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A multifunctional matching algorithm for sample design in agricultural plots.

N Ohana-Levi1, A Derumigny2, A Peeters3

  • 1Independent Researcher, Variability, Ashalim 85512, Israel.

Computers and Electronics in Agriculture
|August 12, 2021
PubMed
Summary
This summary is machine-generated.

A new data-driven method, multifunctional matching (MFM), efficiently selects optimal agricultural sampling locations. MFM improves crop management by accurately representing field variability with minimal data points.

Keywords:
Agricultural samplingPartially-observed dataRepresentative sampling given covariatesSpatial autocorrelationTwo-phase study

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Area of Science:

  • Agricultural Science
  • Geospatial Analysis
  • Data Science

Background:

  • Efficient crop management relies on accurate, representative agricultural field data.
  • Limited grower resources necessitate advanced methods for selecting optimal sampling points.
  • Existing methods may not fully capture spatial variability or covariate distributions.

Purpose of the Study:

  • Develop a data-driven method for selecting representative agricultural sampling locations.
  • Ensure selected points reflect the distribution and spatial variability of field covariates.
  • Create an algorithm to determine the minimal number of observations for desired accuracy.

Main Methods:

  • Developed the multifunctional matching (MFM) criterion based on matching moments (standard deviation, mean, Kendall's tau) between sample and population.
  • Applied MFM to vineyard and peach orchard datasets with covariates like NDVI, soil electrical conductivity, slope, and TWI.
  • Validated MFM against crop water stress index (CWSI) and compared it with conditioned Latin hypercube sampling (cLHS) and random sampling.

Main Results:

  • MFM algorithm determined optimal sampling locations and number of points for 90% representation accuracy.
  • MFM demonstrated superior representation of CWSI distribution compared to cLHS and random sampling.
  • Selected MFM locations showed smaller deviations from population mean and standard deviation, capturing spatial variability.

Conclusions:

  • MFM is an effective data-driven approach for selecting representative agricultural sampling points.
  • The method accurately captures spatial variability and covariate distributions, outperforming other sampling strategies.
  • MFM is adaptable for various moments/functionals and applicable across disciplines needing small sample sizes.