Averages yield across all years at each location. A sample with 180 bu in 2024 and 220 bu in 2025 becomes 200 bu. Best for: Finding patterns that persist across years. Smooths out weather variation.
All Years (Combined)
Each year treated as separate data point. Same location counted multiple times (once per year). Best for: Maximum sample size when years had similar growing conditions. β οΈ Combines good and bad years together - results may be misleading if yields varied significantly.
All Years (Year-Normalized)
Each year treated separately, but yields converted to % of that year's average. Example: 200 bu yield in a 180 bu avg year = 111%. 250 bu yield in a 250 bu avg year = 100%. Best for: Comparing across years with different weather. Isolates nutrient effect from seasonal variation.
Individual Year (e.g., "2024")
Only uses yield from that specific harvest year. Best for: Analyzing a specific season's results.
π€ How should I choose?
Similar yields across years? β "Combined" gives you more data points
Very different yields (drought vs good year)? β "Year-Normalized" controls for weather
Want simplicity? β "Averaged" is most straightforward
Analyzing specific season? β Select individual year
π Understanding Field Normalization×
Why do correlations change when normalized?
WITHOUT normalization:
Correlations include differences between fields. A high-yielding field with high P will inflate the P-yield correlation, even if P isn't the reason that field yields well (could be better soil type, drainage, etc.)
WITH normalization:
Yields are converted to "% of field average", removing field-to-field differences. This isolates the nutrient effect by asking: "Within each field, do higher nutrient levels produce above-average yields?"
Why correlations often decrease:
Some of the original correlation was driven by field productivity differences, not the nutrient itself. The normalized correlation is stricter but more trustworthy for fertilizer decisions.
Example:
Raw correlation: P = +0.35 (includes field effects)
Normalized: P = +0.18 (nutrient effect only)
The 0.18 is more reliable for predicting if adding P will help within a field.
π‘ Recommendation:
Use normalized correlations when deciding on variable-rate fertilizer applications within fields. Use raw correlations when comparing overall field performance.
Individual Correlations: Analyzes each nutrient separately. Shows how each nutrient relates to yield independently - useful for exploring individual relationships.
No yield data available. Import yield maps on the Import page to see correlations.
Scatter Plot: Nutrient vs Yield
bu/ac
Threshold Analysis
Data Verification Report
Multivariate Regression: Analyzes all nutrients together. Shows which nutrients significantly affect yield after controlling for the others - answers "What matters when everything is considered?"
π Select Variables for Model
π Model Summary
β οΈ Collinearity Warning
Some variables are highly correlated with each other, which can make coefficient estimates unreliable.
π Regression Coefficients
Each coefficient shows the nutrient's effect on yield while holding other nutrients constant
Variable
Coefficient
Std Error
t-value
p-value
Sig.
Significance: *** p < 0.001, ** p < 0.01, * p < 0.05, . p < 0.1
π― Yield Predictor
Enter nutrient values to predict yield based on the model.
Yield by Nutrient Level: Groups soil samples into Low, Medium, and High categories for each nutrient, then shows the average yield for each group. Helps identify which nutrient levels are associated with the best yields.
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Select a field and nutrient to analyze spatial changes over time.