Table 1.
Regional provenance and pedigree [6,3,6] of cranberry cultivars in the dataset.
Table 2.
Features and target variables in the Quebec-Wisconsin dataset.
Table 3.
Factor contribution to ML model accuracy.
Table 4.
Intervals of compatibility of quartile nutrient concentrations in plant tissues (leaves and stems) of true negative specimens across factors compared to published ranges.
Fig 1.
Biplot analysis of the Quebec-Wisconsin dataset.
Fig 2.
Dendrogram to analyze macro- and micronutrients separately (Fv = filling value).
Table 5.
Diagnosis of two Quebec and Wisconsin cranberry cultivars against their respective closest Euclidean distances from successful cultivar-specific neighbors.
Fig 3.
Nutrient diagnosis for ‘Stevens’ in Quebec and ‘Crimson Queen’ in Wisconsin at regional scale across factors or at local scale at factor-specific level.
Fig 4.
Relationship between actual and predicted cranberry yields at year t+1.
Table 6.
Accuracy of Random Forest regression models (Yt, Yt+1 for yield, t for current year, Ft for fertilization regime, and Ct for tissue composition) using the Quebec 2014–2018 fertilization trials with cultivar “Stevens”.
Fig 5.
Perturbation vector of the defective Quebec specimen in Table 4 compared to a close successful specimen to attain high yield in the following year.