Agriculture, Ecosystems and Environment 240 (2017) 287–299

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Agriculture, Ecosystems and Environment
journal homepage: www.elsevier.com/locate/agee

Modelling soil water balance and root water uptake in cotton grownunder different soil conservation practices in the Indo-Gangetic Plain
Pramila Aggarwala , Ranjan Bhattacharyyaa,* , Amit Kumar Mishraa , T.K. Dasa ,
J. Šimunekb , P. Pramanika , S. Sudhishria , A. Vashistha , P. Krishnana , D. Chakrabortya ,K.H. Kambleaab

ICAR-Indian Agricultural Research Institute (IARI), New Delhi 110012, India
Department of Environmental Sciences, University of California, Riverside, USA

A R T I C L E I N F O

Article history:
Received 19 October 2016
Received in revised form 17 February 2017
Accepted 21 February 2017
Available online xxx
Keywords:
Permanent broad and narrow beds
Leaf Area Index
Soil hydraulic properties
Potential transpiration rates
HYDRUS-2DA B S T R A C T

Although soil conservation practices are being promoted as better environmental protection
technologies than traditional farmers’ practice, limited information is available on how these practices
affect soil water balance and root water uptake. The root water uptake (RWU) patterns of cotton grownunder soil conservation practices and soil water balance in cotton (Gossypium hirsutum L.) ﬁeldsunder acotton-wheat (Triticum aestivum L.) cropping system were analyzed using the Hydrus-2D model. The
treatments were: conventional tillage (CT), zero tillage (ZT), permanent narrow beds (PNB), permanent
broad beds (PBB), ZT with residue (ZT + R), PNB with residue (PNB + R) and PBB with residue (PBB + R).
Results in the third year of the cotton crop indicated that the surface (0–15 cm layer) ﬁeld saturated
hydraulic conductivity in both PNB and PBB plots were similar and were signiﬁcantly higher than in the
ZT plots. Computed potential transpiration rates (Trp) under CT were lower than in other treatments, due
to less radiation interception and lower Leaf Area Index (LAI). Both PNB and PBB plots had higher Trp andcrop yields than CT plots, which were further improved by residue retention. Predicted soil water content(SWC) patterns during the simulation periods of third and fourth years showed strong correlation(R2 = 0.88, n = 105, P < 0.001, the root mean square error (RMSE) = 0.025, and the average relative error(AVE) = 7.5% for the third year and R2 = 0.81, n = 105, P < 0.001, RMSE = 0.021, and AVE = 9% for the fourth
year) with the actual ﬁeld measured SWCs. Cumulative RWU (mm) were in the order: ZT (143) < CT(157) < PNB (163) < ZT + R (174) < PBB (188) < PNB + R (198) < PBB + R (226). Thus, PBB + R and PNB + R
practices could be adopted for cotton cultivation, as these enhanced root growth and improved radiationinterception and LAI. The Hydrus-2D model may be adopted for managing efﬁcient water use, as it cansimulate the temporal changes in SWC and actual transpiration rates of a crop/cropping system.
© 2017 Elsevier B.V. All rights reserved.

1. Introduction
In the conventional systems involving intensive tillage, there isa gradual decline in soil organic matter (SOM) through accelerated
oxidation and burning of crop residues (which is a common

Abbreviations: CT, conventional tillage; ZT, zero tillage; PNB, permanent narrow
beds; PBB, permanent broad beds; ZT + R, ZT with residue; PNB + R, PNB withresidue; PBB + R, PBB with residue; RWU, root water uptake; RLD, root lengthdensity; SWC, soil water content; BD, bulk density; DAS, days after sowing; Trp,
potential transpiration; Tra, actual transpiration; RMSE, root mean square error; AVE,
average relative error; SOM, soil organic matter; IGP, Indo-Gangetic Plains.
* Corresponding author.
E-mail addresses: ranjan_vpkas@yahoo.com, ranjanvpkas@gmail.com(R. Bhattacharyya).
http://dx.doi.org/10.1016/j.agee.2017.02.028
0167-8809/© 2017 Elsevier B.V. All rights reserved.

practice in the upper Indo-Gangetic Plains). This causes environmental pollution and loss of valuable plant nutrients. Hence,resource conservation issues have drawn the attention of scientists
to devise innovative soil conservation practices for higher
productivity (Bhattacharya et al., 2015). Conservation agriculture(CA), one such novel soil conservation practice, involves minimum
soil disturbance, providing a soil cover through crop residues orother cover crops, and crop rotations for achieving higher
productivity and minimizing adverse environmental impacts(Hobbs et al., 2007; FAO, 2010). When the crop residues are
retained on the soil surface, it initiates processes that lead to
improved soil quality by protecting the soil from raindrops andlimiting water evaporation. Therefore, CA may lead to sustainableimprovements in the efﬁcient use of water by increasing
inﬁltration and soil water retention and reducing water losses

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P. Aggarwal et al. / Agriculture, Ecosystems and Environment 240 (2017) 287–299

due to evaporation, and by improving nutrient balances and theiravailability (Dahiya et al., 2007; Govaerts et al., 2007; Verhulst
et al., 2010; Bhattacharya et al., 2013). CA also improves soil health(mainly by increasing SOM content).
Studies on zero tillage (ZT) and bed planting technologies havelargely been conducted on wheat in the rice (Oryza sativa L.)–wheat(Triticum aestivum L.) cropping system in the Indo-Gangetic Plains(IGP). There is already greater emphasis on crop diversiﬁcation,
due to growing concerns about the unsustainability of the rice–wheat system in this region (Bhandari et al., 2003; Aggarwal et al.,2004). In this context, a crop such as cotton (Gossypium hirsutum L.)
has emerged as a promising alternative to rice in the Kharif (rainy)season (Das et al., 2014). In this region, especially in the states of
Uttar Pradesh, Punjab, Haryana and Rajasthan, >95% of the area ofrice and wheat area under irrigation. All these conditions call for
efﬁcient use of resources to achieve sustainable production thatcan minimize water use. Potential improved productivity andassured high returns could be realized from the cotton–wheat
system, thereby improving the livelihood of farmers in the region(Mayee et al., 2008).
The raised bed technology enhances water use efﬁciency,
improves soil physical environment and encourages root proliferation that gives better yields for upland crops, including maize (Zeamays L.), wheat, soybean (Glycine max L.) and cotton (Sayre andHobbs, 2004; Zhongming and Fahong, 2005; Aggarwal et al., 2006;
Thierfelder and Wall, 2012; Ahmad et al., 2014). Ahmad et al. (2011)indicated that the bed-furrow system (60 cm bed and 30 cm
furrow) is most suited for cotton when the plant-to-plant distance
was maintained at 30 cm. Selected geometries of bed-furrow
planting of cotton were further evaluated for water savings
compared with traditional practices of ﬂat sowing. Results
obtained from cotton ﬁelds showed a 40% water saving and10-15% yield increase in the IGP. The results thus suggested thatthe raised bed technology has sufﬁcient potential to economize
water use and improve water productivity of cotton-based
cropping systems (Tursunov, 2008).
For both ZT and bed planting methods, retention of residues on
the surface can modify soil water content. If the soil surface iscovered with residues, it is shielded from solar radiation and hence
the evaporation rate from the surface is less than bare soils. In
addition, an increase in the thickness of the boundary layer
between soil and (open) atmosphere also decreases evaporation
rates. Although the surface moisture under the residue will
evaporate slower, after the wetting event, cumulative evaporationfrom the residue-covered surface can exceed that of the baresurface (Van Donk et al., 2010).
Numerical simulations are an efﬁcient approach to investigate
soil water dynamics for optimal irrigation management practices(Meshkat et al., 1999; Cote et al., 2003). Many studies have showedthat numerically simulated soil water data agree with ﬁeld data.
For instance, De Silva et al. (2008) used Hydrus (2D/3D) tonumerically evaluate root water uptake (RWU) and soil water
movement in land areas with a mixture of natural vegetative cover(i.e. trees and grasses) and concluded that different irrigation
amounts and frequencies should be used for different plant species
in irrigated strip intercropping ﬁelds. Deb et al. (2013) evaluatedspatio-temporal compensated and uncompensated RWU patterns
of mature pecan trees in a silty clay loam orchard using the Hydrus(2D/3D) model. They concluded that: (i) simulated soil watercontents (SWCs) and temperatures at different times agreed wellwith measured soil water contents and temperatures and that (ii)simulated transpiration and relative transpiration values werestrongly correlated with measured transpiration and plant-based
water stress indicators (stem and leaf water potentials) (Deb et al.,2013). In another ﬁeld study, Buttar et al. (2006) revealed that
wheat productivity per unit irrigation water was higher in the plots

under the bed planting method compared with the conventionallyplanted plots. Irrigation scheduling based on the CERES (‘Crop
Environment Resource Synthesis’) Model (Ritchie, 1998) performed better than a conventional farmers’ practice (Arora et al.,2007).
Skaggs et al. (2004) compared Hydrus-2D simulations of waterinﬁltration and redistribution with ﬁeld data (at the San Joaquin
Valley Agricultural Sciences Center, located near Fresno, California)
and observed that the Hydrus-2D predictions of the water content
distributions were in very good agreement with ﬁeld data. Theirresults supported the use of the Hydrus-2D Model as a tool forinvestigating and designing drip irrigation management practices.
In a study of Bufon et al. (2012), the latest version of the Hydrus-2D
Model was used to simulate soil water movement under different
irrigation rates and environmental conditions during cotton
growth on beds. The results indicated that SWC data simulatedby Hydrus-2D were very accurate (deviations were 3%). Thesestudies conﬁrmed that the Hydrus-2D Model can be used toevaluate irrigation strategies for cotton.
The major goals of this study were: (i) to calibrate and validate
the Hydrus-2D Model for predicting soil water distributions underconventional and soil conservation practices (ZT, and broad andnarrow beds with residue retention) of growing cotton in the IGP
and (ii) to simulate changes in SWC in the cotton root zone and
RWU during drying of an initially watered soil under soil
conservation practices. It was hypothesized that: (i) permanent
beds would promote root growth, RWU and leaf area, due to lesssoil compaction (Mishra et al., 2015) and more solar radiationinterception, and that (ii) retention of previous crop residues
would further improve SWC and RWU due to less evaporation loss.
The speciﬁc objectives of the study were: (i) to analyse the impactsof permanent narrow and broad beds versus conventional tillage(CT) on SWC distributions and RWU patterns for cotton using theHydrus-2D Model and (ii) to evaluate the effects of residue
retention in PNB and PBB plots on RWU by cotton.
2. Materials and methods
2.1. Field experiment
A CA experiment was initiated in May 2010 at the research farmof the Indian Agricultural Research Institute (IARI), New Delhi,
India, on an alluvial sandy clay loam soil (ﬁne loamy, illitic, Typic
Haplustept) with cotton and wheat as successive crops in a year.
This area is characterized by a semi-arid climate, with highsummer ambient temperatures, high wind speed and an erratic
rainfall distribution. The weather data at the experimental site
during the crop growth of the third and fourth years are presented
in Fig. S1 (Supplementary Information). In both years, maximum
and minimum temperatures during crop seasons varied between
24.2–46.5  C and 5.5–32.2  C, respectively. During the entire crop
seasons in 2012 and 2013, total rainfall was 494 and 1350 mm,
respectively. Pan evaporation varied between 1.5–13.8 mm d1 inboth years.
The surface (0–15 cm) soil of the experimental site had pH 7.7,
Walkley-Black organic C 5.2 g kg1, KMnO4 oxidizable N 182.3 kg
ha1, 0.5 M NaHCO3 extractable P 23.3 kg ha1, and 1 N NH4OAc
extractable K 250.5 kg ha1.The proportions of sand, silt and clay in
the 0–15 cm soil layer were 49.6, 23.2, and 27.2, respectively. The
corresponding values for the 15–30 cm layer were 40.4, 24.5 and35.1, and for the 30–45 cm layer were 42.1, 26.5 and 31.4,
respectively. Thus soil texture of the 0–15, 15–30 and 30–45 cm
layers were sandy clay loam, clay loam and clay loam, respectively.
The experimental treatments consisted of conventional tillage(CT), permanent narrow bed (one row of cotton per 40 cm wide bed
and 30 cm wide furrow) (PNB), permanent broad bed (two rows of

P. Aggarwal et al. / Agriculture, Ecosystems and Environment 240 (2017) 287–299

cotton per 100 cm wide bed and 40 cm wide furrow) (PBB), PBB
with residue (PBB + R), and PNB with residue (PNB + R). These
treatments were established in 2010. From 2011 onwards, two
additional treatments involving zero tillage (ZT) either with
(ZT + R) or without residue retention of previous crops were
established. Thus, the study involved six soil conservation
practices. Among these, three (ZT + R, PBB + R and PNB + R) could
be considered CA practices. The experiment was laid-out in arandomized block design (size: 10.0 m  8.4 m) with three
replications for each treatment. The planting design and root
sampling scheme for both years under different treatments arereported in Fig. 1. After harvest, both cotton and wheat residues
were taken away from the residue removal plots. However, tendercotton twigs and leaves (20% of the total cotton residues) and40% of the wheat residues were retained at the soil surface in all
residue retained plots. In both CT and ZT plots with residue
removal, very little cotton stalk and wheat stubble (10–15 cm)
remained anchored after crop harvests. Conventional tillage
involved one deep (30 cm) ploughing with a disc plough followedby two ploughings each with a disc harrow and cultivator, while noploughing was done in the ZT treatment in any year. Fresh raisedbeds were prepared under PBB and PNB in 2010 with a bed planter,
while the beds were reshaped once a year before sowing of cotton
using a bed planter. Other details of the experiment are presented
by Das et al. (2014) and Mishra et al. (2015).
In all years, Bt cotton (cultivar MRC-7017) was sown manuallyby the end of May at 70  60 cm spacing and harvested in thesecond fortnight of November. A common dose of 150 kg N + 60 kg
P2O5 + 40 kg K2O ha1 was applied to cotton, of which, P and K wereapplied as basal along with 50% of the N. The remaining N was
applied in two equal splits at 30 and 60 days after sowing (DAS) of
cotton. Herbicide glyphosate was applied at 1.0 kg ha1 in the ZT

289

plots about one week before sowing of both crops. Pendimethalin
was applied at 0.75 kg ha1 as pre-emergence at 2–3 DAS.
In addition to one pre-sowing irrigation, eight irrigations were
given to all treatments. In the CT and ZT plots, irrigation wasapplied using a ﬂooding method and the total applied water was
more than PNB and PBB plots, where irrigation water was appliedin furrows. The irrigation water depth applied to each experimental plot was usually measured four times during each irrigation
period using a digital velocity meter and the wetted area of theﬁeld channel. The details of the measurement procedure are given
in Das et al. (2014). In 2012, total irrigation water applied(including a pre-sowing irrigation) during the entire cotton crop in
the plots under CT, PNB, PNB + R, PBB, PBB + R, ZT + R and ZT in 2012
(third year) were 621, 549, 540, 527, 504, 558 and 603 mm,
respectively. In 2013, the corresponding values were 1350, 1255,
1248, 1235, 1220, 1290 and 1265 mm, respectively.
2.2. Selection of simulation period
Model simulations of RWU patterns of cotton and SWC
distributions in the soil proﬁle were performed during 25–
122 days after sowing (DAS) in both years. The initial 0–25 DAS
period was omitted, because root growth, presented in terms ofroot length density (RLD) and average root system diameter(important input variables of the model), was very small to makeany signiﬁcant contribution to total RWU of the season.
2.3. Measurement of soil hydraulic properties
In 2012, undisturbed core samples (with a core of 5.0 cm
diameter and 117.5 cm3 volume) were collected from all plots (intriplicate from each plot) from the 0–15, 15–30 and 30–45 cm soil

Fig. 1. Planting design and root sampling scheme for different soil conservation practices: Permanent broad bed (PBB), Permanent narrow bed (PNB), Conventional tillage(CT), and Zero tillage (ZT).

290

P. Aggarwal et al. / Agriculture, Ecosystems and Environment 240 (2017) 287–299

depths at 30, 60 and 90 DAS of the cotton crop for soil bulk density(BD) determination. Soil cores were used for the determination of
SWCs at 0, 0.33, 1, 2, 5, 10 and 15 bar using a pressure plateapparatus (Klute, 1986). Soil water contents in the soil proﬁle(0–45 cm layer) at 15 cm intervals were also measured gravimetrically on 30, 40, 45, 80, 100 and 105 DAS in years 2012 and 2013. Soil
texture was measured following the international pipette method(Gee and Bauder, 1986). Field-saturated hydraulic conductivity (Ks)was determined at 0–15, 15–30 and 30–45 cm soil layers using a
Guelph Permeameter (Reynolds and Elrick, 1991; Reynolds et al.,2002).
2.4. Root sampling, scanning and analysis
At 30, 60 and 90 DAS, destructive samplings for RLD
measurement in all plots were performed. A 5 cm diameter
sampler was used to collect root samples (200 cm3) from the

0–15, 15–30, and 30–45 cm soil layers from all plots to study thevertical and horizontal distribution of roots around plants. Rootsamples were taken at different growth stages of the cotton crop(in the middle of the simulation period). Sampling depths wererestricted to 45 cm since RLD was negligible (<0.05 cm cm3)
below 45 cm depth. Root samples were taken from the centre of arow by placing the core auger, such that the centre of the core
coincided with the base of the cut plant (Aggarwal et al., 2006).
Another four root samples were taken at 2 cm intervals from theleft and right of the central soil core (Fig. 1). After excavation, soilcores were sealed in polythene bags and brought to the laboratory.
Roots present in the soil cores were washed and scanned, and root
images were obtained using a root length scanner using Winrhizo
software (Aggarwal et al., 2006). The horizontal locations of soilcores were between 2.5 to 2.5, 4.5–9.5 and 11.5–16.5 cm,
respectively, for 0–15, 15–30 and 30–45 cm soil depth layers(Fig. 2). Mapping of the RLD distribution, both vertically andFig. 2. Spatial distribution of the root length density of the cotton crop at the ﬂowering stage under different soil conservation practices (ZT = Zero tillage; PNB = Permanentnarrow bed; PBB = Permanent broad bed; CT = Conventional tillage; ZT + R = Zero Tillage + residue retention; PNB + R = Permanent narrow bed + residue retention;
PBB + R = Permanent broad bed + residue retention) in 2012 (top) and 2013 (bottom). The z coordinates 7.5, 22.5, and 37.5 cm represent a center of 0–15, 15–30, and
30–45 cm soil sampling depths, respectively. Similarly, the x coordinates 0, 7, and 14 represent a center of sampling locations between 2.5 to 2.5 cm, 4.5–9.5 cm, and11.5–16.5 cm, respectively.

P. Aggarwal et al. / Agriculture, Ecosystems and Environment 240 (2017) 287–299

horizontally, was performed through interpolation of measured
data using the inverse distance weighting method. In addition,mean root diameter of the cotton crop for different treatments was
also computed.
2.5. Leaf Area Index and photosynthetically active radiation (PAR)
Leaf Area Index (m2 m2) data for all treatments were recorded
using 1 m2 quadrate from three locations in each plot at 30, 60, 90
and 120 DAS (Das et al., 2014) in 2012. A Point Quantum Sensor(Apogee; Model MQ-200) was used to measure incident andintercepted photo synthetically active radiation (PAR) by cotton
canopy. PAR measurements (mmol m2 s1) were taken above thecanopy with the sensor facing the sky to account for received
incident radiation (Io’) and with the sensor facing the canopy toaccount for reﬂected radiation from the canopy (Ir). The actual
incident radiation (Io) is the difference between Io’ and Ir.
Measurements were also taken below the canopy, keeping thesensor just above the soil, but across the rows with the sensor
facing upwards, for the transmitted radiation (It’) through the
canopy and for the emitted and/or reﬂected radiation from the soil
(Ie). Thus, actual transmitted radiation (It) is the difference between
It’ and Ie. Three sets of measurements were recorded in each plot
and averaged. The above measurements were taken on 30, 60, 90
and 120 DAS on clear days between 1100 and 1200 h IST, when
disturbances due to leaf shading and solar angle were at theirminimum. These measurements were used to derive fraction
intercepted PAR (fIPAR):f IPar ¼

Io  I t
Ioð1Þ

The canopy fIPAR and LAI were related using following equation(Monsi and Saeki, 1953):
f IPar ¼ 1  expðk  LAIÞð2Þ

where k is the canopy radiation extinction coefﬁcient and LAI is theLeaf Area Index (Robertson et al., 2001). The k was determined on
30, 60, 90 and 120 DAS from the measured values of fIPAR and LAI
for each treatment. Values for fIPAR and LAI for each day after thestart of simulation were interpolated between actual measurements by linear interpolation throughout the simulation period.
The computed k values were used for measurement of potential
transpiration rates (Trp).

Daily weather data (maximum and minimum temperatures,
rainfall and pan evaporation) were recorded from the meteorological observatory adjacent to the experimental site. Transpiration
rate Tr(t) at a given time t is related to mean transpiration rate
Trmean using the following equations (Fayer, 2000):TðtÞ ¼ 0:24TmeanTðtÞ ¼ 2:75Tmean

 t < 0:264d;
 t > 0:736d ð3aÞ2 pt
p
Þ ð3bÞ
Sint 2 ð0:264d; 0:736d
1day 2

ð3Þ

Hence, the maximum transpiration rate Trmax (which occurs
when u = p/2) is equal to 2.75Trmeanor Trmean = Trmax/2.75

2.7. Hydrus-2D model and its calibration
2.7.1. The Hydrus-2D model
The Hydrus-2D (Šimunek et al., 2008) model is a Microsoft
Windows based software package for simulating temporalvariations in soil water content distributions and RWU. Hydrus2D is a ﬁnite element model that numerically solves the Richards
equation for saturated and unsaturated water ﬂow. Root water
uptake is included as a sink term that can be computed using theFeddes RWU Model (Feddes et al., 1978). Details about the model
can be obtained from the Hydrus-2D technical manual (Šimunek
et al., 2011). Calibration of the Model was performed using datafrom 2012, whereas validation and simulation of different
components of soil water balance were done for both years.
Details of steps of calibration of the model along with the various
input parameters required are discussed below.
2.7.2. Domain geometries and its discretization
Geometries characterizing the soil transport domain fordifferent CA practices used in Hydrus-2D modelling are shownin Fig. S2 (Supplementary Information). A default value (2.2 cm) of
the targeted ﬁnite element size, as suggested in the Hydrus-2D
Model, was used for the spatial discretization and mesh generation(Supplementary Information, Fig. S3). Three observation points
were inserted at (0, 7.5) (0, 22.5) and (0, 37.5) in the transportdomain for the model to report continuous changes in SWCs withtime. Calculated water contents at these three observation points
were compared with experimentally measured water contents(that were measured near the base of the plant) in the 0–15, 15–30
and 30–45 cm soil layers, respectively.
2.7.3. Soil hydraulic properties
In the present study, the van Genuchten-Mualem model (Van
Genuchten, 1980) was used to represent the water retention curve[Se(C)] and the unsaturated hydraulic conductivity function [K
(u)]:
Se ¼

u  uru s  ur
"

1
Se ¼
n
1 þ ðacÞ

ð5Þ

#m

h

m i2
KðuÞ ¼ K s Sle 1  1  Se1=m

2.6. Weather parameters and transpiration rate

(4)

The maximum transpiration rate (Tmax) was measured in theﬁeld using a porometer at 1200 (because the peak transpiration
rate occurs during this period) (Wang et al., 2007) on 45, 75 and105 DAS.

291

ð6Þ

ð7Þ

where, Se is the effective soil water content, us and ur are thesaturated and residual water contents, respectively, Ks is saturated
soil hydraulic conductivity, a is an inverse of the air-entry suction
value (or bubbling pressure), n is a pore-size distribution index, l isa pore-connectivity parameter assumed as 0.5, and m is anotherparameter related to the pore size distribution and is equal to 1-1/
n. All the input parameters Ks, u r, us, a and n were obtained
experimentally.
A. Field saturated hydraulic conductivity (Ks): It wasmeasured in the ﬁeld using a Guelph Permeameter.
B. Residual soil water content (u r): The soil water content at
permanent wilting point was taken as the u r.
C. Saturated soil water content (us): The us was taken as theporosity of soil, which was calculated from the bulk density,
assuming the particle density 2.65 Mg m3.
D. Parameters m, n, and a: We determined soil water contents
at 7 different water potentials and drew the soil water retention

292

P. Aggarwal et al. / Agriculture, Ecosystems and Environment 240 (2017) 287–299(c–u) curve. Then up was determined using the following equation:

up ¼

us þ u r

ð8Þ2

where us and ur were saturated and residual water contents,
respectively. Then the values of cp were read (cp was the value ofsoil water potential for the given value of up, read from the c–u
curve). Then Sp (slope of the curve around point P with coordinates(up, cp)) values were calculated using the following equations:dudu¼ ðln10ÞhdðloghÞ
dh

ð9Þ

1
du
ðus  urÞdðloghÞ

ð10Þ

Sp ¼

After computing Sp, m was calculated using following equations
according to Van Genuchten (1980):


0 < Sp  1
ð11Þ
m ¼ 1  exp 0:8Sp

m¼1

0:5755 0:1 0:025
þ 2 þ
Sp
Sp
S3p



Sp > 1

ð12Þ

n was computed from m using the relation (Van Genuchten,1980):
n¼

1
1m

ð13Þ

The a parameter (i.e. the inverse of the air-entry suction,
bubbling pressure), was computed from cp and m using thefollowing equation:
1m
1  1=m
a¼
2
1

cp

ð14Þ

2.7.4. Root water uptake (RWU) model
Root water uptake (RWU), a sink term in the Richards equation,
was computed from the equation given below (Feddes et al., 1978):Sðc; x; zÞ ¼ aðc; x; zÞSp ðx; zÞ ¼ aðp; x; zÞbðx; zÞT p Lt

ð15Þ

where the water stress response function a(C, x, z) is adimensionless function of the soil water pressure head (C); Sp(x, z) is potential root water uptake (cm3 cm3d1); b(x,z) is thenormalized root water uptake distribution (cm2); Trp is thepotential transpiration rate (cm d1); and Lt (cm) is the width of thesoil surface associated with transpiration (Deb et al., 2013).
According to Feddes et al. (1978), a varied between 0 and 1,
depending on soil water pressure head C. It was assumed to be 0 at
the soil water pressure head from saturation to 50 cm (P0 = avalue of the pressure head below which roots start to extract waterfrom the soil) and then increased linearly between pressure headsof 50 to 200 cm. Optimal RWU was between pressure heads of200 and 2000 cm (Popt = a value of the pressure head below
which roots extract water at the maximum possible potential
transpiration rate). P2H is the limiting pressure head, below whichroots can no longer extract water at the maximum rate (assuming a
potential transpiration rate of Tp2H) and P2L is same as P2H, but forpotential transpiration rate of Tp2L. Tp2H and Tp2L are the maximum
and minimum daily potential transpiration rates corresponding to
the pressure heads of P2H and P2L. RWU then linearly decreases
until it becomes zero at the wilting point, P3. The above values of
the water stress response function, namely P0, Popt, P2H, P2L and P3
for the cotton crop were taken from the HYDRUS internal database.

The two-dimensional function b(x,z) describing the spatial
distribution of potential RWU over the root zone was obtained bynormalizing the measured root length density data. Normalizing b(x,z) ensures that the function integrates to unity over the ﬂow
domain. It is given by the following equation (Vrugt et al., 2001):



x
z
p ðx  x Þ pz ðz  z Þ
þ
1
exp x
ð16Þ
bðx; zÞ ¼ 1 
Xm
Zm
Xm
Zm
where Zm and Xm are the maximum rooting depth and the
maximum width of the root zone, respectively, and px, pz, x* and z*
are empirical parameters. These parameters were included toprovide for zero RWU at z = Zm and x = Xm and also to allow for a
maximum RWU rate at a depth Z0 (0 < Z0 < Zm) and width X0(0 < X0 < Xm). Parameters pz and px were set to unity for values ofz > z* and x > x*, whereas these were ﬁtted values for z < z* andx < x*.
The following inputs were required for determination of b(x,z):
the maximum rooting depth (a depth down to which RLD was>0.25 cm cm3), the depth of maximum root water uptake (a depth
down to which RLD was >1.0 cm cm3), the maximum horizontalroot spread (a horizontal distance on either side of the base of thecotton plant up to which RLD was >0.25 cm cm3) and thehorizontal maximum root water uptake zone (mean root diameterof tap root in cm). By choosing a tap root/primary root diameter as
the horizontal extent of maximum root water uptake, simulationsof RWU from the soil proﬁle became symmetrical in shape,whereas other choices of computing this parameter gave unsymmetrical water uptake around the plant. Computation of Trp isgiven in the next section. The width of the soil surface associatedwith transpiration (Lt, cm) was obtained by measuring the width ofthe canopy cover of 8 plants in each treatment.
2.7.5. Time-variable atmospheric boundary conditions
The Hydrus-2D Model requires daily rainfall, daily potential soil
evaporation (Ep) and Trp as inputs for deﬁning the upper
atmospheric boundary conditions. All weather parameters required for computation of daily reference evapo-transpiration(ET0), including daily precipitation, maximum and minimum
temperature, maximum and minimum relative humidity andpan evaporation rates, were recorded by the meteorologicalobservatory located 1.0 km from the experimental plots. For
computing both these components, ET0 was calculated using the
FAO-56 Penman-Monteith equation (Allen et al., 1998) based on
the weather data. The calculated ET0 was multiplied by a crop
coefﬁcient (Kc) to estimate the evapo-transpiration of the cotton
crop under standard well-watered, fertilized and disease-free
conditions (ETc).
For partitioning ETc into Ep and Trp, the following formula(Ritchie, 1972) was used:
Ep ¼ ET c expðk  LAIÞ

ð17Þ

where k is the constant related to radiation extinction by canopy
cover and was computed from fIPAR and LAI data using Eq. (2).
2.7.6. Initial condition
The initial SWC of the proﬁle for each simulation period was set
as the SWC of the proﬁle measured at the beginning of eachsimulation.
2.7.7. Boundary condition
The upper boundary of the soil domain was set at theatmospheric boundary and the lower boundary was set as thefree drainage boundary. The vertical sides of the domain had noﬂux boundaries.

P. Aggarwal et al. / Agriculture, Ecosystems and Environment 240 (2017) 287–299

293

conservation practices had no impacts on us in the 15–30 and
30–45 cm soil depth layers (Table S1; Supplementary Information).
The Ks values of the 0–15 cm soil layer were higher in the plots withraised bed planting, in the PBB (144 mm d1) and PNB (188 mmd1) plots, than in the CT (102 mm d1) and ZT (59 mm d1) plots.
Retention of crop residues in both bed planting systems increased
Ks by 100% compared with CT. Ks values of the 15–30 and 30–45 cm
soil depth layers did not vary much among treatments and were<100 mm d1.
Ks is highly dependent upon soil BD, porosity and the size,
continuity and arrangement of pores. A higher estimated value of
Ks in the CT plots compared with the ZT plots reﬂected higher
porosity, since soil BDs of the 0–30 cm soil layer were lower in CT
plots. Higher Ks values in the PNB and PBB plots compared with theCT plots were probably due to less settling of soils in the bed
surface, as irrigation water was not applied directly to the beds.
Flooding in the plots under CT probably caused more settlement of
the loose surface soils, leading to lowering of Ks. In contrast, lateralslower movement of water from furrows to beds under PBB andPNB plots caused higher Ks. With retention of residues under both
PNB and PBB plots, Ks values in the 0–15 and 15–30 cm soil depth
layers increased further.

2.7.8. Validation of the model and simulation of root water uptake
For evaluating the accuracy of the output of the model,
predicted values of SWC from observation nodes at (0,7.5),(0,22.5) and (0,37.5) were compared with measured values of
SWC of 0–15, 15–30 and 30–45 cm soil layers measured near theplants on 30, 40, 45, 80, 100 and 105 DAS in 2012 and 2013. The
performance of the model was checked by computing the
coefﬁcient of determination (r2) and the root mean squared error(RMSE) (Skaggs et al., 2004). The r2 values describe the proportionof the total variance explained by the model. It varies between 0(no correlation) and 1 (perfect correlation). The root mean squared
error (RMSE) informs about the actual size of the errors and was
calculated as:
 X
0:5
1
N
2
ð
P

O
Þ
ð18Þ
RMSE ¼iii¼1Nwhere Pi is the ith predicted value, Oi is the ith observed value, and N
is the total number of observations. RMSE has the same units as O
and P. The regression analysis, in the context of the sensitivity
analysis, involves ﬁtting a linear regression to the model output
and, therefore, the standardized regression coefﬁcients can be usedas the direct measure of sensitivity (Saltelli et al., 2000). RWU of the
0–45 cm of soil proﬁle on various days was simulated by inserting
all the needed inputs in both years. The average relative error (AVE)
was also calculated using the following equation (Zhou et al.,2007a,b):
 X

1
100
N
AVE ¼
ðPi  Oi Þ
ð19Þ
i¼1
N
Oi

3.2. Measured parameters of the spatial root distribution
Only the vertical depth of 45 cm and the horizontal distance of
16.5 cm on either side of a cotton plant were used for root
sampling, as preliminary studies indicated that this was the zonethat contained >95% of cotton roots at 90 DAS (ﬂowering andbolling stage). Rogers and Prior (1992) and Zhao et al. (2010)
reported similar results (the maximum root length density of
cotton roots within a 45 cm layer) related to the root length density(RLD) distribution with soil depth for the growth stages of cotton.
Maps showing the vertical and horizontal distribution of the
RLD were prepared using Arc GIS 9.3 (Fig. 2). Maps clearly showedthat residue retention improved root parameters in the ZT, PNB and
PBB plots. In the CT and bed planted plots (with or without
residue), 65–70% of roots resided within the 0–15 cm soil layerand 18-25% resided within the 15–30 cm layer. In the ZT and
ZT + R plots, the 0–15 and 15–30 cm soil layers contained 50 and
30% of total roots, respectively. The mean RLD of the surface
0–15 cm soil layer was highest under CT (2.20 cm cm3) anddecreased by 41, 57 and 61% in the PNB, PBB and ZT plots,
respectively. Addition of crop residues resulted in decreased RLD(compared to CT plots) by 16, 40 and 60%, respectively. Thus,
PNB + R, PBB + R and ZT + R plots had 43, 39 and 27% higher RLD
than PNB (1.28 cm cm3), PBB (0.94 cm cm3) and ZT (0.69 cmcm3) plots, respectively.
More root growth in the CT plots than in the PNB and PBB plots
was probably due to more water application by the ﬂooding

2.8. Statistical analysis
We analysed soil properties using Analysis of Variance (ANOVA)
for a randomized block design. The Tukey’s honestly signiﬁcantdifference test was used as a post hoc mean separation test
(P < 0.05) using SAS 9.1 (SAS Institute, Cary, North Carolina, USA).
The Tukey’s procedure was used where the ANOVA was signiﬁcant.
3. Results and discussion
3.1. Hydraulic parameters used for the model
The measured values of ur, us, and Ks (in 2012) and computed
values of the shape parameters (a, m and n) of the soil water
characteristics curve for all treatments are presented in Table S1(Supplementary Information). Results indicated that ur did notshow signiﬁcant variations among treatments. However, us of the
0–15 cm layer was highest in the PNB + R treated plots (0.44 cm3
cm3) and lowest in the ZT plots (0.37 cm3 cm3). All soil

Table 1
Measured average Leaf Area Indexes (LAI) of cotton (m2m2), fractional intercepted radiation (fIPAR), and radiation extinction coefﬁcient (k) during the simulation period(2012).
Treatments*

LAI (m2 m2 of soil)
30

DAS = Days after sowing
CT
0.17b
PNB
0.17b
PNB + R
0.19ab
PBB
0.17b
PBB + R
0.20a
ZT
0.18b
ZT + R
0.18b

Radiation extinction coefﬁcient k

fIPAR

60

90

120

30

60

90

120

30

60

90

120

0.45b
0.44b
0.47ab
0.44b
0.49a
0.43b
0.45b

1.96c
2.78b
3.01a
2.92ab3.08a
2.87ab
3.15a

2.76c
3.39b
3.46ab
3.56ab
3.72a
3.40ab
3.42ab

0.17
0.17
0.18
0.17
0.18
0.18
0.18 ns

0.28
0.27
0.27
0.27
0.27
0.26
0.26 ns

0.64b
0.77a
0.77a
0.77a
0.76a
0.73a
0.78a

0.79
0.85
0.85
0.84
0.84
0.83
0.84 ns

1.13a
1.07ab1.03b
1.10ab1.01b
1.10ab
1.07ab

0.73
0.71
0.68
0.71
0.65
0.69
0.67 ns

0.52a
0.53a
0.49ab
0.50ab0.46b
0.45b
0.48ab0.56a
0.55a
0.54ab
0.52ab0.50b0.52ab0.54abValues within a column with similar lowercase letters in superscripts are not signiﬁcant at P < 0.05according to the Tukey’s HSD test.
CT = Conventional tillage; PNB = Permanent narrow bed; PNB + R = Permanent narrow bed + residue retention; PBB = Permanent broad bed; PBB + R = Permanent broad
bed + residue retention; ZT = Zero tillage; ZT + R = Zero tillage + residue retention.

294

P. Aggarwal et al. / Agriculture, Ecosystems and Environment 240 (2017) 287–299

Fig. 3. Time-variable atmospheric boundary conditions for simulations of root water uptake and actual transpiration rate for different soil conservation practices (ZT = Zerotillage; PNB = Permanent narrow bed; PBB = Permanent broad bed; CT = Conventional tillage; ZT + R = Zero tillage + residue retention; PNB + R = Permanent narrow
bed + residue retention; PBB + R = Permanent broad bed + residue retention). PE = Potential evaporation; PT = Potential transpiration; Irr = Irrigation; DAS = Days after sowing.

Fig. 4. Predicted soil water contents (SWCs) and measured SWCs at different soil depths under different soil conservation practices (ZT = Zero tillage; PNB = Permanentnarrow bed; PBB = Permanent broad bed; CT = Conventional tillage; ZT + R = Zero Tillage + residue retention; PNB + R = Permanent narrow bed + residue retention;
PBB + R = Permanent broad bed + residue retention; DAS = Days after sowing).

P. Aggarwal et al. / Agriculture, Ecosystems and Environment 240 (2017) 287–299

irrigation method in the CT plots than the furrow application ofirrigation water in the PNB and PBB plots. Despite more water
being applied (through ﬂooding), the root growth in ZT plots wasless compared with PNB and PBB plots, due to more surface sealing(which is the zone of maximum root growth) in the former
treatment (ZT). Residue retention in soil surface improved RLD due
decreased BD, along with an improvement in the SWC, the latterphenomenon occurred due to lower evaporation losses.
These maps were then used to determine root distribution
parameters, such as the maximum rooting depth (a depth with RLD>0.25 cm cm3), the depth of the maximum RLD (a depth with RLD>1.00 cm cm3), the maximum rooting radius (a horizontal
distance on either side of the root base up to which RLD was>0.25 cm cm3) and the radius of the maximum root intensity(mean root diameter of the primary plant root) for computing b(x,z) as described earlier (Vrugt et al., 2001). Among different
treatments, the depth of the maximum RLD was least in the ZT(19.0 cm) and most in the CT (27.7 cm) plots. Compared with the CAplots (PBB + R, PNB + R and ZT + R), plots without crop residue
retention (PBB, PNB and ZT) had shallower maximum RLD depths
and thicker primary roots. Parameters px and pz for computing b(x,z) were chosen, such that RWU around the centre of the tap root
became symmetrical.

295

conservation practices than in the CT plots could be due to thecompounded effects of many factors, such as an additional nutrient
supply, decreased competition for resources due to a lower weed
density, improved soil physical properties and water regimes,
better radiation interception, water extraction, aeration and lessevaporation in the soil conservation practices compared with theCT plots (Unger et al., 1998; Bhattacharya et al., 2012, 2013; Daset al., 2014). Soil structure affects crop yield through a complex ofroot-based mechanisms, that in turn affect above-ground biomass(Passioura, 2002). Continuous ZT with residue removal often leadto poorer soil structural quality (more compact) and lower crop
yields (Lotfollah et al., 2014). Similar trends were also observed inthis study as well (Das et al., 2014).
3.4. Fractional intercepted photo-synthetically active radiation (fIPAR)
and radiation extinction coefﬁcient (k)All soil conservation practices had a similar leaf area per unit
area (m2) and these values were signiﬁcantly higher than thosefor the CT plots (Table 1). A higher Leaf Area Index (LAI) in the soil

Fractions of incident PAR intercepted by a cotton plant canopy(fIPAR values) computed at 90 DAS were similar in the PNB, PBB andZT plots (0.81-0.82) and 30% higher than in the CT plots (Table 1).
With residue retention in the PNB and PBB plots, the magnitude offIPAR increased by 7%. At 120 DAS, fIPAR was also least in the CT
plots (0.84) and it increased by 12% in the PNB and PBB plots, andby 8% in the ZT plots. However, residue retention at this stage of thecotton growth did not signiﬁcantly improve fIPAR compared to theresidue removal plots. There was an increase in the magnitude of k
as crop growth progressed from 90 to 120 DAS. The results weresimilar to those reported byMilroy et al. (2001). Both at 90 and 120
DAS, k values were higher in the PBB + R and PNB + R plots than in

Fig. 5. Comparison of observed and simulated soil water contents (SWCs) under
different soil conservation practices (RMSE = Root mean square error).

Fig. 6. Comparison of observed and simulated actual transpiration (mm d1) during
the cotton growth in 2012 and 2013 (RMSE = Root mean square error; AVE = Averagerelative error).

3.3. Leaf Area Index

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P. Aggarwal et al. / Agriculture, Ecosystems and Environment 240 (2017) 287–299

remaining treatments. Widths of the plant canopy cover associatedwith transpiration measured at 100 DAS were also similar in the
PNB, PBB and ZT plots (65 cm) and were 30% higher than in the CT
plots. Residue retention further increased the width of canopy
cover by 7%.
The partitioning of Etc into Ep and Tp was done to separate theproductive (Tp) and unproductive (Ep) ﬂuxes. Results clearly
showed that after 78 DAS, Ep was higher and Tp was lower in CT
plots compared to plots under soil conservation practices (Fig. 3).
Cumulative Ep (CEp) during the simulation period was 13.2–
26.4 mm higher, whereas cumulative Tp (CTp) was lower by thesame amount in CT plots compared to plots with soil conservation
practices. Residue retention in PBB and PNB increased CTp slightly(0.7–2.5 mm). However, in the ZT + R plots, the magnitude of the
CEp reduction was 13.3 mm higher than in the ZT plots, whichindicated the effectiveness of retained residues in decreasing
evaporation rates in the ZT treatment (Derpsch, 2001).
3.5. Validation of the Hydrus-2D model to predict distributions of SWC
Figs. 4 and 5 compared SWC predicted by Hydrus-2D with thoseobserved in all experimental plots. The results show that the model
under-predicted the SWC of the soil proﬁle. Comparison ofsimulated and observed SWC at different times during crop
growth in 2012 and 2013 showed a relatively strong correlation (i.e.
R2 = 0.88; n = 105: P < 0.001 and 0.81; n = 105; P < 0.001), respectively, and the low RMSEs of 0.025 and 0.021 cm3 cm3, respectively. The AVE between simulated and measured SWC was 7.5

and 9%, indicating that the model performed well in simulating soil
moisture dynamics. Similar trends, although often with slightly
weaker correlation coefﬁcients, have been reported in manystudies (Skaggs et al., 2004; Zhou et al., 2008; Lu et al., 2009;
Buffon et al., 2012; Deb et al., 2013).
Fig. 6 compared mean daily transpiration rates predicted by theHydrus-2D Model for different treatments with estimated valuesof mean daily RWU/transpiration rates obtained from Eq. (4), using
the measured values of maximum transpiration rates (Tr,max) in allplots. Results also showed relatively strong correlations (R2 = 0.86,
n = 28, P < 0.05 and R2 = 0.90, n = 28, RMSE = 0.012 and 0.014 cm3 cm3and AVE = 6 and 8% in 2012 and 2013, respectively) between
measured and calculated mean daily transpiration rates under all
treatments.
3.6. Root water uptake simulations
Actual root water uptake (mm day1) under different soil
conservation practices during the 2013 simulation period(Fig. 7) also showed more daily water uptake by roots in both
bed systems compared with CT and ZT systems, although theamount of water applied in the former treatments was less. The
spatial distribution (and thus a depth-wise contribution ofdifferent soil layers) of RWU on 112 DAS is presented in Fig. 8. It
was observed that in the ZT plots, the contribution of subsurface layers to RWU was less, because of less root penetration
to depth. In CT plots, both surface and sub-surface layers
contributed to cumulative RWU, but the rates of RWU were lessFig. 7. Simulated actual root water uptake (mm d1) (vertical axis) versus DAS (days after sowing) (horizontal axis) for different soil conservation practices (PBB = Permanent
broad bed; PNB = Permanent narrow bed; PBB + R = Permanent broad bed + residue retention; PNB + R = Permanent narrow bed + residue retention; ZT = Zero tillage;
ZT + R = Zero tillage + residue retention; CT = Conventional tillage) during the 2013 simulation period.

P. Aggarwal et al. / Agriculture, Ecosystems and Environment 240 (2017) 287–299

297

Fig. 8. Root water uptake rates (RWU) (cm3cm3d1) by cotton from 0 to 45 cm of the soil proﬁle on 112 DAS in year 2012 under different soil conservation practices (ZT = Zerotillage; ZT + R = Zero Tillage + residue retention; CT = Conventional tillage; PNB = Permanent narrow bed; PNB + R = Permanent narrow bed + residue retention; PBB =Permanent broad bed; PBB + R = Permanent broad bed + residue retention). Vertical and horizontal directions represent the Z and X axes of the soil domain. For more detailed
information about the dimensions of soil domains and planting geometries of different CA treatments refer to Fig. S2 (Supporting Information).

compared with other treatments. This was mainly because ofhigher water losses through evaporation in CT (Fig. 3). In bothPBB and PNB plots, the contribution of surface layers was
greater, and residue retention improved total water uptake by
improving water uptake from deeper layers. The reasons for thehighest cumulative RWU ﬂux in the PBB + R plots are probably
linked to the better soil physical environment, maximum root
proliferation, maximum radiation interception leading to higher
LAI and hence maximum Trp. All these factors contributed toproviding a better microclimate in the PBB + R plots than in the
CT and residue removal plots. Thus, better observed RWU in theplots under PBB + R than in the CT and residue removal plots
probably contributed to realizing greater crop productivity in theformer treatment compared with other plots (Das et al., 2014).
Sensitivity analysis of model outputs to soil hydraulicparameters was also performed to understand changes incumulative root water uptake (CWU; an output variable of theHydrus-2D model) due to changes in the model input parameters(ur, u s, n, a, Ks, and l) for plots under CT during the 90–120 DAS
period. Results showed that when the input parameter n changedby 10%, CWU values changed from 7.83 to +6.85%. Similarchanges in us produced changes in CWU from 5.31 to +3.18%; in afrom 1.12 to +2.30%; in ur from 0.44 to +0.77%; in Ks from 0.59
to +0.29%, and in l from 0.15 to +0.20%. Similar results with a little
different trend were previously reported by Rocha et al. (2006).
Rocha et al. (2006) indicated that the most sensitive soil hydraulicparameter was the shape factor n, followed by us, Ks, u r, and a, withl being the least sensitive.

3.7. Soil water balance studies
The results of simulated soil water balance components clearly
indicated that cumulative RWU (transpiration) values for the ZT(143 mm) and CT (157 mm) plots were signiﬁcantly lower than for
the PBB (188 mm) plots (Table 2). Retention of crop residues in PBB,PNB and ZT treatments signiﬁcantly improved cumulative RWUvalues. Similarly, cumulative evaporation (CE) from soils under
different treatments were in the following order: PNB (278 mm) >PBB (245 mm) > CT = ZT (222 mm). Retention of crop residues inthese conservation treatments signiﬁcantly decreased CE. Among
all treatments, PBB + R had the highest cumulative RWU (226 mm)
and lowest CE (180 mm). Cumulative drainage from plots under
PNB was 50% less than from CT plots (172 mm). The apparent
reason for less drainage in both bed systems was mainly due to lessapplied irrigation, as well as more evaporation from the porous
surface of both systems. It was also evident that both initial andﬁnal SWC values of the proﬁle under CT were highest and thesewere signiﬁcantly higher than in the PBB and PNB plots with orwithout residues, mainly because the amounts of irrigation water
applied to CT was more than in both bed treatments.
4. Conclusions
To our knowledge, this study is the ﬁrst global attempt totest and validate the Hydrus-2D Model for simulating RWU
under CA. Higher RWU was observed in plots under PBB and PNB
compared with CT (farmers’ practice) and ZT plots, due to less

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P. Aggarwal et al. / Agriculture, Ecosystems and Environment 240 (2017) 287–299

Table 2
Soil water balance components computed for different simulation periods during cotton growth in 2012 using Hydrus-2D.
Treatments

DAS

CRWU (mm)CE (mm)

CD (mm)RF/IRR (mm)Initial SWC (mm)Final SWC (mm)

CT

25–48
49–77
78–122
Total

20.8
31.1
115.2
157.0cd

79.7
71.4
70.4
221.6c

0.00
150.0
21.9
171.9a

150.2
232.6
189.6
572.4

87.3
146.8
175.2
409.3a

132.8
155.7
156.3
444.8a

PBB

25–48
49–77
78–122
Total

23.0
44.1
121.3
188.4bc

90.0
80.0
75.0
245.0b

2.60
150.0
16.0
168.6a

150.2
232.6
189.6
572.4

64.0
122.7
155.0
341.7b

102.2
121.1
98.0
321.3cPBB + R

25–48
49–77
78–122
Total

24.5
53.7
147.5
225.8a

70.0
60.0
50.0
180.0d

7.40
132.3
10.2
149.9b

150.2
232.6
189.6
572.4

66.5
128.6
166.2
361.3b

128.6
116.2
138.9
383.7bc

PNB

25–48
49–77
78–122
Total

20.0
29.6
113.0
162.6cd

91.9
140.0
45.7
277.6a

1.4
121.4
0.9
123.6c

150.2
232.6
189.6
572.4

60.1
112.3
112.3
284.7c

104.5
120.0
64.5
289.0cPNB + R

25–48
49–77
78–122
Total

26.8
33.3
137.5
197.6b

93.1
123.9
36.0
25.30b

2.7
81.4
1.0
85.1d

150.2
232.6
189.6
572.4

64.0
101.2
119.1
284.3c

115.6
132.2
68.0
315.8c

ZT

25–48
49–77
78–122
Total

17.4
26.0
100.0
143.4d

87.1
75.9
59.7
222.7c

1.7
120.0
42.9
150.0b

150.2
232.6
189.6
572.4

72.6
128.4
143.3
344.3b

128.4
142.4
137.9
408.6bZT + R

25–48
49–77
78–122
Total

19.6
29.2
125.3
174.1c

81.0
77.3
48.0
206.3cd

0.0
139.0
14.3
153.3b

150.2
232.6
189.6
572.4

72.8
129.3
149.1
351.2b

129.3
149.1
122.8
401.2bDAS = Days after sowing; CRWU = Cumulative root water uptake; CE = Cumulative evaporation; CD = Cumulative drainage; RF/IRR = Rainfall/Irrigation; SWC = Soil watercontent.
CT = Conventional tillage; PBB = Permanent broad bed; PBB + R = Permanent broad bed + residue retention; PNB = Permanent narrow bed; PNB + R = Permanent narrow
bed + residue retention; ZT = Zero tillage; ZT + R = Zero tillage + residue retention.

drainage and better soil physical environment causing more root
growth, along with more radiation interception. All these factors
led to more biomass and hence more transpiration and lower
evaporation rates in PBB and PNB plots compared with CT and
ZT plots. Residue retention in the PNB, PBB and ZT plots (CApractices) further improved RWU values. Hence, this study
explains the reason (physical processes in the soil andatmosphere) behind greater cotton productivity under PBB + R(Das et al., 2014). Among all soil conservation practices, PBB + R
plots used less water and had higher root water uptake, andincreased productivity and water use efﬁciency. Hence, PBB + R isa better environmental protection technology than the farmers’
practice and is recommended under the cotton-wheat system in
the region and similar agroecologies.
Acknowledgements
Authors are grateful to ICAR-Indian Agricultural ResearchInstitute for providing necessary fund to carry out this study.
Authors are also thankful to Dr H.S. Gupta, the former Director andDr M. Dadlani the former Joint Director Research of the Institute,for their kind support.
Appendix A. Supplementary data
Supplementary data associated with this article can be found, inthe online version, at http://dx.doi.org/10.1016/j.agee.2017.02.028.

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