Spatial modeling of risk in natural resource management

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dc.contributor.authorJones, P. G.en
dc.contributor.authorThornton, P. K.en
dc.contributor.departmentSustainable Agriculture and Natural Resource Management (SANREM) Knowledgebaseen
dc.date.accessioned2016-04-19T19:11:43Zen
dc.date.available2016-04-19T19:11:43Zen
dc.date.issued2002en
dc.description.abstractMaking 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 (includes the southern part of Tanzania, Malawi, much of Mozambique, and all of Zimbabwe, and extends west from the Indian Ocean to include Zambia, the southeastern part of the Democratic Republic of Congo and small portions of Angola). 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.en
dc.format.mimetypeapplication/pdfen
dc.identifier1582en
dc.identifier.citationConservation Ecology 5(2): 27en
dc.identifier.issn1195-5449en
dc.identifier.other1582_Spatial_Modeling_of_Risk.pdfen
dc.identifier.urihttp://hdl.handle.net/10919/66690en
dc.language.isoen_USen
dc.publisherOttawa, Ont.: Resilience Allianceen
dc.relation.urihttp://www.consecol.org/vol5/iss2/art27/en
dc.rightsIn Copyrighten
dc.rights.holderCopyright 2002 by the author(s)en
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectRural developmenten
dc.subjectSustainable developmenten
dc.subjectSoil managementen
dc.subjectLivelihoodsen
dc.subjectAgricultureen
dc.subjectLand use managementen
dc.subjectSoilen
dc.subjectModelingen
dc.subjectSustainable agricultureen
dc.subjectResource management toolsen
dc.subjectNatural resource managementen
dc.subjectFarming systemsen
dc.subjectCrop modelingen
dc.subjectDryland agricultureen
dc.subjectGlobal changeen
dc.subjectGlobal circulation modelen
dc.subjectMaizeen
dc.subjectMarkov modelsen
dc.subjectMarksimen
dc.subjectNatural resource managementen
dc.subjectRisken
dc.subjectSouthern africaen
dc.subjectSpatial modelingen
dc.subjectWeather simulationen
dc.subjectEcosystem Farm/Enterprise Scaleen
dc.titleSpatial modeling of risk in natural resource managementen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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