English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

(ProQuest: ... denotes formulae omitted.)IntroductionAgricultural activities differ from other businesses in that the former are subject to much wider range of risks. Specifically, besides commonly known input and output price risk, credit risk, institutional risk etc., farmers are exposed to risks emerging from changes in biophysical environment. Therefore, it is important to foresee the sources of risk, farmers' strategies, and government policies (OECD, 2009). An appropriate interaction among these components of risk management strategy might mitigate loss due to different types of risks.Risk management is of especial importance in the new European Union (EU) Member States, where agricultural sector has been experiencing serious economic, institutional, and social transformations since the 1990s. Indeed, the collapse of planned economy including large-scale collective farming systems induced certain volatility in factor markets and resulted in sub-optimal farming structure in some cases (Bilan & Chmielewska, 2013a,b). Furthermore, accession to the EU allowed receive the funding under the Common Agricultural Policy (Ministry of Agriculture of the Republic of Lithuania, 2015). The latter has been distributed in the form of both direct payments and investment subsidies. Demographic transition implies a decreasing labour supply in rural areas and thus calls for further mechanisation.According to to R. B. M. Huirne et al. (2000) and J. B. Hardaker et al. (2004) there are two broad categories of risk for agricultural activities, viz. business risk and financial risk. Business risk comprises production, market, institutional, and personal risks (Bernat et al. 2014). Financial risk stems from fluctuations at financial markets and farmers' money-related decisions. Specifically, increasing interest rates might render difficulties in repaying loans or create credit constraints. Farmers' decisions regarding capital structure might also impact the financial viability of farms.As regards the agricultural sector, much of literature has been focused on business risk and, particularly, risk aversion (e.g., Moschini, Hennessy, 2001). The estimation of risk aversion can follow either the attitudinal approach, or empirical approach. The attitudinal approach relies on questionnaire surveys or experiments aimed at identifying farmers' choices under different circumstances. The empirical approach relies on the analysis of factual data and can be carried out either parametrically (Bardsley, Harris, 1987; Bar-Shira et al., 1997) or nonparametrically (Gomez-Limon et al., 2003). The analysis of financial risk has been confined to estimation the impacts of certain financial ratios on probability to become unviable (Argiles, 2001). Such a framework rests on the ideas of Altman (1968, 2004). However, such a setting requires a priori specification of the dependent variable, which involves a certain degree of subjectivity. D. Jackson-Smith et al. (2004) and S. Davidova and L. Latruffe (2007) investigated the determinants of financial performance treating different indicators as dependent variables in regression models. However, no aggregate measures were introduced.In Lithuania, as well as in other Central and East European countries, financial risk constitutes an important dimension of farm viability due to investments in response to the aforementioned transformations there. First, excessive investments might be fuelled by investment support measures thus arriving at unreasonable leverage level. Second, credit constraints might be related to increase in interest rates. Therefore, it is important to offer appropriate methodologies for financial risk appraisal in Lithuanian family farms. The following scientific problem, therefore, emerges: even though a variety of techniques for analysis of financial risk are available, these usually require longitudinal data for estimation of variance; however, such data are not readily available for Lithuanian family farms where extensive time series are not available for multiple holdings. …

Benefit of the Doubt Model for Financial Risk Analysis of Lithuanian Family Farms