Quantitative Monitoring And Management System Of Dry Matter Production Based On Yield Performance And Canopy Images In Maize

In order to guide high yield of maize production, we used the theory of production performance to quantificationally monitor and analyse maize growth process. We monitored yield performance index in the whole growth period to analyse reason why did the actual yield not reach standards value (15000 kg ha-1) in spring maize and summer maize by "Quantitative dynamic analysis and high yield and high efficient management system of maize growth". Based on canopy Images, we preliminarily established the real time, nondestructive, fast model for predicting dry matter accumulation (DM) in maize. The main conclusions are as follows,1 It is the main way by improving the leaf area index (LAI) to narrow the gap between the actual yield and thermal production potential. The actual yield of spring maize (Zhengdan 958) in Gongzhuling and summer maize in Langfang (Zhengdan 958) only reached by 46%(12310.8 kg ha-1) and 42% (10980.5 kg ha-1) thermal production potential in 2014. The main way for us to narrow the gap is to improve LAI in spring maize, or to improve LAI and extend its duration in summer maize.2 The results of monitoring performance indexes showed that the main reason of limiting high yield is insufficient photosynthetic capacity after anthesis. The reasons why spring maize yield did not reach the standard are, LAI and net assimilation rate (NAR) decreased after anthesis, DM decreased, grain weight decreased. The reasons why summer maize yield did not reach the standard are, LAI, duration days and NAR decreased after anthesis, DM decreased, grain weight decreased.3 We preliminarily established the digital image pixel calculating canopy cover (CC) model for predicting LAI (y= 3.2812x0.7639). R2, root mean square error (RMSE) and relative estimation error (RE) of simulated and observed LAI based on the 1:1 line were 0.896?0.035 and 1.46%, respectively, which indicated that the model could predict well the dynamic LAI in maize.4 We preliminarily established leaf area duration calculated by LAI and duration days dynamic model for predicting DM. The model, R2, RMSE and RE in spring maize were y= 0.0298x0.8738,0.893,383.4 kg ha-1 and 3.2%. The model, R2, RMSE and RE in summer maize were y= 0.3941x0.7135,0.880, 559.8 kg ha-1 and 6.1%, respectively, which indicated that the model could predict well the dynamic DM in maize.