,SoyBase is a repository for genetics, genomics and related data resources for soybean. It contains current genetic, physical and genomic sequence maps integrated with qualitative and quantitative traits.,SoyBase database was established in the 1990s as the USDA Soybean Genetics Database. Originally, it contained only genetic information about soybeans such as genetic maps and information about the Mendelian genetics of soybean. In time SoyBase was expanded to include molecular data regarding soybean genes and sequences as they became available. In 2010, the soybean genome sequence was published and it and supporting gene sequences have been integrated into the SoyBase sequence browser. SoyBase genetic maps were used in the assembly of both the Williams 82 2010 assembly (Wm82.a1.v1) and the newest genome assembly (Wm82.a2.v1).,SoyBase also incorporates information about mutant and other soybean genetic stocks and serves as a contact point for ordering strains from those populations. As association analyses continue due to various re-sequencing efforts SoyBase will also incorporate those data into the soybean genome browser as they become available. Gene expression patterns are also available at SoyBase through the SoyBase expression pages and the Soybean Gene Atlas. Other expression/transcriptome/methylomic data sets also have been and continue to be incorporated into the SoyBase genome browser.,Project No:3625-21000-062-00D Accession No: 0425040,

,During symbiosis, C that rhizobia respire to power N fixation can be stored as polyhydroxybutyrate (PHB), shown to support rhizobia survival under laboratory starvation. We collected soil in 2015 from four replicate plots per treatment in two long-term experiments at Waseca, Minnesota. Treatments differed in the intervals between soybean (Glycine max (L.) Merr.) hosts. We measured PHB accumulation in eight nodules per plant from four soybean (cv. ‘MN0095’) trap plants per soil sample. Trap plants were arranged in a greenhouse, common-garden experiment, and PHB accumulation was measured using flow cytometry. Treatments sampled after long intervals without soybean (greater than two years) showed a greater relative abundance of high-PHB strains. Treatments sampled after the first year of soybean following five years of a non-host crop showed a decreased relative abundance of high-PHB strains, compared to treatments sampled after long intervals without soybean. The latter result is consistent with the hypothesis (not tested directly here) that some high-PHB strains were “sanctioned” by plants as less-beneficial. Our results suggest that rhizobia strains with the tendency to allocate more C to N fixation at the expense of PHB accumulation may be less likely to persist where soybean is grown infrequently or where soil conditions make PHB particularly valuable. However, with typical two-year rotations in Minnesota, differences in PHB storage are unlikely to have a major effect on the relative survival of strains.,See README.md for a detailed description of data files and scripts.,

,Data and code for "Cover crop inclusion and residue retention improves soybean production and physiology in drought conditions",CONTEXT: Soybean (Glycine max (L.) Merr.) planting has increased in central and western North Dakota despite frequent drought occurrences that limit productivity. Soybean plants need high photosynthetic and transpiration rates to be productive, but they also need high water use efficiency when water is limited. Retaining crop residues and including cover crops in crop rotations are management strategies that could improve soybean drought resilience in the northern Great Plains.,OBJECTIVE: We aimed to examine how a management practice that included cover crops and residue retention impacts agronomic, ecosystem water and carbon dioxide flux, and canopy-scale physiological attributes of soybeans in the northern Great Plains under drought conditions.,METHODS: We compared two soybean fields over two years with business-as-usual and aspirational management that included residue retention and cover crops during a drought year. This comparison was based on yield, aboveground biomass, Phenocam images, and fluxes from eddy covariance and ancillary measurements. These measurements were used to derive meteorological, physical, and physiological attributes with the ‘big leaf’ framework.,RESULTS: Soybean yields were 29% higher under drought conditions in the field managed in a system that included cover crops and residue retention. This yield increase was caused by extending the maturity phenophase by 5 days, increasing agronomic and intrinsic water use efficiency by 27% and 33%, respectively, increasing water uptake, and increasing the rubisco-limited photosynthetic capacity (Vcmax25) by 42%.,CONCLUSIONS: The inclusion of cover crops and residue retention into a cropping system improved soybean productivity because of differences in water use, phenology timing, and photosynthetic capacity.,IMPLICATIONS: These results suggest that farmers can improve soybean productivity and yield stability by incorporating cover crops and residue retention into their management practices because these practices allow soybean plants to shift to a more aggressive water uptake strategy.,Data Half_Hourly.csv: Half hour data from eddy covariance towers,Management.csv: data about field management,Phenocamdata.csv: The output of 1_phenocam.Rmd code,Predicted_Height_LAI.csv: The output of 3_Inferring_LAI_and_Height.Rmd,Vegetation.csv: biomass and yield data,Code 1_phenocam.rmd: Code to download Phenocam data and identify phenophase transition dates.,2_Daily_CO2_Water_Fluxes.Rmd: Code to analyze daily carbon and water fluxes (Figure 1, 2 3 and Table 2).,3_Inferring_LAI_and_Height.Rmd: Code to calculate the predicted LAI and height for each day. The output is used in the big-leaf framework.,4_Big_Leaf.Rmd: Code for the big-leaf ecophysiology estimates (Figure 4, 5 and 6; Table 3 and 4).,4_Data_Dictionary_Variables: Code to identify the data dictionary variables.,

,A stationary camera used to track vegetation phenology overlooking a restored native prairie. This site is located at the Rosemount Research and Outreach Center in Rosemount, Minnesota. Images are taken every 30 minutes. A link to the Phenocam's network FAQ: https://phenocam.sr.unh.edu/webcam/faq,

,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.,

,Information on crop genotype- and phenotype-metabolite associations can be of value to trait development as well as to food security and safety. The unique study presented here assessed seed metabolomic and ionomic diversity in a soybean (Glycine max) lineage representing ~35 years of breeding (launch years 1972–2008) and increasing yield potential. Selected varieties included six conventional and three genetically modified (GM) glyphosate-tolerant lines. A metabolomics approach utilizing capillary electrophoresis (CE)-time-of-flight-mass spectrometry (TOF-MS), gas chromatography (GC)-TOF-MS and liquid chromatography (LC)-quadrupole (q)-TOFMS resulted in measurement of a total of 732 annotated peaks. Ionomics through inductively-coupled plasma (ICP)-MS profiled twenty mineral elements. Orthogonal partial least squares-discriminant analysis (OPLS-DA) of the seed data successfully differentiated newer higher-yielding soybean from earlier lower-yielding accessions at both field sites. This result reflected genetic fingerprinting data that demonstrated a similar distinction between the newer and older soybean. Correlation analysis also revealed associations between yield data and specific metabolites. There were no clear metabolic differences between the conventional and GM lines. Overall, observations of metabolic and genetic differences between older and newer soybean varieties provided novel and significant information on the impact of varietal development on biochemical variability. Proposed applications of omics in food and feed safety assessments will need to consider that GM is not a major source of metabolite variability and that trait development in crops will, of necessity, be associated with biochemical variation.,,

,This dataset consists of growth and yield data for each season when soybean [Glycine max (L.) Merr.] was grown for seed at the USDA-ARS Conservation and Production Laboratory (CPRL), Soil and Water Management Research Unit (SWMRU) research weather station, Bushland, Texas (Lat. 35.186714°, Long. -102.094189°, elevation 1170 m above MSL). In the 1994, 2003, 2004, and 2010 seasons, soybean was grown on two large, precision weighing lysimeters, each in the center of a 4.44 ha square field. In 2019, soybean was grown on four large, precision weighing lysimeters and their surrounding 4.4 ha fields. The square fields are themselves arranged in a larger square with four fields in four adjacent quadrants of the larger square. Fields and lysimeters within each field are thus designated northeast (NE), southeast (SE), northwest (NW), and southwest (SW). Soybean was grown on different combinations of fields in different years. Irrigation was by linear move sprinkler system in 1995, 2003, 2004, and 2010 although in 2010 only one irrigation was applied to establish the crop after which it was grown as a dryland crop. Irrigation protocols described as full were managed to replenish soil water used by the crop on a weekly or more frequent basis as determined by soil profile water content readings made with a neutron probe to 2.4-m depth in the field. Irrigation protocols described as deficit typically involved irrigations to establish the crop early in the season, followed by reduced or absent irrigations later in the season (typically in the later winter and spring). The growth and yield data include plant population density, height, plant row width, leaf area index, growth stage, total above-ground biomass, leaf and stem biomass, head mass (when present), kernel or seed number, and final yield. Data are from replicate samples in the field and non-destructive (except for final harvest) measurements on the weighing lysimeters. In most cases yield data are available from both manual sampling on replicate plots in each field and from machine harvest. Machine harvest yields are commonly smaller than hand harvest yields due to combine losses. These datasets originate from research aimed at determining crop water use (ET), crop coefficients for use in ET-based irrigation scheduling based on a reference ET, crop growth, yield, harvest index, and crop water productivity as affected by irrigation method, timing, amount (full or some degree of deficit), agronomic practices, cultivar, and weather. Prior publications have focused on soybean ET, crop coefficients, and crop water productivity. Crop coefficients have been used by ET networks. The data have utility for testing simulation models of crop ET, growth, and yield and have been used for testing, and calibrating models of ET that use satellite and/or weather data.,See the README for descriptions of each data file.,,

,Soybean aphid (Aphis glycines Matsumura; SA) is a major invasive pest of soybean [Glycine max (L.) Merr.] in northern production regions of North America. Although insecticides are currently the main method for controlling this pest, SA-resistant cultivars are being developed to sustainably manage SA in the future. The viability of SA-resistant cultivars may depend on identifying a diverse set of resistance genes from screening various germplasm sources, including wild soybean (Glycine soja Siebold and Zucc.), the progenitor of cultivated soybean. Data consisted of infestation ratings generated for a total of 337 distinct plant introduction lines of wild soybean that were exposed to avirulent SA biotype 1 for 14 d in 25 separate tests. Individual plants of the test lines were given a common rating by two researchers, based on a rating scale that progressed from 1=0 to 50, 2=51 to 100, 3=101 to 150, 4=151 to 200, 5=201 to 250, and 6 with >250 SA per test plant. Public dissemination of this dataset will allow for further analyses and evaluation of resistance among the test lines.,,

,Data from this project focuses on the evaluation of breeding lines. Significant progress was made in advancing breeding populations directed towards release of improved varieties in Tanzania. Thirty promising F4:7, 1st generation 2014 PIC (Phaseolus Improvement Cooperative) and ~100 F4:6, 2nd generation 2015 PIC breeding lines were selected. In addition, ~300 F4:5, 3rd generation 2016 PIC single plant selections were completed in Arusha and Mbeya. These breeding lines, derived from 109 PIC populations specifically developed to combine abiotic and biotic stress tolerance, showed superior agronomic potential compared with checks and local landraces. The diversity, scale, and potential of the material in the PIC breeding pipeline is invaluable and requires continued support to ensure the release of varieties that promise to increase the productivity of common bean in the E. African region.,Data available includes databases, spreadsheets, and images related to the project.,,