Time series and ensemble models to forecast banana crop yield in Tanzania, considering the effects of climate change
Banana cultivation plays a pivotal role in Tanzania’s agricultural landscape and food security. Precisely forecasting banana crop yield is essential for resource optimization, market stability, and informed policymaking, particularly in the face of climate change. This study employed time series and...
| المؤلفون الرئيسيون: | , , , |
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| التنسيق: | مقال |
| اللغة: | English |
| منشور في: |
Elsevier
2023-12-01
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| سلاسل: | Resources, Environment and Sustainability |
| الموضوعات: | |
| الوصول للمادة أونلاين: | http://www.sciencedirect.com/science/article/pii/S2666916123000312 |
| _version_ | 1827780181213839360 |
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| author | Sabas Patrick Silas Mirau Isambi Mbalawata Judith Leo |
| author_facet | Sabas Patrick Silas Mirau Isambi Mbalawata Judith Leo |
| author_sort | Sabas Patrick |
| collection | DOAJ |
| description | Banana cultivation plays a pivotal role in Tanzania’s agricultural landscape and food security. Precisely forecasting banana crop yield is essential for resource optimization, market stability, and informed policymaking, particularly in the face of climate change. This study employed time series and ensemble models to forecast banana crop yield in Tanzania, offering crucial insights into future production trends. We utilized Seasonal ARIMA with Exogenous Variables (SARIMAX), State Space (SS), and Long Short-Term Memory (LSTM) models, chosen based on regression analysis and data exploration. Leveraging historical banana yield data (1961–2020) and relevant climate variables, we formulated an ensemble model using a weighted average approach. Our findings underscore the potential of time series and ensemble models for accurate banana crop yield forecasting. Statistical evaluation metrics validate their effectiveness in capturing temporal variations and delivering reliable predictions. This research advances agricultural forecasting by demonstrating the successful application of these models in Tanzania. It emphasizes the importance of considering temporal dynamics and relevant factors for precise predictions. Policymakers, farmers, and stakeholders can leverage this study’s outcomes to make informed decisions on resource allocation, market planning, and agricultural policies. Ultimately, our research bolsters sustainable banana production and enhances food security in Tanzania. |
| first_indexed | 2024-03-11T15:02:00Z |
| format | Article |
| id | doaj.art-a5148e371b174951a791e13532d91dc4 |
| institution | Directory Open Access Journal |
| issn | 2666-9161 |
| language | English |
| last_indexed | 2024-03-11T15:02:00Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Resources, Environment and Sustainability |
| spelling | doaj.art-a5148e371b174951a791e13532d91dc42023-10-30T06:08:46ZengElsevierResources, Environment and Sustainability2666-91612023-12-0114100138Time series and ensemble models to forecast banana crop yield in Tanzania, considering the effects of climate changeSabas Patrick0Silas Mirau1Isambi Mbalawata2Judith Leo3Department of Applied Mathematics and Computational Sciences, Nelson Mandela African Institution of Science and Technology, Arusha, Tanzania; Corresponding author.Department of Applied Mathematics and Computational Sciences, Nelson Mandela African Institution of Science and Technology, Arusha, TanzaniaAfrican Institute for Mathematical Sciences, Kigali, RwandaDepartment of Applied Mathematics and Computational Sciences, Nelson Mandela African Institution of Science and Technology, Arusha, TanzaniaBanana cultivation plays a pivotal role in Tanzania’s agricultural landscape and food security. Precisely forecasting banana crop yield is essential for resource optimization, market stability, and informed policymaking, particularly in the face of climate change. This study employed time series and ensemble models to forecast banana crop yield in Tanzania, offering crucial insights into future production trends. We utilized Seasonal ARIMA with Exogenous Variables (SARIMAX), State Space (SS), and Long Short-Term Memory (LSTM) models, chosen based on regression analysis and data exploration. Leveraging historical banana yield data (1961–2020) and relevant climate variables, we formulated an ensemble model using a weighted average approach. Our findings underscore the potential of time series and ensemble models for accurate banana crop yield forecasting. Statistical evaluation metrics validate their effectiveness in capturing temporal variations and delivering reliable predictions. This research advances agricultural forecasting by demonstrating the successful application of these models in Tanzania. It emphasizes the importance of considering temporal dynamics and relevant factors for precise predictions. Policymakers, farmers, and stakeholders can leverage this study’s outcomes to make informed decisions on resource allocation, market planning, and agricultural policies. Ultimately, our research bolsters sustainable banana production and enhances food security in Tanzania.http://www.sciencedirect.com/science/article/pii/S2666916123000312Time seriesEnsembleModelingForecastingBanana crop yieldClimate change |
| spellingShingle | Sabas Patrick Silas Mirau Isambi Mbalawata Judith Leo Time series and ensemble models to forecast banana crop yield in Tanzania, considering the effects of climate change Resources, Environment and Sustainability Time series Ensemble Modeling Forecasting Banana crop yield Climate change |
| title | Time series and ensemble models to forecast banana crop yield in Tanzania, considering the effects of climate change |
| title_full | Time series and ensemble models to forecast banana crop yield in Tanzania, considering the effects of climate change |
| title_fullStr | Time series and ensemble models to forecast banana crop yield in Tanzania, considering the effects of climate change |
| title_full_unstemmed | Time series and ensemble models to forecast banana crop yield in Tanzania, considering the effects of climate change |
| title_short | Time series and ensemble models to forecast banana crop yield in Tanzania, considering the effects of climate change |
| title_sort | time series and ensemble models to forecast banana crop yield in tanzania considering the effects of climate change |
| topic | Time series Ensemble Modeling Forecasting Banana crop yield Climate change |
| url | http://www.sciencedirect.com/science/article/pii/S2666916123000312 |
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