Sensors for Precision Soil Health

Transcription

1 1 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

2 Important indicators of soil health 2 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

3 Soils are variable 3 Standard soil sampling methods only sample one millionth (1/ ) of the soil volume or less!

4 Standard measurements 4 Standard methods require soil sample collection, sample treatment, and laboratory analysis Limits the ability to assess variations in space and time

5 Potential solution: Proximal soil sensing 5 Optical reflectance Soil mechanical resistance Apparent electrical conductivity Electrochemical sensing

6 Applicability of proximal soil sensors Courtesy of Slava Adamchuk, McGill Univ. 6 Soil property EC/ER Optical Mech. Sound Electro Chem H + H + H + H + Soil texture (clay, silt and sand) Good OK Some H + 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

7 Matching indicators with sensors 7 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 Conductivity/ Resistivity Optical Electro- Chemical H + H+ H + H + H + Mechanical Resistance

8 8 Long-term goal: Integrated sensor-based system providing farmers with a soil health assessment

9 Mechanical resistance sensing 9 Soil resistance measured on-thego (PSSI) was only weakly related to bulk density variations 3 30-cm depth PSSI, MPa 2 1 PSSI, MPa Bulk density, Mg m -3 Chung et al., Trans. ASABE, 2006

10 Optical reflectance sensing 10 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

11 Soil electrical conductivity (EC a ) sensing 11 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

12 EC a can be used to map soil texture within fields 12 Note that these results required within-field calibration sampling Nguyen et al., ASABE Irrigation Symposium, 2015

13 Electrochemical sensing 13 Stop-and-go field system for mapping Sensors tested for ph (r 2 = 0.84) and nitrate (r 2 = 0.87) Other soil nutrients might be added Potential for robotic operation Adamchuk et al., Proc. ICPA, 2014

14 Matching indicators with sensors 14 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 Conductivity/ Resistivity Optical Electro- Chemical H + H+ H + H + H + Mechanical Resistance

15 How do we improve results? 15 Sensor fusion Combining data from multiple sensors can provide: Improved accuracy Robustness to operating condition variations Estimates of additional parameters of interest

16 Commercial sensor fusion: Veris MSP3 16 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) EC a Ion-selective electrodes for ph Optical sensors EC coulters ph ISEs

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

18 Off-line data fusion allows integrating multiple sensing campaigns 18 Adamchuk et al., 2011

19 Case study: Improving bulk density estimates by fusion of mobile sensor measurements 19 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

20 Relating BD to WC, EC a and soil resistance 20 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 1.8 Claypan, Min-till Alluvial, Min-till Claypan, No-till Estimated BD, Mg m Site 1 (R 2 =0.76, RMSE=0.05) Measured BD, Mg m-3 Estimated BD, Mg m Site 2 (R 2 =0.18, RMSE=0.08) Measured BD, Mg m-3 Estimated BD, Mg m Site 3 (R 2 =0.66, RMSE=0.07) Measured BD, Mg m-3

21 Case study: Improving soil health estimates by fusion of VNIR, CI, and EC a data 21 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) Veum et al., Geoderma, 2017

22 Sensor data collection Spectral data in the lab EC a and CI data in the field 22

23 Results: R 2 comparison for SMAF scores 23 Top bar = sensor fusion results; bottom bar = single-sensor results In all cases, sensor fusion improved the results compared to single-sensor data. Largest improvement was for Physical SMAF and Total SMAF.

24 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)

25 25 Results: R 2 comparison All data vs Vis-NIR alone Top bar = sensor fusion results; bottom bar = single-sensor results Combining Ec a, CI, and Vis-NIR reflectance data improved estimates of texture fractions and bulk density

26 Exploring the depth dimension: Calibrating EC to layered soil properties 26 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 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

27 Layer-by-layer clay maps from mobile EC data 0-4" 4-8" 27 Complex texture depth relationships Validated by vertical EC probe data 8-12" 12-16" 16-20" 20-24" 24-28" Clay Content (%) 3.0 to to to to to 41.0

28 Summary 28 VNIR spectroscopy provides good estimates for several biological soil health indicators and the biological soil health score. Adding CI and EC a 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).

29 Summary 29 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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