Project Number: 2020-13660-007-000-D
Project Type: In-House Appropriated
Start Date: Jan 16, 2012
End Date: Jan 15, 2017
1. Determine, develop, and/or improve crop coefficients, crop water use efficiencies, and yieldwater-nutrient relationships, and develop efficient irrigation scheduling tools and management, to improve productivity for traditional and bioenergy crops. 2. Develop and verify remote sensing methods, tools, and decision support systems for managing spatially and temporally variable crop water use and stress, and nutrient status in arid irrigated agriculture. 3. Develop and evaluate decision support systems that integrate remote sensing, geographical information systems, and crop growth modeling for assessing crop water and nutrient management alternatives at field-level and watershed scales. 4. Develop engineering concepts, computational procedures, and software tools for analyzing the design and operation of surface irrigation systems and for predicting irrigation-induced soil erosion, and nutrient fate and transport in irrigated systems.
The four objectives in the plan will be carried out using a combination of field experimentation and modeling. During the next five years, the project will direct and conduct seven new field experiments. Additional support will be provided by existing data sets, including data being currently obtained in 2011 from two cotton field experiments in Maricopa. The seven new field experiments will include studies conducted using sprinkler, surface, and subsurface drip irrigation methods. Field data for the soil erosion modeling work will be supplied by ARS in Kimberly, Idaho. Experiment No. Crop Year Irrigation System Primary Treatment Variables 1 Wheat/Camelina 2012 Sprinkler Water and N 2 Wheat/Camelina 2013 Sprinkler Water and N 3* Cotton 2012 Surface Knifing vs Fertigation-applied N 4 Camelina 2014 Surface Spatial vs point-based ETc est 5 Camelina 2015 Surface Spatial vs point-based ETc est 6 Cotton 2015 Drip & surface Drip vs surface 7 Cotton 2016 Drip & surface Drip vs surface *Second of a two-year experiment