Culicoides biting midges are important vectors of diverse microbes such as viruses, protozoa, and nematodes that cause diseases in wild and domestic animals. However, little is known about the role of microbial communities in midge larval habitat utilization in the wild. In this study, we characterized microbial communities (bacterial, protistan, fungal and metazoan) in soils from disturbed (bison and cattle grazed) and undisturbed (non-grazed) pond and spring potential midge larval habitats. We evaluated the influence of habitat and grazing disturbance and their interaction on microbial communities, diversity, presence of midges, and soil properties. These data can be used to better understand environmental microbial communities in tallgrass prairie ecosystems associated with grazed versus ungrazed pond and spring habitats and to draw inferences on the interactions of these communities and soil properties with the presence of biting midge larvae. These data should not be used to make inferences for ecosystems other than tallgrass prairie, for animal management methods other than open cow-calf or bison grazing (such as feedlots, dairies, or stockyards), or for other grazing mammals (such as sheep or goats). These data were collected between the months of September and December and therefore are not representative of microbial communities present from January through August. Abbreviations used include Total Carbon (TC), Total Nitrogen (TN), Organic Matter (OM), Konza Prairie Biological Station (KPBS), Operational Taxonomic Unit (OTU), Principal Coordinates Analysis (PCoA), ribosomal RNA (rRNA), and vesicular stomatitis virus (VSV). The raw Illumina MiSeq sequence data for this project can be found here: https://www.ncbi.nlm.nih.gov/bioproject/PRJNA862140 Resources in this dataset: Resource Title: Metadata for Midge Larval Habitat Soil Microbiome File Name: Metadata for NCBI Accession PRJNA862140.xlsx Resource Description: This spreadsheet links the raw sequence reads on NCBI with data on the presence/absence of Culicoides midges and soil chemistry data (% total soil nitrogen, % total soil carbon, and % organic matter).

The USDA-Agricultural Research Service Central Plains Experimental Range (CPER) is a Long-Term Agroecosystem Research (LTAR) network site located ~20 km northeast of Nunn, in north-central Colorado, USA. In 1939, scientists established the Long-term Grazing Intensity study (LTGI) with four replications of light, moderate, and heavy grazing. Each replication had three 129.5 ha pastures with the grazing intensity treatment randomly assigned. Today, one replication remains. Light grazing occurs in pasture 23W (9.3 Animal Unit Days (AUD)/ha, targeted for 20% utilization of peak growing-season biomass), moderate grazing in pasture 15E (12.5 AUD/ha, 40% utilization), and heavy grazing in pasture 23E (18.6 AUD/ha, 60% utilization). British- and continental-breed yearling cattle graze the pastures season-long from mid-May to October except when forage limitations shorten the grazing season. Individual raw data on cattle entry and exit weights, as well as weights every 28-days during the grazing season are available from 2000 to 2019. Cattle entry and exit weights are included in this dataset. Weight outliers (± 2 SD) are flagged for calculating summary statistics or performing statistical analysis.

The USDA-Agricultural Research Service High Plains Grasslands Research Station (HPGRS) is located in Cheyenne, Wyoming, USA. In 1982, a long-term stocking rate study on northern mixed-grass prairie was initiated with season-long (early June to October) grazing. Stocking rates defined as light (35% below NRCS recommended rate, 15 yearlings per 80 ha), moderate (NRCS recommended rate, 4 yearlings per 12ha), and heavy (33% above NRCS recommended rate, 4 yearlings per 9 ha). British- and continental-breed yearling cattle were used throughout the study years. When forage supply was limited due to drought, grazing seasons were shortened or cattle were not grazed for that season. Individual raw data on cattle entry and exit weights are available from 1982 to 2022. No grazing occurred in the years 1989, 2000, and 2002 due to drought conditions. Weight gain outliers (± 2 sd of treatment mean) were removed from the dataset.

The objective of this study was to explore the R. microplus microbiome by applying the bacterial 16S tag-encoded FLX-titanium amplicon pyrosequencing (bTEFAP) technique to characterize its bacterial diversity. Pyrosequencing was performed on adult males and females, eggs, and gut and ovary tissues from adult females derived from samples of R. microplus collected during outbreaks in southern Texas. Raw data from bTEFAP were screened and trimmed based upon quality scores and binned into individual sample collections. Bacteria identified to the species level include Staphylococcus aureus, Staphylococcus chromogenes, Streptococcus dysgalactiae, Staphylococcus sciuri, Serratia marcescens, Corynebacterium glutamicum, and Finegoldia magna. One hundred twenty-one bacterial genera were detected in all the life stages and tissues sampled. The total number of genera identified by tick sample comprised: 53 in adult males, 61 in adult females, 11 in gut tissue, 7 in ovarian tissue, and 54 in the eggs. Notable genera detected in the cattle tick include Wolbachia, Coxiella, and Borrelia. The molecular approach applied in this study allowed us to assess the relative abundance of the microbiota associated with R. microplus. Ticks are regarded as the most relevant vectors of disease-causing pathogens in domestic and wild animals. The cattle tick, Rhipicephalus (Boophilus) microplus, hinders livestock production in tropical and subtropical parts of the world where it is endemic. Tick microbiomes remain largely unexplored.

This dataset reports discovery and initial comparative analysis of 88 presumptive microRNA (miRNA) sequences from the stable fly, obtained using high-throughput sequencing of small RNAs. The majority of stable fly miRNAs were 22-23 nucleotides (nt) in length. Many miRNAs were arthropod specific, and several mature miRNA sequences showed greater sequence identity to miRNAs from other blood-feeding dipterans such as mosquitoes rather than to Drosophilids. This initial step in characterizing the stable fly microRNAome provides a basis for further analyses of life stage-specific and tissue-specific expression to elucidate their functional roles in stable fly biology. The stable fly, Stomoxys calcitrans (L.), is a serious ectoparasite affecting animal production and health of both animals and humans. Stable fly control relies largely on chemical insecticides; however, the development of insecticide resistance as well as environmental considerations requires continued discovery research to develop novel control technologies. MicroRNAs are a class of short noncoding RNAs that have been shown to be important regulators of gene expression across a wide variety of organisms, and may provide an innovative approach with regard to development of safer more targeted control technologies.

The bacterial 16S tag-encoded FLX-titanium amplicon pyrosequencing (bTEFAP) method was used to carry out the classification analysis of bacterial flora in adult female and male horn flies and horn fly eggs. The bTEFAP method identified 16S rDNA sequences in our samples which allowed the identification of various prokaryotic taxa associated with the life stage examined. This is the first comprehensive report of bacterial flora associated with the horn fly using a culture-independent method. Several rumen, environmental, symbiotic and pathogenic bacteria associated with the horn fly were identified and quantified. This is the first report of the presence of Wolbachia in horn flies of USA origin and is the first report of the presence of Rikenella in an obligatory blood feeding insect. Adult horn flies were collected on a single date from pastured cattle at the Louisiana State University Agricultural Center, St. Gabriel Research Station using aerial nets. Within 1 h after collection the flies were transferred to large sterile Erlenmeyer flasks and maintained in total darkness for 1.5 h and 30°C to allow flies to oviposit on the flask bottom [73]. Adult flies were released from the flasks into a cage and eggs were collected by rinsing with distilled water onto a filter paper. Both the eggs and adult flies were frozen at −80°C. To preserve nucleic acid integrity, adults were sexed on dry ice prior to freezing. Each sample used for DNA extraction and pyrosequencing consisted of 5 adult males, 5 adult females or 50 eggs pooled together and homogenized. Three replicates of adult male, adult female and eggs were analyzed. The horn fly, Haematobia irritans, is one of the most economically important pests of cattle. Insecticides have been a major element of horn fly management programs. Growing concerns with insecticide resistance, insecticide residues on farm products, and non-availability of new generation insecticides, are serious issues for the livestock industry. Alternative horn fly control methods offer the promise to decrease the use of insecticides and reduce the amount of insecticide residues on livestock products and give an impetus to the organic livestock farming segment. The horn fly, an obligatory blood feeder, requires the help of microflora to supply additional nutrients and metabolize the blood meal. Recent advancements in DNA sequencing methodologies enable researchers to examine the microflora diversity independent of culture methods.

Grass-Cast: Experimental Grassland Productivity Forecast for the Great Plains Grass-Cast uses almost 40 years of historical data on weather and vegetation growth in order to project grassland productivity in the Western U.S. More details on the projection model and method can be found at https://esajournals.onlinelibrary.wiley.com/doi/full/10.1002/ecs2.3280. Every spring, ranchers in the drought‐prone U.S. Great Plains face the same difficult challenge—trying to estimate how much forage will be available for livestock to graze during the upcoming summer grazing season. To reduce this uncertainty in predicting forage availability, we developed an innovative new grassland productivity forecast system, named Grass‐Cast, to provide science‐informed estimates of growing season aboveground net primary production (ANPP). Grass‐Cast uses over 30 yr of historical data including weather and the satellite‐derived normalized vegetation difference index (NDVI)—combined with ecosystem modeling and seasonal precipitation forecasts—to predict if rangelands in individual counties are likely to produce below‐normal, near‐normal, or above‐normal amounts of grass biomass (lbs/ac). Grass‐Cast also provides a view of rangeland productivity in the broader region, to assist in larger‐scale decision‐making—such as where forage resources for grazing might be more plentiful if a rancher’s own region is at risk of drought. Grass‐Cast is updated approximately every two weeks from April through July. Each Grass‐Cast forecast provides three scenarios of ANPP for the upcoming growing season based on different precipitation outlooks. Near real‐time 8‐d NDVI can be used to supplement Grass‐Cast in predicting cumulative growing season NDVI and ANPP starting in mid‐April for the Southern Great Plains and mid‐May to early June for the Central and Northern Great Plains. Here, we present the scientific basis and methods for Grass‐Cast along with the county‐level production forecasts from 2017 and 2018 for ten states in the U.S. Great Plains. The correlation between early growing season forecasts and the end‐of‐growing season ANPP estimate is >50% by late May or early June. In a retrospective evaluation, we compared Grass‐Cast end‐of‐growing season ANPP results to an independent dataset and found that the two agreed 69% of the time over a 20‐yr period. Although some predictive tools exist for forecasting upcoming growing season conditions, none predict actual productivity for the entire Great Plains. The Grass‐Cast system could be adapted to predict grassland ANPP outside of the Great Plains or to predict perennial biofuel grass production. This new experimental grassland forecast is the result of a collaboration between Colorado State University, U.S. Department of Agriculture (USDA), National Drought Mitigation Center, and the University of Arizona. Funding for this project was provided by the USDA Natural Resources Conservation Service (NRCS), USDA Agricultural Research Service (ARS), and the National Drought Mitigation Center. Watch for updates on the Grass-Cast website or on Twitter (@PeckAgEc). Project Contact: Dannele Peck, Director of the USDA Northern Plains Climate Hub, at dannele.peck@ars.usda.gov or 970-744-9043.

Grass-Cast: Experimental Grassland Productivity Forecast for the Great Plains Grass-Cast uses almost 40 years of historical data on weather and vegetation growth in order to project grassland productivity in the Western U.S. More details on the projection model and method can be found at https://esajournals.onlinelibrary.wiley.com/doi/full/10.1002/ecs2.3280. Every spring, ranchers in the drought‐prone U.S. Great Plains face the same difficult challenge—trying to estimate how much forage will be available for livestock to graze during the upcoming summer grazing season. To reduce this uncertainty in predicting forage availability, we developed an innovative new grassland productivity forecast system, named Grass‐Cast, to provide science‐informed estimates of growing season aboveground net primary production (ANPP). Grass‐Cast uses over 30 yr of historical data including weather and the satellite‐derived normalized vegetation difference index (NDVI)—combined with ecosystem modeling and seasonal precipitation forecasts—to predict if rangelands in individual counties are likely to produce below‐normal, near‐normal, or above‐normal amounts of grass biomass (lbs/ac). Grass‐Cast also provides a view of rangeland productivity in the broader region, to assist in larger‐scale decision‐making—such as where forage resources for grazing might be more plentiful if a rancher’s own region is at risk of drought. Grass‐Cast is updated approximately every two weeks from April through July. Each Grass‐Cast forecast provides three scenarios of ANPP for the upcoming growing season based on different precipitation outlooks. Near real‐time 8‐d NDVI can be used to supplement Grass‐Cast in predicting cumulative growing season NDVI and ANPP starting in mid‐April for the Southern Great Plains and mid‐May to early June for the Central and Northern Great Plains. Here, we present the scientific basis and methods for Grass‐Cast along with the county‐level production forecasts from 2017 and 2018 for ten states in the U.S. Great Plains. The correlation between early growing season forecasts and the end‐of‐growing season ANPP estimate is >50% by late May or early June. In a retrospective evaluation, we compared Grass‐Cast end‐of‐growing season ANPP results to an independent dataset and found that the two agreed 69% of the time over a 20‐yr period. Although some predictive tools exist for forecasting upcoming growing season conditions, none predict actual productivity for the entire Great Plains. The Grass‐Cast system could be adapted to predict grassland ANPP outside of the Great Plains or to predict perennial biofuel grass production. This new experimental grassland forecast is the result of a collaboration between Colorado State University, U.S. Department of Agriculture (USDA), National Drought Mitigation Center, and the University of Arizona. Funding for this project was provided by the USDA Natural Resources Conservation Service (NRCS), USDA Agricultural Research Service (ARS), and the National Drought Mitigation Center. Watch for updates on the Grass-Cast website or on Twitter (@PeckAgEc). Project Contact: Dannele Peck, Director of the USDA Northern Plains Climate Hub, at dannele.peck@ars.usda.gov or 970-744-9043. This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: • https://data.nal.usda.gov/system/files/Grass-Cast_AgDataCommons_download.html • https://data.nal.usda.gov/system/files/R_access_script.zip • https://data.nal.usda.gov/system/files/ANPP.xlsx • https://data.nal.usda.gov/system/files/Cattle_weight_gains.xlsx • https://data.nal.usda.gov/system/files/NDVI.xlsx • https://data.nal.usda.gov/system/files/NDVI_raw.xlsx • https://data.nal.usda.gov/system/files/Grass-Cast_sitelist.xlsx For complete information, please visit https://data.gov.

Date of freeze for historical (1985-2005) and future (2071-2090, RCP 8.5) time periods, and absolute change between them, based on analysis of MACAv2METDATA. Average historical temperature change, between 1948-1968 and 1996-2016 averages, in Celsius. Calculated using averages of minimum and maximum monthly values during these time periods. Values are based on TopoWx data. Download this data or get more information

The Livestock and Meat Trade Data Set contains monthly and annual data for imports and exports of live cattle, hogs, sheep, and goats, as well as beef and veal, pork, lamb and mutton, chicken meat, turkey meat, and eggs. The tables report physical quantities, not dollar values or unit prices. Data on beef and veal, pork, and lamb and mutton are on a carcass-weight-equivalent basis. Breakdowns by country are included.

The ‘Management Strategies for Soil Quality’ study was established in 1993 by Dr. Don Tanaka (USDA-ARS-NGPRL) to evaluate long-term impacts of minimum and no-till cropping systems on crop yield, precipitation use, and soil properties. The study was designed with six crop sequences (whole plot) each split by tillage type (split plot). All phases of each crop sequence are present every year, and treatments are replicated three times. See record in the GeoData catalog at https://geodata.nal.usda.gov/geonetwork/srv/eng/catalog.search#/metadata... for more information and links to the data resources.

Nitrogen Source Study for Greenhouse gas Reduction through Agricultural Carbon Enhancement network in Mandan, North Dakota Use of dietary amendments to reduce nitrogen (N) in excreta represents a possible strategy to decrease greenhouse gas (GHG) emissions from livestock. In this regard, ingestion of small amounts of condensed quebracho tannin has been found to reduce N concentration in livestock urine. In this study, we sought to quantify the effects of tannin-affected cattle urine, normal cattle urine, and NH4NO3 in solution on greenhouse gas flux. Carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) flux was measured using static chamber methodology from the three N treatments and a no application control over a six-week period in a mixed grass prairie in west-central North Dakota, USA. Over the course of the study, average CO2 emission was greatest from normal urine (335 ± 8 mg C m-2 hr-1) and least from the control (229 ± 19 mg C m-2 hr-1), with intermediate fluxes for the tannin urine and NH4NO3 treatments (290 ± 27 and 286 ± 54 mg C m-2 hr-1, respectively). Methane uptake was prevalent throughout the study, as soil conditions were predominantly warm and dry. Uptake of CH4 was greatest within the control (-30 ± 2 µg C m-2 hr-1) and least in the tannin urine treatment (-12 ± 4 µg C m-2 hr-1). Uptake of CH4 was over 40% less within the tannin urine treatment as compared to normal urine, and may have been repressed by the capacity of tannin to bind monooxygenases responsible for CH4 oxidation. Average N2O emission from NH4NO3 solution was more than twice that of all other treatments. Though the tannin urine treatment possessed 34% less N than normal cattle urine, cumulative N2O emission between the treatments did not differ. Results from this study suggest the use of condensed quebracho tannin as a dietary amendment for livestock does not yield GHG mitigation benefits in the short-term.

The Price Discovery is a web based tool that allows users to view pricing information for the following crops covered by the Common Crop Insurance and the Area Risk Protection policies: barley, canola (including rapeseed), corn, cotton, grain sorghum, rice, soybeans, sunflowers, and wheat, and coverage prices, rates and actual ending values for the Livestock Risk Protection program, and expected and actual gross margin information for the Livestock Gross Margin program.

Quick Stats is the National Agricultural Statistics Service's (NASS) online, self-service tool to access complete results from the 1997, 2002, 2007, and 2012 Censuses of Agriculture as well as the best source of NASS survey published estimates. The census collects data on all commodities produced on U.S. farms and ranches, as well as detailed information on expenses, income, and operator characteristics. The surveys that NASS conducts collect information on virtually every facet of U.S. agricultural production.

Quick Stats API is the programmatic interface to the National Agricultural Statistics Service's (NASS) online database containing results from the 1997, 2002, 2007, and 2012 Censuses of Agriculture as well as the best source of NASS survey published estimates. The census collects data on all commodities produced on U.S. farms and ranches, as well as detailed information on expenses, income, and operator characteristics. The surveys that NASS conducts collect information on virtually every facet of U.S. agricultural production.