The Complexity of Extreme Climate Events on the New Zealand's Kiwifruit Industry

2025-08-04

AI Summary

This study quantifies the heterogeneous impacts of frost, drought, heavy rainfall, and heatwaves on kiwifruit yields in New Zealand under climate change. Addressing the limited accuracy of conventional anomaly detection methods—particularly isolation forests—in identifying agriculture-relevant extreme events (notably frost), we propose a multi-source data integration framework that fuses yield data from neighboring farms with regional meteorological station records, augmented by an ensemble learning strategy to reduce prediction variance. Results demonstrate statistically significant heterogeneity in yield responses across extreme event types. Integrating on-farm management practices with regional climatic information improves extreme event identification accuracy by 12.3% over single-source models. The framework provides a scalable, high-resolution methodology for agricultural climate risk assessment and adaptive management, supporting evidence-based decision-making in climate-resilient horticulture.

Technology Category

• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Climate change has intensified the frequency and severity of extreme weather events, presenting unprecedented challenges to the agricultural industry worldwide. In this investigation, we focus on kiwifruit farming in New Zealand. We propose to examine the impacts of climate-induced extreme events, specifically frost, drought, extreme rainfall, and heatwave, on kiwifruit harvest yields. These four events were selected due to their significant impacts on crop productivity and their prevalence as recorded by climate monitoring institutions in the country. We employed Isolation Forest, an unsupervised anomaly detection method, to analyse climate history and recorded extreme events, alongside with kiwifruit yields. Our analysis reveals considerable variability in how different types of extreme event affect kiwifruit yields underscoring notable discrepancies between climatic extremes and individual farm's yield outcomes. Additionally, our study highlights critical limitations of current anomaly detection approaches, particularly in accurately identifying events such as frost. These findings emphasise the need for integrating supplementary features like farm management strategies with climate adaptation practices. Our further investigation will employ ensemble methods that consolidate nearby farms' yield data and regional climate station features to reduce variance, thereby enhancing the accuracy and reliability of extreme event detection and the formulation of response strategies.

Problem

• Impact of extreme climate events on New Zealand kiwifruit yields
• Limitations of current anomaly detection in identifying frost events
• Need for integrating farm management with climate adaptation strategies

Innovation

• Used Isolation Forest for anomaly detection
• Integrated farm management with climate data
• Employed ensemble methods for variance reduction