Data and code from: Establishment of southern highbush blueberry cultivars and suppression of phytophthora root rot using cover crop and soil amendment treatments

Authors: Smith, Barbara; STAFNE, ERIC - Mississippi State University; Read, Quentin

Submitted to: Ag Data Commons
Publication Type: Database / Dataset
Publication Acceptance Date: 11/20/2024
Publication Date: 11/20/2024
Citation: Smith, B.J., Stafne, E., Read, Q.D. 2024. Data and code from: Establishment of southern highbush blueberry cultivars and suppression of phytophthora root rot using cover crop and soil amendment treatments. Ag Data Commons. https://doi.org/10.15482/USDA.ADC/27695847.v1.
DOI: https://doi.org/10.15482/USDA.ADC/27695847.v1

Interpretive Summary: Phytophthora root rot, a damaging disease of blueberry, can be managed with soil amendments and cover crops. In this dataset, we present all data and code needed to statistically analyze the data from a study comparing how well different soil treatments do at reducing root rot and improving performance of three different highbush blueberry varieties. There are seven different datasets representing different variables we measured throughout the course of the study. We analyze the data from each dataset with a different kind of statistical model, using linear mixed models. We use a combination of classic and Bayesian statistical methods. The R notebook has full explanations of all the statistical models and how to interpret their results.

Technical Abstract: Phytophthora root rot is a major disease of blueberry that often relies on cultural practices including the addition of soil amendments for disease management. We evaluated the effect of three cover crops on the establishment and growth of southern highbush blueberry (SHB) plants and their ability to suppress Phytophthora root rot infection of plants. This dataset includes data from soil chemical analyses (Table 1), soil pH and electrical conductivity measurements (Table 2), plant leaf, flower, crop load, and fruit ratings (Table 3), plant vigor rating and survival rate (Table 3), stem count, length, weight, and survival (Table 4), root length, diameter, and weight (Table 5). In addition, seedling weight and count data from a blueberry seedling assay (Table 6) and a lupine seedling assay (Table 7) are included. The associated RMarkdown notebook includes statistical models to analyze all datasets presented here. Linear mixed models, generalized linear mixed models, generalized additive models, and cumulative logistic mixed models are fit where appropriate. A combination of Bayesian and frequentist methods are used; some of the frequentist models include Bayesian-style prior distributions on fixed effects. Models are described in more detail in the notebook.