2013 Annual Report
1a.Objectives (from AD-416):
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.
1b.Approach (from AD-416):
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, ID.
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
The second linear move irrigation (LMI) experiment was completed. The study evaluated camelina and wheat under a wide range of irrigation and nitrogen (N) applications. Camelina was less sensitive to water and N levels compared to wheat. Strong linear N responses were observed in wheat biomass, vegetative indices, and grain yield in 2013. Yield and water use data for switchgrass collected during field experiments were analyzed. Two years of data have been evaluated for different N fertilizer managements for surface-irrigated cotton. The regional practice of applying N fertilizer through the surface irrigation water (fertigation) has been shown to be as uniform in the direction of plant row as with knifing of liquid N with a ground applicator. Experimental protocols were developed for the 2014 large-field camelina experiments. We developed and tested two remote sensing/Evapotranspiration (ET) models for use in the camelina studies. Reflectance-based strategies for camelina N management are being developed based on data obtained in the LMI camelina experiments. For regional scale ET estimation, we collected irrigation and cropping data for the Eloy Irrigation District, a district adjacent to the Maricopa Stanfield Irrigation and Drainage District (MSIDD). The ready availability of electronic records from Eloy, in contrast to incomplete records from MSIDD facilitated development of water deliveries by volume and date for each turnout. Using a high-clearance tractor, we collected remote sensing data over wheat and camelina in the 2013 LMI experiment. We deployed 10 wireless sensor units and used the temperature data sets to develop a protocol for our 2014 camelina experiment in the same field. Work was conducted on testing Geographic Information System tools for spatial simulations of cotton yield and ET. An optimization strategy was developed to adjust the model parameters that govern Leaf Area Index and ET using remote sensing data. Spatial simulations were conducted with AquaCrop and Decision Support System for Agrotechnology Transfer-CROPGRO-Cotton models to test alternative strategies for managing water and N. Irrigation research on guayule continued. A deterministic design procedure for runoff recovery irrigation systems that takes advantage of WinSRFR's computational capabilities was developed. Irrigation contours were developed to illustrate the sensitivity of irrigation performance to variability of infiltration conditions. An advective transport framework was programmed with computational improvements to reduce numerical dispersion. A complete advection-dispersion (AD) model for non-reactive solutes was developed using this framework. A Sediment Transport module for WinSRFR was built and tested with a Silt Loam soil and compared with results from a stand-alone model developed at the University of Arizona. Procedures were developed for coupling the HYDRUS-1D model to WinSRFR and to the AD model. Users of WinSRFR can now choose to model infiltration with the Richards equation and can also examine non-reactive solute transport problems and the resulting longitudinal and vertical distribution of solutes.
Identifying accurate ways to quantify cropland evapotranspiration (ET) with remote sensing images. Quantifying the water used by crops over time and space is critical for managing scarce water supplies. Two well-known competing remote sensing ET models were evaluated for consistency and ease-of-use by scientists at the Arid Land Agricultural Research Center in Maricopa, Arizona, using experimental data collected for cotton crops grown in 2009 and 2011. The models, Two Source Energy Balance (TSEB) and Mapping Evapotranspiration at high Resolution and with Internalized Calibration (METRIC) were implemented for the 5 ha study and found to return ET estimates consistent to within 1.5 mm/day. The evaluation study highlighted the benefits for having multiple models available to map ET. The TSEB approach is preferred when input data are accurate because it can be applied anywhere with less reliance on site-specific calibrations and the METRIC approach is preferred when inputs are less certain and when implementation simplicity is important. Outcomes from this research will help research scientists at multiple agencies, such as US Department of Agriculture, US Geological Survey, and US Bureau of Reclamation, develop a suite of tools to map and manage scarce water needed for irrigated agriculture in the United States.
Oliveira, L.F., Scharf, P.C., Vories, E.D., Drummond, S.T., Dunn, D.J., Stevens, W.E., Bronson, K.F., Benson, N.R., Hubbard, V.C., Jones, A.S. 2013. Calibrating canopy reflectance sensors to predict optimal mid-season nitrogen rate for cotton. Soil Science Society of America Journal. 77(1):173-183. DOI: 10.2136/sssaj2012.0154.
Guo, W., Maas, S.J., Bronson, K.F. 2012. Relationship between cotton yield and soil electrical conductivity, topography, and landsat imagery. Precision Agriculture. doi:10.1007/s11119-012-9277-2.
Thorp, K.R., French, A.N., Rango, A. 2013. Effect of image spatial and spectral characteristics on mapping semi-arid rangeland vegetation using multiple endmember spectral mixture analysis (MESMA). Remote Sensing of Environment. 132:120-130.