{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T07:19:10Z","timestamp":1783149550544,"version":"3.54.6"},"reference-count":92,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,5,25]],"date-time":"2026-05-25T00:00:00Z","timestamp":1779667200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002338","name":"Ministry of Education of the People&apos;s Republic of China","doi-asserted-by":"publisher","award":["2024JZDZ06"],"award-info":[{"award-number":["2024JZDZ06"]}],"id":[{"id":"10.13039\/501100002338","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001711","name":"Schweizerischer Nationalfonds zur F\u00f6rderung der Wissenschaftlichen Forschung","doi-asserted-by":"publisher","award":["235381"],"award-info":[{"award-number":["235381"]}],"id":[{"id":"10.13039\/501100001711","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["226\\u20132025-00223"],"award-info":[{"award-number":["226\\u20132025-00223"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["226\\u20132025-00004"],"award-info":[{"award-number":["226\\u20132025-00004"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002701","name":"Ministry of Education","doi-asserted-by":"publisher","award":["22JJD790079"],"award-info":[{"award-number":["22JJD790079"]}],"id":[{"id":"10.13039\/501100002701","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004731","name":"Natural Science Foundation of Zhejiang Province","doi-asserted-by":"publisher","award":["LTGG23D020001"],"award-info":[{"award-number":["LTGG23D020001"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004835","name":"Zhejiang University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004835","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013804","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100013804","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100019332","name":"School of Earth Sciences, Ohio State University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100019332","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42406190"],"award-info":[{"award-number":["42406190"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42571529"],"award-info":[{"award-number":["42571529"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U24A20600"],"award-info":[{"award-number":["U24A20600"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Ecological Informatics"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.ecoinf.2026.103860","type":"journal-article","created":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T15:07:31Z","timestamp":1779980851000},"page":"103860","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Diag-STFN: A diagnostic spatiotemporal multimodal fusion network for global pre-harvest crop yield forecasting"],"prefix":"10.1016","volume":"96","author":[{"given":"Hanchen","family":"Zhuang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiyong","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yijun","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhen","family":"Yan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chao","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoyao","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sensen","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liuchang","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Song","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaocan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenhong","family":"Du","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"5","key":"10.1016\/j.ecoinf.2026.103860_bb0005","doi-asserted-by":"crossref","first-page":"965","DOI":"10.1007\/s12571-015-0488-z","article-title":"Factors that transformed maize productivity in Ethiopia","volume":"7","author":"Abate","year":"2015","journal-title":"Food Secur."},{"key":"10.1016\/j.ecoinf.2026.103860_bb0010","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1016\/S1364-8152(00)00003-7","article-title":"Daisy: an open soil-crop-atmosphere system model","volume":"15","author":"Abrahamsen","year":"2000","journal-title":"Environ. Model. Software"},{"key":"10.1016\/j.ecoinf.2026.103860_bb0015","doi-asserted-by":"crossref","first-page":"7672","DOI":"10.1007\/s11263-025-02518-z","article-title":"Analysing satellite imagery classification under spatial domain shift across geographic regions","volume":"133","author":"Al-Emadi","year":"2025","journal-title":"Int. J. Computer Vision"},{"key":"10.1016\/j.ecoinf.2026.103860_bb0020","doi-asserted-by":"crossref","first-page":"7262","DOI":"10.1038\/s41467-024-51555-8","article-title":"Preseason maize and wheat yield forecasts for early warning of crop failure","volume":"15","author":"Anderson","year":"2024","journal-title":"Nat. Commun."},{"key":"10.1016\/j.ecoinf.2026.103860_bb0025","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1111\/j.1538-4632.1995.tb00338.x","article-title":"Local indicators of spatial association\u2014LISA","volume":"27","author":"Anselin","year":"1995","journal-title":"Geogr. Anal."},{"key":"10.1016\/j.ecoinf.2026.103860_bb0030","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/bs.agron.2018.11.002","article-title":"Seasonal crop yield forecast: Methods, applications, and accuracies","volume":"154","author":"Basso","year":"2019","journal-title":"Adv. Agron."},{"key":"10.1016\/j.ecoinf.2026.103860_bb0035","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.gfs.2019.04.010","article-title":"The GEOGLAM crop monitor for AMIS: Assessing crop conditions in the context of global markets","volume":"23","author":"Becker-Reshef","year":"2019","journal-title":"Glob. Food Secur."},{"key":"10.1016\/j.ecoinf.2026.103860_bb0040","doi-asserted-by":"crossref","DOI":"10.1016\/j.rse.2019.111553","article-title":"Strengthening agricultural decisions in countries at risk of food insecurity: The GEOGLAM Crop Monitor for Early Warning","volume":"237","author":"Becker-Reshef","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"10.1016\/j.ecoinf.2026.103860_bb0045","doi-asserted-by":"crossref","first-page":"677","DOI":"10.5194\/gmd-4-677-2011","article-title":"The Joint UK Land Environment Simulator (JULES), model description \u2013 Part 1: Energy and water fluxes","volume":"4","author":"Best","year":"2011","journal-title":"Geosci. Model Dev."},{"key":"10.1016\/j.ecoinf.2026.103860_bb0050","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.agrformet.2013.01.007","article-title":"Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics","volume":"173","author":"Bolton","year":"2013","journal-title":"Agric. For. Meteorol."},{"key":"10.1016\/j.ecoinf.2026.103860_bb0055","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1016\/S1161-0301(02)00110-7","article-title":"An overview of the crop model Stics","volume":"18","author":"Brisson","year":"2003","journal-title":"Eur. J. Agron."},{"key":"10.1016\/j.ecoinf.2026.103860_bb0060","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/j.fcr.2010.07.012","article-title":"Why are wheat yields stagnating in Europe? A comprehensive data analysis for France","volume":"119","author":"Brisson","year":"2010","journal-title":"Field Crops Res."},{"key":"10.1016\/j.ecoinf.2026.103860_bb0065","article-title":"Making optimal use of limited field-scale data for crop yield forecasting using transfer learning and Sentinel-1 and 2 data","volume":"12","author":"Bueechi","year":"2025","journal-title":"Smart Agric. Technol."},{"key":"10.1016\/j.ecoinf.2026.103860_bb0070","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.agsy.2015.10.001","article-title":"Improving operational maize yield forecasting in Hungary","volume":"141","author":"Bussay","year":"2015","journal-title":"Agric. Syst."},{"key":"10.1016\/j.ecoinf.2026.103860_bb0075","article-title":"Out-of-distribution generalization in climate-aware yield prediction with Earth observation data","author":"Chakravarty","year":"2025","journal-title":"arXiv:2510.07350"},{"key":"10.1016\/j.ecoinf.2026.103860_bb0080","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2023.108201","article-title":"Improving yield prediction based on spatio-temporal deep learning approaches for winter wheat: a case study in Jiangsu Province, China","volume":"213","author":"Chen","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.ecoinf.2026.103860_bb0085","doi-asserted-by":"crossref","first-page":"701","DOI":"10.5194\/gmd-4-701-2011","article-title":"The Joint UK Land Environment Simulator (JULES), model description \u2013 Part 2: Carbon fluxes and vegetation dynamics","volume":"4","author":"Clark","year":"2011","journal-title":"Geosci. Model Dev."},{"key":"10.1016\/j.ecoinf.2026.103860_bb0090","author":"De Wit"},{"key":"10.1016\/j.ecoinf.2026.103860_bb0095","doi-asserted-by":"crossref","first-page":"1299","DOI":"10.5194\/hess-22-1299-2018","article-title":"Combining satellite data and appropriate objective functions for improved spatial pattern performance of a distributed hydrologic model","volume":"22","author":"Demirel","year":"2018","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"10.1016\/j.ecoinf.2026.103860_bb0100","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1016\/j.ijforecast.2018.11.010","article-title":"Interpreting the skill score form of forecast performance metrics","volume":"35","author":"Wheatcroft","year":"2019","journal-title":"Int. J. Forecasting"},{"key":"10.1016\/j.ecoinf.2026.103860_bb0105","first-page":"1","article-title":"Machine learning in nutrient management: A review","volume":"9","author":"Ennaji","year":"2023","journal-title":"Art. Intell. Agric."},{"key":"10.1016\/j.ecoinf.2026.103860_bb0110","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pclm.0000401","article-title":"Kicking around in the wreck","volume":"3","author":"Fanzo","year":"2024","journal-title":"PLoS Clim."},{"key":"10.1016\/j.ecoinf.2026.103860_bb0115","series-title":"A Brief Review of Domain Adaptation","first-page":"877","author":"Farahani","year":"2021"},{"key":"10.1016\/j.ecoinf.2026.103860_bb0120","first-page":"2613","article-title":"Spatial-temporal model with heterogeneous random effects","volume":"33","author":"Feng","year":"2023","journal-title":"Statistica Sinica"},{"key":"10.1016\/j.ecoinf.2026.103860_bb0125","article-title":"Crop models and their use in assessing crop production and food security: A review","volume":"13","author":"Gavasso-Rita","year":"2024","journal-title":"Review"},{"key":"10.1016\/j.ecoinf.2026.103860_bb0130","doi-asserted-by":"crossref","first-page":"2918","DOI":"10.1038\/ncomms3918","article-title":"Distinguishing between yield advances and yield plateaus in historical crop production trends","volume":"4","author":"Grassini","year":"2013","journal-title":"Nat. Commun."},{"key":"10.1016\/j.ecoinf.2026.103860_bb0135","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.jhydrol.2009.08.003","article-title":"Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling","volume":"377","author":"Gupta","year":"2009","journal-title":"J. Hydrol."},{"key":"10.1016\/j.ecoinf.2026.103860_bb0140","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1016\/j.agrformet.2016.12.015","article-title":"Data requirement for effective calibration of process-based crop models","volume":"234","author":"He","year":"2017","journal-title":"Agric. For. Meteorol."},{"key":"10.1016\/j.ecoinf.2026.103860_bb0145","doi-asserted-by":"crossref","first-page":"2157","DOI":"10.5194\/gmd-15-2157-2022","article-title":"Soil-related developments of the Biome-BGCMuSo v6.2 terrestrial ecosystem model","volume":"15","author":"Hidy","year":"2022","journal-title":"Geosci. Model Dev."},{"key":"10.1016\/j.ecoinf.2026.103860_bb0150","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":"10.1016\/j.ecoinf.2026.103860_bb0155","series-title":"Domain Adaptation Across Geographic Regions Through Region-Specific Feature Learning and Distribution Matching","author":"Horihata","year":"2025"},{"key":"10.1016\/j.ecoinf.2026.103860_bb0160","series-title":"Adaptation to Climate Change in Agriculture: Research and Practices","first-page":"97","article-title":"Recent Improvements to Global Seasonal Crop Forecasting and Related Research","author":"Iizumi","year":"2019"},{"key":"10.1016\/j.ecoinf.2026.103860_bb0165","doi-asserted-by":"crossref","DOI":"10.1088\/1748-9326\/11\/3\/034003","article-title":"Changes in yield variability of major crops for 1981\u20132010 explained by climate change","volume":"11","author":"Iizumi","year":"2016","journal-title":"Environ. Res. Lett."},{"key":"10.1016\/j.ecoinf.2026.103860_bb0170","doi-asserted-by":"crossref","first-page":"47","DOI":"10.3354\/cr01751","article-title":"Launch of the global preharvest crop forecast of climate-induced yield variations","volume":"94","author":"Iizumi","year":"2025","journal-title":"Climate Res."},{"key":"10.1016\/j.ecoinf.2026.103860_bb0175","doi-asserted-by":"crossref","first-page":"1936","DOI":"10.1007\/s10618-020-00710-y","article-title":"Inceptiontime: finding AlexNet for time series classification","volume":"34","author":"Ismail Fawaz","year":"2020","journal-title":"Data Min. Knowledge Disc."},{"key":"10.1016\/j.ecoinf.2026.103860_bb0180","doi-asserted-by":"crossref","first-page":"2466","DOI":"10.3390\/electronics14122466","article-title":"AgriTransformer: A transformer-based model with attention mechanisms for enhanced multimodal crop yield prediction","volume":"14","author":"J\u00e1come Galarza","year":"2025","journal-title":"Electronics"},{"key":"10.1016\/j.ecoinf.2026.103860_bb0185","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."},{"key":"10.1016\/j.ecoinf.2026.103860_bb0190","doi-asserted-by":"crossref","first-page":"11132","DOI":"10.1038\/s41598-021-89779-z","article-title":"Simultaneous corn and soybean yield prediction from remote sensing data using deep transfer learning","volume":"11","author":"Khaki","year":"2021","journal-title":"Sci. Rep."},{"key":"10.1016\/j.ecoinf.2026.103860_bb0195","first-page":"1873","article-title":"The SPAtial EFficiency metric (SPAEF): Multiple-component evaluation of spatial patterns for optimization of hydrological models","volume":"11","author":"Koch","year":"2018","journal-title":"Eur. Geosci. Union"},{"key":"10.1016\/j.ecoinf.2026.103860_bb0200","doi-asserted-by":"crossref","first-page":"3822","DOI":"10.1038\/s41467-023-39463-9","article-title":"Climate-driven changes in the predictability of seasonal precipitation","volume":"14","author":"Le","year":"2023","journal-title":"Nat. Commun."},{"key":"10.1016\/j.ecoinf.2026.103860_bb0205","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/j.agsy.2018.03.002","article-title":"Assessing the information in crop model and meteorological indicators to forecast crop yield over Europe","volume":"168","author":"Lecerf","year":"2019","journal-title":"Agric. Syst."},{"key":"10.1016\/j.ecoinf.2026.103860_bb0210","doi-asserted-by":"crossref","DOI":"10.1016\/j.gfs.2022.100643","article-title":"Maize yield forecasts for Sub-Saharan Africa using Earth Observation data and machine learning","volume":"33","author":"Lee","year":"2022","journal-title":"Global Food Secur."},{"key":"10.1016\/j.ecoinf.2026.103860_bb0215","doi-asserted-by":"crossref","first-page":"690","DOI":"10.1038\/s41597-025-05001-z","article-title":"HarvestStat Africa\u2013harmonized subnational crop statistics for sub-Saharan Africa","volume":"12","author":"Lee","year":"2025","journal-title":"Sci. Data"},{"key":"10.1016\/j.ecoinf.2026.103860_bb0220","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/j.agrformet.2017.02.025","article-title":"From ORYZA2000 to ORYZA (v3): An improved simulation model for rice in drought and nitrogen-deficient environments","volume":"237-238","author":"Li","year":"2017","journal-title":"Agric. For. Meteorol."},{"key":"10.1016\/j.ecoinf.2026.103860_bb0225","article-title":"Machine learning crop yield models based on meteorological features and comparison with a process-based model","volume":"1","author":"Liu","year":"2022","journal-title":"Artif. Intell. Earth. Syst."},{"key":"10.1016\/j.ecoinf.2026.103860_bb0230","doi-asserted-by":"crossref","first-page":"1443","DOI":"10.1016\/j.agrformet.2010.07.008","article-title":"On the use of statistical models to predict crop yield responses to climate change","volume":"150","author":"Lobell","year":"2010","journal-title":"Agric. For. Meteorol."},{"key":"10.1016\/j.ecoinf.2026.103860_bb0235","series-title":"International Conference on Learning Representations (ICLR 2020)","article-title":"Multi-scale representation learning for spatial feature distributions using grid cells","author":"Mai","year":"2020"},{"key":"10.1016\/j.ecoinf.2026.103860_bb0240","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1080\/13658816.2021.2004602","article-title":"A review of location encoding for GeoAI: methods and applications","volume":"36","author":"Mai","year":"2022","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"10.1016\/j.ecoinf.2026.103860_bb0245","doi-asserted-by":"crossref","first-page":"e6","DOI":"10.1017\/eds.2022.5","article-title":"Why we need to focus on developing ethical, responsible, and trustworthy artificial intelligence approaches for environmental science","volume":"1","author":"McGovern","year":"2022","journal-title":"Environ. Data Sci."},{"key":"10.1016\/j.ecoinf.2026.103860_bb0250","doi-asserted-by":"crossref","first-page":"756","DOI":"10.1016\/j.oneear.2022.06.008","article-title":"Research priorities for global food security under extreme events","volume":"5","author":"Mehrabi","year":"2022","journal-title":"One Earth"},{"key":"10.1016\/j.ecoinf.2026.103860_bb0255","doi-asserted-by":"crossref","DOI":"10.1038\/s41467-022-29838-9","article-title":"Machine learning-based global maps of ecological variables and the challenge of assessing them","volume":"13","author":"Meyer","year":"2022","journal-title":"Nat. Commun."},{"key":"10.1016\/j.ecoinf.2026.103860_bb0260","doi-asserted-by":"crossref","DOI":"10.1016\/j.ecolmodel.2019.108815","article-title":"Importance of spatial predictor variable selection in machine learning applications\u2013moving from data reproduction to spatial prediction","volume":"411","author":"Meyer","year":"2019","journal-title":"Ecol. Model."},{"key":"10.1016\/j.ecoinf.2026.103860_bb0265","series-title":"Notes on Continuous Stochastic Phenomena","first-page":"17","volume":"37","author":"Moran","year":"1950"},{"key":"10.1016\/j.ecoinf.2026.103860_bb0270","series-title":"ICCV 2025 Workshop on Sustainability with Earth Observation and AI","article-title":"From general to specialized: the need for foundational models in agriculture","author":"Nedungadi","year":"2025"},{"key":"10.1016\/j.ecoinf.2026.103860_bb0275","doi-asserted-by":"crossref","DOI":"10.3390\/ijgi10090600","article-title":"Machine learning of spatial data","volume":"10","author":"Nikparvar","year":"2021","journal-title":"ISPRS Int. J. Geo-Inf."},{"key":"10.1016\/j.ecoinf.2026.103860_bb0280","doi-asserted-by":"crossref","DOI":"10.1016\/j.agsy.2020.103016","article-title":"Machine learning for large-scale crop yield forecasting","volume":"187","author":"Paudel","year":"2021","journal-title":"Agr. Syst."},{"key":"10.1016\/j.ecoinf.2026.103860_bb0285","doi-asserted-by":"crossref","DOI":"10.1088\/1748-9326\/acf50e","article-title":"A weakly supervised framework for high-resolution crop yield forecasts","volume":"18","author":"Paudel","year":"2023","journal-title":"Environ. Res. Lett."},{"key":"10.1016\/j.ecoinf.2026.103860_bb0290","first-page":"1","article-title":"CY-Bench: A comprehensive benchmark dataset for sub-national crop yield forecasting","volume":"2025","author":"Paudel","year":"2025","journal-title":"Earth Syst. Sci. Data Discuss."},{"key":"10.1016\/j.ecoinf.2026.103860_bb0295","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1038\/s41477-020-0625-3","article-title":"Towards a multiscale crop modelling framework for climate change adaptation assessment","volume":"6","author":"Peng","year":"2020","journal-title":"Nature Plants"},{"key":"10.1016\/j.ecoinf.2026.103860_bb0300","doi-asserted-by":"crossref","DOI":"10.1016\/j.agrformet.2023.109652","article-title":"Improving generalisability and transferability of machine-learning-based maize yield prediction model through domain adaptation","volume":"341","author":"Priyatikanto","year":"2023","journal-title":"Agric. For. Meteorol."},{"key":"10.1016\/j.ecoinf.2026.103860_bb0305","doi-asserted-by":"crossref","first-page":"438","DOI":"10.2134\/agronj2008.0140s","article-title":"AquaCrop\u2014the FAO crop model to simulate yield response to water: II. Main algorithms and software description","volume":"101","author":"Raes","year":"2009","journal-title":"Agron. J."},{"key":"10.1016\/j.ecoinf.2026.103860_bb0310","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0066428","article-title":"Yield trends are insufficient to double global crop production by 2050","volume":"8","author":"Ray","year":"2013","journal-title":"Plos One"},{"key":"10.1016\/j.ecoinf.2026.103860_bb0315","doi-asserted-by":"crossref","first-page":"5989","DOI":"10.1038\/ncomms6989","article-title":"Climate variation explains a third of global crop yield variability","volume":"6","author":"Ray","year":"2015","journal-title":"Nat. Commun."},{"key":"10.1016\/j.ecoinf.2026.103860_bb0320","doi-asserted-by":"crossref","first-page":"1623","DOI":"10.5194\/essd-16-1623-2024","article-title":"Harmonized European Union subnational crop statistics can reveal climate impacts and crop cultivation shifts","volume":"16","author":"Ronchetti","year":"2024","journal-title":"Earth Syst. Sci. Data"},{"key":"10.1016\/j.ecoinf.2026.103860_bb0325","series-title":"Handbook of Climate Change and Agroecosystems: The Agricultural Model Intercomparison and Improvement Project (AgMIP) Integrated Crop and Economic Assessments","first-page":"3","article-title":"The Agricultural Model Intercomparison and Improvement Project: Phase I activities by a global community of science","volume":"3","author":"Rosenzweig","year":"2015"},{"key":"10.1016\/j.ecoinf.2026.103860_bb0330","doi-asserted-by":"crossref","first-page":"2553","DOI":"10.1038\/s41467-019-10105-3","article-title":"Inferring causation from time series in Earth system sciences","volume":"10","author":"Runge","year":"2019","journal-title":"Nat. Commun."},{"key":"10.1016\/j.ecoinf.2026.103860_bb0335","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2025.110633","article-title":"Harnessing multi-source data and machine learning for enhanced rice yield estimation","volume":"237","author":"Saha","year":"2025","journal-title":"Comput. Electr. Agric."},{"key":"10.1016\/j.ecoinf.2026.103860_bb0340","doi-asserted-by":"crossref","DOI":"10.1016\/j.eja.2020.126153","article-title":"A systematic review of local to regional yield forecasting approaches and frequently used data resources","volume":"120","author":"Schauberger","year":"2020","journal-title":"Eur. J. Agron."},{"key":"10.1016\/j.ecoinf.2026.103860_bb0345","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.gfs.2014.10.007","article-title":"The potential of Russia to increase its wheat production through cropland expansion and intensification","volume":"3","author":"Schierhorn","year":"2014","journal-title":"Global Food Security"},{"key":"10.1016\/j.ecoinf.2026.103860_bb0350","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.eja.2018.01.006","article-title":"Towards improved calibration of crop models\u2013Where are we now and where should we go?","volume":"94","author":"Seidel","year":"2018","journal-title":"Eur. J. Agron."},{"key":"10.1016\/j.ecoinf.2026.103860_bb0355","doi-asserted-by":"crossref","first-page":"2249","DOI":"10.3390\/rs13122249","article-title":"Modeling the impact of climate changes on crop yield: irrigated vs. non-irrigated zones in Mississippi","volume":"13","author":"Shammi","year":"2021","journal-title":"Remote Sen."},{"key":"10.1016\/j.ecoinf.2026.103860_bb0360","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":"Agronomy J."},{"key":"10.1016\/j.ecoinf.2026.103860_bb0365","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/S1161-0301(02)00109-0","article-title":"CropSyst, a cropping systems simulation model","volume":"18","author":"St\u00f6ckle","year":"2003","journal-title":"Eur. J. Agron."},{"key":"10.1016\/j.ecoinf.2026.103860_bb0370","doi-asserted-by":"crossref","first-page":"E760","DOI":"10.1175\/BAMS-D-22-0106.1","article-title":"Enhancing global food security: opportunities for the American meteorological society","volume":"105","author":"Stuart","year":"2024","journal-title":"Am. Meteorol. Soc."},{"key":"10.1016\/j.ecoinf.2026.103860_bb0375","doi-asserted-by":"crossref","DOI":"10.1016\/j.oneear.2025.101233","article-title":"Transdisciplinary coordination is essential for advancing agricultural modeling with machine learning","volume":"8","author":"Sweet","year":"2025","journal-title":"One Earth"},{"key":"10.1016\/j.ecoinf.2026.103860_bb0380","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1038\/s43247-023-00992-2","article-title":"Satellite forecasting of crop harvest can trigger a cross-hemispheric production response and improve global food security","volume":"4","author":"Tanaka","year":"2023","journal-title":"Commun. Earth Environ."},{"key":"10.1016\/j.ecoinf.2026.103860_bb0385","doi-asserted-by":"crossref","first-page":"1076","DOI":"10.1038\/s41597-025-05436-4","article-title":"Comprehensive collection of EU-27 crops statistics: a harmonized regional dataset of area and production","volume":"12","author":"Tani","year":"2025","journal-title":"Sci. Data"},{"key":"10.1016\/j.ecoinf.2026.103860_bb0390","series-title":"Predicting Yield Before Harvest: How Does the USDA Forecast Corn and Soybean Yield?","author":"Thessen","year":"2008"},{"key":"10.1016\/j.ecoinf.2026.103860_bb0395","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.agsy.2018.06.009","article-title":"Performance of the MARS-crop yield forecasting system for the European Union: Assessing accuracy, in-season, and year-to-year improvements from 1993 to 2015","volume":"168","author":"Van der Velde","year":"2019","journal-title":"Agric. Syst."},{"key":"10.1016\/j.ecoinf.2026.103860_bb0400","article-title":"Crop yield prediction using machine learning: A systematic literature review","volume":"177","author":"Van Klompenburg","year":"2020","journal-title":"Agriculture"},{"key":"10.1016\/j.ecoinf.2026.103860_bb0405","series-title":"Attention is All You Need","first-page":"30","author":"Vaswani","year":"2017"},{"key":"10.1016\/j.ecoinf.2026.103860_bb0410","article-title":"Skillful us soy yield forecasts at presowing lead times","volume":"2","author":"Vijverberg","year":"2023","journal-title":"Am. Meteorol. Soc."},{"key":"10.1016\/j.ecoinf.2026.103860_bb0415","article-title":"The chaos in calibrating crop models: Lessons learned from a multi-model calibration exercise","volume":"145","author":"Wallach","year":"2021","journal-title":"Software"},{"key":"10.1016\/j.ecoinf.2026.103860_bb0420","series-title":"Time Series Forecastability Measures.","author":"Wang","year":"2025"},{"key":"10.1016\/j.ecoinf.2026.103860_bb0425","first-page":"411","article-title":"Decreasing dynamic predictability of global agricultural drought with warming climate","volume":"15","author":"Wu","year":"2025","journal-title":"Nat. Clim. Chang."},{"key":"10.1016\/j.ecoinf.2026.103860_bb0430","first-page":"81437","article-title":"Torchspatial: A location encoding framework and benchmark for spatial representation learning","volume":"37","author":"Wu","year":"2024","journal-title":"arXiv"},{"key":"10.1016\/j.ecoinf.2026.103860_bb0435","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.isprsjprs.2025.10.020","article-title":"Corn yield estimation under extreme climate stress with knowledge-encoded deep learning","volume":"231","author":"Xiong","year":"2026","journal-title":"ISPRS J. Photogram. Rem. Sens."},{"key":"10.1016\/j.ecoinf.2026.103860_bb0440","doi-asserted-by":"crossref","DOI":"10.1016\/j.earscirev.2021.103828","article-title":"Spatiotemporal forecasting in earth system science: Methods, uncertainties, predictability and future directions","volume":"222","author":"Xu","year":"2021","journal-title":"Earth Sci. Rev."},{"key":"10.1016\/j.ecoinf.2026.103860_bb0445","article-title":"Multi-modal data fusion and deep ensemble learning for accurate crop yield prediction","volume":"38","author":"Yewle","year":"2025","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"10.1016\/j.ecoinf.2026.103860_bb0450","doi-asserted-by":"crossref","first-page":"8455","DOI":"10.5194\/gmd-17-8455-2024","article-title":"GNNWR: an open-source package of spatiotemporal intelligent regression methods for modeling spatial and temporal nonstationarity","volume":"17","author":"Yin","year":"2024","journal-title":"Geosci. Model Dev."},{"key":"10.1016\/j.ecoinf.2026.103860_bb0455","series-title":"A Comprehensive Survey on Transfer Learning","first-page":"43","volume":"109","author":"Zhuang","year":"2020"},{"key":"10.1016\/j.ecoinf.2026.103860_bb0460","doi-asserted-by":"crossref","DOI":"10.1016\/j.agrformet.2021.108736","article-title":"Early season prediction of within-field crop yield variability by assimilating CubeSat data into a crop model","volume":"313","author":"Ziliani","year":"2022","journal-title":"Agric. For. Meteorol."}],"container-title":["Ecological Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1574954126002669?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1574954126002669?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T06:55:34Z","timestamp":1783148134000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1574954126002669"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":92,"alternative-id":["S1574954126002669"],"URL":"https:\/\/doi.org\/10.1016\/j.ecoinf.2026.103860","relation":{},"ISSN":["1574-9541"],"issn-type":[{"value":"1574-9541","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Diag-STFN: A diagnostic spatiotemporal multimodal fusion network for global pre-harvest crop yield forecasting","name":"articletitle","label":"Article Title"},{"value":"Ecological Informatics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ecoinf.2026.103860","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"103860"}}