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ARS Home » Plains Area » Bushland, Texas » Conservation and Production Research Laboratory » Soil and Water Management Research » Research » Publications at this Location » Publication #317711

Research Project: IMPROVING WATER PRODUCTIVITY AND NEW WATER MANAGEMENT TECHNOLOGIES TO SUSTAIN RURAL ECONOMIES

Location: Soil and Water Management Research

Title: Acquisition and management of continuous data streams for crop water management

Author
item O`shaughnessy, Susan
item Colaizzi, Paul
item Andrade, Manuel - Orise Fellow
item Evett, Steven - Steve

Submitted to: ASA-CSSA-SSSA Annual Meeting Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: 8/1/2015
Publication Date: 11/16/2015
Citation: Oshaughnessy, S.A., Colaizzi, P.D., Andrade, M.A., Evett, S.R. 2015. Acquisition and management of continuous data streams for crop water management [abstract]. ASA-CSSA-SSSA Annual Meeting. Paper No. 88-2.

Interpretive Summary: Wireless plant and soil water sensors are now readily avaible for agricultural production. However, collecting and making use of large amounts of data from multiple sensors located in a cropped field is challenging. In this presentation, we offer tips on best management practices for data collection, storage, quality control and quality assurance to aide in making irrigation scheduling decisions.

Technical Abstract: Wireless sensor network systems for decision support in crop water management offer many advantages including larger spatial coverage and multiple types of data input. However, collection and management of multiple and continuous data streams for near real-time post analysis can be problematic. This presentation will discuss key practices that we implemented to expedite management of large amounts of quality data-- to include an adequate sampling rate, data averaging, a standardized output format for data capture, centralized data collection, structured files for data storage, and automated daily file organization and execution of algorithms for data processing. Although imperfect, algorithms can be used to compare and validate data from different sources, and address quality assurance and quality control issues. These key practices facilitated near real-time decision support for site-specific crop water management.