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TRANSCRIPT
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Sensors for Precision Soil Health
Ken SudduthKristen Veum and Newell Kitchen
USDA-ARS Cropping Systems & Water Quality Research Unit, Columbia, MO
InfoAg Conference, 17 July 2018, St. Louis, MO
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Important indicators of soil health
¨ Physical ¤ Bulk density¤ Water filled pore space¤ Water stable aggregates
¨ Biological ¤ Organic carbon¤ β-glucosidase¤ Microbial biomass carbon
¨ Chemical & nutrient¤ pH¤ Electrical conductivity¤ Mineralizable nitrogen¤ Extractable P¤ Extractable K
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Soils are variable
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Standard soil sampling methods only sample one millionth (1/1000000) of the soil volume or less!
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Standard measurements
¨ Standard methods require soil sample collection, sample treatment, and laboratory analysis
¨ Limits the ability to assess variations in space and time
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Potential solution: Proximal soil sensing
Opticalreflectance
Soil mechanical resistanceApparent electrical conductivity
Electrochemical sensing
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Applicability of proximal soil sensorsCourtesy of Slava Adamchuk, McGill Univ.
Soil propertyEC/ER Optical Mech. Sound Electro
Chem
Soil texture (clay, silt and sand) Good OK Some
Soil organic matter or total carbon Some Good
Soil water (moisture) Good Good
Soil salinity (sodium) OK Some
Soil compaction (bulk density) Good Some
Depth variability (hard pan) Some OK Some
Soil pH Some Good
Residual nitrate (total nitrogen) Some Some OK
Other nutrients (potassium) Some OK
CEC (other buffer indicators) OK OK
H+
H+ H+H+
H+
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Matching indicators with sensors
¨ Physical ¤ Bulk density¤ Water filled pore space¤ Water stable aggregates
¨ Biological ¤ Organic carbon¤ β-glucosidase¤ Microbial biomass carbon
¨ Chemical and nutrient¤ pH¤ Electrical conductivity¤ Mineralizable nitrogen¤ Extractable P¤ Extractable K
Optical
H+
Electro-Chemical
H+H+H+
H+
Mechanical Resistance
Conductivity/Resistivity
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Long-term goal: Integrated sensor-based system providing farmers with a soil health assessment
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Mechanical resistance sensing
Soil resistance measured on-the-go (PSSI) was only weakly related to bulk density variations
Chung et al., Trans. ASABE, 2006
AA
B
B
0 20 40 60 80 100ECa, mS m-1
0
0.5
1
1.5
2
PSSI
, MPa
0 20 40 60 80 100ECa, mS m-1
0
1
2
3
PSSI
, MPa
0 20 40 60 80 100ECa, mS m-1
0
1
2
3
4
PSSI
, MPa
1.2 1.3 1.4 1.5 1.6 1.7Bulk density, Mg m-3
0
0.5
1
1.5
2
PSSI
, MPa
1.1 1.2 1.3 1.4 1.5 1.6 1.7Bulk density, Mg m-3
0
1
2
3
PSSI
, MPa
1.2 1.3 1.4 1.5 1.6 1.7Bulk density, Mg m-3
0
1
2
3
4
PSSI
, MPa
0 10 20 30 40 50Gravimetric soil water content, %
0
0.5
1
1.5
2
PSSI
, MPa
0 10 20 30 40 50Gravimetric soil water content, %
0
1
2
3
PSSI
, MPa
0 10 20 30 40 50Gravimetric soil water content, %
0
1
2
3
4
PSSI
, MPa
10-cm depth 30-cm depth 50-cm depth
0 0.5 1 1.5 2 2.5 3CI, MPa
0
0.5
1
1.5
2
PSSI
, MPa
0 1 2 3 4 5CI, MPa
0
1
2
3
PSSI
, MPa
0 1 2 3 4 5 6CI, MPa
0
1
2
3
4
PSSI
, MPa
10-cm depth 30-cm depth 50-cm depth
10-cm depth 30-cm depth 50-cm depth
10-cm depth 30-cm depth 50-cm depth
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Optical reflectance sensing
Visible and near-infrared (Vis-NIR) diffuse reflectance spectroscopy was effective in estimating biological indicators but generally not indicators in other categories
Veum et al., SSSAJ, 2015
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Soil electrical conductivity(ECa) sensing
¨ Physical and chemical properties of the soil affect the soil’s ability to transmit electricity
¨ Also important properties for soil health assessment¤ clay content/cation exchange capacity (CEC), pore size, shape, and
distribution, clay type
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ECa can be used to map soil texture within fields
Note that these results required within-field calibration sampling
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Nguyen et al., ASABE Irrigation Symposium, 2015
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Electrochemical sensing
¨ Stop-and-go field system for mapping
¨ Sensors tested for pH (r2 = 0.84) and nitrate (r2 = 0.87)
¨ Other soil nutrients might be added
¨ Potential for robotic operation
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Adamchuk et al., Proc. ICPA, 2014
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Matching indicators with sensors
¨ Physical ¤ Bulk density¤ Water filled pore space¤ Water stable aggregates
¨ Biological ¤ Organic carbon¤ β-glucosidase¤ Microbial biomass carbon
¨ Chemical and nutrient¤ pH¤ Electrical conductivity¤ Mineralizable nitrogen¤ Extractable P¤ Extractable K
Optical
H+
Electro-Chemical
H+H+H+
H+
Mechanical Resistance
Conductivity/Resistivity
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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
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Commercial sensor fusion: Veris MSP3
¨ Two-band optical sensor for soil organic matter/carbon¤ Red at 660 nm, NIR at 940 nm
¨ Shallow (0.3 m) and deep (1.0 m) ECa¨ Ion-selective
electrodes for pH
EC coulterspH ISEs
Optical sensors
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Commercial sensor fusion: Veris P4000
¨ Vis-NIR spectrometers for soil reflectance (343-2202 nm)¨ Dipole contacts for ECa¨ Force sensor for cone
index (CI)
¨ Insertion to ~ 1 m depth
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Off-line data fusion allows integrating multiple sensing campaigns
Adamchuk et al., 2011
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¨ Goal: Relate BD to soil ECa 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 fieldsin central MO
Case study: Improving bulk density estimates by fusion of mobile sensor measurements
Cho et al., Biosystems Engineering, 2016
Veris 3100
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Relating BD to WC, ECa 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
1 1.2 1.4 1.6 1.8Measured BD, Mg m-3
1
1.2
1.4
1.6
1.8
Estim
ated
BD
, Mg
m-3
Site 1(R2=0.76, RMSE=0.05)
1 1.2 1.4 1.6 1.8Measured BD, Mg m-3
1
1.2
1.4
1.6
Estim
ated
BD
, Mg
m-3
Site 2(R2=0.18, RMSE=0.08)
1 1.2 1.4 1.6 1.8 2Measured BD, Mg m-3
1
1.2
1.4
1.6
1.8
Estim
ated
BD
, Mg
m-3
Site 3(R2=0.66, RMSE=0.07)
Claypan, Min-till Alluvial, Min-till Claypan, No-till
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Study site & lab analysis
¨ 30 research plots in central Missouri
¨ Four grain cropping systems and 3 perennial grass systems
¨ Sampled at 0-5 cm and 5-15 cm depth intervals
¨ Lab analysis and calculation of SMAF scores from lab data(SMAF = Soil Management Assessment Framework)
Case study: Improving soil health estimates by fusion of VNIR, CI, and ECa data
Veum et al., Geoderma, 2017
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¨ Spectral data in the lab
¨ ECa and CI data in the field
Sensor data collection
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Results: R2 comparison for SMAF scores
¨ In all cases, sensor fusion improved the results compared to single-sensor data. Largest improvement was for Physical SMAF and Total SMAF.
Top bar = sensor fusion results; bottom bar = single-sensor results
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400 800 1200 1600 2000Wavelength (nm)
0 20 40EC (mS/m)
0 2000 4000Force (kPa)
0 40 80Sand & Clay (%)
-0.8
-0.7
-0.6
-0.5
-0.4
-0.3
-0.2
-0.1
Dep
th (m
)
-2.5
-2
-1.5
-1
-0.5
Dep
th (f
t)
Case study: Profile sensor fusion of Vis-NIR, CI, and ECadata (Veris P4000)
Cho et al., Trans. ASABE, 2017
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Results: R2 comparison All data vs Vis-NIR alone
¨ Combining Eca, CI, and Vis-NIR reflectance data improved estimates of texture fractions and bulk density
Top bar = sensor fusion results; bottom bar = single-sensor results
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Exploring the depth dimension:Calibrating EC to layered soil properties
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¨ Use mobile EC data and commercial software for “laterally constrained inversion”
¨ Extract layer conductivities and compare inversion–based layer conductivities with EC from penetrometer
¨ Calibrate to layer texture data from soil samples
0 0.2 0.4 0.6 0.8 1Fraction of total response from shallower depth
1.5
1.25
1
0.75
0.5
0.25
0
Dep
th, m
Geonics EM38 verticalVeris 3100 shallowVeris 3100 deepDUALEM-2S shallow (PRP)DUALEM-2S deep (HCP)
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Layer-by-layer clay maps from mobile EC data
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¨ Complex texture – depth relationships
¨ Validated by vertical EC probe data
Clay Content (%) 3.0 to 6.8 6.8 to 9.9 9.9 to 12.8 12.8 to 16.2 16.2 to 41.0
0-4"
4-8"
8-12"
12-16"
16-20"
20-24"
24-28"
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Summary
¨ VNIR spectroscopy provides good estimates for several biological soil health indicators and the biological soil health score.
¨ Adding CI and ECa data improved results, especially for the physical and overall soil health scores.
¨ Sensor fusion appears promising for soil health estimation, both overall and to estimate individual indicator variables (e.g., P, K, BD).
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Summary
¨ Further work will evaluate completely in-field sensing packages, both for profile and for surface data.
¨ Addition of other sensors (e.g., gamma radiation) will be investigated.
¨ Our goal is farmer-friendly soil health information efficiently provided in 4 dimensions¤ Time¤ Space¤ Depth
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