Irrigation water demand of common bean on field and regional scale under varying climatic conditions
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B Meteorologische Zeitschrift, Vol. 25, No. 4, 365–375 (published online December 3, 2015)
© 2015 The authors
BioMet
Irrigation water demand of common bean on field and
regional scale under varying climatic conditions
Michael Wagner∗ , Sabine J. Seidel and Niels Schütze
Institute of Hydrology and Meteorology, Technische Universität Dresden
(Manuscript received April 30, 2015; in revised form September 21, 2015; accepted September 29, 2015)
Abstract
Crop irrigation plays an important role in the world’s food production and its role is expected to increase still
further. For policy makers, the quantification of the irrigation water demand and the water availability on a
regional scale is crucial. In the project ‘SAPHIR’, a new stochastic framework was developed to upscale
crop yield and crop water demand from irrigation experiments with common bean to the regional scale
using the one-dimensional mechanistic crop model Daisy. The crop model parameters – derived based on a
comprehensive experimental data collection and a sound calibration of the crop model – were used to simulate
potential bean yield, yield reduction due to drought stress, and crop water demand in mid and northern Saxony,
Eastern Germany, using the dominant soil characteristics. The stochastic relationship between irrigated water
and crop yield (stochastic crop water production function) enabled the prediction of the crop productivity
on a regional scale. Furthermore, the available water resources for irrigation on the catchment scale were
compared to the predicted irrigation water requirements to estimate the degree of local water self sufficiency.
The simulation results show that an irrigation of common bean has high yield effects especially in locations
with low precipitation during the growing season or for soils with a low water storage capacity. Especially in
the drier northern parts of Saxony with its lower soil water storage capability, a decrease in non-irrigated fresh
matter bean yield up to 40 % is predicted for the future. Irrigation and the projected increasing temperature
can enhance the bean yield in southern Saxony. However, the required amount of irrigation water in northern
Saxony can only be delivered by down to 20 % and less from the local precipitation. The presented framework
enables policy makers to compare water demand and available water which allows a precise estimation of
relevant indicators for a considered region, e.g., the degree of local water self sufficiency.
Keywords: irrigation, crop growth modelling, stochastic crop water production function, regionalization,
common bean, climate change
1 Introduction for plants. Extensive dry periods increase the impor-
tance of the water storage capacity of the soil. Moreover,
Modern agriculture now feeds over 7,000 million peo- the importance of crop irrigation will increase world-
ple. With the projected demographic growth, a further wide (Tilman et al., 2002). Haverkort and Verhagen
increase in agricultural output is essential for global (2008) recommend to invest in breeding of new varieties
political and social stability and equity. Beside all un- adapted to extremes in weather (heat, drought) and in ir-
certainties concerning the future climate there exists a rigation equipment.
broad agreement that the worldwide climate will warm Drought and high temperature stress are consid-
up (van der Linden and Mitchell, 2009). A more un- ered to be the two major environmental factors limit-
balanced precipitation regime with an increased number ing crop growth and yield (Prasad et al., 2008). Tem-
of days with heavy precipitation is expected in Europe perature stress has devastating effects on plant growth
(Sillmann et al., 2013) due to a higher water holding and metabolism, as these processes have optimum tem-
capacity in a warmer atmosphere (Semenov, 2009). Ac- perature limits in every plant species (Hasanuzzaman
cording to the authors, more dry periods in spring and et al., 2013). Common bean (Phaseolus vulgaris L.),
summer are expected. Higher temperatures as well as the focus crop of this study, gained markedly lower
the projected higher global radiation lead to a higher fruit set when temperature was elevated during flower
evaporative demand of the atmosphere, but also to a development (Gross and Kigel, 1994). Bean yield is
higher yield potential for some crops if enough water is also influenced by the duration of the vegetative and
available (Nendel et al., 2014). Precipitation extremes reproductive stages which highly depends on tempera-
may harm the crop mechanically and produce events ture (Rosales-Serna et al., 2004), and the redistribu-
with more direct runoff whose water is not available tion of assimilates into economically important organs.
Drought is another major yield constraint in common
∗ Corresponding author: Michael Wagner, Institute of Hydrology and Mete- bean (Cuellar-Ortiz et al., 2008; Porch et al., 2009;
orology, Technische Universität Dresden, 01062 Dresden, Germany, e-mail: Rosales et al., 2012). Beans are particularly susceptible
michael.wagner@tu-dresden.de to drought stress in the reproductive stage during flow-
© 2015 The authors
DOI 10.1127/metz/2015/0698 Gebrüder Borntraeger Science Publishers, Stuttgart, www.borntraeger-cramer.com366 M. Wagner et al.: Irrigation of common bean Meteorol. Z., 25, 2016
Figure 1: Work flow within SAPHIR project and the corresponding sections. The water availability was estimated in the KliWES project.
ering (Graham and Ranalli, 1997) and in the terminal conducted with several crops at three experimental sites
stages (Rosales et al., 2012). in Germany. In this study, common bean was chosen
The study area of this article is Saxony, a federal exemplary. Model parameters were derived based on
state of Germany located in the east. Especially in the field irrigation experiments using the crop model Daisy
northern and eastern parts of Saxony with sandier soils (Abrahamsen and Hansen, 2000). The crop model pa-
and precipitation equal to or below 600 mm a−1 , crop rameters and the stochastic relationship between irri-
irrigation is relevant. According to the Land Statistical gated water and simulated crop yield (SCWPF - stochas-
Office, common bean was cultivated on an area of about tic crop water production function) served as a basis for
300 ha in Saxony in 2014. Due to the high drought the regional simulations. The stochastic approach ex-
susceptibility of common bean, 70 % of the Saxonian plained in detail in Section 2.6 allows the calculation of
area cultivated with that crop were irrigated in 2011 the probability of a specific crop yield at a certain irri-
(Jäkel, 2013). gation water amount. The intersection of the crop water
The objective of this study was to quantify the ir- demand and the corresponding water availability allow
rigation water demand exemplary for one crop, com- to derive the degree of local water self sufficiency. The
mon bean, and intersect it with the available water in latter was estimated for mid and northern Saxony. Fig. 1
Saxony. Crop growth model simulations are widely ac- depicts the work flow of all methods, grouped in field
cepted tools for projections of climate change impacts and regional scale, and annotates the appropriate sec-
on crop yields. Asseng et al. (2013) simulated wheat tions. The procedure will be explained in detail in the
growth under climate change. Regional studies for ce- following. Fertilizing effects on yields due to changes
reals and maize also include the simulation of the water of the atmospheric carbon dioxide concentrations in the
demand for irrigated crops (Nendel et al., 2014; Köst- future and yield increase due to technological improve-
ner et al., 2014). For upscaling from field to regional ments are not considered.
scale, a classification and identification of homogeneous
simulation units is a common method. Wesseling and 2.1 Experimental site, design and data
Feddes (2006), amongst others, used SWAP model and collection
GIS to gather all information for regional water pro-
ductivity. Leclère et al. (2014) carried out an analogue Common bean (cultivar Stanley) was cultivated to esti-
simulation approach for simulating worldwide impacts mate the irrigation water requirements. The experimen-
of climate change on agricultural systems and possible tal site described in this study is located in Pillnitz, Dres-
adaptations to it on a larger scale. A simulation approach den in Germany (51 ° N, 13.9 ° E and 120 m altitude).
is used here, too. The stochastic relationship between ir- The experimental site shows an average annual precip-
rigated water and simulated crop yield based on field ex- itation of about 650 mm and an average temperature of
periments and crop model simulations was used to deter- 10.4 °C. The loamy sand soil is composed of 35 % sand,
mine the crop productivity on the field and the regional 39.5 % silt and 25.5 % clay (soil depth from 0–60 cm)
scales. The intersection of water demand for irrigation with the sand content increasing in deeper soil depths
and available water allows the calculation of the degree and a deep ground water table.
of local water self sufficiency. Beans were sown on 13 May 2014 and harvested af-
ter 77 days on 29 July 2014. The crop rows were spaced
2 Materials and methods 50 cm apart with a between-plant spacing of 6.1 cm. Two
In the project ‘SAPHIR – Saxonian Platform for High sprinkler irrigated (SWB and SVAT) and one rain-fed
Performance Irrigation’, irrigation experiments were treatment (RF) were tested (see Table 1). Each treatmentMeteorol. Z., 25, 2016 M. Wagner et al.: Irrigation of common bean 367
was replicated four times. A linear move irrigation sys- Water flow in the soil is described by the Richards equa-
tem (Gierhake, Germany) was used to sprinkler irrigate tion (Richards, 1931). The soil heat model simulates
the plots. Irrigation scheduling was based on the soil frost as well as thaw processes in the soil. Daisy is very
water balance approach according to Paschold et al. flexible and allows conditional crop management dec-
(2010) (treatment SWB) and a real-time simulation- larations including irrigation scheduling based on pre-
based irrigation scheduling approach (treatment SVAT, dicted soil water potential similar to treatments T 200
described in detail in Seidel et al. (accepted)). More- and T 350. As Daisy’s source code is open, it was com-
over, a NMC-Pro drip irrigation system (Netafim, Is- piled on a high performance computing system to simu-
rael) with a discharge rate of 1.6 l h−1 per emitter and a late regional crop growth.
emitter spacing of 30 cm was installed in two treatments
in a field nearby. The drip lines were placed next to 2.3 Model calibration and validation
each crop row. In these treatments, irrigation of 10 mm
was triggered automatically when a soil water potential The experimental data (yield, partitioned above-ground
of −200 hPa (treatment T 200) or -350 hPa (treatment biomass, LAI, plant height, development stage, and soil
T 350) at a 20 cm soil depth was reached. The plants water potential measurements) of the five treatments
were fertilized and insect pests were controlled with pes- were used for model calibration and validation. Hereby,
ticides according to standard grower practice. the treatments RF and SWB were used for model cali-
The comprehensive plant data collection included bration, whereas the treatments SVAT, T 350 and T 200
measurements of the leaf area index (LAI), measured were used for validation (see Table 1). For this study,
with a AccuPAR LP-80, leaf stomatal conductance (gs ) the model Daisy was set up one dimensionally accord-
measured with a SC-1 Steady State Leaf Porometer ing to the conducted experiment. The model calibration
(both Decagon Devices Inc., USA) and plant heights, but included the fitting of several sensitive plant parameters
also measurements of the above-ground biomass parti- and the soil hydraulic parameters namely saturated hy-
tioned into reproductive organs, leafs and stem during draulic conductivity and the Mualem-van Genuchten pa-
the growing season. At harvest, fresh matter and dry rameters (van Genuchten, 1980). The soil hydraulic
matter content were measured for samples of all repli- parameters were calibrated in advance using measured
cates. According to Vazifedoust et al. (2008), the crop soil water potential data and plant data of white cab-
water productivity (WPobs ) was estimated as the actual bage (which was cultivated on the same plots in 2013
dry matter pod yield divided by the sum of irrigation wa- and 2014), together with model Daisy and the global op-
ter applied and precipitation during the growing season. timization algorithm AMALGAM (Vrugt and Robin-
Moreover, the soil water potential was measured contin- son, 2007), see Seidel et al. (accepted). The bean plant
uously with tensiometers (T4e, UMS, Germany) in treat- parameters were derived using the mentioned Mualem-
ments SWB, SVAT, T 200 and T 350 in three soil depths. van Genuchten parameters. Only the saturated hydraulic
The weather data were collected at the research site and conductivity and the soil layering had to be adapted
the precipitation measured by a Hellmann precipitation slightly to the bean experiment. Possible yield declines
gauge was corrected according to Richter (1995). due to nutrient limitation were excluded.
2.4 Area of investigation
2.2 Crop growth model description and setup
The investigated area is situated in Saxony, Eastern Ger-
For simulating crop growth including root water uptake many. According to the Statistical Office of Saxonia,
and evapotranspiration, the crop model Daisy (Abra- about 50 % of Saxony (904,200 ha) was agricultural land
hamsen and Hansen, 2000; Abrahamsen, 2012) was in 2014. On 715,200 ha of the arable land, crops like ce-
applied. Daisy simulates plant growth, soil water dy- reals, maize, rape and potatoes were produced. As the
namics, soil temperature, and the carbon and nitrogen southern mountainous part is not likely to need irrigation
cycle of the root zone. The crop development stage is due to higher precipitation values and extensive grass-
related to its morphological appearance and depends land cultivation, a criterion was developed to mask the
on temperature and day length. The LAI is a function area, where irrigation is potentially required under cli-
of the specific leaf area and the leaf weight. The root mate change. Based on HAD (2000), the agricultural
system is characterized by the root weight, the rooting area was intersected with the recent climatic water bal-
depth, and the root density distribution. The calcula- ance of CWB < 200 mm a−1 calculated as precipitation
tion of daily plant assimilation is based on Goudriaan minus FAO reference crop evapotranspiration (Allen
and van Laar (1978). The evapotranspiration is esti- et al., 1994). Only areas with both conditions were con-
mated based on the Penman-Montheith formula (Pen- sidered in the following. The resulting 5 by 5 km grid
man, 1948; Monteith, 1965). Under water-limited cells are shown in Fig. 2.
conditions, the actual photosynthesis is reduced propor- In the considered northern part of Saxony, 1,248
tionally to the quotient of actual to potential evapotran- different soil types are defined according to the Bo-
spiration. The surface water balance considers snow, in- denkonzeptkarte (Saxonian State Office for Environ-
terception, evaporation, infiltration and surface runoff. ment, Agriculture and Geology). For one grid cell, the368 M. Wagner et al.: Irrigation of common bean Meteorol. Z., 25, 2016
used. WEREX V provides station based data derived
from the global climate model ECHAM5 (Roeckner
et al., 2003) of scenario A1B for the years 1961–2100.
The years 1961–2000 have similar statistical proper-
ties as the observed climate. To regionalise WEREX V-
data, external drift kriging was used for mean, minimum
and maximum temperatures, global radiation, vapour
pressure and wind speed. Precipitation in WEREX V
has a spatial cover ratio exceeding observed ratios
for smaller precipitation events. Therefore, a non-
interpolating nearest neighbour procedure was chosen
to regionalise precipitation. Due to the high density of
precipitation gauges no inaccuracy in the regional repre-
sentation is assumed. A resolution of 5 by 5 km is used.
For statistical inferences, we refer to 30-year periods.
1961–1990 (P1) is used as recent climate, 1991–2020
(P2) is the actual situation, 2021–2050 (P3) is the near
Figure 2: Area of investigation, Saxony, Eastern Germany. The grid- future and 2071–2100 (P4) the far future projection.
ded area (grey lines, 5 by 5 km) is characterized by a climatic water Each time period is thought to be statistically homoge-
balance CWB < 200 mm a−1 and agricultural land. The experimen- neous to allow statistical analyses. In future scenarios
tal site Pillnitz is located 5 kilometres south east of Dresden. The the climate will become warmer and precipitation tends
coloured elevation ranges from 30 m to 1200 m. to decrease in spring and summer (see Section 3.3). The
other effect is an increasing uncertainty with time about
the precise development of implied anthropogenic car-
bon dioxide emissions and therefore the whole climate
(see van der Linden and Mitchell, 2009).
2.6 Stochastic crop water production
functions
Stochastic crop water production functions (SCWPF)
describe the relationship between simulated crop yield Y
and irrigation water applied I for one specific crop and
one specific site. In arid climates with small amounts of
plant available water from precipitation, SCWPFs can
be used to estimate optimal calendar-based irrigation
schedules (Schütze et al., 2012). However, in semi-
arid and humid climates the precipitation contributes a
considerable amount of plant available water. Due to
Figure 3: Mean sand fraction of the upper soil layer (approx. 0 to the high variability of precipitation irrigation scheduling
−30 cm depth) of all chosen soil types in the particular grid cells. should be adaptive to precipitation (Seidel et al., in
print).
Irrigation scheduling can be controlled by measure-
number of soil types ranges from 1 to 57 with a median ments of the soil water potential which closely relates
of 14. As it is not feasible to simulate each soil type, only to plant stress (Jones, 2004). In sensor-based irrigation
the five predominant soil types of each grid cell were scheduling, irrigation events are triggered when a cer-
chosen. The particle-size distribution plays an important tain soil water potential threshold ΨS is reached (Kloss
role for the amount of plant available water. Fig. 3 shows et al., 2014). This irrigation strategy was applied in treat-
the mean sand fraction of all chosen soil types of the ments T 200 and T 350 but also for the model predictions
grid cells. The values apply for the upper soil layer with of irrigation events on regional scale.
a depth of approximately 0 to −30 cm which is supposed SCWPFs can be drawn from tuples of I and Y.
to contain the highest amount of bean roots. For each 30-year period (P1 to P4), 30 tuples can
be taken from one crop model run. If multiple
2.5 Climate data thresholds for soil water potential ΨS are taken, the
number of 30 tuples multiplies by the number of
For the evaluation of recent and future crop irriga- thresholds. For a wide spread of different irrigation
tion water demand and available water, climate data amounts, nine soil water potential thresholds were cho-
from the statistical regional climate model WEREX V sen (ΨS = −100 hPa, −300 hPa, −500 hPa, −1,000 hPa,
(Kreienkamp et al., 2013; Spekat et al., 2012) was −3,000 hPa, −5,000 hPa, −7,500 hPa, −10,000 hPa,Meteorol. Z., 25, 2016 M. Wagner et al.: Irrigation of common bean 369
Table 1: Average observed fresh (FMobs ) and dry matter (DMobs ), water productivity (WPobs , in kg m−3 ) and simulated (DMsim ) dry matter
pod yield (DMsim ), all in t ha−1 , pod dry matter content (DMC, in %), irrigation water applied (irr, in mm), maximal leaf area index (LAI, in
m2 m−2 ), maximal plant height (height, in cm) and range of leaf stomatal conductance during the growing season (gs , in mmol m−2 s−1 ) of
common bean cultivated in Pillnitz, Germany in 2014. Rainfall during the growing season (77 days) was 194 mm.
treatment FMobs DMobs WPobs DMsim DMC irr LAI height gs
RF 20.8 2.1 1.08 2.1 10.4 0 3.7 40 320–823
SVAT 23.8 1.9 0.57 2.1 8.0 137 4.9 45 608–711
SWB 25.2 2.1 0.80 2.1 8.4 70 4.7 43 491–1035
T 350 28.2 2.2 0.72 2.2 7.9 110 7.0 56 571–882
T 200 29.1 2.3 0.63 2.3 7.7 170 7.7 60 586–929
−14,000 hPa). Moreover, in one scenario no irrigation was taken from easier accessible ground water sources
took place. In total, 300 simulation runs (10 times 30 in Saxony.
tuples) per soil type were conducted. Over all points a The estimated available water can be considered as
two-dimensional kernel density smoothing (as described a boundary condition. The term water self sufficiency is
in Wilks, 2011) was carried out and can be interpreted defined as the ratio of the water availability and the water
as the stochastic density of the points. The density can be demand. However, it is neither technically possible nor
integrated and forms a 2D distribution function. The 2D sustainable to withdraw 100 % of the available water.
distribution has to be converted in a set of 1D curves for In the following, the available water for crop irrigation
specific I and varying Y where all have different ranges was set to 50 % of the total available ground and surface
(0, Ymax ) due to different maximum values Ymax . All 1D water.
curves must be transformed to a particular range of (0, 1)
to be interpreted as conditional distribution functions.
The resulting conditional distributions deliver quantiles 3 Results and Discussion
of F(Y|I) and form the SCWPF for one crop, one cli-
mate, that is to say one grid cell, and one soil. 3.1 Experimental results
Irrigation increased fresh matter bean yield significantly
2.7 Water availability (see Table 1). The drip irrigated treatments achieved
much higher fresh matter yield, plant heights and LAI
The concept of SCWPF allows to estimate the crop values compared to the other treatments. Treatment
water demands under specific conditions (climate, soil) T 200 achieved a about 40 % higher marketable fresh
for different crops. However, the water demand has to matter yield compared to treatment RF. However, bean
be compared with the potential water availability. For dry matter yield differed less between the treatments due
a sustainable water use, the annual water use should to decreasing dry matter contents with increasing irriga-
not exceed the annually available water. In the KliWES tion water applied. The leaf stomatal conductance de-
project, the water balance for Saxony in the recent cli- creased in treatments RF and SWB during a dry period
mate as well as some future climate projections were shortly before flowering indicating reduced transpiration
simulated (Schwarze et al., 2011; Schwarze et al., due to drought stress (not shown).
2014).
This data was used in this study for an estimation of 3.2 Model calibration and validation
ground water recharge and surface water availability for
each grid cell (see Fig. 2). The regional water demand In total, 12 plant model parameters were adapted to the
from Section 3.3 was then intersected with the locally experimental data including parameters for the devel-
available water. Every time period contains 30-year av- opment rate, maximum assimilation rate, crop height
erages and therefore balances over whole years, rather as a function of the development stage, the specific
than over months. From the ground water recharge, only leaf weight and the water stress effect, amongst oth-
the areal fraction of agricultural land for one grid cell ers. Moreover, the assimilate partitioning which defines
was used for ground water withdrawal since the vegeta- what fraction of the assimilate is distributed to the root,
tion on non-agricultural area requires water, too. Surface leaf, stem and storage organ as a piecewise linear func-
water can be generated from the whole grid cell but can tion of development stage was adjusted. The observed
only be taken from surface water reservoirs. The amount and the simulated dry matter pod yields are shown
of water the reservoirs in one grid cell cannot store is in Table 1. The modelling efficiency and the RMSE
routed downstream to the next grid cell. This causes for dry matter yield prediction according to Wallach
a heterogeneous surface water availability compared to (2006) estimated for all five treatments were 89 % and
the relatively evenly distributed available ground water. 0.11 t ha−1 . Predictions of the day of the beginning of
This finding is confirmed by Gramm (2014) who noted flowering and maturity were exact or differed only one
that in 2010 about three quarters of the irrigation water day. However, the study lacks a model validation against370 M. Wagner et al.: Irrigation of common bean Meteorol. Z., 25, 2016
independent experimental data. An integration of addi-
tional experimental data of different growing seasons or
experimental sites into the model calibration and a vali-
dation against more data would increase the model pre-
diction reliability and accuracy.
3.3 From field to regional SCWPF
Field scale
In this study, the main reasons for varying SCWPFs are
the climatic variability and the changing climate in the
future as described in Section 2.5. Fig. 4 shows the bi-
variate density of temperature and precipitation change
for one particular grid cell in the north western part
of the area of investigation. Obviously, the tempera-
ture raises with time (up to a ΔT ∼ 4 K in P4 com-
pared to P1). Beside the right shifting, the winterly den-
sity function does not change its circular shape, which
implicates a similar precipitation regime. The summer
shows a more towed density towards lower precipitation
values. Thus, the climate gets drier in the main grow-
ing season (spring, summer). The global radiation in-
creases from 10 to 15 % from P4 compared to P1 (not
shown). The elevated temperatures as well as the higher
global radiation also increase the evaporative demand
as the warmer air can hold more water vapour and the
transpiration of plants is coupled with the global radi- Figure 4: Bivariate probability density functions for monthly pre-
ation. For a first estimation, the FAO reference evapo- cipitation and temperature change for future time periods P2 till P4
transpiration of a hypothetical grass (ET 0 ) was calcu- in comparison to recent P1. The left column shows changes in winter
lated according to Allen et al. (1994). For the differ- (November to April), the right column in summer (May to October).
ent time periods the following average values were cal- Yellow stands for a low density and red for a high density of points.
culated: ET 0 P1 = 410 mm a−1 , ET 0 P2 = 425 mm a−1 ,
ET 0 P3 = 450 mm a−1 and ET 0 P4 = 520 mm a−1 . Both
effects – less precipitation in the growing season and an estimated (Eq. 3.1). For that, additional data of former
increased evaporative demand – decreased the amount experiments with the same cultivar were used.
of plant available water.
368.1
The crop model Daisy was used to simulate bean DMC = − 354.0 (3.1)
growth for specific climates and soils. The result are (P + I)0.002851
SCWPFs for all grid cells for the five dominant soils for
The predicted yield increase due to irrigation is
the time periods P1 to P4. Fig. 5 illustrates the SCWPFs
shown also for fresh matter yield applying the men-
for the same grid cell that was used for an exemplary cli-
tioned relationship (Fig. 5 lower row). The marketable
mate evaluation (Fig. 4). The potential dry matter yield
(Fig. 5 upper row) shows only a minor increase with fresh matter yield increased by 3.7, 4.0, 3.7 and 6.0 t ha−1
irrigation in P1 to P3, as enough precipitated water is (15, 16, 15 and 28 %, respectively) with I = 200 mm
available for bean growth (median gain at 0.06, 0.07 and from P1 to P4. The projected drier but warmer climate
in P4 (see Fig. 4) enhanced the potential bean yield if
0.06 t ha−1 , hence 2.6, 3.0, and 2.7 % of yield). In P4,
sufficient irrigation water is available.
the growing season becomes drier and the median bean
yield increases by 0.26 t ha−1 (13 %) with irrigation.
Crop models simulate dry (but not fresh) matter car- Regional scale
bon assimilate increase and flow. In the bean experi-
ments, the dry matter content decreased with the amount The local properties of the future climate in different
of irrigation water applied, the differences of the ob- time periods that were shown in Fig. 4 are similar for
served dry matter yield between the treatments were the whole area of investigation (not shown).
relatively low. Marketable fresh matter increased much If the estimation of the SCWPFs explained for the
more than dry matter due to irrigation (see Table 1). field scale are done for each grid cell, a regional view
To deal with this problem, the relationship between ob- on the crop yield can be developed. Fig. 6 shows the
served dry matter content (DMC) and the sum of irriga- estimated median bean yields (50 % quantile of the
tion (I) and precipitation (P) in the growing period was SCWPF) for Saxony. The figure on the upper left showsMeteorol. Z., 25, 2016 M. Wagner et al.: Irrigation of common bean 371
Figure 5: SCWPF for bean in time periods P1 till P4 for a grid cell in north west Saxony. The upper row shows the dry matter, the bottom
row shows the fresh matter common bean yield. The median is the black line, the dark grey area shows the range from 25 % to 75 % quantiles
and the light grey area depicts the range from 5 % to 95 % quantiles
the non-irrigated regional crop yield in P1. A yield de- The southern part shows a special behaviour. There are
crease from the south to the north due to a drier climate several grid cells with larger crop yields in P4 compared
and a lower soil water storage capacity of the upper soil to P1 (see left column in Fig. 6, greenish grid cells in
is obvious. The figures below depict differences in non- the figure on the bottom). These grid cells need only
irrigated crop yield for P2 to P4. Especially the north minor irrigation water to reach the irrigated crop yield
eastern part of Saxony shows small yield decreases in level in P1 and benefit from higher temperatures. In all
P2 and P3 whereas in P4 most parts in the area of inves- regions the crop yield can be increased under future cli-
tigation reveal a significant decrease up to 5 t ha−1 and mate projections when irrigation water is applied. How-
higher for larger regions. ever, especially in P3 and P4, a high crop water demand
Irrigation can be an appropriate measure to increase is expected.
yields. As the second column (Fig. 6) shows, irrigation
compensates for the drier climate and results in yield in- 3.4 Intersection of water demand and water
creases up to 5 t ha−1 for larger areas in P1 to P3. Though availability
in P4 several regions show yield decreases to some ex-
tent due to increasingly drier and hotter conditions. The For a sustainable water use, the water withdrawal should
last column in Fig. 6 indicates the necessary irrigation not exceed the rechargable water. In this study, the
water volume that has to be applied per year, if the crop regional water demand was intersected with the lo-
yield shall not decrease compared to the recent yield in cal available water. Fig. 7 summarizes the intersection
P1 with an irrigation volume of 60 mm a−1 . In P2 are of all grid cells, and the graphs can be interpreted as
only a few regions in the north east with a higher irri- exceedance distribution functions. The ordinate spans
gation demand and the western part of Saxony requires from no (0 %) to all grid cells (100 %) and the abscissa
even less irrigation. In P3, the finding is strengthend with covers the ratio of water self sufficiency from no avail-
larger areas in the east were more than 100 mm a−1 of able water (0 %) to enough water for the simulated irri-
water are required, although the western part still needs gation water requirement (100 %). The latter computes
less water compared to P1. The decreasing irrigation wa- as irrigation in each time period that is necessary to de-
ter demand in the western grid cells is mainly due to liver the same crop yield as the crop yield in P1 with
small changes of the precipitation regime (see Fig. 4) I = 60 mm a−1 . For instance, the supply of 40 % of the
and beneficial temperatures in P3. However, the climate required irrigation water amount is possible for 86 % of
in P4 gets drier and warmer and therefore more irriga- all grid cells in P1, but only for 55 % in P4. The available
tion water is required. Again, the north eastern region water can exceed the necessary water and negative avail-
is particularly affected. The higher sand fractions in the able water amounts are possible due to a high evapora-
soil and the lower pojected precipitation contribute to tive water loss. These two situations lead to the fact, that
the higher irrigation water requirements in the north. the graphs do not necessarily hit the upper left and lower372 M. Wagner et al.: Irrigation of common bean Meteorol. Z., 25, 2016
Figure 6: 1st column: regional fresh matter bean yield (1st row) and yield differences between future time periods (2nd to 4th row) with no
irrigation. 2nd column: fresh matter yield differences for each time period if no irrigation water is applied to an application of 60 mm a−1 of
irrigation water. 3rd column: required irrigated water amount, if the crop yield shall not decrease under the yield level reached in P1 with
I = 60 mm a−1 . All results are averaged medians over all soils of the particular grid cells.
right corner of the diagram. P2 shows a higher water the water balance due to formerly coal mining. P1 shows
self sufficiency than P1 because in that scenario higher some deficits in water self sufficiency in mid northern
yields are reached with less irrigation water. Major de- Saxony (only 43 % of the grid cells are fully self suf-
creases of water self sufficiency appear in P3 and P4. ficient). For P2, a rather high degree of water self suf-
Fig. 8 shows the degree of water self sufficiency in ficiency was estimated due to the lower irrigation wa-
the area of investigation. The water balance values are ter requirements (79 % of grid cells with full water self
not available for the whole area of investigation. For in- sufficiency). In P3, a major decrease of grid cells with
stance, in the north western part and some parts in the full water self sufficiency appears (33 %). P4 shows an
north east there are major anthropogenic influences of even lower degree of water self sufficiency (overall 43 %Meteorol. Z., 25, 2016 M. Wagner et al.: Irrigation of common bean 373
comprehensive experimental data and further analyzed
on the basis of stochastic crop water production func-
tions. These production functions relate the required ir-
rigation water demand to specific yield levels under con-
sideration of different climatic conditions and soils and
can be applied for the upscaling from local crop growth
on field scale to a regional scale. Moreover, the avail-
able water resources on the catchment scale for irriga-
tion were compared to the predicted irrigation water re-
quirements to allow spatially differentiated statements
about the capabilities of water withdrawal from local
sources.
The experimental and simulation results show that
Figure 7: Exceedance probabilities for water self sufficiency over an irrigation of common bean has high yield effects es-
all grid cells for simulated bean. The crop water demand results from pecially in locations with low precipitation during the
the same crop yield in all time periods as in P1 with IP1 = 60 mm a−1 . growing season or for soils with a low water storage
The available water covers 50 % of the groundwater recharge plus capacity. Already 10 to 40 mm of irrigation water ap-
50 % of the available surface water. plied can increase crop yield by 3 to 5 t ha−1 in larger
regions compared to non-irrigated crop yields in P1
(1961–1990). In the drier northern parts of Saxony with
of grid cells with full water self sufficiency). Although its lower water storage capability, a decrease in non-
mainly the northern part of Saxony shows a low degree
irrigated fresh matter bean yield by 5 t ha−1 is predicted
of water self sufficiency while the southern part displays
from P3 (period 2021–2050) to P4 (2071–2100). How-
a high water self sufficiency degree. There are always
ever, the degree of water self sufficiency for crop irriga-
some grid cells with a very high degree of water self suf-
tion is supposed to decrease in many parts of Saxony in
ficiency. This is a result of larger water reservoirs with a
the future. Assuming that common bean would be culti-
high available water volume.
vated on the whole agricultural land, only less than half
With a water self sufficiency below 100 % it is not (33 % and 43 % in P3 and P4) of the investigated area
possible to irrigate the whole agricultural area, and could deliver 100 % of the required irrigation water.
thus yield decreases are likely (compared to P1 at I =
We conclude that in the future, the required amount
60 mm a−1 ). There are different approaches to face the of water often (and especially in Northern Saxony) can-
increasing dryness including: (i) acceptance of a lower not be delivered by precipitation alone. Measures like
crop yield, (ii) cultivation of less area with crops with water saving soil cultivation, a change in crop rotations,
high water demands, (iii) change of the crop rotations, and the cultivation of crops with lower water require-
crop types and varieties and (iv) acquiration of larger ar- ments but also expansion of water storage capabilities
eas for water withdrawal. and long-distance water pipes will play a major role in
One limitation of this study is the concentration on the future. The presented results can support policy mak-
only one crop. Of course, it is not realistic that common ers in water demand regulations (e.g. water rights, water
bean is cultivated on the whole arable land. Different prizes and subsidies). Moreover, farmers can draw in-
peaks of the water demands of different crops would formation about the profitability and potentials of irriga-
lead to different results. However, this approach can tion systems for their farms. According to our findings,
be transferred to other crops and competition between an appropriate experimental design with a comprehen-
crops (irrigation water, area) can be considered (see sive experimental data collection is necessary for deriv-
Stange et al., 2015). ing reliable crop parameters for the crop model. Further
research is required to improve the model predictions of
4 Summary and Conclusions complex plant-soil-climate interactions, like the impacts
of combined heat and drought stress, on plant develop-
The projected climate change is expected to have an ment and yields.
effect on agriculture and the water balance in North and
Central Europe. For the investigated area of Saxony, Acknowledgements
Eastern Germany, an increase in temperature of 4 K
and a decrease in precipitation of 50 to 150 mm a−1 The authors are very thankful for the help of Stefan
(especially during the growing season) are projected Werisch, Verena Wommer, Anne Hartman and Her-
until 2100. mann Laber and the numerous helpers of the research
The future yield development and associated irriga- site of the Sächsische Landesamt in Pillnitz for their
tion water requirements were investigated for common valuable help with the field experiments and the harvest.
bean in Saxony. The relationship between the applied Thanks to the Center for Information Services and High
irrigation water amount and the resulting yield was as- Performance Computing (ZIH) of the Technische Uni-
sessed with a biophysical crop model calibrated using versität we were able to simulate regional crop growth.374 M. Wagner et al.: Irrigation of common bean Meteorol. Z., 25, 2016
Figure 8: Predicted water self sufficiency over all grid cells for common bean. The water demand results from the same crop yield in all
time periods as in P1 with IP1 = 60 mm a−1 .The available water covers 50 % of the groundwater recharge plus 50 % of the available surface
water.
For the study presented here we used several thousand T 200 treatment, automatic irrigation when soil
CPU hours. These investigations are part of the research water potential of 200 hPa is observed
project ‘SAPHIR – Saxonian Platform for High Perfor- T 350 treatment, automatic irrigation when soil
mance Irrigation’ funded by the EU ESF ‘Nachwuchs- water potential of 350 hPa is observed
forschergruppen’ program under grant no. 100098204.
WPobs crop water productivity in kg m−3
We acknowledge support by the German Research
Foundation and the Open Access Publication Funds of Y crop yield in t ha−1
the TU Dresden.
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