Identifying climate adaptation strategies for agri-food value chain actors using hybrid Machine Learning and process-based modelling approaches 

Introduction

Climate change poses risks to food security by affecting various aspects of the agri-food value chain. Importantly, extreme droughts and heat waves reduce yields and nutritional value of crops, accelerate pests, diseases, and crop senescence, and disrupt supply chains and reduce shelf-life of products. Also, these impacts are higher when droughts and heat waves occur simultaneously (i.e., compound events). Climate adaptation strategies adopted by different actors in the agri-food value chain may alleviate negative impacts of climate change. Strategies include alternative farm management, pest and disease control, the use of new genotypes or alternative crops, shifting supply lines, and post-harvest measures.

Project description

The ability to forecast the effects of adaptation strategies is essential in evaluating their effectiveness. With increasingly available data on crop phenotyping, proximal and remote sensing, transportation, delivery, consumption, etc., data-driven models based on Machine Learning (ML) are gaining importance in guiding the decision-making in various aspects of the agri-food value chain. The use of ML-based approaches for evaluating climate adaptation strategies is currently hampered by two things. Firstly, ML-based models are unreliable when extrapolating beyond the range of the training data. Extrapolation is however essential given that climate change leads to conditions that previously were rare or non-existent and of which we hence have little data. The use of hybrid approaches combining ML and process-based modelling that codify domain knowledge may remedy this drawback while using the strengths of ML and the available data. Secondly, data are scattered, noisy, and heterogeneous. The standardization of data schemata through ontologies and knowledge graphs would benefit the organization of data and provide new information for training ML-based models.

The aim of this proposal is to develop and apply hybrid approaches based on ML and process-based modelling for the assessment of climate adaptation strategies for different actors in the agri-food value chain. Four work packages (WPs) are proposed that will focus on tailored solutions for different aspects. WP1 is an overarching WP that focuses on the standardization of data schemata and the joint development of ontologies and integrated knowledge graphs to harmonize terminology and data sources and facilitate faster and higher-quality integration of data within as well as across hybrid ML models. WP2 focuses on the development of hybrid ML and process-based models for crop growth from time series data that involve essential genotype-by-environment (GxE) interactions to describe crop responses to heat waves and droughts. WP3 focuses on assessing the effectiveness of low-cost, nature-based management practices that are applicable by farmers in low-income countries for climate change adaptation, such as crop rotation, crop diversity, alternate genotypes for stable yields, varying sowing dates, tillage, and mulching. Lastly, WP4 focuses on better supply chain strategies for reducing food waste resulting from climate change.