This article analyses the interactions between agricultural policy measures in the EU and the factors affecting GHG emissions from agriculture on the one hand, and the adaptation of agriculture to climate change on the other. To this end, the article uses Slovenia as a case study, assessing the extent to which Slovenian agricultural policy is responding to the challenges of climate change. All agricultural policy measures related to the 2007–2013 programming period were analysed according to a new methodological approach that is based on a qualitative (expert evaluation) and a quantitative (budgetary transfers validation) assessment. A panel of experts reached consensus on the key factors through which individual measures affect climate change, in which direction and how significantly. Data on budgetary funds for each measure were used as weights to assess their relative importance. The results show that there are not many measures in (Slovenian) agricultural policy that are directly aimed at reducing GHG emissions from agriculture or at adaptation to climate change. Nevertheless, most affect climate change, and their impact is far from negligible. Current measures have both positive and negative impacts, but overall the positive impacts prevail. Measures that involve many beneficiaries and more budgetary funds had the strongest impact on aggregate assessments. In light of climate change, agricultural policy should pay more attention to measures that are aimed at raising the efficiency of animal production, as it is the principal source of GHG emissions from agriculture.
COBISS.SI-ID: 3836552
The agri-environmental programme (AEP) is the European Union policy instrument used for the delivery of environmental services expected by the society, but societal expectations for these environmental services are insufficiently assessed. In order to realistically meet the expectations of AEP, this research utilises analytical hierarchy process based web survey to assess the importance that various societal stakeholders in the European countries of Slovenia and Croatia give to specific environmental services and to also identify the agricultural practices that have the greatest potential to realise the expectations that society values the most. All stakeholders (overall group) from Slovenia and Croatia ranked water quality and availability as the first most important environmental service, and reduction of pesticides as the most important agricultural practice to deliver societal expectations. The results indicate that there is similar demand for environmental services in these two countries that differ in their agricultural settings.
COBISS.SI-ID: 8388217
Analysing economic efficiency of farm production always faces a problem of insuffi cient information. Th is is particularly true when the analysis is performed on the reference farm where estimates are based on the average aggregated data. The paper illustrates how the combination of diff erent mathematical programming methods could be effi ciently used to analyse the farm-production plan with the lack of the on-farm accounting data. The utilised approach shows how the holistic analysis of production planning as a multi-criteria problem could be conducted. The estimation of the missing information and the disaggregation of the endogenous farm data is enabled through diff erent models that are based on the constrained optimisation. The developed models are linked into the spreadsheet modular tool enabling systematic analyses of the farm decision making under risky conditions. Illustration of the modular tool application is given via the analyses of three hypothetic dairy farms. Th e obtained results indicate that the developed approach enables holistic analyses of the production planning. The methodology applied provides also important information for the measures aimed to increase effi ciency as well as to benchmarking the performance of diff erent farm types. The results point to a discrepancy between the solutions obtained through diff erent objective functions and shows the advantage of the multi-criteria approach.
COBISS.SI-ID: 3826312
This study presents an application of artificial neural network and regression modeling techniques for forecasting grassland dry matter yield. Using data from a field plot experiment on semi-natural grassland in Maribor (Slovenia), the multiple regression and artificial neural network methodologies were employed to explain the patterns of dry matter yield during a 6-year period. On the basis of the two proposed approaches forecasts were conducted for the independent, validation year (6). The results in terms of Theil inequality coefficient, mean absolute error, and correlation coefficient show a better forecasting performance for the artificial neural network (likely due to the non-linear relationships prevailing among regressors and regressand) while relationships between observables can be better explained by regression modeling results.
COBISS.SI-ID: 4068396
A laboratory filtration system equipped with a 0.80 %m nitrocellulose membrane for wort filtration and a 0.45 %m mixed cellulose ester microporous membrane for beer filtration was used for this study. The critical granularity distributions of wort and beer before and after filtration were determined by the Beckman Coulter Multisizer 3 counter and particle size analyzer. Using Matlab 7.0 software, a mathematical model of the wort filtration performance and granularity relationship was established, which is applicable for predicting and improving the wort filtration performance. The granularity distribution and percentages of particle numbers and volumes of the wort before and after filtration had great impact on the wort filtration performance. The percentages of particle numbers and particle volumes were used to characterize the wort filtration performance based on a mathematical model. The correlation between real and predicted wort filtration performance values was very close. R2, the relative error of real value and prediction value on wort filtration performance, was defined as (predicted value real value)/real value. When we used the particle number percentage, a relative variation R2 was between 1.67 and 3.01%. When we used the particle volume percentage, the relative variation R2 was between 2.89 and 3.87%.
COBISS.SI-ID: 4193836