Maize cultivation significantly contributes to global food security and sustains human livelihoods. Efficient early-season maize mapping is pivotal for forecasting production and informed pre-harvest decisions. Existing approaches rely on prolonged phenological data or available crop labels, limiting their applicability in areas lacking comprehensive data. Thus, an automated, dynamic, and accurate maize identification method for the early growing season is crucial. This study explores spectral bands to distinguish maize early in terms of water content and chlorophyll levels. A novel composite index for dynamic maize identification independent of labels was proposed. Utilizing this index with a multi-temporal Gaussian Mixture Model facilitated early-season maize mapping and identification. Assessments across diverse global regions revealed the method's robustness, consistently achieving 90% accuracy and F1-score. NDCI outperformed other indices, enhancing F1-score by up to 30%. NDCI-mGMM accurately generated maize maps two months pre-harvest, promising an F1 score of at least 77%. Operating autonomously from labels, this framework offers swift and precise maize identification in data-deficient regions, revolutionizing global food security and trade forecasts.

How to cite: Gao, Y., Pan, Y., Ren, S., and Zhao, C.: A novel composite Index for early-season maize mapping, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1392, https://doi.org/10.5194/egusphere-egu24-1392, 2024.

For a better understanding of the environmental impacts of the agricultural sector and based on federal regulation, the monitoring of the agri-environmental system of Switzerland (MAUS) is tracking the development of different environmental indicators, among them regional GHG emissions. For this purpose, we developed a GHG emission model to calculate farm-scale, yearly, management-influenced emissions.

The considered categories of greenhouse gas emissions largely follow the approach of Switzerland's national greenhouse gas inventory under the UNFCCC (FOEN, 2023), while adaptations in the calculation of emissions from manure management were implemented. Among them, the ALFAM2 (Hafner et al., 2019) methodology was used for slurry application emission estimation and slurry storage emission factors were revised based on the publication from Kupper et al. 2020.

The manure management part of the model depicts nitrogen flows along the manure cascade. At each step (1. barn, pasture, yard; 2. storage; 3. application), a fraction of total ammoniacal nitrogen is being lost as N₂O, NH₃, NO_x or N₂. CH₄ emissions from manure management are calculated in parallel to the nitrogen containing emissions, following the methods of Soliva et al., 2006. NH₃, N₂O and CO₂ emissions originating from mineral fertilizer, organic products and harvest residues are calculated by multiplying nitrogen or carbonate inputs with respective emission factors. Further, CH₄ from enteric fermentation is implemented according to the 2019 IPCC guidelines for greenhouse gas inventories, taking into account gross energy intake. As exact and exhaustive data is not available for every single Swiss farm, data from various sources were combined and averaged on different levels if necessary.

Farm-based calculations allow to monitor the effects of management changes on GHG emissions and to summarize the results at different geographical resolutions depending on the goals of the according study. To analyze regional differences for MAUS, the emissions were summarized per municipality and set in relation to utilized agricultural area. Monte-Carlo-like simulations were run to examine sensitivities of individual input variables and uncertainties, which showed generally a large influence of animal numbers and milk urea concentrations on total farm GHG emissions.

Within MAUS, it is planned to calculate emissions annually to detect potential trends. Further, newly available data sources, like farm specific mineral fertilizer applications, will be considered to make more detailed calculations.

FOEN, 2023: Switzerland’s Greehouse Gas Inventory 1990-2021: National Inventory Document. Submission of April 2023 under the United Nations Framework Convention on Climate Change. Federal Office for the Environment, Bern. URL: https://www.bafu.admin.ch/bafu/en/home/topics/climate/state/data/climate-reporting/ghg-inventories/latest.html (20.12.2023).

Hafner, S.D., Pacholski, A., Bittman, S., Carozzi, M., Chantigny, M., Génermont, S., Häni, C., Hansen, M.N., Huijsmans, J., Kupper, T., Misselbrook, T., Neftel, A., Nyord, T., Sommer, S.G., 2019. A flexible semi-empirical model for estimating ammonia volatilization from field-applied slurry. Atmospheric Environment 199, 474-484.

Kupper, T., Häni, C., Neftel, A., Kincaid, C., Bühler, M., Amon, B., VanderZaag, A., 2020. Ammonia and greenhouse gas emissions from slurry storage - A review. Agriculture, Ecosystems and Environment 300

Soliva, C.R., 2007. Dokumentation der Berechnungsgrundlage von Methan aus der Verdauung und dem Hofdünger landwirtschaftlicher Nutztiere. Federal Office for the Environment, Bern.

How to cite: Schneuwly, J., Gilgen, A., and Bretscher, D.: Modelling greenhouse gas emissions at farm level across Switzerland, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2876, https://doi.org/10.5194/egusphere-egu24-2876, 2024.

Vegetation phenology, encompassing critical events like leaf emergence and maturity, serves as an important indicator of adaptive plant responses to environmental factors. In the context of Saudi Arabia, existing crop phenology retrieval methods encounter several challenges related to local farm management operations. These can include unstable crop calendars with planting and harvesting at any time throughout the year, uncertainty in sub-field management with independent control of areas within a center-pivot field, and diverse crop rotations between fodder and non-fodder crops. To address these challenges, we present an innovative framework utilizing machine learning and Sentinel-2 NDVI time series data for mapping phenology stages of key crops at a national scale. The framework is composed of three modules that are implemented step-wise, including: (1) a within-field dynamic clustering module (termed WithinFDy) that monitors fields for potential subdivision based on pixel-level NDVI temporal dynamics; (2) a phenology estimation module (termed PhenoEst) that segments NDVI time series into growing seasons and extracts essential phenology stages (e.g., planting and harvesting dates) for each season; and (3) a crop type discrimination module (termed CropDis) that utilizes extracted phenology information as input features to discriminate between different crop types. Evaluated on 1,000 randomly selected fields in northern Saudi Arabia, our framework achieved overall accuracies of 93.38%, 96.40%, and 94.39% for WithinFDy, PhenoEst, and CropDis modules, respectively. When applied nationwide in 2020, the framework revealed valuable insights. In terms of field management, 21.8% of the fields were divided into two distinct subfields, featuring different planting and harvesting dates - and sometimes crop type, while 73.2% showed consistent practices across the entire field. For seasonal dynamics, 53.4%, 36.3%, and 8.7% of fields supported crops for one, two, and three seasons annually, respectively. Main planting and harvesting activities occurred during winter seasons (November to February), with another peak observed in June. Approximately 30% of fields were under production for 5 to 6 months, and 15.7% were under production year-round. The dominant crop types in 2020 were fodder crops (e.g. alfalfa and Rhodes grass), followed by winter crops like winter wheat. Our methodology represents a substantial advancement over previous approaches, expanding applicability beyond crops with regular growth patterns. The results not only enrich agricultural datasets in Saudi Arabia but also hold promise for enhancing food and water security studies globally.

How to cite: Li, T., Lopez Valencia, O. M., Johansen, K., and McCabe, M. F.: Mapping the nationwide crop phenology stages in Saudi Arabia using machine learning and Sentinel-2 NDVI time series, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5171, https://doi.org/10.5194/egusphere-egu24-5171, 2024.

There is currently a lack of high-resolution pan-European information on land use management, especially in terms of how intensively and extensively cropland and grassland are managed. This is partly due to the lack of ground-based information, which is needed to downscale these types of management practices (some of which are captured in different types of agricultural censuses and surveys) as well as the inability of remote sensing to capture different kinds of land use. This type of information is needed for economic land use modelling and for assessing policy impacts, such as the latest reforms from the Common Agricultural Policy (CAP) and other European Union (EU) Green Deal targets. These types of analyses are undertaken using economic land use models such as GLOBIOM and CAPRI, which is one of the main aims of the Horizon Europe funded LAMASUS project (https://www.lamasus.eu/).  

This presentation will provide an overview of the ongoing developments in creating high-resolution spatially explicit layers on agricultural and grassland management for Europe to support the LAMASUS project. The proposed cropland and grassland management classes will be outlined along with the methodology for how they have been implemented using existing data layers from remote sensing, statistical data from Eurostat, the Joint Research Centre of the EU, agricultural ministries, and other sources. One of the key challenges is ensuring that the high-resolution data matches official statistics at the national (and NUTS2 level where available) so that they can be used by the economic land use models in LAMASUS. A method will be presented for how this is achieved using priors in the form of integrated layers of cropland and grassland probability created from existing high-resolution remotely sensed input layers.

How to cite: See, L., Yashchun, O., Romanchuk, Z., Balkovič, J., Skalsky, R., Malek, Ž., Schepaschenko, D., Deppermann, A., Kriztin, T., and Havlík, P.: Improving high-resolution spatial information on agricultural land use management in Europe for economic land use modelling and the assessment of policy impacts, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5448, https://doi.org/10.5194/egusphere-egu24-5448, 2024.

Previous works on vegetation mapping from optical satellite images use training and test datasets within the same year. We think that from a practical perspective, this experimental setting is not realistic due to (i) crop growth changes from year to year (also like from region to region), therefore test assessment does not fully reflect real-world cases and (ii) obviously it is not possible to apply the algorithm current year if it is trained with current year data. Thus a cross-year experimental setting should be de-facto for this line of research then we can readily apply developed algorithms in real-world applications. In this work, we evaluate a state-of-the-art crop classification method from optical satellite (Sentinel-2) image time series data - a hierarchical multi-stage deep learning method, i.e. ms-convSTAR which we introduced in [1] - in a cross-year experimental setting. The deep learning model is trained with the entire 2021 crop dataset in Switzerland and during test time it is applied to the 2022 crop dataset. Our results show that our method performs reasonably well in this experimental setting achieving ~83% accuracy at the pixel level. 

References

[1] Turkoglu, M. O., D'Aronco, S., Perich, G., Liebisch, F., Streit, C., Schindler, K., & Wegner, J. D. (2021). Crop mapping from image time series: Deep learning with multi-scale label hierarchies. Remote Sensing of Environment, 264, 112603.

How to cite: Turkoglu, M. O., Aasen, H., Schindler, K., and Wegner, J. D.: Country-wide Cross-Year Crop Mapping from Optical Satellite Image Time Series, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6031, https://doi.org/10.5194/egusphere-egu24-6031, 2024.

Grassland ecosystems shape the landscape in large parts of Germany and provide numerous services that are relevant for the carbon cycle, water quality and biodiversity, apart from being the main source of fodder for the dairy and meat industry. Annual yields between grasslands vary strongly because their productivity depends on the management and environmental conditions. Information on grassland yields are not freely and extensively available in Germany but would be relevant for comprehensive assessments of grassland ecosystem services including the impact of extreme events on yields. With satellite remote sensing, grassland productivity and yields can be extensively and multi-temporally estimated. Within our project (SUSALPS, https://www.susalps.de/en/), grassland yields are estimated in a grassland-dominated area in southern Germany using ground-truth measurements of above-ground biomass and Sentinel-2 time series data. Field data was collected on 12 differently used grassland parcels in the region in 2019-2021. We aim to overcome limitations of previous research – caused by the heterogenous nature of grasslands due to varying use intensities in Germany – by including management information and a large gradient of field samples trough multiple measurements throughout the vegetation growth period into the modelling. We tested empirical model based on the field and accompanying Sentinel-2 data (n=74) to estimate grassland biomass. The best model was applied to all available Sentinel-2 scenes in the region in 2019. Random Forest and Artificial Neural Network models showed the highest accuracy (R²_cv = 0.7). A novel input feature was the mowing date which is available as 6-year dataset (Reinermann et al. 2022 & 2023). Next, the multi-temporal biomass estimations are aggregated to annual yield estimates to enable spatially discrete and multi-annual yields are estimated and compared (2018-2023). First results show that the inclusion of mowing date information supports the reliable estimation of grassland yields and its assessment on fine spatial scale substantially. In the future, the results are coupled with modelled plant biodiversity information to gain a complementary picture on grassland ecosystem services.

How to cite: Reinermann, S., Schucknecht, A., Gessner, U., Asam, S., Kiese, R., and Kuenzer, C.: Estimation of annual grassland yields with Sentinel-2 time series, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7646, https://doi.org/10.5194/egusphere-egu24-7646, 2024.

The "Tool for Agroecology Performance Evaluation" (TAPE) was developed under the coordination of the Food and Agriculture Organisation of the United Nations (FAO) to assess the sustainability performance of agroecosystems. The assessment is mainly based on a 2-3-hour farm interview, in which a wide variety of data is collected. The environmental dimension has so far been represented in TAPE by two simple indices: A soil index, which is based on a visual analysis of the soil, and a biodiversity index, which is primarily based on the Gini-Simpson index of crops grown and animals kept. While the TAPE biodiversity index is crucial, it does not yet take into account so-called unplanned biodiversity, i.e. the impact of on-farm management practices on wild species. We have therefore expanded TAPE to include this aspect.

Direct surveys of wildlife biodiversity in the field were not possible in TAPE, as this would have far exceeded the time required for data collection. Consequently, we based the newly developed biodiversity index on the indirect European BioBio method. The new index consists of ten indicators, which can take values between 0 and 100% and be aggregated to form the overall index. Examples of these indicators are field size, nitrogen application or stocking density. The new index was developed and tested on selected Swiss farms, where the comparison with a much more comprehensive and time-consuming method showed a positive correlation (r = 0.56, p-value = 0.009).

The new index has so far been used in Switzerland (21 farms) and in Kenya (103 farms). In Switzerland, the field size and land use change indicators performed best (values > 75%), while the indicators tree habitat, nitrogen application, field operations and grazing intensity performed poorly (values > 50%). In Kenya, the field size, land use change, pesticide and field operations indicators reached values above 75%, while the tree habitat, grazing intensity and semi-natural habitat indicators had values clearly below 50%.

How to cite: Baumgartner, S., Gilgen, A., Felder, R., Herzog, F., Jeanneret, P., Séchaud, R., Paunovic, S., Lucatoni, D., Cluset, R., Mottet, A., and Merbold, L.: An improved biodiversity index for FAO’s Tool of Agroecology Performance Evaluation (TAPE), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9284, https://doi.org/10.5194/egusphere-egu24-9284, 2024.

Remote sensing technology presents unique possibilities for monitoring agricultural systems, providing accurate information like crop type distribution, crop planting area, crop rotation, etc. Extracted from remote sensing imagery, previous efforts generally produce crop information based on pixel-based classification strategy without considering spatial context of objects. Further incorporation of object-based image analysis in crop type mapping could improve mapping accuracy and reduce disturbance caused by uncertainties caused by pixel-based methods. Here we aim to combine the advantages of pixel-based and object-based approaches for further improving crop type maps over Northeast China based on Sentinel-2 imagery, simple non-iterative clustering (SNIC), random forest classifier and Google Earth Engine platform. The results showed in the majority of cropland, object-based mapping results had higher accuracies and reduced obvious errors at parcel level. Overall accuracies improved by 0.5% and the Kappa coefficient improved by 9% in Sanjiang Plain. However, soybean and maize intercropping with small parcels could be ignored in object-based methods when clustering objects. Therefore, an integration of pixel and object-based approaches was adopted considering different landscapes and patch areas to generate an unprecedentedly accurate crop type map in Northeast China.

How to cite: Di, Y., Dong, J., Fu, P., and Marsh, S.: A hybrid framework for improved crop mapping over a large scale by combining pixel-based and object-based approaches, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9951, https://doi.org/10.5194/egusphere-egu24-9951, 2024.

In the domain of precision agriculture, land-use planning, and resource management, the precise delineation of field boundaries is pivotal for informed decision-making. The dynamic nature of agricultural landscapes, particularly in smallholder farming, introduces seasonal changes that pose challenges to accurately identify and update field boundaries. The conventional approach of relying on high-resolution imagery for this purpose proves to be economically impractical on a seasonal basis. We propose a framework that utilizes a spatiotemporal series of medium-resolution public imagery (e.g., Sentinel-2) in conjunction with an outdated high-resolution image as a reference for super-resolution reconstruction. The developed methodology incorporates super-resolution techniques to enhance the spatial resolution while simultaneously performing semantic segmentation at the higher resolution. We evaluate the proposed model's performance in predicting seasonal field boundaries at a pan-India level. The validity of these findings is established through assessment by a team of human annotators.

Our approach aims to offer a scalable spatiotemporal solution for accurate field boundary identification at a national level by combining information from different satellites at different resolutions.

How to cite: Saxena, N., Annkah, A., Deshpande, I., Wilson, A., and Talekar, A.: Monitoring Changes In Agricultural Field Boundaries Using Spatiotemporal Remote Sensing Data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15697, https://doi.org/10.5194/egusphere-egu24-15697, 2024.

Improving the sustainability of agriculture requires an advanced assessment of the ecological impacts of pesticides at both policy and scientific levels. This can be achieved by integrating ecological considerations into the assessments of plant protection products beyond plot or experimental sites. Among plant protection products, pesticides are often the most harmful and toxic due to the chemical properties of their active substances (AS), which can range from non- to extremely toxic depending on the organism affected. Our study applies the Pesticide Load Index (PLI), as applied in Denmark and in the United Kingdom , to quantify pesticide risks to environmental health and biodiversity across the European Union. The PLI is defined as the sum of the application rate (AR) for each applied AS (k) divided by the toxicity (TOX) for a number of non-target taxa such as birds, mammals, fish, algae and agricultural beneficial insects like bees and natural enemies of pests, using the formula: PLI = Σ (ARk / TOXk,i).

Our methodology bridges the gap between ecological health and pesticide risk assessment using three extensive data sets (Figure 1). The first includes EU-wide estimations of AS emissions, as geospatial layers at 1km resolution, representing the most extensive data collection available for the entire European Union. The second dataset provides unparalleled granularity in AS use, capturing field-level information across France for the year 2018, including details on crop type distribution. The third dataset, sourced from the Pesticide Properties DataBase, assesses the ecological impacts of pesticide use by linking usage to ecotoxicological endpoints.

Figure 1: Overview of the methodology for the Pesticide Load Index (PLI) Study. 

By integrating acute toxicity, chronic toxicity, and environmental fate, our approach moves towards a thorough understanding of pesticide impacts. Acute toxicity, indicative of short-term exposure, highlights immediate and potentially severe effects, while chronic toxicity addresses the long-term consequences of prolonged and continuous exposure. The environmental fate sheds light on the pesticides' behaviour and transformation in the environment, considering their distribution, degradation, accumulation, and transport across air, water, and soil.

The outcomes of this study provide a new perspective on pesticide use within the EU. The highly granular nature of the PLI maps makes them key tools for identifying areas with high ecotoxic levels, and therefore informing where additional risk mitigation measures are necessary. Detailed analyses are done by i) identifying predicted hotspots of pesticide use across the EU, ii) analysing variations in bio-climatic regions, and iii) breaking down the results by crop type and region. The role of this approach in monitoring the progress towards the European Union Farm to Fork and Biodiversity strategies targets is therefore clear, particularly in relation to the ambitious target of reducing pesticide use and toxicity by 50% by 2030. 

Our framework provides essential ecological insights for biodiversity conservation and ecosystem preservation, providing policy-makers with spatially explicit data for better-tailored strategies. Additionally, by enabling comparisons between crops, regions, and EU Member States it can contribute as to developing protective, realistic, and scientifically sound regulatory frameworks.  

How to cite: Catarino, R., Galimberti, F., Bopp, S., Fellmann, T., Klinnert, A., Olvedy, M., Paracchini, M. L., Pistocchi, A., Rotllan-Puig, X., Terres, J.-M., Van Der Velde, M., and d'Andrimont, R.: Applying the Pesticide Load Index to characterize ecotoxicological impact from pesticide use in the EU, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15816, https://doi.org/10.5194/egusphere-egu24-15816, 2024.

The conservation and creation of landscape features is recognised as a key conservation tool to halt the loss of agricultural biodiversity in European farmland.

This study introduces a new indicator to quantify landscape features in EU agricultural land, based on the LUCAS Landscape Feature survey. We developed a comprehensive methodology to measure and categorise landscape features, distinguishing Woody, Grassy, Wet, and Stony LF types. Our approach gives a robust and reproducible estimate of the indicator at the EU Member State and possibly regional levels, based on a reliable and statistically representative sample of landscape features.

The methodology combines office-based photo-interpretation with field surveys collecting 3.8 millions field points, ensuring accuracy in determining the presence and type of landscape features within agricultural contexts. Together with information on biodiversity and ecosystem services, it will play a crucial role in evaluating the performance of major policies related to biodiversity conservation in agricultural lands, aligning with the Common Agricultural Policy and the EU Biodiversity Strategy for 2030. Besides, it will play a role in the assessment of natural based solutions for mitigating climate change effects, biodiversity loss and crop production (food) security.

Our findings reveal that, in 2022, landscape features covered 5.6% of EU agricultural land. Woody features were the most prevalent, followed by Grassy, Wet, and Stony features. The percentages of landscape features varied across EU Member States, with Malta and Cyprus exhibiting higher values.

The novel indicator developed is based on a comprehensive and reproducible method for quantifying these features, providing essential insights for policy and decision-making in sustainable agriculture.

How to cite: d'Andrimont, R., Skøien, J., Musavi, T., Iordanov, M., Gallego, J., De Marchi, D., Koeble, R., Guerrero, I., Montero-Castaño, A., Terres, J.-M., and Czúcz, B.: Quantitative Measurement of Landscape Features in EU Agriculture: A Novel Indicator Approach , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16882, https://doi.org/10.5194/egusphere-egu24-16882, 2024.

Obtaining specific field calendars for each crop is very useful information for farmers and public administration to understand and manage harvests. This information can be collected manually from each farm, but this approach is highly time and money consuming. It is possible to acquire it more efficiently using phenology estimates obtained through remote sensing. Common Agrarian Policy (CAP) and Geographical Information System of the Common Agrarian Policy (GISCAP) data were used to know the location of principal cereal plots in Andalusia, Spain. It included common wheat, durum wheat, triticale, oat, rye, barley, sorghum, maize and rice.  Several phenometrics from Sentinel-2 were obtained: start of the season (SOS), middle of the season (MOS), length of the season (LOS) and end of the season (EOS). This dataset was correlated and compared with sowing and harvesting data collected by the Spanish government.  The results showed a high correlation between SOS and sowing and between EOS and harvest for most of the studied crops.

Sowing for common wheat, durum wheat, triticale, oat, rye, and barley took place between October and December according to government calendars, while SOS generally started one month later, between November and January. However, in these crops, harvest and EOS occurred simultaneously, mostly in June. In the case of sorghum, maize and rice, which are summer cereals, their phenometrics differed from the others. Sowing and SOS for sorghum mostly occurred in April and March, and harvest and EOS in September and October, in typically at the same time. Maize sowing took place in March, SOS in April, and harvest and EOS in September. Finally, rice sowing occurred in May, SOS in June, harvest in October and EOS in November. This study shows that obtaining accurate crop calendars from Sentinel-2 phenological trajectories is feasible, providing valuable information for farmers and public administrations.

How to cite: García Pérez, M. A., Caparros-Santiago, J. A., and Rodriguez-Galiano, V.: Comparing crop calendars: phenology derived from Sentinel-2 data vs official data: The case of cereals in Andalusia., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17135, https://doi.org/10.5194/egusphere-egu24-17135, 2024.

The European agricultural census  in 2020 collected a large number of variables from the major share of all the farms within the European Union. There are many potential applications of such a data set, from direct estimates of agricultural indicators to use as input in more complex analytical models. However, the individual responses in the data set cannot be shared directly, as they are regarded as confidential information. Instead, the data must be aggregated to a level where individual responses cannot be identified, typically NUTS regions or grid cells. As a minimum, each aggregated value must be estimated from at least 10 farms (frequency rule). Additionally, a dominance rule requires an aggregated value to be treated as confidential if the 2 largest farms are responsible for more than 85% of the value within a grid cell.

Whereas such requirements are clear, there are many methods for creating grids that respect them. The distribution of data is usually not homogeneous, and different methods have varying effects on the result.  We will outline the advantages and drawbacks of certain methods and present the most promising one that involves grid cells of varying sizes. Whereas there are some examples of this method in the past, it will be the first time it is applied on a continental scale and high-resolution data set such as the European agricultural census data.

How to cite: Skøien, J. O., Lampach, N., Ramos, H., Seljak, R., Koeble, R., and van der Velde, M.: Optimized resolution of gridded data from European agricultural census, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17997, https://doi.org/10.5194/egusphere-egu24-17997, 2024.

The agricultural sector holds the greatest reduction potential to limit adverse effects of reactive nitrogen in the environment. Assessing the negative impacts of excessive release of reactive nitrogen into the biosphere requires spatially explicit information to capture e.g. hot spots of nitrogen surplus, nitrogen use efficiency, the impact on sensitive ecosystems or on ground/drinking water quality.

The agricultural economic model CAPRI is one of the main tools applied by the European Commission for the ex-ante analysis of the impact of agricultural policies and agro-environmental legislation at regional level (NUTS2) in Europe. CAPRI builds on long-term time series of regional, national and international agricultural statistics (e.g. crop and livestock production, fertilizer use), market and trade data. Inputs (e.g. inorganic fertilizer) in CAPRI are explicitly linked to production which delivers the basis for connecting environmental indicators (e.g. nitrogen surplus) directly to individual activities.

To provide the link between the agro-economic model CAPRI and the impact assessment of nitrogen use in agriculture on the environment at higher spatial resolution, we developed a procedure to disaggregate CAPRI regional data and provide maps of nitrogen input/output, crop and livestock production for the time series 2000 – 2018 at the level of Farm Structure Units (FSU) for 26 EU member states and the UK. The FSU are built by the spatial intersection of a 10 x 10 km² INSPIRE compliant grid, the CAPRI NUTS2 region borders and the soil mapping units of the Harmonized World Soil Data Base (FAO/IIASA/ISRIC/ISS-CAS/JRC, 2009), having a median area of 12 km² (minimum 1 km², maximum 100 km²).

The disaggregation procedure for 36 crop types and 18 livestock categories from the regional level to the FSU is driven by information from the gridded Farm Structure Survey (FSS, 2010) crop and livestock data at 10 x 10 km² resolution, CORINE (2018) non-agricultural land cover shares, altitude and slope constraints for individual crops / crop classes derived from LUCAS survey data (https://ec.europa.eu/eurostat/web/lucas).

Nitrogen inputs from mineral fertilizer and manure are disaggregated from the regional level to the FSU following the crops’ requirements. N input from atmospheric deposition and N supply by biological fixation is taken into account at FSU level. N from mineralization of soil organic matter could not be taken into account due to lack of data. N losses from volatilization and surface run-off, N removal by harvest, N in crop residues complete the N flow data set in agricultural areas at FSU level.

How to cite: Koeble, R., Leip, A., Kempen, M., Bielza, M., Catarino, R., Paracchini, M. L., See, L., and Van Der Velde, M.: European maps of crop / livestock categories and N budget parameters (timeseries 2000 – 2018) based on disaggregated CAPRI model data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18505, https://doi.org/10.5194/egusphere-egu24-18505, 2024.

In the context of climate change, high expectations have been put on the agricultural sector for reducing greenhouse gas (GHG) emissions and enhance carbon sequestration. Consequently, a large and growing number of studies have evaluated the efficacy of various agricultural practices for climate change mitigation. However, the scientific evidence is often heterogeneous and frequently contradictory, making it difficult to use to support policy decisions. Meta-analyses synthesise large data sets and have become the gold standard for providing scientific evidence to inform environmental and agricultural policies. However, a growing number of meta-analyses are now available on a specific topic, occasionally with conflicting conclusions, requiring a further level of synthesis to consolidate the findings.

We present the results of a systematic review of 693 published meta-analyses on the effect of farming practices on climate change mitigation. After a systematic search and review of the literature, we extracted data assessing the climate impacts of 34 farming practices and 123 comparisons of sub-practices with corresponding control practices, for a wide range of cropping and livestock systems around the world. From this dataset, we selected the farming practices that showed overall significantly positive effects on the reduction of GHG emission and/or on the increase of carbon sequestration. For cropland and grassland, we were able to identify a set of 35 mitigation sub-practices , including cover and catch crops, intercropping, leguminous crops, the use of enhanced efficiency fertilisers, soil amendment with lime and gypsum, different crop residue management techniques, water management practices, different conservation, restoration and management measures in grasslands, conservation and restoration of peatlands and wetlands, the conservation and creation of landscape features, as well as organic farming systems. For livestock, we identified seven effective mitigation practices, including livestock feeding techniques, manure land application techniques, manure storage techniques. A limited number meta-analyses reported the effect of a given practice on more than one GHG or on GHGs coupled with carbon sequestration together, limiting the exploration of interacting effects.

The systematic evidence map provides robust and encompassing literature based evidence on farming practices with established positive effect on climate change mitigation to support a wide community of inventory compilers, modellers and policymakers. Our review also identifies farming practices with remaining knowledge gaps and research priorities.

How to cite: Bosco, S., Schievano, A., Pérez-Soba, M., Montero-Castaño, A., Chen, M., Tamburini, G., Catarino, R., Guerrero, I., Bielza, M., Angileri, V., Assouline, M., Koeble, R., Mantegazza, O., Dentener, F., Van der Velde, M., Parracchini, M. L., Weiss, F., Terres, J.-M., and Makowski, D.: Meta-analytic evidence on effective farming practices for climate change mitigation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19472, https://doi.org/10.5194/egusphere-egu24-19472, 2024.

Based on the background of climate change and rapid population & land urbanization, we developed the theoretical hypothesis and questions. As we know, urban usually have good education, hospital and housing, so many rural people flow to urban and indirectly take away industry and property. So rural communities face many challenges, such as less people, land, industry and property at least. For climate change, challenges from flooding, drought, heat wave, lower temperature, and frost timing. And opportunities maybe from Increasing and northward accumulated annual temperature (AAT10), and lengthen the growing season. For urbanization, challenges from abandonment, and less cropping frequency. And opportunities maybe from well-facilitated farmland, land unified management, and intelligent agriculture. Rural revitalization and urban-rural integration are two good measures for rural development policy not only in China but in many global counties. Yet, how to catch the rural people and agro-workers' eye, and improve the food supply ability are important questions. We will show some recent study results about geo-spatial change of croplands and its impacts on farming developments in China over the past two decades in this congress.

How to cite: Liu, Z.: Potential impacts of cropland dynamics on farming developments in China by analyzing a fused crop production data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-22310, https://doi.org/10.5194/egusphere-egu24-22310, 2024.

Ensuring compliance with China's “1.8 billion mu”(120 million hectares) cultivated land preservation policy is a fundamental goal of land policy. Northeast China has experienced significant cultivated land expan-sion due to rigorous compensation policies over the past two decades, re-sulting in sustainable increases in grain output. This research employs re-mote sensing data to examine the spatial-temporal pattern and vulnerability of newly increased cultivated land expansion in Northeast China and its potential impact on food security. Results indicate a 3.08% increase in newly increased cultivated land from 2000 to 2020, with the majority located in the Sanjiang Plain's humid area and Inner Mongolia's arid and semiarid regions. with 58.54% of it being at grade 6-10, and the reduced cultivated land all at grade 1. Additionally, 62.84% of the newly increased cultivated land was in ecologically fragile areas, while the rest were in mildly and severely vulnerable areas. Temperature insta-bility was negatively correlated with cultivated land expansion, while grain production was negatively correlated with cultivated land vulnerability. The increase in grain production at the expense of cultivated land ecology is a potential threat to national food security. The vulnerability of cultivated land is negatively and significantly related to grain yield, suggesting an adverse impact on national food security. The poor quality of newly increased cultivated land in Northeast China, characterized by ecological fragility, may lead to short-term gains in grain yield but not guarantee long-term stability. This study found a significant negative correlation between grain yield and cultivated land ecological vulnerability in Northeast China. Thus, protection measures should focus on increasing high-quality and ecologically sound cultivated land to ensure long-term grain production stability. Priority should be given to high-quality and ecologically sound cultivated land for inclusion in high-standard cultivated land construction zones to enhance protection efforts.

How to cite: Wang, J., Liu, Z., Li, Z., and Liao, X.: Identify the spatial-temporal pattern of the increased Cultivated land and its vulnerability in Northeast China from 2000 to 2020 , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-22312, https://doi.org/10.5194/egusphere-egu24-22312, 2024.

Modern-day agriculture presents numerous challenges for small rural farmers, including labor issues, fluctuating costs, unpredictable weather, and the complexities of managing fertilizers, pesticides, and market dynamics. These challenges often hinder farmers from making timely decisions that could maximize their revenue and minimize costs.

The Makara app stands out as a groundbreaking tool in agricultural management, providing a digital platform meticulously designed to meet the diverse needs of farming. It enables farmers to create individual accounts and input detailed information about their land, including soil type, soil health parameters, water sources (such as borewells, rivers, or pipelines), and preferred irrigation methods. The app's versatility allows farmers to manage multiple lands and configure various crops for each, supporting both mixed cropping and multi-cropping systems. Additionally, farmers can adjust crop configurations annually and seasonally, offering unparalleled flexibility in digital farming management.

Makara's interface is exceptionally adaptable and capable of digitizing and tracking any farming setup. Beyond just planning, the app excels in financial management, assisting farmers in budgeting and recording expenses. This includes costs for seeds, fertilizers, and various labor activities like irrigation, weeding, land preparation, applying fertilizers and pesticides, harvesting, transportation, and storage. These financial features are detailed at the crop level for each season and year, providing farmers with a comprehensive view of their agricultural expenses and aiding in more informed financial decision-making.

A standout feature of Makara is its day-to-day advisory service, which provides guidance based on best practices, and insights into planting seasons, crop varieties, and growth stages. The app's recommendations on fertilizers and pesticides aim to promote sustainable farming and maximize yields. Additionally, Makara assists in activity planning and journaling, enabling farmers to maintain systematic records of their farming activities. The app emphasizes budgeting, cost and revenue management, and farm resource optimization, positioning itself as an all-encompassing agricultural tool.

The app's risk prediction module offers farmers valuable insights into expected crop yields for upcoming seasons, allowing them to estimate the likelihood of achieving specific yield targets and the corresponding potential income and profits. This feature is complemented by a historical market price database, enabling farmers to make informed decisions based on predicted yields and market trends.

Makara is crafted for user-friendliness and accessibility, featuring a multilingual interface that not only displays content but also provides audio readouts to farmers in their native languages. Mindful of the connectivity issues often encountered in rural settings, the app is equipped with an offline mode. This ensures uninterrupted operation and access to previously stored data, even in the absence of network connectivity, making it a reliable tool for farmers regardless of their location.

This paper introduces the Makara app, detailing its main functionalities, deployment strategies, and the considerations behind its design choices. We also present an analysis of the app's deployment and usage, highlighting its impact on alleviating the cognitive burden faced by farmers in making crucial decisions that significantly affect their costs and revenues.

How to cite: Guntha, R., Aravindakshan, A., Adla, S., Presannakumar, M., Alberto Ponce Pacheco, M., and Pande, S.: Makara: Navigating Agricultural Challenges through Digital Innovation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14243, https://doi.org/10.5194/egusphere-egu24-14243, 2024.

The 'European Monitoring of Biodiversity in Agricultural Landscapes' (EMBAL) is a monitoring initiative initiated by the European Commission that gathers information on the state of biodiversity in agricultural landscapes across EU member states. Developed within the EU Pollinator Monitoring Framework, EMBAL is a standardized and sample-based in-situ survey of 500x500m landscape sections (plots).

EMBAL provides comprehensive data, including general information on land use and land cover at parcel level, information about landscape elements, and specific vegetation data on a transect level in grassland and arable habitats. Both the methodology and the sampling frame are harmonized with LUCAS (Land Use and Coverage Area frame Survey).

Following a successful pilot in 2020, EMBAL was applied in all 27 EU member states in 2022 and 2023, surveying a total of 3,000 selected plots in both years. This extensive rollout served to gather harmonised baseline data on biodiversity across EU27 and provided a comprehensive field test of the EMBAL methodology across different European landscapes.

In this contribution, we offer an overview of the EMBAL 2022 and 2023 rollout, the EMBAL survey methods and parameters and provide an outlook on the results.

How to cite: Kleinewillinghöfer, L., Baier, C., Haub, C., Lindemann, D., Oppermann, R., Roggon, L., Sutcliffe, L., and Buck, O.: EMBAL - European Monitoring of Biodiversity in Agricultural Landscapes, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16407, https://doi.org/10.5194/egusphere-egu24-16407, 2024.