Integrating canopy dynamics, soil moisture, and soil physical properties to improve irrigation scheduling in turfgrass systems

Abstract

This dissertation explores some components of the soil water balance in turfgrass systems that remain poorly understood. Specifically, rainfall interception of the turfgrass canopy and the canopy response to soil moisture deficits. Two research studies were conducted at Kansas State University at the Rocky Ford Turfgrass Research Center, Manhattan, KS. The first research study (Chapter 1) investigated the magnitude of canopy interception and the role that meteorological conditions and plant canopy characteristics play in turfgrass systems. Canopy interception has largely been ignored in turfgrass irrigation scheduling programs and the magnitude of interception effects remains unknown. Canopy interception of two common turfgrass species, zoysiagrass (Zoysia japonica L.) and creeping bentgrass (Agrostis stolonifera L.), was measured during various precipitation events in the fall of 2019 and spring of 2020. Canopy throughfall amount resulted in a strong (r = 0.98) positive linear relationship with precipitation total. On average, zoysiagrass and creeping bentgrass canopies intercepted a minimum amount of 5 mm before throughfall occurs. This indicates that no precipitation reaches the soil surface for precipitation events < 5 mm. Nearly 60% of the contiguous United States could result in annual precipitation interception of 50% within a turfgrass canopy. This study provides detailed insights to understanding the interception dynamics in turfgrass and highlights the inefficient nature of small precipitation and irrigation events in turfgrass systems. The second research study (Chapter 2) was conducted throughout the 2019 and 2020 growing season which focused on integrating soil moisture, canopy information, and forecasted precipitation to guide our water management decisions in ‘Meyer’ zoysiagrass (Zoysia japonica L.). We discovered that incorporating soil moisture, plant canopy conditions, and forecasted precipitation into a decision tree resulted in water savings of 81% in 2020 and 66% in 2019 compared to a traditional fixed-amount irrigation scheduling. The decision tree is simple decision-support tool that enhanced our ability to identify and apply irrigation at the most opportune time without sacrificing turfgrass quality.