climate role in crop productivity in salt affected soils.docx

1. Role of climate in crop productivity in salt affected soils
Bhaskar Narjary
Email: Bhaskar@cssri.ernet.in
Introduction
Climate is fundamental to crop growth. Crops are dependent on light, temperature, moisture and
carbon dioxide (CO2) concentration to produce the grains and other crop products that are
essential to our nutrition and health. Moisture stimulates seeds to germinate, the time to
emergence being temperature-dependent. In rain fed crops rainfall drives water availability and
determines optimum sowing time. The rate of growth of roots, stem and leaves depends on the
rate of photosynthesis, which in turn depends on light, temperature, moisture and carbon dioxide
(CO2). Temperature and day length also determine when plants produce leaves, stems and
flowers, and consequently the filling of grain or the expansion of fruit. Pest and diseases
incidence on crop, its spreading mainly depends on the prevailing temperature and humidity.
Wet & dry spells cause significant impact on standing crops, physiology, and loss of economic
products. The yield of grain crops depends on grain number and grain weight at harvest, which in
turn depends on biomass at anthesis and the availability of moisture post-anthesis.
Significance of Light
Solar radiation is the primary driver of plant photosynthesis. Decreased light can became a
limiting factor to plant growth when shading occurs.
Significance of temperature
It has been well recognized the importance of temperature in regulation the rates of physiological
process and influencing growth and development of plants. Lercher (1980) stated that sufficient
but not excessive heat is a basic prerequisite of life. Each vital process is restricted to certain
temperature range and has an optimal operating temperature on either side of which performance
declines. Temperature affects yields through five main pathways.
i. Higher T causes faster crop development and thus shorter crop duration, which in
most cases is associated with lower yields (Stone, 2001).
ii. T impacts the rates of photosynthesis, respiration, and grain filling. Crops with a
C4 photosynthetic pathway (e.g. maize and sugarcane [Saccharum officinarum])
have higher optimum T for photosynthesis than C3 crops (e.g. rice and wheat),
but even C4 crops see declines in photosynthesis at high T (Crafts-Brandner and
Salvucci, 2002). Warming during the day can increase or decrease net
photosynthesis (photosynthesis-respiration), depending on the current T relative
to optimum, whereas warming at night raises respiration costs without any
potential benefit for photosynthesis.
iii. Third, warming leads to an exponential increase in the saturation vapor pressure
of air. Assuming a constant relative humidity, warming raises the vapor pressure
deficit (VPD) between air and the leaf, which is defined as the simple difference
between the saturation vapor pressure and the actual vapor pressure of the air.
Relative humidity has remained roughly constant in recent decades over large
spatial scales and is projected to change minimally in the future as well. Increased
VPD leads to reduced water-use efficiency, because plants lose more water per
unit of carbon gain (Ray et al., 2002). Plants respond to very high VPD by closing

2. their stomata’s, but at the cost of reduced photosynthesis rates and an increase in
canopy T, which in turn may increase heat-related impacts.
iv. T extremes can directly damage plant cells. Warming shifts the T probability
distribution, such that hot and cold extremes become more and less likely,
respectively. The reduction of spring and autumn frost risk will lead to a
beneficial extension of the frost-free growing season in several temperate and
boreal regions. For example, projections indicate a 2-week increase in the
growing season for Scandinavia by 2030 compared with the late 20th century
(Trnka et al., 2011). Northern China, Russia, and Canada are also expected to see
large gains in the frost-free period suitable for crop growth (Ramankutty et al.,
2002). On the other end of the spectrum, warming increases the likelihood of heat
stress during the critical reproductive period, which can lead to sterility, lower
yields, and the risk of complete crop failure (Teixeira et al., 2012).
Significance of moisture
An increased incidence of agricultural drought will increase crop water stress. An expansion of
irrigation is a likely response in some regions, although many areas lack irrigation infrastructure,
and water access can often be curtailed during periods of severe drought. In situations with
shallow or medium depth to groundwater, plants may also be able to escape drought by accessing
moisture below the surface. In general, though, crop plants will respond to reduced soil moisture
by closing their stomata and slowing carbon uptake to avoid water stress, thereby raising canopy
T and potentially increasing heat-related impacts. Water stress during the reproductive period of
cereal crops may be particularly harmful (Stone, 2001; Hatfield et al., 2011), while changes in
the timing of the rainy season, particularly in tropical areas, may confound traditional techniques
for farmers to determine appropriate planting dates. Finally, more intense rainfall events may
lead to flooding and waterlogged soils, also pathways for damaged crop production.
Significance of CO2
Rising atmospheric CO2 concentrations provide some counteracting tendencies to the otherwise
negative impacts of rising T and reduced soil moisture. First, higher CO2 has a fertilization effect
in C3 species such as wheat, rice, and most fruit and vegetable crops, given that photo
respiratory costs in the C3 photosynthesis pathway are alleviated by higher CO2. Elevated CO2
also has the benefit of reducing stomatal conductance, thereby increasing water-use efficiency in
both C3 and C4 crops (Ainsworth and Long, 2005). Yields are estimated to be enhanced by
approximately 15% in C3 plants under an approximately 200 µLL-1
atmospheric CO2 increase,
although the relative benefit of this effect varies widely between studies and is still a subject of
considerable debate in the scientific literature (Long et al., 2006). Another debate surrounds the
concern that CO2 fertilization may reduce the nutritional quality of crops, especially in nutrient-
poor cropping systems, through reduced nitrate assimilation and lower protein concentrations in
harvestable yield (Taub et al., 2008).
Air pollutants such as nitrogen oxides, carbon monoxide, and methane, react with hydroxyl
radicals in the presence of sunlight to form tropospheric Ozone causes oxidative damage to
photosynthetic machinery in all major crop plants (Wilkinson et al., 2012). Aerosols from air
pollution can also reduce plant-available radiation. These pollution-related impacts are likely to
be highest in agricultural areas downwind of urban regions, but O3 precursors can also be
transported across continents. In fact, tropospheric O3 concentrations above preindustrial levels
are currently found in most agricultural regions of the globe (Van Dingenen et al., 2009).

3. Interaction effects may also occur between O3 and elevated CO2. For example, reduced stomatal
conductance under elevated CO2 will reduce O3 uptake by crop plants, thereby limiting damage
to the plant and maintaining biomass production (McKee et al., 2000). However, empirical
evidence is mixed regarding the ability of elevated CO2 to reduce the impact of O3 on final yields
(McKee et al., 1997). A related concern is that variety improvement in crops such as wheat has
favored increased stomatal conductance, given that higher transpiration fluxes are generally
associated with increased photosynthesis rates and final yields (Reynolds et al., 1994). However,
a higher stomatal conductance implies more uptake of O3, increasing the sensitivity of more
recent varieties to O3 damage (Biswas et al., 2008).
Climate Change and climatic variability
Climate is changing naturally at its own pace, since the beginning of the evolution of earth, 4–5
billion years ago, but presently, it has gained momentum due to inadvertent anthropogenic
disturbances. These changes may culminate in adverse impact on human health and the
biosphere on which we depend. The multi-faceted interactions among the humans, microbes and
the rest of the biosphere have started reflecting an increase in the concentration of greenhouse
gases (GHGs) i.e. CO2, CH4 and N2O causing warming across the globe along with other
cascading consequences in the form of shift in rainfall pattern, melting of ice, rise in sea level
etc. The above multifarious interactions among atmospheric composition, climate change and
human, plant and animal health need to be scrutinized and probable solutions to the undesirable
changes may be sought.
Vulnerability is the degree to which a system is susceptible to, or unable to cope with adverse
effects of climate change, including climate variability and extremes. Vulnerability is a function
of the character, magnitude, and rate of climate change and variation to which a system is
exposed as well as the system’s sensitivity and adaptive capacity. Vulnerability to climate
change varies across regions, sectors, and social groups. Understanding the regional and local
dimensions of vulnerability is essential to develop appropriate and targeted adaptation efforts. At
the same time, such efforts must recognize that climate change impacts will not be felt in
isolation, but in the context of multiple stresses. In particular, the dramatic economic and social
changes associated with globalisation themselves present new risks as well as opportunities.
Trends in climatic parameter in Karnal
long term rainfall(1972-2010) analysis revealed that Karnal district receives an annual rainfall
757.6 mm, with a considerable variation (CV=34.3%) in total amount of rainfall from as low as
340.7 mm during 2006 (driest year) to as high as 1339.9 mm in 1988 (wettest year so far) (Table
1). Monsoon rainfall contributed 79% of the total annual rainfall with high coefficient of
variation (CV= 40.1). Monsoon rainfall categorization based on long period average (LPA) and
coefficient of variation (CV) of the corresponding monsoon season in Karnal observed that in the
recent decades (2001-2010), 6 year received deficit rainfall (18-57 % lower than long term
average), 2 year normal rainfall and only 2 year excess rainfall event (9-70% higher than long
term average) (Fig. 1). In early two decades (1981-1990 and 1991-2000), one day maximum
rainfall (≥ 50 mm) mainly confined in June and July months but in recent decades (2001-2010) it
was shifted in September months (Fig. 2). Average maximum and minimum temperature of
Karnal is 30 °C and 17 °C. But there is 0.2°C and 0.5°C increase in maximum and minimum
temperature in last decade which may affect crop growth period and yield during kharif and rabi
season. Trend analysis of BSS hours during 1972- 2010 showed that average daily BSS of Karnal
is 7.4 hr and it is decreasing @ 0.0.06 hr per day during the study period (Fig. 3). All these affects
may not be significant now but if this trend continues, yield of crop may be affected badly in future.

4. In the long term, the projected changes will result in greater instability in food production and will
threaten livelihood security of farmers.
Table 1: Statistical analysis of Maximum temperature, Minimum temperature, rainfall and rainy
days at Karnal (1972-2010)
Ma
x T
Min
T
Total Monsoon Summer Post Monsoon
Rainfal
l
Rainy
Days
Rainfal
l
Rainy
Days
Rainfal
l
Rain
y
days
Rainfal
l
Rain
y
days
Mean 29.9
16.
9
757.6 47.6 595.9 32.0 67.7 6.9 94.0 8.7
SE 0.1 0.1 41.6 2.2 38.2 1.6 9.0 0.8 8.8 0.6
Median 30.0
16.
9
706.3 45.0 585.1 29.0 55.0 5.0 84.5 9.0
SD 0.6 0.4 259.8 14.0 238.8 10.3 56.2 5.0 55.0 3.7
Minimum 28.2
16.
1
340.7 24.0 215.3 11.0 0.2 0.0 14.9 1.0
Maximu
m 31.3
18.
3
1399.9 81.0 1271.3 51.0 252.9 20.0 233.0 16.0
CV 1.9 2.6 34.3 29.5 40.1 32.1 83.0 72.3 58.5 42.4
To understand the epochal behavior of rainfall series for different monsoon months, 38-year
running means of each of the monsoon months was calculated and decadal means of each of the
monsoon months was compared (Fig. 4). There was notable shifting in monthly rainfall during
the monsoon period. The rainfall of June and September during 2001-2010 increased by 3 and 36
% while July and August months’ rainfall declined by 33 and 32 % respectively, over long term
averages. There was an opposite trend in the September rainfall. During two decades (1972-80
and 1981-90) September month rainfall was in negative phase but recent two decades (1991-
2000 and 2001-10) it was changed dramatically from negative phase to positive phase with 36 %
surplus rainfall from the long term mean. For decadal behavior of evapotranspiration, 30 years’
means of reference evapotranspiration was calculated by modified Penman-Monteith method and
it was compared with the decadal means (Fig. 5). It was observed that, although in the nineties
(90-2000) total evapotranspiration was in the negative phase but in the last decade total
evapotranspiration increased by 10 mm from mean of 1392 mm of the study period. Both
summer (March-May) and post monsoon (Oct-Dec) evapotranspiration were in positive phase in
the recent decades while monsoonal evapotranspiration remains unchanged.

5. Fig. 1: Monsoon rainfall categorization as deficient, normal and Excess at Karnal, Haryana
Fig. 2: Frequency of one day maximum rainfall in monsoon months at Karnal, Haryana.
Fig. 3: Trend analysis of BSS during 1981- 2010 at Karnal, Haryana.
Fig. 4: Decadal means of monsoon months rainfall at Karnal, Haryana.
0
200
400
600
800
1000
1200
1400
Monsoon
Rainfall
(mm)
Year
Exces Deficit Monsoon
0
50
100
150
200
250
1970 1980 1990 2000 2010
1
day
maximum
rainfall,
mm
Year
June
July
August
September
y = -0.0604x + 128.1
R² = 0.7415
4.0
6.0
8.0
10.0
1980 1985 1990 1995 2000 2005 2010
BSS
(hr)
Year
-100.0 -80.0 -60.0 -40.0 -20.0 0.0 20.0 40.0 60.0 80.0 100.0
72-80
81-90
90-2000
2000-2010
Decade
Decadal means of monsoon rainfall (mm) ( departure from mean)
Jun Jul Aug Sep

6. Fig 5: decadal means of reference evapotranspiration (1981- 2010) at Karnal, Haryana
Regional Climate Scenarios for salt affected soil (Haryana) Using PRECIS
PRECIS is the Hadley Centre portable regional climate model, developed to run on a PC with a
grid resolution of (0.44° x 0.44°). High-resolution limited area model is driven at its lateral and
sea-surface boundaries by output from global coupled atmosphere-ocean (HadCM3) and global
atmospheric (HadAM3) general circulation models. PRECIS captures important regional
information on summer monsoon rainfall missing in its parent GCM simulations.
The PRECIS data on precipitation, maximum and minimum temperature have been analyzed for
Haryana. Preliminary inferences on the variations of these entities have been presented in Figure
6. Mean maximum temperature is projected increase by 1.30
C and mean minimum temperature
by 2.10
C towards mid century. The increase in mean maximum temperature is projected to be
4.20
C and mean minimum temperature 4.70
C towards end century respectively. Decrease is
projected for average annual rainfall by 3.0% for mid century scenario and increase by 17% for
end century scenario.
Fig. 6: Projected Change in mean annual precipitation and temperature in Haryana source:
Haryana State Action Plan on Climate Change, 2012)
-40.0 -30.0 -20.0 -10.0 0.0 10.0 20.0 30.0 40.0
81-90
90-2000
2000-2010
Decadal means of reference Evapotranspiration (mm)
Decade
Total ET South west Winter Summer Post Monsoon

7. In the seasonal and monthly scale it was observed that in mid century (2021-2050) mean daily
maximum temperature in winter, summer, monsoon and post monsoon months projected
increase by 1.3, 2.1, 1.7 and 0.4 0
C, respectively (table 2), while at the end of century (2071-
2099) projected change was 4.3, 4.6, 4.3 and 3.70
C in winter, summer monsoon and post
monsoon months, respectively. Mean daily minimum temperature projected to be increased by
1.8, 2.5, 1.8 and 2.10
C in winter, summer monsoon and post monsoon months at mid century
while at end century projected increase of minimum temperature was 4.5, 5.2, 4.1 and 50
C,
respectively.
Table 2: Projected mean daily maximum and minimum temperature in Haryana (source: Haryana
State Action Plan on Climate Change, 2012)
Haryana receives most of its rain during the monsoon season, which starts in late June. The mean
seasonal precipitation amounts simulated by PRECIS are as shown in figure 7. Under the A1B
scenario, mean annual rainfall is projected to decrease marginally for Haryana by about 63 mm
(3%) by mid century and increase by about 347 mm (17%) by end century. Monsoon months,
JJAS show marginal to 14% increase in mid and end century scenarios respectively.
Figure 7: Projected Change in mean seasonal and annual precipitation in Haryana (source:
Haryana State Action Plan on Climate Change, 2012)

8. Climate and salt salinity and sodicity
High temperature and high ET has been found to cause accumulation of salts in the upper soil
horizon with decreased rate of downward leaching resulting into soil salinization/alkalization
even in places that were not found affected earlier (Dregne, 1976). There is also a problem of
secondary salinization with increase in canal irrigation. The process of salinization due to lateral
movement of water is well explained by Rengasamy et al. (2002) figure 8.
Fig. 8: Different form of soil salinity (Rengaswamy, 2002)
Seepage salinity is the visual scalding of soil surfaces associated with a rising saline water table.
On foot slopes and valley floors of the landscape, the water table is shallower and closer to the
surface. Under native ecosystem, leaching of salts from the permeable soil due to natural
processes led to the accumulation of salts in the shallow groundwater. The salinity of the
groundwater is often very high ranging from EC (electrical conductivity) 15 to 150 dS/m. As
long as the water table was 4 m below the surface, saline groundwater did not affect native
vegetation. With the clearance of perennial native vegetation and introduction of agriculture the
equilibrium levels (Canal Irrigation) of the water table have changed. In low-lying regions,
where water tables were shallower, more water, with salt, has leaked to the groundwater from the
upper horizons. Groundwater levels have risen as a result. Introduction canal irrigation led to a
lower utilization of captured water from rainfall and leakage of still more water down the profile.
As the saline groundwater approached the surface, soil layers (top 1 m) were Stalinized and
waterlogged.
Salt accumulation negatively affects the soil properties and processes and reduces land potential
to be cultivated or for any other use (Varallyay, 1994). High salt content in the soil water
associated with impaired uptake of other essential elements like ca in case of high Na becomes
unavailable to plants. Soil water phase of salt affected soils show signs of low nutrient ion
activities (Grattan and Grieve, 1999).
Climate plays an important role in maintaining the soil properties. It can have adverse effects on
all type of soils yet can have even more deleterious effects on sodic lands. Increased sodicity
affects the soil physical properties like dispersion and slaking, and cause dispersion of aggregates
and loss of carbon within aggregates and physically protected from decomposition (Tisdall and
Oades, 1982). Smith et al. (2009) have estimated that agricultural soil would lose upto 62-164 Tg
carbon by 2100 with the changed climate scenario using Century Model. Sodic soils with high

9. amount of sodium on exchangeable sites affects the plant growth (Gupta and Abrol, 1990) and
climate change may aggravate the problem. Altered pattern of rainfall can affect the capacity of
soil to maintain the required level of organic carbon and also the soil structure. Sodic soils suffer
from ponding on surface due to their lower infiltration rates. High evapo-transpiration causes rise
in salt concentration in soil solution. The soil moisture available to plant is in very low amount
and presence of salts in this water raises the osmotic potential of soil solution. Water becomes
physiologically unavailable to plants and may generate water stress and other nutrients
deficiencies. Nitrogen is an important element for crop growth especially in sodic soils (Curtin
and Naidu, 1998), but the rate of loss of N through volatilization increases in soils with high pH
and waterlogged conditions (Grattan and Grieve, 1999). Also presence of high level of chloride
may also limit the uptake of nitrate (Grattan and Grieve, 1999). All these factors may altogether
affects the plant growth, reproduction and senescence by affecting plant's physiological and
biochemical functions Owing to rigorous structural degradation, presence of high salt content
and consequently developed imbalance in water availability limits the biomass production on
sodic soils. Climate change will aggravate the degradation of sodic soils by accelerating salt
accumulation in susceptible regions.
Climate change impacts on crop yield and productivity
The changes in crop production related climatic variables will possibly have major influences on
regional as well as global food production. The likely impacts of climate change on crop yield
can be determined either by experimental data or by crop growth simulation models. To predict
future impacts on crop yields, crop models present valuable approaches. A number of crop
simulation models, such as CERES-Maize (Crop Environment Resource Synthesis), CERES-
Wheat, SWAP (soil–water–atmosphere–plant), Crop Syst and Info Crop, have been widely used
to evaluate the possible impacts of climate variability on crop production, especially to analyze
crop yield-climate sensitivity under deferent climate scenarios. Table 3 shows a summary of the
crop growth models used to study climate change impacts on crop yields in recent studies.
Challinor et al. (2007) mainly discussed the temperature effect on the crop yield in India with the
regional climate model PRECIS and the GLAM crop model under present (1961–1990) and
future (2071– 2100) climate conditions. The result shows that the mean and high temperature are
not the main factors to decide the crop yield, but extreme temperature has a negative effect on
crop yield when irrigation water is available for the extended growing period. Kalra et al. (2008)
shows that productivity of wheat, mustard, barley, and chickpea has decreased due to rise in
seasonal temperature in northern states of India; namely Punjab, Haryana, Rajasthan, and Uttar
Pradesh. Hundal and Prabhjyot-Kaur (2007) based con Dynamic crop growth simulation models
CERES-Rice and CERES-Wheat predicted that an increase in minimum temperature up to 1.0 to
3.0 degrees Celsius above normal has led to decline in productivity of rice and wheat by 3% and
10% respectively in Punjab.
Based on Climates in A1b, A2, B1 and B2 emission scenarios as per a global climate model
(MIROC3.2.HI) and a regional climate model (PRECIS) for future projected climate and
simulated with InfoCrop-rice model, Soora et al. (2013) reported that in India, climate change is
likely to reduce irrigated rice yields by ~4 % in 2020 (2010–2039), ~7 % in 2050 (2040–2069),
and by ~10 % in 2080 (2070–2099) climate scenarios. On the other hand, rainfed rice yields in
India are likely to be reduced by ~6 % in the 2020 scenario, but in the 2050 and 2080 scenarios
they are projected to decrease only marginally (<2.5 %). At regional level, states like Punjab,

10. Haryana and Rajasthan are projected to lose more yields (6–8%) in the 2020 scenario. Similarly
in the 2050 scenario, projected yield loss is expected to stand at 15–17 % in the above three
states. However, towards the end of the century, many regions in India are projected to lose
yields in the range of 6–21 %.
Table: Summary of crop models used for the study of climate change impacts
Climatic model Crop model Objective crop Predicted impacts Researchers
PRECIS GLAM Rice, Wheat Extreme temperature
has a negative effect
on crop yield
Challinor et al.
(2007)
CERES-Rice
CERES-Wheat
Rice, Wheat Increase in minimum
temperature up to 1.0
to 3.0 degrees Celsius
above normal has led
to decline in
productivity of rice
and wheat by 3% and
10% respectively in
Punjab.
Hundal and
Prabhjyot-Kaur
(2007)
Global climate
model
(MIROC3.2.HI)
and a regional
climate model
(PRECIS)
InfoCrop-rice
model
Rice In the 2050 scenario,
projected yield loss is
expected to stand at
15–17 % in Punjab
and Haryana
Soora et al.
(2011)
HadCM3,
CSIRO-Mk2
and CCCMA-
CGCM2
Crop-Syst Rice, Wheat 7%, 15% and 25%
decrease in rice and
10%, 20% and 34%
in wheat for the years
2020, 2050 and 2080
respectively in
Central Punjab
Jalota et al.
(2013)
A1FI scenario
of IPCC
WOFOST Potato In 2055, a mean
decrease of 17.9
(Kufri Badshah), 21.1
(Kufri Jyoti) and
22% (Kufri Pukhraj)
is likely in the
productivity due to
rise in temperature in
Punjab
Dua et al.
(2013),
Based on General Circulation Models (GCMs), viz. HadCM3, CSIRO-Mk2 and CCCMA-
CGCM2, Jalota et al. (2013) reported that the projected averaged annual T was found to increase
by 1.1 ± 0.5°C, 2.5 ± 0.7°C and 3.5 ± 0.8°C in 2020, 2050 and 2080 respectively. Similarly,

11. projected increase in average annual T min max was 1.7 ± 0.5°C, 3.0 ± 0.4°C and 4.1 ± 0.6°C in
2020, 2050 and 2080 respectively. CropSyst model-simulated crop yields of rice–wheat system
showed 7%, 15% and 25% decrease in rice and 10%, 20% and 34% in wheat for the years 2020,
2050 and 2080 respectively. Using the WOFOST crop growth model for potential production
potato in Punjab based on A1FI scenario of temperature and atmospheric CO2 of the years 2020
and 2055, Dua et al. (2013), reported that estimated rise in temperature alone will result in
change in productivity of Kufri Badshah from +11.6% (Amritsar) to –10% (Fatehgarh) in 2020,
whereas the change in productivity of Kufri Jyoti will be from +11.6% (Amritsar) to –11.6%
(Fatehgarh) and of Kufri Pukhraj from +12% (Amritsar) to –11.5% (Mansa). During this period,
CO2 fertilization is expected to increase tuber productivity from +3.9% to +4.5%, depending
upon cultivar and location. However, in 2055, a mean decrease of 17.9 (Kufri Badshah), 21.1
(Kufri Jyoti) and 22% (Kufri Pukhraj) is likely in the productivity due to rise in temperature
only, while the expected rise in CO2 is likely to bring about 17.3 (Kufri Badshah) to 18.5%
(Kufri Jyoti) increase in potato productivity.
Conclusion:
Climate change impacts on crop yield are often integrated with its effects on water productivity
and soil water balance. Global warming will influence temperature and rainfall, which will
directly have effects on the soil moisture status and groundwater level. Crop yield is constrained
to crop varieties and planting areas, soil degradation, growing climate and water availability
during the crop growth period. With temperature increasing and precipitation fluctuating, water
availability and crop production will decrease in the future. If the irrigated areas are expanded,
the total crop yield will increase; however, food and environmental quality may degrade. Soil
evaporation and plant transpiration will be changed with climate change; thus, water use
efficiency may decrease in the future. Improving water productivity and keeping stable relations
with global food suppliers will be vital for food security.
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