Smart Agrivoltaics with IoT-Guided Irrigation for the Food-Energy-Water Nexus: A Simulated Field Study

This empirical-style paper presents a simulated study at the intersection of Agricultural Engineering, Renewable Energy, and Water Management. The study addresses a current research direction in which digital and data-driven methods are being integrated with domain-specific decision systems. Thirty-six simulated plots generated 3,528 plot- day observations. The agrivoltaic structures were modeled at a 30% ground coverage ratio with elevated bifacial modules. A 14-week simulated field experiment represented 36 vegetable plots assigned equally to open-field conventional irrigation, fixed agrivoltaics with scheduled irrigation, and agrivoltaics with IoT-guided irrigation. The crop profile approximated a moderately shade-tolerant leafy vegetable. Hourly radiation, temperature, vapor- pressure deficit, soil moisture, rainfall probability, and photovoltaic output were generated from bounded seasonal profiles. The IoT treatment triggered irrigation from root-zone soil moisture and forecast evapotranspiration while constraining watering during predicted rainfall windows. The IoT-guided agrivoltaic scenario used 28.4% less irrigation water than the open-field baseline while maintaining a slightly higher simulated marketable yield. It also achieved the highest water-use efficiency and a land-equivalent ratio of 1.62 when crop and electricity outputs were normalized against single-use land systems. Midday canopy temperature was lower under both agrivoltaic treatments, and soil-moisture variability was smallest under sensor-guided control. All numerical observations, experimental assignments, outcomes, and performance values are simulated for methodological illustration; no real participants, institutions, farms, factories, hospitals, or city systems were studied. The results are intended to demonstrate a coherent research design, provide testable hypotheses, and show how a future real-world study could report evidence transparently. The paper concludes with implementation constraints, methodological limitations, and priorities for empirical validation.