A new study from Lingxin Bu at Ningxia University offers a breakthrough in how farmers and energy providers might better understand—and manage—crop water use in water-scarce regions. Published in *Agricultural Water Management* (《农业水管理》), the research introduces a unified framework that tracks maize transpiration not just during the day, but around the clock, using solar-induced chlorophyll fluorescence (SIF) as a key indicator.
For decades, irrigation scheduling and water resource planning have relied on daytime-only models of crop water use. But as Lingxin Bu and the team at the School of Civil and Hydraulic Engineering discovered, nearly 10% of total water loss in maize can occur at night—through a process called nocturnal transpiration. Ignoring this nighttime flow leads to underestimations of actual water demand, which can mislead irrigation scheduling and strain energy resources used for pumping.
“Most existing models only look at the sunlit hours,” says Bu. “But plants don’t shut down at night. They still breathe, and in semi-arid regions like Ningxia, every drop counts.”
The team began by evaluating two SIF-based approaches—SIF–Gc (linking SIF to canopy conductance) and SIF–GPP (linking SIF to gross primary productivity)—for estimating daytime transpiration. The SIF–Gc model outperformed SIF–GPP, achieving an R² of 0.72 and RMSE of 1.25 mm/day. But Bu and colleagues knew they could do better.
Through sensitivity analysis, they found that incorporating a nonlinear term for volumetric soil water content (VWC²) significantly improved model accuracy. “Soil moisture isn’t just a linear driver,” explains Bu. “It has a threshold effect—once it drops below a certain level, transpiration falls off sharply.”
The real innovation came in modeling nighttime transpiration (Tn). The team discovered that longwave radiation, residual stomatal conductance, and CO₂ inhibition all play measurable roles in nocturnal water loss. By integrating these factors into the SIF–Gc framework, they developed a robust Tn model that achieved R² values of 0.81–0.84 across multiple growing seasons.
When combined, the improved daytime and new nighttime models formed a 24-hour transpiration framework that accurately estimated total water use (Ttotal) with R² values of 0.82–0.90 during 2023–2025. This level of precision could allow farmers to fine-tune irrigation schedules, reduce overwatering, and cut energy costs associated with pumping—especially in energy-intensive regions like Ningxia, where irrigation relies heavily on groundwater and electricity.
For energy providers and water utilities, this research signals a shift toward smarter, data-driven demand forecasting. If nocturnal transpiration can be reliably monitored using satellite-based SIF data, it opens the door to real-time irrigation optimization and demand response strategies that align crop water use with energy availability—particularly during peak load periods.
“This isn’t just about saving water,” says Bu. “It’s about aligning agricultural practices with energy systems in a way that’s sustainable for both.”
As climate pressures intensify and water becomes a scarcer resource, frameworks like this one—published in *Agricultural Water Management*—could become essential tools for balancing food production, energy use, and environmental stewardship in semi-arid agricultural powerhouses.