The agricultural baseline database provides longrun, 10-year projections from USDA's annual long-term projections report. The database covers projections for major field crops (corn, sorghum, barley, oats, wheat, rice, soybeans, and upland cotton), and livestock (beef, pork, poultry and eggs, and dairy).

The Cover Crop Chart (v. 2.0) is designed to assist producers with decisions on the use of cover crops in crop and forage production systems. The chart, patterned after the periodic table of elements, includes information on 58 crop species that may be planted individually or in cocktail mixtures. Information on growth cycle, relative water use, plant architecture, seeding depth, forage quality, pollination characteristics, and nutrient cycling are included for most crop species. The Cover Crop Chart is easy to use, requiring only Adobe Acrobat software. Using the chart as a guide, users can select individual crop species by clicking on the name which will direct them to additional information about the selected crop. Icons within each crop page return the user to the chart, thereby easily allowing comparisons of different crops. The Cover Crop Chart represents a compendium of information from multiple sources throughout the U.S. and Canada, and is not based on research conducted at the USDA-ARS Northern Great Plains Research Laboratory (NGPRL). Primary sources of information included the Midwest Cover Crops Council, USDA - Sustainable Agriculture Research & Education (SARE), USDA - Natural Resources Conservation Service (NRCS) PLANTS Database, relevant peer-reviewed journal articles, and the 3rd edition of *Managing Cover Crops Profitably* (Andy Clark, Editor). Information on specific crops is occasionally generalized and/or approximate to accommodate wide variation in geographic/agronomic conditions, and therefore may not reflect performance in on-farm conditions. Accordingly, USDA - Agricultural Research Service (ARS) makes no guarantee to the performance of specific crops based on information provided within the Cover Crop Chart. The Cover Crop Chart is produced and distributed by the staff of the USDA-ARS NGPRL, Mandan, ND. Mark Liebig and Holly Johnson contributed to the design and content of the chart with input from NGPRL staff and producers and technicians from the Area IV Soil Conservation Districts of North Dakota and NRCS staff at the Bismarck and Dickinson Field/Area Offices.

A systematic search of the literature using Google Scholar, (https://scholar.google.com/) and Web of Science was used to identify studies that examined the effects of individual compounds or mixtures of MVOCs on the behavioral responses of stored-product arthropods. Stored-product arthropods were defined as those insects and arachnids attacking stored, durable commodities in the post-harvest supply chain at any of the successive links, including storage, transportation, processing, and marketing. Where applicable, we parsed studies into component experiments where behavioral responses or other factors such as type of assays or measured variables may have differed (e.g. dosage, compound, etc.). We classified each test as resulting in statistically significant attraction (+), repellence (−), or neither (○) compared to a negative or positive control. We excluded any studies lacking appropriate negative or positive controls, lacking replication, or lacking sufficient details on the identity of tested substrates to enable appropriate interpretation. Terms used to search databases included the following singly and/or in combination: “fungal”, “volatiles”, “stored products”, “insect behavior”, “insect-microbe”, “interactions”, “semiochemicals”, “mycotoxin”, “behavioral response”, “attraction”, and “postharvest”, and combinations thereof. In addition, we kept track of methodology used for tests, response variables, target insect, insect stage, and microbial taxon. We split our analysis up between tests with complex (but usually uncharacterized) blends of MVOCs, and those with known individual or known component mixtures of MVOCs.

Data reported in research published in Crop Science, “Mapping the quantitative field resistance to stripe rust in a hard winter wheat population ‘Overley’ × ‘Overland.’” Authors are Wardah Mustahsan, Mary J. Guttieri, Robert L. Bowden, Kimberley Garland-Campbell, Katherine Jordan, Guihua Bai, Guorong Zhang from USDA Agricultural Research Service and Kansas State University. This study was conducted to identify quantitative trait loci (QTL) associated with field resistance to stripe rust, also known as yellow rust (YR), in hard winter wheat. Stripe rust infection type and severity were rated in recombinant inbred lines (RILs, n=204) derived from a cross between hard red winter wheat cultivars ‘Overley’ and ‘Overland’ in replicated field trials in the Great Plains and Pacific Northwest. RILs (n=184) were genotyped with reduced representation sequencing to produce SNP markers from alignment to the ‘Chinese Spring’ reference sequence, IWGSC v2.1, and from alignment to the reference sequence for ‘Jagger’, which is a parent of Overley. Genetic linkage maps were developed independently from each set of SNP markers. QTL analysis identified genomic regions on chromosome arms 2AS, 2BS, 2BL, and 2DL that were associated with stripe rust resistance using multi-environment best linear unbiased predictors for stripe rust infection type and severity. Results for the two linkage maps were very similar. PCR-based SNP marker assays associated with the QTL regions were developed to efficiently identify these genomic regions in breeding populations. Field response to YR was evaluated in seven trials: Rossville, KS (2018 and 2019), Hays, KS (2019), Pullman, WA (2019 and 2020) and Central Ferry, WA (2019 and 2020). An augmented experimental design was used at Rossville, KS with highly replicated checks and two full replications of RILs (n=187 in 2018; n=204 in 2019). The field experiment at Hays was arranged in a partially replicated augmented design with one or two replications of each RIL (n=194). The parental checks (Overley and Overland) were represented in three blocks for each of the two field replications at Hays, and RILs were distributed among blocks; not all RILs were present in each replication. RILs were arranged in an augmented design with two replications at Pullman (n=204 RILs) and Central Ferry (n=155 RILs in 2019; n=204 in 2020). At Pullman and Central Ferry. The trials at Rossville, KS were inoculated using an inoculum consisting of equal parts of four isolates that were all virulent to Yr9. Two isolates were collected in Kansas in 2010 and had virulence to Yr17 but not QYr.tamu-2B. The other two isolates were from Kansas in 2012 and had virulence to QYr.tamu-2B, but not Yr17. Susceptible spreader rows (KS89180B, carrying Yr9) were inoculated several times during the tillering stage in the evenings with an ultra-low volume sprayer using a suspension of 2 mL of fresh urediniospores in 1 L of Soltrol 170 isoparaffin oil. Trials at Pullman, WA and Central Ferry, WA were evaluated under natural inoculum supplemented by a mixture of isolates collected in the previous field season. The trial at Hays, KS was evaluated under natural infection. Data collection at Rossville, KS began once the susceptible check (KS89180B) had an infection severity coverage of ~10% and continued until senescence. In Rossville, disease ratings (IT and SEV) were collected on 16, 22, and 28th of May 2019. Most ratings in Rossville were taken some time after heading from Zadoks stages 55 to 70. In Pullman, disease ratings were collected on July 1 and 12. In Central Ferry, disease ratings were taken on 12th and 18th of June 2019. The second rating date was used for subsequent statistical analysis. In Hays, disease ratings were taken on June 1, 2019, when the plants were in early booting or heading stages (Zadoks 31-41). Stripe rust evaluations were measured using two disease rating scales: IT (0-9; from no infection to highly susceptible, Line and Qayoum, 1992) and SEV based on visual estimation of the percent flag leaf area affected by the pathogen including associated chlorosis and necrosis (0-100%). DNA was extracted from seedlings, and genotyping-by-sequencing was conducted as described previously (Guttieri, 2020) on a subset of 189 lines (187 RILS and 2 parents) of which 23 RILs were F6-derived and 164 RILs were F9-derived. Single nucleotide polymorphisms (SNPs) were identified in parallel using reference-based calling in the TASSEL pipeline (Bradbury et al., 2007) using both the IWGSC v2.1 reference genome (Zhu et al., 2021) and the Jagger reference sequence (Wheat Genomes Project (http://www.10wheatgenomes.com/10-wheat-genomes-project-and-the-wheat-ini...). The TASSEL pipeline was executed with the following parameters: minimum read count = 1, minimum quality score = 0, minimum locus coverage = 0.19, and minimum minor allele frequency = 0.005, minimum heterozygous proportion = 0, and removal of minor SNP states. The resulting SNP datasets from each reference sequence were filtered in TASSEL by taxa (RILs) and sites (SNPs). The RILs were filtered to include those RILs for which at least 20% sites were present. The sites were filtered to include sites for which > 60% of RILs were called, minor allele frequency (MAF) > 0.25, maximum allele frequency < 0.75, maximum heterozygous proportion = 0.25, and removal of minor SNP states. The ABH plugin in TASSEL was applied to this reduced dataset to identify parental genotypes. Resources in this dataset: Resource Title: Multilocation Stripe Rust Data File Name: MultiLocRawData_Yr.xslx Resource Title: OvOv_CS_TasselSNPCalls File Name: KSM17-OvOv-parents_merge1.hmp_.txt Resource Description: Output of TASSEL GBS SNP calling pipeline using Chinese Spring v2 refseq. Starting point for map construction pipeline. Resource Title: OvOv GBS SNP Calls Jagger RefSeq File Name: KSM17-OvOv-Jagger_pmerge1.hmp_.txt Resource Description: TASSEL output from reference-based SNP calling using the Jagger reference sequence Resource Title: QTL-Associated KASP Markers with IT and SEV BLUPs File Name: KASP_Data_IT_SEV.xlsx Resource Description: Multilocation best linear unbiased predictors (BLUPs) for stripe rust infection type and severity of recombinant inbred lines. KASP assay results for QTL-associated SNPs, coded Overley = 2, Overland = 0, Het = 1, Missing = "."

Farming Systems Study for Greenhouse gas Reduction through Agricultural Carbon Enhancement network in Morris, Minnesota Tillage is decreasing globally due to recognized benefits of fuel savings and improved soil health in the absence of disturbance. However, a perceived inability to control weeds effectively and economically hinders no-till adoption in organic production systems in the Upper Midwest, USA. A strip-tillage (ST) strategy was explored as an intermediate approach to reducing fuel use and soil disturbance, and still controlling weeds. An 8-year comparison was made between two tillage approaches, one primarily using ST the other using a combination of conventional plow, disk and chisel tillage [conventional tillage (CT)]. Additionally, two rotation schemes were explored within each tillage system: a 2-year rotation (2y) of corn (Zea mays L.), and soybean (Glycine max [L.] Merr.) with a winter rye (Secale cereale L.) cover crop; and a 4-year rotation (4y) of corn, soybean, spring wheat (Triticum aestivum L.) underseeded with alfalfa (Medicago sativa L.), and a second year of alfalfa. These treatments resulted in comparison of four main management systems CT-2y, CT-4y, ST-2y and ST-4y, which also were managed under fertilized and non-fertilized conditions. Yields, whole system productivity (evaluated with potential gross returns), and weed seed densities (first 4 years) were measured. Across years, yields of corn, soybean and wheat were greater by 34% or more under CT than ST but alfalfa yields were the same. Within tillage strategies, corn yields were the same in 2y and 4y rotations, but soybean yields, only under ST, were 29% lower in the fertilized 4y than 2 yr rotation. In the ST-4y system yields of corn and soybean were the same in fertilized and non-fertilized treatments. Over the entire rotation, system productivity was highest in the fertilized CT-2y system, but the same among fertilized ST-4y, and non-fertilized ST-2y, ST-4y, and CT-4y systems. Over the first 4 years, total weed seed density increased comparatively more under ST than CT, and was negatively correlated to corn yields in fertilized CT systems and soybean yields in the fertilized ST-2y system. These results indicated ST compromised productivity, in part due to insufficient weed control, but also due to reduced nutrient availability. ST and diverse rotations may yet be viable options given that overall productivity of fertilized ST-2y and CT-4y systems was within 70% of that in the fertilized CT-2y system. Closing the yield gap between ST and CT would benefit from future research focused on organic weed and nutrient management, particularly for corn.

This product summarizes fertilizer consumption in the United States by plant nutrient and major fertilizer products—as well as consumption of mixed fertilizers, secondary nutrients, and micronutrients—for 1960 through the latest year for which statistics are available. The share of planted crop acreage receiving fertilizer, and fertilizer applications per receiving acre (by nutrient), are presented for major producing States for corn, cotton, soybeans, and wheat (data on nutrient consumption by crop start in 1964). Fertilizer farm prices and indices of wholesale fertilizer prices are also available.

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.

What is Stored Grain Advisor? Stored Grain Advisor (SGA) is a decision support system for the managemement of insect pests of farm-stored wheat. SGA predicts the likelihood of insect infestation, and recommends preventative and remedial action. It also provides advice on how to sample and identify insect pests of stored wheat. Computer models of insect population growth allow SGA to predict future insect populations in the grain bin, as well as the breakdown of insecticides, the effects of fumigation, and cooling the wheat with aeration. The ability of Stored Grain Advisor to graphically show insect population trends makes it a powerful educational tool. Requirements Version 3.04 runs under Microsoft Windows 98, 2000, XP, and 32 bit Vista. Instructions Remove any previous versions of SGA using the uninstaller included with the program. Download SgaSetup.exe to your computer. Run SgaSetup.exe and follow the Installer's instructions. Delete SgaSetup.exe.

What is Stored Grain Advisor? Stored Grain Advisor (SGA) is a decision support system for the managemement of insect pests of farm-stored wheat. SGA predicts the likelihood of insect infestation, and recommends preventative and remedial action. It also provides advice on how to sample and identify insect pests of stored wheat. Computer models of insect population growth allow SGA to predict future insect populations in the grain bin, as well as the breakdown of insecticides, the effects of fumigation, and cooling the wheat with aeration. The ability of Stored Grain Advisor to graphically show insect population trends makes it a powerful educational tool. Requirements Version 3.04 runs under Microsoft Windows 98, 2000, XP, and 32 bit Vista. Instructions Remove any previous versions of SGA using the uninstaller included with the program. Download SgaSetup.exe to your computer. Run SgaSetup.exe and follow the Installer's instructions. Delete SgaSetup.exe. SGA Pro SGA Pro was designed for use in commercial elevators as part of the Areawide IPM Project for stored grain. Grain samples are taken with a vacuum probe and processed over an inclined sieve. SGA Pro analyzes the insect data, grain temperatures and moistures, and determines which bins need to be fumigated. (NOTE: available but unsupported.) This program runs under Microsoft Windows 98, 2000, XP, Vista, and Win7. Note: Win7 may require Windows Classic theme to display properly. SGA Pro was designed for use in commercial elevators (concrete silos, etc). This system takes a sampling based approach to managing insect pests. Grain samples are taken with a vacuum probe, and processed over an inclined sieve. SGA Pro analyzes the insect data, grain temperatures and moistures, and determines which bins need to be fumigated. This software was developed for the Areawide IPM Project.

This data product contains statistics on wheat-including the five classes of wheat: hard red winter, hard red spring, soft red winter, white, and durum-and rye. Includes data published in the monthly Wheat Outlook and previously annual Wheat Yearbook. Data are monthly, quarterly, and/or annual depending upon the data series. Most data are on a marketing year basis, but some are calendar year.