Agricultural Water Management 96 (2009) 1096–1104
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Integrated effect of transplanting date, cultivar and irrigation on yield, water saving and water productivity of rice (Oryza sativa L.) in Indian Punjab: Field and simulation study S.K. Jalota a,*, K.B. Singh a, G.B.S. Chahal a, R.K. Gupta a, Somsubhra Chakraborty a, Anil Sood b, S.S. Ray c, S. Panigrahy c a b c
Punjab Agricultural University, Ludhiana, India Punjab Remote Sensing Center, Ludhiana, India Space Applications Centre, Ahmedabad, India
A R T I C L E I N F O
A B S T R A C T
Article history: Received 28 June 2008 Accepted 3 February 2009 Available online 21 March 2009
Individual effect of different field scale management interventions for water saving in rice viz. changing date of transplanting, cultivar and irrigation schedule on yield, water saving and water productivity is well documented in the literature. However, little is known about their integrated effect. To study that, field experimentation and modeling approach was used. Field experiments were conducted for 2 years (2006 and 2007) at Punjab Agricultural University Farm, Ludhiana on a deep alluvial loamy sand Typic Ustipsamment soils developed under hyper-thermic regime. Treatments included three dates of transplanting (25 May, 10 June and 25 June), two cultivars (PR 118 inbred and RH 257 hybrid) and two irrigation schedules (2-days drainage period and at soil water suction of 16 kPa). The model used was CropSyst, which has already been calibrated for growth (periodic biomass and LAI) of rice and soil water content in two independent experiments. The main findings of the field and simulation studies conducted are compared to any individual, integrated management of transplanting date, cultivar and irrigation, sustained yield (6.3–7.5 t ha1) and saved substantial amount of water in rice. For example, with two management interventions, i.e. shifting of transplanting date to lower evaporative demand (from 5 May to 25 June) concomitant with growing of short duration hybrid variety (90 days from transplanting to harvest), the total real water saving (wet saving) through reduction in evapotranspiration (ET) was 140 mm, which was almost double than managing the single, i.e. 66 mm by shifting transplanting or 71 mm by growing short duration hybrid variety. Shifting the transplanting date saved water through reduction in soil water evaporation component while growing of short duration variety through reduction in both evaporation and transpiration components of water balance. Managing irrigation water schedule based on soil water suction of 16 kPa at 15–20 cm soil depth, compared to 2day drainage, did not save water in real (wet saving), however, it resulted into apparent water saving (dry saving). The real crop water productivity (marketable yield/ET) was more by 17% in 25th June transplanted rice than 25th May, 23% in short duration variety than long and 2% in irrigation treatment of 16 kPa soil water suction than 2-days drainage. The corresponding values for the apparent crop water productivity (marketable yield/irrigation water applied) were 16, 20 and 50%, respectively. Pooled experimental data of 2 years showed that with managing irrigation scheduling based on soil water suction of 16 kPa at 15–20 cm soil depth, though 700 mm irrigation water was saved but the associated yield was reduced by 277 kg ha1. ß 2009 Elsevier B.V. All rights reserved.
Keywords: Rice Date of transplanting Cultivar Crop duration Irrigation Water saving Crop water productivity
1. Introduction Sustenance of rice–wheat system is of immense importance for food security and livelihood in South Asia. In Indo-Gangetic Basin
* Corresponding author at: Department of Soils, Punjab Agricultural University, Ludhiana, Punjab 141 004, India. E-mail address:
[email protected] (S.K. Jalota). 0378-3774/$ – see front matter ß 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.agwat.2009.02.005
of India, area under rice crop is 10 million ha, out of that 2.6 million ha falls in Indian Punjab (52% of whole geographical area of the state of Punjab). In this semi-arid tropical region, the rice is grown on coarse- to fine-textured puddled soils during the kharif season (summer) by transplanting 30 days old seedlings. The date of transplanting is staggered from May to June because of the limited availability of labour for transplanting and power for pumping ground water during the peak periods. Due to temporal and erratic rains and insufficient and irregular surface water supplies through
S.K. Jalota et al. / Agricultural Water Management 96 (2009) 1096–1104
canal system, the main source of irrigation is ground water. Early transplanted rice (in the month May) requires more frequent irrigations drawn from ground water to meet the emanating higher crop water requirement (Chahal et al., 2007) due to higher evaporative demand (8–12 mm day1). This causes over-exploitation of the ground water, resulting into decline in water table at an alarming rate, which was 0.65 meter year1 from 1998 to 2005 (Singh, 2006). At present, the yield of rice is almost stagnated and extraction of ground water is unsustainable as its lifting is becoming costlier due to rapid hike in prices of electric power and diesel. Moreover, the ability to develop surface water resources is economically limited. Therefore, it is of prime importance to save water and enhance crop water productivity. Water saving can be achieved through reduction in (i) the unproductive water loss from paddy fields by soil water evaporation (E) and (ii) evapotranspiration (ET). Such saving is known as wet saving because the water is lost for future use in the basin (Seckler, 1996). In practice, it can be achieved by shifting the transplanting date to the period having relatively lower evaporative demand (Chahal et al., 2007) and growing short duration variety requiring less water (Tuong, 1999). Another type of water saving known as dry saving is achieved through reduction in irrigation water applied by submergence non-submergence (SNS) technology or intermittent irrigation applied, a few days after water has disappeared from the surface (Sandhu et al., 1980; Singh et al., 1996; Tabbal et al., 2002) and based on soil water suction (SWS) in the root zone (Tuong, 1999; Kukal et al., 2005). Scheduling irrigation based on soil water suction (16 kPa) to save irrigation water without loss in yield of rice (variety, PR 113) has been suggested by Kukal et al. (2005) in north-west India. While evaluating a water saving technology, it is important that one should know the nature of water saving (wet or dry) and the components of water balance. The later are not easily measured in the field and vary widely with soil type, location and season (Jalota and Arora, 2002; Jalota et al., 2006). Under such situations, combination of field experimentation and modelling is the powerful approach that can give the conclusive results (Arora, 2006; Chahal et al., 2007). Sufficient literature on these management interventions under field conditions is available but is mostly limited to managing the one at a time. It is hypothesized that the effects of these management interventions may be additive and the magnitude of water saving in rice will be more with integration of these interventions, i.e. changing transplanting date, cultivar and irrigation compared to that by individual. Therefore, the present study aimed at (1) investigating the single and integrated effects of date of transplanting, variety and irrigation regime on yield, water saving, and crop water productivity of rice under field conditions and (2) simulation of crop biomass, water balance components, crop water productivity and rice yield in relation to different management interventions.
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solu-bridge (Chopra and Kanwar, 1976), pH (water soil ratio of 2:1) with potentiometer (Jackson, 1973) and soil organic carbon by wet digestion methods (Walkley and Black, 1934). Ammonical and nitrate nitrogen were determined by Kjeldahl distillation method (Keeney, 1982). Daily weather data on maximum and minimum temperature, maximum and minimum relative humidity, wind speed and rainfall during the crop growth period were obtained from meteorological station at Punjab Agricultural University. Solar radiation was generated by ClimGen model (Stockle and Nelson, 1999). A total of 12 treatments were replicated thrice in 36 plots of size 6.5 m 4 m in split plot design. The treatments included three dates of transplanting viz. 25 May (D1), 10 June (D2) and 25 June (D3), two varieties, inbred PR 118 (V1) and hybrid RH 257 (V2) and two irrigation regimes viz. intermittent irrigation at 2-days drainage period (I1) and irrigation based on soil water suction (SWS) of 16 kPa (I2). In the climate of Punjab the crop duration for V1 and V2 during the kharif season usually takes 120 and 90 days (from transplanting to harvest). Soil water suction (SWS) was measured with tensiometers installed at 15–20 cm soil depth. In each irrigation treatment, lateral movement of water was minimized by keeping a buffer strip. Seedlings of 30 days old nursery of both the varieties were transplanted on three dates of transplanting with 20 cm row and 15 cm plant spacing on two times puddled soil. Puddling was done by running cultivator in the standing water followed by planking. At each date of transplanting, 40 kg N and 30 kg P2O5 and 30 kg K2O per hectare were applied at the time of transplanting by broadcasting. Second and third (40 kg ha1 each) and an additional dose of N (30 kg ha1) were applied at 22, 43 and 70 days after transplanting respectively in D1, D2 and D3 transplanting date treatments. Irrigation treatments were started following continuous flooding for 15 days after transplanting. The amount of irrigation water applied at each irrigation from transplanting till maturity was monitored with Parshal flume. Each plot was embanked with earthen bund of 15 cm height to avoid runoff loss or runoff gain. The wet and dry savings were calculated as saving in ET and irrigation water, respectively (Seckler, 1996). To control weeds (Echinochloa crusgalli L.), butachlor 50EC 3000 ml ha1 was applied 2 days after transplanting. Monocrotophos (1400 ml ha1), Chloropyriphos (2.5 l ha1), Padan (18 kg ha1) and Tilt 25 EC (500 ml ha1) were used periodically to control insect-pests and diseases. Biomass at 30, 60 and 90 days after transplanting was measured from 1-m row length in each treatment. It was determined after oven drying at 60 8C to constant weight. At maturity, the crop was harvested from the whole plot excluding border lines and rice yield was determined at 14% moisture content. The numbers of ears/ spikelets unfilled and filled with grains were counted from three hills comprising of 20–30 ears each. Rice yield and percent unfilled ears with grains in different treatments were analyzed statistically using split design (Steel and Torrie, 1960).
2. Materials and methods 2.2. Simulation study 2.1. Field study Field experiments were conducted at the Research Farm, Punjab Agricultural University, Ludhiana (308560 N, 758520 E and 247 m m.s.l.) in India during kharif season of the years 2006 and 2007. The soil was deep alluvial loamy sand Typic Ustipsamment developed under hyper-thermic regime (USDA classification). At the start of the experiment, soil physical (texture, bulk density and hydraulic conductivity) and chemical (EC, pH, OC, ammonical and nitrate nitrogen) properties of the field were determined, up to a depth of 1.8 m, at an interval of 0.15 m, following the standard procedures. The sand, silt and clay contents were determined by Pipette method, bulk density with cores and hydraulic conductivity with constant head methods (Jalota et al., 1998). EC was measured with
2.2.1. Description of the CropSyst model CropSyst model was chosen as it is a process based, simple, multi-year, multi-crop, daily time step cropping system simulation model. Further, its performance for periodic biomass and leaf area index of rice crop in rice–wheat system (Jalota et al., in press) and soil water storage (Chakraborty, 2008) have already been tested (Fig. 1). The model is designed to study the effect of cropping system management on crop productivity, water and N balance and the environment (Stockle et al., 1994; Stockle and Nelson, 1999). Simulations were made by selecting a location and soil, and building crop rotations with management schedule. The location parameters included longitude, latitude, weather files and ET models. The soil parameters included specification of soil layers,
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S.K. Jalota et al. / Agricultural Water Management 96 (2009) 1096–1104 Table 1 Crop parameters (Estimated from the experiments, standard calibrated) used in validation. Sr. No. Growth 1 2 3 4 5 6 7 8
Crop parameters
Rice
Units
Above ground biomass-transpiration coefficient Light to above ground biomass conversion Actual to potential transpiration ratio that limit leaf area growth Actual to potential transpiration ratio that limit root growth Optimum mean daily temp. for growth Maximum water uptake Leaf water potential at the onset of stomatal closure Wilting leaf water potential
7.5
kPa kg/m2
5.0 0.20
g/MJ
Morphology 1 Maximum rooting depth 2 Initial leaf area index 3 Maximum expected leaf area index 4 Fraction of maximum leaf area index at physiological maturity 5 Specific leaf area 6 Stem/leaf portioned coefficient 7 Leaf duration(degree days) 8 Extinction coefficient for solar radiation 9 Leaf duration sensitivity to water stress 10 ET crop coefficient at full maturity
Fig. 1. Observed and CropSyst model-simulated periodic biomass, LAI and soil water storage in rice.
thickness, texture, bulk density, cation exchange capacity, pH, volumetric water content at water potentials of 30 kPa (Field Capacity) and 1500 kPa (Wilting Point). The management options in the model included cultivar selection, crop rotation, irrigation, nitrogen fertilization, tillage operations and residue management. The crop file comprised of common set of parameters related to classification, growth, morphology and phenology of the crop to represent different crops and crop cultivars. Model outputs taken were biomass and water balance components on daily basis and rice yield at the end. 2.2.2. Calibration and validation The CropSyst model was calibrated for biomass and yield of two varieties of rice using the observed phenological parameters (flowering, grain filling and physiological maturity) and harvest (harvest index) of the crop from the experiment during the year of 2006. The other parameters for the crop file were taken as default with slight adjustments. These adjustments were made within the range from the experience or reported by other researchers so that the periodic crop growth like phenological stages, periodic biomass production and final grain yield were matched with the experimentally observed values. The crop parameters used in the model are given in Table 1. During the first step of calibration simulated phenological stages (germination, flowering and physiological maturity) were matched with the observed by adjusting the degree days. The observed degree-days in the two varieties V1 and V2 were 1100 and 1050 for beginning of flowering, 1500 and 1250 for grain filling and 1700 and 1500 8C-days for physiological maturity, respectively. Soil file for the experimental site was prepared using
0.20 30 10 1000
8C mm/day J/kg
1500
J/kg
1 0.100 6.0 1.0
m m2/m2 m2/m2
20 3.0 1700 0.50 1.50 1.20
m2/kg
Phenology 1 2 3 4 5 6 7 8
Degree days Emergence Degree days Peak leaf area index Degree days begin flowering Degree days grain filling Degree days Physiological maturity Base temperature Cut off temperature Phenological sensitivity to water stress
1 950 1100 1500 1700 15 35 1.0
Harvest 1
Unstressed harvest index
0.35
Baseline reference atmospheric CO2 concentration
350
CO2 1
8C - days
8C 8C 8C 8C 8C 8C 8C
-
days days days days days
ppm
the actually observed data on soil texture, bulk density and hydraulic conductivity, EC, pH, OC, ammonical-nitrogen and nitrate-nitrogen, which are given in Table 2. Location file was prepared from the weather parameters of rainfall, maximum temperature, minimum temperature, maximum and minimum relative humidity and wind speed actually recorded at the station. The solar radiation for the years (missed data) was generated with ClimGen model using the real data on solar radiation from 1991 to 1995 at Ludhiana. In a separate study it has been found that ClimGen model generates solar radiation close to the observed in different climatic situations in Punjab (Bal et al., 2008). The crop management file for rice crop was also prepared from the management operations performed on different dates in the experiment. The calibrated model was validated on an independent experiment conducted during the year 2007 on rice crop. 2.2.3. Model evaluation criteria The following indicators of model performance were used to test the prediction capability of the model. 2.2.3.1. Root mean square error. The value of root mean square error (RMSE) was calculated using the following equation hP n RMSE ¼
i¼1
ðP i Oi Þ2 =n O¯
i0:5 (1)
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Table 2 Physical and chemical properties of the soil profile of experimental site. Depth, cm
Sand, %
Silt, %
Clay, %
Texture
Bulk density, Mg m3
Hydraulic conductivity, mm h1
Physical properties 0–15 15–30 30–45 45–60 60–75 75–90 90–105 105–120 120–135 135–150 150–165 165–180
86.3 80.0 82.5 81.3 82.5 81.3 82.5 81.3 82.3 82.5 83.8 80.0
8.8 12.5 11.3 12.5 10.0 10.0 11.3 10.0 8.8 10.0 8.8 10.0
5.0 7.5 6.3 6.3 7.5 8.8 6.3 8.8 9.0 7.5 7.5 10.0
ls ls ls ls ls ls ls ls ls ls ls ls
1.83 1.83 1.72 1.64 1.64 1.62 1.61 1.61 1.63 1.70 1.62 1.63
9.0 18.0 12.7 28.2 26.5 30.8 28.2 28.6 24.6 23.8 21.3 16.2
Depth, cm
pH
EC, dS m1
OC, %
NH4-N, kg ha1
NO3-N, kg ha1
Chemical properties 0–15 15–30 30–45 45–60 60–75 75–90 90–105 105–120 120–135 135–150 150–165 165–180
8.6 8.6 8.7 8.7 8.6 8.6 8.7 8.7 8.7 8.7 8.9 9.0
0.237 0.174 0.164 0.158 0.169 0.182 0.137 0.123 0.136 0.173 0.206 0.255
0.285 0.215 0.135 0.105 0.030 0.075 0.276 0.135 0.150 0.108 0.060 0.044
15.68 7.84 21.57 16.80 11.76 13.73 8.02 3.92 3.92 5.94 17.36 1.97
23.52 19.60 15.68 23.52 25.49 27.44 41.17 30.58 23.52 35.28 27.44 33.33
where Pi and Oi are the predicted and observed values, respectively, O¯ is the average of the observed data, and n is the number of observations. The value equal to zero for a model showed perfect fit between the observed and predicted data. 2.2.3.2. Model efficiency. The model efficiency (EF), the measure of the deviation between model predictions and measurements relative to the scattering of the observed data (Wu et al., 1999) was calculated using the Nash and Sutcliffe (1970) relationship (Eq. (2)). Its value 1.0 showed a perfect fit between measured and predicted by the model hP i n 2 i¼1 ðP i Oi Þ i EF ¼ 1 hP (2) n ¯ 2 i¼1 ðOi OÞ
2.2.3.3. Coefficient of residual mass. This indicator shows the difference in observed and predicted data relative to the observed data (Eq. (3)). Its zero value denotes perfect fit, negative and positive values over- and under-prediction, respectively Pn Pn i¼1 Oi i¼1 P i CRM ¼ (3) Pn i¼1 Oi 2.2.4. Simulations Simulations were run for 10 years using the weather data of Ludhiana from 1998 to 2007. The calibrated and validated model was applied to simulate the effects of three dates of transplanting in rice, two varieties and two irrigation regimes on the water balance and water productivity. The seasonal water balance was estimated as the following equation I þ R ¼ E þ T þ D þ DS
(4)
where I is irrigation, R is rainfall, E is soil water evaporation, T is transpiration from the canopy, D is drainage beyond root zone and DS is change in soil water storage in the root zone. Real (wet) and
apparent (dry) water saving were calculated as reduction in ET and Irrigation water, respectively (Seckler, 1996). Real crop water productivity (RCWP) and apparent crop water productivity (ACWP), as reported in the literature (Jalota et al., 2006; Chahal et al., 2007; Mahajan et al., 2009), were estimated as RCWP ðkg m3 Þ ¼
marketable grain yield evapotranspiration
(5)
ACWP ðkg m3 Þ ¼
marketable grain yield irrigation water applied
(6)
3. Results and discussion 3.1. Field study 3.1.1. Rice yield Rice yields in different treatments as influenced by transplanting date, variety and irrigation regime during the years of 2006 and 2007 are presented in Table 3. Averaged over treatments, the rice yields were 7158 in 2006 and 6932 kg ha1 in 2007, which were statistically at par. Pooled analysis of the 2 years data indicated that by shifting date of transplanting from D1 to D3 did not affect rice yields significantly, but yield was more in D3 than D2. These observations confirm the field and simulated results reported by Chahal et al. (2007), who found a significant relation between rice yield and number of days having temperature greater than 37 8C, during the period of post-transplanting. In the present study the number of days with temperature greater than 37 8C during the crop growth in D2 and D3 were 53 and 26% during year 2006 and 42 and 8% during 2007 of the D1, respectively. However, the date of transplanting showed a significant interaction with the year. In the year 2006, yield in the D2 treatment declined significantly compared to D1 and D3. It may be attributed to two reasons (i) in that season total solar radiation received was less 1942 (MJ m2) in D2 compared to 2178 in D1 and 2040 in D3 and (ii) during flowering to anthesis growth stage of rice, out of six showers of
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Table 3 Effects of transplanting date, variety and irrigation regimes on yield (kg ha1) and % ears unfilled with grain of rice in years 2006 and 2007. 2006
2007
PR 118
25 May 10 June 25 June Mean LSD (0.05) Year DOT Year DOT Variety Year variety DOT variety Y DOT variety Irrigation Y irrigation DOT irrigation Year DOT irrigation Variety irrigation Y variety irrigation DOT variety irrigation Y DOT variety irrigation Unfilled ears, % 25 May 10 June 25 June LSD (0.05)
RH 257
Pooled
PR 118
RH 257
PR 118
RH 257
2-d D
SWS
2-d D
SWS
Mean
2-d D
SWS
2-d D
SWS
Mean
2-d D
SWS
2-d D
SWS
Mean
8469 6631 7860 7653
7576 5623 6672 6624
7271 6327 8216 7271
7285 6302 7667 7085
7650 6221 7604 7158
6550 6574 6324 6483
6691 7030 6098 6606
7122 7995 6864 7327
6284 8291 7357 7311
6661 7473 6661 6932
7510 6603 7092 7068
7134 6327 6385 6615
7197 7161 7540 7299
6785 7297 7512 7198
7156 6847 7132 7045
476
NS
NS
431
NS
NS
231
NS
NS
NS
327
NS
NS
NS
6.7 10.6 4.9 DOT = 1.8
8.0 12.5 6.6
7.1 14.6 5.4
8.4 12.4 8.3
NS NS 521 319 451 553 NS 245 346 NS NS NS 490 NS NS
7.5 12.5 6.3
rain, i.e. 9.0, 5.4, 9.0, 11.0, 62.6, 39.6 totaling 185 mm, the two heavy showers (40 mm) have damaged the pollens and consequently increased the proportion of ears without grains. In the D2 treatment the numbers of ears without grains were 12% of the total compared to 6% in D1 and 7% in D3. In addition to rain, soil water stress (Wopereis et al., 1996); and temperature stress during flowering to anthesis stage (Yoshida, 1981; De Datta, 1981) may have affected the spikelet sterility. In the present study, it was found that with increasing number of days having temperature >37 8C during flowering to anthesis period, spikelet sterility was increased by 5% day1. These results are in line with those of Jiangtao et al. (2006), who reported that quantity and quality of spikelets during the blossom period play a critical role in grain formation of rice. In the year 2007 solar radiation in D2 was more than D3 and there were only three rainfall events, i.e. 24.2, 14.0 and 16.1 totaling 54 mm from flowering to anthesis stage, which occurred in light showers and resulted into significantly higher yield in D2 than that in D1 and D3 treatments. Amongst the two varieties, V2 yielded 407 kg ha1 more rice than V1, which was significantly different at 0.05 levels. This is due to higher biomass (24%) and harvest index (6%) of the former variety. Varieties also showed a significant interaction with the year. In year 2006, the difference in varieties was not significant however; in 2007 yield of V2 variety was significantly higher by 775 kg ha1 than V1. Yield was also influenced significantly by irrigation treatments. In the I2 irrigation treatment, yield was significantly decreased by 277 kg ha1 than I1. Significant interactions between year irrigairrigation, and year variety irrigation were also observed. In the year 2006 yield was significantly reduced by 608 kg ha1 in I2 treatment than I1 and in 2007 both the treatments were at par. Interaction between year variety irrigation regimes indicated that yield was more in V1 variety with combination of I1 irrigation treatment during 2006 and in V2 with I2 during 2007. These interactions seem to be due to differential rainfall showers in the 2 years (Fig. 2). The numbers of small/light showers (
