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India’s Minimum Support Price (MSP) framework rests on a cost estimation system that exhibits measurable gaps when compared with present-day agricultural conditions. At its core lies the Comprehensive Scheme for Studying Cost of Cultivation, implemented by the Directorate of Economics and Statistics. The scheme relies on a triennial block sampling design in which selected villages are observed over a three-year cycle before rotation. The Commission for Agricultural Costs and Prices (CACP) use these data to compute C2, which includes paid-out expenses along with imputed values for family labour, land rent, and a fixed 10 per cent interest on owned capital. Although methodologically sound, this framework has remained largely unchanged since decades, despite significant structural transformations in Indian agriculture.
The most immediate concern is the time lag embedded in the estimation process. Because data are collected, validated, and aggregated over multiple years, MSP recommendations are often based on cost conditions that are two to three years old. Under relatively stable price conditions, this lag introduces only modest deviations, typically in the range of 2-5 per cent. However, during periods of input price volatility, the gap widens substantially. The 2021-22 input shock offers a clear example: global fertiliser prices surged, diesel prices increased before partial relief measures, and labour costs rose moderately. Yet MSP decisions for subsequent marketing seasons relied on earlier cost projections. Field-level observations in major procurement states suggested that actual cultivation costs rose faster than those reflected in official estimates, compressing farmer margins despite continued procurement at MSP. This indicates that the MSP system is structurally stable in normal periods but becomes systematically misaligned during input price shocks, with potential compression of MSP-to-cost margins by several percentage points.
Empirical comparisons in scholarly work (Kamra & Ramakumar, 2019) have already highlighted the scale of the problem. For cotton, A2+FL costs were underestimated by as much as 32 per cent in some years (e.g., CACP estimate of Rs 1,970/quintal against actual plot-level costs of Rs 2,882/quintal in 2012-13). Similar underestimation was observed for soybean (up to 58 per cent in one year), as well as for crops like maize and groundnut. The degree of underestimation was generally higher for C2 than for A2+FL. These gaps imply that, had costs been more accurately captured, MSP for several crops would have needed to be 20-30 per cent higher to maintain the intended margin over costs. Despite the introduction of a correction factor (CF) by the Commission, the situation has remained the same over the years.
Another issue lies in the continued use of a fixed 10 per cent interest rate on owned capital. In recent years, benchmark government security yields have remained closer to 7-7.5 percent. Even after incorporating a reasonable risk premium, the opportunity cost of capital would likely fall within 8-8.5 per cent. The current assumption therefore introduces a mild upward bias in cost estimation. While the aggregate impact is limited generally less than one per cent of total cost for low-capital crops such as paddy and wheat it becomes somewhat more pronounced for capital-intensive crops such as groundnut and cotton. This bias is not large enough to distort MSP fundamentally, but it does contribute to a systematic deviation between estimated and actual cost structures.
A more subtle but increasingly important gap arises from changes in mechanisation patterns. The existing framework distinguishes between owned machinery (accounted for through depreciation and interest) and hired machinery services (captured under paid-out costs), which in principle avoids double counting. However, the rapid expansion of custom hiring centres has shifted machinery access patterns, particularly among small and marginal farmers. In many regions, hiring has replaced ownership as the dominant model. The triennial sampling design, however, may still reflect older ownership-heavy distributions. As a result, it may assign excess weight to imputed machinery costs in areas where hiring now prevails. The resulting bias appears region-specific and moderate likely in the range of a few percentage points—but it highlights a growing disconnect between static sampling structures and evolving production practices.
These methodological gaps, while real, should be interpreted with care. They influence the accuracy of cost estimation but do not, by themselves, explain broader structural outcomes such as cropping patterns. Evidence from across states suggests that crop diversification is driven far more by procurement assurance, irrigation conditions, input subsidies, and market linkages than by marginal differences in estimated costs. States such as Gujarat and Karnataka have achieved relatively higher shares of pulses and oilseeds due to stronger market ecosystems and processing linkages, whereas Punjab continues to be dominated by the rice-wheat system. As noted in earlier CACP assessments two decades ago, intensive paddy cultivation in Punjab and Haryana has long been associated with groundwater depletion and declining soil health, yet diversification has remained limited due to assured procurement, subsidised power for irrigation, and relatively stable returns from paddy cultivation.
This contrast underscores a critical insight: MSP in practice operates less as a pure cost-based price signal and more as a procurement-backed assurance mechanism. Even where MSPs are announced for alternative crops such as pulses and oilseeds, weak procurement limits their effectiveness in shaping farmer decisions. Historical evidence shows that market prices for several crops have frequently fallen below MSP in multiple regions, particularly where procurement infrastructure is weak. Recent data from agricultural market platforms such as the Unified Portal for Agricultural Statistics (UPAg) further reinforce this pattern, showing that mandi (wholesale market) prices for crops like soybean and pulses often remain below MSP across several states, whereas paddy and wheat prices tend to align more closely with MSP in regions with strong procurement. Thus, even if cost estimation were perfectly updated and aligned with real-time conditions, diversification outcomes would remain largely unchanged unless procurement and market support systems evolve in parallel.
The implications for MSP outcomes are therefore twofold. First, during stable periods, the gap between MSP and actual cost remains limited, and the system functions broadly as intended. Second, during periods of input price volatility, lagged cost estimation can compress the MSP-to-cost margin, reducing real profitability even when nominal MSP increases are announced. This reinforces a broader analytical point: the MSP debate in India is often framed as a pricing problem, whereas it is fundamentally a problem of measurement and transmission. Measurement gaps arise from lagged and imperfect cost estimation, while transmission gaps stem from uneven procurement and market access.
Addressing these issues requires calibrated rather than disruptive reform. A phased approach could begin with greater transparency, including a detailed methodological review and structured stakeholder consultations. In the next stage, selective refinements such as updating the interest rate assumption and introducing limited indexing for volatile inputs like fuel and fertilisers could be piloted for crops where diversification is desired, such as pulses and oilseeds. In the final stage, these refinements could be extended across all MSP crops, accompanied by gradual improvements in sampling frequency and regional representation. The fiscal implications of such changes would remain modest relative to their potential to enhance policy credibility and precision.
At a deeper level, however, technical refinements alone cannot resolve the central policy ambiguity. MSP simultaneously serves multiple objectives: providing income support, stabilizing prices, and influencing production patterns. Greater accuracy in cost estimation can improve its functioning, but it cannot substitute for clarity in policy intent. If MSP is expected to promote diversification toward less water-intensive or nutritionally desirable crops, then procurement policies, market infrastructure, and input incentives must be aligned accordingly. Without aligning procurement incentives, even a perfectly measured MSP will have limited influence on cropping decisions.
India’s MSP system has played a critical role in ensuring food security and stabilising farm incomes over decades. Yet a framework designed for earlier production conditions now faces the risk of gradual misalignment as agricultural systems evolve. A carefully sequenced modernisation focused on improving cost estimation without disrupting institutional continuity can strengthen both its credibility and effectiveness. When cost estimates more closely reflect observed realities, MSP can function not only as a safety net but also as a more reliable guide for long-term agricultural transformation.
Mohit Sharma is Assistant Professor, RPCAU, Pusa, Bihar
Views expressed are the author’s own and don’t necessarily reflect those of Down To Earth