Estimation of farm economic sustainability and viability became more topical when redesigning the Common Agricultural Policy which should stabilise farm income and make agribusiness more viable and sustainable (typically in Czech areas facing natural constraints). The key question is how to calculate the income of farms or farm households not only to survive but also to grow sustainably. The article summarises and compares knowledge from 51 studies to provide a comprehensive discussion on different ways how to measure economic viability and sustainability to set income support for farms in the areas with natural constraints optimally. The authors found family farms and off-farm income as important limitations of FADN database (Farm Accountancy Data Network) for evaluation of the economic sustainability of farm household. Moreover, some financial ratios (Return on Assets – ROA and assets turnover) are not suitable viability indicators for farms with a high share of hired land (typically large legal entities). Joining family farms and legal entities, the authors recommend using modified Farm Net Value Added (MFNVA) allowing for opportunity costs of own land and non-land assets. The average wage in the economy or region is a better proxy for opportunity labour costs of unpaid work rather than average agricultural wage.<br />

The aim of the article is to evaluate production efficiency and its determinants of specialised dairy farming among the EU regions. In the most of European regions, there is a relatively high significance of small specialised farms including dairy farms. The DEAVRS method (data envelopment analysis with variable returns to scale) reveals efficient and inefficient regions including the scale efficiency. In the next step, the two-sample t-test determines differences of economic and structural indicators between efficient and inefficient regions. The research reveals that substitution of labour by capital/contract work explains the variability of the farm net value added per AWU (annual work unit) income indicator by more than 30%. The significant economic determinants of production efficiency in specialised dairy farming are farm size, herd size, crop output per hectare, productivity of energy, and capital (at α = 0.01). Specialised dairy farms in efficient regions have significantly higher farm net value added per AWU than inefficient regions. Agricultural enterprises in inefficient regions have a more extensive structure and produce more noncommodity output (public goods). Specialised dairy farms in efficient regions have a slightly higher milk yield, specific livestock costs of feed, bedding, and veterinary services per livestock unit.

PurposeInnovation ecosystems face many environmental challenges. The literature review shows that innovation ecosystems accelerate innovation activity, but empirical studies have not provided enough case studies focusing on the minimum-waste business strategy as one aspect of the circular economy. Various forms of interaction between members occur in the innovation ecosystems, which determines the level of cooperation. This paper aims to show the structure and forms of cooperation in an innovation ecosystem using the Czech Hemp Cluster (CHC) and its surroundings and suggest research directions in the field of interaction between members in an innovation ecosystem. Although hemp is associated with the production and distribution of narcotics, it is a versatile plant supporting the minimum-waste business strategy.Design/methodology/approachThe research is based on a theoretical part of a literature review of major scientific articles on innovation ecosystems from 2016 to 2021. The case study of the CHC and the hemp ecosystem is based on qualitative research in the form of a content analysis of the mission of the cluster members. In addition to content analysis, the classic multidimensional scaling method and hierarchical cluster analysis were used to reveal ecological guilds.FindingsThe case study highlighted the specific relationship between the cluster and the ecosystem. The cluster does not determine the ecosystem boundaries, but the ecosystem is a much broader system of cooperation and interaction between organisations. Clusters emerge after an ecosystem has existed for a particular time to coordinate collaboration and information between organisations and stakeholders. The analysis of the CHC revealed the specific role of non-profit organisations (NPOs) in the innovation ecosystem. NPOs are not engaged in primary functions in the value chain, but they provide supporting activities through coordinated networking, disseminating information on innovation, awareness-raising and stakeholder education. Compared to natural ecosystems, innovation ecosystems are typically characterised by higher forms of collaboration between members.Research limitations/implicationsAn exciting opportunity for research on innovation ecosystems is the ecological guilds taken from natural ecosystems and whose identification can help define the boundaries of innovation ecosystems. An opportunity for further research is the comparison of NPO-based and government-based clusters playing a central role in developing innovation ecosystems. Regarding the problematic generalisability of the case study to the entire agricultural production, a challenge is a search for minimum-waste business models in agriculture characterised by the biological nature of production.Originality/valueTheoretical and empirical studies have not yet considered innovation ecosystems in the minimum-waste context to a sufficient extent. The paper builds on previous scholarly studies focusing on innovation ecosystems and, for the first time, discusses the role of NPOs in the innovation ecosystem. The CHC case study adds a suitable minimum-waste business model to the still very scarce literature on sustainable innovation ecosystems. The article discusses the purpose and forms of cooperation in an innovation ecosystem, identifies a complementarity of roles in the innovation cluster and describes the interrelationship between the cluster and the ecosystem. Discussion of the ecosystem leader in the cluster-based innovation ecosystem shows the differences between Czech, Polish and German life science ecosystems.

The paper deals with weather derivatives as the potentially effective risk management tool for agricultural enterprises seeking to mitigate their income exposure to variations in weather conditions. Design and valuation of the weather derivatives is an interdisciplinary approach covering agrometeorology, statistics, mathematical modeling, and financial and risk management. This paper first offers an overview of data sources and then methods of design and valuation of weather derivatives at the regional level. The accompanied case study focuses on cultivation of cereals (wheat and barley) in the Czech Republic. However, its generalizability is straightforward. The analysis of key growing phases of cereals is based on regression analysis using weather indices as the independent variables and crop yields as dependent variables. With the bootstrap tool, the burn analysis is considered as useful tool for estimating uncertainty about the payoff, option price, and statistics of probability distribution of revenues. The results show that the spatial and production basis risks reduce the efficiency of the weather derivatives. Finally, the potential for expansion of weather derivatives remains in the low income countries of Africa and Asia with systemic weather risk.

Th e aim of the article is to identify the key structural, yield and economic determinants of the change in regional effi ciency of specialized milk farms over the period 2007 to 2011. Th e following quantitative methods were applied on the regional FADN data (panel data of 100 regions): the Malmquist productivity index, the Welsh two-sample t-test, and the linear regression analysis. Th e article put emphasis on the investment activity and investment subsidies allocated in the sample of regions. Th e results reveal that regions with a positive change in the production effi ciency have a signifi cantly higher average milk yield, maize yield, long-term debt ratio, income level, and investment activity than the regions with a negative change in the production effi ciency. On the contrary, investment subsidies per livestock unit do not signifi cantly diff er between the progressive and other regions. Investment subsidies are slightly higher in the regions with a negative change in the production effi ciency and continuously help them to mitigate the drop in technical effi ciency.

The impact evaluations of public investments are essential for policymakers to evaluate the effectiveness of public resource allocation. European public investment subsidies target small companies to enhance their competitiveness and viability in the market. This article uses the average treatment effect and the difference-in-difference approach to evaluate the impacts of investment support from the Rural Development Programme and the Operational Programme Enterprise and Innovation on structural and economic indicators of small enterprises. This representative case study of 550 supported small companies from the Czech food and beverage industry during 2007-2015 clearly shows that investment subsidies increase the fixed assets, the credit-to-debt ratio and the labour productivity of supported companies versus nonparticipants. However, the discussion with recent studies indicates that this is not always positive for participants since high growth versus nonparticipants could result in crowding-out effects and increasing long-term and short-term debt that negatively impact technical efficiency.

The redesign of the Common Agricultural Policy (CAP) allows for more room to address issues related to stabilising farmers’ income and developing their viability in areas facing natural constraints (ANC). Maintaining income levels, developing farm economies in rural areas, and encouraging competitive agricultural practises are the challenges facing the new CAP. ANCs in the Czech Republic are characterised by a lower level of income compared to areas outside ANCs and their generally prevailing specialisation in livestock production, which has been facing a relatively turbulent development in the last decade. The main aim of this paper is to evaluate the economic viability with regard to the level of natural disadvantage and with regard to farm specialisation. The database of Farm Accountancy Data Network (FADN) was used for assessment; the authors built the Farm Economic Viability indicator, which is based on modified Farm Net Value Added. The differences between the farm groups were tested through analysis of variance. Significantly lower viability was found in ANCs compared to farms outside ANCs. Field crops achieved significantly higher levels, both in and outside ANCs. The most threatened group of farms are grazing livestock in ANCs.

Purpose Risk attitude is an elementary attribute of entrepreneurial behaviour. Determinants of risk-taking propensity have been widely investigated in the group of entrepreneurs and non-entrepreneurs so far. There is a lack of evidence on determinants of risk-taking propensity in the farming business, which is considered as risky business because of the ongoing climate change and epidemic outbreaks. Alternatively, the risk of lower European Union budget raised the question, how to implement publicly supported financial instruments for micro and small farmers which have lower credit rating. The purpose of this study is to find socio-demographic determinants of the risk-taking propensity of the Czech micro farms, controlling for the type of farming. Design/methodology/approach The survey of 747 micro farmers was processed through ordinal logistic regression. The study is based on the subjective self-assessment of the risk-taking behaviour which is frequently used to measure risk-taking attitude. The results are representative from the type of farming point of view. Findings The model provided clear evidence that age, household size, living with the partner/wife/husband and level of education have a significant relationship with risk-taking propensity. The most risk-tolerant farmers are young with less formal education and living in small households. The risk-taking propensity varies by the type of farming. Specialized crop farms have significantly higher risk-taking propensity than farms with a substantial share of livestock production. Alternatively, gender, feeling about household income and religion are not significantly related to the risk-taking propensity of the Czech micro farms. Research limitations/implications The main limitation of the study is the number of explanatory variables and the use of self-assessment of risk-taking attitude. The risk attitude can be explained by other variables which require in-depth qualitative research, such as past risk experience, the structure of decision problems, market orientation and operation under subsistence conditions. Practical implications The significant determinants of risk-taking attitude of micro farmers are important for banks, the Czech Support and Guarantee Fund for Farmers and Forestry and for policymakers who design the rules for post-2020 common agricultural policy. The study is original and valuable for the Central and Eastern European countries’ implementation of financial instruments as new rules for investment support are being prepared and research on the risk-taking attitude of the most vulnerable segment of farmers has not been conducted. Originality/value The originality of this study is from the perspective of agricultural sector as well as from the micro farms point of view. The results have commercial and political implications. Younger farmers, singles and lower-educated farmers have significantly higher risk-taking propensity and can be potentially risky clients for banks. Such farmers represent the financial gap in the credit market, and their viable development projects could be subject for implementation of financial instruments co-financed by the European Agricultural Fund for Rural Development in the forthcoming programming period past 2020.