Impacts of extreme agroclimatic indicators on the performance of coffee (Coffea arabica L.) aboveground biomass in Jimma Zone, Ethiopia
Estimating crop biomass is critical for countries whose primary source of income is agriculture. It is a valuable indicator for evaluating crop yields and provides information to growers and managers for developing climate change adaptation strategies. The objective of the study was to model the imp...
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Elsevier
2022-08-01
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844022014244 |
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| author | Fedhasa Benti Chalchissa Girma Mamo Diga Gudina Legese Feyisa Alemayehu Regassa Tolossa |
| author_facet | Fedhasa Benti Chalchissa Girma Mamo Diga Gudina Legese Feyisa Alemayehu Regassa Tolossa |
| author_sort | Fedhasa Benti Chalchissa |
| collection | DOAJ |
| description | Estimating crop biomass is critical for countries whose primary source of income is agriculture. It is a valuable indicator for evaluating crop yields and provides information to growers and managers for developing climate change adaptation strategies. The objective of the study was to model the impacts of agroclimatic indicators on the performance of aboveground biomass (AGB) in Arabica coffee trees, a critical income source for millions of Ethiopians. One hundred thirty-five coffee tree stump diameters were measured at 40 cm above ground level. The historical (1998–2010) and future (2041–2070) agroclimatic data were downloaded from the European Copernicus climate change services website. All datasets were tested for missing data, outliers, and multicollinearity and were grouped into three clusters using the K-mean clustering method. The parameter estimates (coefficients of regression) were analyzed using a generalized regression model. The performance of coffee trees' AGB in each cluster was estimated using an artificial neural network model. The future expected change in AGB of coffee trees was compared using a paired t-test. The regression model’s results reveal that the sensitivity of C. arabica to agroclimatic variables significantly differs based on the kind of indicator, RCP scenario, and microclimate. Under the current climatic conditions, the rise of the coldest minimum (TNn) and warmest (TXx) temperatures raises the AGB of the coffee tree, but the rise of the warmest minimum (TNx) and coldest maximum (TXn) temperatures decreased it (P < 0.05). Under the RCP4.5, the rise of consecutively dry days (CDD) and TNx would increase the AGB of the coffee tree, while TNx and TXx would decrease it (P < 0.05). Except for TXx, all indicators would significantly reduce the AGB of coffee trees under RCP8.5 (P < 0.05). The average values of AGB under the current, RCP4.5, and RCP85 climate change scenarios, respectively, were 26.66, 28.79, and 24.41 kg/tree. The predicted values of AGB under RCP4.5 and RCP8.5 will be higher in the first and third clusters and lower in the second cluster in the 2060s compared to the current climatic conditions. As a result, early warning systems and adaptive strategies will be necessary to reduce the detrimental consequences of climate change. More research into the effects of other climatic conditions on crops, such as physiologically effective degree days, cold, hot, and rainy periods, is also required. |
| first_indexed | 2024-04-11T14:13:03Z |
| format | Article |
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| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-04-11T14:13:03Z |
| publishDate | 2022-08-01 |
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| spelling | doaj.art-f640366077c540ab86ed08d74e3baf962022-12-22T04:19:37ZengElsevierHeliyon2405-84402022-08-0188e10136Impacts of extreme agroclimatic indicators on the performance of coffee (Coffea arabica L.) aboveground biomass in Jimma Zone, EthiopiaFedhasa Benti Chalchissa0Girma Mamo Diga1Gudina Legese Feyisa2Alemayehu Regassa Tolossa3Jimma University, Department of Natural Resource Management, Jimma, Ethiopia; Corresponding author.Ethiopia Agricultural Research Institute, Addis Ababa, EthiopiaAddis Ababa University, Center for Environmental Science, Addis Ababa, EthiopiaJimma University, Department of Natural Resource Management, Jimma, EthiopiaEstimating crop biomass is critical for countries whose primary source of income is agriculture. It is a valuable indicator for evaluating crop yields and provides information to growers and managers for developing climate change adaptation strategies. The objective of the study was to model the impacts of agroclimatic indicators on the performance of aboveground biomass (AGB) in Arabica coffee trees, a critical income source for millions of Ethiopians. One hundred thirty-five coffee tree stump diameters were measured at 40 cm above ground level. The historical (1998–2010) and future (2041–2070) agroclimatic data were downloaded from the European Copernicus climate change services website. All datasets were tested for missing data, outliers, and multicollinearity and were grouped into three clusters using the K-mean clustering method. The parameter estimates (coefficients of regression) were analyzed using a generalized regression model. The performance of coffee trees' AGB in each cluster was estimated using an artificial neural network model. The future expected change in AGB of coffee trees was compared using a paired t-test. The regression model’s results reveal that the sensitivity of C. arabica to agroclimatic variables significantly differs based on the kind of indicator, RCP scenario, and microclimate. Under the current climatic conditions, the rise of the coldest minimum (TNn) and warmest (TXx) temperatures raises the AGB of the coffee tree, but the rise of the warmest minimum (TNx) and coldest maximum (TXn) temperatures decreased it (P < 0.05). Under the RCP4.5, the rise of consecutively dry days (CDD) and TNx would increase the AGB of the coffee tree, while TNx and TXx would decrease it (P < 0.05). Except for TXx, all indicators would significantly reduce the AGB of coffee trees under RCP8.5 (P < 0.05). The average values of AGB under the current, RCP4.5, and RCP85 climate change scenarios, respectively, were 26.66, 28.79, and 24.41 kg/tree. The predicted values of AGB under RCP4.5 and RCP8.5 will be higher in the first and third clusters and lower in the second cluster in the 2060s compared to the current climatic conditions. As a result, early warning systems and adaptive strategies will be necessary to reduce the detrimental consequences of climate change. More research into the effects of other climatic conditions on crops, such as physiologically effective degree days, cold, hot, and rainy periods, is also required.http://www.sciencedirect.com/science/article/pii/S2405844022014244Agroclimatic indicatorBove ground biomassArtificial neural network modelCoffee treeMicroclimates |
| spellingShingle | Fedhasa Benti Chalchissa Girma Mamo Diga Gudina Legese Feyisa Alemayehu Regassa Tolossa Impacts of extreme agroclimatic indicators on the performance of coffee (Coffea arabica L.) aboveground biomass in Jimma Zone, Ethiopia Heliyon Agroclimatic indicator Bove ground biomass Artificial neural network model Coffee tree Microclimates |
| title | Impacts of extreme agroclimatic indicators on the performance of coffee (Coffea arabica L.) aboveground biomass in Jimma Zone, Ethiopia |
| title_full | Impacts of extreme agroclimatic indicators on the performance of coffee (Coffea arabica L.) aboveground biomass in Jimma Zone, Ethiopia |
| title_fullStr | Impacts of extreme agroclimatic indicators on the performance of coffee (Coffea arabica L.) aboveground biomass in Jimma Zone, Ethiopia |
| title_full_unstemmed | Impacts of extreme agroclimatic indicators on the performance of coffee (Coffea arabica L.) aboveground biomass in Jimma Zone, Ethiopia |
| title_short | Impacts of extreme agroclimatic indicators on the performance of coffee (Coffea arabica L.) aboveground biomass in Jimma Zone, Ethiopia |
| title_sort | impacts of extreme agroclimatic indicators on the performance of coffee coffea arabica l aboveground biomass in jimma zone ethiopia |
| topic | Agroclimatic indicator Bove ground biomass Artificial neural network model Coffee tree Microclimates |
| url | http://www.sciencedirect.com/science/article/pii/S2405844022014244 |
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