Journal Identification = FIELD Article Identification = 5443 Date: April 21, 2011 Time: 3:6 pm
Et
Sa
b
c
a
ARRA
KAWEDW
1
fl
oT
(
0d
Field Crops Research 122 (2011) 104–117
Contents lists available at ScienceDirect
Field Crops Research
journa l homepage: www.e lsev ier .com/ locate / fc r
valuation and application of ORYZA2000 for irrigation scheduling of puddledransplanted rice in north west India
udhir -Yadava,∗, Tao Lib, E. Humphreysb, Gurjeet Gill a, S.S. Kukalc
The University of Adelaide, Adelaide, AustraliaInternational Rice Research Institute, Los Banos, PhilippinesPunjab Agricultural University, Ludhiana, India
r t i c l e i n f o
rticle history:eceived 10 December 2010eceived in revised form 6 March 2011ccepted 8 March 2011
eywords:lternate wetting and dryingater savings
vapotranspirationeep drainageater productivity
a b s t r a c t
Water-saving technologies that increase water productivity of rice are urgently needed to help farmersto cope with irrigation water scarcity. This study tested the ability of the ORYZA2000 model to simulatethe effects of water management on rice growth, yield, water productivity (WP), components of thewater balance, and soil water dynamics in north-west India. The model performed well as indicated bygood agreement between simulated and measured values of grain yield, biomass, LAI, water balancecomponents and soil water tension, for irrigation thresholds ranging from continuous flooding (CF) to70 kPa soil water tension.
Using weather data for 40 different rice seasons (1970–2009) at Ludhiana in Punjab, India, the modelpredicted that there is always some yield penalty when moving from CF to alternate wetting and drying(AWD). With an irrigation threshold of 10 kPa, the average yield penalty was 0.8 t ha−1 (9%) comparedwith CF, with 65% irrigation water saving, which increased to 79% at 70 kPa with a yield penalty of 25%.The irrigation water saving was primarily due to less drainage beyond the root zone with AWD comparedto CF, with only a small reduction in evapotranspiration (ET) (mean 60 mm).
There were tradeoffs between yield, irrigation amount and various measures of WP. While yield wasmaximum with CF, water productivity with respect to ET (WPET) was maximum (1.7 g kg−1) for irrigationthresholds of 0 (CF) to 20 kPa, and irrigation water productivity (WPI) increased to a maximum plateau(1.3 g kg−1) at thresholds ≥30 kPa.
Because of the possibility of plant stress at critical stages known to be sensitive to water deficit (panicleinitiation (PI) and flowering (FL)), treatments with additional irrigations were superimposed for 2 weeksat one or both of these stages within the 10, 20 and 30 kPa AWD treatments. Ponding for two weeks at FLwas more effective in reducing the yield penalty with AWD than ponding at PI, but the biggest improve-
−1
ment was with ponding at both stages. This reduced the average yield loss from 9% (0.8 t ha ) to 5%(0.5 t ha−1) for AWD with thresholds of 10 and 20 kPa. However, maximum WPI (1.1 g kg−1) was achievedwith an irrigation threshold of 20 kPa combined with more frequent irrigation at FL only, but with agreater yield penalty (8%). Thus the optimum irrigation schedule depends on whether the objective is tomaximise yield, WPET or WPI, which depends on whether land or water are most limiting. Furthermore,the optimum irrigation schedule to meet the short term needs of individual farmers may differ from that
ater r
needed for sustainable w
. Introduction
The high input water requirement of conventional, continuouslyooded puddled transplanted rice (PTR) has become a major threat
∗ Corresponding author at: School of Agriculture, Food and Wine, The Universityf Adelaide, Waite Campus, PMB 1, Glen Osmond, South Australia 5064, Australia.el.: +61 8 8303 7744; fax: +61 8 8303 7109.
to the sustainability of rice production in regions facing currentor future water scarcity. This is especially the case in the north-west Indo-Gangetic plains (IGP) of India, where the production ofirrigated rice and wheat is critical for food security of the country(Humphreys et al., 2010). The steady decline of ground water (Hiraand Khera, 2000; Hira et al., 2004; Ambast et al., 2006; Hira, 2009)
has led to general acceptance of the need to find ways to reduceirrigation water input while maintaining yield. One way to reducewater input to rice is by improved irrigation management suchas reduction in ponded water depth (Kukal and Aggarwal, 2002),use of saturated soil culture (Borrell et al., 1997) and alternate
Journal Identification = FIELD Article Identification = 5443 Date: April 21, 2011 Time: 3:6 pm
s Rese
wTsbtlnsetfspc1
uw(i1fw2wSmha1sFnow
eillmtstmmcSepuwwoaEt(cdsw
cfm
S. -Yadav et al. / Field Crop
etting and drying (AWD) or intermittent irrigation (Bouman anduong, 2001). Alternate wetting and drying involves allowing theoil to dry out for few days after the disappearance of ponded waterefore the crop is irrigated again (Feng et al., 2007). All these irriga-ion water saving technologies reduce seepage and deep drainageosses, more so on more permeable soils (Tuong et al., 1994). Alter-ate wetting and drying is a proven technology for the tropics andub-tropics, with practical guidelines for its application (Boumant al., 2008). It is widely practised in regions of China where irriga-ion water is scarce (Li and Barker, 2004). There are many reportsrom small plot studies in the IGP showing large irrigation wateravings (15–40% of the applied water) with AWD in PTR in com-arison with continuous flooding (CF), with no or minor decline inrop yield (Sandhu et al., 1980; Sharma, 1989, 1999; Choudhary,997; Hira et al., 2002; Humphreys et al., 2008).
Rice cultivars perform well when grown under CF or in sat-rated soil, but yield declines as the soil dries below saturation,ith a critical threshold of around 10 kPa soil water tension (SWT)
Bouman and Tuong, 2001). In some studies, no significant declinen grain yield has been observed with irrigation thresholds up to6 kPa (Kukal et al., 2005) or 20 kPa (Sudhir-Yadav et al., 2011a). Aew studies have even reported an increase in crop yield with AWDhen irrigation was carefully managed (Wu, 1999; Zhang et al.,
008). Recently, guidelines for ‘safe AWD’ have been developedhich aim to ensure that yield is maintained (Bouman et al., 2008).
afe AWD includes short periods of ponding at critical develop-ental stages of the crop (for 2 weeks after transplanting, and from
eading to anthesis). The rest of the time, the irrigations are man-ged so that the soil water tension does not increase beyond about0 kPa at about 15 cm depth. In reality, the irrigation threshold forafe AWD varies with soil type, crop growth stage and weather.urthermore, the tradeoffs between yield and water productivityeed to be understood to identify the optimum irrigation thresh-ld, which will vary depending on which is more limiting, land orater.
Crop growth simulation models provide the opportunity toxplore irrigation thresholds and the tradeoffs between irrigationnput, yield and water productivity. By running scenarios usingong term historical weather data, they enable analysis of theikelihood of different outcomes. Modelling studies can help deter-
ine the nature of any irrigation water savings, and in particularhe effects of management options on water depletion from theoil/groundwater system, and thus on water availability for irriga-ion elsewhere and alternative uses. Well-calibrated and validated
odels can be used to estimate components of the water balance,any of which are not easily measured in the field, and which
an vary widely with management, soil type, location and season.everal modelling studies have already been used to explore theffects of various management practices on rice yield and com-onents of the water balance in Punjab, India. Arora et al. (2006)sed ORYZA2000 to explore the effects of puddling intensity, alongith two irrigation regimes (CF and irrigation 2 d after the pondedater had dissipated “2 d”), on yield and water balance components
f PTR. Chahal et al. (2007) used the CROPMAN (Crop productionnd management) model (developed by Blackland Research andxtension Center, Temple, TX, USA) to explore the effects of riceransplanting date. Jalota et al. (2009) used the CropSyst modelStockle et al., 1994) to study the effects of transplanting date,ultivar and irrigation (2 d and 16 kPa SWT) on yield and water pro-uctivity of rice. However, to date there have been no studies whicheek to optimize irrigation scheduling for PTR in relation to land and
ater productivity.
In this paper we present the results of a modelling study toalibrate and evaluate the ORYZA2000 rice growth model for PTRor a range of irrigation thresholds, and then use the calibrated
odel to study the effects of irrigation management on the trade-
arch 122 (2011) 104–117 105
offs between yield, irrigation water productivity (WPI) and WP withrespect to ET (WPET).
2. Materials and methods
2.1. Field experiment
Data from replicated field experiments were used to parame-terize and evaluate the performance of the ORYZA2000 (V 2.13,released in 2009) model. The experiments were carried out on aclay loam soil at Punjab Agricultural University (PAU), Ludhiana,India (30◦54′N, 75◦98′E, 247 m AMSL) during 2008 and 2009. Theclay loam topsoil (0–10 cm) with 0.5% organic carbon overlies clayat 30 cm and silty clay at 60 cm. The soil properties, crop manage-ment, and crop and water monitoring methods are fully describedin Sudhir-Yadav et al. (2011a,b), with details pertinent to the modelevaluation summarized here.
The field experiments included puddled transplanted rice (PTR)with 4 irrigation schedules viz. (i) continuous flooding (CF), andintermittent (AWD) irrigation when the SWT at 20 cm depthincreased to (ii) 20, (iii) 40, and (iv) 70 kPa. After puddling, rice(PAU201) was transplanted on the same day, on 5th and 6th Julyin 2008 and 2009, respectively, in rows 20 cm apart with plantto plant spacing in the rows of 15 cm. All treatments receiveda basal fertilizer application (40 kg N ha−1 as urea, 13 kg P ha−1asdiammonium phosphate, 25 kg K ha−1 as murate of potash and15 kg Zn ha−1 as zinc sulphate) broadcast after puddling. A further80 kg N ha−1 as urea was broadcast in 2 splits, 21 and 42 d aftertransplanting (DAT). Weeds, pests and diseases were well con-trolled using recommended practices. All treatments were keptcontinuously flooded (topped up daily to 50 mm standing waterdepth) for the first 15 DAT prior to commencement of the irriga-tion scheduling treatments. The CF treatments were topped up to50 mm standing water depth throughout the season. The amount ofirrigation water applied to all AWD treatments was 50 mm at eachirrigation.
The volume of irrigation (I) water applied to each plot at eachirrigation was measured with a Woltman® helical turbine meter.Rainfall (R) was measured using an automatic rain gauge installedat the experimental site. All other water balance components (deepdrainage, runoff, seepage, soil water depletion between the daybefore puddling and harvest) were calculated from measurementof related parameters (Sudhir-Yadav et al., 2011b). Evapotranspi-ration (ET) was calculated as the residual term in the water balanceequation. Soil water tension was measured with tube tensiometerswith the ceramic cup at 18–20 cm.
2.2. ORYZA2000 model
2.2.1. DescriptionORYZA2000 is an explanatory and dynamic eco-physiological
simulation model of the ‘School of De Wit’ (Bouman et al., 1996;van Ittersum et al., 2003). It simulates the growth, development,and water balance of rice under potential production, and in water-limited and nitrogen-limited environments. For all productionsituations, it is assumed that the crop is free from disease, pestsand weeds. A detailed explanation of the model and program codeis given in Bouman et al. (2001), and the key modules for potential-and water-limited-production are well explained in the literature(Arora et al., 2006; Boling et al., 2007; Feng et al., 2007).
The water dynamics in our study were simulated using the‘PADDY’ soil water balance module. This is a one-dimensionalmulti-layer (up to 10) integral model that simulates soil waterbalance for a variety of growing conditions viz., puddled or non-puddled, with free or impeded drainage at some depth in the soil
Journal Identification = FIELD Article Identification = 5443 Date: April 21, 2011 Time: 3:6 pm
106 S. -Yadav et al. / Field Crops Research 122 (2011) 104–117
Table 1Effect of irrigation schedule on crop growth stages of puddled transplanted rice in field experiments (Sudhir-Yadav et al., 2011a).
2008 CFa 09 June 12 June 05 July 15 August 15 September 13 October 21 October20 kPa 09 June 12 June 05 July 21 August 23 September 22 October 30 October40 kPa 09 June 12 June 05 July 22 August 27 September 25 October 02 November70 kPa 09 June 12 June 05 July 24 August 29 September 25 October 02 November
2009 CF 09 June 12 June 06 July 15 August 14 September 15 October 22 October20 kPa 09 June 12 June 06 July 21 August 22 September 23 October 30 October
2201
p(
2
apclce
uafepeleCcmbltltoiTidttalS
TV
40 kPa 09 June 12 June 06 July70 kPa 09 June 12 June 06 July
a CF, continuous flooding.
rofile. The ‘PADDY’ module is well explained by Bouman et al.2001) and Arora (2006).
.2.2. ParameterizationIn ORYZA2000, most of the crop parameters for rice are generic
nd can be used for all varieties (Bouman et al., 2001). Using croparameters of IR72 as the standard (Bouman et al., 2001), therop development rates, assimilate partitioning factors, specificeaf area, leaf death rate, and fraction of stem reserves are bestalibrated specifically for the variety-environment under consid-ration.
ORYZA2000 was parameterized for the rice cultivar (PAU201)sed in our field experiments. Crop development rate, which isgenotype × environment × management (G × E × M) interaction
actor, was calculated using the observed crop phenology param-ters viz. dates of emergence, panicle initiation, flowering, andhysiological maturity (Table 1) for each treatment in each year ofxperimentation. Genetic parameters viz. specific leaf area, assimi-ate partitioning, leaf death rate, fraction of stem reserves, and lightxtinction coefficient were calibrated with measurements from theF (non-stressed) treatment each year. Specific leaf area was cal-ulated from the measured leaf area and leaf dry weight. The dryatter partitioning factors were first estimated from the measured
iomass of green leaf blades, yellow plus brown leaf blades (deadeaves), stems (including leaf sheath), and panicles, and further fineuned by model fitting (refining the parameter value until simu-ated biomass and LAI best agreed with measured values). Finally,he leaf death coefficient was calculated as a function of devel-pment stage. The maximum depth of rooting was set to 0.6 mrrespective of irrigation treatments (Bouman and van Laar, 2006).he light extinction coefficient, which is a function determining thenterception and transmission of active photosynthetic radiation,epends on the canopy structure of the cultivar. From the calibra-
ion within ±20% of measured values, the light coefficient was seto 0.45 up to PI (DVS = 0.65) and to 0.65 for later stages. Similarly,mong the drought stress parameters, the upper limit of death ofeaves was set to 40 and 60 kPa in 2008 and 2009, respectively, andWIRTRF (which scales the transpiration changes under drought
able 2an Genuchten parameters and saturated hydraulic conductivity of the field experiment
August 26 September 23 October 30 OctoberSeptember 01 October 31 October 08 November
stress) was set to 0.015597 (2008) and 0.010597 (2009) instead of0.003297 (default value). As there was not much difference in thecrop phenology during the two years of experimentation, the aver-age values of the two years were used to calculate the developmentrate used for simulation scenarios.
The soil parameters were derived from the properties of undis-turbed soil cores collected from the side walls of a pit in a buffer areain the experimental field. The cores were collected from the middleof 9 depth intervals over the 0–150 cm profile using steel rings of7.5 cm inner diameter and 5 cm height. The soil water characteris-tic was determined using pressure plate apparatus, and saturatedhydraulic conductivity (Ks) was determined by maintaining a con-stant water head at the soil surface (Sudhir-Yadav et al., 2011b). Themeasured Ks of the plough sole was further fine tuned by modelfitting against measured soil water tension in the root zone. TheVan Genuchten parameters (Table 2) were derived from the soilparticle size analysis and organic matter content (Wösten et al.,2001).
Daily potential evaporation was calculated using the Penman–Monteith method and weather data from the PAU meteorologicalstation (1.5 km from the experimental site). The measured weatherparameters were total daily sunshine hours, daily maximum andminimum air temperature, morning vapor pressure and daily aver-age wind speed. Daily sunshine hours were converted to solarradiation using the Ångström formula (Angström, 1924).
2.2.3. ValidationThe performance of ORYZA2000 was evaluated for 3 AWD irri-
gation regimes using 2 years of field experimental data. Following(Bouman and van Laar, 2006) a combination of graphical analysesand statistical measures was used to appraise model performance.The simulated and measured aboveground biomass, weight of stor-age organs, soil water potential, and grain yield were compared
graphically. For the same variables, we computed the slope (ˇ),intercept (˛), and coefficient of determination R2 of the linearregression between simulated (Y) and measured (X) values. Wealso calculated the Student’s t-test of means assuming unequal vari-ance (P(t*)), and the absolute (RMSEa) and normalized (RMSEn) root
Journal Identification = FIELD Article Identification = 5443 Date: April 21, 2011 Time: 3:6 pm
S. -Yadav et al. / Field Crops Rese
Table 3Description of treatments for simulation scenarios.
Treatment ID Description
T0 Given SWT throughout the seasonT1 T0 + additional irrigation (CF at PI)T2 T0 + additional irrigation (2d at PI)T3 T0 + additional irrigation (CF at FL)T4 T0 + additional irrigation (2d at FL)T5 T0 + additional irrigation (CF at PI and FL)T6 T0 + additional irrigation (2d at PI and FL)
Cit
m
R
R
w
ttt
2
1dmc4tt
2pr
2oo0dwflt1w
2ccbFbt1
wt2ai
F, continuous flooding from 1 week before to 1 week after the growth stage; 2d,rrigation 2 d after the floodwater has dissipated, from 1 week before to 1 week afterhe growth stage; PI, panicle initiation; FL, flowering.
ean square errors between simulated and measured values:
MSEa =(
1n
∑(Yi − Xi)2
)0.5(1)
MSEn = 100 ×(
(1/n)∑
(Yi − Xi)2)0.5
∑Xi/n
(2)
here n is the number of observations.The model reproduces experimental data best when ˛ is close
o 0, ˇ close to 1, R2 close to 1, P(t*) larger than 0.05, RMSEa similaro the standard errors of measured values, and RMSEn similar tohe coefficient of variation of measured values.
.2.4. Scenario analysesFor all scenario analyses, the emergence date was set to the
64th day of the year (DOY) with seed bed duration of 23 d and plantensity was 33 hills m−2 with 2 plants per hill, as in the field experi-ents. The model was run with nitrogen non-limiting on the same
lay loam soil as the field experiments, using weather data from0 different rice seasons (1970–2009) from the PAU weather sta-ion. The simulations were run from one day before transplantingo physiological maturity each year.
.2.4.1. Yield potential. ORYZA2000 was used to estimate the yieldotential of puddled transplanted rice (PAU201) for 40 differentice seasons.
.2.4.2. Irrigation threshold. The effect of irrigation managementn grain yield, water balance components and various measuresf WP (WPI, WPI+R, and WPET) was investigated using thresholds of(CF) to 70 kPa at increments of 10 kPa (at 20 cm soil depth). Pud-ling was done one day prior to transplanting, with an irrigationater input for puddling of 150 mm (Tuong, 1999). The crop wasooded for the first 15 DAT, and during this period a 50 mm irriga-ion was applied whenever the standing water depth decreased to0 mm. For the tensiometer based scheduling, irrigation (50 mm)as applied whenever the given SWT threshold was reached.
.2.4.3. Irrigation threshold plus supplementary irrigation at criti-al growth stages. The effect of the above irrigation thresholds, inombination with additional irrigation at critical stages known toe sensitive to water deficit (panicle initiation, PI and flowering,L), was also investigated. It is well-established that water stressetween PI and flowering causes floret sterility and thus reduceshe number of grains per panicle and yield (Garrity and O’Toole,994; Boonjung and Fukai, 1996; Bouman and Tuong, 2001).
Based on these critical stages, there were six sub-treatments
ithin each SWT based AWD treatment (Table 3). The sub-
reatments involved ponding or ‘2 d’ water management (irrigationd after the floodwater has dissipated) from a week before toweek after PI and/or FL. The 2 d option was included as this
s recommended practice from 15 DAT to shortly before harvest
arch 122 (2011) 104–117 107
in north-west India. For these simulations the irrigation man-agement module was modified to add a function for combiningmultiple irrigation management switches within the growth sea-son. In ORYZA2000, there are seven water management optionsbased on standing water depth, soil water potential, soil water con-tent, time step (Julian days) or crop development stage, but onlyone option can be used for the entire growing season. With thenew modifications, any combination of management options canbe used, including a mixture of development stage, Julian days andsoil water potential.
3. Results
3.1. Parameterization and validation of ORYZA2000
3.1.1. Crop variablesTrends of periodic biomass partitioning in measured and sim-
ulated observations are shown in Fig. 1 (2008) and Fig. 2 (2009).In general, all crop variables were reproduced very well with CFand an irrigation threshold of 20 kPa, and generally satisfactorily at40 and 70 kPa (Table 4). There was slight overestimation of greenand dead leaf biomass with the 40 and 70 kPa thresholds, whichresulted in slight overestimation of total biomass (in 2008) and LAI(both years). The quantitative goodness-of-fit parameters showeda reasonable relationship between simulated and measured val-ues across all treatments, with slope (ˇ) close to 1 (0.84–1.29) anda relatively small intercept (˛). The regression coefficients for allgrowth variables ranged from 0.88 to 0.99 and were usually morethan 0.94.
The scatter plots of measured versus simulated total biomass(Fig. 3a) and panicle biomass (Fig. 3b) throughout each season showthat the simulated values were within the magnitude of variationin the field observations, and generally close to the 1:1 line. TheRMSEa was 0.1–0.3 t ha−1 with RMSEn of 3–6% for observed andsimulated grain yield of all treatments (Fig. 4 and Table 4).
3.1.2. Soil water and water balance componentsIn general, there was good agreement between trends in
measured and simulated drainage, runoff and ET as affected byirrigation treatment, and generally good agreement in absolute val-ues, more so in 2009 (Fig. 5). In 2008, the model predicted higherdeep drainage and lower runoff and ET than the measured values inall treatments, while in the CF treatment in 2009, runoff was overpredicted and ET under predicted. Over the two seasons and fourirrigation treatments, the RMSEn for deep drainage was 20%, 23%for ET and 43% for runoff (Table 5). It should be noted that thereis some uncertainty in the estimates of runoff in the experimentalplots (and therefore ET). Runoff was probably overestimated in thefield plots as one of the inputs in the calculation was water depthwhich was measured each morning. When the rains commencedlater in the day or at night, the water depth would have been less,thus the capacity of the plots to capture rainfall would have beengreater. As all plots were continuously flooded for the first 15 DAT,and there was a lot of rain during this period in 2008, there is there-fore likely to be overestimation of runoff in 2008 in the AWD plots.However, the bunds in the CF plots were damaged, with the netresult that runoff was not overestimated by as much as the AWDplots, or perhaps even underestimated. As ET was calculated as theresidual term in the water balance equation, over estimation ofrunoff (or any other parameter) would result in underestimation
of ET. The reasons for the disagreement between simulated andmeasured values of runoff and thus ET in the CF treatment in 2009are not known.
The dynamics of soil water tension at 20 cm depth were sim-ulated well in all treatments each year (Fig. 6). The estimated
Journal Identification = FIELD Article Identification = 5443 Date: April 21, 2011 Time: 3:6 pm
108 S. -Yadav et al. / Field Crops Research 122 (2011) 104–117
0
3000
6000
9000
12000
15000
18000
160 180 200 220 240 260 280 300
Bio
mas
s (
kg h
a-1 )
Day of year
CF
0
1
2
3
4
5
160 180 200 220 240 260 280 300
LAI
Day of year
CF
0
3000
6000
9000
12000
15000
18000
160 180 200 220 240 260 280 300
Bio
mas
s (k
g ha
-1)
Day of year
20 kPa
0
1
2
3
4
5
160 180 200 220 240 260 280 300
LAI
Day of year
20 kPa
0
3000
6000
9000
12000
15000
18000
160 180 200 220 240 260 280 300
Bio
mas
s (
kg h
a-1 )
Day of year
40 kPa
0
1
2
3
4
5
160 180 200 220 240 260 280 300
LAI
Day of year
40kPa
0
3000
6000
9000
12000
15000
18000
160 180 200 220 240 260 280 300
Bio
mas
s (
kg h
a-1 )
Day of year
70 kPa
0
1
2
3
4
5
160 180 200 220 240 260 280 300
LAI
Day of year
70 kPa
F (×), st
vssitT
ig. 1. Simulated (lines) and measured dry biomass of the whole crop (�), leavesreatments in 2008.
alues of SWT were often within the uncertainty range of mea-
ured values. However in 2009, there was one unexplained peak ofimulated SWT during active tillering in all AWD treatments whichndicated much stronger drying than was observed. The quantita-ive goodness-of-fit parameters for soil water tension are given inable 5.
3.2.1. Potential yieldPotential yield varied from 7.8 to 11.6 t ha−1 over the 40
years, depending on seasonal conditions (Fig. 7a). For example,the low yields in 1977 were associated with low total radiation
Journal Identification = FIELD Article Identification = 5443 Date: April 21, 2011 Time: 3:6 pm
S. -Yadav et al. / Field Crops Research 122 (2011) 104–117 109
0
3000
6000
9000
12000
15000
18000
160 180 200 220 240 260 280 300
Bio
mas
s (
kg h
a-1)
Day of year
CF
0
1
2
3
4
5
160 180 200 220 240 260 280 300
LAI
Day of year
CF
0
3000
6000
9000
12000
15000
18000
160 180 200 220 240 260 280 300
Bio
mas
s (
kg h
a-1 )
Day of year
20 kPa
0
1
2
3
4
5
160 180 200 220 240 260 280 300
LAI
Day of year
20 kPa
0
3000
6000
9000
12000
15000
18000
160 180 200 220 240 260 280 300
Bio
mas
s (
kg h
a-1)
Day of year
40 kPa
0
1
2
3
4
5
160 180 200 220 240 260 280 300
LAI
Day of year
40 kPa
0
3000
6000
9000
12000
15000
18000
160 180 200 220 240 260 280 300
Bio
mas
s (
kg h
a-1 )
70 kPa
0
1
2
3
4
5
160 180 200 220 240 260 280 300
LAI
70 kPa
stems
(dsasdp
Day of year
Fig. 2. Simulated and measured dry biomass of the whole crop (�), leaves (×),
2273 MJ m−2) compared to high yields in 1979 (3159 MJ m−2)uring the rice season. There was a significant decline in
2
olar radiation with time since 1979 (R = 0.58, p < 0.05) withn average reduction of 11 MJ m−2 year−1. Potential yield wasignificantly correlated with radiation (R2 = 0.55, p < 0.05), andeclined at an average rate of 73 kg ha−1 year−1 (R2 = 0.64,< 0.05).
3.2.2. Effect of irrigation thresholdAverage yield with CF was the same as potential yield
−1
(9.4 t ha ). There was a fairly constant, gradual decline in yieldas the irrigation threshold increased from 0 (CF) to 70 kPa(Fig. 7b). Changing from CF to AWD resulted in a yield penaltyof around 9% with mild water stress (10–20 kPa irrigation thresh-old), which increased to 25% yield loss at 70 kPa, on average.
Journal Identification = FIELD Article Identification = 5443 Date: April 21, 2011 Time: 3:6 pm
110 S. -Yadav et al. / Field Crops Research 122 (2011) 104–117
Table 4Quantitative goodness-of-fit parameters for ORYZA2000 simulation of crop growth variables over the growing season for different irrigation regimes pooled over 2 years.
Abbreviations: N, number of data pairs; Xmean, mean of measured values in whole population; Xsd, mean of simulated values in whole population; Ymean, mean of simulatedvalues in whole population; Ysd, standard deviation of simulated values; ˛, intercept of linear relation between simulated and measured values; ˇ, slope of linear relationbetween simulated and measured value; R2, adjusted linear correlations coefficient between simulated and measured values; P(t), significance of paired t-test; RMSEa,absolute root mean square error; RMSEn, normalized root mean square error (%).
0
4000
8000
12000
16000
20000 a b
0 4000 8000 12000 16000 20000
Sim
ulat
ed c
rop
biom
ass
(kg
ha-1
)
Measured crop biomass (kg ha-1)
0
2000
4000
6000
8000
10000
0 2000 4000 6000 8000 10000
Sim
ulat
ed p
anic
le b
iom
ass
(kg
ha-1
)
Mesured panicle biomass (kg ha-1)
Fig. 3. Simulated vs. measured crop biomass (a) and panicle biomass (b) during the whole crop season for all years and treatments. The solid line is the 1:1 relationship; thedotted lines are plus and minus the measured standard error around the 1:1 line.
Table 5Quantitative goodness-of-fit parameters for ORYZA2000 simulation of soil water tension and water loss components pooled over two growing season.
Abbreviations: N, number of data pairs; Xmean, mean of measured values in whole population; Xsd, mean of simulated values in whole population; Ymean, mean of simulatedvalues in whole population; Ysd, standard deviation of simulated values; ˛, intercept of linear relation between simulated and measured values; ˇ, slope of linear relationbetween simulated and measured value; R2, adjusted linear correlations coefficient between simulated and measured values; P(t), significance of paired t-test; RMSEa,absolute root mean square error; RMSEn, normalized root mean square error (%).
Journal Identification = FIELD Article Identification = 5443 Date: April 21, 2011 Time: 3:6 pm
S. -Yadav et al. / Field Crops Research 122 (2011) 104–117 111
0
2000
4000
6000
8000
10000 a b
CF 20 kPa 40 kPa 70 kPa
Gra
in y
ield
(kg
ha-
1 )
Irrigation threshold
0
2000
4000
6000
8000
10000
CF 20 kPa 40 kPa 70 kPa
Gra
in y
ield
(kg
ha-
1)
Irrigation threshold
Fig. 4. Measured (black column) and simulated (gray coulmn) grain yield (kg ha−1) as affected by irrigation schedule in 2008 (a) and 2009 (b). Vertical bars indicate standarderror of measured values.
Journal Identification = FIELD Article Identification = 5443 Date: April 21, 2011 Time: 3:6 pm
112 S. -Yadav et al. / Field Crops Research 122 (2011) 104–117
0
10
20
30
40
50
60
70
80
90
100 a
196 206 216 226 236 246 256 266 276 286
Soi
l wat
er te
nsio
n (k
Pa)
Day of year
20 kPa
0
10
20
30
40
50
60
70
80
90
100
196 206 216 226 236 246 256 266 276 286
Soi
l wat
er te
nsio
n (k
Pa)
Day of year
20 kPa d
0
10
20
30
40
50
60
70
80
90
100
196 206 216 226 236 246 256 266 276 286
Soi
l wat
er te
nsio
n (k
Pa)
Day of year
40 kPa
b e
0
10
20
30
40
50
60
70
80
90
100
196 206 216 226 236 246 256 266 276 286
Soi
l wat
er te
nsio
n (k
Pa)
Day of year
40 kPa
0
10
20
30
40
50
60
70
80
90
100
196 206 216 226 236 246 256 266 276 286
Soi
l wat
er te
nsio
n (k
Pa)
70 kPa
c(f)
0
10
20
30
40
50
60
70
80
90
100
196 206 216 226 236 246 256 266 276 286
Soi
l wat
er te
nsio
n (k
Pa)
70 kPa
f
20 cm
T27go7
ttr
mi1ia
Day of year
Fig. 6. Measured (�) and simulated (solid line) soil water tension at
he average water input with CF was 3040 mm, which included540 mm of irrigation water (Fig. 8a and b). There were 65 and2% reductions in irrigation water in the 10 and 20 kPa irri-ation regimes, respectively. Irrigation water savings increasednly slightly to 79% as the threshold increased from 20 to0 kPa.
Of the total water input with CF, 6% was evaporated directly fromhe ponded water, 12% was transpired, and 81% drained beyondhe root zone (0–60 cm). The rest (1%) was either lost as runoff oretained in the soil profile.
Evapotranspiration with CF ranged from 480 to 820 mm with a
ean of 580 mm (Fig. 8c). There was a small decline (mean 60 mm)
n ET in switching from CF to AWD with an irrigation threshold of0 kPa, and a very slight decline in ET as the irrigation threshold
ncreased from 10 to 70 kPa. The ET loss decreased by 10% at 10 kPand 14% at 70 kPa compared with CF.
Day of year
depth in 2008 (a–c) and 2009 (d–f) for different AWD treatments.
Average deep drainage in the CF treatment was 2470 mm, whichdeclined greatly to 820 mm under AWD at 10 kPa SWT (Fig. 8e).Drainage decreased steadily from 820 to 500 mm as the irrigationthreshold increased from 20 kPa to 70 kPa.
Irrigation water productivity increased sharply from a meanof 0.37 g kg−1 with CF to 0.99 g kg−1 at 10 kPa, with a furthersmall increase when the irrigation threshold was increased to20 kPa (1.19 g kg−1) (Fig. 9a). At irrigation thresholds beyond20 kPa, WPI was almost constant (around 1.30 g kg−1). The trendin WPI+R was similar to that of WPI, although less pronounced(Fig. 9b). Input water productivity was much lower than WPI
in all AWD treatments because rainfall (which ranged from179 to 1085 mm over the 40 years) was usually a large com-ponent of input. The effect of irrigation threshold on WPETwas negligible for irrigation thresholds from CF to 20 kPa, andthere was a small but steady decline in WPET as the thresh-
Journal Identification = FIELD Article Identification = 5443 Date: April 21, 2011 Time: 3:6 pm
S. -Yadav et al. / Field Crops Research 122 (2011) 104–117 113
Fig. 7. Simulation results over 40 years for (a) potential grain yield and (b) grain yield as affected by irrigation threshold. The error bars represent the range, ( ) represent75th percentile, ( ) represent 50th percentile and ( ) represent the mean of 40 years data.
Fig. 8. Simulation results of water balance components over 40 years as affected by irrigation threshold. The error bars represent the range, ( ) represent 75th percentile,( ) represent 50th percentile and ( ) represent the mean of 40 years data.
Journal Identification = FIELD Article Identification = 5443 Date: April 21, 2011 Time: 3:6 pm
114 S. -Yadav et al. / Field Crops Research 122 (2011) 104–117
F ationp e, (m
o(
3c
ttop
tTbsbi
c1
4
4
4
attO
ig. 9. Simulation results over 40 years for (a) water productivity based on irrigroductivity based on evapotranspiration (WPET). The error bars represent the rangean of 40 years data.
ld increased from 20 kPa (1.65 g kg−1) to 70 kPa (1.41 g kg−1)Fig. 9c).
.2.3. Irrigation threshold plus supplementary irrigation atritical growth stages
With all irrigation thresholds (10, 20, and 30 kPa), supplemen-ary irrigation at one or both critical stages did not increase yield tohat with CF throughout the season (Table 6). Even with a thresholdf 10 kPa and ponding at both PI and Fl, there was an average yieldenalty of 5%.
Additional irrigation at PI had only a very small effect on yield ofhe AWD treatments. Additional irrigation at FL was more effective.here was a further small yield benefit with additional irrigation atoth PI and Fl. There was a consistent trend for ponding at criticaltages to produce higher yield than the 2 d treatment, and for aigger response to supplementary irrigation at these stages as the
rrigation threshold increased.Irrigation and input WP with 2 d supplementary irrigation at
ritical stages was always higher than with ponding, by factors of.7–3.5 (WPI) and 1.5–2.3 (WPI+R).
. Discussion
.1. Model performance
.1.1. Calibration
In the current version of ORYZA2000 (V 2.13), development rates
nd drought stress sensitivity coefficients are G × E × M parame-ers, and therefore the model needs to be calibrated against stressreatment for accurate simulation (Bouman et al., 2001). Also,RYZA2000 does not produce yield components such as panicle
(WPI), (b) water productivity based on total water input (WPI+R), and (c) water) represent 75th percentile, ( ) represent 50th percentile and ( ) represent the
density, which limits the ability to diagnose the causes of differ-ent treatment responses. Therefore, model improvements whichincorporate development rates and stress coefficients into geneticparameters would be highly desirable to enable application of thecalibrated model across a wider range of environmental and man-agement conditions.
4.1.2. Model evaluationThe evaluation of ORYZA2000 was by far the most comprehen-
sive that has been reported for this model under Punjab (India)conditions, against a range of crop growth, yield, water and soilwater parameters. Performance of the calibrated model was goodfor a range of irrigation thresholds from continuous flooding up to70 kPa soil tension. In particular, biomass accumulation, leaf areaindex and yield were very accurately simulated for thresholds upto 20 kPa, and generally well for thresholds up to 70 kPa. The simu-lation of soil water tension at 20 cm was excellent in all treatments,and simulation of components of the water balance was generallygood. Perhaps most importantly, the effect of increasing irrigationthreshold was captured well in the simulations of all crop, soil waterand other water parameters.
Simulated potential grain yield in our study was similar tothat simulated by Chahal et al. (2007) for PR 118 transplanted onJuly 1 using the CROPMAN model and 23 years of weather data(1982–2004) at Ludhiana. We used PAU201, which is about 15 dshorter duration than PR 118, and with 0.25 t ha−1 higher yield
potential.
There was a gradual decline in average rice yield as the irriga-tion threshold increased from CF to 10 kPa (8% yield decline) to20 kPa (9%), 40 kPa (19%) and 70 kPa (25%). The yield decline wasprimarily associated with reduced above ground biomass, and was
Journal Identification = FIELD Article Identification = 5443 Date: April 21, 2011 Time: 3:6 pm
S. -Yadav et al. / Field Crops Research 122 (2011) 104–117 115
Table 6Simulated grain yield, irrigation, and irrigation (WPI) and input water productivity (WPI+R) of puddled transplanted rice as influenced by AWD irrigation threshold andsupplementary irrigation at panicle initiation and/or flowering.
igher (p < 0.05) in drier seasons (Fig. 10), when the frequency ofry down to the irrigation thresholds was higher. The poor correla-ion (R2 = 0.29–0.38) probably reflects variation in the distributionf rainfall across years. The yield decline at 20 kPa SWT ranged
y = -0.0091x + 13.804R² = 0.38
0
5
10
15
20
25
30
35
40
45a
12009006003000
Gra
in y
ield
pen
alty
(%
)
Rainfall (mm)
Gra
in y
ield
pen
alty
(%
)
y = -0.0174x + 33.526R² = 0.31
0
5
10
15
20
25
30
35
40
45
12009006003000
Gra
in y
ield
pen
alty
(%
)
Rainfall (mm)
c
ig. 10. Variation in the degree of yield penalty in relation to total rainfall amount in a sension.
from 6% (967 mm rainfall) to 15% (228 mm rainfall). The simulatedyield response to irrigation threshold is generally consistent withthe findings of the field experiments. In their review, Bouman andTuong (2001) also found that rice performs best when grown under
b
y = -0.0128x + 24.668
R² = 0.29
0
5
10
15
20
25
30
35
40
45
12009006003000
Rainfall (mm)
eason with irrigation thresholds of (a) 20 kPa, (b) 40 kPa and (c) 70 kPa soil water
Journal Identification = FIELD Article Identification = 5443 Date: April 21, 2011 Time: 3:6 pm
1 s Rese
Csifhe
6fC2wteBil
ap−o
t4Cbi(Tfi
4
iwapPwifwbfa(auoFdwadcdtgg2ctwb
16 S. -Yadav et al. / Field Crop
F or in saturated soil, and that yield declines as the soil dries belowaturation, with a critical threshold of around 10 kPa. Field studiesn Punjab, India, show similar or slightly lower yield of PTR withrequent AWD (e.g. irrigation scheduled 2 d after the floodwateras dissipated, or with a threshold of 20 kPa) than with CF (Arorat al., 2006; Singh et al., 2009; Sudhir-Yadav et al., 2011a).
Shifting from CF to AWD reduced irrigation water input by5–78%, larger than that of 15–40% irrigation water saving reportedrom many field studies in the region (Sandhu et al., 1980;houdhary, 1997; Sharma, 1999; Kukal et al., 2005; Won et al.,005; Humphreys et al., 2008). The reduction in irrigation inputith AWD was primarily due to reduction in the drainage beyond
he root zone, consistent with the modelling studies of Boumant al. (2007) for PTR in the north China plain. Many studies (e.g.ouman et al., 1994; Kukal and Aggarwal, 2002) emphasize the
mportance of reduction of hydrostatic pressure to reduce the waterosses in the form of drainage.
The change in soil water storage between transplanting datend physiological maturity was small compared with other com-onents of the water balance, ranging from ∼70 mm for CF to 0 to43 mm for 70 kPa, and of similar order of magnitude to the resultsf the simulations of Arora (2006) and Jalota et al. (2009).
The ET was relatively stable over a wide range of irrigationhresholds and only declined slightly from 579 mm under CF to97 mm at 70 kPa, with the biggest decline of 56 mm going fromF to a threshold of 10 kPa which was associated with reducediomass. Similar values of ET for rice grown with CF or frequent
rrigation were observed in the simulations of other researchersArora, 2006; Chahal et al., 2007; Jalota et al., 2009) in Punjab.he trend in effect of irrigation threshold on ET was similar to thendings of Bouman et al. (2007) for PTR.
.2. Optimum irrigation threshold
There were tradeoffs between yield, irrigation amount and var-ous measures of WP. While yield was maximum with CF, WPET
as maximum (1.7 g kg−1) for irrigation thresholds of 0–20 kPa,nd irrigation water productivity (WPI) increased to a maximumlateau (1.3 g kg−1) at thresholds ≥30 kPa. Additional irrigation atI or FL slightly reduced the yield penalty with AWD in comparisonith CF, with higher yield with ponding at these stages than allow-
ng the soil to dry for 2 d between irrigations. Continuous floodingor two weeks at FL was more effective in reducing the yield penaltyith AWD than CF at PI, but the biggest improvement was with CF at
oth stages. This additional irrigation reduced the average yield lossrom 9 to 5% (0.5 t ha−1) for AWD with thresholds of 10 and 20 kPa,nd from 14 to 9% for AWD at 30 kPa. However, maximum WPI1.3 g kg−1) was achieved with AWD at thresholds ≥30 kPa, with nodditional irrigation at PI or FL. Thus the optimum irrigation sched-le depends on whether the objective is to maximise yield, WPETr WPI, which depends on whether land or water are most limiting.or individual farmers for whom irrigation water is limiting, eitherue to limited physical availability or affordability, the objectiveill be to maximise WPI of their fields. For a water resource man-
ger, the objective will be to maximise WP with respect to waterepletion from a higher spatial scale such as a sub-catchment oratchment. This normally means maximising WPET. Although deeprainage is a loss from individual fields, this water will re-enterhe groundwater and could be available for reuse, as in the ricerowing areas of north west India where almost all the rice is irri-ated using groundwater (Ambast et al., 2006; Humphreys et al.,
010). Similarly, runoff to adjacent fields or surface water systemsan be re-used, and water stored in the soil profile can be used byhe next crop. Therefore, reducing ET is necessary to produce realater savings at the catchment scale, and, maximising WPET should
e a high priority in this region (Loeve et al., 2004). The modelling
arch 122 (2011) 104–117
studies suggest that for PTR, this will be achieved through frequentirrigation to keep the soil close to saturation (around 10 kPa).
5. Conclusions
The study shows that ORYZA2000 performs well in predictingthe effects of irrigation schedule on crop growth, yield, water bal-ance components and water productivity of puddled transplantedrice (PTR) in north-west India, when calibrated for the range ofstresses × seasonal conditions. The scenario analysis for 40 rice sea-sons always indicated some yield penalty when changing from CF toAWD. However, this yield penalty can be reduced to 5%, on average,with ponding for 2 weeks at both panicle initiation and flower-ing. The results of the simulations are consistent with the findingsof field studies that AWD has great potential to deliver large irri-gation water savings and increase irrigation water productivity incomparison with CF. The main cause of the irrigation water savingis greatly reduced drainage in AWD, with a relatively small (60 mm)decrease in ET. Since the effects of the irrigation treatments on ETwere small, and drainage water is likely to be internally recycledin this region, the results suggest no impact of changing irrigationmanagement from frequent to less frequent AWD on groundwaterdepletion.
Acknowledgements
This work was supported by the Australian Centre for Inter-national Agricultural Research (ACIAR) and the John AllwrightFellowship fund. We thank A. Boling for her support to assist inthe ORYZA training. The authors are also grateful to Bas Boumanfor providing support and guidance for this study.
References
Ambast, S.K., Tyagi, N.K., Raul, S.K., 2006. Management of declining groundwater inthe Trans Indo-Gangetic Plain (India): some options. Agric. Water Manage. 82,279–296.
Angström, A., 1924. Solar and atmospheric radiation. Q. J. R. Meteorol. Soc. 50, 121.Arora, V.K., 2006. Application of a rice growth and water balance model in an irri-
gated semi-arid subtropical environment. Agric. Water Manage. 83, 51–57.Arora, V.K., Gajri, P.R., Uppal, H.S., 2006. Puddling, irrigation, and transplanting-time
effects on productivity of rice–wheat system on a sandy loam soil of Punjab,India. Soil Till. Res. 85, 212–220.
Boling, A.A., Bouman, B.A.M., Tuong, T.P., Murty, M.V.R., Jatmiko, S.Y., 2007. Mod-elling the effect of groundwater depth on yield-increasing interventions inrainfed lowland rice in Central Java, Indonesia. Agric. Syst. 92, 115–139.
Boonjung, H., Fukai, S., 1996. Effects of soil water deficit at different growth stages onrice growth and yield under upland conditions 2. Phenology, biomass productionand yield. Field Crops Res. 48, 47–55.
Borrell, A., Garside, A., Fukai, S., 1997. Improving efficiency of water use for irrigatedrice in a semi-arid tropical environment. Field Crops Res. 52, 231–248.
Bouman, B.A.M., Feng, L.P., Tuong, T.P., Lu, G.A., Wang, H.Q., Feng, Y.H., 2007. Explor-ing options to grow rice using less water in northern China using a modellingapproach—II. Quantifying yield, water balance components, and water produc-tivity. Agric. Water Manage. 88, 23–33.
Bouman, B.A.M., Kropff, M.J., Tuong, T.P., Wopereis, M.C.S., ten Berge, H.F.M., van Laar,H.H., 2001. ORYZA2000: Modelling Lowland Rice. International Rice ResearchInstitute, Wageningen University and Research Centre, Los Banos, Philippinesand Wageningen, Netherlands.
Bouman, B.A.M., Lampayan, R.M., Tuong, T.P., 2008. Water Management in Irri-gated Rice: Coping with Water Scarcity. Interantional Rice Research Institute,Los Banos, Philippines.
Bouman, B.A.M., Tuong, T.P., 2001. Field water management to save water andincrease its productivity in irrigated lowland rice. Agric. Water Manage. 49,11–30.
Bouman, B.A.M., van Laar, H.H., 2006. Description and evaluation of the rice growthmodel ORYZA2000 under nitrogen-limited conditions. Agric. Syst. 87, 249–273.
Bouman, B.A.M., van Keulen, H., van Laar, H.H., Rabbinge, R., 1996. The ‘School of DeWit’ crop growth simulation models: a pedigree and historical overview. Agric.
Syst. 52, 171–198.
Bouman, B.A.M., Wopereis, M.C.S., Kropff, M.J., Tenberge, H.F.M., Tuong, T.P., 1994.Water-use efficiency of flooded rice fields. 2. Percolation and Seepage losses.Agric. Water Manage. 26, 291–304.
Chahal, G.B.S., Sood, A., Jalota, S.K., Choudhury, B.U., Sharma, P.K., 2007. Yield, evap-otranspiration and water productivity of rice (Oryza sativa L.)–wheat (Triticum
Journal Identification = FIELD Article Identification = 5443 Date: April 21, 2011 Time: 3:6 pm
s Rese
C
F
G
H
H
H
H
H
H
J
K
K
L
L
S. -Yadav et al. / Field Crop
aestivum L.) system in Punjab (India) as influenced by transplanting date of riceand weather parameters. Agric. Water Manage. 88, 14–22.
houdhary, T.N., 1997. Water Management in Rice for Efficient Production. Direc-torate of Water Management Research, Patna, India.
eng, L.P., Bouman, B.A.M., Tuong, T.P., Cabangon, R.J., Li, Y.L., Lu, G.A., Feng, Y.H.,2007. Exploring options to grow rice using less water in northern China usinga modelling approach—I. Field experiments and model evaluation. Agric. WaterManage. 88, 1–13.
arrity, D.P., O’Toole, J.C., 1994. Screening rice for drought resistance at the repro-ductive phase. Field Crops Res. 39, 99–110.
ira, G.S., 2009. Water management in northern states and the food security of India.J. Crop Improv. 23, 136–157.
ira, G.S., Jalota, S.K., Arora, V.K., 2004. Efficient Management of Water Resources forSustainable Cropping in Punjab. Research Bulletin, Department of Soil, PunjabAgriucultural University, Ludhiana, Punjab, p. 20.
ira, G.S., Khera, K.L., 2000. Water Resource Management in Punjab UnderRice–Wheat Production System. Research Bulletin, Department of Soil, PunjabAgricultural University, Ludhiana, Punjab, India.
ira, G.S., Rachhpal, S., Kukal, S.S., 2002. Soil matric suction: a criterion for schedulingirrigation to rice (Oryza sativa). Indian J. Agric. Sci. 72, 236–237.
umphreys, E., Kukal, S.S., Amanpreet, K., Sudhir, T., Sudhir, Y., Yadvinder, S., Bal-winder, S., Timsina, J., Dhillon, S.S., Prashar, A., Smith, D.J., 2008. Permanentbeds for rice–wheat systems in Punjab India. 2: water balance and soil waterdynamics. In: ACIAR Proceedings Series , pp. 37–61.
umphreys, E., Kukal, S.S., Christen, E.W., Hira, G.S., Balwinder-Singh, Sudhir-Yadav,Sharma, R.K., 2010. Halting the groundwater decline in north-west India—whichcrop technologies will be winners? Adv. Agron. 109, 155–217.
alota, S.K., Singh, K.B., Chahal, G.B.S., Gupta, R.K., Chakraborty, S., Sood, A., Ray, S.S.,Panigrahy, S., 2009. Integrated effect of transplanting date, cultivar and irrigationon yield, water saving and water productivity of rice (Oryza sativa L.) in IndianPunjab: field and simulation study. Agric. Water Manage. 96, 1096–1104.
ukal, S.S., Aggarwal, G.C., 2002. Percolation losses of water in relation to pud-dling intensity and depth in a sandy loam rice (Oryza sativa) field. Agric. WaterManage. 57, 49–59.
ing to rice (Oryza sativa). Irrig. Sci. 23, 153–159.
i, Y., Barker, R., 2004. Increasing water productivity for paddy irrigation in China.Paddy Water Environ. 2, 187–193.
oeve, R., Dong, B., Molden, D., Li, Y.H., Chen, C.D., Wang, J.Z., 2004. Issues of scale inwater productivity in the Zhanghe irrigation system: implications for irrigationin the basin context. Paddy Water Environ. 2, 227–236.
arch 122 (2011) 104–117 117
Sandhu, B.S., Khera, K.L., Prihar, S.S., Singh, B., 1980. Irrigation needs and yield of riceon a sandy-loam soil as affected by continous and intermittent submergence.Indian J. Agric. Sci. 50, 492–496.
Sharma, B.R., 1999. Water saving irrigation techniques for paddy rice in India. In:International Symposium on Water Saving Irrigation for Paddy Rice ,. ChineseNational Committee on Irrigation and Drainage, Beijing, People’s Republic ofChina, pp. 72–83.
Sharma, P.K., 1989. Effect of periodic moisture stress on water-use efficiency inwetland rice. Oryza 26, 252–257.
Singh, Y., Humphreys, E., Kukal, S.S., Singh, B., Kaur, A., Thaman, S., Prashar, A., Yadav,S., Timsina, J., Dhillon, S.S., Kaur, N., Smith, D.J., Gajri, P.R., 2009. Crop perfor-mance in permanent raised bed rice–wheat cropping system in Punjab, India.Field Crops Res. 110, 1–20.
Stockle, C.O., Martin, S.A., Campbell, G.S., 1994. CropSyst, a cropping systemssimulation-model-water nitrogen budget and crop yield. Agric. Syst. 46,335–359.
Sudhir-Yadav, Gill, G., Humphreys, E., Kukal, S.S., Walia, U.S., 2011a. Effect of watermanagement on dry seeded and puddled transplanted rice. Part 1: crop perfor-mance. Field Crops Res. 120, 112–122.
Sudhir-Yadav, Humphreys, E., Kukal, S.S., Gill, G., Rangarajan, R., 2011b. Effect ofwater management on dry seeded and puddled transplanted rice. Part 2: waterbalance and water productivity. Field Crops Res. 120, 123–132.
Tuong, T.P., 1999. Productive water use in rice production: opportunities and limi-tations. J. Crop Prod. 2, 241–264.
Tuong, T.P., Wopereis, M.C.S., Marquez, J.A., Kropff, M.J., 1994. Mechanisms and con-trol of percolation losses in irrigated puddled rice fields. Soil Sci. Soc. Am. J. 58,1794–1803.
van Ittersum, M.K., Leffelaar, P.A., van Keulen, H., Kropff, M.J., Bastiaans, L., Goudri-aan, J., 2003. On approaches and applications of the Wageningen crop models.Eur. J. Agron. 18, 201–234.
Won, J.G., Choi, J.S., Lee, S.P., Son, S.H., Chung, S.O., 2005. Water saving by shallowintermittent irrigation and growth of rice. Plant Prod. Sci. 8, 487–492.
Wösten, J.H.M., Pachepsky, Y.A., Rawls, W.J., 2001. Pedotransfer functions: bridgingthe gap between available basic soil data and missing soil hydraulic character-istics. J. Hydrol. 251, 123–150.
Wu, X., 1999. Popularization of water saving irrigation techniques to farmers. In: Xu,Z. (Ed.), Proceedings of the International Symposium on Water-saving Irrigationfor Paddy. Guilin, China, pp. 41–46.
Zhang, H., Zhang, S.F., Yang, J.C., Zhang, J.H., Wang, Z.Q., 2008. Postanthesis moderatewetting drying improves both quality and quantity of rice yield. Agron. J. 100,726–734.
![[XLS] viewCENTRE NIFT IRRIGATION,FC PHE RAVI TAWI IRRIGATION CANAL MAJ/MED. IRRIGATION JAMMU MAJ/MED IRRIGATION KMR. MINOR IRRIGATION JAMMU MINOR IRRIGATION KASHMIR MINOR IRRIGATION LADAKH ...](https://static.documents.pub/doc/80x56/5b095a277f8b9ac90f8debc7/xls-viewcentre-nift-irrigationfc-phe-ravi-tawi-irrigation-canal-majmed-irrigation.jpg)
