesSchool of MathematicsPan African University Institute of Basic SciencesMaize yield prediction in the sub-Saharan region is imperative for mitigation of risks emanating from crop loss due to changes in climate. Temperature, rainfall amount, and reference evapotranspiration are major climatic factors affecting maize yield. They are not only interdependent but also have significantly changed due to climate change, which causes nonlinearity and nonstationarity in weather data. Hence, there exists a need for a stochastic process for predicting maize yield with higher precision. To solve the problem, this paper constructs a joint stochastic process that acquires joints effects of the three weather processes from joint a probability density function (pdf) constructed using copulas that maintain interdependence. Stochastic analyses are applied on the pdf and process to account for nonlinearity and nonstationarity, and also establish a corresponding stochastic differential equation (SDE) for maize yield. The trivariate stochastic process predicts maize yield with R2=0.8389 and MAPE=4.31% under a deep learning framework. Its aggregated values predict maize yield with R2 up to 0.9765 and MAPE=1.94% under common machine learning algorithms. Comparatively, the R2 is 0.8829% and MAPE=4.18%, under the maize yield SDE. Thus, the joint stochastic process can be used to predict maize yield with higher precision.http://dx.doi.org/10.1155/2022/3633658 |
| spellingShingle | Patrick Chidzalo Phillip O. Ngare Joseph K. Mung’atu Trivariate Stochastic Weather Model for Predicting Maize Yield Journal of Applied Mathematics |
| title | Trivariate Stochastic Weather Model for Predicting Maize Yield |
| title_full | Trivariate Stochastic Weather Model for Predicting Maize Yield |
| title_fullStr | Trivariate Stochastic Weather Model for Predicting Maize Yield |
| title_full_unstemmed | Trivariate Stochastic Weather Model for Predicting Maize Yield |
| title_short | Trivariate Stochastic Weather Model for Predicting Maize Yield |
| title_sort | trivariate stochastic weather model for predicting maize yield |
| url | http://dx.doi.org/10.1155/2022/3633658 |
| work_keys_str_mv | AT patrickchidzalo trivariatestochasticweathermodelforpredictingmaizeyield AT phillipongare trivariatestochasticweathermodelforpredictingmaizeyield AT josephkmungatu trivariatestochasticweathermodelforpredictingmaizeyield |