Mapping projects at the European Soil Data Centre
This is a modal window.
The media could not be loaded, either because the server or network failed or because the format is not supported.
Formal Metadata
Title |
| |
Title of Series | ||
Number of Parts | 57 | |
Author | ||
Contributors | ||
License | CC Attribution 3.0 Germany: You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal purpose as long as the work is attributed to the author in the manner specified by the author or licensor. | |
Identifiers | 10.5446/55259 (DOI) | |
Publisher | ||
Release Date | ||
Language | ||
Producer | ||
Production Place | Wageningen |
Content Metadata
Subject Area | ||
Genre | ||
Abstract |
| |
Keywords |
11
32
38
40
50
53
54
57
00:00
Texture mappingGame theoryMultiplicationSuite (music)AreaBitPresentation of a groupOpen sourceQuicksortProcess (computing)Time zoneStrategy gameContext awarenessTexture mappingEndliche ModelltheorieOrder (biology)Set (mathematics)Category of beingComputer animation
01:01
Scale (map)Organic computingDiffusionStrategy gameGreen's functionHorizonSet (mathematics)Self-organizationVisualization (computer graphics)Computer animation
01:41
Data managementData integrityMultiplicationInformationComplex (psychology)Order (biology)ResultantSlide ruleDifferent (Kate Ryan album)Bit1 (number)Medical imagingSelf-organizationOrbitComputer animation
02:29
ImplementationScale (map)Core dumpStaff (military)DatabaseEndliche ModelltheorieIndependence (probability theory)State of matterEndliche ModelltheorieScaling (geometry)State of matterGroup actionResultantUniform resource locatorPressureFigurate numberWeb 2.0Instance (computer science)Musical ensembleGoodness of fitRight angleLimit (category theory)Military baseStability theoryOpen sourceComputer animation
03:45
Process (computing)Kolmogorov complexityComplex (psychology)Data managementUniqueness quantificationResultantOrder (biology)Expert systemScaling (geometry)Process (computing)InformationPrice indexClassical physicsAreaOperator (mathematics)Observational studyHome pageFood energyComputer animation
04:32
System programmingProcess (computing)Human migrationTransformation (genetics)Digital signalInformation securityFood energyCivil engineeringDigitizingFood energySupersymmetryCasting (performing arts)Inheritance (object-oriented programming)Human migrationIncidence algebraExtreme programming1 (number)Closed setTerm (mathematics)Process (computing)Event horizonInformation securityDenial-of-service attackMetrePoint (geometry)Different (Kate Ryan album)TouchscreenWeb browserCASE <Informatik>FamilyArmSource codeComputer animation
06:07
Insertion lossStrategy gameComa BerenicesSpherical capIntegrated development environmentCondition numberObservational studyCompact spaceSelf-organizationInsertion lossStrategy gameInternet service providerMetric systemThermodynamicsProjective planeExpert systemPhysical lawObject (grammar)Green's functionVideo gameSoftware developerCondition numberIntegrated development environmentLogic gateOrder (biology)QuicksortSpecial unitary groupComputer animation
07:27
Food energyStrategy gameIntegrated development environmentGreen's functionBuildingObject (grammar)Green's functionPosition operatorInformationIntegrated development environmentNormal (geometry)Metropolitan area networkForm (programming)FamilyArmCircleCausalityPrisoner's dilemmaSuite (music)Machine visionComputer animation
08:24
Strategy gameReduction of orderInsertion lossLimit (category theory)PlanningTheory of relativityResultantInsertion lossObject (grammar)RoutingGroup actionPhysical lawReduction of orderHazard (2005 film)Multiplication signComputer animation
09:42
Service (economics)Transformation (genetics)ImplementationInternet forumConsistencyTelecommunicationHorizonStrategy gamePressure volume diagramGreen's functionHorizonWeightInternet forumGreen's functionInformationAxiom of choiceGraph coloringTheory of relativityFood energySelf-organizationComputer animation
10:57
Distribution (mathematics)Process modelingContent (media)Total S.A.Organic computingChannel capacityThermal conductivityMultiplicationSpectrum (functional analysis)Internet service providerParticle systemObject (grammar)Category of beingConnectivity (graph theory)Set (mathematics)Bell and HowellInformationState of matterComputer animation
12:01
Integrated development environmentData modeloutputEndliche ModelltheorieData managementCovering spaceMusical ensembleHypermediaComputer animation
12:34
AverageBit rateArithmetic meanTotal S.A.Insertion lossAreaData managementSpherical capStrategy gameIntegrated development environmentData managementBit rateEndliche ModelltheorieOrder (biology)Internet service providerAssociative propertyPoint (geometry)Mountain passView (database)MereologyComputer animation
13:35
MaizeResidue (complex analysis)PermanentFood energyGrass (card game)Active contour modelCovering spaceReduction of orderCovering spaceDistribution (mathematics)Data managementOrder (biology)AreaGrass (card game)Game controllerProcedural programmingMachine visionDomain nameComputer animation
14:14
Spherical capMaxima and minimaParameter (computer programming)Group actionStatisticsBuildingSelf-organizationState of matterOrder (biology)Texture mappingContext awarenessDialectPrice indexSpherical capComputer animation
15:04
Distribution (mathematics)Integrated development environmentData modelTerm (mathematics)Process (computing)Organic computingDisplacement MappingAreaScale (map)System programmingForestMetropolitan area networkEndliche ModelltheorieLocal ringRule of inferenceOrder (biology)AreaComputer animation
15:48
Multitier architectureDigital signalObject (grammar)StatisticsDatabaseProcess (computing)Process modelingScaling (geometry)Traffic reportingAreaResultantAuthorizationDifferent (Kate Ryan album)Group actionMathematicsEndliche ModelltheorieField (computer science)Software developerDirection (geometry)Texture mappingDigitizingWebsiteComputer animation
17:04
DatabaseLeast squaresMathematical analysisNormed vector spaceVarianceResultantFlow separationMathematicsEndliche ModelltheorieMereologyConnectivity (graph theory)Mathematical analysisPoint (geometry)Sampling (statistics)Computer animation
18:30
DisintegrationEuclidean vectorPressure volume diagramExtension (kinesiology)Population densityPoint (geometry)Archaeological field surveyData managementLatent heatSample (statistics)Endliche ModelltheorieInformationSystem programmingModemTwitterDevice driverState of matterObject (grammar)Point (geometry)Dependent and independent variablesGoodness of fitMachine visionOrder (biology)Physical systemDifferent (Kate Ryan album)SummierbarkeitSource codeResultantEndliche ModelltheorieNumberState observerCategory of beingData managementInformationNetwork topologyArchaeological field surveyAreaProcess (computing)WordPower (physics)GoogolGodComputer animation
20:43
ForestWeightGreen's functionSystem on a chipSequenceGame controllerForcing (mathematics)Line (geometry)Game theoryGreen's functionEstimatorComputer animation
21:25
MathematicsProcess modelingSystem on a chipAreaOrganic computingResidue (complex analysis)Covering spaceData managementRotationData conversionPrediction8 (number)Point (geometry)MathematicsInformation securityData structureINTEGRALOrder (biology)Data conversionMathematical analysisObject (grammar)Uniform resource locatorElectronic mailing listComputer clusterComputer animation
22:33
DatabaseData modelCellular automatonSystem on a chipMereologyFluxBarrelled spaceMathematicsChemical equationDialectMereologyProcess (computing)Endliche ModelltheorieRandom matrixObject (grammar)Software maintenanceReduction of orderComputing platformSelf-organizationFlow separationProduct (business)Power (physics)Rule of inferenceComputer animation
23:17
Smith chartDirected graphSource codeElement (mathematics)Distribution (mathematics)DisintegrationSound effectDirected setBounded variationFrequencyCycle (graph theory)Source codeDirected graphSign (mathematics)DemosceneProcess (computing)Insertion lossDivisorProduct (business)Sound effectComputer animation
24:15
Data managementMaxima and minimaOrganic computingAsynchronous Transfer ModeSoftware maintenanceMathematical analysisPressure volume diagramImplementationFrequencySpherical capVector potentialRotationType theoryCondition numberReduction of orderPrice indexCategory of beingCondition numberMaxima and minimaSelf-organizationData managementAxiom of choiceGraph coloringGame theoryMachine visionMathematicsStapeldateiGroup actionMereologyOcean currentRevision controlProcess (computing)Computer animation
25:33
Correlation and dependenceTexture mappingOrganic computingData managementTotal S.A.Integrated development environmentSource codeLocal ringLiquidData miningAreaLocal ringResultantError messageEmailOpen sourceDivision (mathematics)Decision theoryOrder (biology)Sheaf (mathematics)Particle systemConcentricData miningSource codeLatent heatCategory of beingComputer animation
26:32
Thresholding (image processing)Level (video gaming)Content (media)Distribution (mathematics)Organic computingIntegrated development environmentTotal S.A.OutlierNormal (geometry)Distribution (mathematics)Product (business)Data miningOutlierCategory of beingNatural numberWeightComputer animation
27:20
Integrated development environmentCASE <Informatik>DatabaseSource codeThomas BayesInheritance (object-oriented programming)WeightDistribution (mathematics)ResultantAreaFluxCombinational logicTexture mappingElement (mathematics)Endliche ModelltheorieComputer animation
28:10
Workstation <Musikinstrument>Image resolutionScaling (geometry)AreaSelf-organizationResultantSet (mathematics)Local ringComputer fileInformationMachine visionBit rateArithmetic meanFood energyComputer animation
29:06
Observational studyFocus (optics)Maxima and minimaDivisorBit rateTotal S.A.Endliche ModelltheorieSpeech synthesisLine (geometry)Military baseoutputPlotterScaling (geometry)Observational studyMathematicsDevice driverState of matterMusical ensembleComputer animation
30:05
Total S.A.Focus (optics)ForestData conversionMathematicsEvoluteSystem callComputer animation
30:39
Discrete groupTape driveSimulationContent (media)Insertion lossHand fanSystem programmingInsertion lossRoutingECosProcess (computing)Visualization (computer graphics)Forcing (mathematics)Multiplication signLimit (category theory)Texture mappingEstimatorDialectKey (cryptography)Range (statistics)Different (Kate Ryan album)Computer animation
31:49
Game theoryEndliche ModelltheorieSound effectMassTraffic reportingRule of inferenceMereologyProjective planeExecution unitHypermediaMathematicsScaling (geometry)Endliche ModelltheorieWorkstation <Musikinstrument>outputArithmetic meanObservational studyRange (statistics)Computer animation
32:48
Process (computing)Linear regressionDistribution (mathematics)Linear mapEndliche ModelltheorieSpline (mathematics)Multivariate AnalyseAdaptive behaviorRandom numberForestStatisticsMathematical analysisProcess modelingVector potentialDiffusionSystem programmingData modelDisintegrationIntegrated development environmentData managementDevice driverConservation lawInsertion lossEuclidean vectorPressure volume diagramGreen's functionStrategy gameKeyboard shortcutSoftware frameworkWorkstation <Musikinstrument>Set (mathematics)Endliche ModelltheorieMathematical analysisGreen's functionObject (grammar)Linear regressionCategory of beingOrder (biology)PhysicalismKeyboard shortcutStrategy gameReduction of orderInterpolationDifferent (Kate Ryan album)Software frameworkProcess (computing)MathematicsKrigingDiffuser (automotive)Self-organizationAutocovarianceSoftware developeroutputData managementMultiplication signPredictabilityMaxima and minimaState observerPhysical lawLocal ringMarginal distributionProduct (business)OrbitForcing (mathematics)Condition numberWebsiteInsertion lossArmMereologyShared memorySpacetimeThomas BayesTransport Layer SecurityDialectComputer animation
36:39
Green's functionInternet forumStudent's t-testLevel (video gaming)Open setEvent horizonDialectObject (grammar)Different (Kate Ryan album)Very-high-bit-rate digital subscriber lineDecision tree learningLogic gateReal numberComputer animation
37:53
Multiplication signComputer animation
38:30
Physical lawWage labourMeeting/Interview
39:04
Image resolutionMoment (mathematics)Meeting/InterviewLecture/Conference
39:44
Image resolutionMoment (mathematics)Field (computer science)Information privacySampling (statistics)NumberCollaborationismoutputSet (mathematics)MetreInformationPixelDifferent (Kate Ryan album)Covering spaceLevel (video gaming)Object (grammar)Point (geometry)Constraint (mathematics)Associative propertyCategory of beingType theoryINTEGRALVulnerability (computing)MathematicsWave packetForm (programming)DigitizingWeightRule of inferencePlanningSource codeRevision controlCopyright infringementPosition operatorMeeting/InterviewLecture/Conference
Transcript: English(auto-generated)
00:10
Thank you, Tom. It is my pleasure to be here. As you say, we are ex-colleges, we know each other very well and we hope to continue to collaborate because we do really interesting things and I follow the presentations and really they are exciting for me.
00:27
Just to warn you that my presentation will be a little bit... We have a lot of data, we present all the data sets, most of the data sets that we produce here, that we produce quite a lot, but I will put them in a policy context.
00:43
I mean that we don't produce just to produce, but we produce them in order to support various policies in the European Union. Just to introduce myself, I am working in the European Commission in the Joint Research Centre, we do research for policy and today my topic will have to do with the modelling and mapping soil properties of the European Union.
01:02
A small introduction from our side, who we are, what is this body, the Joint Research Centre. The main policies that drive our research, which have to do with the soil thematic strategy, the European Green Deal, the Common Agriculture Policy, the Soils in Eurasia Europe and the missions.
01:20
The recent launched EU Soil Observatory, I will present some examples of data sets like soil erosion land degradation, which are my competencies, climate change in the soil organic carbon, diffused soil pollution, of course all this has to do with the Lucas data set produced mainly by Lucas. And I will give a flavour of what we are doing as well at GlobalScape.
01:42
So who we are, just to, I will try to be sorry for this, but somehow you have to know who we are and why we do this kind of work. So the first slides will be a little bit dry, they will have to do with policy, but the next slides after the first ones I am approaching that I will give you some exciting results. So we are a policy-driven organisation, I mean that we provide evidence, science
02:09
evidence results to our colleagues in Brussels in order to design the European Union policies. So GRC as a body try to anticipate the emerging issues, to understand the complexities and to bridge the silos between different policies.
02:26
Of course we have to address challenges in the research and very fast some facts and figures from ourselves starting from the clockwise from the right. We are located in six locations in EU, in Italy, Belgium, Germany, Netherlands and Spain.
02:42
The headquarters are in Brussels but I am locating LISPA which is the biggest GSC site. The important, we have to be policy neutral, not to be influenced by member states. I mean we have to produce our results without any influence from various groups or various pressure teams or member states or politics.
03:05
We have 42 large scale research facilities and more than 110 online databases, 83% of them have a PhD, over 1400 scientific publications per year. More than 100 economic and biophysical models are produced here, 125, I give you an example, instances of policy support per year we produce.
03:27
30% of our activities have to do with policy preparation, 70% with implementation, monitoring of the policies. We have a budget of 380 million euro, 62 million euro and budget from Horizon Europe and other research activities.
03:45
So in practice you see here a very complex diagram, how we try from various, from a lot of social people, web models, best scientific practices,
04:01
try to crowdsource data from citizens, industry, expert knowledge, data base, industrial knowledge, somehow to streamline all this through various processes in order to give some results and some advice to policymakers. This is one of the main important knowledge, I think it's unique at this scale because we operate at a continent scale
04:26
and as I told you we have to put all this information somehow in order to produce the best policy information. Just the things that we deal with have to do with energy, there are 10 priority nexus, energy and transport, educational skills,
04:42
innovation systems, food, industrial and health. We are dealing in the thing on resource cost, climate change and sustainability, people governance, civil security, data and digital transformation, migration, economy, finance and markets. I'm going directly to our subject which has to do with land degradation and soils.
05:00
So it's not a fancy thing, it's not something very fast that can attract people. Climate change or biodiversity is a very nice thing and we see that easily attract the policymakers. So we have quite a difficult job to convince the policymakers that soil is important, it's important for climate change, it's important for food security and we have seen recent events like the ones in Germany with extreme floods,
05:28
you can see here some farms as well what's happening if you have high incidence of soil erosion or floods in Germany. For example, the land degradation can take place either with an immediate event
05:44
like in terms of storms or a flood, but can be a long-time process like you can see on the screen, on the picture from my hometown, close to La Sallegrizo, where a farmer cultivates his farm for 50 years and you see the difference of one point meter depth between the two farms.
06:02
This means inappropriate practices by humans to destroy our soils. So the main policy in the EU is the soil thermodynamics study which will have a new thermodynamics strategy in 2021. We are preparing, we are working on this, but according to the existing one,
06:23
the metrics on soil soil erosion, the loss of organic matter, the contamination, the biodiversity loss, the soil seeding, the landslides, the sanitisation, the compaction. Therefore, we as experts, as modelists here, we have to provide policy support in those subjects for our projects.
06:40
Not so much happened during the last 20 years regarding soils, and I can say that it was one of the things that was somehow neglected in the EU in relevant policies. There was the soil thermodynamics study in 2006, before maybe we have seen some notion about agricultural environmental condition in the common agricultural policy, but after 2013, we see some interest from the common agricultural policy about soils.
07:07
We have seen the sustainable development goals to improve the life on land and on how to combat this specific objective out of the 17 objectives regarding the soils. And now we see a lot of movements and a lot of discussion about soil,
07:23
starting with the future common agricultural policy and the European EU green asset will tell us now. These are the eight objectives of the EU Green Deal, and as you see out of the eight objectives, the one in brown are relevant to soils. For example, they increase the EU climate ambition of 2013-15,
07:41
and soil we know that has a carbon pool. The soils in the circular economy, because mobilising industry for a clean and circular economy includes somehow aspects of soils. A zero pollution ambition for a toxic environment, the soil contamination is much relevant here. The biodiversity strategy, which is also an objective to preserve and restore ecosystems and biodiversity,
08:05
is fully linked to soil biodiversity. And of course, the farm to fork strategy, which has an objective for a fair health and environmental friendly food system, here we have to model and to give information about nutrient access, fertilisation, etc.
08:20
So we see that soils are a cross-cutting thing within the EU Green Deal. Therefore, last summer we had the idea to start positioning how soils are important in the EU Green Deal. And below you can see some issues relevant to soil. We know that erosion is two times higher than soil formation, so we have an issue there.
08:42
So we lose soil, the carbon stocks are decreasing and are reduced during the last years, and especially in pitlands, where earthworms and biodiversity is affected by land-use intensity. We see an important issue about carbon fertilisation and copper in the EU, so we speak about contamination. 23% of EU soils have critical densities, so soil compaction.
09:05
And of course, we seal our soil because we build cities and we expand out to various other environments, so it's an important thing. So we try to link all those aspects with the main objectives of the EU Green Deal, which you have to do.
09:25
For example, in the EU 2030 by device strategy, we have to plant 30% of the land in a protected area, we have to plant 3 billion trees, to increase organic carbon, to reduce the use of hazardous pesticides, nutrient losses, and of course, how to reduce CO2 emissions in relation to climate law.
09:43
We have an important other policy issue, which has to do with soils in the horizon of European missions. Soil is one out of the five missions currently recognised in the horizon of Europe, the other one has to do with cancer, with healthy oceans, with climate net. So this gives us the opportunity, somehow, for the next years, we have quite a big budget for research,
10:05
and this is very important for research activities in the EU. And I'm going directly to our activities, which have to do with the recently launched EU Soil Observatory, and there we have asked five main goals, starting from goal one, to develop an EU-wide soil information system,
10:21
to goal two, to have a stronger European soil data centre. We operate currently in the European soil data centre where we want a stronger one. Goal three, to monitor soil-related policies, which of course do our activities, to various policies within the EU green deal and the common educational policy.
10:42
Of course, the goal four, to support the research and innovation, and the goal five, to involve the citizens and to have a forum where all these issues take place and discussion takes place about the soil-related issues. Therefore, we produce large data sets, you may be aware of some of them
11:02
because they are publicly available in the European soil data centre, and our base for this is the Lucas that I will present in a few minutes. So, our objective is to create, for a monitor system, to create an EU monitoring system, which includes physical, chemical and biological soil properties. For the moment, we model them with Lucas, we model the cost fragments,
11:24
the particle size distribution, the pH, the organic carbon, the carbon content, the total nitrogen, the phosphorus content, calcium chase capacity, heavy metals. For most of them, we provide maps. There are various publications of Bell Lab in 2019, and Joe Dermot, with the chemical properties and the physical properties of 2016,
11:41
we provide information. Of course, we are ambitious and we have other objectives, so we want to provide information, such kind of data sets for pesticides, for biodiversity like nematodes, from fungicides, herbicides, antibiotics, and soil biodiversity. All this, we hope that can take place with Lucas because Lucas said component
12:01
that is enriched with many other properties. I will go to soil erosion and give you some examples, because we have, from one side, the activities, the model activities, which take place, we have as a base in the Lucas, but also we do other modeling activities with empirical or process-based model, and one empirical model is the Roosle,
12:22
which allows us to estimate the soil loss by water erosion in the EU, takes as input parameters, soil erosivity, the rainfall erosivity, and the thickness, the cover management and support practice. Based on this, we produce the soil loss by water erosion in the EU, and we have produced this, not just for OAS,
12:40
but we have produced for 2010, 2016. We monitor somehow erosion with these modeling activities, and we see that the erosion is much higher than soil formation rate, so if the soil formation rate is considered around 1.4 and 2 tons per hectare, the soil erosion currently in the EU is 245 tons per hectare per year.
13:03
Therefore, for 25% of EU soils, we have non-sustainable erosion rates, and we have something to do, and of course, this is our role here to provide policy advice to our policymakers to say, look, we have a problem there, those are the hot spots, and please introduce those, the X or Y management practice,
13:22
in order to reduce erosion risk. This may happen somehow during the last years, because we have seen in the Common Agricultural Policy, there are more and more management practices introduced and incentivized, which somehow allow to decrease erosion in the EU. And which are those management practices? You see the farmers on the top part, you see the crop distribution.
13:44
There are crops which are less erosion, the permanent grass, the alpha, the wheat, but crops which are much more erosion, like the sugar beets or the tobacco. On the bottom part, you see the main practice that we advise,
14:00
that can have an important impact in reducing erosion, like reduced tillage, the plant residues, the cover crops, the stone walls, the grass management, the control farm. Those are recommended in specific areas of the EU, or if you have hot spots, in order to reduce erosion. Of course, this map is not just for scientific purposes, we have done a lot of publications on this, but is used for developing indicators for poll support,
14:24
as I told you, one of the main initiatives in the EU was the United Nations Standard Development Goals, and this map fits one of the indicators there. We monitor the Common Agricultural Policy till now, and we provide cap context indicators.
14:40
We plan the future Common Agricultural Policy, and we do raw scenarios. This map is taken also by the European Parliament Green Group, various statistics in Eurostat, regional statistics. Of course, the cultural outlook that the GGRP provides, and of course, by various international organizations, like the OECD, the IPBES, the UNEP, and the EUCD,
15:01
we collaborate quite a lot with those. Of course, soil erosion is not just what the soil is, it's eroding from the field, but how much of this soil is added to the river basin, or to the sea, and there we apply another process-based model, which is the bottom setting, and we want to see the transport of soil sediments
15:26
to the river basin, and we see that almost 60% of the eroding soil is finished to the river basin. This takes place by calibrating 25 catchments, and of course, in this modeling approach,
15:42
we should take into account that we don't model yet other processes, like galley erosion, or man's lights. This was a process, a vehicle model that we estimated, so we want to go much farther, and much farther means that we want to include the latest developments in Copernicus,
16:01
which have to do with remote sensing and technological changes in crops, so currently, we are aimed with Copernicus on how to see the crops at the field scale, and we want to produce those, taking as simple as those digital land maps to know exactly where it's cultivated to what,
16:21
because it's important for us. You remember my screen, when I told you the crops have different erosivity inputs, and based on, in the phenological, and take also to account phenological sites, because the crop is different in Germany, which is the crops, the phenological site is much different in Germany than in Greece,
16:41
and with objective-related approach, we want to produce a much more dynamic approach to estimated erosion on a field scale basis, and on a yield base, and this will take place with Copernicus, take also interannual variability, and of course, this allows us to add some climate and land use, and that is very important for policy.
17:02
So now, we will go in this direction. Lucas, as I told, I gave you the notion before, what is Lucas? Lucas is the biggest EU survey, but there was a nice proposal in 2006, 2007 in GRC,
17:21
and we proposed in Europe. Europe has monitors the land use changes with Lucas in the EU, everything is going to see the land use, but we have the idea, we are not monitoring the soils. I mean, you take a sample from the soil field, analyze it, immediately you have excellent results. So, we start this exercise in 2009,
17:41
we collected 20,000 songs, and based on this exercise, 20,000 songs, we have produced a lot of results, and then we see that this exercise is successful, we repeat in 2015. So, again, the 2015 data are available as point data, and now we repeat in 2018, the 2018 data are expected quite soon,
18:01
and as I told you, it's a dynamic component because in the first Lucas, we had only the physical and chemical distribution, I mean, chemical properties. Then we added analysis spectra, then we added analysis of heavy metals. Now, for 2018, this is very easily preceded by diversity aspects and analysis. Now, the policymakers ask to have some analysis
18:21
of pesticides and microplastics. So, Lucas is a dynamic component that allows us to, it is well-seen by the policymakers as well. It has also some advantages that we assure that they will have monitoring capability. I mean, we have experience in the past before arriving in Lucas. We have different other surveys,
18:43
like IPC forest, where different sums were coming from different member states with different depths, analyzed by different laboratories, and of course, they were not compiled. But at least, Lucas, before, we started methodology, we have harmonized methodology, points are repeated, most of the points, 70% of the points are repeated, and we analyze them by one laboratory
19:02
which is certified, to collaborate with member states and to see the, to have better observability of Lucas, to increase the number of points. This is not, this is really not easy.
19:22
And now, of course, the Lucas allows us to cross validate the results of our models. Important, you don't have only Lucas sources at all. You have also other information related to this. You have, as I said, various goals there, he knows the crop,
19:40
he knows if there are management practices, he knows the tree coverage, he knows issues about biodiversity. So there are important info that are linked to Lucas, which are used by us to see what are the main drivers for having some trends in soil properties. It's a systematic approach and we want, our vision is to have not only 22,000 points,
20:06
but we also know that we see some good, some good response from member states because of course doing this exercise is quite heavy for us and requires a lot of resources. Of course,
20:20
the last objective is to debate all these, the Lucas with national soil monitoring system because we know that France has its own monitoring system, has its own Lucas, something like Lucas. And we want somehow to debate those points that we surveyed, we have surveyed points, and somehow to hear the convergence on how we do the analysis, how do we survey
20:40
in order to provide the scientific community much more data science. About soil organic carbon and green deal, I mean, now the soil organic carbon is important for, as you know, the soil organic carbon is important because for climate change. For as we know, that's a carbon sink, but agriculture emits 10%
21:01
gas emissions. So therefore, we have to somehow to provide some estimations how we can decrease this, this trade. And we don't see any decrease in terms, unfortunately, I mean, the emissions coming from the catchers are stable or increased. So we need to reduce the non-CO2 emissions and the sequestered carbon in soils.
21:20
We know that soils with certain practice can sequester carbon. And in this context, in GSC we have developed and described in global change biology where with the Lucas points as well included, we try to estimate the soil organic carbon in the EU and we see that in topsoil
21:41
around 17.6 gigatons of soil are included. Important to see also the scenario analysis. I mean, if you follow one practice or another practice, what will happen in soil organic carbon. So therefore, we learn some scenarios like the conversion from Allard to Dresden,
22:01
the reduced tillers, the combined residual integration of reduced tillers, the lane rotation, the cover crop. So we see what will be the impact of following one X or Y practice in soil organic carbon. And this is important in order to propose the policy makers which is the best structure. Of course, you propose,
22:20
but you don't know if this is efficient because our proposal would be to convert most of the arctic grass, but this is not possibly you and your food security. So our objective here in GSE is not only to have separate silos like modeling erosion,
22:40
modeling soil organic carbon, but also how to integrate them. An important issue to cap our erosion estimates with the century by chemical model to see, I mean, how much carbon is eroded, how much carbon we use with soil erosion processes. And we have done this in a integrated platform
23:01
where the agricultural soils are losing very lost, a small part of carbon. Of course, there are some hotspots where carbon is lost and we know that agricultural practices are important to prevent this reduction of erosion and to maintain soil productivity by maintaining soil organic carbon. There is an important debate about erosion and the carbon cycle.
23:23
If erosion is a carbon sink or a carbon source, there is quite a big debate between the studies, one claiming the one side of the carbon sink, the other claiming the source on our side. Somehow we try to estimate the impact of human activities
23:41
and erosion effects on productivity and soil restoration. And we try to see what will be the future rainfall erosion. I mean, rainfall erosion is an important factor that affects soil erosion in the future. And we know that we have more storms and more rainfall intensity
24:01
of this natural process in increasing carbon losses in the EU. We run various scenarios with the accelerator carbon soil erosion scenarios. This was published by Ligato in Science and Science 2018. An important policy that drives our work, both in soil erosion,
24:20
carbon and other soil properties. On soil, Lucas is the carbon capture policy. And we see now that the carbon capture policy has some specific objectives, like we want sustainable development, we want a climate change mitigation. Those are translated
24:40
in specific scenarios. So for the first time, we have sorry relevant impact indicators in the carbon capture policy, like how to reduce soil erosion with the percentage of lab in moderated civilians, soil erosion in agricultural lab. It has carbon sequestration which increases the soil organic carbon. And then we have to propose
25:02
specific best agricultural environmental conditions in the carbon capture policy, which are the best practices to increase carbon and to reduce erosion. And among them, we can see to protect the carbon rich soils, to maintain the soil organic matter, to be careful using
25:22
some tools on nutrient controls, some minimum lab management and the protection of soils in winter, the preserve of soil protection and the crop protection. I will go very fast in soil contamination because we have done some nice work here. So as I told you, Lucas has been analysed for heavy metals
25:40
and among them we have seen the copper is one of the important heavy metals that has been analysed. It is correlated with soil properties like pH, specific sources for copper contamination like
26:00
the Fujiside treatments specifically for vinegars, the liquid manure, the silica sludge, of course the atmospheric deposition, the mining activities, and some local industries and of course the particles come from carbon here in copper soils. Vinegars unfortunately has the highest copper concentration
26:22
in the EU and mainly due to the seasonal treatments, olive growths and orchards, and this was published by this map. Recently we also published the mercury distribution in the EU and this again takes place with Lucas because mercury was analysed there.
26:42
It is not mercury, it doesn't seem to be even you see that the mercury is not a serious issue in the EU, but mercury somehow is relevant to natural properties. Here we see all the outliers close to old mine production
27:02
of mercury like the Monta Miata in Tuscany, in Spain some pot spot, in Slovenia and Slovakia and some coal power plant. So we have seen with various analyses, we have seen where the outliers and then we investigate why we have those outliers.
27:21
As I told you, we don't stay just in silos, but we try to link the two modeling results. So here you see the previous map of mercury combines soil erosion with sediment fluxes. So we try to combine those two modeling activities and try to see how much mercury is released in the seas.
27:42
We investigated that the Mediterranean Sea received most of the mercury, almost half of the mercury, because it is a toxic element and has health issues. In some areas in Italy, in Slovenia, there you have
28:01
hotspots of soil erosion, so unfortunately you have this combination which may provoke quite high release of methane-specific seas. At global scale, I will give some information what we are doing. For us it is very challenging that the work done at very local scale or farm level,
28:22
then we try to upscale this work at national level, and we produce our results at the EU level, which is our main area of interest. But of course we do some work at global scale as well, because we have this expertise and sometimes we can be contacted by file or other organizations and we produce these data sets also at global scale. So I will give you
28:41
some examples. Similar to the EU, we collected high temporary rainfall data. High temporary means 30 different organizations, and we collected this kind of data for almost 3,625 stations, which
29:02
speak about 60,000 years of high-temporal data. And based on those data, we produced the first global rate for Rosintima, which has to do with as an important factor for soil erosion, but of course also for other aspects, climate change as well, because we can see where the rainfall intensity is higher, and of
29:20
course in tropical, you see areas. And this was an important input to produce the global soil erosion map, which was produced by Borrell in 2017. We see that soil erosion at the global scale is much higher than the EU, at almost 36
29:42
billion tons of soil that is loaded per year. Another driver besides the rainfall intensity is the land use change. We see that in crop lands, the soil erosion is quite high. I mean, how many years have the soil erosion is taking? I mean, 11 percent of
30:01
clover plots are responsible for 50 percent of the soil erosion. So this study is not just a map, it's much more because we see the earth system dynamics, we see this study, which are the main earth system dynamics, which are the main changes, land use changes that provoke this erosion change,
30:21
because we didn't monitor just erosion for 2010, but we monitored also the change, you can see right, bottom right, the change between 2012, and we see that there is an increase of soil erosion portion due to conversion of much forest land to crop land.
30:40
Again, here, we try to combine our erosion, global erosion staff, with an existing phosphorus estimate map to see how much phosphorus is lost. Why phosphorus? Because phosphorus is an essential platform, it's essential for plant growth, it's a key limit for nutrient future
31:00
food, it's a treat for ecosystem health, and of course it's lost with soil erosion, it may provoke a lot of issues in the seas, and as you know, phosphorus is limited because you have a certain mindset, there is a loss of geopolitical interest on this topic. Therefore, we want to estimate, to combine those,
31:20
to estimate how much phosphorus was lost, is lost, and from different agriculture systems, so the range is between 112 kilos per hectare per year, very high loss in eastern China, Indonesia, where you have a lot of erosion, but also a lot of phosphorus
31:40
inputs, and some high loss as well in India regions. This was recently promised as well by Ali, one at a time. Just coming out of the press, we do also some soil erosion projections, meaning that our erosion in that model I showed you, the 3,625 stations are projected
32:01
towards the future, and what will be the erosivity projections in the future, because we have, we take into account the climatic models, we input in our model the climatic models, and try to do this projection for erosivity, and this is important to show that erosion will increase at global scale between 30 and 66
32:22
percent, this range you get because you have a lot of erosion that can be 2.6, 4.5, and 8.5. Similar study has been done recently in EU, the first one at global scale has been published by Borrelim, NAS, the recent one has been published by me recently, and we show that in EU the
32:41
change will be much less, but of course the erosion is not such a big issue in EU like in at a global scale. So some, I'll go towards the end and give you some notion, some examples, a lot of maps, a lot of data sets. We have followed
33:01
different modeling approaches for spatial interpolation, spatial interpolation is critical, I mean you have a lot of points, many times you look at other data sets like the rainfall erosivity, we have the stations with erosivity base we have to interpolate, so we have followed the Gaussian process regression for global erosivity and the European rainfall erosivity, the cubist
33:26
physical properties that are represented, I mean the clay cetam sat, the Gaussian process regression for the chemical properties for the nitrogen phosphorus, the generalized linear models for the copper distribution, the random
33:41
phosphorus for the material, of course why you have this variability, you have this variability because of the performance of the model, because of the inputs that we have, because of the covariance we have to conclude the remarks, I will have to conclude the remarks, one about modeling, one about policy,
34:01
regarding the future we see that we go towards object oriented solutions, so from a pixel or from a shapefile we go now to see what is captivating the land, it is very important for us, the farmer what he is doing, so we follow more this
34:22
land parcel approach will be very important for us. The Lucas 2018 Earth observation has a lot of potential to be combined with Copernicus, another on crop calendar, it promises to run some policy scenarios, some scenario analysis of climate or land use change, I mean the main
34:42
scenario analysis, either climate or the land use change or the policies, we are close to policy interfaces, we know what the main policies are predicting, we focus also on model integration, we have different soil processes and try to integrate them,
35:00
like carbon loss, biodiversity loss, erosion, diffuse pollution, phosphorus, and incorporate them in the earth science modeling, and more to come, a lot of things will come now, as you have seen a lot of stuff in soil organic carbon, in the diffuse pollution, you will see more and more carbon also by diversity and DNA analysis, because around 1000
35:22
DNA, they give quite interesting results, which are relevant also for policy, but are really exciting for science. Some completely max in the organic policy, the common potential policy will have a strong environmental component, which allows us now to have some impact in the provisions or
35:41
predictions of how various processes on soil will go in the picture, so we have to do a lot of work there, how we can protect our soils, which better management practices, that we catch on management practices are important for soil conservation, EU is a front runner in the sustainable development course, and we produce a lot of stuff there, the EU green is the main
36:02
model now, with the main strategies like the biodiversity loss, the climate law in order to reduce greenhouse gas emissions, the farm to fork in order to reduce the nutrient losses, and of course the zero pollution in order to reduce
36:22
the emissions. We expect now the new soil thematic strategy that will come very soon, and we give quite a lot of work on how we can achieve that degradation in 2030, but unfortunately, a binding legal framework of soil is missing, and this is quite important for our work.
36:40
As I told you, we established now the European Soil Observatory, which has a lot of work and has different objectives from research, policy, data, et cetera, and one important event that takes place, and we are working to participate, because an open event, and everybody can register, is the European Soil Observatory Stakeholders Forum, so there will be one, I think, high
37:02
policy, high level policy speaker, but the next of the days will be, we try to involve all policy, all stakeholders, policymakers, researchers, regional authorities, farmers, NGOs, citizens, everybody is working to do that. We have
37:23
a nice idea that during this three days soil forum, we have a young researcher forum, so we will dedicate one day, one half-day in young researchers, so if you are a PhD student, or you have taken a PhD normal three years ago, please submit an abstract, because we
37:41
dedicate half-day in young researchers, and we want to listen to you, to the young researchers, how they see the research on soil, so in different environmental aspects, so these are my contact points, all the data that I presented are available in ESDAF, you can contact me on any issue, so thank you Tom for
38:01
giving me this excellent opportunity. Thank you Panas, it's amazing how much work you do here in your group, it's difficult to follow. Yes, I know. So, but it was really good overview, and we have some time for questions, I was just looking at the chat, if there's any questions, please post them
38:22
in the chat, and I'm also looking here at the room, the conference, if there's any hands in the air. Yes, there's one question. We will bring the person for the sake of the hearing everything, we bring the
38:40
person next to the microphone, so here's a question by Lena Halunova, professor from CTU Prague. No, I cannot hear you, officially, no. No, nothing? No, no, no. Wait, wait.
39:01
Yeah, a little louder, a little louder, I see, okay, please. No, very, very, very, I see very clear, I listen very clear, Tom, but not you. You heard me, but you didn't. Yes, yes, yes. Very interesting, can I see?
39:21
Tell me the question, I will repeat. Okay, yes? Is that the question? So just about that? Okay, there are two questions.
39:43
Can you hear me, Panos? Yes, yes, yes, very, very clear. One question is how you related with IUSS, so how do you work with them? Yes, yes. And then the second question is for the solar erosion, what is the best resolution you have at the moment for EU? Okay, if I go for solar erosion, it's 100 meters that we provide.
40:01
Of course, as I told you, this is at pixel level, but we would prefer to have it at farm level, so our next objective is to go on farm level. The main reason for having the 100 meters is because it's driven by land use changes, and there we have the covering plus other data sets that drive
40:20
us at 100 meters. Now, regarding the IUSS, I call it the International Union of Soil Science, if I'm not wrong. So we have some exchange of mail, I'm trying to involve in the European Soil Observatory, where the President of the EU says that Constantine will come and present there.
40:40
We have some informative, I mean, we exchange mails, they take some of our information in their newsletters, but it's a different type of organization, I mean, we are researchers, we provide support, but they're quite an association that keeps all the Soil Sciences Society under their umbrella. Okay, thank you, and one more
41:03
question for me. What are the biggest constraints for estimating, like, soil degradation, land degradation over Europe? What are the biggest constraints? I mean, like, you know, we have lots of talks in this workshop, which is about land cover modeling, biomass
41:22
emissions and things, but those are hidden, I mean, it's a really different thing. So it's hidden, Tom, it's hidden because, yes, yes, I'll give you the next slide, then it's very easy. It's a privacy issue, it's the privacy issue which gives us a lot of problems. I mean, we have this
41:40
privacy issue, and I think with Luca's going to adjust in the gray zone, because if you go in the field and you sample of a farmer field, as soon as you analyze the physical and chemical properties, I mean, if you analyze pH or maybe soil organic carbon or clay or silk, it's okay, but since you start giving information about heavy metals, about
42:02
pesticides, then we have an issue with soil, it's a transversality, it's not just a private, I mean, if you have one field with X or Y properties or X or Y problems, this should somehow be a close, because in this field you produce food, and this is important. So the
42:21
privacy issue for us is an important issue, but the privacy which does not allow to have a lot of inputs. Another issue is that we should have a better collaboration on integrating all these monitoring systems, I mean, we have the 20,000 soil samples of Luca's, but why not have 150,000 soil
42:41
samples, because we know that France has another 50,000 samples, or Germany has another, so it's very important that we integrate all these efforts in one effort and we go with much higher number of points.