地球信息综合分析

基于网格的精准农业数据库及示范应用——以黑龙江农场双山基地为例

展开
  • 中国科学院地理科学与资源研究所, 北京 100101
齐清文(1963-),男,博士,研究员,研究方向地图学,遥感,地理信息等.E-mail:qiqw@igsnrr.ac.cn

收稿日期: 2011-10-01

  修回日期: 2011-11-28

  网络出版日期: 2011-12-25

基金资助

中国科学院知识创新工程重大项目"双山基地新型精准农业技术应用".

Grid-based Precision Agriculture Database and Its Demonstrative Application: Taking Shuangshan Farm in Heilongjiang Province as an Example

Expand
  • Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China

Received date: 2011-10-01

  Revised date: 2011-11-28

  Online published: 2011-12-25

摘要

精准农业是指按照田间每一操作单元的具体条件,精细准确地调整各项土壤和作物管理措施,最大限度地优化使用各项农业投入(如化肥、农药、水、种子和其他方面的投入量),以获取最高产量和最大经济效益,同时减少化学物质使用,保护农业生态环境,保护土地等自然资源.本文从精准农业对数据的要求入手,提出以格网为单元的精准农业数据库的设计思想.采用地理空间定位网格的形式组织和应用农业信息,对于支持地理数据的共建共享,方便多源、多尺度农业空间信息的整合与应用分析,实现网格内的土壤本底和作物种植研究,从而实现农业的精准操作有着十分重要的意义.本文通过研究田间(不同网格内)各种因素(气候、土壤、种子、农机、化肥、农药、能源等)的变化,找出它们之间的相关性,以达到选定最佳种植方案,进行自适应喷水、施肥、施药,保持作物良好的生长条件,并获取最高产量和最大经济效益,同时保护农业生态环境及土地等农业自然资源的目的.

本文引用格式

姜莉莉, 齐清文, 张岸 . 基于网格的精准农业数据库及示范应用——以黑龙江农场双山基地为例[J]. 地球信息科学学报, 2011 , 13(6) : 804 -810 . DOI: 10.3724/SP.J.1047.2011.00804

Abstract

Precision agriculture adjusts soil and crop management practices accurately to pursue the optimal use of agricultural inputs (such as fertilizers, pesticides, water, seeds and other inputs) and obtain maximum yield and maximum economic efficiency in accordance with the specific conditions of each operating unit, while reducing chemical use, protecting agricultural ecological environment, land and other natural resources. In this paper, starting with the requirements of precision agriculture data, based on the geographic grid system, we made the precision agriculture database design with the grid as the basic unit. Grid database accurately reflects the need to obtain plots of differences in soil within meters of the bottom level, yield, fertility, micro-topography, crop, water, worms, grass and other information, so the stored data using a grid is in line with the needs of precision agriculture. The use of geo-spatial positioning of the grid to support the sharing of geographic data, to facilitate the integration and application of spatial information analysis of the grid within the soil background and crop research in order to achieve precision agriculture has a very important significance. We studied changes of various factors (climate, soil, seeds, agricultural machinery, fertilizers, pesticides, energy, etc.) in the field (different grid), and the relationship between them, in order to adopt the best planting program and the adaptive water, fertilizer and pesticide, to maintain good crop growing conditions as well.

参考文献

[1] 赵春江,薛绪掌,王秀,等. 精准农业技术体系的研究进展与展望[J]. 农业工程学报,2003,19(4):7-12.

[2] 王克林,李文祥.精确农业发展与农业生态工程创新[J]. 农业工程学报,2000,16(1):5-8.

[3] 赵军,王熙,庄卫东. 基于GPS 的变量施肥播种机的试验研究[J]. 农机化研究,2006,28(12):154 -156.

[4] 马晓蕾,范广博,李永玉,等. 精准施肥决策模型与数据库系统[J]. 农业机械学报,2011,42(5):193-197.

[5] 陈立平. 精准农业变量施肥理论与试验研究 . 中国农业大学,2003.

[6] 吴才聪,马成林,张书慧,等. 精确农业倾斜网格划分及其应用[J]. 农业工程学报,2003,19(1):137 -141.

[7] Franzen D W. Summary of Grid Sampling Project in Two Illinois Fields[J]. NDSU Technical Bulletin, NDSU Extension Service, Fargo, ND, 2008.

[8] Clay D E, Chang J, Carlson C G, Malo D, Clay S A and Ellsbury M. Precision Farming Protocols (Part 2), Comparison of Sampling Approaches for Precision Phosphorus Management[J]. Communications in Soil Science and Plant Analysis, 2000, 31: 2969-2985.

[9] 奚廷孔,张艳新. 土壤样品的采集和处理技术[J]. 广西农学报,2007,22(3):36-43.

[10] 杨俐苹,白由路. 土壤测试实验室数据自动采集处理与推荐施肥系统[J]. 中国土壤与肥料,2008,45(4):65-68,72.

[11] 林芬芳.不同尺度土壤质量空间变异机理、评价及其应用研究 . 浙江大学博士论文,2009.

[12] 尹兰香.同安区耕地土壤化学性质空间变异特征及插值模型效果的研究 .福建农林大学硕士论文,2006.

[13] 王绍强,朱松丽,周成虎.中国土壤土层厚度的空间变异特征[J].地理研究,2001,20(2):161-169.

[14] 史利江.基于GIS和地统计学的土壤养分空间变异特征研究 .上海师范大学硕士论文,2006.

[15] 左继林,刘苑秋,胡松竹,等.龟峰镇土壤养分空间变异特征的研究[J].西北农业学报,2004,13(3):131-136.

[16] Mallarino A P and Wittry D J. Efficacy of Grid and Zone Soil Sampling Approaches for Site-specific Assessment of Phosphorus, Potassium, pH, and Organic Matter[J]. Precision Agriculture, 2004, 5: 131-144.

[17] Moges S M, Raun W R, Mullen R W, Freeman K W, Johnson G V and Solie J B. Evaluation of Green, Red, and Near Infrared Bands for Predicting Winter Wheat Biomass, Nitrogen Uptake, and Final Grain Yield[J]. Journal of Plant Nutrition, 2004, 27: 1431-1441.

[18] Lobell D B, Ortiz-Monasterio J I, Asner G P, Naylor R L and Falcon W P. Combining Field Surveys, Remote Sensing, and Regression Trees to Understand Yield Variations in an Irrigated Wheat Landscape[J]. Agronomy Journal,2005,97:241-249.
文章导航

/