Evaluating the seasonal accumulation of Heat units as an agroclimatic indicator on Baby corn (Zea mays L.) under different sowing windows

Sankar, T. Gouri; Ramanathan, S.; Kokilavani, S.; Chandrakumar, K.; Kalarani, M. K.

doi:10.31018/jans.v15i1.4273

Cited by 1 publication

(1 citation statement)

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“…Thus, the phenological phases became longer than the early sowing of sorghum. This might be attributed to the variation in temperature that prevailed under the different sowing dates (Sankar et al, 2023).…”

Section: Crop Variablesmentioning

confidence: 99%

Phenology, phyllochron and productivity of sorghum in response to varying growing environments and nitrogen levels in the semiarid irrigated condition

Ammaiyappan,

Geethalakshmi,

Bhuvaneswari

et al. 2023

JANS

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Climate change is likely to affect agricultural production in many parts of the world. Sorghum is the stable food crop in semiarid areas, and climate change has significant adverse effects on sorghum yields. The optimum time of sowing is one of the important technologies that allow the crop to better utilise natural resources by the crop to maximise productivity. For optimizing the sowing time, the field investigation was carried out in Tamil Nadu Agricultural University, Coimbatore, under summer irrigated conditions in 2022. The experiment was laid out in a split-plot design with three replications. The main plot consists of five dates of sowing and the subplot includes different nitrogen levels. The results revealed that advanced sowing (first fortnight of April) reduced the yield by 13.73% and delayed sowing (second fortnight of May) recorded the maximum yield (4230.3kg ha-1) with higher phyllochron values. GDD manifested a significant negative relationship with a high correlation coefficient (r) value of -0.98** (p = 0.01) for days to anthesis, -0.98** (p = 0.01) for days to physiological maturity and -0.88* (p = 0.05) for phyllochron. Average temperature exhibited a significant negative correlation with days to anthesis (-0.75*), physiological maturity (-0.97**) and phyllochron (-0.89*). The best-fit regression model showed that a GDD-based model could better predict the phenology of sorghum compared to an average temperature-based model. The study indicated that understanding sorghum's phenological response and productivity level under varied dates of sowing and the nitrogen levels in semiarid environments can help determine the appropriate sowing time and optimum nitrogen level for achieving better yield in summer-irrigated sorghum.

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Section: Crop Variablesmentioning

confidence: 99%