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Successful farm business managers must understand the determinants of profitability and have an overall long‐term or strategic management focus. The objective of this research was to explore the use of an e‐learning tool to help producers understand the impacts of different production, pricing, cost control, and investment decisions on their farm’s financial performance. This objective was accomplished by developing and testing a computer‐based training and application tool to facilitate determination of the financial health of farm businesses using the DuPont profitability analysis model. The results of the two experiments indicate that the computer software was effective for teaching techniques of profitability analysis contained within the DuPont model.

The purpose of this paper is to explore the linkage between agricultural sector and macroeconomic factors with farm financial health. It considers whether agricultural lenders can more accurately anticipate changes in the credit quality of their portfolios by considering broad economic indicators outside the agriculture sector.

This paper examines firm, sector, and macroeconomic drivers of probability of default (PD) migrations from a sample of 153 grain farms of actual lender data from Farm Credit Mid-America’s portfolio. A series of ordered logit models are developed.

Farm-level and sector-level variables have the most significant impact on PD migrations. Equity to asset ratios, working capital to gross farm income ratios, and gross corn income per acre are found to be the most significant drivers of PD migrations. Macroeconomic variables are shown to unreliably forecast PD migrations, suggesting that agricultural lenders should emphasize firm and sector variables over macroeconomic factors in credit risk models.

This paper builds the literature on agricultural credit risk by testing a broader set of sector and macroeconomic variables than previous articles. Also, prior articles measured the direction but not magnitude of PD migrations; the ordered model in the analysis measures both.

This paper examines credit products, operational performance and business models employed by nontraditional lenders (NTLs) in agricultural credit markets.

Two research methods were employed in this study: (1) an executive interview to collect primary data and (2) a case study approach to analyze the findings and develop insights.

The findings indicate the presence of significant differences among lenders across and within three categories of NTLs (large volume, vendor financing and collateral-based NTLs). For example, collateral-based NTLs employ different strategies focusing on types of loans, funding sources, commodities they support and geographic coverage to further segment the market. NTLs in this study were able to capture market by successfully identifying gaps in the supply side of agricultural credit and developing products that meet the needs of that niche (e.g. heavy renters, large operations, producers seeking fixed interest rates for term loans, financially fragile producers). Most of the interviewed NTLs had credit standards comparable to those of traditional lenders and consider them both competitors and partners since many NTLs partner with traditional lenders on participation loans, loan servicing and/or sourcing funds.

The supply side of a nontraditional lending has not been studied extensively due to the proprietary nature of data. The executive interviews conducted in this study allowed for accumulation of industry data, which is not available otherwise.

Research in the field of indigenous value chains is limited in theory and empirical research. The purpose of this paper is to interpret values that may inform a new approach to considering value chains from New Zealand Maori kin community contexts.

The paper derives from research that develops Indigenous research methods on positionality. By extending the “included researcher” (Kawharu, 2016) role, the research recognises the opportunity of being genealogically connected to one of the communities, which may enable “deep dive research” relatively easily. Yet practical implications of research also obligate researchers beyond contractual terms to fulfil community aspirations in innovation.

Research findings show that a kin community micro-economy value chain may not be a lineal, progressive sequence of value from supplier to consumer as in Porter’s (1985) conceptualisation of value chains, but may instead be a cyclical system and highly consumer-driven. Research shows that there is strong community desire to connect lands and resources of homelands with descendant consumers wherever they live and reconnect consumers back again to supply sources. Mechanisms enabling this chain include returning food scraps to small community suppliers for composting, or consumers participating in community working bees, harvesting days and the like.

The model may have implications and applicability internationally among indigenous communities who are similarly interested in socio-economic growth and enterprise development.

The apper’s originality, therefore, derives from addressing a research gap, showing that indigenous values may provide a new approach to conceptualising value chains and developing them in practice.

The purpose of this paper is to shed light on the role of and relationships between human, structural and relational capital assets for strategic management in a farm business. In particular, it analyzes the interaction between human capital’s creativity skills and the introduction of climate-smart technologies for the competitiveness of the firm.

An explorative case study was conducted on one of the largest Italian farm businesses to gain an understanding of the drivers of intellectual capital (IC) and of their implications for strategic management. Full-time employees’ perception of the skills required to achieve strategic goals and their perception of whether they possessed these abilities were investigated to determine if an alignment was present. The skills were subsequently classified using the framework of Amabile (1988) into domain-relevant and creativity-relevant skills. Then, two linear regression models were used to investigate the effects of training on the acquisition of these two sets of skills.

The analysis confirmed the strategic role of interactions among human capital assets to effectively exploit the structural capital of the company. When investigating employees’ perceptions, a gap emerged about informatics capabilities and knowledge of soils. As the company’s investments in innovation are oriented to ICT technologies, the company could strengthen informatics training to enable its employees to implement effective innovation.

The paper contributes to the literature on IC by highlighting the role of interconnections of assets to align organizations with their strategic goals. Therefore, the provision of IC accounting contributes to the strategic management of human capital.

The purpose of this paper is to examine changes in the structures of US farms and lenders and identify prospective implications for federal credit.

Data from US farm operations for 1996-2014 were adjusted to 2014 values using commodity price indices. Farm size groups were constructed by value of farm production to analyze changes in farm numbers, production, assets, debt, leverage, liquidity, profitability, land tenure, commodity type, contract production, organization type, and use of Farm Service Agency (FSA) direct and guaranteed loans by farm size. Bank, Farm Credit System (FCS), and FSA data from 1996 to 2015 were adjusted to 2014 values. Lender size groups were constructed to analyze changes in bank and association numbers, farm loans, and use of FSA guaranteed loans by lender size.

The greatest consolidation has been by farms with over $2 million in production. More farm debt is held by large, complex organizations, frequently with multiple operators, more variable income, and greater reliance on production contracts and operating and nonreal estate credit. Large farms have greater leverage, are more profitable, and have a larger share of household income from the farm. Banks and FCS institutions are fewer and larger, yet smaller institutions use FSA guarantees to a greater extent. Larger farms tend to be more reliant on both direct and guaranteed FSA loans and are likely to become more dependent on FSA credit.

Changing farm and lender structure together with softening farm income may require FSA farm loan program changes to meet any increase in loan demand. Policy alternatives are provided to meet changing demand for farm credit.

Automated body condition scoring (BCS) through extraction of information from digital images has been demonstrated to be feasible; and commercial technologies are being developed. The primary objective of this research was to identify the factors that influence the potential profitability of investing in an automated BCS system.

An expert opinion survey was conducted to provide estimates for potential improvements associated with technology adoption. A stochastic simulation model of a dairy system, designed to assist dairy producers with investment decisions for precision dairy farming technologies was utilized to perform a net present value (NPV) analysis. Benefits of technology adoption were estimated through assessment of the impact of BCS on the incidence of ketosis, milk fever, and metritis, conception rate at first service, and energy efficiency.

Improvements in reproductive performance had the largest influence on revenues followed by energy efficiency and then by disease reduction. The impact of disease reduction was less than anticipated because the ideal BCS indicated by experts resulted in a simulated increase in the proportion of cows with BCS at calving 3.50. The estimates for disease risks and conception rates, obtained from literature, however, suggested that this increase would result in increased disease incidence. Stochastic variables that had the most influence on NPV were: variable cost increases after technology adoption; the odds ratios for ketosis and milk fever incidence and conception rates at first service associated with varying BCS ranges; uncertainty of the impact of ketosis, milk fever, and metritis on days open, unrealized milk, veterinary costs, labor, and discarded milk; and the change in the percentage of cows with BCS at calving 3.25 before and after technology adoption. The deterministic inputs impacting NPV were herd size, management level, and level of milk production. Investment in this technology may be profitable but results were very herd‐specific. A simulation modeling a deterministic 25 percent decrease in the percentage of cows with BCS at calving ≤3.25 demonstrated a positive NPV in 86.6 percent of 1,000 iterations.

This investment decision can be analyzed with input of herd‐specific values using this model.

The recent fluctuations in farm income remind us of the boom-bust nature of the agricultural sector. To better understand these fluctuations in farm income, the purpose of this paper is to examine the relationship between farm income and influential factors from 1964 to 2010 allowing for structural breaks in the data.

The authors estimate error-correction models for an overarching model and several sub-models at different scales based on their relationship with farm income: micro, meso, and macro. The authors then provide a series of impulse response functions (IRFs) that combine short- and long-run impacts in a rigorous framework indicating the response of farm income to shocks from any of the explanatory variables.

Results indicate that prices paid (PP) and received by farmers, technological change, interest and exchange rates (ERs), gross domestic product (GDP) and land prices all influence farm income. Results using IRFs show how increases in farm income arise from shocks to prices received and GDP; while PP, interest rates, and land prices have a negative impact on farm income. Technological progress and ERs switch from having a negative short-run impact, to a positive long-run impact.

This paper takes a fresh look at the single, overarching model for farm income determinants. The authors break this model into three separate levels, with results indicating that these sub-groups perform better than the one overarching model of all variables.

The purpose of this paper is to develop a dynamic, stochastic, mechanistic simulation model of a dairy business to evaluate the cost and benefit streams coinciding with technology investments. The model was constructed to embody the biological and economical complexities of a dairy farm system within a partial budgeting framework. A primary objective was to establish a flexible, user‐friendly, farm‐specific, decision‐making tool for dairy producers or their advisers and technology manufacturers.

The basic deterministic model was created in Microsoft Excel (Microsoft, Seattle, Washington). The @Risk add‐in (Palisade Corporation, Ithaca, New York) for Excel was employed to account for the stochastic nature of key variables within a Monte Carlo simulation. Net present value was the primary metric used to assess the economic profitability of investments. The model was composed of a series of modules, which synergistically provide the necessary inputs for profitability analysis. Estimates of biological relationships within the model were obtained from the literature in an attempt to represent an average or typical US dairy. Technology benefits were appraised from the resulting impact on disease incidence, disease impact, and reproductive performance. In this paper, the model structure and methodology were described in detail.

Examples of the utility of examining the influence of stochastic input and output prices on the costs of culling, days open, and disease were examined. Each of these parameters was highly sensitive to stochastic prices and deterministic inputs.

Decision support tools, such as this one, that are designed to investigate dairy business decisions may benefit dairy producers.

Many good sustainability entrepreneurial ideas and projects fail to reach their objectives due to problems with feasibility. This chapter investigates the effectiveness of methods used to evaluate the feasibility of entrepreneurial ventures in the context of sustainable urban development. Traditional methods and tools based on cost–benefit analysis could provide some guidance for entrepreneurs and intrapreneurs when evaluating sustainability strategies. Yet, such methods rely on restrictive assumptions, which cast doubt on their suitability for real-world sustainability applications. Traditional methods are far from really enabling entrepreneurs to make informed optimal decisions. New integrated methods are necessary for drawing conclusions vis-a-vis the practicality of entrepreneurial ideas by quantifying and analyzing the benefits and costs of all options in a given scenario. This chapter evaluates the effectiveness of current feasibility study methods and their suitability for sustainable urban planning and development. It surmises that caution is advised concerning their reflection of real-life applications, given the complexity and dynamicity of solving sustainability-related problems. It is shown that such methods can arguably be a useful tool when evaluating the viability of investing in innovation and sustainability if they are enriched with advanced modelling techniques, such as system dynamics and optimization methods. For this purpose, an entrepreneurial venture for promoting sustainable mobility via information and communication technology (ICT) is used as a case study. The proposed integrated ‘Sustainability Entrepreneurship’ approach for evaluating feasibility can prove to be very useful for entrepreneurs when assessing the efficacy of complex sustainable-related ventures.