Climate change is projected to notably impact water requirements and crop yield; therefore, it is imperative to quantify climate risk and devise climate-resilient field management practices. This study applied the AquaCrop model to Tulsipur, a sub-metropolitan city located in Western Nepal. The model was calibrated and validated on a field scale, and various scenarios were analysed for baseline (2010–2020) and future (2021–2100) periods to formulate workable management strategies for irrigation and fertilizer applications. Results showed that a deficit irrigation strategy could lead to 81% fewer requirements for irrigation in rice and 24% in wheat at the cost of a minimal (∼1%) reduction in yield. Water requirement is projected to decrease and crop yield to increase for both crops for all future scenarios, except wheat water requirement, where water requirement is projected to increase by up to 13% in the future. Rainfed irrigation leads to extremely high variance in crop yields. Deficit irrigation under the nationally recommended fertilizer dose is recommended as a better option to develop climate resiliency in cereal yield in the study area.

  • Optimal irrigation and fertilizer strategies devised under climate change scenarios.

  • Deficit irrigation reduces water requirements, maintaining high rice and wheat yields.

  • Climate change increases yield and decreases water requirements in nearly all cases.

  • Water requirement in winter wheat trends upward in the future in Western Nepal.

Agriculture contributes 21% to the Gross Development Product (GDP) of Nepal and engages 62% of the total employed population (World Bank 2023). Cereal crops alone account for 44% of the total national crop production (MoALD 2023). However, poor irrigation infrastructure and low fertilizer application rates have led to increasing cereal crop imports in recent times (Basukala & Rasche 2022). Amidst concerns of rapidly increasing food demand, business-as-usual agricultural production systems could exacerbate the already weak food security in Nepal (Economist Impact 2022). In this context, strengthening agricultural productivity is paramount for many reasons, including enhancing resilience to climate change.

Climate change is projected to adversely affect agriculture and food security across the world (Molotoks et al. 2021). Water pollution and soil erosion due to extreme wet and dry droughts are expected to detriment agricultural productivity (Boxall et al. 2009). Changes in precipitation patterns could impact groundwater recharge (Bates et al. 2008). Increasing temperatures can shorten the crop cycle, increase irrigation water requirements, and consequently reduce crop yield (Fischer et al. 2007). Knox et al. (2012) estimated a decline of nearly 8% in crop yield in South Asia by the 2050s. Other studies have also found robust evidence of reduced crop yield due to climate change (Parry et al. 2004; Mendelsohn 2008; Minh et al. 2023; Leon & Iv 2024).

Increasing CO2 and temperature coupled with precipitation variability, a result of climate change, can have a wide range of impacts on crop yield, with potential variation in results depending on the crop type and local climatic conditions (Bouras et al. 2019). For example, higher atmospheric CO2 promotes faster photosynthesis and better regulation of crop transpiration (Drake et al. 1997); on one hand, increased temperature can lead to higher rates of crop transpiration and water demand (Peng et al. 2004). Predicting which phenomenon outshines the other in a complex eco-biological interplay of competing variables is a challenging task.

Developing countries experiencing rapid urbanization are at a high risk of climate change because of weak adaptability and increased pressure on natural resources (Yohe & Tol 2002). Nepal, in particular, faces higher risks due to sharp variations in topography and susceptibility to extreme events (Leduc et al. 2008; Eriksson et al. 2009). Moreover, 79% of the Nepali population that lives in rural areas are likely to face climate-induced compromise in the rural economy that is highly dependent on climate-sensitive resources (Dulal et al. 2010). Climate change poses a considerable risk to rainfed (RF) farming systems (Falloon & Betts 2010), which is a concern for Nepal, which has access to irrigation on only 40% of arable land, of which only 47% has access to year-round irrigation (MoEWRI 2019). On top of that, rapid temperature increase (Malla 2008), erratic precipitation patterns, and an increase in the frequency of hydrological extremes (Bhattarai et al. 2023) are all likely to impact crop yield and irrigation requirements.

Despite the immense risk of climate change on crop yield and productivity, there are only limited studies in Nepal on assessing climate change impacts on crop yield and suggesting climate-resilient pathways. Malla (2008) used the Decision Support System for Agro-technology Transfer (DSSAT) to study the effects of climate change on crop yield and found an increase of up to 3.4% in rice and 41.5% in wheat in the Terai region of Nepal. That study used potential CO2, temperature, and rainfall scenarios instead of using outputs from global climate model (GCM) simulations. Acharya & Bhatta (2013) used statistical time series analysis techniques to establish a direct positive relationship between precipitation and agricultural GDP. Karn (2014) used multiple linear and quadratic regression to examine the effects of weather variables on rice production and observed a 4.2% decrease in rice yield. Shrestha et al. (2013) used a detailed crop yield simulation using AquaCrop (Steduto et al. 2009), a crop water productivity model, to simulate the yield of rice, maize, and wheat in response to irrigation water and soil fertility management in the Terai region of Nepal. However, the study did not include climate change impacts. Palazzoli et al. (2015) employed the Soil Water and Assessment Tool (SWAT) in the Indrawati basin under three representative concentration pathways scenarios and found varying estimates of crop yield (−17 to +12% for rice and −36 to +18% for wheat). Shrestha & Shrestha (2017) simulated crop yield using AquaCrop under a single Special Report Emissions Scenarios (SRES) A1B scenario and found a −23 to +29% variation in wheat yield and a −66 to +25% variation in rice yield under RF conditions.

As the global community continues to evolve in analyzing, evaluating, and improving climate projections (Arias et al. 2021) and in the context of the availability of new shared socioeconomic pathways (SSP) scenarios in the coupled model intercomparison project phase 6 (CMIP6) family of GCM outputs, there remains a research gap in the application of the latest CMIP6 climate projections to assess cereal yields in Nepal. This study aimed to fill that gap by applying the AquaCrop model to conduct a comprehensive scenario analysis of cereal yield and water requirement in the climate change context, with a case of Tulsipur, Dang in Western Nepal, and answering the following research questions: (i) What are the best attainable field management strategies to improve crop yield in current scenarios? (ii) How is climate change projected to impact crop yield and water requirements? (iii) Which management scenarios are the most resilient to climate change? The outcome of this study is expected to be useful in understanding the impacts of climate change on agricultural production and irrigation systems in Nepal and devising workable adaptation strategies to best address the associated risks. Though the geographic focus of this study is relatively narrow, the applicability of the approach applied in this study is wide. Furthermore, the practice of taking small geographic regions for crop models and crop-related hydrometeorological parameters is quite common (e.g., Shrestha & Shrestha 2017; Alsafadi et al. 2023; Zare et al. 2023). In addition, it contributes by documenting scientific literature from local areas, with local data, which are not documented in scientific literature.

This study was conducted in Tulsipur sub-metropolitan city, Dang, situated in the mid-western Terai region of Lumbini province, Nepal, spanning from latitude 27°37′N to 28°29′N and longitude 82°02′E to 82°54′E. A field-scale area of 270 hectares in the command area of the Satgaule Sir Kulo irrigation project (SSK-IP) was chosen for this study (Figure 1). The region is primarily characterized by the prevalence of sandy loam soils. The primary crops cultivated in the region are paddy and wheat. Approximately 55% of the total arable land is dedicated to paddy (i.e., rice) cultivation during the monsoon season, while wheat is grown on around 20% of the arable land during the winter season. Between 2010 and 2020, the average yield for monsoon rice and winter wheat was recorded at 4.66 and 2.38 t/ha, respectively (MoALD 2023). Two primary irrigation canals, namely SSK-IP and Shir Kulo IP, serve as the principal sources of irrigation in the region. Twenty-nine years of daily climatic data (1992–2020) were collected from Tulsipur meteorological station (Index: 508; Lat: 28°08′E, Lon: 82°18′N). Based on an analysis of data from the station, the area receives an average annual rainfall of 1,530 mm, with over 90% of it during the monsoon (June–September) period. The highest rainfall (483 mm) is recorded in the month of July. The average monthly maximum temperature is 27.29 °C, peaking in May (33.9 °C) and dropping to its lowest in December (20.2 °C). The average monthly minimum temperature is 15.2 °C, with the highest in May (20.2 °C) and the lowest in December (6.3 °C).
Figure 1

Location and topography of the study area (Tulsipur) with field study locations.

Figure 1

Location and topography of the study area (Tulsipur) with field study locations.

Close modal
Comprehensive scenario-based analyses were conducted on the crop yield and water requirements of two staple cereal crops of Nepal, namely, rice and wheat, by calibrating and validating the AquaCrop model (Raes et al. 2009) in the study area. In addition to the historical observation data, climate model (CMIP6) data are also utilized in this study. Future climate projections by a multi-model ensemble (MME) of five GCMs under the SSPs were fed to the calibrated model under multiple irrigation and soil fertility management scenarios to conduct a comprehensive impact assessment. The overall methodological framework is shown in Figure 2.
Figure 2

The methodological framework of the study. GCM; Shared Socioeconomic Pathways (SSP); near future (NF); mid future (MF); far future (FF); National Recommended Fertilizer Dose (NRFD); net irrigation requirement (NIR); pedo-transfer function (PTF).

Figure 2

The methodological framework of the study. GCM; Shared Socioeconomic Pathways (SSP); near future (NF); mid future (MF); far future (FF); National Recommended Fertilizer Dose (NRFD); net irrigation requirement (NIR); pedo-transfer function (PTF).

Close modal

Simulation of baseline crop yield and net irrigation requirement (NIR)

Model description and set-up

The AquaCrop model (Raes et al. 2009), a multi-crop crop water productivity model developed by the Land and Water Division of the Food and Agriculture Organization (FAO), was used to simulate baseline rice and wheat yield and net irrigation requirement (NIR). It uses a relatively small number of explicit parameters and mostly intuitive input variables to balance simplicity, accuracy, and robustness (Steduto et al. 2009). It describes the interaction between plants and soil, linking the upper boundary of the system with weather conditions and the lower boundary with groundwater. The processes involved and the calculation scheme of AquaCrop are described in detail in Raes (2017). As this model has wide applicability and has demonstrated suitability in Nepal (e.g., Shrestha et al. 2013; Shrestha & Shrestha 2017), alternative models are not considered for comparison of the results.

The AquaCrop model set-up requires basic climate data, such as daily rainfall, air minimum and maximum temperature, sunshine duration, wind speed, and relative humidity. The latter five are used to evaluate reference evapotranspiration (ET0) using ET0 calculator, an in-built tool in AquaCrop, which is based on the FAO Penman–Monteith equation. Other input parameters incorporate crop types and characteristics, soil properties (type, layer depth, and water-retaining capacity), and field management practices such as irrigation schedules and fertilizer applications. The transplantation date of rice is set up after the third day of >40 mm rainfall is observed in a window of 4 days, any time between 20 June and 20 July. Similarly, the sowing date of wheat is set up based on the common local practice.

Model calibration and validation

Before applying AquaCrop to simulate crop yield, it must be calibrated to local conditions by fine-tuning its parameters. In this respect, AquaCrop offers two sets of parameters: conservative and non-conservative. Conservative parameters are unique to crops and do not change with cultivar-specific climate, geography, and management practices. AquaCrop comes with FAO-recommended conservative parameters, and hence the parameters for rice and wheat have been used as recommended (Vanuytrecht et al. 2014). The non-conservative parameters, on the other hand, are cultivar-specific and have to be fine-tuned based on detailed field experiments. Shrestha et al. (2013) and Shrestha (2014) obtained non-conservative parameters for rice, maize, and wheat in Chitwan. Given the similarities in both the species of crop planted (Joshi 2008) and the climate of Chitwan and Dang, as they both lie in the Terai region of Nepal, the non-conservative parameters from Shrestha et al. (2013) have been used. A comparison of the climate of Chitwan and Dang is provided in Figure S1 (Supplementary Material). Other studies (e.g., Shrestha & Shrestha 2017) have also used a similar approach. The crop parameters fine-tuned for the local environment in Nepal, obtained from Shrestha (2014) provided in Table 2, and the goodness-of-fit analysis to ensure the accuracy of parameters is provided in Table S1-1 in Supplementary Materials.

Local field management practices in Tulsipur could still cause slight changes in the non-conservative parameters. Therefore, a field survey was carried out in the form of in-person interviews and questionnaire surveys to collect data related to crop phenology, irrigation practices, fertilizer applications, and field management practices, including the use of pesticides and insecticides. On top of that, data on the historical yield of rice and wheat were also collected to serve as a basis for rough validation of site-specific fine-tuning. Additionally, soil samples were collected at a depth of 30 cm, which were later analyzed by hydrometer tests at the laboratory of the Institute of Engineering, Pulchowk Campus, to conduct textural analysis. Hydraulic characteristics of soil were obtained from its texture via the use of Saxton & Rawls's (2006) pedo-transfer functions (PTFs), which is a common practice in the absence of laboratory equipment that facilitates direct measurement of soil hydraulic properties. The dominant soil type was used in simulations assuming a uniform profile of 1 m.

Shrestha et al. (2013) validated AquaCrop's ability to reproduce yield in a climatologically similar area of Chitwan by conducting comprehensive field experiments. In the absence of a detailed experimental set-up, similarly comprehensive experiments could not be conducted. However, since we predominantly used the crop phenology parameters calibrated by Shrestha et al. (2013) in the same species of rice and wheat (Savitri rice and Gautam wheat), their experimental validation can be considered adequate. Nonetheless, the model's ability to reproduce farmer survey yield of 3 years for the farmers applying different fertilizer dosages was used as a rough validation measure. A similar practice has been reported previously in Nepal (Shrestha & Shrestha 2017). The district-averaged crop yield of Dang district (MoALD 2023) has also been used as a reference for qualitative discussion of the model's ability to reproduce general trends in yield.

Baseline period scenario analysis

After model calibration and validation, crop yield and water requirements were simulated for the baseline period of 2011–2020. Comprehensive scenario analyses were conducted to assess the field management strategies best suited to improve cereal yield under limited water application. These scenarios mainly incorporate irrigation water management and fertilizer application schemes to determine the most advisable and adaptable management strategies for optimum crop production. Four irrigation water management scenarios were studied: RF, fully irrigated (FI), and two deficit irrigation schemes (D1 and D2). Similarly, four fertilization application scenarios were also studied with reference to National Recommended Fertilizer Dose (NRFD). These scenarios were devised by taking into account the current farmer practice and credible future pathways to cope with climate change. A total of 16 scenarios were simulated, and the yield and NIR were evaluated to devise the optimal field management strategy for the baseline period. These scenarios are described in detail in Table 1.

Table 1

Irrigation management and fertilizer application scenarios used for simulations

CropIrrigation managementFertilizer application
Monsoon rice RF 150% of NRFD/non-limiting 
FI: irrigation applied up to 20 mm excess of FC when soil water content (SWC) drops to FC 100% of NRFD 
Deficit irrigation D1: irrigation applied up to 20 mm excess of FC when SWC drops to 40% TAW 60% of NRFD 
Deficit irrigation D2: irrigation applied up to 20 mm excess of FC when SWC drops to 50% TAW 0% of NRFD 
Winter wheat RF 150% of NRFD/non-limiting 
FI: SWC maintained at FC 100% of NRFD 
Deficit irrigation D1: two applications up to FC 22 and 70 days after sowing 50% of NRFD 
Deficit irrigation D2: three applications up to FC 22, 70, and 85 days after sowing 0% of NRFD 
CropIrrigation managementFertilizer application
Monsoon rice RF 150% of NRFD/non-limiting 
FI: irrigation applied up to 20 mm excess of FC when soil water content (SWC) drops to FC 100% of NRFD 
Deficit irrigation D1: irrigation applied up to 20 mm excess of FC when SWC drops to 40% TAW 60% of NRFD 
Deficit irrigation D2: irrigation applied up to 20 mm excess of FC when SWC drops to 50% TAW 0% of NRFD 
Winter wheat RF 150% of NRFD/non-limiting 
FI: SWC maintained at FC 100% of NRFD 
Deficit irrigation D1: two applications up to FC 22 and 70 days after sowing 50% of NRFD 
Deficit irrigation D2: three applications up to FC 22, 70, and 85 days after sowing 0% of NRFD 

Future climate projection

GCMs have been developed by several leading institutions around the world to model fluctuations in future climate under various forcings. The CMIP provides the most reliable database of future climatic outputs based on projections from numerous GCMs (Meehl et al. 2000). The latest phase CMIP6 (O'Neill et al. 2016) provides insights into future climate under the new SSPs and hence are widely used in climate change impact assessment studies. Five CMIP6 GCMs; ACCESS-CM2, EC-EARTH3, MIROC6, MPI-ESM1-2-HR, and MRI-ESM2-0; were chosen based on the study by Mishra et al. (2020). These models have been shown to perform well in the model performance evaluation phase by various studies within Nepal (Chhetri et al. 2021; Talchabhadel 2021; Thapa et al. 2021; Bhattarai et al. 2023). Downscaled daily precipitation and temperature (maximum and minimum) data in two scenarios, SSP245 and SSP585, were obtained from the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6) at a horizontal resolution of 0.25° (Thrasher et al. 2022).

The baseline period of 1991–2014 was used based on the availability and quality of historical observed precipitation and temperature data at Tulsipur station. The future climatic change was evaluated for the period of 2021–2100 which was further divided into three periods: near future (NF) (2021–2050), mid future (MF) (2051–2075), and far future (FF) (2076–2100). Although the World Meteorological Organization recommends a minimum of 30-year period for climate studies, some periods of less than 30 years (MF and FF) were used to make this study comparable to other studies in Nepal (Pandey et al. 2021; Bhattarai et al. 2023; Shrestha et al. 2023).

The downscaled CMIP6 data were further bias-corrected to ensure that local features were captured adequately. Robust empirical quantile mapping with linear interpolation was the chosen algorithm due to its well-documented superiority over other quantile mapping methods (Enayati et al. 2021). The bias-corrected results were pooled together to form a MME to reduce uncertainty in model outputs (Tebaldi & Knutti 2007). Among the multiple approaches to forming an MME, a simple arithmetic average with equal weights was employed to leverage its simplicity and ease of computation. The performance of individual GCMs and MME was evaluated by comparing it to the historical climate and calculating the following statistical performance indicators: Nash–Sutcliffe efficiency (NSE) (Nash & Sutcliffe 1970), percentage bias (PBIAS) (Gupta et al. 1999), and coefficient of determination (R2) (Moriasi et al. 2007). Finally, annual and seasonal changes in the projected future climate of Tulsipur were discussed. Moriasi et al. (2007) outline calculations and performance ratings for the statistical parameters.

Assessing climate change impacts on crop yield and NIR

The outputs of future climate projections (precipitation and temperature) were inputted into the calibrated AquaCrop model. Additionally, the CO2 forcing time series (RCP4.5 in the SSP245 scenario and RCP8.5 in the SSP585 scenario) was also fed to the crop yield simulation model. The 16 scenarios, described in Section 3.1.3, were kept the same for future scenario analysis, in conjunction with two future SSP scenarios. A total of 32 scenarios were thus generated and simulations were carried out for every scenario. Finally, the projected crop yield and NIR in all three future periods (NF, MF, and FF) were compared to the corresponding outputs in the baseline scenario analysis to obtain a comprehensive quantification of climate change impacts.

Identification of climate-resilient future pathways

Finally, the scenarios least affected by climate change were analyzed and discussed further. Not all well-performing scenarios are viable in the future due to, for example, stress on groundwater recharge or limited water availability for irrigation, Hence the practical implications of observed climate-resilient pathways were discussed in terms of its implication for water and fertilizer requirements. Climate change impacts on the best field management practices recommended in the baseline period were studied and climate change resilience of these scenarios was identified to understand whether the same practices can still be applied in the future. Finally, potential adaptation strategies were devised based on the results of scenario analyses.

Data and sources

Data used in this study, with their corresponding sources, are listed in Table S1.

Model performance

The conservative parameters as recommended by FAO and non-conservative parameters fine-tuned to the local environment are listed in Table 2 and goodness-of-fit analysis is provided in Table S1-1 in the Supplementary Material.

Table 2

Conservative (Vanuytrecht et al. 2014) and non-conservative parameters fine-tuned to local data (Shrestha et al. 2013)

Conservative crop parametersRiceWheat
Base temperature (°C) 8.0 0.0 
Upper temperature (°C) 30.0 26 
Crop coefficient when canopy is complete but prior to senescence 1.1 1.1 
Water productivity normalized for ET0 and CO2 (gm/m219 15 
Possible increase (%) of HI due to water stress before flowering None Small 
Coefficient describing the positive impact of restricted vegetative growth during yield formation on HI Small Small 
Coefficient describing the negative impact of stomatal closure during yield formation on HI Moderate Moderate 
Allowable maximum increase (%) of specified HI 15 15 
Soil water depletion threshold for canopy expansion – upper threshold 0.2 
Soil water depletion threshold for canopy expansion – a lower threshold 0.4 0.65 
Soil water depletion threshold for stomatal closure – upper threshold 0.5 0.65 
Soil water depletion threshold for canopy senescence – upper threshold 0.55 0.7 
Minimum growing degrees required for full biomass production (°C day) 10 14 
Fine-tuned non-conservative parameters Rice Wheat 
Initial plant density (no. of plants per m245 160 
Recovery/emergence (DAP) 
Maximum canopy cover (%) (DAP) 56 50 
Senescence (DAP) 99 90 
Maturity (DAP) 113 122 
Flowering day (DAP) 68 77 
Duration of flowering 14 18 
Maximum rooting depth (m) 0.5 0.6 
Time to reach maximum rooting depth (DAP) 99 84 
Reference harvest index (%) 41 33 
Conservative crop parametersRiceWheat
Base temperature (°C) 8.0 0.0 
Upper temperature (°C) 30.0 26 
Crop coefficient when canopy is complete but prior to senescence 1.1 1.1 
Water productivity normalized for ET0 and CO2 (gm/m219 15 
Possible increase (%) of HI due to water stress before flowering None Small 
Coefficient describing the positive impact of restricted vegetative growth during yield formation on HI Small Small 
Coefficient describing the negative impact of stomatal closure during yield formation on HI Moderate Moderate 
Allowable maximum increase (%) of specified HI 15 15 
Soil water depletion threshold for canopy expansion – upper threshold 0.2 
Soil water depletion threshold for canopy expansion – a lower threshold 0.4 0.65 
Soil water depletion threshold for stomatal closure – upper threshold 0.5 0.65 
Soil water depletion threshold for canopy senescence – upper threshold 0.55 0.7 
Minimum growing degrees required for full biomass production (°C day) 10 14 
Fine-tuned non-conservative parameters Rice Wheat 
Initial plant density (no. of plants per m245 160 
Recovery/emergence (DAP) 
Maximum canopy cover (%) (DAP) 56 50 
Senescence (DAP) 99 90 
Maturity (DAP) 113 122 
Flowering day (DAP) 68 77 
Duration of flowering 14 18 
Maximum rooting depth (m) 0.5 0.6 
Time to reach maximum rooting depth (DAP) 99 84 
Reference harvest index (%) 41 33 

HI, harvest index; DAP, days after planting.

The hydraulic properties of soil samples collected in this study area were determined using Saxton and Rawls' PTF. Soil was categorized into five types based on the total available water (TAW) as detailed in Table S2. Given the abundance of soil type S4, it was taken as the representative soil type. S4 is of sandy loam type with permanent wilting point of 10.8%, field capacity of 23.1%, saturation point of 45%, TAW of 123 mm, and saturated hydraulic conductivity of 464 mm/day. The transplanting date of rice and wheat was also obtained from a farmer survey.

In the absence of long-term yield data in this study area, insights from farmer surveys for 3 years (2020, 2019, and 2010) were used as a reference to compare the results. A clear split among farmers was observed in terms of fertilizer application, therefore, they were divided into two categories: farmers applying 60% NRFD and 100% NRFD. The model was calibrated to the yield of farmers applying 60% NRFD and validated with the model's ability to reproduce the yield of farmers applying 100% NRFD. Results of the comparison in monsoon rice and winter wheat are shown in Figure S2.

The simulated yield shows a good match with the observed one obtained from the farmer survey for both rice and wheat. District-averaged yield is on the lower side of the simulated yield for both rice and wheat, except for a few cases. This is easily explained by the fact that this study area receives ample irrigation as compared to the average field in Dang. The trend of district-averaged yield over the years matches with the simulated yield's trend except for the outlier in 2015 in rice and 2015–2017 in wheat. The outlier below the simulated yield can be explained by potential pest infection or hydrological extreme, which have been shown to significantly reduce crop yields in Nepal (Shrestha et al. 2000; Adhikari et al. 2024) and outlier (in wheat) above the simulated yield might be due to timely onset in conjunction with propitious weather and high fertilizer application, which are known to enhance crop productivity (Shrestha & Nepal 2016). While simulated and observed yields do not match perfectly, several studies (Park et al. 2018; Basukala & Rasche 2022; Devkota et al. 2024) indicate that the inherent variability in farm conditions as differences in soil types, water availability, and farming practices- may lead to generally acceptable but not perfectly matching results. Nonetheless, experimental validation by Shrestha et al. (2013), combined with comparison in this study offers a robust assessment given the current level of data availability. A statistical summary of experimental validation is reported by Shrestha et al. (2013).

Baseline crop yield and NIR

The results of scenario analysis conducted in the baseline period are detailed in the following sub-sections.

Monsoon rice

The yield of monsoon rice under 16 scenarios is presented in Figure 3. The district average (of 10 years) yield of 4 tons/ha lies in the simulated yield under 60% NRFD and RF scenario, which shows that a majority of farmers in Dang depend on rainfall. There is a considerable difference in yield under RF and irrigated scenarios – as observed in Jha et al. (2016) and Risal et al. (2022) – however, different levels of irrigation have no significant influence on crop yield. This finding, indeed, suggests that the D2 irrigation strategy will be advisable in the case of water scarcity, as NIR in D2 is 222 mm compared to 1,158 mm in a FI scenario (Table S3). As expected, an explicit increase in yield has been observed under increasing fertilizer application scenarios. In the FI case (currently practiced), an increment in fertilizer dose from 0 to 150% of the NRFD has resulted in an increment of yield (from 3.68 to 7.60 tons/ha). Even in the RF case, the yield has increased from 3.12 to 5.51 tons/ha. This indicates that the farmer's crop production is limited initially by water scarcity, and subsequently by soil fertility. This finding is consistent with other literature (e.g., Adhikari et al. 1999; Basyal et al. 2019). Adhikari et al. (1999) suggested that sufficient water conditions followed by high fertilizer input could increase rice yield in Nepal. As per Basyal et al. (2019), farmers in Nepal believe dependance on rainfall and lack of fertilizers to be the major constraints of rice production. Also, in the case of RF, error bars of the box plots increase with the increase of fertilizer dose, while, in the case of all irrigation scenarios, a trivial increase is observed. This implies high variability in RF yields, similar to results reported by Guntukula & Goyari (2021) in India. RF crops are naturally subject to higher yield variability due to yearly changes in seasonal rainfall (Anderson et al. 2016).
Figure 3

Baseline rice yield under all irrigation management and fertilizer application scenarios.

Figure 3

Baseline rice yield under all irrigation management and fertilizer application scenarios.

Close modal

To level the variability, irrigation is essential, and thus in the case of 100 and 150% of the NRFD fertilizer scenarios, the D2 scenario is recommended for high and stable yield. The baseline NIR for different irrigation scenarios with varying fertilizer applications is shown in Figure S3. Baseline NIR under irrigation scenarios FI, D1, and D2 are 1,158, 310, and 223 mm, respectively, for 60% of the NRFD (most widely adopted scenario). In the FI scenario, NIR undergoes an insignificant change as the fertilizer dose increases; however, NIR increases slightly with an increase in fertilizer dose for D1 and the D2 irrigation scenarios. This is explained by the increase in water uptake of fertilized crops (Barraclough et al. 1989).

Winter wheat

The yield of winter wheat under 16 scenarios is presented in Figure 4. District average (for 10 years) yield of 2.35 tons/ha lies in the simulated yield under 50% NRFD and RF scenario, once again confirming the dependance of the average farmer on rainfall in Dang district. Similar to monsoon rice, there is a significant difference in wheat yield under RF and irrigated scenarios. However different levels of irrigation itself have no significant difference in yield under each fertilizer scenario. Some variation is observed in the 150% NRFD scenario because plants flourish vigorously in the abundance of nutrients (Chapin et al. 1987), which also increases its water demands (Barraclough et al. 1989) that can be met in FI than D1 and D2 scenario. However, in other fertilizer scenarios, deficit irrigation performs equally as well as the FI scenario. For all irrigation scenarios, an explicit increasing trend in yield is observed under increasing fertilizer dose. In RF condition, there are wide error bars in the box plot with irregular trends, which implies that applying large doses of fertilizer to RF wheat without adequate irrigation to complement it is inimical to yield stability. Li et al. (2004) also showed that fertilizer application is only effective in wheat if supplemented by sufficient irrigation, which was further corroborated in southern Nepal by Shrestha et al. (2013).
Figure 4

Baseline wheat yield under all irrigation management and fertilizer application scenarios.

Figure 4

Baseline wheat yield under all irrigation management and fertilizer application scenarios.

Close modal

A remarkable finding, in the 100% NRFD scenario, is that deficit irrigation (D1) has a slightly higher average yield (3.93 tons/ha) than FI (3.92 tons/ha) (Table S3), which can be attributed to the upward adjustment of harvest index. This is because when soil moisture is below the stomata threshold, water stress affects leaf growth, and carbohydrates are then used to fill the grains instead of making new leaves, resulting in a positive and upward adjustment of the harvest index (Hsiao 1973). Given almost equal yield of FI to deficit irrigation (D1) in all fertilizer scenarios and in consideration of both fertilizer cost and yield demand, 100% NRFD with a D1 irrigation strategy is recommended.

Projected future climate

Bias-corrected climate model outputs (precipitation and temperature) were compared to the historical observed data (1991–2014) to evaluate the effectiveness of the bias correction procedure. The performance evaluation of bias correction is presented in Table 3. MME of bias-corrected model outputs was used to project the future climate in Tulsipur. The results of future climate projections in terms of annual and seasonal climatic changes are discussed in the following sub-sections. Uncertainties in the long-term projections may exist for various reasons such as the simplification of real-world systems in climate models and selection of the climate models for future climate projection. Performance criteria have been adopted in selecting climate models to reduce uncertainty. However, epistemic uncertainty stemming from our incomplete understanding of the earth system, and subsequent representation in climate models, should not be a deterrent to conducting impact studies in a high-risk area like Nepal.

Table 3

Statistical indicators before and after applying bias correction

Climatic variableParameterACCESS-CM2
EC-EARTH3
MIROC6
MPI-ESM1-2-HR
MRI-ESM2-0
Pre-correctionPost-correctionPre-correctionPost-correctionPre-correctionPost-correctionPre-correctionPost-correctionPre-correctionPost-correction
Precipitation NSE 0.99 0.99 0.92 0.94 0.9 0.99 0.95 
PBIAS −0.6 3.2 −2.4 0.2 −11.2 0.1 −13.9 3.7 −10.8 0.2 
R2 0.99 0.99 0.92 0.97 0.93 0.99 0.98 
Maximum temperature NSE 0.71 0.71 0.68 0.74 0.68 
PBIAS −7.5 −7.6 −8 −7 −7.9 
R2 0.99 0.98 0.98 0.98 0.98 
Minimum temperature NSE 0.95 0.95 0.93 0.94 0.93 
PBIAS −7.8 −7.8 −9.4 −7.9 −9.2 
R2 0.99 0.99 0.99 0.99 0.99 
Climatic variableParameterACCESS-CM2
EC-EARTH3
MIROC6
MPI-ESM1-2-HR
MRI-ESM2-0
Pre-correctionPost-correctionPre-correctionPost-correctionPre-correctionPost-correctionPre-correctionPost-correctionPre-correctionPost-correction
Precipitation NSE 0.99 0.99 0.92 0.94 0.9 0.99 0.95 
PBIAS −0.6 3.2 −2.4 0.2 −11.2 0.1 −13.9 3.7 −10.8 0.2 
R2 0.99 0.99 0.92 0.97 0.93 0.99 0.98 
Maximum temperature NSE 0.71 0.71 0.68 0.74 0.68 
PBIAS −7.5 −7.6 −8 −7 −7.9 
R2 0.99 0.98 0.98 0.98 0.98 
Minimum temperature NSE 0.95 0.95 0.93 0.94 0.93 
PBIAS −7.8 −7.8 −9.4 −7.9 −9.2 
R2 0.99 0.99 0.99 0.99 0.99 

Projected change in average annual values

Average annual precipitation (P) is projected to be erratic, with rising trends more noticeable in the future under the SSP585 scenario (Figure 5(a)). Under the medium emissions scenario (SSP245), P is projected to increase by −15 to 60% in NF, −6 to 60% MF, and −14 to 56% in FF with respect to the baseline. Under the high emissions scenario of SSP585, the projected increase is in the same ball park in NF and MF (−8 to 41% and −12 to 59%). However, there is a sharp increase in FF, in both the mean and range, of 11–111% compared to the baseline. These results are in line with the erratic trend in future precipitation projected by multiple studies in southern Nepal (Gentle & Maraseni 2012; Bhattarai et al. 2023). An increase in the frequency and intensity of rainfall extremes has also been projected in the region, for instance, by Talchabhadel et al. (2021), among others.
Figure 5

Annual (a) precipitation, (b) Tmax and (c) Tmin trends during historical and future periods. The black represents historical values whereas the solid blue and red lines represent projected future values in SSP245 and SSP585 scenarios, respectively. The color band represents uncertainty in projections (spread of minimum and maximum values among five GCMs).

Figure 5

Annual (a) precipitation, (b) Tmax and (c) Tmin trends during historical and future periods. The black represents historical values whereas the solid blue and red lines represent projected future values in SSP245 and SSP585 scenarios, respectively. The color band represents uncertainty in projections (spread of minimum and maximum values among five GCMs).

Close modal

A pronounced increasing trend is observed in projected Tmax and Tmin (Figure 5(b) and 5(c)). This rise is projected to be high in FF under SSP585. Under SSP245, a change of −0.4 to 1.2 °C is projected in Tmax in NF which sharply escalates to 1.9–3.0 °C in FF. This rise is further exacerbated in SSP585 where the projected change of −0.2 to 1.5 °C in NF shoots up to 2.9 to 5.4 °C in FF. Tmin also shows a similar trend of a mild increase in NF followed by a sharp rise in FF. Under SSP585, the rise in Tmin increases abruptly from 0.3 to 2.8 °C in NF to 4.6–8.8 °C in FF. Similar results have been obtained by Bhattarai et al. (2023) in the same region of Southwestern Nepal. Rising temperatures are known to cause an increase in the rate of transpiration in plants, consequently increasing water demand (Peng et al. 2004) and potentially challenging water security in the region.

Projected change in seasonal values

Annual changes alone are not enough to characterize future climate as they dilute seasonal fluctuations which are dominant in this region (Pandey et al. 2019). Seasonal changes in four seasons – pre-monsoon (March–May, MAM), monsoon (June–September, JJAS), post-monsoon (October–November, ON), and winter (December–February, DJF) – were studied.

Future post-monsoon is projected to be wetter; however, a higher degree of uncertainty exists in the projection as indicated by the boxplots (Figure 6(a)). The range of changes in post-monsoon precipitation shows a wide variability, with a −66 to 183% increase in NF that balloons up to −18 to 320% increase under SSP585 by the end of the 21st century. Monsoon precipitation is projected to have a steady increase, with 13% in NF and 20% in FF under SSP245 and 12% in NF and 51% in FF under SSP585 scenarios. Pre-monsoon also shows a similar trend as monsoon, with the same characteristic abrupt jump in SSP585 in FF. Winter precipitation, however, shows the least significant increase that decreases toward the end of the 21st century for both scenarios. On average, winter precipitation is projected to increase by 23% under SSP245 and 14% under SSP585 in NF. Precipitation projection shows a conspicuous decreasing trend toward the future with an increase of only 7% under SSP245 and 6% under SSP585, on average, in FF. This projected trend of decreasing winter precipitation in the future is consistent with other studies in the area (e.g., Bajracharya et al. 2018; Bhattarai et al. 2023). These findings suggest that the implications of climate change for winter crops will be greater if supplemental irrigation is not provided. An increase in irrigation water requirement in wheat has already been reported by Kaini et al. (2022).
Figure 6

Seasonal changes in (a) precipitation, (b) maximum temperature, and (c) minimum temperature compared to the seasonal historical baseline. MAM denotes the pre-monsoon season, JJAS the monsoon, ON the post-monsoon, and DJF denotes the winter.

Figure 6

Seasonal changes in (a) precipitation, (b) maximum temperature, and (c) minimum temperature compared to the seasonal historical baseline. MAM denotes the pre-monsoon season, JJAS the monsoon, ON the post-monsoon, and DJF denotes the winter.

Close modal

Both minimum and maximum temperature projections show an increasing trend toward the future. More confidence in these projections can be ascertained by the fact that, on average, every single GCM in every future scenario in all seasons gives an increasing temperature. Winter, post-monsoon, and pre-monsoon seasons show an average increase in Tmax of 3.8, 3.9, and 3.9 °C, respectively, by the end of the 21st century under high emissions scenarios. Monsoon temperature shows a slightly sharper increase in FF, with average Tmax rise reaching 4.9 °C in FF. Minimum temperature also shows a similar increasing trend in all the seasons. Hence, progressive warming can be expected in Tulsipur in all the seasons in the future. Similar conclusions in terms of temperature have also been reported by Almazroui et al. (2020) for South Asia. Year-round progressive warming paints a harsh future for crop yield, both in terms of increasing water demand and climatic extremes (Abbass et al. 2022).

Projected changes in crop yield and NIR

Outputs of climate projection in Section 4.3 were fed to the calibrated AquaCrop model and comprehensive scenario analyses were performed. The projected changes in crop yield and NIR are presented in this section.

Monsoon rice

For almost all the SSPs, fertilizer application, and irrigation scenarios, simulated NIR is projected to decrease for all three future periods (Table 4). As seen in Figure S5, an overall decrease in NIR in the majority of scenarios has been observed which bodes well for climate resiliency in the region. In SSP245, no notable effect of fertilizer application on NIR is projected in NF and MF; while in FF, NIR in FI condition varies from 432 to 428 mm. For both SSP scenarios, NIR is projected to decrease in NF by 58, 80, and 78% for FI, D1, and D2 irrigation scenarios, respectively, while by the end of the 21st century they are projected to increase up to 83, 97, and 98%. This significant decline in NIR is a result of adequate rainfall in the future time windows. These findings are consistent with the findings of Shrestha et al. (2014) in Myanmar. Gerten et al. (2011) and Olesen et al. (2007) presented comparable findings, suggesting a decline in irrigation water requirements ranging from 4 to 82%.

Table 4

Projected changes in net irrigation requirements of monsoon rice compared to the scenario-specific historical baseline

Near future
Mid future
Far future
Fertilizer scenarioIrrigation scenarioBaseline NIR (mm)SSP245SSP585SSP245SSP585SSP245SSP585
Change in %
0% NRFD FI 1,158.30 −58.38 −58.44 −61.90 −64.29 −62.46 −82.04 
D1 309.15 −80.60 −79.82 −85.95 −88.92 −83.16 −97.94 
D2 222.63 −78.31 −79.80 −89.24 −86.29 −87.26 −98.41 
60% NRFD FI 1,158.07 −58.38 −58.45 −61.91 −64.62 −62.64 −82.21 
D1 309.74 −80.63 −79.89 −85.99 −88.94 −83.19 −97.94 
D2 222.78 −79.58 −79.83 −89.27 −86.30 −87.31 −98.42 
100% NRFD FI 1,157.62 −58.51 −58.45 −61.92 −64.97 −62.85 −83.31 
D1 313.90 −79.27 −79.32 −86.18 −87.15 −83.45 −97.95 
D2 233.72 −78.17 −80.68 −89.84 −86.85 −84.92 −98.48 
150% NRFD FI 1,155.37 −58.44 −58.37 −61.87 −64.92 −62.98 −83.27 
D1 314.04 −79.26 −79.30 −85.15 −87.10 −83.46 −97.95 
D2 237.87 −78.56 −81.01 −90.02 −87.01 −83.70 −98.51 
Near future
Mid future
Far future
Fertilizer scenarioIrrigation scenarioBaseline NIR (mm)SSP245SSP585SSP245SSP585SSP245SSP585
Change in %
0% NRFD FI 1,158.30 −58.38 −58.44 −61.90 −64.29 −62.46 −82.04 
D1 309.15 −80.60 −79.82 −85.95 −88.92 −83.16 −97.94 
D2 222.63 −78.31 −79.80 −89.24 −86.29 −87.26 −98.41 
60% NRFD FI 1,158.07 −58.38 −58.45 −61.91 −64.62 −62.64 −82.21 
D1 309.74 −80.63 −79.89 −85.99 −88.94 −83.19 −97.94 
D2 222.78 −79.58 −79.83 −89.27 −86.30 −87.31 −98.42 
100% NRFD FI 1,157.62 −58.51 −58.45 −61.92 −64.97 −62.85 −83.31 
D1 313.90 −79.27 −79.32 −86.18 −87.15 −83.45 −97.95 
D2 233.72 −78.17 −80.68 −89.84 −86.85 −84.92 −98.48 
150% NRFD FI 1,155.37 −58.44 −58.37 −61.87 −64.92 −62.98 −83.27 
D1 314.04 −79.26 −79.30 −85.15 −87.10 −83.46 −97.95 
D2 237.87 −78.56 −81.01 −90.02 −87.01 −83.70 −98.51 

As can be seen in Figure S5c, the NIR in the D2 scenario would even drop to near zero in some years. On top of that, the average NIR is projected to decrease greatly from the baseline of 238 to 45 mm in NF, 31 mm in MF, and 4 mm in FF under SSP585 scenarios. Likewise, in the case of SSP245, it would reduce to 51, 24, and 39 mm in NF, MF, and FF, respectively. It further bolsters the promise of employing a D2 irrigation strategy. The projected decrease in NIR for future climate scenarios indicates the need for an improved water management plan concerning the diversion of canal water to other sectors to meet growing water demand.

For all fertilizer and water management scenarios, simulation results show an increasing rice yield for all three-time windows considered (NF, MF, and FF), and for both SSPs (Figure 7). The projected changes in monsoon rice yield have been summarized in Table S4. Besides a few scenarios in 100% NRFD under SSP245 that show fluctuating yield trends toward the future, the overwhelming majority of scenarios show a pronounced increase in crop yield. Irrigation management strategies have minimal impact on yield in the future. For instance, in the 100% NRFD scenario, by the end of the 21st century, yield is projected to increase to 6.20–6.33 tons/ha under SSP245 and 6.95–6.98 tons/ha under SSP585. This limited variability across irrigation scenarios, even including RF irrigation, is a result of ample precipitation projected in future monsoons. Also, rice (a C3 crop) responds positively to increasing CO2, which overpowers the negative effect of temperature. Similar results of increasing rice yield in response to increasing CO2 have been well-documented in South Asia (Krishnan et al. 2007; Yao et al. 2007).
Figure 7

The projected yield of monsoon rice in SSP245 and SSP585 scenarios under various fertilizer application scenarios and (a) RF, (b) FI, (c) D1, and (d) D2 irrigation scenarios.

Figure 7

The projected yield of monsoon rice in SSP245 and SSP585 scenarios under various fertilizer application scenarios and (a) RF, (b) FI, (c) D1, and (d) D2 irrigation scenarios.

Close modal

Such a high projected yield under RF conditions is in direct contrast to the baseline, where the irrigation scenario played a major role. With the projected increase in rainfall in the future, fertility stress will dominate water stress for rice yields. For example, in FF, RF rice yields are projected to vary from 3.8 to 7.9/4.3–8.9 tons/ha (SSP245/585) across different fertilizer application rates. This underscores the need for equal priority for soil fertility management for future rice yields. While RF yields are comparable to irrigated yields in the future, the erratic nature of future precipitation that led to multiple outliers in the boxplot of RF yields (Figure 7(a)) must be noted. In this regard, an irrigation strategy that helps stabilize yield needs to be adopted. Therefore, a deficit irrigation plan would be the best suited to this region in the future, keeping in mind the growing water demands in other sectors.

Table 5 presents a comparison of the findings of this study with previous crop modeling studies in the climate change context in Nepal.

Table 5

Comparison of the findings of this study with the earlier studies

Reference(s)Study areaMethodKey finding(s)Finding(s) of this study
Malla (2008)  Nepal DSSAT 
  • Rice yield increased by 3.4% in the Terai region of Nepal

  • Wheat yield increased by 41.5% in the Terai region of Nepal

  • Found CO2 enrichment technology led to increased yield

 
  • Rice yield is projected to increase by 25.5%/41.5% in SSP245/ SSP585 scenarios by the end of the 21st century

  • Wheat yield is projected to increase by 12.2%/24.0% in SSP245/ SSP585 scenarios by the end of the century

  • Deficit irrigation is found to be the most climate-resilient strategy to increase yield

 
Karn (2014)  Terai, Nepal Multi-variate regression 
  • Rice yield projected to decrease by 4.2% by 2100

  • High-temperature tolerant rice species were recommended to develop climate resilience

 
  • Rice yield is projected to increase by 25.5%/41.5% in SSP245/ SSP585 scenarios by the end of the 21st century

  • Deficit irrigation and NRFD fertilizer are recommended.

 
Palazzoli et al. (2015)  Indrawati basin, Nepal SWAT forced by three GCMs in representative concentration pathway (RCP) scenarios 
  • Fluctuating rice yield (−17% to +12%) by the end of the century in different scenarios

  • Rice yield is estimated to be climate-resilient in the future

  • Fluctuating wheat yield with a decrease in most scenarios, wheat is the most insecure crop in changing climate

 
  • Increasing rice yield in the future is projected in all scenarios comprehensively up to a 42% increase

  • RF rice yield is still way too unstable and deficit irrigation is needed to further improve resilience.

  • Wheat yield is insecure given decreasing precipitation up to the far future and deficit irrigation can stabilize wheat yield

 
Shrestha & Shrestha (2017)  Central Nepal AquaCrop forced by regional climate models (RCMs) in SRES scenarios 
  • Fluctuating rice yield in different scenarios (−66 to +25%)

  • Erratic trends in future wheat yield (−23% to +29%)

  • Shifting cropping dates to combat climate change

 
  • Overwhelming increase in rice yield in all scenarios (up to 41%) thanks to increased rainfall and CO2

  • Increase in wheat yield in the future (up to 24%) which flattens out in the far future due to low rainfall

  • Deficit irrigation and NRFD fertilization to build climate resilience

 
Reference(s)Study areaMethodKey finding(s)Finding(s) of this study
Malla (2008)  Nepal DSSAT 
  • Rice yield increased by 3.4% in the Terai region of Nepal

  • Wheat yield increased by 41.5% in the Terai region of Nepal

  • Found CO2 enrichment technology led to increased yield

 
  • Rice yield is projected to increase by 25.5%/41.5% in SSP245/ SSP585 scenarios by the end of the 21st century

  • Wheat yield is projected to increase by 12.2%/24.0% in SSP245/ SSP585 scenarios by the end of the century

  • Deficit irrigation is found to be the most climate-resilient strategy to increase yield

 
Karn (2014)  Terai, Nepal Multi-variate regression 
  • Rice yield projected to decrease by 4.2% by 2100

  • High-temperature tolerant rice species were recommended to develop climate resilience

 
  • Rice yield is projected to increase by 25.5%/41.5% in SSP245/ SSP585 scenarios by the end of the 21st century

  • Deficit irrigation and NRFD fertilizer are recommended.

 
Palazzoli et al. (2015)  Indrawati basin, Nepal SWAT forced by three GCMs in representative concentration pathway (RCP) scenarios 
  • Fluctuating rice yield (−17% to +12%) by the end of the century in different scenarios

  • Rice yield is estimated to be climate-resilient in the future

  • Fluctuating wheat yield with a decrease in most scenarios, wheat is the most insecure crop in changing climate

 
  • Increasing rice yield in the future is projected in all scenarios comprehensively up to a 42% increase

  • RF rice yield is still way too unstable and deficit irrigation is needed to further improve resilience.

  • Wheat yield is insecure given decreasing precipitation up to the far future and deficit irrigation can stabilize wheat yield

 
Shrestha & Shrestha (2017)  Central Nepal AquaCrop forced by regional climate models (RCMs) in SRES scenarios 
  • Fluctuating rice yield in different scenarios (−66 to +25%)

  • Erratic trends in future wheat yield (−23% to +29%)

  • Shifting cropping dates to combat climate change

 
  • Overwhelming increase in rice yield in all scenarios (up to 41%) thanks to increased rainfall and CO2

  • Increase in wheat yield in the future (up to 24%) which flattens out in the far future due to low rainfall

  • Deficit irrigation and NRFD fertilization to build climate resilience

 

Winter wheat

Like monsoon rice, NIR for winter wheat is also projected to decrease under both SSPs in the future (Figure S6, Table 6). Due to the much lower projected increase in winter rainfall compared to monsoon rainfall, the projected decrease in NIR is less significant in wheat than in rice. Similar to the baseline, the FI scenario is found to be insensitive to fertilizer application, while the D1 and D2 scenarios project an increase in NIR for higher fertilizer doses. In the case of SSP245, NIR projects a clearly increasing trend toward the future in FI and an erratic trend in D1 and D2 scenarios. For instance, in the 100% NRFD scenario, NIR is projected to decrease in both D1 and D2 scenarios by 27.07 and 26.55% in NF, 16.76 and 12.87% in MF, and 21.02 and 20.34% in FF. Wide fluctuation in the boxplots of D1 and D2 scenarios is due to inconsistent rainfall during the growing period, which in turn causes fluctuations in NIR in the irrigation application period. Unlike in the case of rice, near-zero NIR is not projected in any of the irrigation scenarios. However, the projected NIR in the D1 scenario is still very low compared to other scenarios. Even though an overall decrease in NIR has been projected in the future as compared to the baseline, NIR in wheat trends upward toward FF. Future NIR in this study is projected below the historical baseline because of the projected increase in winter rainfall in the future. However, other studies in the same region have projected a decrease in winter rainfall (Almazroui et al. 2020; Bhattarai et al. 2023), which would have led to an increase in wheat NIR compared to the historical baseline (Kang et al. 2009). Hence, proper adaptation strategies must be devised with respect to winter wheat irrigation.

Table 6

Projected changes in net irrigation requirements of winter wheat compared to scenario-specific historical baseline

Near future
Mid future
Far future
Fertilizer scenarioIrrigation scenarioBaseline NIR (mm)SSP245SSP585SSP245SSP585SSP245SSP585
Change in %
0% NRFD FI 205.97 −36.62 −34.21 −33.54 −33.39 −33.26 −32.28 
 D1 74.19 −26.20 −24.65 −12.59 −17.23 −19.41 −16.38 
 D2 97.76 −25.99 −22.10 −15.58 −20.34 −19.63 −19.31 
50% NRFD FI 206.25 −36.56 −34.14 −33.44 −33.23 −33.13 −32.05 
 D1 73.51 −26.34 −24.82 −12.89 −17.40 −19.56 −16.55 
 D2 96.87 −25.89 −22.03 −15.55 −20.16 −19.46 −19.07 
100% NRFD FI 205.22 −36.68 −34.34 −33.80 −34.00 −33.57 −33.70 
 D1 83.08 −26.56 −24.76 −12.87 −18.11 −20.35 −17.30 
 D2 109.59 −27.07 −22.95 −16.77 −21.95 −21.01 −21.10 
150% NRFD FI 204.78 −36.72 −34.42 −33.95 −34.35 −33.76 −34.47 
 D1 84.89 −26.06 −24.46 −12.39 −17.83 −20.28 −17.39 
 D2 112.38 −27.13 −23.08 −16.84 −22.10 −21.28 −21.83 
Near future
Mid future
Far future
Fertilizer scenarioIrrigation scenarioBaseline NIR (mm)SSP245SSP585SSP245SSP585SSP245SSP585
Change in %
0% NRFD FI 205.97 −36.62 −34.21 −33.54 −33.39 −33.26 −32.28 
 D1 74.19 −26.20 −24.65 −12.59 −17.23 −19.41 −16.38 
 D2 97.76 −25.99 −22.10 −15.58 −20.34 −19.63 −19.31 
50% NRFD FI 206.25 −36.56 −34.14 −33.44 −33.23 −33.13 −32.05 
 D1 73.51 −26.34 −24.82 −12.89 −17.40 −19.56 −16.55 
 D2 96.87 −25.89 −22.03 −15.55 −20.16 −19.46 −19.07 
100% NRFD FI 205.22 −36.68 −34.34 −33.80 −34.00 −33.57 −33.70 
 D1 83.08 −26.56 −24.76 −12.87 −18.11 −20.35 −17.30 
 D2 109.59 −27.07 −22.95 −16.77 −21.95 −21.01 −21.10 
150% NRFD FI 204.78 −36.72 −34.42 −33.95 −34.35 −33.76 −34.47 
 D1 84.89 −26.06 −24.46 −12.39 −17.83 −20.28 −17.39 
 D2 112.38 −27.13 −23.08 −16.84 −22.10 −21.28 −21.83 

In terms of yield, winter wheat yield is projected to increase compared to the scenario-specific baseline yield in all scenarios considered (Figure 8, Table S5). Under the currently adopted practice of 50% NRFD and D2 irrigation, yield is projected to increase by 7.2/9.4% in NF, 10.3/16.7% in MF, and 10.7/22.3% in FF under SSP245/585 scenarios, respectively. A characteristic spike in SSP585 as compared to SSP245 is projected. This can be explained by the higher degree of CO2 emission in the SSP585 scenario, which bodes well for a C3 crop like wheat. Evidence of increasing wheat yield as a result of increasing CO2 concentration has been reported previously in studies throughout the world (Eitzinger et al. 2003; Luo et al. 2003). Fertilizer application is also found to be crucial in wheat yield. For instance, by the end of the 21st century, wheat yields under D2 irrigation are projected to vary from 2.2 to 5.7/2.4 to 6.3 tons/ha (SSP245/585) across varying fertilizer application rates.
Figure 8

Projected yield of winter wheat in SSP245 and SSP585 scenarios under various fertilizer application scenarios and (a) RF, (b) FI, (c) D1, and (d) D2 irrigation scenarios.

Figure 8

Projected yield of winter wheat in SSP245 and SSP585 scenarios under various fertilizer application scenarios and (a) RF, (b) FI, (c) D1, and (d) D2 irrigation scenarios.

Close modal

Unlike monsoon rice, RF wheat yields are much lower compared to irrigated yields. For example, even under a non-limiting fertility scenario (150% NRFD), yield under irrigated conditions (D2) is projected to be 44.04/55.43, 57.14/41.76, and 46.49/37.69% greater than RF yields under SSP245/SSP585 scenarios in NF, MF, and FF, respectively. This implies that fertilizer treatment is more effective for wheat if applied together with irrigation. Additionally, wide variability in RF yields, as seen in the boxplots (Figure 8(a)) suggests that RF irrigation is detrimental to yield stability. Furthermore, deficit irrigation strategies (both D1 and D2) are projected to show almost the same yield as in the case of full irrigation for all the scenarios. Therefore, given the high yields under limited water usage and apparent yield stability, a deficit irrigation plan is projected to be viable in the future.

Identification of climate-resilient future pathways

RF irrigation systems are projected to cause large variability in the yields of winter wheat and, also in some cases, of monsoon rice. Results of climate projection show a greatly fluctuating rainfall in the future, which has led to destabilization in wheat yields in the future. Similar fluctuations have been documented in studies conducted on RF yield variability (Kwesiga et al. 2019; Guntukula & Goyari 2021). Therefore, it can be concluded that RF scenarios have poor climate resilience, and accessible irrigation is a must to curb climate change impacts.

Deficit irrigation D2 with 100% NRFD fertilizer application was recommended for monsoon rice in the baseline period. Future projections of this scenario show stable and increasing yield with decreasing net irrigation requirements, further bolstering the climate resiliency of this scenario. It should be noted that RF yields are practically identical to the yield of the recommended scenario in the future. However, with precipitation and streamflow extremes projected to grow in frequency in the region (Bhattarai et al. 2023), an irrigation strategy must be implemented to combat the variability in rainfall. Similarly, there would be no significant difference in yields under FI, D1, and D2 irrigation; however, the NIR in the D2 scenario is significantly lower, reaching near-zero values in multiple future scenarios. Hence, for these reasons, deficit irrigation D2 with NRFD fertilizer application is recommended for climate-resilient monsoon rice production. It is also important to note that, with rainfall projected to increase in the future, lower fertilizer dosage might be sufficient to meet the food demand. Given the negative impact of fertilizer application on water quality, future studies are recommended to optimize nationally recommended fertilizer dosage.

Similarly, the baseline period recommendation for winter wheat was deficit irrigation D1 with 150% NRFD fertilizer application. Projected irrigation requirements of wheat, although below the historical baseline, are projected to increase further into the future. This suggests that potential adaptation strategies must be devised to meet the increasing water demand. The results of projected future scenario analyses show that deficit irrigation D1 helps achieve a nearly identical yield to full irrigation at a much lower NIR. These findings are consistent with Fereres & Soriano (2007), who highlighted the effectiveness of deficit irrigation in improving water productivity without significantly compromising crop yield. Additionally, the efficacy of deficit irrigation in increasing yield under limited water expenditure has been reported by several studies all around the world (Costa et al. 2007; Stepanovic et al. 2021). Therefore, for all these reasons, D1 irrigation with 150% NRFD promises to be a viable strategy to optimize winter wheat yield in a changing climate.

This research focused on physical and environmental analyses but did not perform an economic analysis to assess the cost-benefit aspects of the proposed irrigation strategies. It can be considered as a limitation and/or recommendations for future research.

Research on climate change impacts on crop yield and water requirements is relatively scant in Nepal. As climate change and climate-induced disasters take the forefront in Nepali policy and management, it is imperative to assess their impacts for designing climate-resilient interventions. This study examined field management practices to optimize crop yield and water requirements using the AquaCrop model. Recommended practices were assessed for climate resilience, focusing on maximizing yield under constrained water availability. This research, however, did not perform a comparison of the AquaCriop model with alternative modeling approaches and economic analysis of proposed irrigation strategies.

Deficit irrigation strategies proved effective in stabilizing yields and reducing water requirements for both monsoon rice and winter wheat during the baseline period, cutting irrigation needs by up to 81 and 24%, respectively, with minimal yield reductions (1.19 and 1.34%). Increased fertilizer application under reduced water stress further enhanced yields.

Projected warmer (in all the seasons and scenarios) future, erratic precipitation trends, and decreasing winter rainfall trends (i.e., drier winter) could be detrimental to the winter crop cycle in the future. Rice is projected to benefit from increased monsoon rainfall, requiring less irrigation while achieving higher yields. Soil fertility is anticipated to be a key constraint on monsoon rice yield. Wheat irrigation demands are expected to rise in the long term, particularly under dry winter conditions.

RF irrigation is projected to result in more variability in yield due to erratic precipitation in the future, further cementing the need for suitable irrigation strategies in building climate-resilient production systems. Deficit irrigation strategies (with NRFD fertilizer application), recommended in this study, are found to be resilient in the projected future climatic context and are therefore recommended as a resilient management scenario to meet the growing food demands.

The authors would like to thank the Department of Hydrology and Meteorology (DHM), Government of Nepal for providing meteorological data. The authors would also like to thank Mr Arshad Ansari for his guidance on bias correction of projected climate. The authors are grateful to colleagues Ms Bhakti Lekhak, Mr Bishal Bastola, Ms Chandra Zyoti Karna, and Mr Dipesh Banjade for helping with the farmer survey. Finally, heartfelt gratitude is extended to the respondent farmers of Tulsipur for their valuable input and warm hospitality.

This research was supported by a Collaborative Research Grant from the Research Division, Research Coordination and Development Council (RCDC), Tribhuvan University, Nepal.

Data cannot be made publicly available; readers should contact the corresponding author for details.

The authors declare there is no conflict.

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Author notes

Md. Zuber and Nabin Kalauni have contributed equally to this study.

This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY-NC 4.0), which permits copying, adaptation and redistribution for non-commercial purposes, provided the original work is properly cited (http://creativecommons.org/licenses/by-nc/4.0/).

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