【有关概念】：农业资源数据是用来描述农业资源的数字、字母、符号、图表、图形或其他模拟量。农业资源数据里包含着农业资源信息。【目前研究现状】：目前，新疆生产建设兵团正在大力开展农业大数据研究与应用建设工作，在此过程中，传统的农业资源数据采集与整合方式表现出采集标准不一致，数据质量不高，信息碎片化、流动性低等问题，迫切需要研究出一套经济、可行、高效的农业资源数据采集与整合方法。【本文的内容概括】：本研究综述了国内现阶段农业数据资源的研究进展情况，讨论了农业资源数据采集与整合方法研究的意义。在对新疆生产建设兵团现有农业资源数据进行系统观察、分析和梳理主要问题的基础上，将农业资源数据采集与整合方法拆分成技术指标规范模块、农业资源采集模块、数据质量检查模块、异构数据转换模块、数据分类编码模块、数据管理模块、决策支持模块、农业资源共享模块，建立了一套农业资源数据采集与整合方法模型，搭建了新疆兵团农业资源整合与共享平台。【展望】：农业资源数据的数量、质量和实效性关系到农业大数据的发展基础。本文已针对现阶段农业资源数据采集与整合情况，对分散、异域的农业资源数据进行初步采集与整合，后期将在实践中不断改进其中不必要和不合理的部分，寻求一种更经济、更合理、更有效的工作程序，为挖掘、揭示和再组织不同农业资源数据间的关联关系做好充分的数据准备。

Agricultural resource data include quantities, text, symbols, charts, graphs or other analog inputs that describe agricultural resources. Agricultural resource data yield agricultural resource information. The Xinjiang Production and Construction Corporations are vigorously promoting the study and construction of applications using agricultural big data. Traditional methods for collecting and integrating agricultural data reveal problems such as inconsistent collection standards, poor data quality, information fragmentation, and low fluidity. A set of economic, feasible and efficient methods for agricultural data collection and integration is urgently needed. This study reviewed the research progress on agricultural data resources in China, particularly research on methods for collecting and integrating agricultural resource data. On the basis of systematic observation and analysis of existing agricultural resource data from the Xinjiang Production and Construction Corps, important concerns and necessary functions were identified. Through this study, agricultural data collection and integration methods were divided into a technical indicator specification module, an agricultural resource collection module, a data quality inspection module, a heterogeneous data conversion module, a data classification coding module, a data management module, a decision support module, and an agricultural resource sharing module. An agricultural resources data collection and integration method model was established and the Xinjiang Corps’ agricultural resources integration and sharing platform was built. The quantity, quality, and effectiveness of agricultural resource data are related to the development foundation of agricultural big data. On the basis of progress in the study of data collection and integration methods, this paper describes preliminary collection and integration of dispersed and unique agricultural resource data. This research will continue to identify ways to improve and streamline data collection and integration practices. The goal is an economical and effective working procedure that effectively prepares data for mining, and reveals relationships among different agricultural resource data elements.