Productivity and Econometric Analysis of Leguminous Monkey-nut (Arachis hypogaea L.) as Influenced by Growth Retardant

India is a second-largest groundnut producer globally, trailing only behind China, holds a pivotal role in the country's agricultural landscape (APEDA, 2018). Groundnut, scientifically known as Arachis hypogaea L., is a self-pollinating legume known by various names such as monkeynut and peanut. It belongs to the Fabaceae family and is commonly referred to as "moongphali" in India (Hamakareem et al., 2016). This remarkable crop ranks among the most important oilseed crops, boasting an oil content ranging from 44% to 56% and a protein content of 22% to 30% on a dry seed basis. In India, groundnut takes the lead in both production and cultivation area, claiming approximately 45% of the total oilseed area and contributing to 55% of the total oilseed production (Lenka et al., 2023).
         
Groundnut cultivation in India spans all three primary agricultural seasons: kharif, rabi and summer, primarily under rainfed conditions. Among these seasons, kharif cultivation alone constitutes a substantial 75% share of the total groundnut production (Barman et al., 2017). Groundnut, characterized as a semi-determinate crop, exhibits persistent vegetative growth even after entering the reproductive stage. This prolonged vegetative phase is a key factor contributing to a sink-source imbalance, ultimately resulting in reduced yields. Furthermore, during the kharif season, mutual shading due to excessive vegetative growth hampers groundnut production (Gatan and Gonzales, 2015). The excessive allocation of dry matter to the stems exacerbates the crop's harvest index, posing additional challenges.
         
Plant growth regulators are the chemical substances, when applied at low concentration modify the growth of plants usually by stimulating or inhibiting part of the natural growth regulatory system. About sixty plant growth regulators are now commercially available and several of them have reached considerable importance in crop production (Ajaykumar and Krishnasamy, 2019). The growth regulators include both growth promoters and retardants which modify the canopy structure and their expression in the form of yield.
         
The utilization of growth retardants presents an opportunity to modulate the allocation of assimilates, potentially increasing production and enhancing crop management. Paclobutrazol (PBZ), a member of the triazole family, acts by inhibiting gibberellin biosynthesis, thereby reducing shoot length and facilitating the efficient partitioning of assimilates (Ziauka and Kuusiene, 2010). The objective of this research was to investigate the effects of different concentrations of PBZ and varying application timings on plant growth, yield and economic considerations in groundnut cultivation.

A field experiment was conducted at Perunthalaivar Kamaraj Krishi Vigyan Kendra, Puducherry during 2020 (Kharif) and 2021 (Kharif) to scrutinize the influence of various concentrations and timings of paclobutrazol (PBZ) application on the growth and yield of groundnut. The experimental layout adhered to a split-plot design, featuring six primary plots and three subplots, all replicated three times. The main plots encompassed distinct concentrations of Paclobutrazol, denoted as follows: T₁: 25 ppm, T₂: 50 ppm, T₃: 100 ppm, T₄: 150 ppm, T₅: 200 ppm and T₆: Control. The subplots, on the other hand, were devoted to exploring three distinct timings of application, designated as S₁: Single spraying at 30 DAE (Days after emergence), S₂: Single spraying at 50 DAE and S₃: Double spraying at both 30 and 50 DAE.
         
Groundnut variety VRIGn -6 was used for the study. Prior to sowing, the entire recommended fertilizer dose (20:50:75 kg of NPK per hectare) was evenly applied as a basal application. This dose consisted of urea, single superphosphate and muriate of potash. Gypsum, at a rate of 500 kilograms per hectare, was applied in two equal portions: one during basal application and the other during earthing up, which took place around 40 days after sowing (DAS). All other agronomic practices were meticulously executed in accordance with the specific requirements of the groundnut crop. Within each plot, ten plants were systematically chosen at random and duly tagged for the purpose of recording vital observations related to growth, yield attributes and overall yield. At the point of maturity, the crop was skillfully harvested, threshed and the resulting seed yield, measured in kilograms per hectare, was meticulously recorded. The dataset encompassing diverse parameters was subjected to thorough statistical analysis using Fisher's ANOVA method, as advocated by Gomez and Gomez (2010).
 
Quantitative variables analysis
 
Correlation analysis was employed to assess the degree of association between different variables (Ravi et al., 2023). Specifically, the Pearson correlation coefficient (PCC) was used to quantify the strength and direction of these relationships. The variables under consideration in this research encompassed pod yield (kg ha^-1), pods plant^-1 (No.), 100 kernel weight (g), shelling (%), SMK (%), number of branches plant^-1, root dry weight (g) and DMP (g plant^-1) (Ajaykumar et al., 2022). The application of correlation analysis aimed to unveil the associations among these variables, shedding light on their impact on pod yield. By employing this comprehensive approach, we sought to gain deeper insights into the factors influencing pod yield. It was expressed mathematically as, Where,
r_xy = The strength of the linear association between the variables x and y.
S_x and S_y = The sample standard deviations.
S_xy = The sample covariance;
x_i and y_i = The respective values of the x and y variables within the sample drawn from the population.
  and  = The sample means.
         
Furthermore, another valuable econometric technique employed in this study was regression analysis, which rigorously examines the interrelationship between a dependent variable and a set of independent variables. This approach has been substantiated by previous research conducted by Maulud and Abdulazeez (2020) and it was represented as:
 
Where,
y_i = The dependent variable, which pertains to pod yield.
x_i = The array of independent variables encompassing the number of pods plant^-1, 100 kernel weight (grams), Shelling (%), SMK (%), Number of branches plant^-1, root dry weight (g) and DMP (g plant^-1).
b = The slope coefficient associated with each independent variable.
a = The constant or intercept term.
u_i = The error term.
         
This structured regression model equation may be elegantly reformulated as follows:
 
Pod yield (Kg ha^-1) = α + β₁ no. of pods plant^-1 + α₂ 100 kernel weight (g) + β₃  shelling (%) + β₄ SMK (%) + β₅ number of branches plant^-1+ β₆ root dry weight (g) + β₇ DMP (g plant^-1) + u_i

Effect of growth characters
 
Plant height
 
The application of paclobutrazol had a impact on several key growth parameters, as delineated in Table 1. A clear trend emerged, demonstrating that plant height experienced a gradual reduction as the paclobutrazol concentration  increased, with the 200 ppm dosage leading to the most significant decrease (45.7 cm) compared to the control group (62.5 cm). This outcome was consistent with the heights observed with other paclobutrazol concentrations, namely T₄ (150 ppm), T₃ (100 ppm) and T₂ (50 ppm). Notably, when considering different application timings, S₃ (Double spraying at 30 and 50 DAE) resulted in the shortest plants, measuring 49.5 cm, in contrast to the other timings. The observed reduction in plant height due to the double application of paclobutrazol could be attributed to a decrease in the number of stem nodes per plant. The reduction in plant height is likely attributable to the anti-gibberellin action of PBZ, which inhibits cell division and cell elongation processes generally influenced by gibberellins. These findings align with the research conducted by Hegazi and El-Shraiy (2007).
 

 
Number of branches and root dry weight
 
Understanding a crop's branching and root system is vital for comprehending various crop production challenges. The number of branches and the dry root weight of plants exhibited significant variation among the different treatments involving various concentrations of paclobutrazol (Table 1). Among the primary plots, the application of paclobutrazol at 200 ppm resulted in the highest number of branches plant^-1 (5.0) and the greatest root dry weight plant^-1 (3.78 g), closely followed by paclobutrazol at 150 ppm (4.8 branches and 3.57 g root dry weight plant^-1) when compared to the control group. In terms of the timing of spraying, the treatment involving double spraying at 30 and 50 DAE recorded the highest values for branches plant^-1 (4.8) and root dry weight plant^-1 (3.35), followed by single spraying at 50 DAE (4.5 branches and 3.14 g root dry weight plant^-1). These findings are in line with the results reported by Lenka et al., (2023). The application of paclobutrazol may have contributed to increased root activity, resulting in enhanced root growth and vigor.

Dry matter production
 
Dry matter production (DMP) in a crop serves as an indicator of its efficiency in utilizing available resources such as solar radiation, moisture, nutrients and other environmental factors. The application of paclobutrazol at 200 ppm resulted in the highest DMP (35.24 g plant^-1), followed by paclobutrazol at 150 ppm (33.17 g plant^-1) when compared to the other treatments (Table 1). Similarly, when considering different application timings, the application of paclobutrazol at 30 and 50 DAE yielded the highest DMP (34.62 g plant^-1), followed by single spraying at 50 DAE. These findings align with the results obtained by Barman et al., (2017).
 
Effect of yield attributes
 
The various concentrations of paclobutrazol application on groundnut was found to be significant on yield attributes (Table 2). Notably, the application of paclobutrazol at 200 ppm led to the highest pod count per plant (21.7), test weight (54.08 g), shelling percentage (74.8%) and kernels (91.24%). In contrast, the control group exhibited the lowest values for these attributes, with 16.3 pods per plant, a test weight of 49.78 g, shelling percentage of 67.2% and kernels at 85.13%. This could be attributed to paclobutrazol having a substantial influence on enhancing yield-related attributes. The application of PBZ may have facilitated the more efficient conveyance of photosynthates to reproductive parts during pod development stages, leading to these observed outcomes, which are in agreement with the findings of Hua et al., (2014). In the context of spraying timing, significantly higher pod count per plant (20.7), test weight (52.71 g), shelling percentage (72.4%) and kernels (91.25%) were recorded in the treatment involving double spraying at 30 and 50 DAE compared to the other treatments.
 

 
Yield
 
The application of paclobutrazol resulted in a significant increase in groundnut yield at various concentration levels, as detailed in Table 2. All concentrations of PBZ demonstrated superior results compared to the control group. Notably, the treatment involving paclobutrazol at 200 ppm (T₅) yielded the highest pod yield (2724 kg ha^-1) and kernel yield (1210 kg ha^-1). Following closely behind was the application of paclobutrazol at 150 ppm. When evaluating different application timings, it was observed that the highest pod yield (2568 kg ha^-1) and kernel yield (1150 kg ha^-1) were achieved when PBZ was applied at both 30 DAE and 50 DAE (S₃). Notably, the interaction between these factors resulted in significantly higher yield attributes being recorded in the T₅S₃ treatment combination. Conversely, the treatment combination T₆S₁ exhibited the lowest number of pods plant^-1, test weight, shelling % and SMK (%). This variation in canopy coverage, influenced by the decreased plant height due to paclobutrazol application, likely played a role in these yield outcomes. Similar results have been observed in studies conducted by Hua et al., (2014) and Barman et al., (2017).
 
Correlation and regression analysis
 
The mean of growth and yield attributes are graphically illustrated in Fig 1. The correlation results (Table 3) showed that all the variables included in the model were positively significant at 1% level of significance and these signs emphasize all the variables would attribute to the yield of the groundnut. The correlation coefficients between pod yield and various factors, including pods plant^-1 (0.92), 100 kernel weight (0.79), shelling (0.78), SMK (0.72), number of pods plant^-1 (0.91), root dry weight (0.91) and DMP (0.94), underscore the strong positive associations among these attributes (Fig 2). This empirical evidence strongly supports the notion that an increase in these variables corresponds to an augmented groundnut yield. The objective of the multiple linear regressions was to quantitatively assess the relationships and elucidate the extent of influence each prescribed parameter exerts on pod yield (Ajaykumar et al., 2023). The multiple linear regression equation, consequently derived, is as follows:
 
Pod yield (Kg ha^-1) =70.87 + 20.56 no. of pods plant^-1 + 3.33 100 kernel weight (g) + 4.63 shelling (%) + 3.SMK (%) + 191.45 no. of branches plant^-1 + 250.48 root dry weight (g) + 48.59 DMP (g plant^-1)
 

 

 

         
All variables except 100-kernel weight, shelling and SMK, exhibited statistically significant associations. Specifically, the slope coefficient for the number of pods per plant suggests that for every one-unit increase, we can anticipate a substantial 20.56 unit rise in pod yield, assuming all other variables remain constant  (Table 4). Similarly, a one-per cent increment in the number of branches plant^-1, root dry weight and dry matter production leads to increases of 191.45, 250.48 and 48.59 units in pod yield, respectively, (Tittonell et al., 2007). This compelling econometric evidence underscores the significant impact of variables such as the number of pods plant^-1, number of branches plant^-1, root dry weight and dry matter production on groundnut pod production. Our regression model enabled us to estimate the predicted grain yield, which we compared against field-level pod yield (Fig 3). Additionally, we examined regression diagnostic plots to assess the model's validity. These plots include residual vs. fitted values, normal quantile-quantile (Q-Q) plots, scale-location plots and residuals vs. leverage values. Their consistent patterns reveal that the model maintains constant error variance, adheres to normal distribution assumptions and is free from outliers (Fig 4).
 

 

 

Both the years of experiments concluded that the application of paclobutrazol @ 200 ppm concentration at 30 and 50 DAE in groundnut significantly reduced plant height to an optimum level with highest yield. The application of paclobutrazol led to a remarkable increase in yield, up to 36% with increasing PBZ concentration and up to 31% with double application compared to single application. Correlation and regression analyses consistently highlight a positive relationship between all examined parameters and pod yield. The inference of this experiment can be drawn as double spraying at 30 and 50 DAE with 200ppm concentration of PBZ showed positive upshots in case of different yield parameters which ultimately resulted on the yield of groundnut.