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Despite Cambodia's rapid economic growth in the most recent decade, levels of poverty and malnutrition remain high. Home garden vegetable production combined with nutrition training has the potential to abate these problems through the increased production and intake of nutrient-dense vegetables by household members and the creation of income for women from selling some of the produce. Evidence for the nutritional impact of home garden interventions is available for low income countries in Africa and Asia, but the evidence base remains small (Ruel and Alderman 2013; DFID 2014). For Cambodia, Olney et al. (2009) previously evaluated the impact of the homestead food production program of Hellen Keller International. Their study found that households included in the program produced and consumed more fruit and vegetables and that this was associated with greater dietary diversity, though not with improved maternal and child health and nutritional outcomes. The USAID-funded project "Deploying vegetable seed kits to tackle malnutrition in Cambodia" (USAID Award Number: AID-BFS-IO-12-00004) aims to contribute to reduced malnutrition, especially of women and children, through diet diversification by promoting the production and consumption of vegetables as affordable sources of essential vitamins and micronutrients. The project aims to achieve this through the deployment of diet-enhancing vegetable home garden seed kits and the development of household gardens in combination with nutrition training in each country in partnership with private seed companies, local and national governments, nutrition & health partners, and USAID mission and programs. The project is planned for a period of 3 years from October 2015 to September 2018. The objective of this study is to evaluate the impact of the project on key nutritional outcome indicators by applying a randomized controlled trial design. This document describes the intervention design and study implementation plan.

Agricultural commercialization in Asia has led to an increased dependence on synthetic pesticides, especially for high-value fruit and vegetable crops. The present study uses the multi-agent modeling software MPMAS to ex-ante assess the impact of different pesticide use reduction strategies. The model is parameterized with data from an intensive and diverse production systems in the mountainous north of Thailand, where the adoption of cash crops has been accompanied by very high levels of pesticide use. The objective of this study is to compare different policy interventions in terms of their impact on pesticide use, farm incomes and land use. The adoption of integrated pest management (IPM) is assessed in combination with tax instruments and with adoption incentives, such as bio-pesticide subsidies and price premiums. The results show that a smart policy package can reduce pesticide use by up to 34% over five years without income trade-offs for farm households.

This study quantifies private and social levels of agricultural pesticide overuse by combining an abatement function approach to estimate the marginal benefits of pesticide use with the Pesticide Environmental Accounting (PEA) tool to estimate marginal social costs. We applied the method to one intensive vegetable production system in the mountainous north of Thailand by using farm and plot level survey data. We find that the exponential specification for the abatement effect of pesticides gives more realistic outcomes than the logistic specification. Based on an exponential specification, we estimate that private overuse is 78-79% of the applied quantify of pesticides, while social overuse is 79-80%. The difference between private and social overuse is small as the exponential form reaches an optimum at a relatively low level of pesticide use.

Litchi is an important crop in the mountainous part of northern Thailand yet its profitability has declined during the last 15 years. The replacement of litchi fruit orchards for seasonal flowers and vegetables has external costs related to increased levels of soil erosion, pesticides, and irrigation water use. Using a combination of financial analysis and agent-based modeling, the paper ex-ante assesses the impact of four technologies—artificial flower induction, small-scale cooperative fruit drying, post-harvest treatments to extend the shelf-life of fresh fruits, and greater irrigation efficiency—in terms of profits, farm incomes, litchi acreage, soil erosion, and pesticide use. The model was calibrated to one watershed in Chiang Mai province where economic development has been rapid. Although each technology substantially increases the profitability of litchi growing, scenario analysis shows that this is not enough to stem the decline in litchi orchards in the study area. To achieve this, the innovations would have to be combined with an increase in the fresh fruit price from about 9 baht/kg at present to at least 12 baht/kg. The sensitivity of the results is tested for variations in the irrigation water supply and liquidity. We report about farmers’ discussions about these results and discuss the implications for other areas in northern Thailand.

Multi-Agent Systems for the Simulation of Land Use Change and Policy Interventions

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.

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.

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.