Estimation of yield, phenology and agro-meteorological indices of Quality Protein Maize (Zea mays L.) under different nutrient omissions in temperate ecology of Kashmir

2.1 Experimental location

A field experiment was conducted to examine the impact of nutrient omission on phenology, yield and agro-meteorological indices of Quality Protein Maize at the Agronomy farm of FoA, Wadoora, Sopore, SKUAST-Kashmir during 2021 kharif season.

The location is falling under a temperate climate and an annual precipitation of 812 mm, with a latitude and longitude of 34° 34′ N and 74° 40′ E and an altitude of 1580 m amsl (above mean sea level). The variety tested for the experiment was LQMH-1. Soil texture of experimental farm was silty-clay-loam with a pH of 6.8 (neutral), medium content of organic carbon, medium availability of N, P, K, and S but with low availability of Zn (Table 1).

2.2 Weather of the experimental site

The weather was found highly variable over the entire crop growing period, with weekly minimum temperature variation between 9.50 and 18.91 °C and weekly maximum temperature between 26 and 32 °C. The total precipitation of 184.60 mm was received during the entire crop growing period, whereas mean sunshine hours recorded were 144.54 and the average maximum (RH₁) and minimum (RH₂) relative humidity recorded have been 75.98% and 48.07%, respectively (Fig. 1).

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Fig. 1

Weekly average meteorological data for Kharif-2021 crop growth season.

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2.3 Details of the experimental design and treatments

Randomized complete block design (RCBD) was used in our study with three replications, and comprised of eight treatments viz., (T₁-Absolute control, T₂-N₁₂₀P₇₅K₄₀, T₃-N₁₂₀P₇₅K₄₀Zn₅S₁₀, T₄-P₇₅K₄₀Zn₅S₁₀ (-N), T₅-N₁₂₀K₄₀Zn₅S₁₀ (-P), T₆-N₁₂₀P₇₅Zn₅S₁₀ (-K), T₇-N₁₂₀P₇₅K₄₀S₁₀ (-Zn) and T₈-N₁₂₀P₇₅K₄₀Zn₅ (-S)).

2.4 Management practices of crop

The experimental plots were setup in accordance with the treatments. To achieve the desired depth, a tractor was used for two ploughings, which were followed by levelling. Bunds and replication borders of experimental plots were manually prepared. N, P, K, Zn and S nutrients were applied through urea (46% N), DAP (46% P₂O₅), MOP (60% K₂O), zinc oxide (80.3% Zn) and potassium sulphate (18.4% S), respectively. Among the nutrient omission treatments, the treatments, T₄ and T₆, phosphorus and sulphur were applied through rock phosphate (20% P₂O₅) and gypsum (18.6% S), respectively. At sowing time, all nutrients were applied except for nitrogen, which was treated in three splits, 1/3rd was added as basal, and the remaining 2/3rd in two equal splits (knee high stage and prior to tasseling). The presence of enough soil moisture for seed germination was ensured prior to seed sowing. Seed that had been pre-soaked and treated with Metalaxyl 35 SD @ 4 g kg⁻¹ and Bavistin + Captan 1:1 ratio @ 2 g kg⁻¹ seed were sown with a spacing of 75 cm × 20 cm in lines.

After sowing, the lines were covered with soil and slightly pressed to ensure that the seeds had adequate contact with the soil. As per the crop requirement, irrigation water was used in the field. At 3 DAS, pre-emergence atrazine spray (0.75 kg a.i. ha⁻¹) plus hand weeding at 21 days after sowing (DAS) were used to control weeds effectively. Chlorpyriphos (0.05%) was sprayed as a preventive approach to control insect infestations. After eliminating the two border rows from the experimental plots, the net plot area was calculated. Crop samples were manually harvested from the net plot area for the purpose of yield measurement, and hand shelling was done to separate the grains from the cobs.

2.5 Biometric observations of crop

When the crop had reached 50% of each phonological stage, observations on the number of days to complete each phenological stage were noted. After removing the stalk and husk from the cobs, grain yield was measured. All the cobs pertaining to each net plot area were sun dried, and the grains were then manually shelled. Grain yield noted in kg⁻¹ was lowered to 15% moisture, and then indicated as tonnes ha⁻¹ (t ha⁻¹). After harvesting, weight of each net plot’s bundle was noted after three to four days of sun drying, and the weight was then transformed into tonnes ha⁻¹ (t ha⁻¹) for measuring biological yield.

2.6 Agro-meteorological indices calculation and heat use efficiency

Meteorological variables (viz. daily maximum and minimum temperature, sunshine hours, average relative humidity and day length) recorded through automatic weather station located in the research farm of Agronomy were used to calculate the agro-meteorological indices, using the formulas provided below, at different phenological stages. With a base temperature of 10 °C, agro-met indices were assessed from sowing time to each phenological stage. (Nuttonson, 1995).

• T_Max. = Maximum temperature,

• T_min = Minimum temperature.

• T_b = Base temperature i.e. a temperature below which growth ceases.

2.7 Statistical analysis

By using the statistical method of randomized complete design, data on QPM phenology and grain yield were evaluated using the analysis of variance (ANOVA) technique. The SPSS software, version 27.0, was used to perform the operation of statistical analysis. The stepwise regression and correlation heat maps between yield and agro-meteorological indices were analysed using R software. The statistical model of the experimental design is given as: $$Y_{\mathit{ij}}=\mu +{\alpha }_i+{\beta }_j+e_{\mathit{ij}}$$ µ is overall mean; α_i is treatment effect (Random Variable); β_j is block effect (Fixed variable); e_ij is the error.

3.1 Crop phenology

Data (Table 2) from the nutrient omission treatments demonstrated a significant variation in QPM so as to complete different phenophases. The prevalent temperature has a significant impact on the rate of plant development. Treatment (T₃) N₁₂₀P₇₅K₄₀Zn₅S₁₀ took 121 days to complete various phenophases when compared with the rest of treatments. The reason could be due to the sufficient and balanced nutrient availability that resulted in ideal vegetative growth prior to the beginning of reproductive stage. Whereas absolute control took least number of days (110.33) to complete different phenophases, that might be due to the best crop response under nutrient stress conditions, thus completing its duration in shorter span in comparison to the rest of treatments.

Among nutrient omission treatments, nitrogen omission treatment took a minimum days (115.00 Days) to achieve various phenophases, therefore, emerged as the most restricted nutrient, followed by phosphorus and potassium, showing most significant impact on growth characters as compared to other nutrients. The omission of nitrogen followed by P and K reduced the vegetative phase of crop and consequently the calendar days and thermal regime of different phenological stages in QPM. Phenological stages are highly dependent on proper nutritional balance therefore any deviation can cause malfunctioning in their expression (Kaur et al., 2010).

3.2 Agro-meteorological indices

3.3 Yield (t ha⁻¹)

Nutrient omission treatments differed significantly in terms of grain-yield as exhibited by the data (Table 7). Treatment N₁₂₀P₇₅K₄₀Zn₅S₁₀ achieved the higher grain yield of 9.78 tonnes ha⁻¹ however, lowest grain-yield of 4.22 tonnes ha⁻¹ were recorded under absolute control treatment. However, it was observed that the omission of nutrients (-Nitrogen, -Phosphorus, -Potassium, -Zinc and –Sulphur) significantly reduced the grain yield. In comparison to Nitrogen, Phosphorus and Potassium omission treatments, omission of zinc (-Zn) and sulphur (-S) treatments had the least influence on grain yield reduction. Furthermore, significant variation was noticed among the various nutrient omission treatments with respect to biological yield. The treatment N₁₂₀P₇₅K₄₀Zn₅S₁₀ recorded the biological yield (22.05 t ha⁻¹), though statistically par to N₁₂₀P₇₅K_40, and lowest biological yield of 10.29 t ha⁻¹ was achieved under absolute control treatment. Further, it was found that biological yield was significantly reduced when nutrients were omitted (-Nitrogen, -Phosphorus, -Potassium, -Zinc and –Sulphur). However, zinc (-Zn) and sulphur (-S) omitted plots demonstrated least impact on biological yield reduction when compared to (-N, -P, and -K). Adequate availability of nutrients through balanced nutrient management system at critical phenophases lead to the efficient photosynthate translocation from source to sink that in turn improved growth characters and yield attributes, thereby resulting in enhanced grain-yield. Experimental results examined by Sinha (2016) support these findings. Increased leaf area results in maximum interception of resulting in increased production and accumulation of dry matter and yield attributing characters that consequently results in higher yield (both grain and biological). Findings of Gani et al. (2018) also supported these results. Omission of nutrients particularly nitrogen effectively disturbs the metabolism of plants, disrupts chlorophyll structure and destabilize pigment protein complex which results in yield reduction in maize followed by phosphorus omission. Moreover, nitrogen omission shortens the vegetative phase that result in low grain yield. These results are supported by Reddy and Dakota (2007). Under the exclusion of primary macronutrients plants do not attain proper vegetative growth resulting in expression of poor yield attributes and yield. (Gangaiah, 2019). Plants subjected to improper growth at early stages affect biomass production with yield reduction because of poorly developed root system, under the omission of P. Further, more yield decline under N, P and K omission might be accounted to availability of soil nutrients in the respective omission plots, making it difficult to meet the higher requirements of these nutrients for maize. Mousumi and Ghosh (2018) explained similar findings in their research.

3.4 Stepwise regression analysis

Stepwise regression analysis was made to find critical agro-meteorological indice responsible for higher yield of quality protein maize at different phenological stages and different the most predictable and probable model obtained for the parameter influencing the yield largely have been presented in Table 8. Among all the parameters affecting yield, NOD (Number of days) and PTU were added due to their higher coefficient of determination (R²). As per the results obtained PTU at knee high and harvest stage explained 92.6% and 83% of total variation in the yield whereas, NOD explained 88.3% and 82.3% of total variation in yield at tasseling and silking stage respectively (Table 5). These results are supported by the findings of Jan et al. (2022) and Bhat et al. (2015).

3.5 Correlation studies

The correlation studies between yield and agro-meteorological indices at different growth stages of quality protein maize were interpreted in Fig. 2. The results analysed on heat maps indicated that grain yield, stover yield and biological yield exhibited strongly positive and significant correlation with all the agrometeorological indices calculated viz. GDD, NOD, HTU, PTI etc except at knee high and tasseling stage where PTI was found to have week and non-significant correlation with yields. Further, the different agrometeorological indices showed positive and significant correlation with each other at all the phonological stages except at knee high stage where they exhibited non-significant relation between each other. These results indicated that agro-meteorological indices have greater and significant impact on the yield of quality protein maize and acts a key factor in influencing it through the heat unit requirement, heat use efficiency, sunshine hours and day length. These results are supported by the findings of Jan et al. (2022) and Bhat et al. (2015).

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Fig. 2

Heat map of the Pearson correlation between Grain yield (GY), Stover yield (SY), Biological yield (BY) and agrometeorological indices viz. Number of days (NOD), Helio-thermal units (HTU), Photothermal units (PTU), Pheno-thermal index (PTI) at (A) Knee High Stage; (B) Tasseling stage; (C) Silking stage; (D) Maturity stage.

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