A Process-Based Simulation Model On Barley Growth And Development

The research and application of crop growth model would be important for facilitating development of informational and digital agriculture. In the present study, the relationships of growth and development to environment factors were analyzed and integrated by using the field experiments data with different genotypes, sowing dates and nitrogen application levels in different ecosites. By adopting advanced modeling technology abroad and the methodology of crop growth model developed by our lab, a physiological process-based barley simulation model (BarleyGrow) was developed through the system analysis and mathematical modeling. The present study should be useful for prediction of growth performance under different conditions and development of barley digital management system in barley crop.Based on the data from Yangzhou field experiment with five cultivars at spring sowing date, three processes of thermal effectiveness, photoperiod and vernalization in barley were quantified, and a physiological development time (PDT) based barley model for phasic and phenological development was developed. Seven cuitlvar parameters were used in the model, including the accumulated temperature from sowing to germination (GDDo), basic temperature in filling period (Tbmax), physiological vernalization time (PVT), critical day length (DLc), start time of photoperiod response (PPs), minimum time from emergence to heading (EHmin) and from heading to maturity (FDmin). The ratio of daily increment of PDT and the deficit of both water and nitrogen was used to estimate the effect of environment on phasic and phenological development. A nonlinear function was adopted to describe vernalization and thermal effectiveness, and the sinusoidal function was used to describe photoperiod curve cluster for different cultivars. Physiologically, our model estimated 2.6 days to reach the single ridge stage,5.6 days to the double ridge stage,11.3 days to the stamen and pistil initiation stage,13.1 days to the anther separation stage,15.3 days to the pollen mother cells stage and 18.2 days to the tetrad stage. Phenologically, estimations were 13.1 days to reach the jointing stage,28.7 days to the heading stage,32.8 days to the grain filling stage and 51.5 days to the maturity stage. PDT was consequently used as a unified scale for measuring developmental progress of different cultivars under different climate and cultural practices.In BarleyGrow, the optimum values of the model parameters were obtained through genetic-simulated annealing algorithms. Based on the field experiments with 14 barley cultivars on different sowing dates at 4 ecosites (Nanjing, Yangzhou, Wuhan and Kunming), the submodel for apical and phenological development was validated by comparing with YDmodel and SUCROS model. As a whole, the BarleyGrow model had an accurate and stable estimation on apical and phenological development. The root mean square error (RMSE) with the BarleyGrow model was ranged between 1.06 and 8.13 days for various cultivars, compared to 6.26 and 13.35 days with YDmodel, and 8.84 and 20.28 days with SUCROS. Compared with YDmodel and SUCROS model, the BarleyGrow model was quite sensitive to basic temperature in grain filling time, physiological vernalization time, critical daylength and minimum time from emergence to heading. The BarleyGrow model gave good predictions of apical and phenological development for a diverse range of temperature and photoperiod conditions across China. Especially, effects in anther separation, pollen mother cells, tetrad, heading, grain filling and maturity stages were better predicted.Accurate simulation of leaf area index (LAI) is critical for reliable prediction of crop growth and yield using a crop growth model. Based on the systematic analysis of barley experimental data from different cultivars and sowing dates at Wuhan and Yangzhou, the submodel for LAI estimation was developed. Along with the expansion coefficient of LAI for cultivar genetic properties, the relationships of leaf area index dynamic in barley under high yield to physiological development time (PDT), the accumulation of photosynthetic available radiation after sowing (∑PAR) and the optimum LAI at booting were simulated. The actual dynamic of LAI was modified with water and nitrogen factors based on the dynamic of LAI under high yield. The internal and external factors of leaf growth and development in barley were integrated into the model. The internal factor was genetic property of LAI expansion. Environmental conditions included daily temperature difference, sunlight,∑PAR, water and nitrogen factor. The submodel was tested with the different cultivars under different sowing dates and different nitrogen rates at Yangzhou, Nanjing and Kunming. The results showed that this submodel gave good predictions of LAI in barley under different ecosites, climates and cultivation practices. Based on the merits of existing crop simulation models, a submodel for photosynthesis and dry matter accumulation was developed. In the submodel, a day was separated into 96 time segments, and the corresponding photosynthetically active radiation during each time segment was calculated. The daily canopy assimilation was simulated using complex Simpson integration method, and the simulation result was evaluated by comparing the method of Gauss integration. Testing results showed that the Simpson integration method was better than Gauss integration method, and featured with higher predictability and broader applicability. Thus the Simpson integration method could be used as a new method to accurately simulate crop dry matter accumulation.Based on the systematic analysis of barley experimental data from various cultivars at different treatments in Nanjing and Yangzhou, a process-based submodel was developed for predicting dry matter partitioning and organ growth in barley. Along with harvest index and partitioning coefficient of leaf for cultivar genetic properties, the relationships of dry matter partitioning dynamic under high yield to physiological development time (PDT), the accumulation of photosynthetic available radiation after sowing (∑PAR), and cultivar genetic properties. The actual dry matter partitioning indices of organs in barley were modified by water and nitrogen limitation factors based on dry matter partitioning dynamic under high yield. Organ weights were the products of corresponding organ partitioning index and biomass. The submodel for organ partitioning indices and organ weights was tested with dataset from different cultivars at different sowing dates in Wuhan and Kunming, and showed good predictions of partitioning indices and organ weights under various conditions.Submodel for yield prediction was established through the method of yield components. Based on the experiment dataset from different cultivars under the optimal condition at Wuhan, Yangzhou and Kunming, the regression equations were built between the relative values of ear per plant, kernel per ear, and thousand-grain weight, and accumulated photosynthetic effective radiation (∑PAR). Ears per plant, kernels per ear, and thousand-grain weight under the actual condition were the equations of their potential values under the optimal condition, and water and nitrogen factor at the actual condition. The internal and external factors of yield components formation on barley were integrated into the model. The internal factors were genetic properties of cultivar, including potential ears per plant, potential kernels per ear, potential thousand grain weight and grain filling duration. Environmental conditions included∑PAR, water and nitrogen factor. The submodel was calibrated and validated with field experimental data from different cultivars at different sowing dates in Wuhan, Kunming and Yangzhou. The results showed that the model could well simulate the yield components and theoretic yield with high applicable levels.