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

1

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

2

Soils are variable

3

Standard soil sampling methods only sample one millionth (1/1000000) of the soil volume or less!

Standard measurements

¨ Standard methods require soil sample collection, sample treatment, and laboratory analysis

¨ Limits the ability to assess variations in space and time

4

Potential solution: Proximal soil sensing

Opticalreflectance

Soil mechanical resistanceApparent electrical conductivity

Electrochemical sensing

5

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+

6

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

7

Long-term goal: Integrated sensor-based system providing farmers with a soil health assessment

8

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

12

Nguyen et al., ASABE Irrigation Symposium, 2015

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

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

14

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

15

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)

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"

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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