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Among these, the impact of climate change on agricultural production is dynamic over time and space, making it a major challenge to food security. Taking the U.S. Corn Belt as an example, we introduce a geographically and temporally weighted regression (GTWR) model that can handle both temporal and spatial non-stationarity in the relationship between corn yield and meteorological variables. With a high fitting performance (adjusted R2 at 0.79), the GTWR model generates spatiotemporally varying coefficients to effectively capture the spatiotemporal heterogeneity without requiring completion of the unbalanced data. This model makes it possible to retain original data to the maximum possible extent and to estimate the results more reliably and realistically. Our regression results showed that climate change had a positive effect on corn yield over the past 40 years, from 1981 to 2020, with temperature having a stronger effect than precipitation. Furthermore, a fuzzy c-means algorithm was used to cluster regions based on spatiotemporally changing trends. We found that the production potential of regions at high latitudes was higher than that of regions at low latitudes, suggesting that the center of productive regions may migrate northward in the future.<\/jats:p>","DOI":"10.3390\/ijgi11080433","type":"journal-article","created":{"date-parts":[[2022,8,1]],"date-time":"2022-08-01T21:01:24Z","timestamp":1659387684000},"page":"433","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Effects of Climate Change on Corn Yields: Spatiotemporal Evidence from Geographically and Temporally Weighted Regression Model"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2278-6780","authenticated-orcid":false,"given":"Bing","family":"Yang","sequence":"first","affiliation":[{"name":"School of Earth Sciences, Zhejiang University, Hangzhou 310027, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9322-0149","authenticated-orcid":false,"given":"Sensen","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Earth Sciences, Zhejiang University, Hangzhou 310027, China"},{"name":"Zhejiang Provincial Key Laboratory of Geographic Information Science, Hangzhou 310028, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0091-6265","authenticated-orcid":false,"given":"Zhen","family":"Yan","sequence":"additional","affiliation":[{"name":"Center of Agricultural and Rural Development, School of Public Affairs, Zhejiang University, Hangzhou 310058, China"},{"name":"Laboratory of Agricultural and Rural Development Intelligent Computation, School of Public Affairs, Zhejiang University, Hangzhou 310058, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1665","DOI":"10.1016\/j.pnsc.2009.08.001","article-title":"Climate change impacts on crop yield, crop water productivity and food security\u2014A review","volume":"19","author":"Kang","year":"2009","journal-title":"Prog. Nat. Sci."},{"key":"ref_2","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":"ref_3","doi-asserted-by":"crossref","first-page":"2007GB002947","DOI":"10.1029\/2007GB002947","article-title":"Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000","volume":"22","author":"Monfreda","year":"2008","journal-title":"Glob. Biogeochem. Cycles"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.fcr.2015.10.013","article-title":"Historical data provide new insights into response and adaptation of maize production systems to climate change\/variability in China","volume":"185","author":"Tao","year":"2016","journal-title":"Field Crops Res."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Alahacoon, N., and Edirisinghe, M. (2021). Spatial Variability of Rainfall Trends in Sri Lanka from 1989 to 2019 as an Indication of Climate Change. ISPRS Int. J Geo-Inf., 10.","DOI":"10.3390\/ijgi10020084"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"108340","DOI":"10.1016\/j.agrformet.2021.108340","article-title":"Understanding the non-stationary relationships between corn yields and meteorology via a spatiotemporally varying coefficient model","volume":"301\u2013302","author":"Jiang","year":"2021","journal-title":"Agric. For. Meteorol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"31","DOI":"10.13031\/2013.2684","article-title":"Spatio-temporal analysis of yield variability for a corn-soybean field in iowa","volume":"43","author":"Bakhsh","year":"2000","journal-title":"Trans. ASAE"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.agee.2007.06.006","article-title":"Agro-ecoregionalization of Iowa using multivariate geographical clustering","volume":"123","author":"Williams","year":"2008","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1038\/nature10452","article-title":"Solutions for a cultivated planet","volume":"478","author":"Foley","year":"2011","journal-title":"Nature"},{"key":"ref_10","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":"Nat. Plants"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"616","DOI":"10.1126\/science.1204531","article-title":"Climate trends and global crop production since 1980","volume":"333","author":"Lobell","year":"2011","journal-title":"Science"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"54013","DOI":"10.1088\/1748-9326\/10\/5\/054013","article-title":"The impact of climate extremes and irrigation on US crop yields","volume":"10","author":"Troy","year":"2015","journal-title":"Environ. Res. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.fcr.2016.04.004","article-title":"Can crop simulation models be used to predict local to regional maize yields and total production in the U.S. Corn Belt?","volume":"192","author":"Morell","year":"2016","journal-title":"Field Crops Res."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2325","DOI":"10.1111\/gcb.14628","article-title":"Excessive rainfall leads to maize yield loss of a comparable magnitude to extreme drought in the United States","volume":"25","author":"Li","year":"2019","journal-title":"Glob. Chang. Biol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.agrformet.2016.12.022","article-title":"Statistical emulators of maize, rice, soybean and wheat yields from global gridded crop models","volume":"236","author":"Blanc","year":"2017","journal-title":"Agric. For. Meteorol."},{"key":"ref_16","first-page":"230","article-title":"Estimating the Spatially Varying Responses of Corn Yields to Weather Variations using Geographically Weighted Panel Regression","volume":"39","author":"Cai","year":"2014","journal-title":"J. Agric. Resour. Econ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"14390","DOI":"10.1073\/pnas.1509777112","article-title":"An analysis of ozone damage to historical maize and soybean yields in the United States","volume":"112","author":"Mcgrath","year":"2015","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1016\/j.fcr.2011.07.001","article-title":"Methodologies for simulating impacts of climate change on crop production","volume":"124","author":"White","year":"2011","journal-title":"Field Crops Res."},{"key":"ref_19","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":"ref_20","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":"ref_21","doi-asserted-by":"crossref","unstructured":"Haghverdi, A., Leib, B., Washington-Allen, R., Wright, W., Ghodsi, S., Grant, T., Zheng, M., and Vanchiasong, P. (2019). Studying Crop Yield Response to Supplemental Irrigation and the Spatial Heterogeneity of Soil Physical Attributes in a Humid Region. Agriculture, 9.","DOI":"10.3390\/agriculture9020043"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1177\/0962280212446318","article-title":"A Bayesian latent model with spatio-temporally varying coefficients in low birth weight incidence data","volume":"21","author":"Choi","year":"2012","journal-title":"Statal Methods Med. Res."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.agrformet.2018.09.021","article-title":"Spatio-temporal downscaling of gridded crop model yield estimates based on machine learning","volume":"264","author":"Folberth","year":"2018","journal-title":"Agric. For. Meteorol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.apenergy.2018.01.036","article-title":"GDP and energy consumption: A panel analysis of the US","volume":"213","author":"Mahalingam","year":"2018","journal-title":"Appl. Energy"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Congdon, P. (2014). Analysis of panel data. Applied Bayesian Modelling, John Wiley & Sons.","DOI":"10.1002\/9781118895047"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Anselin, L., Gallo, J.L., and Jayet, H. (2008). Spatial Panel Econometrics. The Econometrics of Panel Data, Springer.","DOI":"10.1007\/978-3-540-75892-1_19"},{"key":"ref_27","first-page":"255","article-title":"Modeling crop yield potential of Eastern Anatolia by using geographically weighted regression","volume":"55","author":"Olgun","year":"2009","journal-title":"Arch. Acker Pflanzenbau Bodenkd."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"4026","DOI":"10.3390\/w7084026","article-title":"Spatiotemporal Correlations between Water Footprint and Agricultural Inputs: A Case Study of Maize Production in Northeast China","volume":"7","author":"Duan","year":"2015","journal-title":"Water"},{"key":"ref_29","unstructured":"Yu, D. (2010, January 26\u201328). Exploring Spatiotemporally Varying Regressed Relationships: The Geographically Weighted Panel Regression Analysis. Proceedings of the Joint International Conference on Theory, Data Handling and Modelling in GeoSpatial Information Science, Hong Kong."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1080\/13658810802672469","article-title":"Geographically and temporally weighted regression for modeling spatio-temporal variation in house prices","volume":"24","author":"Huang","year":"2010","journal-title":"Int. J. Geogr. Inf. Sci. IJGIS"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1186","DOI":"10.1080\/13658816.2013.878463","article-title":"A geographically and temporally weighted autoregressive model with application to housing prices","volume":"28","author":"Wu","year":"2014","journal-title":"Int. J. Geogr. Inf. Sci. IJGIS"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1111\/gean.12071","article-title":"Geographical and Temporal Weighted Regression (GTWR)","volume":"47","author":"Fotheringham","year":"2015","journal-title":"Geogr. Anal."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1927","DOI":"10.1080\/13658816.2018.1471607","article-title":"A spatiotemporal regression-kriging model for space-time interpolation: A case study of chlorophyll-a prediction in the coastal areas of Zhejiang, China","volume":"32","author":"Du","year":"2018","journal-title":"Int. J. Geogr. Inf. Sci. IJGIS"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1130","DOI":"10.1038\/nclimate3115","article-title":"Similar estimates of temperature impacts on global wheat yield by three independent methods","volume":"6","author":"Liu","year":"2016","journal-title":"Nat. Clim. Chang."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"882","DOI":"10.1016\/j.agrformet.2011.02.010","article-title":"Crop management and phenology trends in the U.S. Corn Belt: Impacts on yields, evapotranspiration and energy balance","volume":"151","author":"Sacks","year":"2011","journal-title":"Agric. For. Meteorol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.rse.2012.12.017","article-title":"MODIS-based corn grain yield estimation model incorporating crop phenology information","volume":"131","author":"Sakamoto","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Buhini\u010dek, I., Kau\u010di\u0107, D., Kozi\u0107, Z., Juki\u0107, M., Gunja\u010da, J., \u0160ar\u010devi\u0107, H., Stepinac, D., and \u0160imi\u0107, D. (2021). Trends in Maize Grain Yields across Five Maturity Groups in a Long-Term Experiment with Changing Genotypes. Agriculture, 11.","DOI":"10.3390\/agriculture11090887"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.agrformet.2017.02.001","article-title":"Detrending crop yield data for spatial visualization of drought impacts in the United States, 1895\u20132014","volume":"237\u2013238","author":"Lu","year":"2017","journal-title":"Agric. For. Meteorol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/S0168-1923(03)00072-8","article-title":"An evaluation of agricultural drought indices for the Canadian prairies","volume":"118","author":"Quiring","year":"2003","journal-title":"Agric. For. Meteorol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1016\/S0168-1923(97)00027-0","article-title":"Growing degree-days: One equation, two interpretations","volume":"87","author":"Mcmaster","year":"1997","journal-title":"Agric. For. Meteorol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1038\/nclimate1585","article-title":"Adaptation of US maize to temperature variations","volume":"3","author":"Butler","year":"2013","journal-title":"Nat. Clim. Chang."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"15594","DOI":"10.1073\/pnas.0906865106","article-title":"Nonlinear temperature effects indicate severe damages to U.S. crop yields under climate change","volume":"106","author":"Schlenker","year":"2009","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_43","first-page":"431","article-title":"Geographically weighted regression. \u00d0modelling spatial non-stationarity","volume":"47","author":"Brunsdon","year":"1998","journal-title":"J. R. Stat. Soc. Ser. D"},{"key":"ref_44","unstructured":"Fotheringham, A.S., Brunsdon, C.F., and Charlton, M.E. (2003). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships, John Wiley & Sons."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Pascucci, S., Carfora, M., Palombo, A., Pignatti, S., Casa, R., Pepe, M., and Castaldi, F. (2018). A Comparison between Standard and Functional Clustering Methodologies: Application to Agricultural Fields for Yield Pattern Assessment. Remote Sens., 10.","DOI":"10.3390\/rs10040585"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.agrformet.2016.05.010","article-title":"Drought impact on rainfed common bean production areas in Brazil","volume":"225","author":"Heinemann","year":"2016","journal-title":"Agric. For. Meteorol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"107682","DOI":"10.1016\/j.fcr.2019.107682","article-title":"Integrating remote sensing-based process model with environmental zonation scheme to estimate rice yield gap in Northeast China","volume":"246","author":"Wang","year":"2020","journal-title":"Field Crops Res."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1870","DOI":"10.3390\/ijgi4041870","article-title":"Exploratory Method for Spatio-Temporal Feature Extraction and Clustering: An Integrated Multi-Scale Framework","volume":"4","author":"Luo","year":"2015","journal-title":"ISPRS Int. J. Geo-Inf."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Chen, Z., Ma, X., Wu, L., and Xie, Z. (2019). An Intuitionistic Fuzzy Similarity Approach for Clustering Analysis of Polygons. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8020098"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Liu, Y., Yuan, Y., and Gao, S. (2019). Modeling the Vagueness of Areal Geographic Objects: A Categorization System. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8070306"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.agrformet.2017.07.010","article-title":"Assessing causes of yield gaps in agricultural areas with diversity in climate and soils","volume":"247","author":"Mourtzinis","year":"2017","journal-title":"Agric. For. Meteorol."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"107615","DOI":"10.1016\/j.agrformet.2019.107615","article-title":"Improving corn yield prediction across the US Corn Belt by replacing air temperature with daily MODIS land surface temperature","volume":"276\u2013277","author":"Pede","year":"2019","journal-title":"Agric. For. Meteorol."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"516","DOI":"10.1126\/science.1251423","article-title":"Greater Sensitivity to Drought Accompanies Maize Yield Increase in the U.S. Midwest","volume":"344","author":"Lobell","year":"2014","journal-title":"Science"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"4189","DOI":"10.1080\/00036846.2017.1279266","article-title":"Geographically weight seemingly unrelated regression (GWSUR): A method for exploring spatio-temporal heterogeneity","volume":"49","author":"Wei","year":"2017","journal-title":"Appl. Econ."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.agrformet.2018.01.031","article-title":"Assessment of the agro-climatic indices to improve crop yield forecasting","volume":"253\u2013254","author":"Mathieu","year":"2018","journal-title":"Agric. For. Meteorol."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1016\/j.scitotenv.2012.10.029","article-title":"Climate change impacts on crop production in Iran\u2019s Zayandeh-Rud River Basin","volume":"442","author":"Gohari","year":"2013","journal-title":"Sci. Total Environ."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1016\/j.agee.2011.05.026","article-title":"Climatic and non-climatic drivers of spatiotemporal maize-area dynamics across the northern limit for maize production\u2014A case study from Denmark","volume":"142","author":"Odgaard","year":"2011","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.fcr.2014.02.010","article-title":"Climate-induced yield variability and yield gaps of maize (Zea mays L.) in the Central Rift Valley of Ethiopia","volume":"160","author":"Kassie","year":"2014","journal-title":"Field Crops Res."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Shrestha, D., Brown, J.F., Benedict, T.D., and Howard, D.M. (2021). Exploring the Regional Dynamics of U.S. Irrigated Agriculture from 2002 to 2017. Land, 10.","DOI":"10.3390\/land10040394"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Ordu\u00f1a Alegr\u00eda, M.E., Sch\u00fctze, N., and Niyogi, D. (2019). Evaluation of Hydroclimatic Variability and Prospective Irrigation Strategies in the U.S. Corn Belt. Water, 11.","DOI":"10.3390\/w11122447"}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/11\/8\/433\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:00:51Z","timestamp":1760140851000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/11\/8\/433"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,1]]},"references-count":60,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["ijgi11080433"],"URL":"https:\/\/doi.org\/10.3390\/ijgi11080433","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,1]]}}}