Introduction

This analysis explores the relationships of agricultural commodity loss, at a county level, from 1989-2015, for the 26 county region of the Palouse, in Washington, Idaho, and Oregon. Here we explore the entire range of commodities and damage causes, identifying the top revenue loss commodities and their most pertinent damage causes - as indicated from the USDA’s agricultural commodity loss insurance archive.

We perform several steps in Phase 2 to explore missing data, which is a continuation from Phase 1, steps 1-9.

Step 10. Missing Data Examination. In this step we examine missing data in relationship to all of our factors (year, county, damagecause) for each individual commodity we are examining (wheat, apples, cherries, and dry peas)

Step 10: Missing Data Examination and filling in select zeros as NA

In Steps 7 and 8, we explore the missing data by county, damage cause, and year, and then fill in missing data for damage cause where commodity loss exists in a county. For example, if wheat is grown in a county for a particular year, but NO wheat loss claims were submitted - we consider this a zero (there where zero claims - vs this data being missing). We do this in order to ensure that we have as complete a dataset as possible.


WHEAT Missing Data examination by damage cause, year, and county, using two dimensions

APPLES Missing Data examination by damage cause, year, and county, using two dimensions