The impact of free-air CO_2 enrichment (FACE) and N supply on growth, yield and quality of rice crops with large panicle

Because CO_2 is needed for plant photosynthesis, the increase in atmospheric [CO_2] has the potential to enhance the growth and development of plant. However, the resultant effects on growth, yield and quality of field-grown rice remain unclear, especially under differing nitrogen (N) availability and/or using cultivars with large panicles. To investigate these, a Free-Air CO_2 Enrichment (FACE) experiment was performed at Wuxi, Jiangsu, China, in 2001-03. A japonica cultivar with large panicle was exposed to two [CO_2] (ambient, ambient+200μmol mol^) at three levels of N supply (15, 25, 35gNm^). FACE accelerates phenology significantly, with 3-5 days earlier in heading and 6-9 days earlier in maturity across 3 years. FACE significantly increased the grain yield by 12.8%, which was mainly due to substantially increased panicle number per square meter (+19%) as result of significant increases in tillering occurrence speed. However the spikelet number per panicle was greatly reduced (-8%), which was due mainly to the significant increase in degenerated spikelets per panicle (+52%) while differentiated spikelets per panicle showed no change. Overall DM accumulation at harvest was stimulated somewhat more (+16%) by FACE, compared to grain yield, by an average of 13% by FACE, thus resulting in 3% reduction in harvest index. FACE caused significant reduction in shoot N concentration (-7%) and significant increase in P concentration (+14%) at grain maturity, resulting in significant increase in N use efficiency and significant reduction in P use efficiency. Both shoot N uptake (+9%) and P uptake (+33%) showed significant increase at harvest, which was mainly due to significant enhanced N and P uptake during early growth stage. On a per plant basis, FACE significantly increased cumulative root volume, root dry weight, adventitious root length and adventitious root number at heading, which was mainly associated with significant increases in root growth rate during early growth period, while total surface area, active adsorption area and root oxidation activity per unit root dry weight showed significant reduction. As for grain quality, FACE cause deterioration of processing suitability and appearance quality drastically, the nutritive value of grain was also negatively influenced by FACE due to a reduction in grain protein and Cu concentration. By contrast, FACE resulted in better eating/cooking quality. For most cases, no [CO_2]×N interaction was detected for the growth, yield and quality parameters. Data from this study has important implications for fertilizer (e.g. N, P) management and variety selection in rice production systems under future elevated [CO_2] conditions.Special Revie

Continuous Multiagent Control using Collective Behavior Entropy for Large-Scale Home Energy Management

With the increasing popularity of electric vehicles, distributed energy generation and storage facilities in smart grid systems, an efficient Demand-Side Management (DSM) is urgent for energy savings and peak loads reduction. Traditional DSM works focusing on optimizing the energy activities for a single household can not scale up to large-scale home energy management problems. Multi-agent Deep Reinforcement Learning (MA-DRL) shows a potential way to solve the problem of scalability, where modern homes interact together to reduce energy consumers consumption while striking a balance between energy cost and peak loads reduction. However, it is difficult to solve such an environment with the non-stationarity, and existing MA-DRL approaches cannot effectively give incentives for expected group behavior. In this paper, we propose a collective MA-DRL algorithm with continuous action space to provide fine-grained control on a large scale microgrid. To mitigate the non-stationarity of the microgrid environment, a novel predictive model is proposed to measure the collective market behavior. Besides, a collective behavior entropy is introduced to reduce the high peak loads incurred by the collective behaviors of all householders in the smart grid. Empirical results show that our approach significantly outperforms the state-of-the-art methods regarding power cost reduction and daily peak loads optimization

Efficient Deep Reinforcement Learning via Adaptive Policy Transfer

Transfer Learning (TL) has shown great potential to accelerate Reinforcement Learning (RL) by leveraging prior knowledge from past learned policies of relevant tasks. Existing transfer approaches either explicitly computes the similarity between tasks or select appropriate source policies to provide guided explorations for the target task. However, how to directly optimize the target policy by alternatively utilizing knowledge from appropriate source policies without explicitly measuring the similarity is currently missing. In this paper, we propose a novel Policy Transfer Framework (PTF) to accelerate RL by taking advantage of this idea. Our framework learns when and which source policy is the best to reuse for the target policy and when to terminate it by modeling multi-policy transfer as the option learning problem. PTF can be easily combined with existing deep RL approaches. Experimental results show it significantly accelerates the learning process and surpasses state-of-the-art policy transfer methods in terms of learning efficiency and final performance in both discrete and continuous action spaces.Comment: Accepted by IJCAI'202