Conclusions

The availability of new technologies like yield monitors, yield mapping software, GPS, satellite and aerial images and GIS has made possible to measure crop growing conditions as well as grain yield within a field at a very high spatial resolution, allowing very fine and precise description of the spatial variability (Roel and Plant 2004). A number of research groups around the globe are seeking to apply seasonal climate forecasts to improve management of food production systems and security of farmer livelihood in the face of climatic risk. One of the tools frequently employed by these efforts is dynamic crop simulation models (Hansen 2000). In this study we integrate this tool with the mentioned advancements regarding the capability of a precise description of yield spatial variability. We consider that this integration may increase the possibility to evaluate the application of seasonal climate forecasts at a regional scale. This integration will allow to study if in fact regions within the scale at which climate prediction model output are given, react uniformly to different climatic conditions. Consequently, evaluations can be made as to whether uniform recommendations can be made from a given forecast or if certain areas within the scale of the forecast should be treated differently.

This study showed that the distribution of national rice yield averages varied with ENSO phases (Fig. 10.2). The frequency of high national rice yields average was more than two times higher in La Niña years than in neutral years. The study conducted in the 12 ha rice field showed that this field presented certain yield spatial pattern with high yielding areas at the north and center portion of the field and a low yielding areas at the south portion of the field. The DSSAT v3.5 CERES-Rice model showed to be able to capture satisfactorily rice yield variability at the spatial and temporal levels. When the model was run spatially, at the different locations within the field and temporally along the different growing seasons, the same pattern of low yield spatial variation can be observed in the southern part of the field. Overall, this suggests that there is no interaction between temporal and spatial effects, there were no climatic conditions (temporal variability) that can make that the south portion of the field achieve a higher yield than the northern portion.

Fig. 10.5. Yield spatial variability; red years correspond to El Niño, blue years correspond to La Niña and black years to neutral conditions

The chief difficulty in linking climate forecasts scenarios with crop simulation models is the substantial mismatch between the forecast output spatial and temporal scales and crop simulation model input requirements. This study was able to demonstrate that for this rice field although we were able to characterize its yield spatial variability very precisely this pattern of spatial variability did not change with different climatic conditions. Therefore, yield spatial variability within this field seems to be regulated by factors related to the soil and not with the climatic conditions. Consequently, at the scale of this field forecast output scale did not constitute a problem. We believe that the approach used in this study can be implemented at a larger spatial scale to evaluate at which level of spatial resolution forecast output scale starts to become a problem.

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