Jennifer Olson

East African food security as influenced by future climate change and land use change at local to regional scales

Climate change impacts food production systems, particularly in locations with large, vulnerable populations. Elevated greenhouse gases (GHG), as well as land cover/land use change (LCLUC), can influence regional climate dynamics. Biophysical factors such as topography, soil type, and seasonal rainfall can strongly affect crop yields. We used a regional climate model derived from the Regional Atmospheric Modeling System (RAMS) to compare the effects of projected future GHG and future LCLUC on spatial variability of crop yields in East Africa. Crop yields were estimated with a process-based simulation model. The results suggest that: (1) GHG-influenced and LCLUC-influenced yield changes are highly heterogeneous across this region; (2) LCLUC effects are significant drivers of yield change; and (3) high spatial variability in yield is indicated for several key agricultural sub-regions of East Africa. Food production risk when considered at the household scale is largely dependent on the occurrence of extremes, so mean yield in some cases may be an incomplete predictor of risk. The broad range of projected crop yields reflects enormous variability in key parameters that underlie regional food security; hence, donor institutions’ strategies and investments might benefit from considering the spatial distribution around mean impacts for a given region. Ultimately, global assessments of food security risk would benefit from including regional and local assessments of climate impacts on food production. This may be less of a consideration in other regions. This study supports the concept that LCLUC is a first-order factor in assessing food production risk.

Distributed Modeling Architecture of a Multi-Agent-Based Behavioral Economic Landscape (MABEL) Model

The authors discuss a distributed modeling architecture in a multi-agent-based behavioral economic landscape (MABEL) model that simulates land-use changes over time and space. Based on agent-based modeling methodologies, MABEL presents a bottom-up approach to allow the analysis of dynamic features and relations among geographic, environmental, human, and socioeconomic attributes of landowners, as well as comprehensive relational schematics of land-use change. The authors adopt a distributed modeling architecture (DMA) in MABEL to separate the modeling of agent behaviors in Bayesian belief networks from task-specific simulation scenarios.Through a client-server infrastructure, MABEL provides an efficient and scalable decision request-response mechanism among heterogeneous agents, scenarios, and behavioral models. As an important part of the land-use change model, a market-bidding system and an adaptive land partition algorithm for land transactions are also discussed.

Contemporary Challenges of Participatory Field Research for Land Use Change Analyses: Examples from Kenya

This article discusses the evolution of participatory methods and their benefits and pitfalls in contributing to land use and land cover change (LULCC) analyses. Participation has become a practical means of developing a more complete assessment of societal change by bringing local peoples narratives and understandings to bear on the interpretation of data collected using more extractive methods, such as the household survey, or data collected remotely, such as satellite images. Their methodological value lies in their ability to provide insights into the local mediation of external political, economic, and cultural processes. However, the realization of these contributions to LULCC analysis requires sensitivity to community differentiation, competing narratives of change, and the broader social context in which participatory forums take place. Examples from Kenya suggest that participatory feedback workshops present distinct empirical advantages that allow researchers to develop an understanding of critical intersections of social and environmental change through a dialogical process whereby participants themselves frame the central categories and change processes.

Developing land use/land cover parameterization for climate–land modelling in East Africa

Regional climate modeling studies now have numerous choices in selecting land use/land cover (LULC) products to provide land surface parameter information. The various LULC products were developed with different objectives, methods and data sources. Not all new LULC products have land classes that match the land class types defined in climate models. More importantly, when used in regional climate models, simulation results can vary significantly depending on the LULC products. Thus, developing appropriate LULC parameterization for climate models becomes critical depending on objectives and efforts. The objective of this paper is to develop the most accurate LULC scheme possible for East Africa for implementation in the Regional Atmospheric Modeling System (RAMS). A crosswalk procedure, based on assessments of various LULC products, was performed connecting land class types in RAMS and the newly created LULC scheme. No simulations are discussed here; rather, we present an outline of the procedures that were carried out to take advantage of the strengths of currently available LULC products, Africover and Global Land Cover 2000, for the purpose of conducting regional climate simulations.

AGROCLIMATIC RESOURCE ASSESSMENT: AN EXAMPLE FOR PEACH CULTIVATION IN THE LOWER PENINSULA OF MICHIGAN

Detailed resource assessments can assist agricultural planners and growers select appropriate crops and cultivars for a region. This study employs a geographic information system (ARC/INFO) to develop a rating scheme suitable for evaluating the climatic resources of the Lower Peninsula of Michigan for commercial peach production. The rating scheme presented here improves on many previously developed indices by placing greater emphasis on identifying critical growth stages and the weather conditions that impact the critical stages. The use of a vector-based geographic information system in the analysis provided an efficient and flexible means for constructing the agroclimatic index. Suitability categories easily can be modified to meet requirements of different users, and additional climate variables readily can be added to the analysis. In addition, a computerized geographic information system allows the climatic suitability ratings to be combined easily with edaphic and topographic information. The clima...