This dataset consists of agronomic calendars for each growing season (year) 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 1995, 2003, 2004, and 2010, 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, each in the center of a 4.4 ha square fields. The four fields were contiguous. The fields were designated northeast (NE), southeast (SE), northwest (NW), and southwest (SW), and were themselves arranged in a larger square with the fields in four adjacent quadrants of the larger square. Irrigation was by linear move sprinkler system in 1995, 2003, 2004, and 2010. In 2019, the NE and SE fields were irrigated using subsurface drip irrigation (SDI), while the NW and SW fields were irrigated using a linear move system. Irrigations designated 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. Irrigations designated as deficit typically involved full irrigation to establish the crop. A crop calendar for each season lists by date the pertinent agronomic and maintenance operations (e.g., planting, thinning, fertilization, pesticide application, lysimeter maintenance, harvest). For each season there is one crop calendar for each two lysimeters (NE and SE, and/or NW and SW). 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 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.

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

[Note 2023-08-14 - Superseded by Version 2, https://doi.org/10.15482/USDA.ADC/1529421 ] This dataset contains all code and data necessary to reproduce the analyses in the manuscript: Mengistu, A., Read, Q. D., Sykes, V. R., Kelly, H. M., Kharel, T., & Bellaloui, N. (2023). Cover crop and crop rotation effects on tissue and soil population dynamics of Macrophomina phaseolina and yield under no-till system. Plant Disease. https://doi.org/10.1094/pdis-03-23-0443-re The .zip archive cropping-systems-0.0.zip contains data and code files. Data Quentin combined data with Yld 2011-2015.xlsx: MS Excel spreadsheet with all data averaged by experimental plot Fred Allen combined data with Yld 2011-2015.xlsx: MS Excel spreadsheet with data at the individual plant level Code cropping_system_analysis_v2.1.Rmd: RMarkdown notebook with the majority of the data processing, analysis, and visualization code marginal_means_byyear.Rmd: RMarkdown notebook with additional analyses producing estimated marginal means for individual years equations.Rmd: RMarkdown notebook with formatted equations formatted_figs.R: R script to produce figures formatted exactly as they appear in the manuscript The Rproject file cropping-systems.Rproj is used to organize the RStudio project. Scripts and notebooks used in older versions of the analysis are found in the testing/ subdirectory.

[Note 2023-08-14 - Supersedes version 1, https://doi.org/10.15482/USDA.ADC/1528086 ] This dataset contains all code and data necessary to reproduce the analyses in the manuscript: Mengistu, A., Read, Q. D., Sykes, V. R., Kelly, H. M., Kharel, T., & Bellaloui, N. (2023). Cover crop and crop rotation effects on tissue and soil population dynamics of Macrophomina phaseolina and yield under no-till system. Plant Disease. https://doi.org/10.1094/pdis-03-23-0443-re The .zip archive cropping-systems-1.0.zip contains data and code files. Data stem_soil_CFU_by_plant.csv: Soil disease load (SoilCFUg) and stem tissue disease load (StemCFUg) for individual plants in CFU per gram, with columns indicating year, plot ID, replicate, row, plant ID, previous crop treatment, cover crop treatment, and comments. Missing data are indicated with . yield_CFU_by_plot.csv: Yield data (YldKgHa) at the plot level in units of kg/ha, with columns indicating year, plot ID, replicate, and treatments, as well as means of soil and stem disease load at the plot level. Code cropping_system_analysis_v3.0.Rmd: RMarkdown notebook with all data processing, analysis, and visualization code equations.Rmd: RMarkdown notebook with formatted equations formatted_figs_revision.R: R script to produce figures formatted exactly as they appear in the manuscript The Rproject file cropping-systems.Rproj is used to organize the RStudio project. Scripts and notebooks used in older versions of the analysis are found in the testing/ subdirectory. Excel spreadsheets containing raw data from which the cleaned CSV files were created are found in the raw_data subdirectory.

This dataset contains all data and code necessary to reproduce the analysis described under the heading "Experiment 3" in the manuscript: Taliercio, E., Eickholt, D., Read, Q. D., Carter, T., Waldeck, N., & Fallen, B. (2023). Parental choice and seed size impact the uprightness of progeny from interspecific Glycine hybridizations. Crop Science. https://doi.org/10.1002/csc2.21015 The attached files are: G_max_G_soja_seedweight_seedcolor_analysis.Rmd: RMarkdown notebook containing all analysis code. The CSV data files should be placed in a subdirectory called data within the working directory from which the notebook is rendered. G_max_G_soja_seedweight_seedcolor_analysis.html: Rendered HTML output from RMarkdown notebook, including figures, tables, and explanatory text. counts_seedwt.csv: CSV file containing the number of progeny selected and average 100-seed weight data for each combination of cross, size class, and replicate. Columns are: F3_location: text identifier of F3 nursery location, either "CLA" or "FF" plot: numeric ID of plot pop: numeric ID of population max: name of G. max parent soja: name of G. soja parent F2_location: text identifier of F2 nursery location, either "Caswell" or "Hugo" n_planted: number of seeds planted (raw) n_selected: number of progeny selected size_ordered: seed size class, to be converted to an ordered factor size_combined: seed size class aggregated to fewer unique levels ave_100sw: average 100-seed weight for the given size class n_planted_trials: number of seeds planted rounded to nearest integer seedcolor.csv: CSV file with additional data on number of seeds of each color by population. Columns are: cross: text identifier of cross line: text identifier of line light: number of light seeds mid: number of mid-green seeds brown: number of brown seeds dark: number of dark or black seeds population: identifier of population type (F2 derived or selected) max: name of G. max parent n_total: sum of the light, mid, brown, and dark columns soja: name of G. soja parent The data processing and analysis pipeline in the RMarkdown notebook includes: Importing the data (slightly cleaned version is provided) Creating boxplots of proportion selected by cross, nursery location, and size class Fitting logistic GLMM to estimate the probability of selection as a function of parent, 100-seed weight, and their interactions Extracting and plotting random effect estimates from model Calculating and plotting estimated marginal means from model Taking contrasts between pairs of estimated marginal means and trends Calculating Bayes Factors associated with the contrasts Generating figures and tables for all above results Additional seed color analysis: importing data (slightly cleaned version is provided) Additional seed color analysis: drawing exploratory bar plot Additional seed color analysis: fitting multinomial GLM modeling the proportion of seeds with each color as a function of population Additional seed color analysis: generating expected value predictions from GLM and taking contrasts Additional seed color analysis: creating figures and tables for model results This research was funded by CRIS 6070-21220-069-00D, United Soybean Board Project # 2333-203-0101, and falls under National Program NP301.

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'.

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.

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. 

This dataset contains water balance data for each year when soybean [Glycine max (L.) Merr.] was grown at the USDA-ARS Conservation and Production Laboratory (CPRL), Soil and Water Management Research Unit (SWMRU), Bushland, Texas (Lat. 35.186714°, Long. -102.094189°, elevation 1170 m above MSL). Soybean [Glycine max (L.) Merr.] was grown on two large, precision weighing lysimeters, each in the center of a 4.44 ha square field in 1995, 2003, 2004 and 2010. Soybean was grown on four large, precision weighing lysimeters and their surrounding 4.4-ha fields in 2019. Irrigation in 1995, 2003, 2004, and 2010 was by linear move sprinkler system. Irrigation in 2019 was by subsurface drip irrigation (SDI) system on the northeast (NE) and southeast (SE) weighing lysimeters an fields, while irrigation was by linear move sprinkler system on the northwest (NW) and southwest (SW) lysimeters and fields. Full irrigations 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. Deficit irrigations were less than full - see crop calendars and irrigation data in these files for details. The weighing lysimeters were used to measure relative soil water storage to 0.05 mm accuracy at 5-minute intervals, and the 5-minute change in soil water storage was used along with precipitation and irrigation amounts to calculate crop evapotranspiration (ET), which is reported at 15-minute intervals. Because the large (3 m by 3 m surface area) weighing lysimeters are better rain gages than are tipping bucket gages, the 15-minute precipitation data are derived for each lysimeter from changes in lysimeter mass. The land slope is <0.3% and flat. The water balance data consist of 15-minute and daily amounts of evapotranspiration (ET), dew/frost fall, precipitation (rain/snow), irrigation, scale counterweight adjustment, and emptying of drainage tanks, all in mm. The values are the result of a rigorous quality control process involving algorithms for detecting dew/frost accumulations, and precipitation (rain and snow). Changes in lysimeter mass due to emptying of drainage tanks, counterweight adjustment, maintenance activity, and harvest are accounted for such that ET values are minimally affected. The ET data should be considered to be the best values offered in these datasets. Even though ET data are also presented in the "lysimeter" datasets, the values herein are the result of a more rigorous quality control process. Dew and frost accumulation varies from year to year and seasonally within a year, and it is affected by lysimeter surface condition [bare soil, tillage condition, residue amount and orientation (flat or standing), etc.]. Particularly during winter and depending on humidity and cloud cover, dew and frost accumulation sometimes accounts for an appreciable percentage of total daily ET. 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 crop 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. See the README for descriptions of each data file.

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.

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.

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.

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.