Argyris Kanellopoulos

A Generic Bio-Economic Farm Model for Environmental and Economic Assessment of Agricultural Systems

Bio-economic farm models are tools to evaluate ex-post or to assess ex-ante the impact of policy and technology change on agriculture, economics and environment. Recently, various BEFMs have been developed, often for one purpose or location, but hardly any of these models are re-used later for other purposes or locations. The Farm System Simulator (FSSIM) provides a generic framework enabling the application of BEFMs under various situations and for different purposes (generating supply response functions and detailed regional or farm type assessments). FSSIM is set up as a component-based framework with components representing farmer objectives, risk, calibration, policies, current activities, alternative activities and different types of activities (e.g., annual and perennial cropping and livestock). The generic nature of FSSIM is evaluated using five criteria by examining its applications. FSSIM has been applied for different climate zones and soil types (criterion 1) and to a range of different farm types (criterion 2) with different specializations, intensities and sizes. In most applications FSSIM has been used to assess the effects of policy changes and in two applications to assess the impact of technological innovations (criterion 3). In the various applications, different data sources, level of detail (e.g., criterion 4) and model configurations have been used. FSSIM has been linked to an economic and several biophysical models (criterion 5). The model is available for applications to other conditions and research issues, and it is open to be further tested and to be extended with new components, indicators or linkages to other models.

The role of farmers’ objectives in current farm practices and adaptation preferences: a case study in Flevoland, the Netherlands

Abstract The diversity in farmers’ objectives and responses to external drivers is usually not considered in integrated assessment studies that investigate impacts and adaptation to climate and socio-economic change. Here, we present an approach to assess how farmers’ stated objectives relate to their currently implemented practices and to preferred adaptation options, and we discuss what this implies for assessments of future changes. We based our approach on a combination of multi-criteria decision-making methods. We consistently assessed the importance of farmers’ objectives and adaptation preferences from what farmers say (based on interviews), from what farmers actually do (by analysing current farm performance) and from what farmers want (through a selected alternative farm plan). Our study was performed for six arable farms in Flevoland, a province in the Netherlands. Based on interviews with farmers, we reduced the long list of possible objectives to the most important ones. The objectives we assessed included maximization of economic result and soil organic matter, and minimization of gross margin variance, working hours and nitrogen balance. In our sample, farmers’ stated preferences in objectives were often not fully reflected in realized farming practices. Adaptation preferences of farmers largely resembled their current performance, but generally involved a trend towards stated preferences. Our results suggest that in Flevoland, although farmers do have more objectives, in practical decision-making they focus on economic result maximization, while for strategic decision-making they account for objectives influencing long-term performance and indicators associated with sustainability, in this case soil organic matter.

Labour not land constrains agricultural production and food self-sufficiency in maize-based smallholder farming systems in Mozambique

Despite abundant land and favourable climatic conditions, Mozambique remains food insecure. We investigated the diversity, constraints and opportunities to increase smallholder productivity and achieve food self-sufficiency in maize-based farming systems in two Posts in central Mozambique. We identified four farm types in each village based on cultivated area and labour. Farm type 1 cultivated relatively large areas, owned cattle and hired in labour. Farm type 2 cultivated moderate areas and both hired in and hired out labour. Farms of type 3a and 3b cultivated the smallest areas. Farm type 3a shared labour while Farm type 3b only hired out labour. For each farm type, we calculated land and labour productivities of maize, sunflower and sesame and assessed maize self-sufficiency. Access to labour during weeding was the main constraint. The hiring out of labour by small farms caused severe reductions in both land and labour productivity. Yield reductions on these farms were due to delayed weeding in own fields. In one Post, Farm type 3b was not maize self-sufficient. Labour quality was probably impaired by excess alcohol consumption among the poorer farmers (both men and women). Our results showed that production can be increased based on current agricultural practices. Farmers did not cultivate all of their land, suggesting that lack of labour constrained intensification by smallholder farmers.

A Generic Farming System Simulator

The aim of this chapter is to present a bio-economic modelling framework established to provide insight into the complex nature of agricultural systems and to assess the impacts of agricultural and environmental policies and technological innovations. This framework consists of a Farm System Simulator (FSSIM) using mathematical programming that can be linked to a cropping system model to estimate at field level the engineering production and environmental functions. FSSIM includes a module for agricultural management (FSSIM-AM) and a mathematical programming model (FSSIM-MP). FSSIM-AM aims to define current and alternative activities and to quantify their input output coefficients (both yields and environmental effects) using a cropping system model, such as APES (Agricultural Production and Externalities Simulator) and other sources (expert knowledge, surveys, etc.). FSSIM-MP seeks to describe the behaviour of the farmer given a set of biophysical, socio-economic and policy constraints and to predict its reactions under new technologies, policy and market changes. The communication between these different tools and models is based on explicit definitions of spatial scales and software for model integration.

Compromise programming: Non-interactive calibration of utility-based metrics

Utility functions have been used widely to support multi-objective decision-making. Expansion of a general additive utility function around the ideal results in a composite linear-quadratic metric of a compromise programming problem. Determining the unknown parameters of the composite linear-quadratic metric requires substantial interaction with the decision maker who might not always be available or capable to participate in such a process. We propose a non-interactive method that uses information on observed attribute levels to obtain the unknown parameters of the composite linear-quadratic metric and enables forecasting and scenario analysis. The method is illustrated with a small scale numerical example.

“Combining equity and utilitarianism”—additional insights into a novel approach

Recently, a novel approach (to be referred to as CEU) was introduced for the frequently arising problem of combining the conflicting criteria of equity and utilitarianism. This paper provides additional insights into CEU and assesses its added value for practice by comparing it with a commonly used extended goal programming (EGP) approach. The comparison comprises the way of balancing equity and utilitarianism, the number and spacing of solutions, discrete versus continuous nature, method-specific parameters, distance to the Pareto front, and computational effort. CEU balances between equity and utilitarianism in a way that is basically different from using a convex combination of these two criteria. Moreover, CEUs parameter has an intuitive interpretation. The set of solutions generated by CEU is smaller and more widely spaced than EGPs set of solutions, which can be an advantage for the decision maker. CEU generates solutions on the Pareto front of the decision makers n-criteria problem. However, CEUs way of balancing equity and utilitarianism causes a (small) distance to the Pareto front of the associated bicriteria problem on the aggregate criteria. Reporting this distance will support the decision maker to assess whether the achieved balance is worth its price. Using CEU may require a larger computational effort than using EGP.