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Faculty of Environmental Sciences, Institute of Hydrology and Meteorology
German irrigation project SAPHIR
Sabine SeidelICID Korea, 17 September 2014
Project overview Virtual �eld Regional crop water production functions and available water
Table of Contents
1 Project overview
2 Virtual �eld
3 Regional crop water production functions and available water
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Project overview Virtual �eld Regional crop water production functions and available water
SAPHIR: Saxonian Platform for High Performance IRrigation
• EU �nanced project (2012-14), 7 young scientists employed
• focus area: Saxony
• 20% of area was irrigated before 1990, now 11% of vegetables
• more dry periods in spring/summer, higher variability of rainfall
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Project overview Virtual �eld Regional crop water production functions and available water
Project overview• Main intention: get information about potential yields andirrigation water reqirements under climate change
sortenspezifischePflanzenparameter
standortspezifischeBodenparameter
virtuelles Feld
ExperimenteOptimierung des Bewässerungsmanagements
ÖkonomieFeldskala
ÖkonomieRegionalskala
€
Q
Q
YUnsicherheitKlima
UnsicherheitBoden
KostenKosten
BewässerungswürdigkeitZusatzwasserbedarf
Gewinnfunktion
Erträge Erträge
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Project overview Virtual �eld Regional crop water production functions and available water
Virtual �eld
• Simulation of processes in the �eld (model↔�eld observations)
• use of mechanistic models→ spatial and temporal transfer
2
INTRODUCTION The loss of agrochemicals into aquifers and surface waters in humid regions is an inevitable consequence of intensive agriculture. In large parts of Europe, for instance, the input of nitrogen to agricultural systems and subsequent losses are so large that they constitute a threat to both the quality of surface and ground waters (EEA, 1995). In most agricultural systems the main loss of nitrogen is due to leaching of nitrate from the fields. The fact that laboratory and field measurements necessary for assessment of nitrogen leaching from agricultural fields are expensive have prompted the development of agro-ecosystem models capable of simulating the nitrogen dynamics in agricultural soils and in particular simulating the leaching. In Denmark this led to the development of the Daisy model (Hansen et al., 1990, 1991a). This model has since then been used extensively (e.g. Blicher-Mathiesen et al., 1990; Blicher-Mathiesen et al., 1991; Hansen et al., 1991b; Hansen et al., 1992; Hansen and Svendsen, 1994, 1995a,b,c; Hansen et al., 1999, Jensen and Østergaard, 1993; Jensen et al., 1992; Jensen et al., 1993; Jensen et al., 1994a,b; Jensen et al., 1996; Magid and Kølster, 1995; Mueller et al., 1997; Petersen et al., 1995; Refsgaard et al., 1999, Styczen and Storm, 1993a,b). The model applications comprise both scientific studies and management related studies aimed at decision support. In addition, the model has been validated in a number of major comparative tests (Vereecken et al., 1991; Hansen et al., 1991a,c; Willigen, 1991; Diekkrüger et al., 1995; Svendsen et al., 1995; Smith et al., 1997; Jensen et al., 1997). Hence, Daisy can be considered a well-tested model. Daisy is a one-dimensional agro-ecosystem model that, in brief, simulates crop growth, water and heat balances, organic matter balance, the dynamics of ammonium and nitrate in agricultural soil based on information on management practices and weather data, Fig. 1. Recently, the simulation of the fate of pesticides has been included in the model. The simulation of the organic matter balance and the nitrogen dynamics is strongly interconnected, hence the organic matter model is considered an integral part of the overall nitrogen balance model. Weather data are used as driving variables. The minimum data requirement is daily values of global radiation, air temperature and precipitation. However, much more detailed information can be utilized by the model, e.g. hourly values of global radiation, air temperature, relative humidity, wind speed, and precipitation. The present chapter offers a relatively detailed description of the Daisy model. Figure 1. Schematic representation of the agro-ecosystem model Daisy. The model comprises three main modules, viz. a bioclimate, a vegetation, and a soil component.
Mac
ropo
res
Pes
ticid
e
Mac
ropo
res
Nitr
ate
Mac
ropo
res
Am
mon
ium
Mac
ropo
res
Org
anic
Mat
ter
Mac
ropo
res
Hea
t
Soi
l m
atrix
Mac
ropo
res
Wat
er
SoilUptake
Turnover
Sorption
Transport
Phase change
BioclimateSVAT
Light distributionInterception
Snow accumulation
VegetationGrowth
PhotosynthesisRespiration
Uptake
Numeric layer
Parameters:Soil Data
VegetationData
Driving variables:
Weather DataManagement
Data
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Project overview Virtual �eld Regional crop water production functions and available water
Irrigation experiments
• in 2013/14 conduction of �eld experiments with white cabbage
• three di�erent scheduling strategies were tested:
• irrigation schedules using di�erent kc values (SWB)• automatically drip irrigated using a tensiometer∗(T)• using the mechanistic model Daisy calibrated against �eld dataof 2012/13∗(D)
• T performed best, SWB overestimated crop waterrequirements
∗an irrigation of 15mm was triggered when a soil tension of -250 hPa (or400 hPa) at 30 cm soil depth was reached
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Project overview Virtual �eld Regional crop water production functions and available water
Experimental data collection
• leaf area index (LAI)
• plant heights
• stomatal conductivity
• biomass partitioning and yield
• soil tension (every 15min, at 30, 60 and 90 cm soil depth)
Figure: Tensiometers installed in white cabbage
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Project overview Virtual �eld Regional crop water production functions and available water
Model calibration - plant variables
0
10
20
30
40
50
60
13 49 55 62 70 74 83 90 97 105
118
126
day after transplanting
plan
t hei
ght [
cm]
Abbildung: Observed (boxplots) and simulated plant heights (2013)
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Project overview Virtual �eld Regional crop water production functions and available water
Model calibration - soil hydraulic dynamics
77 91 108 122 138
0
10
20
30
40
days after transplanting
rain
fall
and
irrig
atio
n [m
m] -600
-500
-400
-300
-200
-100
0
soi
l ten
sion
Ψ [h
Pa]
predictedobserved
Abbildung: Observed and simulated soil tension at 30 cm soil depth
• �eld data+mechanistic crop model→ adequate simulation ofprocesses, transfer (climate change scenarios)
• overcome gap between scienti�c research and farmers
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Project overview Virtual �eld Regional crop water production functions and available water
Site analysis - economic decision support tool• generation of synthetic climate data based on observed data• using the stochastic weather generator LARS-WG• drip irrigation water requirements ranged from 30 to 195mm
0
0.2
0.4
0.6
0.8
1
7 7.5 8 8.5 9 9.5 10
ψ(y
ield
)
DM yield in t/ha
histogramkde
Figure: Simulated yield (DM, in t ha−1) of white cabbage (300 years)10 / 16
Project overview Virtual �eld Regional crop water production functions and available water
Simulation based estimation of Kc values
• 300 Kc curves for 300 synthetic years (fully irrigated cabbage)
• red curve: applied in 2014; green: recommended (SWB)
0 20 40 60 80 100 1200
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
Tage nach Pflanzung
k c
90 %80 %50 %medianGeisenheim
14.5. 2.6. 23.6. 13.7. 2.8. 22.8. 11.9.
95% Quantil
25% Quantil Bereiche:
90% Quantil
75% Quantil
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Project overview Virtual �eld Regional crop water production functions and available water
Regional crop water production functionsBasics Generating SCWPF Regional SCWPF SCWPF variation Water availabilityInput data
regions with climatic water balanceP − ETa < 200mm
agricultural regions
masked soils and masked raster cells fromdisaggregated climate data
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• Mask: CWB<200mm, agriculture, raster: 5x5 km, 5 mostimportant soils per masked cell, several crops
• climate: WEREX V, soil: Bodenkonzeptkarte, land use: InVeKoS
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Project overview Virtual �eld Regional crop water production functions and available water
Potato crop water production function
• simulated using the physically-based crop growth model Daisy
050
100150
200250
300350
400
8
10
12
14
16
180
0.2
0.4
0.6
0.8
1
I in mm/a
DM Y in t/ha
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Project overview Virtual �eld Regional crop water production functions and available water
Potato yield (no irrigation, WEREX A1b EH5)
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Project overview Virtual �eld Regional crop water production functions and available water
Potato yield with irrigation (210 mm, WEREX A1b EH5)
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Project overview Virtual �eld Regional crop water production functions and available water
Regionalised water availability
• Planned: combination with progonosis of water balance
• water balance from KliWES www.wasserhaushaltsportal.sachsen.de
• RD (in�ow to direct runo� storage) and RG2 (in�ow to slow aquifer- interpretation as groundwater recharge)
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