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Tomato Response Evaluation through Fertilization and PGRs Application under Temperature Differentiation in Late Winter

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26 September 2023

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28 September 2023

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Abstract
This study evaluated the exogenous application of PGRs substitute chemical fertilization without compromising the growth and yield of tomato in fluctuated day-night temperature and humidity stressed late winter. Two-factor experiment comprising chemical fertilizers at 100, 110, 90 and 80 % of recommended doses besides control and PGRs of GA3; NAA, 4-CPA and SA @ 50 ppm including control was conducted where treatments were assigned in triplicates. Results revealed no significant variation among the fertilizer doses (80% to 110% of recommendation) regarding growth and yield contributing traits while among the PGRs, GA3 @ 50 ppm produced maximum number of flower clusters plant-1 (16.85), flowers (8.80) and fruits (5.79) cluster-1, single fruit weight (67.83 g) and fruit yield (6.61 kg plant-1) of tomato that was statistically identical with the findings of SA. But significant reduction in yield was noted in NAA and 4-CPA (1.20 kg and 1.21 kg plant-1, respectively). Interestingly, GA3 and SA in combination with any doses of the studied fertilizers maximize the tomato morphological and reproductive traits while fertilizer plus NAA and 4-CPA interaction gave the inferior results. Further, correlation matrix and PCA findings revealed that five fertilizer doses have no distinctiveness whereas GA3 and SA has distinct position than other PGRs with the maximum dependent variables those were contributed positively in the total variations. The study findings suggested that 20% fertilizer requirement could be reduced with the substitution of GA3 and SA @ 50 ppm for successful cultivation of tomato in late winter having the extreme environmental issues.
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Subject: Biology and Life Sciences  -   Horticulture

1. Introduction

Tomato (Solanum lycopersicum L.) is one of the most extensively used and cultivated vegetables in the globe. The nutritional importance of tomato can be largely explained by its content of various health-promoting compounds, including vitamins, carotenoids, and phenolic compounds [1]. These bioactive compounds have a wide range of physiological properties, including anti-inflammatory, anti-allergenic, antimicrobial, vasodilatory, antithrombotic, cardio-protective, and antioxidant effects [2]. Tomatoes are also rich in carotenoids, representing the main source of lycopene in the human diet [3]. Tomatoes also have the naturally occurring antioxidants vitamin C and E [4] as well as large amounts of metabolites, such as sucrose, hexoses, citrate, malate and ascorbic acid [1]. But the recent phenomenon of global warming across the world has posed severe challenges to vegetable production including tomato. Among others, the challenges include increase in air temperature (AT), fluctuation in atmospheric humidity (RH) and intensity of solar radiation [5]. In Bangladesh, winter occurs for a short period being no longer than three months and temperature rise drastically in the post winter months with a variation between high and low pick for more than 15 ºC that experienced in the recent years [6]. These extremities in temperature and humidity phenomena sometimes becomes severe at the late winter and pre-summer period for successful cultivation of tomato. The optimal growth temperature for tomato growth is 18.3 to 32.2 °C, and the relative humidity is 50% to 70% [7], however above 35 °C the growth is slow, and at 40 °C the plants stop growing [8]. Excessive temperature rise causes poor pollination and reduces fruit setting [9], plant dwarfing, senescence [10] and quality deterioration [11].
About 251.69 million tons of tomato has been produced from 6.16 million hectares of land across the globe [12] with the gross annual production of 4.16 lakh tons from an area of 28.53 thousand hectares in Bangladesh [13]. Although, dietary shortage of vegetables is still evident in the country. Again, to meet the daily vegetable requirement of 235 g person-1 day-1 the growers are encouraged to increase the total production but the only thing farmers are used to practice for increasing the yield and production is the excessive and indiscriminate use of inorganic fertilizers neglecting the recommended guidelines [14]; thereby rendering the soil health in danger for future production. Such overuse of inorganic fertilizers has caused soil, air, and water pollutions through nutrient leaching, destruction of soil physical characteristics, accumulation of toxic chemicals in water bodies, and so on [15], as well as causing severe environmental problems and loss of biodiversity [16]. The continuous and steady application of inorganic fertilizers leads plant tissues to frequently absorb and accumulate heavy metals, which consequently decreases the nutritional and grain quality of crops too [17,18]. Therefore, reduction of using agrochemicals especially the use of chemicals fertilizer is very much urgent in the country as any means not only to improve the soil health but also to get quality vegetable product.
Again, plants use several physiological adaptive mechanisms such as hormonal changes, cellular or molecular adaptive mechanisms to survive environmental implications [19]. Plant growth regulators (PGRs) perform a significant role in plant developmental process and thus modulate plant replies to abiotic stresses along with normal growth and developmental processes. PGRs have been implicated in efficient utilization of nutrients and translocation of photo-assimilates [20]. Exogenous PGRs can help to manage balance of phytohormones and thereby they trigger the plants’ tolerance to different stress. More specific responses include alteration of C partitioning, greater root: shoot ratios, enhanced photosynthesis, altered nutrient uptake, improved water status and altered crop canopy [21]. In recent years, exogenous plant growth regulator (PGR) treatment has been used to effectively improve crop drought and heat tolerance and preserve yield under salinity and drought stress [22]. Thus, plant hormones are the key regulators of plant growth and developmental processes as well as crucial for biotic and abiotic stress response throughout their life cycle [23]. However, the comprehensive study on the PGRs application in response to the fertilization minimization for crop cultivation are still scare. Therefore, it has been hypothesized that application of certain PGRs like auxin, gibberellic acid (GA3) and others might ascertain excellent tomato production under adverse environmental conditions with low chemical fertilizer requirements. Considering these, the present research was designed to assess the responses of tomato plants to varied levels of fertilizers from 20 % less to 10 % excess than recommendation after the foliar application of plant growth regulators.

2. Results

Weather Condition in Late Winter

Remarkable fluctuations and differences between maximum and minimum temperature as well as maximum and minimum relative humidity were noticed during the late winter growing period of tomato (Figure 1A and 1B). Both high and low temperature indexes showed gradual decreased down from 30.0 °C and 16.5 °C, respectively in the 1st December, 2022 to 19.0 °C and 10.0 °C in the mid-January 2023. Thereafter, a steady increase in temperature was noticed up to the end of the harvest at April 2023 up to a maximum of 38.5 °C. Daily maximum temperature was noted below 30 °C up to the first week of February and from the second week of February 2023 maximum daily temperature climbed up. It was also noticed that temperature differentiation between high and low pick never went below 12.0 °C. Temperature variations in almost throughout the vegetative growth stage and reproductive phase were more than 15.0 °C (early-January to mid-March) (Figure 1A). Again, relative humidity report exhibited that except few deviations maximum relative humidity was more or less stable in 85-95 % index. While, extreme fluctuation was recorded in minimum daily relative humidity where the lowest humidity was observed 37 % in March 12, 2023. Throughout the growing period minimum relative humidity remained close to 60 % where relative minimum atmospheric humidity was recorded below 50 % during February and March 2023 (Figure 1B).

Plant Height

Plant height of tomato at 45 and 75 DAT was significantly (p≤0.05) influenced by the application of nutrients and plant growth regulators however plant height was not varied significantly at 20 DAT (Figure 2). Control plants (N1) for fertilizer treatment had shorter plants at both 45 and 75 DAT (38.45 cm and 63.41 cm, respectively) while the other tomato plants treated with 80 % to 110 % of FRG’2018 recommended fertilizer doses (N2 to N5) exhibited statistically similar and higher plant height compared to control (Figure 2A). Among the PGRs, maximum plant height was measured in P2 treatment at both 45 and 75 DAT (49.08 cm and 87.90 cm, respectively) which had statistical harmony with P5 (49.01 cm and 86.61 cm, respectively) followed by P1 treatment. Plant height at both the phases was recorded minimum in P4 treatment (31.35 cm and 46.78 cm, respectively) having statistical consistency with P3 treatment (33.27 cm and 47.67 cm, respectively) (Figure 2B). Further, interaction revealed that the tallest tomato plant was recorded in N5P5 at 45 DAT (50.70 cm) being statistically at par with N1P5, N2P2, N2P5, N3P2, N3P5, N4P2, N4P5 and N5P2 combinations and in N3P2 treatment at 75 DAT (90.63 cm) that was statistical parity with the P5 of N2, N3, N4 and N5 and P2 combination of N2, N4 and N5. Contrarily, the shortest plant at 45 DAT was noted in N1P4 (29.97 cm) having statistical unity with the interaction of P3 with N1, N3, N4 and N5 and P4 of N2, N3, N4 and N5. Again, at 75 DAT, interaction of P3 and P4 with N1, N2, N3, N4 and N5 had statistically similar plant height of which minimum been recorded in N3P4 combinations (46.13 cm). Plant height at 20 DAT didn’t vary significantly in interaction effect of fertilizers and PGRs (Table 1).

Base diameter

Main effect of fertilizer exhibited non-significant variation in base diameter of tomato at 20 and 45 DAT. But base diameter at 75 DAT varied statistically where maximum base diameter was noted in N3 (2.39 cm) being statistically similar with N2 (2.37 cm), N4 (2.37 cm) and N5 (2.38 cm) while minimum base diameter was measured in control (2.13 cm) (Figure 3A). Again, base diameter differed significantly among the PGR treatments with the exception at 20 DAT (Figure 3B). At 45 and 75 DAT, maximum base diameter was recorded in P2 PGR (1.77 cm and 2.72 cm, respectively) which had statistical uniformity with P5 (1.75 cm and 2.67 cm, respectively) while minimum base diameter was measured in P3 at 45 DAT (1.29 cm) and in P5 at 75 DAT (1.91 cm). Interaction effect also showed significant difference in base diameter at 45 and 75 DAT (Table 1). At 45 DAT, N3P3, N3P4, N4P3 and N4P4 treatment combinations had significantly minimum same base diameter (1.27 cm) which had statistical similarity with N1, N2 and N5 interacted P3 and P4 as well as N1, N4 and N5 interacted P1. Whereas, maximum base diameter at 45 DAT was noted in N4P2 (1.80 cm) having statistical conformity with nutrient and P2 and P5 interaction. Again, at 75 DAT, significantly maximum base diameter was recorded in N5P2 (2.83 cm) being identical to P2 and P5 interaction of N2, N3, N4 and N5 treatments while minimum base diameter in N1P4 (1.83 cm) and P3 and P4 interaction of nutrient doses had statistical parity with that treatment combination (Table 1).

Number of Branches

Number of branches didn’t vary with the variation in fertilizer doses rather it was varied remarkably due to PGR variations (Figure 4). At 45 DAT, maximum number of branches was found in P3 treatment (3.91 plant-1) having statistical conformity with P5 while minimum number of branches was counted in P4 (2.71 plant-1) and P3 (2.75 plant-1). Statistical superiority in branch number at 75 DAT was observed again in P3 treatment (5.85 plant-1) whereas P3 and P4 had statistically same and inferior branch number (3.67 plant-1) (Figure 4B). It was observed from the interaction effect that N1, N2, N3, N4 and N5 interacted P2 and P5 along with N3P1 expressed statistical similarity for number of branches plant-1 where N5P2 and N3P2 had maximum number of branches (4.23 and 6.13 branches plant-1, respectively) at 45 and 75 DAT. The rest P1, P3 and P4 interaction of all the nutrient treatment had statistically uniform and minimum number of branches plant-1 (Table 1).

Number of Leaves

Number of leaves plant-1 of tomato at 45 and 75 DAT was significantly (p≤0.05) influenced by the application of nutrients and PGRs but non-significant variation was evident at 20 DAT (Figure 5). Among the fertilizer doses, maximum number of leaves at 45 and 75 DAT was counted in N3 treatment (22.36 and 36.44 plant-1) having statistical consistency with N2 and N4 treatments while minimum number of leaves at those two stages was noted in N1 fertilizer dose (19.78 and 31.44 plant-1) (Figure 5A). Again, P2 among the studied PGR exhibited statistically superior number of leaves at both 45 and 75 DAT (27.69 and 47.13 plant-1) being different from all other treatments while statistically inferior number of leaves at both 45 and 75 DAT was recorded in P4 treatment (16.44 and 25.77 plant-1) being statistically uniform with P3 at 45 DAT and with P1 and P3 at 75 DAT (Figure 5B). Moreover, interaction revealed that except N3P1, PGRs like P1, P3 and P4 interacted with N1 to N5 nutrient doses had statistical parity for number of leaves plant-1 at 45 DAT. Of them, plants under N5P4 interaction produced the lowest number of leaves (16.00 plant-1). On the contrary, N2P2 combination had the highest number of leaves (29.00 plant-1) at 75 DAT and P2 and P3 combined with N3 and N4 and P1 interacted with N1, N2 and N5 had statistical consistency among them. Reversely, N1P4 treated plants obtained the lowest number of leaves (21.00 plant-1) at 75 DAT having statistical uniformity with N1P1, N1P3, N2P3, N4P3, N4P4, N5P1, N5P3 and N5P4 combinations (Table 2).

Canopy Spread

Fertilizer doses and PGRs implied no statistical variation on canopy spread at 20 DAT considering their main and interaction effect, but significant (p≤0.05) differences were noticed at 45 and 75 DAT. Tomato plants treated with N3 fertilizer dose attained maximum canopy spread at both 45 and 75 DAT (49.27 and 58.49 cm plant-1, respectively). Rest of the nutrient doses except control (N1) got statistical harmony with N3 treatment. Minimum canopy spread was estimated in N1 fertilizer treated plants (44.09 cm and 53.39 cm at 45 and 75 DAT, respectively) (Figure 6A). On the other side, P2 PGR resulted in maximum canopy spread of tomato at 45 and 75 DAT (55.01 and 69.29 cm, respectively) which had statistical uniformity with P5 treatment (53.14 and 67.51 cm, respectively) while canopy spread measured the lowest in P4 (41.10 and 44.09 cm, respectively) having statistical parity with P3 treatment (41.32 and 46.07 cm, respectively) (Figure 6B). Further, among nutrient-PGR interactions, canopy spread of tomato was estimated the utmost in N3P2 combinations at 45 DAT (57.13 cm) and in N2P2 at 75 DAT (71.27 cm). Interactions like N1P2, N1P5, N2P2, N2P5, N3P5, N4P2, N4P5, N5P2 and N5P5 at 45 DAT and N1P2, N2P5, N3P5, N4P2, N4P5, N5P2 and N5P5 at 75 DAT had statistical unity with the best combination. On the contrary, plants under N1P4 combination attained minimum canopy spread at both 45 and 75 DAT (36.42 and 41.43 cm, respectively) which got statistical similarity with N1P1, N1P3, N2P1, N2P3, N2P4, N3P3, N3P4, N4P3, N4P4, N5P3 and N5P4 combinations at 45 DAT and with N1P3, N2P3, N2P4, N3P3, N3P4, N4P3, N4P4, N5P3 and N5P4 combinations at 75 DAT (Table 2).

Internode Length

Internode length of tomato varied non-significantly with fertilizer application at different doses but variation was significant in terms of PGR treatment (Table 3). Due to fertilizer feeding internode length of tomato ranged between 4.92 cm and 5.13 cm. In case of PGR treatment, long (6.17 cm) internode was recorded in P2 followed by P5 treatment (5.53 cm). While, short internode was measured in P4 treatment (4.20 cm) being statistically identical with P3 treatment (4.32 cm). Interaction of nutrient and PGR also revealed significant variation in internode length of tomato where plants under N2P2 treatment combination had the longest internode (6.53 cm) having statistical parity with N1P2, N3P2, N3P5, N4P2, N5P2 and N5P5 combinations. Conversely, the shortest internode was noticed in N1P4 interaction (4.03 cm) which had statistical similarity with N1P1, N1P3, N2P3, N2P4, N3P3, N3P4, N4P3, N4P4, N5P3 and N5P4 treatments (Table 4).

Number of Leaflets

Significant variation (p≤0.05) in number of leaflets leaf-1 in tomato was noted due to the application of varied fertilizer doses and different types of PGRs (Table 3 and Table 4). Fertilizer treatment N5 resulted in maximum number of leaflets (8.58 leaf-1) which exhibited statistical harmony with N3 and N4 treatments. While, minimum number of leaflets leaf-1 was obtained in N1 treatment having statistical similarity with N2 and N3 treatments. Among the PGR treatments, maximum 10.19 leaflets leaf-1 was counted in plants under P2 treatment which was statistically dissonant from all other PGR treatments. On the reverse side, plants treated with P3 of PGRs had the least number of leaflets leaf-1 (6.38 leaflets leaf-1) followed by P4 treatment (6.91 leaflets leaf-1). Additionally, being significantly varied, interaction of nutrient and PGR revealed that plants under N5P2 had the highest leaflets (10.67 leaf-1) having statistical parity with N2P2, N3P2, N4P2 and N5P5 combinations. On the contrary, the least number of leaflets leaf-1 was noticed in N1P3 interaction (5.93 leaflets leaf-1) which had statistical similarity with N2P3, N3P3, N3P4 and N5P3 treatments (Table 4).

Leaf Area

Remarkable variation (p≤0.05) among fertilizer doses, types of PGRs and their interactions in terms of single leaf area of tomato was observed (Table 3 and Table 4). Except N1 fertilization, all the fertilizer treatments had statistically similar leaf area where plants applied with N3 nutrient dose had the highest leaf area (306.13 cm2). Oppositely, N1 plants exhibited the lowest leaf area (286.33 cm2). Main effect of PGR showed that leaves of P2 treated plants possessed maximum area (337.66 cm2) being statistically identical with P5 treatment (327.75 cm2) while, minimum leaf area was determined in P4 treatment (247.89 cm2) followed by P3 treatment (270.77 cm2). Among the interactions, significantly the largest leaf was measured in N3P2 combination (357.33 cm2) which had statistical parity with N2P5, N3P1, N4P2, N4P5, N5P2 and N5P5 treatment combinations. While, the smallest leaf was attained in N3P4 treatment (225.03 cm2) being statistical consistent with N1P4, N4P3, N4P4, N5P3 and N5P4 interactions.

Leaf SPAD value

Leaf relative greenness as per SPAD value of leaf didn’t differ significantly in case of main effect of fertilizer dose and interaction but variations were significant against PGR treatment (Table 3 and Table 4). Statistical superiority in leaf SPAD value was recorded in plants treated with P2 PGR (53.08) which exhibited statistical unity with P5 treatment (52.01). On the other hand, SPAD value of leaf was estimated in P4 treatment (47.87) having statistical parity with P1 (49.42) and P3 (48.15) treatments.

Shoot Weight

Fertilizer and PGR application significantly (p≤0.05) influenced the fresh and dry weight of shoot of tomato under study (Table 3 and Table 4). Maximum fresh and dry weight of shoot was recorded in N3 treatment (283.94 g and 67.91 g, respectively) which was statistically alike with N2, N4 and N5 treatments while, minimum fresh and dry weight of shoot was resulted in N1 treatment (265.61 g and 62.34 g, respectively). Among the PGR treatments, P2 exhibited the highest fresh and dry weight of shoot (328.13 g and 73.20 g, respectively) which was statistically at par with P5 treatment for both fresh and dry weight of shoot (323.45 g and 72.49 g, respectively). Whereas, minimum shoot weight in fresh and dry basis was obtained in P4 treatment (216.82 g and 57.86 g, respectively) having statistical parity with P3 treatment for shoot dry weight (58.74 g). In addition, plants under N3P2 interaction had the highest fresh and dry weight of shoot (343.20 g and 76.17 g, respectively) which expressed statistical similarity with N2P2, N2P5, N4P2, N4P5, N5P2 and N5P5 combinations. Reversely, minimum fresh and dry weight of shoot was obtained in N3P4 (204.88 g) and N1P4 (53.47 g), respectively where other interactions of fertilizer with P3 and P4 had statistical similarity with those two combinations.

Root Weight

Root fresh weight of non-fertilized (N1) tomato plants was measured statistically minimum (33.84 g) while all other fertilized plants had statistical similarity of which N3 had the maximum root fresh weight plant-1 (35.81 g). Root dry weight had non-statistical variation for fertilizer application (Table 3). In terms of PGR treatments, P2 exhibited the highest fresh and dry weight of root (41.47 g and 20.77 g, respectively) which was statistically at par with P5 treatment (41.10 g and 20.61 g, respectively). Whereas, minimum weight of root in fresh and dry basis was obtained in P4 treatment (27.39 g and 17.43 g, respectively) having statistical parity with P3 treatment (28.65 g and 17.79 g, respectively). Interaction of fertilizer and PGR exhibited that significantly the highest fresh and dry weight of root was registered in N3P2 (42.82 g) and N4P2 (21.49 g), respectively where N2P2, N2P5, N4P2, N4P5, N5P2 and N5P5 had statistical harmony for root fresh weight and N1P2, N1P5, N2P2, N2P5, N3P1, N3P2, N3P5, N4P5, N5P2 and N5P5 got statistical unity for root dry weight with the best combination. Contrarily, the lowest fresh and dry weight of root was obtained from N3P4 (26.12 g) and N1P4 (16.46 g) combinations, respectively (Table 4).

Days Required to Flowering

Fertilizer application didn’t influence the number of days required to flowering in tomato; rather types of PGR significantly (p≤0.05) affected the floral induction date (Table 5). Due to fertilization, days required to flowering ranged from 47.33 days (N3) to 48.44 days (N1). Again, the earliest flowering within 44.02 days was found in P4 which expressed statistical unity with P3 treatment (44.27 days) while, delayed flowering was recorded in P5 treatment (51.35 days) having statistical affinity with P1 (50.05 days) and P2 (50.68 days) treatments. Moreover, nutrient-PGR interaction significantly influenced transplanting to floral induction duration in tomato where flowering occurred in the shortest possible time of 43.00 days in N3P3 combinations being statistically consistent with P3 and P4 interacted all fertilizer doses. On the other hand, N4P5 combination required maximum time (51.67 days) from transplanting to flowering in tomato which showed statistical similarity with the interaction treatments of all fertilizer doses and P1, P2 and P5 (Table 6).

Number of Flower Clusters

Nutrient, PGR and their interaction significantly (p≤0.05) stimulated the attainment of flower clusters in tomato (Table 5 and Table 6). Number of flower clusters was counted minimum in non-fertilized (N1) plants (10.93 plant-1), while it was recorded maximum in N3 treatment (12.55 plant-1) being statistically at par with N2, N4 and N5 treatments. Among the PGR treatments, maximum number of flower clusters was noted in plants applied with P2 (16.85 plant-1) which had statistical unity with P5 treatment (16.58 plant-1), while minimum number of flower clusters was observed in P4 treatment (5.93 plant-1) being similar to that of P3 treatment (5.98 plant-1). Besides, interaction exhibited that plants under N3P2 treatment produced maximum number of flower clusters (17.53 plant-1) which had statistical harmony with N2P2, N2P5, N3P5, N4P2, N4P5, N5P2 and N5P5 treatment combinations. Whereas, N1P3 and N1P4 treated plants developed same minimum number of flower clusters (5.67 plant-1) being statistically consistent with all other interactions between fertilizer and P2 and P4 (Table 6).

Number of Flowers and Fruits Cluster-1

Number of flowers and fruits cluster-1 didn’t vary significantly for fertilizer application but variation was significant in terms of PGR treatment and interaction of nutrient and PGR (Table 5 and Table 6). As a result of fertilization, number of flowers and fruits cluster-1 ranged from 7.08 to 7.47 and 4.61 to 5.04. Again, plants under P2 treatment had maximum 8.80 flowers and 5.79 fruits cluster-1 which showed statistical parity with P5 treatment (8.57 flowers and 5.59 fruits cluster-1). Oppositely, minimum number of flowers and fruits (5.58 and 3.89 cluster-1, respectively) was observed in P3 treatment having statistical harmony with P4 treatment. Once again, maximum 8.93 flowers and 5.93 fruits cluster-1 was counted in N2P2 and N3P2 interactions and N3P2 combination, respectively which exhibited statistical similarity with N1P1, N1P2, N1P5, N2P5, N3P1, N4P2, N4P5, N5P1, N5P2 and N5P5 interactions.

Single Fruit Weight

Fertilizers and PGRs application resulted in statistical variation in single fruit weight of tomato (Table 5). Plants under control fertilizer treatment (no fertilizer) produced the lightest fruit (56.50 g), while rest other fertilizer treatments produced fruits having statistically identical weight of which N3 had heavier fruits (61.89 g). Among the PGR treatments, single tomato was weighed maximum in P2 treatment (67.83 g) which had statistical similarity with P5 PGR treated plants (67.51 g). On the other hand, tomatoes having minimum weight were harvested from P3 treated plants (51.38 g) being statistically at par with P4 Treatment (51.63 g). Furthermore, interaction treatment showed that plants under N3P5 treatment produced the heaviest tomato (69.70 g) having statistical parity with that of N1P2, N1P5, N2P2, N2P5, N3P1, N3P2, N4P2, N4P5, N5P2 and N5P5 combinations. All the nutrient treatments interacted with P3 and P4 PGRs exhibited statistical unity for producing tomatoes having minimum individual fruit weight where the lightest tomato was found in N2P3 interaction (48.33 g) (Table 6).

Fruit Yield

As a result of variations in the vegetative and reproductive behaviors of tomato due to the application of fertilizers and PGRs, fruit yield of tomato also varied significantly (p≤0.05) in single effect as well as in interaction (Table 5 and Table 6). It was noticed that N1 fertilizer treatment had statistically minimum fruit yield (3.32 kg plant-1). Fertilizer doses like N2, N3, N4 and N5 had statistical harmony with respect to fruit yield where maximum yield was estimated in N3 treatment (4.33 kg plant-1). Besides, showing statistical disparity among all the PGR treatments maximum fruit yield was attained in P2 treatment (6.61 kg plant-1) followed by P5 treatment (6.25 kg plant-1) while minimum tomato yield was noted in P3 treatment (1.20 kg plant-1) having statistical similarity with P4 treatment (1.21 kg plant-1) and followed by P1 treatment (4.85 kg plant-1). Once again, interaction revealed that plants under N4P2 combination produced the highest yield (7.02 kg plant-1) and N1P3 combination had the lowest yield (0.87 kg plant-1). Treatment combinations like N2P2, N2P5, N3P2, N3P5, N4P5, N5P2 and N5P5 showed statistical parity with the best treatment, while N1P4, N2P3, N2P4, N3P3, N3P4, N4P3, N4P4, N5P3 and N5P4 interactions had statistical consistency with the worst yielding treatment combination in tomato under the study.

Correlation Coefficient Analysis

Pearson’s correlation co-efficient indicated the interrelationships among the studied 29 variables including growth and yield attributing characters of tomato upon fertilizer and PGR treatment (Figure 7). It was noted that plant height, base diameter, number of branches and leaves plant-1 and canopy spread at 20 DAT (PH1, BD1, BN1, LN1 and CS1) exhibited very weak correlation (-0.09 to 0.52) to other vegetative and reproductive parameters indicating that initial plant growth hardly influenced the yield contributing attributes of tomato. Reproductive trait namely days required to flowering (DFL) was not affected much with the change in the plant growth attributes as there had low to moderate positive correlation with DFL to vegetative traits. SPD also had moderate positive correlation (0.46 to 0.66) with yield characters and low to moderate positive correlation with morphological growth features demonstrating that plant growth didn’t influence the leaf SPAD value and SPAD value didn’t influence the flowering and fruiting in tomato largely. Again, PH2, PH3, BD2, BD3, BN2, BN3, LN2, LN3, CS2, CS3, LLF, ITD, LFA, SFW, RFW, SDW and RDW expressed moderate to very strong positive correlation (0.67 to 0.95) with the yield and yield contributing variables namely FCN, FLC, FTC, FWT and FYP indicating that plant height, base diameter, number of branches and leaves plant-1, canopy spread at 45 and 75 DAT, leaflets leaf-1, internode length and single leaf area at full blossom as well as shoot and root fresh and dry weight had great impact on the flower clusters plant-1, flowers and fruits cluster-1, single fruit weight and fruit yield plant-1 in tomato upon the application of fertilizers and PGRs at vegetative growth stages.

Principal Component Analysis

Principal component analysis (PCA) was employed to depict the relationship and impact of different fertilizer treatments and PGR types on growth and yield of tomato under high day and low night temperature condition in the late winter. The first four principal components, namely PC1 to PC4 (eigenvalues ≥ 1), account for 85% of the total variance in the data set of which PC1 (Dim1) and PC2 (Dim2) explained 65.4 % and 12 % of the total variation, respectively (8A). As seen in Figure 8, both Dim1 (PC1) and Dim2 (PC2) were positively correlated with plant height, base diameter, number of branches and leaves plant-1, canopy spread at 45 and 75 DAT (PH2, PH3, BD2, BD3, LN2, LN3, CS3), leaflets leaf-1, internode length and single leaf area at full blossom (LLF, ITD, LFA), shoot and root fresh weight (SFW, RFW), flower clusters plant-1 (FCN), flowers and fruits cluster-1 (FLC, FTC), single fruit weight (FWT) and fruit yield plant-1 (FYP) in tomato after the application of fertilizers and PGRs. These variables were, therefore, the most contributing factors for determining the best treatment of fertilizer and PGR.
Again, the PCA-biplot exhibited that the entire fertilizer treatments outlays one-another without forming any distinct separate clusters expressing slight deviation of N2, N3, N4 and N5 from N1 in relation to the Dim1 and Dim2 which represents those fertilizers had little or no influence on the growth and yield of tomato under studied condition (Figure 9A). On the other hand, Figure 9B shows that the five PGR treatments including control are grouped into three different clusters namely cluster I (P3 and P4), cluster II (control) and cluster III (P2 and P5) where P2 is located at a distinct position in the positive quadrant with relation to Dim1 and Dim2. The treatment P5 overlaps P2 in the right quadrants (Q1 and Q2) exhibiting that there existed close statistical similarity having positive correlations among the plant growth and yield contributing characters in tomato. Meanwhile, P3 and P4 outlaying one-another also had a distinguished position at the left side in the PCA-biplot which demonstrate that these two PGRs had negative impact on tomato at the studied condition. Whereas, P1 obtained the central position in the PCA and distributed in all the quadrants having slight overlapping with P2 and P5 at the right side which represent that the treatment contributed positively for most of the parameters. Further, the longer vector length of the parameters could better represent PC1 and PC2, and the results indicated the relationship between parameters by confirming the angle between two vectors (0° < positively correlated < 90°; uncorrelated, 0°; 90° < negatively correlated < 180°). Therefore, it can be concluded that P2 and P5 treatments had significant influence on promoting tomato growth and yield at the fluctuating temperature condition in the late winter.

3. Discussion

Light, temperature and humidity are the major environmental factors influencing the plant physiological functions [24,25], photosynthesis and hormonal balance [26] and key active processes in plants life [27,28] including the transition from one development stage to the next [29,30,31] . Besides environmental issues, edaphic elements also have remarkable influence on plant growth and development [32] as soil nutrient availability is one of the absolute needs of plants [33,34]. But imbalances or deviations of these prime requirements from the optimum levels create stresses causing disruption in the physiological functioning, break down the hormonal balance and ultimately assert negative impact on crop yield and produce quality [35,36,37].
In the present research, tomato var. BARI Tomato-14, a regular winter crop [38], has been grown in the late winter treating with different degrees of fertilization from 80 % to 110 % of recommendation [14]. The plants were further foliarly applied with GA3, NAA, 4-CPA and SA @ 50 ppm at the vegetative stage. It was observed that growth and yield contributing traits didn’t vary statistically with the increment of fertilizer dose from 20 % less (80 %) to 10 % extra (110 %) of the recommended dose (100 %), though numerical enhancement in growth and yield of tomato was noticed with fertilizer increase. Rather, variations were only noted from control which means that fertilized plants performed better than non-fertilized plants but fertilizer increment or reduction to a certain level didn’t influence the plant responses statistically. On the other hand, the four types of PGRs exhibited notable statistical variations in morphological and reproductive responses of tomato under studied condition. Researchers addressed that nutrient application has significant positive impact on growth, reproduction and yield of tomato and other related crops of similar growth habit [39,40,55,56,57] which are in resemblance with the present findings in terms of control versus fertilized plants and inconsistent in terms of the effect of the varied fertilizer doses. In the present observation, fluctuating as well as unstable temperature and humidity conditions during the late winter might have obstacle the efficient nutrient uptake by the tomato plants to response differently against varied levels of fertilization because plant performs negatively to any sorts of stresses. In addition, Kim et al. [41] and Loudari et al. [42] investigated that physiological functions, stomatal opening and hormonal regulations all get disrupted in imbalance weather conditions which restrict the nutrient uptake, plant growth and development as noted here with tomato.
Again, among the five PGR treatments including control, gibberellic acid (GA3) exhibited excellent vegetative and reproductive flourishment and salicylic acid (SA) had statistical resemblances with GA3 in most cases. Tomato plants under these two treatments had statistically maximum height, internode length, branching, leaves, canopy cover, fresh and dry biomass as well as flowers, fruits and finally tomato yield. Reversely, naphthalene acetic acid (NAA) and 4-chlorophenoxy acetic acid (4-CPA) treated plants showed retarded growth and development giving the ultimate lowest fruit yield. Plants receiving no PGR had mid-range responses. Tomato is a thermo-sensitive crop and plants are very much susceptible to changes in temperature, humidity and light and respond meticulously against stresses [36,43]. Again, plants’ responses to PGRs, especially when applied as spray, depend on environmental conditions like temperature, humidity, wind speed and light intensity [44]. Following foliar application of GA3 and SA, the growth characteristics of tomato plants were improved because these two PGRs had various effects on promoting cell division, cell enlargement/expansion, tissue differentiation, organ creation, vascular development, nutrient absorption, photosynthesis and biomass accumulation [36,45,46,47]. Again, GA3 and SA’s influence on stimulating the vegetative and reproductive behaviors of tomato under differential temperature and humidity conditions might be due to their ability to promote plants defense mechanisms against stresses [48]. Ogugua et al. [48] and Singh et al. [49] noted that GA3, among different exogenous plant growth regulators, exhibited significant positive impact on superior growth and yield in tomato. In addition, Guo et al. [36] and Ali et al. [50] examined GA3 success in fluctuating summer temperature stress mitigation in tomato. GA3 is important for tomato production to boost yield and improve fruit quality under unfavorable climatic conditions of high temperature [51]. Besides, salicylic acid (SA) is one of the multifunctional hormones whose supplemental use in plants regulate physiological, biochemical, and photosynthetic pigments and molecular mechanisms in response to stressful conditions [52] and this ability of SA stimulate the tomato growth and yield in the present research under unstable temperature and humidity regimes. Endogenous hormone levels in plants, especially auxins, are also degraded with day-night temperature fluctuation and humidity alteration [43]. In the present investigation, shorter plant growth and subsequent lower yield in tomato under NAA and 4-CPA might be due to the environmental unrest that restrict tomato plants to response positively upon applications. Reduce plant height, branching, canopy led to the lower number of flowers and fruits in plants which ultimately inferior fruit yield with NAA and 4-CPA treatment though flourishing results are available with NAA and 4-CPA application [50,53]. Again, flowering as well as fruit setting in tomato and other crops was promoted by GA3 at low concentration [54] as found in the present investigation too. Further, profound vegetative flourishment with higher number of branches and leaves as well as higher plant biomass in GA3 and SA treated plants accelerated the nutrient uptake from soil by plants [58]. Additionally, high fresh biomass and better canopy dimensions accounted for enhanced rate of photosynthesis and the latter process of accumulation and translocation of photosynthates to the sink resulting in the significant quantity of fruits having better quality in tomato during the late winter. Regulation in photosynthesis and source-sink translocation due to plant growth regulator application also noted in several studies [47,59]. Consequently, PGRs application could suppress the fertilizer efficiency in the adverse situation and our correlation and PCA findings also substantiate these phenomena. Therefore, the use of GA3 and SA among studied PGRs revealed effective to minimize the fertilization up to 20% for enhancing the morphological and reproductive traits of tomato.

4. Materials and Methods

Study Area and Planting material

The field and laboratory work of experiment was carried out at the research field and laboratory of the Department of Horticulture, Bangabandhu Sheikh Mujibur Rahman Agricultural University (BSMRAU), Gazipur-1706, Bangladesh during November 2022 to April 2023. Geographically the site was in 24.0379 ºN and 90.3996 ºE and characterized as a mix of tropical and sub-tropical climate with hot dry summer, long humid monsoon and short and dry winter [60]. Healthy, insect-pest and pathogen free seeds of tomato var. BARI Tomato-14 were collected from Bangladesh Agricultural Research Institute (BARI), Gazipur, Bangladesh prior to initiation of the experiment.

Crop Management

Pot cultivation was practiced under semi-protected environment. Grey colored plastic pots of 30 cm depth and 30 cm diameter were filled with planting media prepared by mixing well decomposed cowdung, vermicompost and garden soil in the ratio of 2:1:4 (v/v). Nutrient fertilizers were also added to the media as per treatment. Pretreated seeds of the variety “BARI Tomato-14” were soaked overnight and sown in a plastic tray on 12th November 2022. Seeds were germinated within five days; young tender seedlings were then transplanted to polybags (4 cm × 6 cm) to get strong, healthy seedlings for planting in the main pots. Uniform growth seedling at twenty five-day old were transplanted to the previously prepared pots on 7th December 2022. Two seedlings were transplanted in each pot and single plant was retained upon establishment. Stalking was provided on 20th December 2022 to protect the crop from lodging. All other intercultural operations like weeding, watering, insect-pest management etc. were performed as per commercial guides [38].

Experiment Design and Layout

The pot experiment was set in a factorial randomized complete block design (RCBD) with three replicates where three pots each containing single plant were considered as a replication under each treatment. Treatments consisted of five different doses of fertilizers including control for the factor one and in factor two four separate types of plant growth regulators (PGRs) namely Gibberellic acid (GA3), Naphthalene acetic acid (NAA), 4-Chlorophenoxy acetic acid (4-CPA) and Salicylic acid (SA) @ 50 ppm of each PGRs were used along with control or no PGR. All the pots with tomato plants were placed 60 cm apart from each other for convenient intercultural operations.

Treatment Preparation and Application

Nutrient treatment was given as soil feeding of fertilizers. Recommended fertilizer doses for tomato were accrued from FRG (Fertilizer Recommendation Guide), 2018 [14] of Bangladesh. Besides control (no fertilizer), the four fertilizer doses for individual pots of each replication were 100 %, 110 %, 90 % and 80 % of FRG’2018 where 100 % of FRG’2018 was denoted as 12 g of urea, 10 g of TSP, 5 g of MoP, 3 g of Gypsum, 0.5 g of ZnSO4 and 0.5 g of Boric acid plant-1. For applying nutrients; full of TSP, Gypsum, ZnSO4 and Boric acid and 1/3rdof MoP was mixed with the planting media during pot preparation. Urea and rest of the MoP was applied as side dressing in three and two installments, respectively. Urea was applied at the base of the plants and mixed with the media at 10, 25 and 40 days after transplanting (DAT). While, the 2/3rd of MoP was mixed with the pot soil at 25 and 40 DAT. Immediately after fertilization, light irrigation was applied every time.
On the other hand, the PGR treatment was applied as foliar spray at the vegetative growth stage of the plants for twice at 23 and 38 DAT i.e., two days before fertilizer application so that PGR mediated physiological and hormonal activities could influence the nutrient uptake from the soil. All the PGRs were applied at 50 ppm concentration. Two litre (2 L) solution of each of the PGRs was prepared to spray 45 plants under each treatment. For preparing 1 L solution, exactly 50 mg of PGR powder was weighed in a 50 mL beaker and 5 mL of Methanol (70 %) was added into it and agitated in a magnetic stirrer until completely dissolved. Then, it was poured in a 1 L volumetric flask and filled up to the mark by adding distilled water. The procedure was repeated to make the solution volume to 2 L. Plants were sprayed with the solutions as per treatments at the both sides of the leaf until runoff from the leaves was noticed. Control plants were applied with equal volume of distilled water only at the similar way adding 10 mL of methanol in 2 L water.

Measurement of Growth Characteristics

Influence of fertilizers and PGRs on vegetative growth characters of tomato were assessed by measuring plant height (cm), base diameter (cm), number of branches and leaves plant-1 and canopy spread (cm) periodically at 20, 45 and 75 DAT. Further, number of leaflets leaf-1, internode length (cm), individual leaf area (cm2) and leaf greenness (SPAD value) were determined at fruiting stage. Individual leaf area (cm2) and SPAD value of five leaves leaving 5-6 leaves from top were measured by an electric area meter (LI 3000, USA) and portable chlorophyll meter (SPAD-502Plus; Konica Minolta, Japan), respectively. Individual leaf area was estimated as per Aurdal et al. [61] where leaflets of the large tomato leaf were separated and differently run under the electric area meter and averaged. Again, after the final fruit harvest at 29th March, plants were carefully removed out of the pots, root systems and shoots were separated, they were washed under running water and fresh weight (g) was recorded. Dry weight (g) of shoot and root was measured too through drying the samples at 60ºC in an electric oven (SANYO Drying Oven, MOV202, Japan) for a week.

Assessment of Reproductive Traits and Fruit Yield

Flowering initiated on 19th January 2023 and various reproductive data namely number of days required from transplanting to the first flowering, number of flower clusters plant-1, number of flowers and fruits cluster-1were noted against each replication under treatment. Harvesting was initiated on 23rd February 2023 and continued till 9th April 2023. Immediately after harvest, fruits were weighed (g) and averaged to get total fruit yield plant-1 by multiplying the individual fruit weight with the number of fruits cluster-1 and number of flower clusters plant-1.

Statistical Analysis

Two-way analysis of variance (ANOVA) was performed for hypothesis test. Data were presented as the average of three replicates plus standard error (SE) where three observations were done per replication (n=3). The treatment means were separated by Fisher’s protected least significant difference (LSD) test, using a p-value of ≤ 0.05 to be statistically significant. In addition, correlation matrix and cluster analysis were conducted to examine the interrelationships among the studied dependent variables with respect to the fertilization and PGRs treatments where the strength of correlations among the properties were assessed according to Pearson’s correlation co-efficient. Afterwards, multivariate analysis like principal component analysis (PCA) was done to see more insight into the data matrix and sort out the most effective variables contribute in the total variations. Correlation matrix, cluster analysis and PCA were performed using different packages (agricole, facatominer, factoextra, ggplot2, corrplot) of ‘R’ program (version 4.1.2).

Ethical statement

Tomato var. BARI Tomato-14 (registered tomato variety of Bangladesh released by Bangladesh Agricultural Research Institute) was used as plant material and fertilizers at varied amounts and plant growth regulators namely GA3, NAA, 4-CPA and SA were used as treatment materials in the present study. Plastic pots were filled with soil media where tomato seedlings were transplanted and grown to obtain yield. The lab and field experiments in this study were carried out following guidelines and recommendations of “Biosafety Guidelines of Bangladesh” published by Ministry of Environment and Forest, Government of the People’s Republic of Bangladesh (2005).

5. Conclusions

In conclusion, the exogenous application of GA3 and SA @ 50 ppm remarkably influenced vegetative growth, yield-related reproductive traits and yield of tomato. Aside from this, fertilizer application 80-110 % of the recommended doses have no significant impact in compare to the PGRs that revealed that exogenous application of GA3 and SA (salicylic acid) could reduce 20% of chemical fertilization without compromising the tomato yield. Therefore, it can be suggested that application of GA3 and SA would be used as substitute of chemical fertilizer for enhancing the growth and yield of tomato even under adverse temperature and humidity differentiation in late winter in Bangladesh. Since this is an initial investigation on the use of PGRs as substitution of chemical fertilizer, further studies would be carried out with a wider range of PGRs concentrations focusing on the other abiotic stress conditions for comprehensive understanding of the interactive mode of action of the respective PGR in response to the specific growing stages of tomato.

Author Contributions

J Hassan and J Gomasta conceived the idea of the study, design the experiment, conduct the study and wrote the manuscript. J Hassan, J Gomasta and H Sultana contributed in sample collection, preparation and laboratory analyses. J Hassan and J Gomasta analyze the data and made necessary interpretation. Y Ozaki, S Alamri, A T Alfagham and L A Al-Humaid reviewed and edited the manuscript for further improvement. All authors have read, edited the manuscript and approved it for submission.

Funding

The publication charge was supported by the the Researchers Supporting Project number (RSP2023R194), King Saud University, Riyadh, Saudi Arabia.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Acknowledgments

The authors are highly grateful to the Ministry of Science and Technology (MoST), Bangladesh for the financial support to continue this study under the project entitled “Plant growth regulators (PGRs) mediated approach in reducing chemical fertilizers for Tomato and Brinjal cultivation addressing adverse climate in Bangladesh” (Project ID: SRG-221180). The authors further would like to extend their sincere appreciation to the Researchers Supporting Project number (RSP2023R194), King Saud University, Riyadh, Saudi Arabia.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Li, Y.; Wang, H.; Zhang, Y.; Martin, C. Can the world’s favorite fruit, tomato, provide an effective biosynthetic chassis for high-value metabolites? Plant Cell Rep. 2008, 37, 1443–1450. [Google Scholar] [CrossRef] [PubMed]
  2. Braga, P.C.; Scalzo, R.L.; Dal Sasso, M.; Lattuada, N.; Greco, V.; Fibiani, M. Characterization and antioxidant activity of semi-purified extracts and pure delphinidin-glycosides from eggplant peel (Solanum melongena L.). J. Funct. Foods 2016, 20, 411–421. [Google Scholar] [CrossRef]
  3. Viuda-Martos, M.; Sanchez-Zapata, E.; Sayas-Barberá, E.; Sendra, E.; Pérez-Álvarez, J.A.; Fernández-López, J. Tomato and tomato byproducts. Human health benefits of lycopene and its application to meat products: a review. Crit. Rev. Food Sci. Nutr. 2014, 54, 1032–1049. [Google Scholar] [CrossRef] [PubMed]
  4. Khan, U.M.; Sevindik, M.; Zarrabi, A.; Nami, M.; Ozdemir, B.; Kaplan, D.N.; .... Sharifi-Rad, J. Lycopene: Food sources, biological activities, and human health benefits. Oxid. Med. Cell. Longev. 2021, 2021, 2713511. [CrossRef]
  5. Meena, R.K.; Vashisth, A.; Singh, R.; Singh, B.; Manjaih, K.M. Study on change in microenvironment under different colour shade nets and its impact on yield of spinach (Spinacia oleracea L.). J. Agrometeorol. 2014, 16, 104–111. [Google Scholar] [CrossRef]
  6. Khan, M.H.R.; Rahman, A.; Luo, C.; Kumar, S.; Islam, G.M.A.; Hossain, M.A. Detection of changes and trends in climatic variables in Bangladesh during 1988–2017. Heliyon 2019, 5, e01268. [Google Scholar] [CrossRef]
  7. Shamshiri, R.R.; Jones, J.W.; Thorp, K.R.; Ahmad, D.; Man, H.C.; Taheri, S. Review of optimum temperature, humidity, and vapour pressure deficit for microclimate evaluation and control in greenhouse cultivation of tomato: a review. Int Agrophys. 2018, 32, 287–302. [Google Scholar] [CrossRef]
  8. Firon, N.; Pressman, E.; Meir, S.; Khoury, R.; Altahan, L. Ethylene is involved in maintaining tomato (Solanum lycopersicum) pollen quality under heat-stress conditions. AoB Plants 2012, 2012, pls024. [Google Scholar] [CrossRef] [PubMed]
  9. Singh, U.; Patel, P.K.; Singh, A. K.; Tiwari, V.; Kumar, R.; Rai, N.; .... Singh, B. Screening of tomato genotypes underhigh temperature stress for reproductive traits. Veg. Sci. 2015, 42, 52–55. [Google Scholar]
  10. Lin, W.A.N.G.; Zai-qiang, Y.A.N.G.; Ming-tian, W.A.N.G.; Shi-qiong, Y.A.N.G.; Xia, C.A.I.; Jie, Z.H.A.N.G. Effect of air humidity on nutrient content and dry matter distribution of tomato seedlings under high temperature. Chin. J. Agrometeorol. 2018, 39, 304. [Google Scholar]
  11. Mulholland, B.J.; Edmondson, R.N.; Fussell, M.; Basham, J.; Ho, L.C. Effects of high temperature on tomato summer fruit quality. J. Hortic. Sci. Biotechnol. 2003, 78, 365–374. [Google Scholar] [CrossRef]
  12. FAOSTAT. FAOSTAT Production Database-2022. Available online at: https://www.fao.org/faostat/en/#home. (Accessed on 20th June 2022).
  13. BBS. Yearbook of Agricultural Statistics-2021, Bangladesh Bureau of Statistics (BBS), Statistics and Informatics Division (SID), Ministry of Planning, Government of the People’s Republic of Bangladesh. Available at: https://www.bbs.gov.bd.
  14. Ahmed, S.; Jahiruddin, M.; Razia, S.; Begum, R.A.; Biswas, J.C.; Rahman, A.S.M.M.; Ali, M.M.; Islam, K.M.S.; Hossain, M.M.; Gani, M.N.; Hossain, G.M.A.; Satter, M.A. Fertilizer Recommendation Guide-2018. Bangladesh Agricultural Research Council (BARC), Farmgate, Dhaka-1215. 223p.
  15. Kakar, K.; Xuan, T.D.; Noori, Z.; Aryan, S.; Gulab, G. Effects of organic and inorganic fertilizer application on growth, yield, and grain quality of rice. Agriculture 2020, 10, 544. [Google Scholar] [CrossRef]
  16. Mozumder, P.; Berrens, R.P. Inorganic fertilizer use and biodiversity risk: An empirical investigation. Ecol. Econ. 2007, 62, 538–543. [Google Scholar] [CrossRef]
  17. Abebe, T.G.; Tamtam, M.R.; Abebe, A.A.; Abtemariam, K.A.; Shigut, T.G.; Dejen, Y.A.; Haile, E.G. Growing use and impacts of chemical fertilizers and assessing alternative organic fertilizer sources in Ethiopia. Appl. Environ. Soil Sci. 2022, 2022, 1–14. [Google Scholar] [CrossRef]
  18. Lolamo, T.; Senbeta, A.F.; Keneni, Y.G.; Sime, G. Effects of Bio-Slurry and Chemical Fertilizer Application on Soil Properties and Food Safety of Tomato (Solanum lycopersicum Mill.). Appl. Environ. Soil Sci. 2023, 2023, 1–16. [Google Scholar] [CrossRef]
  19. Yadav, S.; Sharma, K.D. Molecular and morphophysiological analysis of drought stress in plants. Plant Growth 2016, 10, 65246. [Google Scholar]
  20. Siddiqui, M.A.; Shah, Z.H.; Tunio, S.; Chacchar, Q. Effect of different nitrogen (N) phosphorus (P) fertilizer and plant growth regulators gibberellic acid (GA3) and indole-3-acetic acid (IAA) on qualitative traits of Canola (Brassica napus L.) genotypes. Ind. J. Pure App. Biosci. 2016, 4, 238–244. [Google Scholar] [CrossRef]
  21. Meshram, J.H.; Singh, S.B.; Raghavendra, K.P.; Waghmare, V.N. Drought stress tolerance in cotton: Progress and perspectives. Climate Change and Crop Stress 2022, 135–169. [Google Scholar]
  22. Zhang, H.; Sun, X.; Dai, M. Improving crop drought resistance with plant growth regulators and rhizobacteria: Mechanisms, applications, and perspectives. Plant Commun. 2022, 3, 1–15. [Google Scholar] [CrossRef]
  23. Hassan, J.; Miyajima, I. Induction of parthenocarpy in pointed gourd (Trichosanthes dioica Roxb.) by application of plant growth regulators. J. Hortic. Plant Res. 2019, 8, 13. [Google Scholar] [CrossRef]
  24. Chiang, C.; Bånkestad, D.; Hoch, G. Reaching natural growth: The significance of light and temperature fluctuations in plant performance in indoor growth facilities. Plants 2020, 9, 1312. [Google Scholar] [CrossRef] [PubMed]
  25. Mondaca, P.; Valenzuela, P.; Quiroz, W.; Valdenegro, M.; Abades, S.; Celis-Diez, J.L. Environmental conditions and plant physiology modulate Cu phytotoxicity in field-contaminated soils. Ecotoxicol. Environ. Saf. 2022, 246, 114179. [Google Scholar] [CrossRef] [PubMed]
  26. Driesen, E.; Van den Ende, W.; De Proft, M.; Saeys, W. Influence of environmental factors light, CO2, temperature, and relative humidity on stomatal opening and development: A review. Agronomy 2020, 10, 1975. [Google Scholar] [CrossRef]
  27. Qaderi, M.M.; Martel, A.B.; Dixon, S.L. Environmental factors influence plant vascular system and water regulation. Plants 2019, 8, 65. [Google Scholar] [CrossRef] [PubMed]
  28. Pant, P.; Pandey, S.; Dall'Acqua, S. The influence of environmental conditions on secondary metabolites in medicinal plants: A literature review. Chem. Biodivers. 2021, 18, e2100345. [Google Scholar] [CrossRef]
  29. Chia, S.Y.; Lim, M.W. A critical review on the influence of humidity for plant growth forecasting. IOP Conference Series: Materials Science and Engineering 2022, 1257, 012001. [Google Scholar] [CrossRef]
  30. Dinu, M.D.; Mazilu, I.E.; Cosmulescu, S. Influence of Climatic Factors on the Phenology of Chokeberry Cultivars Planted in the Pedoclimatic Conditions of Southern Romania. Sustainability 2022, 14, 4991. [Google Scholar] [CrossRef]
  31. Upadhyay, H.; Juneja, A.; Turabieh, H.; Malik, S.; Gupta, A.; Bitsue, Z.K.; Upadhyay, C. Exploration of crucial factors involved in plants development using the fuzzy AHP Method. Math. Probl. Eng. 2022, 2022. [Google Scholar] [CrossRef]
  32. Triantafyllidis, V.; Zotos, A.; Kosma, C.; Kokkotos, E. Effect of land-use types on edaphic properties and plant species diversity in Mediterranean agroecosystem. Saudi J. Biol. Sci. 2020, 27, 3676–3690. [Google Scholar] [CrossRef]
  33. Gao, J.; Wang, J.; Li, Y. Effects of Soil Nutrients on Plant Nutrient Traits in Natural Pinus tabuliformis Forests. Plants 2023, 12, 735. [Google Scholar] [CrossRef]
  34. Smith, M.R.; Reis Hodecker, B.E.; Fuentes, D.; Merchant, A. Investigating nutrient supply effects on plant growth and seed nutrient content in common bean. Plants 2022, 11, 737. [Google Scholar] [CrossRef] [PubMed]
  35. Cannavo, P.; Recous, S.; Valé, M.; Bresch, S.; Paillat, L.; Benbrahim, M.; Guénon, R. Organic fertilization of growing media: response of N mineralization to temperature and moisture. Horticulturae 2022, 8, 152. [Google Scholar] [CrossRef]
  36. Guo, T.; Gull, S.; Ali, M.M.; Yousef, A.F.; Ercisli, S.; Kalaji, H.M.; .... Ghareeb, R.Y. Heat stress mitigation in tomato (Solanum lycopersicum L.) through foliar application of gibberellic acid. Sci. Rep. 2022, 12, 11324. [Google Scholar] [CrossRef] [PubMed]
  37. Gupta, A.; Yadav, D.S.; Agrawal, S.B.; Agrawal, M. Individual effects of high temperature and tropospheric ozone on tomato: a review. J. Plant Growth Regul. 2023, 42, 1421–1443. [Google Scholar] [CrossRef]
  38. Azad, A.K.; Miaruddin, M.; Wohab, M.A.; Sheikh, M.H.R.; Nag, B.L.; Rahman, M.H.H. (Eds.) (2020). KRISHI PROJUKTI HATBOI (Handbook on Agro-Technology). Bangladesh Agricultural Research Institute, Gazipur-1701, Bangladesh. p 562.
  39. Gao, F.; Li, H.; Mu, X.; Gao, H.; Zhang, Y.; Li, R.; .... Ye, L. Effects of Organic Fertilizer Application on Tomato Yield and Quality: A Meta-Analysis. Appl. Sci. 2023, 13, 2184. [Google Scholar]
  40. Traoré, A.; Bandaogo, A.A.; Savadogo, O.M.; Saba, F.; Ouédraogo, A.L.; Sako, Y.; .... Ouédraogo, S. Optimizing Tomato (Solanum lycopersicum L.) Growth with Different Combinations of Organo-Mineral Fertilizers. Front. Sustain. Food Syst. 2022, 5, 694628. [Google Scholar] [CrossRef]
  41. Kim, Y.X.; Son, S.Y.; Lee, S.; Lee, Y.; Sung, J.; Lee, C.H. Effects of limited water supply on metabolite composition in tomato fruits (Solanum lycopersicum L.) in two soils with different nutrient conditions. Front. Plant Sci. 2022, 13, 983725. [Google Scholar] [CrossRef]
  42. Loudari, A.; Mayane, A.; Zeroual, Y.; Colinet, G.; Oukarroum, A. Photosynthetic performance and nutrient uptake under salt stress: Differential responses of wheat plants to contrasting phosphorus forms and rates. Front. Plant Sci. 2022, 13, 1038672. [Google Scholar] [CrossRef]
  43. Zheng, Y.; Yang, Z.; Xu, C.; Wang, L.; Huang, H.; Yang, S. The interactive effects of daytime high temperature and humidity on growth and endogenous hormone concentration of tomato seedlings. HortScience 2020, 55, 1575–1583. [Google Scholar] [CrossRef]
  44. Cline, J.A.; Bijl, M. Diurnal spray timing does not affect the thinning of apples with carbaryl, benzyladenine, and napthaleneacetic acid. Can. J. Plant Sci. 2002, 82, 437–441. [Google Scholar] [CrossRef]
  45. Bagautdinova, Z.Z.; Omelyanchuk, N.; Tyapkin, A.V.; Kovrizhnykh, V.V.; Lavrekha, V.V.; Zemlyanskaya, E.V. Salicylic acid in root growth and development. Int. J. Mol. Sci. 2022, 23, 2228. [Google Scholar] [CrossRef] [PubMed]
  46. Kaya, C.; Ugurlar, F.; Ashraf, M.; Ahmad, P. Salicylic acid interacts with other plant growth regulators and signal molecules in response to stressful environments in plants. Plant Physiol. Biochem. 2023, 196, 431–443. [Google Scholar] [CrossRef] [PubMed]
  47. Shah, S.H.; Islam, S.; Alamri, S.; Parrey, Z.A.; Mohammad, F.; Kalaji, H.M. Plant growth regulators mediated changes in the growth, photosynthesis, nutrient acquisition and productivity of mustard. Agriculture 2023, 13, 570. [Google Scholar] [CrossRef]
  48. Ogugua, U.V.; Kanu, S.A.; Ntushelo, K. Gibberellic acid improves growth and reduces heavy metal accumulation: A case study in tomato (Solanum lycopersicum L.) seedlings exposed to acid mine water. Heliyon 2022, 8. [Google Scholar] [CrossRef]
  49. Singh, J.; Dwivedi, A.K.; Devi, P.; Bajeli, J.; Tripathi, A.; Maurya, S.K. Effect of plant growth regulators on growth and yield attributes of tomato (Solanum lycopersicom Mill.). Int. J. Curr. Microbiol. Appl. Sci. 2019, 8, 1635–1641. [Google Scholar] [CrossRef]
  50. Ali, M.R.; Quddus, M.A.; Trina, T.N.; Salim, M.M.R.; Asaduzzaman, M. Influence of plant growth regulators on growth, yield, and quality of tomato grown under high temperature in the tropics in the summer. Int. J. Veg. Sci. 2022, 28, 59–75. [Google Scholar] [CrossRef]
  51. Gelmesa, D.; Abebie, B.; Desalegn, L. Regulation of tomato (Lycopersicon esculentum Mill.) fruit setting and earliness by gibberellic acid and 2, 4-dichlorophenoxy acetic acid application. Afr. J. Biotechnol. 2012, 11, 11200–11206. [Google Scholar]
  52. Chen, S.; Zhao, C.B.; Ren, R.M.; Jiang, J.H. Salicylic acid had the potential to enhance tolerance in horticultural crops against abiotic stress. Front. Plant Sci. 2023, 14, 1141918. [Google Scholar] [CrossRef]
  53. Jha, R.K.; Thapa, R.; Shrestha, A.K. Effect of GA3 and NAA on tomato production under protected cultivation in Kaski, Nepal. J. Agric. Food Res. 2022, 10, 100450. [Google Scholar] [CrossRef]
  54. Maboko, M.M.; Du Plooy, C.P. Effect of plant growth regulators on growth, yield, and quality of sweet pepper plants grown hydroponically. HortScience 2015, 50, 383–386. [Google Scholar] [CrossRef]
  55. Sultana, N.; Mannan, M.A.; Khan, S.A.K.U.; Gomasta, J.; Roy, T. Effect of Different Manures on Growth, Yield and Profitability of Small Scale Brinjal (Egg-Plant) Cultivation in Gunny Bag. Asian J. Agric. Hort. Res. 2022, 9, 52–60. [Google Scholar] [CrossRef]
  56. Lin, W.; Lin, M.; Zhou, H.; Wu, H.; Li, Z.; Lin, W. The effects of chemical and organic fertilizer usage on rhizosphere soil in tea orchards. PloS one 2019, 14, e0217018. [Google Scholar] [CrossRef] [PubMed]
  57. Apu, S.C.; Biswas, M.S.; Bhuiyan, M.A.B.; Gomasta, J.; Easmin, S.; Kayesh, E. Effect of Organic Amendments and Arbuscular Mycorrhizal Fungi on Plant Growth, Yield and Quality of Strawberry. Ann. Bangladesh Agric. 2022, 26, 71–82. [Google Scholar] [CrossRef]
  58. Emamverdian, A.; Ding, Y.; Mokhberdoran, F. The role of salicylic acid and gibberellin signaling in plant responses to abiotic stress with an emphasis on heavy metals. Plant Signal. Behav. 2020, 15, 1777372. [Google Scholar] [CrossRef]
  59. Katel, S.; Mandal, H.R.; Kattel, S.; Yadav, S.P.S.; Lamshal, B.S. Impacts of plant growth regulators in strawberry plant: A review. Heliyon 2022, 8, e11959. [Google Scholar] [CrossRef] [PubMed]
  60. Khan, M.N.E.A.; Hassan, J.; Biswas, M.S.; Khan, H.I.; Sultana, H.; Suborna, M.N.; .... Anik, A.A.M. Morphological and anatomical characterization of colchicine-induced polyploids in watermelon. Hortic. Environ. Biotechnol. 2023, 2023, 1–14.
  61. Aurdal, S.M.; Foereid, B.; Sogn, T.; Børresen, T.; Hvoslef-Eide, T.; Fagertun Remberg, S. Growth, yield and fruit quality of tomato Solanum lycopersicum L grown in sewage-based compost in a semi-hydroponic cultivation system. Acta Agric. Scand. - B Soil Plant Sci. 2022, 72, 902–912. [Google Scholar] [CrossRef]
Figure 1. Temperature (A) and relative humidity (B) status of the experimental site during the growing period of tomato (December 01, 2022 to April 30, 2023). Here, T. Max. and T. Min. indicate maximum and minimum temperature, respectively and RH Max. and RH Min. represent maximum and minimum relative humidity, respectively.
Figure 1. Temperature (A) and relative humidity (B) status of the experimental site during the growing period of tomato (December 01, 2022 to April 30, 2023). Here, T. Max. and T. Min. indicate maximum and minimum temperature, respectively and RH Max. and RH Min. represent maximum and minimum relative humidity, respectively.
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Figure 2. Plant height of tomato at different days after transplanting as influenced by the application of fertilizers (A) and plant growth regulators (B). Vertical bars on the top of the columns represent the standard errors of means of three replicates. Different letters indicate the statistical differences among the treatments at p≤0.05. Here, N1, N2, N3, N4 and N5 represent control (no fertilizer), 100, 110, 90 and 80 % of FRG’2018, respectively and P1, P2, P3, P4 and P5 indicate control (no PGR), GA3, NAA, 4-CPA and SA at 50 ppm, respectively.
Figure 2. Plant height of tomato at different days after transplanting as influenced by the application of fertilizers (A) and plant growth regulators (B). Vertical bars on the top of the columns represent the standard errors of means of three replicates. Different letters indicate the statistical differences among the treatments at p≤0.05. Here, N1, N2, N3, N4 and N5 represent control (no fertilizer), 100, 110, 90 and 80 % of FRG’2018, respectively and P1, P2, P3, P4 and P5 indicate control (no PGR), GA3, NAA, 4-CPA and SA at 50 ppm, respectively.
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Figure 3. Base diameter of tomato at different days after transplanting as influenced by the application of fertilizers (A) and plant growth regulators (B). Vertical bars on the top of the columns represent the standard errors of means of three replicates. Different letters indicate the statistical differences among the treatments at p≤0.05. Here, N1, N2, N3, N4 and N5 represent control (no fertilizer), 100, 110, 90 and 80 % of FRG’2018, respectively and P1, P2, P3, P4 and P5 indicate control (no PGR), GA3, NAA, 4-CPA and SA at 50 ppm, respectively.
Figure 3. Base diameter of tomato at different days after transplanting as influenced by the application of fertilizers (A) and plant growth regulators (B). Vertical bars on the top of the columns represent the standard errors of means of three replicates. Different letters indicate the statistical differences among the treatments at p≤0.05. Here, N1, N2, N3, N4 and N5 represent control (no fertilizer), 100, 110, 90 and 80 % of FRG’2018, respectively and P1, P2, P3, P4 and P5 indicate control (no PGR), GA3, NAA, 4-CPA and SA at 50 ppm, respectively.
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Figure 4. Number of branches plant-1 of tomato at different days after transplanting as influenced by the application of fertilizers (A) and plant growth regulators (B). Vertical bars on the top of the columns represent the standard errors of means of three replicates. Different letters indicate the statistical differences among the treatments at p≤0.05. Here, N1, N2, N3, N4 and N5 represent control (no fertilizer), 100, 110, 90 and 80 % of FRG’2018, respectively and P1, P2, P3, P4 and P5 indicate control (no PGR), GA3, NAA, 4-CPA and SA at 50 ppm, respectively.
Figure 4. Number of branches plant-1 of tomato at different days after transplanting as influenced by the application of fertilizers (A) and plant growth regulators (B). Vertical bars on the top of the columns represent the standard errors of means of three replicates. Different letters indicate the statistical differences among the treatments at p≤0.05. Here, N1, N2, N3, N4 and N5 represent control (no fertilizer), 100, 110, 90 and 80 % of FRG’2018, respectively and P1, P2, P3, P4 and P5 indicate control (no PGR), GA3, NAA, 4-CPA and SA at 50 ppm, respectively.
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Figure 5. Number of leaves plant-1 of tomato at different days after transplanting as influenced by the application of fertilizers (A) and plant growth regulators (B). Vertical bars on the top of the columns represent the standard errors of means of three replicates. Different letters indicate the statistical differences among the treatments at p≤0.05. Here, N1, N2, N3, N4 and N5 represent control (no fertilizer), 100, 110, 90 and 80 % of FRG’2018, respectively and P1, P2, P3, P4 and P5 indicate control (no PGR), GA3, NAA, 4-CPA and SA at 50 ppm, respectively.
Figure 5. Number of leaves plant-1 of tomato at different days after transplanting as influenced by the application of fertilizers (A) and plant growth regulators (B). Vertical bars on the top of the columns represent the standard errors of means of three replicates. Different letters indicate the statistical differences among the treatments at p≤0.05. Here, N1, N2, N3, N4 and N5 represent control (no fertilizer), 100, 110, 90 and 80 % of FRG’2018, respectively and P1, P2, P3, P4 and P5 indicate control (no PGR), GA3, NAA, 4-CPA and SA at 50 ppm, respectively.
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Figure 6. Canopy spread plant-1 of tomato at different days after transplanting as influenced by the application of fertilizers (A) and plant growth regulators (B). Vertical bars on the top of the columns represent the standard errors of means of three replicates. Different letters indicate the statistical differences among the treatments at p≤0.05. Here, N1, N2, N3, N4 and N5 represent control (no fertilizer), 100, 110, 90 and 80 % of FRG’2018, respectively and P1, P2, P3, P4 and P5 indicate control (no PGR), GA3, NAA, 4-CPA and SA at 50 ppm, respectively.
Figure 6. Canopy spread plant-1 of tomato at different days after transplanting as influenced by the application of fertilizers (A) and plant growth regulators (B). Vertical bars on the top of the columns represent the standard errors of means of three replicates. Different letters indicate the statistical differences among the treatments at p≤0.05. Here, N1, N2, N3, N4 and N5 represent control (no fertilizer), 100, 110, 90 and 80 % of FRG’2018, respectively and P1, P2, P3, P4 and P5 indicate control (no PGR), GA3, NAA, 4-CPA and SA at 50 ppm, respectively.
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Figure 7. Correlation matrix of growth and yield related 29 variables of tomato. [PH, BD, BN, LN and CS represent plant height, base diameter, number of branches plant-1, number of leaves plant-1 and canopy spread plant-1, respectively and the adjacent digits 1, 2 and 3 indicate 20, 45 and 75 DAT, respectively; LLF, ITD, LFA and SPD allude number of leaflets leaf-1, internode length, single leaf area and leaf SPAD value, respectively; SFW, SDW, RFW and RDW indicate shoot and root fresh and dry weight, respectively; DFL, FCN, FLC, FTC, FWT and FYP represent days required to flowering, number of flower clusters plant-1, number of flowers cluster-1, number of fruits cluster-1, individual fruit weight and fruit yield plant-1, respectively].
Figure 7. Correlation matrix of growth and yield related 29 variables of tomato. [PH, BD, BN, LN and CS represent plant height, base diameter, number of branches plant-1, number of leaves plant-1 and canopy spread plant-1, respectively and the adjacent digits 1, 2 and 3 indicate 20, 45 and 75 DAT, respectively; LLF, ITD, LFA and SPD allude number of leaflets leaf-1, internode length, single leaf area and leaf SPAD value, respectively; SFW, SDW, RFW and RDW indicate shoot and root fresh and dry weight, respectively; DFL, FCN, FLC, FTC, FWT and FYP represent days required to flowering, number of flower clusters plant-1, number of flowers cluster-1, number of fruits cluster-1, individual fruit weight and fruit yield plant-1, respectively].
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Figure 8. Principal component analysis (PCA) (A) and factor loadings for the first two principal components (Dim 1 and Dim 2) (B) of growth and yield attributes of tomato. [PH, BD, BN, LN and CS represent plant height, base diameter, number of branches plant-1, number of leaves plant-1 and canopy spread plant-1, respectively and the adjacent digits 1, 2 and 3 indicate 20, 45 and 75 DAT, respectively; LLF, ITD, LFA and SPD allude number of leaflets leaf-1, internode length, single leaf area and leaf SPAD value, respectively; SFW, SDW, RFW and RDW indicate shoot and root fresh and dry weight, respectively; DFL, FCN, FLC, FTC, FWT and FYP represent days required to flowering, number of flower clusters plant-1, number of flowers cluster-1, number of fruits cluster-1, individual fruit weight and fruit yield plant-1, respectively].
Figure 8. Principal component analysis (PCA) (A) and factor loadings for the first two principal components (Dim 1 and Dim 2) (B) of growth and yield attributes of tomato. [PH, BD, BN, LN and CS represent plant height, base diameter, number of branches plant-1, number of leaves plant-1 and canopy spread plant-1, respectively and the adjacent digits 1, 2 and 3 indicate 20, 45 and 75 DAT, respectively; LLF, ITD, LFA and SPD allude number of leaflets leaf-1, internode length, single leaf area and leaf SPAD value, respectively; SFW, SDW, RFW and RDW indicate shoot and root fresh and dry weight, respectively; DFL, FCN, FLC, FTC, FWT and FYP represent days required to flowering, number of flower clusters plant-1, number of flowers cluster-1, number of fruits cluster-1, individual fruit weight and fruit yield plant-1, respectively].
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Figure 9. PCA-Biplot of the Nutrients and PGRs treatment [PH, BD, BN, LN and CS represent plant height, base diameter, number of branches plant-1, number of leaves plant-1 and canopy spread plant-1, respectively and the adjacent digits 1, 2 and 3 indicate 20, 45 and 75 DAT, respectively; LLF, ITD, LFA and SPD allude number of leaflets leaf-1, internode length, single leaf area and leaf SPAD value, respectively; SFW, SDW, RFW and RDW indicate shoot and root fresh and dry weight, respectively; DFL, FCN, FLC, FTC, FWT and FYP represent days required to flowering, number of flower clusters plant-1, number of flowers cluster-1, number of fruits cluster-1, individual fruit weight and fruit yield plant-1, respectively].
Figure 9. PCA-Biplot of the Nutrients and PGRs treatment [PH, BD, BN, LN and CS represent plant height, base diameter, number of branches plant-1, number of leaves plant-1 and canopy spread plant-1, respectively and the adjacent digits 1, 2 and 3 indicate 20, 45 and 75 DAT, respectively; LLF, ITD, LFA and SPD allude number of leaflets leaf-1, internode length, single leaf area and leaf SPAD value, respectively; SFW, SDW, RFW and RDW indicate shoot and root fresh and dry weight, respectively; DFL, FCN, FLC, FTC, FWT and FYP represent days required to flowering, number of flower clusters plant-1, number of flowers cluster-1, number of fruits cluster-1, individual fruit weight and fruit yield plant-1, respectively].
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Table 1. Interaction effect of fertilizer and PGR on plant height, base diameter and branch number of tomato at different days after transplanting (DAT).
Table 1. Interaction effect of fertilizer and PGR on plant height, base diameter and branch number of tomato at different days after transplanting (DAT).
Treatment combination Plant height at different DAT Base diameter at different DAT Branch number at different DAT
20 45 75 20 45 75 20 45 75
N1 P1 19.60 ± 1.39 39.10 ± 1.55de 61.97 ± 2.48e 0.57 ± 0.03 1.47 ± 0.12d-f 2.13 ± 0.05fg 1.30 ± 0.00 3.00 ± 0.17c-g 4.00 ± 0.17ef
P2 18.53 ± 1.36 45.73 ± 1.86bc 80.97 ± 3.06b-d 0.57 ± 0.03 1.73 ± 0.09a-c 2.43 ± 0.01d-f 1.43 ± 0.13 3.87 ± 0.30a-c 5.23 ± 0.29a-d
P3 18.07 ± 1.45 31.57 ± 1.07fg 47.90 ± 3.85f 0.57 ± 0.03 1.33 ± 0.09ef 1.90 ± 0.06g 1.30 ± 0.00 2.57 ± 0.30g 3.67 ± 0.20ef
P4 18.23 ± 0.93 29.97 ± 0.95g 46.57 ± 3.45f 0.57 ± 0.03 1.30 ± 0.06ef 1.83 ± 0.09g 1.57 ± 0.13 2.33 ± 0.20g 3.67 ± 0.38ef
P5 18.40 ± 1.47 45.87 ± 1.87a-c 79.67 ± 2.95cd 0.57 ± 0.03 1.70 ± 0.12a-d 2.37 ± 0.15ef 1.57 ± 0.13 3.53 ± 0.39a-f 5.13 ± 0.30a-d
N2 P1 18.47 ± 1.84 43.80 ± 1.94cd 72.00 ± 3.53d 0.57 ± 0.03 1.53 ± 0.09b-e 2.43 ± 0.09d-f 1.43 ± 0.13 3.57 ± 0.70a-f 4.57 ± 0.47c-e
P2 18.40 ± 1.24 50.13 ± 1.64ab 89.93 ± 3.58ab 0.57 ± 0.03 1.77 ± 0.09ab 2.77 ± 0.02a-c 1.43 ± 0.13 3.67 ± 0.20a-e 5.87 ± 0.30ab
P3 19.47 ± 0.65 35.17 ± 1.23ef 48.43 ± 2.69f 0.57 ± 0.03 1.30 ± 0.06ef 2.00 ± 0.12g 1.33 ± 0.03 2.57 ± 0.30g 3.47 ± 0.39f
P4 19.53 ± 0.71 33.03 ± 1.26fg 47.33 ± 3.44f 0.57 ± 0.03 1.33 ± 0.09ef 1.97 ± 0.09g 1.30 ± 0.00 2.77 ± 0.23e-g 3.43 ± 0.43f
P5 18.97 ± 1.74 48.17 ± 2.73a-c 89.20 ± 3.52ab 0.60 ± 0.06 1.77 ± 0.09ab 2.70 ± 0.12a-d 1.57 ± 0.13 4.00 ± 0.40ab 5.67 ± 0.49ab
N3 P1 20.67 ± 0.73 45.83 ± 2.00bc 76.83 ± 3.09d 0.57 ± 0.03 1.60 ± 0.06a-d 2.50 ± 0.01b-e 1.57 ± 0.01 4.00 ± 0.17ab 5.67 ± 0.20ab
P2 20.10 ± 0.87 50.33 ± 2.07ab 90.63 ± 2.95a 0.57 ± 0.03 1.77 ± 0.09ab 2.80 ± 0.12ab 1.57 ± 0.13 4.00 ± 0.40ab 6.13 ± 0.43a
P3 19.30 ± 1.10 34.10 ± 1.49fg 47.63 ± 3.06f 0.53 ± 0.03 1.27 ± 0.09f 1.97 ± 0.09g 1.30 ± 0.00 2.87 ± 0.30d-g 3.67 ± 0.33ef
P4 19.10 ± 0.60 31.00 ± 1.59fg 46.13 ± 3.23f 0.57 ± 0.03 1.27 ± 0.09f 1.90 ± 0.06g 1.43 ± 0.13 2.67 ± 0.20fg 3.57 ± 0.13ef
P5 19.60 ± 0.78 49.97 ± 2.08ab 88.77 ± 2.87a-c 0.57 ± 0.03 1.77 ± 0.12ab 2.77 ± 0.15a-c 1.43 ± 0.13 3.87 ± 0.30a-c 5.47 ± 0.23a-c
N4 P1 19.10 ± 0.59 45.73 ± 1.41bc 73.23 ± 4.04d 0.57 ± 0.03 1.50 ± 0.06c-f 2.47 ± 0.09c-e 1.43 ± 0.13 3.23 ± 0.23b-g 4.57 ± 0.30c-e
P2 20.37 ± 0.96 49.67 ± 2.41ab 89.20 ± 3.44ab 0.57 ± 0.03 1.80 ± 0.12a 2.77 ± 0.15a-c 1.40 ± 0.10 3.77 ± 0.23a-d 5.90 ± 0.20ab
P3 19.53 ± 0.61 32.83 ± 1.60fg 47.30 ± 2.70f 0.60 ± 0.00 1.27 ± 0.09f 1.93 ± 0.15g 1.43 ± 0.13 3.00 ± 0.17c-g 3.87 ± 0.30ef
P4 20.07 ± 1.11 31.93 ± 1.31fg 47.60 ± 2.80f 0.57 ± 0.03 1.27 ± 0.09f 1.93 ± 0.12g 1.33 ± 0.03 3.00 ± 0.17c-g 4.00 ± 0.40ef
P5 17.90 ± 0.92 50.33 ± 2.59ab 86.83 ± 3.56a-c 0.57 ± 0.03 1.77 ± 0.09ab 2.77 ± 0.09a-c 1.43 ± 0.13 3.67 ± 0.33a-e 5.10 ± 0.10b-d
N5 P1 19.63 ± 0.75 44.17 ± 1.33c 71.97 ± 3.85d 0.57 ± 0.03 1.50 ± 0.06c-f 2.43 ± 0.09d-f 1.43 ± 0.13 3.20 ± 0.49b-g 4.43 ± 0.59d-f
P2 17.53 ± 0.58 49.53 ± 1.85ab 88.77 ± 3.12a-c 0.57 ± 0.03 1.77 ± 0.09ab 2.83 ± 0.09a 1.43 ± 0.13 4.23 ± 0.23a 6.10 ± 0.49ab
P3 19.07 ± 0.83 32.70 ± 1.39fg 47.10 ± 2.51f 0.57 ± 0.03 1.30 ± 0.06ef 1.93 ± 0.09g 1.30 ± 0.00 2.77 ± 0.23e-g 3.67 ± 0.20ef
P4 19.60 ± 1.25 30.80 ± 0.76fg 46.27 ± 2.95f 0.53 ± 0.03 1.33 ± 0.09ef 1.93 ± 0.09g 1.43 ± 0.13 2.77 ± 0.23e-g 3.67 ± 0.33ef
P5 19.60 ± 0.78 50.70 ± 1.77a 88.57 ± 3.08a-c 0.57 ± 0.03 1.73 ± 0.12a-c 2.77 ± 0.12a-c 1.37 ± 0.07 3.90 ± 0.49a-c 5.43 ± 0.59a-d
LS ns * * ns * * ns * *
Values are means ± standard errors of three replications. Different letters within the column indicate statistically significant differences among the treatments according to LSD at p≤0.05. Here, N1, N2, N3, N4 and N5 represent control (no fertilizer), 100, 110, 90 and 80 % of FRG’2018, respectively and P1, P2, P3, P4 and P5 indicate control (no PGR), GA3, NAA, 4-CPA and SA at 50 ppm, respectively. LS: Level of significance, ns: Not significant, *: Significant at 5 % level of probability.
Table 2. Fertilizer and PGR interactions influencing the number of leaves and canopy spread plant-1 of tomato at different days after transplanting (DAT).
Table 2. Fertilizer and PGR interactions influencing the number of leaves and canopy spread plant-1 of tomato at different days after transplanting (DAT).
Treatment
combination
Number of leaves plant-1 at different DAT Canopy spread plant-1 at different DAT
20 45 75 20 45 75
N1 P1 6.20 ± 0.49 17.57 ± 0.87cd 25.20 ± 1.65de 21.30 ± 1.47 41.77 ± 2.72d-f 52.60 ± 2.63ef
P2 6.00 ± 0.40 25.47 ± 1.18ab 45.00 ± 1.65ab 20.23 ± 1.44 52.20 ± 2.86ab 64.40 ± 3.41a-c
P3 6.43 ± 0.57 16.00 ± 0.68d 23.33 ± 1.82de 19.77 ± 1.52 38.33 ± 2.69ef 45.90 ± 2.72fg
P4 5.57 ± 0.30 14.67 ± 0.52d 21.00 ± 2.48e 19.93 ± 1.03 36.42 ± 2.317f 41.43 ± 2.66g
P5 6.00 ± 0.17 25.20 ± 1.16b 42.67 ± 2.03b 20.10 ± 1.56 51.67 ± 2.78a-c 62.63 ± 2.02b-d
N2 P1 5.90 ± 0.20 17.67 ± 1.36cd 27.67 ± 2.20cd 20.17 ± 1.91 43.43 ± 3.19d-f 56.03 ± 2.74de
P2 6.10 ± 0.42 29.00 ± 1.82a 49.00 ± 3.06a 20.10 ± 1.16 55.03 ± 2.24ab 71.27 ± 3.12a
P3 6.33 ± 0.20 17.33 ± 1.05cd 26.00 ± 1.91de 21.17 ± 0.68 42.80 ± 2.15d-f 47.77 ± 2.06fg
P4 5.80 ± 0.10 17.87 ± 1.44cd 28.33 ± 2.05cd 21.23 ± 0.69 43.30 ± 1.68d-f 47.17 ± 1.97fg
P5 6.33 ± 0.52 25.67 ± 1.93ab 42.33 ± 2.73b 20.77 ± 1.91 52.73 ± 3.00ab 67.00 ± 2.69ab
N3 P1 5.67 ± 0.52 20.53 ± 1.18c 33.00 ± 2.40c 22.37 ± 0.77 48.70 ± 2.92b-d 59.97 ± 2.96cd
P2 6.00 ± 0.40 28.63 ± 2.03ab 47.67 ± 2.51ab 21.80 ± 0.79 57.13 ± 3.08a 71.10 ± 3.10a
P3 5.67 ± 0.20 18.00 ± 1.25cd 28.33 ± 2.33cd 20.90 ± 1.17 43.20 ± 2.16d-f 47.30 ± 1.80fg
P4 5.10 ± 0.10 17.33 ± 1.25cd 27.20 ± 1.73cd 20.80 ± 0.66 42.13 ± 2.38d-f 44.33 ± 2.41g
P5 5.43 ± 0.30 27.30 ± 1.15ab 46.00 ± 2.40ab 21.30 ± 0.87 55.20 ± 2.76ab 69.77 ± 2.80a
N4 P1 6.00 ± 0.40 17.63 ± 1.45cd 27.67 ± 2.19cd 20.80 ± 0.50 44.13 ± 3.09de 57.17 ± 2.60de
P2 6.33 ± 0.52 27.67 ± 1.53ab 46.33 ± 3.18ab 22.07 ± 0.97 56.33 ± 2.38a 69.97 ± 2.84a
P3 6.33 ± 0.20 16.33 ± 0.88d 26.67 ± 0.67de 21.33 ± 0.61 41.67 ± 2.01d-f 44.97 ± 1.86g
P4 6.00 ± 0.40 16.33 ± 0.69d 26.33 ± 1.20de 21.77 ± 1.19 42.20 ± 2.27d-f 44.20 ± 2.49g
P5 6.10 ± 0.61 25.67 ± 1.05ab 43.67 ± 1.93ab 19.60 ± 1.01 51.87 ± 2.40ab 68.73 ± 2.34ab
N5 P1 5.43 ± 0.30 17.67 ± 1.05cd 27.00 ± 1.15c-e 21.33 ± 0.84 44.60 ± 2.34c-e 56.17 ± 2.73de
P2 5.47 ± 0.39 27.67 ± 1.63ab 47.67 ± 2.33ab 19.23 ± 0.50 54.37 ± 2.66ab 69.70 ± 2.44a
P3 5.33 ± 0.33 15.67 ± 0.69d 26.33 ± 1.45de 20.77 ± 0.90 40.60 ± 2.02ef 44.43 ± 2.14g
P4 6.63 ± 0.69 16.00 ± 1.15d 26.00 ± 1.53de 21.20 ± 1.35 41.40 ± 2.09ef 43.30 ± 1.78g
P5 6.23 ± 0.39 26.33 ± 1.33ab 42.67 ± 1.45b 21.30 ± 0.87 54.23 ± 1.85ab 69.43 ± 2.27ab
Level of significance ns * * ns * *
Values are means ± standard errors of three replications. Different letters within the column indicate statistically significant differences among the treatments according to LSD at p≤0.05. Here, N1, N2, N3, N4 and N5 represent control (no fertilizer), 100, 110, 90 and 80 % of FRG’2018, respectively and P1, P2, P3, P4 and P5 indicate control (no PGR), GA3, NAA, 4-CPA and SA at 50 ppm, respectively. ns: Not significant, *: Significant at 5 % level of probability.
Table 3. Number of leaflets leaf-1, internode length, leaf area, leaf SPAD value and fresh and dry weight of shoot and root of tomato as influenced by the application of fertilizers and plant growth regulators.
Table 3. Number of leaflets leaf-1, internode length, leaf area, leaf SPAD value and fresh and dry weight of shoot and root of tomato as influenced by the application of fertilizers and plant growth regulators.
Treatment No. of leaflets leaf-1 Internode length (cm) Leaf area (cm2) Leaf SPAD value Shoot weight (g) Root weight (g)
Fresh Dry Fresh Dry
Fertilizer dose
N1 7.94 ± 0.37b 4.92 ± 0.22 286.33 ± 8.10b 48.59 ± 1.21 265.61 ± 11.6b 62.34 ± 2.20b 33.84 ± 1.64b 18.87 ± 0.50
N2 8.05 ± 0.40b 5.08 ± 0.28 305.54 ± 7.07a 50.44 ± 1.14 282.37 ± 10.9a 66.17 ± 1.78a 35.61 ± 1.46a 19.33 ± 0.42
N3 8.31 ± 0.46ab 5.13 ± 0.22 306.13 ± 13.9a 51.34 ± 1.09 283.94 ± 13.8a 67.91 ± 2.23a 35.81 ± 1.74a 19.16 ± 0.54
N4 8.47 ± 0.37a 5.08 ± 0.22 300.33 ± 12a 49.94 ± 0.95 278.38 ± 13.9a 66.54 ± 1.91a 35.30 ± 1.76a 19.46 ± 0.40
N5 8.58 ± 0.46a 5.06 ± 0.22 300.16 ± 12.5a 50.21 ± 0.96 277.60 ± 14.5a 65.92 ± 1.98a 35.25 ± 1.77a 19.00 ± 0.48
LS * ns * ns ** * * ns
Plant growth regulator
P1 8.51 ± 0.15c 5.06 ± 0.10c 314.41 ± 5.99b 49.42 ± 1.02bc 289.98 ± 4.78b 66.59 ± 1.34b 37.19 ± 0.57b 19.21 ± 0.36b
P2 10.19 ± 0.13a 6.17 ± 0.14a 337.66 ± 5.24a 53.08 ± 1.00a 328.13 ± 4.46a 73.20 ± 1.28a 41.47 ± 0.48a 20.77 ± 0.26a
P3 6.38 ± 0.20e 4.32 ± 0.13d 270.77 ± 7.18c 47.87 ± 0.83c 229.52 ± 4.61c 58.74 ± 0.92c 28.65 ± 0.50c 17.79 ± 0.29c
P4 6.91 ± 0.15d 4.20 ± 0.13d 247.89 ± 6.47d 48.15 ± 0.83c 216.82 ± 3.55d 57.86 ± 0.95c 27.39 ± 0.42c 17.43 ± 0.28c
P5 9.36 ± 0.16b 5.53 ± 0.12b 327.75 ± 6.64ab 52.01 ± 1.03ab 323.45 ± 3.82a 72.49 ± 1.15a 41.10 ± 0.52a 20.61 ± 0.29a
LS ** ** ** ** ** ** ** **
CV (%) 6.79 10.72 6.22 7.49 4.88 6.44 5.01 6.11
Values are means ± standard errors of three replications. Different letters within the column indicate statistically significant differences among the treatments according to LSD at p≤0.05. Here, N1, N2, N3, N4 and N5 represent control (no fertilizer), 100, 110, 90 and 80 % of FRG’2018, respectively and P1, P2, P3, P4 and P5 indicate control (no PGR), GA3, NAA, 4-CPA and SA at 50 ppm, respectively. LS: Level of significance, * and **: Significant at 5 % and 1 % level of probability, respectively, ns: Not significant.
Table 4. Interactive influence of fertilizer and PGR on number of leaflets leaf-1, internode length, leaf area, leaf SPAD value and fresh and dry weight of shoot and root of tomato.
Table 4. Interactive influence of fertilizer and PGR on number of leaflets leaf-1, internode length, leaf area, leaf SPAD value and fresh and dry weight of shoot and root of tomato.
Treatment
combination
No. of leaflets leaf-1 Internode length (cm) Leaf area (cm2) SPAD value Shoot weight (g) Root weight (g)
Fresh Dry Fresh Dry
N1 P1 8.50 ± 0.17fg 4.87 ± 0.42f-j 286.40 ± 11.58g-j 49.50 ± 2.68 273.89 ± 14.32g 64.27 ± 4.29e-g 35.66 ± 1.59f 19.32 ± 1.06b-g
P2 9.67 ± 0.07b-d 6.00 ± 0.38a-d 321.11 ± 9.84c-f 51.77 ± 2.67 305.34 ± 7.29d-f 68.70 ± 3.61b-e 39.69 ± 1.17b-e 20.49 ± 0.60a-c
P3 5.93 ± 0.29k 4.40 ± 0.26h-j 264.40 ± 10.66jk 45.63 ± 2.83 220.10 ± 5.61i-k 56.04 ± 3.04hi 27.45 ± 1.44h-j 17.75 ± 1.06g-j
P4 6.93 ± 0.37ij 4.03 ± 0.09j 248.43 ± 5.18kl 45.17 ± 2.18 214.52 ± 4.67jk 53.47 ± 2.75i 26.49 ± 0.85ij 16.46 ± 0.10j
P5 8.67 ± 0.24e-g 5.30 ± 0.4c-g 311.31 ± 6.89d-h 50.90 ± 2.40 314.22 ± 6.42b-e 69.24 ± 3.92a-e 39.90 ± 1.56b-d 20.32 ± 0.29a-d
N2 P1 8.47 ± 0.29fg 5.13 ± 0.13d-h 310.94 ± 10.6d-h 47.50 ± 3.95 285.94 ± 4.94fg 64.67 ± 3.90d-g 36.46 ± 0.76f 18.83 ± 0.98c-h
P2 9.80 ± 0.31a-c 6.53 ± 0.41a 326.65 ± 6.67b-f 53.03 ± 2.13 327.29 ± 8.89a-d 73.43 ± 1.89a-c 41.36 ± 0.98a-c 20.79 ± 0.27ab
P3 6.13j ± 0.84k 4.23 ± 0.38ij 282.99 ± 11.2h-j 49.33 ± 1.20 239.70 ± 5.50hi 60.43 ± 2.05f-h 29.64 ± 0.38gh 18.42 ± 0.57d-i
P4 6.93 ± 0.18ij 4.03 ± 0.52j 274.43 ± 6.23i-k 49.73 ± 1.14 233.68 ± 3.07h-j 59.93 ± 0.41f-i 29.14 ± 0.54g-i 17.79 ± 0.06g-j
P5 8.93 ± 0.18c-f 5.47 ± 0.29b-f 332.68 ± 9.24a-e 52.60 ± 3.38 325.24 ± 1.99a-d 72.37 ± 2.19a-c 41.46 ± 0.30a-c 20.81 ± 0.91ab
N3 P1 8.87 ± 0.47d-f 5.30 ± 0.26c-g 342.52 ± 9.79a-c 52.33 ± 1.74 310.56 ± 7.28c-e 70.67 ± 2.99a-e 39.52 ± 1.12c-e 19.80 ± 1.11a-f
P2 10.40 ± 0.23ab 6.07 ± 0.35a-c 357.33 ± 13.97a 54.93 ± 2.58 343.20 ± 8.21a 76.17 ± 3.08a 42.82 ± 0.81a 21.23 ± 0.86ab
P3 6.47 ± 0.29jk 4.30 ± 0.26h-j 306.13 ± 15.85e-h 48.37 ± 2.80 249.97 ± 12.71h 60.00 ± 1.99f-i 30.76 ± 1.05g 17.67 ± 0.46g-j
P4 6.27 ± 0.29jk 4.27 ± 0.19h-j 225.03 ± 27.33l 47.77 ± 0.73 204.88 ± 11.86k 58.00 ± 0.96g-i 26.12 ± 1.36j 16.70 ± 0.27ij
P5 9.53 ± 0.35b-e 5.73 ± 0.23a-f 299.61 ± 12.05f-i 53.30 ± 2.00 311.11 ± 10.24c-e 74.70 ± 3.02ab 39.84 ± 1.31b-d 20.37 ± 0.87a-c
N4 P1 7.87 ± 0.24gh 5.03 ± 0.18e-i 318.56 ± 4.79c-f 47.93 ± 1.21 292.15 ± 8.49e-g 66.60 ± 1.30c-f 37.38 ± 1.07d-f 19.32 ± 0.57b-g
P2 10.40 ± 0.12ab 6.23 ± 0.23ab 344.00 ± 12.25a-c 55.03 ± 2.41 333.93 ± 7.8ab 75.80 ± 3.12a 41.93 ± 1.01a-c 21.49 ± 0.19a
P3 6.93 ± 0.12ij 4.43 ± 0.43g-j 251.63 ± 8.48kl 48.30 ± 0.81 220.02 ± 7.85i-k 59.20 ± 1.31g-i 27.84 ± 0.80h-j 18.17 ± 0.38e-j
P4 7.47 ± 0.29hi 4.40 ± 0.47h-j 245.97 ± 5.47kl 49.27 ± 1.97 216.72 ± 5.31jk 59.50 ± 2.42g-i 27.51 ± 0.69h-j 17.90 ± 0.59f-j
P5 9.67 ± 0.29b-d 5.30 ± 0.3c-g 341.50 ± 12.24a-d 49.17 ± 1.66 329.08 ± 7.23a-c 71.60 ± 0.81a-d 41.84 ± 1.04a-c 20.42 ± 0.46a-c
N5 P1 8.87 ± 0.18d-f 4.97 ± 0.15e-i 313.64 ± 9.31c-g 49.83 ± 1.43 287.39 ± 9.56fg 66.77 ± 2.33c-f 36.94 ± 1.12ef 18.79 ± 0.75c-h
P2 10.67 ± 0.18a 6.00 ± 0.29a-d 339.22 ± 7.97a-d 50.63 ± 1.89 330.91 ± 6.53a-c 71.90 ± 1.56a-c 41.56 ± 0.82a-c 19.85 ± 0.59a-e
P3 6.43 ± 0.52jk 4.23 ± 0.26ij 248.70 ± 9.74kl 47.70 ± 1.57 217.82 ± 6.47i-k 58.00 ± 2.11g-i 27.58 ± 0.76h-j 16.93 ± 0.63h-j
P4 6.93 ± 0.35ij 4.27 ± 0.19h-j 245.58 ± 1.62kl 48.80 ± 2.71 214.29 ± 4.74jk 58.40 ± 1.94g-i 27.70 ± 0.27h-j 18.31 ± 1.04e-j
P5 10.00 ± 0.12ab 5.83 ± 0.15a-e 353.63 ± 12.04ab 54.10 ± 2.23 337.58 ± 8.55a 74.53 ± 2.31ab 42.46 ± 1.23ab 21.14 ± 0.87ab
LS ** * ** ns * * * *
Values are means ± standard errors of three replications. Different letters within the column indicate statistically significant differences among the treatments according to LSD at p≤0.05. Here, N1, N2, N3, N4 and N5 represent control (no fertilizer), 100, 110, 90 and 80 % of FRG’2018, respectively and P1, P2, P3, P4 and P5 indicate control (no PGR), GA3, NAA, 4-CPA and SA at 50 ppm, respectively. * and **: Significant at 5 % and 1 % level of probability, respectively, ns: Not significant, LS: Level of significance.
Table 5. Reproductive behaviors and yield of tomato as influenced as influenced by the application fertilizers and plant growth regulators.
Table 5. Reproductive behaviors and yield of tomato as influenced as influenced by the application fertilizers and plant growth regulators.
Treatment Days required to flowering No. of flower clusters plant-1 No. of flowers cluster-1 No. of fruits cluster-1 Single fruit weight (g) Fruit yield plant-1
Fertilizer dose
N1 48.44 ± 1.12 10.93 ± 1.18b 7.08 ± 0.29 4.61 ± 0.34 56.50 ± 2.04b 3.32 ± 0.57b
N2 48.07 ± 1.10 12.27 ± 1.36a 7.37 ± 0.30 4.85 ± 0.26 61.23 ± 1.84a 4.13 ± 0.66a
N3 47.33 ± 1.24 12.55 ± 1.44a 7.47 ± 0.34 4.91 ± 0.25 61.89 ± 2.02a 4.33 ± 0.67a
N4 48.28 ± 1.12 12.49 ± 1.40a 7.46 ± 0.28 4.97 ± 0.22 60.04 ± 2.22a 4.21 ± 0.67a
N5 48.25 ± 1.09 12.35 ± 1.43a 7.47 ± 0.27 5.04 ± 0.18 60.03 ± 2.18a 4.13 ± 0.64a
LS ns ** ns ns * **
Plant growth regulator
P1 50.05 ± 0.78a 15.24 ± 0.28b 8.19 ± 0.29b 5.19 ± 0.12b 61.34 ± 1.03b 4.85 ± 0.17c
P2 50.68 ± 0.85a 16.85 ± 0.30a 8.80 ± 0.33a 5.79 ± 0.12a 67.83 ± 0.84a 6.61 ± 0.17a
P3 44.27 ± 0.63b 5.98 ± c0.14 5.58 ± 0.23c 3.89 ± 0.15c 51.38 ± 0.81c 1.20 ± 0.06d
P4 44.02 ± 0.67b 5.93 ± 0.14c 5.71 ± 0.37c 3.92 ± 0.17c 51.63 ± 0.78c 1.21 ± 0.08d
P5 51.35 ± 0.78a 16.58 ± 0.31a 8.57 ± 0.25a 5.59 ± 0.10a 67.51 ± 0.93a 6.25 ± 0.18b
LS ** ** ** ** ** **
CV (%) 6.21 5.08 6.67 9.56 7.21 10.66
Values are means ± standard errors of three replications. Different letters within the column indicate statistically significant differences among the treatments according to LSD at p≤0.05. Here, N1, N2, N3, N4 and N5 represent control (no fertilizer), 100, 110, 90 and 80 % of FRG’2018, respectively and P1, P2, P3, P4 and P5 indicate control (no PGR), GA3, NAA, 4-CPA and SA at 50 ppm, respectively. LS: Level of significance, * and **: Significant at 5 % and 1 % level of probability, respectively, ns: Not significant.
Table 6. Fertilizer-PGR interaction influencing the reproductive behaviors and yield of tomato var. BARI Tomato-14.
Table 6. Fertilizer-PGR interaction influencing the reproductive behaviors and yield of tomato var. BARI Tomato-14.
Treatment combination Days required to flowering No. of flower clusters plant-1 No. of flowers cluster-1 No. of fruits cluster-1 Single fruit weight (g) Fruit yield
(kg plant-1)
N1 P1 49.00 ± 2.82a-c 13.57 ± 0.59f 8.27 ± 0.18ab 5.27 ± 0.18a-c 57.37 ± 2.35ef 4.08 ± 0.14d
P2 49.67 ± 2.53ab 14.90 ± 0.67e 8.77 ± 0.41a 5.87 ± 0.33ab 64.73 ± 1.97a-d 5.63 ± 0.11b
P3 43.00 ± 1.73d 5.67 ± 0.20g 4.83 ± 0.18e 3.13 ± 0.18h 48.33 ± 2.28g 0.87 ± 0.11e
P4 43.67 ± 1.86d 5.67 ± 0.20g 5.03 ± 0.52de 3.27 ± 0.48gh 48.90 ± 1.76g 0.89 ± 0.09e
P5 51.33 ± 2.23a 14.87 ± 0.81e 8.50 ± 0.17ab 5.50 ± 0.17a-c 63.17 ± 2.43a-e 5.15 ± 0.29bc
N2 P1 50.00 ± 2.08ab 15.33 ± 0.20de 7.87 ± 0.35b 4.90 ± 0.38c-e 61.50 ± 2.65c-e 4.64 ± 0.47cd
P2 50.67 ± 1.56a 17.20 ± 0.49ab 8.93 ± 0.26a 5.93 ± 0.26a 68.90 ± 3.69ab 7.01 ± 0.38a
P3 44.67 ± 1.59cd 6.13 ± 0.30g 5.67 ± 0.22cd 3.77 ± 0.20f-h 53.53 ± 0.92fg 1.23 ± 0.06e
P4 43.67 ± 1.59d 6.10 ± 0.42g 5.77 ± 0.47cd 3.97 ± 0.39fg 53.77 ± 1.55fg 1.33 ± 0.24e
P5 51.33 ± 2.03a 16.57 ± 0.59a-c 8.63 ± 0.19ab 5.67 ± 0.18ab 68.47 ± 2.77a-d 6.43 ± 0.42a
N3 P1 50.67 ± 1.76a 16.10 ± 0.49b-d 8.47 ± 0.41ab 5.40 ± 0.35a-c 64.30 ± 3.34a-e 5.55 ± 0.19b
P2 51.43 ± 2.5a 17.50 ± 0.25a 8.93 ± 0.35a 5.80 ± 0.29ab 68.47 ± 3.76a-d 6.94 ± 0.47a
P3 44.57 ± 1.6cd 6.10 ± 0.49g 5.77 ± 0.24cd 3.97 ± 0.24fg 53.87 ± 2.08fg 1.29 ± 0.02e
P4 44.43 ± 1.74cd 5.90 ± 0.42g 5.63 ± 0.35cd 3.83 ± 0.35f-h 53.13 ± 2.05fg 1.23 ± 0.23e
P5 51.10 ± 1.72a 17.13 ± 0.30ab 8.53 ± 0.35ab 5.57 ± 0.33a-c 69.70 ± 2.54a 6.62 ± 0.27a
N4 P1 50.10 ± 1.33ab 15.67 ± 0.20c-e 8.17 ± 0.24ab 5.17 ± 0.24b-d 61.40 ± 1.34de 4.96 ± 0.11bc
P2 51.30 ± 2.65a 17.33 ± 0.20a 8.90 ± 0.38a 5.87 ± 0.35ab 68.87 ± 2.35ab 7.02 ± 0.61a
P3 43.77 ± 1.13d 6.23 ± 0.29g 5.83 ± 0.24c 4.17 ± 0.27ef 50.53 ± 0.93fg 1.32 ± 0.16e
P4 44.57 ± 1.79cd 6.00 ± 0.40g 5.83 ± 0.26c 4.13 ± 0.26ef 50.83 ± 2.06fg 1.26 ± 0.12e
P5 51.67 ± 1.37a 17.20 ± 0.49ab 8.57 ± 0.26ab 5.53 ± 0.23a-c 68.57 ± 3.30a-c 6.51 ± 0.27a
N5 P1 50.47 ± 1.75a 15.53 ± 0.39c-e 8.20 ± 0.29ab 5.20 ± 0.29a-c 62.13 ± 2.94b-e 5.00 ± 0.19bc
P2 50.33 ± 1.37a 17.33 ± 0.20a 8.47 ± 0.26ab 5.47 ± 0.26a-c 68.17 ± 2.70a-d 6.45 ± 0.28a
P3 45.33 ± 1.7b-d 5.77 ± 0.29g 5.80 ± 0.23cd 4.43 ± 0.23d-f 50.63 ± 1.81fg 1.29 ± 0.05e
P4 43.77 ± 1.78d 6.00 ± 0.17g 6.27 ± 0.31c 4.40 ± 0.31ef 51.53 ± 1.88fg 1.35 ± 0.06e
P5 51.33 ± 2.68a 17.13 ± 0.30ab 8.60 ± 0.38ab 5.70 ± 0.38ab 67.67 ± 4.13a-d 6.56 ± 0.14a
Level of significance * ** * * * **
Values are means ± standard errors of three replications. Different letters within the column indicate statistically significant differences among the treatments according to LSD at p≤0.05. Here, N1, N2, N3, N4 and N5 represent control (no fertilizer), 100, 110, 90 and 80 % of FRG’2018, respectively and P1, P2, P3, P4 and P5 indicate control (no PGR), GA3, NAA, 4-CPA and SA at 50 ppm, respectively. * and **: Significant at 5 % and 1 % level of probability, respectively.
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