Location: Soil and Water Management ResearchTitle: Overview of advances in water management in agricultural production:Sensor based irrigation management Author
Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 8/16/2013
Publication Date: N/A
Citation: N/A Interpretive Summary:
Technical Abstract: Technological advances in irrigated agriculture are crucial to meeting the challenge of increasing demand for agricultural products given limited quality and quantity of water resources for irrigation, impacts of climate variability, and the need to reduce environmental impacts. Multidisciplinary approaches will require choosing crops that are suitable for the region of cultivation, implementing responsible agronomic practices, and applying advanced technology for irrigation management. Sensor-based irrigation management can utilize plant, soil and atmospheric measurements and algorithms for irrigation scheduling and management. When fully integrated with a pressurized irrigation system, sensor-based irrigation management provides a system that delivers precise amounts of irrigation to specific locations within a field when a healthy crop demands it or withholds irrigations in the case of the detection of diseased or otherwise nonviable plants. This level of irrigation control can manage crop water use and improve irrigation water use efficiency while minimizing runoff and deep percolation. This presentation will provide an overview of recent advances made at the USDA-ARS Conservation and Production Research Laboratory in Bushland, Texas in the areas of sensor development and irrigation management algorithms. More specifically, the presentation will describe the practical deployment of wireless infrared and multiband radiometers on moving irrigation systems, algorithms for temperature scaling and plant feedback, and the integration of sensor networks with variable rate irrigation systems. Finally, a summary of crop responses (yield and water use efficiency) to sensor-based irrigation scheduling using plant-feedback algorithms from 1996 to 2012 will be presented along with future directions for more robust scheduling methods.