S-Space College of Agriculture and Life Sciences (농업생명과학대학) Dept. of Plant Science (식물생산과학부) Theses (Master's Degree_식물생산과학부)
Assessment of ORYZA-based rice models under organic fertilizer management
- 농업생명과학대학 식물생산과학부
- Issue Date
- 서울대학교 대학원
- 학위논문 (석사)-- 서울대학교 대학원 : 농업생명과학대학 식물생산과학부, 2018. 2. 김광수.
- Environmental issues associated with intensive agriculture lead to more interests in organic agriculture which is one of sustainable agriculture practices. Organic farms would benefit from the model that simulates soil nutrient dynamics as well as crop growth, which would be useful to optimize the fertilizer application. Few studies have been conducted to simulate crop growth using organic fertilizer, especially for rice which would be an important staple crop. The objectives of this study were to integrate a simple soil nutrient model into the ORYZA 2000 model and to compare with the ORYZA (v3) model under the organic fertilizer application. Crop growth and yield data were obtained at an experiment farm in National Institute of Agricultural Science (NAS) from 2015 to 2017. Another set of rice yield data were obtained at a commercial farm in Suncheon from 2015 to 2016. These data were used to compare estimates of crop yield. Parameter values for both ORYZA 2000 model and ORYZA (v3) model were determined to represent actual crop management and field condition. ORYZA 2000 model tended to have more reliable estimates of crop yield than the ORYZA (v3) model. Both models had relatively large errors in estimating soil inorganic nitrogen. These models also underestimated nitrogen uptake and crop biomass, especially during late vegetative stage and reproductive stage. Nevertheless, the ORYZA 2000 model had greater degree of agreement statistics and less error in rice yield estimation than the ORYZA (v3) model. It appeared that estimation error of crop yield resulted from inaccurate estimation of soil inorganic nitrogen, which would be caused by uncertainties of soil parameters. Weather data measured at a distant weather station to the commercial farm could cause considerable uncertainty in estimation rice yield.