Fedhasa Benti Chalchissa, Girma Mamo Diga, A. R. Tolossa
{"title":"埃塞俄比亚Jimma地区咖啡(Coffea arabica L.)分布对当前和未来气候变化的响应模拟","authors":"Fedhasa Benti Chalchissa, Girma Mamo Diga, A. R. Tolossa","doi":"10.20961/stjssa.v19i1.54885","DOIUrl":null,"url":null,"abstract":"Coffee arabica species have already been affected by climate change, with economic and social implications. Small-holder farmers have faced and will continue to face significant challenges in sustaining the production of their coffee plants. This study aimed to determine the optimal bio-climatic factors for coffee production in current and future climate change scenarios by simulating coffee distribution's responses to nine selective bio-climatic factors under the scenarios of moderate representative concentration pathway (RCP4.5) and worst representative concentration pathway (RCP8.5). The Maxent model was used to simulate the distribution of C. arabica. Multiple regression models (path and response optimizers) were used to parameterize and optimize the logistic outputs from the Maxent model. Results showed that climatic factors such as total precipitation, precipitation seasonality, and mean temperature are the most important climatic factors in influencing C. arabica farming. Under the current condition, total precipitation significantly benefits C. arabica whereas precipitation seasonality significantly affects it (P < 0.001). The annual mean temperature has neither benefited nor affected it. Under the RCP4.5, C. arabica would positively react to the rising annual mean temperature and total precipitation but adversely react to the rising precipitation seasonality. For current, RCP4.5, and RCP8.5, the average five top-optimal multiple responses of C. arabica were 75.8, 77, and 70%, respectively. Under RCP8.5, the maximum optimal response of the plant will be an annual temperature of 23.77°C, total precipitation of 1806 mm, and 77% precipitation seasonality. In comparison to the current and RCP8.5 climatic scenarios, the distribution responses of C. arabica to the climatic factors would be significantly greater in the RCP4.5 scenario (P > 0.001). As precipitation and temperature-related variables increase, the cultivation of C. arabica will increase by 1.2% under RCP4.5 but decrease by 5.6% under RCP8.5. A limited number of models and environmental factors were used in this study, suggesting that intensive research into other environmental aspects is needed using different models.","PeriodicalId":36463,"journal":{"name":"Sains Tanah","volume":"97 1","pages":""},"PeriodicalIF":0.5000,"publicationDate":"2022-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Modeling the responses of Coffee (Coffea arabica L.) distribution to current and future climate change in Jimma Zone, Ethiopia\",\"authors\":\"Fedhasa Benti Chalchissa, Girma Mamo Diga, A. R. Tolossa\",\"doi\":\"10.20961/stjssa.v19i1.54885\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Coffee arabica species have already been affected by climate change, with economic and social implications. Small-holder farmers have faced and will continue to face significant challenges in sustaining the production of their coffee plants. This study aimed to determine the optimal bio-climatic factors for coffee production in current and future climate change scenarios by simulating coffee distribution's responses to nine selective bio-climatic factors under the scenarios of moderate representative concentration pathway (RCP4.5) and worst representative concentration pathway (RCP8.5). The Maxent model was used to simulate the distribution of C. arabica. Multiple regression models (path and response optimizers) were used to parameterize and optimize the logistic outputs from the Maxent model. Results showed that climatic factors such as total precipitation, precipitation seasonality, and mean temperature are the most important climatic factors in influencing C. arabica farming. Under the current condition, total precipitation significantly benefits C. arabica whereas precipitation seasonality significantly affects it (P < 0.001). The annual mean temperature has neither benefited nor affected it. Under the RCP4.5, C. arabica would positively react to the rising annual mean temperature and total precipitation but adversely react to the rising precipitation seasonality. For current, RCP4.5, and RCP8.5, the average five top-optimal multiple responses of C. arabica were 75.8, 77, and 70%, respectively. Under RCP8.5, the maximum optimal response of the plant will be an annual temperature of 23.77°C, total precipitation of 1806 mm, and 77% precipitation seasonality. In comparison to the current and RCP8.5 climatic scenarios, the distribution responses of C. arabica to the climatic factors would be significantly greater in the RCP4.5 scenario (P > 0.001). As precipitation and temperature-related variables increase, the cultivation of C. arabica will increase by 1.2% under RCP4.5 but decrease by 5.6% under RCP8.5. A limited number of models and environmental factors were used in this study, suggesting that intensive research into other environmental aspects is needed using different models.\",\"PeriodicalId\":36463,\"journal\":{\"name\":\"Sains Tanah\",\"volume\":\"97 1\",\"pages\":\"\"},\"PeriodicalIF\":0.5000,\"publicationDate\":\"2022-02-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Sains Tanah\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.20961/stjssa.v19i1.54885\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"AGRONOMY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sains Tanah","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.20961/stjssa.v19i1.54885","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"AGRONOMY","Score":null,"Total":0}
Modeling the responses of Coffee (Coffea arabica L.) distribution to current and future climate change in Jimma Zone, Ethiopia
Coffee arabica species have already been affected by climate change, with economic and social implications. Small-holder farmers have faced and will continue to face significant challenges in sustaining the production of their coffee plants. This study aimed to determine the optimal bio-climatic factors for coffee production in current and future climate change scenarios by simulating coffee distribution's responses to nine selective bio-climatic factors under the scenarios of moderate representative concentration pathway (RCP4.5) and worst representative concentration pathway (RCP8.5). The Maxent model was used to simulate the distribution of C. arabica. Multiple regression models (path and response optimizers) were used to parameterize and optimize the logistic outputs from the Maxent model. Results showed that climatic factors such as total precipitation, precipitation seasonality, and mean temperature are the most important climatic factors in influencing C. arabica farming. Under the current condition, total precipitation significantly benefits C. arabica whereas precipitation seasonality significantly affects it (P < 0.001). The annual mean temperature has neither benefited nor affected it. Under the RCP4.5, C. arabica would positively react to the rising annual mean temperature and total precipitation but adversely react to the rising precipitation seasonality. For current, RCP4.5, and RCP8.5, the average five top-optimal multiple responses of C. arabica were 75.8, 77, and 70%, respectively. Under RCP8.5, the maximum optimal response of the plant will be an annual temperature of 23.77°C, total precipitation of 1806 mm, and 77% precipitation seasonality. In comparison to the current and RCP8.5 climatic scenarios, the distribution responses of C. arabica to the climatic factors would be significantly greater in the RCP4.5 scenario (P > 0.001). As precipitation and temperature-related variables increase, the cultivation of C. arabica will increase by 1.2% under RCP4.5 but decrease by 5.6% under RCP8.5. A limited number of models and environmental factors were used in this study, suggesting that intensive research into other environmental aspects is needed using different models.