Effects of increased phosphorus fertilizer on C, N, and P stoichiometry in different organs of bluegrass (Poa L.) at different growth stages

IntroductionThe application of phosphorus (P) fertilizer can promote photosynthesis in forage grasses and accelerate their establishment.MethodsTo improve the utilization efficiency of P fertilizer for bluegrass (Poa L.) in alpine regions, the effects of P fertilizer on their growth, and carbon (C), nitrogen (N) and P distribution in their different organs of them are tested at six P fertilization levels (3, 6, 9, 12, 15, 18 g·m−2).Results(1) The nutrient content in each organ of bluegrass varies during different growth stages, with the lowest nutrient content occurring in the wilt stage. (2) The response of the nutrient content and ratio of each organ of bluegrass to different P fertilization levels varies. When the P application rate was 15 g·m−2, the contents of N and P in roots were the highest, and their C/N and C/P ratios were the lowest. When the P application rate was 12 g·m−2, the contents of N and P in the stems were the highest, and their C/N and C/P ratios were the lowest. When the P application rate was 9 g·m−2, the contents of N and P in leaves were the highest, and their C/N and C/P ratios were the lowest. When the P application rate was 6 g·m−2 the contents of N and P in the panicle were the highest, and their C/N and C/P ratios were the lowest.DiscussionThese results provide a better understanding of the effect of P fertilization in the nutrient partitioning pattern of perennial forage plant organs in alpine regions. The information from this study can support a more reasonable P fertilization for the establishment of early grassland. For example, in artificial forage grassland, the application of low-concentration P fertilizer (6~9 g·m−2) can promote the nutrient content in spikes and stems of forage grass; for ecological management, the application of high-concentration P fertilizer (15 g·m−2) can promote the nutrient content in roots and enhance the ecological benefits of forage grassland

Information Recovery Algorithm for Ground Objects in Thin Cloud Images by Fusing Guide Filter and Transfer Learning

Ground object information of remote sensing images covered with thin clouds is obscure. An information recovery algorithm for ground objects in thin cloud images is proposed by fusing guide filter and transfer learning. Firstly, multi-resolution decomposition of thin cloud target images and cloud-free guidance images is performed by using multi-directional nonsubsampled dual-tree complex wavelet transform. Then the decomposed low frequency subbands are processed by using support vector guided filter and transfer learning respectively. The decomposed high frequency subbands are enhanced by using modified Laine enhancement function. The low frequency subbands output by guided filter and those predicted by transfer learning model are fused by the method of selection and weighting based on regional energy. Finally, the enhanced high frequency subbands and the fused low frequency subbands are reconstructed by using inverse multi-directional nonsubsampled dual-tree complex wavelet transform to obtain the ground object information recovery images. Experimental results of Landsat-8 OLI multispectral images show that, support vector guided filter can effectively preserve the detail information of the target images, domain adaptive transfer learning can effectively extend the range of available multi-source and multi-temporal remote sensing images, and good effects for ground object information recover are obtained by fusing guide filter and transfer learning to remove thin cloud on the remote sensing images

Does endophyte symbiosis resist allelopathic effects of an invasive plant in degraded grassland?

201

Detection of diseased pine trees in unmanned aerial vehicle images by using deep convolutional neural networks

This study presents a method that uses high-resolution remote sensing images collected by an unmanned aerial vehicle (UAV) and combines MobileNet and Faster R-CNN for detecting diseased pine trees. MobileNet is used to remove backgrounds to reduce the interference of background information. Faster R-CNN is adopted to distinguish between diseased and healthy pine trees. The number of training samples is expanded due to the insufficient number of available UAV images. Experimental results show that the proposed method is better than traditional machine learning approaches, such as support vector machine and AdaBoost, and methods of DCNN, such as Alexnet, Inception and Faster R-CNN. Through sample expansion and background removal, the proposed method achieves effective detection of diseased pine trees in UAV images by using deep learning technology

Effect of <em>Epichloë</em> Endophyte on the Growth and Carbon Allocation of Its Host Plant <em>Stipa purpurea</em> under Hemiparasitic Root Stress

Epichloë endophytes not only affect the growth and resistance of their host plants but also confer nutrient benefits to parasitized hosts. In this study, we used Pedicularis kansuensis to parasitize Stipa purpurea, both with and without endophytic fungi, and to establish a parasitic system. In this study, endophytic fungal infection was found to increase the dry weight of the leaf, stem, and leaf sheath, as well as the plant height, root length, tiller number, aboveground biomass, and underground biomass of S. purpurea under root hemiparasitic stress. Meanwhile, the 13C allocation of the leaf sheaths and roots of S. purpurea increased as the density of P. kansuensis increased, while the 13C allocation of the leaf sheaths and roots of E+ S. purpurea was lower than that of E− S. purpurea. The 13C allocation of the stem, leaf sheath, and root of E+ S. purpurea was higher than that of its E− counterpart. Furthermore, the content of photosynthetic 13C and the 13C partition rate of the stems, leaves, roots, and entire plant of S. purpurea and P. kansuensis transferred from S. purpurea increased as the density of P. kansuensis increased. These results will generate new insights into the potential role of symbiotic microorganisms in regulating the interaction between root hemiparasites and their hosts