Networking 13 Berry Minerals to Sustain a High Yield of Firm Cranberry Fruits

The N fertilization to reach high cranberry (Vaccinium macrocarpon) yields resulted in high proportions of soft berries. Our objective was to define the mineral nutrient balance of cranberry to reach a high yield of firm berries. The database comprised 393 observations on cv. ‘Stevens’. Berries were analyzed for total S, N, P, K, Ca, Mg, B, Cu, Zn, Mn, Fe, Al, and Si. Random Forest and XGBoost machine learning models were run to predict yield and firmness classes using raw concentrations, centered log ratios (clr) accounting for nutrient interactions, and weighted log ratios (wlr) that also considered the importance of each dual interaction. The wlr returned the most accurate models. The wlr standards elaborated from the high-yielding and nutritionally balanced subpopulation most often differed between the high-yield class and the high-firmness class. The wlr Cu level was significantly (p ≤ 0.01) too high to reach the high-yielding class in the nutritionally imbalanced subpopulation. There was excessive Al and shortage of Si and Mg to reach high berry firmness in the nutritionally imbalanced subpopulation (p ≤ 0.01), indicating the large influence of soil genesis on berry firmness. Despite statistical evidence, cranberry response to Al and Si corrective measures should be tested to elaborate site-specific recommendations based on soil and tissue tests.

Authors

Institutions

Publication Details

Journal
Horticulturae
Published
2025-06-18
DOI
https://doi.org/10.3390/horticulturae11060705
Citations
2
Primary Topic
Berry genetics and cultivation research
Type
article
Field-Weighted Citation Impact
3.50

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Networking 13 Berry Minerals to Sustain a High Yield of Firm Cranberry Fruits

Léon‐Étienne Parent
2 citations
Horticulturae
Berry genetics and cultivation research
3.50
article

Networking 13 Berry Minerals to Sustain a High Yield of Firm Cranberry Fruits

Léon‐Étienne Parent
article en
2 citations

Abstract

The N fertilization to reach high cranberry (Vaccinium macrocarpon) yields resulted in high proportions of soft berries. Our objective was to define the mineral nutrient balance of cranberry to reach a high yield of firm berries. The database comprised 393 observations on cv. ‘Stevens’. Berries were analyzed for total S, N, P, K, Ca, Mg, B, Cu, Zn, Mn, Fe, Al, and Si. Random Forest and XGBoost machine learning models were run to predict yield and firmness classes using raw concentrations, centered log ratios (clr) accounting for nutrient interactions, and weighted log ratios (wlr) that also considered the importance of each dual interaction. The wlr returned the most accurate models. The wlr standards elaborated from the high-yielding and nutritionally balanced subpopulation most often differed between the high-yield class and the high-firmness class. The wlr Cu level was significantly (p ≤ 0.01) too high to reach the high-yielding class in the nutritionally imbalanced subpopulation. There was excessive Al and shortage of Si and Mg to reach high berry firmness in the nutritionally imbalanced subpopulation (p ≤ 0.01), indicating the large influence of soil genesis on berry firmness. Despite statistical evidence, cranberry response to Al and Si corrective measures should be tested to elaborate site-specific recommendations based on soil and tissue tests.

HorticulturaeVol. 11(6)
Université Laval (CA)
Natural Sciences and Engineering Research Council of Canada
Zero hunger
Openalex Percentile: Top 8%
Berry genetics and cultivation research
3.50
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.