Exploring Kharif maize productivity through agronomic management

Introduction

Maize (Zea mays L.) is a foundational global cereal providing food, feed, and industrial resources, predominantly cultivated during the monsoon-dependent kharif season in tropical regions. However, climate change-induced weather variability, characterized by thermal fluctuations and erratic rainfall, increasingly threatens productivity by disrupting critical phenology (Swetha Sree et al., 2018; Sab et al., 2020) [13,18] . Physiologically, grain production peaks when source capacity matches kernel sink demand; weather stresses compromise this balance by restricting leaf area expansion, dry matter production, and photo-assimilate translocation. Consequently, delayed sowing exposes the crop to unfavourable microclimates, significantly compressing the vegetative growth phase and driving down final grain and stover biomass yields (Girijesh et al., 2011; Leela Rani et al., 2013; Jajoria et al., 2020) [6,10,12] . Strategic agronomic management offers a vital defence against these climate risks through the integration of optimized planting schedules, population densities, and genotypes. Aligning the sowing window with ideal seasonal weather ensures optimal thermal conditions during flowering and grain-filling. Adjusting plant population structures such as high-density, paired-row configurations maximizes solar radiation capture and cumulative biomass while mitigating individual plant resource competition (Sannagoudar & Murthy, 2018 [14] ; Alimuddin et al., 2020; Bernhard & Below, 2020) [2,3] . Because density responses are highly dependent on genetic maturity groups, pairing these spatial arrangements with late-maturing cultivars extends the vegetative phase to maximize dry matter accumulation (Jat et al., 2019 [11] ; Sab et al., 2020) [13] . Ultimately, quantifying these synergistic Genotype × Environment × Management (G×E×M) interaction effects provides a resilient, multi-factor framework to optimize spatial-temporal resource efficiency and stabilize kharif maize yields (Singh et al., 2022) [17] . Hence, optimization of agronomic factors like plant population density and genotype selection is essential to maximize spatial and temporal resource capture. This study evaluates the combined impacts of sowing times, population densities, and maturity-group genotypes to establish best management configurations for maize productivity

Materials and Methods

Field trials were executed across two experimental seasons (2021 and 2023) using a structured layout to evaluate three main treatment factors: weather variability via four sowing schedules viz., normal sowing (S1), 10 days after normal sowing time (S2), 20 days after normal sowing time (S3), and 30 days after normal sowing time (S4); three plant population densities viz., high density (P1: 133,333 plants ha -1 using paired-row planting at 60-90 × 10 cm), intermediate density (P2: 111,111 plants ha -1 using single-row planting at 60 × 15 cm), and low density (P3: 83,333 plants ha -1 using single-row planting at 60 × 20 cm); and three maturity-group genotypes viz., early-maturing BIO-605 (G1), mid-late ADV-756 (G2), and late-maturing BIO-9682 (G3). At harvest, the grain and stover yield were recorded, and a subsequent pooled analysis was performed to determine stable cross-seasonal trends across the treatments. The soil of the experimental field was medium black with clayey texture and was slightly alkaline in nature (2021: 7.82 and 7.71 soil pH) Soil EC was 00.34 dSm -1 in 2021 and 0.39 dSm

Yield studies

The rate of translocation of photosynthates from source to sink during the reproductive stage directly drives maize grain yield. Yield depends on the balance between source capacity (leaf area and dry matter) and sink demand (developing kernels). When the source capacity matches sink demand, grain production increases. Changes in either side alter the final harvest.

Grain yield

The data on grain yield of maize (kg ha -1 ) as influenced by the different treatments are presented in Table 1. Mean grain yield of maize was 5174 and 4551 kg ha -1 during 2021 and 2023, respectively. In pooled analysis it was 4863 kg ha -1 .

Weather variability

Weather variability created through different sowing time significantly influenced the maize grain yield. Normal sowing time (S1) recorded the highest grain yield during both 2021 and 2023 (6734 and 6268 kg ha -1 , respectively), accordingly it recorded the highest grain yield in pooled analysis (6501 kg ha -1 ) and was found to be significantly superior over the subsequent delayed sowings. Delaying maize sowing time by 10 (S2), 20(S3), and 30 (S4) days past the normal schedule (S1) led to progressive and significant reductions in grain yield. The lowest grain yield was produced in the most delayed sowing (S4) during both years and the pooled analysis (3156, 2578 and 2867 kgha -1 , respectively). Each 10-day step lowered the final grain yield compared to the time before it because each step progressively missed the best weather window for the crop to grow, flower, and make grain. The grain yield reduction of maize due to delayed sowing was to the extent of 5%, 35% and 53% in 2021; 9%, 42% and 59% in 2023 and 7%, 38% and 56% in pooled analysis, in the crop sown after 10, 20 and 30 days after normal sowing, respectively. Crop economic yield is a function of dry matter production and how well the crop translocate and accumulate it in yield attributing traits. Yielding ability of a crop is reflections of yield attributing characters. Superior yield level with normal sowing was due to relatively better expression of vegetative and reproductive attributes that enabled the crop to express the inherent potential to the maximum as compared to later sowings, especially 20-day and 30-day delay, where crop growing period was also notably squeezed limiting potential output. Other authors Girijesh et al. (2011) [6] , Leela Rani et al. (2013) [12] , Swetha Sree et al. (2018) [18] , Sab et al. (2020) [13] , Shingne et al. (2020) [15] . and Sharma et al. (2022) [16] . have also reported reductions in yield attributes and yield of kharif maize with delayed sowing due to less favourable weather variables.

Population density

Maize grain yield increased with increase in population density. The highest plant population density of 133333 plants ha -1 (P1: paired row planting at 60-90x10 cm) achieved maximum grain yield of maize during both years (6286 and 5492 kg ha -1 , respectively) and in pooled analysis (5889 kg ha -1 ), It was significantly more over population densities of 111111 plants ha - 1 (P2: single row planting at 60 x 15 cm) and 833333 plants ha -1 (P3: single row planting at 60 x 20 cm). The lowest population density (P3) yielded the least amount of grain during both years (3949 and 3495 kg ha -1 , respectively) and in pooled analysis (3722 kg ha -1 ). Intermediate population density (P2) yielded significantly higher amount of grain than the lowest density (P3) during both years (5288 and 4666 kg ha -1 , respectively) and in pooled analysis (4977 kg ha -1 ). Higher plant population density increased the cumulative number of ears and biological mass per unit area. Even though individual plant yield may have decreased due to competition for vital resources, the gains from a greater number of plants or harvested ears per unit area significantly outweighed the lower output per individual plant, maximizing overall grain yield per hectare through high cumulative spatial efficiency. The aforesaid results are in agreement with the outcome of Bharud et al. (2014) [5] , Honnappa (2016) [7] , Sannagoudar and Murthy (2018) [14] , Akil et al. (2019) [1] , Alimuddin et al., (2020) [2] , Bernhard and Below (2020) [3] . and Singh et al. (2022) [17] .

Genotype

All the maize genotypes differed from each other for grain yield ha -1 . Grain yield increased with extended maturity period of maize genotype during both years. Accordingly, during 2021 and 2023, late maturing genotype, BIO-9682 (G3) recorded significantly higher grain yield (5784 and 5100 kg ha -1 , respectively) than the mid-late genotype, ADV-756, G2 (5150 and 4521 kg ha -1 , respectively) and early genotype BIO-605, G1 (4589 and 4033 kg ha -1 , respectively). Similar trend of genotype wise grain yield was observed in pooled analysis (5442 > 4836 > 4311 kg ha -1 ). Grain yield in maize relies heavily on key traits like grains per row, total grains per cob, and test weight. Genetic differences drive variations in these traits. The superior grain yield of late genotype BIO-9682 primarily stemmed from stronger expression of yield components as compared to medium ADV-756 and early BIO-605. Hossein et al. (2014) [8] , Jadhav et al. (2015) [9] , Jat et al. (2019) [11] , Jajoria et al. (2020) [10] , Sab et al. (2020) [13] . and Bhakne et al. (2023) [4] . also found highly significant differences among different maturity group genotypes for yield contributing traits and grain yield which strongly support the present finding.

Interaction

Interaction effects of weather variability x population density (S x P) and weather variability genotype (S X G) significantly influenced grain yield during 2021, 2023 and in pooled analysis. Remaining interaction effects were statistically not significant.

Weather variability and population density (S X P) interaction

Data presented in Table (1a) indicated that interaction effect of normal sowing time and highest population density of 133333 plants ha -1 at paired row planting 60-90X10 cm (S1P1) recorded significantly higher grain yield during 2021(8124 kg ha -1 ), 2023(7526 kg ha -1 ) and in pooled analysis (7825 kg ha -1 ). However, it was statistically on par with S2P1 interaction only during both years but proved significantly superior to other treatment combinations during both years and when pooled. The lowest grain yield was observed in treatment interaction of most delayed sowing with lowest population density (S4P3) during both years and when pooled. Normal sowing time combined with high plant density of 133333 plants ha⁻¹ under adequate spatial arrangement in paired rows maximizes resource capture and grain yield per unit area, offsetting any reduction in individual plant yield potential through a superior number of productive ears per hectare. while a slight 10-day delay sustained similar high-density compensatory growth potential before severe environmental or developmental constraints set in. Whereas, lower densities or further delayed sowings restrict source-sink capacity and yield output.

Data in Table 18 (b) revealed that the interaction effect normal on time sowing with late-maturing genotype (S1G3) recorded significantly higher grain yield over other treatment interactions during 2021, 2023 and when pooled (7575, 7123 and 7349 kg ha - 1 , respectively).Treatment interaction S4G1 recorded the lowest grain yield during both years and when pooled (2719, 2323 and 2521 kg ha -1 , respectively), being at par with S4G2 and S4G3 interactions during 2023 season. Pairing normal on-time sowing with a late-maturing maize genotype maximizes grain yield as this interaction provides an optimal growing duration, efficient resource capture, and favorable thermal conditions during critical grain-filling stages compared to delayed planting or earlier-maturing alternatives.

Stover yield

Maize stover yield is the total aboveground non-grain biomass (leaves, stalks, and tassels) at harvest. It directly relies on the cumulative development of key vegetative growth parameters over the entire crop season. Better early and middle growth leads to a higher total stover weight per hectare. Data on stover yield of maize as influenced by different treatments have been set out in Table 1. The mean straw yield of maize during 2021, 2023 and the in the pooled analysis was 9632, 8936 and 9284 kg ha -1 , respectively.

Weather variability

Stover yield (kg ha -1 ) of maize was significantly influenced by weather variability due to sowing times during both years. In 2021, normal sowing time and 10 days after normal sowing time being at par with each other produced significantly higher stover yield (S1:11238 and S2:11064 kg ha -1 , respectively) over the subsequent delayed sowings by 20 and 30 days after normal sowing time (S3: 9219 and S4: 7005 kg ha -1 , respectively). Similar trend with respect to stover yield was noticed in 2023 and in pooled analysis. Normal sowing and sowing 10 days later comparatively provide a longer and more favourable growth period. This boosts plant height, leaf area, and dry matter. Subsequent delayed sowings (S3 and S4) expose the crop to less favourable seasonal weather conditions, shortening the active vegetative growth period. Similar results were also observed Moosavi et al. (2012), Vikhe (2016), Prasad et al. (2017), Jajoria et al., (2020) [10] , Sab et al. (2020) [13] .

and Sahu et al. (2024)

Population density

Differences in maize stover yield due to different plant population densities were significant. Stover yield of maize was significantly increased with increase in population density in both 2021 and 2023 seasons and in pooled analysis also. The highest population density (P1: 133333 plants ha -1 ) with paired row planting at 60-90 x 10 cm proved significantly superior and produced maximum stover yield (2021: 11574; 2023: 10780 and pooled: 11177 kg ha - 1 ) followed by P2 (111111 plants ha -1 ) with single row planting at 60 x 15 cm distance (2021: 9932; 2023: 8988; pooled: 9460 kg ha -1 ) and the least stover yield was recorded in P3 (83333 plants ha -1 ) with single row planting at 60 x 20 cm (2021: 7389; 2023: 7038, pooled: 7214 kg ha -1 ). This could be attributed to the fact that higher plant population captures more solar radiation and utilizes growth resources more efficiently per unit area, and more efficient spatial distribution in paired row layout minimizes shaded leaves between rows and higher population density maximizes the total number of stalks harvested. These results are in conformity with findings of Norwood (2001), Abuzar et al. (2011), Sannagoudar and Murthy (2018) [14] , Marahatta (2021) and Singh et al. (2022) [17] .

Genotype

Differences in stover yield due to different maturity group genotypes were significant. During both years, stover yield was significantly more with longer maturity period of the genotype. Thus, significantly higher stover yield was produced in late maturing genotype. BIO-9682(G3) during 2021, 2023 and in pooled data (10488, 9739 and 10114 kg ha -1 , respectively) as compared to the medium duration genotype ADV-756 (G2: 9586, 8872, 9229 kg ha -1 , respectively) and early duration genotype BIO-605 (G1:8820, 8196 and 8508 kg ha -1 , respectively). Genotype ADV-756 also yielded significantly higher stover than BIO-605 genotype. This could be because longer durations allow for extended growth periods and higher cumulative dry matter accumulation. Similar results were recorded by Nik et al. (2010), Jadhav et al. (2015) [9] , Jat et al. (2019) [11] , Sab et al. (2020) [13] .

Interaction

Interaction effects of weather variability with population density (SxP) during 2023 and pooled analysis, weather variability with genotype (SxG) during both years and pooled analysis were significant in respect of stover yield.

Weather variability and population density (S X P) interaction

Data given in Table (1a) indicated that interaction effect of normal sowing time and highest population density of 133333 plants ha -1 (S1P1) recorded significantly higher stover yield during 2023(8124 kg ha -1 ), 2023(12858 kg ha -1 ) and in pooled analysis (13163 kg ha -1 ). However, it was statistically on par with S2P1 interaction and significantly superior to other treatment combinations during both 2023 and when pooled. The lowest stover yield was observed in treatment interaction of most delayed sowing with lowest population density (S4P3) during 2023 and when pooled.

Data presented in Table 1(d) revealed that the interaction effect S1G3 i.e. normal sowing time with late-maturing genotype BIO-9682 recorded significantly higher stover yield over other treatment interactions during 2021, 2023 and when pooled (12750, 12436 and 12593 kg ha -1 , respectively).Interaction of most delayed sowing time and early maturing genotype (S4G1) recorded the lowest stover yield during both years and when pooled (6680, 6196 and 6438 kg ha -1 , respectively), being at par with S4 G2 and S4 G3 interactions during both years and when pooled.

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