Evaluating Decision Support Tools for Precision Nitrogen Management on Creeping Bentgrass Putting Greens.

Abstract

Nitrogen (N) is the most limiting nutrient for turfgrass growth. Few tools or soil tests exist to help managers guide N fertilizer decisions. Turf growth prediction models have the potential to be useful, but the lone turfgrass growth prediction model only takes into account temperature, limiting its accuracy. This study investigated the ability of a machine learning (ML)-based turf growth model using the random forest (RF) algorithm (ML-RF model) to improve creeping bentgrass ( Agrostis stolonifera ) putting green management by estimating short-term clipping yield. This method was compared against three alternative N application strategies including (1) PACE Turf growth potential (GP) model, (2) an experience-based method for applying N fertilizer (experience-based method), and (3) the experience-based method guided by a vegetative index, normalized difference red edge (NDRE)-based method. The ML-RF model was built based on a set of variables including 7-day weather, evapotranspiration (ET), traffic intensity, soil moisture content, N fertilization rate, NDRE, and root zone type. The field experiment was conducted on two sand-based research greens in 2020 and 2021. The cumulative applied N fertilizer was 281 kg ha -1 for the PACE Turf GP model, 190 kg ha -1 for the experience-based method, 140 kg ha -1 for the ML-RF model, and around 75 kg ha -1 NDRE-based method. ML-RF model and NDRE-based method were able to provide customized N fertilization recommendations on different root zones. The methods resulted in different mean turfgrass qualities and NDRE. From highest to lowest, they were PACE Turf GP model, experience-based, ML-RF model, and NDRE-based method, and the first three methods produced turfgrass quality over 7 (on a scale from 1 to 9) and NDRE value over 0.30. N fertilization guided by the ML-RF model resulted in a moderate amount of fertilizer applied and acceptable turfgrass performance characteristics. This application strategy is based on the N cycle and has the potential to assist turfgrass managers in making N fertilization decisions for creeping bentgrass putting greens.

Plant phenotyping relevance

MLモデルで芝草の短期刈り取り収量を推定し、その性能を複数の管理戦略と比較しているため、植物形質推定手法が実質的に中心である。

Code and data availability

The supplied blocks describe field phenotyping (clipping yield, NDRE, turf quality) and an ML-RF model, but contain no data availability statement, no public dataset, image, or code deposit, and no authors' public URL for paper-specific assets. The only URL present (http://sterf.golf.se) is a cited reference, not an作者-

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