SWAT Literature Database for Peer-Reviewed Journal Articles

Title:Rice parameters describing crop performance of four U.S. cultivars 
Authors:Kiniry, J.R., G. McCauley, Y. Xie and J.G. Arnold 
Journal:Agronomy Journal 
Volume (Issue):93(6) 
Article ID: 
URL (non-DOI journals): 
Broad Application Category:crop/plant/tree growth or production 
Primary Application Category:crop, forest and/or vegetation growth/yield and/or parameters 
Secondary Application Category:none 
Watershed Description:None 
Calibration Summary: 
Validation Summary: 
General Comments:Describes crop parameters for four different types of rice, that can be used in SWAT and similar models. 
Abstract:Parameters describing processes of crop growth and yield production provide modelers with the means to simulate crops and provide breeders with a system of comparing cultivars. Such values for rice (Oryza sativa L.) are especially important for some regions in the southern USA. Accordingly, the objective of this study was to quantify key biomass and yield production processes of four rice cultivars common in this region. We measured the leaf area index (LAI), the light extinction coefficient (k) for Beer's law, N concentrations, and the harvest index (HI) for the main and ratoon crops in 1999 and 2000 at Eagle Lake, TX. Dry matter was linearly related to intercepted photosynthetically active radiation (IPAR) for all of the data sets. The mean radiation use efficiency (RUE) was 2.39 g aboveground biomass MJ-1 IPAR. Maximum LAI values ranged from 9.8 to 12.7, and the mean k value for the main crop was 0.37. The highest main crop yields were 7.04 Mg ha-1 for Cocodrie in 1999 and 7.22 Mg ha-1 for Jefferson in 2000. Yield differences among cultivars were due to HI differences and were not related to RUE values. The mean HI was 0.32 for all four cultivars over the two harvests in each of the 2 yr. Consistency in values of RUE, k, N concentrations, and HI among the cultivars in this study and between this study and values reported in the literature will aid modelers simulating rice development and yield and aid breeders in identifying key traits critical to rice grain yield improvement.