Laura Riesgo

An MCDM analysis of agricultural risk aversion

Abstract In modelling farm systems it is widely accepted that risk plays a central role. Furthermore, farmers’ risk aversion determines their decisions in both the short and the long run. This paper presents a methodology based on multiple criteria mathematical programming to obtain relative and absolute risk aversion coefficients. We rely on multiattribute utility theory to elicit a separable additive multiattribute utility function and estimate the risk aversion coefficients, and apply this methodology to an irrigated area of Northern Spain. The results show a wide variety of attitudes to risk among farmers, who usually exhibit decreasing absolute risk aversion and constant relative risk aversion.

Economic and agronomic impact of commercialized GM crops: a meta-analysis

The present paper presents a meta-analysis of the economic and agronomic performance of genetically modified (GM) crops worldwide. Bayesian, classical and non-parametric approaches were used to evaluate the performance of GM crops v. their conventional counterparts. The two main GM crop traits (herbicide tolerant (HT) and insect resistant (Bt)) and three of the main GM crops produced worldwide (Bt cotton, HT soybean and Bt maize) were analysed in terms of yield, production cost and gross margin. The scope of the analysis covers developing and developed countries, six world regions, and all countries combined. Results from the statistical analyses indicate that GM crops perform better than their conventional counterparts in agronomic and economic (gross margin) terms. Regarding countries’ level of development, GM crops tend to perform better in developing countries than in developed countries, with Bt cotton being the most profitable crop grown.

A non-interactive elicitation method for non-linear multiattribute utility functions: Theory and application to agricultural economics

Practical implementation of Multiattribute Utility Theory is limited, partly for the lack of operative methods to elicit the parameters of the Multiattribute Utility Function, particularly when this function is not linear. As a consequence, most studies are restricted to linear specifications, which are easier to estimate and to interpret. We propose an indirect method to elicit the parameters of a non-linear utility function to be compatible with the observed behaviour of decision makers, rather than with their answers to direct surveys. The idea rests on approaching the parameter estimation problem as a dual of the decision problem by making the observed decisions to be compatible with a rational decision making process.

Distances needed to limit cross-fertilization between GM and conventional maize in Europe

volume 28 number 8 AuGuST 2010 nature biotechnology We first compiled a database of crossfertilization rates and distance by collating different publications and unpublished studies on maize cross-fertilization, to obtain a total of 1,174 observations covering four European countries (Germany, Italy, Spain and Switzerland). Details on the sources of data used are given in Supplementary Table 2. The database covered studies with a variety of experimental designs (mostly receptor and donor fields side by side, but also donor and receptor fields dispersed in actual agricultural landscapes) and that had been performed in different growing seasons (2001–2006). Data originate from experimental designs representing worst-case scenarios (receptor fields situated downwind from donor fields and coincidence of flowering between donor and receptor fields) in Europe. The relationship between distances and cross-fertilization rates for the database shows a negative relationship between these two variables (Fig. 1). This reciprocal relationship between cross-fertilization rates and distance was pointed out previously by several other authors4,5,7–9. For further analyses, cross-fertilization rates were analyzed for 10 m distance intervals (Supplementary Table 3). Because of the lack of sufficient observations from 50 m upwards, the size of intervals was increased to 20 m. Supplementary Table 3 shows that data on maize cross-fertilization are mostly available for short distances, close to the donor (84.1% of the data set, or 985 observations, are taken between 0 m and 20 m). In contrast, only EU countries have decided to establish mandatory separation distances between GM and non-GM maize fields as the preferred single measure to limit cross-fertilization6. An overview of mandatory separation distances adopted by EU member states (Supplementary Table 1) shows a remarkable range of variation, 25–600 m, between the different countries. Although climatic and landscape parameters in maize cultivation (that affect cross-fertilization rates) are variable in the EU, often there is little sciencebased evidence that the distances adopted are proportional to achieve the desired purity standards. To test the proportionality of the separation distances established by EU member states, we perform a statistical analysis of data obtained from a number of recent studies on maize cross-fertilization performed in different European countries. Although the various studies recorded different variables, we analyzed only data on cross-fertilization rates (measured as percentage of seeds in the sample) in the receptor field as a function of distance from the edge of the pollen source. The aim of the analysis was to estimate distances necessary to keep cross-fertilization below different arbitrary tolerance thresholds and with different confidence levels. The results should inform debate on whether current distances between GM and non-GM maize fields stipulated by member states to meet legal EU labeling thresholds are supported by scientific data. Distances needed to limit cross-fertilization between GM and conventional maize in Europe

Methodology and Criteria for the Allocation of Reused Water in Agriculture

This chapter provides a methodology to analyse the allocation of reused water. The tool has been developed for the Guadalquivir River Basin Authority, allowing decision makers to rank the actions on the reutilization of urban water for agriculture. The decision support is based on four groups of attributes: (1) resource supply, (2) environmental impact, (3) technical and economic feasibility and (4) social and institutional impact. A multicriteria decision method is proposed to aggregate all selected indicators. The results allow the River Basin Authority to classify different water requests of reused water, according not only to their technical knowledge, but also to the experience of different experts and stakeholders in water management.