## 4 Results
### Spatial Variations in Crop Yield at the Sub-National Scale in Morocco  
The validation outputs of the spatial patterns of crop yield based on the MOSAICC simulator of the CANESM model and on the RCP 4.5 and 8.5 scenarios basically reproduced the same spatial variation in the vulnerability of yields, as they showed that there was a tendency for the yields of most crops to increase towards the north of the country (Figure 8). However, while this model reproduced the same spatial pattern for barley at both RCP 4.5 and 8.5, there were slight differences in the case of wheat. For example, under the RCP 4.5 scenario, Rabat-Sale-Zemmour was grey and showed between 6 and less than 8 t/ha of wheat between 1991-2020. However, in the RCP 8.5 scenario, the yields for wheat fell to between 4 and less than 6 t/ha for wheat for the period 1990-2020. Another change was observed for the Oriental region, which recorded between 4 and less than 6 t/ha under RCP 4.5 and dropped to between 0 and less than 2 t/ha for the RCP 8.5 scenario during the period 1990-2020 (Figure 8). This goes to support the observations of progressive higher yields when we moved from stations in the south towards the north (Figures 9 and 10). The models used here are considered reliable as they reproduced most of the spatial variations in yield, and the error terms of the regression equations are considered valid. Figure 11: Cont. Figure 10: Variations in barley yield over time at various stations in Morocco. Spatial Variations in Vulnerability Indexes and Adaptive Capacity at the Sub-National Scale in Morocco  
At the sub-national scale, the results were consistent with what was obtained at the national scale. For example, wheat showed the lowest vulnerability indexes and the highest adaptive capacity indexes as we moved from one part of the country to another. In Figure 11, it can be observed that the vulnerability indexes for wheat--shown by the blue curve--declined consistently from 0.38, to 0.33, to 0.28, and to 0.28, as we moved from latitudes 20.5 \({}^{\circ}\) N, to 25 \({}^{\circ}\) N, to 30 \({}^{\circ}\) N, and 36 \({}^{\circ}\) N, respectively (Figure 11a-c). This indicates that at the sub-national scale, vulnerability declined as we move northwards in the context of wheat. Still in the context of wheat, the adaptive capacity increased as we move northwards from 0.87, to 1.1, to 1.4, and to 1.8 for latitudes 20.5 \({}^{\circ}\) N, 25 \({}^{\circ}\) N, 30 \({}^{\circ}\) N, and 36 \({}^{\circ}\) N, respectively. Therefore, while the vulnerability indexes reduced northwards, adaptive capacity increased. In the context of precipitation, it could also be observed that the normalized  
Figure 11: Spatial/latitudinal variations in vulnerability indexes, adaptive capacity indexes, and normalized rainfall for (**a**) barley, (**b**) maize, and (**c**) wheat in Morocco. growing season precipitation increased northwards from 0.7, to 0.8, to 1.3, and to 1.6 for latitudes 20.5 \({}^{\circ}\) N, 25 \({}^{\circ}\) N, 30 \({}^{\circ}\) N, and 36 \({}^{\circ}\) N, respectively. This finding shows that for wheat the growing season increased as we moved northwards (Figure 11c). The observations in the case of wheat show that wheat is generally less vulnerable and more adaptive. Wheat was also associated with higher growing season precipitation. When compared to wheat, barley had the second lowest vulnerability indexes and the second highest adaptive capacity indexes, while maize had the highest vulnerability indexes and the lowest adaptive capacity indexes (Figure 11a). However, all these crops showed a declining vulnerability index and increasing adaptive capacity and normalized growing season precipitation from the south to the north. It is important to underscore the fact that these crops showed decreasing vulnerability and increasing adaptive capacity and normalized growing season precipitation from the south to the north.