Analysis of the field-scale spatial pattern of peanut smut in Argentina - Paredes, Juan Andrés; Cazon, Luis Ignacio; Conforto, Erica Cinthia; Monguillot, Joaquín Humberto; Asinari, Florencia; González, Noelia R.; Rago, Alejandro Mario; Pérez, Agustín; Camiletti, Boris Xavier (Springer, 2025)
Peanut smut, caused by the soilborne pathogen Thecaphora frezzii, poses a significant threat to Argentina’s peanut production. As a monocyclic disease, the infections are restricted to pegs and pods, with no direct plant-to-plant spread. Spore dissemination occurs exclusively during harvest when infected pods release spores, which can persist in the soil for many years. The lack of detailed knowledge about the spatial pattern of peanut smut in commercial fields limits the design of efficient and cost-effective experiments, accurately monitoring disease progression, and evaluating the effectiveness of management strategies. This study integrates field-scale experiments with statistical tools to investigate the spatial patterns of peanut smut across different scales, and their association with crop practices and host–pathogen interactions. Peanut smut incidence (percentage of smutted pods in a sample) was assessed at both small and large scales. Binary power law (BPL) analysis was used to analyze data from the surveyed field samples. Spatial analysis using heterogeneity, dispersion, autocorrelation, and SADIE statistics revealed that peanut smut tends to exhibit a random spatial pattern at medium-to-high disease incidence levels (> 20%), whereas localized clustering patterns occur at lower incidences (< 6%), as confirmed by the BPL. Higher disease incidences were often recorded near field entrances, likely influenced by harvesting practices and activities that promote spore concentration in specific areas. These findings highlight the importance of avoiding field edges or entrances during sampling to ensure unbiased data collection for disease monitoring. Understanding the spatial dynamics of peanut smut enhances the ability to design accurate experiments, improve sampling methods and contributes to developing better disease management strategies.