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Xiaomin Zhao
Key Laboratory of Crop Physiology, Ecology, Genetics and Breeding, Ministry of Education, Jiangxi Agricultural University, Nanchang, China

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Journal article
Published: 06 April 2021 in Journal of Soils and Sediments
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In order to determine the interactive effect of Phyllostachys pubescens (moso bamboo) expansion to adjacent coniferous forest (Cryptomeria japonica, Japanese cedar) on soil fauna communities and soil food web, we conducted a tangible investigation combined with natural abundance isotope analysis to examine how moso bamboo expansion process affects soil faunal composition and underground soil food web in Lushan mountain, southeast China. Exact treatments are as follows: (1) moso bamboo forest, (2) ecotone area and (3) Japanese cedar forest. We collected 74 arthropod groups from the field. The groups of Acari and Collembola were the two main soil fauna taxa with the highest abundance which accounted for 18.86–98.9% of the relative total abundance among various habitats. Peak of soil faunal density in moso bamboo and Japanese cedar forests appeared in May and November. Soil fauna community in ecotone was more similar to that in moso bamboo forest, indicating that the expansion process was still in infancy stage, and there was no significant difference in soil fauna community diversity index among the three forest types. Moso bamboo expansion did not affect the nutrient level of Collembola and Oribatida, but decreased that of Megsostigmata. The nutrient level of Hymenoptera and Coleoptera increased in ecotone, and Diptera kept in the third nutrient level in all three forest types, while the Hemiptera, Araneida and Pseudo-scorpionidea remained at a high level. The results demonstrated that in moso bamboo expansion process, soil fauna groups with low nutrient levels were more affected, while the soil faunas with high nutrient level were less affected.

ACS Style

Wei Liu; Liqin Liao; Yuanqiu Liu; Qiong Wang; Philip J. Murray; Xueru Jiang; Guiwu Zou; Junhuo Cai; Xiaomin Zhao. Effects of Phyllostachys pubescens expansion on underground soil fauna community and soil food web in a Cryptomeria japonica plantation, Lushan Mountain, subtropical China. Journal of Soils and Sediments 2021, 21, 2212 -2227.

AMA Style

Wei Liu, Liqin Liao, Yuanqiu Liu, Qiong Wang, Philip J. Murray, Xueru Jiang, Guiwu Zou, Junhuo Cai, Xiaomin Zhao. Effects of Phyllostachys pubescens expansion on underground soil fauna community and soil food web in a Cryptomeria japonica plantation, Lushan Mountain, subtropical China. Journal of Soils and Sediments. 2021; 21 (6):2212-2227.

Chicago/Turabian Style

Wei Liu; Liqin Liao; Yuanqiu Liu; Qiong Wang; Philip J. Murray; Xueru Jiang; Guiwu Zou; Junhuo Cai; Xiaomin Zhao. 2021. "Effects of Phyllostachys pubescens expansion on underground soil fauna community and soil food web in a Cryptomeria japonica plantation, Lushan Mountain, subtropical China." Journal of Soils and Sediments 21, no. 6: 2212-2227.

Journal article
Published: 01 March 2021 in Journal of Resources and Ecology
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Maintaining an adequate security level of cultivated land is essential for the healthy and sustainable survival of China's large and growing population. We constructed a cultivated land security evaluation index system, combined with an improved TOPSIS method by taking into account the balance and stability of quantitative, qualitative, and ecological security. We applied this improved method to an evaluation of the state of cultivated land security and analyzed its spatiotemporal variation in Yingtan City (Jiangxi Province, China) from 1995 to 2015. The drivers of the changes in cultivated land security were investigated via a spatial regression model, which can eliminate the effect of spatial autocorrelation. The results showed that cultivated land security decreased rapidly from 1995 to 2005, although it tended to rise slowly in the subsequent period from 2005 to 2015. Areas deemed to be in a highly dangerous state were mainly distributed in the Yuehu District, while those that were secure appeared primarily in the southern mountainous area, with the area in a generally dangerous state extending to the west in the same direction as urban development. Among the examined drivers, social-economic factors and policy factors significantly influenced the cultivated land security. Our work suggests that government managers should take appropriate measures to improve cultivated land security according to its spatiotemporal variations and the underpinning drivers in this region.

ACS Style

Kuang Lihua; Ye Yingcong; Guo Xi; Xie Wen; Zhao Xiaomin. Spatiotemporal Variation of Cultivated Land Security and Its Drivers: The Case of Yingtan City, China. Journal of Resources and Ecology 2021, 12, 280 -291.

AMA Style

Kuang Lihua, Ye Yingcong, Guo Xi, Xie Wen, Zhao Xiaomin. Spatiotemporal Variation of Cultivated Land Security and Its Drivers: The Case of Yingtan City, China. Journal of Resources and Ecology. 2021; 12 (2):280-291.

Chicago/Turabian Style

Kuang Lihua; Ye Yingcong; Guo Xi; Xie Wen; Zhao Xiaomin. 2021. "Spatiotemporal Variation of Cultivated Land Security and Its Drivers: The Case of Yingtan City, China." Journal of Resources and Ecology 12, no. 2: 280-291.

Journal article
Published: 17 January 2021 in Sustainability
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Sloping farmland is prevalent in hilly red soil areas of South China. Improper tillage patterns induce decreased soil organic matter, soil aggregate breakdown, and nutrient imbalance, thereby restricting crop production. However, the stoichiometric characteristics could reflect the nutrient availability which was mostly studied on bulk soil. The stoichiometric characteristics of soil aggregates with multiple functions in farmlands has rarely been studied. The study was to reveal the impact of tillage patterns on the size distribution, nutrient levels, and stoichiometric ratios of soil aggregates after 20 years’ cultivation. Soil samples of 0–20 cm and 20–40 cm from five tillage patterns, bare-land control (BL), longitudinal-ridge tillage (LR), conventional tillage + straw mulching (CS), cross-ridge tillage (CR), and longitudinal-ridge tillage + hedgerows (LH) were collected. The elemental content (C, N and P) and soil aggregate size distribution were determined, and the stoichiometric ratios were subsequently calculated. Through our analysis and study, it was found that the nutrient content of >2 mm soil aggregates in all plots was the highest. In the hedgerow plots, >2 mm water-stable soil aggregate content was increased. Therefore, LH plots have the highest content of organic matter and nutrients. After 20 years of cultivation, stoichiometric ratio of each plot showed different changes on soil aggregates at different levels. the C:N, C:P, and N:P ratios are lower than the national average of cultivated land. Among of them, the stoichiometric ratio in the LH plot is closer to the mean and showed better water-stable aggregate enhancement. Therefore, longitudinal-ridge tillage + hedgerows can be recommended as a cultivation measure. This study provides a reference for determining appropriate tillage measures, balancing nutrient ratios, and implementing rational fertilization.

ACS Style

Jie Zhang; Yaojun Liu; Taihui Zheng; Xiaomin Zhao; Hongguang Liu; Yongfen Zhang. Nutrient and Stoichiometric Characteristics of Aggregates in a Sloping Farmland Area under Different Tillage Practices. Sustainability 2021, 13, 890 .

AMA Style

Jie Zhang, Yaojun Liu, Taihui Zheng, Xiaomin Zhao, Hongguang Liu, Yongfen Zhang. Nutrient and Stoichiometric Characteristics of Aggregates in a Sloping Farmland Area under Different Tillage Practices. Sustainability. 2021; 13 (2):890.

Chicago/Turabian Style

Jie Zhang; Yaojun Liu; Taihui Zheng; Xiaomin Zhao; Hongguang Liu; Yongfen Zhang. 2021. "Nutrient and Stoichiometric Characteristics of Aggregates in a Sloping Farmland Area under Different Tillage Practices." Sustainability 13, no. 2: 890.

Research article
Published: 28 August 2020 in Computational Intelligence and Neuroscience
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Symptoms of nutrient deficiencies in rice plants often appear on the leaves. The leaf color and shape, therefore, can be used to diagnose nutrient deficiencies in rice. Image classification is an efficient and fast approach for this diagnosis task. Deep convolutional neural networks (DCNNs) have been proven to be effective in image classification, but their use to identify nutrient deficiencies in rice has received little attention. In the present study, we explore the accuracy of different DCNNs for diagnosis of nutrient deficiencies in rice. A total of 1818 photographs of plant leaves were obtained via hydroponic experiments to cover full nutrition and 10 classes of nutrient deficiencies. The photographs were divided into training, validation, and test sets in a 3 : 1 : 1 ratio. Fine-tuning was performed to evaluate four state-of-the-art DCNNs: Inception-v3, ResNet with 50 layers, NasNet-Large, and DenseNet with 121 layers. All the DCNNs obtained validation and test accuracies of over 90%, with DenseNet121 performing best (validation accuracy = 98.62 ± 0.57%; test accuracy = 97.44 ± 0.57%). The performance of the DCNNs was validated by comparison to color feature with support vector machine and histogram of oriented gradient with support vector machine. This study demonstrates that DCNNs provide an effective approach to diagnose nutrient deficiencies in rice.

ACS Style

Zhe Xu; Xi Guo; Anfan Zhu; Xiaolin He; Xiaomin Zhao; Yi Han; Roshan Subedi. Using Deep Convolutional Neural Networks for Image-Based Diagnosis of Nutrient Deficiencies in Rice. Computational Intelligence and Neuroscience 2020, 2020, 1 -12.

AMA Style

Zhe Xu, Xi Guo, Anfan Zhu, Xiaolin He, Xiaomin Zhao, Yi Han, Roshan Subedi. Using Deep Convolutional Neural Networks for Image-Based Diagnosis of Nutrient Deficiencies in Rice. Computational Intelligence and Neuroscience. 2020; 2020 ():1-12.

Chicago/Turabian Style

Zhe Xu; Xi Guo; Anfan Zhu; Xiaolin He; Xiaomin Zhao; Yi Han; Roshan Subedi. 2020. "Using Deep Convolutional Neural Networks for Image-Based Diagnosis of Nutrient Deficiencies in Rice." Computational Intelligence and Neuroscience 2020, no. : 1-12.

Journal article
Published: 31 May 2020 in Journal of Soils and Sediments
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The massive mining of ion-adsorption-type rare earth ore has led to a series of problems such as soil acidification, land degradation, and water pollution, and thus, an effective method of treating rare earth tailings is urgently needed. The goal of this study was to explore the rehabilitation effect of the combined application of bamboo biochar (BB) and coal ash (CA) on ion-adsorption-type rare earth tailings. We collected samples from an abandoned ion-adsorption-type rare earth tailing deposit in Jiangxi, China. Four treatment experiments involving amendments of 0%, 1%, 2.5%, and 5% CA and 1% BB and a control were performed. The soil mechanical composition (MC), bulk density (BD), pH, organic carbon (OC), cation exchange capacity (CEC), available nitrogen (AN), available phosphorus (AP), available potassium (AK), cabbage growth, fresh weight, and rare earth element (REE) concentrations of the cabbage were measured. The cabbage did not grow when the soil was only treated with bamboo biochar, but it grew when both bio-charcoal and coal ash were applied. The REE concentrations of the cabbage decreased with increasing coal ash dosage. The pH, OC, and CEC of the amended tailings were significantly increased, while the BD and AP of the amended tailings were significantly decreased compared with those of the control. Moreover, the AN and AK of all of the treatment experiments, except for the application of only BB, were significantly decreased. The application of a combination of biochar and coal ash effectively improves rare earth tailings and promotes the growth of Chinese cabbage. These results are likely related to the improvement of the basic physical and chemical properties of the tailings.

ACS Style

Qin Zhang; Guangyue Wan; Caiyun Zhou; Jie Luo; Jianping Lin; Xiaomin Zhao. Rehabilitation effect of the combined application of bamboo biochar and coal ash on ion-adsorption-type rare earth tailings. Journal of Soils and Sediments 2020, 20, 3351 -3357.

AMA Style

Qin Zhang, Guangyue Wan, Caiyun Zhou, Jie Luo, Jianping Lin, Xiaomin Zhao. Rehabilitation effect of the combined application of bamboo biochar and coal ash on ion-adsorption-type rare earth tailings. Journal of Soils and Sediments. 2020; 20 (9):3351-3357.

Chicago/Turabian Style

Qin Zhang; Guangyue Wan; Caiyun Zhou; Jie Luo; Jianping Lin; Xiaomin Zhao. 2020. "Rehabilitation effect of the combined application of bamboo biochar and coal ash on ion-adsorption-type rare earth tailings." Journal of Soils and Sediments 20, no. 9: 3351-3357.

Research article
Published: 29 November 2019 in Computational Intelligence and Neuroscience
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Deep learning is characterized by its strong ability of data feature extraction. This method can provide unique advantages when applying it to visible and near-infrared spectroscopy for predicting soil organic matter (SOM) content in those cases where the SOM content is negatively correlated with the spectral reflectance of soil. This study relied on the SOM content data of 248 red soil samples and their spectral reflectance data of 400–2450 nm in Fengxin County, Jiangxi Province (China) to meet three objectives. First, a multilayer perceptron and two convolutional neural networks (LeNet5 and DenseNet10) were used to predict the SOM content based on spectral variation and variable selection, and the outcomes were compared with that from the traditional back-propagation neural network (BPN). Second, the four methods were applied to full-spectrum modeling to test the difference to selected feature variables. Finally, the potential of direct modeling was evaluated using spectral reflectance data without any spectral variation. The results of prediction accuracy showed that deep learning performed better at predicting the SOM content than did the traditional BPN. Based on full-spectrum data, deep learning was able to obtain more feature information, thus achieving better and more stable results (i.e., similar average accuracy and far lower standard deviation) than those obtained through variable selection. DenseNet achieved the best prediction result, with a coefficient of determination (R2) = 0.892 ± 0.004 and a ratio of performance to deviation (RPD) = 3.053 ± 0.056 in validation. Based on DenseNet, the application of spectral reflectance data (without spectral variation) produced robust results for application-level purposes (validation R2 = 0.853 ± 0.007 and validation RPD = 2.639 ± 0.056). In conclusion, deep learning provides an effective approach to predict the SOM content by visible and near-infrared spectroscopy and DenseNet is a promising method for reducing the amount of data preprocessing.

ACS Style

Zhe Xu; Xiaomin Zhao; Xi Guo; Jiaxin Guo. Deep Learning Application for Predicting Soil Organic Matter Content by VIS-NIR Spectroscopy. Computational Intelligence and Neuroscience 2019, 2019, 1 -11.

AMA Style

Zhe Xu, Xiaomin Zhao, Xi Guo, Jiaxin Guo. Deep Learning Application for Predicting Soil Organic Matter Content by VIS-NIR Spectroscopy. Computational Intelligence and Neuroscience. 2019; 2019 ():1-11.

Chicago/Turabian Style

Zhe Xu; Xiaomin Zhao; Xi Guo; Jiaxin Guo. 2019. "Deep Learning Application for Predicting Soil Organic Matter Content by VIS-NIR Spectroscopy." Computational Intelligence and Neuroscience 2019, no. : 1-11.

Journal article
Published: 20 September 2018 in JOURNAL OF NATURAL RESOURCES
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ACS Style

丽花 匡; Kuang Li-Hua; 英聪 叶; 小敏 赵; 熙 郭; Ye Ying-Cong; Zhao Xiao-Min; Guo Xi. 基于改进TOPSIS方法的耕地系统安全评价及障碍因子诊断. JOURNAL OF NATURAL RESOURCES 2018, 33, 1627 -1641.

AMA Style

丽花 匡, Kuang Li-Hua, 英聪 叶, 小敏 赵, 熙 郭, Ye Ying-Cong, Zhao Xiao-Min, Guo Xi. 基于改进TOPSIS方法的耕地系统安全评价及障碍因子诊断. JOURNAL OF NATURAL RESOURCES. 2018; 33 (9):1627-1641.

Chicago/Turabian Style

丽花 匡; Kuang Li-Hua; 英聪 叶; 小敏 赵; 熙 郭; Ye Ying-Cong; Zhao Xiao-Min; Guo Xi. 2018. "基于改进TOPSIS方法的耕地系统安全评价及障碍因子诊断." JOURNAL OF NATURAL RESOURCES 33, no. 9: 1627-1641.