Incorporating root distribution factor to evaluate soil water status for winter wheat

Agricultural Water Management 153 (2015) 32–41

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Incorporating root distribution factor to evaluate soil water status for winter wheat Xiaoyu Zhang, Xiying Zhang ∗ , Xiuwei Liu, Liwei Shao, Hongyong Sun, Suying Chen Key Laboratory of Agricultural Water Resources, The Center for Agricultural Resources Research, Institute of Genetics and Developmental Biology, The Chinese Academy of Sciences, 286 Huaizhong Road, Shijiazhuang 050021, China

a r t i c l e

i n f o

Article history: Received 11 September 2014 Received in revised form 16 December 2014 Accepted 1 February 2015 Keywords: Crop water stress index (CWSI) Infrared thermometry Fraction of transpirable soil water (FTSW) Relative available soil water (RASW) Relative root abundance

a b s t r a c t Many indicators have been developed to determine when and how much to irrigate to improve the irrigation efficiency. The relative available soil water (RASW) is an indicator that can be easily calculated but takes the availability of soil water to crops equally throughout the root zone profile without considering the soil water uptake ability as related to the root distribution. This study introduced a root parameter related to the relative root length abundance at different soil depth into RASW to evaluate the soil moisture conditions on crop performance (simplified as RASWr). The field study was carried out in the North China Plain (NCP) from 2010 to 2014 for four growing seasons of winter wheat (Triticum aestivum L.) under six irrigation treatments to create different soil moisture conditions. The results showed that RASWr was more closely related than RASW to the leaf water potential and stomatal conductance, and it was less affected by daily weather fluctuations as compared to crop water stress index (CWSI). Compared to the fraction of transpirable soil water (FTSW), RASWr was more easily obtained. The simple RASW indicator was improved by incorporating the relative root abundance factor which can be simulated by a form parameter of root distribution and maximum rooting depth at different growth stages of the crop. © 2015 Elsevier B.V. All rights reserved.

1. Introduction The North China Plain (NCP), which is one of the most important regions for winter wheat production, accounts for 60% of the national wheat production (Zhang et al., 2013). However, most of the producing areas depend on irrigation and generally 2–4 times irrigations are applied during the growing period, which has significantly decreased the groundwater table and threatens the sustainable irrigation agriculture in this region (Liu et al., 2002; Sun et al., 2006). Therefore, improving irrigation efficiency is particularly important for conservation of groundwater resources in the NCP (Li et al., 2005). When and how much to irrigate are two critical factors to improve the irrigation water use efficiency under limited water supply conditions. Many indicators have been developed and used to determine the timing of irrigation and can be divided into two types: indicators based on crop water status or soil moisture situations. The indicators that are based on crop water status have more advantages because of their relationship with both the soil,

∗ Corresponding author. Tel.: +86 311 85871762; fax: +86 311 85815093. E-mail address: [email protected] (X. Zhang). http://dx.doi.org/10.1016/j.agwat.2015.02.001 0378-3774/© 2015 Elsevier B.V. All rights reserved.

plant and weather characteristics, such as gas exchange parameters (photosynthesis, stomatal conductance, and transpiration rate), relative water content, and leaf water potential (Farooq et al., 2009). Since the rapid development of infrared thermometry technology (IRT), canopy and atmosphere temperature difference has been used to calculate the crop water stress index (CWSI) and schedule irrigation in practice (Gontia and Tiwari, 2008). The estimation of CWSI has many methods based on different theories (Idso et al., 1981). There are two commonly used methods for calculating the CWSI. The Idso empirical method was less suitable for detecting plant water stress in NCP due to its large fluctuation (Jones, 1999; Yuan et al., 2004). The Jackson theoretical method has a solid theoretical foundation and has received wide acceptance (Jones, 1999). However, the key factor limiting the broad application of this method is that there are many environmental variables that need to be considered (Wang et al., 2010; O’Shaughnessy et al., 2012). With the conception of the non-water-stressed baseline, approaches have been developed to improve the sensitivity of the CWSI. Wang et al. (2005) developed an approach to evaluate crop water stress by introducing a plastic leaf without transpiration; Jones (1999) replaced theoretical estimates of the non-transpiring baseline and non-water-stressed baseline by measuring temperatures of appropriate representative wet and dry reference leaves in the canopy.

X. Zhang et al. / Agricultural Water Management 153 (2015) 32–41

However, the CWSI still fluctuates sharply with altered canopy temperature (Tc ) due to attenuated shortwave radiation (Agam et al., 2013). In addition, bare soils in the field increase the calculation errors (Jones et al., 2002), which consequently limits the application of the CWSI for irrigation scheduling (Al-Faraj et al., 2001). Crops depend on soil moisture to meet their water requirements. Soil moisture status is often used as a direct indicator for irrigation scheduling. The common indicators that are based on soil water conditions include the fraction of transpirable soil water (FTSW) (Ritchie, 1981), the soil water potential and the relative available soil water (RASW) (Jensen et al., 1998). RASW only reflects the percentage of soil moisture as measured between the soil field capacity and the wilting point, which is more correlated with the soil characteristics but less related to the plant status (Jensen et al., 1998). The extractable water is defined as the difference between the field capacity and the lowest measured soil water content when plants are very dry (Ritchie, 1981). FTSW has been widely used to analyze the differences in plant responses to drought stress (Augusto and Maus, 2006; Belko et al., 2012; Kashiwagi et al., 2014) and to evaluate the drought degree in field experiments (Braatne et al., 1992; Liu et al., 2003; Jacobsen et al., 2009). It was also used as criteria for drought-tolerance assessment of different cultivars (Pellegrino et al., 2004; Kholova et al., 2010; Belko et al., 2012; Gholipoor et al., 2012; Kashiwagi et al., 2014). However, the determination of the FTSW requires the lower limit of total transpirable soil water (TTSW), which often changes with growing stages and the depth of the soil (Lacape et al., 1998). The availability of soil water to crops depends on the root system (Passioura, 2006; Yadav et al., 2009; White and Kirkegaard, 2010). Under favorable soil water conditions, plants generally extract water at rates that reflect the root distribution pattern. Previous studies have demonstrated that soil water stored before sowing contributes to 40–50% of the total evapotranspiration for winter wheat under limited irrigation in the NCP (Zhang et al., 2013). The enhancement of soil water use efficiency mainly depends on the development of the root system. Generally, the availability of soil water to crops in the upper soil profile is much higher than that in the deeper parts due to the greater root length density in the upper soil layers (Panda et al., 2003; Li et al., 2011). Therefore, the contribution of soil moisture to plant transpiration is sharply distinct from different soil depth (Jemai et al., 2013). RASW reflects the availability of soil water itself. Soil availability to crops depends not only on soil water availability, but also on the root distribution and the ability of root water uptake (Augusto and Maus, 2006; Jacobsen et al., 2009). Thus, the introduction of root distribution factor into the calculation of RASW might increase its accuracy in assessing the soil water availability to crops. Roots are plastic and affected by many factors. However, for most plants, they usually exhibit a similar pattern along the soil profile. Several common models, such as Gerwitz and Page’s (1974) model and Gale and Grigal’s (1987) model, have been developed to describe the vertical distribution of roots. These models have been modified to fit different situations. A 2D model was developed based on the modification of the root density equation proposed by Gerwitz and Page (1974) and a shape parameter was introduced to describe the distribution of root length vertically and horizontally in the soil profile (Pedersen et al., 2010). The shape parameter varied from 0 to 8, indicated root density being evenly distributed through the soil profile, or virtually all roots being found near the soil surface. Zhang et al. (2012) used the shape parameter and also incorporated a bulk density factor to simulate the distribution of root length density for winter wheat and maize in the NCP and good results were obtained. Jackson et al. (1996) summarized data on the root distributions, densities and biomass for major terrestrial biomes and determined an “extinction coefficient” for Gale and Grigal’s (1987) model. The results showed that the root distribution

33

can be simulated by some simple models to be used in study of soil water distribution and uptake (Dupuy et al., 2010). Therefore, the objectives of this study are (i) to develop a soil water indicator combining relative root distribution and relative available soil water; and (ii) to analyze its practicability and accuracy for indicating crop water status under field conditions by comparison with other indicators. 2. Materials and methods 2.1. Field treatments Field experiments were conducted at the Luancheng Agricultural Ecosystem Experimental Station (Fig. 1), the Chinese Academy of Sciences (CAS), in the NCP (37◦ 53 N latitude, 114◦ 40 E longitude, 50.1 m elevation) during the four winter wheat growing seasons of 2010–2011, 2011–2012, 2012–2013 and 2013–2014. The mean annual precipitation is 473 mm, mainly concentrated in the summer season, and during the winter wheat growing season, rainfall is approximately 120 mm. Irrigation is critical for the high yielding of this crop. The soil in this area is a loamy soil with a well-drained structure. The field capacity is 36% in volume, and the wilting point is 13% in volume for the upper 2 m of the soil profile. The detailed soil characters are listed in Table 1. The NCP is an alluvial plain, and distinct soil characters were found for different layers of soil at the experimental site. Winter wheat (Kenong 199, a wide-grown cultivar in the region) was planted in early October and harvested in the middle of June the following year. The row spacing was set at 15 cm with a density of 300 viable seeds m−2 . Before planting, diammonium phosphate (DAP) at 400 kg ha−1 and urea at 150 kg ha−1 were applied as base fertilizers. Six irrigation treatments, from no irrigation during the entire growing season up to five irrigations, were set up to create different soil water conditions, as shown in Table 2. Seasonal rainfall for the four seasons was 53.1, 82.3, 94.6 and 46.3 mm, respectively. 2011–2012 and 2013–2014 were two very dry seasons. Each treatment had four replicates, and 24 plots were used and randomly arranged. The area of each plot was 5 m × 10 m. To minimize the effects of lateral water seepage, a protective zone with 2 m in width without irrigation was established between two adjacent plots. Irrigation was applied using plastic pipe transportation system from a nearby pumping well, and a flow meter was used to record the irrigation. 2.2. Weather conditions An automatic weather station near the experimental field provided local weather data. Wind speed, sunshine duration, air temperature, radiation, humidity and precipitation were recorded every 5 min. With the Crop Water Program (developed by FAO), daily reference evapotranspiration (ET0 ) was calculated using the FAO Penman-Monteith equation, which defines the concept of grass reference (albedo = 0.23, height = 0.12 m, and surface resistance = 70 s m−1 ) (Allen and Wright, 1996). Daily maximum and minimum temperatures, daily sunshine hours, wind speed and relative humidity were used in this calculation. 2.3. Canopy temperature (Tc ) During the winter wheat growing season, the Tc under different irrigation treatments was measured by an infrared camera (IRC; NEC Avio Technologies Co., Tokyo, JPN) from recovery to maturity, with emissivity set at 0.98. The thermal images were collected from 11:00 to 13:00 local time, with four replications for each treatment on clear days (Idso et al., 1981). The view angle was set at 45◦ with the canopy. Each round of measurement took less than

34

X. Zhang et al. / Agricultural Water Management 153 (2015) 32–41

Fig. 1. The location of Luancheng Agricultural Ecosystem Experimental Station in the North China Plain. Table 1 Soil characteristics at the experimental site. Depth (cm) 0–25 25–40 40–60 60–85 85–120 120–165 165–200

Texture

Bulk density (g cm−3 )

Porosity (%)

Field capacity (v v−1 )

Wilting point (v v−1 )

Saturated hydraulic conductivity (m day−1 )

Loam Loam Loam Loam Silty clay loam Clay loam Silty clay loam

1.39 1.50 1.46 1.49 1.54 1.63 1.55

49 46 46 46 46 42 44

0.36 0.35 0.33 0.34 0.34 0.39 0.38

0.096 0.114 0.139 0.139 0.129 0.139 0.164

1.100 0.430 0.730 0.710 0.020 0.003 0.016

5 min to complete, then the sunlight variation was negligible. At anthesis, the diurnal variations in Tc were measured from 8:00 to 17:00 for several days. Twet and Tdry were considered as the upper and lower boundaries for Tc , respectively. In this experiment, real leaves covered with Vaseline on two sides and filter paper sprayed with water exposed to the sun under similar conditions were taken as dry and wet reference surfaces, respectively (Jones 1999). The temperatures of the wet and dry reference surfaces were measured at the beginning and end of every set of measurements, averaged as the values of Twet and Tdry , respectively. Thermal images were analyzed using the InfReC Analyzer NS9500 Lite software, the temperatures from the underlying soil, crop stem and spikes were excluded. The average leaf temperature from each thermal image was determined as Tc .

2.4. Leaf water potential, stomatal conductance and soil water contents Stomatal conductance, transpiration and leaf water potential were measured at midday on sunny days. Stomatal conductance and transpiration rate were measured using a gas exchange system (Li-6400, Li-COR, USA), with leaves from the non-shaded upper canopy in full extension. With a ZLZ5 pressure chamber (produced by Lanzhou University, China), four flag leaves from each replication were sampled to measure leaf water potential. In a cycle of 7–15 days, soil volumetric water contents were monitored at an increment of 20 cm to a depth of 2 m using a neutron meter (CPN 503 DR, USA). The access tubes were installed in the middle of each plot.

Table 2 Irrigation timing and amount for the six irrigation treatments during the four growing seasons of winter wheat from 2010 to 2014. Treatments

Irrigation numbers

I0 0 1 I1 2 I2 3 I3 4 I4 5 I5 Seasonal rainfall amount (mm)

Irrigation timing

Jointing Jointing/Flowering Jointing/Heading/Grainfill Winter/Jointing/Heading/Grainfill Winter/Jointing/Booting/Flowering/Grainfill

Total irrigation amounts (mm) 2010/2011

2011/2012

2012/2013

2013/2014

0 75 150 210 280 350 53.1

0 90 165 240 350 425 82.3

0 90 160 235 310 380 94.6

0 90 170 255 340 420 46.3

X. Zhang et al. / Agricultural Water Management 153 (2015) 32–41

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2.5. Root sampling

2.8. Incorporating root factor into the RASW (RASWr)

Roots were sampled at the main growing stages of winter wheat during the four growing seasons for each treatment. The sampling procedure followed the description of Böhm (1979). The diameter of the core was 10 cm. Four cores were taken for each treatment down to 1.5 m at 10-cm increments. The cores were taken to the laboratory and washed to obtain the roots. The live roots were separated from other debris. Root length was measured based on the line-intersect method (Tennant, 1975). The root length density (RLD) at different depth was calculated by dividing the root length by the sampled soil volume (cm/cm3 ).

A root parameter was introduced to optimize the function of RASW to evaluate the actual soil water availability to crops. The relative root length abundance was incorporated into the RASW to obtain the modified RASW, simplified as RASWr. A normalized equation was used to simulate the relative abundance of roots throughout the soil profile by Gerwitz and Page (1974) as the following:

2.6. Grain yield and water use efficiency (WUE) The plot was harvested at maturity, the kernels were air-dried to a constant weight and the weight was recorded. Total water use, also defined as seasonal evapotranspiration (ET), was calculated based on the following equation (Zhang et al., 2008): ET = P + I + W − R − D + CR

(1)

where W is the difference between the initial and final soil water contents throughout the root zone profile, P is precipitation, I is irrigation, R is runoff, D is drainage and CR is capillary rise. R and CR were negligible, due to the less rainfall and deep groundwater table. D was calculated based on the relationship of soil moisture with unsaturated hydraulic conductance at the bottom of the root zone profile. WUE was calculated by dividing the grain yield by ET. 2.7. Calculation of different indicators This study selected an analogous index to calculate CWSI by substituting Tdry for D1 (the lower bound) and Twet for D2 (the upper bound) (Jones 1999) as the following: CWSI =

Tc − Twet Tdry − Twet

ATSW TTSW

actual − pw fc − pw

Zr

(5)

where RLDz is the root length density (RLD) at soil depth of z (cm) and RLDmax is the maximum RLD throughout the soil profile, which usually occurred in the surface soil layer. RLDz /RLDmax represents the relative root length abundance at depth Z in contrast with the maximum root length density. ı was a form parameter used for the root distribution simulation and its value was set at 3 for the winter wheat in this study based on Zhang et al. (2012). The average maximum rooting depth (Zr ) for winter wheat was determined by the days after germination (DAG). Zr was equal to the DAG in cm and reached the maximum depth of 50 cm before over-wintering. After winter dormancy (at the end of February the following year), Zr increased at the rate of 1.5 cm per day until maturity (Zhang et al., 2012). Here, the average maximum rooting depth was used as the effective rooting depth. For each soil layer, RASWr was calculated using the equation: RASWr(z) = RASW(z) × RLDz /RLDmax = RASW(z) × exp[(−ı(Z/Zr )]. The weighted average of RASWr(z) (RASWr(z) divided by the sum of the root factor along the root zone profile) was the value of RASWr. This calculation involves the active rooting depth and the form parameter for root distribution. The value of RASWr was in the range of 0 to 1. 2.9. Statistical analysis For each treatment, the least significant differences (LSD) test (P < 0.05 and 0.01) was performed, and data were analyzed for correlated different traits using Excel software (Clewer and Scarisbrick, 2001). 3. Results

(3)

where total transpirable soil water (TTSW) was estimated as the stored soil water between an upper and a lower limit in the root zone during different growing stages. The upper limit was identified as the soil water content at the field capacity. The lower limit was defined as the lowest soil moisture that the crop could not uptake. Under the condition of this study, the soil moisture at harvest along the root zone profile for the rain-fed treatment (I0) was taken as the lower limit when the soil was very dry. This lower limit was different from the permanent wilting point, because it weighted the root distribution and was related to the crop uptake ability of soil water at different soil depth. The actual transpirable soil water (ATSW) was determined as the soil moisture minus this lower limit baseline down to the maximum rooting depth at different growth stages. RASW was calculated as RASW =

 Z 

(2)

where Twet and Tdry are the measured temperatures of the similarly exposed wet and dry reference surfaces, respectively, as the basis for comparison with Tc (the measured average leaf temperatures). FTSW was calculated as (Ritchie, 1981): FTSW =



RLDz = exp −ı RLDmax

(4)

where  actual represents the actual soil water content along the root zone profile,  fc represents the soil water content at field capacity and  pw represents the soil water content at the permanent wilting point (Jensen et al., 1998).

3.1. Changes in soil moisture and reference evaporation (ET0 ) during the four growing seasons Various soil moisture conditions were created by different irrigation managements during the four seasons (Fig. 2). The changing in soil water contents in the rooting zone is both related to water supply and demand. Soil moisture conditions were maintained at relatively stable levels during the earlier growing season of winter wheat due to the lower reference ET0 (Fig. 2) and the smaller crop canopy. After winter dormancy, a remarkable decrease was found in the soil moisture for I0, suggesting that the rainfall could not support the normal growth of wheat, especially during the rapid growing duration from jointing (beginning of April) to grain-fill (middle of May). For other treatments, a much longer high-moisture period was maintained by the irrigation applied at the earlier jointing stage. With the rapid increase in canopy size and the rise in atmospheric evaporation demand, the soil moisture decreased which was more apparent under limited irrigation treatments (such as I1 and I2). For the I3, I4 and I5 treatments, the more frequent irrigation resulted in a much higher and stable level of soil moisture during most of the growing season. During maturity, the higher ET0 resulted in the higher water use and soil moisture decreased significantly (Fig. 2). For the less irrigated treatments of

Reference ET (mm/d)

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X. Zhang et al. / Agricultural Water Management 153 (2015) 32–41 Grainfill to maturity

Anthesis to earlier Jointing to grain fill heading

10 9 8 7 6 5 4 3 2 1 0

Recovery Seedling

Winter dormance

Soil water contents (v/v)

0.4

Days after sowing 0.3 0.2 0.1

No irrigation (I0)

One irrigation (I1)

Two irrigations (I2)

Three irrigations (I3)

Four irrigations (I4)

Five irrigations (I5)

0.0 0

50

100

150

200

250

Days after sowing

Fig. 2. The average daily reference evapotranspiration (ET0 ) and the average soil water contents for the active rooting zone at the main growing stages of winter wheat during the four growing seasons of winter wheat under the six irrigation treatments from 2010 to 2014.

I0 and I1, there was not much water available for crop water use and then soil moisture was maintained at constant lower level. 3.2. Root distribution and relative root abundance simulation The distribution of RLD determines the soil water availability to crops along the root-zone profile. Fig. 3a shows the distribution of the relative root length abundance at the recovery, jointing and maturity stages using the root data sampled from the field and the simulated values calculated from Eq. (5) for a typical season (the average of six irrigation treatments). The shape of the relative root abundance distribution throughout the soil profile at the three stages was similar, except for the maximum rooting depth. Fig. 3b shows that the general distribution of root length density for winter wheat under various soil water conditions was similar. For a crop at a given site, the distribution of the relative root abundance could be simulated using a form parameter and the rooting depth. Then it was relatively easier to incorporate the root factor into the RASW. Fig. 3a and b showed that there was relatively a large difference in

a

b

Fig. 3. (a) Measured and simulated relative root length abundance along the soil profile (average value of different treatments) and the simulated values at the recovery, jointing and maturity stages in 2011–2012 growing season; (b) Measured and simulated value relative root length abundances for five irrigation treatments (from non-irrigation I0 up to four irrigations I4) and the simulated values at maturity in 2010–2011 growing season.

Fig. 4. Root length density and soil volumetric water contents along the root zone profile at three different growth stages of winter wheat for treatment without irrigation (I0 treatment) during 2010–2011 season. (The shaded parts indicated the distribution of soil water uptake during the two periods from recovery to jointing and from jointing to maturity.)

measured and simulated relative root abundance from 20 to 60 cm soil layer. The simulated ones were greater than the measured ones which might be caused by the higher soil bulk density at these soil layers (Table 1). Zhang et al. (2012) demonstrated that subsoil compaction significantly reduced the root growth and interrupted the normal root distribution. Incorporating soil bulk density into Eq. (5) could improve the accuracy in simulating the root distribution (Zhang et al., 2012). Although there was some discrepancy between the measured and simulated root distribution, the simulated results could represent the general distribution of root length density for winter wheat under various soil water conditions Fig. 4 shows the changes in the RLD and soil water contents within the root zone soil profile from recovery to maturity for the I0 treatment during the 2010–2011 season, a very dry season. The effective rainfall was 4 mm from recovery to jointing and around 20 mm from jointing to maturity during that season. The crop water use mainly came from the stored soil water before sowing. From recovery to jointing stages, the root water uptake occurred in the top soil layers where the root abundance and soil water contents were both greater. From jointing to maturity, with the increase in root growth in deeper soil layers (100–140 cm) and the decrease in the soil water contents in the upper soil layers, more water was extracted from the deep soils. The soil water availability to crop depends not only on the soil water contents, but also on the relative abundance of the crop root system in a certain soil layer. Therefore, the inclusion of root factor into the RASW would improve the relationship of the RASW with crop water status. 3.3. Indicators of crop water status as calculated by different methods The changes in the CWSI, FTSW, RASW and RASWr in a typical season (taken 2010–2011 season as an example) are shown in Table 3 under three irrigation treatments (no irrigation, two irrigations and five irrigations) after winter dormancy. Under no irrigation condition (I0), winter wheat encountered a serious water stress, with a seasonal mean CWSI that was approximately 25% higher than the other treatments and nearly approached nontranspiring point at maturity. While under conditions with more water supply (five irrigations), an average CWSI of 0.491 was observed, which was much lower than that of I0, which had an average value of 0.614. Due to the lower soil water contents in the root zone, the FTSW, RASW and RASWr values under I0 were consistently lower than those under other treatments. Under less

X. Zhang et al. / Agricultural Water Management 153 (2015) 32–41

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Table 3 Changes in crop water stress index (CWSI), fraction of transpirable soil water (FTSW), RASWr, relative available soil water (RASW) under three irrigation treatments from recovery to maturity in 2010–2011 growing seasons.* Indicators

Treatments

Growth stage Recover

Jointing

Heating

Anthesis

Grain-fill

Maturity

CWSI

No irrigation Two irrigations Five irrigations

0.45a 0.48a 0.37b

0.54a 0.41b 0.32c

0.73a 0.56b 0.47b

0.68a 0.51b 0.43b

0.77a 0.68b 0.61b

0.93a 0.89a 0.76b

FTSW

No irrigation Two irrigations Five irrigations

0.25c 0.39b 0.47a

0.20c 0.46b 0.54a

0.11c 0.32b 0.50a

0.13c 0.42b 0.58a

0.08c 0.27b 0.51a

0.08b 0.09b 0.22a

RASW

No irrigation Two irrigations Five irrigations

0.36c 0.45b 0.51a

0.33b 0.49a 0.55a

0.27c 0.41b 0.53a

0.29c 0.55b 0.68a

0.22c 0.36b 0.55a

0.24b 0.29b 0.39a

RASWr

No irrigation Two irrigations Five irrigations

0.21c 0.30b 0.41a

0.15c 0.49b 0.61a

0.10c 0.28b 0.58a

0.25c 0.59b 0.74a

0.12c 0.30b 0.68a

0.13c 0.21b 0.30a

*

Under the same column for the same indicator, the values followed with the same letter were not significant at P < 0.05.

irrigation, with the decrease in the soil water conditions (as shown in Fig. 2), the CWSI increased, and the FTSW, RASW and RASWr decreased (Table 3). Table 3 also shows that the difference among different treatments was greater for the RASWr than for the RASW. The incorporation of the root factor to the RASW improved its sensitivity to the variations in the soil water conditions related to crop water use. 3.4. Correlation of different indicators with crop water status As shown in Fig. 5, a similar relationship between leaf stomatal conductance (SC) and leaf water potential (LWP) with CWSI, FTSW RASWr and RASW was found. However, a significant linear correlation was found for the three factors: the CWSI, FTSW and RASWr with SC and LWP. RASW had less significant relationship with SC or LWP, whereas the RASWr was more closely related to the changes in SC and LWP. The incorporation of root factor into the RASW greatly improved its sensitivity in indicating the crop water status. CWSI had a distinct diurnal change pattern that was closely related to the daily radiation change. When the atmospheric evaporation demand was greater, the difference in the CWSI was also greater among the different irrigation treatments (Fig. 6); otherwise, the difference was reduced. Therefore, the time set in the thermometry had a major impact on the value of the CWSI for estimating crop water status. Fig. 6 also shows that from 11:30 to 13:30 local time, the CWSI was more stable during the day. Therefore, it is better to use the CWSI during that time to indicate crop water status. Considering the small RLD in the deep soil layer, the RASW was again calculated based on the soil water conditions in only the upper half part of the root zone (RASW-half) and were compared with the RASWr under three irrigation treatments from recovery to maturity, by taking the example in 2012–2013 growing season. During the entire growing season, the RASW was always greater than the RASW-half and RASWr, and the RASWr was between the RASW and RASW-half under sufficient irrigation treatments. In contrast, under less irrigation treatments, the RASWr and RASWhalf were similar, and the RASW was much greater than the other two parameters. The results showed that the soil water availability to crops along the root zone profile should not be taken equally and the root distribution factor has played an important role in determining the soil availability to crops. 3.5. Correlation of different indicators with crop yield The grain yield, seasonal evapotranspiration (ET) and WUE during the four seasons under the six irrigation treatments are

listed in Table 4. The ET generally increased with the increase in irrigation, while the grain yield did not respond to the irrigation in the same way as did the ET. The yield of winter wheat increased with the increase in irrigation, and when irrigation reached a certain level, the yield was stabilized or even slightly reduced with a further increase in irrigation. The highest yield was achieved when the irrigation was applied three times in the dry seasons of 2010–2011 and 2013–2014, and twice in the normal rainfall seasons of 2011–2012 and 2012–2013. The WUE was usually greater under the less-irrigated treatments, except for that in 2010–2011, when the seasonal rainfall was not only small, but mostly fell during the late grain fill stage of winter wheat. The treatment without any irrigation (I0) produced the lowest yield, and the WUE also significantly decreased. Table 4 also shows that more irrigation did not necessarily produce the highest yield under the growing conditions of NCP, where the grain-fill duration was shorter. Many studies in the same region have demonstrated that a moderate water deficit accelerated the development of winter wheat and anthesis was advanced by 2–4 days, extending the grain-fill duration. The mobilization of the assimilate stored in the vegetative tissues to grains was increased, resulting in greater grain yield and WUE (Zhang et al., 2008). In the serious water shortage regions of the NCP, moderate irrigation should be adopted for a greater yield and higher WUE of winter wheat. Therefore, it is important to determine when to irrigate for maximum yield and irrigation water-saving based on the indicators of the crop water status. The relationships between the final grain yield with the corresponding crop water indicators (as evaluated with the CWSI, FTSW, RASW and RASWr) are presented in Fig. 7. A significant correlation existed between the grain yields and these indicators. All of the indicators were calculated during the grain-fill stage. During this period, the soil moisture conditions were more representative, it might be considered as the cumulative effects of all of the irrigations during the growing season on the soil moisture conditions. The highest yield of winter wheat was not achieved with the best soil moisture conditions of all the four indicators, similar to the results that are listed in Table 4. The correlation coefficient of the four indicators with the grain yield of wheat reached a significant level, while the relationship of the RASWr with the grain yield was much closer than RASW. The results strongly suggested that similar to the CWSI and FTSW, the RASWr based on soil water changes and plant root development can be considered a stabilized indicator to evaluate the soil water availability to the crop and to guide the timing of irrigation.

X. Zhang et al. / Agricultural Water Management 153 (2015) 32–41

1.2

1.2

1.0

1.0

0.8

0.8 CWSI

38

0.6 y = 0.133x + 0.589 R² = 0.426*

0.4

0.4

0.2

0.2

-2

-1

y = 0.204x + 0.910 R² = 0.455*

0

0 1.2

1.2

1.0

1.0

0.8

0.8

FTSW

-3

0.6

0.4

0.2

0.2

y = 0.144x + 0.9489 2

R = 0.3387

1.2

1.2

1.0

1.0

0.8

0.8

0.4

0.2

0.2

y = 0.3552x + 1.2954 R2 = 0.5514**

0 1.2

1.0

1.0

0.8

0.8

0.4 0.2

-2

-1

0.4

0.6

0.8

1

0.2

0.4

0.6

0.8

1

y = 0.707x - 0.037 R² = 0.617**

0.6 0.4 0.2

0.0 -3

0.2

y = 0.2817x + 0.4919 2 R = 0.1434

0

1.2

0.6

-4

1

0.0

-1

RASWr

-2

0.8

0.6

0.4

0.0 -3

0.6

y = 0.563x + 0.189 R² = 0.345*

0

0

0.6

-4

0.4

0.0

-1

RASW

-2

0.2

0.6

0.4

0.0 -3

-4

y = -0.303x + 0.720 R² = 0.422*

0.0

0.0 -4

0.6

0.0 0

Leaf water potential (MPa)

0

0.2

0.4

0.6

0.8 -2

1 -1

Stomatal conductance (mol H2O m s )

Fig. 5. Relationships of leaf water potential and stomatal conductance with crop water stress index (CWSI), fraction of transpirable soil water (FTSW), RASWr, relative available soil water (RASW) at heading stage during 2012–2013 growing season of winter wheat (significant at *P < 0.05 and **P < 0.01).

3.6. Optimizing the irrigation schedule of winter wheat using the RASWr The treatment with the highest grain yield in the four seasons was used as the optimized irrigation schedule; its RASWr was used as the optimized soil water indicator. The RASWr from the treatment with one additional irrigation more than the optimized treatment was used as the upper limit. Correspondingly, the RASWr from the treatment that had one irrigation less than the optimized treatment was used as the lower limit. The average lines of the three RASWr during the four seasons were shown in Fig. 8. Although the optimal irrigation schedule was different among the four seasons, the average values of the optimized RASWr at different stages

were similar (Fig. 8). An average RASWr of 0.5 can be used as the threshold value for irrigation for winter wheat. Generally, before sowing winter wheat in the NCP, the heavy rainfall in the summer monsoon season can bring the soil water contents near field capacity. During the seedling and overwintering stages, the crop transpiration was small, and the soil water contents were generally sufficient for crop water use; therefore, the RASWr remained at a higher level. After recovery, the rapid development in the crop canopy and the increase in the evaporation demand resulted in a greater evapotranspiration rate. The soil moisture decreased steadily under the less-irrigated treatments, especially in the top soil layers. In contrast, with the increased rooting depth, the soil water stored in the deep soil layer

X. Zhang et al. / Agricultural Water Management 153 (2015) 32–41

39

Table 4 Grain yield (GY, kg ha−1 ), water use efficiency (WUE, kg/m3 ) and evapotranspiration (ET, mm) of winter wheat under six irrigation treatments for four growing seasons.* Treatments

GY

0 irri. 1 irri. 2 irri. 3 irri. 4 irri. 5 irri.

2010–2011 3017.5d 6141.0c 6613.1b 7845.0a 7755.1a 7920.5a

0 irri. 1 irri. 2 irri. 3 irri. 4 irri. 5 irri.

2012–2013 4629.8d 5771.5c 6817.0a 6483.0ab 6223.6b 6272.1b

*

WUE

ET

GY

WUE

ET

1.27c 1.83a 1.75a 1.81a 1.42b 1.43b

237.0d 335.9c 378.6c 433.6b 546.2a 550.9a

2011–2012 4597.2c 6244.2b 6796.9a 6363.7b 6520.8b 6618.6b

2.14a 1.95a 1.78b 1.54c 1.47c 1.53c

215.1c 320.0b 381.8b 412.5ab 473.5a 487.0a

1.80a 1.82a 1.87a 1.69b 1.62b 1.51c

251.1c 321.9bc 364.5b 383.0ab 385.1ab 442.0a

2013–2014 5621.7c 6965.4b 7643.3a 7981.4a 7715.2a 7687.0a

2.26a 2.07ab 1.90b 1.73bc 1.43c 1.47c

261.5c 348.2bc 440.3b 486.1ab 588.4a 574.5a

In the same column under the same season, the values followed with the same letter were not significant at P < 0.05.

Fig. 6. Daily changes of solar radiation and crop water stress index (CWSI) of the four irrigation treatments from 8:00 am to 17:00 pm local time at flowering stage in 2012–2013.

gradually became available for crop water use. Although the RASWr decreased slightly, it was maintained at a relatively stable value under the optimized irrigation management. 4. Discussion and conclusions With the increase in fresh water shortage around the world, it is essential to develop the most suitable irrigation schedule to produce the optimum plant yield. Such a schedule should be

developed for different ecological regions, as plant water consumption depends mostly on plant growth, soil and climatic conditions (Bellot and Chirino, 2013). Deficit irrigation, defined as the application of water below full crop-water requirements, is an important tool for reducing irrigation water use. Extensive studies have demonstrated that deficit irrigation does not necessarily reduce crop production (e.g., Candogan et al., 2013; Fereres and Soriano, 2007). Especially under the growing conditions of winter wheat in the NCP, a moderate water deficit accelerates crop development and advances the timing of anthesis to extend the grain-fill duration. Zhang et al. (2008) demonstrated that the highest grain yield of winter wheat in this region was produced with 84% of the seasonal full ET. This study further demonstrates that the highest yield of winter wheat is usually produced with a moderate irrigation supply (Table 4). More than the optimized irrigation supply, the yield of winter wheat was not increased, and the WUE was significantly reduced. Therefore, it is important to apply an optimized irrigation schedule for maximum yield and better WUE in the NCP, which would favor the conservation of groundwater resources in this region. Due to the influence of weather factors and the changes in seasonal rainfall and its distribution, using indicators of crop and soil water status to decide when to irrigate is important for the application of optimized irrigation schedule. Since Idso et al. (1981) first defined the crop water stress index, the CWSI has been widely used

Fig. 7. Relationships of grain yield with drought–stress different indicators (crop water stress index (CWSI), fraction of transpirable soil water (FTSW), RASWr, relative available soil water (RASW)) calculated during the grain-fill stage for four growing seasons under different irrigation treatments (**: P < 0.01; ***: P < 0.001).

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X. Zhang et al. / Agricultural Water Management 153 (2015) 32–41

Fig. 8. Upper and lower limit values of RASWr with the maximum grain yield under during the main growth stages of winter wheat.

as an indispensable tool for irrigation scheduling and yield estimation. The results from this study demonstrate that both the CWSI and RASWr are closely related to the crop water status, such as leaf water potential and stomatal conductance (Fig. 5). However, the CWSI fluctuates daily and is easily influenced by the weather conditions (Fig. 6), which increases the difficulty of using the CWSI to evaluate the plant water status. Previous studies have demonstrated that the canopy conditions also impact the variation in the CWSI (Gontia and Tiwari, 2008; Chen et al., 2010; Taghvaeian et al., 2014). Thus, when using the CWSI as an indicator for irrigation, other factors, such as weather fluctuation and crop characteristics, should also be considered (Farooq et al., 2009). Compared to the CWSI, which mainly focuses on the aboveground parts of plants, FTSW is more correlated with the available soil water to crops. Because soil water content is not directly influenced by the diurnal weather fluctuation, indicators based on soil water status might be more stable. FTSW has been widely used to evaluate the crop water deficit degree and has also been used as a screening criterion for the drought tolerance of different cultivars (e.g., Jacobsen et al., 2009; Gholipoor et al., 2012). However, the determination of the FTSW requires the lower limit of total transpirable soil water, which is related to crop type, growing stages and active rooting depth (Lacape et al., 1998). In this study, the lower limit of the total transpirable soil water was determined using the soil water contents at harvest for the least irrigated treatment (I0), and significant correlations were found between the FTSW with the crop water status and the final grain yield. Then, the soil water contents along the root zone profile under rain-fed conditions at harvest could be considered as the lower limit of the total transpirable soil water under the conditions of this study. RASW was easily obtained, but only indicates the total amount of soil available moisture and is not directly related to crop characters. Many studies have demonstrated that the availability of the soil moisture to crops depends not only on the soil moisture conditions, but also on the root system (Bengough et al., 2011; White and Kirkegaard, 2010). Zhang et al. (2004) found that when the RLD was less than 0.8 cm/cm3 , root was a limiting factor for crops to fully utilize the soil moisture. In the NCP, during the growing period of winter wheat, rainfall was far less than the crop water requirements, and the soil moisture that was stored before sowing played an important role in supplying the water use of this crop. Zhang et al. (2013) showed that soil water depletion contributed approximately 40–50% of the seasonal ET. In the deep soil layers, the RLD was usually smaller, and the soil water availability to crops would be limited by the root factor. Thus, the modification of RASW by combining the root distribution factor greatly improved its relationship with crop water status. The calculation of RASW was also simplified by introducing the relative root abundance concept and by using a simple form parameter to simulate the root distribution.

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