数字农科院2.0

Spatial distribution prediction and benefits assessment of green manure in the Pinggu District, Beijing, based on the CLUE-S model

文献类型： 外文期刊

作者： Zhang Li-ping;Zhou Zhi-ming;Hou Sen;Huang Yuan-fang;Zhang Shi-wen;Cao Wei-dong

作者机构：

关键词： CLUE-S model;green manure;spatial distribution;prediction;benefits assessment

期刊名称： JOURNAL OF INTEGRATIVE AGRICULTURE

ISSN：

年卷期：

页码：

收录情况： SCI

摘要： Green manure use in China has declined rapidly since the 1980s with the extensive use of chemical fertilizers. The deterioration of field environments and the demand for green agricultural products have resulted in more attention to green manure. Human intervention and policy-oriented behaviors likely have large impacts on promoting green manure planting. However, little information is available regarding on where: at what rates, and in which ways (i.e., intercropping green manure in orchards or rotating green manure in cropland) to develop green manure and what benefits could be gained by incorporating green manure in fields at the county scale. This paper presents the conversion of land use and its effects at small region extent (CLUE-S) model, which is specifically developed for the simulation of land use changes originally, to predict spatial distribution of green manure in cropland and orchards in 2020 in Pinggu District located in Beijing, China. Four types of land use for planting or not planting green manure were classified and the future land use dynamics (mainly croplands and orchards) were considered in the prediction. Two scenarios were used to predict the spatial distribution of green manure based on data from 2011: The promotion of green manure planting in orchards (scenario 1) and the promotion of simultaneous green manure planting in orchards and croplands (scenario 2). The predictions were generally accurate based on the receiver operating characteristic (ROC) and Kappa indices, which validated the effectiveness of the CLUE-S model in the prediction. In addition, the spatial distribution of the green manure was acquired, which indicated that green manure mainly located in the orchards of the middle and southern regions of Dahuashan, the western and southern regions of Wangxinzhuang, the middle region of Shandongzhuang, the eastern region of Pinggu and the middle region of Xiagezhuang under scenario 1. Green manure planting under scenario 2 occurred in orchards in the middle region of Wangxinzhuang, and croplands in most regions of Daxingzhuang, southern Pinggu, northern Xiagezhuang and most of Mafang. The spatially explicit results allowed for the assessment of the benefits of these changes based on different economic and ecological indicators. The economic and ecological gains of scenarios 1 and 2 were 175 691 900 and 143 000 300 CNY, respectively, which indicated that the first scenario was more beneficial for promoting the same area of green manure. These results can facilitate policies of promoting green manure and guide the extensive use of green manure in local agricultural production in suitable ways.

分类号： S

  • 相关文献

[1]Prediction of nitrogen and phosphorus contents in citrus leaves based on hyperspectral imaging. Liu Yanli,Liu Yanli,Lyu Qiang,He Shaolan,Yi Shilai,Liu Xuefeng,Xie Rangjin,Zheng Yongqiang,Deng Lie. 2015

[2]Analysis of promoters of microRNAs from a Glycine max degradome library. Han, Yi-qiang,Gao, Ya-mei,Hu, Zheng,Zheng, Dian-feng. 2014

[3]Quantitative structure-toxicity relationship study of lethal concentration to tadpole (Bufo vulgaris formosus) for organophosphorous pesticides. Yan, Dongyun,Xu, Shaohui,Yan, Dongyun,Jiang, Xin,Bian, Yongrong,Yu, Guifen,Wang, Ligang.

[4]Artificial Neural Network to Predict Leaf Population Chlorophyll Content from Cotton Plant Images. Li Shao-kun,Wang Ke-ru,Wang Chong-tao,Suo Xing-mei,Jiang Ying-tao,Yang Mei. 2010

[5]Prediction of heterosis using QTLs for yield traits in rapeseed (Brassica napus L.). Shen, Jin-Xiong,Fu, Ting-Dong,Yang, Guang-Sheng,Tu, Jin-Xing,Ma, Chao-Zhi. 2006

[6]EARLY DETECTION OF GREY MOULD DEVELOPMENT IN 'RED GLOBE' GRAPES DURING STORAGE. Guan, Wenqiang,Wu, Xinling,Guan, Wenqiang,Wang, Zhidong,Wang, Xiaotuo,Yan, Ruixiang. 2015

[7]Variability in climatology and agricultural production in China in association with the East Asian summer monsoon and El Nino Southern Oscillation. Tao, FL,Yokozawa, M,Zhang, Z,Hayashi, Y,Grassl, H,Fu, CB.

[8]Equations to predict methane emissions from cows fed at maintenance energy level in pasture-based systems. Stergiadis, Sokratis,Zou, Caixia,Chen, Xianjiang,Wills, David,Yan, Tianhai,Stergiadis, Sokratis,Zou, Caixia,Chen, Xianjiang,Allen, Michelle.

[9]Major controlling factors and predictions for cadmium transfer from the soil into spinach plants. Liang, Zhenfei,Ding, Qiong,Wei, Dongpu,Li, Jumei,Chen, Shibao,Ma, Yibing,Ding, Qiong.

[10]An agent-based population model for China. Chen, Zhongxin,Tang, Huajun,Li, Wenjuan,Chen, Zhongxin,Tang, Huajun,Holm, Einar,Makila, Kalle,Li, Wenjuan,Liu, Shenghe. 2008

[11]Supercritical CO2 dyeing of ramie fiber with disperse dye. Liu, Zhao-Tie,Zhang, Lili,Liu, Zhongwen,Gao, Ziwei,Dong, Wensheng,Xiong, Heping,Peng, Yuande,Tang, Shouwei.

[12]Rapid analysis of layer manure using near-infrared reflectance spectroscopy. Xing, L.,Chen, L. J.,Han, L. J.,Xing, L.,Chen, L. J.,Han, L. J.. 2008

[13]Prediction Of Peanut Protein Solubility B.ased On The Evaluation M odel Established By Supervised Principal Component Regression. Wang, L, Liu, HZ, Liu, L, Wang, Q, Li, SR, Li, QZ. 2017

[14]An Improved Combination Model for the Multi-Scale Prediction of Slope Deformation. Xiangyu Li,Tianjie Lei,Jing Qin,Jiabao Wang,Weiwei Wang,Dongpan Chen,Guansheng Qian,Jingxuan Lu. 2022

[15]Prediction of China's Grain Consumption from the Perspective of Sustainable Development-Based on GM(1,1) Model. Zhang, Xiaoyun,Bao, Jie,Xu, Shiwei,Wang, Yu,Wang, Shengwei. 2022

[16]Transcriptomic and methylomic analyses provide insights into the molecular mechanism and prediction of heterosis in rice. Fu, Chongyun,Ma, Ce,Zhu, Manshan,Liu, Wuge,Ma, Xiaozhi,Li, Jinhua,Liao, Yilong,Liu, Dilin,Gu, Xiaofeng,Wang, Haiyang,Wang, Feng. 2023

[17]ResNetKhib: a novel cell type-specific tool for predicting lysine 2-hydroxyisobutylation sites via transfer learning. Xiaoti Jia,Pei Zhao,Fuyi Li,Zhaohui Qin,Haoran Ren,Junzhou Li,Chunbo Miao,Quanzhi Zhao,Tatsuya Akutsu,Gensheng Dou,Zhen Chen,Jiangning Song. 2023

[18]Integration of high-throughput phenotyping , GWAS, and predictive models reveals the genetic architecture of plant height in maize. Wang, Weixuan,Guo, Weijun,Le, Liang,Yu, Jia,Wu, Yue,Li, Dongwei,Wang, Yifan,Wang, Huan,Lu, Xiaoduo,Qiao, Hong,Gu, Xiaofeng,Tian, Jian,Zhang, Chunyi,Pu, Li. 2023

[19]GC/QQQ coupling with metabolomics for selection of predicator of tea fermentation. Yingqing Ye,Jiazheng Lin,Junfeng Yin,Hua Feng He. 2023

[20]Coupling Kinetic Modeling with Artificial Neural Networks to Predict the Kinetic Parameters of Pine Needle Pyrolysis. Langui Xu,Lin Zhang,Xiangjun He,Wenbin He,Ziyong Wang,Weihua Niu,Dong Wei,Yi Ran,Wendan Wu,Mingjun Cheng,Jundou Liu,Ruyi Huang. 2024

作者其他论文 更多>>