Abstract

The present study employs a process-based DSSAT (Decision Support System for Agrotechnology Transfer) model for estimating agriculture water use and its evaluation for simulating groundwater fluctuations. A field experiment was performed during kharif season with PR 121 variety (rice) at Punjab Agricultural University, Ludhiana. There were six treatments with three dates of transplanting – June 5 (D1), June 20 (D2) and July 5 (D3), one cultivar – PR121 (V) and two irrigation regimes – 2-day drainage (I1) and tensiometer-based (I2). Both simulation and experimental results showed that paddy transplanted on June 20 (D2) and tensiometer-based irrigation treatment (I2) gave 23 and 32% higher yield in comparison to June 5 (D1) and July 5 (D3) with maximum water use efficiency of 1.69 kg/m3. The validated DSSAT model was simulated for a historical time slice (1998–2014) and indicated that 66% of applied water in the form of rainfall or irrigation in paddy fields was lost as drainage component. The simulated change in storage (rainfall minus evapotranspiration) was able to explain groundwater level fluctuations in terms of trends/magnitude in most of the years.

HIGHLIGHTS

  • In this research, a process based DSSAT (Decision Support System for Agro technology Transfer) model was used to estimate crop water use in paddy fields.

  • Both simulation and experimental results showed that paddy transplanted on June 20 (D2) and tensiometer based irrigation treatment (I2) gave 23 and 32% higher yield in comparison to June 5 (D1) and July 5 (D3) with maximum water use efficiency of 1.69 kg/m3.

  • The novelty of the research is that it tries to correlates the water use in paddy fields to explain the groundwater level fluctuations in the region.

Graphical Abstract

Graphical Abstract
Graphical Abstract

INTRODUCTION

Long-term abstraction of groundwater resources, in particular for irrigation, has resulted in severe decline and deterioration of water resources in many arid and semi-arid regions of the world (Konikow & Kendy 2005; Rodell et al. 2009; Scanlon et al. 2012; Feng et al. 2013; Yang et al. 2013; Famiglietti 2014; Chinnasamy & Ganapathy 2018). Rice (Oryza sativa L.), an important cereal and staple food for more than half of the global population, accounts for 40% of all global irrigation, and 17% of global groundwater depletion. The global average water footprint per kilogram of rice is 2,500 litres (Bouman 2009), with India at 2,800 litres and the State of Punjab has the highest footprint of 4,500 litres. Here, rice covers nearly 75% of the total cropped area and requires nearly 800–2,000 mm of irrigation water depending upon climate and soil type. The rainfall and surface water resources are not sufficient to meet the total irrigation needs. The percentage of irrigated area from canals and tube wells in the state is 28 and 72%, respectively. This has led to a ‘silent revolution of intensive groundwater use’ (Lamas & Martinez-Santos 2005) with the number of energized tube wells increasing from 0.19 in 1970 to 1.475 million in 2019 (Anon 2019). Consequently, there is huge pressure on the groundwater aquifers, more so in the central districts of the state where the water table has registered a steep decline (>1 m/year). Many researchers in the past have emphasized water saving in rice through adoption of improved technologies, such as laser land levelling, alternate wetting and drying (AWD), use of tensiometers for irrigation scheduling, delayed transplanting, shorter duration rice varieties and even drip irrigation systems (Singh et al. 2001a, 2001b; Kukal et al. 2005; Aggarwal et al. 2009; Minhas et al. 2010; Eberbach et al. 2019). It is often likely that these practices may reduce deep drainage, with very little effect on evapotranspiration (ET). Reducing deep drainage may not ‘save water’ or reduce the rate of decline of the water table (Humphreys et al. 2010). Researchers have also argued that the major loss, i.e., ET in rice, ranges about 30–40% (Jalota et al. 2018) and the drainage from paddy fields may not be considered as a loss because the water can be captured and reused downstream (Bouman et al. 2007). In a study elsewhere, in Iran, it was concluded that water saving irrigation systems (e.g,. drip and sprinkler) conserve water but they can reduce potential groundwater recharge (PGR) compared with traditional systems (e.g., furrow) and may impair sustainability, particularly in arid and semi-arid regions (Porhemmat et al. 2018). Further, rice is grown in kharif season and the region receives nearly 80% of the annual rains during this period. As rice is a tolerant crop, its yield is not adversely affected and the standing rainwater in the fields slowly joins the water table. Seasonal trends of the region also showed that the water table rose after kharif season (mid-June to mid-October) and fell during the rabi season (mid-October to mid-June) (Jalota et al. 2018). It becomes imperative to establish a relationship between water balance dynamics and groundwater changes (Yang et al. 2006), but systematic studies on this aspect are seldom seen for the region. This requires complex information about the soil vegetation system, which is usually lacking, and there are financial and technical limitations involved in gathering a large amount of data. In most of the earlier studies, groundwater use has been related to the number of tubewells and unit draft (Aggarwal et al. 2005; Miglani et al. 2015), and these assessments do not indicate the desired saving in water in order to decrease or stop groundwater decline which require continuous measurements of long-term agricultural water use. Alternatively, process-based crop models such as CERES (Crop Environment Resource Synthesis), DSSAT (Decision Support System for Agrotechnology Transfer), EPIC (Environmental Policy Integrated Climate), etc., can provide insights into crop water use and drainage phenomena from a simplified input of climate, soil and water management. For instance, Jalota et al. (2014) used CropSyst to simulate the effect of climate change on the global irrigation water use under designed climate change scenarios. Dar (2016) examined the applicability of the DSSAT model in analysing the impact of water-related options on rice yield and water use for enhancing water productivity in irrigated environments of Punjab and found the model performed reasonably well. With the above background, the present research was planned to evaluate the DSSAT model for quantifying the impact of water balance components on groundwater levels in rice crop.

MATERIAL AND METHODS

Experimental details

The field experiment was conducted at the Research Farm of the Department of Soil Science, Punjab Agricultural University, Ludhiana (30°56′ N, 75°52′ E and 247 m above mean sea-level) during the rainy (kharif) season of 2014. The soil of the experimental field was deep alluvial loamy sand Typic Ustipsamment developed under hyperthermic regime (USDA classification). At the start of the experiment, depthwise soil physical and chemical properties of the field were determined following the standard procedures (Table 1). The meteorological data (daily maximum and minimum temperatures, rainfall, pan evaporation, sunshine hours, morning and evening relative humidity) were collected from the meteorological observatory of the Punjab Agricultural University, Ludhiana located at a distance of 100 m from the experimental field.

Table 1

Physico–chemical properties of the experimental soil

Depth (cm)pHEC (dS−1)OC (%)NH4-N (ppm)NO3-N (ppm)P (kg ha−1)K (kg ha−1)Bulk density (g cm−3)Hydraulic sonductivity (cm hr−1)Cation exchange capacity (meq/100 g)Texture
0–15 6.67 0.230 0.26 12.84 25.67 44.0 117.6 1.52 0.04 9.91 Sandy loam 
15–30 6.33 0.077 0.17 12.84 16.34 25.3 72.8 1.60 1.26 8.72 Sandy loam 
30–60 7.00 0.114 0.08 14.00 16.34 22.4 89.6 1.55 1.83 11.89 Sandy clay loam 
60–90 7.76 0.184 0.08 11.67 14.00 15.8 56 1.54 1.89 13.10 Sandy clay loam 
90–120 7.74 0.242 0.06 8.17 12.84 14.9 72.8 1.45 0.41 11.12 Sandy loam 
120–150 7.87 0.150 0.03 9.34 12.84 10.8 33.6 1.29 0.82 6.74 Sandy loam 
150–180 7.72 0.158 0.04 7.00 10.50 6.8 50.4 1.45 1.36 7.91 Sandy loam 
Depth (cm)pHEC (dS−1)OC (%)NH4-N (ppm)NO3-N (ppm)P (kg ha−1)K (kg ha−1)Bulk density (g cm−3)Hydraulic sonductivity (cm hr−1)Cation exchange capacity (meq/100 g)Texture
0–15 6.67 0.230 0.26 12.84 25.67 44.0 117.6 1.52 0.04 9.91 Sandy loam 
15–30 6.33 0.077 0.17 12.84 16.34 25.3 72.8 1.60 1.26 8.72 Sandy loam 
30–60 7.00 0.114 0.08 14.00 16.34 22.4 89.6 1.55 1.83 11.89 Sandy clay loam 
60–90 7.76 0.184 0.08 11.67 14.00 15.8 56 1.54 1.89 13.10 Sandy clay loam 
90–120 7.74 0.242 0.06 8.17 12.84 14.9 72.8 1.45 0.41 11.12 Sandy loam 
120–150 7.87 0.150 0.03 9.34 12.84 10.8 33.6 1.29 0.82 6.74 Sandy loam 
150–180 7.72 0.158 0.04 7.00 10.50 6.8 50.4 1.45 1.36 7.91 Sandy loam 

Rice was grown in sandy loam soil under different irrigation conditions, since the genetic parameters for cultivar ‘PR111’ of rice have been calibrated previously (Vashisht et al. 2015). In the experiment, the effects of three sowing dates (June 5, D1, June 20, D2 and July 5, D3) and two irrigation regimes (2-day drainage (I1) and tensiometer-based, (I2) level were evaluated on growth and yield of rice.

Each treatment was replicated three times with a plot size 6 × 4 m2 in a split-plot design. Irrigation treatments were started following continuous flooding for 15 days after transplanting. The amount of irrigation each time was 75 mm. For tensiometer-based irrigation, the soil water suction was measured with tensiometers installed at 20 cm soil depth. In 2-day drainage, irrigation was applied 2 days after the ponded water had infiltrated into the soil. Fertilizer dose of N, P, K was applied following the procedure given in a package of practices for crops of Punjab (www.pau.edu). Plant parameters (plant height, number of tillers m−2 and dry matter accumulation) and yield attributes (grain loop length, per cent filled grains, thousand grain weights and grain and straw yield) were recorded.

The actual ET was computed from the water balance equation:
formula
(1)
where P, I, R, D, ΔS and ET are precipitation, irrigation, runoff, drainage, change in soil water storage and evapotranspiration, respectively. The precipitation and actual irrigation were recorded. Changes in soil moisture storage were computed at regular intervals by gravimetric method. The amount of runoff was negligible. Drainage was experimentally computed using the equation in Ogata & Richards (1957) between soil water storage (W) and time (T):
formula
(2)
Drainage rate was obtained as
formula
(3)

To get the above constants of A and B, a bunded and fully saturated plot on the bare soil was covered with a polyethylene sheet in order to stop the evaporation completely. Profile soil moisture storage was estimated from the plot at the depths of 15, 30, 45, 60, 90, 120, 150 and 180 cm at days 1, 3, 5 and 7 up to 39 days. From the relation between W and T, the values of A and B were 38.13 and 0.09 (Figure 1).

Figure 1

Relation between soil water storage and time.

Figure 1

Relation between soil water storage and time.

Water use efficiency (WUE) was calculated as:
formula
(4)

Simulation study

Simulations for yield and water balance were run using the DSSAT model (Jones et al. 2003). In the model, certain crop parameters were modified slightly (within the given range) to match the periodic biomass and yield and the rest of the parameters were equivalent to those previously reported (Vashisht et al. 2015). The location parameters included longitude, latitude, daily weather data files and ET models. The methods selected in the simulation were Priestley–Taylor – evapotranspiration, Soil Conservation Service – infiltration, canopy curve (daily) photosynthesis, Ritchie water balance – hydrology, CERES–Godwin – organic matter and Ritchie-CERES – soil evaporation.

Treatment-specific irrigation management operations, performed on different dates in the experiments, were entered in the crop management file. The model performance was evaluated for yield and water balance component in rice transplanted during 2014 using the Nash–Sutcliffe modelling efficiency (ME) (Nash 1970) and the root mean square error (RMSE), respectively.

Estimating seasonal groundwater level fluctuations using CERES-Rice

Rice yield and water balance components were simulated for 16 years (1998–2014) using the validated CERES-Rice model. Before 2008, the rice was planted in the first week of June but after the implementation of Preservation of Sub-Soil Water Act in 2008 (Singh 2009), the rice planting was shifted to after June 10. Hence, in the model, the transplanting date was kept as June 5 until 2007 and afterwards was kept as June 20. The water use was simulated based on IW/CPE ratio which was considered as 2.5 for sandy loam and 2.0 for loam and silt loam soil, respectively. The change in groundwater storage (m) was estimated as:
formula
(5)

In the present study, the difference in subsurface inflow and outflow is taken as nil (Kaur et al. 2015) and irrigation from groundwater was not considered as a fraction of the total withdrawal contribution to water table decline which is used as ET. The specific yield was assumed to be 0.20 (Kaur et al. 2014).

Site-specific groundwater level data (pre- and post-monsoon) from 1998 to 2014 was obtained by employing krigging interpolation technique (Dhillon et al. 2018) on the nearby observation wells.

RESULTS

Experimental results

Rice yield was significantly influenced by the date of transplanting. The maximum yield of rice was observed on June 20 (D2), which was higher than that in June 5 (D1) and July 5 (D3), i.e., 24 and 31% (Table 2). These observations confirm the field and simulated results reported by other researchers (Singh et al. 2001a, 2001b; Chahal et al. 2007; Safdar et al. 2008; Jalota et al. 2009). Late planting (July) showed a decline in grain yield due to delayed panicle formation and grain filling in the season where temperature and solar radiation are less (IRRI 1993). With respect to irrigation, I2 recorded 11% more yield than I1, although irrigation was 21% higher in 2-day drainage-based treatment. Kukal et al. (2005) also reported that irrigation at 160 ± 20 cm soil metric suction helped save 30–35% irrigation water as compared to 2-day drainage-based irrigation with no yield penalty. ET observed was higher in D1 (550 mm) than D2 (514 mm) and D3 (546 mm).

Table 2

Measured yield (t ha−1), water balance components (mm) and water use efficiency (%) under different treatments

TreatmentYieldPIETDΔSWUE (%)
D1I1 5.86 418 1,995 552 1,601 260 1.06 
D1I2 5.92 418 1,854 549 1,461 262 1.08 
D2I1 6.7 418 2,226 520 1,851 273 1.29 
D2I2 7.88 418 1,764 509 1,399 274 1.55 
D3I1 5.12 420 1,959 550 1,575 254 0.93 
D3I2 5.9 420 1,492 543 1,115 254 1.09 
D1 5.89 418 1,924 550 1,531 261 1.07 
D2 7.29 418 1,995 514 1,625 273 1.42 
D3 5.57 420 1,725 546 1,345 254 1.02 
I1 5.89 419 2,060 541 1,676 262 1.09 
I2 6.56 419 1,703 534 1,325 263 1.23 
TreatmentYieldPIETDΔSWUE (%)
D1I1 5.86 418 1,995 552 1,601 260 1.06 
D1I2 5.92 418 1,854 549 1,461 262 1.08 
D2I1 6.7 418 2,226 520 1,851 273 1.29 
D2I2 7.88 418 1,764 509 1,399 274 1.55 
D3I1 5.12 420 1,959 550 1,575 254 0.93 
D3I2 5.9 420 1,492 543 1,115 254 1.09 
D1 5.89 418 1,924 550 1,531 261 1.07 
D2 7.29 418 1,995 514 1,625 273 1.42 
D3 5.57 420 1,725 546 1,345 254 1.02 
I1 5.89 419 2,060 541 1,676 262 1.09 
I2 6.56 419 1,703 534 1,325 263 1.23 

D1 – June 5, D2 – June 20, D3 – July 5; I1 (2-day drainage), I2 (tensiometer-based irrigation).

The water balance components and WUE changed with treatments (Table 2). Precipitation was nearly the same for all the dates of transplanting. D2 received 16 and 4% more irrigation than that of D3 and D1, respectively. Measured ET by water balance equation was higher in D1, i.e., 550.5 mm than D2 and D3. The highest water use efficiency in D2 is ascribed to relatively greater decrease in ET (denominator) compared to the yield (numerator). Averaged across irrigation treatments, WUE in I2 was 13% more than I1.

Simulation study

Simulated grain yield and ET had trends similar to measured responses, although there were differences in their magnitude (Figure 2 and Table 3). Statistical parameters such as RMSE and Nash–Sutcliffe ME during validation were 0.09 and 0.99 for grain yield. The coefficient of determination (R2) between simulated and observed yield was 0.96 and was 0.86 in the case of ET.

Table 3

Simulated and computed ET (Equation (1)) and yield under different sowing dates and irrigation regimes

TreatmentET (mm)
Yield (t/ha)
ObservedSimulatedObservedSimulated
D1I1 427 552 5.8 5.9 
D1I2 433 549 6.1 5.9 
D2I1 414 520 6.4 6.7 
D2I2 408 509 6.9 7.9 
D3I1 430 550 5.6 5.1 
D3I2 423 543 5.9 5.9 
D1 430 550 6.0 5.9 
D2 411 514 6.6 7.3 
D3 427 546 5.8 5.5 
Average 423 537 6.1 6.2 
TreatmentET (mm)
Yield (t/ha)
ObservedSimulatedObservedSimulated
D1I1 427 552 5.8 5.9 
D1I2 433 549 6.1 5.9 
D2I1 414 520 6.4 6.7 
D2I2 408 509 6.9 7.9 
D3I1 430 550 5.6 5.1 
D3I2 423 543 5.9 5.9 
D1 430 550 6.0 5.9 
D2 411 514 6.6 7.3 
D3 427 546 5.8 5.5 
Average 423 537 6.1 6.2 

D1 – June 5, D2 – June 20, D3 – July 5; I1 (2-day drainage irrigation), I2 (tensiometer-based irrigation).

Figure 2

Comparison of simulated and measured grain yield for the year 2014.

Figure 2

Comparison of simulated and measured grain yield for the year 2014.

Effect of crop water balance on groundwater level changes

The model simulated crop yield, irrigation, daily ET and drainage from 1998 to 2014 for the site (Table 4), using the historic climate data. The highest yield of paddy was obtained in 2014, i.e., 6.6 t/ha, the lowest yield in 2010, i.e., 5.5 t/ha and the average yield of 17 years is 6.0 t/ha which closely agrees with the data provided by government agencies (Statistical Abstracts Punjab, Various issues).

Table 4

Yield and water balance components during different years

YearYield (t/ha)PIETDΔSWS
1998 5.8 758 1,317 435 1,364 276 
1999 6.0 542 1,393 419 1,275 241 
2000 6.4 269 1,960 597 1,354 278 
2001 6.0 846 1,237 398 1,449 236 
2002 6.5 293 1,942 452 1,539 243 
2003 5.7 580 1,282 402 1,224 236 
2004 5.9 348 1,630 410 1,303 264 
2005 6.5 596 1,331 430 1,260 237 
2006 5.8 496 1,408 400 1,268 236 
2007 5.8 409 1,459 387 1,222 259 
2008 6.1 887 1,189 479 1,323 274 
2009 6.5 817 1,389 529 1,423 254 
2010 5.5 906 1,020 391 1,271 264 
2011 5.7 905 1,060 397 1,308 260 
2012 6.2 305 1,662 440 1,289 238 
2013 5.9 720 1,262 462 1,242 278 
2014 6.6 418 1,994 426 1,696 290 
Average 6.0 594 1,443 438 1,342 257 
YearYield (t/ha)PIETDΔSWS
1998 5.8 758 1,317 435 1,364 276 
1999 6.0 542 1,393 419 1,275 241 
2000 6.4 269 1,960 597 1,354 278 
2001 6.0 846 1,237 398 1,449 236 
2002 6.5 293 1,942 452 1,539 243 
2003 5.7 580 1,282 402 1,224 236 
2004 5.9 348 1,630 410 1,303 264 
2005 6.5 596 1,331 430 1,260 237 
2006 5.8 496 1,408 400 1,268 236 
2007 5.8 409 1,459 387 1,222 259 
2008 6.1 887 1,189 479 1,323 274 
2009 6.5 817 1,389 529 1,423 254 
2010 5.5 906 1,020 391 1,271 264 
2011 5.7 905 1,060 397 1,308 260 
2012 6.2 305 1,662 440 1,289 238 
2013 5.9 720 1,262 462 1,242 278 
2014 6.6 418 1,994 426 1,696 290 
Average 6.0 594 1,443 438 1,342 257 

The average kharif season's rainfall computed from the daily records (for the years 1989–2014) was 594 mm. The irrigation requirements of rice ranged from 1,020 to 1,994 mm with an average of 1,443 mm. The ET, which is one of the major losses, from the rice field varied from 387 to 597 mm during different years with an average loss as 438 mm. The drainage component which is assumed equal to potential recharge ranged between 61 and 70% of total water applied (precipitation + irrigation) during different years, with an average drainage of 66%, i.e., 1,342 mm (Table 4), of which, precipitation contributes 19.2%. However, a significant variation in contribution to potential recharge individually from precipitation and irrigation is witnessed for different years (Figure 3). Increased precipitation bears a linear positive relationship for contribution to potential recharge and vice versa relationship for irrigation.

Figure 3

Impact of precipitation on contribution to potential recharge.

Figure 3

Impact of precipitation on contribution to potential recharge.

An average post-monsoon rise of 0.74 m in groundwater levels at the site was observed with exceptions in the years 2000, 2002 and 2012, when the water table dropped post-monsoon. These exceptional years were due to deficit rainfall coupled with high irrigation water use. Although a graph between groundwater fluctuations in response to rainfall and irrigation water use for the experimental site indicates that June to October is the period when groundwater level responds relative poorly to irrigation water requirement, since soil water from the rainy season is also absorbed, yet, there was general agreement between potential recharge and seasonal groundwater level changes (Figure 4).

Figure 4

Relation between simulated water input (P + I), potential recharge and groundwater (GW) level changes.

Figure 4

Relation between simulated water input (P + I), potential recharge and groundwater (GW) level changes.

The simulated change in storage (Equation (5)) was able to fairly estimate the groundwater water fluctuations rather than potential recharge (drainage − irrigation) alone (Figure 5), more so when years with rainfall greater than 800 mm were excluded. In a recent study by Jalota et al. (2018), the annual water table fluctuations were approximated by computing the water deficit [evapotranspiration − (rainfall + surface water)], which can approximate the water table decline/rise rather than groundwater withdrawal alone.

Figure 5

Observed versus simulated rise/fall (m) in water table.

Figure 5

Observed versus simulated rise/fall (m) in water table.

CONCLUSIONS

In view of shrinking water resources and the anticipated effects of climate change, there is a need to have a better understanding of crop water balance relationship with groundwater dynamics. The outcome of the study indicates that delaying the transplanting date to June 20 and tensiometer-based irrigation can result in higher yield (34%) and water saving (10%) in comparison to June 5 and 2-day drainage-based irrigation. The findings are in tune with the government recommendations of delaying the transplanting date of paddy to mid-June. Simulated CERES-Rice model was then applied to quantify change in storage while analysing groundwater fluctuations. The estimated change in storage was able to explain groundwater level fluctuations in terms of trends/magnitude in most of the years barring the high rainfall as there are still limitations when we directly try to link groundwater level changes and crop water use. First, the simulation results might be more accurate for recent years than for earlier years. Second, the effect of different crop cultivars was not used in historical simulations and the genetic parameters of PR 121 were used for the whole simulation period. Third, if actual groundwater level observations were available we could have computed more realistic regression. Aside from the application of the agricultural model, a more accurate estimation of groundwater use to reflect the change of groundwater level in multiple aquifers through the application of a groundwater model might be necessary. However, with all these limitations, such type of study still provides information on crop behaviour under different conditions, and thus can be helpful for formulating management intervention for increasing productivity and water use efficiency and also give insights on agricultural water use and groundwater fluctuations.

ACKNOWLEDGEMENTS

The authors are very grateful to the Indian Institute of Water Management, Bhubneshwar – ICAR for funding the research under All India Coordinated Research Project of Irrigation Water Management.

DATA AVAILABILITY STATEMENT

All relevant data are included in the paper or its Supplementary Information.

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