Evaluating the Potential of Whole-Farm Insurance Over Crop-Specific Insurance Policies

Since 1996, different formats of whole-farm insurance (WFI) have been launched in North America and Spain. Their rationale is to pool all farm's insurable risks into a single policy that provides cheaper coverage against the farm's revenue losses. We evaluate the gains of moving from a situation of full insurance coverage delivered by crop-specific policies to WFI. Based on the records of individual farmers gathered by the Spanish Agricultural Insurance Agency (ENESA), we select two representative farms in Valencia that have consistently purchased insurance during 1993-2004 for three crops (apricots, plums and wine grapes). WFI is designed to deliver exactly the same expected revenue than does the combined effects of three crop-specific multiple-peril insurance policies, covering from the same risks. We carry out Monte-Carlo simulations to compare crop-specific insurance with WFI, looking at premium differences, farms' revenues, and farmers' utilities (DARACRRA). From ENESA's database we evaluate the parameters of the yield distribution functions, the eligible losses distribution functions and their correlation. Results show that WFI is slightly superior to crop-specific insurance. Premia are 20% cheaper, and certainty equivalents slightly larger. Yet, the left tail of the revenue distribution is only weakly reduced by either insurance strategy, due to crop risks that are not covered by either policy. The main conclusion is that, if crop-specific insurance is sufficiently mature, farmers would benefit from WFI and Governments would enhance the efficiency of their insurance subsidies.agricultural insurance, whole-farm insurance, simulation, crop risks, Spanish agriculture, Risk and Uncertainty, Q14, G, Q18,

Income Stabilisation in a Changing Agricultural World: Policy and Tools

This paper attempts to draw conclusions regarding Risk Management instruments (RMI) for potential development or expansion in the EU (Garrido and Bielza, 2007). Using data from EU countries, compiled in the course of two EU research projects about RMIs, we perform a cross-sectional analysis of the role of agricultural insurance and ad hoc payments. Tests of comparisons of means of key insurance data reveal the impact of insurance policies and the degree of competitiveness in supply side. While the presence of subsidies explains differences across EU member states' (MSs) insurance data, the degree of competitiveness is not a differentiating factor. In the last part of the paper, we rate a number of RMIs on the basis of a number of criteria. We conclude that RMIs on EU scale should be flexible enough to accommodate very diverse risk contexts, farmers’ demands and ongoing national programmes. Our conclusions may be useful in defining RMIs within the scope of European Agricultural Policy, and as an extension of similar studies (Cafiero et al. 2005; European Commission (2006a).

Revenue Insurance as an Income Stabilization Policy: An Application to the Spanish Olive Oil Sector

Various forms of revenue insurance have been applied in Canada and in the US with relative success. In this paper different combinations of traditional agricultural policies and revenue and yield insurance are analysed for the Spanish olive oil sector. Taking a database containing about half million Spanish olive growers during 8 campaigns, five possible policies are studied and the results are examined according to different criteria including average revenue and its variability, growers utility gains, taxpayers cost and the transfer efficiency of support. The policies analysed are: (1) non-intervention; (2) the policy currently in force in Spain that combines a production aid with a yield insurance; (3) a revenue insurance, only; (4) revenue insurance combined with a production aid; and (5) an aid per tree in combination with revenue insurance. The methodology is based on Monte-Carlo simulations performed on about 100 groups of growers that have been grouped according to their expected yields and variability. Assuming and estimating olive oil price and yields correlations for each group of growers, the analysis allows for consistent policy comparisons at a very disaggregate level. Using the results for all analysed groups, policies are ranked based on the above criteria at the provincial and national levels. Results show that the current regime of EU production aids of olive oil eliminates the advantage of extending the current yield insurance to a revenue insurance. It is also shown that the level of support delivered by production aids cannot be reached with revenue insurance even with completely subsidised premiums. Finally, it is shown that the policy that combines tree aids with revenue insurance exhibits good results for all examining criteria.Agricultural policy, revenue insurance, olive oil sector, Risk and Uncertainty,

Agricultural risk management in Europe

Replaced with revised version of paper 11/18/08Risk management policy, agricultural insurance, calamity funds, ad-hoc aids, natural disaster, Production Economics, Risk and Uncertainty,

Regional Yield Insurance for Arable Crops in EU-27

Replaced with revised version of paper 11/18/08.Area yield insurance, index insurance, yield risk, Agricultural Finance, Risk and Uncertainty,

Hydrological drought index insurance for irrigation districts in Spain

Hydrological droughts are a major risk for irrigated agriculture in many regions of the world. The aim of this article is to propose an insurance tool to help irrigators manage the risk of water scarcity in the framework of the Spanish Crop Insurance System (SCIS). Only the United States Insurance System provides this type of coverage, but has very restrictive conditions. To determine the type of insurance scheme that better fits with the SCIS and to the Spanish irrigated agriculture, an expert panel was held with the participation of all stakeholders involved in crop insurance. Following the expert panel conclusions, an hydrological drought index insurance (HDII) addressed to irrigation districts (ID) is proposed. It would compensate water deficits suffered in the whole ID. We detail the conditions that the ID should fulfill to be eligible for HDII. HDII is applied to the Bardenas Irrigation District V (ID-V) in Spain, and the hedging effectiveness of the instrument is analyzed comparing ID-V’s gross margins with and without the insurance contract. Results suggest that the proposed insurance scheme could provide an effective means of reducing farmers’ vulnerability to water shortages and there is no major impediment for it to be included as a new line in the SCIS. This type of insurance can be generalized to any ID fulfilling the conditions mentioned in this paper

Mapping climatic risks in the EU agriculture

Replaced with revised version of paper 11/18/08.Agrometeorological models, climatic risk, European Union, Vegetation indices, Environmental Economics and Policy, Risk and Uncertainty,

Maximizing the number of polychronous groups in spiking networks

In this paper we investigate the effect of biasing the axonal connection delay values in the number of polychronous groups produced for a spiking neuron network model. We use an estimation of distribution algorithm (EDA) that learns tree models to search for optimal delay configurations. Our results indicate that the introduced approach can be used to considerably increase the number of such groups

Multi-dimensional classification with super-classes

The multi-dimensional classification problem is a generalisation of the recently-popularised task of multi-label classification, where each data instance is associated with multiple class variables. There has been relatively little research carried out specific to multi-dimensional classification and, although one of the core goals is similar (modelling dependencies among classes), there are important differences; namely a higher number of possible classifications. In this paper we present method for multi-dimensional classification, drawing from the most relevant multi-label research, and combining it with important novel developments. Using a fast method to model the conditional dependence between class variables, we form super-class partitions and use them to build multi-dimensional learners, learning each super-class as an ordinary class, and thus explicitly modelling class dependencies. Additionally, we present a mechanism to deal with the many class values inherent to super-classes, and thus make learning efficient. To investigate the effectiveness of this approach we carry out an empirical evaluation on a range of multi-dimensional datasets, under different evaluation metrics, and in comparison with high-performing existing multi-dimensional approaches from the literature. Analysis of results shows that our approach offers important performance gains over competing methods, while also exhibiting tractable running time

Interval-based ranking in noisy evolutionary multiobjective optimization

As one of the most competitive approaches to multi-objective optimization, evolutionary algorithms have been shown to obtain very good results for many realworld multi-objective problems. One of the issues that can affect the performance of these algorithms is the uncertainty in the quality of the solutions which is usually represented with the noise in the objective values. Therefore, handling noisy objectives in evolutionary multi-objective optimization algorithms becomes very important and is gaining more attention in recent years. In this paper we present ?-degree Pareto dominance relation for ordering the solutions in multi-objective optimization when the values of the objective functions are given as intervals. Based on this dominance relation, we propose an adaptation of the non-dominated sorting algorithm for ranking the solutions. This ranking method is then used in a standardmulti-objective evolutionary algorithm and a recently proposed novel multi-objective estimation of distribution algorithm based on joint variable-objective probabilistic modeling, and applied to a set of multi-objective problems with different levels of independent noise. The experimental results show that the use of the proposed method for solution ranking allows to approximate Pareto sets which are considerably better than those obtained when using the dominance probability-based ranking method, which is one of the main methods for noise handling in multi-objective optimization