Impacts of COVID-19 and Price Transmission in U.S. Meat Markets.

Coronavirus 2019 (COVID-19) has caused ongoing disruptions to U.S. meat markets via demand and supply-side shocks. Abnormally high prices have been reported at retail outlets and meat packers have been accused of unfair business practices because of widening price spreads. Processing facilities have experienced COVID-19 outbreaks resulting in shutdowns. Using weekly data on wholesale and retail prices of beef, pork, and poultry, we characterize the time series behavior and dynamic linkages of U.S. meat prices before the COVID-19 pandemic. We model vertical price transmission using both linear and threshold autoregressive (AR) models and vector error correction (VEC) models. With the estimated models, we then compare price movements under COVID-19 to model predictions. All three meat markets are well-integrated and we observe unexpected, large price movements in April and May of 2020. Early COVID-19 related shocks appear to be transitory with prices returning to expected levels at a pace consistent with the speed of transmission prior to the pandemic. This well-functioning market process suggests a degree of resilience in U.S. meat supply chains.

Corrigendum: Peanut Leaf Wilting Estimation From RGB Color Indices and Logistic Models.

[This corrects the article DOI: 10.3389/fpls.2021.658621.].

Peanut Leaf Wilting Estimation From RGB Color Indices and Logistic Models.

Peanut (Arachis hypogaea L.) is an important crop for United States agriculture and worldwide. Low soil moisture is a major constraint for production in all peanut growing regions with negative effects on yield quantity and quality. Leaf wilting is a visual symptom of low moisture stress used in breeding to improve stress tolerance, but visual rating is slow when thousands of breeding lines are evaluated and can be subject to personnel scoring bias. Photogrammetry might be used instead. The objective of this article is to determine if color space indices derived from red-green-blue (RGB) images can accurately estimate leaf wilting for breeding selection and irrigation triggering in peanut production. RGB images were collected with a digital camera proximally and aerially by a unmanned aerial vehicle during 2018 and 2019. Visual rating was performed on the same days as image collection. Vegetation indices were intensity, hue, saturation, lightness, a∗, b∗, u∗, v∗, green area (GA), greener area (GGA), and crop senescence index (CSI). In particular, hue, a∗, u∗, GA, GGA, and CSI were significantly (p ≤ 0.0001) associated with leaf wilting. These indices were further used to train an ordinal logistic regression model for wilting estimation. This model had 90% accuracy when images were taken aerially and 99% when images were taken proximally. This article reports on a simple yet key aspect of peanut screening for tolerance to low soil moisture stress and uses novel, fast, cost-effective, and accurate RGB-derived models to estimate leaf wilting.