Sample Bunch Weight Scaling Tonnage

Scale stratified measured bunch mass using caller known sample inclusion probabilities and actual represented bunch counts, explicitly Hájek weighted mean; not equal-weight count method, exact unbiased population total or random-sample claim.

Description

Unequal sample inclusion probabilities can bias a simple bunch-weight average.

Unequal sample inclusion probabilities can bias a simple bunch-weight average. An inverse-probability ratio mean accounts for those supplied sampling weights.

When to use Sample Bunch Weight Scaling Tonnage

Use this tool to scale stratified measured bunch mass using caller known sample inclusion probabilities and actual represented bunch counts, explicitly Hájek weighted mean; not equal-weight count method, exact unbiased population total or random-sample claim.

Prepare vineyard measurements, dates or block records for the intended growing period. Keep weights, counts and weather observations in the stated units.

How to use Sample Bunch Weight Scaling Tonnage and understand its results

Prepare vineyard measurements, dates or block records for the intended growing period. Keep weights, counts and weather observations in the stated units.
Enter sampleBunchMassesKgByStratum, representedHarvestBunchCounts, sampleInclusionProbabilities using the field table and calculation rules, then run the tool.
Read inverseProbabilityMeanBunchMassesKg, stratumScaledYieldTonnes, totalStratifiedYieldTonnes. The explanation below defines the units, signs, ordering and any flags or placeholder values.
If the tool reports an input issue, correct the values and run it again. Apply the stated assumptions when interpreting a successfully returned result.

Input field	Meaning	Value type; see rules below	Requirement
sampleBunchMassesKgByStratum	Sample bunch masses kg by stratum	list of list of number	Required
representedHarvestBunchCounts	Represented harvest bunch counts	list of number	Required
sampleInclusionProbabilities	Sample inclusion probabilities	list of list of number	Required

Align strata, sample mass rows and inclusion probabilities in (0,1]. Use nonnegative masses and integer represented bunch counts. Compute the Hájek mean in each stratum, then scale and sum yield.^[1]

Paired lists must be nonempty and have equal lengths.

weighted mean = Σ(mass/probability)/Σ(1/probability); tonnes = mean × represented bunches/1000

Units, indexing, boundary conditions and exceptional cases are defined in the calculation rules above.

Output field	Meaning	Value type
inverseProbabilityMeanBunchMassesKg	Inverse probability mean bunch masses kg	list of number
stratumScaledYieldTonnes	Stratum scaled yield tonnes	list of number
totalStratifiedYieldTonnes	Total stratified yield tonnes	number

json

{
  "input": {
    "sampleBunchMassesKgByStratum": [
      [
        1,
        3
      ],
      [
        2,
        2
      ]
    ],
    "representedHarvestBunchCounts": [
      100,
      200
    ],
    "sampleInclusionProbabilities": [
      [
        0.5,
        1
      ],
      [
        1,
        1
      ]
    ]
  },
  "output": {
    "inverseProbabilityMeanBunchMassesKg": [
      1.6666666666666667,
      2
    ],
    "stratumScaledYieldTonnes": [
      0.16666666666666669,
      0.4
    ],
    "totalStratifiedYieldTonnes": 0.5666666666666667
  }
}

Limitations and assumptions

Known inclusion probabilities are required; this is not an exactly unbiased total or proof of random sampling.

Numbers and computed results must be finite; overflow produces an error. Matrices are limited to 128 rows and 128 entries per row; other numeric lists are limited to 100,000 entries. Missing required fields, extra fields, wrong value types and violations of the calculation rules are input errors.

Alternative or Complementary methods

Use Crop Estimate Cluster Count Method: Expand equal-probability sampled vine cluster counts to block yield with caller mature-cluster mass and count-sampling error only; no weight/selection uncertainty, harvest guarantee or optimum crop load. For a complementary task, use Lag Phase Weight Yield Forecast: Forecast heterogeneous lag-phase cluster masses using supplied calibrated maturity growth factors and represented cluster inventories; unlike count-only yield expansion, weight growth is explicit and not guaranteed.

References

Sample Bunch Weight Scaling Tonnage — tool.io

Similar or alternative tools

Crop Estimate Cluster Count Method
Expand equal-probability sampled vine cluster counts to block yield with caller mature-cluster mass and count-sampling error only; no weight/selection uncertainty, harvest guarantee or optimum crop load.
Lag Phase Weight Yield Forecast
Forecast heterogeneous lag-phase cluster masses using supplied calibrated maturity growth factors and represented cluster inventories; unlike count-only yield expansion, weight growth is explicit and not guaranteed.