Impacts of climate change on phenology, yield, and water productivity of wheat in a semi-arid region of India using the CERES-Wheat model

Climate change impact on agriculture has become a critical issue for the global scientific community, governments, and policymakers (Wang et al. 2024). It is significantly influencing agricultural productivity and thereby posing substantial threats to national and global food security in the 21st century (Tubiello et al. 2007; Bouteska et al. 2024). Food and water security are two issues that the world is already dealing with and that are predicted to get worse in the future, primarily as a result of global climate change (Foley et al. 2011). According to the Sixth Assessment Report (AR6) of the Intergovernmental Panel on Climate Change (IPCC 2021), the global surface temperature will continue to increase until at least the mid-century under all the climate change scenarios. The IPCC report also projected a decrease in water availability for food production in arid and semi-arid regions (IPCC 2014).

Wheat (Triticum aestivum L.) plays a critical role in ensuring India's food and nutritional security, ranking as the second most significant cereal crop after rice and contributing approximately 13% to the global wheat supply (Zaveri & Lobell 2019). Wheat is primarily grown during the Rabi season, which is the winter cropping season in India. It is sown from October to December and harvested from March to May. The total area under wheat cultivation in India is substantial, with 30 million hectares dedicated to wheat farming (Jat et al. 2020). Wheat production in India faces challenges such as water scarcity, soil degradation, pests and diseases, and climate variability. Ensuring sustainable wheat production while conserving resources and adapting to changing climate conditions remains a significant challenge for Indian farmers. Overall, wheat production in India plays a vital role in ensuring food security and meeting the dietary needs of its population.

Crop water productivity (CWP) of wheat refers to the efficiency with which water is used to produce wheat crops. According to Kijne et al. (2003), CWP is the marketable crop yield over the water uses by actual crop evapotranspiration (ET). It is typically measured as the amount of wheat yield (in terms of grain or biomass) obtained per unit of water consumed or applied during the crop's growth period. The estimation of water productivity may be a crucial approach to tackling the problems of both food and water security. Water productivity is an important metric in agriculture as it reflects the sustainable and efficient use of water resources, especially in regions facing water scarcity or where irrigation is necessary. Efforts to improve water productivity in wheat cultivation are therefore crucial for sustainable agriculture and food security, particularly in regions where water resources are limited or subject to competition from other sectors. In water-scarce regions, especially in arid and semi-arid regions, farmers may need to adopt more efficient water management practices to achieve higher water productivity. The increasing temperatures (Asseng et al. 2015; Zhao et al. 2017), shifts in rainfall patterns (Batool et al. 2019), and scarcity of water resources for irrigation have a negative impact on the yields (Daloz et al. 2021) and water productivity of wheat. Consideration of climate change impacts on CWP is gradually becoming substantial for scientists and governments all over the world. Research and innovation continue to play a vital role in developing practices and technologies that optimize water use while maintaining or increasing wheat yields.

Several assessments have been done so far on the changes in CWP using a crop simulation modelling approach (Rosenzweig et al. 2014). Kang et al. (2009) studied the influences of climate change on crop productivity and water-use efficiencies (WUEs) in the context of food safety, indicating that WP may decline in the future owing to temperature rise and rainfall pattern changes. It could potentially lead to a further decrease in crop duration (Chandran et al. 2021) and amplify yield variability, causing a shift in growing conditions (Olesen & Bindi 2002). The magnitude and effects of climate change can vary with agro-climatic zones (Jalota & Vashisht 2016). Kumar et al. (2014) assessed the impact of changes in temperature and rainfall in India and projected a wheat yield reduction of up to 25% by 2080. However, greater atmospheric carbon dioxide concentrations may be able to partially offset the negative consequences of future temperature increases (Chandran et al. 2021). Impacts of climate change on food production are likely to be severe especially for farmers with small landholdings in developing countries like India because of their low capability to cope with adverse climatic conditions.

To adapt crop systems to the changing climate, it is important to know how climate change affects agricultural production and water productivity. Process-based crop simulation models are useful tools to quantitatively assess the impacts of climate change and develop effective adaptation and mitigation strategies (Shi et al. 2013; Nouria et al. 2017; Qian et al. 2019) as they consider the interaction between climatic variables and crop management and their effects on crop productivity (Jones et al. 2013). These models can dynamically and quantitatively describe the process of crop growth, development, and yield formation using mathematical equations. Numerous uncertain factors, such as uncertainty in the global climate model (GCM), soil water storage predictions, climate variables, and enhanced atmospheric CO₂ levels, affect the impacts of climate change on CWP. General circulation models (GCMs) are increasingly capable of making such predictions of seasonal and long-term climate variability as well as improving the prospects of predicting the impact on yields. Crop simulation models can predict CWP under various crop management options and changing climatic parameters using GCMs data (Islam et al. 2022).

The Decision Support System for Agrotechnology Transfer (DSSAT) is a well-known agricultural modelling framework that includes various crop models for simulating crop growth and yield under different environmental and management conditions (Hoogenboom et al. 2019). The Crop Environment Resource Synthesis (CERES)-Wheat model is one of the crop models within the DSSAT system specifically designed for simulating the growth and development of wheat crops. It has been frequently used by researchers over different agro-climatic regions to simulate the growth, development, and yield of wheat in response to soil characteristics, water availability, crop management, climate, varieties, and so on. (Babel et al. 2018). Kumar et al. (2024) employed the CERES-Wheat model to simulate crop growth, yield, and nitrogen dynamics in a long-term conservation agriculture (CA)-based wheat system and concluded that the DSSAT-CERES-Wheat model has significant potential to assess the impacts of tillage and nitrogen management practices on crop growth, yield, and soil nitrogen dynamics in the western Indo-Gangetic Plain (IGP) region. The study reviewed showed that the DSSAT-CERES has capability in modelling wheat yield and crop growth parameters.

The reviewed literature suggested that there is a knowledge gap in the evaluation of the CERES-Wheat model under varying irrigation regimes in the semi-arid climatic conditions. Also, the capability of the CERES-Wheat model needs to be assessed under different climate change scenarios for determining effective strategies for optimum wheat growth and yield and for improving water productivity. Therefore, the present study was taken up with the objective of simulating the climate change impacts on wheat growth, yield, and water productivity during the three future periods of the 2030s (2020–2039), 2050s (2040–2059), and 2070s (2060–2079), relative to the baseline (1981–2010) under the representative concentration pathway (RCP) 4.5 and 8.5 scenarios using the validated DSSAT-CERES-Wheat model. Our study's originality arises from its unique methods to calibrate and validate the DSSAT crop growth model for wheat crop. We focused on key aspects of wheat crop like phenology, grain yield, biomass, and CWP under different climate change scenarios. By carefully designing irrigation strategies to match real-world farming practices, our findings are more relevant to today's agricultural challenges. This multifaceted approach not only enhances the comprehensiveness of our findings but also underscores the practical relevance of our research in addressing contemporary agricultural challenges amidst evolving climate scenarios.

The CERES-Wheat, which is a radiation use efficiency (RUE)-based crop simulation model of the DSSAT version 4.7 (Jones et al. 2003; Hoogenboom et al. 2004), was used for the study. It is a dynamic process-based model with the capability of simulating the growth and development of wheat, on a daily time step. It uses carbon, nitrogen (N), and water balance principles to simulate crop growth stages, total aboveground biomass, yield, and water and nitrogen balances. It simulates the development of different phenological stages based on accumulated degree-days that are calculated using the daily minimum and maximum temperatures. The daily product of photosynthetically active radiation (PAR) and a crop-specific RUE is used to calculate the accumulation of biomass. The development of the leaf area depends on the specific leaf area and the availability of assimilates. The daily temperature, growing degree-days, atmospheric CO₂ concentration, and water/nitrogen status are used to model the rate of expansion of the leaf area. Grain number, plant population, and grain weight at physiological maturity are used to simulate grain yield. If the daily carbon pool is inadequate for the maximum possible growth rate, it is assumed that some carbon can be remobilized from vegetative to reproductive sinks. Input parameters used for the CERES-Wheat model were site information (latitude, longitude, and elevation), daily weather data (solar radiation, maximum and minimum air temperatures, and rainfall), soil profile data (layer-wise physical and chemical characteristics of soil), crop genetic characteristics, and crop management data (sowing and tillage data: date, depth, method; plant population; irrigation; fertilizer data: dates, depth, amount, and methods; and harvest data: date, stage, method, etc.).

A high value for the d-index and a low value for RMSEn indicate a good fit between the simulated and observed values.

In the model calibration process, the genetic coefficients were derived for wheat cultivar HD 2967 using the generalized likelihood uncertainty estimator (GLUE) module of DSSAT 4.7 (Table 2). An iterative approach was used to obtain reasonable genetic coefficients through trial-and-error adjustment until the simulated and measured values matched or were within predefined error limits (Bisht & Shaloo 2022).

The impact of climate change on water productivity of wheat (Table 4) indicated that, under both the RCPs, the CWP decreased with the baseline CO₂ concentration by 9, 17.6, and 18.3% for RCP 4.5 and 10.6, 20.6, and 22.6% for RCP 8.5 during the 2030s, 2050s, and 2070s, respectively. But at future CO₂ concentration, the extent of CWP is decreased by only 6.8, 12.6, and 13% for RCP 4.5 during the 2030s, 2050s, and 2070s, respectively. However, the positive effect of increased CO₂ on water productivity was noticed under RCP 8.5 and the reductions observed were only 5.4 and 2.2% during the 2050s and 2070s, respectively. IWP has also shown similar results of less reductions in grain yield under increased CO₂ concentration as compared with the baseline concentrations under both the future scenarios (RCP 4.5 and RCP 8.5). The increased CO₂ concentration leads to reduction in leaf stomatal conductance and thus the rate of ET that eventually improves the efficiency of water use by the crops (Gerten et al. 2007). Although increased CO₂ at moderate air temperatures increases WUE, these positive effects are diminished when the temperature is raised above the optimal level for the particular crop (Hatfield & Dold 2019). Similar findings were also observed by Mubeen et al. (2020) who used the DSSAT model for the cotton–wheat cropping system in semi-arid conditions. Using the AquaCrop 5.0 model under the RCP 8.5 scenario, Islam et al. (2022) studied future wheat WP under the changing climate conditions along with adaptation options in north-western Bangladesh. In the near future (2020–2039), the mid-future (2040–2059), and the far future (2060–2079), they reported that WP of wheat decreased by 6.6, 21.2, and 33.6%, respectively. Additionally, they recommended that changing sowing dates and introducing heat-tolerant varieties would be effective adaptation strategies to increase wheat's yield and WP in the face of climate change.

In order to simulate the CWP of irrigated wheat and maize in the semi-arid region of Iran, Vaghef et al. (2017) employed a coupled SWAT-MODSIM model. According to the findings, crop yield and CWP had a close linear connection that climbed and subsequently decreased as consumptive water use continued to rise. Liu et al. (2021) used the Soil and Water Assessment Tool (SWAT) to examine the yield and CWP of wheat in the Heihe River basin for three future time periods (2025–2049, 2050–2074, and 2075–2099) under RCP 4.5 and RCP 8.5. They discovered that under both scenarios, the effects of future climate change on crop yield and CWP of wheat would be detrimental.

The results from this study indicated that air temperatures are projected to rise under both the RCP scenarios for all three time slices (2030s, 2050s, and 2070s), whereas rainfall amount is projected to increase under RCP 4.5 and decrease under RCP 8.5. There was a considerable reduction in wheat grain and biomass yield under future climate change scenarios. But the increased CO₂ concentration in future could minimize the reduction in grain yield, biomass yield, and CWP because of the CO₂ fertilization effect. However, no influence of CO₂ fertilization was seen on the crop phenology (days to anthesis and physiological maturity). The yield reductions were more in RCP 8.5 compared with RCP 4.5. Furthermore, the yield reduction was more in the 2070s time slices compared with the 2050s. In conclusion, climate change poses a significant threat to the yield and CWP of wheat, with far-reaching implications for food security, the economy, and the environment. Addressing these challenges through sustainable and adaptive agricultural practices is essential to ensure a stable and secure food supply in the face of a changing climate. By integrating climate projections into decision-making processes, farmers, policymakers, and other stakeholders can better prepare for and mitigate the challenges posed by climate change. It is therefore advised that strategies for adapting to climate change such as altering the date and age of seedling planting, selecting cultivars and heat-tolerant varieties, determining the quantity and timing of fertilizer applications, and managing irrigation be found and implemented in order to maximize wheat yields in the future. Nevertheless, this study uses a single wheat crop cultivar with CMIP5 projections data for impacts assessment. Therefore, impact analysis of climate change using corresponding varieties under different crop management conditions and CMIP6 projection data can be considered as the future scope of this study.

All authors contributed to the study conception and design. Conceptualization, investigation, data collection, and analysis were performed by H.B., S.P., B.K., and D.K.S. The first draft of the article was written by H.B. and all authors commented on previous versions of the article. Writing – review and editing was done by S.P., L.V., R.N.S., M.T., and S.G. All authors read and approved the final article.

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

The authors declare there is no conflict.