This is digital research data corresponding to a published manuscript, Cover cropping history affects cotton boll distribution, lint yields, and fiber quality, in Crop Science, Vol. 63 p. 1209–1220. There has been limited introduction of new cover crop species into cotton (Gossypium hirsutum L.) production within the last 30 years. Mounting evidence shows that traditional cover cropping species may be detrimental to cotton production, either by depleting soil fertility with crop removal, immobilizing minerals from high carbon residue, or excessive quantity of residue remaining at planting. The objective of this study was to determine the effects of growing a novel cover crop species, carinata (Brassica carinata A. Braun), as a winter annual cover crop for cotton rotation in the southeastern Coastal Plain. Over a 2-year period, carinata, winter wheat (Triticum aestivum L.), and fallow covers were maintained over winter months, then rotated into cotton. Each year, seedcotton and lint yields were collected, along with subsamples for ginning and subsequent fiber quality analyses. Additionally, end-of-season plant mapping was conducted on plants from 1-m of row per plot to determine cover crop effects on boll formation, retention, and distribution, as well as canopy architecture.

The datasets and code presented in this Data in Brief article are related to the research article entitled "Comprehensiveness of conservation of useful wild plants: an operational indicator for biodiversity and sustainable development targets". The indicator methodology includes five main steps, each requiring and producing data, which are fully described and available here. These data include: species taxonomy, uses, and general geographic information (dataset 1); species occurrence data (dataset 2); global administrative areas data (dataset 3); eco-geographic predictors used in species distribution modeling (dataset 4); a world map raster file (dataset 5); species spatial distribution modeling outputs (dataset 6); ecoregion spatial data used in conservation analyses (dataset 7); protected area spatial data used in conservation analyses (dataset 8); and countries, sub-regions, and regions classifications data (dataset 9). These data are available at http://dx.doi.org/10.17632/2jxj4k32m2.1. In combination with the openly accessible methodology code (https://github.com/CIAT-DAPA/UsefulPlants-Indicator), these data facilitate indicator assessments and serve as a baseline against which future calculations of the indicator can be measured. The data can also contribute to other species distribution modeling, ecological research, and conservation analysis purposes.

Blueberry is an economically important berry crop. Both production and consumption of blueberries have increased sharply worldwide in recent years at least partly due to their known health benefits. The development of improved genomic resources for blueberry, such as a well-assembled genome and transcriptome, could accelerate breeding through genomic-assisted approaches. To enrich available transcriptome data and identify genes potentially involved in fruit quality, RNA sequencing was performed on fruit tissue from two northern-adapted hybrid blueberry breeding populations. RNA-seq was carried out using the Illumina HiSeqTM 2500 platform. Because of the absence of a reference-grade genome for blueberry, a transcriptome was de novo assembled from this RNA-seq data and other publicly available transcriptome data from blueberry downloaded from the National Center for Biotechnology Information (NCBI) Short Read Archive (SRA) using Trinity. After removing redundancy, this resulted in a dataset of 91,861 blueberry unigenes. This unigene dataset was functionally annotated using the NCBI-Nr protein database. All raw reads from the breeding populations were deposited in the NCBI SRA with accession numbers SRR6281886, SRR6281887, SRR6281888, and SRR6281889. The de novo transcriptome assembly was deposited at NCBI Transcriptome Shotgun Assembly (TSA) database with accession number GGAB00000000. These data will provide real expression evidence for the blueberry genome gene prediction and gene functional annotation and a reference transcriptome for future gene expression studies involving blueberry fruit.

This data set contains 32 million annotated SNPs having an average SNP density of 30 SNPs per kb and 12 non-synonymous SNPs per gene model. These SNPs were identified from a genetically diverse, worldwide, collection of soybean germplasm representing wild, landrace, and improved cultivars. A combination of new and publicly available re-sequencing data was used in this analysis. The accession genotypes and their annotations are described in the manuscript titled: 'Analysis and characterization of 1500 diverse genome sequences as a versatile resource for post-genomics research'.

Watermelon (Citrullus lanatus) and other cucurbits are important crops grown in Guatemala for local consumption and export. The whitefly (Bemisia tabaci) vector of Cucurbit yellow stunting disorder virus (CYSDV), Melon chlorotic leaf curl virus (MCLCuV), and Squash vein yellowing virus (SqVYV) was observed in fields with numbers increasing during the season. Four samplings of crowns, peduncles, and/or leaves of symptomatic plants were made in March and April 2015. Total RNA was extracted from symptomatic plant tissue and tested by RT-PCR for SqVYV, CYSDV, Papaya ringspot virus (PRSV), and/or begomoviruses. Primers specific for the coat protein gene of SqVYV (1020 bp), CYSDV (707 bp), or PRSV (511 bp), and degenerate begomovirus primers (1159 or 533 bp) amplified products of the expected sizes from 15 of 24, 20 of 24, 4 of 24, or 8 of 8 plants, respectively. SqVYV amplicons from six individual plants from the fourth sampling, SqVYV and CYSDV amplicons from a pool of plants from the third sampling, and degenerate begomovirus amplicons from the second sampling were cloned in the pGEM-T vector. Five clones of each amplicon were sequenced in both directions and representative consensus sequences were deposited in GenBank (Accession Nos. KT007178 to KT007183). Sequence analysis demonstrated that SqVYV coat protein gene sequences from Guatemala shared 99 to 100% nucleotide (nt) identity with each other, and 97 to 98% nt identity with divergent SqVYV isolates previously described from Florida (e.g., WM2005aHi, GenBank Accession No. JF897974) and California (GenBank Accession No. KP218061), but only 90% nt identity with the predominant SqVYV isolate found in Florida (e.g., Sq2003Hi, GenBank Accession No. EU259611) . Tissue blots were prepared from crowns and peduncles from the first, third, and fourth samplings, and tested by tissue blot nucleic acid hybridization assay for SqVYV. Tissue blots indicated SqVYV infection in an additional 48 of 102 watermelon samples, and cylindrical inclusions typical of SqVYV were observed in phloem tissue from the fourth sampling by light microscopy, confirming the identification of SqVYV. This is the first report of SqVYV infecting watermelon in Central America.

Sweetpotato (Ipomoea batatas) plays a critical role in food security and is the most important root crop worldwide following potatoes and cassava. In the United States (US), it is valued at over $700 million USD. There are two sweetpotato germplasm collections (Plant Genetic Resources Conservation Unit and US Vegetable Laboratory) maintained by the USDA, ARS for sweetpotato crop improvement. To date, no genome-wide assessment of genetic diversity within these collections has been reported in the published literature. In our study, population structure and genetic diversity of 417 USDA sweetpotato accessions originating from 8 broad geographical regions (Africa, Australia, Caribbean, Central America, Far East, North America, Pacific Islands, and South America) were determined using single nucleotide polymorphisms (SNPs) identified with a genotyping-by-sequencing (GBS) protocol, GBSpoly, optimized for highly heterozygous and polyploid species. Population structure using Bayesian clustering analyses (STRUCTURE) with 32,784 segregating SNPs grouped the accessions into four genetic groups and indicated a high degree of mixed ancestry. A neighbor-joining cladogram and principal components analysis based on a pairwise genetic distance matrix of the accessions supported the population structure analysis. Pairwise FST values between broad geographical regions based on the origin of accessions ranged from 0.017 (Far East – Pacific Islands) to 0.110 (Australia – South America) and supported the clustering of accessions based on genetic distance. The markers developed for use with this collection of accessions provide an important genomic resource for the sweetpotato community, and contribute to our understanding of the genetic diversity present within the US sweetpotato collection and the species.

R scripts and associated data used to select the sweetpotato core sets described in: Slonecki, T. J., Rutter, W. B., Olukolu, B. A., Yencho, G. C., Jackson, D. M., & Wadl, P. A. (2023). Genetic diversity, population structure, and selection of breeder germplasm subsets from the USDA sweetpotato (Ipomoea batatas) collection. Frontiers in Plant Science, 13. https://doi.org/10.3389/fpls.2022.1022555 Resources in this dataset: Title: Sweetpotato_phenotype_classification_scripts File name: SP_phenotype_classification_scripts_7-29-21.R Description: R Scripts used to format, classify, and retain individuals with 'rare' phenotypes into the core collections, generating the reduced 508 accession dataset from which the core sets were selected Title: Sweetpotato_core_set_selection_scripts File name: Core_set_selection_permutations_8-24-20.R Description: Core set sampling scripts derived from previous R scripts and data generated in "SP_phenotype_classification_scripts" Title: Original Phenotype data File name: Core_Sets_VanRaden_Complete_Passport_Phenotype_July_2021.xlsx Description: Phenotype data downloaded from GRIN sweetpotato collection and used for core set selection Title: SP_core_selection_Rdata File name: SP_core_selection_data_7-30-21.zip Description: Data sets used in conjunction with Rscripts from Slonecki et. al. 2022

This collection contains supplementary information for the manuscript “Genetic mapping and QTL analysis for peanut smut resistance”, which reports the genetic map and quantitative trait loci associated with resistance to peanut smut, a disease caused by the fungus Thecaphora frezii. The information includes genotyping data of a 103 recombinant inbred line (RIL) population {susceptible Arachis hypogaea subsp.hypogaea × resistant synthetic amphidiploid [(A. correntina × A. cardenasii) × A. batizocoi]⁴ˣ} and parental lines, generated with the Axiom_Arachis2 SNP array. For more information about this dataset contact: Renee Arias: Renee.Arias@usda.gov or Alicia Massa: Alicia.Massa@usda.gov

This data is from the manuscript titled: "Genetic variation among 481 diverse soybean accessions, inferred from genomic re-sequencing". SNP calls were obtained from resequencing 481 diverse soybean lines comprising 52 wild (Glycine soja) and 429 cultivated (Glycine max). This dataset contains 6 gzipped VCF (Variant Call Format) files with variant calls for all 481 USB accessions, all G. max accessions, G. soja accessions, accessions sequenced at 15x coverage, accessions sequenced at 40x coverage, and 106 accessions re-sequenced from a previous study (Valliyodan et al. 2016). SNPs were called using the Haplotype caller algorithm from the Genome Analysis Toolkit (GATK) version gatk-2.5-2-gf57256b. A total of 7.8 million SNPs were identified between the 481 re-sequenced accessions. SNPs were assigned IDs using the script "assign_name.awk" available at https://github.com/soybase/SoySNP-Names. SNP effects were predicted using SnpEff 3.0. Dataset also available at https://soybase.org/data/v2/Glycine/max/diversity/Wm82.gnm2.div.Valliyod... Funding support provided by the United Soybean Board for the large-scale sequencing of soybean genomes (project #1320-532-5615), Bayer (previously Monsanto and Bayer), and Corteva (previously Dow AgroSciences), with in-kind support for analysis from USDA Agricultural Research Service project 5030-21000-069-00-D.

Phenotypic Data A subset of 264 lines from the National Small Grains Collection global hexaploid winter wheat germplasm collection was evaluated under controlled growth chamber conditions for reaction to the pathogens Parastagonospora nodorum and Pyrenophora tritici-repentis. Both infiltrations and inoculations were performed on plants planted in plastic cones and when seedlings were at the second leaf stage. Plants were infiltrated with the P. nodorum necrotrophic effectors (NEs) SnTox1, SnToxA, SnTox3, SnTox267, and SnTox5; and the P. tritici-repentis NE Ptr ToxB. The scoring system was 0-3, with reaction types of 2 and 3 considered sensitive and 0 to 1 were insensitive. Plants were inoculated with the P. nodorum isolates Sn4, Sn2000, AR2-1, SnIr05H71a, and NOR4 and P. tritici-repentis isolates Pti2, 86-124, DW5, and AR CrossB10. After inoculation, plants were placed in a 100 % humidity growth chamber at 21 °C for 24 hours under constant light, then moved to a controlled growth chamber at 21 °C with a 12 h photoperiod. Plants were scored at 7 days post inoculation. For P. nodorum, plants were scored using a 0 to 5 scale, with 0 being highly resistant and 5 being highly susceptible. For P. tritici-repentis, plants were scored using a 1 to 5 scale, with 1 being highly resistance and 5 being highly susceptible. Three homogeneous replicates (determined by Bartlett’s chi squared analysis) were used to calculate an average value for each trait. This value was used for the rest of the analysis. Genotypic Data DNA of the winter wheat panel was extracted and genotyped using the Illumina iSelect 90k wheat SNP array. Clustering data was analyzed using GenomeStudio 2.0.5 from Illumina, Inc. SNPs were ordered based on their physical position in the Chinese Spring IWGSC RefSeq v2.0. In TASSEL v5.2, SNP markers were filtered with a minor allele frequency greater than 0.01 and missing data less than 50%. For the remaining markers, missing values were imputed using the LD-KNNi method. Genome-wide association analysis data Association mapping was conducted using the R package GAPIT v.3. The filtered hapmap file was used for the association mapping, along with the average value for each phenotypic trait. The models GLM, MLM, MLMM, FarmCPU, and Blink were run on the averages for each trait. ** Resources in this dataset: Resource Title: Phenotypic data collected from 264 lines in the NSGC global hexaploid winter wheat collection Resource Description: The phenotypic file consists of the lines in this panel, their accession numbers, their ACIMPT designation (improvement status), country and continent of origin, and the scores for each phenotypic trait evaluated, in both replicate from and average of all the replicates. SnToxA, Ptr ToxB, SnTox1, SnTox267, SnTox3, and SnTox5 are all purified necrotrophic effectors and were scored on a 0-3 scale. Sn4, Sn2000, AR2-1, SnIr05H71a, and NOR4 are Parastagonospora nodorum isolates and were scored on a 0-5 scale. Pti2, 86-124, DW5, and AR CrossB10 are Pyrenophora tritici-repentis isolates and were scored on a 1-5 scale. An entry of ‘NA’ indicated missing data. Resource Title: Necrotrophic effector Ptr ToxB GWAS data output from the winter wheat global panel Resource Title: Necrotrophic effector SnTox1 GWAS data output from the winter wheat global panel Resource Title: Necrotrophic effector SnTox3 GWAS data output from the winter wheat global panel Resource Title: Necrotrophic effector SnTox5 GWAS data output from the winter wheat global panel Resource Title: Necrotrophic effector SnTox267 GWAS data output from the winter wheat global panel Resource Title: Necrotrophic effector SnToxA GWAS data output from the winter wheat global panel Resource Title: P. nodorum isolate AR2-1 GWAS data output from the winter wheat global panel Resource Title: P. nodorum isolate NOR4 GWAS data output from the winter wheat global panel Resource Title: P. nodorum isolate Sn4 GWAS data output from the winter wheat global panel Resource Title: P. nodorum isolate Sn2000 GWAS data output from the winter wheat global panel Resource Title: P. nodorum isolate SnIr05H71a GWAS data output from the winter wheat global panel Resource Title: P. tritici-repentis isolate 86-124 GWAS data output from the winter wheat global panel Resource Title: P. tritici-repentis isolate AR CrossB10 GWAS data output from the winter wheat global panel Resource Title: P. tritici-repentis isolate DW5 GWAS data output from the winter wheat global panel Resource Title: P. tritici-repentis isolate Pti2 GWAS data output from the winter wheat global panel Resource Description for GWAS data output files: The GWAS output file consists of the SNP (SNP), chromosome assignment (Chromosome), position on that chromosome (Position), p-value (P.value), minor allele frequency (maf), number of observations (nobs), FDR adjusted p-values (FDR_Adjusted_P-values), and marker effect. Markers are sorted in order of significance, with the most significant first. Sheet 1 consists of the GLM output, sheet 2 MLM, sheet 3 MLMM, sheet 4 FarmCPU, and sheet 5 Blink. Resource Title: Winter wheat panel_264 lines_90k_refv2 Resource Description: The genotypic hapmap data file consists of the raw SNP data after cluster analysis and includes 79,103 SNPs. The file consists of the rs# (SNP name), alleles, chrom (1 to 21 with 1 being chromosome 1A, 2 1B, 3 1D, so forth until 21 is 7D), pos (chromosome position based on Chinese Spring IWGSC RefSeq v2.0), strand (+ or NA for negative), assembly, center, protLSID, assayLSID, panelLSID, QCcode, and the 264 lines used in this panel with their SNP allele calls. ‘NA’ means no SNP call was detected. Resource Title: winterwheatpanel_264 lines_genotype90k_refv2_filtered_KNNimp.hmp Resource Description: The filtered genotypic hapmap data file consists of the SNP data after cluster analysis, filtering for a minor allele frequency greater than 0.01 and missing data less than 50%, and imputation using the LD-KNNi method. The filtered data consists of and includes 42,022 SNPs. The file consists of the rs# (SNP name), alleles, chrom (1 to 21 with 1 being chromosome 1A, 2 1B, 3 1D, so forth until 21 is 7D), pos (chromosome position based on Chinese Spring IWGSC RefSeq v2.0), strand (+ or NA for negative), assembly, center, protLSID, assayLSID, panelLSID, QCcode, and the 264 lines used in this panel with their SNP allele calls. ‘NA’ means no SNP call was detected.

This collection contains supplementary data for the manuscript "Genotypic characterization of the U.S. Peanut Core Collection", which describes genotyping results for the USDA peanut core collection. Each accession was genotyped with the Arachis_Axiom2 SNP array, yielding 14,430 high-quality, informative SNPs across the collection. Additionally, a subset of the core collection was replicated genotyped in replicate, using between two and five seeds per accession to assess heterogeneity within an accession. Supplementary files include: descriptive information about the genotyped accessions, SNP genotype calls in several formats, a phylogenetic tree calculated from the genotype data, Structure analysis, PCA analysis, and comparisons with the diploid progenitors. This research was co-funded by the National Institute of Food and Agriculture and the National Peanut Board.

Phenotypic Data Two durum × cultivated emmer recombinant inbred line (RIL) populations were evaluated for grain yield components under field conditions in North Dakota, USA. The BP025 population was developed by crossing Ben (PI 596557), a North Dakota hard amber durum variety, with PI 41025, a cultivated emmer accession collected near Samara, Russia. The BP025 population consists of 200 RILs developed by single seed-descent and was advanced to the F7:8 generation. The RP883 population was developed by crossing the durum line Rusty (PI 639869) with PI 193883, a cultivated emmer wheat accession collected near Shewa, Ethiopia. The RP883 population consists of 190 RILs developed by single seed-descent and was advanced to the F7:8 generation. The two populations were evaluated under field conditions in a total of three seasons for each and were grown in a randomized complete block design (RCBD) with three replicates per season. Plants were grown in hill plots with each plot consisting of 10-15 seeds and considered an experimental unit. The BP025 population was planted and evaluated in 2017, 2018, and 2019. The RP883 population was evaluated in 2018, 2019, and 2020. The 2017, 2018, and 2019 plots were grown at the North Dakota State University field site near Prosper, ND (47.002°N, 97.115°W), and the 2020 plots were grown at the NDSU agronomy seed farm near Casselton, ND (46.880°N, 97.243°W). The BP025 and RP883 populations and parental lines were evaluated for 11 traits including days to heading (DTH), plant height (PHT), the total number of spikelets per spike (SPS), kernels per spike (KPS), grain weight per spike (GWS), thousand kernel weight (TKW), kernel area (KA), kernel width (KW), kernel length (KL), kernel circularity (KC), and kernel length:width ratio (KLW). DTH was measured as the number of days from planting until 50 % of the spikes emerged completely beyond the flag leaf. PHT was measured in centimeters from the base of the plot to the tip of the tallest spike (excluding awns). Eight heads were used for phenotypic evaluations. SPS was counted as the total number of spikelets per spike. KPS, GWS, TKW, KA, KW, KL, KC, and KLW were obtained using a MARVIN grain analyzer (GAT Sensorik GMBH, Neubrandenburg, Germany). For KPS and GWS, the value obtained by the MARVIN for each sample was divided by the number of spikes in that sample to obtain KPS and GWS for data analysis. Genotypic Data DNA of the BP025 population was extracted and genotyped using the Illumina iSelect 9k wheat SNP array. The genotypic data file consists of the marker names, the chromosome assignments of the markers, the linkage map position of the markers, and the genotypic calls for each marker within each RIL where “A” represents an allele from PI 41025, “B” represents an allele from Ben, and “-“ indicates missing data. The data was used to assemble the linkage-based genetic maps for the 14 durum wheat chromosomes and further used in statistical analysis to identify chromosome regions harboring genes associated with the various phenotypic traits mentioned in the phenotypic file. DNA of the RP883 population was extracted and genotyped using the Illumina iSelect 90k wheat SNP array. The genotypic data file consists of the marker names, the chromosome assignments of the markers, the linkage map position of the markers, and the genotypic calls for each marker within each RIL where “A” represents an allele from Rusty, “B” represents an allele from PI 193883, and “-“ indicates missing data. The data was used to assemble the linkage-based genetic maps for the 14 durum wheat chromosomes and further used in statistical analysis to identify chromosome regions harboring genes associated with the various phenotypic traits mentioned in the phenotypic file. Resources in this dataset: Resource Title: Genotypic data for the durum x cultivated emmer wheat recombinant inbred population BP025 File Name: Genotypic data for the durum x cultivated emmer wheat recombinant inbred population BP025.xlsx Resource Description: The genotypic data file consists of the marker names, the chromosome assignments of the markers, the linkage map position of the markers, and the genotypic calls for each marker within each RIL where “A” represents an allele from PI 41025, “B” represents an allele from Ben, and “-“ indicates missing data. Resource Title: Genotypic data for the durum x cultivated emmer wheat recombinant inbred population RP883 File Name: Genotypic data for the durum x cultivated emmer wheat recombinant inbred population RP883.xlsx Resource Description: The genotypic data file consists of the marker names, the chromosome assignments of the markers, the linkage map position of the markers, and the genotypic calls for each marker within each RIL where “A” represents an allele from Rusty, “B” represents an allele from PI 193883, and “-“ indicates missing data. Resource Title: Phenotypic data collected from the durum x cultivated emmer wheat recombinant inbred populations BP025 and RP883 File Name: Phenotypic data collected from the durum x cultivated emmer wheat recombinant inbred populations BP025 and RP883.xlsx Resource Description: In the data file, column headings indicate the trait evaluated, the year, and replicate or average of all three replicates. For example, “SPS2017rep1” indicates rep 1 of the spikelets per spike trait collected in the 2017 trial. Sheet 1 consists of the BP025 population data, and sheet 2 consists of the RP883 population data. An entry of ‘NA’ indicates missing data.

Phentoypic data The durum × cultivated emmer recombinant inbred line (RIL) population (referred to as DP527) was evaluated for grain yield components under greenhouse and field conditions in North Dakota, USA. The DP527 population was developed by crossing Divide (PI 642021), a North Dakota hard amber durum variety, with PI 272527, a cultivated emmer accession collected near Pest, Hungary. The DP527 population consisted of 219 RILs developed using the single-seed descent method to the F7 generation and bulked to produce F7:8 RILs. The DP527 population was evaluated under field conditions in a total of three seasons and were grown in a randomized complete block design (RCBD) with three replicates each season. Plants were grown in hill plots, with each plot consisting of 10-15 seeds and considered an experimental unit. The 2017 and 2019 plots were grown at the North Dakota State University (NDSU) field site near Prosper, ND (47.002°N, 97.115°W). The 2020 plots were grown at the NDSU agronomy seed farm near Casselton, ND (46.880°N, 97.243°W). The DP527 population and parental lines were phenotyped for 11 traits including days to heading (DTH), plant height (PHT), total number of spikelets per spike (SPS), kernels per spike (KPS), grain weight per spike (GWS), thousand kernel weight (TKW), kernel area (KA), kernel width (KW), kernel length (KL), kernel circularity (KC), and kernel length:width ratio (KLW). DTH was measured as the number of days from planting until 50% of the spikes emerged completely beyond the flag leaf. PHT was measured from the base of the hill plot to the tip of the highest spike (excluding awns) in the plot in centimeters. Eight heads from each replicate were used for phenotypic evaluations. SPS was counted as the total number of spikelets divided by the number of heads in the sample. KPS, GWS, TKW, KA, KW, KL, KC, and KLW data were obtained using a MARVIN grain analyzer (GAT Sensorik GMBH, Neubrandenburg, Germany). KPS and GWS data from the MARVIN was divided by the number of heads in the sample to obtain an average per wheat head. For the 2019 environment, planting occurred in late May, and by early September about one third of the lines were not mature. Therefore, only DTH, PHT, and SPS were evaluated in the 2019 field season. The DP527 population and parents were evaluated under greenhouse conditions in two greenhouse seasons (2018 and 2019) with two replicates per season. Plants were grown in 15 cm diameter pots in a greenhouse with 16-h photoperiod and a temperature of 21 °C. All plants were grown in a completely randomized design (CRD) with one plant per pot, which was one experimental unit. DTH was measured as the number of days from planting until the emergence of the first spike beyond the flag leaf, and PHT was measured from the base of the plant to the tip of the highest spike in centimeters. Plants were hand harvested and four heads per plant were used for the rest of the phenotypic evaluations, which were measured as described for field environments. In the data file, column headings indicate the trait evaluated, the year, field vs greenhouse, and replicate or average of all three replicates. For example, “SPS2017Frep1” indicates rep 1 of the spikelets per spike trait collected in the 2017 field trial. Sheet 1 consists of the field data, and sheet 2 is the greenhouse data. An entry of ‘NA’ indicates missing data. Genotypic data DNA of the DP527 population was extracted and genotyped using the Illumina iSelect 90k wheat SNP array. The genotypic data file consists of the chromosome assignments of the markers, the marker names, the linkage map positions of the markers, and the genotypic calls for each marker within each RIL where “1” represents an allele from Divide, “2” represents an allele from PI 272527, and “3” indicates missing data. This data was used to assemble the linkage-based genetic maps for the 14 durum wheat chromosomes and further used in statistical analyses to identify chromosome regions harboring genes associated with the various phenotypic traits mentioned in the phenotypic data file. Resources in this dataset: Resource Title: Genotypic data for the durum x emmer wheat recombinant inbred population DP527 File Name: DP527 genotypic data.xlsx Resource Description: The genotypic data file consists of the chromosome assignments of the markers, the marker names, the linkage map positions of the markers, and the genotypic calls for each marker within each RIL where “1” represents an allele from Divide, “2” represents an allele from PI 272527, and “3” indicates missing data. Resource Title: Phenotypic data collected from the durum x emmer wheat recombinant inbred population DP527 File Name: DP527 phenotypic data.xlsx Resource Description: In the data file, column headings indicate the trait evaluated, the year, field vs greenhouse, and replicate or average of all three replicates. For example, “SPS2017Frep1” indicates rep 1 of the spikelets per spike trait collected in the 2017 field trial. Sheet 1 consists of the field data, and sheet 2 is the greenhouse data. An entry of ‘NA’ indicates missing data.

[Note: this dataset has been superseded by version 2: https://doi.org/10.15482/USDA.ADC/1522673 ] This collection contains supplementary data for the manuscript "Legacy genetics of Arachis cardenasii in the peanut crop shows profound benefits of international seed exchange," which describes the impact of alleles from a wild relative of peanut, Arachis cardenasii, through analysis of those alleles across cultivars and breeding lines across many countries. The initial challenging cross, between tetraploid cultivated peanut (Arachis hypogaea) and the diploid species A. cardenasii, was carried out in the late 1960s. Subsequent work produce a tetraploid line that contained introgressed regions from A. cardenasii. Those chromosomal regions, several containing important resistance genes, were used in numerous breeding lines. The genetic legacy from A. cardenasii is documented in the files in this collection. The information includes genotyping data across peanut cultivars and breeding lines, generated through both genotyping arrays ("SNP chips") and whole-genome sequencing. Information in this collection also includes data related to impact of A. cardenasii on disease- and pest resistance in modern peanut varieties.

[Note: This version supersedes version 1: https://doi.org/10.15482/USDA.ADC/1520889 Changes in version 2: A new Dataset was added - DataSet1-CardAlleles-iii.xlsx Datasets were renamed and renumbered in accordance to article revisions, and some changes made (see below for details): SupplementaryData1-Worldwide-genotypes-ii.xlsx => DataSet3-Worldwide-genotypes-ii.xlsx (Data with very minor changes, including the removal of three of 710 markers) SupplementaryData2-Lineages-FieldData-ii.xlsx => DataSet4-Lineages-FieldData-iv.xlsx (this file underwent minor revisions, with some extra comments added) SupplementaryData3-Fingerprints.tar => DataSet5-Fingerprints.tar (Data unchanged) SupplementaryData4-Austp183.xlsx => DataSet6-Austp183.xlsx (Data unchanged) SupplementaryData5-introgression.tar => DataSet2-WGSIntrogression.tar (A Dataset more stringently filtered with more control genotypes was used, cutting the number of markers from 2,566,180 to 2,337,866. More genotype output files were added, including controls. A genome-ordered list of A. cardenasii GKP 10017 diagnostic bases was added (Acard-diag_bases.txt.gzip).] Description This collection contains supplementary data for the manuscript "Legacy genetics of Arachis cardenasii in the peanut crop shows profound benefits of international seed exchange," which describes the impact of alleles from a wild relative of peanut, Arachis cardenasii, through analysis of those alleles across cultivars and breeding lines across many countries. The initial challenging cross, between tetraploid cultivated peanut (Arachis hypogaea) and the diploid species A. cardenasii, was carried out in the late 1960s. Subsequent work produce a tetraploid line that contained introgressed regions from A. cardenasii. Those chromosomal regions, several containing important resistance genes, were used in numerous breeding lines. The genetic legacy from A. cardenasii is documented in the files in this collection. The information includes genotyping data across peanut cultivars and breeding lines, generated through both genotyping arrays ("SNP chips") and whole-genome sequencing. Information in this collection also includes data related to impact of A. cardenasii on disease- and pest resistance in modern peanut varieties.

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 = "."

USDA-ARS coordinated regional wheat (Triticum aestivum L.) breeding trials examine agronomic performance and adaptation over a wider geographic range than single breeding programs can achieve. The trials provide an evaluation of experimental breeding lines in alternate test sites that are environmentally similar or dissimilar to the program of origin. Data from USDA-ARS Hard Winter Wheat Regional Nurseries grown in 1987 to 2014 were used to identify similarities among Great Plains test sites. Mean correlations of entry grain yields across locations and years were used in principal factor analyses to cluster them into production zones. The procedures used were identical to those of a previously published analysis using test data from 1959 to 1989. Five factors explained 67% of the variance in the correlation matrix among Southern Regional Performance Nursery (SRPN) locations. The analysis divided the SRPN into four major Great Plains production zones, designated Southeast, Northwest, Southwest and Northeast. The remaining minor production zone consisted of only two central South Dakota locations, both outside the typical target area and selection site of SRPN entries. In the Northern Regional Performance Nursery (NRPN), five production zones were established, with location separation predominantly resulting from east–west differences in performance. The SRPN and NRPN wheat production zones closely follow previously described ecological zones of adaptation of native Great Plains plant species. Wheat breeding programs and growers may continue to use the production zones established via the USDA-ARS coordinated winter wheat regional nurseries to target and select germplasm for crossing and for production.

To study the impact of wheat streak mosaic virus on global gene expression in wheat curl mite, we generated a de novo transcriptome assembly using 50 x 50 paired end reads from the Illumina HiSeq 2500. Reads were assembled using Trinity (version 2.0.6) and contigs greater than 200 nt were retained. All assembled transcripts were annotated using the Trinotate pipeline using blastp searches against the Swiss-prot/Uni-Prot database, blastx searches against the Swiss-prot/Uni-Prot databases, HMM searches against the Pfam-A database, blastp searches against the non-redundant protein database, and signalP and tmHMM predictions. To reduce noise from low abundance transcripts not well supported by the data, we filtered the assembly to retain only those transcripts with TPM values >=0.5.

Genetic fingerprinting of 184 Aspergillus section Flavi isolates from Ethiopia screened with 24 Insertion/Deletion markers located within the aflatoxin-biosynthesis gene cluster. Each file name contains in this order: isolate number, marker number, range of base pairs on the aflatoxin-biosynthesis cluster where the marker is located, and well position within the 384 microplate used for capillary electrophoresis.

A subset of ~30 inbreds were evaluated in 2014 and 2015 to develop an image based ear phenotyping tool. The data is stored in CyVerse. Data types in this directory tree are: dimension and width profile data collected from scanned images of ears, cobs, and kernels collected from the Genomes To Fields (G2F) project cooperators. G2F is an umbrella initiative to support translation of maize (Zea mays) genomic information for the benefit of growers, consumers and society. This public-private partnership is building on publicly funded corn genome sequencing projects to develop approaches to understand the functions of corn genes and specific alleles across environments. Ultimately this information will be used to enable accurate prediction of the phenotypes of corn plants in diverse environments. There are many dimensions to the over-arching goal of understanding genotype-by-environment (GxE) interactions, including which genes impact which traits and trait components, how genes interact among themselves (GxG), the relevance of specific genes under different growing conditions, and how these genes influence plant growth during various stages of development.

Phenotypic, genotypic, and environment data for the 2014 field season: The data is stored in CyVerse. Data types in this directory tree are: dimension and width profile data collected from scanned images of ears, cobs, and kernels collected from the Genomes To Fields (G2F) project cooperators. G2F is an umbrella initiative to support translation of maize (Zea mays) genomic information for the benefit of growers, consumers and society. This public-private partnership is building on publicly funded corn genome sequencing projects to develop approaches to understand the functions of corn genes and specific alleles across environments. Ultimately this information will be used to enable accurate prediction of the phenotypes of corn plants in diverse environments. There are many dimensions to the over-arching goal of understanding genotype-by-environment (GxE) interactions, including which genes impact which traits and trait components, how genes interact among themselves (GxG), the relevance of specific genes under different growing conditions, and how these genes influence plant growth during various stages of development.

Phenotypic, genotypic, and environment data for the 2015 field season: The data is stored in CyVerse. Data types in this directory tree are: hybrid and inbred agronomic and performance traits; inbred genotypic data; and environmental (soil, weather) data collected from the Genomes To Fields (G2F) project cooperators. G2F is an umbrella initiative to support translation of maize (Zea mays) genomic information for the benefit of growers, consumers and society. This public-private partnership is building on publicly funded corn genome sequencing projects to develop approaches to understand the functions of corn genes and specific alleles across environments. Ultimately this information will be used to enable accurate prediction of the phenotypes of corn plants in diverse environments. There are many dimensions to the over-arching goal of understanding genotype-by-environment (GxE) interactions, including which genes impact which traits and trait components, how genes interact among themselves (GxG), the relevance of specific genes under different growing conditions, and how these genes influence plant growth during various stages of development.

Phenotypic, genotypic, and environment data for the 2016 field season: The data is stored in CyVerse. Data types in this directory tree are: hybrid and inbred agronomic and performance traits; inbred genotypic data; and environmental (soil, weather) data collected from the Genomes To Fields (G2F) project cooperators. G2F is an umbrella initiative to support translation of maize (Zea mays) genomic information for the benefit of growers, consumers and society. This public-private partnership is building on publicly funded corn genome sequencing projects to develop approaches to understand the functions of corn genes and specific alleles across environments. Ultimately this information will be used to enable accurate prediction of the phenotypes of corn plants in diverse environments. There are many dimensions to the over-arching goal of understanding genotype-by-environment (GxE) interactions, including which genes impact which traits and trait components, how genes interact among themselves (GxG), the relevance of specific genes under different growing conditions, and how these genes influence plant growth during various stages of development.

The Germplasm Resources Information Network (GRIN) is an online portal for information about agricultural genetic resources that are managed by the Agricultural Research Service of USDA, along with U.S. partnering organizations. The content includes general information about ARS animal, microbial and plant germplasm collections, most notably the U.S. National Plant Germplasm System (NPGS). The NPGS curates more than 600,000 active accessions of living plant material at 20 genebank locations around the U.S., and makes small quantities available globally to plant breeders and other professional scientists. GRIN also documents activities of Crop Germplasm Committees (CGC) that support the NPGS. The CGCs are comprised of public and private sector subject matter experts for a given crop (there are currently 44 CGCs) who voluntarily provide input on technical and operational matters to the NPGS. The site includes two searchable datasets: the ARS Rhizobium collection and Plant Variety Protection Certificates. The Rhizobium collection is living bacteria that nodulate the roots of leguminous plants symbiotically to provide nitrogen fixation. Samples are available to research scientists globally upon request. The Plant Variety Protection (PVP) Certificates are issued by the Agricultural Marketing Service (AMS) of USDA to provide intellectual property protection to registered new varieties of plants that are propagated by seed or tubers. The GRIN site allows queries of PVPs by certificate number, name of the crop, variety name, or certificate holder, all using data provided by the AMS.

The Germplasm Resources Information Network (GRIN) is an online portal for information about agricultural genetic resources that are managed by the Agricultural Research Service of USDA, along with U.S. partnering organizations. The content includes general information about ARS animal, microbial and plant germplasm collections, most notably the U.S. National Plant Germplasm System (NPGS). The NPGS curates more than 600,000 active accessions of living plant material at 20 genebank locations around the U.S., and makes small quantities available globally to plant breeders and other professional scientists. GRIN also documents activities of Crop Germplasm Committees (CGC) that support the NPGS. The CGCs are comprised of public and private sector subject matter experts for a given crop (there are currently 44 CGCs) who voluntarily provide input on technical and operational matters to the NPGS. The site includes two searchable datasets: the ARS Rhizobium collection and Plant Variety Protection Certificates. The Rhizobium collection is living bacteria that nodulate the roots of leguminous plants symbiotically to provide nitrogen fixation. Samples are available to research scientists globally upon request. The Plant Variety Protection (PVP) Certificates are issued by the Agricultural Marketing Service (AMS) of USDA to provide intellectual property protection to registered new varieties of plants that are propagated by seed or tubers. The GRIN site allows queries of PVPs by certificate number, name of the crop, variety name, or certificate holder, all using data provided by the AMS.

GrainGenes is a popular repository for information about genetic maps, mapping probes and primers, genes, alleles and QTLs for the following crops: wheat, barley, rye and oat. Documentation includes such data as primer sequences, polymorphism descriptions, genotype and trait scoring data, experimental protocols used, and photographs of marker polymorphisms, disease symptoms and mutant phenotypes. These data, curated with the help of many members of the research community, are integrated with sequence and bibliographic records selected from external databases and results of BLAST searches of the ESTs. Records are linked to corresponding records in other important databases, e.g. Gramene's EST homologies to rice BAC/PACs, TIGR's Gene Indices and GenBank. In addition to this information within the GrainGenes database itself, the GrainGenes homepage at http://wheat.pw.usda.gov provides many other community resources including publications (the annual newsletters for wheat, barley and oat, monographs and articles), individual datasets (mapping and QTL studies, polymorphism surveys, variety performance evaluations), specialized databases (Triticeae repeat sequences, EST unigene sets) and pages to facilitate coordination of cooperative research efforts in specific areas such as SNP development, EST-SSRs and taxonomy. The goal is to serve as a central point for obtaining and contributing information about the genetics and biology of these cereal crops

Gramene is a curated, open-source, integrated data resource for comparative functional genomics in crops and model plant species.

A set of PYTHON programs to implement image processing of ground and aerial images by offering via graphical user interface (GUI) 1) plot-level metrics extraction through a series of algorithms for image conversion, band math, radiometric/geometric calibrations, segmentation, masking, adaptive region of interest (ROI), gridding, heatmap, and batch process, 2) GIS interface for GeoTIFF pixels to Lat/Lon, UTM conversion, read/write shapefile, Lat/Lon to ROI, grid to polygon, and 3) utility GUI functions for zooming, panning, rotation, images to video, file I/O, and histogram.

This is an identification key to genera for seeds and fruits of the legume family. The coverage is world wide, and for each genus there are descriptions of the seeds and fruits, distribution data, and images. The interactive software system INTKEY is used for accessing the data and images. The key can be used for identifying to genus unknown legume samples or for querying the data and images for legume genera, and is designed for seed analysts, technicians, port inspectors, weed scientists, ecologists, botanists, and researchers who need to identify isolated legume fruits and seeds. Procedures relating to preparation, collection, and authentication of data are provided in the 'Procedures' resource file. In order to utilize the identification key the entire folder needs to be downloaded and extracted with all internal structure unmodified.

The Legume Information System (legumeinfo.org) is the USDA-ARS genetics and genomics database for legume crops and relatives. Researchers can also submit their data directly. LIS houses data for more than a dozen species such as common bean and chickpea, peanut, and soybean, with genome sequences, genes and predicted functions, families of related genes, views of evolutionary relationships between genomic regions, genetic maps, markers, and links to germplasm resources.

The Maize Genetics Cooperation Stock Center is operated by USDA/ARS, located at the University of Illinois, Urbana/Champaign, and integrated with the National Plant Germplasm System (NPGS). The center serves the maize research community by collecting, maintaining and distributing seeds of maize genetic stocks, and providing information about maize stocks and the mutations they carry through the Maize Genetics and Genomics Database (MaizeGDB). Users can browse to obtain detailed information about the following stocks: Chromosome 1 Markers Chromosome 2 Markers Chromosome 3 Markers Chromosome 4 Markers Chromosome 5 Markers Chromosome 6 Markers Chromosome 7 Markers Chromosome 8 Markers Chromosome 9 Markers Chromosome 10 Markers Unplaced Genes Multiple Genes Rare Isozyme B-Chromosome Alien Addition Trisomic Tetraploid Cytoplasmic-Sterile / Restorer Cytoplasmic Trait Toolkit B-A Translocations (Basic Set) B-A Translocations (Others) Inversion Reciprocal Translocations (wx1 and Wx1 marked) Stock records include information on availability, annotations, related records (genotypic variations, phenotypes), GRIN (Germplasm Resources Information Network) information, and offsite resources.

MaizeGDB is a community-oriented, long-term, federally funded informatics service to researchers focused on the crop plant and model organism Zea mays. Genomic, genetic, sequence, germplasm, gene product, metabolic pathways, functional characterization, literature reference, diversity, and expression are among the datatypes stored at MaizeGDB. At the project's website are custom interfaces enabling researchers to browse data and to seek out specific information matching explicit search criteria. First released in 1991 with the name MaizeDB, the Maize Genetics and Genomics Database, now MaizeGDB (since 2003), is funded, developed, and hosted by the USDA-ARS located at Ames, Iowa.

Insects impact American agriculture both as destructive and beneficial organisms. Insect pests, parasites, predators, products, and pollinators are all economically important. It is critically important to distinguish between different species, races, stocks, strains, biotypes, and other genetic entities and to document their different interactions with agriculture and the environment. The goals of the National Invertebrate Genetic Resources Program include: Preservation of reference specimens Maintenance of genetically important germplasm Documentation of specific insect stocks Management of databases Distribution of material to researchers and breeders

The goal of the National Microbial Germplasm Program is to ensure that the genetic diversity of agriculturally important microorganisms is maintained to enhance and increase agricultural efficiency and profitability. The program collects, authenticates, and characterizes potentially useful microbial germplasm; preserves microbial genetic diversity; and facilitates distribution and utilization of microbial germplasm for research and industry. The Agricultural Research Service maintains several microbial germplasm collections including: USDA ARS Culture Collection USDA ARS Collection of Entomopathogenic Fungal Cultures (ARSEF) Query or Download the Rhizobium Database US National Fungus Collections

Global food availability and security is based on intensive agricultural production. Over the past century, this intensification has relied heavily on producing crops with increasing genetic uniformity. Although these practices have benefits, they also include the risks of increasing the vulnerability of crops to pests, diseases, and environmental stress. Plant breeding and associated scientific research is essential to meet the ongoing challenges of producing plants for food, fiber, animal feeds, industrial and medicinal purposes, and for landscape and ornamental uses. It is important to collect and conserve living plant material, both to help solve immediate agricultural production problems as well as safeguard plant genetic diversity for future needs. This mission is more essential than ever because the loss of genetic diversity is accelerating with threats from many factors including global urbanization, habitat changes associated with climate, and changes in land use related to population growth and economic development. The U.S. National Plant Germplasm System (NPGS) is collaborative effort to safeguard the genetic diversity of agriculturally important plants. The NPGS is managed by the Agricultural Research Service (ARS), the in-house research agency of the United States Department of Agriculture (USDA). Funding for the NPGS comes primarily through appropriations from the U.S. Congress. However, the NPGS is a partnership between the public and private sectors. Many NPGS genebanks are located at state land-grant university sites, which contribute lab, office, greenhouse and field space for operations, as well as staff for technical and support services. The private sector is a major user of the NPGS collections and is the primary means by which new and improved plants are commercialized. The mission of the NPGS is to support agricultural production by: acquiring crop germplasm conserving crop germplasm evaluating and characterizing crop germplasm documenting crop germplasm distributing crop germplasm

Panzea is an NSF-funded project called "Biology of Rare Alleles in Maize and its Wild Relatives". We are investigating the connection between phenotype (what we see) and genotype (the genes underlying the phenotype) - of complex traits in maize and its wild relative, teosinte, and specifically in how rare genetic variations contribute to overall plant function. These studies will enrich our knowledge of evolution, sustainable agriculture, and genetic diversity and conservation. Over the 10 years of the project, we have trained many new scientists at all levels and generated key resources for the public, teachers, and scientific researchers.

PeanutBase (peanutbase.org) is the primary genetics and genomics database for cultivated peanut and its wild relatives. It houses information about genome sequences, genes and predicted functions, genetic maps, markers, links to germplasm resources, and maps of peanut germplasm origins. This resource is being developed for U.S. and International peanut researchers and breeders, with support from The Peanut Foundation and the many contributors that have made the Peanut Genomics Initiative possible. Funded by The Peanut Foundation as part of the Peanut Genomics Initiative. Additional support from USDA-ARS. Database developed and hosted by the USDA-ARS SoyBase and Legume Clade Database group at Ames, IA, with NCGR and other participants.

An easy-to-customize, low-cost, low disturbance, motorized, and adjustable proximal sensing cart for field-based high-throughput phenotyping is described. General dimensions, motor specifications, and a remote operation application are given. The cart, named "Professor", supports mounting multiple proximal sensors and cameras for characterizing plant traits grown under field conditions. Professor easily adapts to multiple sensor configurations supporting detection of multiple target traits and has two axes of adjustable clearance by design. Professor is useful as a field-based phenotyping platform and offers a framework for customized development and application.

Genomic, Genetic and Breeding Resources for Pulse Crop Improvement. Crops supported include Adzuki bean, Bambara bean, Chickpea, Common bean, Cowpea, Faba bean, Lentil, Lupin, Pea, Pigeon pea, Vetch, and others. The Pulse Crop Database (PCD), formerly the Cool Season Food Legume Database (CSFL), is being developed by the Main Bioinformatics Laboratory at Washington State University in collaboration with the USDA-ARS Grain Legume Genetics and Physiology Research Unit, the USDA-ARS Plant Germplasm Introduction and Testing Unit, the USA Dry Pea and Lentil Council, Northern Pulse Growers and allied scientists in the US and across the world, to serve as a resource for Genomics-Assisted Breeding (GAB). GAB offers tools to identify genes related to traits of interest among other methods to optimize plant breeding efficiency and research, by providing relevant genomic, genetic and breeding information and analysis. Therefore, tools such as JBrowse and MapViewer can be found in this database, as well as key resources to provide the access to the annotation of available transcriptome data, helping pulse breeders and researchers to succeed in their programs.

Raw genome sequencing data of 17 isolates of Aspergillus flavus and Aspergillus parasiticus collected from peanuts from Ethiopia. These isolates were selected as representative of the genetic diversity of Aspergillus section Flavi found colonizing peanut seeds in four peanut-farming districts of Ethiopia, these are Darolabu, Gursum, Fedis, and Babile.

The Releases Of Beneficial Organisms (ROBO) database consists of documented importations and releases of beneficial insects, mites, and microorganisms, as biological control agents of invasive pests and weeds, and as pollinators. It also includes information on importation and release of microbial natural enemies, and as such is related to the National Microbial Germplasm Program. The goal of the ROBO program is to provide information on invertebrate and microbial germplasm for biological control of invertebrate pests and weeds and ecological research. Users of this database are expected to be foreign and domestic scientists and students involved in biological control and ecological studies, public and private biological control practitioners, and officials in state and federal regulatory agencies dealing with issues involving the introduction of non-indigenous organisms. The primary organisms covered by this database include: arthropod (insects, mites, and ticks), nematode and other invertebrate pests, weedy plants, and their microbial (bacteria, viruses, fungi, protozoa, etc.), arthropod, nematode and other invertebrate natural enemies. The primary emphasis of the database is collection, introduction, release, culture, establishment, recolonization and impact of non-indigenous organisms on pests in the United States, and their shipment to other countries. The database also includes information on the collection, introduction and release of non-indigenous invertebrate pollinators in the United States. The ROBO site, originally at https://www.ars-grin.gov/nigrp/robo.html, contained descriptions with links to the database search interface and guidelines, but is in the process of being modernized. The link presented here as part of this resource explains the timeline for this activity, and will be updated once the project is complete.

Ricebase (https://ricebase.org) is an integrative genomic database for rice (Oryza sativa) with an emphasis on combining datasets in a way that maintains the key links between past and current genetic studies. Ricebase includes DNA sequence data, gene annotations, nucleotide variation data and molecular marker fragment size data. Rice research has benefited from early adoption and extensive use of simple sequence repeat (SSR) markers; however, the majority of rice SSR markers were developed prior to the latest rice pseudomolecule assembly. Interpretation of new research using SNPs in the context of literature citing SSRs requires a common coordinate system. A new pipeline, using a stepwise relaxation of stringency, was used to map SSR primers onto the latest rice pseudomolecule assembly. The SSR markers and experimentally assayed amplicon sizes are presented in a relational database with a web-based front end, and are available as a track loaded in a genome browser with links connecting the browser and database. The combined capabilities of Ricebase link genetic markers, genome context, allele states across rice germplasm and potentially user curated phenotypic interpretations as a community resource for genetic discovery and breeding in rice.

The Sol Genomics Network (SGN) is a clade-oriented database dedicated to the biology of the Solanaceae family which includes a large number of closely related and many agronomically important species such as tomato, potato, tobacco, eggplant, pepper, and the ornamental Petunia hybrida. SGN is part of the International Solanaceae Initiative (SOL), which has the long-term goal of creating a network of resources and information to address key questions in plant adaptation and diversification. A key problem of the post-genomic era is the linking of the phenome to the genome, and SGN allows to track and help discover new such linkages. Data: Solanaceae and other Genomes SGN is a home for Solanaceae and closely related genomes, such as selected Rubiaceae genomes (e.g., Coffea). The tomato, potato, pepper, and eggplant genome are examples of genomes that are currently available. If you would like to include a Solanaceae genome that you sequenced in SGN, please contact us. ESTs SGN houses EST collections for tomato, potato, pepper, eggplant and petunia and corresponding unigene builds. EST sequence data and cDNA clone resources greatly facilitate cloning strategies based on sequence similarity, the study of syntenic relationships between species in comparative mapping projects, and are essential for microarray technology. Unigenes SGN assembles and publishes unigene builds from these EST sequences. For more information, see Unigene Methods. Maps and Markers SGN has genetic maps and a searchable catalog of markers for tomato, potato, pepper, and eggplant. Tools SGN makes available a wide range of web-based bioinformatics tools for use by anyone, listed here. Some of our most popular tools include BLAST searches, the SolCyc biochemical pathways database, a CAPS experiment designer, an Alignment Analyzer and browser for phylogenetic trees. The VIGS tool can help predict the properties of VIGS (Viral Induced Gene Silencing) constructs. The data in SGN have been submitted by many different research groups around the world. A web form is available to submit data for display on SGN. SGN community-driven gene and phenotype database: Simple web interfaces have been developed for the SGN user-community to submit, annotate, and curate the Solanaceae locus and phenotype databases. The goal is to share biological information, and have the experts in their field review existing data and submit information about their favorite genes and phenotypes.

As part of our project, “Developing Association Mapping in Polyploid Perennial Biofuel Grasses” (DOE-USDA Plant Feedstock Genomics for Bioenergy Program grant DE-A102-07ER64454)*, two SNP discovery initiatives were carried out. The earlier one (2009) was an approach based on EST sequences. The latest initiative (2011-12) adopted a more powerful approach, based on GBS (Genotyping by Sequencing). We believe that the SNP markers identified in these studies will greatly enhance breeding efforts that target the improvement of key biofuel traits and the development of new switchgrass cultivars. To enable genome-wide association study (GWAS) and genomic selection (GS) in switchgrass, we genotyped a full-sib population (n =130), a half-sib population (n =168) and association populations (66 pops, n =540). The parents of the linkage populations are upland tetraploids. The association populations are primarily of the upland ecotype, both tetraploid and octoploid, with a few lowland tetraploids as well. A total of 350 GB of sequence was generated from 840 individuals using GBS. Over 1.2 million putative SNPs were discovered with the UNEAK pipeline. In addition, ultra-high density paternal and maternal linkage maps, of 41K and 46K SNPs, respectively, were also constructed based on the conserved synteny between switchgrass and foxtail millet. The data associated with this study are listed here: Genotype calls from the full-sib population [65 MB] Genotype calls from the half-sib population [80 MB] Genotype calls from the association populations [164 MB] Paternal and maternal linkage maps of the full-sib population [8 MB]

GRIN-Global is an ongoing international collaborative project to develop shared and open-source applications that help manage plant germplasm collections. The software was jointly developed by the Agricultural Research Service of USDA, Global Crop Diversity Trust, and Bioversity International, with the first version released in December 2011. The ARS has used GRIN-Global to manage its plant germplasm collections, the U.S. National Plant Germplasm System, since November 2015. GRIN-Global is an extension of Germplasm Resources Information Network (GRIN) information management system, which was first developed by ARS beginning in the mid-1980s. GRIN-Global is comprised of a suite of computer applications that are used internally by genebank staff to curate collections, as well as a public website through which scientists can query the database and request samples of germplasm through a shopping cart process.

The Triticeae Toolbox (T3) webportal hosts data generated by the Triticeae Coordinated Agricultural Project (CAP), funded by the National Institute for Food and Agriculture (NIFA) of the United States Department of Agriculture (USDA). T3 contains SNP, phenotypic, and pedigree data from wheat and barley germplasm in the Triticeae CAP integrating rapidly expanding DNA marker and sequence data with traditional phenotypic data to provide access to predictive analyses mapping genotype to phenotype and enabling breeders to select on marker data alone. T3 will also link to related genomic and crop diversity databases (GrainGenes, Gramene, Ensembl Plants, and GRIN) for functional analyses to identify causal polymorphisms and networks affecting phenotypes. The software and data structure for T3 were developed as part of the Barley CAP project for its database, The Hordeum Toolbox (THT), carried forward as part of the Triticeae CAP in the T3 databases. T3 Barley holds data generated for Hordeum vulgare L. T3 Wheat holds data generated for Triticum spp. T3 Oat holds data generated for Avena. All are being enhanced in database performance, community curation and user tools. T3 contains germplasm line information, pedigree, genotype and phenotypic data from breeding programs participating in the CAP and core germplasm collections maintained by the USDA National Small Grains Collection.

Our mission is to support application of low-input sustainable agriculture by: Providing, to the best of our ability, technical information about rhizobia, their preservation, and cultural and symbiotic characteristics; Acquiring and preserving the nitrogen-fixing bacterial symbionts of leguminous plants with the goal of maintaining widest possible genetic diversity; Maintaining quality control of new and existing germplasm by evaluation of microbiological purity and by examination of nodulation of the original trap host plant; Distributing cultures to the public and private sectors without charge for these services; Developing or adapting techniques in molecular biology for the determination of genetic diversity of rhizobia, to investigate interactions with their host plants and to identify novel characteristics; Acquiring, maintaining, evaluating quality, and distributing type strains for all the different taxa of nitrogen-fixing legume symbionts; Participating in the UNESCO program.

The Uniform Soybean Tests, Northern Region, in place since 1941, evaluate yield, disease resistance, and quality traits of public breeding lines from northern states of the USA and Canadian provinces. The annual reports which compile the test results (PDF format) are available, and new reports are added annually. The Uniform Soybean Tests are conducted and managed as a component of a CRIS project on Enhancing Resistance to Root Rot Pathogens of Soybeans in the USDA-ARS Crop Production and Pest Control Unit at West Lafayette, Indiana. The purpose of the Uniform Soybean Tests is to critically evaluate the best of the experimental soybean lines developed by federal and state research personnel in the U.S. and Canada, for their potential release as new varieties. Locations include Iowa, Illinois, Indiana, Kansas, Michigan, Minnesota, Missouri, North Dakota, Nebraska, Ohio, Ontario, Quebec, Tennessee. Germplasm exchange among breeding programs is the foundation of breeding progress. The purpose of the Uniform Soybean Test is to facilitate the free exchange of germplasm in an effort to maximize genetic diversity and provide well-adapted, stable breeding lines and varieties in the pursuit of breeding progress. Participants are encouraged to exchange germplasm within the legal guidelines pertaining to transgenic strains.

The Uniform Soybean Tests, Southern States, in place since 1943, evaluate yield, disease resistance, and quality traits of public breeding lines from the southern states of the USA. The annual reports which compile the test results (PDF format) are available, and new reports are added annually. The Uniform Soybean Testing Program has been directed toward the testing of elite breeding lines that ultimately leads to the release of varieties. Breeding lines are developed and evaluated in several participating federal and state research programs. As breeding lines demonstrate specific qualities in the individual programs, they are advanced to the preliminary and uniform regional tests conducted in cooperation with research workers in the southern states. This testing program enables breeders to evaluate new strains under a wide variety of conditions, and permits new strains to be put into production in a minimum amount of time. A wide range of soil and climatic conditions exists in the regions. As an aid in recognizing regional adaptation, the region has been subdivided into five rather broad areas which still represent a wide range of soil types. These are: (1) the East Coast, consisting of the Coastal Plain and Tidewater areas of the eastern shore of Maryland, Virginia, North Carolina, and the upper half of South Carolina; (2) the Southeast, consisting primarily of the Coastal Plain soils of the Gulf Coast area, but also including similar soil from South Carolina, southward; (3) the Upper and Central South, including the Piedmont and loessial hill soils east of the Mississippi River; (4) the Delta area, composed of the alluvial soils along the Mississippi River from southern Missouri, southward; and (5) the West, comprising Arkansas and Louisiana (outside the Delta), Kansas, Oklahoma, and Texas. In the West, the potential soybean-growing areas would include alluvial soils, and the Gulf Coast of Louisiana. Germplasm exchange among breeding programs is the foundation of breeding progress. The purpose of the Uniform Soybean Test is to facilitate the free exchange of germplasm in an effort to maximize genetic diversity and provide well-adapted, stable breeding lines and varieties in the pursuit of breeding progress. Participants are encouraged to exchange germplasm within the legal guidelines pertaining to transgenic strains.

GUI-based software coded in PYTHON to automate image stitching and alignment processes from a set of tile images for the high throughput image analytics by implementing a series of algorithms: 1) deskewing the image acquired in an oblique view angle, 2) row alignment of the geometrically drifted image due to acquisition errors by detecting the crop row using Hough Transformation, and 3) options for omnidirectional overlap trimming and resizing.