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The aim of this article is to investigate the relationship between size and farm growth. The existing theories of the association between size and farm growth give mixed results by countries and over time. This paper pursues a twofold objective: on one hand, to test the validity of Gibrat's Law for French, Hungarian and Slovenian specialized dairy and crop farms during the pre- and post-accession period to the European Union membership. Dairy and crops farms are prevailing in the farming structure of these countries. Using Farm Accountancy Data Network datasets makes it necessary to avoid biases due to heterogeneous structures across the farming systems. Thus we use quantile regressions to control for farm size related heterogeneity in the samples. On the other hand, the main novelty of this paper is the comparative analysis of the relationship between farm size and farm growth between transition Hungarian and Slovenian and non-transition French farming sectors, characterized by rather different farm structures. The results reject the validity of Gibrat's Law for crop farms in Hungary and to a lesser extent in France, and for French and Slovenian dairy farms. We provide evidence that smaller farms grew faster than larger ones over the studied period 2001-2007 for France, 2001-2008 for Hungary, and 2004-2008 for Slovenia. Conversely, the results for Slovenia suggest that the rate of growth of crop farms in terms of its land is independent from its size.

The aim of this article is to investigate the relationship between size and farm growth. The existing theories of the association between size and farm growth give mixed results by countries and over time. This paper pursues a twofold objective: on one hand, to test the validity of Gibrat's Law for French, Hungarian and Slovenian specialized dairy and crop farms during the pre- and post-accession period to the European Union membership. Dairy and crops farms are prevailing in the farming structure of these countries. Using Farm Accountancy Data Network datasets makes it necessary to avoid biases due to heterogeneous structures across the farming systems. Thus we use quantile regressions to control for farm size related heterogeneity in the samples. On the other hand, the main novelty of this paper is the comparative analysis of the relationship between farm size and farm growth between transition Hungarian and Slovenian and non-transition French farming sectors, characterized by rather different farm structures. The results reject the validity of Gibrat's Law for crop farms in Hungary and to a lesser extent in France, and for French and Slovenian dairy farms. We provide evidence that smaller farms grew faster than larger ones over the studied period 2001-2007 for France, 2001-2008 for Hungary, and 2004-2008 for Slovenia. Conversely, the results for Slovenia suggest that the rate of growth of crop farms in terms of its land is independent from its size.

Investing in farmers – or agriculture human capital – is crucial to addressing challenges in our agri-food systems. A global study carried out by the FAO Investment Centre and the International Food Policy Research Institute, with support from the CGIAR Research Programme on Policies, Institutions and Markets and the FAO Research and Extension Unit, looks at agriculture human capital investments, from trends to promising initiatives. One of the nine featured case studies is the Twigire Muhinzi National Extension System in Rwanda. Twigire Muhinzi is the government's homegrown, decentralized and farmer-oriented national system based on two complementary types of farmer-to-farmer extension approaches: farmer promoters and farmer field schools. The model showcases how an extension approach can improve farmer skills, knowledge and empowerment and thus lead to enhanced adoption of relevant technologies and practices. In Rwanda, mainstreaming the farmer field school approach into the national extension system along with financial support from public-private partnerships contributed to its scaling up. This publication is part of the Country Investment Highlights series under the FAO Investment Centre's Knowledge for Investment (K4I) programme. ; Non-PR ; IFPRI2; DCA; 5 Strengthening Institutions and Governance; Capacity Strengthening; Rwanda SSP ; DSGD; PIM ; CGIAR Research Program on Policies, Institutions, and Markets (PIM)

The aim of this article is to investigate the relationship between size and farm growth. The existing theories of the association between size and farm growth give mixed results by countries and over time. This paper pursues a twofold objective: on one hand, to test the validity of Gibrat's Law for French, Hungarian and Slovenian specialized dairy and crop farms during the pre- and post-accession period to the European Union membership. Dairy and crops farms are prevailing in the farming structure of these countries. Using Farm Accountancy Data Network datasets makes it necessary to avoid biases due to heterogeneous structures across the farming systems. Thus we use quantile regressions to control for farm size related heterogeneity in the samples. On the other hand, the main novelty of this paper is the comparative analysis of the relationship between farm size and farm growth between transition Hungarian and Slovenian and non-transition French farming sectors, characterized by rather different farm structures. The results reject the validity of Gibrat's Law for crop farms in Hungary and to a lesser extent in France, and for French and Slovenian dairy farms. We provide evidence that smaller farms grew faster than larger ones over the studied period 2001-2007 for France, 2001-2008 for Hungary, and 2004-2008 for Slovenia. Conversely, the results for Slovenia suggest that the rate of growth of crop farms in terms of its land is independent from its size.

The tables in Section 1 of the report contain information on the whole farm economic performance of all co-operators in the sample. The results have been stratified by natural region in order to gain an insight into the influence of the different agro-ecological zones on the overall performance of farmers in the survey

This article considers the value of the Leftist filmmaking collective Ogawa Productions' interdisciplinary practice, which combined filmmaking and farming as an activist project of advocacy for social and environmental justice in 1980s Japan. It argues that Ogawa Pro, as the collective was known, integrated agriculture and film culture to construct a radically inclusive ecosystemic understanding of humans, plants, animals, and the climate. Viewed today, their approach exemplifies an early model of ecological thinking that speaks to the recent multispecies turn in the arts, humanities, and social sciences. But Ogawa Pro's turn to the land is also riddled with ambivalence: the films harbour agrarian romanticism bordering on a politics of nostalgia and ethnic environmentalism. Torn between what we might today call progressive and reactionary traditionalist politics, Ogawa Pro's enmeshed filming and farming practices constitute an important example of what I call Land Cinema—that is, film entangled in territorial, ecological, and aesthetic aspects of land. Though the collective's earlier and more militant films have received critical acclaim in recent years, its later land-based work merits further attention for the way it exposes political tensions over how to cultivate, represent, and share space responsibly.

AbstractThe farm sector balance sheet, income statement, and the Farm Costs and Returns Survey (FCRS) data are utilized to evaluate the financial conditions of American agriculture. This study compares estimated balance sheet and income statements using USDA and FCRS constructs with actual farm records for farms participating in the Farm Business Farm Management (FBFM) record‐keeping program. Results suggest a tendency for USDA and FCRS to report lower amounts of both assets and liabilities than FBFM records show, but this is explained in part by the inclusion of only farm business assets in the FCRS and USDA data sets. Income estimates do not vary greatly between the data sources.