Modeling at farm level: Positive Multi-Attribute Utility Programming

This article proposes a new mathematical programming model for the simulation of farmers’ decision-making. We have developed a model based on a multi-attribute utility approach that takes into consideration the most relevant attributes of farmers within a positive framework. This approach overcomes the limitations found in some mathematical models used in the literature to simulate farmers’ behavior. A five-step procedure is presented in order to elicit the utility function that reproduces farmers’ current decision-making. We illustrate this positive multi-attribute approach using a sample of farmers in an irrigated area in southern Spain, where our simulations demonstrate the accurateness of the model in reproducing actual farmers’ decision-making. We also find evidence that the model is able to explain the heterogeneous behavior of farmers within a homogeneous agricultural syste

Essays on economic modelling of farmers’ behaviours

Multiple socio-economic and biophysical factors affect farmers’ behavioural responses to economic policy and technology adoption, but personal beliefs and expert opinions are also important for making actual choices on farm policy and agricultural innovations. Using econometric and mathematical programming models, this thesis assessed farmers’ behavioural responses to farm structural changes, technology adoption and policy interventions. For structural changes, the results showed that conversion to organic farming in the European Union (EU) is a two-tier decision, with a first choice of staying conventional or moving to conversion and a second choice of mixed or organic production. This gradual conversion process encourages the presence of nested structures within the process, largely influenced by socio-economic and biophysical variables such as milk prices, policy incentives and technology factors. Following milk quota abolition under the EU Common Agricultural Policy (CAP) reform, risk of price uncertainty emerged as another key factor for the organic conversion process of Swedish dairy farms. In econometric modelling of farmers’ choices and impacts of responses to adopting agricultural technology, the parametric econometric specification is commonly applied. However, it cannot guarantee the true specification of behavioural responses to agricultural innovation. In this thesis, a recently developed nonparametric (NP) kernel density estimator was applied in impact assessment of agricultural technology adoption, using the case of zero tillage technology in the rice-wheat cropping system of the Indo-Gangetic Plains (IGP) region. This estimator can capture possible nonlinearities in the data generation process that cannot be known a priori. The results showed that the NP specification outperformed the parametric specification in predicting propensity scores and produced impact estimates with small standard error. For the study area, the results showed that introduction of the new technology generated the economic benefits of markedly lower tillage costs yield in zero-tilled plots. In a study of policy interventions, the wealth effect of the CAP direct payment system on agricultural crop production decisions was analysed using Bayesian econometrics and positive mathematical programming (PMP). Under risk, lump-sum payments may influence risk-averse farmers on crop production decisions. In simulations, no direct payment in a risky environment caused a shift in land use away from risky crops towards low-risk crops, altering the crop mix. Moreover, the farm-level wealth effect varied greatly between farms, although its magnitude was influenced by regional characteristics, e.g. historical farm structure and region-specific conditions

Essays on village development in rural Thailand

The process of development is accompanied by urbanization through shifting labor from agriculture to the industrial and service sector. Thus, rural villages in developing countries, where most agriculture takes place, are often seen as a place unattractive for work and living. Especially younger people seek employment in the cities where they find better infrastructure and more leisure possibilities. Left behind in the villages are often the elderly and minors. Most of the development investments are made in urban agglomerates while villages are given lower priority. Hence, the role of rural villages is often underrated in developing countries and therefore some villages remain pockets of poverty. Considering the experience of many European countries with well-targeted rural development policies, villages can become modernized and they can become an attractive living place with low prices for land and a better environment than in polluted cities. Hence, there is a need to study the role that rural villages can play in economic development of an emerging market country like Thailand. Therefore, this thesis takes an in-depth look at the mechanism, constraints and opportunities that govern socioeconomic development of rural villages in Thailand. The dissertation contains four essays. Each essay deals with a different aspect of the development of rural villages. The first essay is based on panel data from a single village, and tests, by means of two econometric models, the standard assumption that out-migration is a driver for increase in welfare and reducing poverty in villages. The second paper, using the same village than in the first paper applies a mathematical programming model to investigate the future role of agriculture under two likely external economic scenarios. The third article takes a broader view, by using a sample of 220 villages in Northeast Thailand, and explores the factors that on the one hand, can facilitate transformation from backward to progressive rural villages and that on the other hand, can hinder modernization and development. The fourth essay is focused on villages along the Mekong River in both, Thailand and Laos and presents an account of the impact of the recent Covid-19 pandemic on Mekong villages. The results of the four essays can be summarized as follows. Firstly, against the hypotheses suggested by the theory of migration, migration was not a significant factor driving income growth in the village. In-migration exceeded out-migration and business investment in the village was a major driver of income growth. Secondly, while income growth was well in line with Thailand’s national rate of economic growth, inequality has risen and poverty decline was minimal, much behind the aggregate rate of poverty decline in the country. In addition, household debt on average has more doubled between 2009 and 2018. Thirdly, households who diversified into wage and self-employment experienced better progress in terms of income growth and were less likely to be poor, compared to households whose primary occupation was farming. While all village households are engaged in farming, income from agriculture is not the only source of household income, i.e. majority of households are following a multiple income, part-time farming system. Results of a village-level, positive mathematical programming model showed that even drastic price increases in maize, which is the main agricultural commodity of the village, will not reverse the trend away from agriculture and towards a more diversified livelihood strategy. On the other hand, households are not likely to give up farming altogether in the foreseeable future. Fourthly, using panel data of 220 villages between 2007 and 2017, results of the descriptive and the model analysis demonstrate the importance of infrastructure investment in the development of rural villages in North East Thailand. On average of all the villages, poverty has been halved and household income has doubled. Key infrastructures are good quality roads, significantly related to basically all welfare parameters. Agricultural productivity growth is facilitated by improvements in irrigation infrastructure. A major infrastructure is improvements in telecommunication which is still emerging but was found to be crucial for a modernization of the village economy. However, progress in this area is lagging behind the advances made in urban areas. Finally, the paper describing the implications of the Covid-19 pandemic in villages along the Mekong River in Northeast Thailand and Laos, showed that while the economic impact of Covid-19 in rural villages was minimal, the pandemic has exposed the weakness of rural economies in the Mekong villages under lockdown conditions and due to their past threats from the exploitation of the river as a source of hydropower. This makes it even harder for these villages to cope with other ongoing processes such as climate change and natural resource depletion. In conclusion, the studies of rural villages in Thailand have opened up a new perspective of development for emerging market economies. It also provides an entry point to a policy debate for a new rural development policy. Such policy should recognize the opportunities that exist in rural villages as a means of inclusive economic growth and not, as in the past, just as a source of cheap labor for urban development

The EU-Wide Individual Farm Model for Common Agricultural Policy Analysis (IFM-CAP v.1): Economic Impacts of CAP Greening

This report presents the first EU-wide individual farm level model (IFM-CAP) aiming to assess the impacts of CAP towards 2020 on farm economics and environmental effects. The rationale for such a farm-level model is based on the increasing demand for a micro simulation tool capable to model farm-specific policies and to capture farm heterogeneity across the EU in terms of policy representation and impacts. Based on Positive Mathematical Programming, IFM-CAP seeks to improve the quality of policy assessment upon existing aggregate and aggregated farm-group models and to provide assessment of distributional effects over the EU farm population. To guarantee the highest representativeness of the EU agricultural sector, the model is applied to every EU-FADN (Farm Accountancy Data Network) individual farm (83292 farms). The report provides a detailed description of the first IFM-CAP model version (IFM-CAP V.1) in terms of design, mathematical structure, data preparation, modelling livestock activities, allocation of input costs, modelling of the CAP post-2013 and calibration process. The theoretical background, the technical specification and the outputs that can be generated from this model are also briefly presented and discussed. Model capability is illustrated in this study with an analysis of the EU farmers' responses to the greening requirements introduced by the 2013 CAP reform.JRC.D.4-Economics of Agricultur

Economic analysis of Dutch agricultural land use in a changing policy environment

Abstract This study empirically investigates farmers’ decision-making on agricultural land use change in the Netherlands. Five driving factors influencing decision making on both on-farm land adjustments and changes to the size of the farm are selected: increased price volatility, milk quota, land prices, direct payments and insurance possibilities. By analysing the influence of increased output price volatility and risk on land use change, it is first shown that opposite effects between complementing and substituting land uses are present, leading to competition within the dairy sector and within crop production. Second, by employing a duration model over the period before, during and towards milk quota abolition, it is empirically shown that quota hamper the pace of change in land used for milk production on dairy farms. Third, the price of agricultural land is analysed by taking into account four categories that all influence the price of land: the direct influence via the returns from land, institutional regulations, the spatial environment and local market conditions. It is shown that the financial crisis leads to a decline in the effects of local market conditions, but the announcement of milk quota abolition in 2008 has led to an increase in the effects of the spatial environment. The last two driving factors analyse policy measures using a mathematical programming model. Fourth, it is found that the 2013 Common Agricultural Policy reforms will cause farmers to shift away from crops previously eligible for payments, with the initial shift of the direct payment reform enhanced by the move towards direct payments combined with a green payment. Fifth, small changes in land allocation towards more volatile crops are observed when the possibility of whole farm and crop-specific insurance is offered. In general it is found that when production risk is decreased, this opens the possibility to increase risk in other areas, such as farm expansion. When the total amount of farmland is treated as variable, changes to land cannot be explained by changes in revenues and risk alone. Land use change is now also influenced by long-term decision making based on expectations on future costs and revenues, and other factors such as farm characteristics, institutional and transaction characteristics and the influence of location and economic conjecture.</p

Calibration of agricultural risk programming models using positive mathematical programming

Mathematical programming models of farmers’ cropping decisions must first be calibrated before they can be used to examine agricultural producer responses to policy changes. In this paper, we compare three calibration approaches for disentangling the risk parameter from the parameters of the cost function: one assumes a logarithmic utility function, while the others employ an exponential utility function. Historical crop insurance data for southern Alberta, Canada, are used to assess the calibration performance of the three approaches, and sensitivity analysis is implemented to test whether the changes in the optimal land allocation caused by the changes in the values of the parameters are practically reasonable. Only one of the three models is of practical use for policy analysis because it can recover the true values of the parameters and the results of sensitivity analysis are reasonable

Calibration of Agricultural Risk Programming Models Using Positive Mathematical Programming

Beginning in the 1960s, agricultural economists used mathematical programming methods to examine producers responses to policy changes. Today, positive mathematical programming (PMP) employs observed average costs and crop allocations to calibrate a nonlinear cost function, thereby modifying a linear objective function to a nonlinear one to replicate observed allocations. The standard PMP approach takes into account producers risk aversion, which is not a very satisfying outcome because it intricately entangles the cost parameters and the producer s attitudes biophysical aspects of production and human behavior are intertwined so that one cannot study the impact of policy on one in the absence of the other. Several approaches that calibrate both the risk coefficient and cost function parameters have been proposed. In this paper, we discuss two methods mentioned in literature one based on constant absolute risk aversion (exponential utility function) and the other on decreasing absolute risk aversion (logarithmic utility function). We compare these methods to an approach that employs maximum entropy method. Then we use historical data from a region in Alberta s southern grain belt to compare the different outcomes to which the three approaches lead. We find that the latter approach is robust and easier to employ. Acknowledgement

Calibration of agricultural risk programming models using positive mathematical programming: a reply

In this reply, we briefly clarify some points raised in the comment regarding the goal of the paper, model estimation and comparison