Improving Rice Yield by Promoting Pre-anathesis Growth in Subtropical Environments

Abstract

Rice yield is greatly influenced by climatic factors and soil fertility in the location where it is grown, but information about the individual effects of climatic factors and soil fertility variables is difficult to distinguish because they are often not independent. The objective of this study was to demonstrate the effect of climatic factors on grain yield when soil fertility was not a confounding factor for explaining yield differences across two subtropical environments. Field and pot experiments with six rice cultivars were conducted in Deyang and Luzhou, Sichuan Province, China. We found that rice yield was higher in Deyang than in Luzhou by 7.0–16.8% for field experiments and by 57.6–87.4% for pot experiments. Biomass production rather than harvest index was responsible for the yield difference. Maximum and minimum temperatures and cumulative solar radiation (CSR) before heading (HD) were higher in Deyang than in Luzhou, whereas after HD, maximum and minimum temperatures were lower in Deyang than in Luzhou. Rice yield was more closely related to maximum and minimum temperatures and CSR before HD than to these parameters after HD. There was no difference in yield between soil types from Deyang and Luzhou within the same ecological condition. Thus, the yield difference between the two subtropical environments was mainly caused by the difference in climatic factors. The differences in biomass between Deyang and Luzhou were mostly due to variations in pre-heading crop growth rate (pre-CGR) and pre-heading radiation use efficiency (pre-RUE), which were induced by varying temperatures and CSR. We concluded that lower yield in Luzhou was associated with lower pre-CGR and pre-RUE. Our study suggests that developing rice cultivars with high pre-CGR and pre-RUE through a breeding program may also be a feasible approach to achieve high yield in subtropical environments.

Advancing UAV Communications: A Comprehensive Survey of Cutting-Edge Machine Learning Techniques

“…Meanwhile, another advantage of swarm UAV systems is their ability to perform complex tasks. For example, a Joint offloading and trajectory design for UAV mobile edge computing networks [148] 2019 Energy efficient for UAV-enabled mobile edge computing networks [149] 2022 Deep-Graph-Based RL for Joint Cruise Control for Aerial Edge Internet of Things [150] 2022 A Survey on the Convergence of Edge Computing and ML for UAVs [151] 2022 Multi-UAV network assisted intelligent edge computing Space-based networks (satellite) [152] 2017 Space-based information service in Internet Plus Era [153] 2020 Cell Free Satellite assisted UAV networks for 6G wide area IoT [154] 2022 Satellite-and Cache-assisted UAV trajectory optimization for 6G aerial networks [155] 2024 UAV and satellite remote sensing for inland water quality Swarm UAV system [156] 2018 Review of UAV swarm communication and control architectures [157] 2018 Channel estimation and self-positioning for UAV Swarm syste, [158] 2022 Recent advances and future trends of UAV swarm communication [159] 2023 Comparative analysis of different UAV swarm control methods on unmanned farms [160] 2023 Air-to-ground relay communication planning method for UAVs swarm applications swarm of UAVs can be used for search and rescue missions, where they can quickly and efficiently search large areas for survivors. Additionally, a swarm of UAV can be used for monitoring and surveillance tasks, where they can cover large areas and provide real-time data to a central hub.…”