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TEMPOROSPATIAL ANALYSISOF AGRICULTURAL SYSTEMSAT REGIONAL WATERSHED LEVEL
30 YEARS OF DATA TO CHARACTERIZE THE MEUSE & MOSELLE WATERSHEDS (FRA)
Session 5: FARM SYSTEM DESIGN
•• Temporospat ia l ana lys i s o f agr i cu l tura l systems • RIZZO et al. •• ESA Session 5 • August 26th 2014 •• bit.ly/ESA_2014
Aim & background
Davide
Guillaume
Marc
SAD-ASTER Mirecourt
1
RIZZOGODFROY
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~24 000 Km2)
Rizzo, Godfroy, Benoît 2013 DynAMM’Eau project report
•• Temporospat ia l ana lys i s o f agr i cu l tura l systems • RIZZO et al. •• ESA Session 5 • August 26th 2014 •• bit.ly/ESA_2014
A landscape agronomy perspective…2
…to address interactions between farming practices and natural resource (e.g., water)
management through spatially-explicit modeling at the landscape level
wheatbarleyrapeseedmaizegrassland
% of land cover (on total study area) land use changes?
time and spatial regional scalesrelevant for watershed managers
30%
50%
23%
47%
MATERIAL & METHODS
•• Temporospat ia l ana lys i s o f agr i cu l tura l systems • RIZZO et al. •• ESA Session 5 • August 26th 2014 •• bit.ly/ESA_2014
Method #1: a time dominant approach3
Space dominant
sequence of (land cover)
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 (land cover)
sequences
logics of human driven activities
Mari et al. 2006 Temporal and spatial data mining
•• Temporospat ia l ana lys i s o f agr i cu l tura l systems • RIZZO et al. •• ESA Session 5 • August 26th 2014 •• bit.ly/ESA_2014
4
Method #2: clustering land cover return time
81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 08 10
01 02 03 15
possible return time every© x x x xx x x x ©© x x x ©x © x x xx x x © x© x x © xx © x x ©x x © x x© x © x ©x © x © x
presence
5 years wheatbarley & other cerealsrapeseedmaizeother cropsset-asidegrasslands (3 types)artificial & undet.semi-natural
04 05 06 07 08 09 10 11 12 13 14
29 possible combinations over 15 5-year periods
of major land covers
23 583 points
TerUti 1981-1990
23 586 points
TerUti 1992-2003
11 588 points
TerUti-Lucas 2006-10
absence
4 years
3 years
2 years
5-year sliding
windows
TerUti is a yearlyland use stratified
sampling on a regular grid
Hierarchical clustering on Principal Components
FactoMiner, Lê et al., 2008
cf. Slak & Lee, 2003
data-mining
multivariate analysis
•• Temporospat ia l ana lys i s o f agr i cu l tura l systems • RIZZO et al. •• ESA Session 5 • August 26th 2014 •• bit.ly/ESA_2014
5
Method #3: evaluation of nitrogen pressure
plot balance per year
Using the survey « Pratiques culturales » (PK)
Balance Azotée Spatialisée des Systèmes de Culture de l’Exploitation
BASCULE Benoît, 1992
INPUT OUTPUT
fertilization(mineral + organic)* yield x N content*
* Integrated with literature
Plot level information on: - fertilization & yield (crop of the year)- preceding crop
TerUti subsample • • 1994, 2001 & 2006
plot BASCULEaverage (median) of plot
balance over multiple years
area BASCULESum of the (positive) plot
BASCULE over the target area *
NB negative values do not subtract
*farm, farm system, watershed, cluster, etc.
1
2
3
time
space
RESULTS
•• Temporospat ia l ana lys i s o f agr i cu l tura l systems • RIZZO et al. •• ESA Session 5 • August 26th 2014 •• bit.ly/ESA_2014
Results #1: major land use dynamics6
barley
seq.
4 crop
seq.
crops & urban
semi-natural
rapeseed& wheat
mixedfarming
crops & urban
semi-natural
80’s 90’s 00’s
1981-1990 1992-2003 2006-2010
6 clusters4 regions2 periods
•• Temporospat ia l ana lys i s o f agr i cu l tura l systems • RIZZO et al. •• ESA Session 5 • August 26th 2014 •• bit.ly/ESA_2014
axe 1 (56% var.)
no crops
Results #1bis: cluster dynamic per watershed
6
max crops
2n
dax
is (
31
% v
ar.)
no pastures
perm. pastures
1st axis (56% var.)
•• Temporospat ia l ana lys i s o f agr i cu l tura l systems • RIZZO et al. •• ESA Session 5 • August 26th 2014 •• bit.ly/ESA_2014
Results #2: evaluation of nitrogen pressure7
BASCULEglobal 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)
CONCLUSIONS
•• Temporospat ia l ana lys i s o f agr i cu l tura l systems • RIZZO et al. •• ESA Session 5 • August 26th 2014 •• bit.ly/ESA_2014
Conclusions8
synthetic representation of long time series over a wide area
spatial explicit evaluation that can be coupled with vulnerability maps
scalable on 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
Lazrak et al., in prep. for SAGEO congress
•• Temporospat ia l ana lys i s o f agr i cu l tura l systems • RIZZO et al. •• ESA Session 5 • August 26th 2014 •• bit.ly/ESA_2014
DATA| TerUti (1981-2004)
.015
1 2 3 4 5
13 14 15 16 17
4 700 mailles pour la couverture du
territoire…
la maille
la photo aerienne
… 8 positions de photographies
par maille …
… 36 points à enquêterpar 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 1981-1990555 903 points 1992-2003154 501 points 2004
about 0.5 million of points
•• Temporospat ia l ana lys i s o f agr i cu l tura l systems • RIZZO et al. •• ESA Session 5 • August 26th 2014 •• bit.ly/ESA_2014
DATA| TerUti-Lucas (2006-...)
.016
Sous-echantillon
18 km (Lucas) 1
6 km 1+2
6 km doublé 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
•• Temporospat ia l ana lys i s o f agr i cu l tura l systems • RIZZO et al. •• ESA Session 5 • August 26th 2014 •• bit.ly/ESA_2014
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 -
Maïs tête 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
19
92
-19
96
19
99
-20
03