Agriculture is challenged by large scale issues, such as the impacts of wide land system changes on environmental resources, urging agronomy to evolve. Landscape agronomy emerged as a new perspective to address these issues through spatially-explicit modeling of the interactions between farming practices and natural resources at the landscape level (Benoit, Rizzo et al. 2012 http://bit.ly/LAabstract). In this study we aimed at characterizing agricultural systems focusing major crop sequences and the related fertilization practices to so as to map the potential pressure on water quality.We used 30 years of data to characterize the Meuse and Moselle watersheds (northeastern France). _______________ suggested citation: Rizzo D, Godfroy G, Benoît M (2014) Temporospatial analysis of agricultural systems at regional watershed level: 30 years of data to characterize the Meuse and Moselle watersheds, France. 13th Congress of the European Society for Agronomy, Session 5: farming system design. Debrecen (Hungary). http://bit.ly/1oG0vI2

1. TEMPOROSPATIAL ANALYSISOF AGRICULTURAL SYSTEMSAT REGIONAL WATERSHED LEVEL
30 YEARS OF DATA TO CHARACTERIZE THE MEUSE & MOSELLEWATERSHEDS (FRA)
Session 5: FARM SYSTEM DESIGN

2. ••Temporospatialanalysis of agricultural systems •RIZZO et al. ••ESA Session 5•August 26th2014••
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Aim & background
Davide
Guillaume
Marc
SAD-ASTER Mirecourt
1
RIZZO
GODFROY
BENOÎT
Water protection issues urge agronomy to deal with large scale impacts on environmental resources
Characterizing agricultural systems
major land use sequences & related fertilization practices
map of potential pressure on water quality
Agricultural dynamics in 23 primary watersheds (north-eastern France
~24000 Km2)
Rizzo, Godfroy, Benoît2013 DynAMM’Eauprojectreport

3. ••Temporospatialanalysis of agricultural systems •RIZZO et al. ••ESA Session 5•August 26th2014••
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A landscapeagronomyperspective…
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…to address interactions between farming practices and natural resource (e.g., water) management through spatially-explicit modeling at the landscape level
wheat
barley
rapeseed
maize
grassland
% of landcover (on totalstudyarea) landuse changes?
time and spatial regional scales
relevant for watershed managers
30%
50%
23%
47%

4. MATERIAL & METHODS

5. ••Temporospatialanalysis of agricultural systems •RIZZO et al. ••ESA Session 5•August 26th2014••
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Method #1: a time dominant approach
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Space dominant
sequenceof (landcover) images
Software for LUCC analysis generally implement unsupervised clustering on spatial entities based on space-dominant attributes
Data mining approaches involving clustering of sequences allowed knowledge extraction to get a landscape description in terms of land-use patterns
Time dominant
image of (landcover) sequences
logics of human driven activities
Marietal.2006Temporalandspatialdatamining

6. ••Temporospatialanalysis of agricultural systems •RIZZO et al. ••ESA Session 5•August 26th2014••
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4
Method #2: clustering land cover return time
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possible return time every
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presence
5 years
wheat
barley& othercereals
rapeseed
maize
othercrops
set-aside
grasslands(3 types)
artificial& undet.
semi-natural
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29 possible combinations
over 15 5-year periods
of major landcovers
23 583 points
TerUti1981-1990
23 586 points
TerUti1992-2003
11 588 points
TerUti-Lucas2006-10
absence
4 years
3 years
2 years
5-year sliding windows
TerUtiisa yearlyland use stratifiedsamplingon a regulargrid
Hierarchicalclusteringon Principal Components
FactoMiner, Lêet al., 2008
cf. Slak& Lee, 2003
data-mining
multivariate analysis

7. ••Temporospatialanalysis of agricultural systems •RIZZO et al. ••ESA Session 5•August 26th2014••
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5
Method #3: evaluation of nitrogen pressure
plot balanceper year
Using the survey «Pratiquesculturales» (PK)
Balance Azotée Spatialisée des Systèmes de Culture de l’Exploitation
BASCULE
Benoît, 1992
INPUT
OUTPUT
fertilization
(mineral+ organic)*
yieldxN content*
* Integrated withliterature
Plot level information on:
-fertilization & yield (crop of the year)
-preceding crop
TerUtisubsample • • 1994, 2001 & 2006
plot BASCULE
average (median) of plot balance over multiple years
area BASCULE
Sum of the (positive) plot BASCULE over the target area *
NB negative values do not subtract
*farm, farm system, watershed, cluster, etc.
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time
space

8. RESULTS

9. ••Temporospatialanalysis of agricultural systems •RIZZO et al. ••ESA Session 5•August 26th2014••
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Results#1: major landuse dynamics
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barleyseq.
4 cropseq.
crops& urban
semi-natural
rapeseed& wheat
mixedfarming
crops& urban
semi-natural
80’s
90’s
00’s
1981-1990
1992-2003
2006-2010
6 clusters
4 regions
2 periods

10. ••Temporospatialanalysis of agricultural systems •RIZZO et al. ••ESA Session 5•August 26th2014••
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axe 1 (56% var.)
no crops
Results#1bis: cluster dynamicper watershed
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max crops
2ndaxis (31% var.)
no pastures
perm. pastures
1staxis (56% var.)

11. ••Temporospatialanalysis of agricultural systems •RIZZO et al. ••ESA Session 5•August 26th2014••
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Results #2: evaluation of nitrogen pressure
7
BASCULE
global average
other land uses
Cluster BASCULE
example of 2006 data
30 ] 38[ 51
30 ] 39 [ 53
39 ] 50 [ 64
44 ] 63 [ 100
Kg N / Ha / year
min ] median[ max
threshold for ground water pollution (≈ 50 mg NO3-/ liter)

12. CONCLUSIONS

13. ••Temporospatialanalysis of agricultural systems •RIZZO et al. ••ESA Session 5•August 26th2014••
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Conclusions
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syntheticrepresentation of long time series over a wide area
spatial explicit evaluation that can be coupled with vulnerability maps
scalableon available input data(e.g., within the watershed)
(mitigating) effects of contextual land uses(e.g., forest)
inclusion of other practices(e.g., catch crops, pesticides)
influence of specific climate conditionsand other potential practice drivers
using custom group of pixels to better understand the land system architecture
Leverages…
…and potentials
to match action spaces of relevant managers and coordinating landscape level decision-making
Turner II et al., 2013 Land system architecture
Future applications
Reinterpreting watershed hierarchy (Strahler)
Rizzo et al., 2013 Farming systems designing landscapes
Lazraket al., in prep. for SAGEO congress

14. Thank you for the attention
ridavide@gmail.com
bit.ly/ESA_2014
@pievarino
REF
DavideRIZZO

15. ••Temporospatialanalysis of agricultural systems •RIZZO et al. ••ESA Session 5•August 26th2014••
bit.ly/ESA_2014
DATA| TerUti (1981-2004)
.015
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2
3
4
5
13
14
15
16
17
4 700 maillespour la couverture du territoire…
la maille
la photo aerienne
… 8 positions de photographiespar maille …
… 36 points à enquêter
par photographie
6
18
7
8
9
10
11
12
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
553 250 points 1981-1990
555 903 points 1992-2003
154 501 points 2004
about 0.5 million of points

16. ••Temporospatialanalysis of agricultural systems •RIZZO et al. ••ESA Session 5•August 26th2014••
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DATA|TerUti-Lucas (2006-...)
.016
Sous-echantillon
18 km
(Lucas) 1
6 km
1+2
6 kmdoublé
1+2+3
maître 3 km
1+2+3+4
11
12
13
14
15
21
22
23
24
25
31
32
33
34
35
41
42
43
44
45
51
52
53
54
55
18 Km
300 m

17. ••Temporospatialanalysis of agricultural systems •RIZZO et al. ••ESA Session 5•August 26th2014••
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Analyses des successions de cultures
22
cluster 6CB(O) ‘90
cluster 3 polycult.élev.
cluster 4cultures ’90 (urbain)
sN-
Blé -
Jach-
PPP -
autres -
P.Art-
P.Tem-
Maïs -
Colza -
Orge -
Art -
Maïstête de rotation
sN-
Blé -
Jach-
PPP -
autres -
P.Art-
P.Tem-
Maïs -
Colza -
Orge -
Art -
sN-
Blé -
Jach-
PPP -
autres -
P.Art-
P.Tem-
Maïs -
Colza -
Orge -
Art -
sN-
Blé -
Jach-
PPP -
autres -
P.Art-
P.Tem-
Maïs -
Colza -
Orge -
Art -
sN-
Blé -
Jach-
PPP -
autres -
P.Art-
P.Tem-
Maïs -
Colza -
Orge -
Art -
sN-
Blé -
Jach-
PPP -
autres -
P.Art-
P.Tem-
Maïs -
Colza -
Orge -
Art -
CBO, maïs-blé
CBO, MBCB
1992-1996
1999-2003