Texas A&M AgriLife Extension Service was formally established in 1915 after the 1914 passing of the Smith-Lever Act and in conjunction with Texas A&M University. Originally named Texas Agricultural Extension Service, then later Texas Cooperative Extension, the name Texas AgriLife Extension Service was adopted on January 1, 2008. A&M was added to the agency name on September 1, 2012 as a result of a Texas A&M University System change to strengthen the association with Texas A&M. The primary mission of AgriLife Extension is to provide educational outreach programs and services to the citizens of Texas. In conjunction with Texas A&M AgriLife Research, the Extension faculty members conduct research and bring practical applications of those research findings to the people of Texas.
Forecasting crop performance through non-destructive tools is crucial for enabling timely, data-driven decisions for sustainable cotton production. This study evaluated physiological, PIs [chlorophyll index (CI), nitrogen balance index (NBI)], vegetation, VIs [normalized difference vegetation index (NDVI), normalized difference red edge (NDRE)], and biochemical, BIs [leaf nitrogen, petiole nitrate-N] indicators to forecast aboveground biomass accumulation (AGB), nitrogen (N) uptake, and lint yield in cotton. We hypothesized that the predictive strength of each indicator would vary by type and growth stage. Field experiments were conducted across five site-years in West and North Florida, USA, using six N rates (0-252 kg N/ha) in four replications. Quadratic regression (QR) model identified PIs at peak flowering and VIs at first flower and cutout growth stages as the most robust predictors of AGB. A random forest regression (RF) model also showed similar results, with PIs at peak flowering and VIs at peak flowering and first flower as the best indicators for AGB prediction. A strong relationship of BIs with N uptake at bloom and post-bloom growth stages evaluated through QR and RF supports their use for early reproductive assessment. Lint yield predicted using QR and RF was best forecasted by VIs at peak flowering, and cutout. Principal component analysis confirmed mid-to-late season VIs as major drivers of AGB and lint yield variability, while as early reproductive BIs as major indicators of N uptake. This research establishes a robust framework for real-time indicator and growth stage-based biomass, N uptake, and yield forecasting in cotton.
Legume-Rhizobia symbiosis plays a crucial role in both agricultural and ecological contexts, providing a sustainable solution for improving soil fertility and mitigating climate change through carbon storage. This thorough examination delves into the complex mechanisms of this mutually beneficial relationship, investigating the molecular, physiological, and ecological aspects that govern the interaction between leguminous plants and nitrogen-fixing rhizobia bacteria. In this review, we analysed the key elements of this symbiosis, including the processes of nodulation, the pathways of communication, and the genetic factors that enable nitrogen fixation. Furthermore, we scrutinize the factors that influence the success of this partnership, which range from soil conditions and the diversity of host plants to the effectiveness of specific strains of rhizobia. Additionally, the broader implications of legume-rhizobia symbiosis on carbon storage were also reviewed. This mutualistic relationship not only enhances plant growth and productivity but also stimulates the release of substances from the roots that enrich the organic matter in the soil and contribute to long-term carbon retention. We assess the potential of this process to reduce greenhouse gas emissions and combat climate change, emphasizing its significance in sustainable agriculture and ecosystem management. Moreover, we also explore recent advancements in biotechnology and microbial engineering, which offer promising opportunities to optimize legume-rhizobia interactions for increased agricultural productivity and enhanced carbon storage. This comprehensive examination synthesizes the current state of knowledge on legume-rhizobia symbiosis and its role in carbon storage, shedding light on the multifaceted advantages of this ecological partnership for a sustainable and resilient future.
Background Expanded Food and Nutrition Education Program (EFNEP) curricula are mandated to follow evidence-based guidelines such as the Dietary Guidelines for Americans (DGA). Content analysis methods can be used to assess curriculum alignment with established guidelines and corresponding evaluation measures. However, this is a time-intensive process that typically relies on multiple human reviewers. Artificial intelligence (AI) tools present opportunities to compare human and AI content analyses. Objective Apply a sequential human- then AI-based content analysis to assess alignment of commonly used EFNEP adult curriculum to the 2020–2025 DGA. Study Design, Setting, Participants A 20-item instrument assessing the coverage of DGA messages was developed by EFNEP and nutrition experts; each item was rated using a 3-point scale (0 = not mentioned; 1 = briefly mentioned; 2 = explicitly taught with supporting discussion/activities). Two nutrition experts independently rated the coverage in the 8-lesson curriculum; any disagreements were resolved through consensus. Parallel analyses were conducted using Google Gemini AI. Measurable Outcome/Analysis Coverage frequencies of 20 DGA items (i.e., how often they were coded as 0, 1, or 2) were identified and descriptively compared with assess agreement between human and AI ratings by using Excel. Results Most frequently mentioned items in the curriculum were increasing fruit and vegetables, consuming a variety of vegetables, varying protein in nutrient-dense forms, consuming fat-free dairy/fortified soy alternatives, and choosing whole grains. Agreement between human and AI reviews was 35%, 20%, and 20% for explicitly taught, briefly mentioned, and not mentioned content, respectively. Discrepancies in agreement occurred mostly in the first and last lessons, where concepts were mentioned indirectly or embedded within another topic. Artificial intelligence scored items based on semantic comparisons, while human reviewers also considered the intent of the DGA messages. Conclusions Artificial intelligence demonstrated stronger agreement with human-based analyses for clearly explicit content, but was less consistent when nuanced interpretation was required. These findings support AI use as a complementary tool, while emphasizing human presence to ensure comprehensive curriculum evaluation. Funding US Department of Agriculture’s National Institute of Food and Agriculture, Hatch Multistate Research
Background Food insecurity is a critical indicator of well-being, affecting 13.5% of US households and contributing to health disparities, health care costs, and mortality. Concurrently, alcohol misuse remains a significant public health concern, leading to a wide range of adverse outcomes, including physical, mental, and social problems, as well as financial hardship. Given the substantial societal and economic burdens of food insecurity and alcohol misuse, understanding how alcohol consumption may exacerbate or interact with food insecurity is essential for designing effective public health and policy interventions. Objective To examine whether alcohol use increases the likelihood of food insecurity among a nationally representative sample of US adults (>18 years). Study Design, Setting, Participants A cross-sectional study was conducted among 16,218 adults using data from 2 National Health and Nutrition Examination Survey (NHANES) cycles (2017–2023). The final analytical sample included participants with complete data on food security status and alcohol use. Measurable Outcome/Analysis The primary outcome was food security status, classified as secure or insecure in the past 12 months. Alcohol use was measured by average daily drinks and classified as never, former, moderate, heavy, or excessive drinking. We conducted multivariate logistic regression analysis, controlling for age, gender, education level, poverty quintile, and race/ethnicity. Results Overall, 21% of the sample was food insecure, and this was most prevalent among individuals in the highest poverty group. Compared with never drinkers, former drinkers (odds ratio [OR] = 1.28 [95% confidence interval (CI), 1.12–1.45]), heavy (OR = 1.25 [95% CI, 1.00–1.57]), and excessive (OR = 1.84 [95% CI, 1.36–2.50]) had higher odds of food insecurity, with excessive drinking showing the highest likelihood. Moderate drinking was not significant after adjustment (OR = 1.03 [95% CI, 0.93–1.14]). Conclusions Overall, our findings suggest that alcohol use was associated with higher odds of food insecurity, with the magnitude of the association varying by drinking pattern. These findings underscore the importance of considering alcohol use and drinking patterns when planning and implementing programs or policies to address food insecurity. Funding This material is based on work supported by the Texas A&M AgriLife Institute for Advancing Health Through Agriculture, Institute for Advancing Health Through Agriculture, and the US Department of Agriculture, Agricultural Research Service.
Alzheimer's disease presents a public health challenge, with disparities in dementia literacy and resource access, particularly in rural communities. This study examines Extension professionals' perceptions of the adequacy, availability, and awareness of clinical and non-clinical dementia resources, focusing on potential urban-rural and role-based differences. Using an existing dataset (N = 132), ordinal logistic regression assessed associations between resource ratings and county rurality as well as professional role. Results indicate professionals in rural counties reported significantly lower ratings for the adequacy (OR = 0.47, p < .05), availability (OR = 0.42, p < .05), and awareness (OR = 0.49, p < .05) of clinical dementia resources compared to urban counterparts. No significant differences emerged for non-clinical resources. Findings highlight geographic and professional disparities in clinical dementia resources.
