Little is known about the impact of direct sowing under vegetation cover on the production and quality of New Rice for Africa （NERICA） on poor oxisol. In this study, two NERICA varieties （NERICA 3 and NERICA 8） w...Little is known about the impact of direct sowing under vegetation cover on the production and quality of New Rice for Africa （NERICA） on poor oxisol. In this study, two NERICA varieties （NERICA 3 and NERICA 8） were grown under tropical oxisol soil with very low nutrient contents. Four cultivation systems were used in completely randomized block design, including plowing （control）, unplowed soil with dead vegetation cover （DVC）, unplowed soil with live vegetation cover （LVC） and unplowed soil with mixed vegetation cover （MVC）. DVC significantly improved the exponential growth of NERICAs. NERICA 3 was the more productive （2.16–3.05 t/hm2） compared with NERICA 8 （0.71–1.21 t/hm2）. Cultivation systems improved the nutritional quality of NERICAs. The total protein content of NERICA 3 under DVC and MVC was 84.8% and 75.0% higher than control, respectively. The total soluble carbohydrate contents of NERICA 8 under LVC and MVC was 73.2% and 57.3% higher than control, respectively. These results suggested that conservative approach like direct sowing on unplowed soil with vegetation cover systems can improve the nutritional quality of rainfed NERICAs and their sustainable production under poor oxisol soil in sub-Saharan Africa.展开更多
Indoor environmental quality has always been the focus of people’s long-term attention. How to monitor the indoor environmental level conveniently and accurately is a problem that people pay attention to now. After r...Indoor environmental quality has always been the focus of people’s long-term attention. How to monitor the indoor environmental level conveniently and accurately is a problem that people pay attention to now. After research, an indoor environment level monitoring system based on LoRa communication is designed. The system is mainly divided into two parts, the detection node, and the monitoring terminal. Temperature, humidity, light intensity, noise, formal-dehyde, and carbon dioxide are detected through the node with STM32F103ZET6 microcontroller as the controller;the data is sent to the monitoring terminal for display through LoRa communication. At the same time, the T-S fuzzy neural network (TSFNN) is improved by the particle swarm optimization (PSO) algorithm to classify the indoor environment quality level. Experimental test: the total error of the improved TSFNN model test set is reduced by 8.6007. The system can monitor the indoor environment level objectively and reliably, and has high practical value.展开更多
基金the Laboratory of Biotechnology and Environment as well as the Food and Nutrition Research Center(CRAN)of Institute for Medical Research and Study of Medicinal Plants in Cameroon for their support
文摘Little is known about the impact of direct sowing under vegetation cover on the production and quality of New Rice for Africa （NERICA） on poor oxisol. In this study, two NERICA varieties （NERICA 3 and NERICA 8） were grown under tropical oxisol soil with very low nutrient contents. Four cultivation systems were used in completely randomized block design, including plowing （control）, unplowed soil with dead vegetation cover （DVC）, unplowed soil with live vegetation cover （LVC） and unplowed soil with mixed vegetation cover （MVC）. DVC significantly improved the exponential growth of NERICAs. NERICA 3 was the more productive （2.16–3.05 t/hm2） compared with NERICA 8 （0.71–1.21 t/hm2）. Cultivation systems improved the nutritional quality of NERICAs. The total protein content of NERICA 3 under DVC and MVC was 84.8% and 75.0% higher than control, respectively. The total soluble carbohydrate contents of NERICA 8 under LVC and MVC was 73.2% and 57.3% higher than control, respectively. These results suggested that conservative approach like direct sowing on unplowed soil with vegetation cover systems can improve the nutritional quality of rainfed NERICAs and their sustainable production under poor oxisol soil in sub-Saharan Africa.
文摘Indoor environmental quality has always been the focus of people’s long-term attention. How to monitor the indoor environmental level conveniently and accurately is a problem that people pay attention to now. After research, an indoor environment level monitoring system based on LoRa communication is designed. The system is mainly divided into two parts, the detection node, and the monitoring terminal. Temperature, humidity, light intensity, noise, formal-dehyde, and carbon dioxide are detected through the node with STM32F103ZET6 microcontroller as the controller;the data is sent to the monitoring terminal for display through LoRa communication. At the same time, the T-S fuzzy neural network (TSFNN) is improved by the particle swarm optimization (PSO) algorithm to classify the indoor environment quality level. Experimental test: the total error of the improved TSFNN model test set is reduced by 8.6007. The system can monitor the indoor environment level objectively and reliably, and has high practical value.
