Assessment of DRAINMOD-NII model for prediction of nitrogen losses through subsurface drained sandy clay under cultivation in south west Punjab, India Open Access

A major source of surface and groundwater pollution has been attributed to drained agricultural lands (Randall & Mulla 2001; Stoate et al. 2001; Liu et al. 2014). Extensive use of manure and fertilizers to boost the production of food can increase the risk of nitrogen (N) contamination of surface water and groundwater, promoting eutrophication due to unused nitrogen entering the waterbodies (Carpenter et al. 1998; Mrdjen et al. 2018). Nitrate-nitrogen (NO₃-N)-polluted drainage waters have been described as a key non-point source of surface water contamination (Jacobs & Gilliam 1985; David et al. 1997; Saadat et al. 2018). In highly productive agricultural areas with poorly drained soils, seasonal perched water tables, or shallow groundwater, subsurface drainage is the most widely used water management technique. In US 39.5% of the total cropped area is under subsurface drainage (Rabalais & Turner 2019). This method of water management boosts crop production, lowers risk, and enhances crop producers’ income.

In Punjab, the problem of waterlogging and salinity is widespread across all the Muktsar blocks, which is one of the districts of Southwest Punjab (Krishan et al. 2021). In the past three decades, the water table has risen steadily, to come closer to 1 meter or less from the surface over larger areas (Government of Punjab 2008; CGWB 2019). The percentage of waterlogged areas increased from 62.54% of Punjab's total waterlogging area in 1998 to 89.17% in Muktsar district in 2006. (Singh 2012; Krishan et al. 2021). The issue is widespread throughout all blocks of the Muktsar district (Muktsar, Lambi, Gidderbaha and Malout). In several villages, the water level rises nearly to the surface during the rainy season, causing significant harm to standing crops (CGWB 2019). Water logging and soil salinity are thus an inevitable off-flow of irrigation and have a detrimental effect on the output and productivity of irrigation controlled areas in the south-west of Punjab, resulting in enormous economic losses. Therefore, serious questions about the long-term viability of irrigated agriculture have been rightly raised, unless the problem of environmental degradation is not properly addressed. Initiation of the water logging problem, its scope and degrees, in this part of Punjab are regulated by several variables. The vast network of unlined canal distribution and their field channels recharge the ground water body due to infiltration from and return of irrigation into the fields, accounts for one of the reasons of water logging in this part of Punjab (CGWB 2017). The two major lined canals, i.e., the Rajasthan feeder and Sirhind feeder running parallel to the east of the district of Muktsar, although lined cause major damage to the region due to cracks in the lining of their bed and sides, by the inflow of excess water into the area. Water logging is also responsible for the inadequate operation of the current surface drainage system. With the development of the canal network, the parallel drains have not been properly built. The maintenance of these drains is of very low standard, except where drains have been properly installed. Owing to low groundwater quality, there is much less withdrawal of underground water for irrigation. The water table rise in this area is also caused by the lateral movement of groundwater flow from South-West to North-Eastern areas, the water table depth contours from North-West Punjab to Bathinda and Malout towns with an average travel rate of about 0.29 km per year (Uppal & Mangat 1981; Singh 2012). The cropping pattern of the district Muktsar, which was commonly known as the cotton belt in the South-West area of Punjab, has also changed. For several reasons, the share of other crops decreased from 40.13% in 1995–96 to 10.51% in 2009–10 (Gupta 2002; Singh 2013). Wheat, the main food grain, has always remained very environment friendly. In the district, it has been the major rabi crop. Roughly 44.50% of the total cropped area in that district was under wheat in 1995–96, which increased marginally to 45.65% in 2009–10 (Ladha et al. 2000; Singh 2013). Paddy, which was originally grown in the district's waterlogged field, is now the second main crop. It was barely grown in 1995–96 on 1.57% of the area and is now the second-dominant crop with 22.27% of the total cropped area recorded in 2009–10. Cotton has always remained the traditional crop of the Muktsar district. The area under cotton occupied 15.21% of the gross cropped area increased in 1995–96 to 21.38% in 2009–10 (Gupta & Kamra 2006; GOP 2017). However, due to changing climate conditions, and the rising water table, the annual variations in the yield and area under various major crops declined (Singh 2013; GOP 2017). The water table is increasing at an alarming pace in this region of Punjab. In the irrigation control area of this region in Punjab, water logging adversely affects the cropping pattern and crop productivity, resulting in enormous socio-economic losses. Cotton is completely substituted by paddy due to the problem of water logging. Earlier cotton covered 80% of the region's total area, but now it is completely eliminated from the cultivation process, due to its sensitiveness to excess water stress. Due to relative drawbacks, sugarcane, serson and other crops were also eliminated from cultivation. The yields of the most important crops such as wheat and paddy have decreased to almost 50% compared to normal soil (Gupta & Kamra 2006; Sekhon et al. 2018). This has raised production costs because more fertilizers, pesticides and insecticides are being used by farmers to improve crop productivity. NO₃-N is the common contaminant to the groundwater reservoir, due to excess use of fertilizers, in the region. Waterlogging has led to a significant decrease in net returns from crop production and income from farmers, thereby affecting the well-being of the rural population in this Punjab region. A burning problem in the Punjab economy is debt and suicides, especially among marginal and small farmers, due to low net returns from agriculture. Since 2016, the government of Punjab has initiated many schemes for the reclamation of these areas, mostly affected by waterlogging and excess salinity. Subsurface drainage technology is one among the initiatives, which has been undertaken to reclaim 12,882 acres of waterlogging affected areas in this district of Punjab. Subsurface drainage technology leads to excessive nitrogen loss from the areas. The DRAINMOD-NII model, which is a field-scale model, has been developed for poorly and artificially drained lands to simulate their hydrology and nitrogen losses (Golmohammadi et al. 2016; Youssef et al. 2018; Singh et al. 2020). The calibration area should be of field-scale size, with an installed subsurface drainage system, which should reflect field conditions. The calibration area for agricultural fields should usually consist of not less than three lateral drains, developed to continuously measure drain flow rates and water table behaviour in between the laterals (Skaggs et al. 2012a, 2012b). Furthermore, the modeled region should represent normal drainage boundary conditions. Keeping all this in consideration, the main objective of this study was to evaluate the DRAINMOD-NII model for Punjab conditions, determining the volume of drainage and nitrogen losses from a newly installed subsurface drainage system at Thehri, Muktsar, Punjab, to validate its design for better crop conditions and, as a result, minimize nitrogen loss to surface and subsurface waters from these reclaimed areas.

Model simulations were performed, using the data from the study area (Figure 1). Input parameters included soil properties, meteorological parameters, crop characteristics, drainage system design parameters and irrigation (Table 1). Parameters related to nitrogen, including N transport and transformation, organic matter parameters and crop management were required for the DRAINMOD-NII simulation. Precipitation data was obtained from Bathinda weather station, Punjab Agricultural University. Irrigation was applied to the crops on weekly basis for rice and after a fortnight for wheat crop. Potential evapotranspiration (PET) depends on net radiation, wind velocity and humidity within the region. Daily PET was computed, using Thornthwaite method (1948), in the model using weather data from 2018–2020. Soil samples were collected for various soil parameters and nitrate content form different locations using gps based, sampling points, considering the latitude and longitude of that particular point. The samples were collected from various depths to a maximum depth of 1.8 m, between the laterals. Hydraulic conductivity was measured in situ, during both the rice and wheat seasons, using the augur hole method and an average representative value was selected for the whole study area. Soil properties at the study area are listed in Table 2. DRAINMOD-NII simulates cropping systems, comprising more than one crop. The simulation study was based on rice-wheat cropping rotation, which takes almost one year to complete. The study was repeated for the two years having rice-wheat cropping system. The input data for each crop consisted of the major dates of planting, the effective rooting depth, harvesting and stress counting parameters. Crop parameters included N uptake and yield parameters. Yield parameters of harvest index (HI), root/shoot ratio (RSR), N content of plant grains, roots, shoots and potential crop yield were included in the model. The N uptake during the entire growing season is estimated from yield parameters by DRAINMOD-N II model. The maximum crop yield obtained in absence of soil water related stresses is defined as the potential crop yield by Evans et al. (1991). In DRAINMOD the crop yield was calculated based on the product of potential yield and the DRAINMOD predicted relative yield. The ratio of crop yield to the total above-ground biomass is defined as the Plant HI by Hay (1995). Hoad et al. (2001) described the RSR as the mass ratio between root dry matter and shoot dry matter. The HI and RSR are used by the DRAINMOD-N II model to estimate non-grain above-ground dry matter and below-ground dry matter from DRAINMOD-N II predicted or field-measured crop yields (Youssef et al. 2005). Table 3 shows the possible yields and nitrogen content of rice wheat based on field measurements. Salazar et al. (2009) listed the popular ranges of rice wheat crop N, C, and lignin contents as in Table 3. The N-uptake tabulated feature proposed by Youssef (2003) and Shedekar et al. (2021) was used for crop modeling in addition to the harvest index, root/shoot ratio, shoot N, and root N in rice wheat that were calculated based on observed field data.

Denitrification, nitrification, fertilizer dissolution, pH regulation, and volatilization were among the carbon and nitrogen transformation parameters considered in the model simulation. Organic matter parameters define the possible rates of decomposition (Kdec) and C/N ratios organic matter and litter in soils (SOM) pools. The model was initialized with NO₃-N, NH₄-N, and Organic Carbon (OC) concentrations measured in the region. The model was simulated using the procedure defined by Youssef et al. (2006). The nitrogen initial transport parameters and NH₄ distribution coefficient input parameters for DRAINMOD-NII are shown in Table 4. Table 5 shows the values chosen during model calibration. All of the values mentioned were derived from Salazar et al. (2009) and Shedekar et al. (2021).

O_i represents the observed value at time i, P_i represents the simulated value at time i. Obar represents the observed mean value, and n represents the number of paired observed-simulated values. When the RMSE (Equation (1)) equals 0 (zero), it implies a perfect match between observed and expected values, whereas increasing RMSE values imply a worsening match. According to Singh et al. (2004), RMSE values less than half the standard deviation of the observed (measured) data are considered low and suggest a strong model prediction. Nash–Sutcliffe efficiency (Equation (2)) can range from −∞ to 1, according to Nash & Sutcliffe (1970). An efficiency of 1 (E = 1) corresponds to a perfect match between the simulated and observed results. When the efficiency is zero (E = 0), the model predictions are as accurate as the mean of the observed results. Efficiency less than zero (E < 0) means that the observed mean is a better predictor than the model. The coefficient of determination, R², (Equation (3)), ranges from 0 to 1 that defines how much of the variance in the calculated data is explained by the model, with higher values meaning less error variance, R² > 0.5 is usually considered appropriate (Santhi et al. 2001; Van Liew et al. 2003). The average tendency of the simulated data to be greater or smaller than their observed counterparts is measured by the percentage of bias (PBIAS) (Gupta et al. 1999). PBIAS optimal value is 0, with low magnitude values suggesting a good model simulation. Underestimation bias is indicated by positive values, while overestimation bias is indicated by negative values (Gupta et al. 1999). The results of a statistical analysis were used to verify the model's reliability during the calibration and validation periods, and the results are shown in Tables 6 and 7. The significance of R² was determined using a partial F test, which revealed a substantial correlation at a 5% level of significance, as shown in Tables 6 and 7 and discussed in sections 3.1.1. and 3.2.1 respectively.

In the calibration period, the average of simulated daily drainage discharges during the rice-wheat season (0.88 cm day⁻¹) was closer to that observed (1.02 cm day⁻¹). Flow events during the growing season are predicted well, but there are discrepancies in the magnitude of some events, especially during wet months, such as June and July, when drainage was under-estimated. Since subsurface drainage and evapotranspiration are the two main pathways of water loss considered in this simulation study's water balance, any over- or under-estimation of subsurface drainage is balanced by adjustments in simulated ET. Actual ET is calculated using daily potential ET and soil moisture availability within the crop rooting depth by the DRAINMOD model. Daily potential ET was calculated using the Thornthwaite equation and crop coefficients by (Allen et al. 1998) as input to the model in this modeling research. Actual crop water use varies on year-to-year basis depending on moisture availability, and crops can change their water use by their rooting depth. In the summers of 2018 and 2019, simulated drain flow depths matched well with the observed drain flow depths, but drainage was underestimated in June and July. This implies that better ET estimation will help the model perform better. However, this is only possible if more accurate weather data or calculated crop ET are available at the desired location.

DRAINMOD-NII was successfully calibrated and validated over a two-year cultivation period in Thehri, Muktsar, Punjab, using data sets from conventional drained plots. The statistical comparison of simulated and observed drain flows and nitrogen losses revealed a stronger agreement with some discrepancies between the two data sets. For the DRAINMOD-N II, statistical goodness-of-fit measurements such as RMSE, PBIAS, NSE, and R² confirm that the model results and field observations are in strong agreement. The findings show that DRAINMOD-N II has the ability to simulate drainage rates and nitrogen losses from agricultural lands in Indian conditions for newly reclaimed lands. However, since this model was only tested for a shorter time period and with a single cropping method, inconsistencies that caused conflict with a specific set of values must be resolved for wider application of this model.

The authors would like to express their gratitude to every person associated with this study, especially Dr Vinayak Shedekar, Research Scientist in the Department of Food, Agricultural and Biological Engineering at the Ohio State University, Drainage Department, Govt. of Punjab, India, for providing the subsurface drainage facility and the farmers involved at the study location. The research presented in this paper was supported financially in part by the HRDG CSIR, New Delhi under the Senior Research Fellowship programme with award letter 09/272(0137) 2018-EMR-1 is duly acknowledged.

The research presented in this paper was supported financially in part by the HRDG CSIR, New Delhi under the Senior Research Fellowship (direct) programme with award letter 09/272(0137) 2018-EMR-1.

Methodology, Software, writing-original draft, visualization: Mehraj U Din Dar

Conceptualization, investigation: Mehraj U Din Dar

Supervision: J.P. Singh

Resources: J.P. Singh

Review and editing, result interpretation: Mehraj U Din Dar, J.P. Singh

Review and editing: Mehraj U Din Dar, J.P. Singh

The codes used if any will be shared upon request.

Not applicable.

Consent taken from the authors.

Consent given by all authors.

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

The authors declare there is no conflict.