Comparative resource use structure and resource use efficiency of conventional Vs high density planting system of bt cotton in Yavatmal District of Maharashtra

Introduction

Drawing from previous Agricultural experiences, Indian farmers are increasingly adopting alternative cultivation techniques, with HDPS emerging as a promising method to enhance cotton yields. HDPS is being viewed as a viable alternative to traditional practices, offering advantages such as suitability for mechanical harvesting, lower labour costs, better resource International Journal of Agriculture and Food Science https://www.agriculturaljournals.com efficiency, and higher productivity and profitability. This method has already gained widespread acceptance in major cotton-producing countries worldwide. Efficient water management plays a critical role in the success of HDPS, especially in the face of limited freshwater availability. Proper irrigation practices are necessary to regulate excessive vegetative growth and to ensure that nutrients and assimilates are directed toward the plant's reproductive parts. In this regard, deficit irrigation an approach that conserves water while maintaining yield levels has proven to be effective. It involves using less water than conventional evapotranspiration demands, contributing to more sustainable cotton farming. This review emphasizes the significance of integrating HDPS with deficit irrigation to develop a sustainable cotton production model. • X 3 = Machine hours (hrs)

Cobb-Douglas type of production function to determine the resource use structure.

Human labour

Labour use is significantly higher in HDPS, with 245.99 days compared to 205.70 days in the conventional system. This suggests that HDPS is more labour-intensive, possibly due to the higher plant population and more frequent operations like pruning, spraying, or harvesting.

Bullock Power

The use of bullock power is slightly lower in HDPS (10.76 hours) than in the conventional system (11.27 hours), indicating a marginal shift away from traditional animaldrawn implements, possibly due to changes in field layout or increased mechanization.

Machine Power

Machine use is also lower in HDPS (8.99 hours) compared to conventional farming (11.96 hours). This could imply reduced mechanized operations in HDPS or better optimization of machinery usage due to higher plant density.

Seed

Seed requirement in HDPS is almost double, at 4.96 kg versus 2.49 kg in the conventional system. This reflects the nature of HDPS, which involves closer spacing and a higher number of plants per unit area, requiring more seed.

Manure

The application of manure is higher under HDPS (23.47 quintals) compared to conventional farming (16.96 quintals). This increase likely supports the higher nutrient demand of densely planted crops.

Nitrogen

Surprisingly, the nitrogen application is lower in HDPS (125.03 kg) compared to the conventional system (230.32 kg). This might be due to more efficient nutrient management or the use of organic inputs in HDPS.

Phosphorus

The use of phosphorus is also reduced in HDPS, with 68.78 kg compared to 159.50 kg in conventional farming. This further indicates a shift in nutrient management practices under HDPS.

Potassium

Potassium application follows the same trend, with HDPS using 43.47 kg, significantly less than the 75.00 kg in the conventional system. This suggests that overall chemical fertilizer usage is lower in HDPS.

Plant Protection Chemicals

Expenditure on plant protection is much higher in HDPS, with a cost of ₹12,635.51 compared to ₹7,100.20 in conventional farming. The denser plant population in HDPS may create a more humid microclimate, increasing pest and disease risk and thereby necessitating more frequent pesticide applications. These results indicate general trend of comparative resource use structure similar findings were obtained by Rao and Meena (2020) [8] .

Resource Use Productivities in Cotton Production

The analysis of Cotton cultivation using the Cobb-Douglas production function provides a comprehensive view of how various inputs affect yield. The results detailed in Table No.2 include estimated parameters related to the elasticities of yield, standard errors of regression coefficients, their significance levels and the coefficient of multiple determination (R 2 ). The (R 2 ) expressed impact of the independent variable on cotton yield. The regression coefficient for input reflect the production elasticity, indicating percentage change in yield associated with a one unit change in the respective input, assuming other factors remain constant. These results are crucial for estimating the impact of resource use on yield. For production function analysis, nine inputs were evaluated: human labour, bullock power, machine power, seed, manure, nitrogen, phosphorus, potash and plant protection. By using Cobb-Douglas production function gives detailed insights into the relationship between various inputs and yield.

Coeffient of Determination (R ) indicates proportion of total variation in cotton yield explained by input variables. Higher value of it signifies a better fit of the model to the data. Production elasticities which means regression coefficients for each variable measures percentage change in yield resulting from a one unit change in respective input, assuming other factors remains constant.

Results of Cobb-Douglas Production Function for Cotton

Farmers For conventional the coefficient of multiple determination (R²) was calculated to be 0.7522, indicating that 75% of the variation in output could be explained collectively by the nine independent resource variables included in the model. This value reflects the proportion of yield variation attributable to changes in input levels, assuming all other factors remain constant. These findings are particularly valuable, as they offer clear insights into how adjustments in resource use may influence crop yield. Among the inputs analysed, the regression coefficients for Manure (X5), Fertiliser K (X 8 ), and Plant protection (X 9 ) were found to be positive and statistically significant at the 1% level and Machine power (X 3 ) statistically significant at the 10% level, suggesting that increasing the application of these resources could lead to higher production. Conversely, the coefficient for X 1 , X 2 , X4, X6 and X 7 was statistically insignificant, indicating limited or no potential for yield improvement through increased use of this input.

For the conventional group specifically, the significant encouraging coefficients suggest that a one-unit increase in machine power, manure, potassium and plant protection usage could enhance yield by approximately 0.13%, 0.47%, 0.62%, and 0.06%, respectively. (Figures in parentheses are standard errors of respective regression coefficient) For HDPS the coefficient of multiple determination (R²) was calculated to be 0.6400, indicating that 64% of the variation in output could be explained collectively by the nine independent resource variables included in the model. This value reflects the proportion of yield variation attributable to changes in input levels, assuming all other factors remain constant. These findings are particularly valuable, as they offer clear insights into how adjustments in resource use may influence crop yield. Among the inputs analysed, the regression coefficients for Seed (X4), Manure (X 5 ) and Fertiliser K (X 8 ) were found to be negative(X 4 ), others positive and statistically significant at the 1% level, suggesting that increasing the application of these resources could lead to higher production. Conversely, the coefficient for X 1, X 2 , X3 , X 6, X 7 and X9 was statistically insignificant, indicating limited or no potential for yield improvement through increased use of this input.

For HDPS, the significant positive coefficients suggest that a one-unit increase in manure and potassium usage could enhance yield by approximately 0.66%, 0.84% or unitary increase in input seed express negative impact on yield due to overcrowding at 0.69% level respectively.

Resource Use Efficiency in Cotton Production

Production function analysis is commonly applied to assess how efficiently resources are utilized. This involves calculating the marginal value product (MVP) of each input. A resource is deemed to be used efficiently when its MVP is equal to its corresponding cost. Thus, achieving efficiency in resource use requires that the MVP of each input matches its factor cost. In this context, Table No Conventional system indicates that the marginal value product to marginal cost (MVP/MC) ratio was positive and more than unity for inputs such as human labour, bullock hours, machine, manure, nitrogen, potassium and plant protection, Indicating that these resources were underutilized and that enhancing their use could improve resource efficiency and potentially increase output. However, the MVP/MC ratio for seed and phosphorus was less than unity, indicating it was over utilised. These inputs were overuse of seed and phosphorus implies that further increasing its application would not contribute to higher yields and may instead be inefficient.

In case of HDPS indicates that the marginal value product to marginal cost (MVP/MC) ratio exceeded one for inputs such as labour, manure, nitrogen, potassium and plant protection This suggests these resources were underutilized and that enhancing their use could improve resource efficiency and potentially increase output. However, the MVP/MC ratio for bullock hour, machine, seed, and phosphorus was below one, indicating it was over utilised. In this group, the overuse of bullock, machine, seed, and phosphorus implies that further increasing its application would not contribute to higher yields and may instead be inefficient.

Conclusion and policy implications

The study revealed that, HDPS practice is profitable as compare to the conventional most of the farmers are unaware about CICR recommended technologies for cotton crop, therefore there is need to disseminate the technology through extension workers of the state to increase the cotton productivity. The study conclude that there is existence of high wages and labour scarcity problems so it can be overcomes by provision of subsidies machines and implements like cotton picking implements because cotton harvesting is most expensive operation. Promotion of high density planting system among the farmers because it has more potential to increase benefit of farmers as compared to the conventional method.

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