Participation begets integration: lessons learned from incorporating ethnography into linear programming David Wilsey PhD, University of Minnesota; Tom Gill PhD, Penn State University; Alfredo Rios PhD, The Ohio State University; Peter Hildebrand PhD, University of Florida Keywords: Livelihoods, farming systems, modeling, extension, ELP. Abstract Over the last decade, Ethnographic Linear Programming (ELP) emerged as an innovative method to explore farmer responses to varied livelihood system “disruptions,” such as new technology, market interventions, and shocks. Modelers develop and use scenario-driven models that integrate social, ecological, and economic considerations of farm-based livelihood systems and strategies. ELP models have been developed for diverse livelihood systems across a broad geographic range. We analyzed three recent ELP modeling experiences to better understand and articulate the perceived strengths and limitations of the method. The three models reflected specific farming systems and addressed, respectively, the effect of HIV/AIDS on food security in western Kenya, commercial feasibility of a locally gathered palm in central Mexico, and potential adoption of ecologically based alternatives to chemical pesticides in highland Peru. ELP differs from linear programming in its use of ethnographic methods to capture, quantify, and integrate qualitative social considerations into biological and/or economic models. The greatest strength of the method was the use of participatory, ethnographic approaches. Community participation has intrinsic value but also greatly enhances representation of diverse social agents and facilitates integration of social, economic, ecological, and political systems in the model, all of which enhance model validity. In addition, the modeling process was flexible and amenable to the modeler’s creativity. Conversely, participation increased time investments – in data collection and processing, model elaboration, and validation. Emphasis on intra-system rather than inter-system diversity limited broader application of the models. However, we recognize that the needs of academic researchers differ from those of field practitioners and expect that the latter could minimize observed limitations without substantial sacrifice to the numerous benefits outlined above. 1.0 Introduction 1.1 Linear programming Linear programming (LP) is a mathematical procedure that optimizes (maximizes or minimizes) an objective function subject to a set of constraints and available resources. The emergence and continued growth of applied LP parallels the development of computing, as the method relies on high-speed computers (once mainframes, now laptops) to efficiently work through extensive iterations. LP modeling is a basic tool for analyzing smallholder farming systems. Models simulate the complex farming and livelihood systems of smallholder by including the many and diverse crops, cropping systems, and other activities. LP models allow researchers and practitioners to understand why households choose particular livelihood strategies, on the basis of their available resources and constraints (Gladwin, et al., 2001). Moreover, LP models help users predict the effects of possible changes (interventions) to the system by testing potential scenarios, or “What if?” questions. Despite increasing computational power of linear programming, smallholder systems presented a challenge to its practical application. Practically speaking, smallholder systems are businesses, but they are also households. Relative to a business, smallholder objectives may be less clear, more diverse, or altogether different. Additionally, smallholder management directly relates to household composition,