Agronomic Spatial Variability and Resolution Data Normalization and Temporal Variability

Data Normalization Frequently, measurements made at different locations or at the same location at different times differ in magnitude because of other factors. For example yield varies because: –rainfall differs between years or locations –Different crop cultivars (varieties) are used –Freezes occur at flowering –Different rates of fertilizer are applied

Data Normalization Data normalization adjusts the measured variable to a common scale to reduce biases when making comparisons. Data are normalized by dividing each measurement by the average value of all measurements or dividing all measurements by the maximum value. –Dividing by the mean yields an average value of 1 –Dividing by the maximum yields a maximum value of 1

Data Normalization Generally, measurements should be normalized, when making multitemporal comparisons –This is particularly important when investigating spatial variability

Temporal Variability Agronomic measurements can change over time with measurements changing at different rates within a field during a growing season. This variability is termed temporal variability. Measurements can also change differently within a field between crop years. This variability is frequently termed multi- temporal variability.

Temporal Variability Temporal variability may be described by: –Time series analysis –Descriptive statistics

Temporal Variability Field Average Yield

Satellite Estimated Yields Temporal Variability By Field

Red Rock, Oklahoma

Pond Creek, Oklahoma

Cherokee, Oklahoma

Hitchcock, Oklahoma East Field

Hitchcock, Oklahoma East Field East Field

Hitchcock, Oklahoma East Field

Hitchcock, Oklahoma East Field

Hitchcock, Oklahoma West Field

Hitchcock, Oklahoma West Field

Hitchcock, Oklahoma West Field

Hitchcock, Oklahoma West Field