{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T22:22:51Z","timestamp":1768083771592,"version":"3.49.0"},"reference-count":40,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2025]]},"DOI":"10.1109\/access.2025.3562397","type":"journal-article","created":{"date-parts":[[2025,4,18]],"date-time":"2025-04-18T17:38:22Z","timestamp":1744997902000},"page":"70018-70043","source":"Crossref","is-referenced-by-count":3,"title":["Joint Prediction of U.S. Rice Yields and Methane Emissions: A Machine Learning Approach"],"prefix":"10.1109","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6055-779X","authenticated-orcid":false,"given":"Jameson","family":"Augustin","sequence":"first","affiliation":[{"name":"Department of Agricultural and Applied Economics, University of Georgia, Athens, GA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6249-7548","authenticated-orcid":false,"given":"Munisamy","family":"Gopinath","sequence":"additional","affiliation":[{"name":"Department of Agricultural and Applied Economics, University of Georgia, Athens, GA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7969-7506","authenticated-orcid":false,"given":"Berna","family":"Karali","sequence":"additional","affiliation":[{"name":"Department of Agricultural and Applied Economics, University of Georgia, Athens, GA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuhan","family":"Rao","sequence":"additional","affiliation":[{"name":"North Carolina Institute for Climate Studies, Asheville, NC, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1080\/15226510701374831"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2022.107340"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1186\/2048-7010-2-10"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1007\/s40011-017-0867-7"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2017.11.182"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2023.e18512"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2024.e25112"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1038\/s43017-023-00482-1"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.2134\/jeq2005.0208"},{"key":"ref10","volume-title":"Rice Sector at a Glance","year":"2024"},{"key":"ref11","year":"2024","journal-title":"U.s. Rice Facts"},{"key":"ref12","volume-title":"Sources of Greenhouse Gas Emissions","year":"2023"},{"key":"ref13","volume-title":"Principal Rice Exporting Countries Worldwide in 2023\/2024","year":"2024"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1017\/S0021859606006691"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.3390\/s19204363"},{"issue":"1","key":"ref16","first-page":"1","article-title":"Deep Gaussian process for crop yield prediction based on remote sensing data","volume-title":"Proc. AAAI Conf. Artif. Intell","volume":"31","author":"You"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.agrformet.2023.109458"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2023.3303643"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1002\/bbb.2087"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.fcr.2016.04.003"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1021\/acs.est.3c05138"},{"key":"ref22","volume-title":"Soil pH in H2O at 6 standard depths (0, 10, 30, 60, 100 1755 and 200 cm) at 250 m resolution","author":"Hengl","year":"2018"},{"key":"ref23","volume-title":"Soil water content (volumetric percent) for 1757 33kpa and 1500kpa suctions predicted at 6 standard depths (0, 10, 30, 60, 1758 100 and 200 cm) at 250 m resolution","author":"Hengl","year":"2019"},{"key":"ref24","volume-title":"Soil texture classes (usda system) for 6 soil depths (0, 10, 1760 30, 60, 100 and 200 cm) at 250 m","author":"Hengl","year":"2018"},{"key":"ref25","volume-title":"Noaa climate data record (CDR) of avhrr normalized difference vegetation index (NDVI)","author":"Vermote","year":"2019"},{"key":"ref26","volume-title":"NOAA climate data record (CDR) of avhrr leaf area index (LAI) and fraction of absorbed photosynthetically active radiation (FAPER)","author":"Vermote","year":"2019"},{"key":"ref27","volume-title":"Cropland Data Layer","year":"2024"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2021.112514"},{"key":"ref29","article-title":"PiML toolbox for interpretable machine learning model development and diagnostics","author":"Sudjianto","year":"2023","journal-title":"arXiv:2305.04214"},{"key":"ref30","article-title":"InterpretML: A unified framework for machine learning interpretability","author":"Nori","year":"2019","journal-title":"arXiv:1909.09223"},{"key":"ref31","first-page":"1","article-title":"A unified approach to interpreting model predictions","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Lundberg"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/4235.996017"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1016\/j.agrformet.2020.108275"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2020.105471"},{"key":"ref35","article-title":"Tackling climate change through livestock: A global assessment emissions mitigation opportunities","volume-title":"Tech. Rep.","author":"Gerber","year":"2013"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.2307\/2699986"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1016\/j.landusepol.2024.107381"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1023\/A:1024557205871"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1515\/intag-2017-0010"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1016\/j.geoderma.2014.07.019"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/10820123\/10969758.pdf?arnumber=10969758","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,29]],"date-time":"2025-04-29T04:33:29Z","timestamp":1745901209000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10969758\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":40,"URL":"https:\/\/doi.org\/10.1109\/access.2025.3562397","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}