Modelling the climate change impact of mitigation (RCP 2.6) and high emission (RCP 8.5) scenarios on maize yield and possible adaptation measures in different agroclimatic zones of Punjab, India

Abstract

BACKGROUNDA simulation study was performed for assessing climate change impact on maize under Representative Concentration Pathways (RCPs 2.6 and 8.5) for Punjab, India. The study area comprised five agroclimatic zones (AZs) including seven locations. The bias corrected temperature and rainfall data from four models (CSIRO‐Mk‐3‐6‐0, FIO‐ESM, IPSL‐CM5A‐MR and Ensemble) were used as input in CERES‐Maize model which was run with constant management practices for two Punjab maize hybrids (PMH 1 and PMH 2). The maize yield for upcoming 70 years (2025–2095) was simulated and its deviations from the baseline (2010–2021) yield were computed under optimized sowing (early‐May to early‐July) and current sowing (end‐May to end‐June) period.RESULTSWith current sowing dates, the maize yield declined under both RCP 2.6 and RCP 8.5 scenarios, respectively in all the AZs, that is, by 4–23% and 60–80% in AZ II, by 5–60% and 60–90% in AZ III, by 9–30% and 50–90% in AZ IV and by 13–40% and 30–90% in AZ V. Though yield decline was lesser under RCP 2.6 as compared to RCP 8.5, but still it indicates that adaptive strategy such as shifting of sowing dates may be helpful in stabilizing the maize yield.CONCLUSIONThe results for iterative combinations of sowing period revealed that early June sowing in AZ II for both the hybrids, mid‐ to end‐June (Ludhiana and Amritsar) and end‐May to mid‐June (Patiala) sowings for PMH 1 were able to nullify the negative impact of climate change. Maize cultivation in AZ IV and AZ V would not be a suitable venture for farmers of the region. © 2023 Society of Chemical Industry.

Optimising Sowing Window for Wheat Cultivars Under RCP 4.5 and RCP 6.0 Scenarios During the 21st Century in Indian Punjab

“…The CERES-Wheat module of Decision Support System for Agrotechnology Transfer (DSSAT v4.7) was used for simulating the yield (Hoogenboom et al 2019). It has been widely validated and used in various environments for simulating the growth and yield of wheat (Basso, Liu, and Ritchie 2016;Qu et al 2019;Prabhjyot-Kaur et al 2023). Based upon the daily meteorological data, soil physical properties, crop management practices and cultivar-specific genotypic traits of the wheat crop, it simulates the growth and yield (Jones and Kiniry 1986;Jones et al 2003).…”

“…org/ MarkS imGCM/ for different locations in Punjab. However, the futuristic data obtained from GCM's first need to be corrected for bias removal before further applications (Christensen et al 2008;Kaur et al 2023). So the ensemble model data on maximum, minimum temperature and rainfall was bias corrected by procedures described by Kaur et al (2020a).…”

“…The results depicted a shortening of the vegetative growth period of wheat crop under RCP 8.5 by 10 days during 2050s and by 14 days during 2080s which ultimately affected the yield by reductions upto 20% and 30% under RCP 4.5 and RCP 8.5, respectively, during the 2080s. In Punjab state, Prabhjyot- Kaur et al (2023) evaluated the impact of climate change on wheat under four climatic scenarios (RCPs 2.6, 4.5, 6.0 and 8.5) during three time slices (2030-2050, 2050-2070 and 2070-2090). They reported that a shift in sowing date with a supplemented nitrogen dose would be a viable agronomic option to sustain wheat productivity under RCPs 2.6, 4.5 and 6.0 scenarios in the state.…”

“…With the advancement in the science of crop modelling, the impact of interactions between meteorological parameters and plant physiological processes are being simulated quite accurately. So these crop models can predict such interactive effects as well as simulate the suitable adaptation measures to counter the negative impacts (Tacka, Barkley, and Nalley 2015;Liu, Zhang, and Ge 2021;Kothiyal et al 2023). For assessing the impact of projected changes in climate on crop growth processes and yield (Zhenga et al 2020;Jia et al 2022), the predicted climate data simulated by GCMs (Global Climate Models) is used as an input in crop models (Stocker et al 2013).…”

How does climate change affect potential yields of four staple grain crops worldwide by 2030?

“…Zhang et al (2019) used different climatic crop models for evaluating Chinese yield loss of maize in the future [25], while Yin and Leng (2020) found that climate variability controls 42.0% of global maize yield variations from 1980 to 2010 and historical climate trend leading to the yield loss by 1.5-3.8% during the period 1980-1990 in China [26]. Zhu et al (2021) first used hierarchical linear modeling (HLM) method for predicting maize yield in Jilin Province of China [27], Xu et al (2022) put forward that the detrimental effect of global warming on maize yield might reduce the capacity of bio-energy with carbon capture and storage (BECCS) and threaten food security in the future [28], and Kothiyal et al (2023) modeled the climate change impact of mitigation (RCP 2.6) and high emission (RCP 8.5) scenarios on maize yield and possible adaptation measures in different agroclimatic zones of Punjab, India [29].…”