Crop parameter estimation for DSSAT CERES rice through a data assimilation technique | |
| Author | Chudech Losiri |
| Call Number | AIT Thesis no.RS-10-04 |
| Subject(s) | Agriculture--Technology transfer--Decision making--Thailand Decision support systems--Thailand |
| Note | A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Remote Sensing and Geographic Information Systems |
| Publisher | Asian Institute of Technology |
| Series Statement | Thesis ; no. RS-10-04 |
| Abstract | Crop simulation models are a curtail tool for studying the agricultural production. It provides the information to the decision maker for deciding the environment that related to the farm. Running crop simulation model is al ways limited by the uncertainties in input parameters which affect the yield prediction. To overcome this problem, this study develops a new methodology to estimate unknown crop parameters for DSSAT CERES- Rice through data assimilation which give s the new way to incorporate between observation data and crop model for improving the crop model performance and yield estimation. In this study the DSSAT, is an agro-technology transfer model, is select ed to integrate with genetic algorithm (GA) for unknown crop parameters estimation. The unknown crop parameters consist of crop activity parameters (planting date, harvesting date, planting population at emergency, planting depth) and so il water parameters (drained upper limit, lower limit, saturation and bulk density) at difference 4 layers (3, 12, 28 and 60 cm). To develop DSSAT-GA, the LAI is used as a reference data in the assimilation process. The GA proposes a set of unknown crop parameters to simulate in the DSSAT model. The simulated LAI from the model is compared with the LAL reference data based on objective function. Finally, the DSSAT –G A is successfully developed. The three assimilation cases (every day, ever y 8 days and every 10 days LAI data) are applied to estimate the unknown crop parameters from the DSSAT-GA. The result shows that the good estimated parameter value can be obtained from the assimilating with every day LAI data. However, assimilating with ever y 8 days and every 10 days remains give the appropriated parameters value when comparing with the reference parameter. In conclusion, the DSSAT-GA, data assimilation, gives the good performance to estimate unknown parameters for the DSSAT model. This technique, the DSSAT-GA, can solve the uncertainties in input parameters problem for running the DSSAT model at many scales of the study. |
| Year | 2010 |
| Corresponding Series Added Entry | Asian Institute of Technology. Thesis ; no. RS-10-04 |
| Type | Thesis |
| School | School of Engineering and Technology |
| Department | Department of Information and Communications Technologies (DICT) |
| Academic Program/FoS | Remote Sensing (RS) |
| Chairperson(s) | Honda, Kiyoshi |
| Examination Committee(s) | Tripathi, Nitin Kumar;Voratas Kachitvichyanukul;Daroonwan Kamthonkiat |
| Scholarship Donor(s) | Thailand (HM King) |
| Degree | Thesis (M.Sc.) - Asian Institute of Technology, 2010 |