USDA Agricultural Research Service Research Articles

National dairy research programs: what is their role in animal health and sustainability research?

The US dairy industry has made great strides in improving animal health over many decades, which has driven substantial improvements in economic, social, and environmental sustainability. As consumer and corporate focus on climate continues to grow, the continued need to research and improve animal health and understand its connection with the environment is integral to the success of the dairy industry. Research to address these areas can be supported by national research programs and collaboration between them. The USDA and Dairy Management Inc established a collaborative research agreement in 2007; to date, this collaboration has not explicitly focused on animal health or its intersection with the environment. It is integral to the success of animal agriculture in an ever-changing sustainability landscape that animal health is addressed as a key piece of socioeconomic and environmental sustainability. An academic-industry stakeholder committee reached a consensus that supported this idea and identified that it is equally important to communicate these research findings with consumers in a way that resonates. The purpose of this Viewpoint article is to highlight that national research programs at the USDA Agricultural Research Service's National Animal Disease Center and Dairy Management Inc can and should play an important role in supporting and facilitating research at the intersection of animal health and sustainability broadly.

115 Gene editing: Where are we on campus and where can it go?

Abstract Editing the genome of livestock provides solutions to otherwise intractable problems and can provide a tool to better understand the role of specific proteins during development. The potential benefit for production agriculture is so great that the USDA Agricultural Research Service has invested in 4 positions now located in a research unit at the University of Missouri with a focus currently on swine and poultry. Genome editing can result in introducing alleles from divergent populations or novel alleles to produce animals that have new proteins, modified proteins or animals that no longer produce a protein. Applications of genome editing include improving disease resistance (viral and bacterial), improving digestion, disseminating valuable genetics, improved thermoregulation, changing the body/milk composition, and creating hypoallergenic meat. Changes in these animals will improve animal welfare, animal productivity and the sustainability of animal agriculture. In addition to improving animal agriculture, gene editing has enabled a new understanding of maternal recognition of pregnancy in the pig and a better understanding of molecules involved in growth and development of pigs. With new developments in editing of just somatic cells [somatic cell gene editing (SCGE), rather than germline editing], it will be possible to edit some cells in existing animals and thus change their phenotype. SCGE technologies such as this are being developed via the biomedical community as models to lead to human clinical applications. One such application is to correct cells in the lungs that produce the CFTR protein. Correction of just 10% of the cells in the lungs is estimated to correct the lung symptoms associated with cystic fibrosis. Similar applications in production agriculture might produce animals that have novel phenotypes of an altered carcass composition or better use of nutrients. Application of genetic engineering technologies such as genome editing is limited by biology and the imagination of the investigator.

Comparison effects of Natural and Artificial lighting on selected blood physiological variables of broilers grown to 56 days of age

Light is one of the major enviro-climatic factors in poultry houses that influences physiology and metabolic processes and impact poultry welfare, health, production and behaviors. This study compared the effects of natural and artificial lighting on selected blood physiological variables of broilers grown to 56 days of age. In each of the two trials, a total of 704 (352 males/352 females) 1-d- old Ross x Ross 708 chicks were obtained from a commercial hatchery on day of hatch. The design structure was a randomized complete block design with two treatments and eight replications per trial. Birds were equally and randomly distributed into 16 environmentally controlled rooms in groups of 44 (22 males/22 females) per room. Thus, each of the two treatments were represented by eight rooms with total of sixteen replicates. Temperature and RH on d 1 were maintained at 90 ± 2.1°F and 50 ± 5%, respectively across all treatments rooms. Temperature was decreased by 4 °F per week until it reached 66 °F at 56 d of age. Feed and water were provided ad libitum. Birds were provided a four phase-feeding program (Starter: 0-14 d, Grower: 15-28 d, Finisher: 29-42 d, and Withdrawal: 43-56 d). Blood samples (3 ml) were collected from the brachial wing vein of 3 males and 3 females birds per room on d 28, 42, and 56 which were then analyzed immediately for whole blood physiological variables. Blood plasma samples were analyzed for T3, T4, and corticosterone concentrations. Results showed that artificial light significantly increased sO2 (P < 0.049), SaO2 (P < 0.031), and decreased RHb (P < 0.044) which may be due to slight difference in body weights, but the changes were all within physiological ranges for this species. In addition, blood glucose and plasma corticosterone concentrations were not affected by treatments, signify a similar level of physiological stress between the two light types. In conclusion, physiological variables examined in this study were unaffected by both natural and artificial lighting. However, birds exposed to artificial lighting had slightly increased body weight in comparison with birds reared under natural lighting, which may have a significant impact in commercial poultry production. USDA Agricultural Research Service. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

A short history of the phosphorus index and Andrew Sharpley's contributions from inception through development and implementation.

In the 1980s, growing recognition of agricultural phosphorus (P) sources to surface water eutrophication led to scrutiny of animal feeding operations. In 1990, the USDA-Natural Resources Conservation Service (NRCS) invited prominent scientists to find a solution. It was at an initial meeting that Dr. Andrew Sharpley suggested that P assessment could be modeled after the Universal Soil Loss Equation, where a matrix of factors influencing P loss would be associated with farm nutrient management recommendations. After codifying the P assessment into the USDA-NRCS 590 Nutrient Management Standard some 10years later, 48 states chose to develop their own P Index. Sharpley, working with many others, helped develop several state P Indices. In 2000, Sharpley secured funding from the USDA-Agricultural Research Service to support the National P Research Project, which conducted in-field P runoff assessments using standardized rainfall simulated studies across 20 states; this allowed individual trials to be aggregated for agroecological regions that were then incorporated into specific state P Indices. Eventually, comparison of P Indices across state boundaries led to a white paper at the behest of USDA-NRCS that resulted in three regional projects evaluating modeling approaches to support or replace P Indices. Sharpley's national umbrella project pointed to shortcomings in water quality models, such as APEX or TBET, as a replacement for state P Indices, which remain a key part of the USDA-590 standard. As a selfless leader, capable of attracting and assembling diverse, productive interdisciplinary teams, Sharpley was essential to the inception, development, and implementation of the P Index.

156 The Impact of Sire Parasite Resistance on Offspring Gastrointestinal Nematode Indicators, and Relative Impact of Lamb Breeding Values on sale Value of Ram Lambs

Abstract Genetic selection of sheep for resistance to gastrointestinal nematodes (GIN) has become a priority for pasture-based production of lambs to minimize the need for deworming. The first objective of this experiment was to determine the impact of sire weaning or post-weaning fecal egg count (FEC) estimated breeding value (EBV; WFEC and PFEC, respectively) from the National Sheep Improvement Program (NSIP) on GIN infection in Katahdin lambs born in fall (Oct – Nov; n = 459) or winter (Jan – Feb; n = 378) of 2018 through 2021 at the USDA Agricultural Research Service (ARS). FAMACHA scores were determined, and blood samples and feces were collected from the lambs to determine packed cell volume (PCV), and FEC at 60, 90, 120, and 150 days of age, and lambs were selectively dewormed if anemic. Data were analyzed using mixed models. The offspring FEC was positively (P < 0.001) and the PCV negatively (P < 0.001) related to the sire WFEC and PFEC. This implies that offspring parasitism would be less and survival greater from highly resistant sires. A second objective was to examine effects on sale prices of breeding-quality ram lambs from four farms, including ARS, of lamb EBV for WFEC and ewe productivity trait (EPT) designed to identify ewes with superior maternal ability. Sale price effects were dominated by sale type with a premium of $1,086/animal relative to direct sales from the farm for animals in NSIP or specialty sales. EPT EBV was impactful yielding a-SD increase of 2.3 units or an extra $39/animal. WFEC had no effect on sale value. Thus, while more parasite-resistant sires produced offspring with decreased FEC, the sale value of these ram lambs appeared to have been more strongly associated with EPT, or with a desire to obtain ram lambs with balanced EBV rather than elite EBV for parasite resistance.

Does the upper respiratory tract microbiome change in dairy calves fed milk replacer containing probiotics?

Abstract Our overall hypothesis is that feeding probiotics to dairy calves will alter overall immunity and may additionally change the microbiome in the respiratory tract as well as the gut. A group of 20 dairy calves were split into two treatment groups: Control (N=10, milk replacer), and Probiotic (N=10, milk replacer + 0.5 g/day Bovamine Dairy). On day 0 birth weight was obtained and the calves were provided colostrum as per the dairy SOP. On day 2, probiotics were added to the milk replacer of the treated group. Blood was collected along with nasal and tonsil swabs on days 0, 7, 14, 21, 28, 42, and 48. Calves were monitored daily for fecal, nasal, and ocular discharge scores. Our preliminary data showed no significant differences (P<0.5) between the Control and Probiotic groups in immune cell populations in peripheral blood. DNA was extracted from nasal and tonsil swabs to evaluate the bacterial populations in the upper respiratory microbiome. Hypervariable regions 1 through 3 along the 16S ribosomal RNA gene were amplified by PCR and sequenced by Illumina MiSeq to determine the bacterial taxa present. Evaluation of these samples revealed a greater percentage of operational taxonomy units (OTU) were classified at the genus level in the tonsil compared to the nasal samples. The microbiome of the tonsils was more stable over time points compared to the nasal samples. Evaluation of each timepoint for nasal and tonsil samples identified bacterial taxa that changed in relative abundance with the addition of the probiotic compared to the control diet. Addition of probiotic changed the relative abundance of OTU (P<0.01) in the nasal and tonsil sampling sites and at multiple timepoints. Analyses are ongoing. Project supported by fund from the USDA Agricultural Research Service.

Registration of ‘Empire’ big bluestem

Abstract‘Empire’ (Reg. no. CV‐18, PI 700112) big bluestem (Andropogon gerardii Vitman) is a 54‐clone synthetic selected for high biomass yield. Empire is broadly adapted to USDA hardiness zones 3–7, ranging from the Great Plains to the eastern United States. Although Empire was developed as a high‐biomass cultivar for use in establishing perennial biomass production for conversion to bioenergy, it has a heading date similar to many other big bluestem cultivars, suggesting that it could be used as a forage crop as well for either hay or pasture production. Under an optimal single‐harvest biomass production management, Empire ranked 1st in biomass yield for 8 of 12 locations in multiple‐year trials averaged over a total of 40 environments (location‐trial‐years). Biomass yield of Empire, averaged over 12 locations, was 30% higher than ‘Goldmine’, 46% higher than ‘Champ’, and 52% higher than ‘Bonanza’. Ground cover of Empire was slightly higher than for most check cultivars, averaging 80% across all 12 locations. Empire is broadly adapted to USDA hardiness zones 3–6, extending from the Great Plains to the eastern United States and was released to the public by the USDA Agricultural Research Service in 2021.

Cover Image, Volume 115, Issue 2

On the cover: Michael Bartolo (left) and Ardell Halvorson examine onions in drip‐irrigated research plots at the CSU Arkansas Valley Research Center. To learn about available management strategies that can reduce onion bacterial diseases, see the review article, “Reducing the risk of onion bacterial diseases: A review of cultural management strategies”, by Belo et al. Photo Credit: Bill Cotton, USDA Agricultural Research Service. image

Estimating WEPP Cropland Erodibility Values From Soil Properties

Highlights Very fine sand is the single most important variable for predicting rill and interrill erodibility values. Slope steepness is the most important property for predicting critical shear for rill erosion. Erodibility prediction equations with the best goodness-of-fits have both soil texture and mineralogy terms. Equations to predict erodibility from texture, organic carbon, cation exchange, slope, and taxonomy are proposed. Abstract. In the late 1980s, the USDA Agricultural Research Service, along with other federal agencies and multiple universities, collaborated to develop a new physically based soil erosion model, the Water Erosion Prediction Project (WEPP) Model. The WEPP model was intended to replace the Universal Soil Loss Equation and was to include estimates for upland runoff and erosion, sediment delivery to first order channels, and runoff and sediment routing through a downstream channel network. The WEPP technology estimated erosion from raindrop splash and sheet flow (interrill erosion) and concentrated channel flow (rill erosion). To make these erosion estimates, WEPP required new soil erodibility values for interrill erodibility (Ki). rill erodibility (Kr), and critical shear (𝜏c) for concentrated flow erosion. The WEPP Core team determined that they needed to estimate these three erodibility values from measurable soil properties for a wide range of soil conditions. To develop relationships between WEPP soil erodibility variables and other soil properties, a field study was carried out using rainfall and runoff simulation to measure the three erodibility values for 36 soils. Sites were identified on croplands from Washington to Georgia and Maine to California, USA for erodibility measurement. Concurrently, the USDA Soil Conservation Service (SCS) carried out detailed soil surveys and laboratory analyses for all sites to provide a large database of soil physical, chemical, and engineering properties. Correlation and regression analyses were carried out to develop relationships between SCS measurable soil properties and WEPP soil erodibility values. This article provides a summary of the field procedures, data analyses, and subsequent predictive equations that were developed. The predictive equations that were finalized in the WEPP User Summary used sand, very fine sand, clay, and organic carbon contents to predict cropland soil erodibility, but the Coefficient of Determination (r2) values were 0.55 or less. More complex predictive equations were developed with soil physical, chemical, mineralogical, and geomorphic properties, with r2 values up to 0.81. Most of the better predictive equations included terms for soil texture and clay mineralogy, often with additional chemical properties. A set of simplified erodibility equations using only the readily available properties of soil texture, organic carbon, cation exchange capacity, slope steepness, and taxonomic order were derived for use within the WEPP Model, with r2 values greater than 0.5 for all three equations for estimating soil erodibility from measurable soil properties. Keywords: Critical Shear, Interrill Erodibility, Rill Erodibility, Soil Properties, WEPP.

Forecasting of Canopy Temperatures Using Machine Learning Algorithms

Highlights This study analyzes the feasibility of using Artificial Neural Networks (ANNs) to estimate canopy temperatures. A methodology is introduced to forecast canopy temperatures using historical canopy temperatures. ANNs can predict canopy temperatures with satisfactory accuracy for plant stress-based irrigation scheduling. The methodology can be useful to add redundancy to plant stress-based irrigation scheduling methods. Abstract. Recent advances can provide farmers with irrigation scheduling tools based on crop stress indicators to assist in the management of Variable Rate Irrigation (VRI) center pivot systems. These tools were integrated into an Irrigation Scheduling Supervisory Control and Data Acquisition System (ISSCADAS) developed by scientists with the USDA-Agricultural Research Service (ARS). The ISSCADAS automates the collection of data from a network of wireless infrared thermometers (IRTs) distributed on a center pivot’s lateral and in the field irrigated by the center pivot, as well as data from a wireless soil water sensor network and a microclimate weather station. This study analyzes the use of Artificial Neural Networks (ANNs), a type of machine learning algorithm, for the forecasting of canopy temperatures obtained by a wireless network of IRTs mounted on a three-span VRI center pivot irrigating corn near Bushland, TX, during the summer of 2017. Among the predictors used by the ANNs were weather variables relevant to the estimation of evapotranspiration (i.e., air temperature, relative humidity, solar irradiance, and wind speed), irrigation management variables (irrigation treatment, irrigation scheduling method, and the amount of water received during the last 5 days as irrigation or rainfall), and days after planting. Two case studies were conducted using data collected from periodic scans of the field performed during the growing season by running the pivot dry. In the first case, data from the first three scans were used to train an ANN, and canopy temperatures estimated using the ANN were then compared against canopy temperatures measured by the network of IRTs during the fourth scan. In the second case, data from the first six scans were used to train ANNs, and canopy temperatures estimated using the ANN were then compared against canopy temperatures measured by the network of IRTs during the seventh scan. The Root of the Mean Squared Error (RMSE) of ANN predictions in the first case ranged from 1.04°C to 2.49°C, whereas the RMSE of ANN predictions in the second case ranged from 2.14°C to 2.77°C. To assess the impact of ANN accuracy on irrigation management, estimated canopy temperatures were fed to a plant-stress-based irrigation scheduling method, and the resulting prescription maps were compared against prescription maps obtained by the same method using the canopy temperatures measured by the network of IRTs. In the first case, no difference was found between both prescription maps. In the second case, only one plot (out of 26) was assigned a different prescription. Results of this study suggest that machine learning techniques can be used to assist the ISSCADAS in situations where canopy temperatures cannot be measured by the network of IRTs due to poor visibility conditions, or because the center pivot cannot traverse the field within a reasonable amount of time. Keywords: Artificial Neural Network, Canopy temperature sensing, Center pivot irrigation, Irrigation scheduling, Machine learning, Metamodeling, Variable rate irrigation.

Invasive annual grasses—Reenvisioning approaches in a changing climate

For nearly a century, invasive annual grasses have increasingly impacted terrestrial ecosystems across the western United States. Weather variability associated with climate change and increased atmospheric carbon dioxide (CO₂) are making even more difficult the challenges of managing invasive annual grasses. As part of a special issue on climate change impacts on soil and water conservation, the topic of invasive annual grasses is being addressed by scientists at the USDA Agricultural Research Service to emphasize the need for additional research and future studies that build on current knowledge and account for (extreme) changes in abiotic and biotic conditions. Much research has focused on understanding the mechanisms underlying annual grass invasion, as well as assessing patterns and responses from a wide range of disturbances and management approaches. Weather extremes and the increasing occurrences of wildfire are contributing to the complexity of the problem. In broad terms, invasive annual grass management, including restoration, must be proactive to consider human values and ecosystem resiliency. Models capable of synthesizing vast amounts of diverse information are necessary for creating trajectories that could result in the establishment of perennial systems. Organization and collaboration are needed across the research community and with land managers to strategically develop and implement practices that limit invasive annual grasses. In the future, research will need to address invasive annual grasses in an adaptive integrated weed management (AIWM) framework that utilizes models and accounts for climate change that is resulting in altered/new approaches to management and restoration.

Bibliometric Analysis of Research on the Main Genes Involved in Meat Tenderness.

Tenderness is one of the main characteristics of meat because it determines its price and acceptability. This is the first bibliometric study on the trend of research on the role of genes in meat tenderness. A total of 175 original and English-language articles published up to 2021 were retrieved from Scopus. The bibliometric analysis was carried out with VOSviewer (version 1.6.18, Eck and Waltman, Leiden, Netherlands) and complemented with the Analyze search results service from Scopus. Erroneous and duplicate data were eliminated, and incomplete information was added to standardize the results. Scientific production was evaluated by means of quantity, quality and structure indicators. As a first glance, 8.816% of authors have published more than 50% of papers mainly related to genes encoding the calpain (CAPN)-calpastatin (CAST) system and single nucleotide polymorphisms (SNPs). Among other findings, a strong link was found between the contribution of the main countries (led by the United States with) and their institutions (led by the USDA Agricultural Research Service with) to their gross domestic product. Most studies on the topic are published in the Journal of Animal Science, and other journals with high impact according to the number of citations and different metrics. Finally, when evaluating the most cited articles, the occurrence and association of the main keywords, it was confirmed that research is focused on the role of CAPN and CAST genes and of SNPs in beef tenderness. The change in science was emphasized; although marker-assisted selection is still used, genes have an infinitesimal effect on complex traits. Therefore, since about 2010, new research groups adopted genomic selection to evaluate dense panels of SNPs and better explain genetic variation in meat tenderness.

The impact of sire fecal egg count estimated breeding values on indicators of offspring gastrointestinal nematode infection, and relative impact of lamb estimated breeding values on sale value of ram lambs

Genetic selection of sheep for resistance to gastrointestinal nematodes (GIN) has become a priority for pasture-based production of lambs to minimize the need for deworming. The objective of this experiment was to determine the impact of sire weaning or post-weaning fecal egg count (FEC) estimated breeding value (EBV; WFEC and PFEC, respectively) from the National Sheep Improvement Program (NSIP) on GIN infection in Katahdin lambs born in fall (Oct – Nov; n = 459) or winter (Jan – Feb; n = 378) of 2018 through 2021 at the USDA Agricultural Research Service (ARS). FAMACHA scores were determined, and blood samples and feces were collected from the lambs to determine packed cell volume (PCV), and FEC at 60, 90, 120, and 150 days of age, and lambs were selectively dewormed if anemic. Data were analyzed using mixed models containing fixed effects of year, sex, age at sampling (fitted as a repeated measure), and their interactions, and continuous effects of sire FEC EBV. The FEC and PCV of offspring was positively (P < 0.001) and negatively (P < 0.001), respectively, related to the sire WFEC and PFEC. A second objective was to examine effects on sale prices of breeding-quality ram lambs from four farms, including ARS, of lamb EBV for WFEC and a ewe productivity trait (EPT) designed to identify ewes with superior maternal ability. Sale price effects were dominated by sale type with a premium of $1086/head relative to direct sales from the farm for animals in NSIP or Katahdin Hair Sheep International Expo sales. Producers likewise assigned a premium of $107/head to animals retained for use in their own breeding herds. The next most impactful variable was the EPT EBV. A 1-SD increase of 2.3 units in EPT yielded an extra $39/head whereas a similar 1-SD decrease in EPT resulted in a $32/head discount. Lambs born in fall were discounted by $6/head relative to those born in winter. Lambs that were not dewormed received a $29/head premium over lambs that were dewormed and was presumably taken as a phenotypic signal that the lamb was resistant. A ± 1-SD change in WFEC had no effect on sale value, which changed by less than $0.01/head. This result may have been confounded with whether or not the lamb had been dewormed. Thus, while more parasite-resistant sires produced offspring with lower FEC, the sale value of these ram lambs appeared to have been more strongly associated with other EBV such as EPT or with a desire to obtain ram lambs with balanced EBV rather than elite EBV for parasite resistance.

TMAO Response to a High-Fat High-Sugar Meal Challenge in Generally Healthy United States Adults

ObjectivesTrimethylamine n-oxide (TMAO) is linked to the gut microbiome's metabolism of choline, carnitine, and betaine and is associated with adverse cardiovascular health. In a secondary analysis, we assessed the plasma TMAO response to a high-fat high-sugar meal challenge devoid of choline, carnitine, or betaine in metabolically healthy male (n = 48) and female (n = 49) participants residing near Davis, California, USA.MethodsThe standardized meal challenge was a 3000-gram shake containing 60% kilocalories from fat (shortening and palm oil), 25% kilocalories from carbohydrate (granulated white sugar), and 15% kilocalories from protein (pasteurized egg white). The shake contained limited amounts of direct TMAO-precursors including 2.9 mg of choline and 0.76 mg of betaine. Blood samples were collected at fasting and 0.5, 3, and 6 hours after consuming the meal challenge. TMAO was measured by liquid chromatography-high resolution mass spectrometry using the MxP Quant 500 kit (Biocrates, Innsbruck, Austria). Differences in TMAO concentration by time were assessed by ANOVA and values are means ± standard deviation.ResultsTMAO response demonstrated a post-prandial rise in concentration that peaked at 3 hours and returned to fasting levels by 6 hours. Mean TMAO levels at fasting, 0.5, 3, and 6 hours were 5.27 ± 3.43 μM, 6.09 ± 6.01 μM, 6.87 ± 8.11 μM, 4.76 ± 4.00 μM, respectively. Significant differences were observed between fasting and 3 hours (P = 0.039), and 3 and 6 hours (P = 0.011).ConclusionsPlasma TMAO concentration was affected by a high-fat high-sugar meal challenge despite providing limited amounts of direct TMAO-precursors. The circadian rhythm of the gut microbiome or the host, or the gut microbiome's utilization of phosphatidylcholine from biological sources may contribute to these results. Understanding the regulation of TMAO and its relationship to meals is critical to identifying its role in the etiology of diabetes and cardiovascular disease.Funding SourcesThis work was funded in part by the USDA-Agricultural Research Service (2032–51,530-025–00D) and the National Institute of Health (RO1HL148110).

Chokeberry Reduces Inflammation in Human Pre-adipocyte Cells

ObjectivesChokeberry, Aronia melanocarpa, consumed as a fruit and used as a traditional food by some American Indian tribes has greater levels of anthocyanin compared to cranberries and blueberries. Animal and human studies demonstrate that chokeberry juice extract (CBE) has cardioprotective, anti-diabetic, and anti-inflammatory properties. Saturated fatty acids including palmitate (PA, 16:0) and lipopolysaccharide (LPS) produces pro-inflammatory effects by inducing inflammatory gene expression. The objective of this study was to determine the effect that CBE, Cyanidin 3-O-galactoside (C3Gal) and Cyanidin 3-glucoside(C3G) have on PA or LPS induced inflammation as measured by interleukin-6 (IL-6). MethodsHuman primary subcutaneous pre-adipocyte cells (ATCC, Manassas, Virginia) were pretreated with either 2 uM C3Gal or CBE for three hours. Following pretreatment, cells were treated with 0.17 mM bovine serum albumin (BSA) for control; 0.1 mM PA; BSA in presence of 2uM C3Gal or CBE; and PA in presence of 2 uM C3Gal or CBE. RT-qPCR analysis was conducted to determine IL-6 mRNA expression normalized to 18s rRNA expression. One-way ANOVAs were run to determine significance of group differences. ResultsTreatment of cells with PA increased (P < 0.0001) IL-6 mRNA compared to control and CBE groups. Treatment of cells with CBE decreased (P < 0.0001) PA-induced IL-6 mRNA while C3Gal did not (p = 0.4657). Treatment of cells with CBE decreased (P < 0.0001) LPS-induced IL-6 mRNA while C3G treatment did not (p = 0.4537). ConclusionsPA induces inflammation, as measured by IL-6 mRNA expression, in human primary subcutaneous pre-adipocyte cells. This inflammation is reduced by treatment with CBE while isolated anthocyanin such as C3- galactoside or glucoside has partial inhibition. Future studies will identify additional chokeberry anthocyanins and flavonoids important in anti-inflammatory effects. Funding SourcesThis work was funded by grant support from the USDA Agricultural Research Service Project and NIH National Institute of General Medical Sciences, The Centers of Biomedical Research Excellence Award.

WebStart WEPS: WEPS with Remote Data Access and Cloud-Computing Functionality

HighlightsWebStart WEPS is a Java WebStart enabled browser-accessible version of WEPS.This model-as-a-service cloud-based system provides remote access to model inputs and remote model execution.Default QuickPlot reports are provided to quickly visualize model results.MapViewer revisions are provided to improve map-based site selections.Abstract. The Wind Erosion Prediction System (WEPS) is a physical process-based wind erosion model developed by the USDA Agricultural Research Service (USDA-ARS). WEPS simulates daily climate and management-driven changes to the surface, vegetation, and soil. It estimates erosion losses when surface conditions and wind speeds are sufficient for soil movement. WEPS was released in 2010 to the USDA Natural Resources Conservation Service (USDA-NRCS) for assisting land managers in controlling wind erosion, establishing field-level plans for soil conservation, and determining wind erosion susceptibility as a part of national conservation programs to conserve the soil resource. The WEPS interface allows easy user access to national-scale input data. Four primary inputs are required to set up and execute a WEPS run: (1) field dimensions; (2) field location, which automatically determines applicable local climate data; (3) a representative soil; and (4) time series of management practices. These inputs require access to a significant amount of data, which are typically stored locally; however, maintaining these data on every computer running WEPS and keeping them current were problematic for the NRCS. To address these concerns, remote access functionality was incorporated into WEPS to deliver climate and wind data, as well as soil, operation, crop/residue, and management (a date-sequenced list of management operations) records. In addition, WebStart WEPS also features the option of running WEPS components remotely “in the cloud” as web services. The Java WebStart framework provides a simplified delivery mechanism to users’ computers and offers automatic updating capability. Keywords: Cloud services, CSIP, Erosion, Java WebStart, Modeling, WEPS, Wind erosion.

OUP accepted manuscript

Born in the backwoods of rural Mississippi to an illiterate sharecropper, W. Joe Lewis graduated from Mississippi State University at the age of twenty-four with a Ph.D. in entomology. He immediately accepted a research position with the USDA–Agricultural Research Service, which resulted in a forty-year career, culminating with the highest professional grade for a USDA scientist, acknowledging his meritorious service. During those four decades, he also held joint faculty appointments with the University of Georgia and University of Florida. Lewis earned three degrees in entomology from Mississippi State University within a rapid span of seven years (B.S., 1964; M.S., 1965; Ph.D., 1967). His USDA career had numerous research firsts: isolation and identification of the kairomones for larval and egg parasitoids; demonstration of a parasitoid to use kairomones to enhance its searching ability in the field; development of a laboratory system for oriented in-flight behavioral studies of parasitoids; demonstration of the key role that associative learning plays in parasitoid responses to chemical and/or visual host cues; discovery that plants, in response to herbivore feeding damage, can actively emit information-rich chemical distress signals that are used by a parasitoid to locate and attack the herbivore; and demonstration that plant signals can be herbivore-specific and selectively used by parasitoids. Additionally, Lewis led the formulation and successful demonstration, using cotton production as a model, of the economic and environmental benefits of a fundamental shift to a total system approach to sustainable pest management.

Experimental Investigation of a Moored, Circular Pipe Breakwater

Floating breakwaters are commonly used for shoreline protection in coastal areas and may offer a relatively cost-effective method for embankment protection in irrigation reservoirs. This study explored the potential use of a moored cylindrical floating breakwater design constructed from corrugated irrigation pipe and included a preliminary investigation of the performance of a floating breakwater that was subjected to regular waves of varying height and period in a laboratory wave tank. Experiments were carried out at the USDA Agricultural Research Service (ARS), National Sedimentation Laboratory in Oxford, Mississippi. The model breakwater was made of a 17.8-cm outer diameter, high-density polyethylene (HDPE) corrugated pipe section, filled with water and restrained under two mooring configurations using steel mooring lines attached to the floor of the flume, either vertically or at an angle on each side of the breakwater. The draft of the breakwater was varied between approximately 87% and 143% of the outer diameter by adjusting the tension of the mooring lines. Additional controlled experiments were performed using the same pipe section under fixed conditions. Waves were measured using capacitance-type wave staffs located both upwave and downwave of the breakwater, and mooring forces were measured using force gauges. Experimental results indicated that the floating breakwater arrangements studied show potential for usefulness in field application, as wave heights were reduced by as much as 60% in some cases. Cable-moored models performed best when fully submerged relatively close to the still water surface. Mooring line slackness reduced the effectiveness of the model in wave attenuation but also reduced the amount of force incurred by the cable mooring system. The cylindrical model best attenuated shorter waves of low to moderate steepness.

Assessing Daily Evapotranspiration Methodologies from One-Time-of-Day sUAS and EC Information in the GRAPEX Project.

Daily evapotranspiration (ETd) plays a key role in irrigation water management and is particularly important in drought-stricken areas, such as California and high-value crops. Remote sensing allows for the cost-effective estimation of spatial evapotranspiration (ET), and the advent of small unmanned aerial systems (sUAS) technology has made it possible to estimate instantaneous high-resolution ET at the plant, row, and subfield scales. sUAS estimates ET using “instantaneous” remote sensing measurements with half-hourly/hourly forcing micrometeorological data, yielding hourly fluxes in W/m2 that are then translated to a daily scale (mm/day) under two assumptions: (a) relative rates, such as the ratios of ET-to-net radiation (Rn) or ET-to-solar radiation (Rs), are assumed to be constant rather than absolute, and (b) nighttime evaporation (E) and transpiration (T) contributions are negligible. While assumption (a) may be reasonable for unstressed, full cover crops (no exposed soil), the E and T rates may significantly vary over the course of the day for partially vegetated cover conditions due to diurnal variations of soil and crop temperatures and interactions between soil and vegetation elements in agricultural environments, such as vineyards and orchards. In this study, five existing extrapolation approaches that compute the daily ET from the “instantaneous” remotely sensed sUAS ET estimates and the eddy covariance (EC) flux tower measurements were evaluated under different weather, grapevine variety, and trellis designs. Per assumption (b), the nighttime ET contribution was ignored. Each extrapolation technique (evaporative fraction (EF), solar radiation (Rs), net radiation-to-solar radiation (Rn/Rs) ratio, Gaussian (GA), and Sine) makes use of clear skies and quasi-sinusoidal diurnal variations of hourly ET and other meteorological parameters. The sUAS ET estimates and EC ET measurements were collected over multiple years and times from different vineyard sites in California as part of the USDA Agricultural Research Service Grape Remote Sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX). Optical and thermal sUAS imagery data at 10 cm and 60 cm, respectively, were collected by the Utah State University AggieAir sUAS Program and used in the Two-Source Energy Balance (TSEB) model to estimate the instantaneous or hourly sUAS ET at overpass time. The hourly ET from the EC measurements was also used to validate the extrapolation techniques. Overall, the analysis using EC measurements indicates that the Rs, EF, and GA approaches presented the best goodness-of-fit statistics for a window of time between 1030 and 1330 PST (Pacific Standard Time), with the Rs approach yielding better agreement with the EC measurements. Similar results were found using TSEB and sUAS data. The 1030–1330 time window also provided the greatest agreement between the actual daily EC ET and the extrapolated TSEB daily ET, with the Rs approach again yielding better agreement with the ground measurements. The expected accuracy of the upscaled TSEB daily ET estimates across all vineyard sites in California is below 0.5 mm/day, (EC extrapolation accuracy was found to be 0.34 mm/day), making the daily scale results from TSEB reliable and suitable for day-to-day water management applications.

Opportunities for Partnership in Local Food Procurement: Mississippi Delta Schools

Abstract Objectives Procuring food for school meals is costly in a region like the fertile MS Delta. Hence, efforts to procure foods locally may yield substantial benefits in terms of economic return to the area and positive impacts on children's diets. The objective of this study was to determine food procurement amounts in two Mississippi Delta school districts, one participating in the Fresh Fruit and Vegetable Program (FFVP) and illustrate potential economic impacts of purchasing local. Methods Procurement data from two Mississippi Delta school districts, academic years 2018–2019 and 2019–2020, were compiled and grouped into 8 food categories. The food categories were fruits, vegetables, grains, protein, dairy, added sugars, fats, and miscellaneous. Amounts of procured foods were converted to weight in pounds and cost in dollars. Results For the FFVP district, dairy accounted for the largest cost (34%) followed by protein (23%), grains (19%), fruits (13%), and vegetables (10%) in 2018–2019. In 2019–2020, grains accounted for the largest cost (26%) followed by fruit (20%), protein (19%), and vegetables (12%). For the non-FFVP district, protein accounted for the largest cost (25%) followed by grains (24%), dairy (18%), fruits (14%), and vegetables (14%) in 2018–2019. In 2019–2020, grains accounted for the largest cost (26%) followed by protein (24%), dairy (19%), fruits (16%), and vegetables (11%). Conclusions Although school districts purchase substantial amounts of dairy, grains, protein, fruits and vegetables, very little if any is procured locally. Encouraging school food service administrators to connect with and purchase foods from local farmers may provide an economic boost to the agricultural community and will facilitate serving fresh, nutrient rich foods, such as fruits and vegetables, to school children. Funding Sources This research was funded by the USDA Agricultural Research Service.