[Comparison of artificial neural network with compatible biomass model for predicting aboveground biomass of individual tree]. / 人工神经网络与相容性生物量模型预测单木地上生物量的比较.

Forest biomass is an important index in forest development planning and forest resource monitoring. In order to provide a more efficient and low-biased method for estimating individual tree biomass, we introduced artificial neural network here. We used the data of aboveground biomass of 101 Larix olgensis trees harvested from the Dongzhelenghe Forest Farm in Heilongjiang Province to develop four aggregation model systems (AMS), based on different combination of the variables (diameter at breast height, tree height, crown width). The weighted functions were used to eliminate heteroscedasticity. Then, we trained artificial neural network (ANN) biomass model based on the optimal combination. The models were tested by the leave-one-out cross-validation method to compare the accuracy of the two biomass estimation methods. The results showed that biomass model based on only one variable, diameter at breast height, could accurately estimate the biomass of L. olgensis. Adding two indices, tree height and crown width, could improve the fitting performance of models, with AMS4 performing the best among the four addictive model systems. The biomass models developed by the two methods both could estimate biomass at tree level accurately, with the coefficient of determination (R2) of each component was higher than 0.87. Compared with the AMS4, R2 of leaf biomass model was about 0.05 higher, and that of other organs were also about 0.01 higher in artificial neural network model system. In addition, the root mean square error (RMSE) and other indicators were also significantly smaller. For example, the RMSE of tree stem and aboveground biomass were smaller by 2.135 kg and 3.908 kg, respectively. The model's validation statistics mean relative error (MRE) performed better. In general, ANN was a flexible and reliable biomass estimation method, which was worthy consideration when predicting tree component biomass or aboveground biomass.

iTRAQ Proteomic Analysis of Continuously Cropped Soybean Root Inoculated With Funneliformis mosseae.

Soybean (Glycine max) is susceptible to root rot when subjected to continuous cropping, and this disease can seriously diminish the crop yield. Proteomics analyses can show the difference of protein expression in different treatment samples. Herein, isobaric tag for relative and absolute quantitation (iTRAQ) labeling and liquid chromatography-tandem mass spectrometry (LC-MS/MS) were employed for proteomic analysis of continuously cropped soybean inoculated with the arbuscular mycorrhizal fungus (AMF) Funneliformis mosseae. The AMF can reduce the incidence of root rot and increase plant height, biomass index in 1, 2, and 4 year of continuous cropping. Differential expression of proteins in soybean roots was determined following 1 year of continuous cropping. A total of 131 differentially expressed proteins (DEPs) were identified in F. mosseae-treated samples, of which 49 and 82 were up- and down-regulated, respectively. The DEPs were annotated with 117 gene ontology (GO) terms, with 48 involved in biological processes, 31 linked to molecular functions, and 39 associated with cell components. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis mapped the DEPs to 113 mainly metabolic pathways including oxidative phosphorylation, glycolysis, and amino acid metabolism. Expression of glucan 1,3-beta-glucosidase, chalcone isomerase, calcium-dependent phospholipid binding and other defense-related proteins was up-regulated by F. mosseae, suggesting inoculation promotes the growth and development of soybean and increases disease resistance. The findings provide an experimental basis for further research on the molecular mechanisms of AMF in resolving problems associated with continuous soybean cropping.

Effects of Funneliformis mosseae on Root Metabolites and Rhizosphere Soil Properties to Continuously-Cropped Soybean in the Potted-Experiments.

Continuous cropping in soybean is increasingly practiced in Heilongjiang Province, leading to substantial yield reductions and quality degradation. Arbuscular mycorrhizal fungi (AMF) are soil microorganisms that form mutualistic interactions with plant roots and can restore the plant rhizosphere microenvironment. In this study, two soybean lines (HN48 and HN66) were chosen as experimental materials, which were planted in different years of continuous cropping soybean soils and were inoculated or not with Funneliformis mosseae in potted-experiments. Ultimately, analysis of root tissue metabolome and root exudates, soil physicochemical properties, plant biomass, as well as rhizosphere soil properties in different experimental treatments, inoculated or not with F. mosseae, was performed. Experimental results showed that: (a) The disease index of soybean root rot was significantly lower in the treatment group than in the control group, and there were differences in disease index and the resistance effect of F. mosseae between the two cultivars; (b) compared with the control, the root tissue metabolome and root exudates remained unchanged, but there were changes in the relative amounts in the treatment group, and the abundant metabolites differed by soybean cultivar; (c) soybean biomass was significantly higher in the treatment group than in the control group, and the effect of F. mosseae on biomass differed with respect to the soybean cultivar; and (d) there were differences in the physiochemical indexes of soybean rhizosphere soil between the treatment and control groups, and the repairing effect of F. mosseae differed between the two cultivars. Therefore, F. mosseae can increase the biomass of continuously cropped soybean, improve the physicochemical properties of the rhizosphere soil, regulate the root metabolite profiles, and alleviate barriers to continuous cropping in potted-experiments of soybean.