Implications of Agricultural Intensification and Climate Change on Water Quality-A Global Assessment
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Uniting agriculture and nature for poverty reduction
Hua Xie*, Claudia Ringler and Gauthier Pitois
Implications of Agricultural Intensification and Climate
Change on Water Quality-A Global Assessment
Sustainability in the Water-Energy-Food Nexus
International Food Policy Research InstituteMay 19-20, 2014 in Bonn, Germany
Uniting agriculture and nature for poverty reduction
Outline
Water quantity and water quality- two sides of one coin
Global water quality assessment – an emerging research field
Non-point source agricultural pollution
Uniting agriculture and nature for poverty reduction
Nitrogen and phosphorus in agriculture
Essential elements for life Excess nitrogen and phosphorus in aquatic environment cause
water quality problems
Photo: Wikimedia Commons.
Uniting agriculture and nature for poverty reduction
Methodology
Land model
Transport model
N&P concentrations in water environment
N&P emissions from agricultural production system on land
Hydrologic model
Stream flow
Uniting agriculture and nature for poverty reduction
Methodology Process-based simulation
of nitrogen and phosphorus in agricultural production
Soil and Water Assessment Tool (SWAT)
Spatial resolution: 0.5˚ × 0.5˚ lat/long grid
Uniting agriculture and nature for poverty reduction
Input data-base period (2000-2005)Data SourceTopography HydroSHEDSSoil HWSDPrecipitation GPCPTemperature GEOS-4 & GEOS-5Solar radiation GEWEX SRB 3.0Fertilizer use1 University of Minnesota (Muller et al.,
2012)Cropland area McGill University (Monfreda et al.,
2008) Nitrogen atmospheric deposition
ORNL
1. Simulated crops: maize, wheat, rice, cotton, sorghum, millet, soybean
Uniting agriculture and nature for poverty reduction
Input data-base period (2000-2005)
Uniting agriculture and nature for poverty reduction
Six agricultural intensification pathways
Input data –scenario analysis
Socioeconomic growth Climate change
Optimistic CSIRO (A1B)Medium MIROC (A1B)
Pessimistic
Uniting agriculture and nature for poverty reduction
Input data –scenario analysis (population)
2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 20502
4
6
8
10
12
CSIRO&MIROC-medium CSIRO&MIROC-optimistic CSIRO&MIROC-pessimistic
Popu
latio
n (b
illio
n pe
ople
)
Uniting agriculture and nature for poverty reduction
Input data –scenario analysis (GDP)
2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 20500
40
80
120
160
200
CSIRO&MIROC-medium CSIRO&MIROC-optimistic CSIRO&MIROC-pessimistic
GD
P (b
illio
n U
SD)
Uniting agriculture and nature for poverty reduction
Input data –scenario analysis (cropland)
2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 20501400
1450
1500
1550
1600
1650
1700
1750
1800
CSIRO-medium CSIRO-optimistic CSIRO-pessimisticMIROC-medium MIROC-optimistic MIROC-pessimistic
Crop
ping
are
a (m
illio
n ha
)
Uniting agriculture and nature for poverty reduction
Input data-scenario analysis (livestock)
2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050200
250
300
350
400
450
500
CSIRO-medium CSIRO-optimistic CSIRO-pessimisticMIROC-medium MIROC-optimistic MIROC-pessimistic
Tota
l mea
t pro
ducti
on (m
illio
n to
ns/y
r)
Uniting agriculture and nature for poverty reduction
Input data-scenario analysis (NUE)
─ Optimistic scenario: 40% NUE improvement
─ Medium scenario: 20% NUE improvement
─ Pessimistic scenario: no NUE improvement
𝑁𝑢𝑡𝑟𝑖𝑒𝑛𝑡𝑢𝑠𝑒𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦 (𝑁𝑈𝐸 )= 𝑐𝑟𝑜𝑝 𝑦𝑖𝑒𝑙𝑑(𝑘𝑔)𝑛𝑢𝑡𝑟𝑖𝑒𝑛𝑡 𝑎𝑝𝑝𝑙𝑖𝑒𝑑(𝑘𝑔)
(Partial Productivity Factor, PFP)
Uniting agriculture and nature for poverty reduction
Input data-climate change
CSIRO change in precipitation
Uniting agriculture and nature for poverty reduction
Input data-climate change
CSIRO change in temperature
Uniting agriculture and nature for poverty reduction
Input data-climate change
MIROC change in precipitation
Uniting agriculture and nature for poverty reduction
Input data-climate change
MIROC change in temperature
Uniting agriculture and nature for poverty reduction
Input data-climate change CNRM-CM3 model with A1B scenario for 2050CNRM-CM3 model with A2 scenario for 2050CNRM-CM3 model with B1 scenario for 2050CSIRO-Mk3.0 model with A1B scenario for 2050CSIRO-Mk3.0 model with A2 scenario for 2050CSIRO-Mk3.0 model with B1 scenario for 2050ECHam5 model with A1B scenario for 2050ECHam5 model with A2 scenario for 2050ECHam5 model with B1 scenario for 2050MIROC 3.2 (medium resolution) model with A1B scenario for 2050MIROC 3.2 (medium resolution) model with A2 scenario for 2050MIROC 3.2 (medium resolution) model with B1 scenario for 2050
(Jones et al., 2009)
Uniting agriculture and nature for poverty reduction
Nitrogen loading-base period
46 million tons/yr
Uniting agriculture and nature for poverty reduction
Phosphorus loading-base period
2.7 million tons/yr
Uniting agriculture and nature for poverty reduction
Nitrogen loading-2050
Baselin
e
CSIRO_optimistic
CSIRO_medium
CSIRO_pessimisti
c
MIROC_optimisti
c
MIROC_medium
MIROC_pessi
mistic
0
10
20
30
40
50
60
70
80
90
46
6268
7368
7481
Mill
ion
tons
/yr
Uniting agriculture and nature for poverty reduction
Phosphorus loading-2050
Baselin
e
CSIRO_optimistic
CSIRO_medium
CSIRO_pessimisti
c
MIROC_optimisti
c
MIROC_medium
MIROC_pessi
mistic
0
0.5
1
1.5
2
2.5
3
3.5
4
2.7 2.8 2.9 3.0 3.13.3 3.4
Mill
ion
tons
/yr
Uniting agriculture and nature for poverty reduction
Nitrogen loading growth rate by country
Uniting agriculture and nature for poverty reduction
Phosphorus loading growth rate by country
Uniting agriculture and nature for poverty reduction
Key messages
The outlook is alarming—large additional N and P emissions from agriculture projected by 2050
What can be done
─ better fertilizer policy and knowledge of fertilizer use
─ land conservation
─ Manure management
Uniting agriculture and nature for poverty reduction
Acknowledgement
This study is supported by Veolia North America under the CGIAR Research Program on Water, Land and Ecosystems (WLE)
Thank you！
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