ABDULLAH MOHAMMAD GHAZI AL KHATIB

Scopus Publications

Forecasting tomato production in major Asian producers: a comparative study of ARIMA, exponential smoothing, score-driven models, and XGBoost
Abdullah Mohammad Ghazi Al khatib, Bayan Mohamad Alshaib, Pradeep Mishra, Shiwani Tiwari, Motee Asaad Alshalaby, Binita Kumari
Scientific Reports, 2026
Tomato production is a crucial component of the agricultural sector in Asian countries. Accurate forecasting of tomato production is essential for effective agricultural planning, resource allocation, and ensuring food security in the region. This study aims to investigate the patterns and forecast tomato production in five major Asian producing countries: Bangladesh, China, India, Pakistan, and Sri Lanka, utilizing advanced time series models and machine learning techniques. A comprehensive time series dataset spanning from 1961 to 2021 was employed, partitioned into a training period (1961–2014) and a validation period (2015–2021). The study applied various modeling techniques, including ARIMA, Exponential Smoothing, Score-Driven models, and XGBoost. Model performance was evaluated using information criteria, error metrics, and diagnostic tests. Results indicate that while XGBoost yielded the lowest validation errors for several nations due to recent volatility, Exponential Smoothing was selected as the optimal practical model for forecasting Bangladesh’s production to properly account for long-term structural trend extrapolation. Score-Driven models exhibited superior performance for China, India, Pakistan, and Sri Lanka. The selected models generated forecasts up to 2028, revealing continuing upward trajectories for Bangladesh, China, India, and Pakistan, and stabilization for Sri Lanka. This study contributes to the understanding of tomato production dynamics in major Asian producers and offers guidance for agricultural planning, resource allocation, and food security policies. The findings provide valuable insights into the future trends of tomato production in the region, enabling stakeholders to make informed decisions and adapt to potential changes in the agricultural landscape.
The heterogeneous effects of artificial intelligence on labor markets: A calibrated simulation of skills, tasks, and wages
Abdullah Mohammad Ghazi Al khatib, Bayan Mohamad Alshaib
International Review of Economics and Finance, 2026
Artificial Intelligence (AI) is reconfiguring labor markets with profound, heterogeneous effects on skills and wages. While existing models offer qualitative insights, they often lack the quantitative rigor for testable predictions. This paper develops a novel simulation model extending the standard task-based framework from a one-dimensional continuum to a two-dimensional space (cognitive complexity and codifiability) with a dynamic AI capability frontier. Calibrated to match empirical stylized facts, such as the high-skill wage premium, our model generates three key findings. First, AI's advance consistently widens the wage gap between high- and low-skill workers. Second, the rate of inequality growth slows when AI becomes a stronger substitute for human labor, as broad displacement compresses wage growth across the skill spectrum. Third, counterfactual policy simulations reveal a strict efficiency-equity trade-off: interventions managing AI's adoption pace (e.g., an "AI tax") effectively mitigate inequality but dampen aggregate output, while training subsidies and labor supply expansions prove less effective against structural automation. Our work provides a methodologically transparent tool for analyzing the future of work. • Calibrated model reveals rising wage inequality between high/low skills. • Inequality growth slows when AI acts as a strong labor substitute. • Low-skill wages stagnate while high-skill wages rise with AI expansion. • AI tax reduces inequality 4× more effectively than training subsidies. • Provides open-source Python code for replicable AI-labor market analysis.
Machine learning analysis of digital financial inclusion: Identifying determinants, barriers, and sustainability links
Chadi Azmeh, Abdullah Mohammad Ghazi Al khatib, Rabeh Morrar, Bayan Mohamad Alshaib
Social Sciences and Humanities Open, 2026
Provincial drivers of green total factor productivity in China: Policy insights from panel data analysis
Abdullah Mohammad Ghazi Al khatib, Bayan Mohamad Alshaib, Priyanka Lal, Pradeep Mishra, Shikha Yadav
Social Sciences and Humanities Open, 2026
China's transition to high-quality development requires decoupling economic growth from environmental degradation. This study investigates the drivers of Green Total Factor Productivity (GTFP) across 30 Chinese provinces from 2005 to 2020, specifically examining the non-linear transmission mechanisms of technological change. Employing a dynamic panel System GMM estimator to control for endogeneity and persistence, we uncover a robust U-shaped relationship between green technology innovation (GTI) and GTFP. This finding indicates that provinces must overcome an initial “innovation trap” characterized by high adjustment costs before innovation yields net productivity gains (threshold GTI ≈ 5.077). Furthermore, our results validate the Porter Hypothesis, showing that environmental regulation significantly boosts GTFP, whereas traditional infrastructure expansion and high energy intensity currently exert negative structural drags. These insights challenge linear growth models and inform a differentiated policy framework: we advocate for “bridge financing” to support lagging regions through the innovation valley and a strategic pivot from physical road density to digital and green infrastructure investments.
Editorial: Regenerative agriculture and support in changing policy environments: farmers' rights, contract farming, and navigating towards sustainable practices
Abdullah Mohammad Ghazi Al khatib, Pradeep Mishra, Priyanka Lal, Pradip Kumar Sahu
Frontiers in Sustainable Food Systems, 2026
The global agri-food system stands at a critical juncture, confronted by the intersecting pressures of climate change, biodiversity loss, resource scarcity, and persistent socio-economic inequities. The imperative to transition from conventional production models to systems that are not only economically viable but also environmentally sustainable and socially just has never been more urgent. This transformation demands a holistic approach, integrating top-down policy frameworks with bottom-up, on-the-ground innovations that empower the farmers at the heart of the system. This Research Topic brings together a collection of studies from diverse global contexts-spanning Asia, Africa, and Europe-to explore the multifaceted pathways toward building more resilient, equitable, and sustainable agri-food systems. The contributing articles illuminate the complex interplay between governance, farmer behavior, collaborative ecosystems, and consumer perspectives that will define the future of food and agriculture (Figure 1).Effective governance is the bedrock upon which sustainable agricultural practices are built. The articles in this collection demonstrate that while policy is a powerful driver of change, its success hinges on design, motivation, and implementation. planting scale and risk attitudes. Together, these studies underscore that effective policy must be multi-layered, well-coordinated, and precisely targeted to influence farmer decisionmaking.Beyond policy, the success of agricultural transformation depends on the adoption of innovative farming models and the cultivation of farmer entrepreneurship. In Northwestern Ethiopia, Gidelew et al. assess the impact of cluster farming on smallholder commercialization. Their findings show a clear positive association, with participants earning significantly higher incomes and selling a greater proportion of their crops. This model of collective farming on adjacent lands enhances productivity, market linkages, and bargaining power, offering a scalable strategy for transforming subsistence agriculture.A different approach is explored by Xi et al. in Laifeng County, China, who examine the link between ecological specialty industries and farmers' livelihoods. Their case study of the local vine tea industry reveals that capitalizing on unique regional resources can dramatically increase farmer incomes, particularly for higher-income participants who possess greater livelihood capital. This highlights the potential of niche, high-value industries to drive rural revitalization. At the individual level, the entrepreneurial spirit of farmers is a key enabler of growth. Thakur et al. delve into the entrepreneurial behavior of polyhouse vegetable growers in India, identifying leadership ability, planning, and innovativeness as core traits. Factors such as farm income, experience, and access to extension services were found to be significantly correlated with these behaviors, pointing to the need for support systems that nurture these essential skills.Innovation in agriculture does not happen in a vacuum; it requires a robust ecosystem of collaboration. Proposing a novel framework, Chen and Li introduce a "Four-Helix + Intermediary" model for green agriculture, comprising government, academia, industry, and farmers, with intermediaries playing a crucial coordinating role. Their case study of the soapberry industry in China demonstrates that this synergistic model is effective for driving rural development, though its collaborative harmony can be further optimized.The vital function of intermediaries is further detailed by Cai et al. in their study on land conservation in China. They find that land intermediary organizations, such as cooperatives and legal institutions, positively influence farmers' willingness to conserve cultivated land by enhancing the stability of their land tenure. Interestingly, they also note that farmers with higher human and social capital are less dependent on these intermediaries, suggesting that interventions must be tailored to farmers' existing capacities. These studies collectively argue for a systems-level approach where interconnected actors work in concert to facilitate knowledge transfer, resource sharing, and risk mitigation.Ultimately, a sustainable food system must be one that is perceived as fair by all its participants, including the end consumer. Mouchtaropoulou et al. investigate consumer perceptions of fairness and sustainability in the agri-food chains of five Mediterranean countries. Their research uncovers a stark disconnect: an overwhelming 87% of consumers believe the current revenue distribution-where farmers receive an estimated 15% of the final price-is unfair. A choice experiment centered on olive oil revealed strong consumer preferences for products that are local, traditionally farmed, produced by family companies, and reflect a fair price for their quality. This indicates a powerful, market-driven demand for transparency and equity that, if harnessed, could realign value chains to better reward producers.
Comparing Forecasting Models for Potato Production: Evaluating T-ARMA, ARIMA-ARCH, Weibull and Score-Driven Approaches in Major Global Producers
Abdullah Mohammad Ghazi Al khatib, Bayan Mohamad Alshaib, Neha Mishra, Pradeep Mishra, Walid Emam, et al.
Potato Research, 2025
Modelling Onion Price Volatility and Market Interdependencies: Insights from Indian Markets
Abdullah Mohammad Ghazi Al khatib
Indian Journal of Economics and Development, 2025
Onion is one of the most market-sensitive agricultural commodities in India, with price fluctuations affecting both producers and consumers. This study examined the volatility and price transmission mechanisms in nine Indian onion markets using monthly data from January 2010 to December 2020. Through Johansen cointegration tests, the analysis focused a long-term co-movement of prices across these markets. Additionally, the multivariate GARCH model highlighted significant conditional volatility and strong, time-varying positive price connections, with the Delhi market being the most influential. Any price disruption in Delhi rapidly spreads to other markets. These findings emphasized the need for policymakers to understand price dynamics better and design effective measures to prevent market inefficiencies, such as artificial price inflation due to hoarding or collusion. The study offered valuable insights into market interdependencies and volatility, helping to inform policy decisions and improve forecasting in onion production systems, particularly following unusual volatility events like that of December 2010.
FISH PRODUCTION MODELING AND FORECASTING IN INDIA USING THE XGBOOST ALGORITHM
S Yadav, B Kumari, A.M.G. Al-Khatib, Y.S. Raghav, D Sharma, B.M. Al-Shaib, H Nayak, S Ray, T Biswas, N Mishra, P Mishra
Journal of Animal and Plant Sciences, 2025
Time series analysis using machine learning is vital for forecasting in commodity sciences. This research leverages advanced machine learning models for time series forecasting of fish production at both state and national levels in India. The study developed and compared traditional models, like the autoregressive integrated moving average (ARIMA) and state space models, with the advanced machine learning model, extreme gradient boosting (XGBoost), using training and test data sets. Results showed that XGBoost and state space models significantly outperformed the ARIMA model. Specifically, XGBoost had the highest accuracy in eight of eighteen series, followed by state space models (seven out of eighteen), and ARIMA models (three out of eighteen). This confirms that applying diverse machine learning models can enhance forecasting accuracy for fish production. After identifying the best-performing models, forecasts for fish production were extended to 2030, indicating that India’s total and marine fish production would likely continue to grow, with minimal change expected in key producing states. This data-driven analysis offers valuable insights for food security planning and policy-making in the region. Keywords: Fish production, Time series, machine learning, forecasting, ARIMA, XGBoost.
Assessing the suitability of different modeling techniques for meteorological forecasting on Chickpea wilt
Promil Kapoor, M. L. Khichar, Surender Dhankar, Abdullah Mohammad Ghazi Al Khatib, Bayan Mohamad Alshaib, Dhar Mender, Priyanka Lal, Sunil Rajamani, Ashok Kumar Chhabra, Vikram Singh, Shikha Yadav, Swapnil Panchabhai, Krishan Kumar, Pradeep Mishra
Mausam, 2025
The daily climate data collected for Hisar district between November 1, 1977 and April 30, 2022, has been analyzed and presented in this study. The data set was divided into two parts: training and testing data. This study presents the results of ARIMA, state space, and seasonal Holt-Winters models fitted for maximum temperature, minimum temperature, relative humidity (M), relative humidity (E), bright sunshine hours, and rainfall. The models were trained on data spanning from November 1977 to April 2013. The top selected ARIMA models were chosen based on evaluation criteria, such as the Akaike information criterion, root mean squared error, mean absolute error, mean absolute percentage error, in-sample MSE, and the maximum number of significant coefficients. The state space models were selected based on minimum values of the Akaike information criterion (AIC), Bayesian information criterion (BIC), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), in-sample Mean Squared Error (MSE), and Mean Absolute PercentageError (MAPE). The seasonal Holt-Winters models were fitted with additive specifications and a period of 365. Global chickpea production is highly dependent on various biotic and abiotic stresses. One of the critical biotic stresses, Fusarium wilt, significantly limits chickpea productivity causing economic losses ranging from 10 to 40% in many countries and escalates to 100% when temperature and humidity are favourable. Weather forecasting is crucial in plant disease management as it helps to predict disease outbreaks by analyzing how weather conditions influence pathogen development and spread, allowing farmers to take timely preventative measures.
Beyond linearity: a critical review of the finance–growth nexus
Abdullah Mohammad Ghazi Al Khatib
Cogent Economics and Finance, 2025
This critical review synthesizes over a century of research on the finance–growth nexus, arguing that this relationship is not simply linear but a complex, multifaceted phenomenon contingent on institutional quality, non-linear dynamics and technological innovations – a thesis substantiated by the extensive literature reviewed. Employing a systematic literature review methodology, the article defines financial development as improving financial system quantity, quality and efficiency and economic growth as sustained output increases. It critically analyzes the bidirectional and often non-linear interplay between finance and growth, tracing classical theories to modern endogenous models and highlighting persistent methodological challenges. Special attention is given to financial crises, the transformative impact of financial technology (fintech) and artificial intelligence (AI), and evolving policy implications. Findings underscore the nexus’s context-dependent nature, challenging simplistic assumptions and emphasizing the need for nuanced, evidence-based policy implications to foster sustainable and inclusive economic growth in an increasingly digital global economy.
Data-Driven Farming: Harnessing Big Data for Agriculture
Abdullah Mohammad Ghazi Al khatib, Bayan Mohamad Alshaib
Transforming Agriculture Through Artificial Intelligence for Sustainable Food Systems, 2025
Smart Irrigation Systems: Optimizing Water Use with AI
Abdullah Mohammad Ghazi Al khatib, Bayan Mohamad Alshaib
Transforming Agriculture Through Artificial Intelligence for Sustainable Food Systems, 2025
Big Data in Agriculture: Acquisition, Processing and Implications
Aditya Pratap Singh, Tanushree Biswal, Umakanta Maharana, Abdullah Mohammad Ghazi Al Khatib, Pradeep Mishra
Smart Farming Smarter Solutions Revolutionizing Agriculture with Artificial Intelligence, 2025
Multi-model forecasting framework for agricultural nutrient dynamics in India: a comparative analysis of ML and hybrid approaches for NPK consumption
Pradeep Mishra, Diaa Salman, Binita Kumari, Abdullah Mohammad Ghazi Al khatib, Bayan Mohamad Alshaib
Cogent Food and Agriculture, 2025
Decoding Potato Power: A Global Forecast of Production with Machine Learning and State-of-the-Art Techniques
Shikha Yadav, Abdullah Mohammad Ghazi Al khatib, Bayan Mohamad Alshaib, Sushmita Ranjan, Binita Kumari, Naief Alabed Alkader, Pradeep Mishra, Promil Kapoor
Potato Research, 2024
Correction to: Forecasting Potato Production in Major South Asian Countries: a Comparative Study of Machine Learning and Time Series Models
Pradeep Mishra, Abdullah Mohammad Ghazi Al khatib, Bayan Mohamad Alshaib, Binita Kuamri, Shiwani Tiwari, Aditya Pratap Singh, Shikha Yadav, Divya Sharma, Prity Kumari
Potato Research, 2024
Forecasting Potato Production in Major South Asian Countries: a Comparative Study of Machine Learning and Time Series Models
Pradeep Mishra, Abdullah Mohammad Ghazi Al khatib, Bayan Mohamad Alshaib, Binita Kuamri, Shiwani Tiwari, Aditya Pratap Singh, Shikha Yadav, Divya Sharma, Prity Kumari
Potato Research, 2024
Modeling and forecasting rainfall patterns in India: a time series analysis with XGBoost algorithm
Pradeep Mishra, Abdullah Mohammad Ghazi Al Khatib, Shikha Yadav, Soumik Ray, Achal Lama, Binita Kumari, Divya Sharma, Ramesh Yadav
Environmental Earth Sciences, 2024
Farm Management Production and Resource Economics
Farm Management Production and Resource Economics, 2024
Global control of electrical supply: A variational mode decomposition-aided deep learning model for energy consumption prediction
Abul Abrar Masrur Ahmed, Nadjem Bailek, Laith Abualigah, Kada Bouchouicha, Alban Kuriqi, Alireza Sharifi, Pooya Sareh, Abdullah Mohammad Ghazi Al khatib, Pradeep Mishra, Ilhami Colak, El-Sayed M. El-kenawy
Energy Reports, 2023
The complexity of financial development and economic growth nexus in Syria: A nonlinear modelling approach with artificial neural networks and NARDL model
Abdullah Mohammad Ghazi Al khatib
Heliyon, 2023
The Interaction Between Financial Development and Economic Growth: A Novel Application of Transfer Entropy and Nonlinear Approach in Algeria
Abdullah Mohammad Ghazi Al khatib, Bayan Mohamad Alshaib, Ali Mohamad Kanaan
Sage Open, 2023
An Overview of Pulses Production in India: Retrospect and Prospects of the Future Food with an Application of Hybrid Models
Pradeep Mishra, Abdullah Mohammad Ghazi Al Khatib, Priyanka Lal, Ayesha Anwar, Korakot Nganvongpanit, Mostafa Abotaleb, Soumik Ray, Veerasak Punyapornwithaya
National Academy Science Letters, 2023
Fiscal Sustainability and Its Implications for Economic Growth in Egypt: An Empirical Analysis
Bayan Mohamad Alshaib, Abdullah Mohammad Ghazi Al khatib, Alina Cristina Nuta, Mohamad Hamra, Pradeep Mishra, Rajani Gautam, Sarfraz Hussain, Cristina Gabriela Zamfir
Sage Open, 2023
Prediction of Fruit Production in India: An Econometric Approach
Soumik Ray, Pradeep Mishra, Hicham Ayad, Prity Kumari, Rajnee Sharma, Binita Kumari, Abdullah Mohammad Ghazi Al Khatib, Anant Tamang, Tufleuddin Biswas
Journal of Horticultural Research, 2023
The Relationship between Financial Development and Inflation Rate in Egypt
Abdullah Mohammad Ghazi Al khatib
Indian Journal of Economics and Development, 2023
Modeling and Forecasting of Sugarcane Production in South Asian Countries
Pradeep Mishra, Khder Mohammed Alakkari, Achal Lama, Soumik Ray, Monika Singh, Claris Shoko, Mostafa Abotaleb, Abdullah Mohammad Ghazi Al khatib, Kadir Karakaya
Current Applied Science and Technology, 2023
MODELING AND FORECASTING OF TEA PRODUCTION IN INDIA
Abdullah Mohammad Ghazi Al khatib
Journal of Animal and Plant Sciences, 2022
Modeling and Analyzing the Dynamic Impact of Financial Development on Economic Growth in Syria
Pradeep Mishra
Economic Affairs New Delhi, 2022
Modelling and Forecasting of Maize Production in South Asian Countries
Sourakanti Sarkar
Economic Affairs New Delhi, 2022
Modeling and forecasting meteorological factors using BATS and TBATS models for the Keonjhar district of Orissa
Monika Ray, K.C. Sahoo, Mostafa Abotaleb, Soumik Ray, P.K. Sahu, Pradeep Mishra, AbdullahMohammad GhaziAlKhatib, Soumitra SankarDas, Vikas Jain, Ritisha Balloo
Mausam, 2022
STATE OF ART OF SARIMA MODEL IN SECOND WAVE ON COVID-19 IN INDIA
International Journal of Agricultural and Statistical Sciences, 2022
Modelling and Forecasting of Pulses Production in South Asian Countries and its Role in Nutritional Security
Yashpal Singh Raghav, Pradeep Mishra, Khder Mohammed Alakkari, Monika Singh, Abdullah Mohammad Ghazi Al Khatib, Ritisha Balloo
Legume Research, 2022
Modeling and forecasting of milk production in different breeds in Turkey
HARUN YONAR, AYNUR YONAR, PRADEEP MISHRA, MOSTAFA ABOTALEB, ABDULLAH MOHAMMAD GHAZI AL KHATIB, TATIANA MAKAROVSKIKH, MUSTAFA CAM
Indian Journal of Animal Sciences, 2022
Modeling and Forecasting of Sugarcane Production in India
Pradeep Mishra, Abdullah Mohammad Ghazi Al Khatib, Iqra Sardar, Jamal Mohammed, Kadir Karakaya, Abhiram Dash, Monika Ray, Lakshmi Narsimhaiah, Anurag Dubey
Sugar Tech, 2021
Time Series SARIMA Modelling and Forecasting of Monthly Rainfall and Temperature in the South Asian Countries
Soumik Ray, Soumitra Sankar Das, Pradeep Mishra, Abdullah Mohammad Ghazi Al Khatib
Earth Systems and Environment, 2021
Modeling and forecasting of egg production in India using time series models
Abdullah Mohammad Ghazi Al Khatib, Harun Yonar, Mostafa Abotaleb, Pradeep Mishra, Aynur Yonar, Kadir Karakaya, Amr Badr, Vinti Dhaka
Eurasian Journal of Veterinary Sciences, 2021

ABDULLAH MOHAMMAD GHAZI AL KHATIB

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