Enhancing Agricultural Water Management Through Soil Moisture Monitoring and Irrigation Scheduling
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1 Enhancing Agricultural Water Management Through Soil Moisture Monitoring and Irrigation Scheduling Mary Love Tagert, J. Caleb Rawson, Anna Linhoss, Gretchen Sassenrath*, and Billy Kingery Mississippi State University and *Kansas State University 2015 Mississippi Water Resources Conference April 8, 2015
2 Background: Mississippi Delta Increasing reliance on irrigation and expansion Pumpage exceeds annual recharge - interior county water levels are dropping by a foot a year (ymd.org, 2014) Overdraft Mississippi River Valley alluvial aquifer (MRVA). Decreasing water availability Higher costs associate with pumping Heightened environmental concerns and scrutiny on agricultural water diversion
3 Background: Water Management Irrigation is a necessity Rainfall untimely and spatially variable (annual >50 ) Periodic summer drought Water retention canals and holding ponds Costly to install Can take farm land out of production Maintenance issues Pipe Hole and Universal Crown Evaluation Tool (PHAUCET) or Delta Plastic Pipe Planner Calculates pipe pressure and flow rates for each furrow (hole size)
4 Background: Mississippi Irrigation Scheduling Tool (MIST) MIST development began in 2011, with ongoing improvements. ET calculated through the Modified Penman-Montieth equation with climate data from over 19 weather stations located within the Delta and precipitation data from Nexrad (Allen et. al., 1998; Addresses the humid region s unpredictable precipitation through incorporation of Nexrad data and Runoff calculation (Vories, 2005). Grower input is minimized through the use of automated data use. MIST is available through a web-based interface.
5 Research Objectives Objective One: Collect 2014 soil and soil moisture data. Objective Two: Improve runoff estimation and soil water balance, especially for high-rainfall events.
6 Materials and Methods Data Collection May 2014 biweekly/weekly Setup and installation. Watermark Sensor Setup Tools Fabrication. Southern Loop Satartia Redgum Northern Loop Jonestown Dublin Sunnyside
7 Materials and Methods Conversion and Evaluation of Soil Moisture Data Evaluate previously collected data Conversion of soil moisture data Determination of data needs for application and testing Precipitation Data Incorporation of Nexrad into the spreadsheet version of the MIST model Method of Nexrad data collection dependent on ease of accessibility
8 Materials and Methods Irrometer, Inc., Riverside, CA Watermark 200SS Electrodes within a Granular Matrix Resistance centibars Model-900 datalogger 8 sensor connections
9 Materials and Methods: Sensor Setup Programing Data Loggers ABE Shop May 25, 2014 Complete Sensor Cycling before installation. Two complete saturation and drying cycles before installation.
10 Materials and Methods: Field Setup Two sets per field 6 sensors from 6-36 Datalogger F u r r o w
11 Materials and Methods: Installation and Collection Sunnyside and Jonestown site May 21, 2014
12 MLT2 Materials and Methods Soil Moisture Release Curves are needed to convert pressure data from sensors to inches of water for comparison to model. In 2011, composite 12-inch samples from several sites were analyzed by Decagon Devices, Inc. Model considers: Soil Type Soil Runoff Potential (hydrologic soil group) Average Available Water Capacity (AWC)
13 Slide 12 MLT2 Define? Mary Love Tagert, 4/6/2015
14 MIST Modeled Field Water Balance for Three Separate Soils at Redgum 2012 Soybean Pivot Water in. (Inches) WB MIST Da WB MIST Fd WB MIST Ac /21/12 5/11/12 5/31/12 6/20/12 7/10/12 7/30/12 8/19/12 9/8/12 Date Water Balance (WB), Forestdale Silty Clay Loam (Fd).18, Dowling Clay (Da).12, Alligator Clay (Ac).11. /Users/johnrawson/Documents/School/MIST/Graphs_Data/Calibration-Input-vs-Output_Current.xlsx
15 Materials and Methods: Data Conversion [1] cbar = 1 cmh 2 O [2] pf = Log 10 W P [3] H 2 O in./ft. Soil = W C *(12in./ft.) Centibar (cbar) Water Potential (W P ) cmh 2 O Soil Moisture Tension (pf) unit less pf value is logarithm of the absolute value of soil matric potential Werner, H. (1992). Measuring soil moisture for irrigation water management. Cooperative Extension Service, South Dakota State University, US Department of Agriculture.
16 Materials and Methods: Soil Moisture Release Curves /Users/johnrawson/Documents/School/MIST/Soil Data/2014 Soil Tests/Redgum 6 in
17 Materials and Methods: Depth-Specific SMRCs Test Site Soil Series Horizon (a) Depths Site Jonestown Redgum Soil Series Bosket Forestdale Horizons and Depth Ap inches AB inches Bt inches Ap inches Btg inches Btg inches (a) Horizon separate soil layers by obvious physical features, chiefly color and texture. Texture affects soil physical properties: Infiltration rates, Water holding Capacity, and Average Available Water Content. /Users/johnrawson/Documents/School/MIST/Soil Data/2014 Bulk Density Worksheets
18 Materials and Methods: Soil Sampling 2014 Depth Specific Curves Six samples per calibration site (6-36 ) Redgum Jonestown Decagon Devices, Inc. >250 cm 3 soil per sample Bulk Density of each sample
19 Results Table 2. Test Bulk Density for Model Application and Testing Sites Depth Jonestown Bosket Series Field Bulk Density NRCS (a) Soil Data Bulk Density Depth Redgum Forestdale Series Field Bulk Density NRCS (a) Soil Data Bulk Density 6 inch (0-9in.) 6 inch (0-6in.) 12 inch inch inch (9-25in.) 18 inch (6-26in.) 24 inch inch inch (25-30 inch (26-36 inch in.) 36 inch in.) (a) Natural Resource Conservation Service NRCS Soil Data available at: soilseries.sc.egov.usda.gov /Users/johnrawson/Documents/School/MIST/Soil Data/2014 Bulk Density Worksheets.xlsx
20 Pressure (cbar) Pressure (cbar) Comparison of Sensor Field Water Balances for Data Loggers A&B at 2012 Redgum Soybean Pivot and Jonestown Corn Furrow R 2 = /11/12 5/31/12 6/20/12 7/10/12 7/30/12 8/19/12 9/8/12 Date 200 R 2 = /11/12 5/31/12 6/20/12 7/10/12 7/30/12 8/19/12 Date /Users/johnrawson/Documents/School/MIST/Graphs_Data/Nash-Sutcliffe box comparison.xlsx Redgum Forestdale Silty Clay Loam Box A Box B Jonestown Bosket Very Fine Sandy Loam Box A Box B
21 Redgum Sensor Water Balances for Data Loggers A&B Calculated from Composite 12-inch and Depth-Specific SMRCs 25 Water in. (Inches) Forestdale Silty Clay Loam DSC BoxA DSC BoxB C12 BoxA C12 BoxB 0 5/21/12 6/15/12 7/10/12 8/4/12 8/29/12 Date /Users/johnrawson/Documents/School/MIST/Graphs_Data/2012_Calibration_Watermark_Conversions_with_updated_SWRC_Current.xlsx
22 Jonestown Sensor Water Balances for Data Loggers A&B Calculated from Composite 12-inch and Depth-Specific SMRCs Bosket Very Fine Sandy Loam Water in. (Inches) DSC BoxA DSC BoxB C12 BoxA C12 BoxB 0 5/21/12 6/5/12 6/20/12 7/5/12 7/20/12 8/4/12 8/19/12 Date /Users/johnrawson/Documents/School/MIST/Graphs_Data/2012_Calibration_Watermark_Conversions_with_updated_SWRC_Current.xlsx
23 Water in. (Inches) Water in. (Inches) Jonestown and Redgum Sensor Water Balances for Data Loggers A&B Calculated from Composite 36-inch and Depth-Specific SMRCs /21/12 6/15/12 7/10/12 8/4/12 8/29/12 9/23/12 Date BoxB R 2 = BoxA R 2 = BoxA R 2 = BoxB R 2 = /21/12 6/15/12 7/10/12 8/4/12 8/29/12 Date Forestdale Silty Clay Loam DSC BoxA DSC BoxB C36 BoxA C36 BoxB Bosket Very Fine Sandy Loam DSC BoxA DSC BoxB C36 BoxA C36 BoxB /Users/johnrawson/Documents/School/MIST/Graphs_Data/2012_Calibration_Watermark_Conversions_with_updated_SWRC_Current.xlsx
24 Composite 12-inch, Depth-Specific, and MIST Calculated Water Balance Inputs for Jonestown 2012 Corn Furrow with Nexrad Precipitation Water (in.) Water (in.) (a) R 2 = /11/12 5/26/12 6/10/12 6/25/12 7/10/12 7/25/12 8/9/12 8/24/ Date (a) R 2 = /11/12 5/26/12 6/10/12 6/25/12 7/10/12 7/25/12 8/9/12 8/24/12 Date (a) Assuming three remaining spikes are irrigation events /Users/johnrawson/Documents/School/MIST/Graphs_Data/Nash-Sutcliff Box Comparisons_Old_SMRC.xlsx /Users/johnrawson/Documents/School/MIST/Graphs_Data/Calibration-Input-vs-Output_Current.xlsx Bosket Very Fine Sandy Loam C12 Inputs MIST Inputs Nexrad 2012 Bosket Very Fine Sandy Loam MIST Inputs Nexrad 2012 DSC Inputs
25 Conclusions Value of MIST to address water irrigation use efficiency for Mississippi s humid climate and soil spatial variability conditions found in the region Model s use of NRCS Runoff Coefficient applies to a larger range of field soil types Nexrad increases reliability of rainfall measurements Use of Penman-Monteith equation for ET calculated with data from Delta weather stations. MIST modeled water balance inputs for soil conditions that were able to be fully evaluated Hypothetical R 2 = assuming irrigation events 0.28 in. un-model runoff possible result rainfall distribution or increased bulk density from soil compaction Water balance losses appear be isolated to crop water use Soil Sampling / Soil Moisture Release Curves Composite 36-inch soil samples collected in the same manner should provide accurate SMRCs for the profile Jonestown R 2 = 0.98 for Box A and Box B, each Redgum R 2 = 0.99 for Box A and 0.96 for Box B
26 Future Work Participating Grower Irrigation Data Alternate Runoff Coefficients or manual adjustments for field soil compaction Change in sensor arrangements for high clay soils Removal of PVC Horizontal Installation for 6, 12, and 18-inches Crop Coefficients
27 Acknowledgements MS Soybean Promotion Board MS Corn Promotion Board Yazoo Mississippi Delta Join Water Management District and Dave Kelly Mr. Turner Massey Mr. Darrington and Mr. Byron Seward Mr. Roosevelt Lee Mr. Billy Walker Mr. John Michael Pillow
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