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Updated: Jan 30, 2026

In vivo and In vitro Infection of Potato Roots with Plant Parasitic Nematodes for the Assessment of Induced Structural Changes
Published on: February 28, 2025
Prediction of Potato Tuber Damage by Root-Knot Nematodes using Quantitative DNA Assay of Soil
Frank S Hay1, 2, Kathy Ophel-Keller2
1School of Integrative Plant Science, Section of Plant Pathology and Plant-Microbe Biology, Cornell University, Geneva, NY 14456.
Abstract:
Root-knot nematodes (Meloidogyne fallax and M. hapla) cause significant reductions in potato yield by reducing tuber quality. Concentrations of M. fallax and M. hapla DNA in soil were determined by quantitative polymerase chain reaction following sampling at planting and harvest within 78 fields across 3 years in Australia. Meloidogyne spp. were also detected using a tomato bioassay. M. fallax was more prevalent than M. hapla and DNA concentrations of M. fallax in soil were significantly higher in samples collected at harvest compared with those at planting. In contrast, M. hapla DNA in soil did not significantly change from planting to harvest. Using receiver operating characteristic curve analysis, M. fallax DNA in soil at planting and harvest was a highly accurate predictor of tuber damage at harvest and galling on tomato. Prediction accuracy for tuber damage was highest for M. fallax DNA compared with M. hapla or M. fallax + M. hapla. Both Meloidogyne spp. were detected in the peel of asymptomatic certified seed. For M. fallax, the addition of seedborne inoculum did not improve tuber damage predictions. This suggested that soilborne M. fallax populations contributed most substantially to tuber damage. These findings highlight the utility of this approach for predicting risk of crop damage from nematodes. The use of this technique as a practical management tool is discussed.
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Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

