Economic-environmental trade-offs in cotton-based cropping systems: Evidence from Telangana, India

Introduction

Agriculture continues to play a pivotal role in the Indian economy, providing livelihoods to nearly half of the population and contributing substantially to national income and food security. Within the agricultural sector, commercial crops such as cotton assume particular importance due to their strong linkages with agro-processing industries, export markets, and employment generation across rural and urban regions. Cotton (Gossypium spp.), often referred to as "white gold," is one of the most important fiber crops cultivated in India, accounting for nearly one-fourth of the global cotton area. India has consistently ranked among the world's leading cotton producers, exporters, and consumers, underscoring the crop's strategic relevance to the national economy. Globally, cotton production during 2022-23 was estimated at approximately 114.4 million bales cultivated over 32.5 million hectares, with China, India, Brazil, and the United States emerging as the major producers. India alone accounted for nearly 25.5 million bales, cultivated over an area exceeding 12.5 million hectares (USDA, 2023). Cotton contributes significantly to India's agricultural exports, supporting a vast textile industry that employs millions of workers and contributes around 2% to national GDP and over 10% to total exports. In addition to economic challenges, environmental concerns associated with cotton cultivation have gained increasing attention in recent years. Cotton is widely recognized as an input-intensive crop with a relatively high environmental footprint. The extensive use of nitrogen fertilizers contributes significantly to nitrous oxide (N₂O) emissions, a potent greenhouse gas with a global warming potential far exceeding that of carbon dioxide. Energy consumption for irrigation, mechanization, and fertilizer production further adds to carbon dioxide emissions. Several studies have identified cotton as one of the major contributors to agricultural greenhouse gas emissions in India, particularly in regions characterized by intensive input use and groundwater exploitation. India's commitment to climate change mitigation under national and international frameworks, including the www.extensionjournal.com Nationally Determined Contributions (NDCs), necessitates the identification of agricultural strategies that can reduce emissions without compromising farm incomes. This dual challenge has shifted the policy focus from mere productivity enhancement to sustainable intensification, where economic viability and environmental performance are jointly considered. Telangana has emerged as a major cotton-producing state in India over the last decade. The state witnessed a rapid expansion in cotton area following its formation, driven by favorable prices, improved market access, and policy support as shown in table 1. During 2022-23, Telangana accounted for nearly 16% of India's total cotton area, with production increasing steadily across districts such as Nagarkurnool, Adilabad, Nalgonda, Sangareddy, and Warangal. However, cotton cultivation in Telangana is predominantly rain-fed and resource-intensive, raising concerns about long-term sustainability, groundwater depletion, and environmental degradation. The present study seeks to address this gap by conducting an integrated economic and environmental assessment of cotton-based cropping systems in semi-arid Telangana. By combining farm-level cost and return analysis with greenhouse gas emission estimation and Pareto-based multiobjective optimization, the study provides empirical evidence on the trade-offs between profitability and environmental sustainability. The findings are expected to inform policy interventions aimed at promoting sustainable cotton cultivation while ensuring livelihood security for farmers.

1.1 Objectives of the study a. To estimate the economic returns from cotton-based cropping systems followed by farmers in Nagarkurnool district of Telangana. b. To quantify greenhouse gas (GHG) emissions from cotton-based cropping systems during the Kharif and Rabi seasons. c. To optimize both economic returns and environmental impacts of cotton-based cropping systems using a multiobjective framework and develop optimal model farm scenarios for policy guidance.

Hypotheses of the study Null hypotheses (H₀)

 The existing cotton-based farming practices do not optimize economic returns and environmental sustainability.  The choice of cotton-based cropping systems and management practices is independent of agro-climatic zones and farm size categories.

Alternate hypotheses (H₁)

 Trade-offs exist between farm profitability and environmental impacts in cotton-based cropping systems.

 Adoption of optimal model farms can enhance farmer profitability while minimizing environmental impacts.

Research gap

Cotton cultivation supports livelihoods for millions of farmers in Telangana and across India. However, it is characterized by high input intensity, significant labour requirements, and substantial environmental impacts, including greenhouse gas emissions and water consumption.

While several studies have documented the costs, returns, and GHG emissions of cotton (Ramasamy et al., 2019; Birthal et al., 2021) [6,1] , few have explicitly examined the trade-offs between profitability and environmental sustainability at the farm level in semi-arid regions. Furthermore, there is limited empirical evidence on optimal model farms that integrate economic and environmental objectives to guide decisionmaking by policymakers and farmers. Addressing this gap is crucial for promoting cotton-based farming systems that are both economically viable and environmentally sustainable, particularly in regions facing water scarcity, climate variability, and high agrarian risk (Youssef et al., 2019) [10] .

Review of Literature

A substantial body of research has examined the economics of cotton cultivation across India, consistently highlighting its high-cost and high-risk nature. Cotton production relies heavily on purchased inputs such as fertilizers, pesticides, and labour, making it more expensive than competing crops like maize and pulses (Ramasamy et al., 2019) [6] . Variable costs constitute over 70% of total cultivation expenses, with human labour, fertilizers, and pesticides as the dominant components (Birthal et al., 2021) [1] . Comparative studies of cotton-based cropping systems indicate that diversification can enhance profitability. For instance, cotton-groundnut systems often generate higher net returns than cotton monocropping due to favorable groundnut prices and riskspreading benefits, whereas cotton-maize systems tend to be less profitable but carry lower production risks, given maize's shorter duration and reduced susceptibility to pests (Sankar & Naidu, 2017) [8] . These findings underscore the economic trade-offs and management challenges inherent in cottonbased farming, particularly in semi-arid regions where climatic variability further influences productivity and income stability. The environmental dimension of cotton cultivation has been increasingly examined using life cycle assessment (LCA) and www.extensionjournal.com IPCC-based emission coefficient approaches. Fertilizerinduced nitrous oxide emissions have been identified as a major source of agricultural greenhouse gases (Paustian et al., 2004; Pathak et al., 2010) [3,5] , while crop-specific studies indicate that cotton exhibits higher emissions per hectare than cereals due to intensive input use (Zhang et al., 2018) [11] .

Cotton-based systems in semi-arid regions significantly contribute to regional emission profiles, emphasizing the need for improved input-use efficiency and alternative cropping strategies (Paramesh et al., 2023) [4] . The inherent trade-off between profitability and environmental performance has motivated the application of Pareto-based multi-objective optimization in agriculture. Such approaches identify efficient solutions that balance economic and environmental objectives, with intermediate cropping strategies offering compromise solutions that retain substantial economic benefits while reducing emissions (Ray et al., 2020; Li et al., 2024) [7,2] . Despite this progress, empirical applications of integrated economic-environmental frameworks in cotton-based systems remain limited in India, a gap that the present study addresses using farm-level data and Pareto optimization techniques.

Materials and Methods

Study area

The study was conducted in Nagarkurnool district of Telangana, located in the semi-arid agro-climatic zone of southern India. The district is characterized by erratic rainfall, limited irrigation coverage, and predominance of small and marginal farmers. Agriculture is the primary livelihood source, with cropping decisions strongly influenced by monsoon performance and groundwater availability. Cotton is the dominant kharif crop in the district, occupying approximately 1,46,009.8 hectares. During the Rabi season, groundnut and maize are the major crops, occupying 51,673.8 hectares and 3,821.4 hectares, respectively. Achampet mandal was selected for the study as it represents typical cotton-based cropping systems prevalent in the district.

Sampling and data collection

Primary data were collected from 40 sample farmers during the agricultural year 2022-23 using a structured questionnaire. The sample consisted of 20 farmers practicing Cotton-Groundnut systems and 20 farmers practicing Cotton-Maize systems. Data were collected on input use, labour employment, costs, yields, prices, and crop management practices.

Economic analysis

Costs of cultivation were estimated following standard farm management accounting procedures. Costs were classified into variable costs (human labour, bullock and machine labour, seeds, fertilizers, pesticides, irrigation, and interest on working capital) and fixed costs (rental value of land, depreciation, land revenue, and interest on fixed capital). Gross returns were calculated based on yield and prevailing market prices, while net returns were obtained by deducting total costs from gross returns.

Environmental impact assessment

Greenhouse gas emissions were estimated using the Cool Farm Tool (CFT), which calculates emissions from fertilizer use, energy consumption, field operations, and residue management. Emissions were expressed in kg CO₂equivalent per hectare. Crop-wise emissions were aggregated to obtain total emissions for each cropping system. Water footprint was also estimated to assess water use intensity.

Pareto optimization framework

A multi-objective optimization framework was employed with the objectives of maximizing net returns and minimizing GHG emissions. The optimization was subject to land availability constraints and non-negativity conditions. Pareto-optimal solutions were generated to identify trade-offs and balanced cropping system combinations.

Here's a general formulation along with the mathematical equations:

Problem: Maximizing and minimizing objective functions simultaneously subjected to constraints. Formulation: Let:

Decision variables

𝑥 𝑖 =proportion (or area share) of the i-th cropping system, where i=1,2,…,n

In the present study, the decision variables represent alternative cotton-based cropping systems (e.g., cottongroundnut and cotton-maize).

Objective functions Objective 1: Maximize net returns

Maximize This ensures that the total allocated area across cropping systems does not exceed 1 ha

Non-negativity Constraints

Solution Approach

Since the objectives of maximizing net returns and www.extensionjournal.com minimizing GHG emissions are inherently conflicting, no single solution simultaneously optimizes both objectives. Therefore, a Pareto-based multi-objective optimization approach was adopted to generate a set of Pareto-optimal solutions. These solutions represent efficient trade-offs where any improvement in net returns would lead to an increase in GHG emissions and vice versa.

From the Pareto set, extreme solutions (maximum returns and minimum emissions) and an intermediate knee-point solution were identified to aid policy-relevant interpretation and practical decision-making for sustainable cotton-based cropping systems.

Results and Discussion

Costs and returns of cotton-based cropping systems

The economic analysis revealed notable differences in cost structure and profitability between the two cotton-based systems as shown in table 2. The total cost of cultivation was ₹2,81,018 per hectare for the Cotton-Groundnut system and ₹2,44,998 per hectare for the Cotton-Maize system. Variable costs accounted for nearly three-fourths of total costs in both systems, highlighting the input-intensive nature of cotton cultivation.

Human labour constituted the largest cost component, reflecting labour-intensive operations such as weeding, pesticide application, and harvesting. Seed costs were significantly higher in the Cotton-Groundnut system due to the high seed rate and price of groundnut. Fertilizer and manure costs were higher in the Cotton-Maize system, indicating greater nutrient requirements. Despite higher costs, the Cotton-Groundnut system generated higher gross returns (₹3,97,907/ha) and net returns (₹1,16,889/ha), compared to ₹3,42,107/ha and ₹97,109/ha for the Cotton-Maize system. This indicates the superior economic performance of cotton-groundnut systems driven by favorable market prices and diversification benefits.

Environmental impacts of cotton-based cropping systems

The environmental assessment revealed that the Cotton-Groundnut system emitted 7,043 kg CO₂e/ha, while the Cotton-Maize system emitted 5,820 kg CO₂e/ha. Carbon dioxide emissions constituted the largest share in both systems, followed by nitrous oxide emissions arising from fertilizer use. Methane emissions were negligible in all cropping systems The higher emissions in the Cotton-Groundnut system can be attributed to greater input use and longer crop duration. The Cotton-Maize system, although less profitable, exhibited better environmental performance, indicating a trade-off between income and emissions.

Conclusion and policy implications

The study demonstrates that cotton-based cropping systems in semi-arid Telangana involve inherent trade-offs between economic profitability and environmental sustainability. While cotton-groundnut systems offer higher incomes, they are associated with higher greenhouse gas emissions. Cottonmaize systems, on the other hand, provide environmental benefits at the cost of reduced profitability. Importantly, Pareto-optimal mixed cropping strategies offer a viable compromise, enabling farmers to reduce emissions without substantial income losses. Policy interventions should focus on promoting such diversified systems through incentives, extension services, and support for input-use efficiency. Encouraging balanced cotton-based systems can contribute to climate mitigation while ensuring livelihood security in semi-arid regions.

References

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