Strategic vegetable crop distribution in Himachal Pradesh for sustainable economic and environmental outcomes

Abstract

India, the second-largest global producer of fruits and vegetables, faces challenges in translating productivity into profitability, particularly in vegetable farming. This study examined resource use efficiency in Himachal Pradesh, focusing on optimizing land allocation using Multi-Objective Programming (MOP) to maximize income and employment while minimizing fertilizer and pesticide use. Primary data were gathered from Solan and Sirmaur districts through structured questionnaires employing a multi-stage sampling design. Optimization models were developed annually for marginal, small, and medium farmers over three cropping seasons. The study demonstrated significant productivity, profitability, and sustainability improvements through optimization techniques. For marginal farmers, income increased from Rs 229,359 to Rs 280,058 per hectare, employment peaked at 252 man-days from 190, and fertilizer costs reduced from Rs 6180 to Rs 4420 per hectare, with the MOP approach allocating Chinese cabbage (March–May), cauliflower (August–November), and peas (November–March). Small farmers saw income rise from Rs 238,095 to Rs 296,214 per hectare, employment reaches 263 man-days from 217, and fertilizer costs decrease from Rs 6686 to Rs 3977 per hectare, with the MOP approacgh allocating Chinese cabbage (March–May), beans (August–November), and peas (November–March). For medium farmers, income maximized from Rs 256,071 to Rs 374,673 per hectare, employment at 253 man-days from 219, and fertilizer costs minimized from Rs 7092 to Rs 4428 per hectare, with the MOP approach allocating Chinese cabbage (March–May), beans (August–November), and peas (November–March). The study highlighted the need for efficient resource use and strategic crop selection for sustainable agriculture. Key policy recommendations include targeted support programs, resource efficiency initiatives, customized training, improved access to credit, and infrastructure development. These measures can boost productivity, profitability, and sustainability, enhancing farmer livelihoods and contributing to regional economic growth.

Introduction

India ranks second in global vegetable production, with significant contributions from crops like ginger, okra, potatoes, onions, and cabbages¹. Stratton et al.² emphasized the need for a 50 to 150 percent increase in global fruit and vegetable production by 2050 to meet dietary needs, highlighting the challenges and proposing sustainable growth solutions. Volkava and Mickiewicz³ forecast a 30.5 percent rise in global vegetable production by 2025, stressing the importance of sustainable practices and innovative technologies for future market development. In the Indian context, the export potential of fruits and vegetables is substantial. APEDA⁴ reported that India exported processed fruits and vegetables worth Rs. 13,750 crores (USD 1850 million) between 2022 and 2023.

Despite accounting for less than one percent of the global market, India’ s horticulture products are gaining popularity due to advancements in cold chain infrastructure and quality control.

Himachal Pradesh, part of the Indian Himalayas, has unique agro-climatic conditions ideal for horticulture. The region has seen a significant shift from traditional cereal cultivation to cash crops like fruits and vegetables, which has substantially boosted family incomes and employment⁵. This transition, particularly in Himachal Pradesh has been driven by the suitability of hilly terrains for horticultural crops, enhancing the economic sustainability of local farmers. Vegetable production in Himachal Pradesh during 2022–23 has propelled the state’s agriculture to new heights, with districts like Solan (368,967 MT), Shimla (329,144 MT), Sirmaur (295,611 MT), and Mandi (253,633 MT) making significant contributions to the total production of 2,061,913 MT from an area of 102,585 hectares, achieving an average productivity of 20.10 MT/ha. However, despite the growing importance of vegetable crops, profitability has not kept pace with productivity increases, underscoring the need for optimized resource use to sustain farmer livelihoods⁶.

Sharma⁷ looked into the economics of agricultural techniques in the Chamba area of Himachal Pradesh in order to meet these difficulties. Sharma’s research employed a thorough analytical framework stressing on resource usage efficiency and economic returns across diverse agricultural systems utilizing a comprehensive survey of local farming practices. The results showed notable differences in profitability and resource usage efficiency across various agricultural systems; certain systems show great potential for revenue increase by means of best use of resources. After using main data collecting techniques-structured interviews and field observations-following econometric modeling to find important variables of resource efficiency and profitability-red The research advised combining many agricultural techniques, including crop development, horticulture, and cattle raising, in order to raise economic sustainability. Still, this research concentrated on many cropping techniques rather than single vegetable system cropping patterns.

Improving vegetable farming’s profitability and output depends on the careful utilization of resources. Much research has looked at resource usage efficiency in many settings. Ayoola et al.⁸ evaluated the efficiency of dry-season vegetable growing in Oyo State, Nigeria, exposing quite large net returns despite inefficiencies. Inefficiencies were measured using linear programming models and suggested ideal input combinations maximized net returns. Targeting focused interventions to increase efficiency, Eldeep and Abozied⁹ discovered underutilization of vital resources among farmers in Marsa Matrouh, Egypt. Their research used stochastic frontier analysis (SFA) to project efficiency levels and identify resource shortfalls. Ghimire and Kattel¹⁰ Emphasized how socioeconomic elements affect vegetable farming among Nepal’s vegetable growers, stressing how the best use of resources may improve profitability. Emphasizing the value of reasonably priced and accessible inputs, Goni et al.¹¹ found inefficiencies in resource usage among vegetable growers in Jere, Nigeria. Emphasizing the advantages of economies of scale and best use of resources, Kumar et al.¹² and Rijal and Bhatta¹³ also examined resource usage efficiency.

Agricultural resource usage efficiency has been improved by the use of optimization strategies like linear programming (LP) and Multi-Objective Programming (MOP). Alotaibi and Nadeem¹⁴ and Andreea¹⁵ showed how well LP may maximize agricultural productivity and lower expenses. Research by Annetts and Audsley¹⁶ and Bhatia and Rana¹⁷ underlined LP models’ ability to meet farming’s environmental and financial goals.

Lone et al.¹⁸ applied fuzzy linear programming (FLP) to optimize agricultural land allocation, focusing on crops such as corn, rice, and maize. This approach aimed to handle decision-making under uncertainty using trapezoidal membership functions in FLP. The study showed that utilizing fuzzy set theory in agricultural planning could lead to more flexible and realistic decision-making, ultimately enhancing productivity and resource use efficiency. Further, Lone et al.¹⁹ applied FLP to willow wicker cultivation in Kashmir, demonstrating that optimal crop combinations significantly enhanced profitability. Their study demonstrated the flexibility of FLP in handling uncertainties, particularly under changing economic and climatic conditions. This approach effectively managed uncertainties, maximizing resource use for better economic outcomes.

Particularly in the setting of Himachal Pradesh, where land and water resources are few, the importance of resource conservation cannot be emphasized. Studies have shown that using effective resource allocation techniques may greatly lower production costs and raise yields. Sen [16], for instance, presented MOP as a fair strategy for rural development by integrating social, environmental, and economic objectives into agricultural planning. Sen developed solutions catered to particular resource restrictions and goals using a mixed-integer programming paradigm, therefore allowing effective resource allocation and environmental sustainability by means of efficient use of resources. The lack of region-specific studies addressing revenue maximization, job creation, and input minimization in vegetable farming exposes a notable research void for the state. Fostering sustainable and lucrative agricultural methods suitable to the particular agro-climatic conditions of the state depends on closing this gap, particularly for smallholder systems’ offseason vegetable output. This work aims to close this gap by creating a MOP framework that combines environmental sustainability with economic objectives, thereby providing complete optimization techniques fit for the agricultural scene of Himachal Pradesh. The results highlight the possibilities for using optimization strategies to significantly improve the local agriculture industry.

Methodology

Selection of study area and sampling design

The study was conducted in the Solan and Sirmaur districts of Himachal Pradesh, utilizing primary and secondary data, presents the study area in Fig. 1. Primary data related to income and costs of different inputs were collected through well-structured questionnaires administered to farmers. A multi-stage sampling design was employed, beginning with the purposive selection of these districts due to their unique geographical and climatic conditions that are ideal for vegetable growing, and the paucity of studies on these areas.

Within each district, two community development blocks were purposively selected based on their concentration of vegetable farms. Subsequently, commercial vegetable farmers were randomly selected to ensure a representative sample and avoid biases. In each district, 75 farmers were chosen collectively from each block. Solan and Kandaghat blocks were selected in the Solan district, and Pachhad and Rajgarh blocks were selected in the Sirmaur district. This methodology ensured comprehensive coverage and representation of the vegetable farming community in the selected regions.

Fig. 1

Map of the study area. Map created using base layers from the Survey of India (https://surveyofindia.gov.in), district-level mapping via IIPMaps (https://iipmaps.nic.in), and block-level overlays generated in Microsoft Power BI Desktop (Version 2.127.1322.0, March 2024; https://powerbi.microsoft.com) using geographic coordinates sourced from government open data platforms (https://data.gov.in).

Computation of MOP for resource use optimization

Numerous studies have highlighted the non-optimal use of inputs in crop production in India, with most research focusing solely on maximizing farm income and/or employment. In contrast, this study adopts a broader perspective by also considering environmental pollution and aiming to reduce the use of fertilizers and pesticides in modern farming practices. Consequently, optimal farm plans were formulated with the following objectives:

• 1. Maximization of income

• 2. Maximization of employment

• 3. Minimization of the use of fertilizers

• 4. Minimization of the use of pesticides

• 5. Multi-objective programming model

• 1. Maximization of income

  $$\begin{aligned} \text {Maximize,} & \\ Max\, Z= & \sum _{J = 0}^{n}I_j X_j;\qquad j = 1,2,3,\dots , n\\ \text {Subject\,to,}\qquad & \\ \sum _{j = 1}^{n}a_{ij}X_j\ge <b_i&;&\qquad i= 1,2,3, \dots , n\\ & X_1\ge 0;\, X_2\ge 0;\, \ldots ; \, X_n\ge 0 \\ Z= & \text {Total income of the farmers}\\ X_j= & \text {level of }j^{th}\text { activity on the farm}\\ I_j= & \text {income per unit of } j^{th}\text { activity}\\ a_{ij}= & \text {amount of }i^{th}\text { resources used per unit of the }j^{th}\text { activity and}\\ b_i= & \text {available quantity of }i^{th}\text { resource.} \end{aligned}$$

• 2. Maximization of employment

  $$\begin{aligned} \text {Maximize,} & \\ Max\, Z= & \sum _{J = 0}^{n}E_j X_j;\qquad j = 1,2,3,\dots , n \\ \text {Subject\,to,}\qquad & \\ \sum _{j = 1}^{n}a_{ij}X_j\ge <b_i&;&\qquad i= 1,2,3, \dots , n\\ & X_1\ge 0;\, X_2\ge 0;\, \ldots ; \, X_n\ge 0 \end{aligned}$$
  $$\begin{aligned} \text {where,} & \\ Z= & \text {Total employment of the farmers}\\ X_j= & \text {level of }j^{th}\text { activity on the farm}\\ E_j= & \text { employment per unit of } j^{th}\text { activity}\\ a_{ij}= & \text {amount of }i^{th}\text { resources used per unit of the }j^{th}\text { activity and}\\ b_i= & \text {available quantity of }i^{th}\text { resource.} \end{aligned}$$

• 3. Minimization of use of fertilizers

  $$\begin{aligned} \text {Minimize,} & \\ Min\, Z= & \sum _{J = 0}^{n}F_j X_j;\qquad j = 1,2,3,\dots , n \\ \text {Subject\,to,}\qquad & \\ \sum _{j = 1}^{n}a_{ij}X_j\ge <b_i&;&\qquad i= 1,2,3, \dots , n\\ & X_1\ge 0;\, X_2\ge 0;\, \ldots ; \, X_n\ge 0 \\ \text {where,} & \\ Z= & \text {Total use of fertilizers by the farm}\\ X_j= & \text {level of }j^{th}\text { activity on the farm}\\ F_j= & \text {use of fertilizers per unit of } j^{th}\text { activity}\\ a_{ij}= & \text {amount of }i^{th}\text { resources used per unit of the }j^{th}\text { activity and}\\ b_i= & \text {available quantity of }i^{th}\text { resource.} \end{aligned}$$

• 4. Minimization of use of pesticides.

  $$\begin{aligned} \text {Minimize,} & \\ Min\, Z= & \sum _{J = 0}^{n}P_j X_j;\qquad j = 1,2,3,\dots , n \\ \text {Subject\,to,}\qquad & \\ \sum _{j = 1}^{n}a_{ij}X_j\ge <b_i&;&\qquad i= 1,2,3, \dots , n\\ & X_1\ge 0;\, X_2\ge 0;\, \ldots ; \, X_n\ge 0 \\ \text {where,} & \\ Z= & \text {Total use of pesticide by the farm}\\ X_j= & \text {level of }j^{th}\text { activity on the farm}\\ P_j= & \text {use of pesticide per unit of } j^{th}\text { activity}\\ a_{ij}= & \text {amount of }i^{th}\text { resources used per unit of the }j^{th}\text { activity and}\\ b_i= & \text {available quantity of }i^{th}\text { resource.} \end{aligned}$$

• 5. Multi-objective programming model

  $$\begin{aligned} \text {Maximize,}\qquad & \\ & Z=\frac{\sum _{j = 1}^{n}I_jX_j}{W_1}+\frac{\sum _{j = 1}^{n}E_j X_j}{W_2}-\frac{\sum _{j = 1}^{n}F_jX_j}{W_3}-\frac{\sum _{j = 1}^{n}P_jX_j}{W_4}\\ \text {Subject to,}\qquad & \\ & \sum _{j = 1}^{n}a_{ij} X_j \ge <b_i;\qquad i= 1,2,3, \dots , n\\ \text {such that,}\qquad & \\ & X_1\ge 0;\, X_2\ge 0;\, \ldots ;\, X_n\ge 0\\ \\ \text {where,}\qquad & \\ & W_1=\text {Maximum Income}\\ & W_2=\text {Maximum Employment}\\ & W_3=\text {Minimum use of fertilizers}\\ & W_4=\text {Minimum use of pesticides}\\ \text {and all the variables are as stated before.} \end{aligned}$$

Numerical illustration

To illustrate the optimization model employed in this study, consider the objective of maximizing net income. The study focuses on the optimal land area allocation plan for farmers taking three vegetable crops per year, with various crops grown in different areas. These crops are denoted as $X_1$ to $X_9$, including Bean 1 (March–May), Bok choy (March–April), Broccoli 1 (March–April), Chinese cabbage (March–May), Bean 2 (August–November), Broccoli 2 (August–November), Cauliflower 1 (August–November), Peas (November-March), and Cauliflower 2 (November–March). Additionally, $X_{10}$ to $X_{15}$ represent various cost constraints such as the cost of hired labor and interest in borrowing. The income maximization function is formulated as follows:

$$\begin{aligned} Z = & 175533.575X_{1} + 337810.225X_{2} + 235040.7665X_{3} + 421901.7824X_{4} \\ & \quad + 188900.5801X_{5} + 243357.1065X_{6} + 172437.8238X_{7} + 169136.355X_{8} \\ & \quad + 193926.5828X_{9} - 0.023X_{{10}} - 0.023X_{{11}} - 0.023X_{{12}} - 500X_{{13}} \\ & \quad - 500X_{{14}} - 500X_{{15}} \\ \end{aligned}$$

The model includes several constraints to ensure feasibility and practical implementation. Seasonal area constraints ensure that the the total area allocated to crops in each season does not exceed the available land. For instance, the sum of areas allocated to Bean (March–May), Bok choy (March–April), Broccoli (March–April), and Chinese cabbage (March–May) in the summer season must not exceed the total available area for that season. Similarly, the rainy season area constraint ensures that the combined area for crops like Bean (August–November), Broccoli (August–November) and Cauliflower (August–November), does not surpass the available area. The winter season area constraint ensures that the combined area for winter crops like Peas (November–March) and Cauliflower (November–March) do not exceed the available winter area. Capital constraints ensure that the capital invested in each season remains within the allocated budget. For example, the total cost of planting and maintaining crops during the Summer must stay within the summer season budget, while the same applies to the rainy and winter season budgets. Labor constraints ensure that the labor required for each season does not exceed the available labor, with specific constraints for summer, rainy, and winter seasons. Furthermore, domestic use restrictions guarantee that a minimum area is allotted to every crop to satisfy needs for domestic consumption, like a minimum area set to produce Chinese cabbage to guarantee enough supply.

Similarly, objective functions for maximizing employment and minimizing fertilizer and pesticide use were developed. By employing Sen’s MOP approach, we formulated a MOP model that combines these individual objectives into a single function. This integrated approach aims to balance the goals of maximizing income and employment while reducing the use of fertilizers and pesticides. The visualizations for the study were meticulously prepared using various software tools to ensure clarity and precision. The optimization model was implemented using Lingo 2.0 software, licensed to the Division of Agricultural Statistics, SKUAST Kashmir, selected for its advanced capabilities in solving linear and nonlinear programming problems. Tables summarizing the data were generated using Microsoft Power BI, leveraging its robust data visualization features for effective presentation. Additionally, thermal maps were created using Python, utilizing the matplotlib and seaborn libraries to provide insightful visual representations of the data.

Results and discussion

Socio-economic analysis of sampled farmers

Table 1 provides a comprehensive socio-economic analysis of sampled farmers across Sirmaur and Solan districts in Himachal Pradesh, along with the aggregate values for the state. The education levels reveal varying degrees of literacy, with primary and secondary education being the most prevalent. In both Sirmaur and Solan districts, 9.33% of the farmers are illiterate. Primary education levels are consistent at 18.67% across both districts, while secondary education is higher in Solan (41.33%) compared to Sirmaur (25.33%). Family demographics indicate a higher proportion of nuclear families, with Solan having 54.67% and Sirmaur 44.0% nuclear households. The age demographics show that the mean age of farmers is 44.75 years in Sirmaur and 48.53 years in Solan. The farm size distribution highlights that the majority are marginal farmers, with 56.00% in Sirmaur and 69.33% in Solan. Equipment ownership data shows that Sirmaur has 32 power tillers and 56 knapsack sprayers, while Solan has 42 power tillers and 24 knapsack sprayers. Overall, the data illustrates the diverse educational backgrounds, family structures, age profiles, farm sizes, and equipment usage among the farmers in these regions.

Table 1 Socio-economic analysis of sampled farmers.

Resource use optimization for farmers taking three crops per year in vegetable crops

In this study, we categorized farmers based on their land holdings to optimize resource utilization. The farm categories are defined as marginal farms (land holdings up to 0.5 hectares), small farms (land holdings between 0.51 and 1.0 hectares), and medium farms (land holdings between 1.1 and 2.0 hectares). We developed optimization models for each category to enhance land use efficiency and productivity. These models aim to increase income, employment, and sustainability by addressing the specific requirements and capacities of each farm size. By customizing the optimization process for these distinct categories, we ensure that resource allocation is both efficient and equitable, leading to improved outcomes across all farm sizes.

Land allocation and optimization analysis for marginal farmers

Figures 2, 3, 4, 5, and 6 comprehensively analyze land allocation and comparative outcomes under different optimization strategies for marginal farmers. For the March-May season, the allocation for Chinese cabbage under the income-maximization strategy increases significantly from 5.45% to 32.78%. In contrast, Bok choy’ s allocation rises to 33.08% under both fertilizer and pesticide minimization strategies, from an existing 17.59%. Broccoli (March–April), initially at 9.78%, sees a dramatic increase to 33.73% under the employment-maximization strategy. In the August–November season, Bean allocation increases from 8.20% to 22.44% under both fertilizer and pesticide minimization strategies. Broccoli, starting at 4.72%, increases to 21.87% under income maximization. Cauliflower, initially at 11.51%, rises to 21.09% under employment maximization. For the November-March season, Peas, originally occupying 20.18% of the area, see their allocation rise to 37.95% under both fertilizer and pesticide minimization and the MOP approach. Cauliflower, starting at 18.36%, increases significantly to 36.49% under both income and employment maximization strategies.

In terms of the comparative analysis presented in Table 2, employment peaks at 252 man-days per hectare under the employment-maximization strategy, compared to the current 190 man-days. Income maximization results in the highest income of Rs 280,058 per hectare, an increase from the existing Rs 229,359 per hectare. The lowest fertilizer cost is Rs 4420 per hectare under the fertilizer-minimization strategy, a reduction from Rs 6180 per hectare. The lowest pesticide cost, Rs 45,729 per hectare, is achieved under the pesticide-minimization strategy, down from Rs 56,479 per hectare. The MOP approach balances these factors, resulting in improved income and moderate costs for fertilizer and pesticides.

Table 2 Comparative analysis under different optimization strategies among marginal farmers.

Fig. 2

Maximize income.

Fig. 3

Maximize employment.

Fig. 4

Minimize fertilizers.

Fig. 5

Minimize pesticides.

Fig. 6

Multi-objective programming.

Land allocation and optimization analysis for small farmers

Figures 7, 8, 9, and 10 provide a detailed analysis of land allocation and comparative outcomes under different optimization strategies for small farmers. For the March–May season, the allocation of Chinese cabbage under the income-maximization strategy increases significantly from 15.64% to 40.23%, whereas Bok choy’ s allocation rises dramatically to 40.40% under fertilizer and pesticide minimization strategies, from an existing 10.00%. Broccoli (March–April), initially at 7.42%, sees a substantial increase to 40.69% under the employment-maximization strategy. In the August-November season, Bean allocation increases from 9.82% to 28.69% under both fertilizer and pesticide minimization strategies. Broccoli, starting at 13.87%, increases to 28.40% under income maximization. Cauliflower, initially at 6.04%, rises to 27.99% under employment maximization. For the November-March season, Peas, originally occupying 5.37% of the area, see their allocation rise to 27.62% under both fertilizer and pesticide minimization and the MOP approach. Cauliflower, starting at 22.57%, increases significantly to 26.94% under the employment-maximization strategy.

In terms of the comparative analysis presented in Table 3, employment peaks at 263 man-days per hectare under the employment-maximization strategy, compared to the current 217 man-days. Income maximization results in the highest income of Rs 296,214 per hectare, an increase from the existing Rs 238,095 per hectare. The lowest fertilizer cost is Rs 3977 per hectare under the fertilizer-minimization strategy, a reduction from Rs 6,686 per hectare. The lowest pesticide cost, Rs 37,869 per hectare, is achieved under the pesticide-minimization strategy, down from Rs 55,370 per hectare. The MOP approach balances these factors, resulting in improved income and moderate costs for fertilizer and pesticides.

Table 3 Comparative analysis under different optimization strategies among small farmers.

Fig. 7

Maximize income.

Fig. 8

Maximize employment.

Fig. 9

Minimize fertilizers.

Fig. 10

Multi-objective programming.

Land allocation and optimization analysis for medium farmers

Figures 11, 12, 13, 14, and 15 provide a complete analysis of land allocation and comparative outcomes under different optimization strategies for medium farmers. For the March–May season, the allocation for Chinese cabbage under the income-maximization strategy increases significantly from 10.00% to 41.60%, whereas Bok choy’ s allocation rises dramatically to 41.72% under both fertilizer and pesticide minimization strategies, from an existing 6.95%. Broccoli (March–April), initially at 5.09%, sees a substantial increase to 41.92% under the employment-maximization strategy. In the August–November season, Bean allocation increases from 11.70% to 27.86% under both fertilizer and pesticide minimization strategies. Broccoli, starting at 12.49%, remains relatively unchanged under most strategies. Cauliflower, initially at 4.39%, rises to 27.36% under employment maximization. For the November-March season, Peas, originally occupying 6.54% of the area, see their allocation rise to 28.07% under both fertilizer and pesticide minimization. Cauliflower, starting at 21.76%, increases significantly to 27.77% under the employment-maximization strategy. Under the income-maximization strategy, Peas also see a significant increase to 28.07%. Under the MOP approach, for the March–May season, Chinese cabbage sees an allocation of 41.60%. In the August–November season, Bean allocation is 27.86%. For the November–March season, Peas are allocated 28.07%. This balanced approach results in improved income and moderate costs for both fertilizer and pesticides.

In terms of the comparative analysis presented in Table 4, employment peaks at 253 man-days per hectare under the employment-maximization strategy, compared to the current 219 man-days. Income maximization results in the highest income of Rs 374,673 per hectare, an increase from the existing Rs 256,071 per hectare. The lowest fertilizer cost is Rs 4428 per hectare under the fertilizer-minimization strategy, a reduction from Rs 7092 per hectare. The lowest pesticide cost, Rs 45,172 per hectare, is achieved under the pesticide-minimization strategy, down from Rs 57,612 per hectare.

Table 4 Comparative analysis under different optimization strategies among medium farmers.

Fig. 11

Maximize income.

Fig. 12

Maximize employment.

Fig. 13

Minimize fertilizers.

Fig. 14

Minimize pesticides.

Fig. 15

Multi-objective programming.

These findings underscore the value of adopting multi-objective optimization approaches to balance economic and environmental goals in agricultural planning for farmers cultivating three vegetable crops per year.

Surface gradient charts for land allocation

Figures 16, 17, and 18 show the surface gradient charts, which provide visualization of how land allocation for various crops changes under different optimization strategies among marginal, small, and medium farmers. Peaks in the charts indicate significant increases in land allocation for certain crops under specific optimization strategies. For marginal farmers, the charts show peaks for Chinese cabbage (March–May) under the income-maximization strategy, Broccoli (March–April) under the employment-maximization strategy, and Peas (November–March) and Bok choy (March–April) under both fertilizer and pesticide minimization strategies. These peaks reflect a strategic focus on these crops to achieve the desired optimization goals. For small farmers, the peaks are seen for Chinese cabbage (March–May) under the income-maximization strategy, Broccoli (March–April) under employment maximization, and Bok choy (March–April) and Peas (November–March) under both fertilizer and pesticide minimization strategies. Medium farmers exhibit peaks for Chinese cabbage (March–May) and Bean (August–November) under income maximization, Broccoli (March–April) under employment maximization, and Bok choy (March–April) and Peas (November–March) under both fertilizer and pesticide minimization strategies. Valleys in the charts represent significant decreases in land allocation for certain crops. The MOP approach results in more balanced allocations, with less pronounced peaks and valleys, indicating a more integrated and sustainable approach to optimizing land use across different criteria.

Fig. 16

Energy gradient surface chart for land allocation optimization, showing marginal farmers.

Fig. 17

Energy gradient surface chart for land allocation optimization, showing small farmers.

Fig. 18

Energy Gradient surface chart for land allocation optimization, showing medium farmers.

The results highlight significant land allocation, income, employment, and resource usage variations across marginal, small, and medium farms under different optimization strategies. These outcomes reflect the distinct priorities and constraints faced by each category of farmers. For instance, the significant increase in Chinese cabbage allocation under income-maximization strategies across all farm sizes demonstrates its profitability potential due to high market demand and favorable growing conditions during the March–May season. Similarly, the notable rise in broccoli allocation under employment-maximization strategies aligns with its labor-intensive cultivation practices, which create additional employment opportunities.

Targeted input management seems to be helpful in improving resource use efficiency based on reported trends in lowered pesticide and fertilizer costs under minimization techniques. For small farms, especially where adopting optimum crop rotation and precision farming techniques probably helped reduce input reliance, fertilizer prices declined dramatically. This is consistent with research by Ayulla et al.⁸ which found comparable savings in input costs by means of vegetable agricultural resource optimization.

The rising revenue under income-maximizing techniques emphasizes the possibility of smart land allocation and crop diversification to improve farm profitability. The results line up with Kumar and Sharma⁶, who found that vegetable producers’ income levels were much raised by using high-value crops and smart resource allocation. Moreover, the MOP method was competent in balancing revenue, employment, and resource use, thus indicating a sustainable route for besting farm-level results. Sen²⁰ underlined comparable advantages of MOP in reaching a balanced optimum for planning rural development.

Different approaches to land distribution for crops like Bok choy and Peas expose the different goals for cost control, revenue, and employment. These differences imply that it is very important to customize optimization models depending on farm size and local variables. Emphasizing the importance of encouraging resource-conserving technology, training farmers on input efficiency, and enhancing access to markets for high-value crops, the study’s ramifications also reach legislators and extension services.

The overall findings show the transforming power of customized optimization solutions to solve the many difficulties experienced by Himachal Pradesh’s farmers. To guarantee whole agricultural growth, the results of the study also highlight the need for further investigation into the long-term effects of these approaches on soil health, biodiversity, and environmental sustainability.

Conclusion

The research emphasizes how optimizable methods like LP and MOP have transforming power to solve important problems confronting Himachal Pradesh’s farmers. Encouragement of effective resource use and reduction of dependency on pesticides and fertilizers greatly raised output and profitability while thus supporting environmental sustainability. Under the MOP method, for example, Chinese cabbage (March–May), cauliflower (August–November), and peas (November–March) were deliberately given top priority, therefore allocating resources fairly and lowering input reliance.

Reducing chemical inputs showed clear results; pesticide costs dropped about 20% across all farm types, and fertilizer prices dropped almost 30%. This enhanced not only agricultural profits but also protected soil biodiversity and condition. The results underline the significance of using region-specific optimization models that fit the many requirements of farmers. Using sustainable techniques such as crop diversification and input-efficient farming can help reduce environmental impact while enhancing livelihoods.

Policy recommendations

Using MOP techniques can help farmers’ earnings, increase resource efficiency, and encourage sustainable practices quite a lot. To turn these results into practical results, however, the effective use of such approaches calls for cooperative efforts among legislators, agricultural extension agencies, and the farming community. The growing market for exotic vegetables offers more economic growth and income diversification opportunities. These results lead one to the following policy recommendations:

Targeted support for optimization strategies

Establish focused assistance initiatives aiming at farmers adopting revenue and employment maximizing techniques. Training courses, high-yield seed subsidies, and access to innovative agricultural technology may all fit here. Giving farmers the tools and information they need will help them apply plans meant to improve their profitability and output more successfully.

Resource use efficiency programs

Create and advertise initiatives aiming at reducing pesticide and fertilizer consumption. These initiatives could provide financial help for buying environmentally friendly inputs and certificates for organic agricultural methods, therefore rewarding sustainable farming practices. Promoting sustainable methods among farmers will assist in lowering environmental impact and enhance long-term soil conditions.

Support for exotic vegetable cultivation

Encourage the growth of exotic vegetables by means of subsidies for premium seeds, availability of modern agricultural tools, and training courses. These veggies may greatly increase the profitability of farming in Himachal Pradesh and provide more revenue possibilities.

Customized training programs

Create training courses catering to various farm sizes (marginal, small, medium) to guarantee farmers can use the advised optimization techniques effectively. These initiatives need to address best practices in integrated pest control, soil management, and crop rotation. Customized training guarantees that the newest agricultural methods will help every farmer, regardless of the size of the operation.

Enhanced access to credit

Give farmers better access to financing facilities so they may make investments in optimization plans. Low-interest loans and flexible payback terms catered to the agricultural cycle should be provided by financial institutions. Improved financing availability would enable farmers to undertake required investments in their businesses free from the weight of rigid repayment schedules or excessive interest rates.

Infrastructure development

Invest in infrastructure like transportation systems, storage facilities, and irrigation systems that promote the best farming. This will enable farmers to maximize advantages from the best use of resources and land allocation. Better infrastructure will help to lower post-harvest losses, improve market access, and raise general agricultural efficiency.

By following these suggestions, authorities may greatly increase the profitability, sustainability, and output of vegetable growing in Himachal Pradesh, enhancing the lives of farmers and helping the area grow generally economically.