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3C dataverse: Community capitals, cover crops, & conservation agriculture in the U.S. corn-soybean belt, version 2.2
<p><strong>What? </strong></p> <p>A dataset containing 315 total variables from 33 secondary sources. There are 262 unique variables, and 53 variables that have the same measurement but are reported for a different year; e.g. average farm size in 2017 (CapitalID: N27a) and 2022 (N27b). Variables were grouped by the community capital framework's seven capitals—Natural (96 total variables), Cultural (38), Human (39), Social (40), Political (18), Financial (67), & Built (15)—and temporally and thematically ordered. The geographic boundary is NOAA NCEI's corn and soybean belt (figure below), which stretches across 18 states and includes N=860 counties/observations. Cover crop data for the 80 Crop Reporting Districts in the boundary are also included for 2015-2021.</p> <p><strong>Why? </strong></p> <p>Comprehensively assessing how community capital clustered variables, for both farmers and nonfarmers, impact conservation practices (and perennial groundcover) over time helps to examine county-level farm conservation agriculture practices in the context of community development. We contribute to the robust U.S. cover crop literature a better understanding of how overarching cultural, social, and human factors influence conservation agriculture practices to encourage better farm management practices. Analyses of this Dataverse will be presented as recomendations for farmers, nonfarmers, ag-adjacent stakeholders, and community leaders.</p> <p><strong>How? </strong></p> <p>Variables used in this dataset range 20 years, from 2004-2023, though primary analyses focus on data collected between 2017-2024, primarily 2017 and 2022 (NASS Ag Census years). First, JAM-K requested, accessed, and downloaded data, most of which was already publically available. Next, JAM-K cleaned the data and aggregated into one dataset, and made it publically available on Google Drive and Zenodo. </p> <p><strong>What is 'new' or corrected in version 2.2? </strong></p> <p><em>Edited/amended</em>: Carroll, KY is now spelled correctly (two 'l's, not one); variable names, full and abbreviated, were updated to include the data year; Pike County's (IL) FIPS has been corrected from its wrong 17153 (same as Pulaski County) to 17149 (correct fips), and all Pike County (IL) data has been correctly amended; Farming dependent (ERS) updated for all variables; Data for built capital variables irrCorn17, irrSoy17, irrHcrp17, tractor17, and combine17 were incorrect for v.1, but were corrected for v.2; Several variable labels aggregated by Wisconsin University's Population Health Institute's County Health Rankings and Roadmaps were corrected to have the data's original source and years included, rather than citing CHR&R as the source (except for CHR&R's originally-produced values such as quartiles or rank scores); variables were reorganized by hypothesized community capital clusters (Natural -> Built), and temporally within each cluster. </p> <p><em>Added</em>: 55 variables, mostly from the 2022 Ag Census, and v 2.2 added a .pdf file with descriptives of data sources and years, and a .sav file. </p> <p><em>Omitted</em>: Four variables deemed irrelevant to the study; V1 codebook's "years internally available" column. Variable herbac22 for 55079, Milwaukee, WI, incorrectly had the value 2,049.612. That value was correctly changed to missing, with no data in the cell.</p> <p><strong>CRediT</strong>: conceptualization, CBF, JAM-K; methodology, JAM-K; data aggregation and curation, JAM-K; formal analysis, JAM-K; visualization, JAM-K; supervision, CBF; funding acquisition, CBF; project administration, CBF; resources, CBF, JAM-K</p> <p><strong>Acknowledgements</strong>: This research was funded by the Agriculture and Food Research Initiative Competitive Grant No. 2021-68012-35923 from the United States Department of Agriculture National Institute for Food and Agriculture. Any opinions, findings, conclusions, or recommendations expressed in this presentation are those of the authors and do not necessarily reflect the view of the U.S. Department of Agriculture. Much thanks to Corteva for granting data access of OpTIS 2.0 (2005-2019), and Austin Landini for STATA code and visualization assistance. </p>
Water chemistry data including nitrate stable isotopes sampled from zero-tension lysimeters in an Iowa corn-soybean field in 2017 and 2018
These data were used in the manuscript titled "Mechanisms underlying episodic nitrate and phosphorus leaching from poorly drained agricultural soils" published in the Journal of Environmental Quality. We measured nitrate, ammonium, and phosphate concentrations in zero-tension lysimeters installed along a topographic gradient in a corn and soybean field in north-central Iowa, USA, during 2017 and 2018. We measured nitrate stable isotope compositions in a subset of lysimeter samples. Concentrations of nitrate, ammonium, and ferrous and ferric iron were measured in periodic soil extractions co-located with the lysimeters.
Planting Date Maps (Low Resolution) and County Data for the US Corn Belt
<p>Aggregated dataset on planting dates for maize and soybean crops in the US Corn Belt from 2000-2020. This dataset was generated from Landsat satellite data and covers 12 states. The methodology is described in <a href="https://doi.org/10.1016/j.rse.2023.113551">Deines et al. 2023 </a>in Remote Sensing of Environment (open access).</p> <p>Note: Version 2 is identical to Version 1, but includes a file missing from v1 (soybeans_progressByCounty_2000-2020_DeinesEtAl_vRSE_formatted.csv)</p> <p><strong>Preferred citation:</strong></p> <p>Deines, J.M., A. Swatantran, D. Ye, B. Myers, S. Archontoulis, & D.B. Lobell. 2023. Field-scale dynamics of planting dates in the US Corn Belt from 2000 to 2020. <em><strong>Remote Sensing of Environment. </strong></em><a href="https://doi.org/10.1016/j.rse.2023.113551">https://doi.org/10.1016/j.rse.2023.113551</a></p> <p><strong>Contents</strong></p> <ol> <li>Gridded map datasets of planting dates aggregated to 10 km resolution. Map datasets are contained in zip files by crop type. Each zip file contains 21 annual geotiff rasters covering the full study region. <ol> <li>Maize_plantingDates_2000-2020_10000m_DeinesEtAl_vRSE.zip</li> <li>Soybeans_plantingDates_2000-2020_10000m_DeinesEtAl_vRSE.zip</li> </ol> </li> <li>Tabular county planting date statistics derived from the high-resolution (30 m) planting date maps. This data is presented in .csv format and includes the day-of-year when 10, 25, 50, 75, and 90 percent of crop area was planted for each year. There is one record for each county-year; data files are separated by crop type. <ol> <li>maize_countyStats_doyPercentPlanted_2000-2020_DeinesEtAl_vRSE.csv</li> <li>soybeans_countyStats_doyPercentPlanted_2000-2020_DeinesEtAl_vRSE.csv</li> </ol> </li> <li>Tabular planting progress by county. These .csv's present the area planted for each crop by county for each day. They can be considered the "raw" version of item #2 (county stats) and can therefore be used to derive other planting progress percentiles other than those provided in item #2. <ol> <li>maize_progressByCounty_2000-2020_DeinesEtAl_vRSE_formatted.csv</li> <li>soybeans_progressByCounty_2000-2020_DeinesEtAl_vRSE_formatted.csv</li> </ol> </li> </ol> <p><strong>Metadata</strong></p> <p>1. Planting date maps</p> <ul> <li>Value: day-of-year of planting</li> <li>No Data value = -999 (grid cells outside of the study area or lacking maize/soybeans that year)</li> <li>Map projection: EPSG:5070, CONUS Albers Equal Area</li> <li>Maps for 2008-2018 use the USDA's Cropland Data Layers to identify maize pixels; maps for 1999-2007 use the Corn-Soy Data Layer produced by <a href="https://www.nature.com/articles/s41597-020-00646-4">Wang et al. 2000</a> (data + manuscript are open access).</li> </ul> <p>2. Tabular datasets</p> <ul> <li>GEOID = 5 digit county FIPS, or the unique identifier assigned to each county by the US TIGER county dataset</li> <li>county_code = 3 digit county identifier. Represents places 3-5 in the 5 digit code.</li> <li>state_code = 2 digit state identifier. Represents places 1-2 in the 5 digit code.</li> <li>DOY = day-of-year</li> <li>area_daily_m2 = area of that crop planted that day in that county, in square meters</li> <li>doy_X = day-of-year when X% of area was planted that year</li> </ul> <p><br> </p>
Figure 6 in Molecular and morphological characterization of Tylenchus zeae n. sp. (Nematoda: Tylenchida) from Corn (Zea mays) in South Carolina
Figure 6: Phylogenetic relationships of Tylenchus zeae n. sp. with other select Tylenchidae, as inferred from a 418 bp alignment of mitochondrial COI sequences, according to the GTR + I + G model of nucleotide substitution and incorporated into MrBayes (MB) as described. A 50% majority rule consensus tree was generated with posterior probabilities (PP) shown on appropriate branches, with Bursaphelenchus cOnicaudatus as the outgroup. New sequences are indicated in bold.
Figure 4 in Molecular and morphological characterization of Tylenchus zeae n. sp. (Nematoda: Tylenchida) from Corn (Zea mays) in South Carolina
Figure 4: Phylogenetic relationships of Tylenchus zeae n. sp. with other select Tylenchidae, as inferred from a 1585 bp alignment of 18S rRNA sequences, according to the GTR + I + G model of nucleotide substitution and incorporated into MrBayes (MB) as described. A 50% majority rule consensus tree was generated with posterior probabilities (PP) shown on appropriate branches, with AphelenchOides besseyi as the outgroup. New sequences are indicated in bold.
Figure 3 in Molecular and morphological characterization of Tylenchus zeae n. sp. (Nematoda: Tylenchida) from Corn (Zea mays) in South Carolina
Figure 3: Line drawings of Tylenchus zeae n. sp. A: Female pharyngeal region; B: Female lip region showing stylet; C: Areolated lateral field; D: Male spicule, gubernaculum, and bursa. E: Vulval region showing vulva, uterus, and spermatheca; F–G: female tails.
Figure 2 in Molecular and morphological characterization of Tylenchus zeae n. sp. (Nematoda: Tylenchida) from Corn (Zea mays) in South Carolina
Figure 2: Photomicrographs of Tylenchus zeae n. sp. males and females. A–B: Anterior end with arrows pointing toward the excretory pore; C: Excretory pore; D: Areolated lateral field; E: Entire female body; F: Female basal bulb; G: Female gonad; H–I: female posterior end with arrow pointing the anal area (H); J: Female vulva region with arrow pointing toward the spermatheca; K: Male spicule.
Figure 5 in Molecular and morphological characterization of Tylenchus zeae n. sp. (Nematoda: Tylenchida) from Corn (Zea mays) in South Carolina
Figure 5: Phylogenetic relationships of Tylenchus zeae n. sp. with other select Tylenchidae, as inferred from an 822 bp alignment of 28S rRNA sequences, according to the GTR + I + G model of nucleotide substitution and incorporated into MrBayes (MB) as described. A 50% majority rule consensus tree was generated with posterior probabilities (PP) shown on appropriate branches, with Bursaphelenchus mucrOnatus as the outgroup. New sequences are indicated in bold.
Figure 1 in Molecular and morphological characterization of Tylenchus zeae n. sp. (Nematoda: Tylenchida) from Corn (Zea mays) in South Carolina
Figure 1: Scanning electron micrograph (SEM) images of Tylenchus zeae n. sp. A: Female specimen, anterior end, arrow pointing toward the excretory pore; B: Female specimen, head; C: Female specimen, face view; D: Lateral field (midbody); E: Female specimen, anal opening; F: Female specimen, vulval opening; G: Male specimen, spicule; H: Female specimen, arrow showing the anal opening; I: Female specimen, tail; J: Male specimen, posterior end.
Figure 2 in Influence of Mycorrhizae and Irrigation on Growth and Mineral Uptake by Corn (Zea mays L.) Seedlings in a Calcareous Soil
Figure 2. Average of macronutrients N, P, K, Mg, and Ca uptake (mg/plant) by shoots of corn seedlings grown in Guam cobbly clay soil, either inoculated (■) or not inoculated (♦) with Glomus aggregatum and provided one of four volumes of water: W1=7200 mL, W2=3600 mL, W3=1800 mL, and W4=900 mL during the 3-week experiment. Crossbars represent standard deviations of means of four replications.
Figure 3 in Influence of Mycorrhizae and Irrigation on Growth and Mineral Uptake by Corn (Zea mays L.) Seedlings in a Calcareous Soil
Figure 3. Average of micronutrients Fe, Mn, B, Cu, and Zn uptake (mg/plant) by shoots of corn seedlings grown in Guam cobbly clay soil, either inoculated (■) or not inoculated (♦) with Glomus aggregatum and provided one of four volumes of water: W1=7200 mL, W2=3600 mL, W3=1800 mL, and W4=900 mL during the 3-week experiment. Crossbars represent standard deviations of means of four replications.
Figure 3 in Does narrow row spacing suppress weeds and increase yields in corn and soybean? A meta-analysis
Figure 3. The overall effect of narrow row spacing (<76 cm) on weed density, weed biomass,weed control,weed seed production,and crop yield.The vertical black dashed line indicates zero effect. The black dots represent mean effect sizes (log of response ratios [lnðRRÞ]), and the black lines represent their respective 95% confidence intervals (CIs). The numbers in parentheses indicate the number of observations followed by the number of studies for each effect size. The effect sizes were considered significantly different when their 95% CIs did not overlap or contain zero.
Figure 6 in Does narrow row spacing suppress weeds and increase yields in corn and soybean? A meta-analysis
Figure 6. The effect of narrow row spacing (<76 cm) on crop yield as explained by subgroups of the crop, tillage, weed type, weed management method, herbicide application frequency, and time. The vertical black dashed line indicates zero effect. The black dots represent mean effect sizes (log of response ratios [lnðRRÞ]) for each subgroup, and the black lines represent their respective 99% confidence intervals (CIs). The numbers in parentheses indicate the number of observations followed by the number of studies for each effect size. The effect sizes were considered significantly different when their 99% CIs did not overlap or contain zero.
Figure 2. A in Does narrow row spacing suppress weeds and increase yields in corn and soybean? A meta-analysis
Figure 2. A map of the states in the midwestern and eastern United States showing experimental sites for the 35 corn and soybean narrow row spacing studies included in the meta-analysis.
Figure 5 in Does narrow row spacing suppress weeds and increase yields in corn and soybean? A meta-analysis
Figure 5. The individual effect sizes (natural log of response ratios [lnðRRÞ]) of (A) weed density, (B) weed biomass,(C) weed control, (D) weed seed production, and (E) crop yield as a function of crop row spacing. The green and red dots represent individual effect sizes for corn and soybean, respectively. The horizontal black dashed line represents zero effect,while the vertical black line represents 76-cm row spacing (control).The black bold line shows the relationship between individual effect sizes and crop row spacing,which is given as R (Pearson's correlation) with a P-value. The gray-shaded area represents 95% confidence intervals (CIs) of the linear relationship.
Figure 8 in Does narrow row spacing suppress weeds and increase yields in corn and soybean? A meta-analysis
Figure 8. Sensitivity analysis showing the variation in overall effect sizes (log of response ratios [ln(RR)]) (mean ± 95% confidence intervals [CIs]) of narrow row spacing effects on (A) weed density, (B) weed biomass, (C) weed control, (D) weed seed production, and (E) crop yield when any specific study was excluded from the analysis. The vertical red solid and dashed lines represent the mean ± 95% CIs, respectively, of overall effect sizes with all the studies included in the analysis.
Figure 3 in Critical period of weed control in an interseeded system of corn and alfalfa
Figure 3. Interseeded alfalfa total dry biomass yield as a percentage of the weed-free control over the critical duration of weedy treatments averaged over corn hybrid (pendulum and upright) for a 2-yr study (2020–2021). Interseeded corn and alfalfa were established in 2019 and 2020,(establishment years),and alfalfa was harvested four times the following season, in 2020 and 2021. In weedy interseeded treatments, weeds emerged with the crop and were then removed at different dates, creating the critical timing of weed removal (green circles). In weed-free interseeded treatments, weeds were added later in the crop, creating the critical weed free period (black triangles). An interseeded untreated and a weed-free check were included within these treatments. The critical period times are based on a 5% acceptable yield loss and are denoted by the dashed vertical lines, averaged over years and effect of corn hybrid; the boxes denote the SE for each of the growing degree–day estimates. Points represent observed mean values; lines represent the fitted models calculated using the DRC package in R (R Core Team 2020).
Figure 2 in Critical period of weed control in an interseeded system of corn and alfalfa
Figure 2. Interseeded alfalfa dry biomass yield for the first cutting as a percentage of the weed-free interseeded corn and alfalfa control over the critical duration of weedy treatments averaged over corn hybrid (pendulum and upright), for a 2-yr study (2020–2021). Interseeded corn and alfalfa were established in 2019 and 2020 (establishment years), and alfalfa was harvested the following season, in 2020 and 2021. In weedy treatments, weeds emerged with the crop and were then removed at different dates, creating the critical timing of weed removal (green circles).In weed-free interseeded treatments,weeds were added later in the crop, creating the critical weed-free period (black triangles). An interseeded untreated and a weed-free check were included within these treatments. The critical period times are based on a 5% acceptable yield loss and are denoted by the dashed vertical lines, averaged over years and effect of corn hybrid; the boxes denote the SE for each of the growing degree–day estimates. Points represent observed mean values; lines represent the fitted models calculated using the DRC package in R (R Core Team 2020).
Figure 1 in Critical period of weed control in an interseeded system of corn and alfalfa
Figure 1. Interseeded corn silage dry biomass yield as a percentage of the weed-free interseeded corn and alfalfa control over the critical duration of weedy treatments with differing leaf architecture, pendulum (black circles) or upright (green triangles), for 2019 (A) and 2020 (B). In weedy treatments, weeds emerged with the crop and were then removed at different dates,creating the critical timing of weed removal (CTWR;dashed line).In weed-free interseeded treatments,weeds were added later in the crop,creating the critical weed-free period (CWFP; solid line). An interseeded untreated and a weed-free check were included within these treatments. The CTWR based on a 5% acceptable yield loss, averaged over hybrids, is denoted by the dashed vertical line (black); the boxes denote the SEs of those estimates. The CWFP estimates are not shown, because they were greater than the harvest date. Points represent observed mean values; lines represent the fitted models calculated using the DRC package in R (R Core Team 2020).
Figure 1 in Does narrow row spacing suppress weeds and increase yields in corn and soybean? A meta-analysis
Figure 1. PRISMA (Preferred Reporting Items for Systematic Reviews and MetaAnalyses; Page et al. 2021) flow diagram showing the stepwise procedure used for selecting 35 studies for meta-analysis.
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