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Transcript of Sensors for Precision Soil Health - InfoAg Important indicators of soil health آ¨ Physical آ¤...

  • Sensors for Precision Soil Health

    Ken Sudduth Kristen 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

    Optical reflectance

    Soil mechanical resistance Apparent electrical conductivity

    Electrochemical sensing

    5

  • Applicability of proximal soil sensors Courtesy of Slava Adamchuk, McGill Univ.

    Soil property EC/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

    A A

    B

    B

    0 20 40 60 80 100 ECa, mS m-1

    0

    0.5

    1

    1.5

    2

    PS SI

    , M Pa

    0 20 40 60 80 100 ECa, mS m-1

    0

    1

    2

    3

    PS SI

    , M Pa

    0 20 40 60 80 100 ECa, mS m-1

    0

    1

    2

    3

    4

    PS SI

    , M Pa

    1.2 1.3 1.4 1.5 1.6 1.7 Bulk density, Mg m-3

    0

    0.5

    1

    1.5

    2

    PS SI

    , M Pa

    1.1 1.2 1.3 1.4 1.5 1.6 1.7 Bulk density, Mg m-3

    0

    1

    2

    3

    PS SI

    , M Pa

    1.2 1.3 1.4 1.5 1.6 1.7 Bulk density, Mg m-3

    0

    1

    2

    3

    4

    PS SI

    , M Pa

    0 10 20 30 40 50 Gravimetric soil water content, %

    0

    0.5

    1

    1.5

    2

    PS SI

    , M Pa

    0 10 20 30 40 50 Gravimetric soil water content, %

    0

    1

    2

    3

    PS SI

    , M Pa

    0 10 20 30 40 50 Gravimetric soil water content, %

    0

    1

    2

    3

    4

    PS SI

    , M Pa

    10-cm depth 30-cm depth 50-cm depth

    0 0.5 1 1.5 2 2.5 3 CI, MPa

    0

    0.5

    1

    1.5

    2

    PS SI

    , M Pa

    0 1 2 3 4 5 CI, MPa

    0

    1

    2

    3

    PS SI

    , M Pa

    0 1 2 3 4 5 6 CI, MPa

    0

    1

    2

    3

    4

    PS SI

    , M Pa

    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

    9

  • 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

    10

  • 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

    11

  • 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

    13

    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 coulters pH ISEs

    Optical sensors

    16

  • 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

    17

  • Off-line data fusion allows integrating multiple sensing campaigns

    Adamchuk et al., 2011

    18

  • ¨ 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 fields in central MO

    Case study: Improving bulk density estimates by fusion of mobile sensor measurements

    Cho et al., Biosystems Engineering, 2016

    Veris 3100

    19

  • 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 1.8 Measured BD, Mg m-3

    1

    1.2

    1.4

    1.6

    1.8

    Es tim

    at ed

    B D

    , M g

    m -3

    Site 1 (R2=0.76, RMSE=0.05)

    1 1.2 1.4 1.6 1.8 Measured BD, Mg m-3

    1

    1.2

    1.4

    1.6

    Es tim

    at ed

    B D

    , M g

    m -3

    Site 2 (R2=0.18, RMSE=0.08)

    1 1.2 1.4 1.6 1.8 2 Measured BD, Mg m-3

    1

    1.2

    1.4

    1.6

    1.8

    Es tim

    at ed

    B D

    , M g

    m -3

    Site 3 (R2=0.66, RMSE=0.07)

    Claypan, Min-till Alluvial, Min-till Claypan, No-till

    20

  • 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

    21

  • ¨ Spectral data in the lab

    ¨ ECa and CI data in the field

    Sensor data collection

    22

  • 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

    23

  • 400 800 1200 1600 2000 Wavelength (nm)

    0 20 40 EC (mS/m)

    0 2000 4000 Force (kPa)

    0 40 80 Sand & Clay (%)

    -0.8

    -0.7

    -0.6

    -0.5

    -0.4

    -0.3

    -0.2

    -0.1

    D ep

    th (m

    )

    -2.5

    -2

    -1.5

    -1

    -0.5

    D ep

    th (f

    t)

    Case study: Profile sensor fusion of Vis-NIR, CI, and ECa data (Veris P4000)

    Cho et al., Trans. ASABE, 2017

    24

  • Results: R2