The stakes are considerable. Agriculture still employs nearly 47 percent of India’s workforce and contributes roughly 18 percent of gross domestic product. Cotton and sugarcane sit at the heart of the country’s industrial economy: cotton feeds a textile sector that accounts for about 2.3 percent of GDP, 13 percent of industrial production, and nearly 12 percent of export earnings, while supporting more than 45 million jobs. Sugarcane underpins the second-largest agro-based industry after textiles and has become central to India’s ethanol blending program, which reached approximately 15 percent in 2024–25, reducing petroleum imports and conserving foreign exchange. Any instability in the economics of these crops therefore ripples far beyond the farm gate.

The study draws on the Comprehensive Scheme on Cost of Cultivation maintained by the Directorate of Economics and Statistics under the Ministry of Agriculture. For cotton, the analysis covers Andhra Pradesh, Gujarat, Karnataka, Maharashtra, and Punjab, which together accounted for roughly 66 percent of cultivated area and 65 percent of national production in 2021–22. For sugarcane, it covers Uttar Pradesh, Maharashtra, Karnataka, and Tamil Nadu, which together represent more than 80 percent of area and output. The dataset spans 130 cotton observations and 104 sugarcane observations across five-year intervals, allowing the researcher to isolate phase-wise transformations tied to major policy regimes.

Methodologically, the study combines descriptive statistics, compound annual growth rates, and coefficients of variation with a Cobb–Douglas production function estimated through a fixed-effects panel regression. The fixed-effects specification assigns each state its own intercept, stripping out time-invariant characteristics such as soil, climate, and agrarian structure that would otherwise bias the estimates. Cluster-robust standard errors at the state level were used for cotton, and robust standard errors for sugarcane, to guard against heteroskedasticity and serial correlation within states. The dependent variable is the value of output per hectare, encompassing both main products and by-products, while the explanatory variables capture distinct cost components including seed, labour, machinery, irrigation, and fertilizers.

The descriptive results paint a picture of relentless cost escalation. Cultivation costs rose persistently for both crops across all states, with cotton exhibiting higher growth and greater instability in paid-out costs and total cultivation costs than sugarcane. Maharashtra recorded the steepest cost growth for both crops, but also the highest volatility, a pattern the study links to recurrent droughts and volatile cotton prices. Punjab, by contrast, achieved the highest growth in cotton value of output with moderate instability, reflecting better irrigation infrastructure, market integration, and institutional support. For sugarcane, Karnataka and Tamil Nadu showed more moderate growth and lower variability, while Uttar Pradesh experienced rapid cost expansion accompanied by even greater instability.

Profitability improved over time, but the picture changes dramatically depending on which cost concept is used. When profits are measured against Cost A2, the actual paid-out expenses, farmers generally remained in the black throughout the study period. When the imputed value of family labour is added, the margins narrow. But against Cost C2, the comprehensive measure that includes the rental value of owned land, interest on fixed capital, and family labour, the results turn sobering: Andhra Pradesh, Maharashtra, and Punjab registered negative long-term growth rates in cotton profitability, while for sugarcane, Maharashtra and Tamil Nadu showed weak performance despite strong growth under simpler measures. This divergence is the classic cost-price squeeze, in which rising land values, wages, and capital charges outpace gains in gross output.

The structural decomposition of costs reveals a deeper transformation. Operational costs have grown faster than fixed costs across both crops, meaning that escalation is driven by recurring expenditures on labour, fertilizers, irrigation, and machinery services rather than capital deepening. In Maharashtra and Gujarat, operational costs exceed 70 percent of total cotton cultivation costs, leaving farmers acutely exposed to input price inflation. Human labour remains the largest cost share everywhere, but the declining share of animal labour and rising machine labour in Punjab and Gujarat signal a gradual mechanization transition. The moderation of insecticide expenditure after 2011–12 in cotton regions likely reflects the widespread adoption of Bt technology and improved pest management.

The econometric results deliver the study’s most consequential findings. For cotton, seed expenditure emerges as the dominant determinant of value of output, with an elasticity of 0.42, meaning a 1 percent increase in seed investment raises output value by 0.42 percent. Machine and animal labour follow with an elasticity of 0.30, family labour contributes 0.28, and irrigation shows a small but positive elasticity of 0.04, suggesting that water investments pay off only when complemented by better seeds, nutrients, and management. For sugarcane, casual labour cost is the most influential determinant, underscoring the crop’s labour-intensive nature across planting, weeding, harvesting, and transport. Fertilizer and manure expenditure exerts a positive influence, but irrigation spending shows a negative association with output value, a result the author attributes to inefficient water use, excessive irrigation, groundwater depletion, and poor resource allocation in water-stressed regions.

That negative irrigation coefficient challenges the conventional view of water as an unambiguously productivity-enhancing input and carries significant policy weight in a country where sugarcane is among the most water-intensive crops. The study also finds that mechanization has not yet generated substantial gains for sugarcane, with machine and animal labour coefficients statistically insignificant, possibly due to fragmented landholdings and underutilized machinery. Roughly 77 percent of the variation in sugarcane output value is attributable to state-specific factors, and the model explains about 90 percent of within-state variation over time, indicating strong explanatory power for the selected inputs.

The policy implications are clear. Improving input-use efficiency, strengthening seed systems, promoting appropriate mechanization, and enhancing irrigation management emerge as priorities for sustaining the value of output in commercial crop cultivation. For cotton, continued investment in quality seeds and hybrid technologies offers the highest return; for sugarcane, raising labour productivity through mechanization and skill development could relieve the structural constraint imposed by rising rural wages and labour scarcity. Because cost structures differ so sharply across states, the study argues that policy design must be sensitive to regional production conditions rather than uniform. The author acknowledges limitations, noting that the Cobb–Douglas specification imposes constant elasticities and may not capture non-linear or dynamic relationships, and calls for future research using more flexible production functions. Yet even with those caveats, the analysis offers a rare long-term, comparative accounting of why India’s commercial farmers keep producing more while earning precariously, and where the next rupee of investment is most likely to pay off.

Subject of Research: Structural change in input use, cost instability, and farm profitability in Indian cotton and sugarcane cultivation from 1996–97 to 2021–22

Article Title: Structural change in input use instability and farm profitability in cotton and sugarcane in India

Article References: Mandal, A. (2026). Structural change in input use instability and farm profitability in cotton and sugarcane in India. Discover Agriculture, 4(1), Article 304. https://doi.org/10.1007/s44279-026-00747-5

Image Credits: AI Generated

DOI: 10.1007/s44279-026-00747-5

Keywords: cotton, sugarcane, India, cost of cultivation, farm profitability, input use, Cobb-Douglas production function, fixed-effects panel regression, Bt cotton, irrigation efficiency, agricultural economics, value of output

The research, published in the journal Discover Agriculture, examined the sources of total factor productivity (TFP) and profit growth in Beninese cotton production between 2000 and 2018. A team led by Idelphonse O. Saliou of the University of Abomey-Calavi assembled an impressive dataset: 482 cotton producers surveyed across three major agroecological zones—the central region around Savalou, the northern district of Banikoara, and the western Atacora region around Cobly. To qualify for the study, producers had to be at least 40 years old with a minimum of 19 consecutive years of farming experience, ensuring that each participant could reconstruct nearly two decades of production history.

Reconstructing nineteen years of farm records in a setting where most smallholders keep no formal accounts is a formidable challenge. The researchers used retrospective recall techniques, dividing the study period into four sub-periods aligned with successive presidential regimes—a practical aid to memory in a country where agricultural policy shifts with political transitions. Producers identified years of high and low performance within each sub-period, then supplied production details for the remaining years. Where available, farmers supplemented their recollections with accounting books, input purchase invoices, and labor contracts. The final estimation sample included 5,577 observations, reduced from a theoretical maximum of 9,158 by recall limitations, missing variables, and strict data-cleaning procedures that the authors argue should not introduce systematic bias under standard missing-data assumptions.

Methodologically, the study employed a parametric translog cost frontier approach, a flexible second-order approximation of the true cost function that imposes few prior restrictions on the underlying technology. The framework, following the decomposition methods of Kumbhakar and colleagues, separates productivity growth into three distinct components: technical efficiency change, which measures how close farmers operate to the best-practice frontier; technological change, which captures shifts in that frontier itself; and scale effects, which reflect economies or diseconomies of expanding production. Input prices for fertilizer, labor, and a Laspeyres index of other inputs—including seeds, insecticides, herbicides, and animal or mechanical traction—were normalized by land prices to satisfy linear homogeneity constraints. Crucially, the model accounted for unobserved heterogeneity among farms, distinguishing persistent inefficiency rooted in structural conditions from time-varying inefficiency that fluctuates year to year.

The headline result is modest but meaningful: TFP in Beninese cotton production grew by an average of 1.26 percent per year between 2000 and 2018. Decomposition reveals that this growth was powered almost entirely by technological progress, which advanced at 2.81 percent annually, complemented by a modest gain in technical efficiency of 0.24 percent per year. The technological momentum reflects concrete changes in the field: continuous varietal improvement programs that guarantee quality seed to producers, the gradual replacement of hand tools with animal traction and mechanical power, and intensified use of mineral fertilizers and pesticides for weed control and plant health. The technical efficiency gains, meanwhile, are credited in part to Benin’s dense extension network—nearly all cotton farmers belong to Village Cotton Producers Cooperatives, and extension agents are evaluated on the basis of cotton production performance.

But there is a troubling counterweight. The scale component exerted a negative effect of 1.83 percent per year on TFP, indicating that Beninese cotton farms are operating under decreasing returns to scale. When farmers increased the use of all inputs, output rose proportionally less, driving up unit costs for each additional kilogram of cotton harvested. In plain terms, farms are growing beyond their most efficient size, and unexploited economies of scale represent a pool of unrealized productivity. The authors note similar findings in Chinese agriculture after reforms, and contrast them with European and Finnish dairy farms where scale effects contributed positively to productivity—evidence that lower average costs could be achieved by producing at more optimal scales.

The study also traced how productivity translated, or failed to translate, into the bottom line. Farm profits grew by an average of 1.65 percent per year over the period, but the sources of that growth were largely external rather than internal. Rising cotton prices, which climbed at 3.03 percent annually, were the dominant driver, aided by Benin’s price stabilization mechanism that guarantees a minimum income for producers and shields them from world market volatility. Output quantity growth of 0.89 percent per year and the TFP gains also helped. Working against these gains, input prices rose at 2.91 percent per year, steadily eroding profitability—a pattern the authors note mirrors Kumbhakar and Lien’s findings in Norwegian dairy farming. Profit growth was strongest between 2011 and 2015, driven by favorable price movements, while the earliest sub-period saw profits squeezed by input cost inflation.

One finding stands out for its starkness: the estimated overall cost efficiency of Beninese cotton farms is only about 23.5 percent, combining persistent efficiency of 28 percent with time-varying efficiency of 83.8 percent. Actual production costs remain substantially above the minimum attainable frontier. The authors caution that this does not simply reflect poor management. Rather, the persistent inefficiency component likely captures structural and systemic constraints beyond individual farmers’ control: poor rural infrastructure, high transportation and input transaction costs, imperfect access to mechanization services, climate variability, credit market imperfections, and institutional rigidities. Similar low-efficiency findings across West Africa support this interpretation, with prior research showing that institutional environments—particularly access to credit, inputs, and marketing channels—significantly shape producer performance.

The researchers confirmed both of their formal hypotheses: technical efficiency gains contributed positively to TFP growth, and rising cotton prices positively influenced farm profits. Yet they are candid about the study’s limitations. Retrospective data collection risks recall bias and measurement error; the sample of experienced, older producers may overestimate efficiency; the cost frontier framework does not address potential endogeneity, including the possibility that government-set cotton prices are not truly exogenous; and the assumption of full allocative efficiency—that farmers use optimal input combinations—may not hold in practice. Future work using profit frontier models could test how sensitive these conclusions are to those assumptions.

For policymakers, the recommendations are clear. The authors call for strengthening producer capacities through training, promoting technological innovations such as mechanization and pest-resistant varieties, and implementing incentive-compatible price policies that support optimal input use. The stagnation of TFP growth in the final sub-period of the study, driven by losses in technical efficiency and negative scale effects, serves as a warning that past gains are not guaranteed to persist. With nearly three million livelihoods tethered to the cotton plant, Benin’s experience offers a broader lesson for agricultural development across West Africa: productivity growth is possible even under structural constraints, but converting it into durable farmer prosperity requires tackling the institutions, infrastructure, and input markets that determine whether efficiency gains reach the farm gate.

Subject of Research: Total factor productivity and profit growth in cotton production in Benin, West Africa

Article Title: Total factor productivity and profit growth in cotton production in Benin, West Africa

Article References: Total factor productivity and profit growth in cotton production in Benin, West Africa. (n.d.). https://doi.org/10.1007/s44279-026-00749-3

Image Credits: AI Generated

DOI: 10.1007/s44279-026-00749-3

Keywords: Benin, cotton production, total factor productivity, technical efficiency, profit growth, cost frontier, agricultural economics, West Africa, technological change, economies of scale, farm profitability, agricultural policy