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The increased availability and adoption of precision agriculture technologies has left researchers to grapple with how to best utilize the associated high-frequency large-volume of data. Since the wealth of information from precision equipment can easily be aggregated in real-time, this poses an interesting question of how aggregates of high-frequency data may complement, or substitute for, publicly released periodic reports from government agencies.

This study utilized advances in event study and yield projection methodologies to test whether simulated weekly harvest-time yields potentially drive futures price that are significantly different from the status quo. The study employs a two-step methodology to ascertain how corn futures price reactions and price levels would have evolved if market participants had access to weekly forecasted yields. The marginal effects of new information on futures price returns are first established by exploiting the variation between news in publicly available information and price returns. Given this relationship, the study then estimates the counterfactual evolution of corn futures price attributable to new information associated with simulated weekly forecasted yields.

The results show that the market for corn exhibits only semi-strong form efficiency, as the “news” provided by the monthly Crop Production and World Agricultural Supply and Demand Estimates reports is incorporated into prices in at most two days after the release. As expected, an increase in corn yields relative to what was publicly known elicits a futures price decrease. The counterfactual analysis suggests that if weekly harvest-time yields were available to market participants, the daily corn futures price will potentially be relatively volatile during the harvest period, but the final price at the end of the harvest season will be lower.

The study uses simulation to show the potential evolution of corn futures price if market participants had access to weekly harvest-time yields. In doing so, the study provides insights centered around the ongoing debate regarding the economic value of USDA reports in the presence of growing information availability within the private sector.

Federally subsidized crop insurance aims to mitigate farm risks of crop producers. A body of literature has examined informational problems under this program. However, few studies empirically link crop insurance participation with farm financial performance. Most use county-level aggregates to argue that crop insurance participation is associated with increased farm financial debt. Using farm-level data, this study provides empirical evidence of crop insurance's effects on farm financial risk.

The impact of crop insurance on farm financial risks is assessed using farm-level data from Kansas. The sample consists of at least 1,600 farms each year from 2002 to 2015. Financial risks are measured using the probability of falling into the critical zone of five different financial ratios. The study uses two matching estimators to estimate the causal effects of crop insurance participation on farm financial risks. Several alternative empirical approaches account for unobserved heterogeneity and potential endogeneity.

Crop insurance participation has reduced the farm's likelihood of being in the critical liquidity risk by 8%. This result is robust across matching estimators and alternative specifications to account for unobserved heterogeneity and potential endogeneity.

This is one of the few studies to examine whether crop insurance reduces farm financial risks. This study provides empirical evidence of the extent to which crop insurance enrollment impacts farm financial risks. Findings suggest that crop insurance is critical to maintaining the financial well-being of crop producers, and significantly reduces the likelihood of producers being in a critical liquidity risk.