HarvestChoice

Farming entails a great deal of risk and uncertainties. Weather varies, price fluctuates, soil degrades, pest damages, and, even climate changes. Farmers everywhere must cope with these uncertainties. Throughout the history of agriculture, many options, such as fertilizer application, irrigation, improved varieties, and farming machinery have been developed to help manage the risks, increase yields, increase efficiency, and, increasingly, promote sustainability of the overall system.

With these techniques and tools in mind, each farmer must assess their local context and analyze the costs and benefits of adopting them, such as the additional labor and/or investment required. Careful and informed assessment is especially more important for farmers with limited resources, like smallholder farmers in developing countries. Even if farmers recognize that new approaches will likely improve production versus their traditional practices, they may still be highly risk averse for many legitimate reasons (e.g., lack of insurance markets, bad experiences in the past, or conceiving the inherently uncertain nature of farming).

Likewise, at a higher level, the national and international donors and policymakers share the farmers’ goal of improving food security cost-effectively. They, like farmers, must strategically assess the feasibility and profitability of available strategic and policy options and decide which ones to promote and where, with even greater deals of risks and uncertainties. If reliable estimates could be made of the current and potential patterns of crop productivity, many agricultural development investment and policy decisions would be significantly improved, or made with greater confidence.

HarvestChoice and its partners develop databases, tools, analyses, and syntheses designed to improve strategic investment and policy decisions. The overriding objective is to accelerate and enhance the performance of those crops and cropping systems most likely to bring significant benefits to the world's poor and undernourished.[3]

The use of spatially‐referenced data and spatially‐explicit analysis to generate spatially specific knowledge is a cornerstone of the HarvestChoice initiative. A fundamental characteristic of agriculture (particularly subsistence agriculture) is the close coupling of its performance with prevailing biophysical conditions, conditions that can vary widely over space and time. HarvestChoice relies on its own and its partners' spatial datasets to provide new information on:

• the location of the poor and undernourished in relation to major crop production systems
• the dependence of both urban and rural poor on specific crops and crop products
• the incidence and severity of major production constraints such as drought and disease in focus crops and locations
• the potential benefits to the poor from alleviating such constraints
• an inventory and characterization of existing and prospective technologies that might help address
• an economic evaluation of the potential crop production, consumption, price, and trade, as well as the likely hunger and income consequences of a range of technology scenarios, and
• the potential commercialization challenges that promising technology options might face.

HarvestChoice makes available spatially (and socio-economically) explicit estimates of the potential welfare benefits of a range of interventions (e.g., on-farm, market and market access, and national policy).

These maps (alongside tables, graphs, and text) provide information of direct relevance to agricultural development investors and policymakers. They do this by detailing the potential scale and distribution of economic benefits – including the identification of locations and social groups whose welfare might be impacted negatively. These outputs will, however, be supplemented by a larger collection of novel spatial data products that represent key, intermediate factors;

• the baseline location and performance of agriculture worldwide (for at least 15 crops),
• the incidence and severity of production constraints (for at least 30 abiotic and biotic constraints),
• the potential response to on-site technology and management interventions (e.g., for different crop varieties, different levels of nutrient, water, and pest management), and
• the influence of changing transportation costs on profitability.

This amounts, potentially, to several thousand maps and associated datafiles.

• Alston, Julian M., P.G. Pardey, J.S. James, and M.A. Andersen. 2009. The economics of agricultural R&D. Annual Review of Resource Economics 1: 537-565.
• Wood, S. 2007. A Harvest Choice Primer on Agricultural Potential
• Wood, S. 2007. A Harvest Choice Primer on Geographical Targeting/ Segmentation and Development Domains
• You, L., and S. Wood. 2006. An entropy approach to spatial disaggregation of agricultural production. Agricultural Systems 90(1-3): 329-347.
• You, L., S. Wood, and U. Wood-Sichra. 2009. Generating plausible crop distribution maps for Sub-Saharan Africa using a spatially disaggregated data fusion and optimization approach. Agricultural System 99, Issues 2-3, p. 126-140.

• HarvestChoice
• HarvestChoice Labs
• AgMarketFinder
• Spatial Production Allocation Model (SPAM)
• International Science and Technology Practice and Policy (InSTePP) Center at the University of Minnesota
• Global Food and Natural Resource Systems research program of the International Food Policy Research Institute (IFPRI)
• Resource of Choice: The Harvest Choice Website, CGIAR News, April 2009, accessed on May 19, 2010