Mitigating Drought Impacts in Drylands: Quantifying the Potential for Strengthening Crop- and Livestock-Based Livelihoods by Federica Carfagna Raffaello Cervigni & Pierre Fallavier

Author:Federica Carfagna, Raffaello Cervigni & Pierre Fallavier
Language: eng
Format: epub

Source: Africa Risk Capacity calculations, using IFPRI’s DSSAT Cropping System Model v4.5.

Note: ARV = Africa RiskView; BAU = business as usual.

To estimate the impacts of crop-farming technologies on vulnerable populations, the DSSAT model simulated how the various technologies affect the mean level and distribution of yields. Distributions of the drought-affected population estimated using the yield values from the 25 simulation years for each technology can be compared to distributions of drought-affected populations estimated under the baseline scenario in which yields do not benefit from adoption of any of the technologies.

The differences in estimated numbers of people affected show the impact of each technology on the drought-hit population or, in other words, on households’ resilience. Figure 2.9 shows, again for the case of Mauritania, the effects of adopting one of the interventions considered in the analysis (specifically, the adoption of a crop variety that is both drought-tolerant and heat-tolerant). As can be seen, the difference between the 2010 and 2030 no-intervention (BAU) scenarios is given only by the difference in vulnerability profiles; hence it is proportional to the level of drought experienced in any given year. Instead, compared to the 2030 BAU scenario, the number of drought-affected people declines in many years; in some years, the result is only to slow down the increase in the number of drought-affected people, while in other years the number of drought-affected people actually falls below the 2010 baseline. Overall, adopting the drought- and heat-tolerant variety leads to an 11 percent decrease in the number of drought-affected people. This example shows the benefit of a single intervention adopted in all polygons where, although it cannot prevent the drought from occurring, it can definitely reduce its impact on the population living in the area.

The model assumes that a technology is considered for adoption in a given area if and only if the number of people affected after its implementation is actually lower than in the case of non-intervention. Benefits are maximized when the entire set of interventions is considered, and in each area the technology adopted is the one that leads to the largest reduction in the number of drought-affected people. The results presented in this book are based on the latter approach.

To estimate the number of people affected and to evaluate the impact of adoption of each crop technology, the livestock model described above was used to create a vulnerability profile for aridity zones 1, 2, and 3. The number of people affected in these areas was calculated using the percentage of drought-affected people out of the total vulnerable population observed in the crop model for the same admin unit or a neighboring one.

Irrigation is considered to be an intervention that saves people from being affected by drought. Thus the irrigation model output (see “Resilience analysis for irrigation”) can be directly considered as the benefit of implementing irrigation interventions, and the number of people “saved” can be compared to the reduction in people affected because of adoption of a technology or livestock intervention.

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