Agriculture Information Service Built on Geospatial Data Infrastructure and Crop Modeling
Agriculture Information Service Built on Geospatial Data Infrastructure and Crop Modeling
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基于地理空间数据基础设施和作物建模的农业信息服务
DOI：
10.1145/2637064.2637094
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
Kumpee Teeravech
中科院分区：
文献类型：
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作者：
Kiyoshi Honda;Amor V. M. Ines;Akihiro Yui;Apichon Witayangkurn;Rassarin Chinnachodteeranun;Kumpee Teeravech
正在建立一个名为FieldTouch的农业信息服务平台，并在地理空间数据基础设施和作物建模框架上进行测试。日本北海道的100多名农民一直在参与这一开发，并正在利用这些服务来优化他们的日常农业实践，例如，规划和确定哪些地区需要更多地施肥，以提高田间作物的生长均质性和稳健性。FieldTouch集成了用于田间监测的多尺度传感器数据，提供记录农业实践的功能，然后支持农民决策，例如肥料管理。正在使用RapidEye卫星图像监测植被状况，每两周更新一次。来自25个节点的现场传感器数据每10分钟记录不同土壤深度的土壤水分和温度数据，以及一整套气象变量，如降雨量、最低和最高温度、太阳辐射、风等。国家气象观测网AMeDAS的数据也是每日天气数据的来源。我们使用了“CloudSense”传感器后台服务，通过一个标准的Web服务SOS(传感器观测服务)向FieldTouch提供元数据和数据，这带来了极大的灵活性和增强了系统操作的自动化。利用试验站的农艺数据，利用数据同化技术对当地小麦品种的品种参数(遗传系数)进行了校准。这些都建立在一个名为明日小麦(TMW)的基于网络的DSSAT小麦作物模型中，用户可以在该模型中探索在给定气候条件下播种时机、土壤和作物管理的影响。TMW访问从在线观测站到最新存档的长期天气数据，对内置的天气生成器进行参数化，然后生成100个天气情景，然后在选定的播种日期运行小麦模型，然后是两周，以及前后一周。产量表示为这些不同种植方案下的产量分布。未来的发展将使系统更加个性化，以便用户可以输入肥料情景，还能够应用季节性气候预报，并链接到25个传感器节点，以模拟给定管理情景的当前植物条件。通过这种方式，用户可以更好地了解如何管理其领域中的漏洞来源。
An agricultural information service platform, called FieldTouch, is being built and tested on geospatial data infrastructure and crop modeling framework. More than 100 farmers in Hokkaido, Japan, have been participating on this development and are utilizing the services for optimizing their daily agricultural practices, e.g., planning and targeting areas where to apply fertilizer more to enhance homogeneity of growth and robustness of crops in their fields.FieldTouch integrates multi-scale sensor data for field monitoring, provides functionality for recording agricultural practices, then supports farmers in decision making e.g., fertilizer management. RapidEye satellite images are being used for monitoring vegetation status updated every two weeks. Field sensor data from 25 nodes record soil moisture and temperature data at different soil depths, and suites of meteorological variables e.g., rainfall, minimum and maximum temperature, solar radiation, wind, etc. every 10 minutes. Data from national weather observation network, AMeDAS, is also a source of daily weather data. We used "cloudSense" sensor backend service that serves meta-data and data to FieldTouch via a standard web service called SOS (Sensor Observation Service), which brought great flexibility and enhanced automation of system's operation.Using agronomic data from experimental station, the cultivar parameters (genetic coefficients) of a local wheat variety were calibrated for the DSSAT (Decision Support System for Agrotechnology Transfer) crop model using data assimilation. These were built in a web-based DSSAT wheat crop model called Tomorrow's Wheat (TMW) where in a user can explore the effects of timing of sowing at a given climatic condition, soil and crop management. TMW accesses long-term weather data from the on-line observation station up to the most recent archive, parameterize a built-in weather generator, then generate 100 weather scenarios then runs the wheat model at the chosen planting date, then two weeks, and one week before and after that. The yields are presented as distribution of yields at these different planting options. Future developments are going-on to personalize more the system so that the user can input fertilizer scenario, and be able also to apply seasonal climate forecast, and link to the 25 sensor nodes to simulate current plant conditions given a management scenario. In this way, the user can be informed better on how to manage their sources of vulnerabilities in their fields.