The research team, led by Ramalingareddy Vijaya Kumari and Sreenivas Akula at Professor Jayashankar Telangana State Agricultural University in Hyderabad, combined decades of official agricultural statistics with sophisticated econometric tools normally reserved for financial markets. They applied compound annual growth rates, decomposition analysis, and instability indices to measure how area, output, and yield changed over time. Then they turned to wholesale price data from January 2020 to December 2025, sourced from Agmarknet and the Department of Consumer Affairs, to probe the invisible architecture of India’s sesame trade using unit root tests, Johansen co-integration, Granger causality, and Vector Error Correction Models.

The headline result is striking: India’s sesame production growth has been driven almost entirely by yield improvements rather than land expansion. Decomposition analysis showed that at the national level, the yield effect contributed 106.34 percent of production growth while the area effect was actually negative at −1.42 percent. In practical terms, farmers are coaxing more grain from less soil. Productivity rose from roughly 368 kilograms per hectare in 1980–81 to 613 kilograms per hectare in 2025–26, reflecting the adoption of improved varieties and better crop management practices. Meanwhile, the cultivated area contracted from about 2,508 thousand hectares to roughly 1,526 thousand hectares over the same span.

Not every state followed the same script. West Bengal and Madhya Pradesh recorded consistent production growth of 3.82 and 3.96 percent respectively over the full study period, with moderate instability. Telangana, by contrast, saw its area shrink at −4.07 percent annually, yet productivity surged at 5.21 percent per year, meaning technology and management largely compensated for lost acreage. Gujarat’s productivity climbed 2.43 percent despite wild swings in production, which registered an instability index of 48.51. Rajasthan, once a sesame stronghold, posted a substantial negative area effect of −58.41 percent in the overall decomposition, suggesting farmers there have shifted toward more remunerative crops like paddy, wheat, and soybean where assured procurement exists.

Price behavior across the five major markets told its own seasonal story. Using a 12-month moving average and multiplicative time-series decomposition, the researchers found that prices generally dipped during peak arrival months from March through June, when fresh harvests flooded the markets, and peaked during lean supply periods from September through December. Rajasthan showed the sharpest seasonal variation, with prices in November reaching 112.15 percent of the annual average while June bottomed out at 86.22 percent. The Cuddy–Della Valle Index revealed that instability was most pronounced during the monsoon months of June through September, with Telangana recording the highest monthly figure of 24.04 percent in July. West Bengal remained comparatively stable throughout the year, suggesting better storage behavior or more consistent market arrivals.

The real technical fireworks came from the market integration analysis. Correlation coefficients between state-level wholesale prices ranged from 0.88 to 0.95, all highly significant, indicating that sesame prices moved in near-lockstep across geographically separated markets. Even after stripping out trend and systematic effects, residual correlations stayed between 0.854 and 0.940, confirming strong short-run linkages. The Augmented Dickey–Fuller test showed all price series were non-stationary at level but became stationary after first differencing, classifying them as integrated of order one, I(1). This satisfied the prerequisite for Johansen co-integration testing, which rejected the null hypothesis of no co-integration at every level, with a trace statistic of 132.86 for the “None” hypothesis and a p-value of 0.000.

Co-integration means the markets share a stable long-run equilibrium, even if prices temporarily drift apart. The Vector Error Correction Model then quantified how quickly deviations get corrected. Madhya Pradesh showed a significant negative error-correction coefficient of −0.194, meaning roughly 19.4 percent of any disequilibrium from the previous period was eliminated each period. Telangana (0.193) and Gujarat (0.250) also displayed statistically significant adjustment responses. Rajasthan and West Bengal, however, showed no significant error-correction behavior, suggesting they respond to price signals differently or rely on other mechanisms to restore balance. Residual diagnostics confirmed the model was sound, with no autocorrelation or heteroskedasticity issues, though residuals were not perfectly normally distributed.

Granger causality tests then mapped the direction of price information flow, revealing a predominantly one-way transmission network. Madhya Pradesh, Gujarat, Rajasthan, and Telangana all significantly Granger-cause West Bengal, while the reverse relationships were insignificant. Gujarat, Rajasthan, and Telangana also influence Madhya Pradesh. Rajasthan transmits price signals to Gujarat, and Telangana influences both Gujarat and Rajasthan. In short, Rajasthan, Gujarat, and Telangana act as price leaders, while West Bengal and Madhya Pradesh behave as price followers. The impulse response function showed that a one-standard-deviation shock in Rajasthan produced a strong immediate response there, peaked in the third month, and then propagated to Madhya Pradesh, which responded positively until the fifth month before moderating. West Bengal showed only a mild, stable reaction, while Telangana fluctuated, starting with a brief negative response before a sharp positive increase.

Forecast error variance decomposition added another layer of nuance. Rajasthan’s own shocks accounted for 46.61 percent of its price variability in the first period, but this declined to 37.82 percent by the tenth period, indicating that external influences grow over time. Madhya Pradesh emerged as the dominant external contributor, rising from 24.32 percent to 28.83 percent, while Telangana’s influence climbed from zero to 13.26 percent. Gujarat’s contribution fell from 22.93 percent to 11.58 percent, and West Bengal remained the weakest influence at 8.52 percent. These results suggest that no single market dominates all aspects of price transmission; leadership differs between short-run signaling and longer-term influence, which is useful intelligence for procurement planning and market monitoring.

On the trade front, India remains a major net exporter of sesame, but the picture is shifting. Exports peaked at 389.15 thousand tonnes in 2011 before declining to 218.97 thousand tonnes in 2023. Imports, negligible until 2012, surged thereafter, reaching 149.10 thousand tonnes in 2020 and 124.98 thousand tonnes in 2023. This dual pattern of significant exports and rising imports reflects growing domestic demand, periodic supply shortages, and intensifying global competition rather than a simple decline in India’s export role. World sesame production in 2024–25 is estimated at 6.68 million tonnes, down 7.7 percent year-on-year, and India ranks first in production with 8.66 lakh tonnes, ahead of Myanmar, Sudan, Nigeria, and Pakistan.

The study’s authors argue that sustainable growth of the sesame sector requires a strategic focus on climate-resilient cultivars suited to diverse agro-ecological conditions, stronger post-harvest infrastructure including storage and processing, and wider adoption of digital marketing platforms like e-NAM to improve transparency and price discovery. Given rising consumer interest in cold-pressed sesame oil, driven by perceived health benefits and the presence of natural antioxidants such as sesamin and sesamol, value-chain strengthening and export promotion could unlock significant economic potential. Encouraging sesame cultivation in rainfed and resource-constrained regions through appropriate policy support may foster crop diversification, improve farm profitability, and contribute to sustainable agricultural development across India.

Subject of Research: Production dynamics, trade behavior and spatial market integration of sesamum in India

Article Title: Production dynamics, trade behavior and spatial market integration of sesamum in India

Article References: Vijaya Kumari, R., Akula, S., Gundu, R., & Panasa, V. (2026). Production dynamics, trade behavior and spatial market integration of sesamum in India. Frontiers in Sustainable Food Systems, 10, Article 1938242. https://doi.org/10.3389/fsufs.2026.1938242

Image Credits: AI Generated

DOI: 10.3389/fsufs.2026.1938242

Keywords: Production, dynamics, trade, behavior, spatial, market, integration, sesamum, India, scientific research