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Field Crops Research
jou rna l h om epage: www.elsev ier .com/ locate / fc r
valuation of tradeoffs in land and water productivity of dry seeded rice asffected by irrigation schedule
udhir-Yadava,b,∗, E. Humphreysb, Tao Lib, 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 19 April 2011eceived in revised form 9 January 2012ccepted 9 January 2012
eywords:RYZA2000lternate wetting and dryingater saving
oil water dynamicsrop model
a b s t r a c t
Management strategies that increase water productivity and reduce labour requirement while maintain-ing or increasing land productivity are urgently needed. Dry seeded rice (DSR) has been proposed as one ofthe technologies to achieve these objectives, but little is known about tradeoffs between land and waterproductivity of rice as affected by irrigation schedule. This study tested the ability of the ORYZA2000model to simulate the effects of irrigation schedule of DSR on yield, various measures of water produc-tivity, and soil water dynamics. The study showed that under conditions of no or mild water stress (up to20 kPa SWT) ORYZA2000 performs well in simulating the effects of irrigation schedule on crop growth,yield, soil water dynamics, water balance components and water productivity of DSR in north-west India.However, the model overpredicted crop growth and yield at higher irrigation thresholds (40 and 70 kPa)which was atleast partially due to the fact that the DSR suffered from iron deficiency at higher irrigationthresholds.
Using weather data for 40 rice seasons, the model predicted that there is always some yield penaltywhen switching from continuous flooding (CF) to alternate wetting and drying (AWD) of DSR. The yielddecline varied from 6% with an irrigation threshold of 10 kPa to 26% at 70 kPa. However, there was large
irrigation water saving when changing from CF to alternate wetting and drying (AWD) at only 10 kPa,and only a small rate of decline in irrigation input as the threshold increased from 10 to 70 kPa. The watersaving with AWD was primarily because of less drainage. There were tradeoffs between yield, waterproductivity and water depletion in relation to irrigation schedule. Maximum yield occurred with CF,maximum WPI and WPI+R with an irrigation threshold of 30 kPa and maximum WPET with a threshold of 20 kPa.
. Introduction
Puddling, followed by hand-transplanting of rice seedlings andontinuous flooding, is the traditional method of rice culture inhe Indo-Gangetic Plains (IGP) of South Asia. This establishment
ethod consumes a lot of energy (for intensive tillage), labour andater. In north-west India, where agriculture is highly dependentpon migrant labour, labour scarcity for rice transplanting is now aajor concern for the viability of puddled transplanted rice (PTR),
nd labour costs for hand transplanting have risen sharply in recent
ears. Another serious issue with traditional rice production is theery high water input, with very heavy reliance on groundwateror rice cultivation in north-west India. Farmers often have to use
∗ Corresponding author at: Crop and Environmental Sciences Division, Inter-ational Rice Research Institute, DAPO Box 7777, Metro Manila 4031, Laguna,hilippines.
100–250 mm (Tuong, 2000; Sudhir-Yadav et al., 2011b) of waterjust for the puddling operation before hand-transplanting. The highwater requirement of rice is one of the reasons for the alarming rateof decline of the water table (30–100 cm per year) in some areasof north-west India (Ambast et al., 2006; Hira, 2009). The fall ingroundwater is of concern to farmers because of the costs of deep-ening tubewells and installing pumps able to lift water from deeperdepths. Secondly, groundwater is largely pumped using electric-ity, which is free or highly subsidised for farmers. The inadequateand unreliable electricity supply to rural areas is driving farmers toadopt more water use efficient methods of rice production.
Many farmer trials and experiments have shown that rice canbe successfully dry seeded into non-puddled soils in the north-west IGP, with or without prior cultivation (Hobbs et al., 2002;Qureshi et al., 2004; Saharawat et al., 2009, 2010; Sudhir-Yadav
et al., 2011a). Dry seeded rice (DSR) provides an opportunity formore timely crop establishment in some regions, and eliminatespuddling from the rice–wheat cropping system, to the benefit ofwheat and other upland crops in the rotation (Ladha et al., 2003).
2008 Daily 09 June 12 June 17 August 17 September 14 October 21 October20 kPa 09 June 12 June 22 August 25 September 23 October 30 October40 kPa 09 June 12 June 25 August 30 September 26 October 03 November70 kPa 09 June 12 June 25 August 30 September 26 October 03 November
2009 Daily 09 June 12 June 20 August 18 September 16 October 23 October20 kPa 09 June 12 June 28 August 29 September 02 November 09 November40 kPa 09 June 12 June 02 September 02 October 03 November 09 November70 kPa 09 June 12 June 02 September 02 October 03 November 09 November
Sudhir-Yadav et al. / Field Crops Research 128 (2012) 180–190 183
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ig. 3. Simulated vs. measured crop biomass (a) and panicle biomass (b) during the wn all years. The solid line is the 1:1 relationship; the dotted lines are plus and minu
oreover, DSR involves less intensive tillage than puddling, reduc-ng fuel costs and generation of carbon dioxide, and is conducive to
echanisation of crop establishment (Khade et al., 1993), greatlyeducing labour requirement. Studies in north-west India (Bhushant al., 2007; Choudhury et al., 2007; Sudhir-Yadav et al., 2011b)ave shown that DSR consumes less irrigation water than PTR whensing the same irrigation scheduling criteria. However, yield of DSR
n comparison to PTR is variable. Several studies in north-west Indiaound similar yield of DSR and PTR (Gill, 2008; Saharawat et al.,009; Sudhir-Yadav et al., 2011a). In contrast, other studies in theame region reported lower yield of DSR than PTR (Gupta et al.,003; Sharma et al., 2005; Bhushan et al., 2007; Choudhury et al.,007). The cause of the lower yields is generally unknown, but inome situations it was associated with weed infestation, micro-utrient deficiency, nematodes and water stress (Singh et al., 2008;reye et al., 2009; Sudhir-Yadav et al., 2011a). Whether or howuch of the yield loss was due to water deficit stress is unknown.
he results of Sudhir-Yadav et al. (2011a) on a clay loam soil atudhiana showed that both PTR and DSR are extremely sensitiveo water deficit stress, with yields declining as the threshold forrrigation decreased beyond a soil tension of 20 kPa at 18–20 cmoil depth. DSR was more sensitive to water deficit stress than PTR,hich was at least partly due to iron deficiency, despite numer-
us iron sprays. These results were obtained in years of average orbove average rainfall, and therefore the soil only dried to 20 kPan a few occasions. Other studies suggest that the safe thresholdor irrigation of PTR is 10 kPa (Bouman and Tuong, 2001). To date,here are no scientifically based guidelines for irrigation manage-
ent for DSR, and the irrigation requirement for maximum yield isikely to vary with site conditions (e.g. soil type, weather, depth tohe watertable), variety, growth stage and management.
Crop growth simulation models are useful tools for extrap-lation of the results of field experiments on the effects oflternative management practices across different seasonal andgro-ecological conditions. Modelling studies can help to explorestablishment method and irrigation management for optimizingater and land productivity, and to determine the likely irrigationater savings. Determining the nature of the irrigation water sav-
ngs is important to understand the effects of changed managementn water depletion from the soil/groundwater system, and thus onater availability for alternative uses. Modelling studies also pro-
ide an opportunity to estimate components of the water balance,ost of which are very difficult to determine under field conditions.
n the past, there have been several modelling studies in north-westndia which explored the effects of various management practices
rop season with daily (�), 20 kPa (�), 40 kPa (�) and 70 kPa (�) irrigation thresholdsmeasured standard error around the 1:1 line.
on yield and water productivity of PTR (Arora, 2006; Chahal et al.,2007; Jalota et al., 2009; Sudhir-Yadav et al., 2011b). However, todate, there are no reports of the parameterization and evaluationof crop models for DSR in north-west India, nor in South Asia asa whole. Therefore, this study aimed to calibrate and evaluate theORYZA2000 model for DSR, and to use the model to simulate theeffects of irrigation threshold on land and water productivity ofDSR.
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)model for DSR using the variety PAU201. The experiments wereconducted during 2008 and 2009 at Punjab Agricultural Univer-sity (PAU) research farm, Ludhiana, India (30◦54′N, 75◦98′E, 247 mAMSL) on a clay loam soil. Soil properties, crop management andcrop and water monitoring methods are fully described in Sudhir-Yadav et al. (2011a,b).
The field experiments included 4 irrigation schedules: (i) “daily”irrigation, and intermittent (AWD) irrigation when the soil watertension (SWT) at 20 cm depth increased to (ii) 20, (iii) 40 and (iv)70 kPa. The DSR was sown on 9 June each year. For the first 42DAS, the DSR was irrigated to keep the soil water tension below10–15 kPa at 10 cm soil depth, after which the irrigation treatmentswere commenced. The daily irrigated treatments were topped upto 50 mm standing water depth throughout the season, until abouttwo weeks before harvest maturity. The amount of irrigation waterapplied to all AWD treatments was 50 mm at each irrigation.
2.2. ORYZA2000 model
2.2.1. Parameterization and validationThe methodology for parameterization and validation of
ORYZA2000 for PTR (presented in Sudhir-Yadav et al. (2011c)) wasused to parameterize and evaluate the model for DSR, and onlythose parameters which differed for DSR and PTR are specifiedhere. The SWIRTRF, which scales the transpiration changes under
drought stress, was set to 0.025597 and 0.015597 in 2008 and2009, respectively for DSR. Crop development rates were calculatedusing observed crop phenology parameters (Table 1). The measuredKs of the plough sole was further fine tuned by model fitting of
184 Sudhir-Yadav et al. / Field Crops Research 128 (2012) 180–190
Table 3Quantitative goodness-of-fit parameters for ORYZA2000 simulation of crop growth variables for DSR over the growing season for different irrigation regimes pooled over 2seasons.
Abbreviations: N, number of data pairs; Xmean, mean of measured values in whole population; Xsd, standard deviation of measured values in whole population; Ymean, meano es; ˛,o rrelatt re err
sTs
2
(m(ysimidmt
3
3
3
potdow
f simulated values in whole population; Ysd, standard deviation of simulated valuf linear relationship between simulated and measured value; r2, adjusted linear co-test; RMSEa, absolute root mean square error; RMSEn, normalized root mean squa
imulated soil water tension against measured values (Table 2).he van Genuchten parameters were derived from the soil particleize analysis and organic matter content (Wösten et al., 2001).
.2.2. ScenariosThe performance of DSR under range of irrigation thresholds
continuous flooding (CF) to 70 kPa) was evaluated, in a similaranner to that of PTR (Sudhir-Yadav et al., 2011c). The start time
STTIME)/emergence date of DSR was set to the 165th day of theear (DOY), and plant density was set to 110 plants m−2 with a rowpacing of 20 cm. For the first 30 DAS, all DSR treatments wererrigated 2 d after the disappearance of ponded water, before com-
encing the irrigation treatments, with 50 mm applied at eachrrigation. The CF treatments were topped up to 50 mm waterepth whenever the depth declined to 10 mm. The AWD treat-ents received 50 mm irrigation water whenever the threshold soil
ension was reached.
. Results
.1. Parameterization and evaluation of ORYZA2000 for DSR
.1.1. Crop variablesORYZA2000 performed well in simulating a range of crop
arameters for DSR with daily irrigation or an irrigation thresholdf 20 kPa, with the slope (ˇ) close to 1 (0.88–1.20), and a rela-
ively small intercept (Figs. 1–4 and Table 3). The coefficient ofetermination (r2) was generally more than 0.95, with RMSEn
f 2–14%. In contrast, the values of the same crop parametersere greatly overestimated at 40 and 70 kPa, primarily because of
intercept of linear relationship between simulated and measured values; ˇ, slopeion coefficient between simulated and measured values; P(t), significance of pairedor (%).
overestimation of green leaf biomass and LAI, but stem and paniclebiomass were also overestimated. At 40 and 70 kPa, was almostalways greater than 1 (1.10–1.60), r2 ranged from 0.70 to 0.95, andRMESn was 1.3 and 1.9 t ha−1 for the 40 and 70 kPa treatments,respectively.
3.1.2. Soil water and water balance componentsThere was generally good agreement between the simulated and
measured values of the water balance components for DSR (Fig. 5and Table 4). However, each year the model greatly overestimatedrunoff and underestimated ET in the CF treatment. The dynamics ofsoil water tension at 20 cm depth in DSR was generally simulatedwell each year in all treatments (Fig. 6). However, the deviationof estimated values of SWT from measured values was very high(RMSEn = 104–111%).
3.2. Simulation analysis
Potential grain yield varied from 8 to 12 t ha−1 over the 40 riceseasons (Fig. 7a). Model outputs clearly showed a long term declinein potential yield of DSR, similar to that observed for PTR (Sudhir-Yadav et al., 2011c) due to reduction in net radiation. Potential yieldwas significantly correlated with radiation (R2 = 0.58, p < 0.05), anddeclined at an average rate of 65 kg ha−1 year−1 (R2 = 0.59, p < 0.05).There was a fairly steady decline in yield from 9.8 to 7.2 t ha−1 asthe irrigation threshold increased from CF to 70 kPa (Fig. 7b). Therewas always a yield penalty by changing irrigation schedule from CF
to AWD, regardless of the season, and the average penalty variedfrom 6% at 10 kPa to 26% at 70 kPa, on average.
Irrigation input declined greatly by averages of 59 and 77% whenchanging from CF to 10 kPa or 20 kPa, respectively, and the decline
Sudhir-Yadav et al. / Field Crops Research 128 (2012) 180–190 185
ncreased gradually to 84% at 70 kPa threshold (Fig. 8a). The trendn total water input was similar to that of irrigation input with 48%eduction in total water input with irrigation at 10 kPa comparedith CF (3205 mm) (Fig. 8b).
cted by irrigation schedule in 2008 (a) and 2009 (b). Vertical bars indicate standard
With CF, most of the total water input (76%) drained beyond theroot zone (0–60 cm), 14% was transpired, and 8% was evaporateddirectly from the ponded water, on average. The rest (2%) was eitherlost as runoff or retained in the soil profile.
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y irrigation threshold in 2008 and 2009. Vertical bars indicate standard error of
186 Sudhir-Yadav et al. / Field Crops Research 128 (2012) 180–190
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aada
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Aoot
Fig. 6. Measured (�) and simulated (solid line) soil water tension at
Evapotranspiration of DSR-CF ranged from 590 to 981 mm with mean of 705 mm (Fig. 8c). Under AWD, ET decreased by 10%
t 10 kPa and further decreased by 20% at 70 kPa. Average deeprainage was 2430 mm in the CF treatment and declined to 892 mmt 10 kPa and 272 mm at 70 kPa (Fig. 8e). Only a very small amount
able 4uantitative goodness-of-fit parameters for ORYZA2000 simulation of soil water tension
Variables N Xmean Xsd Ymean Ysd
Water loss componentsEvapotranspiration 8 696 211 643 119
Drainage 8 876 755 893 822
Runoff 8 176 177 158 279
Soil water tension20 kPa 186 12 12 8 8
40 kPa 185 18 19 12 14
70 kPa 189 17 19 13 15
bbreviations: N, number of data pairs; Xmean, mean of measured values in whole populaf simulated values in whole population; Ysd, standard deviation of simulated values; ˛,f linear relationship between simulated and measured value; r2, adjusted linear correlat-test; RMSEa, absolute root mean square error; RMSEn, normalized root mean square err
depth in 2008 (a–c) and 2009 (d–f) for different AWD treatments.
of input water (average 53 mm with daily irrigation declining to28 mm in 70 kPa) which was lost as runoff.
Irrigation water productivity was 0.37 g kg−1 with CF butincreased sharply to around 0.9 and 1.5 g kg−1as the irriga-tion threshold increased to 10 kPa and 20 kPa, respectively
and water loss components in DSR pooled over two growing seasons.
tion; Xsd, standard deviation of measured values in whole population; Ymean, mean intercept of linear relationship between simulated and measured values; ˇ, slopeion coefficient between simulated and measured values; P(t), significance of pairedor (%).
Sudhir-Yadav et al. / Field Crops Research 128 (2012) 180–190 187
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.
188 Sudhir-Yadav et al. / Field Crops Research 128 (2012) 180–190
F ation
p ge,
t
(saCt
4
4
4
baodcd
ggwTiw(meto(a(
ig. 9. Simulation results over 40 years for (a) water productivity based on irrigroductivity based on evapotranspiration (WPET). The error bars represent the ranhe mean of 40 years data.
Fig. 9a). At irrigation thresholds >20 kPa, WPI was almost con-tant (1.6–1.8 g kg−1). The trend in WPI+R was similar to that of WPI,lthough less pronounced (Fig. 9b). WPET was similar (1.4 g kg−1) inF to 30 kPa but declined slightly when SWT increased from 30 kPao 70 kPa (1.3 g kg−1) (Fig. 9c).
. Discussion
.1. Model performance
.1.1. Calibration and evaluation of ORYZA2000 for DSRThe current version of ORYZA2000 (V 2.13) needs to be cali-
rated for each G × E × M situation, which greatly diminishes thebility of a calibrated version to be used to extrapolate findings tother situations (e.g. seasonal conditions) and management (e.g.ifferent sowing dates). Also, the model does not produce yieldomponents such as panicle density, which limits the ability toiagnose the causes of different treatment responses.
Performance of the calibrated model against a range of croprowth, yield, water and soil water parameters was generally veryood for daily irrigation and an irrigation threshold of 20 kPa soilater tension during an average and a wetter than average season.
he over prediction of simulated and measured grain yield in 2008n the daily irrigation treatment might be because of crop lodging,
hich occurred in this treatment about 1 week prior to maturitySudhir-Yadav et al., 2011a). The model performed poorly in esti-
ating growth and yield of DSR with thresholds of 40 and 70 kPaach year. The discrepancy was greater in 2009 (Table 3), whenhe 40 and 70 kPa treatments exhibited strong visual symptoms
f Fe deficiency which were not controlled by repeated Fe spraysSudhir-Yadav et al., 2011a). Iron deficiency in DSR with AWD haslso been reported by other workers in the region and elsewhereHobbs et al., 2002; Yadvinder et al., 2008; Kreye et al., 2009). It
(WPI), (b) water productivity based on total water input (WPI+R), and (c) waterrepresent 75th percentile, represent 50th percentile and represent
may be present even before the symptoms are visible (Nayyar et al.,1990). Therefore we suggest that over prediction of crop growthand yield of DSR by the model was at least partly due to Fe defi-ciency. In the current version of ORYZA (V 2.13), there is no moduleto induce micro-nutrient deficiency along with water deficit stress.The model simulations with irrigation thresholds beyond 20 kPamay indicate yield potential of DSR on similar soil types for situ-ations where Fe deficiency is not a problem (e.g. situations whereground water is high in Fe content, as in parts of Punjab).
4.1.2. Simulations – model performanceThere are no simulation studies for DSR in north-west India
with which to compare our findings. However, similar studies ofirrigation threshold were conducted by Bouman et al. (2007)and Xue et al. (2008) in two different environments in China.Both these simulation studies were with dry seeded aerobic rice(HD297, whose yield potential is almost half that of PAU201).As in our study, Bouman et al. (2007) also found a slightdecline in the grain yield of aerobic rice as irrigation thresholdincreased from 10 to 30 kPa in deep ground water table (190 cm)conditions.
The simulated grain yield of DSR was slightly higher than simu-lated grain yield of PTR under the same environmental conditions(Sudhir-Yadav et al., 2011c) which might be associated with thehigher plant density of DSR (Tekrony and Egli, 1991) and/or avoid-ance of transplanting shock (Ros et al., 2003). There was a gradualdecline in average rice yield with DSR as the irrigation thresholdincreased. The yield decline was primarily associated with reduced
above ground biomass, and was higher in drier seasons (p < 0.05),when the frequency of dry down to the irrigation thresholds washigher. The simulated yield response to irrigation threshold isgenerally consistent with the findings of many field experiments
ops Re
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Bouman and Tuong, 2001; Castillo et al., 2006; Venuprasad et al.,007; de Vries et al., 2010; Sudhir-Yadav et al., 2011a).
With the current options in ORYZA, emergence of the rice cropccurs on the same day as the model run (STTIME) is started andhere is no switch to include pre-tillage and pre-sowing irrigationsn the total water balance. Therefore, the model is likely to underes-imate irrigation input, and overestimate irrigation and input waterroductivity.
Shifting from CF to AWD reduced irrigation water input by9–84% in DSR, primarily because of reduced drainage beyond theoot zone. In Beijing, China, Xue et al. (2008) reported a sharpecline in the irrigation water in aerobic rice when irrigationhreshold increased from 10 kPa to 50 kPa. In our modelling studyith PTR, we also found 65–79% irrigation water saving with AWD
at 10–70 kPa), primarily due to less drainage beyond the root zone.There was an average decline in total ET of around 74 mm in
oing from CF to an irrigation threshold of 10 kPa, and a furtherecline (36 mm) going to a 20 kPa threshold. The decline in ETas associated with a 7% decline in total biomass with 10 kPa in
omparison with CF. Total ET was almost constant over irrigationhresholds from 20 to 70 kPa. Similar trends for the effect of irri-ation threshold on ET were also found in simulation studies forocations in China (Bouman et al., 2007; Xue et al., 2008)
.2. Optimum irrigation threshold
There were tradeoffs between yield, irrigation amount and var-ous measures of WP with alteration of irrigation schedule. In DSRield was maximum with CF, however, measures of water pro-uctivity (WPI, WPI+R and WPET) were maximum under AWD.aximum WPI and WPI+R were achieved when irrigation was
cheduled at ≥30 kPa SWT. However, WPET was maximum at aower irrigation threshold (10–20 kPa). Therefore, for a farmer who
ants to maximise yield, the modelling results suggest that grow-ng DSR with continuous flooding once the crop is established ishe best option. For a farmer for whom irrigation water is limit-ng, the objective will be to maximise WPI of his fields, meaninghat DSR with an irrigation threshold of 30 kPa would be the bestption, despite a 16% yield reduction in comparison with DSR-CF.or a water resource manager, the objective will be to maximise WPith respect to water depletion from a higher spatial scale such as
sub-catchment or catchment. This normally means maximisingPET, and the simulation results suggest that irrigation schedule
t 20 kPa SWT would be the best option. Finally, if water deple-ion (ET) is to be minimised while still growing rice, the simulationesults suggest that this will be achieved with PTR with AWD; ashere is only a very small reduction in ET in increasing the thresholdrom 20 to 70 kPa (30 mm), and a much larger decline in yield, thisuggests that 20 kPa would also be the optimum management.
. Conclusions
The study shows that under conditions of no or mild water stressup to 20 kPa SWT) ORYZA2000 (V 2.13) performs well in simulat-ng the effects of irrigation schedule on crop growth, yield, wateralance components and water productivity of DSR in north-west
ndia. However, the model overpredicted crop growth and yield atigher irrigation thresholds (40 and 70 kPa). The poor performancef the model under water deficit was at least partly due to the facthat these treatments suffered from iron deficiency, which is notimulated by the model. The scenario analysis for 40 rice seasons
redicted that yield declined gradually as the irrigation threshold
ncreased. There was a large irrigation water saving when changingrom CF to AWD, and only a small rate of decline in irrigation inputs the threshold increased from 20 to 70 kPa. There were tradeoffs
search 128 (2012) 180–190 189
between yield, water productivity and water depletion. Maximumyield occurred with CF, maximum WPI and WPI+R with an irrigationthreshold of 30 kPa while maximum WPET was with 20 kPa.
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