Consumptive water footprints, water use efficiencies and productivities of rice under alternate wetting and drying for Kharagpur, West Bengal, India Open Access

In Asia, irrigated agriculture accounts for about 90% of total diverted freshwater, and more than 50% of this is used to irrigate rice (Hatiye et al. 2015). However, water is frequently being wasted as a result of inefficient management, resulting in low water productivity (Yao et al. 2012; Amarasingha et al. 2015). It is estimated that about 2 million ha of Asia's irrigated dry-season rice and 13 million ha of its irrigated wet-season rice will experience water scarcity by 2025 (Bouman & Tuong 2001; Amarasingha et al. 2015). Therefore, major studies (Yadav et al. 2011; Amarasingha et al. 2015; Sharda et al. 2017) suggested few efficient irrigation water management practices that could save a significant quantity of water during rice cultivation. One such water-saving irrigation technique is an alternate wetting-and-drying (AWD) method (Carrijo et al. 2017), in which water is applied to the field a number of days after the disappearance of ponded water (Amarasingha et al. 2015; Carrijo et al. 2017). This means that the rice fields are not kept continuously submerged (i.e. never letting the ponded water disappear) as practiced in conventional irrigation, but are allowed to dry intermittently during the rice-growing stage (Sriphirom et al. 2018). A ‘Safe’ AWD allows the water level in the rice field to drop down to 15 cm below the soil surface before the crop is irrigated again (Amarasingha et al. 2015; Carrijo et al. 2017).

Well-designed AWD water management conditions revealed higher rice water productivity compared to continuously flooded soil conditions in most of the previous instances (Gaydon et al. 2012; Ye et al. 2013; Amarasingha et al. 2015; Thakur et al. 2018). This AWD technique also proved its ability to save up to 30% irrigation water and improve irrigation water use efficiency by 5–35% depending upon the crop cultivar, soil characteristics, climate, and crop water (Cabangon et al. 2011; Liang et al. 2013; Djaman et al. 2018). However, research is still lacking on knowledge about water consumption per unit production of rice under this AWD technique. Recognizing the significance of the above facts discussed, quantification of consumptive water footprint (CWF) of rice under AWD is of utmost importance as it is the reliable way to evaluate real water consumption during rice growth periods.

The CWF of any crop denotes the ratio of the total evapotranspiration during that crop growth period to the quantity of that crop obtained (Cao et al. 2018). The CWF combines blue and green water footprints (Cao et al. 2018). Therefore, it reflects the amount of freshwater resources utilized per unit quantity of agricultural produces from a particular management system. Hence, the lower CWF of a crop under any water management will reveal its efficiency to produce more biological yield with less volume of freshwater (Lovarelli et al. 2016). Therefore, the research on the CWF of rice under AWD irrigation has turned out to be a hot research topic.

In view of the importance of CWF, the previous researches on CWF of crop production were carried out in several regional levels from an irrigation district (Cao et al. 2014a; Suttayakul et al. 2016; Shrestha et al. 2017), a river basin (Bocchiola et al. 2013; Denis et al. 2016; Roux et al. 2017), a city-level region (Marano & Filippi 2015; Lu et al. 2016; Chu et al. 2017), a country (Cao et al. 2014b; Wang et al. 2015; Zhuo et al. 2016) to the global standpoint (Mekonnen & Hoekstra 2011, 2014; Lovarelli et al. 2016). The previous studies on CWF were satisfactorily addressed in terms of research scales and crop types (Cao et al. 2018). Mekonnen & Hoekstra (2011) documented CWF values of 126 crops and derived crop produces for 10 years (1996–2005) at multiple regional levels. However, most of the previous studies used different types of models like the water balance model, hydrological model, and crop model to estimate CWF of crop production (Cao et al. 2018). Although the modeling assessment of CWF is proficient in economy and time to estimate costs for large scale, field evaluation is essential for validation of results obtained from modeling work (Johannessen et al. 2015). Meanwhile, CWF assessment for a specific farm area and real-time estimation of CWF-related parameters are more precise than the model simulated results and can provide a more truthful reference for farmers.

Very few researchers have started to investigate field measurement methods for CWF of rice and its viability in current years. Kar et al. (2015) studied for the first time, CWF of rice under intermittent irrigation management in the East coast region of India. Cao et al. (2018) estimated CWF of rice production under traditional irrigation practice in East China. The above relevant literature principally paid attention to the measurement of rice CWF at the farm level. However, more exploration on such farm level assessment of CWF of rice under different irrigation practices is of utmost importance to provide accurate information on real water consumption of rice under different irrigation management to the local farmers. Recognizing the importance of the above fact, the present study was conducted to investigate the impact of AWD irrigation practice on CWF of rice at farm level during monsoon (kharif) and post-monsoon season (rabi) of Eastern India. Along with CWF, the variations of water input, yield, water use efficiency, and water productivities under AWD were also explored and documented in the present study.

The field experiments were carried out in the Research Farm of Agricultural and Food Engineering Department, Indian Institute of Technology (IIT), Kharagpur, India. The study site is located at 22.32°N latitude and 87.31°E longitude. The average annual rainfall in the study area is around 1,400 mm, 78% of which occurs during the monsoon period (June to September). Winter (December to mid-February) is brief but chilly (10° to 25 °C); summers (March to June) are hot (25° to 40 °C) and relative humidity ranges from 50 to 95%. The climate is mostly sub-humid and subtropical. The soil is of sandy loam type and grouped under alfisol based on the USDA Soil Taxonomy. Table 1 presents the physical and chemical properties of the soil at the experimental site.

The field experiments were carried out during the kharif (July-October) and rabi seasons (January-April) of 2015/16 and 2016/17 in 6 (3 m × 3 m) plots under conventional (CON) and alternate wetting and drying (AWD) practices. These two water treatments (CON and AWD) were replicated three times. The high yielding and popularly cultivated IR 36 rice cultivar was selected for this study. Rice seedlings of 25 days old were transplanted manually by planting 5 seedlings per hill and maintained a grid spacing of 20 cm × 20 cm for both CON and AWD practices. All plots of both treatments were mechanically weeded at 20 days after transplantation (DAT) and manually weeded at 30 and 45 DAT. The recommended dose of N: P: K (120:50:60 kg/ha) was considered (Adhikari et al. 2011) for the experiments under both water treatments. The N-fertilizer was applied in the form of urea (46% N) in four splits; basal (during transplanting), 20, 40, and 60 DAT whereas the full fertilizer doses of phosphorus in the form of Single Super Phosphate (18% P₂O₅) and potassium as Muriate of Potash (62.5% K₂O) were applied in one-split as basal dose (during transplanting).

Groundwater was used to irrigate the experimental plots of both treatments. In CON practice, a maximum of 2–3 cm standing water was maintained during 0–20 DAT for the crop to establish, and 5 cm was maintained during 21–95 DAT. Similar to CON practice, the AWD plots were initially maintained at a ponding depth of 2–3 cm during 0–20 DAT. After that, the AWD plots were irrigated to a depth of 5 cm above the ground surface when the standing water level depleted to 15 cm below the ground surface (i.e. -ve water level). The ponding depth was measured with a measuring scale installed in each plot of both water treatments. In the case of AWD, ‘Safe’ AWD guidelines were followed (Amarasingha et al. 2015) in this study. To record the water level below the ground surface, a perforated PVC pipe of 30 cm long and 15 cm diameter were installed in the AWD plots. The diameter was made large enough for easy visibility, freehand movement inside the tube to clean during clogging of soil. All plots were accommodated with an outlet 5 cm above the soil surface after 20 DAT to facilitate 5 cm of ponding depth in rain events. The excess rainwater in all plots was channelled out and collected in a constructed farm pond, and utilized for irrigation purposes in both treatment plots. All plots were surrounded by 50 cm wide bunds to prevent lateral water seepage and nutrient diffusion between the treatments, followed by 50 cm wide irrigation channels. The bunds of all plots were plugged based on the procedure reported by Patil et al. (2011) for controlling the lateral flow from plot-to-plot. Each plot was provided by the bund height of 80 cm to control the overland flow from the adjacent irrigation and drainage channels.

The crop-related information was collected during the crop growing periods. The information related to tillering, panicle initiation, flowering, and harvesting were collected at different growth stages in both seasons. For obtaining grain yield, rice plants were first harvested from a 1 m × 1 m sampling area marked at three randomly selected spots in each plot. The grains were then separated from the straws using a threshing machine and the average grain yield (grain weight) was recorded for each plot.

The data (yield, CWUE, CWP, TWP, GWF, BWF, and CWF) were statistically analyzed by the analysis of variance (ANOVA). Duncan's multiple range test (DMRT) was employed to assess the differences between the treatment means at the 1, 5, and 10% significance levels. All statistical analyses were performed using SPSS 16.0 software.

Table 2 presents the values of the water balance components of CON and AWD practices during both seasons. The DP loss under AWD was reduced by 31 and 27% in kharif seasons of two respective years; whereas, in the rabi season, it was reduced by 27 and 26% for the respective years. The ET_C under AWD was reduced by 6% during both kharif and rabi seasons of two respective years (Figure 2). The total water input (irrigation + rainfall) for AWD practice was significantly (p < 0.01) reduced by 26 and 29% in kharif seasons and by 22 and 27% in rabi seasons of the two respective years (Figure 2).

Table 2 reveals the variation of the CWUE under AWD and CON during both kharif and rabi seasons. The CWUE in AWD was found to be increased by 27 and 33%, respectively, during the kharif seasons, whereas it was 20 and 29% higher for rabi seasons of the respective two years (Figure 2). The improvement of CWUE in AWD was found to be significant at a 95% level of confidence (p < 0.05) during both kharif and rabi seasons (Table 2).

Table 3 presents the variation of the grain yield, CWP, and TWP under AWD and CON during kharif and rabi seasons of 2015/16 and 2016/17 periods. The grain yield was found to be lower in AWD practice and the reduction was observed as about 4 and 3% in respective kharif seasons; whereas in rabi season, it was about 4 and 2% for the respective two years (Figure 3). However, this yield reduction in AWD during both kharif and rabi was not statistically (p > 0.05) significant (Table 3). The CWP improved by 2 and 3%, respectively in AWD during respective kharif seasons, whereas in rabi season, the percentage increase in CWP under AWD was 2 and 5% for the two respective years (Figure 3). But, this enhancement of CWP under AWD was found to be statistically insignificant (p > 0.05) during both seasons (Table 3). Similarly, TWP increased by 29 and 37%, respectively in AWD during the respective two years' kharif season, whereas in rabi season, the percentage improvement in TWP was found as 23 and 35 for two respective years (Figure 3). This improvement under AWD was found to be statistically significant at a 95% level of confidence (p < 0.05) during both seasons of each year (Table 3).

Table 3 presents the observed data of green water (GW) and blue water (BW) for both irrigation practices. The use of GW under both irrigation managements was found as same during kharif of 2015/16 and rabi of both experimental years. However, the scenario of kharif 2016/17 showed about a 6% reduction in GW use under AWD. The BW usage under AWD was declined by about 11% in kharif of 2015/16; whereas in 2016/17 (kharif), ET_C of both practices was fulfilled by GW only. In the case of the rabi season, about a 7% decrease in BW utilization under AWD for both years (Figure 2).

The GWF during kharif was increased by 6% in 2015/16 and reduced by 3% under AWD in 2016/17; whereas during rabi season, the percentage increase was 4 and 2% for 2015/16 and 2016/17 respectively (Figure 3). But, no substantial change (p > 0.05) of GWF was found between CON and AWD during both kharif and rabi seasons (Table 3). In the case of BWF, the reduction under AWD was estimated at about 6% in 2015/16 kharif and 3 and 5% in the respective two rabi seasons (Figure 3). However, there was no change of BWF between AWD and CON practices in the kharif season of 2016/17. Such reductions of BWF under AWD were also statistically insignificant (p > 0.05) in this study (Table 3). Similarly, CWF under AWD declined by 1 and 3% in the respective two kharif seasons; while in rabi season, the reduction percentage was about 2 and 5% for the respective two years (Figure 3). However, this reduction of CWF under AWD was not statistically significant (p > 0.05) (Table 3).

Instead of following the CON method, the adoption of the AWD method showed a beneficial effect in terms of water-saving in this study. However, following AWD practice is tricky during the monsoon (kharif) season, and maintaining depletion of water may be challenging. However, the study area experienced some dry periods during monsoon and supplementary irrigation was provided when the water level in AWD pipe reached the threshold (i.e. 15 cm below ground surface). Table 2 shows a significant (p < 0.01) reduction in water use (irrigation + rainfall) under AWD for kharif and rabi seasons of both years. This reduction can be attributed to the decline in the number of irrigations under AWD. Similar types of results were also registered by other researchers (Yao et al. 2012; Tan et al. 2013; Lampayan et al. 2015a, 2015b; Pan et al. 2017; Pascual & Wang 2017; Djaman et al. 2018). Zhang et al. (2012) obtained a substantial reduction of 25% in irrigation water application under AWD. Lampayan et al. (2015a, 2015b) reviewed that a 38% reduction in water input under AWD management would be possible if AWD was implemented properly. Umesh et al. (2017) documented that properly implemented AWD management in the rice field could save water up to 30%. Djaman et al. (2018) registered a considerable quantity of water saving by over 27% with this AWD. Similarly, Sriphirom et al. (2018) showed the possibility of 19% water saving using the AWD technique during the tillering stage of rice growth. This reduction in water input under AWD had an impact on the different outflow parameters like ET_C and DP in the field during rice growth. Table 2 and Figure 2 represented the reductions in both ET_C and DP under AWD in this study. These reductions can be attributed to the significant decline in irrigation water under AWD. Alberto et al. (2011) documented the possibility of ET_C reduction by about 11% in aerobic rice fields. Tan et al. (2013) also registered similar types of findings in their study where intermittent irrigation was found superior over traditional methods in terms of percolation reductions by over 15%. Recently, Shekhar et al. (2020b) reported a 13% reduction in ET_C under AWD practice as compared to CON in their study. Therefore, the AWD management had a positive impact on CWUE in rice cultivation. In the present study, CWUE was significantly (p < 0.05) enhanced in AWD during all experimental seasons (Table 2; Figure 2). This improvement could be attributed to a reduction in ET_c and a substantial (p < 0.01) decrease in water input under AWD. Wang et al. (2016) observed a similar kind of improvement of WUE under a mild AWD approach and documented that the reduced unproductive tillers, increased root growth, and enhanced harvest index attributed to the higher WUE under moderate AWD than CON irrigation. Yang et al. (2017) also registered 21–30% enhanced WUE under moderate AWD in their study. Zhou et al. (2017) also obtained significantly higher WUE under AWD management compared to traditional irrigation practices in their study.

The current research work demonstrated that the AWD with significantly (p < 0.01) reduced water input than CON and had a positive impact on rice yield and productivities (Table 3; Figure 3). The adoption of AWD did not reduce yield significantly in the present study (Table 3). Hence, statistically similar grain yield using significantly (p < 0.01) reduced water application and decreased ET_C resulted in enhanced TWP and CWP respectively under AWD in the current study. Other researchers (Yadav et al. 2011; Yao et al. 2012; Lampayan et al. 2015a, 2015b; Pan et al. 2017; Djaman et al. 2018) also documented similar results in their studies. Thakur et al. (2014) registered about 3% higher yield under mild AWD compared to the conventional method. In the study of Lampayan et al. (2015a, 2015b), total water productivity was found to be higher under AWD compared to CON. Wang et al. (2016) also obtained better yield and productivity in moderate AWD than traditional practice in their study and reported that reduced unproductive tillers, increased root growth, and enhanced harvest index attributed to the higher yield under moderate AWD than CON irrigation. Pan et al. (2017) reported that there was no significant reduction of yield under the ‘safe’ AWD method (explain) compared to the farmer's water management practice. The study also documented that the ‘safe’ AWD was advantageous over the farmer's water management practice to enhance water productivity significantly with a reduction in the number of irrigations. According to Zhou et al. (2017), rice yield also improved under moderate AWD practice. Such kind of positive reports of AWD on rice yield and productivity also strengthened the outcomes of the present study.

Since CWF of any crop refers to the real water consumption by crop, only ET_C was considered for farm-level water footprint calculation in the current research work. Figure 2 and Table 3 clearly show the variations of BW usage between the AWD and CON method. The change of BW and ET_C coupled with little reduction of grain yield in AWD made the little decline in BWF and CWF under AWD in this study. But, Kar et al. (2015) obtained a significant (p < 0.05) decrease in CWF under intermittent irrigation method in their study. However, the present study illustrated the statistical similarity of different water footprints between AWD and CON. According to Kar et al. (2015), about 5 and 6% reduction in CWF with 3 and 7% decrease in ET_C was possible under moderate and severe AWD respectively. The current farm level investigation also reported similar kinds of reduction percentages of ET_C and CWF under AWD (Figure 3). Hence, it was inferred that the CWF of rice to a small extent could be influenced by AWD management practice.

The conventional method of rice production has very low water use efficiency, resulting in low productivity, high water footprints, and huge losses of water from the field. Thus, in order to increase the production and water use efficiency and to reduce the water footprints of rice, we need to adopt advanced water-saving irrigation techniques. In this study, the effect of AWD practice on yield, CWUE, water productivity, and water footprints was assessed in comparison to conventional practice. The AWD practice was able to provide significant enhancement of CWUE (27–33% in kharif and 20–29% in rabi) with a considerable reduction in water input (26–29% in kharif and 22–27% in rabi) and deep percolation (27–31% in kharif and 26–27% in rabi). Total water productivity of AWD was found to be significantly improved (29–37% in kharif and 23–35% in rabi) in association with a reduction in ET_C (6% in both kharif and rabi), BWF (6% in kharif and 3–5% in rabi) and CWF (1–3% in kharif and 2–5% in rabi). Therefore, positive responses were achieved under AWD over two years' field experiments in the present study. Since water management in AWD involves wetting and drying phases, water application needs to be adjusted to the local demand as well as local environmental features to obtain more productivity with less water footprint. To fulfil the challenging amount of rice productivity under water-scarce situation in future, water footprints of rice under such kinds of non-conventional water environment should be investigated thoroughly in different environments.

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