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The escalating demand for water, energy, and food, coupled with the imperative for sustainable development, necessitates innovative solutions to address the complex interdependencies within the water–energy–food nexus. In this context, agriculture and photovoltaics (Agri-PV or Agri–voltaics) systems have emerged as a promising approach to promoting sustainable agricultural practices while enhancing energy efficiency and food production. However, limited research, especially on the technical aspects of Agri-PV, has resulted in a knowledge gap regarding how to model and determine the suitability of Agri-PV for different crops based on local conditions. This study presents a novel approach to modeling and simulating Agri-PV systems for various major crops in developing countries, using Uzbekistan as a case study. It provides a blueprint for selecting suitable Agri-PV systems. The research investigates the technical feasibility of Agri-PV technology tailored to Uzbekistan's agricultural landscape, with broader implications for Central Asia. Employing a systematic methodology, the study begins by selecting appropriate sites and crops for Agri-PV system testing, ensuring the relevance and applicability of the research findings to the local context. Using advanced software tools such as PVSyst, the study accurately calculates photosynthetically active radiation (PAR) values specific to selected crops, bridging a significant knowledge gap and providing empirical data essential for informed decision making. The methodology further incorporates an in-depth analysis of economic and technical considerations in selecting PV modules and inverters, enhancing the scientific accuracy of the study. By strategically modeling Agri-PV systems based on parameters like row density, module distance, and tilt angle, this research aims to optimize the integration of photovoltaic technology with agricultural practices in Uzbekistan. Moreover, this study helps to understand the impact of Agri-PV systems on the water–energy–food nexus, providing valuable insights into the potential benefits and challenges specific to the region. The study identifies the positive impact of Agri-PV on major crops and provides a suitable design and modeling approach for sustainable farming practices. [ABSTRACT FROM AUTHOR]

• The WEF nexus is clarified based on the material flow of inputs and outputs. • A generic integrated intensity index for the WEF material nexus is proposed. • The intensity of the WEF nexus and its key aspects in the ADR basin are measured. The input–output relationships among material, energy, and information within the water, energy, and food subsystems are intricately interwoven, constituting an integrated system vital for sustainable development. Various water-energy-food (WEF) nexus indicators have been applied to evaluate the system's internal relationships and state. Nevertheless, these methods fail to aggregate the internal relationships into an indicator. This study proposes a WEF nexus indicator that incorporates the interconsumption into the system using the material flow analysis. The interconsumption consists of energy consumption in the food and water subsystems, water consumption in the energy and food subsystems, and food consumption in the energy subsystem. Moreover, the major factors were identified by the sensitivity factor method. The three countries (Tajikistan, Uzbekistan and Turkmenistan) in the Amu Darya River (ADR) basin exhibit strong interactions in the WEF nexus system, are chosen as a case study. Our results show that in 1992–2018, Uzbekistan's food subsystem consumed the most energy among the three countries; the energy consumption within the water subsystem displays an upward trend across all three countries; the water consumption in the energy subsystem of Tajikistan and Uzbekistan is 2–4 times greater than that in Turkmenistan; and the water withdrawals for agriculture within the three countries have remained relatively stable. The composite index in three countries exhibits an upward trend, with the most pronounced increase occurring in Turkmenistan. Uzbekistan has the strongest WEF nexus relationship, with a WEF nexus index that was 2–3 times greater than that of Tajikistan and Turkmenistan. The most important subsystem of Tajikistan and Turkmenistan is the energy subsystem; however, water resources are the major factors in Uzbekistan. The introduced indicator enables a comparison of the WEF system between regions and temporal variations in a certain region. It can provide intuitive and effective data support for WEF nexus system management. [ABSTRACT FROM AUTHOR]

Soil salinity negatively affects plant growth and leads to soil degradation. Saline lands result in low agricultural productivity, affecting the well-being of farmers and the economic situation in the region. The prediction of soil salinization dynamics plays a crucial role in sustainable development of agricultural regions, in preserving the ecosystems, and in improving irrigation management practices. Accurate information through monitoring and evaluating the changes in soil salinity is essential for the development of strategies for agriculture productivity and efficient soil management. As part of an ex-ante analysis, we presented a comprehensive statistical framework for predicting soil salinity dynamics using the Homogeneity test and linear regression model. The framework was operationalized in the context of the Khorezm region of Uzbekistan, which suffers from high levels of soil salinity. The soil salinity trends and levels were projected under the impact of climate change from 2021 to 2050 and 2051 to 2100. The results show that the slightly saline soils would generally decrease (from 55.4% in 2050 to 52.4% by 2100 based on the homogeneity test; from 55.9% in 2050 to 54.5% by 2100 according to the linear regression model), but moderately saline soils would increase (from 31.2% in 2050 to 32.5% by 2100 based on the homogeneity test; from 31.2% in 2050 to 32.4% by 2100 according to the linear regression model). Moreover, highly saline soils would increase (from 13.4% in 2050 to 15.1% by 2100 based on the homogeneity test; from 12.9% in 2050 to 13.1% by 2100 according to the linear regression model). The results of this study provide an understanding that soil salinity depends on climate change and help the government to better plan future management strategies for the region.