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The purpose of this paper is to evaluate dairy farm financial performance over time utilizing farm financial ratios from three university business analysis programs. The evaluation includes measures of profitability, solvency, and liquidity by herd size.

Financial ratios to reflect profitability (rate of return on assets), solvency (debt to asset ratio), and liquidity (current ratio) were collected from Cornell University, Michigan State University, and the University of Wisconsin for dairy farms from 2000 to 2012. The distribution of farm financial performance using these ratios was examined over time and by herd size. Variance component methods are used to examine the percent of variation due to individual firm and industry aspects. A simple credit risk score is calculated to examine relative farm risk.

Dairy farm profitability performance is similar across herd sizes in poor years but larger herds realized significantly more profitability in good years. Findings were similar with respect to liquidity. Large herds consistently carried relatively more debt. Large herds’ financial performance was more uniform than across smaller herds. Larger herds had more financial risk as measured by credit risk scoring but recovered quickly to industry averages in profitable years.

The variation of dairy farm financial performance in an era of volatile milk and feed price is assessed. The results have important implications for farm financial management and benchmarking farm financial performance. In addition to helping to evaluate the efficacy of various price and income risk management tools, these results have important implications for understanding the benefits of the new federal Margin Protection Program for Dairy that is available to all US dairy farmers.

The purpose of this research is to understand US Upper Midwest dairy farm profitability performance over time and across herd size. Profitability is broken down into asset efficiency and operating profit margin. The primary objective is to determine how much information is required to accurately benchmark farm performance.

Financial ratios to measure profitability (rate of return on assets), profit margin (operating profit margin ratio), and asset efficiency (asset turnover) were collected from Michigan State University and the University of Wisconsin business analysis programs for dairy farms from 2000 through 2016. Financial ratio patterns were examined both across time and herd size. Annual distributions were divided into quartiles and the use of one to five-year averages were used to determine accuracy of quartile rank compared to true long-run farm profitability performance.

Financial performance across large herds was more uniform than across smaller herds. Small and large herd profitability performance converged in poor years but diverged in good years. Using three or more years performance greatly improved accuracy of benchmarking profitability.

The data utilized are very rich in the sense of the amount of variation across years and herd size. The results have important implications for farm financial management and benchmarking farm financial performance. Farm firms should benchmark multiple years of profitability before making major management changes to alleviate deficiencies.

The purpose of this paper is to examine the use of accelerated depreciation deductions, which includes Section 179 and bonus depreciation, taken in the first year of asset life by Michigan farms. The frequency, value and influence of accelerated depreciation on farm investment are also analyzed.

Accrual adjusted income statements, balance sheets, depreciation schedules, and income tax information for 66 Michigan farms from 2004 to 2014 provide data for the analysis. The present value of the accelerated deduction and change in the cost of capital were calculated. Finally, investment elasticities were used to arrive at the change in investment due to accelerated depreciation.

Accelerated depreciation was utilized across all applicable asset classes. Section 179 was used more often than bonus depreciation in part because it was available in all the examined years. Based on actual farm business use, accelerated depreciation lowered the cost of capital for the operations resulting in an estimated increase in investment of 0.27 to 11.6 percent depending on asset class.

The data utilized are of a detail not available in previous investigations which used either aggregate data or estimated rather than the observed use of accelerated depreciation. This analysis reveals that accelerated depreciation as used by commercial farms lowers the cost of capital and thus encourages investment particularly in machinery and equipment.

The structural change of the dairy industry has been a long-term process with fewer, larger dairy herds in all regions. The purpose of this paper is to evaluate whether this structural change is leading to less income and wealth equality across dairy farms and how these factors differ across the USA.

Income and wealth inequality of US dairy farms was estimated by Gini coefficients using data from the 2000 and 2010 ARMS dairy costs and returns data. A population-level quantile regression was estimated at decile increments to determine the factors that affect net farm income (NFI) and net worth (NETW) and if they changed across the time periods.

Adjusted-Gini coefficients were estimated and indicated that income inequality was greater than wealth inequality across US dairy farms. Results of the quantile regressions confirm regional differences exist with dairy farms in Mountain regions consistently having lower NFI and NETW relative to farms in the Lake States region when factors such as herd size were equal. Life cycle effects were not observed for NFI, but present within NETW estimates across the ten years.

This analysis estimates industry-specific-adjusted Gini coefficients to determine if income and wealth inequality exist.

The purpose of this paper is to determine which financial record‐keeping system farmers use, as well as what system attributes farmers value and to what degree.

This research uses a choice experiment to examine farmer's demand for attributes of financial record‐keeping systems. A sample from the general Michigan farm population is compared to samples from university and agribusiness record system clients.

Results reveal that university and agribusiness clients are willing to pay considerably more for a farm‐specific record system to backstop their farm management decisions.

The results provide an understanding of farmer demands for farm financial record systems and can be used to position record‐keeping systems to meet those demands.

This paper describes and analyzes farm financial accounting system use and preferences by type.

The purpose of this paper is to examine the sources and magnitude of variation in accrual adjusted gross farm revenue and farm revenue net of feed purchases on Michigan dairy farms representative of Upper Midwest dairy farms. The paper aims to assess whether adjusted gross revenue‐type insurance instruments meet insurability conditions when applied to dairy farms.

Accrual adjusted dairy farm revenue and revenue net of feed purchased from Michigan dairy farm panel data from 1995 through 2006 were detrended and summarized. Variance decomposition was used to identify sources of variation in adjusted gross revenue and adjusted gross revenue less feed purchases. In‐sample insurance premiums were estimated and Monte Carlo simulations were used to adjust these premiums for out‐of‐sample considerations.

Milk price variation was the largest source of variation while milk production per cow varied little. Farms with smaller herds and those with larger percentages of farm revenue from crop sales had higher relative revenue variability and would trigger a higher frequency of indemnities under a whole farm revenue insurance contract.

Because the data analyzed conclude in 2006, the volatility of the past couple of years is not reflected. Therefore, researchers are encouraged to test the proposed insurance feasibility further with more recent data.

The paper addresses considerations for the development and commercialization of a feasible dairy revenue insurance instrument.

This paper fulfils a need to understand magnitude and source of revenue variation on dairy farms and how insurance might mitigate negative consequences of this variation.

The purpose of this paper is to disseminate a new model that addresses the urgent social challenge of providing adequate long-term care in rural circumstances through innovative use of existing resources, and to suggest future research.

This paper is exploratory in and is based upon the analysis of qualitative observations (interviews and site visits) framed in the financial and operational records of the facility studied, macro- and micro-level demographics, and the scholarly and practice literatures.

Significant cost savings upon implementation, improvements in quality of care and both worker and client satisfaction were observed.

The model has been in operation only one year; the trend has been positive, however, more research is needed to identify its stability and develop a more refined description of its components: while essential features of this innovative model can be applied in any residential long-term care situation, replicating its success is obviously linked with the skill and authority of the director. Evaluation research is currently in progress.

The paper suggests budget-neutral solutions to persistent challenges of caring for older adults in rural circumstances.

Quality and financing of long-term residential care for elders is insufficient and worsening. This model addresses problems central to financing and quality of care by connecting existing resources in new ways. It does not require additional funding or changes in qualifications required for jobs.

The model is the original creation of a residential long-term care facility director working with a network of partnerships that he discovered and developed: partnerships include a broad range of organizations in the public and non-profit sectors, and the state university.

This article quantifies public sentiment for dairy products using online media and investigates potential relationships between online media, both volume and sentiment, and future prices of Class III milk.

Netbase, an online media listening platform, was used to quantify US generated online media sentiment and number of mentions regarding dairy products. Granger-causality tests and Impulse Response Functions (IRFs) were used to study relationships between online media derived data and dairy futures prices.

Milk and cheese have more mentions in online media than yogurt and ice cream. Online media net sentiment around milk was the lowest of the dairy products studied. Granger-causality tests showed that Class III milk price Granger-causes net sentiment of dairy as a whole and of fluid milk. Price additionally Granger-causes mentions of milk, ice cream and yogurt. Notably, milk and ice cream mentions Granger-cause the Class III milk price. IRF's reveals that increases in mentions have a positive, albeit small, effect on the Class III milk price that is statistically significant for ice cream, but not for milk. IRF's directionality of the relationship from price to online media derived data was mixed.

This is the first time that relationships between online media -volume and sentiment- and futures prices of an agricultural commodity are researched. Exploration of futures markets alongside online media advances the use of online media to glean insights in financial, along with food and agricultural markets.

Supply chains (SCs) have been growingly virtualized in response to the market challenges and opportunities that are presented by new and cost-effective internet-based technologies today. This paper designed a virtual closed-loop supply chain (VCLSC) network based on multiperiod, multiproduct and by using the Internet of Things (IoT). The purpose of the paper is the optimization of the VCLSC network.

The proposed model considers the maximization of profit. For this purpose, costs related to virtualization such as security, energy consumption, recall and IoT facilities along with the usual costs of the SC are considered in the model. Due to real-world demand fluctuations, in this model, demand is considered fuzzy. Finally, the problem is solved using the Grey Wolf algorithm and Firefly algorithm. A numerical example and sensitivity analysis on the main parameters of the model are used to describe the importance and applicability of the developed model.

The findings showed that the Firefly algorithm performed better and identified more profit for the SC in each period. Also, the results of the sensitivity analysis using the IoT in a VCLSC showed that the profit of the virtual supply chain (VSC) is higher compared to not using IoT due to tracking defective parts and identifying reversible products. In proposed model, chain members can help improve chain operations by tracking raw materials and products, delivering products faster and with higher quality to customers, bringing a new level of SC efficiency to industries. As a result, VSCs can be controlled, programmed and optimized remotely over the Internet based on virtual objects rather than direct observation.

There are limited researches on designing and optimizing the VCLSC network. This study is one of the first studies that optimize the VSC networks considering minimization of virtual costs and maximization of profits. In most researches, the theory of VSC and its advantages have been described, while in this research, mathematical optimization and modeling of the VSC have been done, and it has been tried to apply SC virtualization using the IoT. Considering virtual costs in VSC optimization is another originality of this research. Also, considering the uncertainty in the SC brings the issue closer to the real world. In this study, virtualization costs including security, recall and energy consumption in SC optimization are considered.

1. Investigates the role of IoT for virtual supply chain profit optimization and mathematical optimization of virtual closed-loop supply chain (VCLSC) based on multiperiod, multiproduct with emphasis on using the IoT under uncertainty.

2. Considering the most important costs of virtualization of supply chain include: cost of IoT information security, cost of IoT energy consumption, cost of recall the production department, cost of IoT facilities.

3. Selection of the optimal suppliers in each period and determination of the price of each returned product in virtual supply chain.

4. Solving and validating the proposed model with two meta-heuristic algorithms (the Grey Wolf algorithm and Firefly algorithm).

Investigates the role of IoT for virtual supply chain profit optimization and mathematical optimization of virtual closed-loop supply chain (VCLSC) based on multiperiod, multiproduct with emphasis on using the IoT under uncertainty.

Considering the most important costs of virtualization of supply chain include: cost of IoT information security, cost of IoT energy consumption, cost of recall the production department, cost of IoT facilities.

Selection of the optimal suppliers in each period and determination of the price of each returned product in virtual supply chain.

Solving and validating the proposed model with two meta-heuristic algorithms (the Grey Wolf algorithm and Firefly algorithm).

Investigates the characteristics of the regular customer cards of four major Finnish retail organizations in the daily product sector with respect to the basic characteristics of (customer) relationship marketing. The purpose is to study whether, and to what degree, relationship marketing is the right framework in this regard for today’s retailing in Finland, and how this “new paradigm” can be applied in future to improve the effectiveness of the regular customer cards. Regular customer cards are found to correspond to relationship marketing mainly on its lowest level, and thus at a level where the benefits of relationship marketing are lowest as well. Alternatively, the regular customer systems and programmes are not yet developed enough to take advantage fully of the possibilities of relationship marketing. There is a lot of potential to improve regular customer systems and cards towards more relationship‐based marketing in the daily product sector in retailing by customization, augmentation, and internal marketing.