Application of a rice growth and water balance model in an irrigated semi-arid subtropical environment

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1 agricultural water management 83 (2006) available at " Application of a rice growth and water balance model in an irrigated semi-arid subtropical environment V.K. Arora * journal homepage: Department of Soils, Punjab Agricultural University, Ludhiana , Punjab, India article info Article history: Accepted 25 September 2005 Published on line 21 November 2005 Keywords: Water productivity Water-saving practices Crop modeling Transplanting time Irrigation management Puddling intensity abstract In order to enhance rice productivity in water-limited environments, there is a need to adopt water-saving practices. This study examines the applicability of the ORYZA2000 model in analyzing impact of water-related options on rice yield and water use for enhancing water productivity in irrigated environments of Punjab. Performance of the model was reasonably good as indicated by close matching between simulated harvest-time grain yield, biomass, and soil profile water use with measured data and trends of simulated water balance components for a range of soil and water management scenarios. Using long-term weather data, cumulative probability distribution of simulated grain yield and evapotranspiration (ET) showed that rice yield of late-transplanted (July 1) crop was comparable to that of earlytransplanted (May 16) crop, and ET-based water productivity was greater in late-planted crop. Though ET-based water productivity with continuous submergence and 2-day interval regimes were comparable, water-input-based productivity was greater under 2-day interval regime. Increasing puddling intensity from P 1 (puddling once) to P 2 (puddling two times) and P 4 (puddling four times) regimes had little effect on ET-based water productivity, but caused a substantial increase in water-input-based productivity. # 2005 Elsevier B.V. All rights reserved. 1. Introduction Rice production in Asia is increasingly constrained by water limitations. In Punjab state of north-west India, where irrigated rice cropping is practiced in alluvial coarse-textured soils since early 1970s, there has been an alarming scarcity of ground water resources. In central Punjab having good quality ground waters, the areas with water table below 10 m depth increased from 3% in 1973 to 76% in 2002 (Hira et al., 2004) thereby threatening the sustainability of rice culture. Thus, there is a need to enhance water productivity at different spatial scales. It can be achieved by adopting water-saving practices that include improved irrigation management, viz., reduction in ponded water depth, saturated soil conditions, and alternate wetting and drying (Bouman and Tuong, 2001), optimum puddling intensity in high permeability soils, and synchronization of growing cycle with low evaporativedemand periods. Use of crop modeling techniques supports field research focused towards evaluating the effects of different practices on water productivity. A number of crop-specific models, which account for soil water dynamics and growth and development in rice, are available in literature (e.g., CERES and ORYZA models). There are some reports of application of ORYZA model in rain-fed environments (Lansigan et al., 1997), but such analyses for irrigated rice are lacking. This paper presents an evaluation and application of the ORYZA2000 model (Bouman et al., 2001) in analyzing impact of waterrelated management options on crop yields and irrigation water use so as to enhance water productivity in rice in semiarid sub-tropical environments of north-west India. * Tel.: ; fax: address: vkaro58@yahoo.com /$ see front matter # 2005 Elsevier B.V. All rights reserved. doi: /j.agwat

2 52 agricultural water management 83 (2006) Methods and materials 2.1. ORYZA2000 model The ORYZA2000 model, an update of the previous models of ORYZA series, simulates growth, development, and water balance of rice under potential production, water-limited and nitrogen-limited environments. A detailed explanation of the model and program code is given in Bouman et al. (2001), and the following text gives a brief description of the key modules for potential- and water-limited-production Crop growth and development The ORYZA2000 model follows a calculation scheme for the rate of dry mass production of the plant organs, and for the rate of phenological development. The rate of CO 2 assimilation is estimated from the daily incoming radiation, temperature, and leaf area index (LAI) by integrating instantaneous rates of leaf CO 2 assimilation over time and depth within canopy. The integration is based on an assumed sinusoidal time course of radiation over the day and the exponential extinction of radiation within the canopy. Photosynthesis of single leaves depends on leaf N, radiation intensity (separated into direct and diffused radiation), stomatal CO 2 concentration, and temperature. Maintenance respiration requirements are subtracted from the gross assimilation rate to obtain net daily growth as carbohydrates ha 1 day 1. The carbohydrates produced are partitioned among roots, leaves, stem, and storage organ (panicle) as a function of development stage (DS). In grain crops, carbohydrate production during grain-fill can be higher or lower than the storage capacity of grains that is determined by the number and maximum growth rate of grains. The number of spikelets at flowering is calculated from biomass accumulation from panicle initiation until first flowering. Spikelet sterility due to either too-high or too-low temperature is considered. Leaf area growth includes a source- and sink-limited phase. In the early phase, leaf area grows exponentially as a function of temperature sum, and relative leaf growth rate. After LAI is larger than one, increase in leaf area during linear phase is computed from increase in leaf mass and specific leaf area (SLA) that depends on DS. From flowering onwards, leaf loss rate is accounted for using a DSdependent loss rate factor and green leaf biomass. When the rice crop is transplanted, LAI and all biomass values are reset based on planting density after transplanting relative to plant density in the seedbed. Crop growth resumes only after a transplanting shock has elapsed duration of which has a linear correspondence with seedling age at transplanting. The phenological development (DS) is tracked as a function of daily ambient temperature and photoperiod. The rice crop has four phenological phases, viz. (i) juvenile phase from emergence (DS = 0) to start of photoperiod-sensitive phase (DS = 0.4), (ii) photoperiod-sensitive phase from DS = 0.4 until panicle initiation (DS = 0.65), (iii) panicle development phase from DS = 0.65 until 50% flowering (DS = 1.0), and (iv) grain-fill phase from DS = 1.0 until physiological maturity (DS = 2.0). Each of these four phases has variety-specific development rate constants (DRC). Differences among varieties in total duration are caused primarily by differences in the duration of the juvenile phase. Sub-optimal photoperiod less than the optimal photoperiod result in a longer photoperiod sensitive phase. In transplanted rice, transplanting shock also causes a delay in phenological development that depends on seedling age Evapotranspiration and water stress effects The evapotranspiration (ET) module computes potential evaporation rates from soil and plant surfaces for the main field crop using either of the three methods, viz., Penman, Priestley and Taylor, and Makkink depending on the availability of meteorological data. The computed reference potential ET can also be modified to account for local effects. The effects of water limitations on crop growth and development are accounted by considering their effects on expansive growth, leaf rolling, spikelet sterility, assimilate partitioning, delayed flowering, and accelerated leaf death. The stress factors for each of these processes are defined as a function of soil water tension in the root zone Soil water dynamics The water dynamics in the ORYZA2000 model is accounted by using a soil water balance module (PADDY). It is a onedimensional multi-layer (up to 10) integral model that simulates soil water balance for a variety of growing conditions, viz., puddled/non-puddled, free draining/impeded drainage soil profile. A typical soil profile of a puddled rice soil consists of a (i) ponded water layer, (ii) a muddy layer with little/no resistance to water flow, (iii) a compacted layer with large resistance to water flow (plough sole), and (iv) nonpuddled subsoil. The amount of ponded water is the starting point of flux calculations. Irrigation is inputted either as (i) user-defined timings and amounts of water, or (ii) fixed amount of water at a given (a) minimum ponded water depth, or (b) minimum soil water tension or content in a given layer, or (c) fixed number of days after disappearance of ponded water. The water gains through rain and/or irrigations are accounted by ET and percolation losses. Daily soil evaporation (E) and plant transpiration (T) from ET module are met preferentially from ponded water layer, and then from top soil layer (for E) and all rooted layers (for T) in the absence of ponded water. The percolation rate from the puddled layers is either inputted as a constant value or calculated using an iterative procedure that makes use of hydraulic characteristics of plough sole and that of underlying non-puddled subsoil. The percolation rate can never be greater than the amount of ponded water after extraction of ET loss for the day. When the depth of ponded water after accounting for ET and percolation loss exceeds bund height, the excess water is assumed lost as runoff. Even in the case of no percolation, there are inter-layer fluxes contributing towards drainage due to redistribution of water in the soil profile. All water input in excess of field capacity is drained from a layer with a maximum rate equal to saturated hydraulic conductivity (K s ) of the layer. If the rate is low, the water content may reach saturation and may develop a perched water table. If the soil profile is not freely draining, one or more layers in the profile restrict water flow. When the outflow flux for a given layer is too low, the excess water is redistributed upward, and may cause ponding at the soil surface. In the case of presence of ground water in the soil profile, soil layers in the subsoil may drain to their field

3 agricultural water management 83 (2006) capacity values. Capillary rise from the ground water to a soil layer is assumed to occur only if soil water tension is greater than field capacity. The model requires input data on cropand cultivar characteristics, soil water retention and drainage, irrigation management, water table depth, and weather conditions Testing data In order to evaluate performance of the ORYZA2000 model, database were used from a field study on rice under variable soil and water management and transplanting-time from 2000 to 2002 (Arora et al., 2006) Experimental details Experiments were conducted at Punjab Agricultural University Research Farm, Ludhiana, India ( N, E, 247 m above mean sea level) on a deep alluvial sandy loam (USDA: Typic ustochrept, FAO: Dystric cambisol) soil developed under hyper-thermic regime. The soil contained 740 g kg 1 sand ( mm) and 110 g kg 1 clay (less than 2 mm) in top 0.30 m layer, was non-saline with EC of 0.2 ds m 1 and organic carbon content of 0.3 g 100 g 1. The ground water at the experimental site was more than 10 m deep, and did not influence water dynamics in the soil profile. Treatments comprised combinations of variable puddling intensity and irrigation regimes in a split plot design with puddling in the main plot, and irrigation in the sub-plot with three replications. In order to avoid changes in micro-climate due to differential transplanting dates, this factor was not randomized with puddling and irrigation regimes. Each subplot measured 12 m 3 m with a bund height of 150 mm to minimize run-off loss and run-on gain. During 2000, puddling regimes included two puddlings dry tillage followed by two runs of a tine cultivator and leveling with a wooden bar in ponded water (P 2 ); and four puddlings dry tillage followed by four runs of a cultivator and leveling with a wooden bar in ponded water (P 4 ). During 2001 and 2002, a lower puddling intensity treatment (P 1 ) was added. Irrigation regimes comprised of maintaining continuous submergence throughout the growing season of rice (I 1 ); and intermittent submergence involving 2 weeks of ponding after transplanting followed by irrigations at 2-day interval after soaking-in of previous irrigation (I 2 ). During 2001 and 2002 years, rice was transplanted on two dates, viz., first fortnight of June (D 1 ), and end June (D 2 ) to generate variation in evaporative demand. Thirty-day old rice seedlings (cv. PR 114) were transplanted 0.15 m apart in 0.20 m wide rows in ponded water on June 10 in 2000, June 8 (D 1 ) and June 30 (D 2 ) in 2001, and June 16 (D 1 ) and July 2 (D 2 ) in The crop received 120 kg N ha 1 (through urea), 13 kg P ha 1 (single super phosphate), 25 kg K ha 1 (potassium chloride), and 5 kg Zn ha 1 (zinc sulphate). The entire amount of P, K, and Zn was incorporated into the top soil with the last run of cultivator, while N was top-dressed in three equal splits, viz., at the time of transplanting, and 3 and 6 weeks after transplanting. The local agronomic recommendations were followed for weed, disease, and pest control, and the crop was harvested in October. Table 1 Cultivar and soil information required in the ORYZA2000 model Development rate constants (DRC) (8C day S1 ) DRCJ = DRCI = DRCP = DRCR = Development rate in juvenile phase Development rate in photoperiod-sensitive phase Development rate in panicle development phase Development rate in reproductive phase Partitioning coefficient Development stage (DS) Shoot as a fraction of dry mass increment DS Leaves as a fraction of shoot increment Stem as a fraction of shoot increment Storage organ as a fraction of shoot increment DS Specific leaf area (ha kg S1 ) Number of soil layers 8 Thickness of soil profile (m) 0.6 Number of puddled layers 4 Thickness of puddle layer (m) 0.20 Depth of plough sole (m) Water content at saturation (m 3 m S3 ) 0.40 Water content at field capacity (m 3 m S3 ) 0.30 Water content at permanent wilting (m 3 m S3 ) 0.10 Water content at air dryness (m 3 m S3 ) 0.02 K s (plough sole) (cm day S1 ) P P P 4 0.2

4 54 transplanted in first fortnight of June. In a given cropping season, puddling and irrigation regimes affected ET slightly. The ET was more under I 1 than I 2 regime apparently due to more frequent irrigations in the former regime. Similarly, ET was also greater under P 4 and P 2 compared to P 1 regime, for a comparable irrigation regime, due to less drainage loss with intensive puddling that made more water available for ET. Seasonal drainage was a substantial component of total water input through irrigation and rainfall. It ranged between 59 and 67% for different treatments in year 2000 and 61 and 79% in more wet year Within a given cropping season, puddling had a greater effect than irrigation regime on drainage component. For example, drainage was 77, 70, and 64% of water input in P 1,P 2, and P 4 regimes under I 1 regime, and 73, 70, and 66% in the three puddling regimes under I 2 regime in D 1 crop during There was a close correspondence between measured and simulated soil water use (depletion) between transplanting and harvesttime. The RMSD between the two were 19 mm for measured values ranging between 75 and 108 mm in m soil profile. These results indicate that simulation of soil water dynamics and crop yields was quite satisfactory. Moreover, there was a correspondence between the trends of simulated and meaagricultural water management 83 (2006) Input data Cultivar-related information on DRC in different phenological phases (Table 1) was arrived at by field observations on crop phenology, viz., panicle initiation, flowering, and physiological maturity for a stress-free (potential production) environment. The values of partitioning coefficients were derived by analyzing the fraction of dry mass increment allocated to the plant organs between successive samplings. The SLA was estimated by concurrent measurements of area and mass of leaf fraction of total biomass at different sampling times during the growing season. The crop-specific information on maximum assimilation rate, its dependence on air temperature and CO 2 concentration, and upper and lower limits of drought stress factors (varying from zero to unity) for different physiological processes derived for IR-72 and IR-20 (Bouman et al., 2001) was used in the present analysis. Soil water content was monitored at transplanting (immediately after disappearance of ponded water) and at harvesting gravimetrically in 0.15 m increments down to 0.60 m depth. Soil water content at specified matric potential was estimated in the pressure plate extractor. Saturated hydraulic conductivity (K s ) of different soil layers (including plough sole) for the three puddling regimes, viz., P 1,P 2, and P 4 was determined on undisturbed soil cores in the laboratory. Depth of irrigation water was computed by monitoring water level before and after irrigation using fixed scales in the field plots. In order to estimate reference ET using Penman method, weather data on total radiation (sunshine hours), maximum and minimum air temperature, vapor pressure, wind speed, and rainfall for the period from 1991 to 2002 was obtained from a meteorological station 2 km south east of the experimental site. 3. Results and discussion 3.1. Model evaluation The ORYZA2000 model was evaluated in respect of simulation of grain yield, biomass, and soil water balance components in variable puddling intensity, irrigation regimes, and transplanting date scenarios. The model was calibrated using data for 2000 growing season, wherein near potential rice yield were realized; while the data for 2001 and 2002 growing seasons for the two transplanting dates were used for model validation. Comparison of harvest-time measured and simulated grain yield (14% moisture) for all the treatments in the 3 years (Fig. 1a) shows that matching between the two was quite reasonable with data scatter close to 1:1 line. The root mean square of deviations (RMSD) was 0.5 tonnes ha 1 and normalized RMSD was 7% for measured yields varying between 6.4 and 8.7 tonnes ha 1. Harvest-time biomass was slightly overpredicted (Fig. 1b) with a RMSD of 1.2 tonnes ha 1 and normalized RMSD of 9% for measured biomass ranging between 12.1 and 15.9 tonnes ha 1. An examination of simulated components of water balance in different treatments brings out interesting facts (Table 2). Seasonal ET was greater in low rainfall years of 2000 and 2002 compared to more wet year Delayed transplanting in end June caused a mean reduction in ET by 50 mm in 2001 and 70 mm in 2002 compared to that Fig. 1 Comparison of simulated and measured harvesttime (a) grain yield (14% moisture) and (b) above-ground biomass in different treatments during the 3 years.

5 agricultural water management 83 (2006) Table 2 Simulated seasonal water balance components and comparison of simulated and measured soil water use between transplanting and harvest under rice in different treatments Treatment Irrigation ET Drainage Soil water use Simulated Measured Year 2000, rainfall 452 mm P 2 I P 2 I P 4 I P 4 I Year 2001 D 1, rainfall 839 mm P 1 I P 1 I P 2 I P 2 I P 4 I P 4 I Year 2001 D 2, rainfall 724 mm P 1 I P 1 I P 2 I P 2 I P 4 I P 4 I Year 2002 D 1, rainfall 271 mm P 1 I P 1 I P 2 I P 2 I P 4 I P 4 I Year 2002 D 2, rainfall 257 mm P 1 I P 1 I P 2 I P 2 I P 4 I P 4 I sured responses of grain yield to changes in puddling intensity, irrigation regime, and transplanting date which suggests that the model can be used to assess the impact of water-related management options Simulation analyses The model was employed to simulate rice yields in relation to transplanting dates and to analyze the impact of change in irrigation depth and regime, and puddling intensity on seasonal water balance and rice yields using 12 years (from 1991 to 2002) weather data of Ludhiana Transplanting date effects Simulated potential rice yield (14% moisture) varied with changes in transplanting dates (Table 3). The range of grain yield in 12-year simulation indicates that there was a substantial year-to-year variation due to climatic factors. Early-transplanted rice had a slight gain in yield by 0.3 tonnes ha 1 apparently due to better radiation regime during the growing season. Cumulative probability distribution of rice yields and seasonal ET in relation to the four transplanting dates with continuous submergence regime and 75 mm irrigation depth and P 2 regime on a sandy loam soil are given in Fig. 2. It shows that median (with 50% probability of exceedance) rice yield was 6.6, 6.5, 6.5, and 6.4 tonnes ha 1, and median ET was 758, 674, 617, and 569 mm for May 16, June 1, June 16, and July 1 transplanted crop. It suggests that the rice yield of latetransplanted (July 1) crop was comparable to that of earlytransplanted (May 16) crop, and ET-based water productivity (yield/et) was also greater in late-transplanted crop. The water productivities values were 0.87, , and 1.12 kg m 3 for the four transplanting dates, and are close to Table 3 Simulated potential rice yields (14% moisture) for different transplanting dates Transplanting date Grain yield (tonnes ha 1 ) Range Mean Standard deviation May June June July

6 56 agricultural water management 83 (2006) that mean yield and ET in continuous submergence regime were same for the two irrigation depths of 50 and 75 mm, thereby leading to same values of ET-based water productivity. However, water input (irrigation + rainfall)-based productivity was substantially greater with 50 mm irrigation depth. This is due to decreased drainage loss and irrigation amount with reduced irrigation depth. Earlier studies (Bouman et al., 1994; Khepar et al., 2000; Kukal and Aggarwal, 2002) documented that a reduction in ponded water depth caused a substantial decrease in percolation (drainage) rates, more so in permeable soils. Simulated mean grain yield and ET was lower with 2-day interval regime compared to continuous submergence (Table 4). Although ET-based water productivity under the two regimes were comparable, water-input-based productivity was greater under 2-day interval regime with both the irrigation depths. This was due to greater reduction in water input due to decreased drainage loss in 2-day interval regime. These values of water-input-based productivity are comparable to measured data reported from other rice growing regions in Asia (Tuong and Bhuiyan, 1999; Bouman and Tuong, 2001). Fig. 2 Cumulative probability distribution of (a) rice yields and (b) evapotranspiration for different transplanting dates on a sandy loam soil with continuous submergence and 75 mm irrigation depth and P 2 regime. those reported by Zwart and Bastiaanssen (2004). Greater reduction in ET compared to that in grain yield with delayed transplanting not only enhanced water productivity, but also implied wet saving (Seckler, 1996) of water Irrigation management effects In order to assess the impact of irrigation management, rice yield, and water balance was simulated under different irrigation regimes and irrigation depths for June 16 transplanted crop and P 2 regime on a sandy loam soil (Table 4). It is interesting Puddling effects Puddling intensity effects on water balance and rice yields were simulated for June 16 transplanted crop with 2-day interval regime and 75 mm irrigation depth. Variations in puddling intensity were captured primarily through variations in K s of plough sole in the model. Increasing puddling intensity from P 1 to P 2 and P 4 regime in a sandy loam soil (reduction in K s of plough sole from 0.40 to 0.25 and 0.20 cm day 1 ) caused a slight increase in simulated mean rice yield and ET (Table 5), but it had far greater effect on reduction in irrigation amount primarily due to decreased drainage loss. This led to greater water-input-based productivities in P 2 and P 4 regimes compared to P 1 regime, although ET-based water productivities under the three puddling regimes were comparable. Mean water-input-based productivity under P 1,P 2, and P 4 regimes through model analysis were 0.32, 0.39, and 0.43 kg m 3, and are close to measured data (Singh et al., 2001; Arora et al., 2006), while ET-based productivities under the three puddling regimes were 0.97, 1.00, and 1.01 kg m 3. The analysis shows that improved irrigation management (2-day interval irrigation regime and reduced ponded water depth), and increased puddling intensity not only enhanced water productivity, but also effected substantial dry saving (Seckler, 1996) of water by reducing irrigation amount. Table 4 Simulated effects of irrigation management on seasonal ET, rice yield, and water productivity for June 16 transplanted crop with P 2 puddling regime on a sandy loam soil Irrigation regime Irrigation depth Grain yield (tonnes ha 1 ) ET Irrigation Water productivity (kg m 3 ) ET-based (Irrigation + rain)-based Continuous submergence (0.7) a 628 (45) 1256 (169) (0.7) 626 (45) 1116 (159) Day interval (0.6) 602 (38) 925 (123) (0.7) 591 (36) 763 (102) Mean rainfall was 600 mm. a Numerals in parentheses are standard deviation around mean values for 12 years ( ) simulation.

7 agricultural water management 83 (2006) Table 5 Simulated effects of puddling intensity on seasonal ET, rice yield, and water productivity for June 16 transplanted crop with irrigation depth of 75 mm and 2-day interval regime on a sandy loam soil Puddling intensity Grain yield (tonnes ha 1 ) ET Irrigation ET-based Water productivity (kg m 3 ) (Irrigation + rain)-based P (0.6) a 585 (35) 1206 (112) P (0.6) 602 (38) 925 (123) P (0.6) 602 (41) 831 (159) Mean rainfall was 600 mm. a Numerals in parentheses are standard deviation around mean values for 12 years ( ) simulation. 4. Conclusions The analysis has demonstrated that performance of the ORYZA2000 model was reasonably good as indicated by close matching between simulated harvest-time grain yield, biomass and soil profile water use (depletion) with measured data and trends of simulated water balance components for a range of soil and water management scenarios. Using long-term weather data, cumulative probability distribution of grain yield and ET showed that rice yield of late-transplanted (July 1) crop was comparable to that of early-transplanted (May 16) crop, and ET-based water productivity was greater in lateplanted crop. Though ET-based water productivity with continuous submergence and 2-day interval regimes were comparable, water-input-based productivity was greater under 2-day interval regime. Similarly, increase in puddling intensity from P 1 to P 2 and P 4 regimes had little effect on ETbased water productivity, but caused a substantial increase in water-input-based productivity. This analysis suggests that delayed transplanting of rice (from mid June to end June) and 2-day interval irrigation regime with medium puddling in coarse-textured soils of Punjab would provide best trade-off of high rice yield and greater water productivity. Acknowledgements This study was funded by National Agricultural Technology Project (PSR No. 1). The author is grateful to Dr. Bas Bouman of International Rice Research Institute, Philippines, for providing software of the ORYZA2000 model. references Arora, V.K., Gajri, P.R., Uppal, H.S., Puddling, irrigation and transplanting-time effects on productivity of rice wheat system on a sandy loam soil of Punjab. India. Soil Tillage Res. 85, Bouman, B.A.M., Kropff, M.J.,Tuong, T.P.,Woperis, M.C.S., ten Berge, H.F.M., van Laar, H.H., Modelling Lowland Rice. International Rice Research Institute, Los Banos, Philippines; and Wageningen University and Research Centre, Wageningen, The Netherlands, 235 pp. Bouman, B.A.M., Tuong, T.P., Field water management to save water and increase its productivity in irrigated lowland rice. Agric. Water Manage. 49, Bouman, B.A.M., Wopereis, M.C.S., Kropff, M.J., ten Berge, H.F.M., Tuong, T.P., Water efficiency of flooded rice fields. II Percolation and seepage losses. Agric. Water Manage. 26, Hira, G.S., Jalota, S.K., Arora, V.K., Efficient Management of Water Resources for Sustainable Cropping in Punjab. Technical Bulletin, Department of Soils, Punjab Agricultural University, Ludhiana, India, 20 pp. Khepar, S.D., Yadav, A.K., Sondhi, S.K., Siag, M., Water balance model for paddy fields under intermittent irrigation practices. Irrig. Sci. 19, Kukal, S.S., Aggarwal, G.C., Percolation losses of water in relation to puddling intensity and depth in a sandy loam rice (Oryza sativa) field. Agric. Water Manage. 57, Lansigan, F.P., Pandey, S., Bouman, B.A.M., Combining crop modeling with economic risk-analysis for the evaluation of crop management strategies. Field Crops Res. 51, Seckler, D., The New Era of Water Resources Management: From Dry to Wet Water Saving. Research Report I. International Irrigation Management Institute, Colombo, Sri Lanka. Singh, K.B., Gajri, P.R., Arora, V.K., Modeling the effects of soil and water management practices on the water balance and performance of rice. Agric. Water Manage. 49, Tuong, T.P., Bhuiyan, S.I., Increasing water-use efficiency in rice production: farm-level perspectives. Agric. Water Manage. 40, Zwart, S.J., Bastiaanssen, W.G.M., Review of measured crop water productivity values for irrigated wheat, rice, cotton and maize. Agric. Water Manage. 69,