Micro-data efficiency evaluation of agricultural companies: The case of Germany and neighbouring countries

This study uses micro-financial data to examine the efficiency of agricultural enterprises in Germany and its neighbouring countries. The aim of the study is to introduce a model for the agricultural sector and conduct an efficiency analysis using these data, interpreting the results with specific knowledge in the management of an agriculture company. Both technical and allocative efficiencies were determined, and the companies were ranked. Possible correlations between company size, measured by turnover, and the determined efficiency were analysed. At present, there is a lack of studies in the agricultural sector with high aggregated financial data, which are the basis and necessity for well-founded decision support to increase efficiency. The data envelopment analysis method was used, as a non-parametric procedure from operations research and economics field. Both the constant returns to scale (CCR) and variable returns to scale (BCC) models were used to calculate the efficiency values. The results showed that large and very large companies achieved the highest levels of efficiency. Interestingly, very large companies lost efficiency compared to large companies, suggesting that the optimal efficiency level lies with the latter. Furthermore, the Netherlands was the absolute efficiency leader, while the other countries achieved similar lower efficiencies. This study contributes to the literature by providing a comprehensive efficiency analysis in the agricultural sector based on financial data, thus offering a basis for future studies and political decisions.

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Publication Details

Journal
Agricultural Economics (Zemědělská ekonomika)
Published
2024-11-29
DOI
https://doi.org/10.17221/190/2024-agricecon
Citations
1
Primary Topic
Efficiency Analysis Using DEA
Type
article
Field-Weighted Citation Impact
0.49

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article

Micro-data efficiency evaluation of agricultural companies: The case of Germany and neighbouring countries

Jitka Janová, Kevin Nowag
1 citations
Agricultural Economics (Zemědělská ekonomika)
Efficiency Analysis Using DEA
0.49
article

Micro-data efficiency evaluation of agricultural companies: The case of Germany and neighbouring countries

Jitka Janová, Kevin Nowag
article en
1 citations

Abstract

This study uses micro-financial data to examine the efficiency of agricultural enterprises in Germany and its neighbouring countries. The aim of the study is to introduce a model for the agricultural sector and conduct an efficiency analysis using these data, interpreting the results with specific knowledge in the management of an agriculture company. Both technical and allocative efficiencies were determined, and the companies were ranked. Possible correlations between company size, measured by turnover, and the determined efficiency were analysed. At present, there is a lack of studies in the agricultural sector with high aggregated financial data, which are the basis and necessity for well-founded decision support to increase efficiency. The data envelopment analysis method was used, as a non-parametric procedure from operations research and economics field. Both the constant returns to scale (CCR) and variable returns to scale (BCC) models were used to calculate the efficiency values. The results showed that large and very large companies achieved the highest levels of efficiency. Interestingly, very large companies lost efficiency compared to large companies, suggesting that the optimal efficiency level lies with the latter. Furthermore, the Netherlands was the absolute efficiency leader, while the other countries achieved similar lower efficiencies. This study contributes to the literature by providing a comprehensive efficiency analysis in the agricultural sector based on financial data, thus offering a basis for future studies and political decisions.

Agricultural Economics (Zemědělská ekonomika)Vol. 70(11)
Mendel University in Brno (CZ)
Mendelova Univerzita v Brně
Openalex Percentile: Top 28%
Efficiency Analysis Using DEA
0.49
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