Conference Agenda

83% of greenhouse gas emissions from cereal and oilseed crops originate from fertiliser when both fertiliser-induced field emissions and emissions from the manufacture of synthetic fertiliser are included [HGCA/AHDB 2012]. Nitrogen Use Efficiency (NUE) is the nitrogen in the crop at harvest as a percentage of the nitrogen supply including both Soil nitrogen supply and applied fertiliser. UK farmers achieve, on average, around 60% nitrogen fertiliser use efficiency, but research suggests 80% efficiency could be attainable. Excess applied nitrogen is lost through leaching into water courses, and as Nitrous oxide emissions, which impacts the environment and farmers’ costs.

NUE-Profits (funded by Innovate-UK/DEFRA) is a farmer-led collaboration with industry specialists and academic experts to combine measurement, monitoring, modelling, and forecasting of crop and soil to enable farmers to improve NUE without compromising yield or grain protein thus improving farmer profit margins and reducing crop environmental impact.

Earth observation data compliments in-field measurements, reducing the number and frequency of measurements a farmer needs to make to monitor crop growth and plant nitrogen uptake and to observe spatial and temporal variation. Leaf Area Index (LAI) can accurately characterise the plant phenology, particularly the timing and rate of crop growth, which provides information about plant nitrogen uptake. We generate gap-filled LAI time-series from Sentinel 2 observations with cloud and cloud shadow removed using a cloud mask we optimised for monitoring cereal crops. We also assimilate LAI, alongside in-situ measurements and other datasets (for example soil moisture), into a physiological crop growth model to localise the model to the field and sub-field level. This allows us to model and forecast crop yield and development based on historical and forecast weather data. The data assimilation framework is adaptable to incorporating additional data sets when they are available and, importantly, allows quantification of uncertainty.

Climate change, environmental degradation, and the rising demand for sustainable food systems urgently demand a transformation of our agricultural practices and supply chains. From ensuring food safety and reducing waste to promoting fair trade, the complexity of our food systems underscores the need for greater transparency and traceability in how we source our food. Earth Observation (EO) has proven a powerful tool for understanding land use, deforestation, and agriculture's environmental impacts. However, emerging technologies like blockchain have the potential to disrupt the sector further, promising unprecedented visibility and accountability. This could empower consumers, farmers, and regulators alike to make informed choices that support sustainable farming practices and facilitate compliance with regulations like the EU Deforestation Regulation (EUDR).

The RUDEO project, a 1-year ESA project carried out by GMV and GMV-SES, proposes use cases to carry out a critical examination of the transformative potential of blockchain within agriculture. The project plans to evaluate existing solutions and propose new workflows that will assess the actual implications of using blockchain technology to streamline certification processes, ensure fair compensation for farmers who adopt sustainable methods, support carbon markets linked to cropland carbon stocks, create transparent platforms connecting producers and consumers, and trace the journey of commodities for ethical sourcing.

The RUDEO project's investigations will shed light on whether blockchain, alongside established EO technologies, can truly revolutionize agricultural practices and even go beyond aligning with the goals of the European Green Deal's Farm to Fork Strategy. Success in this area could offer solutions to the urgent challenges facing agriculture under pressure, facilitating more robust Monitoring, Reporting, and Verification (MRV) systems. In turn, this would support initiatives like the CAP Eco-Schemes by streamlining certification processes and incentivizing innovative agricultural practices with low environmental impact. Additionally, this integrated approach has the potential to reduce GHG emissions, combat biodiversity loss, and promote deforestation-free products. If achievable, the combination of EO data with blockchain could offer significant added value by driving collaboration, fostering trust, and accelerating the adoption of sustainable practices that benefit both farmers and the planet.

The Common Agricultural Policy implementers are on their roadmap to switch to a full Area Monitoring System (AMS). AMS is not just to integrate IT and EO technological novelties into the Integrated Administration and Control System (IACS), it is a real paradigm shift towards a performance-based CAP. The full-scale and time-wise continuous cloud processing has opened a wide range of possibilities to analyse land management and land cover systems with comprehensive territorial and seasonal coverage. The administration must be capable of addressing via the direct payments the challenges of climate mitigation, soil protection, biodiversity and different agro-ecosystem services. Beyond the parcel-level validation duties of CAP Paying Agencies for planning and monitoring the payment schemes, AMS requires further multi-annual EO data analysing capabilities. Extended data sharing and data interoperability are also key conditions of new AI-based modelling techniques.

The proposed Agri-Dashboard implements Data as a Service and visualisation by widget-based apps. It enables Paying Agencies to get fast access to reliable information on agricultural parcel level required for customised monitoring, expert judgements and policy reporting obligations.

We highlight solutions for understanding large scale outputs of Machine Learning products, detections taken along the threshold-driven marker logic, analysing class-based decision models or integrating Geotagged Photos. Agri-Dashboard supports data slicing and data analysis using misclassification matrices, class-level accuracy measures and statistics or analysing the quality of training and test data. These data insight methods are fundamental for AMS Quality Assurance.

The multi-annual EO-data of AMS seriously has the potential to become a solid knowledge-base of a real performance-oriented payment system opening higher flexibility and spatial sensitivity to design eligibility conditions, impact indicators in zonal logic, meaning that AMS products could form the base of strategic planning.

Agricultural landscape features (LF) are small fragments of non-productive (not directly used for agricultural production) and typically, but not only, natural or semi-natural vegetation (hedges, tree, grass margin, stone walls, ponds, ditches etc). They are located in the agricultural landscape and can provide ecosystem services and support for biodiversity. In the latest LUCAS - a harmonized land cover and land use data collection exercise that extends over the whole EU territory - Landscape Features module done in 2022, the survey is comprised of 3.8 million sampling points, distributed over 93,000 one-hectare quadrats and the methodology combines office-based photo interpretation with field surveys . In a first phase, all points for the field survey were photo-interpreted in the office. Surveyors classified points as non-LF or one of four types of LF (woody, grassy, wet, and stony) based on areal EO ortho-photo of ~ 20 cm resolution. This was followed by field observation to confirm/correct the previous step. Based on the outcomes of the LUCAS LF module, we assess the influence of the density and type of landscape features on the biodiversity of pollinators and farmland birds. We also consider information on agricultural intensity and compare the results with previous landscape surveys within LUCAS. We assess how this information can be used to evaluate the performance of the Common Agricultural Policy in halting and reversing the loss of farmland biodiversity for a more sustainable agriculture.

Feeding a growing world population sustainably is crucial, but sustainable practices have varied, ever-changing impacts. This is because agriculture is a complex system, influenced by various local factors such as soil composition, soil health, land use, and climate variability. It is important to understand the heterogeneity of effects of practices at the field-level to help farmers personalise the decisions.

Analysing complex interactions and estimating effects boils down to answering causal queries. In recent years, observation-based causal inference approaches have emerged as a mature field. This research introduces a causal inference framework utilising Double Machine Learning to estimate Conditional Average Treatment Effects. The primary objective is to uncover the nuanced and spatially diverse impacts of crop rotation (farming practices of interest) on Soil Organic Carbon (SOC) levels in Lithuania's heterogeneous regions.

The study uses the farmers' declarations of the crops they cultivate (LPIS) from 2018-2022 to derive a binary crop rotation treatment, determined by cultivating at least 4 different crops in 5 years. We control for climate variables from ERA5 (e.g. surface net short-wave radiation, soil temperature), and soil properties (e.g. clay) to help us identify and estimate the impact of crop rotation on SOC content.

Beyond estimating local effects, the study explores the diversity of outcomes across space and time. This deeper analysis helps identify the key drivers influencing these variations, such as soil composition, climate, or even specific crop rotation combinations. The results indicate that crop rotation appears to be more effective in the western croplands of the country while exhibiting a negative impact on SOC in the central-northern regions.

Estimating the effects of practices at the field level allows farmers to make informed choices tailored to their specific conditions. This local-specific approach contrasts with traditional one-size-fits-all recommendations, empowering farmers to optimise their practices for both environmental and economic sustainability.

Monitoring Land Use Land Cover Change (LULCC) due to oil palm plantations in near-real time is instrumental for regulating palm oil imports to EU in the context of EU Deforestation-free Regulation (EUDR).

Earth Observation satellite missions providing free global imagery with high revisit frequencies, are critical for monitoring tropical rainforests and their conversion to oil palm plantations. SAR imagery (Descals et al. 2021) and LandTrendr algorithms (Du et al. 2022) have been used in previous studies to identify closed-canopy oil palm plantations and planting years. However, these approaches have limitations in accurately mapping young and sparse plantations.

This study aims to fill these gaps by detecting plantations in a timely manner. Thanks to the Proba-V full mission Collection 2 spanning 2014 to 2020, we developed a sensor-agnostic method for monitoring deforestation and plantation rotation. RED, NIR and SWIR wavelength allows computing the NDWVI, a new index for discriminating vegetation from land cleared for plantations. Normalisation reduces atmospheric and seasonal variability, while statistical boundaries identify pixels that deviate from the normal distribution of forest values. The algorithm detects LULCC with a bi-weekly timestep at global scale.

The annual maps depict the dynamics typical for oil palm plantations, from land preparation to mature plantations, including dates of clearing and planting. Validation involves 1016 points randomly selected using a stratified sampling. Preliminary results in Southeast Asia show F1-score and OA of 81% for plantations detection. 70% of detections were accurate for the exact year, surpassing significantly previous studies.

This method can support the EUDR implementation for imports and provides valuable insights into yield estimation. This work demonstrates the suitability of 100m spatial resolution and 5-day temporal frequency for global mapping, particularly in cloudy regions and for perennial crops. Release of this oil palm plantation map series is scheduled for May 2024, with publication.

.