The impact of climate variability on chickpea production in the central highlands of ethiopia using auto regressive distributive lag model
Abstract This study examines how climate variability affects chickpea production in Ethiopia’s central highlands, using a statistical model called the autoregressive distributed lag (ARDL). The goal is to understand how changes in climate factors like temperature and rainfall influence chickpea yiel...
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| Format: | Article |
| Language: | English |
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Springer
2025-05-01
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| Series: | Discover Agriculture |
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| Online Access: | https://doi.org/10.1007/s44279-025-00200-z |
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| author | Tariku Tirfeessa Adeba Gemechu Lama Zemedu |
| author_facet | Tariku Tirfeessa Adeba Gemechu Lama Zemedu |
| author_sort | Tariku Tirfeessa |
| collection | DOAJ |
| description | Abstract This study examines how climate variability affects chickpea production in Ethiopia’s central highlands, using a statistical model called the autoregressive distributed lag (ARDL). The goal is to understand how changes in climate factors like temperature and rainfall influence chickpea yields over time, both in the short and long term. The research applies advanced methods to analyze the relationships between temperature, rainfall, and chickpea yields, testing how these factors move together over time. It also looks at how changes in one factor might cause changes in the others. The ARDL model helps assess the impact of temperature and rainfall on chickpea yields in both the short term and the long term. The findings show that temperature and rainfall have a complex relationship with chickpea production. More rainfall is linked to higher yields, while temperature fluctuations generally have a negative effect over time. The study also finds that temperature changes have the strongest influence on yields. This is confirmed by additional analysis, which shows that temperature plays a major role in shaping chickpea production in the region. In conclusion, the study highlights the need for developing chickpea varieties that can withstand climate change and adopting sustainable farming practices to reduce the impact of climate variability. The findings call for greater international collaboration to address these challenges. This research provides important insights into how climate change affects chickpea production and underscores the importance of integrating climate science into agricultural policy. |
| format | Article |
| id | doaj-art-e4666be8749340baa2da77b4e8abeac9 |
| institution | OA Journals |
| issn | 2731-9598 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Agriculture |
| spelling | doaj-art-e4666be8749340baa2da77b4e8abeac92025-08-20T01:51:36ZengSpringerDiscover Agriculture2731-95982025-05-013111610.1007/s44279-025-00200-zThe impact of climate variability on chickpea production in the central highlands of ethiopia using auto regressive distributive lag modelTariku Tirfeessa0Adeba Gemechu1Lama Zemedu2University in Agricultural Economics, Jimma UniversityDepartment of Agricultural Economics, Jimma UniversityDepartment of Agricultural Economics, Ethiopian Agricultural Research InstituteAbstract This study examines how climate variability affects chickpea production in Ethiopia’s central highlands, using a statistical model called the autoregressive distributed lag (ARDL). The goal is to understand how changes in climate factors like temperature and rainfall influence chickpea yields over time, both in the short and long term. The research applies advanced methods to analyze the relationships between temperature, rainfall, and chickpea yields, testing how these factors move together over time. It also looks at how changes in one factor might cause changes in the others. The ARDL model helps assess the impact of temperature and rainfall on chickpea yields in both the short term and the long term. The findings show that temperature and rainfall have a complex relationship with chickpea production. More rainfall is linked to higher yields, while temperature fluctuations generally have a negative effect over time. The study also finds that temperature changes have the strongest influence on yields. This is confirmed by additional analysis, which shows that temperature plays a major role in shaping chickpea production in the region. In conclusion, the study highlights the need for developing chickpea varieties that can withstand climate change and adopting sustainable farming practices to reduce the impact of climate variability. The findings call for greater international collaboration to address these challenges. This research provides important insights into how climate change affects chickpea production and underscores the importance of integrating climate science into agricultural policy.https://doi.org/10.1007/s44279-025-00200-zUsing an autoregressive distributed lag (ARDL) model |
| spellingShingle | Tariku Tirfeessa Adeba Gemechu Lama Zemedu The impact of climate variability on chickpea production in the central highlands of ethiopia using auto regressive distributive lag model Discover Agriculture Using an autoregressive distributed lag (ARDL) model |
| title | The impact of climate variability on chickpea production in the central highlands of ethiopia using auto regressive distributive lag model |
| title_full | The impact of climate variability on chickpea production in the central highlands of ethiopia using auto regressive distributive lag model |
| title_fullStr | The impact of climate variability on chickpea production in the central highlands of ethiopia using auto regressive distributive lag model |
| title_full_unstemmed | The impact of climate variability on chickpea production in the central highlands of ethiopia using auto regressive distributive lag model |
| title_short | The impact of climate variability on chickpea production in the central highlands of ethiopia using auto regressive distributive lag model |
| title_sort | impact of climate variability on chickpea production in the central highlands of ethiopia using auto regressive distributive lag model |
| topic | Using an autoregressive distributed lag (ARDL) model |
| url | https://doi.org/10.1007/s44279-025-00200-z |
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