<> "The repository administrator has not yet configured an RDF license."^^ . <> . . . "Creating long-term weather data from thin air for crop simulation modeling"^^ . "Simulating crop yield and yield variability requires long-term, high-quality daily weather data, including\r\nsolar radiation, maximum (Tmax) and minimum temperature (Tmin), and precipitation. In many regions,\r\nhowever, daily weather data of sufficient quality and duration are not available. To overcome this limitation,\r\nwe evaluated a new method to create long-term weather series based on a few years of observed daily\r\ntemperature data (hereafter called propagated data). The propagated data are comprised of uncorrected\r\ngridded solar radiation from the Prediction of Worldwide Energy Resource dataset from the National\r\nAeronautics and Space Administration (NASA–POWER), rainfall from the Tropical Rainfall Measuring\r\nMission (TRMM) dataset, and location-specific calibration of NASA–POWER Tmax and Tmin using a limited\r\namount of observed daily temperature data. The distributions of simulated yields of maize, rice, or wheat\r\nwith propagated data were compared with simulated yields using observed weather data at 18 sites in\r\nNorth and South America, Europe, Africa, and Asia. Other sources of weather data typically used in crop\r\nmodeling for locations without long-term observed weather data were also included in the comparison:\r\n(i) uncorrected NASA–POWER weather data and (ii) generated weather data using the MarkSim weather\r\ngenerator. Results indicated good agreement between yields simulated with propagated weather data\r\nand yields simulated using observed weather data. For example, the distribution of simulated yields\r\nusing propagated data was within 10% of the simulated yields using observed data at 78% of locations\r\nand degree of yield stability (quantified by coefficient of variation) was very similar at 89% of locations. In\r\ncontrast, simulated yields based entirely on uncorrected NASA–POWER data or generated weather data\r\nusing MarkSim were within 10% of yields simulated using observed data in only 44 and 33% of cases,\r\nrespectively, and the bias was not consistent across locations and crops. We conclude that, for most locations,\r\n3 years of observed daily Tmax and Tmin data would allow creation of a robust weather data set for\r\nsimulation of long-term mean yield and yield stability of major cereal crops."^^ . "2015" . . "209-10" . "1" . . "Elsevier"^^ . . . "Agricultural and Forest Meteorology"^^ . . . "01681923" . . . . . . . . . . . . . . . . . . . . . . "K G"^^ . "Cassman"^^ . "K G Cassman"^^ . . "J V"^^ . "Wart"^^ . "J V Wart"^^ . . "H"^^ . "Yang"^^ . "H Yang"^^ . . "A"^^ . "Jarvis"^^ . "A Jarvis"^^ . . "L"^^ . "Claessens"^^ . "L Claessens"^^ . . "P"^^ . "Grassini"^^ . "P Grassini"^^ . . . . . . "Creating long-term weather data from thin air for crop simulation modeling (PDF)"^^ . . . . . "AFM_209–210_49–58_2015.pdf"^^ . . . "Creating long-term weather data from thin air for crop simulation modeling (Other)"^^ . . . . . . "Creating long-term weather data from thin air for crop simulation modeling (Other)"^^ . . . . . . "Creating long-term weather data from thin air for crop simulation modeling (Other)"^^ . . . . . . "Creating long-term weather data from thin air for crop simulation modeling (Other)"^^ . . . . . . "Creating long-term weather data from thin air for crop simulation modeling (Other)"^^ . . . . . "HTML Summary of #9057 \n\nCreating long-term weather data from thin air for crop simulation modeling\n\n" . "text/html" . . . "Climate Change"@en . .