In the previous post, we identified studies that we have done that used crop models together with climate models. Here, as promised, we do a brief case study to demonstrate the power of this method to help researchers and policymakers identify specific geographic areas as climate hotpots (areas where productivity declines are projected to be great) and climate opportunities (areas where a crop could not previously grow, but will under climate change; or areas with great yield improvements).
They say a picture is worth a thousand words, and the map in Figure 1 hopefully will prove to be just that. It shows productivity changes for rainfed maize in Kenya as the result of projected climate change. This was generated by computing average yields that would be expected given the soils and climate of the 1950-2000 period, and comparing them to the average yields based on the climate of 2050 that is projected by the CNRM GCM and the A1B scenario. While in our studies of climate change in Africa we use do analysis for 4 different climate models, let us just focus on this one for now.
It’s important to understand what the map in Figure 1 shows. We can see by the legend that
- Green signifies yield gain as a result of climate change
- Orange signifies yield decline
- The darker the green or orange, the bigger the gain or loss.
- Blue is area gained as a result of climate change (unproductive in 2000, productive in 2050)
- Red is area lost due to climate (productive in 2000, unproductive in 2050)
Let us now consider ways in which this kind of pixel-level data based on crop models and climate models might help us in prioritizing landscapes for intervention.
Situation 1: Climate Hotspots
Consider the red areas of the map in the western part of Kenya near the Uganda border. These show areas where rainfed maize will no longer be able to be cultivated due to climate change. This part of the country could end up in severe crisis. For most of Kenya, not only is maize the main crop grown, but it is also the main food consumed. If any subsistence farmers live in this area – and surely there are some if not many – they will be devastated by such an occurrence.
Such an area should receive high attention by policymakers, researchers, and donors, to avoid a future humanitarian crisis (should the CNRM climate model prove to be correct). In the worst case scenario for this type of area, in addition to severe poverty and malnutrition among those living there, we would expect high incentives for climate migration, with the people abandoning their farms in search of a place with better economic prospects. This may put strains on other rural areas as these migrants try to find land that they can settle and cultivate. It could also put a strain on urban areas, both secondary towns and major cities.
Is there anything that can be done for farmers living in this area? A number of possibilities exist. They include:
- Developing new maize varieties suitable to the new climate. Looking at the climate maps for temperature and rainfall, it appears that the yields are primarily hindered by heat stress. So in this particularly case, the development of heat-tolerant varieties would be a good solution, if it proves to be technically feasible.
- If irrigation is possible, then irrigation may allow farmers to plant in a cooler time of the year, thus avoiding the heat stress of the current planting season.
- Experimenting with agroforestry might allow for providing shade for the crops and thus cooling the soils at the hottest part of the day. Such an idea might also apply to green mulch. But experimentation on how to best accomplish this is important.
- If an alternative crop (perhaps millet) is more heat tolerant, farmers could switch to the alternative crop. This would be difficult with a food staple such as maize, because people find it very difficult to change their diets.
- Switching into livestock, if technically feasible for the area, may be an alternative.
- If no alternative farming solution can be found, the government might consider offering voluntary relocation to a different area, or investing in rural industry that might be appropriate for providing alternative employment to the farmers.
Before moving to the next section, it seems appropriate to remind the reader that not only are red areas at risk, but areas with high yield decline are also at risk. In this particular map, there are very few dark orange (yield losses of greater than 25 percent), so it is not as relevant to this particular case. But even yield declines less than 25 percent but still relatively high (perhaps greater than 10 percent) could signal serious stress on the farmers and families in the area.
In the next post, we will continue our case study, but will focus on climate opportunities to be found in the map.