Spatial modeling of risk in natural resource management
| dc.creator | Jones, Peter G. | |
| dc.creator | Thornton, Philip K. | |
| dc.date | 2002 | |
| dc.date | 2014-10-02T08:33:18Z | |
| dc.date | 2014-10-02T08:33:18Z | |
| dc.date.accessioned | 2026-06-27T16:59:25Z | |
| dc.description | Making decisions in natural resource management involves an understanding of the risk and uncertainty of the outcomes, such as crop failure or cattle starvation, and of the normal spread of the expected production. Hedging against poor outcomes often means lack of investment and slow adoption of new methods. At the household level, production instability can have serious effects on income and food security. At the national level, it can have social and economic impacts that may affect all sectors of society. Crop models such as CERES-Maize are excellent tools for assessing weather-related production variability. WATBAL is a water balance model that can provide robust estimates of the potential growing days for a pasture. These models require large quantities of daily weather data that are rarely available. MarkSim is an application for generating synthetic daily weather files by estimating the third-order Markov model parameters from interpolated climate surfaces. The models can then be run for each distinct point on the map. This paper examines the growth of maize and pasture in dryland agriculture in southern Africa. Weather simulators produce independent estimates for each point on the map; however, we know that a spatial coherence of weather exists. We investigated a method of incorporating spatial coherence into MarkSim and show that it increases the variance of production. This means that all of the farmers in a coherent area share poor yields, with important consequences for food security, markets, transport, and shared grazing lands. The long-term aspects of risk are associated with global climate change. We used the results of a Global Circulation Model to extrapolate to the year 2055. We found that low maize yields would become more likely in the marginal areas, whereas they may actually increase in some areas. The same trend was found with pasture growth. We outline areas where further work is required before these tools and methods can address natural resource management problems in a comprehensive manner at local community and policy levels. | |
| dc.identifier | https://hdl.handle.net/10568/44127 | |
| dc.identifier.uri | http://hdl.handle.net/123456789/138983 | |
| dc.language | en | |
| dc.rights | Open Access | |
| dc.source | Jones, P. G. and Thornton, P.K.. 2002. Spatial modeling of risk in natural resource management. Conservation Ecology 5(2): 27. http://www.consecol.org/vol5/iss2/art27/ | |
| dc.subject | simulation models | |
| dc.subject | weather | |
| dc.subject | natural resources | |
| dc.subject | resource management | |
| dc.subject | climate change | |
| dc.subject | dry farming | |
| dc.subject | zea mays | |
| dc.subject | maize | |
| dc.subject | modelos de simulación | |
| dc.subject | tiempo meteorológico | |
| dc.subject | recursos naturales | |
| dc.subject | ordenación de recursos | |
| dc.subject | cambio climático | |
| dc.subject | cultivo en tierras áridas | |
| dc.subject | maíz | |
| dc.subject | africa meridional | |
| dc.title | Spatial modeling of risk in natural resource management | |
| dc.type | Journal Article |