Assessing the Potential of Agrivoltaic in Arid Regions through Long-term Soil Moisture Balance Simulation

Abstract

The co-location of crop and photovoltaic energy generation, or Agrivoltaics (APV), entails making the best use of the available space and resources. APV can solve the growing problems of salinity and aridity in arid and semi-arid regions. Through numerous synergistic feedbacks on water resources, agricultural productivity, marginal land reclamation, and solar energy production efficiency, APV has a promising potential and is projected to increase the efficiency of agriculture and marginal land reclamation. This thesis explores the interactions between soil salinity, water irrigation, evapotranspiration, salinity evolution, crop interception, panel arrangement, and shading impacts by stochastically generating soil water balance partitioning over long durations to investigate how soil salinity, water irrigation needs, relative yield, and irrigation threshold interact.

A simple model that considers the feedback between salinity and evapotranspiration and is mediated by plants' tolerance to salt was provided. To investigate the potential of Agrivoltaic in remediating salt-affected lands, a heavily salinized plot analyzed to examine the consequences of combining the deployment of photovoltaic cells and salt-tolerant crops on the reduction in irrigation water requirements and soil salt mass shows how effective APV is at improving salt-affected soils over time (Perri et al., 2018). Given the dearth of freshwater resources in dry places, APV can save more than 4 mm of irrigation water per day when compared to open-field agriculture. Additionally, reduced water intake is projected to result in less salt input; therefore, less leaching frequency needs and lower groundwater salinization is anticipated. To verify this model, experimental data is required. Moreover, the modeling assumptions for shade, interception, and evapotranspiration need improvements to provide a more realistic representation of the heat and water fluxes.

Date of Award	Dec 2022
Original language	American English
Supervisor	Riaan van der Merwe (Supervisor)