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Angelova, D. (2014). The state-contingent approach to production and choice under uncertainty: usefulness as a basis for economic modeling (Vol. 3). FACCE MACSUR Reports, 3.
Abstract: The state-contingent approach developed by Chambers and Quiggin (2000) constitutes an attractive blend of a theory of production analysis under uncertainty and a theory of decision-making under uncertainty.One of the goals of this contribution is to introduce the reader to the approach by outlining its contents while comparing and contrasting it to related theories. With respect to production analysis: an emphasis is made on the ability of the approach to deliver well defined cost functions corresponding to stochastic production technologies. With respect to decision-making under uncertainty: the comparison with other theories consistent with a rational agent emphasizes the production theoretical basis of the state-contingent approach.It is the author’s belief that appropriately categorizing the state-contingent approach serves the primary goal of this work: to explore its usefulness as a basis for economic modeling. Some challenges regarding an empirical implementation are discussed: challenges in estimating the parameters of a state-contingent technology representation in general, as well as challenges arising from the fact that the approach is constructed around the argument pioneered by Leonard J Savage: that probabilities underlying economic decision-making are inherently subjective.(The financial support of ScienceCampus Halle is gratefully acknowledged.) No Label
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Bojar, W., Knopik, L., & Zarski, J. (2014). Integrated assessment of business crop productivity and profitability for use in food supply forecasting (Vol. 3).
Abstract: Climate change suggests long periods without rainfall will occur in the future quite often. Previous approach on dependence crop-yields from size of rain confirms the existence of a statistically significant relation. We built a model describing the amount of precipitation and taking into account periods of drought, using a mixture of gamma distribution and one point-distribution. Parameter estimators were constructed from rainfall data using the method of maximum likelihood. Long series of days or decades of drought allow to determine the probabilities of adverse developments in agriculture as the basis for forecasting crop yields in the future (years 2030, 2050). Forecasted yields can be used for assessment of productivity and profitability of some selected crops in Kujavian-Pomeranian region. Assumptions and parameters of large-scale spatial economic models will be applied to build up relevant solutions. Calculated with this approach output could be useful to expect decrease in agricultural output in the region. It will enable to shape effective agricultural policy to know how to balance food supply and demand through appropriate managing with stored food raw material and/or import/export policies. Used precipitation-yields dependencies method let verify earlier used methodology through comparison of obtained solutions concerning forecasted yields and closed to it uncertainty analysis.This work was co-financed by NCBiR, Contract no. FACCE JPI/04/2012 – P100 PARTNER No Label
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Bellocchi, G., Martin, R., Shtiliyanova, A., Ben Touhami, H., & Carrère, P. (2014). Vul’Clim – Climate change vulnerability studies in the region Auvergne (France) (Vol. 3).
Abstract: The region Auvergne (France) is a major livestock territory in Europe (beef and dairy cattle with permanent grasslands), with a place in climate change regional studies assisting policy makers and actors in identifying adaptation and mitigation measures. Vul’Clim is a research grant (Bourse Recherche Filière) of the region Auvergne (February 2014-September 2015) to develop model-based vulnerability analysis approaches for a detailed assessment of climate change impacts at regional scale. Its main goal is the creation of a computer-aided platform for vulnerability assessment of grasslands, in interaction with stakeholders from a cluster of eco-enterprises. A modelling engine provided by the mechanistic, biogeochemical model PaSim (Pasture Simulation model) is the core of the platform. An action studies the changes of scales by varying the granularity of the data available at a given scale (e.g. climate data supplied by global scenarios) to let them being exploited at another scale (e.g. high-resolution pixels). Another action is to develop an assessment framework linking modelling tools to entry data and outputs, including a variety of components: data-entry manager at different spatial resolutions; automatic computation of indicators; gap-filling and data quality check; simulation kernel with the model(s) used; device to represent results as maps and integrated indicators. No Label
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Sanna, M., Acutis, M., & Bellocchi, G. (2014). Interrelationship between evaluation metrics to assess agro-ecological models (Vol. 3).
Abstract: When evaluating the performances of simulation models, the perception of the quality of the outputs may depend on the statistics used to compare simulated and observed data. In order to have a comprehensive understanding of model performance, the use of a variety of metrics is generally advocated. However, since they may be correlated, the use of two or more metrics may convey the same information, leading to redundancy. This study intends to investigate the interrelationship between evaluation metrics, with the aim of identifying the most useful set of indicators, for assessing simulation performance. Our focus is on agro-ecological modelling. Twenty-three performance indicators were selected to compare simulated and observed data of four agronomic and meteorological variables: above-ground biomass, leaf area index, hourly air relative humidity and daily solar radiation. Indicators were calculated on large data sets, collected to effectively apply correlation analysis techniques. For each variable, the interrelationship between each pair of indicators was evaluated, by computing the Spearman’s rank correlation coefficient. A definition of “stable correlation” was proposed, based on the test of heterogeneity, allowing to assess whether two or more correlation coefficients are equal. An optimal subset of indicators was identified, striking a balance between number of indicators, amount of provided information and information redundancy. They are: Index of Agreement, Squared Bias, Root Mean Squared Relative Error, Pattern Index, Persistence Model Efficiency and Spearman’s Correlation Coefficient. The present study was carried out in the context of CropM-LiveM cross-cutting activities of MACSUR knowledge hub. No Label
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Rusu, T. (2014). Impact of Climate Change on Crop Land and Technological Recommendations for the Main Crops in Transylvanian Plain, Romania (Vol. 3).
Abstract: The Transylvanian Plain (TP) is an important agricultural production area of Romania that is included among the areas with the lowest potential of adapting to climate changes in Europe. Thermal and hydric regime monitoring is necessary to identify and implement measures of adaptation to the impacts of climate change. Soil moisture and temperature regimes were evaluated using a set of 20 data logging stations positioned throughout the plain. Each station stores electronic data regarding ground temperature at 3 depths (10, 30, 50 cm), humidity at a depth of 10 cm, air temperature (at 1 m) and precipitation. For agricultural crops, the periods of drought and extreme temperatures require specific measures of adaptation to climate changes. During the growing season of crops in the spring (April – October) in the southeastern, southern, and eastern escarpments, precipitation decreased by 43.8 mm, the air temperature increased by 0.37°C, and the ground temperature increased by 1.91°C at a depth of 10 cm, 2.22°C at a depth of 20 cm and 2.43°C at a depth of 30 cm compared with values recorded for the northern, northwestern or western escarpments. Water requirements were ensured within an optimal time frame for 58.8-62.1% of the spring row crop growth period, with irrigation being necessary to guarantee the optimum production potential. The biologically active temperature recorded in the TP demonstrates the need to renew the division of the crop areas reported in the literature. No Label
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