{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T23:52:38Z","timestamp":1768780358185,"version":"3.49.0"},"reference-count":63,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2021,8,17]],"date-time":"2021-08-17T00:00:00Z","timestamp":1629158400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100011929","name":"Foundation for Food and Agriculture Research","doi-asserted-by":"publisher","award":["430871"],"award-info":[{"award-number":["430871"]}],"id":[{"id":"10.13039\/100011929","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100005825","name":"National Institute of Food and Agriculture","doi-asserted-by":"publisher","award":["2017-70005-27191 and 2016-68007-2506"],"award-info":[{"award-number":["2017-70005-27191 and 2016-68007-2506"]}],"id":[{"id":"10.13039\/100005825","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>To ensure agricultural sustainability and desirable environmental outcomes, stakeholders need systems-based model-driven decision support tools. The objective of this study was to develop a global scale web-based geospatial crop modeling application called Food, Agriculture, and Resource Management system (FARMs), to simplify the application of the crop simulation model \u2014Decision Support System for Agrotechnology Transfer (DSSAT) without requiring users to create input weather, climate, and soil files. FARMs was built based on open source Geographic Information System (GIS) technologies and DSSAT to allow for adaptive management through its ability to perform in-season yield predictions for alfalfa and maize, currently. Validation of FARMs against variety trial data in California was acceptable between measured and simulated yields for alfalfa. The work done in this study showed how a complex model like DSSAT can be translated into a useable web-based decision support tool for near-real-time simulation with the help of open-source GIS technologies.<\/jats:p>","DOI":"10.3390\/ijgi10080553","type":"journal-article","created":{"date-parts":[[2021,8,17]],"date-time":"2021-08-17T21:27:40Z","timestamp":1629235660000},"page":"553","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["FARMs: A Geospatial Crop Modeling and Agricultural Water Management System"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8922-8380","authenticated-orcid":false,"given":"Jae Sung","family":"Kim","sequence":"first","affiliation":[{"name":"University Libraries, The Pennsylvania State University, University Park, State College, PA 16802, USA"},{"name":"Department of Land, Air and Water Resources, University of California, Davis, CA 95616, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2460-7777","authenticated-orcid":false,"given":"Isaya","family":"Kisekka","sequence":"additional","affiliation":[{"name":"Department of Land, Air and Water Resources, University of California, Davis, CA 95616, USA"},{"name":"Department of Biological and Agricultural Engineering, University of California, Davis, CA 95616, USA"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Boote, K.J. (2019). The DSSAT crop modeling ecosystem. Advances in Crop Modeling for a Sustainable Agriculture, Burleigh Dodds Science Publishing.","DOI":"10.19103\/AS.2019.0061"},{"key":"ref_2","unstructured":"Hoogenboom, G., Porter, C.H., Shelia, V., Boote, K.J., Singh, U., White, J.W., Hunt, L.A., Ogoshi, R., Lizaso, J.I., and Koo, J. (2019). Decision Support System for Agrotechnology Transfer (DSSAT) Version 4.7.5, DSSAT Foundation. Available online: https:\/\/DSSAT.net."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1016\/S1161-0301(02)00107-7","article-title":"DSSAT Cropping System Model","volume":"18","author":"Jones","year":"2003","journal-title":"Eur. J. Agron."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.envsoft.2018.02.002","article-title":"APSIM Next Generation: Overcoming challenges in modernising a farming systems model","volume":"103","author":"Holzworth","year":"2018","journal-title":"Environ. Model. Softw."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/S1161-0301(02)00108-9","article-title":"An overview of APSIM, a model designed for farming systems simulation","volume":"18","author":"Keating","year":"2003","journal-title":"Eur. J. Agron."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"426","DOI":"10.2134\/agronj2008.0139s","article-title":"AquaCrop\u2014The FAO crop model to simulate yield response to water: I. Concepts and underlying principles","volume":"101","author":"Steduto","year":"2009","journal-title":"Agron. J."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"RZWQM Development Team, Hanson, J.D., Ahuja, L.R., Shaffer, M.D., Rojas, K.W., DeCoursey, D.G., Farahani, H., and Johnson, K. (1998). RZWQM: Simulating the effects of management on water quality and crop production. Agric. Syst., 57, 161\u2013195.","DOI":"10.1016\/S0308-521X(98)00002-X"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1007\/s00271-011-0273-5","article-title":"Use of CropSyst as a decision support system for scheduling regulated deficit irrigation in a pear orchard","volume":"30","author":"Marsal","year":"2011","journal-title":"Irrig. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Kroes, J.G., Van Dam, J.C., Bartholomeus, R.P., Groenendijk, P., Heinen, M., Hendriks, R.F.A., Mulder, H.M., Supit, I., and Van Walsum, P.E.V. (2017). SWAP version 4; Theory Description and User Manual. Wageningen Environmental Research, Report 2780, Wageningen University and Research. Available online: http:\/\/library.wur.nl\/WebQuery\/wurpubs\/fulltext\/416321.","DOI":"10.18174\/416321"},{"key":"ref_10","unstructured":"(2021, August 03). GEMS, Agroinformatics|Data-Driven Agircultural Innovation, n.d. Available online: https:\/\/agroinformatics.org."},{"key":"ref_11","unstructured":"USDA (2021, June 14). 2017 Census of Agriculture. 2018 Irrigation and Water Management Survey, 2019. Volume 3, Special Studies, Part 1, AC-17-SS-1, Available online: https:\/\/www.nass.usda.gov\/Publications\/AgCensus\/2017\/Online_Resources\/Farm_and_Ranch_Irrigation_Survey\/fris.pdf."},{"key":"ref_12","unstructured":"Andales, A.A. (2014, January 25\u201326). Colorado irrigation scheduler. Proceedings of the 26th Annual Central Plains Irrigation Conference, Colby, KS, USA."},{"key":"ref_13","first-page":"15","article-title":"Environmental risk assessment and management system","volume":"28","author":"Arabi","year":"2011","journal-title":"Colo. Water"},{"key":"ref_14","unstructured":"Cahn, M., Hartz, T., Smith, R., Noel, B., Johnson, L., and Melton, F. (2015, January 7\u20138). CropManage: An online decision support tool for irrigation and nutrient management. Proceedings of the Western Nutrient Manage Conference, Reno, NV, USA."},{"key":"ref_15","first-page":"75","article-title":"An interoperable, GIS-oriented, information and support system for water resources management","volume":"3","author":"Cau","year":"2013","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"498","DOI":"10.1016\/j.envsoft.2015.10.032","article-title":"SWATShare\u2013A web platform for collaborative research and education through online sharing, simulation and visualization of SWAT models","volume":"75","author":"Rajib","year":"2016","journal-title":"Environ. Model. Softw."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.envsoft.2012.11.008","article-title":"Enabling collaborative decision-making in watershed management using cloud-computing services","volume":"41","author":"Sun","year":"2013","journal-title":"Environ. Model. Softw."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.envsoft.2019.01.015","article-title":"Application of a hybrid neural-fuzzy inference system for mapping crop suitability areas and predicting rice yields","volume":"114","author":"Dang","year":"2019","journal-title":"Environ. Model. Softw."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.compag.2018.04.026","article-title":"CropGIS\u2013A web application for the spatial and temporal visualization of past, present and future crop biomass development","volume":"161","author":"Machwitz","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"105018","DOI":"10.1016\/j.compag.2019.105018","article-title":"Land suitability assessments for yield prediction of cassava using geospatial fuzzy expert systems and remote sensing","volume":"166","author":"Purnamasari","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.envsoft.2019.02.006","article-title":"A multi-scale and multi-model gridded framework for forecasting crop production, risk analysis, and climate change impact studies","volume":"115","author":"Shelia","year":"2019","journal-title":"Environ. Model. Softw."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"104836","DOI":"10.1016\/j.envsoft.2020.104836","article-title":"A satellite-driven hydro-economic model to support agricultural water resources management","volume":"134","author":"Maneta","year":"2020","journal-title":"Environ. Model. Softw."},{"key":"ref_23","unstructured":"DSSAT Foundation, Inc (2021, June 17). DSSAT Overview|DSSAT.net. Available online: https:\/\/dssat.net\/about."},{"key":"ref_24","unstructured":"NASA (2021, June 17). NASA POWER|Prediction of Worldwide Energy Resources, Available online: https:\/\/power.larc.nasa.gov."},{"key":"ref_25","unstructured":"(2021, June 17). International Research Institute for Climate and Society (IRI); Michigan State University (MSU); HarvestChoice, International Food Policy Research Institute (IFPRI). Global High-Resolution Soil Profile Database for Crop Modeling Applications, Harvard Dataverse, V2, 2015. Available online: https:\/\/doi.org\/10.7910\/DVN\/1PEEY0."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.envsoft.2019.05.012","article-title":"Development of a 10-km resolution global soil profile dataset for crop modeling applications","volume":"119","author":"Han","year":"2019","journal-title":"Environ. Model. Softw."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Liu, W.L., Purdon, K., Stafford, T., Paden, J., and Li, X. (2016). Open Polar Server (OPS)\u2014An Open Source Infrastructure for the Cryosphere Community. ISPRS Int. J. Geo-Inf., 5.","DOI":"10.3390\/ijgi5030032"},{"key":"ref_28","unstructured":"(2021, June 17). Django Software Foundation. Django, 2021. Available online: https:\/\/djangoproject.com."},{"key":"ref_29","unstructured":"The PostgreSQL Global Development Group (2021, June 17). PostgreSQL: The World\u2019s Most Advanced Open Source Database. Available online: https:\/\/www.postgresql.org."},{"key":"ref_30","unstructured":"PostGIS Project Steering Committee (2021, June 17). PostGIS\u2014Spatial and Geographic Objects for PostgreSQL. Available online: https:\/\/postgis.net\/."},{"key":"ref_31","unstructured":"GDAL (2021, June 17). GDAL\u2014GDAL Documentation. Available online: https:\/\/www.gdal.org\/."},{"key":"ref_32","unstructured":"(2021, June 17). Django Software Foundation. Django Overview|Django. Available online: https:\/\/www.djangoproject.com\/start\/overview\/."},{"key":"ref_33","unstructured":"Gillies, S. (2021, June 17). Shapely\u2014Shapely 1.6 Documentation. Available online: https:\/\/shapely.readthedocs.io\/en\/maint-1.6\/."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1038\/s41592-019-0686-2","article-title":"SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python","volume":"17","author":"Virtanen","year":"2020","journal-title":"Nat. Methods"},{"key":"ref_35","unstructured":"(2021, June 17). D3noob. Simple Tooltips in v5\u2014bl.ocks.org. Available online: https:\/\/bl.ocks.org\/d3noob\/4e4485d94aebf63ae8059258c40f2609."},{"key":"ref_36","unstructured":"Miller, D. (2021, June 17). Start Bootstrap\u2014Simple Sidebar. Available online: https:\/\/github.com\/BlackrockDigital\/startbootstrap-simple-sidebar."},{"key":"ref_37","unstructured":"Traversy, B. (2021, August 16). Python Django Dev to Deployment, bt_real_estate_theme. v1.0.0. Available online: https:\/\/www.udemy.com\/course\/python-django-dev-to-deployment\/."},{"key":"ref_38","unstructured":"(2021, August 10). Json.org, Introducing JSON, n.d. Available online: https:\/\/www.json.org\/json-en.html."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Hengl, T., de Jesus, J.M., MacMillan, R.A., Batjes, N.H., and Heuvelink, G.B.M. (2014). SoilGrids1km\u2014Global Soil Information Based on Automated Mapping. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0105992"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Hengl, T., Heuvelink, G.B., Kempen, B., Leenaars, J.G., Walsh, M.G., Shepherd, K.D., Sila, A., MacMillan, R.A., de Jesus, J.M., and Tamene, L. (2015). Mapping soil properties of Africa at 250 m resolution: Random forests significantly improve current predictions. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0125814"},{"key":"ref_41","unstructured":"NASA (2021, June 17). NASA POWER|Docs|Data Services|API v1 Overview, Available online: https:\/\/power.larc.nasa.gov\/docs\/services\/api\/v1\/."},{"key":"ref_42","unstructured":"(2021, June 18). Python Software Foundation. Json\u2014JSON Encoder and Decoder. Available online: https:\/\/docs.python.org\/3\/library\/json.html."},{"key":"ref_43","unstructured":"Ganesan, C. (2021, June 18). Spatializing Your Data with PostGIS, GeoDjango, and OpenLayers, Orelly\u2019s OSCON. Available online: http:\/\/wiki.ptagis.org\/images\/6\/64\/Spatializing-data.pdf."},{"key":"ref_44","unstructured":"Butler, H., Daly, M., Doyle, A., Gillies, S., Hagen, S., and Schaub, T. (2021, June 18). RFC 7946\u2014The GeoJSON Format. Available online: https:\/\/datatracker.ietf.org\/doc\/rfc7946\/."},{"key":"ref_45","unstructured":"Virtualandy (2021, June 18). Ellipsoid Based Changes. GitHub Gist. Available online: https:\/\/gist.github.com\/virtualandy\/1233401#file-geojson-spec-1\u20131."},{"key":"ref_46","unstructured":"OpenLayers Contributors (2021, June 18). Openlayers\u2014Welcome, n.d. Available online: https:\/\/openlayers.org\/."},{"key":"ref_47","unstructured":"You, E. (2021, June 18). Vue.js. Available online: https:\/\/vuejs.org."},{"key":"ref_48","unstructured":"Bostock, M. (2021, June 18). D3.js\u2014Data-Driven Documents. Available online: https:\/\/d3js.org."},{"key":"ref_49","unstructured":"(2021, June 18). The jQuery Foundation. jQuery. Available online: https:\/\/jquery.com."},{"key":"ref_50","unstructured":"Bootstrap Team (2021, June 18). Bootstrap. The Most Popular HTML, CSS and JS Library in the World. n.d. Available online: https:\/\/getbootstrap.com."},{"key":"ref_51","unstructured":"SpryMedia Ltd (2021, June 18). DataTables|Table Plug-In for jQuery. Available online: https:\/\/dataTables.net."},{"key":"ref_52","unstructured":"Bemis, C. (2021, June 18). Feather\u2014Simply Beautiful Open Source Icons, n.d. Available online: https:\/\/feathericons.com."},{"key":"ref_53","unstructured":"Zivolo, F., and Contributors (2021, June 18). Popper\u2014Tooltip & Popover Positioning Engine. Available online: https:\/\/popper.js.org."},{"key":"ref_54","unstructured":"(2021, June 18). EOX IT Service GmbH. Sentinel-2 Cloudless Map of the World by EOX. Available online: https:\/\/s2maps.eu."},{"key":"ref_55","unstructured":"USGS (2021, June 18). USGS ImageryTopo, n.d, Available online: https:\/\/basemap.nationalmap.gov\/arcgis\/rest\/services\/USGSImageryTopo\/MapServer."},{"key":"ref_56","unstructured":"OpenStreetMap Contributors (2021, June 18). Nominatim Demo, n.d. Available online: https:\/\/nominatim.openstreetmap.org\/ui\/search.html."},{"key":"ref_57","unstructured":"Tsuji, G.Y., Uehara, G., and Balas, S. (1994). DSSAT Version 3, International Benchmark Sites Network for Agrotechnology Transfer University of Hawaii."},{"key":"ref_58","unstructured":"USDA (2021, June 18). Description of Gridded Soil Survey Geographic (gSSURGO) Database|NRCS Soils, n.d, Available online: https:\/\/www.nrcs.usda.gov\/wps\/portal\/nrcs\/detail\/soils\/home\/?cid=nrcs142p2_053628."},{"key":"ref_59","unstructured":"(2021, June 18). Python Software Foundation. 17.1. Threading\u2014Thread-Based Parallelism. Available online: https:\/\/docs.python.org\/3.6\/library\/threading.html."},{"key":"ref_60","unstructured":"UCANR (2021, June 18). 2017 California Alfalfa Variety Trial Results. Available online: https:\/\/alfalfa.ucdavis.edu\/+producing\/variety\/apr\/APR325\u20132017-FINAL.pdf."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"125971","DOI":"10.1016\/j.eja.2019.125971","article-title":"Simulating Alfalfa Regrowth and Biomass in Eastern Canada Using the CSM-CROPGRO-Perennial Forage Model","volume":"113","author":"Jing","year":"2020","journal-title":"Eur. J. Agron."},{"key":"ref_62","unstructured":"UCANR (2021, August 07). 2020 California Alfalfa Variety Trial Results. Available online: https:\/\/alfalfa.ucdavis.edu\/+producing\/variety\/apr\/APR-2020%20CMD.pdf."},{"key":"ref_63","unstructured":"NASA (2021, August 03). NASA Power|Docs|Methodology| Data Sources, Available online: https:\/\/power.larc.nasa.gov\/docs\/methodology\/data\/."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/10\/8\/553\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:45:27Z","timestamp":1760165127000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/10\/8\/553"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,17]]},"references-count":63,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2021,8]]}},"alternative-id":["ijgi10080553"],"URL":"https:\/\/doi.org\/10.3390\/ijgi10080553","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,17]]}}}