Simulation as an analytical tool continues to gain popularity in industry, government, and academics. For agricultural economists, the popularity is driven by an increased interest in risk management tools and decision aids on the part of farmers, agribusinesses, and policy makers. Much of the recent interest in risk analysis in agriculture comes from changes in the farm program that ushered in an era of increased uncertainty. With increased planting flexibility and an abundance of insurance and marketing alternatives farmers face the daunting task of sorting out many options in managing the increased risk they face. Like farmers, decision makers throughout the food and fiber industry are seeking ways to understand and manage the increasingly uncertain environment in which they operate. The unique abilities of simulation as a tool in evaluating and presenting risky alternatives together with an expected increase in commodity price risk, as projected by Ray, et al., will likely accelerate the interest in simulation for years to come.

Purpose-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. Design/methodology/approach-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. Findings-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. Originality/value-Decision support tools, such as this one, that are designed to investigate dairy business decisions may benefit dairy producers.

Purpose-The objective of this paper is to improve the method for the strategic planning and management of food and agribusiness chains. Design/methodology/approach-Several research methodologies are used to develop the ChainPlan methodology. The theory (literature review) provided the basis on which to build a preliminary framework ten years prior. Then, empirical application of the initial method provided insights regarding needed additions to and subtractions from the original method. These insights, combined with continued research on advances in the theories, contributed to further development of the ChainPlan methodology Findings-A method is proposed to fill the theoretical gap regarding the strategic planning applied to agribusiness chains. The ChainPlan method is a theoretical-empirical method, built based on the academic literature and perfected over the years through its application in several productive chains Originality/value-Many authors have proposed a method to build strategic plans in organizations, but when planning agribusiness chains is concerned, the academic discussion revolves around the coordination of agribusiness chains and analyses to be applied in this sector. This article fills this theoretical gap and proposes a tool, which is a specific strategic planning method to be applied in agribusiness chain

Innovation and new ventures have been part of the food production and distribution industry for decades if not centuries. In recent times, new ventures under the banner of valueadded agriculture have become the mantra for producers, politicians, and agri-businesses that are searching for better margins and higher incomes than provided by traditional commodity production and distribution. But, the commercial potential of value-added ventures and innovations is not obvious, often is not realized, and may be frequently overestimated. In fact, studies of new business start-ups in agriculture and other sectors indicate that a high proportion of those businesses fail during the first twelve months of operation, and many are not sustainable even after this most vulnerable start-up period (SBA).The objective of this paper is to provide a framework for assessing the commercial potential of innovation in agriculture. We highlight the four critical components of market analysis essential to successful innovation and new ventures, those are the assessment of customers, competitors, sustainable competitive advantage, and internal capabilities. We identify and describe where concepts of economics

PurposeAutomated 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. Design/methodology/approachAn 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. FindingsImprovements 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. Originality/valueThis investment decision can be analyzed with input of herd‐specific values using this model.