Identify gaps in wheat production

Food production relies on many factors such as land suitability, climatic variability, and crop planning. Agroclimatic information is key to national organizations to estimate potential yields and identify areas where food production can be increased. The Food and Agriculture Organization (FAO) has developed the Global Agro-Ecological Zones (GAEZ) dataset to provide key information on land utilization, agroclimatic resources, and agricultural production to analyze and identify opportunities to improve yields and food production. In this tutorial, you'll take on the role of an analyst who'll use the GAEZ data to analyze wheat production trends in the Yaqui Valley, Mexico.

This tutorial was last tested on September 15, 2023, using ArcGIS Pro 3.1. If you're using a different version of ArcGIS Pro, you may encounter different functionality and results

Requirements

• ArcGIS Pro (see options for software access)
• ArcGIS Spatial Analyst or ArcGIS Image Analyst

Access GAEZ data

To begin your analysis, you'll create an ArcGIS Pro project and add the GAEZ image service to your map. Once on the map, you'll filter the data using a definition query to display information for the selected crop, water supply type, and time period. For this first module, you'll display wheat that's been irrigated for the year 2010.

Create a project

In this section, you’ll create an ArcGIS Pro project and load the GAEZ image service to it.

1. Start ArcGIS Pro. If prompted, sign in using your licensed ArcGIS organizational account.

   Note:

   If you don't have access to ArcGIS Pro or an ArcGIS organizational account, see options for software access.

2. On the home page, under New Project, click Map.

   

3. Click Map.

   The Create a New Project window appears. You'll create a project folder for your work.

4. For Name, type Wheat Analysis. For Location, browse to a location on your computer where you want to save the project.

   

5. Click OK.

   A blank map appears. Next, you'll zoom to the Yaqui Valley in Mexico.

6. On the ribbon, click the Map tab. In the Inquiry group, click Locate.

   

   The Locate pane appears. You can search for locations of interest and see them on the map.

   

7. In the Locate pane, in the Search box, type Ciudad Obregon. Press Enter.

   Several results appear and the map zooms to the first location automatically.

   

8. Zoom out and to the south to see the surrounding area.

   

   The visible area is the Yaqui Valley, where your analysis will take place.

9. Close the Locate pane.

   Next, you'll change the basemap to see the general extent of the agriculture in the area.

10. On the Map tab, in the Layer group, click Basemap. Choose Imagery Hybrid.

    

    The areas in dark green are the fields that you'll be analyzing with GAEZ data.

    

Load GAEZ data

Now that you have an ArcGIS Pro project set up and you're focused on the project area, you'll find the GAEZ data online and add it to your map.

1. Open a web browser and go to the FAO GAEZ v4 Data Portal.
2. Scroll down to the GAEZ v4 Image Service and Metadata Listing table.

   

   Several GAEZ variables are available in this table. You'll access Actual Yields and Production data as an image service. The table also contains links to metadata to get a thorough description about the data.

3. Find the row for Theme 5: Actual Yields and Production. For that row, right-click Image Service URL and copy the link's address.

   

   Note:

   The process to copy a URL may vary depending on your web browser. The Image Service URL is https://gaez-services.fao.org/server/rest/services/res06/ImageServer.

4. In ArcGIS Pro, on the ribbon, click the Map tab. In the Layer group, under Add Data, click the drop-down arrow. Choose Data From Path.

   

   The Add Data From Path window appears. Here, you'll paste the URL from the data portal to add its image service to your map.

5. In the Add Data From Path window, for Path, right-click and choose Paste.

   

6. Click Add.

   The GAEZ data is added to the map, but the layer needs to be configured before you can view it. First, you'll rename the layer.

7. In the Contents pane, click the res06 layer and press the F2 key. Type Actual Yields and Production.

   The layer is renamed. Next, you'll explore the layer's attributes.

8. In the Contents pane, right-click Actual Yields and Production, and choose Attribute Table.

   

   The attribute table appears below the map. There are 498 rasters in this image service.

   

   You'll reduce the number of rasters in this table to see only the raster representing irrigated wheat production from 2010.

Query the image service

Now that the GAEZ data has been added to the map, you'll use a Definition Query to reduce the number of rasters from 498 to 1 using a series of SQL clauses. This definition query will display the yield raster for irrigated wheat. Then, you’ll update the symbology to better understand this data.

1. In the Contents pane, right-click Actual Yields and Production, and choose Properties.

   

   The Layer Properties window appears. This is where you can create definition queries.

2. In the Layer Properties window, click the Definition Query tab. Click New definition query.

   

   Next, you'll build a query with a series of clauses to select the yield of wheat crops that were irrigated in 2010. The first clause will select the wheat crops.

3. Create the clause Where Crop is equal to Wheat.

   

   You will add a second clause that will select the areas that have been irrigated.

4. Click Add Clause. Create a clause to select Water Supply is equal to Irrigated.

   

   The third clause will select the yield variable.

5. Click Add Clause. Create a clause to select Variable Name is equal to Yield.

   

   The final clause will select data from the year 2010.

6. Click Add Clause. Create a clause to select Time Period is equal to 2010.

   

7. Click Apply. Click OK.

   Now, only one raster is shown in the attribute table, and it is displayed on the map. It shows the agricultural yield in tons per hectare for irrigated wheat in 2010.

   

   Note:

   If the map does not display the Actual Yields and Production layer, click the Refresh button in the lower right corner of the Map view.

   Due to the layer's configuration and your map's extent, the Actual Yields and Production symbology may look different than the image above.

8. Close the attribute table.

   Now, you'll change the symbology to show the actual yield values in tons per hectare. To do this, you'll start by removing the existing symbology, also known as a Processing Template.

9. In the Contents pane, ensure that the Actual Yields and Production layer is selected.
10. On the ribbon, click the Data tab. In the Processing group, click Processing Templates. Choose None.

    

    The data appears to have the same symbology across the entire study area.

    

    To better visualize the data for this area, you'll use DRA, or dynamic range adjustment. Dynamic range adjustment is a contrast stretch that stretches only the pixel values within your map's current display extent. With this setting activated, the layer's symbology will update as you pan around the map and change its extent.

11. On the ribbon, click the Image Service Layer tab. In the Rendering group, click DRA.

    

    The Actual Yields and Production symbology updates. More of a gradient is seen in the layer's symbology.

    

    Note:

    Your symbology may appear different than the image above. This is due to the dynamic range adjustment. If you pan around the map, you'll see the symbology change based on your map's extent. The underlying pixel values do not change.

    Next, you'll change the symbology from black and white to green to better display your data.

12. On the Image Service Layer tab, in the Rendering group, click Symbology.

    

    The Symbology pane appears. This allows you to change your layer's appearance on the map. You'll change it from a black and white color ramp to varying shades of green.

13. In the Symbology pane, for Color scheme, click the drop-down arrow. Check the box next to Show names. Choose Greens (Continuous).

    

    The map shows areas of high agricultural yield in dark green. In contrast, areas of lower yield are shown in light green. If you click the map, you can see the underlying yield values in tons per hectare.

    

14. Click any of the cells near Ciudad Obregon.

    The Pop-up appears with information about the cell you picked. The yield value, tons per hectare, is the Service Pixel Value in the Pop-up.

    

15. Close the Pop-up.

    The values around Ciudad Obregon are about 5.1 or 5.2 tons per hectare. The values at the southern end of the Yaqui Valley, near the town of Navojoa, are about 5.0 tons per hectare.

16. Click the Save button.

    

In this module, you created an ArcGIS Pro project, and added a GAEZ image service. You also applied a definition query to display a raster of irrigated wheat yield in the Yaqui Valley. Next, you’ll analyze wheat yields across the valley and see how they compare to global yields.

Analyze wheat yield

Next, you’ll analyze wheat yield across the study area. First, you’ll create a histogram for irrigated wheat for the global dataset, then you’ll export a raster to isolate the study area and create a histogram for the Yaqui Valley. Finally, you’ll classify and change the symbology of the yield map.

Create a global histogram of yields

In this section, you’ll create a histogram of wheat yield for the entire world. Later, you'll compare the global results to the local yields.

1. In the Contents pane, right-click the Actual Yields and Production layer. Point to Create Chart and select Histogram.

   

   A blank chart appears below the map. The Chart Properties pane also appears. You'll use this pane to configure your histograms. The chart will pull on the wheat production values; they will be referred to as Band_1.

2. In the Chart Properties pane, under Variable, set Number to Band_1.

   

   The histogram renders in the chart view, but it is skewed to the left. This is caused by the dynamic range adjustment.

   

   Next, you'll turn off the dynamic range adjustment.

3. On the ribbon, on the Image Service Layer tab, in the Rendering group, click DRA.

   The dynamic range adjustment is turned off and the histogram redraws.

   

   This histogram represents the 2010 yield of irrigated wheat globally. The average is about 4.1 tons per hectare. Next, you'll add more statistics to help understand the data.

4. In the Chart Properties pane, under Statistics, check the box next to Std. Dev.

   

   Additional statistics are added to the histogram.

   

   The global mean is 4.2 tons per hectare. From the previous module, you learned that the yield values were near 5.0 (Navojoa) and 5.2 (Ciudad Obregon). These values are above the global mean but are within one standard deviation (< 5.94 ton/ha). This implies that there is room for improved yields.

Create a local histogram of yields

Now you'll create a histogram for the local wheat yield. First, you’ll export a yield raster for the Yaqui Valley. Then, you'll create a histogram of wheat yield for this specific study area. Finally, you’ll compare the local results to the broader global context.

1. In the Contents pane, right-click the Actual Yields and Production layer. Point to Data and select Export Raster.

   

   The Export Raster pane appears. This pane allows you to extract an area of interest from a larger raster dataset. You'll use this pane to create a raster of only the Yaqui Valley. First, you'll give your raster a name and save it to your project's file geodatabase.

2. In the Export Raster pane, for Output Raster Dataset, click Browse.

   

   The Output Location window appears. You can choose where you want to save the raster.

3. Under Project, expand Databases and select Wheat Analysis.gdb.

   

4. For Name, type Irrigated_Wheat_Yield. Click Save.

   Next, you'll set the north (top), east (right), south (bottom), and west (left) extents of the project area. This will be the area exported as an additional raster.

5. Set Clipping Geometry to As Specified Below. Enter the following parameters:
   • For Top, type 27.86.
   • For Right, type -108.80.
   • For Bottom, type 26.63.
   • For Left, type -110.78.

   

   Note:

   If you are conducting this analysis for another part of the world, set your map's extent to your area of interest. Then, set Clipping Geometry to Current Display Extent. The Top, Right, Bottom, and Left parameters will be populated for you automatically based on your map extent.

   Next, you'll set the Pixel Type to ensure that the raster does not remove decimal places from the original dataset during the export process.

6. Set Pixel Type to 32 Bit float.

   

   Finally, you'll set the pixels that don't have a value to equal 0.

7. For NoData value, type 0.

   

8. Click Export.

   The exported raster is added to the Contents pane.

   

   Now, you'll take this raster and use it to create a histogram of its values.

9. In the Contents pane, rename Irrigated_Wheat_Yield to Irrigated Wheat Yield.
10. Right-click Irrigated Wheat Yield, point to Create Chart, and choose Histogram.
11. In the Chart Properties pane, under Variable, set Number to Band_1.
12. Under Statistics, check the box next to Std. Dev.

    A histogram for the Yaqui Valley is created with statistics. You'll use the mean value of 5.1 tons per hectare and the standard deviation values of about 5.1 and 5.44 tons per hectare when you create a map in the next section.

    

13. Close the histograms.

    This histogram is the distribution of yield for irrigated wheat in the Yaqui Valley in 2010. Farms with a yield lower than one standard deviation from the mean can be targets for local projects, such as capacity building. These projects can help farmers improve their agricultural yield over time.

Create a classified map of yields

In this section, you’ll create a map of wheat yield using the mean and standard deviation values that you determined in the previous section.

1. In the Contents pane, turn off the Actual Yields and Production layer.
2. Right-click the Irrigated Wheat Yield layer and choose Symbology.

   

   The Symbology pane appears. You'll map the areas above and below the standard deviations.

3. In the Symbology pane, for Primary symbology, choose Classify.

   

4. For Classes, choose 3.

   

5. For Color scheme, choose Yellow-Green-Blue (Continuous).

   

6. On the Classes tab, for the first Upper value, type 5.1. For the second Upper value, type 5.3, and for the third Upper value, type 5.44.

   

   The map reflects, in yellow, those parts of the Yaqui Valley that have lower yields.

   

   To better display these yield results with the aerial imagery basemap, you'll use blending.

7. In the Contents pane, ensure that the Irrigated Wheat Yield layer is selected.
8. On the ribbon, click the Raster Layer tab. In the Effects group, set Layer Blend to Overlay.

   

   The Irrigated Wheat Yield layer and the imagery can be seen together in the Map view.

   

   The areas in green have an average wheat yield of about 5.3 tons per hectare.

9. In the Contents pane, turn off the Irrigated Wheat Yield layer.
10. Save your project.

You have mapped the irrigated wheat yield in the Yaqui Valley. Using histograms, you compared the area's regional distribution to the broader global wheat yield. In the next module, you’ll identify yield achievement ratios and create maps of production gaps.

Map and analyze production gaps

In the previous module, you created a histogram of irrigated wheat yield in the Yaqui Valley. Now, you’ll calculate and display a map of the yield achievement ratio. This is a measurement that determines how close an area is to maximizing its output as a percentage calculated between an area's actual yield and its potential yield. You'll determine this ratio between 2000 and 2010 to map production gaps. This identifies areas that have reduced their production gap between 2000 to 2010 and the areas that haven’t made progress within this period. Those areas that haven't made progress have the potential to increase production in the upcoming years.

Display the yield achievement ratio

In this section, you’ll display the yield achievement ratio for 2000 and 2010. First, you need to access another image service from the GAEZ v4 Data Portal.

1. In your web browser, return to the FAO GAEZ v4 Data Portal.
2. In the GAEZ v4 Image Service and Metadata Listing table, find the row containing Theme 6: Yield and Production Gaps. Right-click its Image Service URL and copy the link address.

   

   Note:

   The process to copy a URL may vary depending on your web browser.

   The Image Service URL is https://gaez-services.fao.org/server/rest/services/res07/ImageServer.

3. In ArcGIS Pro, on the ribbon, click the Map tab. In the Layer group, click the drop-down menu for Add Data and choose Data From Path.

   The Add Data From Path window appears.

4. In the Add Data From Path window, under path, paste the image service URL. Click Add.

   A raster named res07 is added to the Contents pane.

   

   You'll rename this layer.

5. In the Contents pane, rename the res07 layer to Yield and Production Gaps.

   Next, you'll use a definition query to isolate the data related to irrigated wheat from 2000 for the yield achievement ratio.

6. Right-click Yield and Production Gaps and select Properties.

   The Layer Properties window appears.

7. In the Layer Properties window, if necessary, click the Definition Query tab.
8. Click New definition query and write a clause to select Where Crop is equal to Wheat.
9. Click Add Clause and write a clause to select Water Supply is equal to Irrigated.
10. Click Add Clause and write a clause to select Variable Name is equal to Cropwise Yield Achievement Ratio.
11. Click Add Clause and write a clause to select Time Period is equal to 2000.

    

    This query will isolate the variable of interest for the year 2000.

12. Click Apply. Click OK.

    The map shows, in green, that the yield achievement ratio for the Yaqui Valley is about 70 percent to 85 percent.

    

    Next, you'll explore the yield and production gap for the year 2010. You'll do this by editing the existing definition query.

13. In the Contents pane, right-click the Yield and Production Gaps layer and choose Properties.

    The Layer Properties window appears. You'll edit the existing definition query to change the year from 2000 to 2010.

14. If necessary, click the Definition Query tab. Click the Edit button.

    

    You can update the existing definition query to display a different year.

15. Change the last clause to select Time Period is equal to 2010.

    

16. Click Apply. Click OK.

    The map shows a yield achievement ratio of more than 85 percent for 2010 in dark green.

    

Observe the production gap

In this next section, you’ll map the production gaps for 2000 and 2010. Then, you'll visually compare them. First, you'll update the symbology and change the existing definition query.

1. In the Contents pane, ensure that the Yield and Production Gaps layer is selected.
2. On the ribbon, click the Data tab. In the Processing group, click Processing Templates and choose None.
3. In the Contents pane, right click Yield and Production Gaps and choose Properties.

   The Layer Properties window appears.

4. If necessary, click the Definition Query tab. Click Edit.

   You'll change the Variable Name from Cropwise Yield Achievement to Cropwise Apparent Production Gap.

5. In the third clause, change Cropwise Yield Achievement to Cropwise Apparent Production Gap.

   

6. Click Apply. Click OK.
7. In the Contents pane, ensure that the Yield and Production Gaps layer is selected.
8. In the Symbology pane, for Color scheme, choose Red-Yellow-Green (Continuous).

   

   The colors of the map are misleading; the higher gaps are green and the smaller gaps are red. Next, you'll invert the color ramp so the larger gaps are red.

9. Check the box next to Invert.

   

   The values in the Contents pane are the production gaps. The values should be multiplied by 1,000 and given a unit of tons. For example, if a cell has a value of 4.2, this can be read as that area having a production gap of 4,200 tons of wheat.

   

   The areas in red have the largest production gap. In other words, in 2010, this area had the most potential for additional crops. Now, let’s compare the production gap in 2010 to 2000.

   

10. In the Contents pane, rename Yield and Production Gaps to Yield and Production Gaps 2010.
11. Right-click the Yield and Production Gaps 2010 layer and choose Copy.

    

12. Right-click Map and choose Paste.

    

    A second Yield and Production Gaps 2010 layer is added to the map.

    

13. Rename the pasted Yield and Production Gaps 2010 layer to Yield and Production Gaps 2000.

    

    Now that the original layer showing 2010 production gaps has been copied, you'll update it to show the 2000 yield and production gaps by changing the definition query.

14. Right-click Yield and Production Gaps 2000 and choose Properties.

    The Layer Properties window appears.

15. If necessary, click the Definition Query tab. Click Edit.
16. Change the Time Period is equal to 2010 clause to Time Period is equal to 2000.

    

17. Click Apply. Click OK.

    The map updates to show the yield and production gaps in 2000. Areas in red continue to show where larger gaps previously existed.

    

    The two time periods are on the map, but only one can be seen at a time. To compare the two time periods, you'll use the Swipe tool.

18. In the Contents pane, ensure that the Yield and Production Gaps 2000 layer is selected.
19. On the ribbon, click the Image Service Layer tab. In the Compare group, click Swipe.

    

    The swipe tool allows you to partially reveal one layer that is positioned directly on top of another layer. You'll be able to visually compare the yield and production gaps between 2000 and 2010.

20. Click and drag in the Map view to use the Swipe tool.

    

    The Yield and Production Gaps 2000 layer is dynamically hidden as you move your pointer to reveal the Yield and Production Gaps 2010 layer for comparison. Visually, you can see that the yield and production gap has changed.

    Note:

    To stop using the Swipe tool, switch to the Explore tool. To access this tool, on the ribbon, click the Map tab. In the Navigate group, click Explore.

Calculate the production gap

In this final section, you’ll compute the change in yield and production gaps between the two time periods and determine the areas where the production gap hasn’t been reduced. To do this, you'll use the Raster Calculator geoprocessing tool.

1. In Command Search, type Raster Calculator and choose Raster Calculator.

   

   Note:

   The Raster Calculator tool appears twice. You can choose either option. Both options will take you to the same geoprocessing tool.

   The Geoprocessing pane appears. You'll use the Raster Calculator tool to calculate the difference between the Yield and Production Gaps 2000 and Yield and Production Gaps 2010 layers.

2. Under Map Algebra Expression, under Rasters, double-click Yield and Production Gaps 2000.

   The Yield and Production Gaps 2000 layer is added to the expression under the list of rasters.

   

3. Under Tools, under Operators, double-click the Subtraction operator.

   The subtraction operator is added to the expression.

4. Under Map Algebra Expression, under Rasters, double-click Yield and Production Gaps 2010.

   The Yield and Production Gaps 2010 layer is added to the expression.

   

   Note:

   Your expression should be "Yield and Production Gaps 2000" - "Yield and Production Gaps 2010".

   Next, you'll give the output raster a name.

5. For Output raster, type Yield_Production_Gap_Difference.

   

   The expression to calculate the change in yield and production gaps is complete. Before you run the tool, you'll ensure that it only runs for the Yaqui Valley.

6. Click the Environments tab.

   

   Next, you'll manually enter the minimum and maximum x- and y-values to determine the geographic extent that this tool should process.

7. Under Processing Extent, for Extent, choose As Specified Below. Enter the following parameters:
   • For X Min, type -110.78.
   • For X Max, type -108.80.
   • For Y Min, type 26.63.
   • For Y Max, type 27.86.

   

   Finally, you'll ensure that the raster you're creating doesn't geographically shift during processing.

8. Under Raster Analysis, for Snap Raster, choose Yield and Production Gaps 2000.

   

9. Click Run.

   The Yield_Production_Gap_Difference layer is added in the Map view.

   

   Note:

   Your symbology may differ from the image above.

   To better observe the results, you'll update the symbology.

10. In the Contents pane, right-click the Yield_Production_Gap_Difference layer and choose Symbology.

    The Symbology pane appears.

11. Under Primary symbology, choose Stretch.
12. For Color scheme, choose Condition Number.

    

13. Check the box next to Invert.

    

    The Yield_Production_Gap_Difference layer shows the areas in which the production gap has decreased the most in dark green, or about 3,000 tons per hectare. The areas that didn’t improve are red, or 0 tons per hectare of decrease between 2000 and 2010. These areas have the potential to yield more wheat in the future.

14. Save your project

In this tutorial, you created an ArcGIS Pro project to analyze irrigated wheat yield in the Yaqui Valley, Mexico. You also learned how to access Global Agro-Ecological Zones (GAEZ) image services from the Food and Agriculture Organization (FAO) for visualization. Additionally, you analyzed wheat yield by comparing the local scale to the global scale using histograms. Finally, you mapped the production gap and identified regions in which the gap hasn’t been reduced between 2000 and 2010 to determine where yields could be increased in future years.

You can find more tutorials in the Learn ArcGIS tutorial Gallery.

Acknowledgements

• FAO 2010. Theme 5: Actual Yields and Production. Data Access and Download. September 23, 2022.
• FAO 2010. Theme 6: Yield and Production Gaps. Data Access and Download. September 23, 2022.
• Hybrid Reference Layer sources: Esri, HERE, Garmin, FAO, NOAA, USGS, © OpenStreetMap contributors, and the GIS User Community.
• World Imagery layer sources: Esri, Maxar, Earthstar Geographics, and the GIS User Community