Multivariate and Hierarchical Data Modelling: Effect of Climate Interventions on Pear Quality & Size

Challenge

Climate change poses critical challenges for the agriculture sector. As authorities develop future climate intervention strategies such as carbon removal, sustainable energy transitions, and transportation interventions, it is essential to understand their implications on food production.

Do these interventions affect the quality, size of agricultural produce, if so, can they be studied in detail?

Solution

Ecotrons provide a sophisticated platform for studying complex environmental interactions and ecosystem dynamics under controlled conditions. We used ecotrons to set up the experiment of administering the climate change interventions.

We conduct extensive multivariate and hierarchical data analysis on the data. We fitted linear mixed-effects models (LMMs) to evaluate continuous quality outcomes and generalized linear mixed models (GLMMs) for binary quality. For the longitudinal data of fruit growth, we used linear mixed-effects models (LMMs) with random slopes. Soil data was analyzed using Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) to identify scenario-specific differences in soil properties.

Key Findings

Best Scenarios:

•CO₂ Removal: Increases Quality of Pears ~7.25 pts

•Sustainable Energy by ~7.90 pts

Best Conditions:

•Belgium-grown & Conference pears

•No Intervention had fastest growth and biggest size of pears but poorest soil/quality

Soil Insights:

•PCA: Removing CO2 & Sustainable Energy = high organic matter, microbial activity

•LDA: Nitrate and infiltration = key discriminators

Topics:

Experimental Design, Multivariate Methods, Linear Mixed Models, Generalized Linear Mixed Models, Principal Components Analysis, Linear Discriminant Analysis, Longitudinal Data Analysis

Tools:

R studio, Latex Overleaf

Data:

CSV files

Links

The Report with our findings

PMHD Project.pdf

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