Ocupaciones para Guarderías.

The new PMP variant (EXPMP) presented in this study resulted in lower PAD values than the PAD values achieved by STPMP, in all ex-post exercises. Two major limitations of STPMP (i.e. the underestimation of the value of limiting resources and the assumption of constant marginal gross margin of the non-preferable activity) are overcome and a better justification is attached to the necessary assumptions. As a result, the forecasting capacity of the model improves. The two approaches used in the EXPMP variant to estimate the value of α resulted in similar quality of predictions in both Flevoland and Midi-Pyrenees. Using additional information on supply elasticities to estimate a different value of α for each activity increased the data requirements of the model but also resulted in slightly higher values of PAD compared to the minimum achieved PAD value. Nevertheless, the procedure of determining the value of α is better justified from an empirical point of view. The appropriateness of one of the two approaches depends on data availability. If good quality information on supply elasticities is available, that is, if estimation of supply elasticities is based on longer time series of a dataset relevant for this farm type, then it can be utilized to improve the predictions of the model and to strengthen the economic justification of the assumptions of PMP.

From the ex-post experiments of all farm types calibrated with EXPMP, it can be concluded that given the same values of the model parameters, the model predictions improve as α increases. As α is reciprocally related to the supply elasticities, it can be stated that for this exercise, more inelastic models result in better model predictions. Machinery and managerial capacity of farms do not change that quickly in the short run and for that reason less elastic models are needed. In cases of long term model applications and forecasts, a more elastic response might be more relevant. However, in such cases, factors exogenous to the model, such as changes in the structure of farming systems and the industry, might be more important for good predictions than the elasticity of farm’s supply to price changes. The models presented here are calibrated with PMP and consequently exact calibration is guaranteed. We can only assess the performance of the model based on its forecasting capacity. Hazell and Norton (1986) suggest that, in

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practice, a model that reproduces the base (calibration) year activity levels with PAD values not > 15% can be used for forecasting purposes. It is to be expected that the PAD values of the forecasts of such models will be substantially greater than the PAD values for the calibration year. All farm types tested in this study with the model calibrated by EXPMP resulted in PAD values only marginally > 15% for the forecasting year. We conclude that the forecasting capacity of the resulting model is acceptable.

In this study, the quality of the model predictions is evaluated by comparing observed and simulated cropping patterns. However, assessing other important economic (e.g. average farm income) and environmental (e.g., nitrogen leaching) indicators could be of great interest to model users and policy-makers, because this not only evaluates the modelling methods but also the technical coefficients of the model and hence the quality of the data. FSSIM is used to simulate different farm types across the EU and calculate a number of different indicators relevant for the assessment of a large variety of policy questions. In some cases, the simplifications and the mismatch of data are such that large PMP terms are needed to achieve a satisfactory forecast.

The objective of the SEAMLESS models is to simulate farming systems across Europe. To achieve this, given the available resources, a farm typology was developed and the average farms were simulated with FSSIM. Despite the increased detail of the SEAMLESS typology compared with what is available at EU level, still a lot of the existing diversity between individual farms is not taken into account. In general, the observed cropping pattern of average farms includes more activities than the observed cropping patterns of individual farms. Issues related to farm specific constraints, accessibility of resources and the decision making of individuals are averaged and hence only partially considered. This affects the values of the calibrated parameters of all PMP variants and the results of the analysis. Researchers should be careful with the interpretation of the PMP calibrated parameters since they capture modelling misspecifications.

The suitability of a PMP variant for specific bio-economic analysis depends on various issues, such as justification of PMP assumptions, model characteristics, data availability, type of policy and strategy questions addressed by the model. Gocht (2005), for example, evaluates a number of existing PMP variants with ex-post experiments in Germany, whereas Blanco et al. (2008) design ex-post experiments to test models calibrated with different variants of PMP including activities not observed in the base year. Ex-post

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PMP variant that is more appropriate for each specific case and to increase user's confidence in the model results. From the results of the ex-post exercises presented here, it appears that the EXPMP variant outperformed the STPMP, indicating that EXPMP is an attractive calibration procedure for a bio-economic farm model such as FSSIM.

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Chapter 4