Francisco Areal

Evaluating the Sustainable Intensification of arable farms

Sustainable Intensification (SI) of agriculture has recently received widespread political attention, in both the UK and internationally. The concept recognises the need to simultaneously raise yields, increase input use efficiency and reduce the negative environmental impacts of farming systems to secure future food production and to sustainably use the limited resources for agriculture. The objective of this paper is to outline a policy-making tool to assess SI at a farm level. Based on the method introduced by Kuosmanen and Kortelainen (2005), we use an adapted Data Envelopment Analysis (DEA) to consider the substitution possibilities between economic value and environmental pressures generated by farming systems in an aggregated index of Eco-Efficiency. Farm level data, specifically General Cropping Farms (GCFs) from the East Anglian River Basin Catchment (EARBC), UK were used as the basis for this analysis. The assignment of weights to environmental pressures through linear programming techniques, when optimising the relative Eco-Efficiency score, allows the identification of appropriate production technologies and practices (integrating pest management, conservation farming, precision agriculture, etc.) for each farm and therefore indicates specific improvements that can be undertaken towards SI. Results are used to suggest strategies for the integration of farming practices and environmental policies in the framework of SI of agriculture. Paths for improving the index of Eco-Efficiency and therefore reducing environmental pressures are also outlined.

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We first compiled a database of crossfertilization rates and distance by collating different publications and unpublished studies on maize cross-fertilization, to obtain a total of 1,174 observations covering four European countries (Germany, Italy, Spain and Switzerland).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

Integrating spatial dependence into Stochastic Frontier Analysis

An approach to incorporate spatial dependence into Stochastic Frontier analysis is developed and applied to a sample of 215 dairy farms in England and Wales. A number of alternative specifications for the spatial weight matrix are used to analyse the effect of these on the estimation of spatial dependence. Estimation is conducted using a Bayesian approach and results indicate that spatial dependence is present when explaining technical inefficiency.

Farm technical efficiency under a tradable milk quota system

This paper incorporates the milk quota system into technical efficiency analysis of dairy farms in England and Wales. Our approach accounts for milk quota trade, allowing an investigation of the relationship between the way in which milk quota market is used by farmers and technical efficiency. In addition, several explanatory variables for inefficiency were used. Results obtained from a Bayesian stochastic frontier analysis show that the way in which farmers use the milk quota market is linked to farm efficiency. Other aspects such as environmental payments received by the farmer are linked to inefficiency.

Predicting the impact of the EU common agricultural policy reform in England: Micro and macroeconomic aspects

In this article we investigate the effects of the European CAP reform on a selection of arable crops in England, both at a regional and national level. The results show that the CAP reform will push farmers to adjust to the new market conditions, which will cause a further restructuring of the English agricultural business sector. Our results show that, under the new market conditions, economically‐small farms will increase their output by allocating more land to cereals, whereas economically‐large farms will need to decrease land allocated to cereals to reduce production costs and achieve better returns.

On the Opportunity Cost of Crop Diversification

Distance functions are increasingly being augmented, with environmental goods treated as conventional outputs. A common approach to evaluate the opportunity cost of providing an environmental good is the exploitation of the distance functions dual relationship to the value function. This implies that the opportunity cost is assumed to be non‐negative. This approach also requires a convex technology set. Focusing on crop diversification for a balanced sample of 44 cereal farms in the East of England for the years 2007–2013, this paper develops a novel opportunity cost measure that does not depend on these strong assumptions. We find that the opportunity cost of crop diversification is negative for most farms.

Variation on the Effects of the 2003 CAP Reform and Regional Differences in the Italian Olive Oil Sector

This paper analyses the impacts of the 2003 CAP reform on the production of Italian olive oil controlling for the regional differences in olive oil production as well as for the differences between years. Italian olive oil production time series data from the Farm Accountancy Data Network for the 2000-2010 period at regional level is used to examine the effect of the 2003 Fischler reform on the production of olive oil. Production costs and payments received by farmers to support their income are considered. The data were collected at micro level based on a sample of farms representative of the production systems in the country. In order to consider the differences in production among the regions, eight representative regions in terms of surveyed farms are considered. The authors found that the most important factors affecting the production of olive oil are the area under olive groves and labour productivity. Results also show no evidence that the level of payments have an impact to the level of production, however, the type of payments has.