Simulating Farm Household Poverty: From Passive Victims to Adaptive Agents

Existing microeconomic models for simulating poverty heavily rely on static projection from statistical inference. When used for simulation these models tend to conceive farm households as passive victims and thereby underestimate their resilience and adaptive capacity. Farming systems research has much to contribute to the research on poverty by bringing in a detailed understanding of farm household decision-making, which directly relates to their adaptive capacity. This paper presents a novel methodology to simulate poverty dynamics using a farming systems approach. The methodology is based on mathematical programming of farm households but adds three innovations: First, poverty levels are quantified by including a three-step budgeting system, including a savings model, a Working-Leser model, and an Almost Ideal Demand System. Second, the model is extended with a disinvestment model to simulate farm household coping strategies to food insecurity. Third, multi-agent systems are used to tailor each mathematical program to a real-world household and so to capture the heterogeneity of opportunities and constraints at the farm level as well as to quantify the distributional effects of change. An empirical application to Uganda illustrates the methodology. The method opens exciting new prospects for applying farming systems research and multi-agent systems to poverty analysis and the ex ante assessment of alternative policy interventions.Food Security and Poverty,

From Bioeconomic Farm Models to Multi-Agent Systems: Challenges for Parameterization and Validation

Bioeconomic farm models have been very instrumental in capturing the technical aspects of human-nature interactions and in highlighting the economic consequences of resource use changes. They may elucidate the tradeoffs that farm households face in crop choice and farming practices, assess the profitability of various land-use options and capture the internal costs of adjusting to changes in environmental and market conditions. But they face also limitations when it comes to analyzing situations, in which heterogeneity of households and landscapes is large and increasing. Multi-agent models building on the bioeconomic farm approach hold the promise of capturing more fully the heterogeneity and interactions of farm households. The fulfillment of this promise, however, depends on the empirical parameterization and validation of multi-agent models. Although multi-agent models have been widely applied in experimental and hypothetical settings, only few studies have tried to build empirical multi-agent models and the literature on methods of parameterization and validation is therefore limited. This paper suggests novel methods for the empirical parameterization and validation of multiagent models that may comply with the high standards established in bioeconomic farm modeling. The biophysical measurements (here: soil properties) are extrapolated over the landscape using multiple regressions and a Digital Elevation Model. The socioeconomic surveys are used to estimate probability functions for key characteristics of human actors, which are then assigned to the model agents with Monte-Carlo techniques. This approach generates a landscape and agent populations that are robust and statistically consistent with empirical observations.Farm Management,

A MAS model for optimizing the spatial aspects of livestock production and manure abatement

As a consequence of the EU Nitrates Directive many countries have developed policies to regulate manure production and manure emission on land. Farmers have three allocation options: spreading manure on own land, transporting manure to other farmers’ land and processing manure. To better understand the manure problem as an allocation problem a spatial mathematical programming multi-agent model has been developed. The model is applied for Flanders (Belgium), a highly concentrated livestock area. Using this model, policy alternatives and their cost efficiency can be evaluated. These simulations result in advice on location and type of manure processing and an indicator which creates transparency in the manure and processing market

Exploring strategic priorities for regional agricultural R&D investments in East and Central Africa:

"Agriculture plays a dominant role in nearly all the countries of East and Central Africa, and many face similar agroecological, climatic, and development challenges. As a result, significant scale economies can be made through the regionalization of research and development (R&D) using networks such as the Association for Strengthening Agricultural Research in Eastern and Central Africa. The challenge for such networks, however, is to determine both regional and national research priorities with the highest potential rates of economic return. Methodology to assess regional research priorities is a critical input into this process, particularly when it comes to weighing likely complementarities among individual research programs, thus maximizing impact across countries at the regional level. This paper presents such an approach using spatial analysis and the Dynamic Research Evaluation for Management (Dream) modeling software, which was developed by the International Food Policy Research Institute to assess potential economic returns to agricultural R&D and guide resource allocation decisions. Dream is applied to the East and Central African region to estimate potential economic and technological spillovers from country- and regional-level R&D investments for select commodities based on future projections of supply and demand, trade flows between countries and world markets, and shared agroecologies and farming systems. The results of the study indicate significant potential for agricultural technology spillovers within the region. Countries will therefore reap greater economic benefits in their search for technology solutions if they pool their resources and pursue regional initiatives for the common good." from Author's AbstractDREAM, Technology spillovers, Priority setting, Economic surplus, Agricultural research,

The (Ir)relevance of the Crop Yield Gap Concept to Food Security in Developing Countries : With an Application of Multi Agent Modeling to Farming Systems in Uganda

This thesis scrutinizes the relationship between the width of the crop yield gap and farm household food security. Many researchers have argued that an exploitable gap between average crop yields and the genetic yield potential contributes to food security and that this potential should therefore be improved. Yet, crop yield gaps in developing countries are mostly wide, which is prima facie evidence that factors other than the yield potential are most constraining. A significant negative correlation between the width of the rice yield gap and food security for 19 Indian states confirms this. The concept and pitfalls of the crop yield gaps are further analyzed at the farm household level for the case of improved maize in two village communities in southeast Uganda. Multi-agent systems are used to model the heterogeneity in socioeconomic and biophysical conditions. The model integrates three components: (1) whole farm mathematical programming models representing human decision-making; (2) spatial layers of different soil properties representing the physical landscape; and (3) a biophysical model simulating crop yields and soil property dynamics. The thesis contributes to methodology in four ways: First, it is shown that MAS can be parameterized empirically from farm survey data. Second, it develops a non-separable three-stage decision model of investment, production, and consumption to capture economic trade-offs in the allocation of scarce resources over time. Third, a three-step budgeting system, including an Almost Ideal Demand System, is used to simulate poverty dynamics. Fourth, coping strategies to food insecurity are included. Simulation results show that neither the width of the yield gap nor the change in its width over time relate to food security at the farm household level. The maize yield gap is decomposed in both proximate and underlying factors. It is shown that the existence of maize yield gaps does not signal inefficiencies but poverty can be reduced substantially by addressing the underlying constraints such as access to innovations and credit. Improvements in labor productivity are crucial and are a much better indicator of development than crop yields and yield gaps. The results suggest that a strong focus on crop yields and yield gaps might not only be inefficient but even counterproductive to development

Can water allocation in the Yellow River basin be improved?: Insights from a multi-agent system model

In 1999, the Government of China enforced a cross-provincial, quota-based Water Allocation Agreement that was developed in 1987 and titled Unified Water Flow Regulation (UWFR) to ensure that flow to the Yellow River mouth would not be cut off. This policy was in line with the refocus of the Government, over the last decade, on sustainable water use and keeping the Yellow River healthy. The policy enforcement ended more than two decades of flow-cutoffs, that is, periods when the Yellow River did not reach the Bohai Sea at its mouth, during an increasing number of days every year.Water allocation, river basin management, multi-agent system,

Seed-div: an abstract role-playing game for discussing collective management of agrobiodiversity

L'objectif principal de cette recherche est d'accompagner les paysans dans l'élaboration d'un cadre de gestion collective de la diversité de leurs variétés de céréales vivrières. La gestion semencière et son impact sur la dynamique de la biodiversité sont soumis aux choix individuels des paysans, leurs stratégies d'une part, et au fonctionnement général du système semencier d'autre part. Nous postulons que la compréhension partagée des interactions au sein de ce système complexe est un prérequis pour travailler ensemble à la construction de règles de gestion collective qui participent à la durabilité de l'agriculture via un accès à un large choix de semences. La modélisation participative conceptuelle et les jeux de rôle ont été utilisés durant différents ateliers réunissant chercheurs, ONG, organisations paysannes et agriculteurs. Le résultat de cette série d'ateliers correspond à un modèle, un système multi-agents, représentant un archétype de village malien permettant de simuler la diversité de stratégies individuelles de gestion de semences qui sont ensuite disponibles gratuitement pour la communauté villageoise. Les agents du modèle sont des agriculteurs qui choisissent les variétés à semer ce qui provoque des échanges dans la communauté en fonction de leurs stratégies individuelles ou de facteurs externes. Les paramètres utilisés ont une valeur qualitative c'est pourquoi le modèle sert de support de discussion entre paysans de différentes régions. Le modèle a été construit et validé au travers de ces ateliers avec un impact évident sur les acteurs locaux pour la construction de nouveaux scénarios de gestion de la diversité variétale. Ainsi, le modèle a pu être utilisé en termes de prospective pour simuler des scénarios à partir de nouvelles formes d'action collective. (Résumé d'auteur