ABSTRACT
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.
ABSTRACT
Climate suitability is important for coffee (Coffea arabica L.) production in climate variability-prone regions like Ethiopia. The aim of this study was to assess the current and future climate suitability for the species in the Jimma zone under moderate (RCP4.5) and worst (RCP8.5) climate change scenarios. Field surveys and Worldclim and Paleoclim databases were used to capture 224 C. arabica species' location points and 9 bioclimatic data, respectively. The MaxEnt model with integration of ArcGis was used to simulate and characterize these data. The diagnostic outcome of the model showed that the anticipated climate change will increase the areas of suitability in the first and third coffee sub-zones, while there will be a decrease in the second sub-zone. Net suitability under the RCP4.5 would be decreased by 4.75 and 6.09% in the 2050s and 2070s, respectively. Indeed, under the RCP8.5, total suitability will be expected to be increased by 2.52% and 2.25% in the 2050s and 2070s, respectively. For the 2050s and 2070s, the suitability gap between RCP4.5 and RCP8.5 was estimated to be 401 km2 and 1567 km2, respectively. To summarize, with the exemption of RCP 8.5 within the 2070s, the suitability would be improved and come up short in all circumstances. To keep Arabica coffee in its original habitat, we suggest that the entire climate change adjustment procedures that are prearranged under the RCP4.5 ought to be executed to sustain the crop trees in its origin. Otherwise, moving the crop plant from impeded areas to suitable ones is crucial.
