[Objective] The aim of this study is to evaluate nitrogen efficient cotton germplasms and improve nitrogen use efficiency. [Method] Eighty cotton germplasms were selected and evaluated in the hydroponic experiment under low (0.25 mmol·L-1) and high (5 mmol·L-1) nitrogen concentration. Different traits for screening were identified and nitrogen use efficiency types were classified. Field experiments were also performed for comparison and confirmation of the identified germplasms. [Result] The results showed that there were significant differences in the total plant dry matter, shoot nitrogen accumulation and nitrogen absorption efficiency in cotton germplasms at the two nitrogen levels. Based on coefficient of variation, principal component analysis and correlation, six traits including total plant dry matter, shoot dry matter, root dry matter, total nitrogen accumulation, shoot nitrogen accumulation and nitrogen absorption efficiency were used as screening indicators. According to the Heatmap clustering analysis and the nitrogen efficiency comprehensive index, two germplasms (Lu05R59 and CCRI 69) were identified as low nitrogen tolerant and nitrogen efficient, and two germplasms (Coker 201 and Xinluzhong 30) as low nitrogen sensitive and nitrogen inefficient. The results of field experiment were consistent with the results of the hydroponic culture at the seedling stage. [Conclusion] It was finally determined that Lu05R59 and CCRI 69 were the low nitrogen tolerant and nitrogen efficient germplasms, and Coker 201 and Xinluzhong 30 were low nitrogen sensitive and nitrogen inefficient germplasms. The results of these studies provide the possibility for screening and rapid identification of nitrogen use efficiency in cotton at the seedling stage, and provide the ideal materials and theoretical basis for further study of cotton nitrogen efficient. 相似文献
The main objective of this research was to evaluate the navigation performance of multi-information integration based on a low-end inertial measurement unit (IMU) in precision agriculture by utilizing different auxiliary information (i.e., GNSS real-time kinematic (RTK), non-holonomic constraints (NHC) and dual antenna GNSS). A series of experiments with different operation scenes (e.g., open sky in wet and dry soils) were carried out for quantitative analysis. For the position drift error during a 20-s GNSS outage, the dual-antenna GNSS-assisted approach did not provide a reduction, and the NHC reduced the maximum error in the lateral and vertical directions by over 80% in the dry soil test, but only by approximately 30% in the wet soil test. The heading error with continuous GNSS assistance can be less than 0.03° and be reduced by more than 90% with the aid of dual-antenna GNSS. Additionally, the NHC reduced the heading error from 0.54° to 0.21° and from 0.34° to 0.25° in the dry and wet soil tests respectively. The results suggested that the multi-information integration improved the positioning and orientation reliability. Moreover, the lateral positioning accuracy required for the control of agriculture autonomous vehicles was achieved at approximately 3.0 mm with over a 60% accuracy improvement brought by the dual-antenna GNSS assistance. In contrast to the vulnerability of a single system, multi-information integration can provide comprehensive navigation information with higher reliability and lower costs. Hence, multi-information fusion will be a great opportunity for agriculture to meet the high-accuracy and high-reliability requirements of precision agriculture.