Unravelling spatial heterogeneity of apple replant disease and associated parameters at field scale

<b>Background and Aims:</b> Apple replant disease (ARD), a widespread disorder, arises from repeated apple (Malus domestica) planting in the same area, disrupting physiological and morphological plant functions. In ARD-affected orchards, uneven growth is usually attributed to the patchy distribution of the ARD-causing agent. But, variability in soil properties can also cause differing growth, either directly or by influencing biota involved in ARD. Evaluating phenomena under field conditions that vary at a small scale (such as ARD) is a challenging task. Here, we aimed to identify the drivers of small-scale growth variations. <br/>

<b>Methods:</b> We used a pair-wise design where neighboring trees within two commercial orchards were chosen based on maximum difference in stem diameter growth. With this spatially explicit approach, large-scale site heterogeneity as a reason for differing growth was minimized. Pairs in the plots with grass and Tagetes patula as pre-culture were chosen. <br/>

<b>Results:</b> At one site, neighbors with differing growth were separated due to high particulate organic matter content, likely resulting from localized tree shredding. The worse-growing partners exhibited higher phytoalexin levels, which are general indicators of biotic stress. However, its cause remained unidentified. Amplicon sequencing was performed to assess rhizosphere bacterial community composition (data in the NCBI Sequence Read Archive (SRA) under Bioproject PRJNA1295795). Although microbiome differences were the most likely explanation for the stress response, no compositional changes were detected. At the second site, no differences depending on tree growth occurred. <br/>

<b>Conclusion:</b> Overall, our work demonstrated that sampling neighboring trees with varying stem growth can identify co-occurring differences in related variables. Still, results were highly site-dependent and determined by the variables chosen for analysis.