Effects of Different Chinese Hickory Husk Returning Modes on Soil Nutrition and Microbial Community in Acid Forest Soil

Chinese hickory(Carya cathayensis Sarg.)is an important economic forest in Southeastern China.A large amount of hickory husk waste is generated every year but with a low proportion of returning.Meanwhile,intensive management has resulted in soil degradation of Chinese hickory plantations.This study aims to investigate the effects of three Chinese hickory husk returning modes on soil amendment,including soil acidity,soil nutrition,and microbial community.The field experiment carried out four treatments:control(CK),hickory husk mulching(HM),hickory husk biochar(BC),and hickory husk organic fertilizer(OF).The phospholipid fatty acid(PLFA)biomarker method was employed to determine the soil microbial community.After one year of treatment,the results showed that:(i)HM and BC significantly increased soil pH by 0.33 and 1.71 units,respectively;(ii)HM,BC and OF treatments significantly increased the soil organic carbon,alkaline nitrogen,available phosphorous,and available potassium.The OF treatment demonstrated the most significant improvement in the soil nutrient;(iii)The soil microbial biomass significantly increased in the HM,BC and OF treatments,and all microbial groups showed an increasing trend.HM treatment increased the fungal/bacterial ratio(F/B).The OF treatment significantly decreased the Shannon-Wiener diversity(H’)and evenness index(J)of the microbial community(P<0.05).Considering the treatments effects,costs,and ease of operation,our recommended returning modes of Chinese hickory husk are mulching and organic fertilizer produced by composting with manure.

Dynamic characteristics of dwarf Chinese hickory trees under impact excitations for mechanical fruit harvesting

Mechanical vibration is an effective fruit harvesting method.To evaluate the dynamic characteristics of dwarf Chinese hickory(Carya cathayensis Sarg.)trees and the influence of the tree structure on transmission and attenuation of dynamic response,a new method was proposed based on acceleration admittance measurement on dwarf Chinese hickory trees in orchard environment under impact excitation.The primary resonance frequencies of the tree can be determined based on the acceleration admittance measurement.The effect of the tree structure on the vibratory transmission was quantified using the attenuation ratio of the acceleration admittance.A 5-year-old dwarf Chinese hickory tree sample was tested.The responses at three resonance frequencies(5,9 and 12 Hz)were analyzed because they were identified as the most effective bands of excitation for the main part of the tree specimen.The results reveal that the variation of the dynamic response along the testing tree is greatly related to the Chinese hickory tree structure.The attenuation ratio of the acceleration admittance at the branch crotches indicates the leader top crotch may amplify the acceleration admittance no matter what the crotch angle and the branch diameter is.Unlike the crotches,the branch chain nodes generally have negative influence on the acceleration admittance along the branch chains which heavily depend on the branch chain configuration.The branch chains with a chain angle no less than 150°and a wood diameter ratio close to 1.0 could produce little influence on the vibration transmission.For those branches with chain angle less than 150°,the vibration was generally attenuated at their chain nodes at three resonance frequencies.To impose impact excitations on the tree,high mechanical harvesting efficiency could be achieved on those branch chains which are almost straight and uniform.

Detection of endogenous foreign bodies in Chinese hickory nuts by hyperspectral spectral imaging at the pixel level

It is difficult to differentiate small,but harmful,shell fragments of Chinese hickory nuts from their kernels since they are very similar in color.Including shell fragments of Chinese hickory nuts by mistake may create safety hazards for consumers.Therefore,there is a need to develop an effective method to differentiate the shells from the kernels of Chinese hickory nuts.In this study,a deep learning approach based on a two-dimensional convolutional neural network(2D CNN)and long short-term memory(LSTM)integrated with hyperspectral imaging for distinguishing the shells and kernels of Chinese hickory nuts at the pixel level was proposed.Two classical classification methods,principal component analysis-K-nearest neighbors(PCA-KNN)and the support vector machine(SVM),were employed to establish identification models for comparison.The results showed that the 2D CNN-LSTM model achieved the best performance with an overall classification accuracy of 99.0%.Moreover,the shells in mixtures of shells and kernels were detected based on the proposed deep learning method and visualized for subsequent operations for the removal of foreign bodies.