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,

INDIVIDUAL FARMING AS A LABOR SINK: EVIDENCE FROM POLAND AND RUSSIA

In Poland and Russia, small-scale individual farms employ more labor per hectare of land than large-scale corporate farms, without suffering from lower labor productivity. Individual farming is a labor sink for the rural population, and land policies promoting individualization of agriculture in transition countries can alleviate the social consequences of rural unemployment without sacrificing agricultural productivity.Labor and Human Capital,

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