The need to assess the sustainability attributes of the United States beef industry is underscored by its importance to food security locally and globally. A life cycle assessment (LCA) of the US beef value chain was conducted to develop baseline information on the environmental impacts of the industry includ`ing metrics of the cradle-to-farm gate (feed production, cow-calf, and feedlot operations) and post-farm gate (packing, case-ready, retail, restaurant, and consumer) segments.

The growing importance of rice globally over the past three decades is evident in its strategic place in many countries’ food security planning policies. Still, its cultivation emits substantial greenhouse gases (GHGs). The Indica and Japonica sub-species of Oryza sativa L. are mainly grown, with Indica holding the largest market share. The awareness, economics, and acceptability of Japonica rice in a food-insecure Indica rice-consuming population were surveyed. The impact of parboiling on Japonica rice was studied and the factors which most impacted stickiness were investigated through sensory and statistical analyses. A comparison of the growing climate and greenhouse gas emissions of Japonica and Indica rice was carried out by reviewing previous studies. Survey results indicated that non-adhesiveness and pleasant aroma were the most preferred properties. Parboiling treatment altered Japonica rice’s physical and chemical properties, introducing gelatinization of starch and reducing adhesiveness while retaining micronutrient concentrations. Regions with high food insecurity and high consumption of Indica rice were found to have suitable climatic conditions for growing Japonica rice. Adopting the higher-yielding, nutritious Japonica rice whose cultivation emits less GHG in these regions could help strengthen food security while reducing GHGs in global rice cultivation.

Accurate analysis of livestock feed quality is critical for enhancing productivity and supporting sustainable farming practices. This study explores the integration of red, green, and blue (RGB) and near-infrared (NIR) imaging modalities, leveraging their complementary strengths, RGB for physical properties and NIR for chemical compositions, to predict key nutritional metrics. A novel knowledge distillation model was developed to transfer insights from a complex teacher model to a simpler student model. The approach involved training three types of models: single-channel (RGB or NIR), double-channel (RGB and NIR), and knowledge distillation models. Key evaluation metrics, including mean squared error (MSE), mean absolute error, and root MSE, validated the model's predictive accuracy. Experimental results demonstrated that the knowledge distillation model significantly outperformed both single- and double-channel models, achieving a 91.86% reduction in the MSE compared to RGB single-channel models, an 89.68% reduction compared to NIR single-channel models, and a 63.43% improvement over double-channel models. This study provides a robust, efficient, and cost-effective solution for feed quality assessment, highlighting the transformative potential of multimodal imaging and machine learning in precision agriculture.