ABSTRACT
Kentucky bluegrass (Poa pratensis L.) is the most common perennial turfgrass species grown on playgrounds, municipal and residential lawn areas,
and golf tees, fairways and roughs. Fertilization is the most efficient way to
improve and maintain turfgrass aesthetic quality. Tissue diagnosis can guide
fertilization, but tissue concentration ranges are biased by not taking into
consideration nutrient inter-relationships, carryover effects and other key
features. The centered log-ratio transformation reflects nutrient interactions
in plants and avoids statistical biases. Machine learning (ML) models relate
the target variable to the key features ex ante, and can predict future
events from prior knowledge. The
objective of his study was to predict turfgrass quality from key
features and rank nutrients in the order of their limitations. The experimental
setup comprised four N, three P, and four K rates applied on permanent plots
during three consecutive years. Soils were a loam and an USGA sand. Eleven
elements (N, S, P, K, Ca, Mg, B, Cu, Zn, Mn, Fe) were quantified in clippings collected during spring, summer and autumn every
year. Turfgrass quality was categorized as target variable by color
rating. Concentrations were centered log-ratioed (clr) partitioned into four quadrants in the confusion matrix
generated by the xgboost ML model. The area under curve (AUC) and model
accuracy were high to predict turfgrass color from the nutrient analyses of
clippings collected in the preceding season, facilitating the seasonal
adjustment of the fertilization regime to sustain high turfgrass quality. We
provide a computational example to run the ML model and rank nutrients in the
order of their limitations.