Proposed Modification to Method for Determining Reasonable In-Season Demand for the Surface Water Coalition: Use of the USDA Crop Data Layer Presented to the SWC Methodology Technical Working Group by Matt Anders February 11, 2015 Settlement Document Subject to I.R.E. 408

What is Cropping Pattern? Acreage of each crop type grown by SWC members. Used in the Methodology for calculating Reasonable In-Season Demand (RISD): ∑(% Crop x Irr. Acres x ET Crop) / Project Efficiency = RISD

Current Method Data Source: USDA National Agricultural Statistics Service (NASS). Download tabular crop acreages by county. Calculate crop type averages for by county. Assign crop type percentages to each SWC member based on land area in each county. Drawbacks NASS tabular data are adjusted to protect farmer privacy. Incomplete data since average does not reflect current cropping patterns. Uncertain if county-wide cropping patterns represent SWC member cropping patterns.

Proposed Method: CDL Data Source: USDA National Agricultural Statistic Service (NASS) Download digital Crop Data Layer (CDL) Calculate percentages by crop type for each SWC member. Alternative Methods ET Idaho Delayed availability. METRICDelayed availability and not annually produced.

Processing CDL Downloaded digital Crop Data Layer (CDL) for Data Speckling

Processing CDL Data Smoothing Options Filter 3 by 3 pixel filter. Each pixel in the dataset is assigned a value based on values of neighboring pixels. Zonal Statistics A polygon dataset determines zones. The majority value within each zone is assigned to all pixels in the zone. Based on testing decided to use Zonal Statistics.

Processing CDL Smoothed CDL with Zonal Statistics Overlaid IDWR Irrigated Lands dataset. Assigned the majority crop within each irrigated land polygon to all pixels that fall within the polygon.

Processing CDL Problems with Smoothing IDWR Irrigated Lands dataset not available for every year of CDL data. Used current or most recent Irrigated Lands dataset. Field assigned one crop even if it has multiple crops

SWC Member Irrigated Lands Dataset created by SWC Member Burley Minidoka TFCC Dataset created by IDWR based on water right permissible place of use (PPU) A&B AFRD2 Milner NSCC

Acres for Each SWC Member Processed in ArcGIS with a geoprocessing model CDL_Processing” script in the “CDL_Toolbox.tbx Step 1:Clip CDL with SWC Irrigated Lands Dataset

Acres for Each SWC Member Step 2:Group by Raster Value CDL Attribute Description:generic_cdl_attributes.dbf RISD Group NameRaster ValueRaster Value Description Alfalfa36Alfalfa Barley21Barley Corn1 Developed - Semi Irr Developed/Open Space Developed/Low Intensity Dry Beans42Dry Beans Oats28Oats Pasture/Hay Other Hay/Non Alfalfa --- Historical value no longer used Grass/Pasture --- Historical value no longer used Peas53Peas Potatoes43Potatoes Spring Wheat23Spring Wheat Sugarbeets41Sugarbeets Winter Wheat24Winter Wheat z_Non-Crop Fallow/Idle Cropland Forest Open Water Perennial Ice/Snow Barren Deciduous Forest Evergreen Forest Mixed Forest Shrubland --- Historical value no longer used Woody Wetlands Herbaceous Wetlands z_Developed - No Irr Developed/Med Intensity Developed/High Intensity z_OtherAll other values

Acres for Each SWC Member Step 3: Calculate Acres by RISD Group (acres.xls) Step 4: Compute 3-Year Average (CDL_summary.xlsx) Discussion: Average data to reduce influence of a single year while still being representative of the current cropping pattern. 7 years of data did not indicate a clear relation between cropping pattern and water supply (CDL_summary_SWSI.xlsx), so decided to use a shorter average.

Comparison of Method Results NASS County Data: Average A&BAFRD2BIDMilnerMinidokaNSCCTFCC Alfalfa20.2%42.5%40.4%27.8%36.9%14.0%29.5% Barley11.6%3.3%3.7%12.0%11.9%20.1%12.7% Dry Beans3.4%4.4%2.9%9.2%2.1%3.6%16.7% Silage/Grain Corn3.6%5.7%27.8%14.9%9.1%2.0%11.2% Oats0.2%0.4% 0.1%0.8%0.2% Potatoes12.2%11.9%10.2%10.5%7.1%13.7%6.3% Sugarbeets12.6%11.7%4.0%9.6%12.0%23.0%6.7% Spring Wheat13.2%5.2%5.3%8.3%14.6%15.0%4.2% Winter Wheat23.0%14.9%5.2%7.5%5.5%8.4%12.5% CDL Data: 3-Year Average A&BAFRD2BIDMilnerMinidokaNSCCTFCC Alfalfa19.2%26.6%16.8%20.9%29.9%22.4%22.6% Barley31.1%5.6%4.3%15.4%13.2%6.9%10.9% Corn3.1%28.1%6.8%11.2%2.7%31.3%19.1% Developed - Semi Irr0.9%2.6%4.9%0.4%5.5%2.5%4.6% Dry Beans8.4%2.5%7.6%13.0%3.0%3.6%11.2% Oats0.1%0.7%0.1%0.3%0.2%0.1%1.0% Pasture/Hay2.6%17.1%5.3%1.6%9.0%11.9%15.0% Peas0.1%0.0% 0.4%0.1% 1.6% Potatoes8.7%3.3%10.7%7.6%11.9%7.3%2.8% Spring Wheat3.7% 4.1%3.7%3.6%1.8%0.7% Sugarbeets18.7%4.4%20.6%10.5%12.0%5.6%3.0% Winter Wheat3.5%5.5%18.7%15.0%8.8%6.5%7.4% Change: (NASS County Data: Average ) - (CDL Data: 3-Year Average) A&BAFRD2BIDMilnerMinidokaNSCCTFCC Alfalfa-1.0%-15.9%-23.6%-6.9%-7.0%8.4%-6.9% Barley19.5%2.3%0.6%3.4%1.3%-13.2%-1.9% Corn-0.5%22.4%-21.0%-3.7%-6.4%29.3%8.0% Developed - Semi Irr--- Dry Beans5.0%-1.9%4.7%3.7%0.9%0.0%-5.5% Oats-0.1%0.3%-0.3%0.2%-0.5%-0.1%0.8% Pasture/Hay--- Peas--- Potatoes-3.6%-8.6%0.5%-2.9%4.9%-6.4%-3.5% Spring Wheat-9.4%-1.4%-1.2%-4.6%-11.0%-13.1%-3.5% Sugarbeets6.1%-7.3%16.6%0.9%0.0%-17.5%-3.7% Winter Wheat-19.6%-9.4%13.5%7.6%3.4%-1.9%-5.0%

Information on Website CDL folder Input CDL Datasets CDL_swc_2007_30m_zs.tif(re-sampled from 56 m pixels to 30 m) CDL_swc_2008_30m_zs.tif (re-sampled from 56 m pixels to 30 m) CDL_swc_2009_30m_zs.tif (re-sampled from 56 m pixels to 30 m) CDL_swc_2010_zs.tif CDL_swc_2011_zs.tif CDL_swc_2012_zs.tif CDL_swc_2013_zs.tif Processing Output Output by company and year from the geoprocessing script. GIS Folder Geoprocessing Script:CDL_Toolbox.tbx ArcMap Project:CDL-SWC-TWG.mxd CDL Attribute Key:generic_cdl_attributes.dbf Geoprocessing Script Grouping Calculation:CDL_Toolbox.tbx

Information on Website Irrigated Acres folder Shapefiles of the irrigated acres used for each SWC member BurleyBID_POU_2013.shp Minidokaminidoka acres 3-13.shp TFCCTFCC_2013.shp A&BAB_SW_clip_IDWR_Irr2010.shp AFRD2AFRD2_snake_clip_IDWR_Irr2010.shp Milnermilner_clip_IDWR_Irr2010.shp NSCCnorthside_clip_IDWR_Irr2010.shp Loose files CDL Method Comparison Results (Slide 12):CDL_Comparison_Method_Results.docx This PowerPoint:CDL_Cropping_Pattern.pptx

Questions? (208)