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Examination of pig farm technology by computer simulation
25-29Views:147Agricultural production is among the riskiest production activities. Similarly to other branches of agriculture in animal breeding the finished product is the result of complex procedures. The biological technological procedure, the creation of the product is affected by an outstanding number of environmental factors which also cause uncertainties. In the North Great Plain Region of Hungary, sows, gilts and slaughter pigs are produced on a corporate farm. The reliable operation data of this company provide a stable basis for and estimating future costs and revenue and their distributions. Monte Carlo methods are one of the generally accepted tools for modeling risks. The significant independent variables, their ranges and probability distributions, and the correlation between them were inputs to the model. The values of the variables were produced using a random number generator. The computer simulation was performed using @Ris (PalisadeCorporation) software. The study concentrates on the factors affecting the number of off spring (piglets). Model inputs were the mating, mortality and farrowing rates; the costs and the income values based on these rates have been analysed as the output data of the model.
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Analysis of the influential factors on gross value added in the Hungarian sheep sector
107-112Views:119The competitiveness of the Hungarian sheep sector has been in steady decline for some time now. Crucial has been the problem that the value added in the sector is not generated in Hungary, as most of the produced lambs in Hungary leave the country with an average weight of 21 kilograms, with slaughtering happening abroad.A model has been constructed for our investigations, which introduces the connections between the product cycle phases for mutton in Hungary. This model allows us to calculate the volume of gross value added generated within specific product cycle phases. We used Monte Carlo simulation for our examination, for which the Crystall ball software package was utilized, namely the OptQuest module, for optimization. First, we conducted an optimization of an experiment number of 500,000 for “Gross value added” in the case of the slaughterhouse. During the optimization, Easter, Christmas and August lamb ratio and ewe number, as well as progeny, were set as decision variables and examined as values of gross value added, the decision variables of which contribute to obtaining the best results. The gained decision variables were set in the model and a Monte Carlo simulation was run with an experiment number of 500,000, where only the values of the conditions were changed along the pre-set dispersion; the values of the decision variables were fixed. The most significant aim of our investigation was to identify the volume of gross value added generated during processing in various phases of the product chain and the change of which inputs affected this volume the most. The findings proved that, in the case of capital uniformity, the output of processing was most influenced by sheep progeny on the bottom level of the mutton product chain. This factor is followed by that of weight gain in the source material producing and fattening sub-modules, as well as the gross wage in starter lamb feed and meadow hay in the source material producing sub-modules. Contour plots helped to describe the connections between these factors. By using contour plots, the volume of gross value added might be forecast for various combinations of factors.
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The economic efficiency of apple production in terms of post‑harvest technology
99-106Views:117This study analyses how the level of postharvest technology’s development influences the economic efficiency of apple production with the help of a deterministic simulation model based on primary data gathering in producer undertakings. To accomplish our objectives and to support our hypotheses three processing plant types are included in the model: firstly apple production with no postharvest and prompt sale after the harvest, secondly parallel production and storage combined with an extended selling period and thirdly production and entire postharvest infrastructure (storage, sorting-ranking, packing) with the highest level of goods production and continuous sales. Based on our results it can be stated that the parallel production (plantation) and cold storage, so the second case is proved to be totally inefficient, considering that the establishment of a cold storage carries enormously high costs with resulting a relative low plus profit compared to the first type of processing plant. The reason for this is that this type is selling bulk goods without sorting-grading or packaging; storage itself – as a means of continuously servicing the market – is not covered properly by the consumers. Absolute efficiency ranking cannot be established regarding the other two processing plants: plantation without post-harvest infrastructure resulting lower NPV, but a more favourable IRR, DPP and PI as developing a plantation and a whole post-harvest infrastructure.
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Investment analysis of a piglet producer farm – a Hungarian case study
141-152Views:235The pig population in Hungary was about 8 million in 1990, while this number dropped to only 2.8 million by 2018. The previously so successful integrated domestic pig farming has almost completely disappeared and most of the smaller farms still operating in the 1990s are no longer functioning. At present, a process of concentration can be observed, which was accompanied by the further specialization of pig farming. The main profile of most pig farms is fattening, but there is a smaller number of farms in Hungary today specialized for piglet production, the successful operation of which requires significantly more expertise and more complex technology.
The main aim of this study is to present the production and economic indicators of a pig farm specialized in piglet production in Hungary as a result of a greenfield investment in the current economic environment, on a case study basis. For this purpose, an economic simulation was prepared based on primary data collection, operating on a deterministic basis, modelling the production and economic processes of the farm. The performed calculation does not derive the economic indicators of the activity from accounting records, but assigns the prices of natural inputs used on the basis of technological data. Primary data and information collection (e.g. technological data, input and output prices, unit cost items, etc.) took place between 2018-2019.
At the purchase prices of pigs in the last two years, which have increased significantly due to the African Swine Fever (ASF), the majority of pig farms in Hungary have an outstanding profit-making capacity. The physical efficiency indicators of the analysed pig farm are almost identical to the average data of such farms in the Netherlands, which has one of the most developed pig industry. The income of the examined pig farm at farm level is about 734 thousand EUR, i.e. 232 EUR per sow. Moreover, this activity is profitable even without subsidies. As a result, the greenfield investment pays off in the 8th year by default (average scenario). The investment has a Net Present Value (NPVr=3%) of EUR 2,609 thousand for 10 years, an Internal Rate of Return of 8.5%, and a Profitability Index (PIr=3%) of 1.3. At the same time, risk factors such as sales prices, output and capacity utilization, and feed costs should be taken into consideration as in extreme cases the return on investment may be unfavourable (pessimistic scenario).
JEL code: D24, M11, Q12
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The impact of boundary organizations on decision-making under uncertainty: A multi-agent simulation
13-16Views:114Modern environmental issues imply that decision-makers have the capacity to take into account possibly conflicting information from distinct domains, such as science and economics.As the development of technology increases the temporal and spatial scopes of risks, decision-makers can no longer consider economic and scientific information separately but should encourage experts to work together. Boundary organizations, institutions that cross the gap between two different domains, are able to act beyond the boundaries while remaining accountable to each side (Guston, 2001). By encouraging a flow of information across the boundaries, they permit an exchange to take place, while maintaining the authority of each domain (Cash et al., 2003; Clark et al., 2002). The goal is to simulate boundary organizations to assess their impact on the diffusion of experts’ opinions. The hypothesis tested is whether the existence of a boundary organization eases the decision-making process by reducing the number of opinions expressed. The methodology relies on a multi-agent system based on a model of continuous opinion dynamics (Deffuant et al., 2001) extended over two dimensions. The world is defined by two parameters: the uncertainty, that reflects the possible zone of discussion between experts, and the exchange, which represents the openness of discussions. Agents are described by credibility and conviction: the credibility represents how much other agents may be influenced by an agent, and the conviction represents the resistance of an agent to changing its position. Two kinds of agents are left free to interact, modifying their position in their domain (dimension) through one-to-one exchanges. Agents called borgs are introduced: open to trans-disciplinary discussion, they are able to exchange on both dimensions. The results show that the range of expressed opinions is significantly reduced, even at low levels of experts involved in the boundary organization.
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Factors influencing the gross value added in the sheep production chain
141-146Views:163The competitiveness of the sheep sector in East Europe has been decreasing from year to year. The value added in the sector is not generated in the countries as a high proportion of the lambs are exported. For example, in Hungary, 95% of the lambs, unnecessary for replacement, are sold at an average weight of 21 kg and are slaughtered abroad. A stochastic model was constructed to investigate the connections between the cycle phases of the mutton production. Three modules were distinguished, the lamb production, fattening and slaughtering-processing sub-modules. The aim of our study was to identify the gross value added generated in the three sub-modules and to analyse the main factors influencing its volume using the conditions in Hungary as an example. The major hypothesis of our research was that the profitability of the production chain is mainly determined by the breed. The results showed that, considering market prices, the gross value added in the processing module was mostly influenced by the number of lambs sold per ewe per year at the bottom level of the mutton product chain. The next most important factors were the weight gain in the lamb producing and fattening sub-modules and dressing percentage in slaughtering-processing sub-module. Contour plots were constructed which help to describe the relationship among analyzed factors. Using the contour plots, the gross value added for different combinations of these factors might be forecast.
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Efficiency analysis of dairy farms in the Northern Great Plain region using deterministic and stochastic DEA models
113-122Views:126Running any dairy enterprise is a risky activity: the profitability of the enterprise is affected by the price fluctuation of feed and animal health products from inputs, as well as by the fluctuation of end-product prices. Under these circumstances, it is essential for the cattle breeders, in order to survive, to harness the reserves in management as effectively as possible. In this research the efficiency and risk of 32 sample dairy farms were analysed in the Northern Great Plain Region from the Farm Accountancy Data Network (FADN) by applying classical Data Envelopment Analysis (DEA) and stochastic DEA models. The choice of this method is justified by the fact that there was not such an available reliable database by which production functions could have been defined, and DEA makes possible to manage simultaneously some inputs and outputs, i.e. complex decision problems. By using DEA, the sources that cause shortfall on inefficient farms can be identified, analysed and quantified, so corporate decision support can be reinforced successfully. A disadvantage of the classical DEA model is that the stochastic factors of farming cannot be treated either on the side of inputs or outputs; therefore, their results can be adopted with reservations, especially in agricultural models. This may have been because we could not discover that many agricultural applications. Considering the price of inputs and outputs as probability variables, 5000 simulation runs have been done in this research. As a result, it can be stated that at which intervals of the input and output factors can become competitive and the fluctuation of these factors can cause what level of risk at each farm.
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