The Nonirrigated Lands Regression Model

The nonirrigated land model operates in two phases. In its first phase a regression model estimates coefficients per-region crop yield response based on winter, spring, and summer temperatures and precipitation using region-level monthly input data for a recent period of record depending on the application at hand.

Regression coefficients obtained in the first phase are employed in modeling scenarios runs. Model outputs provide estimated levels of crop production and gross revenues resulting from temperature and changes in precipitation. This phase is run twice: first without precipitation adjustments and then run again with them. The yield change is calculated as the difference between these to remove artifacts introduced by the regression itself.

OpenAg calculates gross revenues based on the new yield estimates in a similar manner to the PMP model’s revenue calculation: crop area (acres) * estimated yield (tons/acre) * adjusted price ($/ton)

Warning

When calculating revenue and area, nonirrigated lands model does not currently take into account crop area adjustment parameters. Those will only affect irrigated lands models.

The regression model uses a more limited dataset than the PMP model, depending on how much agriculture is irrigated or nonirrigated. In order to reduce effects from small samples, it is limited to crops with large amounts of nonirrigated area, and it is only used in regions where nonirrigated agriculture accounts for more than 5% of total agriculture.

Considerations

The regression model can occasionally show higher yields in response to less rainfall if the data for the calibration period had that response. In the Washington model area, this is rare, but has been noted as occurring.