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Updated: Jun 21, 2025

Microplot Design and Plant and Soil Sample Preparation for 15Nitrogen Analysis
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Functional data analysis-based yield modeling in year-round crop cultivation.

Hidetoshi Matsui1, Keiichi Mochida2,3,4

  • 1Faculty of Data Science, Shiga University, Banba, Hikone, Shiga 522-8522, Japan.

Horticulture Research
|July 11, 2024
PubMed
Summary
This summary is machine-generated.

Predicting crop yield using functional data analysis (FDA) helps optimize agricultural management. This method models environmental impacts on strawberry and tomato yields, enhancing resource efficiency and sustainability for climate-adaptive horticulture.

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Area of Science:

  • Agricultural Science
  • Statistical Modeling
  • Horticultural Science

Background:

  • Effective agricultural management relies on accurate crop yield prediction.
  • Longitudinal crop cultivation, especially in controlled environments like plant factories, presents complex dynamics influenced by environmental factors.
  • Understanding these dynamics is crucial for optimizing resource use and ensuring sustainable production.

Purpose of the Study:

  • To develop and validate a methodology for modeling the relationship between environmental parameters and crop yield in longitudinal cultivation.
  • To assess the impact of environmental fluctuations on crop yield using advanced statistical techniques.
  • To provide insights for optimizing growth parameters and enhancing resource efficiency in horticulture.

Main Methods:

  • Functional Data Analysis (FDA) was employed to model the complex relationships.
  • A varying-coefficient functional regression model (VCFRM) was utilized to analyze time-series data.
  • The model visualizes seasonal shifts and the dynamic interplay between environmental factors (solar radiation, temperature) and crop yield.

Main Results:

  • The VCFRM successfully modeled the relationship between environmental variables and crop yield in strawberry and tomato production.
  • Seasonal shifts and the dynamic influence of environmental factors on yield were visualized.
  • The interpretability of the FDA-based model provided actionable insights for optimizing crop production.

Conclusions:

  • The feasibility of VCFRM-based yield modeling for longitudinal crop cultivation was demonstrated.
  • The methodology offers strategies for stable, efficient crop production.
  • This approach is pivotal for addressing climate adaptability challenges in plant factory-based horticulture.