Peter Drucker once said, \"The best way to predict the future is to create it.\" This idea captures the essence of using artificial intelligence (AI) to shape sustainable agricultural futures in a world facing accelerating climate change, resource depletion, and land degradation. Key crops can be made more resilient through effective frameworks that combine environmental science with artificial intelligence and machine learning. As evidenced in the literature, the olive tree has high economic, cultural, and ecological value; however, it is highly sensitive to climate change. Rising temperatures and declining rainfall in drier and semi-drier regions, such as the northern part of Saudi Arabia, are threatening olive cultivation. Al-Jouf is considered a rapidly emerging center for olive production; however, these stresses threaten long-term agricultural sustainability. The framework we propose integrates ecological niche modeling (ENM), maximum entropy (MaxEnt), and geographic information systems (GIS) to capture complex, nonlinear interactions among bioclimatic, topographic, and soil variables. By employing AI and machine learning to enhance ecological modeling, this research establishes a foundation for predictive, data-driven decision -making in sustainable agriculture and contributes to Saudi Vision 2030 objectives for environmental stewardship, food security, and climate resilience. In short, this study develops an AI-driven species distribution model integrated into a geospatial data-science workflow to assess current and future olive suitability in Al-Jouf under CMIP6 climate scenarios.
","abstract_html":"<p>Peter Drucker once said, "The best way to predict the future is to create it." This idea captures the essence of using artificial intelligence (AI) to shape sustainable agricultural futures in a world facing accelerating climate change, resource depletion, and land degradation. Key crops can be made more resilient through effective frameworks that combine environmental science with artificial intelligence and machine learning. As evidenced in the literature, the olive tree has high economic, cultural, and ecological value; however, it is highly sensitive to climate change. Rising temperatures and declining rainfall in drier and semi-drier regions, such as the northern part of Saudi Arabia, are threatening olive cultivation. Al-Jouf is considered a rapidly emerging center for olive production; however, these stresses threaten long-term agricultural sustainability. The framework we propose integrates ecological niche modeling (ENM), maximum entropy (MaxEnt), and geographic information systems (GIS) to capture complex, nonlinear interactions among bioclimatic, topographic, and soil variables. By employing AI and machine learning to enhance ecological modeling, this research establishes a foundation for predictive, data-driven decision -making in sustainable agriculture and contributes to Saudi Vision 2030 objectives for environmental stewardship, food security, and climate resilience. In short, this study develops an AI-driven species distribution model integrated into a geospatial data-science workflow to assess current and future olive suitability in Al-Jouf under CMIP6 climate scenarios.</p>","abstract_has_math":false,"creators":["Alrewily, Fares"],"institution":null,"degree_name":"Information Systems and Technology, PhD","degree_level":"Restricted to Claremont Colleges Dissertation","degree_discipline":"Center for Information Systems and Technology","degree_department":null,"school":null,"contributors":["June Hilton","Itamar Shabtai"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2026,"date_issued":"2026-01-01T08:00:00Z","date_published":"2026-01-01T08:00:00Z","updated_at":"2026-07-24T01:41:17Z","subjects":["Geographic Information Sciences"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarship.claremont.edu/cgu_etd/1085","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["June Hilton","Itamar Shabtai"]},{"key":"dc:creator","label":"Author","values":["Alrewily, Fares"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2028-06-25T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Center for Information Systems and Technology"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Restricted to Claremont Colleges Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Information Systems and Technology, PhD"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Geographic Information Sciences"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarship.claremont.edu/cgu_etd/1085"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Peter Drucker once said, \"The best way to predict the future is to create it.\" This idea captures the essence of using artificial intelligence (AI) to shape sustainable agricultural futures in a world facing accelerating climate change, resource depletion, and land degradation. Key crops can be made more resilient through effective frameworks that combine environmental science with artificial intelligence and machine learning. As evidenced in the literature, the olive tree has high economic, cultural, and ecological value; however, it is highly sensitive to climate change. Rising temperatures and declining rainfall in drier and semi-drier regions, such as the northern part of Saudi Arabia, are threatening olive cultivation. Al-Jouf is considered a rapidly emerging center for olive production; however, these stresses threaten long-term agricultural sustainability. The framework we propose integrates ecological niche modeling (ENM), maximum entropy (MaxEnt), and geographic information systems (GIS) to capture complex, nonlinear interactions among bioclimatic, topographic, and soil variables. By employing AI and machine learning to enhance ecological modeling, this research establishes a foundation for predictive, data-driven decision -making in sustainable agriculture and contributes to Saudi Vision 2030 objectives for environmental stewardship, food security, and climate resilience. In short, this study develops an AI-driven species distribution model integrated into a geospatial data-science workflow to assess current and future olive suitability in Al-Jouf under CMIP6 climate scenarios.
"]},{"key":"dc:title","label":"Title","values":["AI-Driven Suitability Modeling for Sustainable Olive Cultivation: An Environmental Assessment in a Changing Climate"]}]}],"canonical_facts":{"dc:contributor":["June Hilton","Itamar Shabtai"],"dc:creator":["Alrewily, Fares"],"dc:date.available":["2028-06-25T07:00:00Z"],"dc:description.abstract":["Peter Drucker once said, \"The best way to predict the future is to create it.\" This idea captures the essence of using artificial intelligence (AI) to shape sustainable agricultural futures in a world facing accelerating climate change, resource depletion, and land degradation. Key crops can be made more resilient through effective frameworks that combine environmental science with artificial intelligence and machine learning. As evidenced in the literature, the olive tree has high economic, cultural, and ecological value; however, it is highly sensitive to climate change. Rising temperatures and declining rainfall in drier and semi-drier regions, such as the northern part of Saudi Arabia, are threatening olive cultivation. Al-Jouf is considered a rapidly emerging center for olive production; however, these stresses threaten long-term agricultural sustainability. The framework we propose integrates ecological niche modeling (ENM), maximum entropy (MaxEnt), and geographic information systems (GIS) to capture complex, nonlinear interactions among bioclimatic, topographic, and soil variables. By employing AI and machine learning to enhance ecological modeling, this research establishes a foundation for predictive, data-driven decision -making in sustainable agriculture and contributes to Saudi Vision 2030 objectives for environmental stewardship, food security, and climate resilience. In short, this study develops an AI-driven species distribution model integrated into a geospatial data-science workflow to assess current and future olive suitability in Al-Jouf under CMIP6 climate scenarios.
"],"dc:identifier":["https://scholarship.claremont.edu/cgu_etd/1085"],"dc:subject":["Geographic Information Sciences"],"dc:title":["AI-Driven Suitability Modeling for Sustainable Olive Cultivation: An Environmental Assessment in a Changing Climate"],"thesis:degree_discipline":["Center for Information Systems and Technology"],"thesis:degree_level":["Restricted to Claremont Colleges Dissertation"],"thesis:degree_name":["Information Systems and Technology, PhD"]},"updated_at":"2026-07-24T01:41:17Z"}