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Detection of the moisture content (MC) and freshness for loquats is crucial for achieving optimal taste and economic efficiency. Traditional methods for evaluating the MC and freshness of loquats have disadvantages such as destructive sampling and time‐consuming. To investigate the feasibility of rapid and non‐destructive detection of the MC and freshness for loquats, optical fiber spectroscopy in the range of 200–1000 nm was used in this study. The full spectra were pre‐processed using standard normal variate method, and then, the effective wavelengths were selected using competitive adaptive weighting sampling (CARS) and random frog algorithms. Based on the selected effective wavelengths, prediction models for MC were developed using partial least squares regression (PLSR), multiple linear regression, extreme learning machine, and back‐propagation neural network. Furthermore, freshness level discrimination models were established using simplified k nearest neighbor, support vector machine (SVM), and partial least squares discriminant analysis. Regarding the prediction models, the CARS‐PLSR model performed relatively better than the other models for predicting the MC, with R2P and RPD values of 0.84 and 2.51, respectively. Additionally, the CARS‐SVM model obtained superior discrimination performance, with 100% accuracy for both calibration and prediction sets. The results demonstrated that optical fiber spectroscopy technology is an effective tool to fast detect the MC and freshness for loquats. [ABSTRACT FROM AUTHOR]

A typical pepper-forest boundary was selected in the arid valley of Minjiang River upper reaches, and the moisture content in 0-15 cm soil layer was determined by time-domain reflectometry (TDR) during drought, after rain, and different seasons. Moving split-window techniques (MSWT) was employed to detect the depth of edge influence (DEI) on soil moisture and its dynamic variation with time. The results showed that the changes of squared Euclidean distance (SED) curve on the graph tended to become stable when the window width reached 8 - 12, and DEI could be detected. The seasonal variation of soil moisture could be divided into three periods, i.e., rising period (January to April), peak period (May to October), and lessen period (November to December). DEI was smaller during drought, and increased after rain. The DEI on soil moisture ranged from 6 m in pepper field to 2 m in forest during drought and from 12 m in pepper field to 2 m in forest after rain, but ranged from 10 m in pepper field to 2 m in forest field within a year. DEI was different in different seasons, and was dynamic. Under such condition of soil moisture, the forest restoration from pepper land and the seedling planting were not ecologically reasonable. Reducing human disturbance and revegetating with natural shrubs and meadows could be more effective for vegetation conservation in the arid valley of Minjiang River upper reaches.

By using moving split-window techniques (MSWT), this study estimated how far the edge effects penetrated the forest and agricultural fields in the arid valley of upper reaches of Minjiang River, southwestern China. Its aim was to provide general information on vegetation along edge to interior gradients in order to assist in interpretation and prediction of biological phenomena associated with agriculture-forestry boundary, and to improve current management practices in such areas. Three types of boundaries (10 transects) were investigated and sampled. The results showed that when the window width reached 6-10, the change of the SED curve on the graph tended to become stable, and one or two peaks occurred. The depth of edge influence was clearly different for different types of boundaries, and could be estimated within 50 m from the edge to interior. The depth of edge influence (DEI) on vegetation diversity almost varied between 12-30 m, mainly depending on the patch type, topography and microclimate, but seldom on slope orientation. Of the 6 forest transects in the three types of boundaries, the DEI was detected only in the forest part transects M2 and M6, but almost detectable in the agricultural part of all transects. MSWT was considered to be a useful tool for characterizing edge dynamics if enough data was available, and became a simple and powerful technique for analyzing the boundary. The results will provide further knowledge for understanding the interaction between forestry and agriculture in the arid valley.