SOIL MANAGEMENT USING SENSORS. Ken Sudduth, Ag Engineer USDA-ARS Cropping Systems & Water Quality Research Unit, Columbia, Missouri

Transcription

1 1 SOIL MANAGEMENT USING SENSORS Ken Sudduth, Ag Engineer USDA-ARS Cropping Systems & Water Quality Research Unit, Columbia, Missouri

2 2 SOIL (WATER) MANAGEMENT USING SENSORS Ken Sudduth, Ag Engineer USDA-ARS Cropping Systems & Water Quality Research Unit, Columbia, Missouri

3 Soils are variable... 3 Soil Mapping Units Dd = Dundee sandy loam De = Dundee silt loam Re = Reelfoot loam Rf = Reelfoot sandy loam Tp = Tiptonville silt loam (approx. 20 ac field)

4 4.. but common water management approaches aren t Ensure that the area with the smallest available water holding capacity receives adequate water Match the needs of the average (or largest area) soil water conditions Limit applications to avoid overwatering the wettest areas In all cases, parts of the field are either over- or underirrigated

5 5 Equipment accommodates site-specific irrigation..

6 6.. but what data can be used for the prescription? NRCS soil maps Spatial resolution and accuracy may not be sufficient Static soil water content sensors May be difficult/expensive to install enough to characterize highly variable fields Remote sensing Cost, timeliness issues Mobile proximal soil sensors

7 What is mobile proximal soil sensing? 7 Mobile proximal soil sensing (PSS) is the use of fieldbased sensors to obtain signals from the soil when the sensor s detector is in contact with or close (within 2 m) to the soil (Viscarra Rossel and McBratney, 1998; Viscarra Rossel et al., 2011) A number of mobile PSS are commercially available, and development continues on others Once calibration to soil properties of interest is completed or confirmed, mobile PSS can provide very high (10 m or better) spatial resolution

8 What soil properties would we sense for water management? 8 Those affecting available water capacity (AWC) Texture Organic Matter

9 What soil and landscape properties should we sense for water management? 9 Available Water Capacity -soil texture -organic matter Rate of Water Infiltration -soil surface porosity -layers of impermeability -slope Water Redistribution Within the Field -relative elevation -curvature -slope Soil electrical conductivity (EC a ) Visible & nearinfrared (VNIR) soil reflectance Elevation mapping by RTK-GPS

10 What sensors are important? 10 Visible & near-infrared (VNIR) soil reflectance Soil electrical conductivity (EC a ) Elevation mapping by RTK-GPS

11 Soil electrical conductivity (EC a ) 11 As measured by proximal in-field sensors, bulk, apparent soil electrical conductivity measured in situ, abbreviated EC a. (or just EC) A measure of the soil s ability to transmit (conduct) an electrical charge This is not the same as laboratory measurements of soil electrical conductivity, often done as a saturated paste

12 What is soil electrical conductivity? 12 In precision agriculture, we are usually talking about sensor measured bulk, apparent soil electrical conductivity Abbreviated EC a or just EC Units of EC: millisiemens per meter (ms/m) This is not the same as laboratory measurements of soil electrical conductivity, often done as a saturated paste Soil EC is a measure of the soil s ability to transmit (conduct) an electrical charge

13 Soil pathways of electrical conductance Solid phase particle contact 2. Liquid phase water contact, salinity 3. Solid-liquid phase - CEC 13 After Corwin and Lesch, 2002

14 What affects EC? 14 Physical and chemical properties of the soil affect the soil s ability to transmit electricity These same properties are often important contributors to the productive capability of the soil. salinity clay content/cation exchange capacity (CEC) pore size, shape, and distribution clay type soil moisture soil temperature

15 How is EC measured? 15 Contact method Static electrodes inserted into the ground On-the-go measurement with rolling electrodes Non-contact method Electromagnetic induction (EMI) Sensors are operated in-field either touching or at a fixed distance from the soil surface. Penetrating EC sensors Allow measurement as a function of depth A point measurement, but may be useful for calibrating mobile EC mapping

16 Soil EC sensing history s: Used to estimate salinity in CA with four-probe arrays, four-electrode hand probes, and early electromagnetic induction (EMI) instruments Early 1990s: First used to investigate soil variability in non-saline areas Rhoades and van Schilfgaarde, 1976 Carter et al., 1993

17 Veris 3100/MSP 17 Deep reading to approximately 3 ft depth (outside coulters) Shallow reading to approximately 1 ft (inside coulters) MSP also adds other sensors soil color and ph

18 Geonics EM38 18 Non-contact, uses electromagnetic (EM) induction About 5 ft sensing depth in usual operation User must assemble transport and data collection system EM-38 sensor Analog/Digital converter DGPS antenna Laptop computer

19 DUALEM (many models) 19 Like EM38, also uses EM induction Depending on model, may provide 2, 4, or 6 measurements simultaneously for different measurement depths

20 EC maps provide highresolution data 20

21 EC sensors provide information about different soil depths 0 Depth, m Geonics EM38 vertical Veris 3100 shallow Veris 3100 deep DUALEM-2S shallow (PRP) DUALEM-2S deep (HCP) Depth for 90% of total response: Veris shallow: Veris deep: DUALEM shallow: EM38: DUALEM deep: 0.3 m 1.0 m 2.2 m 5 m 10 m Fraction of total response from shallower depth 21

22 Mapped differences among sensors 22 VerisShalowVerisDepEMHorizontal EMVertical

23 23 EC data patterns are consistent.

24 but the numeric values can change April 1994 vs. April Differences in: Soil moisture Soil temperature Crop residue Instrument calibration

25 What can EC tell us? 25 Alone, it only indicates the relative differences in soil EC for that field Site calibration/investigation will help establish the causes of soil EC variation Knowing what causes different soil EC values within a field will point to potential management applications of the information

26 26 Infiltration good PAWC poor Leaching high Infiltration good PAWC good Infiltration poor PAWC poor Denitrification high

27 EC can be used to map soil texture within fields 27 Note that these good results required calibration sampling within the field

28 28 New Zealand research by Hedley et al.

29 EC can also be calibrated to soil properties across scattered locations (with less accuracy) 29 Profile-average clay, g/kg A ECa-em, ms/m Profile-average CEC, cmol/kg B ECa-em, ms/m Missouri Illinois Michigan Wisconsin South Dakota Iowa

30 Current research: Calibrating EC to layered soil properties 30 Use mobile EC data and commercial software to perform laterally constrained inversion of Maxwell s equations Extract layer conductivities and compare inversion based layer conductivities with EC from penetrometer Calibrate to layer texture data from soil samples Depth, m Geonics EM38 vertical Veris 3100 shallow Veris 3100 deep DUALEM-2S shallow (PRP) DUALEM-2S deep (HCP) Fraction of total response from shallower depth

31 Multi-sensor penetrometer data 31 Penetrometer with optical, EC, and force sensors (Veris P4000) Combining P4000 probe data with EC and other mobile sensor data can give a better picture of vertical & horizontal soil variability

32 Multi-sensor penetrometer data Depth (m) Sand & Clay (%) Wavelength (nm) EC (ms/m) Force (kpa) 4000 Depth (ft) -0.2

33 0-4" 4-8" 33 Layer-by-layer clay maps from mobile EC and probe data 8-12" 12-16" 16-20" Complex texture depth relationships 20-24" 24-28" Clay Content (%) 3.0 to to to to to 41.0

34 34 Understanding soil moisture effects to standardize EC Working on combining in-season EC data with point-measured soil moisture to more accurately map soil moisture in space (3d) and time

35 Summary 35 Mobile proximal soil sensing provides an alternative method for measuring soil variability important in sitespecific water management Soil EC data is particularly useful as a measure of texture and available water capacity Current research is refining EC-texture relationships and working toward a more standardized calibration of EC to texture

36 Questions? 36

37 37 Applications Better soil maps

38 Applications Nutrient management zones 38 Create zones based on EC (and perhaps topography); soil sample by zones Appropriate in areas where soil fertility variations are primarily due to landscape and soil-forming factors rather than management factors

39 Applications Variable N 39 Response to N varies by EC zones

40 40 Application: Nematode control in cotton Nematodes are more numerous in sandy soils Classified EC maps used to guide chemical application

41 Proximal Soil Sensing and Sensor Fusion

42 How do we improve results? Sensor fusion Combining data from multiple sensors can provide: Improved accuracy Robustness to operating condition variations Estimates of additional parameters of interest

43 Commercial sensor fusion: Veris MSP3 Two-band optical sensor for soil organic matter Red at 660 nm, NIR at 940 nm Shallow (0.3 m) and deep (1.0 m) soil apparent electrical conductivity (EC a ) Ion-selective electrodes for ph Optical sensors EC coulters ph ISEs

44 Commercial sensor fusion: Veris P4000 Vis-NIR spectrometers for soil reflectance ( nm) Dipole contacts for EC a Force sensor for cone index (CI) Insertion to ~ 1 m depth

45 Off-line data fusion allows integrating multiple sensing campaigns From Adamchuk et al., 2011

46 Case study: Improving bulk density estimates by fusion of mobile sensor measurements Goal: Relate BD to soil EC a as measured by Veris 3100, and soil strength as measured by cone penetrometer or the SSPS developed by Chung et al. (2006) Data collected on alluvial and claypan soil fields in central MO Veris 3100 Cho et al., Biosystems Engineering, 2016

47 Relating BD to WC, EC a and soil resistance Over all depths, better results were obtained for claypan (sites 1 & 3) than for alluvial (site 2) soils. Soil water content (WC) required for accuracy Claypan, Min-till Alluvial, Min-till Claypan, No-till

48 Case study: Profile sensor fusion of Vis-NIR, CI, and ECa data (Veris P4000) Depth (m) Sand & Clay (%) Wavelength (nm) EC (ms/m) Force (kpa) Cho et al., Trans. ASABE, 2017 Depth (ft) -0.2

49 Results: R 2 comparison All data vs Vis-NIR alone Adding EC a and CI to Vis-NIR reflectance data improved the model for organic carbon, texture fractions and bulk density.

50 Questions? 50