Texas A&M AgriLife Research is the agricultural and life sciences research agency of the U.S. state of Texas and a part of the Texas A&M University System. Formerly named Texas Agricultural Research Service, the agency's name was changed January 1, 2008, as part of a rebranding of Texas A&M AgriLife (formerly Texas A&M Agriculture). The A&M was formally added to the agency's name on September 1, 2012, as part of a branding effort by the Texas A&M University System to strengthen the association between the agencies and Texas A&M University.The agricultural experiment station division is headquartered at Texas A&M's flagship campus in College Station, Texas. Texas AgriLife Research serves all 254 Texas counties and operates 15 research centers throughout the state. Texas A&M AgriLife Research specialists in beef cattle have produced the world's largest set of gene-mapping resources for beef cattle and have cloned what is believed to be the first animal—a calf—specifically cloned for disease resistance.
Fusarium wilt, caused by Fusarium oxysporum. f. sp. vasinfectum (FOV) race 4 (FOV4), is a vascular disease and an emerging threat to cotton production in the US Cotton Belt. After the first report of FOV4 in California in 2001 and in Far-west Texas in 2017, a survey of > 600 cotton fields in South New Mexico in 2018 identified FOV4 in more than 10 fields. However, there was a lack of information on the pathology of FOV4, screening methods, sources of FOV4 resistance in tetraploid Upland cotton (Gossypium hirsutum), resistant cultivars, and genetic and genomic basis of resistance. The objective of this review is to summarize research progress in relevant areas with a focus on our success as exemplified from the productive collaboration between breeders and pathologists from New Mexico State University and Texas A M University. Since 2018, > 6000 cotton breeding lines have been evaluated for FOV4 resistance in the greenhouse and a FOV4-infested field in Fabens, Texas, leading to identification of new sources of FOV4 resistance (presumably from Fw1 in Upland and a major QTL on A11 in diploid Asiatic cotton). Several FOV4 resistant breeding lines (possessing Fov7/Fw2) with high yields and good fiber quality were developed through repeated single plant selection and progeny tests. We were the first to compare the infection process among two resistant lines of different genetic sources (carrying Fov4 and Fov7/Fw2) and susceptible lines. Reliable evaluation methods in the field, greenhouse, and growth chambers were developed through studies on optimal temperature, inoculation density, planting date, cotton growth stage including seed germination, root wounding, experimental designs, and evaluation parameters. We have performed linkage mapping and genome-wide association study (GWAS) for FOV4-resistance QTLs. We have recently demonstrated that the FOV7 resistance gene Fov7 (previously named Fw2 and FwR) with the underlying GhGLR4.8A gene on chromosome D03 also confers resistance to FOV4 in Upland cotton. The previously released three FOV4-resistant Upland cultivars (NuMex COT 15 GLS, NuMex COT 17 GLS, and Acala 1517-20) carried Fov7/Fw2. This led to the development of portable markers through both genotyping-by-targeted sequencing, allele-specific (AS) PCR-based SNP typing, and Kompetitive allele specific PCR (KASP) genotyping and used in marker-assisted selection (MAS). Our comprehensive research activities on FOV4 have led to the publication of 37 refereed journal articles including four first reports of different Fusarium spp. infecting cotton, which has paved the way in identifying and utilizing a complete set of FOV4 resistance genes for breeding through an integrated genetic and genomic approach.
Flow Duration Curves (FDCs) provide a statistical relationship between the magnitude and frequency of streamflow in a watershed. The shape and scale of an FDC are dependent upon the dynamic relationship between streamflow regimes and the climatic, geological, topographical, and other environmental or anthropogenic attributes of the watershed. While Machine Learning (ML) models can provide enhanced predictive performance in ungaged watersheds over conventional approaches, validating the models’ comprehension of the underlying hydrologic processes is imperative for any stakeholder involvement. In this study, we employed Random Forest (RF) regression on individual Exceedance Percentiles (EPs) and slope of the FDCs for a large sample of watersheds in the contiguous United States. Then, we explored the interactions between watershed attributes and FDCs using SHapley Additive exPlanations (SHAP) to divulge the local (watershed scale), regional, and global (model scale) influence of attributes and their dependence structures. Results indicate that climate attributes (precipitation and aridity index) were the preeminent drivers in predicting FDCs across all quantiles, primarily affecting the scale of FDCs, followed by the baseflow index and geologic attributes that highly influence the low flow regime and control the shape of FDCs. The dominant controls of other watershed attributes, including precipitation seasonality, % snow, and elevation, vary between EPs and regions that were readily discernible in their SHAP values.
Adoption of cover crops in corn-producing regions of the United States (U.S.) has progressed at an uneven pace. Since corn is a nitrogen (N) demanding crop, a limited understanding of N fertilizer management for corn following cover crops is among the major barriers to widespread adoption of cover crops. To address this critical knowledge gap, we conducted the first meta-analysis to systematically examine the impacts of N fertilization on corn yield and N content following cover crops, considering various agronomic practices and soil conditions. Studies comparing corn production with and without cover crops were selected for analysis, with 392 and 228 individual observations included for corn yield and N content, respectively. Results depicted that the integration of cover crops tends to boost corn yield and N content by 11
This study applies the SWAT+ paddy rice module to simulate the paddy water balance of a Mediterranean irrigation district modeled exclusively as rice paddies, focusing on the Acequia del Oro irrigation community in the Albufera de València (Spain). The model was implemented at high spatial resolution and calibrated against two complementary observations, monthly irrigation volumes (m3) and daily drainage flows (m3 s−1), for 2016–2023. A global Sobol sensitivity analysis identified evapotranspiration (PETCO) and percolation (PERCO) as the most influential parameters, guiding an iterative ensemble calibration that improved model skill. Performance metrics showed NSE above 0.93 and R2 up to 0.94 for drainage flows, and satisfactory agreement for irrigation volumes (NSE = 0.76). Climate impacts were assessed under SSP2 4.5 and SSP5 8.5 using an ensemble of top performing CMIP6 models statistically downscaled by AEMET (ESD and DeepESD). Projections indicate precipitation reductions (9–31%) and potential evapotranspiration increases (8–18%) by mid and late century, leading to higher irrigation requirements (4–10%) and modest yield declines (up to 8%). These results highlight the combined effect of intensified atmospheric demand and reduced water availability, supporting integrated irrigation management and climate adaptation planning in Mediterranean rice systems.
The U.S. poultry industry faces substantial disease management challenges amid expanding production and increasing biological threats, such as highly pathogenic avian influenza. Biosecurity is a set of management strategies that limit the introduction of pathogenic microorganisms to production environments. This study evaluated microbial contamination across common farm fomites and assessed the effectiveness of intervention strategies under relevant field conditions. Environmental samples were collected from five barns over three sampling periods from doorknobs, environmental controller panels, vehicle tires and floormats, tunnel fans, side air inlets, evaporative cooling system water reservoirs, and the air through two methods of dust sampling. Boot covers, footbaths, and tire wash interventions were assessed via laboratory simulations using organic material to reflect typical farm conditions. Total aerobes, Staphylococci, and coliforms were quantified across surfaces and treatments. Results indicated that high contact and vehicle associated samples harbored microbial loads ranging between 0.18 and 3.93 log10 cfu/cm2 of the different microbes assessed. Cooling system reservoirs harbored 5.72, 2.16, and 3.45 log10 cfu/cm2 of total aerobes, Staphylococci, and coliforms, respectively. Dust associated loads varied by sampling method. Boot covers reduced, but did not eliminate, contamination risk to the environment. Both footbath and tire wash applications significantly reduced microbial loads, with longer contact times producing greater reductions than brief exposures. These results describe the potential risk sources in spreading disease, while also confirming that protocols that emphasize cleaning and applying disinfectants to increase contact times should be employed on farms to mitigate disease transmission.
