In this study, we developed a machine learning framework to detect clinical mastitis (CM) at the current milking (i.e., the same milking) and predict CM at the next milking (i.e., one milking before CM occurrence) at the quarter level. Time series quarter-level milking data were extracted from an automated milking system (AMS). For both CM detection and prediction, the best classification performance was obtained from the decision tree-based ensemble models. Moreover, applying models on a data set containing data from the current milking and past 9 milkings before the current milking showed the best accuracy for detecting CM; modeling with a data set containing data from the current milking and past 7 milkings before the current milking yielded the best results for predicting CM. The models combined with oversampling methods resulted in specificity of 95 and 93% for CM detection and prediction, respectively, with the same sensitivity (82%) for both scenarios; when lowering specificity to 80 to 83%, undersampling techniques facilitated models to increase sensitivity to 95%. We propose a feasible machine learning framework to identify CM in a timely manner using imbalanced data from an AMS, which could provide useful information for farmers to manage the negative effects of CM.

Greenhouse gas (GHG) emission-induced climate change, particularly occurring since the mid-20th century, has been considerably affecting short-term weather conditions, such as increasing weather variability and the incidence of extreme weather-related events. Milk production is sensitive to such changes. In this study, we use spatial panel econometric models, the spatial error model (SEM) and the spatial Durbin model (SDM), with a panel dataset at the state-level varying over seasons, to estimate the relationship between weather indicators and milk productivity, in an effort to reduce the bias of omitted climatic variables that can be time varying and spatially correlated and cannot be directly captured by conventional panel data models. We find an inverse U-shaped effect of summer heat stress on milk production per cow (MPC), indicating that milk production reacts positively to a low-level increase in summer heat stress, and then MPC declines as heat stress continues increasing beyond a threshold value of 72. Additionally, fall precipitation exhibits an inverse U-shaped effect on MPC, showing that milk yield increases at a decreasing rate until fall precipitation rises to 14 inches, and then over that threshold, milk yield declines at an increasing rate. We also find that, relative to conventional panel data models, spatial panel econometric models could improve prediction performance by leading to smaller in-sample and out-sample root mean squared errors. Our study contributes to the literature by exploring the feasibility of promising spatial panel models and resulting in estimating weather influences on milk productivity with high model predicting performance.

Low carbon fuel policies such as the U.S. Renewable Fuel Standard (RFS), Canada Clean Fuel Regulations (CFR), and California Low Carbon Fuel Standard (LCFS) as well as the 45Z tax credit are intended to reduce greenhouse gas (GHG) emissions from transportation. Cellulosic feedstocks, optimized biorefineries, and favorable farming locations can significantly reduce biofuel carbon intensity (CI). Despite advances in field-to-fuel GHG monitoring and flexibility in resource allocation within biorefineries (e.g., governing net electricity production), rigid CI accounting procedures in current policies may limit CI responsiveness across candidate sites and processing facilities. This work examines a hypothetical biomass-to-sustainable aviation fuel (SAF) pathway using miscanthus and alcohol-to-jet (i) to demonstrate how GHG accounting requirements drive estimates of biofuel CIs and (ii) to explore potential CI and financial implications of scenario-specific life cycle assessment (LCA). Results demonstrate GHG accounting using the CFR/LCFS can reasonably account for distinct levels of net electricity production by a biorefinery, but only the CFR yields similar CI sensitivity to spatially explicit factors (feedstock CI, grid electricity CI) as scenario-specific LCA: most GHG accounting frameworks do not capture CI variation across candidate sites in the United States. Ultimately, this work demonstrates the importance of LCA methodological specifications in low carbon fuel policies and tax credits.

Greenhouse gas (GHG) emission-induced climate change, particularly occurring since the mid-20th century has been considerably affecting short-term weather conditions, such as increasing weather variability, and incidence of extreme weather-related events. Milk production is sensitive to such changes. In this study, we use spatial panel econometric models, Spatial Error Model (SEM) and Spatial Durbin Model (SDM), with a panel dataset at the state-level varying over seasons, to estimate the relationship between weather indicators and milk productivity, in an effort to reduce the bias of omitted climatic variables that can be time-varying and spatially correlated and cannot be directly captured by conventional panel data models. We find an inverse U-shaped effect of summer heat stress on milk production per cow (MPC), indicating that milk production reacts positively to a low-level increase in summer heat stress, and then MPC declines as heat stress continues increasing beyond a threshold value of 72. Additionally, fall precipitation exhibits an inverse U-shaped effect on MPC, showing that milk yield increases at a decreasing rate until fall precipitation rises to 14 inches and then over that threshold milk yield declines at an increasing rate. We also find that, relative to conventional panel data models, spatial panel econometric models could improve prediction performance by leading to smaller in-sample and out-sample root mean squared errors. Our study contributes to the literature by exploring the feasibility of promising spatial panel models and resulting in estimating weather influences on milk productivity with high model predicting performance.