The Effects Of Sowing Date And Sowing Density On Growth Develop Of Winter Wheat And Simulation Study

It is of great significance to increase wheat (Triticum aestivum L.) yield to ensure our national food security and promote sustainable development of agriculture in the future. In this study, a field experiment was conducted, then made a comparison of phasic development models in Wheat. In additon, it analysed the effect of different sowing dates and density on wheat productivity. The study established the dynamic model of relative leaf area index and the dynamic model of relative dry matter accumulation. This study would lay a foundation for wheat production management and regulation, improving wheat grow model and national food security.A field experiment with one cultivar (Yangmai16) under five sowing dates and five sowing densities was conducted, then analysed the growth period changes of wheat under different sowing dates. The study took wheat physiological development time (PDT) as the time scale of the growth of wheat, then analysed dynamics of population tiller number, dynamics of leaf area index, canopy light transmittance rate, dry matter accumulation (DMA) dynamics, yield and its constituents of wheat under different sowing dates and densities. Different sowing dates had a considerable impact on wheat growth period. Different sowing dates and densities had a significant impact on growth indicators of wheat, thereby affecting the yield formation. Suitable sowing dates and sowing densities were good for yield formation.A field experiment with three cultivars under dfferent sowing dates and different sowing densities was conducted. The dynamic model of relative leaf area index (RLAI) was established with the normalized LAI and PDT from emergence to filling. The wheat RLAI model is y=(0.0435+0.2546x)/(1-3.0684x+2.8572x2). The dynamic model of relative dry matter accumulation (RDMA) was established with the normalized DMA and PDT from emergence to maturity, and the temporal characteristics of DMA changes was quantitatively analyzed based on the RDMA model. The wheat RDMA model is y=1.0262/(1+e1.7164-6.1718x)1/0.4941. It showed that Rational function could reflect the dynamic model of leaf area index. The Richards’ equation could accurately describe the dynamic model of the ground dry matter accumulation of wheat under different sowing dates and densities.By comparing the differences of the phase division, cultivar parameters and model algorithm in CERES-Wheat, APSIM-Wheat and WheatGrow model, the similarities and differences of the phasic development between three models were revealed. The results showed that the development stages were divided by the accumulation of thermal development unites in CERES-Wheat and APSIM-Wheat model, while WheatGrow used physiological development time to divide the development stages. In additon, WheatGrow put forward the growth of terminal spikelet to forecast the phasic development. There were more parameters related to the phasic development in WheatGrow model than the other models, such as the temperature sensitive parameter and so on. Polyline type function was used to describe the development rate to temperature in CERES-Wheat and APSIM-Wheat model, while Sine index equation or Cosine function index were used to describe the development rate to temperature in WheatGrow model. Vernalization response in CERES-Wheat and APSIM-Wheat model was based on the mean daily temperature, while vernalization response was related to the daily eight3-hour temperature in WheatGrow model. It’s consistent that quadratic function was used to describe the photoperiod response in the three models. The simulation results in Yizheng showed that the R2value between simulated and obseved data of the three models were all above0.98, and the simulation errors were in the range of1to2days. In additon, the best performance of the model in simulating maturity is CERES, while the best performance of the model in simulating anthesis is WheatGrow.