Comparison of climate change impact on rainfed maize yield in Kansas using statistical and process-based models

dc.contributor.authorRawat, Meenakshi
dc.date.accessioned2023-06-08T16:22:14Z
dc.date.available2023-06-08T16:22:14Z
dc.date.graduationmonthAugust
dc.date.issued2023
dc.description.abstractChanging climate and the projected increase in variability and frequency of extreme events make an accurate prediction of crop yield critically important for addressing emerging challenges in food security. Precise and timely prediction of crop yield can provide valuable information to agronomists, producers, and decision-makers. Even without considering climate change, several factors including environment, management, and genetics and their complex interactions make the prediction of crop yield challenging. In this study a statistical-based Multiple Linear Regression (MLR) model was developed to predict rainfed maize yield in Kansas and compared with yield predictions of the DSSAT process-based model to assess the impact of synthetic climate change scenarios of 1 and 2 °C temperature rise. Historic weather, soils, and crop management data were collected and converted to model-compatible formats to simulate and compare maize yield using both models. It was found that DSSAT had a large Root Mean Square Error (RMSE) compared to the MLR model whereas the correlation coefficients (r) were 0.93 and 0.70 for MLR and DSSAT, respectively. These results indicated that predicted yields from the MLR model had a stronger association with the observed yields than the simulated yields from DSSAT. Analysis of climate change impact showed that the reduction in rainfed maize yield predicted by DSSAT was 8.7% and 18.3% for the synthetic scenarios of 1 and 2 °C temperature rise respectively. Reduction in rainfed maize yield predicted by the MLR model was nearly 6% in both scenarios. Due to the extreme heat effect, predicted impacts under uniform climate change scenarios were considerably more severe for the process-based model than for the statistical-based model.
dc.description.advisorVaishali Sharda
dc.description.degreeMaster of Science
dc.description.departmentDepartment of Biological & Agricultural Engineering
dc.description.levelMasters
dc.identifier.urihttps://hdl.handle.net/2097/43336
dc.language.isoen_US
dc.publisherKansas State University
dc.rights.uri© the author. This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subjectClimate impacts
dc.subjectDSSAT
dc.subjectModel inter-comparison
dc.subjectMultiple linear regression (MLR) model
dc.titleComparison of climate change impact on rainfed maize yield in Kansas using statistical and process-based models
dc.typeThesis

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