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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.

A simulation study was conducted for two cultivars of maize (PMH1 and PMH2) in four agroclimatic zones of Punjab state of India where climate change depicts a consistent rise in temperature and increased variability in amount and distribution of rainfall. The yield assessment was performed for four agroclimatic zones of Punjab comprising of seven locations because variability in temperature rise and rainfall existed from location to location.Corrected ensemble model weather data (temperature and rainfall) for RCP4.5 and RCP6.0 was used as an input in the calibrated and validated CERES-Maize model and yield was simulated for a period of 70 years. The simulated yield for near as well as far-future was statistically assessed to understand the yield trend in Punjab under current dates of sowing and the results indicated a strong negative correlation between the yield and the weather parameters under the two scenarios at the considered four agroclimatic zones of Punjab. An increase in maximum and minimum temperature was observed ranging 0-4°C and 3-8°C, respectively at all the agroclimatic zones except Faridkot (zone V) where the increase in minimum temperature was observed by 0-3°C during the crop growth season while the rainfall variability ranged from 200-800mm under both the scenarios. At agroclimatic zone II and zone III similar results were obtained with higher yields at later dates of sowing and the rainfall at agroclimatic zone III was higher under RCP6.0 (300-600mm) while the yields for agroclimatic zone IV and V (Abohar) with rainfall variation of 270-450mm and 200-400mm, respectively showed no yield increment. Maize at Faridkot performed well with higher yields at early sowing dates. Among the two cultivars PMH1 showed more high yield years than PMH2 for most of the years. The yield under differential sowing dates showed the rst fortnight of June and end June to be the best sowing dates for most of the locations as the yield for these dates were higher for most of the years. Thus, the study can be further applied to decide the future sowing window of maize for the agricultural state like Punjab.

A simulation study was conducted for two cultivars of maize (PMH1 and PMH2) in four agroclimatic zones of Punjab state of India where climate change depicts a consistent rise in temperature and increased variability in amount and distribution of rainfall. The yield assessment was performed for four agroclimatic zones of Punjab comprising of seven locations because variability in temperature rise and rainfall existed from location to location. Corrected ensemble model weather data (temperature and rainfall) for RCP4.5 and RCP6.0 was used as an input in the calibrated and validated CERES-Maize model and yield was simulated for a period of 70 years. The simulated yield for near as well as far-future was statistically assessed to understand the yield trend in Punjab under current dates of sowing and the results indicated a strong negative correlation between the yield and the weather parameters under the two scenarios at the considered four agroclimatic zones of Punjab. An increase in maximum and minimum temperature was observed ranging 0-4°C and 3-8°C, respectively at all the agroclimatic zones except Faridkot (zone V) where the increase in minimum temperature was observed by 0-3°C during the crop growth season while the rainfall variability ranged from 200-800mm under both the scenarios. At agroclimatic zone II and zone III similar results were obtained with higher yields at later dates of sowing and the rainfall at agroclimatic zone III was higher under RCP6.0 (300-600mm) while the yields for agroclimatic zone IV and V (Abohar) with rainfall variation of 270-450mm and 200-400mm, respectively showed no yield increment. Maize at Faridkot performed well with higher yields at early sowing dates. Among the two cultivars PMH1 showed more high yield years than PMH2 for most of the years. The yield under differential sowing dates showed the first fortnight of June and end June to be the best sowing dates for most of the locations as the yield for these dates were higher for most of the years. Thus, the study can be further applied to decide the future sowing window of maize for the agricultural state like Punjab.

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