Estimating the spatial distribution of possible livestock production level using a mathematical model and the SMAP soil moisture data: the case of Botswana

Ejigu, A. A., Asfaw, M. D. and Kassa, S. M. (2022) Estimating the spatial distribution of possible livestock production level using a mathematical model and the SMAP soil moisture data: the case of Botswana. Communications in Mathematical Biology and Neuroscience, 2022. 124. ISSN 2052-2541 doi: 10.28919/cmbn/7817

Abstract/Summary

In a traditional livestock farming, the livelihood of animals depends highly on existing plant biomass, which is affected by the level and intensity of temperature, rainfall, humidity and other meteorological variables. Understanding the interaction of such meteorological factors and agricultural production in general is an important aspect in planning at the macro and micro levels. Particularly livestock agriculture is heavily affected by the changing climate, and hence the variation in major meteorological variables. However, there is still limited research regarding the impacts of meteorological variables on livestock production in each particular region. Soil moisture is one of the main factors in agricultural production and hydrological cycles with better memory of previous weather conditions. It also involves complex structural characteristics and meteorological factors. In this study, a soil moisture dependent mathematical model for the interaction of plants and herbivores is developed and analysed. The Soil Moisture Active Passive level 4 satellite soil moisture data is used in the model to simulate the possible spatial distribution of plants and the corresponding potential livestock production level for Botswana. A global dynamic sensitivity analysis is employed to study the sensitivity of the solution of the model with a variation in the involved parameter values. The results of the simulations of the model show that estimated livestock harvest in wet regions is more than triple as compared to what is estimated for dry regions. If some important parameters are properly estimated and the soil moisture data is available for each region, it is possible to estimate the livestock production level for each spatial region with better accuracy using the proposed model.

Item Type	Article
URI	https://centaur.reading.ac.uk/id/eprint/122862
Identification Number/DOI	10.28919/cmbn/7817
Refereed	Yes
Divisions	No Reading authors. Back catalogue items Life Sciences > School of Agriculture, Policy and Development > Department of International Development
Publisher	SCIK
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