Rice–wheat (RW) systems are critical to food security and livelihoods of rural and urban poor in south Asia and China, and to regional economies in southeast Australia. The sustainability of RW systems in south Asia is, however, threatened by yield stagnation or decline, and declining partial factor productivity, soil organic C and water availability. Crop models potentially offer a means to readily explore management options to increase yield, and to determine trade-off between yield, resource-use efficiency and environmental outcomes. This paper reviews the performance of CERES-Rice and CERES-Wheat in Asia and Australia in relation to their potential application towards increasing resource use efficiency and yield of RW systems.
The performance of the models was evaluated using simulated and observed data on anthesis and maturity dates, in-season LAI and growth, final grain yield and its components, and soil water and N balances from published studies across Asia and Australia, and then by computing the statistical parameters for the major characters. Over the four data sets examined for anthesis and six for maturity dates, CERES-Rice predicted those dates fairly well (normalised RMSE = 4–5%; D-index = 0.94–0.95), but over the 11 sets for grain and 4 for biomass yield, the predictions were more variable (normalised RMSE = 23% for both; D-index 0.90 and 0.76, for grain and biomass, respectively). Model performance was poorer under conditions of low N, water deficit, and low temperatures during the reproductive stages. Over the three data sets examined, CERES-Wheat predicted the anthesis and maturity dates quite well (normalised RMSE = 4–5%; D-index = 0.94–0.99), and over eight sets for grain and two sets for biomass yield the model predicted them also reasonably well (RMSE = 13–16%; D-index = 0.86–0.97). Only one study evaluated the DSSAT RW sequence model with fairly satisfactory predictions of rice and wheat yields over 20 years with adequate N, but not the long-term change in soil organic C and N. Predictions of in-season LAI and crop growth, and soil and water processes were quite limited to investigate the robustness of model processes.
Application of models to evaluate options to increase water and N use efficiency requires the ability to perform well at the margin where deficit stress begins. While both models generally perform satisfactorily under water and N non-limiting conditions, the little evidence available suggests that they do not perform well under resource-limiting situations. We recommend that the models’ key processes under the water and N limiting conditions be further evaluated urgently. The DSSAT sequence model also needs to be further evaluated against observations for a range of locations and management using data from long-term experiments in RW systems. 相似文献
Background： The minimum set of sub-models for simulating stand dynamics on an individual-tree basis consists of tree-level models for diameter increment and survival. Ingrowth model is a necessary third component in uneven-aged management. The development of this type of model set needs data from permanent plots, in which all trees have been numbered and measured at regular intervals for diameter and survival. New trees passing the ingrowth limit should also be numbered and measured. Unfortunately, few datasets meet all these requirements. The trees may not have numbers or the length of the measurement interval varies. Ingrowth trees may not have been measured, or the number tags may have disappeared causing errors in tree identification. Methods： This article discussed and demonstrated the use of an optimization-based approach to individual-tree growth modelling, which makes it possible to utilize data sets having one or several of the above deficiencies. The idea is to estimate all parameters of the sub-models of a growth simulator simultaneously in such a way that, when simulation begins from the diameter distribution at the first measurement occasion, it yields a similar ending diameter distribution as measured in the second measurement occasion. The method was applied to Pinus patula permanent sample plot data from Kenya. In this dataset, trees were correctly numbered and identified but measurement interval varied from 1 to 13 years. Two simple regression approaches were used and compared to the optimization-based model recovery approach. Results： The optimization-based approach resulted in far more accurate simulations of stand basal area and number of surviving trees than the equations fitted through regression analysis. Conclusions： The optimization-based modelling approach can be recommended for growth modelling when the modelling data have been collected at irregular measurement intervals. 相似文献
Four generalised diameter-height equations were developed and compared for pure and even-aged stands of Tecomella undulata in hot arid region of Rajasthan State in India. The data used to fit the equations consisted of 1 540 diameter-height observations collected from the plots laid out in uniformly stocked stands of varying age and density. The performance of four equations was tested by non-linear least squares regression and evaluated using different statistical criteria. Finally, these equations, with the same values of coefficients ob- tained during the fitting phase, were validated by an independent data set consisting of 854 diameter-height observations. Overall, equation （4） （Hui and Gadow function） was found to perform best for both the fitting data set as well as validation data set. 相似文献
