The Food Security Use Case (UC3) aims to address the impact of Climate Change on the productivity of essential food security crops from the food, social and economic perspective, allowing to determine the most suitable areas for specific crops and thus promoting sustainable management at the territorial level. The project is developed through the identification of specific climate indices for target crops in specific areas, which allow the impact of Climate Change to be assessed and yield trends to be predicted over time. Moreover, it’s possible to determine the most suitable areas for specific crops cultivation over time and to define the sustainability of specific crop cultivation. This information makes it possible to anticipate potential yield declines and helps in the implementation of Climate Change mitigation solutions in larger territorial districts.

Challenge

Climate change is a negative outcome of environmentally damaging food systems, and a threat to the future of food production and the livelihoods that depends on it. Food systems encompass all the interrelated actors and processes involved in the production, processing, distribution, consumption, and disposal of food. Rising temperatures, extreme and unpredictable weather patterns, and disruption to water cycles are already causing reductions in the yields of staple crops in some regions of the world. Climate change presents a key threat to agroecosystems, food security and the many livelihoods dependent on food production, particularly in the global South. In an already flawed and inequitable food system, climate change therefore acts as a threat multiplier, further aggravating existing problems such as hunger and food insecurity. In some geographical hotspots climate change is already undermining food systems, even
where incremental adaptation measures are ramping up. Transformative adaptation in agriculture promotes long-term resilience by continually shifting the geographical locations where specific types of crops and livestock are produced, aligning agricultural production with changing landscapes and ecosystems, and/or introducing resilience-building production methods and technologies across value chains.

Solution

UC3, addressing food security, relies on a multitude of observation data types (from ground, satellite, production and climatic time series) to implement different services:

• Impact analysis, mainly based on observation data
• Risk of loss or damage estimation, through the development of predictive algorithms, based on recent time-series, forecast data and the characterization of the impact estimates of the previous component
• Identification of new areas with favorable climate conditions for specific crops
• Identification of crops suitable for new climate conditions.

Current status

The UC3 focuses on climate-resilient agriculture, initially targeting tomato as the pilot crop due to its high relevance for the Mediterranean diet and its economic importance in Italy.
Subsequently, the same methodology was successfully scaled to maize, another essential food crop with a similar cultivation period, demonstrating the flexibility and transferability of the approach to other crops vital for food security.

Tomato Case study: Tomato was selected as the starting point because of its key role in the Italian agri-food sector. The study areas were chosen to reflect a range of climatic conditions, allowing a thorough analysis of climate change impacts on productivity. The UC3 has achieved steady progress in the tomato use case. It has been fully integrated into the EO4EU platform, which supplies input data for both training and inference phases. The number of climate indices (including climate, soil and elevation parameters) has reached 84, categorized into different growing periods: the entire growing cycle (from April to September, “tot”), the planting and early growth season (from April to June, “q2”), the production season (from July to September, “q3”), and the critical month of May.
To enhance the geographical coverage of the dataset, climatic, soil, altitude, and yield data from Spain - another major tomato producing country - have been integrated, allowing for a broader and more robust comparison. In addition, the time span was increased: climate and productivity data from 2006 to 2023 were used.
A Machine Learning (ML) model has been developed to predict tomato yield classification based on climate and territorial indicators. This model enables yield classification in both new geographical areas and future climate scenarios, providing valuable insights into how climate change may impact tomato cultivation in different regions. The results obtained will aid in identifying areas that remain favorable for tomato production and adapting agricultural strategies accordingly.
The whole workflow has been carried out with the support of stakeholders, who were able to provide crop-specific physiological, agricultural and management information and needs for their working sector. Numerous interactions have been held with different entities, consortia, and associations to better define the use case requirements and to refine the applied methodology.

Maize extension: Following the successful implementation for tomato, the methodology was applied to maize, one of the most widely cultivated crops in Italy and Europe. Maize was chosen not only for its importance in food systems but also because its cultivation period closely aligns with that of tomato, facilitating methodological reuse. The same ML model architecture used in the tomato use case was adopted, but re-trained from scratch using data specific to maize. In particular, the model was trained with maize yield data, climate and territorial indicators all tailored to the maize cultivation cycle and collected from selected Italian and French provinces. These provinces were chosen based on their high productivity and climatic diversity, in order to ensure the relevance of the data and to enable robust comparisons across different agro-climatic zones, as it was previously done for the tomato crop. This extension confirms the scalability of the UC3 approach: by simply adjusting the periods for computing climate indicators to match the crop calendar, the entire workflow can be transferred to other strategic crops, enhancing food security planning under climate uncertainty.

Relevant data for the UC3, including Earth observation, simulations, projections, and statistics, have been selected to train the model and to support the production forecasts, and are described below:

• Copernicus ERA5 (temperature and precipitation)
• ISTAT (Italian crop distribution and production at local scale)
• Copernicus DEM 30 m of resolution
• SoilGrid information (clay, sand, nitrogen, ocd, phh2o)
• Very High-Resolution datasets from CMCC simulations (historical simulations and
• projections of temperature)

Data from the dataset listed are collected from several Italian regions and for the entire Spanish territory from 2006 to 2023 for the tomato crop, in order to obtain more comprehensive statistics and a larger dataset to train the predictive model. For the maize crop, instead, data were collected for the same time period, but from several regions in Italy and France, with the aim of training the model specifically for maize cultivation.

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