Črtomir Rozman

A Multi-Criteria Analysis of Spelt Food Processing Alternatives on Small Organic Farms

ABSTRACT The nutritional, technological and processing characteristics of spelt wheat make it suitable for organic farming. Furthermore, spelt wheat can be directly processed into different kinds of food products on the farm itself, and represents additional business and market opportunities for organic farmers. In this paper we examine different spelt processing alternatives on a chosen organic farm in northeastern Slovenia. Simulation modeling, cost-benefit analysis and multi-criteria decision analysis were applied in order to find the most suitable spelt processing solution for the analyzed farm. The presented methodological framework upgrades the standard financial cost-benefit analysis with a quantitative utility function and a qualitative DEX-i modeling approach. The calculation of utility function (u) and DEX-i evaluation produced the following spelt processing project rankings: spelt husking (u = 2,67) spelt cookies (u = 2,18), spelt flour (u = 1,65) and spelt bread (u = 1,63). The proposed methodology enables multi-criteria spelt processing project ranking; however, further studies are suggested for the development of multi-criteria decision analysis-based methods for this topic.

The Use of Multi Criteria Models for Decision Support on Organic Farms

ABSTRACT The need for reliable information in the organic farm planning process dictates the use of modern decision support methods. The methodology of an integrated deterministic simulation system application for decision-making support on organic farms is described in this article. The deterministic simulation system KARSIM 1.0 consists of 74 deterministic production simulation models that enable different types of costs and financial feasibility calculations for organic production and food processing. The KARSIM 1.0 was applied on a sample organic farm for the simulation of five different business alternatives. The simulation model results are further evaluated with two multi-objective analysis methods: analytical hierarchical process (Expert Choice Decision Support System software) and DEX-i expert system (computer software for multi attribute decision making). Alternative 3 (spelt grain processing) results with the highest multi-objective decision evaluation (Expert Choice = 0.275 and DEX-i evaluation = very good). The combination of deterministic cost simulation model and multi criteria decision analysis with the emphasis on financial, technological, market and risk objective presents a powerful decision support tool for organic farms, however, further improvement and development of proposed methodology would be suggested.

A Multi-Criteria Decision Analysis Framework Tool for the Selection of Farm Business Models on Organic Mountain Farms

Mountain regions are important producers of organic food. For them, reliable decision making regarding business planning necessitates different critical support methods. KARSIM 1.0 (DSM) is a methodology based on an integrated deterministic simulation system application for decision-making support, consisting of 74 deterministic production simulation models. DSM enables different types of cost and financial feasibility calculations for organic production and food processing. KARSIM 1.0 was used to simulate three specific business alternatives for mountain organic farms. (Alternative 1: spelt grain, fruit cider, wine and brandy, plum brandy, calves meat and sheep–soft cheese production, Alternative 2: spelt flour, pear and apple juice, plum brandy, veal and sheeps milk, Alternative 3: spelt grain, dried fruit, calves, soft and hard sheep cheese). Simulation model results were compared using two multi-objective analysis methods: the analytical hierarchical process (Expert Choice Decision Support System software) and DEX-i method. The results showed the highest multi-objective decision evaluation for alternative 2 (Expert Choice = 0.361 and DEX-i evaluation = excellent). We can conclude that the combination of a deterministic cost simulation model and multi-criteria decision analysis presents an acceptable decision support tool for mountain organic farms; however, further research is desirable.

The System Dynamics Model for Development of Organic Agriculture

Organic agriculture is the highest environmentally valuable agricultural system, and has strategic importance at national level that goes beyond the interests of agricultural sector. In this paper we address development of organic farming simulation model based on a system dynamics methodology (SD). The system incorporates relevant variables, which affect the development of the organic farming. The group decision support system (GDSS) was used in order to identify most relevant variables for construction of causal loop diagram and further model development. The model seeks answers to strategic questions related to the level of organically utilized area, levels of production and crop selection in a long term dynamic context and will be used for simulation of different policy scenarios for organic farming and their impact on economic and environmental parameters of organic production at an aggregate level.

The Development of Sugar Beet Production and Processing Simulation Model – a System Dynamics Approach to Support Decision-Making Processes

Abstract Background: The sugar beet is the main field crop used for sugar production in the temperate climatic zone. The abolishment of the quota system will open new investment opportunities in countries that were forced to abandon sugar industry as the result of the reform in 2006. Present paper describes the modeling of sugar beet production and its processing into sugar for purpose of decision support. Methods: A system dynamics methodology was chosen to model impacts of regional sugar factory investment. We present two basic concepts of system dynamics models at causal loop diagram level. The first holistic model deals with regional planning of new product development and the second one deals with factory model. Results: The holistic model presented main feedback loops and dynamics of main elements in the case of regional investment into sugar industry. The factory model considered the specifics of the beet processing which is a) limited period of beet processing and b) initial adjustment to the production capacity at the start of the production season Conclusions: The model seeks answers to strategic questions related to the whole sugar beet production and processing system and will be used for simulation of different scenarios for sugar production and their impact on economic and environmental parameters at an aggregate level.

System dynamics model for policy scenarios of organic farming development

This paper presents the system dynamics model of organic farming development in order to support decision making. The model seeks answers to strategic questions related to the level of organically utilized area, levels of production and crop selection in a long-term dynamic context. The model will be used for simulation of different policy scenarios for organic farming and their impact on economic and environmental parameters of organic production at an aggregate level. Using the model, several policy scenarios were performed.

Business opportunity assessment in Slovene organic spelt processing: application of real options model

Decision making in organic farming is related to risk and uncertainty, and options must be evaluated in the decision-making process. This paper presents the methodology of an integrated deterministic simulation system (KARSIM 1.0) application for decision-making support on organic farms in northeastern Slovenia. An emphasis to modify the net present value (NPVt) criterion by incorporating the real options approach was made. Its application is shown in organic spelt (Triticum aestivum ssp. spelta McKey) production and processing using two real options approaches, the Black–Scholes and binomial models. The NPVt indicates that the decision to process spelt for animal fodder is financially unfeasible, while the real options approach differentiates the results by organic spelt grain and flour production for human nutrition. It may be concluded that the real options approach can be useful when assessing projects with uncertainty, sunk costs and irreversibility, and it can provide for examining agricultural investment decisions.

Comparison of using regression modeling and an artificial neural network for herbage dry matter yield forecasting

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. Copyright

Multi-criteria assessment of less favoured areas: A state level

The paper present a multi-criteria decision DEXi model for assessment of less favoured areas (LFAs). The tool enables easier assessment of farming in different areas of Slovene LFAs with respect to criteria of sustainability. Analysis of LFAs and final integration of the assessment of LFAs depend upon various criteria. In this paper we analyze individual LFAs and farming systems in these areas at the state level with respect to criteria of sustainability and farming potential.

Feasibility analysis of different bedding materials for horses

ABSTRACT The aim of this paper is to assess the economic aspects of bedding materials for sport/recreational horses. Four materials are tested: sawdust in bulk, wood shavings, squared harvest straw, and slice-dusted straw. The process of collecting data for feasibility analysis is structured into two different trials. In Trial 1, daily observations are collected and measured to obtain data about the technical and cleaning characteristics of the four different bedding materials, and in Trial 2, the durability of the bedding materials is tested. From an economic (cost) standpoint, the most suitable bedding materials are squared harvest straw and slice-dusted straw. Unfavourable economic results are found for wood shavings and sawdust in bulk. However, the durability analysis shows wood shavings and slice-dusted straw to be the better options. Thus, the results indicate that some bedding types are better for durability reasons and others are better for economic reasons.