Novel action thresholds of a logistic regression model to forecast dollar spot on bentgrasses

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Abstract

Dollar spot (Clarireedia jacksonii) susceptibility varies among bentgrasses (Agrostis spp.). This trial assessed the ability of five action thresholds of the risk index (RI) of a logistic regression model to forecast dollar spot incidence on bentgrass fairway turf grown on a sandy loam in North Brunswick, NJ. Dollar spot incidence was assessed over 128 observation periods (May to Nov. annually) over 3 yr and related to five action thresholds for prediction accuracy. Action thresholds included a RI threshold of 20% (RI 20%), interpreting the change in the RI over time (RI slope), adjusting the RI upwards to maximize accuracy (RImax), combining RI 20% with RI slope, and combining RImax with RI slope. The RI 20% accurately predicted disease on 63 to 66% of observation periods for highly susceptible creeping bentgrass (A. stolonifera L.) cultivars Shark, Penncross, and Independence; other action thresholds improved accuracy by only 10% or less for these cultivars. Prediction accuracy on lower susceptibility cultivars (007, Declaration, and colonial bentgrass Capri; A. capillaris L.) was substantially improved with four novel action thresholds by reducing over-predictions compared to RI 20%. Accuracy was improved by as much as 32% when the RImax and RI slope were combined for Declaration compared to RI 20%. On low susceptibility cultivars, turf managers may be able to use action thresholds that incorporate a RI greater than 20% and/or RI slope to reduce fungicide inputs and maintain acceptable dollar spot control compared to RI 20%.

Original languageEnglish (US)
JournalCrop Science
DOIs
StateAccepted/In press - 2021

All Science Journal Classification (ASJC) codes

  • Agronomy and Crop Science

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