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Behind the Map:

FAO's AquaCrop
FAQ - AquaCrop
AquaCrop is a crop water productivity model developed by the Land and Water Division of FAO. It simulates yield response to water of herbaceous crops, and is particularly suited to address conditions where water is a key limiting factor in crop production. AquaCrop attempts to balance accuracy, simplicity, and robustness. It uses a relatively small number of explicit and mostly-intuitive parameters and input variables requiring simple methods for their determination.
Climate Change and Agriculture and Food Security
Research Program on Climate Change, Agriculture and Food Security.
The CCAFS is a strategic collaboration researching climate change and food security run by the CGIAR. It is from here that statistically downscaled data was retrieved from for the creation of the crop yield projections. The downscaling of Global Climate Models (GCM's) is an essential step to allow for regional studies where climate predictions are required. Most GCM's have spatial resolutions of 100m or greater, making regional data very course and likley missing large amounts of spatial variability. There are two types of downscaling, statistical and dynamic. Dynamic downscaling fits GCM outputs into regional meteorological models. These are very computational and resource heavy approaches. Statistical downscaling, the approached used in this project, use a series of equations to attempt to predict local values based off of GCM data as well as local variables such as jet stream locations.
How to download cliamte data

Methodology:

01.

Download Climate Projections

Regional Climate predictions were downloaded from CCAFS. The data downloaded consists of Rainfall, and Temperature (Mean, Max and Min). A tutorial of how to download the data can be found here.

02.

Averaging of Data

The monthly averaged climate data is averaged based on soil polygons. The averaged data was transposed to proprietary AquaCrop files (.TMP, .PLU, .EVT).

03.

AquaCrop Model Run For Each Scenario

AquaCrop was run for each scenario. This means that for each year of data (2030 and 2050) AquaCrop was run for each crop and soil types scenario.

04.

AquaCrop Output Processed into ESRI Shapefile

For each crop type and year, the AquaCrop output was converted to a vector shapefile and a column was added and populated for the crop type.

05.

Shapefile Converted to .KML File

The shapefile for each crop yield prediction was converted to KML using shp2kml 2.0.

06.

KML files imported into Google Fusion Tables

KML files were imported into one of two Google Fusion Tables based on their year.