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
3. Progress Report:
A linear move sprinkler irrigation system was used to evaluate camelina and wheat crop water-use, water stress, and nitrogen (N) management under a wide range of irrigation applications. Field data, including soil moisture, irrigation, remote sensing, and crop and soil N, have been processed. The second year of this 2-year experiment will be repeated in 2013. Sorghum field data was analyzed statistically. Sorghum crop coefficients were developed in support of irrigation management for this biofuel crop in Arizona. A 3 year $450,000 Biomass Research and Development Initiative grant was awarded to the Unit in 2012 to conduct research on irrigation management for the natural rubber producing crop, guayule. The 1st year experimental study of cotton N management was completed. Results were unremarkable due to high initial soil nitrate in soil, which precluded nitrogen fertilizer response. Harvest data indicated high internal N use efficiency of Arizona cotton. The second cotton N field study is currently being conducted in 2012. A sub-experiment in a nearby field to the cotton N experiment was conducted to obtain data that will be used to validate a solute transport model for surface irrigation. Substantial progress was made in reducing and processing the extensive data sets generated during the 2011 cotton and remote sensing large-field experiment. This included progress in the analysis of spatial variability of soil infiltration. A field site at the Maricopa Agricultural Center to conduct the 2014 large-field camelina experiment was identified with the farm manager. Multispectral remote sensing data from ASTER, Landsat and MODIS satellite observations were acquired, adjusted for atmospheric properties, and georegistered to UTM coordinates. These data are archived locally at ALARC and remotely at a collaborating institution. Ground-based eddy-covariance and scintillometry data over a central Arizona, Ak-Chin reservation barley field were also acquired. GIS algorithms were developed for collecting, synthesizing, analyzing, and interpreting spatial information on agricultural cropping systems. The software was designed for spatial processing of remote sensing data layers and the incorporation of the spatial data into the simulation of one-dimensional cropping system simulation models. The software is written in Python and is designed as a plug-in tool within the open-source Quantum GIS software environment. Crop modeling validation data were collected for camelina, wheat, and lesquerella during four different field experiments in 2012. Collaboration was initiated with researchers with AgriLife in Weslaco, TX on evaluating the impact of surge irrigation practices in the Rio Grande Basin. Fertigation programming, rather than erosion, was conducted for 2012. The advection framework is the same for both sediment and solutes. It was programmed into WinSRFR 5.0 and adapted to prediction of the longitudinal distribution of fertilizer in solution for irrigated furrows. Several patterns of application were tested, with both variable and constant fertilizer injection rates at the furrow entrance.
1. Irrigation and nitrogen management for camelina. Arid and semi-arid regions of the USA have been targeted to produce camelina as a renewable biodiesel energy source. However, information about camelina’s crop water use and irrigation management for the arid environment was unavailable. Scientists at ARS in Maricopa, Arizona, conducted research that quantified the camelina crop water use requirements and crop coefficients, and determined the effects of different irrigation and nitrogen applications on its oilseed yields. Results show that the water use requirement of camelina can be much lower than that for traditional crops produced in the area, and that irrigation water use can be significantly reduced with little penalty in oil yields. General irrigation scheduling tools and guidelines for camelina were developed to help growers with irrigation management decisions. The research suggests that camelina with its associated water-savings could create opportunities for growers in the arid-west with limited cropping alternatives.
2. Open-source GIS software for spatial extrapolation of simulation models. Geographic information systems (GIS) are an excellent tool for collection, synthesis, analysis, and interpretation of spatial information collected from agricultural crop lands. However, we lack well-designed software tools for harnessing the power of GIS to aid decision making for management of water and nitrogen resources for crop production. In FY12, ARS researchers at Maricopa, Arizona developed a suite of open-source software plug-in tools in the Quantum GIS environment that facilitates processing of remote sensing image data and other spatial data layers within distinct spatial management units across a field. The open-source nature of these algorithms will facilitate their distribution through the channels of the parent open-source project: Quantum GIS. We anticipate these algorithms will be useful for many researchers and practitioners who utilize remote sensing and simulation models to solve agricultural and environmental problems having a spatial component.
3. WinSRFR 4.1 released to the public and Natural Resources Conservation Service (NRCS). The performance of gravity (surface) irrigation, the prevalent method of on-farm water application in the U.S. and worldwide, typically is low but can be substantially improved if systems are designed and/or operated based on hydraulic engineering principles. Researchers at ARS in Maricopa, Arizona, released Version 4.1 of WinSRFR, a surface-irrigation software program that can be used to analyze field evaluation data, estimate field infiltration properties, analyze design alternatives, optimize operations, and conduct simulation studies. The new software features an updated simulation engine (SRFR) that was reprogrammed using a design layout which includes a modern graphical diagnostic and debugging tool. New functionalities include simulation with the Green-Ampt infiltration equation and surge irrigation modeling. Intended users include university extension agents, farm advisors, irrigation consultants, and NRCS irrigation specialists.