Current and next-year cranberry yields predicted from local features and carryover effects
Léon Etienne Parent1,2, Reza Jamaly1, Amaya Atucha3
1Département des Sols et de Génie Agroalimentaire, Université Laval, Québec, Québec, Canada.
Plos One
|May 10, 2021
Summary
Customizing cranberry (Vaccinium macrocarpon) fertilization using machine learning improves yield prediction. Site-specific nutrient management, considering local factors and nutrient carryover, is crucial for optimizing cranberry production and future harvests.
Area of Science:
- Agricultural Science
- Horticultural Science
- Data Science in Agriculture
Background:
- Cranberry cultivation in Wisconsin and Quebec relies on general soil and tissue nutrient tests.
- Existing fertilization strategies do not fully account for site-specific factors like cultivar, location, and nutrient carryover.
- Variability in cranberry yield is influenced by a complex interplay of environmental and management factors.
Purpose of the Study:
- To develop customized nutrient diagnosis and fertilizer recommendations for cranberries at a local scale.
- To predict next-year cranberry production by accounting for local factors and carbon and nutrient carryover effects.
- To improve the accuracy of nutrient management strategies for cranberry stands.
Main Methods:
- Collected 1768 observations from on-farm surveys and fertilizer trials in Quebec and Wisconsin.
- Developed a machine learning model (Random Forest) using minimum datasets to analyze yield-influencing factors.
- Tested nutrient carryover effects using a 5-year fertilizer experiment on permanent plots in Quebec.
Main Results:
- Micronutrients significantly contributed to variations in cranberry tissue composition compared to macronutrients.
- The Random Forest model accurately predicted current-year berry yield based on location, cultivars, climate, fertilization, and soil/tissue tests (0.83 accuracy).
- Site-specific nutrient diagnosis proved more effective than general recommendations, highlighting regional and local variations.
- Next-year yield and nutrient status were accurately predicted using current-year data (R2=0.73, 0.85 accuracy).
Conclusions:
- Machine learning and compositional analysis are effective tools for customizing nutrient diagnosis and predicting site-specific cranberry yields.
- Nutrient standards are not universally transferable across different regions or even between cranberry beds within the same region.
- Large, comprehensive datasets are essential for capturing the multifactorial influences on cranberry yield at the local level.
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