Forecasting yield of selected arabica coffees by multiple regression yield model at Melko, Jima
1997
Gibramu Temesgen (IAR, Addis Abeba (Ethiopia))
Yield of Arabica coffee seems to be influenced mainly by climatic factors prevailing in major producing regions of Ethiopia, as there has been a considerable yearly production variation from a given farm area planted with known cultivars under uniform management. The objective of the study was thus to identify such important variables influencing the production, and develop a model to forecast future coffee yield statistics of the selected cultivars under Melko conditions. The partial regression coefficients (r's) between independent variables that prevailed in the last 17 years (1977-93) and the dependent variable (yield) showed that mean daily minimum temperature in December-January, and May; relative humidity (percent), monthly rainfall distribution (number of rainy days) in December-January were negatively correlated (r=-0.462 to -0.759) with current coffee yield. On the contrary, mean daily maximum temperature, and sunshine hours in December-January; monthly moisture availability index (MAI) in February, March, May; and rainfall distribution in April were positively related (r=+0.403 to +0.558) to the yield. Apart from the climatic factors, 'alternate bearing' or previous year's crop level showed a significant negative effect on current year's coffee yield level. Generally, the multiple regression yield model developed for this study fits the harvested yield during the years with a highly significant (p = 0.01) coefficient of determination (R squared 2) value of 0.900. The validity of the model was further verified on other potential cultivars screened at the location, and similar results were recorded. In conclusion, the model can be used to forecast yield of such potentially selected cultivars with a good degree of success under Melko and neighbouring areas with similar environmental conditions.
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Este registro bibliográfico ha sido proporcionado por Ethiopian Institute of Agricultural Research