By Constantin Zopounidis, Nikos Kalogeras, Konstadinos Mattas, Gert van Dijk, George Baourakis
This publication makes a speciality of using farm point, micro- and macro-data of cooperative platforms and networks in constructing new powerful, trustworthy and coherent modeling instruments for agricultural and environmental coverage research. The efficacy of public intervention on agriculture is basically made up our minds by way of the life of trustworthy details at the results of coverage innovations and industry advancements on farmers' construction judgements and specifically, on key concerns equivalent to degrees of agricultural and non-agricultural output, land use and earning, use of ordinary assets, sustainable-centric administration, structural swap and the viability of relations farms. over the past years, a number of tools and analytical instruments were constructed for coverage research utilizing numerous units of information. Such equipment were in line with built-in ways in order to examine the above key concerns and feature hence tried to provide a robust surroundings for selection making, fairly in an period of radical swap for either agriculture and the broader economy.
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Additional resources for Agricultural Cooperative Management and Policy: New Robust, Reliable and Coherent Modelling Tools
In the initial simulations, only one source of uncertainty, namely crop yield variability within the EU, is examined. Positive correlations among crop yield deviates amplify variations in crop production. This is supported by our results of the experimental simulations in which correlations among crop yield deviates are taken into account and compared to results where they are not addressed. Variability in output value varies widely depending on the aggregation level under consideration. In particular, at levels below where prices are determined, output value variability is larger than either production variability or price variability alone, supporting the potential need for risk management policy.
The state of food insecurity in the world, 2011. Rome: Food and Agricolture Organization of the United Nations. Farm Foundation. (2008). What’s driving food prices? , & Peters, J. (2007). Biodiesel: A new Oildorado? Energy Policy, 35(3), 1675–1684. , Nastari, P. , & Lucon, O. (2004). Ethanol learning curve—the Brazilian experience. Biomass and Bioenergy, 26, 301–304. Granger, C. W. J. (1969). Investigating causal relations by econometric model and cross-spectral methods. Econometrica, 37, 424–438.
4, we can see that the corn produced for domestic use and employed for bioethanol has increased significantly over time. Therefore, 18 A. C. Leucci et al. using as covariates the portion of corn destined for domestic use (d) and that for the production of bioethanol, interesting results emerge: only the equation with corn for domestic use as the response variable presents significant parameters, and the Granger test shows that it is the amount of corn used for the production of bioethanol that influences, in the Granger sense, the quantity for internal use and not vice versa.