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Wet Season Versus Dry Season Livestock Dispersal

An example of the utility of a theoretical or stylized simulation relates to changes in livestock dispersal patterns by Maasai herders in Kajiado, Kenya. Forty years ago, herders moved their animals in a pattern echoing the movements of wildlife of the Amboseli Basin (Worden 2007). In the wet season, livestock were grazed broadly, using ephemeral water sources and eating forage distant from permanent water. As water became limiting, livestock herders moved their animals closer to permanent water sources. During the dry season, herds grazed areas around the permanent water sources. This pattern may be summarized as wet season dispersal of livestock.

It is reasonable to think that animals confined to a relatively small area at the height of resource shortages – the dry season – may reduce forage acquisition and livestock populations. In the 1980s, Maasai adopted a cultural and institutional system where elders imposed a wet season dispersal pattern for livestock (BurnSilver 2007). Herders graze their animals in the areas around permanent water sources (which tend to be near their permanent residences) during the wet season. Areas distant from permanent water are kept as grazing reserves. As the landscape dries and forage is depleted, elders open neighboring areas to grazing, in what is termed a “staged” approach (BurnSilver 2007). Herders graze animals there until forage is depleted, and another stage is opened. By the height of the dry season, higher elevation grazing reserves are being used, and animals are being grazed for 2–3 days, then walked back to permanent water to drink. They then return to the reserve to graze for 2–3 days, and the cycle repeats until new rains arrive.

We sought to assess the utility of wet season versus dry season dispersal for livestock, and chose a stylized ecosystem representation in NetLogo 5.0 (Northwestern University, Evanston, Illinois). Major aspects of the model are introduced here, with minor points omitted for brevity; the full model has been placed in the Community section of the NetLogo web site (ccl.northwestern.edu/netlogo/). A grassland was represented by a grid of patches 41 × 41, with each pixel approximating 1 km2. The grid represented a torus to avoid edge effects, such that an animal moving off one edge of the grid appeared on the opposite edge. In each landscape cell, we implemented a forage growth model following Fryxell et al. (2005). That source provides formula that link stochastic precipitation with rates of growth of grasses. Rainfall included a 25 % inter-annual coefficient of variation, with precipitation distributed evenly throughout the growing season. A modified logistic growth curve represented forage production through time based on precipitation. Animals gained weight from the forage they ate, lost weight if there was insufficient forage, and reproduced at a rate related to their body condition.

A variable number of wells were distributed randomly throughout the grassland simulated. Animals were compelled to return to wells to drink every 3 days; if a measure of thirst for an animal exceeded a threshold (i.e., 7.5 days in this stylized simulations), the animal died. A switch on the simulation interface (Fig. 9.3) allowed the model to adopt a dry season or a wet season dispersal. We simulated dry season and wet season dispersal of livestock, and varied the number of wells on the landscape from 1 to 10. For each combination, we used 30 simulations to yield standard error estimates. Based on these stylized simulations, the utility of the newer dry season dispersal pattern to pastoralists may be questioned (Fig. 9.4). The usefulness of storing vegetation in areas distant from water sources for use in the dry season is outweighed by the ability of animals to graze more freely during the wet season. These results are not definitive, given the stylized application used, but they do suggest that more detailed follow-up analyses would be helpful and that field data be collected.

 
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