Stability for earliness in wheat

Introduction

Early maturity acts as an escape mechanism against late-season droughts, terminal heat stress, and pest cycles. A stable early-maturing crop will always complete its life cycle before these adverse environmental conditions peak. Maharashtra produces 35.10 lakh tonnes of wheat across an area covering 15.27 lakh hectares. (Anonymous,2026) [1] . The Eberhart and Russell model mathematically dissects phenotypic performance across test environments using three primary criteria i.e. the genotypic mean yield, the linear regression coefficient (bi), and the deviation from regression mean square (𝑆 2 𝑑𝑖) (Nyol et al., 2025) [10] . According to this mathematical structure genotypes that maintain a high mean yield paired with a unit regression coefficient and a non-significant, near-zero deviation demonstrate predictable behavior and robust stability across wide geographical or temporal ranges (Eskezia et al., 2026) [6] . In regions like Maharashtra, farmers rely heavily on multi-cropping sequences (such as planting a winter crop after the monsoon harvest). If a variety's earliness is stable, farmers can perfectly time the harvest and immediately plant the next crop without delay. Early-season produce typically fetches a premium market price. If the earliness is unstable and fluctuates, farmers cannot synchronize their harvests to coincide with these lucrative, early-market windows. Crops with a stable, short growing cycle require less water, fertilizer, and fewer pesticide applications, directly lowering operational costs while ensuring the plant reaches maximum yield. In modern agriculture, early-maturing crop varieties act as a critical buffer against climate unpredictability and market fluctuations. Keeping this view in mind, the present investigation was carried out.

Rabi 2024-25 in a randomized block design with three replications. The data from each of these locations were recorded on the number of days to 50 per cent heading, days to maturity. Line sowing was practiced with a row spacing of 20 cm. The gross plot size was 6.00 m × 1.20 m (6 rows), while the net plot size was 6.00 m × 0.80 m (4 rows). A uniform fertilizer dose of 120:60:40 kg NPK/ha was applied alongside standard agronomic practices. Stability analysis based on the Eberhart and Russell, (1966) [5] and Panse and Sukhatme, (1967) [11] model were undertaken using INDOSTAT statistical package at Bajra Research Scheme, Dhule.

Results and discussion

The genotypes exhibited significant differences for both traits days to 50% heading and days to maturity when tested against pooled error (Table -1), confirming a diverse genetic base in the experimental material. The environments differed significantly for days to 50% heading and days to maturity when tested against pooled deviation, pooled error and G X E interaction indicating a high degree of variability between the three locations. G × E interaction was found significant for both characters days to 50% heading and days to maturity when tested against pooled error. Hence stability analysis performed for these two traits. The G X E interactions were significant for both linear and non-linear components across days to 50% heading except for days to maturity. When tested against pooled deviation and pooled error, the environment (linear) effect was found to be significant for both characters. E + G X E effect was significant for the traits viz., days to 50 per cent heading, days to maturity when tested against pooled deviation and pooled error. The significant differences between genotype and environment for major wheat characters were also found in earlier studies by Bains and Gupta (1972) [4] , Krupal (2017) [8] , Rathod (2018) [13] and Patil (2021) [12] . Bains and Gupta (1972) [4] , Kishor et al. (1992) [7] , Mehta et al. (2000) [9] , Ashraf et al. (2001) [3] , and Rathod (2018) [13] ,

Patil (2021) [12] had significant linear and non-linear components of G × E interactions for the majority of characters investigated in wheat. Negative environmental indices at Rahuri location, indicated that it was the favorable location for days to 50% heading. (Table-2) Rahuri location, provided a favorable environment for late maturity, whereas Niphad location and Dhule location tended to favor earlier maturity. Average stability for days to 50% heading was observed in NIAW-4820, NIAW-4857, NIAW-4864, NIAW-4887, NIAW-4891 and NIAW-4898. (Table -3) For days to maturity genotypes NIAW-4809 and NIDW-1585 showed average stability. (Table -4) All above mentioned genotypes had higher mean than parental mean and non-significant regression coefficient and deviation from regression (Mean>population mean, bi=1.00 ns and S 2 di=0 ns) and grouped as average stable for both characters and would be well adapted to all of environments. Genotype NIAW-4840 showed above-average stability for days to 50% heading. (Table -3) Genotypes NIAW-4820, NIAW-4901, and MACS-6222[C] showed above-average stability for days to maturity. (Table -4). All above mentioned genotypes followed the criteria for aboveaverage stability (i.e. Mean>population mean, bi<1.00 and significant and S 2 di=0 ns) and might be adopted to poor environment. The genotypes NIAW -4910 showed below average stability for days to 50% heading. (Table -3) The genotypes NIAW-4840, NIAW-4857, NIAW-4871, NIAW-4887 and NIAW-4891 showed below average stability for days to maturity. (Table -4) All above mentioned genotypes followed the criteria for below-average stability. (i.e. Mean>population mean, bi>1.00 and significant and S 2 di=0 ns) and suitable for poor environment. These findings regarding days to 50 percent heading align with those previously reported by Thakare (2014) [14] and Krupal (2017) [8] . Similar results for days to maturity were obtained by Krupal (2017) [8] and Rathod (2018) [13] .

Table 1: ANOVA for stability as per Eberhart and Russell Model (1966) [5] in wheat. P.E. (Pooled error) 144 1.48 3.13 @, @@: Significant at 5 and 1% level of significance, respectively against G × E #,##: Significant at 5 and 1% level of significance, respectively against the pooled deviation (PD) *, **: Significant at 5 and 1% level of significance, respectively against the pooled error (PE)

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Acknowledgement

AcknowledgementsThe authors gratefully acknowledge Associate Dean -Dr. R. J. Desale and Former associate Dean-Dr. B. M. Ilhe, Govt. College of Agriculture, Dhule. The author expresses gratitude to Dr. K.K. Barhate, Professor (CAS) Plant Breeding Bajra Research Scheme, Agricultural Research Station, College of Agriculture, Dhule, Dr. N.M. Magar, Assistant Professor Agril. Botany Agricultural Research Station, Niphad, Dr. V. R. Awari, Assistant Professor Agril. Botany PG farm MPKV, Rahuri for growing the crop in their respective experimental stations. The authors express sincere thanks to Dr. N. M. Magar Assistant Professor Agril. Botany Agricultural Research Station, Niphad for providing the genotypes used in the study.

References

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