{"id":2983678,"date":"2026-09-30T22:10:35","date_gmt":"2026-10-01T05:10:35","guid":{"rendered":"https:\/\/www.esri.com\/arcgis-blog\/?post_type=blog&p=2983678"},"modified":"2026-09-30T22:34:59","modified_gmt":"2026-10-01T05:34:59","slug":"advancing-sustainable-agriculture-with-spatial-analysis","status":"publish","type":"blog","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/spatial-analyst\/analytics\/advancing-sustainable-agriculture-with-spatial-analysis","title":{"rendered":"Advancing sustainable agriculture with spatial analysis"},"author":7091,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"open","ping_status":"closed","template":"","format":"standard","meta":{"_acf_changed":false,"_searchwp_excluded":""},"categories":[23341],"tags":[42181,778922,39601,781252,727781],"industry":[],"product":[36561,37031],"class_list":["post-2983678","blog","type-blog","status-publish","format-standard","hentry","category-analytics","tag-arcgis-pro","tag-arcgis-spatial-analyst","tag-zonal","tag-zonal-characterization","tag-zonal-statistics","product-arcgis-pro","product-spatial-analyst"],"acf":{"authors":[{"ID":7091,"user_firstname":"Sarmistha","user_lastname":"Chatterjee","nickname":"Sarmistha Chatterjee","user_nicename":"schatterjee17","display_name":"Sarmistha Chatterjee","user_email":"SChatterjee@esri.com","user_url":"","user_registered":"2018-03-02 00:19:17","user_description":"Sarmistha is a Product Engineer in Esri's raster analysis group, where she focuses on raster and scientific multidimensional data analysis. She is especially interested in combining GIS technology, spatial analysis, and software engineering to study changes for conservation and better decision-making. Before joining Esri in 2017, Sarmistha earned her PhD in Geography from the University of Delaware, where she researched fluvial geomorphology, water resources, and spatial modeling.","user_avatar":""},{"ID":367942,"user_firstname":"Nishani","user_lastname":"Moragoda","nickname":"Nishani Moragoda","user_nicename":"nmoragoda","display_name":"Nishani Moragoda","user_email":"nmoragoda@esri.com","user_url":"","user_registered":"2025-01-27 18:56:00","user_description":"Nishani Moragoda is a Product Engineer on the ArcGIS Spatial Analyst team at Esri. Her background is in hydrology, sediment flux modeling, and climate change impacts, with a special focus on multidimensional scientific data analysis. She completed her MS and PhD in Geography from the University of Alabama before joining Esri.","user_avatar":""}],"short_description":"Identify vulnerable agricultural zones using zonal characterization, combining climate and population data to support SDG Zero Hunger decisions.","flexible_content":[{"acf_fc_layout":"content","content":"

Achieving food security is one of the most pressing global challenges. The United Nations Sustainable Development Goal 2 (Zero Hunger) emphasizes the need to improve agricultural productivity, ensure resilience to climate change, and support vulnerable populations. Geographic Information Systems play a critical role in addressing these challenges by enabling spatial analysis of environmental and socio-economic variables.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2983680,"id":2983680,"title":"UN_ZeroHunger","filename":"UN_ZeroHunger.jpg","filesize":34384,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/09\/UN_ZeroHunger.jpg","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/spatial-analyst\/analytics\/advancing-sustainable-agriculture-with-spatial-analysis\/un_zerohunger","alt":"Figure: United Nations Sustainable Development Goal 2","author":"7091","description":"","caption":"Figure 01: United Nations Sustainable Development Goal 2","name":"un_zerohunger","status":"inherit","uploaded_to":2983678,"date":"2026-10-01 05:04:02","modified":"2026-10-01 05:32:18","menu_order":0,"mime_type":"image\/jpeg","type":"image","subtype":"jpeg","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":741,"height":350,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/09\/UN_ZeroHunger-213x200.jpg","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/09\/UN_ZeroHunger.jpg","medium-width":464,"medium-height":219,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/09\/UN_ZeroHunger.jpg","medium_large-width":741,"medium_large-height":350,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/09\/UN_ZeroHunger.jpg","large-width":741,"large-height":350,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/09\/UN_ZeroHunger.jpg","1536x1536-width":741,"1536x1536-height":350,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/09\/UN_ZeroHunger.jpg","2048x2048-width":741,"2048x2048-height":350,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/09\/UN_ZeroHunger.jpg","card_image-width":741,"card_image-height":350,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/09\/UN_ZeroHunger.jpg","wide_image-width":741,"wide_image-height":350}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"

With the Zonal Characterization<\/a> tool in ArcGIS Pro, we can now perform more comprehensive evaluations of geographic regions by summarizing multiple variables across defined zones. This enables analysts to evaluate complex relationships across environmental, climatic, and demographic datasets in a single workflow.<\/p>\n

 <\/p>\n

Supporting Sustainable Development Goal 2: Zero Hunger<\/strong><\/h3>\n

In this example, the goal is to identify vulnerable regions for sustainable agriculture in Kenya.<\/p>\n

To achieve this, the analysis brings together three critical variables:<\/p>\n

    \n
  1. Total population (2018)<\/li>\n
  2. Mean yearly precipitation (1981-2010)<\/li>\n
  3. Number of high-temperature days (1981-2010)<\/li>\n<\/ol>\n

    These variables are summarized across ecosystems, which serve as the zones for the analysis.\u00a0This enables decision-makers to assess not just environmental conditions, but also how many people are impacted in high-risk areas.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2983685,"id":2983685,"title":"Kenya_Inputs","filename":"Kenya_Inputs.jpg","filesize":350864,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/09\/Kenya_Inputs.jpg","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/spatial-analyst\/analytics\/advancing-sustainable-agriculture-with-spatial-analysis\/kenya_inputs","alt":"Variables used for the analysis includes total population, mean historic precipitation and temperature.","author":"7091","description":"Figure: Variables used for the analysis includes total population, mean historic precipitation and temperature.","caption":"Figure 02: Variables used for the analysis includes total population, mean historic precipitation and temperature.","name":"kenya_inputs","status":"inherit","uploaded_to":2983678,"date":"2026-10-01 05:07:07","modified":"2026-10-01 05:32:10","menu_order":0,"mime_type":"image\/jpeg","type":"image","subtype":"jpeg","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":2515,"height":903,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/09\/Kenya_Inputs-213x200.jpg","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/09\/Kenya_Inputs.jpg","medium-width":464,"medium-height":167,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/09\/Kenya_Inputs.jpg","medium_large-width":768,"medium_large-height":276,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/09\/Kenya_Inputs.jpg","large-width":1920,"large-height":689,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/09\/Kenya_Inputs-1536x551.jpg","1536x1536-width":1536,"1536x1536-height":551,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/09\/Kenya_Inputs-2048x735.jpg","2048x2048-width":2048,"2048x2048-height":735,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/09\/Kenya_Inputs-826x297.jpg","card_image-width":826,"card_image-height":297,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/09\/Kenya_Inputs-1920x689.jpg","wide_image-width":1920,"wide_image-height":689}},"image_position":"center","orientation":"horizontal","hyperlink":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/09\/Kenya_Inputs.jpg"},{"acf_fc_layout":"content","content":"

    Step 1: Preparing inputs for analysis<\/strong><\/p>\n

    The Zonal Characterization tool requires a zone layer, and one or more value rasters representing variables of interest. In this workflow, ecosystems define spatial zones while climate and population datasets provide value inputs. Each raster dataset captures a different dimension of vulnerability, making it possible to evaluate regions holistically.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2983693,"id":2983693,"title":"ZC_ToolUI","filename":"ZC_ToolUI.png","filesize":93475,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/09\/ZC_ToolUI.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/spatial-analyst\/analytics\/advancing-sustainable-agriculture-with-spatial-analysis\/zc_toolui","alt":"Figure: Zonal Characterization tool UI","author":"7091","description":"","caption":"Figure 03: Zonal Characterization tool UI","name":"zc_toolui","status":"inherit","uploaded_to":2983678,"date":"2026-10-01 05:19:13","modified":"2026-10-01 05:32:32","menu_order":0,"mime_type":"image\/png","type":"image","subtype":"png","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":789,"height":637,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/09\/ZC_ToolUI-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/09\/ZC_ToolUI.png","medium-width":323,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/09\/ZC_ToolUI.png","medium_large-width":768,"medium_large-height":620,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/09\/ZC_ToolUI.png","large-width":789,"large-height":637,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/09\/ZC_ToolUI.png","1536x1536-width":789,"1536x1536-height":637,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/09\/ZC_ToolUI.png","2048x2048-width":789,"2048x2048-height":637,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/09\/ZC_ToolUI-576x465.png","card_image-width":576,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/09\/ZC_ToolUI.png","wide_image-width":789,"wide_image-height":637}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"

    Step 2: Calculate summary statistics for multiple variables<\/strong><\/p>\n

    The analysis for all the three variables happen in the same spatial reference, cell size, cell alignment and processing extent. The tool processes all variables together and computes appropriate summary statistics for each.<\/p>\n

    For example:<\/p>\n