{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["13(1)"],"submitter":["Bal SK"],"pubmed_abstract":["The present study tests the accuracy of four models in estimating the hourly air temperatures in different agroecological regions of the country during two major crop seasons, kharif and rabi, by taking daily maximum and minimum temperatures as input. These methods that are being used in different crop growth simulation models were selected from the literature. To adjust the biases of estimated hourly temperature, three bias correction methods (Linear regression, Linear scaling and Quantile mapping) were used. When compared with the observed data, the estimated hourly temperature, after bias correction, is reasonably close to the observed during both kharif and rabi seasons. The bias-corrected Soygro model exhibited its good performance at 14 locations, followed by the WAVE model and Tempe"],"journal":["Scientific reports"],"pagination":["7789"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10183030"],"repository":["biostudies-literature"],"pubmed_title":["Identifying appropriate prediction models for estimating hourly temperature over diverse agro-ecological regions of India."],"pmcid":["PMC10183030"],"pubmed_authors":["Sandeep VM","Vijaya Kumar P","Manikandan N","Bal SK","Pramod VP","Sarath Chandran MA","Singh VK","Vanaja M","Subba Rao AVM"],"additional_accession":[]},"is_claimable":false,"name":"Identifying appropriate prediction models for estimating hourly temperature over diverse agro-ecological regions of India.","description":"The present study tests the accuracy of four models in estimating the hourly air temperatures in different agroecological regions of the country during two major crop seasons, kharif and rabi, by taking daily maximum and minimum temperatures as input. These methods that are being used in different crop growth simulation models were selected from the literature. To adjust the biases of estimated hourly temperature, three bias correction methods (Linear regression, Linear scaling and Quantile mapping) were used. When compared with the observed data, the estimated hourly temperature, after bias correction, is reasonably close to the observed during both kharif and rabi seasons. The bias-corrected Soygro model exhibited its good performance at 14 locations, followed by the WAVE model and Tempe","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 May","modification":"2025-04-26T14:25:10.483Z","creation":"2025-04-06T14:36:15.118Z"},"accession":"S-EPMC10183030","cross_references":{"pubmed":["37179371"],"doi":["10.1038/s41598-023-34194-9"]}}