Assessing the Potential for Expanding Agriculture in Washington State

Group Members: Avril Li, Ben Felstein, Paul Levine, Rebecca Mason

Project Proposal and Goals

Our team’s proposal was assessing the potential for expanding agriculture in Washington State.Our project focuses mainly on the agricultural expansion in Washington States. Our project aims to answer question the “Is Washington State fulfilling its agricultural potential or are there significant areas of suitable agricultural land that are not currently being farmed?” We are going to use GIS analysis to assess the extent to which agriculture in WA could be expanded. Our analysis will focus on WA’s top 3 cereal crops: wheat, corn and barley. After determining the potential area for farming, we will then examine the significance of this figure in both a local and a global context.

Team Organization

Stage 1: Data Acquisition

  • data layer acquisition -Paul
  • outline and proposal composition -Becca and Ben
  • climatic data on barley, corn, wheat research -Avril

Stage 2: Map Creation using ArcMap

  • Map 1: climate and soil condition portion Avril   (could we do 3 maps for each crop separately)
  • Map 2: Land uses portion 1 Ben and Becca 
  • Map 3: map 2 and layers with current growing wheat, barley, corn BeccaPaul can help whoever needs help

Stage 3 Data Analysis and Result

  • everyone

To view Final Project Report:

geob-270-final-project-report

flowchart

Final Project Maps:

Part 1:barleypart1 barleypart2 barleypart3 barleytemp barleyprecp

Part 2: cornpart1 cornpart2 cornpart3 cornprecp corntemp

Part 3: wheatpart1 wheatpart2 wheatpart3 wheatprecp wheattemp

soilph

Data management was key in our project. Prior to starting the project, we organized all our data layers (raster or shape files) into one common geodatabase, which helped a lot with organization later on. Even then, we still need to make sure that the outputs of each ArcMap analysis were linked to our geodatabase. In the future, if time permitted, we could adopt other more complex analysis methods such as neighbourhood operations or multi-criteria evaluation. Since we have factors such as temperature, precipitation and pH, it will be interesting to see how the display and data will differ with MCE and our current method. Overall, the project was a success because everyone collaborated in a nice way.