Objectives: Crop production is an important variable in social, economic and environmental analyses. There is an abundance of crop data available for the United States, but we lack a typology of county-level crop production that accounts for production similarities in counties across the country. We fill this gap with a county-level classification of crop production with ten mutually exclusive categories across the contiguous United States. Data description: To create the typology we ran a cluster analysis on acreage data for 21 key crops from the United States Department of Agriculture\u27s 2012 Agricultural Census. Prior to clustering, we estimated undisclosed county acreage values, controlled for acreage in other crop types, and removed counties with low agricultural production to produce proportional scores for each crop type in each county. We used proportional scores to control for the influence of county size in the cluster analysis and used internal and stability measures to validate the analysis. The final dataset features 2922 counties. Future research can leverage this typology as an input for county- or regional-level analysis

Hammond Wagner, Courtney R.

Niles, Meredith T.

Roy, Eric D.

English

UVM ScholarWorks

University of Vermont ScholarWorks @ UVM Rubenstein School of Environment and Natural Resources Faculty Publications Rubenstein School of Environment and Natural Resources 8-30-2019 US county-level agricultural crop production typology Courtney R. Hammond Wagner University of Vermont Meredith T. Niles University of Vermont Eric D. Roy University of Vermont Follow this and additional works at: https://scholarworks.uvm.edu/rsfac  Part of the Community Health Commons, Human Ecology Commons, Nature and Society Relations Commons, Place and Environment Commons, and the Sustainability Commons Recommended Citation Wagner CR, Niles MT, Roy ED. US county-level agricultural crop production typology. BMC research notes. 2019 Dec;12(1):1-3. This Article is brought to you for free and open access by the Rubenstein School of Environment and Natural Resources at ScholarWorks @ UVM. It has been accepted for inclusion in Rubenstein School of Environment and Natural Resources Faculty Publications by an authorized administrator of ScholarWorks @ UVM. For more information, please contact donna.omalley@uvm.edu. Hammond Wagner et al. BMC Res Notes          (2019) 12:552  https://doi.org/10.1186/s13104-019-4594-4DATA NOTEUS county-level agricultural crop production typologyCourtney R. Hammond Wagner1,2, Meredith T. Niles2,3 and Eric D. Roy1,2* Abstract Objectives: Crop production is an important variable in social, economic and environmental analyses. There is an abundance of crop data available for the United States, but we lack a typology of county-level crop production that accounts for production similarities in counties across the country. We fill this gap with a county-level classification of crop production with ten mutually exclusive categories across the contiguous United States.Data description: To create the typology we ran a cluster analysis on acreage data for 21 key crops from the United States Department of Agriculture’s 2012 Agricultural Census. Prior to clustering, we estimated undisclosed county acreage values, controlled for acreage in other crop types, and removed counties with low agricultural production to produce proportional scores for each crop type in each county. We used proportional scores to control for the influ-ence of county size in the cluster analysis and used internal and stability measures to validate the analysis. The final dataset features 2922 counties. Future research can leverage this typology as an input for county- or regional-level analysis.Keywords: Agriculture, Farming systems, Typology, Crop production, United States, County-level© The Author(s) 2019. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creat iveco mmons .org/licen ses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.ObjectiveAgricultural production is an important variable for many different types of analyses, including, for example, regional economic models, environmental assessments of water quality or greenhouse gas emissions, and analyses of social trends. While ample data are available to exam-ine various aspects of agricultural production across the US, we lack a typology of recent agricultural production across the country. We present a dataset with an agricul-tural production typology for counties across the con-tiguous US, derived from a cluster analysis of the 2012 county-level crop data [1].The US Department of Agriculture (USDA) Economic Research Service’s (ERS) Farm Resource Regions, defined in the early 2000s, are examples of agricultural regions partially based on county-level agricultural production [2]. The USDA uses the Farm Resource Regions to exam-ine regional trends and determine program and funding priorities [3]. The ERS Farm Resource Regions were informed by a cluster analysis of county-level farming characteristics in the early 1990s [4]. Cropping patterns have shifted since 1991 due to economic trends, agricul-tural policy and climate change [5–8]. Using USDA crop data from the 2012 Agricultural Census, we provide a more up-to-date crop production typology.The motivation for this dataset came from a need for a single categorical, county-level variable that incorpo-rates the diversity of crop types grown throughout the US, where previous efforts were largely geographically focused. Additionally, we sought to capture similarities in agricultural production, including diversity of production and relative quantity of production, between counties. We anticipate that this dataset will be of use in analyses that seek to understand county-level patterns that may relate to crop production types, as we intend to do in a forthcoming publication focused on nitrogen dynamics.Open AccessBMC Research Notes*Correspondence:  eroy4@uvm.edu1 Rubenstein School for Environment and Natural Resources, University of Vermont, Burlington, Vermont, USAFull list of author information is available at the end of the articlePage 2 of 3Hammond Wagner et al. BMC Res Notes          (2019) 12:552 Data descriptionOur dataset identifies 10 mutually exclusive, agricultural crop production categories for the contiguous US based on crop production values in the 2012 USDA Agricul-tural Census Data [9]. Each crop production category is defined by the crops that emerged from the USDA crop data to drive membership in each county-level crop pro-duction cluster. These ten categories are: (1) corn silage and other crops, (2) tobacco, (3) hay, (4) barley, beans and sugar beets, (5) alfalfa and barley, (6) sorghum, sunflower and wheat, (7) oranges and sugarcane, (8) rice, (9) corn grain and soybeans, and (10) cotton and peanuts.County-level crop acreage data was obtained from the USDA NASS Quick Stats Database from the US Agricul-tural Census of 2012 [9]. We downloaded county level crop acreage data for the 21 crops that, according to International Plant Nutrition Institute (IPNI), account for an average of 95% of harvested cropland acres reported in the agricultural census across the contiguous US [10]. The 21 crops are: apples, barley, canola, beans, corn grain, corn silage, cotton, alfalfa, oranges, peanuts, pota-toes, rice, sorghum, soybeans, sugar beets, sugarcane, sunflower, sweet corn, tobacco, wheat and other hay (i.e. all hay acreage excluding alfalfa). Additionally, we downloaded the total county acreage and created a “22nd crop” which represents acreage of all other crops grown in the county that are unaccounted for in the 21 crops. The 22nd crop category captures acreage in the 55 crops included in the agricultural census that are less preva-lent (i.e. combined represent only 5% of harvested crop-land in the US), for example, cucumber, oats and cherries [9]. The initial data download from Quick Stats included 3060 counties, out of the total 3108 county equivalents in the contiguous US. The data cleaning process resulted in a final dataset of 2922 counties or 94% of the counties in the contiguous US.USDA Agricultural Census data contains withheld data in the form of “(D)” and “(Z)” values in the dataset to avoid disclosing data for individual farms and to repre-sent small figures, respectively [11]. To clean the dataset we changed all (Z) values to zero, as (Z) values represent a value of less than half an acre [11]. Then we followed the IPNI methodology [10] to estimate missing values for all (D)s in the dataset. We describe these methods in detail in the methods and technical validation document (see Table 1). We then created the 22nd crop variable by summing acreage for all 21 crops and subtracting this from the reported county total harvested cropland. To control for differences in county size, we transformed the absolute acreage values to proportional scores.We then performed a k-means cluster analysis [12] on the 22 county-level crop acreage proportional scores. We determined the optimal number of clusters, or crop production categories, to be 10. We analyzed the cluster scree plot, as well as ran internal and stability measures using the clValid R package [13]. We describe in detail the technical validation of our cluster analysis in the methods document (see Table 1).LimitationsWe acknowledge that while we have justified and vali-dated our selection of a 10-cluster solution, the selec-tion of a different number of clusters would change the dataset.AbbreviationsUSDA: United State Department of Agriculture; ERS: Economic Research Service; IPNI: International Plant Nutrition Institute; NASS: National Agricultural Statistics Service.AcknowledgementsWe thank Thomas Wentworth for his map design and assistance in visualizations.Authors’ contributionsCRHW collected (downloaded) and collated the data, ran the cluster analysis and validation, and led the writing of the manuscript. MTN co-designed the study, advised data collation, analysis and validation, and assisted in writing the manuscript. EDR co-designed the study, advised data collation, analysis and validation, and assisted in writing the manuscript. All authors read and approved the final manuscript.Table 1 Overview of data files/data setsLabel Name of data file/data set File types (file extension) Data repository and identifier (DOI or accession number)Data file 1 1_Crop_cluster_data_wrangling_forReposi-tory_071919R script (.R) Figshare: https ://doi.org/10.6084/m9.figsh are.81328 67.v2Data file 2 2_Cluster_Analysis_22crops_forRepository R script (.R) Figshare: https ://doi.org/10.6084/m9.figsh are.81328 67.v2Data file 3 Methods_and_Technical_Validation_US_county_agri-cultural_clusters_figshareWord file (.docx) Figshare: https ://doi.org/10.6084/m9.figsh are.81328 67.v2Data set 1 crop_production_typology_data_and_meta-data_052819Excel file (.xlsx) Figshare: https ://doi.org/10.6084/m9.figsh are.81328 67.v2Page 3 of 3Hammond Wagner et al. BMC Res Notes          (2019) 12:552 • fast, convenient online submission •  thorough peer review by experienced researchers in your field•  rapid publication on acceptance•  support for research data, including large and complex data types•  gold Open Access which fosters wider collaboration and increased citations  maximum visibility for your research: over 100M website views per year •  At BMC, research is always in progress.Learn more biomedcentral.com/submissionsReady to submit your research ?  Choose BMC and benefit from: FundingThis work was supported by a Catalyst Award from the Gund Institute for Environment at the University of Vermont awarded to Meredith T. Niles and Eric D. Roy. The funding body played no role in the design of the study, the collection, analysis, and interpretation of data, or in writing the manuscript.Availability of data materialsThe datasets generated during the current study are publicly accessible in the Figshare repository [1]: https ://doi.org/10.6084/m9.figsh are.81328 67.v2.Ethics approval and consent to participateNot applicable.Consent for publicationNot applicable.Competing interestsThe authors declare that they have no competing interests.Author details1 Rubenstein School for Environment and Natural Resources, University of Ver-mont, Burlington, Vermont, USA. 2 Gund Institute for Environment, University of Vermont, Burlington, Vermont, USA. 3 Department of Food and Nutrition Sciences & Food Systems Program, University of Vermont, Burlington, Vermont, USA. Received: 24 July 2019   Accepted: 22 August 2019References 1. Hammond Wagner CR, Niles MT, Roy ED. US county-level agricultural crop production typology. Figshare; 2019. https ://doi.org/10.6084/m9.figsh are.81328 67.v2. 2. USDA ERS. Farm resource regions. United States Department of Agricul-ture; 2000 Sep. Report No.: Number 760. 3. Wade T, Claassen RL, Wallander S. Conservation-practice adoption rates vary widely by crop and region. United States Department of Agriculture, Economic Research Service; 2015. https ://www.ers.usda.gov/webdo cs/publi catio ns/44027 /56332 _eib14 7.pdf?v=42403 . 4. Sommer JE, Hines FK. Diversity in U.S. agriculture: a new delineation by farming characteristics. Agricultural economic report (USA). 1991. http://agris .fao.org/agris -searc h/searc h.do?recor dID=US915 3983. Accessed date 5 Dec 2018. 5. Aguilar J, Gramig GG, Hendrickson JR, Archer DW, Forcella F, Liebig MA. Crop species diversity changes in the United States: 1978–2012. PLoS ONE. 2015;10(8):e0136580. 6. Iizumi T, Ramankutty N. How do weather and climate influence cropping area and intensity? Glob Food Sec. 2015;1(4):46–50. 7. Plourde JD, Pijanowski BC, Pekin BK. Evidence for increased mono-culture cropping in the Central United States. Agr Ecosyst Environ. 2013;15(165):50–9. 8. Westcott PC. Ethanol expansion in the United States: How will the agricultural sector adjust? Economic Research Service: United States Department of Agriculture; 2007. Report No.: FDS-07D-01. 9. USDA-NASS. Quick Stats. Washington, DC: National Agricultural Statistics Service, U.S. Department of Agriculture; 2019. https ://quick stats .nass.usda.gov/. 10. IPNI. A nutrient use information system (NuGIS) for the U.S. Norcross; 2012. http://www.ipni.net/nugis . 11. USDA-NASS. 2012 Census of Agriculture: United States, Summary and State Data. United States Department of Agriculture; 2014. Report No: AC-12-A-51. 12. Everitt B, Hothorn T. An introduction to applied multivariate analysis with R. Berlin: Springer; 2011. 13. Brock G, Pihur V, Datta S, Datta S. clValid, an R package for cluster valida-tion. J Stat Softw. 2011;25:1–22.Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims in pub-lished maps and institutional affiliations.

US county-level agricultural crop production typology

University of Vermont ScholarWorks @ UVM College of Agriculture and Life Sciences Faculty Publications College of Agriculture and Life Sciences 8-30-2019 US county-level agricultural crop production typology Courtney R. Hammond Wagner University of Vermont Meredith T. Niles University of Vermont Eric D. Roy University of Vermont Follow this and additional works at: https://scholarworks.uvm.edu/calsfac  Part of the Agriculture Commons, and the Sustainability Commons Recommended Citation Hammond Wagner, C.R., Niles, M.T. & Roy, E.D. US county-level agricultural crop production typology. BMC Res Notes 12, 552 (2019). https://doi.org/10.1186/s13104-019-4594-4 This Article is brought to you for free and open access by the College of Agriculture and Life Sciences at ScholarWorks @ UVM. It has been accepted for inclusion in College of Agriculture and Life Sciences Faculty Publications by an authorized administrator of ScholarWorks @ UVM. For more information, please contact donna.omalley@uvm.edu. Hammond Wagner et al. BMC Res Notes          (2019) 12:552  https://doi.org/10.1186/s13104-019-4594-4DATA NOTEUS county-level agricultural crop production typologyCourtney R. Hammond Wagner1,2, Meredith T. Niles2,3 and Eric D. Roy1,2* Abstract Objectives: Crop production is an important variable in social, economic and environmental analyses. There is an abundance of crop data available for the United States, but we lack a typology of county-level crop production that accounts for production similarities in counties across the country. We fill this gap with a county-level classification of crop production with ten mutually exclusive categories across the contiguous United States.Data description: To create the typology we ran a cluster analysis on acreage data for 21 key crops from the United States Department of Agriculture’s 2012 Agricultural Census. Prior to clustering, we estimated undisclosed county acreage values, controlled for acreage in other crop types, and removed counties with low agricultural production to produce proportional scores for each crop type in each county. We used proportional scores to control for the influ-ence of county size in the cluster analysis and used internal and stability measures to validate the analysis. The final dataset features 2922 counties. Future research can leverage this typology as an input for county- or regional-level analysis.Keywords: Agriculture, Farming systems, Typology, Crop production, United States, County-level© The Author(s) 2019. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creat iveco mmons .org/licen ses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.ObjectiveAgricultural production is an important variable for many different types of analyses, including, for example, regional economic models, environmental assessments of water quality or greenhouse gas emissions, and analyses of social trends. While ample data are available to exam-ine various aspects of agricultural production across the US, we lack a typology of recent agricultural production across the country. We present a dataset with an agricul-tural production typology for counties across the con-tiguous US, derived from a cluster analysis of the 2012 county-level crop data [1].The US Department of Agriculture (USDA) Economic Research Service’s (ERS) Farm Resource Regions, defined in the early 2000s, are examples of agricultural regions partially based on county-level agricultural production [2]. The USDA uses the Farm Resource Regions to exam-ine regional trends and determine program and funding priorities [3]. The ERS Farm Resource Regions were informed by a cluster analysis of county-level farming characteristics in the early 1990s [4]. Cropping patterns have shifted since 1991 due to economic trends, agricul-tural policy and climate change [5–8]. Using USDA crop data from the 2012 Agricultural Census, we provide a more up-to-date crop production typology.The motivation for this dataset came from a need for a single categorical, county-level variable that incorpo-rates the diversity of crop types grown throughout the US, where previous efforts were largely geographically focused. Additionally, we sought to capture similarities in agricultural production, including diversity of production and relative quantity of production, between counties. We anticipate that this dataset will be of use in analyses that seek to understand county-level patterns that may relate to crop production types, as we intend to do in a forthcoming publication focused on nitrogen dynamics.Open AccessBMC Research Notes*Correspondence:  eroy4@uvm.edu1 Rubenstein School for Environment and Natural Resources, University of Vermont, Burlington, Vermont, USAFull list of author information is available at the end of the articlePage 2 of 3Hammond Wagner et al. BMC Res Notes          (2019) 12:552 Data descriptionOur dataset identifies 10 mutually exclusive, agricultural crop production categories for the contiguous US based on crop production values in the 2012 USDA Agricul-tural Census Data [9]. Each crop production category is defined by the crops that emerged from the USDA crop data to drive membership in each county-level crop pro-duction cluster. These ten categories are: (1) corn silage and other crops, (2) tobacco, (3) hay, (4) barley, beans and sugar beets, (5) alfalfa and barley, (6) sorghum, sunflower and wheat, (7) oranges and sugarcane, (8) rice, (9) corn grain and soybeans, and (10) cotton and peanuts.County-level crop acreage data was obtained from the USDA NASS Quick Stats Database from the US Agricul-tural Census of 2012 [9]. We downloaded county level crop acreage data for the 21 crops that, according to International Plant Nutrition Institute (IPNI), account for an average of 95% of harvested cropland acres reported in the agricultural census across the contiguous US [10]. The 21 crops are: apples, barley, canola, beans, corn grain, corn silage, cotton, alfalfa, oranges, peanuts, pota-toes, rice, sorghum, soybeans, sugar beets, sugarcane, sunflower, sweet corn, tobacco, wheat and other hay (i.e. all hay acreage excluding alfalfa). Additionally, we downloaded the total county acreage and created a “22nd crop” which represents acreage of all other crops grown in the county that are unaccounted for in the 21 crops. The 22nd crop category captures acreage in the 55 crops included in the agricultural census that are less preva-lent (i.e. combined represent only 5% of harvested crop-land in the US), for example, cucumber, oats and cherries [9]. The initial data download from Quick Stats included 3060 counties, out of the total 3108 county equivalents in the contiguous US. The data cleaning process resulted in a final dataset of 2922 counties or 94% of the counties in the contiguous US.USDA Agricultural Census data contains withheld data in the form of “(D)” and “(Z)” values in the dataset to avoid disclosing data for individual farms and to repre-sent small figures, respectively [11]. To clean the dataset we changed all (Z) values to zero, as (Z) values represent a value of less than half an acre [11]. Then we followed the IPNI methodology [10] to estimate missing values for all (D)s in the dataset. We describe these methods in detail in the methods and technical validation document (see Table 1). We then created the 22nd crop variable by summing acreage for all 21 crops and subtracting this from the reported county total harvested cropland. To control for differences in county size, we transformed the absolute acreage values to proportional scores.We then performed a k-means cluster analysis [12] on the 22 county-level crop acreage proportional scores. We determined the optimal number of clusters, or crop production categories, to be 10. We analyzed the cluster scree plot, as well as ran internal and stability measures using the clValid R package [13]. We describe in detail the technical validation of our cluster analysis in the methods document (see Table 1).LimitationsWe acknowledge that while we have justified and vali-dated our selection of a 10-cluster solution, the selec-tion of a different number of clusters would change the dataset.AbbreviationsUSDA: United State Department of Agriculture; ERS: Economic Research Service; IPNI: International Plant Nutrition Institute; NASS: National Agricultural Statistics Service.AcknowledgementsWe thank Thomas Wentworth for his map design and assistance in visualizations.Authors’ contributionsCRHW collected (downloaded) and collated the data, ran the cluster analysis and validation, and led the writing of the manuscript. MTN co-designed the study, advised data collation, analysis and validation, and assisted in writing the manuscript. EDR co-designed the study, advised data collation, analysis and validation, and assisted in writing the manuscript. All authors read and approved the final manuscript.Table 1 Overview of data files/data setsLabel Name of data file/data set File types (file extension) Data repository and identifier (DOI or accession number)Data file 1 1_Crop_cluster_data_wrangling_forReposi-tory_071919R script (.R) Figshare: https ://doi.org/10.6084/m9.figsh are.81328 67.v2Data file 2 2_Cluster_Analysis_22crops_forRepository R script (.R) Figshare: https ://doi.org/10.6084/m9.figsh are.81328 67.v2Data file 3 Methods_and_Technical_Validation_US_county_agri-cultural_clusters_figshareWord file (.docx) Figshare: https ://doi.org/10.6084/m9.figsh are.81328 67.v2Data set 1 crop_production_typology_data_and_meta-data_052819Excel file (.xlsx) Figshare: https ://doi.org/10.6084/m9.figsh are.81328 67.v2Page 3 of 3Hammond Wagner et al. BMC Res Notes          (2019) 12:552 • fast, convenient online submission •  thorough peer review by experienced researchers in your field•  rapid publication on acceptance•  support for research data, including large and complex data types•  gold Open Access which fosters wider collaboration and increased citations  maximum visibility for your research: over 100M website views per year •  At BMC, research is always in progress.Learn more biomedcentral.com/submissionsReady to submit your research ?  Choose BMC and benefit from: FundingThis work was supported by a Catalyst Award from the Gund Institute for Environment at the University of Vermont awarded to Meredith T. Niles and Eric D. Roy. The funding body played no role in the design of the study, the collection, analysis, and interpretation of data, or in writing the manuscript.Availability of data materialsThe datasets generated during the current study are publicly accessible in the Figshare repository [1]: https ://doi.org/10.6084/m9.figsh are.81328 67.v2.Ethics approval and consent to participateNot applicable.Consent for publicationNot applicable.Competing interestsThe authors declare that they have no competing interests.Author details1 Rubenstein School for Environment and Natural Resources, University of Ver-mont, Burlington, Vermont, USA. 2 Gund Institute for Environment, University of Vermont, Burlington, Vermont, USA. 3 Department of Food and Nutrition Sciences & Food Systems Program, University of Vermont, Burlington, Vermont, USA. Received: 24 July 2019   Accepted: 22 August 2019References 1. Hammond Wagner CR, Niles MT, Roy ED. US county-level agricultural crop production typology. Figshare; 2019. https ://doi.org/10.6084/m9.figsh are.81328 67.v2. 2. USDA ERS. Farm resource regions. United States Department of Agricul-ture; 2000 Sep. Report No.: Number 760. 3. Wade T, Claassen RL, Wallander S. Conservation-practice adoption rates vary widely by crop and region. United States Department of Agriculture, Economic Research Service; 2015. https ://www.ers.usda.gov/webdo cs/publi catio ns/44027 /56332 _eib14 7.pdf?v=42403 . 4. Sommer JE, Hines FK. Diversity in U.S. agriculture: a new delineation by farming characteristics. Agricultural economic report (USA). 1991. http://agris .fao.org/agris -searc h/searc h.do?recor dID=US915 3983. Accessed date 5 Dec 2018. 5. Aguilar J, Gramig GG, Hendrickson JR, Archer DW, Forcella F, Liebig MA. Crop species diversity changes in the United States: 1978–2012. PLoS ONE. 2015;10(8):e0136580. 6. Iizumi T, Ramankutty N. How do weather and climate influence cropping area and intensity? Glob Food Sec. 2015;1(4):46–50. 7. Plourde JD, Pijanowski BC, Pekin BK. Evidence for increased mono-culture cropping in the Central United States. Agr Ecosyst Environ. 2013;15(165):50–9. 8. Westcott PC. Ethanol expansion in the United States: How will the agricultural sector adjust? Economic Research Service: United States Department of Agriculture; 2007. Report No.: FDS-07D-01. 9. USDA-NASS. Quick Stats. Washington, DC: National Agricultural Statistics Service, U.S. Department of Agriculture; 2019. https ://quick stats .nass.usda.gov/. 10. IPNI. A nutrient use information system (NuGIS) for the U.S. Norcross; 2012. http://www.ipni.net/nugis . 11. USDA-NASS. 2012 Census of Agriculture: United States, Summary and State Data. United States Department of Agriculture; 2014. Report No: AC-12-A-51. 12. Everitt B, Hothorn T. An introduction to applied multivariate analysis with R. Berlin: Springer; 2011. 13. Brock G, Pihur V, Datta S, Datta S. clValid, an R package for cluster valida-tion. J Stat Softw. 2011;25:1–22.Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims in pub-lished maps and institutional affiliations.

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US county-level agricultural crop production typology

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