{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T04:29:06Z","timestamp":1775276946630,"version":"3.50.1"},"reference-count":73,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2021,6,1]],"date-time":"2021-06-01T00:00:00Z","timestamp":1622505600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2021,6,1]],"date-time":"2021-06-01T00:00:00Z","timestamp":1622505600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2021,6,1]],"date-time":"2021-06-01T00:00:00Z","timestamp":1622505600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2021,6,1]],"date-time":"2021-06-01T00:00:00Z","timestamp":1622505600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2021,6,1]],"date-time":"2021-06-01T00:00:00Z","timestamp":1622505600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2021,6,1]],"date-time":"2021-06-01T00:00:00Z","timestamp":1622505600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,6,1]],"date-time":"2021-06-01T00:00:00Z","timestamp":1622505600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/100007479","name":"Fondazione Cariparo","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100007479","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computers and Electronics in Agriculture"],"published-print":{"date-parts":[[2021,6]]},"DOI":"10.1016\/j.compag.2021.106126","type":"journal-article","created":{"date-parts":[[2021,4,3]],"date-time":"2021-04-03T19:00:08Z","timestamp":1617476408000},"page":"106126","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":53,"special_numbering":"C","title":["Ten years of corn yield dynamics at field scale under digital agriculture solutions: A case study from North Italy"],"prefix":"10.1016","volume":"185","author":[{"given":"Ahmed","family":"Kayad","sequence":"first","affiliation":[]},{"given":"Marco","family":"Sozzi","sequence":"additional","affiliation":[]},{"given":"Simone","family":"Gatto","sequence":"additional","affiliation":[]},{"given":"Brett","family":"Whelan","sequence":"additional","affiliation":[]},{"given":"Luigi","family":"Sartori","sequence":"additional","affiliation":[]},{"given":"Francesco","family":"Marinello","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"D5","key":"10.1016\/j.compag.2021.106126_b0005","doi-asserted-by":"crossref","DOI":"10.1029\/2005JD006290","article-title":"Global observed changes in daily climate extremes of temperature and precipitation","volume":"111","author":"Alexander","year":"2006","journal-title":"J. Geophys. Res. Atmos."},{"key":"10.1016\/j.compag.2021.106126_b0010","doi-asserted-by":"crossref","unstructured":"Ali, A., Martelli, R., Lupia, F., Barbanti, L., 2019. Assessing multiple years\u2019 spatial variability of crop yields using satellite vegetation indices. Remote Sens. 2019, 11, Page 2384 11, 2384. https:\/\/doi.org\/10.3390\/RS11202384.","DOI":"10.3390\/rs11202384"},{"issue":"12","key":"10.1016\/j.compag.2021.106126_b0015","doi-asserted-by":"crossref","first-page":"1361","DOI":"10.1016\/j.agwat.2008.06.003","article-title":"Nitrate leaching assessment in a long-term experiment under supplementary irrigation in humid Argentina","volume":"95","author":"Aparicio","year":"2008","journal-title":"Agric. Water Manage."},{"key":"10.1016\/j.compag.2021.106126_b0020","first-page":"1","article-title":"Precision agriculture technologies positively contributing to ghg emissions mitigation, farm productivity and economics","volume":"9","author":"Balafoutis","year":"2017","journal-title":"Sustain."},{"key":"10.1016\/j.compag.2021.106126_b0025","unstructured":"Blackmore, S., 2003. The role of yield maps in Precision Farming. PhD thesis, Cranfiled University at Silsoe, UK."},{"issue":"4","key":"10.1016\/j.compag.2021.106126_b0030","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1016\/S1537-5110(03)00038-2","article-title":"The analysis of spatial and temporal trends in yield map data over six years","volume":"84","author":"Blackmore","year":"2003","journal-title":"Biosyst. Eng."},{"issue":"12","key":"10.1016\/j.compag.2021.106126_b0035","doi-asserted-by":"crossref","first-page":"653","DOI":"10.3390\/agriculture10120653","article-title":"Precision and digital agriculture: adoption of technologies and perception of Brazilian farmers","volume":"10","author":"Bolfe","year":"2020","journal-title":"Agriculture"},{"key":"10.1016\/j.compag.2021.106126_b0040","doi-asserted-by":"crossref","first-page":"102646","DOI":"10.1016\/j.agsy.2019.102646","article-title":"LCIS DSS\u2014An irrigation supporting system for water use efficiency improvement in precision agriculture: A maize case study","volume":"176","author":"Bonfante","year":"2019","journal-title":"Agric. Syst."},{"key":"10.1016\/j.compag.2021.106126_b0045","first-page":"205","article-title":"Spatially distributed experimentation","author":"Bramley","year":"2013","journal-title":"Precision Agric. Sustain. Environ. Protection."},{"issue":"2","key":"10.1016\/j.compag.2021.106126_b0050","doi-asserted-by":"crossref","first-page":"332","DOI":"10.2136\/sssaj1990.03615995005400020006x","article-title":"Dielectric properties and influence of conductivity in soils at one to Fifty Megahertz","volume":"54","author":"Campbell","year":"1990","journal-title":"Soil Sci. Soc. Am. J."},{"key":"10.1016\/j.compag.2021.106126_b0055","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.agsy.2018.09.011","article-title":"Precision conservation meets precision agriculture: A case study from southern Ontario","volume":"167","author":"Capmourteres","year":"2018","journal-title":"Agric. Syst."},{"key":"10.1016\/j.compag.2021.106126_b0060","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.compag.2018.05.012","article-title":"Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review","volume":"151","author":"Chlingaryan","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2021.106126_b0065","doi-asserted-by":"crossref","first-page":"780","DOI":"10.1016\/j.apsoil.2017.06.025","article-title":"Field-scale electrical resistivity profiling mapping for delineating soil condition in a nitrate vulnerable zone","volume":"123","author":"Cillis","year":"2018","journal-title":"Appl. Soil Ecol."},{"key":"10.1016\/j.compag.2021.106126_b0070","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3390\/rs11232869","article-title":"Assessing the feasibility of using medium-resolution imagery information to quantify the impact of the heatwaves on irrigated vineyards","volume":"11","author":"Cogato","year":"2019","journal-title":"Remote Sens."},{"key":"10.1016\/j.compag.2021.106126_b0075","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.fcr.2018.01.007","article-title":"Do crop sensors promote improved nitrogen management in grain crops?","volume":"218","author":"Cola\u00e7o","year":"2018","journal-title":"F. Crop. Res."},{"key":"10.1016\/j.compag.2021.106126_b0080","doi-asserted-by":"crossref","first-page":"402","DOI":"10.1080\/00224561.2005.12435833","article-title":"Nitrogen fertilizer management based on site-specific management zones reduces potential for nitrate leaching","volume":"60","author":"Delgado","year":"2005","journal-title":"J. Soil Water Conserv."},{"key":"10.1016\/j.compag.2021.106126_b0085","doi-asserted-by":"crossref","first-page":"105880","DOI":"10.1016\/j.agwat.2019.105880","article-title":"Differential irrigation scheduling by an automated algorithm of water balance tuned by capacitance-type soil moisture sensors","volume":"228","author":"Dom\u00ednguez-Ni\u00f1o","year":"2020","journal-title":"Agric. Water Manag."},{"key":"10.1016\/j.compag.2021.106126_b0090","unstructured":"EUROSTAT, 2019. European statistics on agriculture, forestry and fisheries [WWW Document]. URL https:\/\/ec.europa.eu\/eurostat\/data\/database (accessed 8.17.20)."},{"key":"10.1016\/j.compag.2021.106126_b0095","unstructured":"FAOSTAT, 2020. Food and Agriculture Organization, Statistics Data [WWW Document]. URL http:\/\/www.fao.org\/faostat\/en\/#data\/QC (accessed 8.17.20)."},{"key":"10.1016\/j.compag.2021.106126_b0100","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1023\/A:1011481832064","article-title":"Evaluating farmer defined management zone maps for variable rate fertilizer application","volume":"2","author":"Fleming","year":"2000","journal-title":"Precis. Agric."},{"issue":"1","key":"10.1016\/j.compag.2021.106126_b0105","doi-asserted-by":"crossref","first-page":"33","DOI":"10.3112\/erdkunde.2014.01.04","article-title":"Optimizing irrigation efficiency through the consideration of soil hydrological properties - Examples and simulation approaches","volume":"68","author":"Grashey-Jansen","year":"2014","journal-title":"Erdkunde"},{"key":"10.1016\/j.compag.2021.106126_b0110","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.eja.2019.04.001","article-title":"Improving productivity and increasing the efficiency of soil nutrient management on grassland farms in the UK and Ireland using precision agriculture technology","volume":"106","author":"Higgins","year":"2019","journal-title":"Eur. J. Agron."},{"key":"10.1016\/j.compag.2021.106126_b0115","doi-asserted-by":"crossref","first-page":"138235","DOI":"10.1016\/j.scitotenv.2020.138235","article-title":"Understanding the impact of sub-seasonal meteorological variability on corn yield in the U.S. Corn Belt","volume":"724","author":"Jiang","year":"2020","journal-title":"Sci. Total Environ."},{"key":"10.1016\/j.compag.2021.106126_b0120","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.eja.2017.11.002","article-title":"A review of data assimilation of remote sensing and crop models","volume":"92","author":"Jin","year":"2018","journal-title":"Eur. J. Agron."},{"issue":"2","key":"10.1016\/j.compag.2021.106126_b0125","doi-asserted-by":"crossref","first-page":"303","DOI":"10.2134\/agronj2003.3030","article-title":"Site-specific management zones based on soil electrical conductivity in a semiarid cropping system","volume":"95","author":"Johnson","year":"2003","journal-title":"Agron. J."},{"issue":"3-4","key":"10.1016\/j.compag.2021.106126_b0130","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1016\/S1161-0301(02)00107-7","article-title":"The DSSAT cropping system model","volume":"18","author":"Jones","year":"2003","journal-title":"Eur. J. Agron."},{"issue":"8","key":"10.1016\/j.compag.2021.106126_b0135","doi-asserted-by":"crossref","first-page":"362","DOI":"10.3390\/agriculture10080362","article-title":"Latest advances in sensor applications in agriculture","volume":"10","author":"Kayad","year":"2020","journal-title":"Agriculture"},{"key":"10.1016\/j.compag.2021.106126_b0140","doi-asserted-by":"crossref","first-page":"2873","DOI":"10.3390\/rs11232873","article-title":"Monitoring within-field variability of corn yield using sentinel-2 and machine learning techniques","volume":"11","author":"Kayad","year":"2019","journal-title":"Remote Sens."},{"key":"10.1016\/j.compag.2021.106126_b0145","doi-asserted-by":"crossref","unstructured":"Kayad, A.G., Al-Gaadi, K.A., Tola, E., Madugundu, R., Zeyada, A.M., Kalaitzidis, C., 2016. Assessing the spatial variability of alfalfa yield using satellite imagery and ground-based data. PLoS One 11. https:\/\/doi.org\/10.1371\/journal.pone.0157166.","DOI":"10.1371\/journal.pone.0157166"},{"key":"10.1016\/j.compag.2021.106126_b0150","doi-asserted-by":"crossref","first-page":"S-109","DOI":"10.2135\/cropsci2009.10.0594","article-title":"Eco-efficient agriculture: Concepts, Challenges, And opportunities","volume":"50","author":"Keating","year":"2010","journal-title":"Crop Sci."},{"issue":"3-4","key":"10.1016\/j.compag.2021.106126_b0155","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":"10.1016\/j.compag.2021.106126_b0160","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1080\/00224561.2002.12457486","article-title":"Use of site-specific management zones to improve nitrogen management for precision agriculture","volume":"57","author":"Khosla","year":"2002","journal-title":"J. Soil Water Conserv."},{"issue":"3-4","key":"10.1016\/j.compag.2021.106126_b0165","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1016\/j.jhydrol.2008.01.021","article-title":"Frequency, electrical conductivity and temperature analysis of a low-cost capacitance soil moisture sensor","volume":"352","author":"Kizito","year":"2008","journal-title":"J. Hydrol."},{"key":"10.1016\/j.compag.2021.106126_b0170","doi-asserted-by":"crossref","first-page":"106065","DOI":"10.1016\/j.agwat.2020.106065","article-title":"Effect of irrigation and fertilization regimes on grain yield, water and nitrogen productivity of mulching cultivated maize (Zea mays L.) in the Hetao Irrigation District of China","volume":"232","author":"Li","year":"2020","journal-title":"Agric. Water Manag."},{"key":"10.1016\/j.compag.2021.106126_b0175","doi-asserted-by":"crossref","first-page":"106570","DOI":"10.1016\/j.agwat.2020.106570","article-title":"Optimizing irrigation and nitrogen management strategy to trade off yield, crop water productivity, nitrogen use efficiency and fruit quality of greenhouse grown tomato","volume":"245","author":"Li","year":"2020","journal-title":"Agric. Water Manag."},{"issue":"1-2","key":"10.1016\/j.compag.2021.106126_b0180","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.agwat.2006.12.012","article-title":"Increased nitrogen use efficiencies as a key mitigation alternative to reduce nitrate leaching in north china plain","volume":"89","author":"Li","year":"2007","journal-title":"Agric. Water Manag."},{"key":"10.1016\/j.compag.2021.106126_b0185","doi-asserted-by":"crossref","unstructured":"Luce, M. St., Whalen, J.K., Ziadi, N., Zebarth, B.J., 2011. Nitrogen dynamics and indices to predict soil nitrogen supply in humid temperate soils, 1st ed, Advances in Agronomy. Elsevier Inc. https:\/\/doi.org\/10.1016\/B978-0-12-385538-1.00002-0.","DOI":"10.1016\/B978-0-12-385538-1.00002-0"},{"issue":"5","key":"10.1016\/j.compag.2021.106126_b0190","doi-asserted-by":"crossref","first-page":"743","DOI":"10.22438\/jeb\/38\/5\/MRN-383","article-title":"Seasonal dynamics of surface energy fluxes over a center-pivot irrigated cropland in Saudi Arabia","volume":"38","author":"Madugundu","year":"2017","journal-title":"J. Environ. Biol."},{"issue":"2","key":"10.1016\/j.compag.2021.106126_b0195","doi-asserted-by":"crossref","first-page":"410","DOI":"10.1016\/j.sjbs.2016.10.003","article-title":"Estimation of gross primary production of irrigated maize using Landsat-8 imagery and Eddy Covariance data","volume":"24","author":"Madugundu","year":"2017","journal-title":"Saudi J. Biol. Sci."},{"issue":"1","key":"10.1016\/j.compag.2021.106126_b0200","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1007\/s11119-005-0681-8","article-title":"Future directions of precision agriculture","volume":"6","author":"McBratney","year":"2005","journal-title":"Precis. Agric."},{"key":"10.1016\/j.compag.2021.106126_b0205","first-page":"432","article-title":"An integrated approach to site-specific management zone delineation","volume":"5","author":"Miao","year":"2018","journal-title":"Front. Agric. Sci. Eng."},{"key":"10.1016\/j.compag.2021.106126_b0210","unstructured":"Michalopoulos, S., 2015. Europe entering the era of \u2018precision agriculture\u2019 [WWW Document]. Euractiv.Com. URL http:\/\/www.euractiv.com\/section\/science-policymaking\/news\/europe-entering-the-era-of-precision-agriculture\/ (accessed 6.23.20)."},{"issue":"2","key":"10.1016\/j.compag.2021.106126_b0215","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1016\/j.compag.2010.10.014","article-title":"Site-specific management zones based on the Rasch model and geostatistical techniques","volume":"75","author":"Moral","year":"2011","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2021.106126_b0220","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1016\/j.compag.2018.12.011","article-title":"IoT and agriculture data analysis for smart farm","volume":"156","author":"Muangprathub","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2021.106126_b0225","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.agwat.2017.12.002","article-title":"Using site-specific nitrogen management in rainfed corn to reduce the risk of nitrate leaching","volume":"199","author":"Muschietti-Piana","year":"2018","journal-title":"Agric. Water Manage."},{"key":"10.1016\/j.compag.2021.106126_b0230","doi-asserted-by":"crossref","first-page":"107629","DOI":"10.1016\/j.fcr.2019.107629","article-title":"Soil tillage, residue management and site interactions affecting nitrogen use efficiency in maize and cotton in the Sudan Savanna of Africa","volume":"244","author":"Nafi","year":"2019","journal-title":"F. Crop. Res."},{"key":"10.1016\/j.compag.2021.106126_b0235","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/bs.agron.2017.01.003","article-title":"Delineation of soil management zones for variable-rate fertilization: A review","author":"Nawar","year":"2017","journal-title":"Adv. Agron."},{"key":"10.1016\/j.compag.2021.106126_b0240","doi-asserted-by":"crossref","first-page":"105556","DOI":"10.1016\/j.compag.2020.105556","article-title":"FastMapping: Software to create field maps and identify management zones in precision agriculture","volume":"175","author":"Paccioretti","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2021.106126_b0245","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.compag.2012.07.007","article-title":"Development approach and evaluation of the Nutrient Expert software for nutrient management in cereal crops","volume":"88","author":"Pampolino","year":"2012","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2021.106126_b0250","first-page":"131","article-title":"Precision agriculture: Variable rate nitrogen in Barley","volume":"33","author":"Peralta","year":"2015","journal-title":"Cienc. del Suelo"},{"key":"10.1016\/j.compag.2021.106126_b0255","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1016\/j.compag.2013.09.014","article-title":"Delineation of management zones with soil apparent electrical conductivity to improve nutrient management","volume":"99","author":"Peralta","year":"2013","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2021.106126_b0260","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.compag.2014.10.017","article-title":"Delineation of management zones to improve nitrogen management of wheat","volume":"110","author":"Peralta","year":"2015","journal-title":"Comput. Electron. Agric."},{"issue":"1463","key":"10.1016\/j.compag.2021.106126_b0265","doi-asserted-by":"crossref","first-page":"2021","DOI":"10.1098\/rstb.2005.1752","article-title":"Crop responses to climatic variation","volume":"360","author":"Porter","year":"2005","journal-title":"Philos. Trans. R. Soc. B Biol. Sci."},{"key":"10.1016\/j.compag.2021.106126_b0270","first-page":"1","article-title":"From smart farming towards agriculture 5.0: a review on crop data management","volume":"10","author":"Rubio","year":"2020","journal-title":"Agronomy"},{"key":"10.1016\/j.compag.2021.106126_b0275","first-page":"1","article-title":"Concepts and rationale for regional nitrogen rate guidelines for corn concepts and rationale for regional nitrogen rate guidelines for corn","author":"Sawyer","year":"2006","journal-title":"Iowa State Univ. Univ. Ext."},{"key":"10.1016\/j.compag.2021.106126_b0280","doi-asserted-by":"crossref","unstructured":"Schepers, J.S., Raun, W.R., 2008. Nitrogen in agricultural systems. In: Agron. Monogr. ASA, CSSA, SSSA, Madison, Wisconsin. https:\/\/doi.org\/10.2134\/agronmonogr49.c12.","DOI":"10.2134\/agronmonogr49.c12"},{"key":"10.1016\/j.compag.2021.106126_b0285","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.biosystemseng.2018.04.020","article-title":"Forecasting maize yield at field scale based on high-resolution satellite imagery","volume":"171","author":"Schwalbert","year":"2018","journal-title":"Biosyst. Eng."},{"key":"10.1016\/j.compag.2021.106126_b0290","article-title":"Fertilizer suggestions for corn","author":"Shapiro","year":"2008","journal-title":"Extension Circular EC117"},{"issue":"2","key":"10.1016\/j.compag.2021.106126_b0295","doi-asserted-by":"crossref","first-page":"378","DOI":"10.2134\/agronj2011.0249","article-title":"Generalized algorithm for variable-rate nitrogen application in cereal grains","volume":"104","author":"Solie","year":"2012","journal-title":"Agron. J."},{"issue":"2","key":"10.1016\/j.compag.2021.106126_b0300","doi-asserted-by":"crossref","first-page":"159","DOI":"10.2134\/jeq1973.00472425000200020001x","article-title":"Rationale for optimum nitrogen fertilization in corn production","volume":"2","author":"Stanford","year":"1973","journal-title":"J. Environ. Qual."},{"issue":"5","key":"10.1016\/j.compag.2021.106126_b0305","doi-asserted-by":"crossref","first-page":"618","DOI":"10.1002\/jpln.201500566","article-title":"Validation of topsoil texture derived from agricultural soil maps by current dense soil sampling","volume":"179","author":"St\u0119pie\u0144","year":"2016","journal-title":"J. Plant Nutr. Soil Sci."},{"issue":"3-4","key":"10.1016\/j.compag.2021.106126_b0310","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1016\/S0016-7061(00)00047-1","article-title":"Soil resistivity: A non-invasive tool to map soil structure horizonation","volume":"97","author":"Tabbagh","year":"2000","journal-title":"Geoderma"},{"issue":"5","key":"10.1016\/j.compag.2021.106126_b0315","doi-asserted-by":"crossref","first-page":"1366","DOI":"10.2134\/agronj2007.0070","article-title":"Establishing management classes for broadacre agricultural production","volume":"99","author":"Taylor","year":"2007","journal-title":"Agron. J."},{"issue":"6","key":"10.1016\/j.compag.2021.106126_b0320","doi-asserted-by":"crossref","first-page":"2020","DOI":"10.2134\/agronj15.0116","article-title":"Model and sensor-based recommendation approaches for in-season nitrogen management in corn","volume":"107","author":"Thompson","year":"2015","journal-title":"Agron. J."},{"issue":"2","key":"10.1016\/j.compag.2021.106126_b0325","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1016\/j.sjbs.2016.04.013","article-title":"Characterization of spatial variability of soil physicochemical properties and its impact on Rhodes grass productivity","volume":"24","author":"Tola","year":"2017","journal-title":"Saudi J. Biol. Sci."},{"issue":"1-3","key":"10.1016\/j.compag.2021.106126_b0330","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.agsy.2009.02.001","article-title":"Uncertainty and investment in precision agriculture - Is it worth the money?","volume":"100","author":"Tozer","year":"2009","journal-title":"Agric. Syst."},{"key":"10.1016\/j.compag.2021.106126_b0335","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.agwat.2013.10.005","article-title":"Laboratory and field assessment of the capacitance sensors Decagon 10HS and 5TE for estimating the water content of irrigated soils","volume":"132","author":"Visconti","year":"2014","journal-title":"Agric. Water Manag."},{"issue":"4","key":"10.1016\/j.compag.2021.106126_b0340","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1016\/j.biosystemseng.2005.08.010","article-title":"Management zones based on correlation between soil compaction, yield and crop data","volume":"92","author":"Vrindts","year":"2005","journal-title":"Biosyst. Eng."},{"issue":"2","key":"10.1016\/j.compag.2021.106126_b0345","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/S1537-5110(03)00040-0","article-title":"Analysis of soil and crop properties for precision agriculture for winter wheat","volume":"85","author":"Vrindts","year":"2003","journal-title":"Biosyst. Eng."},{"key":"10.1016\/j.compag.2021.106126_b0350","doi-asserted-by":"crossref","first-page":"126193","DOI":"10.1016\/j.eja.2020.126193","article-title":"Machine learning-based in-season nitrogen status diagnosis and side-dress nitrogen recommendation for corn","volume":"123","author":"Wang","year":"2021","journal-title":"Eur. J. Agron."},{"issue":"6194","key":"10.1016\/j.compag.2021.106126_b0355","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1126\/science.1246067","article-title":"Leverage points for improving global food security and the environment","volume":"345","author":"West","year":"2014","journal-title":"Science"},{"issue":"5","key":"10.1016\/j.compag.2021.106126_b0360","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1007\/s11119-016-9490-5","article-title":"Long-term impact of a precision agriculture system on grain crop production","volume":"18","author":"Yost","year":"2017","journal-title":"Precis. Agric."},{"key":"10.1016\/j.compag.2021.106126_b0365","doi-asserted-by":"crossref","first-page":"105256","DOI":"10.1016\/j.compag.2020.105256","article-title":"Decision support systems for agriculture 4.0: Survey and challenges","volume":"170","author":"Zhai","year":"2020","journal-title":"Comput. Electron. Agric."}],"container-title":["Computers and Electronics in Agriculture"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0168169921001447?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0168169921001447?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,9,29]],"date-time":"2025-09-29T13:23:00Z","timestamp":1759152180000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0168169921001447"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6]]},"references-count":73,"alternative-id":["S0168169921001447"],"URL":"https:\/\/doi.org\/10.1016\/j.compag.2021.106126","relation":{},"ISSN":["0168-1699"],"issn-type":[{"value":"0168-1699","type":"print"}],"subject":[],"published":{"date-parts":[[2021,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Ten years of corn yield dynamics at field scale under digital agriculture solutions: A case study from North Italy","name":"articletitle","label":"Article Title"},{"value":"Computers and Electronics in Agriculture","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compag.2021.106126","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2021 Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"106126"}}