Asset Details
Do you wish to reserve the book?
Demonstrating the Use of the Yield-Gap Concept on Crop Model Calibration in Data-Poor Regions: An Application to CERES-Wheat Crop Model in Greece
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Demonstrating the Use of the Yield-Gap Concept on Crop Model Calibration in Data-Poor Regions: An Application to CERES-Wheat Crop Model in Greece
Do you wish to request the book?
Demonstrating the Use of the Yield-Gap Concept on Crop Model Calibration in Data-Poor Regions: An Application to CERES-Wheat Crop Model in Greece
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Demonstrating the Use of the Yield-Gap Concept on Crop Model Calibration in Data-Poor Regions: An Application to CERES-Wheat Crop Model in Greece
2023
Overview
Yield estimations at global or regional spatial scales have been compromised due to poor crop model calibration. A methodology for estimating the genetic parameters related to grain growth and yield for the CERES-Wheat crop model is proposed based on yield gap concept, the GLUE coefficient estimator, and the global yield gap atlas (GYGA). Yield trials with three durum wheat cultivars in an experimental farm in northern Greece from 2004 to 2010 were used. The calibration strategy conducted with CERES-Wheat (embedded in DSSAT v.4.7.5) on potential mode taking into account the year-to-year variability of relative yield gap Yrg (YgC_adj) was: (i) more effective than using the average site value of Yrg (YgC_unadj) only (the relative RMSE ranged from 10 to 13% for the YgC_adj vs. 48 to 57% for YgC_unadj) and (ii) superior (slightly inferior) to the strategy conducted with DSSAT v.4.7.5 (DSSAT v.3.5—relative RMSE of 5 to 8% were found) on rainfed mode. Earlier anthesis, maturity, and decreased potential yield (from 2.2 to 3.9% for 2021–2050, and from 5.0 to 7.1% for 2071–2100), due to increased temperature and solar radiation, were found using an ensemble of 11 EURO-CORDEX regional climate model simulations. In conclusion, the proposed strategy provides a scientifically robust guideline for crop model calibration that minimizes input requirements due to operating the crop model on potential mode. Further testing of this methodology is required with different plants, crop models, and environments.
