Improving soil moisture monitoring in agricultural systems using hydrogeophysics William Avery, MS Student in School of Natural Resources Advisor: Trenton Franz Asst. Professor of Hydrogeophysics, School of Natural Resources Daugherty Water for Food Institute Faculty Fellow With acknowledgements to Catie Finkenbiner (UCARE Student), Tiejun Wang (Postdoc), Foad Foolad (PhD Student), Chase Johnson (Crop consultant), Darin Desilets (HydroInnova LLC), Gary Womack (HydroInnova LLC), Suat Irmak (BSE), Ayse Kilic (SNR, CE), Adriano Diotto (BSE), Brian Armstrong (Armstrong Farms), Roric Paulman (Paulman Farms) Funding provided by: DWFI MS student program, USGS104b, Layman Award, USACE through CRREL and Great Plains Ecosystem CESU
Motivation By 2050 FAO predicts needed increase of 70% in cereal grains to feed 9 billion people (FAO 2012) 70% of global water consumptive use for agriculture, 40% of global food production is from irrigation agriculture (Mekonnen 2011) Estimated that 60% of 2,500 trillion liters used for agriculture is wasted for non-productive ET (Clay 2004) WHY?????? 2
Hypothesis Difficult to accurately measure water availability in the soil at the measurement scale of water application (individual nozzle and center-pivot) with existing point based technology. That uncertainty is 1 key factor that leads to overwatering as to not adversely affect yield. Right photo courtesy of Valley Irrigation 3
Solution! Only apply water when and where it is needed Turns out that it is not that easy to measure plant water use (ET) and soil water storage at the same scale, or on the scale of water application (i.e. see myriad of ET and soil moisture monitoring methods) 4
Measuring ET and Storage We can measure required ET from temperature, energy, mass or combination approaches (Penman-Monteith, FAO reference crop). Value representative of field. Source: S. Irmak, used with permission 5
Measuring ET and Storage We can measure required ET from temperature, energy, mass or combination approaches (Penman-Monteith, FAO reference crop). Value representative of field. Source: S. Irmak, used with permission However; we can only measure soil water storage at a point. Source: www.shellypark.co.nz 6
Measuring ET and Storage We can measure required ET from temperature, energy, mass or combination approaches (Penman-Monteith, FAO reference crop). Value representative of field. $64,000?: How representative are point soil moisture measurements over entire field or Source: www. Calmit.unl.edu However; we can only measure soil water storage at a point. watering section? Source: www.shellypark.co.nz 7
Lab Group Summary Research: Understand the flow of water through natural and human dominated ecosystems 8
Lab Group Summary Research: Understand the flow of water through natural and human dominated ecosystems Extension: Expose or incorporate useful hydrogeophyscial technologies into practice of stakeholders. How many inches of water can this technology save? 9
Lab Group Summary Research: Understand the flow of water through natural and human dominated ecosystems Extension: Expose or incorporate useful hydrogeophyscial technologies into practice of stakeholders. How many inches of water can this technology save? Industry: Develop entrepreneurial opportunities 10
Cosmic-ray Sensor Summary Pros: Large-area (~1000 ft. radius circle) Soil moisture averaged over top 1-2 ft. Proximal sensor, doesn t interfere with farm operations Mobile for large scale mapping or within field spatial variability for precision agriculture New tool for conducting field scale water balance studies 11
Cosmic-ray Sensor Summary Pros: Large-area (~1000 ft. radius circle) Soil moisture averaged over top 1-2 ft. Proximal sensor, doesn t interfere with farm operations Mobile for large scale mapping or within field spatial variability for precision agriculture New tool for conducting field scale water balance studies Cons: Soil moisture averaged over top 1-2 ft. only Cost? 12
Cosmic-ray Sensor Summary Pros: Large-area (~1000 ft. radius circle) Soil moisture averaged over top 1-2 ft. Proximal sensor, doesn t interfere with farm operations Mobile for large scale mapping or within field spatial variability for precision agriculture New tool for conducting field scale water balance studies Cons: Soil moisture averaged over top 1-2 ft. only Cost? Applications: 1) Irrigation management- Use to optimize irrigation water inputs in May-June for max. irrigation in July-August 2) Precision agriculture- in-season spatial mapping 13
A Comparison of Neutron Probes Essentially same detector but Transient Quasi-static Static 5. Surface Water http://sanangelo.tamu.edu/agronomy/sorghum/neutron.htm 7. Soil Moisture 8. Lattice Water 3. Vegetation 4. Intercepted 1. Water Vapor 6. Layer of Water 9. Soil Carbon Compounds 2. Built-up with updated electronics and high voltage NPMs Same basic physics as in-situ neutron probe Passive sensor, uses cosmic-ray neutrons as source Relates fast neutrons to water content instead of slow or thermal neutrons Footprint is ~1000x larger (density of soil vs. air) Probe sees about top 30 cm In-situ probe considered gold standard in agronomy and soil physics 14
Supporting Evidence June-July 2014, near Central City, NE Installed 12 profiles of Watermark sensors and 1 cosmic-ray sensor In collaboration with S. Irmak, A. Kilic, and A. Diotto 15
Supporting Evidence Field is fairly flat, homogeneous vegetation, sandy loam soil texture, ideal setting for homogeneity? 200m In collaboration with S. Irmak, A. Kilic, and A. Diotto 16
Supporting Evidence In collaboration with S. Irmak, A. Kilic, and A. Diotto 17
Supporting Evidence In collaboration with S. Irmak, A. Kilic, and A. Diotto 18
Supporting Evidence In collaboration with S. Irmak, A. Kilic, and A. Diotto 19
Supporting Evidence ~ 8 Days In collaboration with S. Irmak, A. Kilic, and A. Diotto 20
Supporting Evidence In collaboration with S. Irmak, A. Kilic, and A. Diotto 21
Supporting Evidence In collaboration with S. Irmak, A. Kilic, and A. Diotto 22
Supporting Evidence In collaboration with S. Irmak, A. Kilic, and A. Diotto 23
Supporting Evidence ~ 3 Days In collaboration with S. Irmak, A. Kilic, and A. Diotto 24
1. Irrigation management If I have 50 inches of water over the next 5 years when and how much should I apply it? 25
1. Irrigation management By reducing Watermark point sensor uncertainty with CRS, there can be increased yield and reduced input costs. 26
1. Irrigation management By reducing Watermark point sensor uncertainty with CRS, there can be increased yield and reduced input costs. Yield: How many inches of water can we save in May and June to apply in July and August? 1 inch of water = 12-15 bushels/acre, savings ~ $7k per 130 acres at $4 corn per inch of water 27
1. Irrigation management By reducing Watermark point sensor uncertainty with CRS, there can be increased yield and reduced input costs. Yield: How many inches of water can we save in May and June to apply in July and August? 1 inch of water = 12-15 bushels/acre, savings ~ $7k per 130 acres at $4 corn per inch of water Energy: How much water/energy does that cost us over an entire growing season? 1 center-pivot rotation, ~$1-2k in energy pumping costs 28
1. Irrigation management By reducing Watermark point sensor uncertainty with CRS, there can be increased yield and reduced input costs. Yield: How many inches of water can we save in May and June to apply in July and August? 1 inch of water = 12-15 bushels/acre, savings ~ $7k per 130 acres at $4 corn per inch of water Energy: How much water/energy does that cost us over an entire growing season? 1 center-pivot rotation, ~$1-2k in energy pumping costs Fertilizer: How much Nitrogen can we save from leaching out of the root zone? ~$0.4 per lb of N, need 200 lb per acre, savings ~$50 per lb of N per 130 acres 29
2. Precision Agriculture Problem: we can break field into 5400 management zones but we have very limited or no information on what is happening in each zone! Remember a point sensor represents only a point, takes about 6-12 point samples to estimate a reasonable zone average for each layer Why should I spend an extra ~30k to upgrade my pivot to variable rate if we can t make dynamic water prescription maps? 30
2. Precision Agriculture A point sensor represents only a point unless we tell it otherwise Why not train our point sensors with spatially representative information from the cosmicray sensors 31
B. Armstrong s Farm near Brule, NE. In collaboration with J. Gates, J. Fritton and TNC 800 m Picture of stationary cosmic-ray soil moisture probe being installed at site on 3 Nov. 2014. Field-average SM recorded every hour. Mobile cosmic-ray soil moisture probe capable of 1 minute level measurements. Soil electrical conductivity survey (not at site) using electromagnetic induction. Soil EC is highly correlated to soil clay percentage.
B. Armstrong s Farm near Brule, NE. Preliminary Results In collaboration with J. Gates, J. Fritton and TNC
2. Precision Agriculture Cosmic-ray rover can be mounted to existing farm equipment, ATV, sprayer, destroyer Collect 4 to 8 training datasets to train any existing point sensor and then make dynamic water prescription maps for VRI Landscapes, fields, and management zones, wet up and dry down following similar and now observable patterns 34
Future Steps Refine data collection and data processing currently can mapped in about ~1-2 hours per center-pivot data processing ~10-15 minutes Continue to investigate data merging strategies for combining time series data with spatial mapping (see. Franz et al. 2015 GRL) Work with companies to: make hardware cost effective develop user friendly software develop cost effective business strategies (i.e. crop consultants) 35
Questions? This work is supported by: DWFI MS student program, USGS104b, Layman Award, USACE through CRREL and Great Plains Ecosystem CESU 36