Dates represent an important export crop for Saudi Arabia that attracts foreign exchange. There is an opportunity for Saudi Arabia to extend its date exports to the international market due to production exceeding consumption and comparative advantage in date production. This paper explores the future export market for Saudi dates by analyzing the factors that affect Saudi date exports and highlighting this crop’s comparative advantage. Also, the paper uses autoregressive and distributed lag methodology with data from 1980 to 2017 to predict the future for exported dates between 2020 and 2025. The relationship between the export of dates and other exogenous variables shows there are significant effects from domestic production, domestic consumption of dates, and the price of exported dates in both the long and short term. In contrast, the comparative advantage of Saudi dates shows a significant effect only in the short term, because there has been no active program to promote Saudi dates in global markets. The explanatory variables predict that 2019 Saudi date exports will increase by 3.8-fold in 2025, with the total export amount over 700 thousand tons. Thus, there is an opportunity to develop more programs that target the international market in support of date farms and exporters, and to support market research directed at satisfying the international market.

The increasing integration of Artificial Intelligence (AI) into global economic activities raises significant questions about its environmental impact, particularly in resource-intensive sectors such as agriculture. This study utilized an Environmental Cost-Benefit Analysis (ECBA) framework to assess whether the economic benefits of leading AI models outweigh their ecological costs. We quantified energy consumption, carbon emissions, and water use based on real-world data from state-of-the-art models, monetizing these impacts using global externality values. Our analysis revealed significant differences in environmental efficiency: smaller models demonstrate clear net benefits, whereas large-scale models, such as Grok-3, incur extremely high ecological costs, justifiable only by exceptional economic returns. These findings suggest that the rapid expansion of AI is not compatible with sustainable development. Focusing on agriculture, where resource constraints are severe but AI holds the potential for increased productivity and resilience, we argue that implementing smaller, energy-efficient, domain-specific AI models can facilitate digital transformation without exacerbating environmental pressures. This research underscores the crucial need to integrate robust sustainability principles into AI design, deployment, and policy, particularly in ecologically sensitive sectors such as agriculture. This alignment is crucial to ensure that technological innovation and environmental responsibility work in tandem for a sustainable AI future.

Saudi Arabia implements a wide range of non-tariff measures on imports and exports. Different research articles have quantified the effect of non-tariff measures on trade, but their effect on Saudi Arabia has not been quantified. The major objective of this paper is to quantify the effect of non-tariff measures on the imports of Saudi Arabia. Panel data from 2000 to 2022 for four major regions trading with Saudi Arabia are used to estimate the panel ARDL gravity model. The results of the bound test confirm the presence of a long-run association between the model variables. In the long-run, the per capita income of Saudi Arabia is the main determinant of imports. In contrast, in the short-run the per capita income has no influence, and the non-tariff measures have a negative effect on import value. At the cross-sectional level, the results confirm the negative effect of non-tariff measures on the selected trade partners with varying degrees. The results ascertain the detrimental effect of the application of technical and non-technical measures on Saudi Arabia’s imports. We recommend policymakers in Saudi Arabia adopt a more transparent policy of NTMs application that leads to a sustainable supply of goods and services and ensures sustainable trade.

This study evaluates and compares the empirical performance of three demand system models―Almost Ideal Demand System (AIDS), Quadratic AIDS (QUAIDS), and Rotterdam―in estimating price, income, and cross-price elasticities for potato demand in Saudi Arabia. Given the volatile nature of emerging-market data, the study aims to identify the most suitable model for accurately capturing consumer behavior in the Saudi potato market. The empirical analysis highlights the strengths and limitations of each model in handling price and income fluctuations. While the AIDS and QUAIDS models proved theoretically attractive, both exhibited estimation challenges in this context, including statistically insignificant coefficients, violations of homogeneity and symmetry, and unstable elasticity results. QUAIDS, though capable of capturing nonlinearities, suffered from convergence issues and multicollinearity under volatile conditions. These limitations prompted the adoption of the Rotterdam model, which provided a more stable and theoretically consistent forecast. The study also implements homogeneity and symmetry restrictions within Rotterdam’s framework (RMLE) to enhance model reliability. Diagnostic tests, including Wald tests for coefficient significance and Durbin-Watson tests for residual autocorrelation, confirm the robustness of the findings. In addition, the analysis highlights the practical significance of elasticity estimation for policy design and food security planning. The results offer valuable insights for policymakers and market analysts to understand demand elasticity and improve forecasting in Saudi Arabia’s potato sector.

Chemical fertilizers are essential inputs for agricultural production. Using the correct amounts at the right times improves crop yields and, consequently, farm income. The self-sufficiency rate for nitrogen and phosphate fertilizers averaged about 101.47% and 138%, respectively, from 2008 to 2023. For wheat, nitrogen units reached approximately 85.76 effective units per Feddan (about 0.42 hectares), with a productivity of 0.21 ardebs (1 ardeb of wheat ≈ 150 kg) and a net revenue of roughly 412.66 EGP per nitrogen unit. Similarly, phosphorus units averaged 46.50 effective units per Feddan, producing 0.39 ardebs per unit and generating 772.72 EGP per unit. For cotton, nitrogen units averaged around 79.56 effective units per Feddan, with a productivity of 0.10 quintals (1 quintal ≈ 157.5 kg for seed cotton) per unit and a net revenue of 909.96 EGP per unit. Phosphorus units reached about 52.31 effective units per Feddan, with a productivity of 0.13 quintals per unit and a net revenue of 1390.15 EGP per unit. The production elasticity of these fertilizer units in wheat was 0.963, indicating decreasing returns to scale. For cotton, the total production elasticity was approximately 0.887, also showing reduced returns to scale. The study recommends policies to boost domestic fertilizer production, especially nitrogen fertilizer lines, ensure timely distribution to farmers in sufficient quantities and at appropriate times—particularly during summer—and promote the combined use of organic fertilizers alongside price controls.