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| Assessment of L and Suitability Potentials for Selecting Winter Wheat Cultivation Areas in Beijing, China, Using RS and GIS |
| WANG Da-cheng, LI Cun-jun, SONG Xiao-yu, WANG Ji-hua, YANG Xiao-dong, HUANG Wen-jiang |
1. Institute of Agricultural Remote Sensing and Information Technology, Zhejiang University
2. Beijing Research Center for Information Technology in Agriculture
3. Agricultural Technology Extension Station in Beijing |
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摘要 It is very important to provide reference basis for winter wheat quality regionalization of cultivation area. The aim of this article was based on factors affecting wheat quality and setting realistic spatial models in each part of the land for assessment of land suitability potentials in Beijing, China. The study employed artificial neural network (ANN) analysis to select factors and evaluate the relative importance of selected environment factors on wheat grain quality. The spatial models were developed and demonstrated their use in selecting the most suitable areas for the winter wheat cultivation. The strategy overcomes the non-accurate traditional statistical methods. Satellite images, toposheet, and ancillary data of the study area were used to find tillable land. These categories were formed by integrating the various layers with corresponding weights in geographical information system (GIS). An integrated land suitability potential (LSP) index was computed considering the contribution of various parameters of land suitability. The study demonstrated that the tillable land could be categorized into spatially distributed agriculture potential zones based on soil nutrient and assembled weather factors using RS and GIS as not suitable, marginally suitable, moderately suitable, suitable, and highly suitable by adopting the logical criteria. The sort of land distribution map made by the factors with their weights showed more truthfulness.
Abstract It is very important to provide reference basis for winter wheat quality regionalization of cultivation area. The aim of this article was based on factors affecting wheat quality and setting realistic spatial models in each part of the land for assessment of land suitability potentials in Beijing, China. The study employed artificial neural network (ANN) analysis to select factors and evaluate the relative importance of selected environment factors on wheat grain quality. The spatial models were developed and demonstrated their use in selecting the most suitable areas for the winter wheat cultivation. The strategy overcomes the non-accurate traditional statistical methods. Satellite images, toposheet, and ancillary data of the study area were used to find tillable land. These categories were formed by integrating the various layers with corresponding weights in geographical information system (GIS). An integrated land suitability potential (LSP) index was computed considering the contribution of various parameters of land suitability. The study demonstrated that the tillable land could be categorized into spatially distributed agriculture potential zones based on soil nutrient and assembled weather factors using RS and GIS as not suitable, marginally suitable, moderately suitable, suitable, and highly suitable by adopting the logical criteria. The sort of land distribution map made by the factors with their weights showed more truthfulness.
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Received: 14 October 2010
Accepted:
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| Fund: This research was supported by the National Natural Science Foundation of China (40701120), the Beijing Nova Program, China (2008B33), and the Beijing Natural Science Foundation, China (4092016). |
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Corresponding Authors:
Correspondence WANG Ji-hua, Professor, Tel: +86-10-51503488, Fax: +86-10-51503750, E-mail: wangjh@nercita.org.cn
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| About author: WANG Da-cheng, Ph D, Tel: +86-10-62754134, Fax: +86-10-51503750, E-mail: wdc198206@163.com |
Cite this article:
WANG Da-cheng, LI Cun-jun, SONG Xiao-yu, WANG Ji-hua, YANG Xiao-dong, HUANG Wen-jiang.
2011.
Assessment of L and Suitability Potentials for Selecting Winter Wheat Cultivation Areas in Beijing, China, Using RS and GIS. Journal of Integrative Agriculture, 10(9): 1419-1430.
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| [1]Anderson D, McNeill G. 1992. Artificial Neural Networks Technology. Kaman Sciences Corporation, New York, USA. Bandyopadhyay S, Jaiswal R K, Hegde V S, Jayaraman V. 2009. Assessment of land suitability potentials for agriculture using a remote sensing and GIS based approach. International Journal of Remote Sensing, 30, 879-895. [2]Cao G C, Wu D B, Chen H Q, Qiang X L, Dong M, Kou H, Wang J L, Hou L B, Li M. 2004. Relationship between temperature, sunshine and quality of spring-sowing wheat. Scientia Agricultura Sinica, 37, 663-669. (in Chinese) [3]Environmental System Research Institute (ESRI). 1989. ARC/ INFO Training Course Class Material. vol. 1. Redlands, CA, USA. Food and Agricultural Organization of the United Nations (FAO). 1990. Guidelines for Soil Profile Description. 3rd ed. Rome, Italy. Guo X D, Wang J, Xie J Q, He T, Lian G, Lv C Y. 2005. Land degradation analysis based on the land use changes and land degradation evaluation in the Huan Beijing area. Remote Sensing for Environmental Monitoring, GIS Applications, and Geology, 5983, 598-612. [4]Hammond C M, Walker B H. 1984. A procedure for land capability analysis in Southern Africa, based on computer overlay techniques. Landscape Planning, 11, 269-291. [5]He Z H, Lin Z J, Wang L J, Xiao Z M, Wan F S, Zhuan Q S. 2002. Classification on Chinese wheat regions based on quality. Scientia Agricultura Sinica, 35, 359-364. (in Chinese) [6]Kalogirou S. 2002. Expert systems and GIS: an application of land suitability evaluation. Computers, Environment and Urban Systems, 26, 89-112. [7]Kasturirangan K. 1995. Remote sensing in India-present scenario and future thrust. Journal of the Indian Society of Remote Sensing, 23, 1-6. [8]Krishna A P. 1996. Remote sensing approach for watershed based resource management in Sikkim Himalaya: a case study. Journal of the Indian Society of Remote Sensing, 24, 69-83. [9]Li J X, Fan W Q, Bao G R, Shi W D, Mei Y X. 2002. The influence of climate on wheat quality. Journal of Inner Mongolia University for the Nationalities, 17, 89-91. (in Chinese) [10]Pirbalouti A G, Golparvar A. 2008. Evaluating agroclimatologically variables to identify suitable areas for rapeseed in different dates of sowing by GIS approach. American Journal of Agricultural and Biological Sciences, 3, 656-660. [11]Ramalho-Filho A, Oliveira de R P, Pereira L C. 1997. Use of geographic information systems in (planning) sustainable land management in Brazil: potentialities and user needs. ITC Journal, 3, 295-301. [12]Rockström J, Barron J, Fox P. 2002. Rainwater management for increased productivity among small-holder farmers in drought prone environments. Physics and Chemistry of the Earth, 27, 949-959. [13]Sathish A, Niranjana K V. 2010. Land suitability studies for major crops in Pavagada taluk, Karnataka using remote sensing and GIS techniques. Indian Society of Remote Sensing, 38, 143-151. [14]Shim J P, Warkentin M, Courtney J F, Power D J, Sharda R, Carlsson C. 2002. Past, present, and future of decision support technology. Decision Support Systems, 33, 111-126. [15]Sys C, Verheye W. 1972. Principles of land classification in arid and semi-arid regions. Algemeen Bestuur vande Ontwikkelingss, Ghent, Belgium: International Training Centre for Post-Graduate Soil Scientists. State University of Ghent. Sys C. 1985. Land evaluation. Algemeen Bestuur vande Ontwikkelingss, Ghent, Belgium: International Training Centre for Post-Graduate Soil Scientists. State University of Ghent. Wang D C, Li C J, Song X Y, Wang J H, Huang W J, Wang J Y, Zhou J H, Huang J F. 2010. Analysis of identifying important ecological factors influencing winter wheat protein content based on artificial neural networks. Transactions of the CSAE, 26, 220-226. (in Chinese) [16]Zhang X Y, Chen Y Y, Su Z S, Zhou H Q, Ma Y P. 2001. A study on monitoring frost of main crop in the area of Ningxia by using remote sensing. Remote Sensing Technology and Application, 16, 32-36. (in Chinese) [17]Zhao S Z, Ji S Q, Wang S Z, Lv F R, Guo G J. 2004. Effect of different soil types on the main quality and yields of high fluten wheat. Journal of Henan Agricultural Sciences, 7, 52- 53. (in Chinese) |
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