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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.

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