Small-sized dairy farms (SSDFs) are integral to the agricultural landscape, providing economic, social, and environmental benefits to rural communities. However, they face growing challenges, including market volatility, rising production costs, labor shortages, and complex regulatory demands. This review synthesizes the current literature on the economic and environmental obstacles confronting SSDFs and explores strategies to enhance their sustainability and competitiveness. Key barriers include limited access to capital, high feed and energy expenses, and difficulties in adopting new technologies due to financial constraints. SSDFs also struggle to compete with larger farms benefiting from economies of scale and increased market power. Potential solutions include strengthening cooperative models, implementing diversification strategies, and leveraging policy support for targeted financial assistance and technology adoption. Case studies of successful SSDFs show that transitioning to organic production, adopting climate-smart techniques, and focusing on niche markets can significantly improve profitability and resilience. This review emphasizes the need for tailored policy frameworks, innovative financial models, and collaboration among stakeholders to support SSDFs. Future research should prioritize understanding SSDF-specific financial dynamics, assessing the cost-effectiveness of technology adoption, and developing strategies to enhance market access and long-term sustainability in the U.S. dairy sector.

Projections of climate patterns through the end of the 21st century indicate varying impacts across the U.S. However, a common thread of these projections calls for increasing atmospheric temperatures in every region, some more pronounced than others. The significance of these projections for corn and soybean production cannot be overestimated. This study contributes to our understanding of climate change impacts on production and farm revenues by projecting their impacts on corn and soybean yields in Buchanan County, Iowa, a county in the center of the Corn Belt. Projections indicate that as atmospheric temperatures rise and precipitation levels vary markedly, the result is a significant decline in corn and soybean yields, the latter to a lesser extent, as compared to long-term yield trends. Depending upon the climate change scenario that will materialize, corn yields are projected to decline by up to 29%, while soybean yields are projected to decline by up to 24% from their normal upward trends by the year 2100. Due to the long-term upward trends in yields, corn and soybean yields will increase in absolute terms by the end of the century. Depending upon the climate change scenario, actual corn and soybean yields will increase by 30 to 57% and 30 to 66%, respectively, by the end of the 21st century, significantly less than they would have in the absence of these climate projections.

Texas is the third-largest milk-producing state in the U.S., with Central Texas being the second-largest milk-producing region in Texas. The average size of a dairy herd in Texas is 1829 cows. In Central Texas alone, there are 88,000 dairy cows. However, there is a lack of environmental impact research for this region. The overall objective of this case study is to evaluate the net carbon and carbon equivalent balances for a large dairy operation in Central Texas. The dairy selected for this study has a herd size of 5000 milking cows. The data assumptions were made regarding the selected dairy’s performance and production for the 2021 production year. These data include herd size and management, milk production, crop production, feed purchases, and on-farm energy usage. The USDA-Integrated Farm System Model (IFSM) was used to estimate the daily and annual greenhouse gas emissions and environmental footprint of the dairy by quantifying the operation’s carbon footprint based on its 2021 performance and management practices. Research outcomes identify and quantify sources of greenhouse gas (GHG) emissions produced on the dairy farm. Additionally, the carbon footprint (CF) was determined by estimating the CO₂ equivalents (CO₂-eq) emitted or sunk from animal and manure emissions, direct and indirect land emissions, net biogenic and anthropogenic CO₂ emissions, and the production of resource inputs. The results of this case study indicated that the carbon footprint (CF) of the 5000-milking-head dairy in Central Texas was 0.40 lb. of CO₂ per lb. of fat- and protein-corrected milk (FPCM) when considering biogenic CO₂ and 0.83 lb. of CO₂ per lb. of FPCM without biogenic CO₂.

Nonpoint source pollution from cultivated croplands has often been associated with downstream water quality impairment in various watersheds. Given projected changes in global climate patterns, this study contributes to the existing literature by elucidating the impacts of climate projections on edge-of-field surface runoff and sediment and nutrient losses. We apply a well-tested ecohydrological model, Agricultural Policy Environmental eXtender (APEX), to continuous corn and corn–soybean fields in Buchanan County, Iowa, using climate scenarios developed from three well-known representative concentration pathway (RCP) climate projections: RCP 2.6, RCP 4.5, and RCP 8.5. Our results indicate that there will be a moderate to substantial increase in surface runoff, sediment, and nutrient losses depending upon the reference point of comparison (baseline scenario) and upon which climate scenario actually materializes. However, regardless of which climate scenario materializes and regardless of the baseline for comparison, soluble nitrogen losses are bound to increase, the magnitude depending upon the climate scenario. We find also that nutrient losses will be higher from continuous corn fields than from corn–soybean fields, given the tillage practices implemented on corn versus soybeans in the study area. Similarly, we find that nutrient losses may be higher from fields that receive manure than fields that receive only inorganic fertilizer, though this latter finding may be predicated upon the specific nutrient application rates utilized.

Cardiovascular disease remains a leading cause of morbidity and mortality in the United States (US). Although high-quality data are accessible in the US for cardiovascular research, digital literacy (DL) has not been explored as a potential factor influencing cardiovascular mortality, although the Social Vulnerability Index (SVI) has been used previously as a variable in predictive modeling. Utilizing a large language model, ChatGPT4, we investigated the variability in CVD-specific mortality that could be explained by DL and SVI using regression modeling. We fitted two models to calculate the crude and adjusted CVD mortality rates. Mortality data using ICD-10 codes were retrieved from CDC WONDER, and the geographic level data was retrieved from the US Department of Agriculture. Both datasets were merged using the Federal Information Processing Standards code. The initial exploration involved data from 1999 through 2020 (n = 65,791; 99.98% complete for all US Counties) for crude cardiovascular mortality (CCM). Age-adjusted cardiovascular mortality (ACM) had data for 2020 (n = 3118 rows; 99% complete for all US Counties), with the inclusion of SVI and DL in the model (a composite of literacy and internet access). By leveraging on the advanced capabilities of ChatGPT4 and linear regression, we successfully highlighted the importance of incorporating the SVI and DL in predicting adjusted cardiovascular mortality. Our findings imply that just incorporating internet availability in the regression model may not be sufficient without incorporating significant variables, such as DL and SVI, to predict ACM. Further, our approach could enable future researchers to consider DL and SVI as key variables to study other health outcomes of public-health importance, which could inform future clinical practices and policies.

Background: Rural health disparities in Texas impact population health due to limited healthcare access, insurance, and transportation challenges, especially in medically underserved areas. A shortage of specialists in rural regions worsens these issues, leading to increased morbidity and mortality rates. Objective: Our research aimed to address a knowledge gap by investigating the availability of three medical specialists—cardiologists, pulmonologists, and endocrinologists—in rural counties of Texas and identifying areas where access to healthcare is limited. Methods: Utilizing data from regional, state, and federal sources, the analysis geocoded specialist locations and created GIS maps to visualize the distribution of specialists across Texas’s 254 counties. Physician demand was calculated by considering disease incidence and population size, resulting in a county-level physician availability index to highlight areas with shortages. Results: Our findings demonstrate a significant deficiency of cardiologists in 196 counties when considering a maximum reasonable travel distance of 50 miles. Comparable deficiencies were observed for pulmonologists and endocrinologists, with western rural counties predominantly comprising the deficiency areas for each specialty. These results emphasize a significant rural–urban disparity concerning access to the three investigated health specialists. Conclusions: Addressing geographic disparities can reduce health inequities, improve rural healthcare access, and promote a more equitable healthcare system across Texas. Solutions may include incentives for specialists to work in underserved areas, expanded telemedicine services, and transportation assistance.