A Comparison of the Potential for Scaling Up Irrigation Scheduling Techniques: Substrate Moisture Sensing Versus Predictive Water Use Modeling

Evapotranspiration equations (e.g. Penman-Monteith) are widely used to estimate crop irrigation.  However, crop coefficients that adjust potential evaporation to crop-specific transpiration are empirically derived, absent of physiological response descriptions.  Although complex mechanistic models exist for predicting crop water use (e.g. MAESTRA), their application in commercial nurseries has, so far, only been conceptual.  Alternatively, irrigation scheduling can take place by substrate moisture measurement, triggering irrigation based on predefined volumetric water contents (threshold method).  In this study we grew trees in a containerized pot-in-pot production system and irrigated them with both scheduling methods.  The threshold method maintained substrate volumetric water content between 35% and 42%.  The modeling method used MAESTRA to estimate transpiration on a 15-minute time step, triggering periodic irrigation from crop water use estimates.  Tree growth (stem caliper) and canopy development (m² of leaf area) were measured over the growing season.  In addition, we monitored daily irrigation and leachate for water balance and irrigation application efficiency calculations. We tested the hypothesis that precise characterization of two physiology parameters [minimum stomatal conductance (g₀) and the marginal water cost per unit of carbon gain (g₁)] could yield accurate transpiration estimates (within 10%).  Predictive water use modeling exceeded our 10% error window, but we were able to estimate irrigation within 20% of measured values. Overall, trees irrigated by the MAESTRA method developed more (up to 15%) stem caliper and accumulated up to an additional 25% of leaf area in one growing season.  However, the modeling method applied more water (~20% across species).  Despite the additional amount of water, we found the efficiency of applied irrigation (percentage of water that did not leach) to be similar between the two methods (within 10%).  We conclude that MAESTRA holds promise as an effective means for scheduling irrigation with generalized physiology parameter sets.