Code accompanying the manuscript \"The stability of fatty acid composition in sunflower oil is dependent on environment and affected by structural variation\" (Ingold, Hulke et al.), submitted to Theoretical and Applied Genetics.
The code implements a genome-wide association study of seed fatty acid composition and its stability in the sunflower (Helianthus annuus L.) association mapping (SAM) population of 287 varieties, grown in eight field trials across North America: Vancouver, British Columbia (2010); Moorhead, Minnesota (2015, early and late plantings 2016); Ames, Iowa (2010, 2013, 2014); and Athens, Georgia (2010). Palmitic, stearic, oleic, and linoleic acid were measured by gas chromatography.
Multivariate GWAS was performed on four phenotype sets: mean fatty acid composition within each environment; the same omitting high oleic varieties; within-environment stability quantified by standard errors among replicate samples (alpha stability); and across-environment stability quantified by Eberhart and Russell's beta.
Included are scripts for phenotype preparation, beta stability regression, CHELSA climate data extraction and correlation, genotype and kinship analysis (ADMIXTURE, VanRaden kinship, PCA, LD blocks), multivariate GWAS with GEMMA and univariate GWAS with vcf2gwas, post-GWAS candidate gene identification, and analysis of a chromosome 5 introgression associated with stability under hot, humid conditions.
The code is archived at https://doi.org/10.5281/zenodo.22001495 and developed at https://github.com/BrianSmart/SunflowerFattyAcidStabilityGWAS. SNP genotypes are third-party and available from HelianthOME (http://www.helianthome.org/download/#genotype).
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The code implements a genome-wide association study of seed fatty acid composition and its stability in the sunflower (Helianthus annuus L.) association mapping (SAM) population of 287 varieties, grown in eight field trials across North America: Vancouver, British Columbia (2010); Moorhead, Minnesota (2015, early and late plantings 2016); Ames, Iowa (2010, 2013, 2014); and Athens, Georgia (2010). Palmitic, stearic, oleic, and linoleic acid were measured by gas chromatography.
Multivariate GWAS was performed on four phenotype sets: mean fatty acid composition within each environment; the same omitting high oleic varieties; within-environment stability quantified by standard errors among replicate samples (alpha stability); and across-environment stability quantified by Eberhart and Russell's beta.
Included are scripts for phenotype preparation, beta stability regression, CHELSA climate data extraction and correlation, genotype and kinship analysis (ADMIXTURE, VanRaden kinship, PCA, LD blocks), multivariate GWAS with GEMMA and univariate GWAS with vcf2gwas, post-GWAS candidate gene identification, and analysis of a chromosome 5 introgression associated with stability under hot, humid conditions.
The code is archived at https://doi.org/10.5281/zenodo.22001495 and developed at https://github.com/BrianSmart/SunflowerFattyAcidStabilityGWAS. SNP genotypes are third-party and available from HelianthOME (http://www.helianthome.org/download/#genotype).
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Particulate organic matter (POM) and mineral-associated organic matter (MAOM) fractions were isolated from <2 mm soil and analyzed by diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS). Raw spectra (including bleached NaOCl-oxidized POM and MAOM samples) are stored in SFT_POM_MAOM_raw.rds and processed in Raw_spectra.rmd. Mineral contributions were removed by spectral subtraction (POM \u2013 PX and MAM \u2013 MX), and spectra were transformed using Kubelka\u2013Munk (KM) functions in OMNIC; the resulting spectral subtraction data are stored in SFT_POM_MAOM_sub_KM.rds and SFT_POM_MAOM_sub_KM_ave.rds (replicate spectra are averaged) and processed in Subtracted_Spectra.rmd and Averaged_Spectra.rmd. Soil chemistry and index data (<2 mm soil, POM, MAOM) are in SFT_2023_Metadata.rds and Helix_Index123.rds and analyzed in SFT_Chem.rmd and SFT_stats.rmd. Variables include total carbon (TC), total nitrogen (TN), C:N ratios, SOM, water-holding capacity (WHC), pH, dissolved organic carbon (DOC), dissolved organic nitrogen (DON), microbial biomass carbon (MBC), microbial biomass nitrogen (MBN), and CO2 respiration. These files allow reproduction of DRIFTS spectral analyses (raw, subtraction, and averaged spectra) and statistical analyses of soil chemistry and index values. The READ_ME file describes the main purpose of each data file/analysis, and defines acronyms used.
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Soil was collected in July and August 2023 from four experimental almond orchards that received wood chip amendments by whole orchard recycling (WOR) in 2008 (15 years old), 2017 (6 years old), 2019 (4 years old), and 2020 (3 years old); this serves as a space-for-time (SFT) substitution design, with four replicate blocks sampled for each Treatment \u00d7 Field combination. The orchards are located at the University of California Kearney Agricultural Research and Extension Center (36.5966 \u00b0N, -119.5176 \u00b0W) in Parlier, California (USA), each having 4 replicated WOR and control blocks. WOR treatments received 33 \u2013 85 metric tons of wood chips per acre. Chronologically, the 3-year-old orchard received 60 tons of wood per acre, the 4-year-old orchard received 61 tons of wood per acre, the 6-year-old orchard received 85 tons of wood per acre, and the 15-year-old orchard received 33 tons of wood per acre. Control treatments followed conventional management practices during orchard establishment. For the orchards recycled in 2017, 2019, and 2020, the control soils were unamended and burning served as the control treatment in the 2008 orchard. This yields a chronosequence of soil samples at 15-, 6-, 4-, and 3-years post WOR, as a space for time substitution design. The soil texture primarily consists of fine sandy loam, characterized as coarse-loamy, mixed nonacid thermic Typic Xerorthents of the Hanford series. The National Cooperative Soil Survey characterized the Hanford soil series to typically contain less than 1 % soil organic matter (SOM) content, decreasing with depth (Official Series Description - HANFORD Series, n.d.). The orchard experiences a Mediterranean climate, with dry, hot summers and cool, wet winters and precipitation levels generally falling below almond evapotranspiration requirements for a significant portion of the growing season. On average, the annual rainfall and temperature stand at 285 mm and 17\u00b0C, respectively. Thus, all crop production in the region is irrigated.
Particulate organic matter (POM) and mineral-associated organic matter (MAOM) fractions were isolated from <2 mm soil and analyzed by diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS). Raw spectra (including bleached NaOCl-oxidized POM and MAOM samples) are stored in SFT_POM_MAOM_raw.rds and processed in Raw_spectra.rmd. Mineral contributions were removed by spectral subtraction (POM \u2013 PX and MAM \u2013 MX), and spectra were transformed using Kubelka\u2013Munk (KM) functions in OMNIC; the resulting spectral subtraction data are stored in SFT_POM_MAOM_sub_KM.rds and SFT_POM_MAOM_sub_KM_ave.rds (replicate spectra are averaged) and processed in Subtracted_Spectra.rmd and Averaged_Spectra.rmd. Soil chemistry and index data (<2 mm soil, POM, MAOM) are in SFT_2023_Metadata.rds and Helix_Index123.rds and analyzed in SFT_Chem.rmd and SFT_stats.rmd. Variables include total carbon (TC), total nitrogen (TN), C:N ratios, SOM, water-holding capacity (WHC), pH, dissolved organic carbon (DOC), dissolved organic nitrogen (DON), microbial biomass carbon (MBC), microbial biomass nitrogen (MBN), and CO2 respiration. These files allow reproduction of DRIFTS spectral analyses (raw, subtraction, and averaged spectra) and statistical analyses of soil chemistry and index values. The READ_ME file describes the main purpose of each data file/analysis, and defines acronyms used.
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Trends in records of 50 years or more showed that temperatures have changed from 1900-2020, more for minimum (0.1 - 0.3 \u2103 decade-1) than maximum (-0.1 - 0.2 \u2103 decade-1), more for winter (-0.1 - 0.3 \u2103 decade-1) than summer (-0.1 - 0.1 \u2103 decade-1), and more often in the north than in the south. Except in Mississippi, annual precipitation has increased at rates of 25 mm decade-1 or greater over 1950-2020, but monthly trends were inconsistent. Projected trends suggest continued temperature increases, highlighting the need for research on management systems that are resilient to such increases.
","distribution_titles":["Geodata ISO 19139 metadata","README.txt","README_LOCA2.txt","Station Data.xlsx","NOAA Data and trends.xlsx","LOCA2-LTAR.zip","ESM_Data_trends_AllTrends.xlsx","Climate_Indicators.txt","Coeff_Var_Trender.txt","LOCA_MKTrend.txt","ViolinPlotter.txt"],"harvest_record":"https://catalog.data.gov/harvest_record/73e60f2d-3810-4a41-960d-d3b6150db7c7","harvest_record_raw":"https://catalog.data.gov/harvest_record/73e60f2d-3810-4a41-960d-d3b6150db7c7/raw","has_download":true,"has_spatial":true,"identifier":"10.15482/USDA.ADC/29640977.v1","keyword":["CMIP 6 dataset","LTAR","Trend Analysis Background Data","precipitation","source code","temperature"],"last_harvested_date":"2026-09-30T21:32:32.041052","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"352b4532-793d-4075-a03f-05b778a3c43a","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/usda.png","name":"Department of Agriculture","organization_type":"Federal Government","slug":"usda"},"parent_identifier":null,"popularity":1,"publisher":"Agricultural Research Service","slug":"data-from-are-historical-trends-in-weather-consistent-with-model-predictions-in-the-centra","spatial_centroid":{"lat":39.3375,"lon":-90.45},"spatial_shape":{"coordinates":[[-85.4,42.4],[-93.8,42.3],[-92.1,39.2],[-90.5,33.45]],"type":"MultiPoint"},"theme":["geospatial"],"title":"Data from: Are historical trends in weather consistent with model predictions in the Central United States?","type":"dataset"},{"_score":8.294483,"_sort":[1790803895703,8.294483,4,"1a85cb8e-bfcd-4ee3-b40f-ea15e087a213"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","accrualPeriodicity":"irregular","bureauCode":["005:18"],"contactPoint":{"fn":"Liebig, Mark A.","hasEmail":"mailto:mark.liebig@usda.gov"},"description":"Retaining crop residue on the soil surface is important in semiarid cropping systems, where dry conditions and variable weather can render the soil susceptible to degradation and moisture loss. Conversely, removing crop residue can generate an additional income stream for agricultural producers, while broadening the spectrum of uses from harvestable commodities. Understanding potential tradeoffs of crop residue removal across a range of agroecosystems over the long-term is essential to adequately guide management decisions. A study was conducted to quantify crop and soil responses to continuous spring wheat treatments with and without straw removal, each managed under minimum and no-tillage. Treatments were replicated three times and deployed over a 24-yr period. Soil coverage by crop residue was measured annually using two 25 point transects spaced equally along a 7.6 m cable. Spring wheat aboveground biomass was measured prior to combine harvest using 0.33 m2 frames. Biomass samples were threshed to separate grain from straw. Soil samples were collected in 2018 with a hydraulic probe to a 152.4 cm depth in increments of 0-7.6, 7.6-15.2, 15.2-30.5, 30.5-61.0, 61.0-91.4, 91.4-121.9, and 121.9-152.4 cm. Separate samples for aggregate stability analysis were collected with a trowel from the 0-7.6 cm depth. Soil samples were evaluated for soil bulk density, water-stable aggregates (WSA), electrical conductivity, soil pH, nitrate-nitrogen, available phosphorus, sulfate-sulfur, exchangeable cations (Ca, Mg, K, Na), micronutrients (B, Cu, Fe, Mn, Zn), total soil nitrogen, total carbon, inorganic carbon, and particulate organic matter (POM) carbon and nitrogen. Particulate organic matter (POM) was estimated from material retained on a 0.053 mm sieve analyzed for carbon and nitrogen content by dry combustion. Analyses for POM and WSA were conducted for the 0-7.6 cm depth only. Data may be used to better understand crop and soil property responses to residue management and tillage practices under rainfed conditions within a semiarid continental climate. Applicable USDA soil types include Temvik, Wilton, Grassna, Linton, Mandan, and Williams.
The SQM Data Dictionary describes element/value names, data type, etc. for each spreadsheet tab, and the Metadata files describe attributes and units for their respective data files.
Retaining crop residue on the soil surface is important in semiarid cropping systems, where dry conditions and variable weather can render the soil susceptible to degradation and moisture loss. Conversely, removing crop residue can generate an additional income stream for agricultural producers, while broadening the spectrum of uses from harvestable commodities. Understanding potential tradeoffs of crop residue removal across a range of agroecosystems over the long-term is essential to adequately guide management decisions. A study was conducted to quantify crop and soil responses to continuous spring wheat treatments with and without straw removal, each managed under minimum and no-tillage. Treatments were replicated three times and deployed over a 24-yr period. Soil coverage by crop residue was measured annually using two 25 point transects spaced equally along a 7.6 m cable. Spring wheat aboveground biomass was measured prior to combine harvest using 0.33 m2 frames. Biomass samples were threshed to separate grain from straw. Soil samples were collected in 2018 with a hydraulic probe to a 152.4 cm depth in increments of 0-7.6, 7.6-15.2, 15.2-30.5, 30.5-61.0, 61.0-91.4, 91.4-121.9, and 121.9-152.4 cm. Separate samples for aggregate stability analysis were collected with a trowel from the 0-7.6 cm depth. Soil samples were evaluated for soil bulk density, water-stable aggregates (WSA), electrical conductivity, soil pH, nitrate-nitrogen, available phosphorus, sulfate-sulfur, exchangeable cations (Ca, Mg, K, Na), micronutrients (B, Cu, Fe, Mn, Zn), total soil nitrogen, total carbon, inorganic carbon, and particulate organic matter (POM) carbon and nitrogen. Particulate organic matter (POM) was estimated from material retained on a 0.053 mm sieve analyzed for carbon and nitrogen content by dry combustion. Analyses for POM and WSA were conducted for the 0-7.6 cm depth only. Data may be used to better understand crop and soil property responses to residue management and tillage practices under rainfed conditions within a semiarid continental climate. Applicable USDA soil types include Temvik, Wilton, Grassna, Linton, Mandan, and Williams.
The SQM Data Dictionary describes element/value names, data type, etc. for each spreadsheet tab, and the Metadata files describe attributes and units for their respective data files.
Three .csv files. Two years of data collected on critical thermal maxima and minima (CTmax and CTmin) from 14 colonies in Oklahoma from 2017-2018. Additional data files represent thermal traits from 10 colonies in a short term (10 days) acclimation experiment and projections using environmental data for potential activity differences.
Abstract from paper:
How do individuals tolerate both the hot and the cold climate of our planet? One possibility is that organisms have plastic traits like thermal tolerance that allow them to function in highly variable environments. In this study, we tested whether phenotypic plasticity of temperature tolerance (i.e. acclimatization in the field and acclimation in the lab) occurs in the red harvester ant, Pogonomyrmex barbatus, at two temporal scales. We first measured the upper and lower critical thermal limits (CTmax and CTmin) of ants monthly for two years while concurrently measuring environmental conditions. Both CTmax and CTmin co-varied with temperature in a predictable way; values increased in a positive, linear manner. We then experimentally tested whether CTmax and CTmin could shift within a shorter time period by exposing subcolonies of ants to cool (10\u00b0C), moderate (20\u00b0C), and hot (30\u00b0C) temperatures for 10 days. CTmax increased only slightly at the hottest temperature treatment (+1.2\u00b0C), however CTmin increased considerably under both moderate (+2.6\u00b0C) and hot treatments (+3.8\u00b0C). Combined, our results suggest that thermal tolerance of ants may be more plastic than originally hypothesized, potentially aiding an already thermophilic clade.
Methods from paper:
Study site and environmental temperature
We sampled ant workers monthly during their annual active period in 2017 and 2018 (i.e. March-November) from 14 colonies in a 30-ha grazed prairie in the central Great Plains of Oklahoma (34.5478\u00ba N, -98.2311\u00ba W, 330 m elevation). Over two years, ground temperature was recorded every 10 minutes using HOBO U23 Pro v2 External Temperature Data loggers at three equidistant locations within the sampling area. Temperature values were then averaged per month.
Thermal tolerance across months
During each sampling event (n = 18), we collected ~20 workers directly outside the nest of each colony and used 5 workers to measure critical thermal maximum (CTmax) and 5 workers to measure critical thermal minimum (CTmin). We did so using a heating/cooling assay to determine the temperature at which individuals lost muscle control. Thermal assays were conducted by placing individual ants into 1.5ml microcentrifuge tubes and plugging the tops with cotton to remove a potential thermal refuge in the cap. For CTmax, tubes were placed randomly into a Thermal-Lok 2-position dry heat bath that was prewarmed to 36\u00baC. Every 10 minutes, individuals were checked to see if they had reached their critical thermal limit by rotating the tube to check for a righting response. The temperature was then increased by 2\u00baC, with the process repeated until all individuals had reached their critical thermal maximum. CTmin was assayed in a similar manner, but we used a EchoThermTM IC20 chilling/heating dry bath that was precooled to 20\u00baC, following the methods above except with temperature lowered 2\u00b0C every 10 minutes. Additional ants from each colony were kept at ambient conditions as a control during each thermal assay, all of which survived. During each trial, we also confirmed the interior temperature of one unused vial using a thermocouple attached to an Extech MN35 Digital Mini MultiMeter. CTmax and CTmin values were averaged per colony for each month.
Thermal tolerance within a month
In April of 2019, we collected ~200 workers from each of 10 separate colonies to assess if critical thermal limits could change within a single cohort of ants over a short period of time. We split each group of 200 workers into three sub-colonies containing 50 workers and placed these newly created sub-colonies into three environmental chambers set at 10\u00baC, 20\u00baC, and 30\u00baC with a 12:12 L:D cycle and 85% RH. The selected temperatures span the approximate range of average monthly temperatures at our study site during which ants were active. Each sub-colony was provided with water and 20% sucrose solution ad libitum in cotton plugged vials and a small petri dish with Plaster of Paris that was moistened every other day. Critical thermal limits (CTmax and CTmin) were assayed using five individuals from each colony immediately prior to the start of the experiment and for five individuals from each sub-colony after 10 days in the environmental chambers. CTmax and CTmin values were averaged per colony for each temperature treatment.
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Abstract from paper:
How do individuals tolerate both the hot and the cold climate of our planet? One possibility is that organisms have plastic traits like thermal tolerance that allow them to function in highly variable environments. In this study, we tested whether phenotypic plasticity of temperature tolerance (i.e. acclimatization in the field and acclimation in the lab) occurs in the red harvester ant, Pogonomyrmex barbatus, at two temporal scales. We first measured the upper and lower critical thermal limits (CTmax and CTmin) of ants monthly for two years while concurrently measuring environmental conditions. Both CTmax and CTmin co-varied with temperature in a predictable way; values increased in a positive, linear manner. We then experimentally tested whether CTmax and CTmin could shift within a shorter time period by exposing subcolonies of ants to cool (10\u00b0C), moderate (20\u00b0C), and hot (30\u00b0C) temperatures for 10 days. CTmax increased only slightly at the hottest temperature treatment (+1.2\u00b0C), however CTmin increased considerably under both moderate (+2.6\u00b0C) and hot treatments (+3.8\u00b0C). Combined, our results suggest that thermal tolerance of ants may be more plastic than originally hypothesized, potentially aiding an already thermophilic clade.
Methods from paper:
Study site and environmental temperature
We sampled ant workers monthly during their annual active period in 2017 and 2018 (i.e. March-November) from 14 colonies in a 30-ha grazed prairie in the central Great Plains of Oklahoma (34.5478\u00ba N, -98.2311\u00ba W, 330 m elevation). Over two years, ground temperature was recorded every 10 minutes using HOBO U23 Pro v2 External Temperature Data loggers at three equidistant locations within the sampling area. Temperature values were then averaged per month.
Thermal tolerance across months
During each sampling event (n = 18), we collected ~20 workers directly outside the nest of each colony and used 5 workers to measure critical thermal maximum (CTmax) and 5 workers to measure critical thermal minimum (CTmin). We did so using a heating/cooling assay to determine the temperature at which individuals lost muscle control. Thermal assays were conducted by placing individual ants into 1.5ml microcentrifuge tubes and plugging the tops with cotton to remove a potential thermal refuge in the cap. For CTmax, tubes were placed randomly into a Thermal-Lok 2-position dry heat bath that was prewarmed to 36\u00baC. Every 10 minutes, individuals were checked to see if they had reached their critical thermal limit by rotating the tube to check for a righting response. The temperature was then increased by 2\u00baC, with the process repeated until all individuals had reached their critical thermal maximum. CTmin was assayed in a similar manner, but we used a EchoThermTM IC20 chilling/heating dry bath that was precooled to 20\u00baC, following the methods above except with temperature lowered 2\u00b0C every 10 minutes. Additional ants from each colony were kept at ambient conditions as a control during each thermal assay, all of which survived. During each trial, we also confirmed the interior temperature of one unused vial using a thermocouple attached to an Extech MN35 Digital Mini MultiMeter. CTmax and CTmin values were averaged per colony for each month.
Thermal tolerance within a month
In April of 2019, we collected ~200 workers from each of 10 separate colonies to assess if critical thermal limits could change within a single cohort of ants over a short period of time. We split each group of 200 workers into three sub-colonies containing 50 workers and placed these newly created sub-colonies into three environmental chambers set at 10\u00baC, 20\u00baC, and 30\u00baC with a 12:12 L:D cycle and 85% RH. The selected temperatures span the approximate range of average monthly temperatures at our study site during which ants were active. Each sub-colony was provided with water and 20% sucrose solution ad libitum in cotton plugged vials and a small petri dish with Plaster of Paris that was moistened every other day. Critical thermal limits (CTmax and CTmin) were assayed using five individuals from each colony immediately prior to the start of the experiment and for five individuals from each sub-colony after 10 days in the environmental chambers. CTmax and CTmin values were averaged per colony for each temperature treatment.
","distribution_titles":["File 2 Across Months Pogo Thermal.csv","File 3 Within Month Pogo Thermal.csv","File 1 METADATA Pogo Thermal.csv"],"harvest_record":"https://catalog.data.gov/harvest_record/baca902d-dd40-4535-bead-faa5a68dce8f","harvest_record_raw":"https://catalog.data.gov/harvest_record/baca902d-dd40-4535-bead-faa5a68dce8f/raw","has_download":true,"has_spatial":false,"identifier":"10.15482/USDA.ADC/28459058.v1","keyword":["Critical thermal limits","Pogonomyrmex barbatus","temperature","traits"],"last_harvested_date":"2026-09-30T21:30:45.020435","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"352b4532-793d-4075-a03f-05b778a3c43a","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/usda.png","name":"Department of Agriculture","organization_type":"Federal Government","slug":"usda"},"parent_identifier":null,"popularity":3,"publisher":"Agricultural Research Service","slug":"data-from-temporal-plasticity-of-thermal-tolerance-in-ants","spatial_centroid":null,"spatial_shape":null,"theme":[],"title":"Data from: Temporal plasticity of thermal tolerance in ants","type":"dataset"},{"_score":15.241561,"_sort":[1790803842515,15.241561,1,"a592d3cf-ad2e-4a98-a2bf-e2f7fdd71671"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","bureauCode":["005:18"],"contactPoint":{"fn":"Bandaru, Varaprasad","hasEmail":"mailto:prasad.bandaru@usda.gov"},"description":"The Environmental Policy Integrated Climate (EPIC) model is a comprehensive, field-scale agroecosystem model widely used for both diagnostic and prognostic analyses in agriculture. However, its application at regional scales is limited due to its original design to simulate a limited number of fields. Custom Python or R scripts have attempted to scale EPIC, but they are often inefficient, non-standardized, and not publicly available. To address these issues, a comprehensive Python package, named as GeoEPIC, that streamlines spatial EPIC implementation. GeoEPIC automates input generation from spatial datasets, model calibration, simulation execution, and output post-processing.
","distribution":[{"@type":"dcat:Distribution","downloadURL":"https://smarsgroup.github.io/geo_epic_win/reference/api/core/","mediaType":"text/html","title":"https://smarsgroup.github.io/geo_epic_win/reference/api/core/"}],"identifier":"10779/USDA.ADC.31151371.v1","keyword":["EPIC crop model","Python","Spatial Modeling Approach"],"license":"https://opensource.org/license/BSD-3-Clause","modified":"2026-08-25","programCode":["005:040"],"publisher":{"@type":"org:Organization","name":"Agricultural Research Service"},"temporal":"2024-06-01/2025-11-30","title":"GeoEPIC Python Package"},"description":"The Environmental Policy Integrated Climate (EPIC) model is a comprehensive, field-scale agroecosystem model widely used for both diagnostic and prognostic analyses in agriculture. However, its application at regional scales is limited due to its original design to simulate a limited number of fields. Custom Python or R scripts have attempted to scale EPIC, but they are often inefficient, non-standardized, and not publicly available. To address these issues, a comprehensive Python package, named as GeoEPIC, that streamlines spatial EPIC implementation. GeoEPIC automates input generation from spatial datasets, model calibration, simulation execution, and output post-processing.
","distribution_titles":["https://smarsgroup.github.io/geo_epic_win/reference/api/core/"],"harvest_record":"https://catalog.data.gov/harvest_record/8efe138f-32f1-43cf-ad94-fc669543fae2","harvest_record_raw":"https://catalog.data.gov/harvest_record/8efe138f-32f1-43cf-ad94-fc669543fae2/raw","has_download":true,"has_spatial":false,"identifier":"10779/USDA.ADC.31151371.v1","keyword":["EPIC crop model","Python","Spatial Modeling Approach"],"last_harvested_date":"2026-09-30T21:30:42.515787","organization":{"aliases":["dept"],"code_repo_exempt":false,"code_repo_url":null,"description":null,"id":"352b4532-793d-4075-a03f-05b778a3c43a","logo":"https://raw.githubusercontent.com/GSA/logo/refs/heads/master/usda.png","name":"Department of Agriculture","organization_type":"Federal Government","slug":"usda"},"parent_identifier":null,"popularity":1,"publisher":"Agricultural Research Service","slug":"geoepic-python-package","spatial_centroid":null,"spatial_shape":null,"theme":[],"title":"GeoEPIC Python Package","type":"dataset"},{"_score":8.286104,"_sort":[1790794424534,8.286104,4,"374f22e6-71ff-41c8-ad59-226d7c041e6e"],"access_level":"public","dcat":{"@type":"dcat:Dataset","accessLevel":"public","contactPoint":{"@type":"vcard:Contact","fn":"OpenData","hasEmail":"mailto:no-reply@data.sf.gov"},"description":"A. SUMMARY\nThe Municipal Natural Gas Equipment Inventory serves to catalog natural gas-fueled equipment used in municipally owned buildings. \nThis inventory, implemented by the SF Environment Department, aims to establish an understanding of the scope of work needed to electrify municipal buildings and inform an effective and collaborative planning process.\nThis effort was identified as an action in Section BO-2.4 of the 2021 Climate Action Plan and is included in the Environment Code Chapter 7 (Municipal Green Building Requirements). \n\nB. HOW THE DATASET IS CREATED\nThe list of buildings required to report data for the Municipal Natural Gas Equipment Inventory was compiled by cross-referencing the City\u2019s Facility Systems of Record and the 2020 municipal benchmarking report to identify all city-owned buildings with non-zero carbon emissions. Numerous municipal buildings are exempt from these reporting requirements, including facilities of the Port of San Francisco and buildings with a primary purpose of providing collection, storage, treatment, delivery, distribution, and/or transmission of water, wastewater, and/or power utilities. \nEach department received an inventory template, provided by the Environment Department, to submit high level building data and detailed information on each piece of natural gas equipment in use in these buildings. Departments were asked to self-report the required building and equipment data over the course of a 6-month data collection period in 2023 and are asked to keep this inventory up to date in the following years as equipment is replaced. \n\nC. UPDATE PROCESS\nThe inventory will be regularly updated by department representatives via the inventory PowerApp. When a gas-powered equipment item is retired or replaced, departments are asked to mark it as no longer in use and provide information on any electric replacement equipment, if applicable. While departments have the flexibility to update the inventory at any time, they are encouraged to do so at 6 month intervals at the minimum. \n\nUpdated inventory data will be automatically reflected in this dataset. \n\nD. HOW TO USE THIS DATASET\nIt is important to note that this dataset does not include facilities of the Port of San Francisco and buildings with a primary purpose of providing collection, storage, treatment, delivery, distribution, and/or transmission of water, wastewater, and/or power utilities, in accordance with Environment Code Chapter 7 exemptions.","distribution":[{"@type":"dcat:Distribution","describedBy":"https://data.sf.gov/api/views/vc6r-v7av/columns.json","describedByType":"application/json","downloadURL":"https://data.sf.gov/api/v3/views/vc6r-v7av/query.json?accessType=DOWNLOAD","mediaType":"application/json"},{"@type":"dcat:Distribution","describedBy":"https://data.sf.gov/api/views/vc6r-v7av/columns.xml","describedByType":"application/xml","downloadURL":"https://data.sf.gov/api/v3/views/vc6r-v7av/query.xml?accessType=DOWNLOAD","mediaType":"application/xml"},{"@type":"dcat:Distribution","downloadURL":"https://data.sf.gov/api/v3/views/vc6r-v7av/export.csv?accessType=DOWNLOAD","mediaType":"text/csv"},{"@type":"dcat:Distribution","downloadURL":"https://data.sf.gov/api/v3/views/vc6r-v7av/export.kml?accessType=DOWNLOAD","mediaType":"application/vnd.google-earth.kml+xml"},{"@type":"dcat:Distribution","downloadURL":"https://data.sf.gov/api/v3/views/vc6r-v7av/export.kmz?accessType=DOWNLOAD","mediaType":"application/vnd.google-earth.kmz"},{"@type":"dcat:Distribution","downloadURL":"https://data.sf.gov/api/v3/views/vc6r-v7av/query.geojson?accessType=DOWNLOAD","mediaType":"application/geo+json"}],"identifier":"https://data.sf.gov/api/views/vc6r-v7av","issued":"2024-03-28","keyword":["environment","environmental health","greenhouse gas emissions","natural gas"],"landingPage":"https://data.sf.gov/d/vc6r-v7av","modified":"2026-09-25","publisher":{"@type":"org:Organization","name":"data.sf.gov"},"theme":["Energy and Environment"],"title":"San Francisco Municipal Natural Gas Equipment Inventory"},"description":"A. SUMMARY\nThe Municipal Natural Gas Equipment Inventory serves to catalog natural gas-fueled equipment used in municipally owned buildings. \nThis inventory, implemented by the SF Environment Department, aims to establish an understanding of the scope of work needed to electrify municipal buildings and inform an effective and collaborative planning process.\nThis effort was identified as an action in Section BO-2.4 of the 2021 Climate Action Plan and is included in the Environment Code Chapter 7 (Municipal Green Building Requirements). \n\nB. HOW THE DATASET IS CREATED\nThe list of buildings required to report data for the Municipal Natural Gas Equipment Inventory was compiled by cross-referencing the City\u2019s Facility Systems of Record and the 2020 municipal benchmarking report to identify all city-owned buildings with non-zero carbon emissions. Numerous municipal buildings are exempt from these reporting requirements, including facilities of the Port of San Francisco and buildings with a primary purpose of providing collection, storage, treatment, delivery, distribution, and/or transmission of water, wastewater, and/or power utilities. \nEach department received an inventory template, provided by the Environment Department, to submit high level building data and detailed information on each piece of natural gas equipment in use in these buildings. Departments were asked to self-report the required building and equipment data over the course of a 6-month data collection period in 2023 and are asked to keep this inventory up to date in the following years as equipment is replaced. \n\nC. UPDATE PROCESS\nThe inventory will be regularly updated by department representatives via the inventory PowerApp. When a gas-powered equipment item is retired or replaced, departments are asked to mark it as no longer in use and provide information on any electric replacement equipment, if applicable. While departments have the flexibility to update the inventory at any time, they are encouraged to do so at 6 month intervals at the minimum. \n\nUpdated inventory data will be automatically reflected in this dataset. \n\nD. 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