Figure 1.
Flow chart of the methodology used to derive coffee productivity indices and predicted Arabica coffee yields.
Table 1.
Weighted environmental criteria for evaluating the qualitative Arabica coffee productivity classes and scores (1 to 5 for worst to best)a.
Table 2.
Distribution of Arabica coffee areas (ha) and yields (t ha−1) calculated from the coffee database for 2005 for the ten agro-ecological zones of Rwanda.
Table 3.
Distribution of soil types (ha) and areas of Arabica coffee cultivation in the ten agro-ecological zones of Rwanda.
Table 4.
Distribution of soil types (ha) and areas of Arabica coffee cultivation in the ten agro-ecological zones of Rwanda (Cont'd).
Table 5.
Distribution of slope classes (%) and areas with Arabica coffee in the ten agro-ecological zones of Rwanda.
Figure 2.
Qualitative Arabica coffee productivity indices (low, moderate, and high) generated by combining factors (elevation, slope, soil type, rainfall, and temperature) using weighted overlay analysis in the ten agro-ecological zones.
Figure 3.
Potential Arabica coffee yield (t ha−1) predicted using ordinary kriging in the ten agro-ecological zones based on actual yields (t ha−1) measured at sample sites.
Figure 4.
Relationship between measured and predicted Arabica coffee yields – cross validation using ordinary kriging (Predicted Arabica coffee yield index – CYI (t ha−1) = 0.71x + 0.33; Mean Prediction Error – MPE = 0.0187; Root Mean Square Prediction Error – RMSE = 0.278; Root Mean Square Standardized Prediction Error – RMSSE = 0.99; Mean Standardized Prediction Error - MSE = 0.036; Coefficient of determination – R2 = 0.73; Average Standard Error –Avg. SE = 0.291; Sample points, n = 121).
Figure 5.
Soils are classified using the USDA Soil taxonomy (Source: Data collected from the Ministry of Agriculture and Animal Resources, using the Rwanda soil database) after [32].