Enhancing Water Conservation and Crop Productivity in Irrigated Agriculture

Transcription

1 Enhancing Water Conservation and Crop Productivity in Irrigated Agriculture Kelly R. Thorp Kevin Bronson, Doug Hunsaker, Andrew French USDA-ARS-ALARC, Maricopa, Arizona

2 Background U.S. Department of Agriculture (ARS) Main research arm of USDA 2,200 research scientists 800 research projects 90+ research locations Arid-Land Agricultural Research Center Maricopa, Arizona (south of Phoenix) 3 research units Pest Management and Biocontrol Plant Physiology and Genetics Water Management and Conservation 23 research scientists Co-located at University of Arizona farm

3 Water Unit Research Scientists Kevin Bronson Soil Scientist & Research Leader Nitrogen Fertilizer Management Clinton Williams Research Soil Scientist Wastewater Reuse Doug Hunsaker Research Agricultural Engineer Irrigation Water Management Soil Moisture Measurement? Vacant Research Microbiologist Wastewater Reuse Andrew French Research Physical Scientist Remote Sensing of Evapotranspiration Eduardo Bautista Research Hydraulic Engineer Hydraulic Modeling Kelly Thorp Research Agricultural Engineer Remote Sensing Computer Simulation Modeling Agricultural Information Technologies Fedja Strelkoff Research Hydraulic Engineer Hydraulic Modeling

4 Water Management Research Activities Agronomic Field Investigations Develop crop coefficients for irrigation scheduling Improve crop water use efficiency Understand yield-water-nutrient relationships Improve management of traditional and bioenergy crops Remote and Proximal Sensing Technology Develop crop sensing and data processing techniques Monitor crop water and nutrient stress site-specifically Understand spatial and temporal variability Computational Methods and Analysis Develop and apply cropping system simulation models Integrate crop sensing data with simulation models Develop geographic information systems for spatial data analysis Develop decision support systems for water and nutrient management

5 Precision Irrigation Management EM-38 survey for soil water properties Remote sensing of crop coefficients for ETc EC a Soil water holding capacity 98 mm/m (dark) to 158 mm/m (light) Meters Simulated variation in soil water depletion determines irrigation timing, amount and location Cotton lint yield map (Blue higher yield)

6 Water Use Efficiency for New Crops Bioethanol crops: Agave Biomass sorghum Switchgrass Guayule for natural rubber Installing drip irrigation guayule experiment Transplanting guayule

7 Deficit Irrigation Management Field investigation Crops Durum wheat Camelina (for biodiesel) Gradient irrigation Linear move sprinkler Arizona environment Variable nitrogen rates Grain Yield (raw weight), kg/ha Linear Move Sprinkler Seasonal irrigation, mm

8 Fate and Management of N Fertilizer Field investigations in furrow irrigated cotton UAN fertigation versus side-dress knifing Canopy spectral reflectance observations Nitrous oxide emissions from soil Nitrogen fertigation Side-dress knifing of nitrogen fertilizer Observing nitrous oxide emissions from soil

9 Remote Sensing of Evapotranspiration Airborne Image Data Wireless Sensor Data ET Modeling LST (Thermal IR) 10 min. LST NDVI Daily ET

10 Wireless Networked Sensors Real-time, time continuous, spatially distributed, low-cost monitoring Experiments at MAC and nearby farms Sensors: Infrared thermometers In-field weather Soil moisture Vegetation index radiometers Wireless Network Low-power, low-data rate, lower cost, multiple units Reliable, accurate Semi-automated Network mesh, XML/RPC Zigbee or equivalent Wireless Node Infrared thermometers & VNIR radiometers Soil moisture probe deployment

11 ET Estimation at Multiple Scales Plot Scale Farm Scale Regional Scale Ground-based radiometry Airborne remote sensing surveys Thermal IR satellite imagery Hand-held and Vehiclebased Proximal Sensing Low-altitude airborne remote sensing Basal Crop Coefficient, Kcb Standard FAO-56 Kcb model for wheat NDVI-adjusted Kcb for densely-planted wheat Measured Kcb for densely-planted wheat FAO behind measured 9-16 days FAO ahead of measured days Satellite-based remote sensing Temperature NDVI Day of year, 2005

12 Hyperspectral Remote Sensing Spectroradiometers ASD FieldSpec GER 1500 Hundreds of bands Spectral analysis Find sensitive wavebands Develop vegetation indices and models

13 Cropping System Simulation Modeling Computer simulations Input management, cultivar, weather, and soil information Simulate carbon, nitrogen, and hydrologic processes Simulate crop growth and development processes Simulate on daily or hourly time steps Output crop yield, ET, N fate, etc. Many models available DSSAT, Aquacrop, APSIM, STICS, WOFOST, InfoCrop, CropSyst, Variable complexity and simulation detail Require calibration against field observations Test alternative management scenarios (what if?)

14 Sensor Data and Model Fusion Combine RS and Crop Models Mathematical methods Data assimilation Model optimization Sensor Data Infrequent Spatially distributed Simulation Model Temporal continuity Adjusted to sensor data Forward prediction VIS/NIR Images Thermal Images Assimilation/Optimization PROSAIL Crop Growth Water Balance Energy Balance Cropping Systems Model Nitrogen Balance ET, N Fate, Crop Yield, etc.

15 GIS & Geospatial Analysis Tools

16 Industry Collaboration USDA-ARS Scientist Responsibilities Conduct scientific research for public interest Publish results in peer-reviewed scientific journals Participate in professional societies Technology transfer Mechanisms for Collaboration Participation in tech transfer workshops and demo projects Cooperative Research and Development Agreements Find a scientist in your area of specialization Inexpensive access to skilled research personnel

17 Questions?