september 10-11, 2007 workshop "calculating regional gross nutrient balances" various...

September 10-11, 2007

Workshop "Calculating Regional Gross Nutrient Balances"

Various examples of Surplus calculation

across EU with NOPOLU system2

10-11 September 2007 Eurostat in Luxembourg



NOPOLU Agri product Presentation

Interest of the agricultural balance calculation

• Agricultural practices, such as fertilizing, or animals breeding represent a major source of nutrients and pesticides pollution of the soil, and therefore a potential source of river pollution.

• As an example, in some Britanny areas, almost 95% of the Nitrogen pollution into the rivers, comes from agricultural activities.

• Agricultural loads, also designated as “surpluses”, are a good indicator of the pressure of those agricultural practices, and consequently a indicator of efficiency of policies.



Principle of the agricultural surpluses computation

• An exportation rate (from cultures and animal excretions) and a consumption rate (fertilisers, grass eat by animals) are allocated for every type of crop and every kind of animals.

• The distribution can be spatially distributed and even at closer scales, if the coefficients are available.

• The calculation : consumption minus exportation corresponds to the agricultural balance. The agricultural surplus load (Gross Balance) is obtained by adding the atmospheric deposition

• This surpluses load is a nutrient soil stock, able to be directly brought to the groundwater and the rivers.



NOPOLU Agri Nutrient Balance model principles

• Statistics are available at administrative scale, often very aggregated

• Crops are grown / managed only on some type of land covers,

• Crops and livestock types are under the Eurostat nomenclature, import procedures translate national nomenclatures.

• Relationships CLC types / land use can be set generally and locally adjusted

• Calculation of yearly balance & surplus (Nitrogen & Phosphorus)…

• Desegregation of parameters from any NUTS level to any HYDROLOGIC level using a LAND COVER (CLC, remote sensing….)

• Spatial statistics for all rates• Phenomenon modeled;

– Symbiotic Fixation– Atmospheric deposition – Synthetic application fertilizers rates are modulated by organic fertilizer application rates– Volatilization of animals excreta in cattleshed, storing areas, pasture field,..

• Basic Data– Agricultural activities and practices (crops, livestocks,…) – Land cover – G.I.S. layer, NUTS & sub-catchments topology



Results at level basin

or NUTS

Scenarios

CLC

Surpluses: Disagregation method

Occupation du Sol CORINE Land Cover - IFEN

Disagregation using soil coverage

+

Watersheds Limits

Census at NUTS level



Allocation of fertiliser and exportation rates per culture in NOPOLU



Large scale assessment of non-point nutrients sources using NOPOLU

More than 10 years of experience and models refinementsFirst step: Maturing the model from pilot study to large scale

and detailed processes model The objective was to build a tool capable of assessing large scale

watersheds agricultural nutrients balances• Pilot application was driven in 1977 on Loire- Brittany basin (157000Km2).

The idea was to process the equations for balance calculation from CORPEN institute at sub-catchment level using a disagregation process involving a Land cover ( in fact CLC).

• Compatibility to EC nomenclature was implemented and tested on Elbe basin (147000Km2) covering 3 countries (an as much agricultural census) in 1999.

• Experience driven by Lunven University for JRC on a large part of Europe permitted to prove to very large scale applicability of the model with very aggregated data (NUTS3)

• Many processes were newly modelled in the module since 2003, with SOLAGRO experience. Large-scale assessment with recent datasets RGA 2000 & CLC2000 was then performed on whole France for WFD Characterisation of river basins.

• Green Dairy project (2005-2006) has used NOPOLU for assessing impact of pasture on Nitrogen Gross Balances.



Large scale assessment of non-point nutrients sources using NOPOLU

More than 10 years of experience and models refinements

Latest developments: use of Earth observation data from satellite remote sensing images

The objective was to test the possibility of assessing in real time evolution of agricultural practices by updating land

covers and census with EO data• Experience driven in GEOLAND project permits to use remote

sensing data processed to obtain up-to-date census and Land Cover as basic module datasets. Pilot basin was Adour-Garonne (118000Km2)

• Refinements in the module procedures & in remote sensing data processes were implemented in GMES phase one. Saar-Mosel basin was tested and NOPOLU Nitrogen surpluses results were injected in a water quality model.



Excédents s tructurels Azotés calculés par Aurous s eau 1995en kg N/ha de SA U par Canton

[ 150 ; 200 [ (4)[ 125 ; 150 [ (9)[ 100 ; 125 [ (11)[ 75 ; 100 [ (24)[ 50 ; 75 [ (41)[ 10 ; 50 [ (83)] 0 ; 10 [ (14)

Exédents s tructurels Azotés (Scénario 3)en kg N/ha de SA U par Canton

[ 200 ; 250 [ (2)[ 150 ; 200 [ (11)[ 125 ; 150 [ (13)[ 100 ; 125 [ (12)[ 75 ; 100 [ (37)[ 50 ; 75 [ (55)[ 10 ; 50 [ (54)] 0 ; 10 [ (1)0 (1)

Technical Norms SCEES/CORPEN for fertilizer, excreta rate & export from crops

From CORPEN idea to NOPOLU pilot application

CORPEN rates applied Aurousseau 1995, scénario 3



Sensivity analysis: Constants and rules

With N-fixing

No N-fixing



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Différences en % (Scénario 3 - SNIE 1990)(N calculé - N livré )/N livré

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Différences (Scénario 3 - SNIE 1990)N calculé - N livré (e n tonne s de N)

25 000 000 - 50 000 000 (0)15 000 000 - 25 000 000 (5)10 000 000 - 15 000 000 (8)

5 000 000 - 10 000 000 (7)0 - 5 000 000 (7)

-10 000 000 - 0 (7)-20 000 000 - -10 000 000 (0)-38 400 000 - -20 000 000 (2)

Checking results: Comparison between N fertilizers delivered (SNIE 1990) versus N modeled fertilizer applied

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Différences en % (Scénario 1 - SNIE 1990)(N calculé - N livré )/N livré



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Différences (Scénario 1 - SNIE 1990)N calculé - N livré (e n tonne s de N)

25 000 000 - 50 000 000 (7)15 000 000 - 25 000 000 (10)10 000 000 - 15 000 000 (2)

5 000 000 - 10 000 000 (3)0 - 5 000 000 (6)

-10 000 000 - 0 (5)-20 000 000 - -10 000 000 (1)-38 400 000 - -20 000 000 (2)

•Scénario 1

•Scénario 2 & 3



Adjustment of constants and rules : Elbe basin

Concerned sub sectors by the missing of census data

Czech sub sectors

Official constants

Revised constants



Scenario on Fodder source (1 Elbe Basin)

The difference between the two approaches concerns 882 thousand ha that shifts from negative surplus to a positive surplus, and the surpluses of which contribute then for 32 thousand tonnes to a total increase in 185 thousand tonnes (i.e. an increase of 131% with respect to the baseline scenario).

Limitation by

resources

Unlimited supply



Fodder source (2 Britanny basin)

Limitation by

resources

Unlimited supply

Positive Negative Positive Negative Positive NegativeLoire basin 154 -23 229 -3 75 19Brittany 167 -0.1 225 -0.1 58 0South-Loire 24 0 39 0 15 0

Total 345 -23 492 -3 147 19Germany 80 -6 215 -1 136 5Czech Rep. 50 0 70 0 20 0Shared catchments 11 -5 40 -3 29 1

Total 140 -11 325 -4 185 7

Surplus (with abatement) in thousands T

Surplus (without abatement), in thousands T

Difference



Nitrogen sources Whole France territory

• Organic excreta from livestock: 1487 kt

• Synthetic fertilizer : 2 506 kt

• Atmospheric deposition (EMEP 2000 source): • 670 kt (surface CLC - 14 kg/ha/an),• 430 kt (over arable land)

• Symbiotic Fixation : 569 kt

SAU RA 2000 (ha)

Fixation symbiotique

(tonnes)

Fixation symbiotique

(%)Lupin, vesce, féverole 40 182 5 926 1%

Légumes secs 270 17 0%Soja 76 274 11 283 2%

Prairies mélangées 2 247 453 120 033 21%Prairies légumineuses 379 080 96 944 17%

Pois protéagineux 422 272 47 059 8%Prairies naturelles 6 891 360 273 907 48%STH peu productives 1 396 083 14 171 2%

569 341 100%



Results over France : Surplus (1 255 kt - 42 kg/ha) & Balance (932 kt - 31 kg/ha)

SAU RA 2000 (ha)

Surplus (tonnes)

Surplus (%)

Surplus (kg/ha)

Bilan (tonnes)

Bilan (kg/ha)

Maïs fourrage 1 381 028 150 006 12,0% 109 128 731 93Colza et navet 1 172 270 104 215 8,3% 89 89 526 76

Maïs grain et semences 1 750 431 129 833 10,3% 75 109 439 63Pomme de terre 155 867 11 361 0,9% 73 9 124 59

Seigle 30 447 2 009 0,2% 66 1 631 54Blé dur 334 735 21 504 1,7% 64 18 579 56

Betterave 405 351 23 373 1,9% 60 17 957 44Orge 1 516 501 78 743 6,3% 52 59 403 39

Triticale 240 880 12 310 1,0% 51 9 203 38Sorgho 58 223 2 970 0,2% 51 2 440 42

Blé tendre 4 876 524 224 033 17,8% 46 159 240 33Prairies naturelles 6 891 360 230 095 18,3% 44 168 347 24Prairies mélangées 2 247 453 54 793 4,4% 41 36 432 16Prairies graminées 621 885 5 579 0,4% 28 2 992 5pois protéagineux 422 272 11 727 0,9% 28 6 089 14

Avoine 101 270 1 764 0,1% 19 726 7STH peu productive 1 396 083 11 575 0,9% 18 6 570 5

Tournesol 721 263 12 708 1,0% 18 6 024 8Soja 76 274 1 057 0,1% 16 490 6



Result Maize fodder : balance (129 kt - 93 kg/ha)



Results Maize fodder : Surplus (150 kt – 109 kg/ha)



Results Wheat : balance (159 kt - 33 kg/ha)



Results : Wheat Surplus (1 255 kt - 42 kg/ha)



Results : balance (932 kt - 31 kg/ha)



Results : Surplus (1 255 kt - 42 kg/ha



Earth Observation Datasets Era

• The surplus (Gross balance) is an indicator of this impact : it is based on crops surfaces and animals census, and fertilizing practices.

Detail Agricultural census is done using farmer surveys, they are published every 10 years in France.

• The use of remote sensing data appears to be one of the bridge to fill the gap for quickly assessing large scale basin nutrients balance :

– It gives nearly real time information.

– Its exploitation is much more cheaper and quicker than current process of surveys.

• It has been validated on several pilot basins : Adour-Garonne & Saar-Mosel for surpluses assessments.



Earth Observation datasets : prospects

Treatment of EO data is done by Infoterra who produced temporal update of Land cover! It allows;

• regular update of HYDROSOL soil occupation cover with the support of frequent Spatial units data.

• update of agricultural census, after a soil occupation comparison.

• Possible update of up-to-date pieces of data, providing a much relevant load calculation.



Method to generate agricultural landcover from MERIS sensor



Interface from MERIS images to NOPOLU Agri

• Use of the process chain line developed by INFOTERRA to produce agricultural land cover

• Integrated NOPOLU System2 model in the chain



Treatment of Spatial Coverage datasets

• The spatial images for 2005 supplied by INFOTERRA detail 4 types of soil occupation :

– Winter / Spring

– Summer irrigated zones

– Summer non irrigated zones

– Meadows

• These images are used to update the existing agricultural census RGA for year 2000 available at the district scale (NUTS 4).

• A table link French Agricultural census codes and the 4 defined soil occupations codes. This allows to perform the link with the European crop codes used in NOPOLU Agri , in order to generate census for other years.

• Then the 2000 census can be update for the year 2005 using .

• The agricultural module allows not only to get Nitrogen surpluses values and data rates at different scales NUTS or watershed, but also to obtain the same calculation for any type of culture.

• Once the watershed census is available, the 2005 census is calculated using surfaces proratas.



Cloudy areas Some earth observations

cannotbe processed because of

clouds. No soil occupation is thenavailable for these areas.

For these areas, the values from the census of year 2000

are used in The calculation.



Results of census update from 2000 to 2005

• The analysis of the updates of crops surfaces are quite interesting since :

– Irrigated summer and winter/spring crops surfaces are quite similar to year 2000 census values, with an average variation of less than ± 5% (mostly increases)

– Non irrigated summer surfaces and meadows are inferior to the year 2000 census values. For meadows, this difference may come from forest areas used as meadows, which are not identified distinct in CLC.



Note : The balances & surpluses are superior on 2005 calculation, even if crops surfaces are slightly smaller. This difference only comes from the pastures and meadows which receive animal dejections.

In both cases, the same number of animals on the pastures, but a different amount of exportations, nearly 46 500 Tons of delta!

2000 2005

Only on non cloudy entities:

Arable Land (ha) 3 255 163 2 918 144

Permanent Pastures & meadows (ha) 1 444 323 1 169 328

On all entities

Total crop area (ha) 6 015 984 5 410 635

Surplus (T/yr) 229 554 274 686

SAU Density (Kg/ha) 38 51

Bilan (T/yr) 181 164 229 554

SAU Density (Kg/ha) 30 42

Results of calculation



Adour Garonne basin results with EO data

Density of Agricultural

loads (surplus) projected on

CLC agricultural areas.

Left : Current calculation for

2005.Top : Reference calculation for

2000.



NOPOLU model

• Detailed scheme of surpluxes calculation inside NOPOLU System2 model



Saar Mosel Basin: EO data M2.1 (detailed land cover) integration

M2.1 product (right) is used as a more detailed alternative to CLC 2000 (left). Its integration is performed by :

a. Creation of the HYDROSOL file, by performing an intersection between administrative, hydrographical layers and M2.1 product.

b. Importation of the resulting file into NOPOLU.

c. Affectation of agricultural census crop and animal types to land cover categories.



Results on Saar Mosel Basin covering 3 countriesCrop area (ha) fertilizer

Synthetic fertilizer

Organic fertilizer

Exportation Production Global BilanGlobal Surplus

(SAU)Surplus

Density (SAU)Bilan density

(SAU)

Reference

LU 124 18 093 7 105 10 988 14 542 743 365 6 191 7 988 64 50

DE 265 42 699 21 368 21 331 35 477 1 572 086 15 676 19 971 75 59

FR 759 107 700 68 518 39 182 88 777 4 244 453 37 477 47 512 63 49

Total Reference 1 148 168 492 96 991 71 501 138 796 6 559 904 59 343 75 471 66 52

M3.1 Calculation

LU 124 18 093 7 105 10 988 14 542 743 365 6 191 7 988 64 50

DE 352 49 391 28 058 21 332 40 482 2 136 836 20 432 26 335 75 58

FR 769 108 377 69 140 39 237 87 202 4 356 294 40 044 50 167 65 52

Total M3.1 1 245 175 861 104 304 71 558 142 226 7 236 495 66 666 84 490 68 54

Evolution (%) from initial census to updated census

DE 33.1% 15.7% 31.3% 0.0% 14.1% 35.9% 30.3% 31.9% -0.9% -2.1%

FR 1.3% 0.6% 0.9% 0.1% -1.8% 2.6% 6.8% 5.6% 4.3% 5.5%

Total 8.5% 4.4% 7.5% 0.1% 2.5% 10.3% 12.3% 12.0% 3.2% 3.6%

• Those global results show :– The missing data from the german census.– A global increase of surpluses values, although the increase is not quite

important regarding density.• It’s nevertheless important to notice that no changes have been processed

regarding animals.• Detailed results can also been examined per crop/animal types and per

entity.



Update of Census with EO dataEuropean code Crop culture

Original Census from 2000

Update 2005

D/01->D/08 + Cereals for the production of grain (including seed) 0.0 0.0 0.0%

D/01 ¤ Common wheat and spelt 111 890.8 110 197.7 -1.5%

D/01/a * triticale 5 285.0 6 906.2 30.7%

D/02 ¤ Durum wheat 12.0 12.0 0.1%

D/03 ¤ Rye (including meslin) 1 136.6 1 347.9 18.6%

D/04 ¤ Barley 135 458.2 133 630.5 -1.3%

D/05 ¤ Oats (including summer meslin) 4 754.8 5 516.0 16.0%

D/06 ¤ Grain maize 147 330.1 148 872.5 1.0%

D/13/d ¤ Other industrial plants

D/13/d1 * Oilseeds 0.0 0.0 0.0%

D/13/d11 - Of rape and turnip 139 992.4 135 576.6 -3.2%

D/13/d12 - Of sunflower 1 725.7 1 802.6 4.5%

D/13/d13 - Soya 111.0 111.0 0.0%

D/13/d14 - Oilseed of other oleaginous (flax, ...) 248.9 249.0 0.0%

D/18 + Forages plants 0.0 0.0 0.0%

D/18/a ¤ Temporary grass meadows

D/18/a1 * temporary pure grass meadows and ray grass 2 350.3 2 257.6 -3.9%

D/18/a2 * temporary mixed grass leguminous meadows 34 516.2 31 987.6 -7.3%

D/18/b ¤ Other forage plants 0.0 0.0 0.0%

D/18/b1 * Excluding legumineous plants 358.9 359.0 0.0%

D/18/b2 * Legumineous plants 4 695.3 4 492.3 -4.3%

D/18/b3 * maze fodder 80 422.8 90 774.0 12.9%

D/18/b4 * proteaginous peas 1 193.6 1 600.8 34.1%

D/20 + Potatoes and oil seed plants, other arable crops 0.0 0.0 0.0%

D/21 + Fallows 98 247.7 98 248.6 0.0%

Total Arable Land 783 764.1 788 292.7 0.6%

F PERMANENT PASTURES AND MEADOWS 0.0 0.0 0.0%

F/01 + Excluding rough grazing 522 359.1 513 463.0 -1.7%

F/02 + Rough grazing 17 003.6 17 875.0 5.1%

Total permanent pastures and meadows 539 362.7 531 338.0 -1.5%Total permanent crops 30 825.5 30 828.1 0.0%

1 353 952.3 1 350 458.8 -0.3%Total

Crop area (Ha) Evolution (%)

From 2000 to 2005



Results on Saar-Mosel Basin

Nitrogen Balance density for wheat



Saar-Mosel basin: results for WheatExport for Wheat crop Organic fertilizer applied



Saar-Mosel basin : results for Maize

Density of organic fertilizer applied

Density of surplus



NOPOLU Agri : Pros & cons

• Transparent

• Separate processes

• Responsive to hypothesis

• Surpluses checked on large scale areas

• Response at basin scale

• Non circular referencing

• Easily implemented and run

• Evolving

• Detailed processes non modelled, especially (denitrification …)

• Too rough rate spatial variation can introduce strong border difference.



Merci

september 10-11, 2007 workshop "calculating regional gross nutrient balances" various...

Documents

nopolu slide

nuts level slide

agricultural loads

nutrient soil stock

luxembourg slide

exportation rates

nuts scenarios clc surpluses

nopolu system2