Building Resilience in Jordan’s Agriculture: Harnessing Climate Smart Practices and Predictive Models to Combat Climatic Variability
Anber Abraheem Shlash Mohammad
Digital Marketing Department, Faculty of Administrative and Financial Sciences, Petra University, Amman 961343, Jordan
Electronic Marketing and Social Media, Economic and Administrative Sciences Zarqa University, Zarqa 13110, Jordan; University Department, INTI International University, Nilai 71800, Negeri Sembilan, Malaysia
Department of Accounting, Business School Faculties, Al Ahilya Amman University, Amman 19111, Jordan
Business Administration Department, Collage of Business and Economics, Qassim University, Buraydah 52571, Qassim, Saudi Arabia
Faculty of Business and Communications, INTI International University, Nilai 71800, Negeri Sembilan, Malaysia
Faculty of Liberal Arts, Shinawatra University, Pathum Thani 12160, Thailand
DOI: https://doi.org/10.36956/rwae.v6i2.1628
Received: 25 December 2024; Published: 3 April 2025
Copyright © 2025 Anber Abraheem Shlash Mohammad, Suleiman Mohammad, Khaleel Ibrahim Al‑ Daoud, Badrea Al Oraini, Asokan Vasudevan, Zhidong Feng. Published by Nan Yang Academy of Sciences Pte. Ltd..
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Abstract
Agriculture in Jordan is increasingly vulnerable to climatic variability, including rising temperatures, erratic precipitation, and frequent extreme weather events, threatening crop yields and food security. This study assessed the impacts of climatic factors, evaluated the effectiveness of climatesmart agricultural practices, and explored future scenarios using predictive models. A mixedmethods approach was employed, combining quantitative regression analysis, ANOVA tests, qualitative content analysis, scenario modelling, and geospatial mapping. Regression analysis showed that climatic factors explain 83.2% of the variability in crop yields (R² = 0.832). Temperature and precipitation had positive and significant effects (coefficients: 0.421 and 0.206, p < 0.01), while extreme weather events significantly reduced yields (coefficient: 2.227, p < 0.01). ANOVA revealed no significant differences in crop yields across low, medium, and high adoption levels of climatesmart practices (F = 0.272, p = 0.762), suggesting suboptimal implementation. Qualitative analysis identified key barriers such as water scarcity, financial constraints, and knowledge gaps, alongside opportunities like drip irrigation and crop diversification. Scenario analysis projected yield variations ranging from 64.69 tons/ha in optimistic scenarios to 40.89 tons/ha in pessimistic ones, highlighting the importance of adaptive measures. Geospatial analysis identified regional hotspots with low yields, correlating with climatic stress. These findings emphasize the need for regionspecific interventions, enhanced capacity building, and financial support to optimize climatesmart practices. Predictive models proved valuable for planning under future climatic uncertainties. This study provides actionable insights for developing a resilient agricultural sector in Jordan, aligning with global food security and climate adaptation goals.
Keywords: Agriculture; Jordan; Climate Change; Resilience; ClimateSmart Practices; Predictive Models
