Evaluating Integrated Soybean Cyst Nematode Management Using Spatially Informed Mixed Models

Evaluating management strategies for soybean cyst nematode (SCN) under field conditions remains challenging because the nematode is spatially aggregated and infests fields that are themselves heterogeneous. When left unmodeled, this structure is absorbed into the residual error and erodes the precision of treatment comparisons. This study combined a split-plot field evaluation of two SCN-resistance sources (PI 88788 and Peking) and fluopyram seed treatment with a graded series of spatially explicit linear mixed models, fitted to populations capable of reproducing on both resistance sources in two Ohio environments. Six models spanning a gradient of spatial complexity—from non-spatial through autoregressive and trend-adjusted to tensor-product penalized spline (TPS) structures—were compared; TPS fit best, improving experimental efficiency by 33–39% over the conventional non-spatial analysis. Peking derived cultivars outyielded PI 88788 derived cultivars by 257 kg ha⁻¹ and suppressed nematode reproduction by 36%. Peking nonetheless showed slightly greater yield sensitivity to increasing pre planting SCN egg density [Pᵢ, eggs 100 cm⁻³ soil; −5.1% per unit increase in ln(Pᵢ)] than PI 88788 (−4.0%), revealing a trade-off between reproductive suppression and yield stability. Fluopyram increased yield by 111 kg ha⁻¹ but did not reduce SCN reproduction, indicating a yield-protective effect rather than a population suppressive effect. Effective management of dually adapted populations therefore requires diversification of resistance, with Peking-derived cultivars complementing PI 88788 and seed treatments providing supplementary yield protection. The study also demonstrates that spatially explicit mixed models improve inferential precision for crop yield and SCN reproduction and offer a transferable approach for other soilborne pathogens.