No abstract

Increasing levels of atmospheric nitrogen deposition have greatly affected forest trees. Acer truncatum Bunge has a large distribution in northern China, Korea and Japan and plays an important ecological role in forest ecosystems. We investigated the responses of A. truncatum to a broad range of nitrogen addition regimes with a focus on seedling growth, biomass partitioning, leaf morphology, gas exchange physiology and chlorophyll fluorescence physiology. Moderate nitrogen addition promoted shoot height, stem diameter at ground height, total biomass, size of leaves and chlorophyll fluorescence and gas exchange performance, whereas extreme level of nitrogen addition did not result in such facilitation. Chlorophyll content, pattern of biomass partitioning, ratio of leaf length to width, leaf water content, and specific leaf area did not change among the addition regimes. The critical amount of nitrogen deposition should be defined in the context of a certain time period in a particular region for a certain species at a special developmental stage. The critical amount of N deposition that weakens total biomass facilitation in A. truncatum planted in mixed soil of yellow cinnamon soil and humic soil is approximately 10 g N m −2 y −1 during the first growing season.

Currently, the plant physiological information detection technology and diagnostic systems has gradually become a hot topic in the research of facilities crop disease warning. By collecting the relevant data of changes in growth conditions of cucumber by Junior-PAM of CI-340 portable photosynthesis system, the experiment studied the correlation between leaf chlorophyll content and Photosynthetic rate, nutritional status and we like to search for the relationship between the changes in disease infection and chlorophyll content in the growth process of leaf. The experiment used different modeling methods to establish the relational model between fluorescence data such as FO ', FM', FV / FM, FO, FM and SPAD, and then make predictions. The results show that the prediction of harmful levels of disease employing neural network model is the best with its MAE being 0.0025 and the accuracy up to 100%.