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The average U.S. farm size has risen dramatically over the last three decades. Motives for this trend are the subject of a large body of literature. This study incorporates farm size risk and return analysis into this research stream. In this paper, cross-sectional and temporal relations between farm size and returns are examined and characterized.

Relying on farm level panel data from Kansas Farm Management Association (KFMA) for 140 farms from 1996 to 2018, this article examines the relationship between farm size and returns and investigates whether farm size is related to risk. Two measures of farm returns are used: excess return on equity and risk-adjusted return on equity. Value of farm production and total farm acres are used as measures of farm size.

Findings suggest a significant and positive relationship between farm size and excess return on equity as well as farm size and risk-adjusted return on equity. However, this return premium associated with farm size is not associated with additional risk. Stated differently, farm size can be viewed as a farm characteristic that is associated with higher return without additional risk.

These findings provide further support for ongoing farm consolidation.

The results suggest the trend towards consolidation in production agriculture is likely to continue. Larger farms bear less risk.

Relatively high recent returns to farmland investments have led to substantially elevated interest in farmland investments. Absent, however, is a well‐functioning equity market in farmland real estate, or well‐developed indexes of farmland returns that might contribute to the development of tradable shares tied to farmland returns, or to methods to hedge the value of owned agricultural assets. The purpose of this study is to empirically present relevant measures related to farmland returns and other financial assets to provide a broad context for evaluation of farmland investments in a portfolio context. Issues related to the development of a farmland fund and index construction are discussed along with major risk and transactional factors that are somewhat unique to the asset class.

Returns data from a broad set of financial categories and broad set of agricultural returns measures are developed and presented in multiple frameworks to convey temporal persistence, relatedness, and portfolio considerations related to farmland. Issues related to the construction of claims based on agricultural assets are discussed.

Agricultural real estate investments have performed well compared to most other financial assets on most traditional measures of risk adjusted performance. However, the difficulties in direct investment remain and the need to develop securitized conduit exposures to farmland returns is identified.

The study presents a unique set of farmland returns measures and examines the stability of the statistics used to describe these through time. Novel characterizations of the data compared to traditional assets helps investors and asset owners accurately understand the exposure to farmland returns.

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.

The authors aim to investigate what influences a Farm Credit System association to make a patronage refund payment. In particular, they seek to investigate what causes the regional heterogeneity in the patronage refund payment decision. It is unclear whether patronage refunds have been used more as a capital management tool or as a member recruitment and retention tool. This study aims to bring some clarity to this issue.

The authors use an empirical logistic model to estimate the probability of a positive patronage refund payment by a Farm Credit System association, controlling for variables related to the associations' balance sheet as reported in the associations' quarterly call reports.

The authors find there is evidence that Farm Credit Service associations use patronage refunds as a capital management tool, at least in part. However, they also find that there are still significant regional differences in the patronage refund payment decision even after controlling for variables affecting the associations' balance sheet. The authors conclude that this likely represents member heterogeneity in preferences for patronage refunds versus a discounted interest rate.

The present study is one of the few empirical papers to examine a broad panel of financial cooperatives. Because of this, the authors' paper provides valuable insight into the aggregate behavior of Farm Credit Service associations, particularly into whether they use patronage refunds as a capital management tool, or as a marketing and retention tool.