Search

To investigate the effects of polyaspartic acid salt(PASP)urea on rice yield and nitrogen(N)concentrations in the ponding water of paddy fields, a field experiment was conducted. We determined the effects of PASP-Ca urea on rice growth, N-uptake and utilization, and N concentrations in paddy field ponding water. In addition, Grey Relational Analysis was used to evaluate the effects of PASP-Ca urea on water pollution. The results showed that N concentrations in the ponding water were lower in the reduced-N treatment test than they were in the conventional-N treatment test. In particular, TN concentrations were 12.70% and 17.23% lower in the reduced-N treatment test than in the control on the first day after basal fertilization and topdressing. The reduction of N application was beneficial to rice growth, with a 0.78% higher yield, 0.39% higher cumulative N-uptake, and 9.35 percent points higher efficiency in N use(P>0.05). N concentrations in the field ponding water were lower with the PASP-Ca urea treatment than with the conventional urea treatment, with NH4+ -N concentration decreasing by 24.94%~68.66%(N fertilization ratio of basal:tillering:panicle=5:3:2)and 2.72%~51.30% (N fertilization ratio of basal:tillering=8:2)with the PASP-Ca urea treatment during one week after basal fertilization. The application of PASP-Ca urea was beneficial to rice growth, nutrient absorption, and yield promotion, with a 0.87%~7.27% higher yield, a 7.05%~35.20% higher accumulated N-uptake, and a 6.11~30.26 percent points higher N use-efficiency, compared to the control. In particular, the PASPCa urea treatment, with a N fertilization ratio of 8:2(basal:tillering), enhanced N uptake in grain(30.08%), N absorption in straw (46.27%)and N use-efficiency(30.26 percent points). Considering rice yield, the N absorption rate of paddy field ponding water, and its resulting N concentration, the reduced-N treatment was more effective than the conventional-N treatment, and the PASP-Ca urea treatment was better than the treatment using conventional urea. Moreover, the optimal N application ratio, of base fertilizer to tillering fertilizer, was 8:2, and the effects of field application needed to be further verified.

The main problem in current energy management is the ability of practical application. To address the problem, this paper proposes a reinforcement learning (RL)-based energy management by combining Tubule Q-learning and Pontryagin’s Minimum Principle (PMP) algorithms for a plug-in hybrid electric bus (PHEB). The main innovation distinguished from the existing energy management strategies is that a dynamic SOC design zone plan method is proposed. It is characterized by two aspects: ① a series of fixed locations are defined in the city bus route and a linear SOC reference trajectory is re-planned at fixed locations; ② a triangle zone will be re-planned based on the linear SOC reference trajectory. Additionally, a one-dimensional state space is also designed to ensure the real-time control. The off-line trainings demonstrate that the agent of the RL-based energy management can be well trained and has good generalization performance. The results of hardware in loop simulation (HIL) demonstrate that the trained energy management has good real-time performance, and its fuel consumption can be decreased by 12.92%, compared to a rule-based control strategy.

We investigated the influences of different nitrogen fertilizer rate on maize production, nitrogen use efficiency and soil nitrate nitrogen at straw return farmland for two years. The results showed that maize production increased with the increment of nitrogen fertilizer. The maize production was the highest at 216 kg·hm -2(N216)of nitrogen use and began to decrease when the amount of nitrogen use was beyond 216 kg· hm -2. There were significant interannual differences on maize production in the same treatment. The maize production in 2010 increased 0.69%~4.75% compared with that in 2009. Nitrogen use efficiency, nitrogen agronomic efficiency and nitrogen harvest index improved with the year of straw return. The highest nitrate nitrogen accumulation was found in the treatment of 240 kg· hm -2(N240)in 0~100 cm soil layer. Soil nitrate content increased with the depth of soil. This may potentially increased the risk of nitrate pollution on shallow groundwater. Compared with N240, the nitrate nitrogen accumulation of N168(168 kg·hm -2 ), N192(192 kg·hm -2 )and N216(216 kg·hm -2 )were equally reduced by respectively 39.87%, 35.84% and 29.38% in 0~100 cm soil layer. Considering the maize production, nitrogen use efficiency and ecological environmental benefits, the optimum amount of nitrogen use should be 200 kg·hm -2.