Union “Farmers’ Parliament” (ZSA) was established in 1999 as non-governmental organisation. Today the organisation has grown up to 800 members representing farmers of all agricultural sectors in Latvia. ZSA is the main agriculture lobby organisation in Latvia with long lasting, highly successful and professional ties in rural development sector both in the national and European Union level. ZSA is active in different EU programmes’ projects with the practical experience, ensuring the testing and demonstration activities, working on political recommendations and organizing information and educational activities and dissemination the knowledge in Latvia and EU.
ZSA is actively participating in the AgriDataValue project, but also has other different developments which are related to the AgriDataValue topics.
One important development is ZSA GEO digital map platform
ZSA has created a platform designed for the development and support of various digital technologies. The platform offers farmers the latest satellite images and the ability to compare them to see changes, various spatial data, which will continue to be supplemented with new digital solutions. Potential developments, models and data collected within the AgriDataValue project also could be technically integrated into the platform. Platform is accessible in the Web: https://app.smartagro.lv
The platform provides various spatial data layers, including: soil moisture and vegetation indices, various cadastres data, soil classes and values, information about agricultural fields integrated from the Latvian rural support service (LAD), satellite image maps, orthophoto maps, topographical maps etc.
Screenshot from the platform showing general vegetation index
After registration in the system, it is possible to draw or load your fields, display or add attributes from the LAD register, see the field area on the map and view them together with the data listed above and the latest satellite images. Platform also provides search functionality by the different parameters.
Satellite data is updated weekly and all cloud-free images are added to the platform. For example, in the season from March 2023 to September 2023, at least 10 images are available for comparison.
The app supports also mobile devices.
To the platform also additional information and layers can be added. For instance: The Latvian Nature Foundation (LDF) is starting the creation of a brand of natural meadow products within the project “LIFE-IP LatViaNature” and invites owners of meadows to apply as cooperation partners and potential producers of natural meadow products. The brand of natural meadow products will be a special label that will allow the consumers to recognize products that come from natural meadows, thus giving the opportunity to support the preservation of those meadows. In cooperation with ZSA, meadow owners will have the opportunity to display their farm and detailed information about products and services provided in the ZSA GEO map platform.
ZSA also developed a new meteodata platform which can be easily used on mobile devices and allows user to promptly review weather forecasts anywhere in Latvia: https://meteo.smartagro.lv. Currently, the platform combines the weather forecast services of SLLC “Latvian Environment, Geology and Meteorology Centre” and MET Norway. But since it is possible to add other services to the platform, potential users are free to suggest other service providers of the weather forecasts.
InAgro hosted the second Learning Network meeting of Pilot 5 on the 19th of January. In total, 14 participants from which 10 farmers shared their experiences with smart farming.
AgriDataValue Pilot 5 is situated in Belgium (Flanders). It covers both vegetables and arable crops. The pilot consists of a field with six crops that are rotated yearly called the Optifarm. New technologies and management practices are applied, and data is collected. The field simulates a real farmers’ field while still allowing more risky and experimental treatments. The data acquired includes weather station data, laboratory analyses of soil samples, soil scans and multispectral drone data. Additional IoT sensors may be included during the AgriDataValue lifetime.
The meeting provided inspiring and valuable feedback on the work accomplished, identifying also opportunities and potential challenges. AgriDataValue aims to capitalize on the fruitful discussion and recommendations that were made. Furthermore, during the event, a QGIS workshop was organized.
On Tuesday, January 9, 2024, the chairman of the Board of Directors of NILEAS Producers’ Group, George Kokkinos, was interviewed by the French researcher and documentarian Mathys Hallet, for the realization of his documentary film, that will be published in 2025, on water management in agriculture in the face of climate change.
He emphasized NILEAS involvement as a pilot in AgriDataValue, by installing environmental sensors measuring air temperature and humidity, rainfall, leaf moisture, wind speed and direction as well as soil sensors in the olive orchards. AgriDataValue with the main aim to establish itself as the Game Changer in smart farming digital transformation and agri-environmental monitoring and strengthen the smart-farming capacities, competitiveness and fair income by introducing an innovative, open source, intelligent and multi-technology, fully distributed Agri-Environment Data Space.
The subject of the interview was the investigation of the ways for the proper management of natural resources with the aim of optimal utilization by the olive oil producers, as well as the pro-environmental practices they use, following modern agroecological approaches. Particular importance was given to the effects of climate change, the challenges facing the traditional model of olive cultivation, and the “tools” available to olive oil producers to adapt to the new conditions.
Topics such as the sowing of leguminous plants in the field, the fragmentation of branches, and the return of the by-products of olive cultivation to the field were discussed, as practices that mitigate the effects of climate change and contribute to the increase of soil organic matter. He referred to the optimal utilization of the available water resources by the producers, and the reason why the use of sensors in the plots and the underground irrigation serve to reduce waste. Additionally, the great gradation and the variability of the climatic conditions of the region, combined with the extreme weather phenomena, create the necessary conditions both for the shift of producers to organic farming, and for the search for techniques and means that will help them cope with these rapid developments.
AgriDataValue developed a questionnaire to collect important information. The survey contains 14 questions in total. The responses are valuable in understanding the readiness of farmers to adopt smart farming and precision agriculture, as well as identifying any barriers or concerns they may have, particularly in the case of the AgriDataValue project.
The questionnaire has been translated into 11 different languages (Greek, English, Spanish, Estonian, French, Italian, Romanian, Lithuanian, Latvian, Dutch, and Polish) and uploaded at the EUSurvey.eu. You may find the AgriDataValue questionnaire at the following link https://ec.europa.eu/eusurvey/runner/AgridataValueSurvey or click here .
Agri-climate monitoring for climate change impact analysis
AgriDataValue, a pioneering initiative in Smart Farming, focuses on several multidisciplinary branches in the sector of agriculture, a fundamental one that embraces Agri-climate monitoring to comprehensively analyze the impacts of climate change on agricultural and livestock productions. By harnessing the power of advanced technologies such as Big Data, IoT sensors, Explainable Artificial Intelligence (XAI), and Federated deep machine learning (FDML), AgriDataValue aims to create a fully distributed Agri-Environment Data Space (ADS) as the platform of data platforms in the field. The project recognizes the critical role of climate variability and change in affecting agricultural production, biodiversity, and food security. Through the integration of diverse agricultural data, including micro-climate measurements and projections (small and large scales), soil conditions, and pest developments, AgriDataValue seeks to provide farmers with actionable insights as adaptation strategies to extreme climate changes aiming for risk mitigation. The initiative also aims to achieve the key objectives outlined in the EU Common Agricultural Policy (CAP) strategy for the period 2023-2027. It aspires to be a game-changer in facilitating climate-resilient and sustainable agriculture practices in the face of evolving climate patterns.
A set of Agri-climate monitoring methodologies have been selected for potential application which are presented in figure 1.
Figure 1. Agri-climate monitoring methodologies
AI-based decision support system under the AgriDataValue principles
The AgriDataValue solution proposes a decision-support system (DSS) based on XAI and FDML. The main components of the DSS, its inputs and outputs are presented in Figure 2. The DSS functionality depends on five main components which are:
Climate data analysis
Data processing including data sampling, filtration, and dimension reduction.
Data analysis including correlation analysis and progressive elimination processes
Climate projection models
Air temperature forecasting (small-scale and large-scale projections)
Crop growth, Livestock, and Agro-economic models
Simulation of crop growth and livestock and estimating Agro-economic reactions using the forecasted air temperature.
Vulnerability analysis
Observation of the tendencies in climate and the change in its extreme conditions.
Analyzing vulnerabilities in the simulated and estimated results about the impact of climate change on agriculture and livestock.
Decision support component: adaptation strategies
Providing the user with informed decision support as suggested adaptation strategies to climate impact on crops, soil, livestock, and biodiversity.
Interpretable explanations for the experienced and non-experienced users that led the algorithm to the conclusions.
Following the above steps to reach the aspired results, the AgriDataValue solution ensures a trustworthy interaction of the user since it functions based on XAI and FDML which are highly important wherever interpretability and privacy are crucial, especially and most importantly in the decision-making stage.
In Polish orchards, environmental monitoring systems that would support agrotechnical decision-making have only recently been implemented. One of the pilots under the AgriDataValue project is the 17-hectare family orchard farm, located in the Lodz Heights Landscape Park, in the valley of the Mroga River, the municipality of Dmosin, Lodz province. Some of the harvested fruit is the basis for the production of juices at the “Wind Orchard” press, established in 2011.
Intensive development of fruit growing in this area began after the end of World War II. At that time, the Institute of Orchardery and Floriculture was established, which passed on the knowledge and experience in apple growing to local fruit growers. “Wind Orchard” owned by the family of Wiatr (in Polish “wiatr” means “wind”), refers to the region’s somewhat forgotten tradition of cold-pressing fruit juices, dating back to the first half of the 20th century. In such a process, the fruit must be fresh, whole, and healthy, which places high demands on orchard management. Among today’s technological solutions for agriculture, improving the efficiency of applied agrotechnical treatments, as well as the quantity and quality of yields, are systems for continuous monitoring of environmental parameters.
As part of the AgriDataValue project, environmental sensors measuring air temperature and humidity, rainfall, insolation, leaf moisture, wind speed and direction as well as soil sensors will be installed in the apple orchard located in the Kaleczew village. This system for monitoring environmental conditions, is expected to enable effective reactions and best decisions in the digital “Wind Orchard” management optimizing fruit production to the needs of its owners and the sales market.
The family orchard
The orchard and the “Wind Orchard” fruit press
Ms Grazyna Wiatr – the owner of the orchard and the fruit press
On Thursday, December 12, George Kokkinos, President of NILEAS Producer’s Group, participated as a speaker in an exciting day workshop entitled “Strengthening the agri-food chain through social dialogue” which was organized by the Federation of Industrial Labor Unions in the Serafion Centre of the Municipality of Athens.
The workshop aimed to focus on identifying, disseminating and promoting best practices related to European Social Dialogue and labour relations in the agri-food chain.
During his speech, he had the opportunity to present the AgriDataValue Project. Targeting the new Common Agricultural Policy (CAP) implementation, AgriDataVaue will drive developments in Smart Farming, Precision Farming, Agri-Environmental monitoring and strengthen the Agricultural Digital Transformation at European Level. The project will implement a specialized digital platform for upscaling (real-time) sensor data for EU-wide monitoring of production and agri-environmental conditions. Beyond storage efficiency and semantic interoperability, the multi-technology platform will combine state-of-the-art Big Data/Artificial Intelligence frameworks and Data-Spaces’ Technologies (BDVA/IDSA/GAIA-X) with agricultural knowledge, agri-environment policies and farmers’ engagement campaigns.
NILEAS Producer’s Group is a partner of the AgriDataValue consortium and participates in the Use Case Cluster 3, which aims, in particular, to protect the health and quality of olive orchards by using fewer pesticides and to predict and mitigate the effects of frost. The project started on February 2023 and will last six years.
Artificial Intelligence (AI) offers numerous possibilities in the agriculture sector. It can assist in decision-making, make future predictions, and provide new opportunities for deploying smart farming techniques. To create these AI models, a substantial amount of data is required. Within the AgriDataValue project, we are developing a public platform where this data can converge with AI models. Farmers will be able to access and provide their data, such as sensor or satellite data, on this platform to build AI models. These models can then be offered to farmers on the platform. We emphasize privacy and data protection in this process.
Pilot Projects Across Europe
While the platform takes shape, data are being collected from several pilot projects to train the AI models. Currently, there are 23 pilots from 9 different countries, covering various topics such as agriculture, horticulture, fruit and vineyards, and livestock.
• Delphy, for example, will collect data on onions to improve the control of drip irrigation.
• In Spain, they aim to address climate control in their greenhouses by automatically adjusting window openings based on optimal photosynthesis activity.
• In Greece, they want to provide advice on spraying against the Mediterranean fruit fly based on smart traps and weather data.
Nitrogen Uptake in Leeks
In Belgium, InAgro, aims to better map the nitrogen uptake in leeks. This will help improve the fertilization recommendations and minimize leaching. InAgro uses drones and satellite images to better estimate crop growth throughout the season. Furthermore, they also collect data from their own Optifarm plot to train AI models.
Learning Network for Precision Agriculture
To implement the AI-generated advice and predictions, smart farming and precision agriculture is needed. Last year, InAgro established a learning network where farmers can share their experiences with precision agriculture. Farmers conducted trials in precision agriculture, choosing the techniques they wanted to test, while technical support was provided as needed. The following months, a second meeting will be organized by InAgro, to discuss the experiences of the first year.
Remote sensing indicators play a pivotal role in identifying and assessing agricultural droughts due to their unique ability to provide comprehensive and timely information over large geographical areas (Der Sarkissian et al. 2019). These indicators, derived from satellite imagery and sensor data, offer a bird’s-eye view of vegetation health, moisture content, and temperature variations crucial for gauging agricultural conditions. They enable a deeper understanding of vegetation dynamics, allowing for the early detection of stress in crops and vegetation cover. By capturing essential parameters remote sensing indicators facilitate the monitoring of changes in vegetation vigor and response to moisture stress or temperature fluctuations. Their capacity to provide historical, current, and predictive data aids in decision-making processes, guiding agricultural planning, resource allocation, and mitigation strategies to combat the impacts of droughts on crop yield and food security (Al Sayah et al. 2021). Consequently, these indicators contribute significantly to proactive drought management and resilience-building in agricultural systems.
On the other hand, climatic models play a pivotal role in understanding and predicting the potential impacts of climate change on agriculture, hence providing valuable insights that are crucial for sustainable food production. For agriculture, these predictions are indispensable for anticipating shifts in growing seasons, identifying regions at risk of extreme weather events, and assessing changes in water availability. Farmers, policymakers, and researchers can use this information to develop adaptive strategies, optimize crop selection, and implement resilient farming practices. With their capacity to give insights from the future, corresponding decision making, and implementation of proactive and reactive measures equally is ensured.
In this context, the following study gives a brief example on the capacity of remote sensing indicators and climatic geospatial data for monitoring vegetation health and climatic conditions of heatwaves in St Emilion for the summer of 2023.
2. Methodology
2.1. Remote sensing indicators
The Vegetation Health Index (VHI) combines the Vegetation Condition Index (VCI) and the Temperature Condition Index (TCI) to evaluate both vegetation and temperature stress. VHI is a valuable tool for detecting drought (Kogan 1995) and is particularly noted for its effectiveness in assessing extensive areas (Yan et al. 2016).
The formula for computing VHI is as follows (Eq 1):
Different VHI values correspond to various drought classifications as defined by Kogan (1995): > 40 (no drought), 30–40 (mild drought), 20–30 (moderate drought), 10–20 (severe drought), and < 10 (extreme drought).
To calculate VHI, one must initially compute the VCI and TCI indices. VCI quantifies vegetation dynamics on a scale of 0–100 to reflect moisture changes (Bhuiyan, 2008) using this equation (Eq 2):
The NDVI (Normalized Difference Vegetation Index) calculation is derived from Red and NIR bands, indicating vegetation presence based on spectral reflectance (intensity of red color) (Eq 3):
NDVI ranges from − 1 to 1, higher values signifying healthy vegetation and lower values suggesting sparse vegetation (Singh et al. 2016). The extraction of NDVI was accomplished using the “Raster Calculator” tool in ArcGIS (ESRI 2016) through specific equations based on different LANDSAT platforms.
Once the NDVI rasters were obtained, VCI was calculated for various years using Eq. 2. TCI, on the other hand, represents Temperature Condition Index, capturing vegetation responses to temperature shifts. TCI complements VCI in offering more accurate drought assessments (Sholihah et al. 2016) and is computed using this formula (Eq 4):
where Tc, Tmin, and Tmax are values corresponding to Land Surface Temperatures (LST). TCI values range from 0 to 100, lower values indicating vegetation stress due to temperature or drought elevations (Bhuiyan 2008). TCI’s advantage lies in its applicability throughout any season, unlike VCI, since it relies on Land Surface Temperatures (LST) (Yan et al. 2016). LST, extracted from thermal bands of satellite platforms, particularly LANDSAT, is a crucial element for TCI computation. The thermal sensor of LANDSAT platforms measures top-of-atmosphere radiances, allowing for brightness temperature extraction using Plank’s law (Dash et al. 2002). The steps for LST extraction are outlined in Table 1 according to the LANDSAT user data guide handbook.
2- Conversion of radiance into satellite temperature
𝑻= 𝐾2𝐿𝑛(𝐾1𝐿𝛌+1) – 273.15
3- Calculation of the proportion of vegetation (Pv)
𝑷𝒗= [(𝑁𝐷𝑉𝐼−𝑁𝐷𝑉𝐼𝑚𝑖𝑛)(𝑁𝐷𝑉𝐼max− 𝑁𝐷𝑉𝐼 min )]2
4- Calculation of the surface emissivity (LSE)
LSE = 0.004 * Pv + 0.986
5- Extraction of LST
𝑳𝑺𝑻= 𝑇1+λ∗(𝑇𝑝)∗𝐿𝑛(𝐿𝑆𝐸)
With Lλ the spectral radiation at the sensor aperture in (watts/m2*ster*μm), QCAL is the quantized calibrated pixel value in DN, LMINλ is the spectral radiation which is scaled to QCALMIN (in watts /m2* ster* μm) , LMAXλ is the spectral radiance which is scaled for QCALMAX (in watts/ m2* ster* μm), QCALMIN is the quantized minimum calibrated pixel value (corresponding to LMINλ) in DN, QCALMAX is the quantized maximum calibrated pixel value (corresponding to LMAXλ ) in DN, T is the effective temperature of the satellite in Kelvin (we added -273.15 as a conversion factor from Kelvin to ˚C), K1 is the constant of band-specific thermal conversion (in watts/m2 * ster * μm), K2 is the thermal band-specific thermal conversion constant (in Kelvin), λ is the wavelength of the emitted radiation, ρ is the h * c / σ (1.438 × 10−2 m ∙ K), h is Planck’s constant (6.626 × 10−34 J ∙ s), σ is Boltzmann’s constant (1.38 × 1 0−23 J/K) and c is the speed of light (2.998 × 108 m/s).
After obtaining the LST index for the entire studied period, TCI was calculated using the “Raster Calculator” tool as specified. Finally, with VCI and TCI maps in hand, VHI computation followed.
2.2. Climatic indicator
In the case of St Emilion, frost and icing conditions are becoming major preoccupations. Understanding the evolution of these phenomena is key for anticipatory planning. Accordingly, MeteoFrance’s DRIAS database was used to obtain the “Number of Frost Days” indicator defined as: The number of days for which the daily minimum temperature of day i is less than or equal to 0. The temporal reference was selected for the reference period (1976-2005) and the year 2050.
Data was obtained in shapefile format, then rasterized to be downscaled to 1 km. By transposing the layer points, the gridded layer will be assimilated to a weather station network. Since the different points holding different climatic values can be considered as “spatial weather stations”. Through a specific kriging technique, spatiotemporal variations were calculated. As most data is transposed into GIS format, a detailed spatiotemporal evolution is obtained. Accordingly, areas of vulnerability are highlighted, hence allowing subsequent prioritized proactive and/or reactive measures. That way, evidence and priority-based budget allocation for orienting interventions can be offered. The reference period and the RCP8.5 scenario were chosen for this example. RCP8.5, the pessimistic climatic pathway was chosen to present the most plausible extreme conditions. By subtracting the predicted horizon and the reference period findings, anomalies were obtained.
3. Results and discussions
3.1. Remote sensing indicators
In this section, the findings of the remote sensing indicators will be detailed. NDVI, TCI, VCI and VHI will be discussed with respect to the obtained values and geographical gradients on the territory.
Figure 1: NDVI map of Saint Emilion for summer 2023
The NDVI map reveals the presence of healthy and dense vegetation with increasing values. As can be seen, most of Saint Emilion displays high to very high values, hence indicating healthy vegetation. Linear strips can be observed as these correspond to mulches that are often laid between vineyards by farmers as part of vegetative measures.
Figure 2: TCI map of Saint Emilion for summer 2023
TCI ranges from 0 to 100 with low values indicating vegetation stress due to temperature/drought increases. As can be seen, most of the territory was under thermal vegetation stress particularly in the central and peripheral regions with heat pockets apparent in the Central-Western parts.
Figure 3: VCI map of Saint Emilion for summer 2023
High values of VCI indicate rather a suitable condition of vegetation while lower values indicate stress. As can be seen most of the territory falls under suitable conditions except for parcels located in the North-Central region that display stress.
Figure 4: VHI map of Saint Emilion for summer 2023
As can be seen from Figure 4, the vegetation health index of St Emilion showed no drought class in terms of VHI.
3.2. Climatic indicator: Evolution of heatwaves
The number of days for heatwaves is revealed in the following maps (Figures 5, 6 and 7). From Figure 5, one can see that the number of heatwaves ranged between 0 and 15 days with the Western region particularly affected. For St Emilion, the number of heatwaves is around 7 days. The highest risks are mainly concentrated in the Northern and Eastern parts of St Emilion and decreases with the progression South and West. In Figure 6, findings show that the number of frost days will significantly increase at the national scale. For St Emilion, number of frost days in 2050 under RCP8.5 are expected to significantly increase from 28 minimum and 31 days maximum (from 7 days) with the same geographical gradient.
Figure 5:Number of heatwaves in France and St Emilion during the reference period
Figure 6: Number of frost days in France and St Emilion in 2050 under RCP8.5
Figure 7: Anomalies of frost days in France and St Emilion in 2050 under RCP8.5
In term of anomalies, generally, a significant increasing trend is observed in throughout the national territory. The same is applicable for St Emilion under the same geographical gradient.
4. Conclusion
In conclusion, the utilization of remote sensing in conjunction with climate indicators emerges as an indispensable approach in the assessment of agricultural drought, revolutionizing the methodology employed in comprehending, monitoring, and responding to environmental adversities. Through the application of remote sensing technologies, access to expansive geographical regions is facilitated, enabling the acquisition of real-time data crucial for informed decision-making in agriculture. This amalgamation not only allows for the early detection and precise monitoring of drought conditions but also facilitates the implementation of proactive measures to alleviate its impact on crops and livelihoods. The deployment of these technologies equips policymakers, farmers, and stakeholders with a robust framework for sustainable agricultural practices, ensuring resilience amidst the evolving patterns of climate change. As society navigates the intricate dynamics of a shifting climate, the fusion of remote sensing and climate indicators assumes a pivotal role, fostering adaptive strategies and fortifying food security for future generations.
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The AgriDataValue project presented by Synelixis at the EC-ESA JOINT EARTH SYSTEM SCIENCE INITIATIVE workshop, organized by ESA and the DG-RTD of EC. The event was held on the 22nd -24th of NOVEMBER 2023 in ESA-ESRIN, FRASCATI, ITALY. This initiative is expected to provide a significant scientific contribution to realize the vision of the EU Green Deal. AgriDataValue targets high-level EU impact goals, including call’s expected outcomes, de stination impacts, and the Key Strategic Orientation of “Innovative governance, environmental observations and digital solutions in support of the Green Deal”. The project is designed and structured to significantly contribute to the overall EU agricultural development and the objectives set by the Green Deal, the EU Climate Change policy and new CAP.
