Browsing by Subject "Ackerbau"

Now showing 1 - 4 of 4

A 2014 Social Accounting Matrix (SAM) for Uzbekistan with a focus on the agricultural sector
(2021) Wieck, Christine; Bozorov, Abdurashid; Feuerbacher, Arndt
Social accounting matrices (SAMs) are the core underlying data for economy-wide simulation models such as computable general equilibrium models. This paper reports the development of a SAM for Uzbekistan for the year 2014. The last SAM developed for Uzbekistan is based on the year 2001 (Müller, 2006) and Uzbekistan is listed among the top ten countries by GDP and population by the Global Trade and Analysis Project for which a recent input-output is missing. The SAM documented in this technical paper is characterized by a detailed representation of the agricultural sector. Generally, data availability in Uzbekistan is a challenge and the development process had to rely on myriad data sources. The final SAM values are estimated using an information-theoretic, cross-entropy approach. Using a Bayesian perspective, the degree of uncertainty of cell entries’ prior values reflected the availability and quality of data sources. In total, this SAM consists of 88 accounts. There are 31 commodity accounts and 31 accounts describe economic activities of which 17 activities are part of the agricultural sector. The factor accounts comprise five types of labor, capital, and main natural resources: land and water. There are three household accounts, one government, and five tax accounts. The authors hope that this SAM will allow researchers to investigate research questions that are of high priority for Uzbekistan’s future economic development, particularly those related to the future role of agriculture and water.
Analyse der Maschinenkosten mittels automatisierter und manueller Maschinendokumentation im ackerbaulichen Produktionsprozess
(2023) Lattke, Justus; Böttinger, Stefan
“Smart Farming”, “Agriculture 4.0” and the “Internet of Things” are terms set to define the future of global agriculture. Many studies predict that these new technologies will have more impact on agricultural productivity than the “Green Revolution” of the 1970s. Upstream and downstream agricultural suppliers claim that their products and services will benefit farmers and their businesses. In this study, several farm management software programs were tested for their capacity to add value to the cropping division of a large and diversified farm. The evaluation showed that a farm management software program based on automatic collection of machinery cost data, gave more precise and timely information than a process relying on manual data collection. This conclusion was arrived at by calculating total cropping equipment costs over a full season using two cost accounting methods – an adaptable planned cost calculation and a process cost calculation – and comparing the results from the automatic and the manual collection of data. To determine the most suitable method and to select the various software programs to evaluate, various trade fairs and companies were visited, and websites searched. The most promising software programs were then tested for functionality and ease of use at the Horsch farm, “Agrovation”. It turned out difficult to establish an incentive system for employees to use the new technologies. For this reason, the farm machines at Agrargesellschaft Pfiffelbach were equipped with the 365FarmNet application without employee registration. The entire production period of crops harvested in 2018 was included. Both automatic and manual data collection took place from August 1, 2017 to December 31, 2018. To evaluate the quality of the two methods of data acquisition, the actual hours worked were compared with the hours recorded according to the type of cost collection.
Die Ökobilanz zur Abschätzung von Umweltwirkungen in der Pflanzenproduktion - dargestellt anhand von Praxisversuchen zur konservierenden Bodenbearbeitung und von unterschiedlich intensiv wirtschaftenden konventionellen Betrieben
(2003) Arman, Beate; Claupein, Wilhelm
In the agricultural field difficulties in life-cycle assessment result from the fact that the methods of life-cycle assessment were developed in techno-industrial production. Agricultural production, however, differs from industrial production in that it depends more strongly on natural resources and, moreover, has a direct influence on them. Hence, apart from preparing data for the used production goods, the expansion of environmental impact categories to include specific effects from agriculture is focused on in the adaptation of ecobalances as an agricultural method. Among others deficiencies here include the balancing of effects in agriculturally utilized soil. The ecobalances at hand were carried out with two different goals in mind. For one, the impact of conventional and conservational cultivation methods were to be balanced. The goal of this ecobalance was to show whether life-cycle assessment have adequate selective power in order to be used as a decision criterion in the optimisation of cultural methods and their environmental impact. For another, the intensity of cultivation of three agricultural enterprises was compared. It was to be shown here whether life-cycle assessment can provide transparency as to the environmental effects of various production methods, which would enable the consumer to obtain information on the environmental relevance of these methods. A further goal of this work was the development of a method for the recording of effects on the soil in life-cycle assessment. The examined farms are situated in the Hohenlohe region and were integrated in the subproject "Conservation Tillage" of the "Cultural Landscape Hohenlohe" project group. In order to balance soil working methods, the three methods plow, cultivator and mulch sowing were examined. The data was obtained from two test fields with the same crop rotation on one of the farms. Balancing of the intensity of cultivation was carried out on three conventionally working farms using varying levels of fertilizer, crop protectants and tillage. The balanced crop rotation of the three farms did not vary (sugar-beets, winter wheat, winter barley). When developing methods for balancing environmental effects on agriculturally utilized soil three aspects were decisive in the selection of balanced effects: 1. What soil properties are there? 2. Which of these properties are influenced directly by cultivation measures? 3. For which properties are relevant negative effects caused by agriculture known? Based on the indicated methods the impact was assessed for the following soil properties: - Soil depth is influenced by soil loss. Soil loss was calculated with the universal soil loss equation. - Impact on the nutrient content was assessed with the help of a nutrient field balance, humus content with the help of a humus balance. - Variations in soil density caused by loading were assessed with the help of the weighted soil load. - Soil life is affected by pollutant input, modelling of the effect potential was carried out with the help of the Critical-Surface-Time model. All in all the results show that in order to differentiate between the tillage variants with respect to their environmental impact, it is necessary to also consider effects on the soil. Comparison of the farms showed that life-cycle assessment can reflect the environmental relevance of different cultivation intensities and can make them visible for the consumer.
Rentabilität und Risiko typischer Ackerbaubetriebe in der Russischen Föderation
(2009) Breunig, Peter; Zeddies, Jürgen
Consistent world population growth, changing diets in emerging markets and the growing impact of biofuels led to considerable price increases for agricultural commodities since 2006, in particular for grains and oilseeds. Among other things this results in a growing interest of capital investors in investments in agricultural companies. Capital investments from outside the agricultural sector play ? especially in Russia ? a major role, due to the farm structure and the predominantly capital-oriented farm businesses. Until now, it is not yet clear, how these investments perform in comparison to the capital market regarding risk and return, which is critical for the future volume and sustainability of these investments. Therefore, the aim of this work is to analyze risk and return of typical arable farms in selected regions of the Russian Federation from 2000 to 2007 and make forecasts from 2008 to 2015. Additionally, the risk-return-performance of the analyzed farms is compared to a russian stock market index. This allows for the examination of the relative excellence based on risk and return among the typical farms and in comparison to the russian capital market. The evaluation of investments in arable farms in the Russian Federation based on risk and return analysis is done by two methods. The first method calculates performance ratios that are based on risk and return values of each typical farm. This is done by analyzing and forecasting financial models of the typical farms, which are based on farm surveys in the relevant regions. In total, eight typical farms in four regions (Voronesh, Stavropol, Samara and Omsk) of the Russian Federation are modeled. For the analysis of historic risk and return values, historic price and yield data from official statistics are integrated into the farm models and the relevant performance ratios are thus calculated. Future performance, price and yield data and their volatility is forecasted by using mathematical methods and published forecasts. The performance ratios of each typical farm are subsequently compared to the russian stock market index MICEX. The second method to evaluate investments in arable farms in the Russian Federation is based on the "Capital Asset Pricing Model". With this model, the return that agricultural companies in Russia have to achieve to have the same risk-return-performance as the Russian stock market can be calculated. This is done by analyzing the movement of applicable stocks relative to the total Russian stock market. Additionally investments in arable land are analyzed in the context of investments in arable farms. The results of the thesis show that in the historical period (2000 to 2007) only the typical farms in the Stavropol region are able to exceed the performance of the Russian stock market index. In the forecast period (2008 to 2015) one typical farm in the Voronesh region as well as the typical farms in the Stavropol region are expected to outperform the russian stock market. Furthermore, it can be shown that the arable farms in Samara and Omsk have a considerably lower risk-return-performance compared to the other typical farms in the west and southwest of Russia. The results validate the strong increase in investments in arable farms in the west and southwest of Russia in recent years. Moreover it is assumed that in the Samara and Omsk region similar investment volumes like in the western regions of Russia cannot be expected.