Digvijay Singh Negi

I. Introduction

Given the relatively large share of employment in agriculture and allied activities, the sector remains central to India’s overall growth trajectory. Agriculture and allied activities continue to be a key source of livelihoods for a substantial proportion of the population and contribute significantly to national income and employment generation. Over the past decade, however, there has been considerable variation in the growth of agricultural and allied Gross State Domestic Product across states. Understanding the reasons behind these differences is particularly important in the context of structural transformation within the sector. While the key feature of economic development is a gradual employment shift towards manufacturing and services, this transition requires structural transformation within the agriculture and allied sectors, where higher-value allied activities such as livestock, fisheries, and horticulture play an increasingly important role (Negi et al, 2021). This note examines the growth performance of agriculture and allied activities across states over the last decade and a half and examines the contribution of sub-sectors to overall growth. The note goes on to discuss potential factors that may help explain why growth has been faster in some states than in others.

The analysis uses the State Domestic Product (SDP) series from 2011-12 to 2024-25, published by the Ministry of Statistics and Programme Implementation, Government of India. The sub-sectors are crop (agriculture), livestock, forestry, and fisheries. I compute annual growth rates using this series. In addition, I also calculate the contribution of each sub-sector to overall growth in agriculture and allied activities using the sub-sector SDP data. This note summarizes the cross-state growth experience over the past 14 years and decomposes aggregate agriculture and allied growth into sectoral drivers using a baseline-share weighted formula. Each sub-sector’s contribution is calculated as the product of its baseline share and its estimated growth rate. I use 2013 as the baseline year for sectoral decomposition. I group the northeastern states, i.e., Arunachal Pradesh, Assam, Manipur, Meghalaya, Mizoram, Nagaland, Sikkim, and Tripura, into a single composite category in the analysis.

II. Growth patterns across states

I begin by documenting the well-established empirical fact that state-level growth in total domestic product is positively associated with growth in agriculture and allied activities. Despite the declining share of agriculture in aggregate output, the agriculture and allied sector continues to play a critical role in driving overall income growth across states. Figure 1 illustrates this positive relationship, showing that states experiencing faster growth in agriculture and allied domestic product also tend to record higher growth in overall state GDP. This pattern shows the continued importance of agricultural performance for overall economic growth at the state level.

Figure 1: Relationship between growth in total domestic product and growth in agriculture and allied activities across states

Notes: The figure presents the annual growth in total State Domestic Product and growth in sectoral Agriculture and Allied Gross State Domestic Product (GSDP) across states. Growth rates are computed as compound annual growth rates over the 14-year period from 2011–12 to 2024–25, using the State Domestic Product (SDP) series published by the Ministry of Statistics and Programme Implementation (MoSPI).

Figure 2: Growth in agriculture and allied State Domestic Product across states

Notes: Figure presents the average annual growth rate of Agriculture and Allied Gross State Domestic Product (GSDP) across states, measured at constant 2011–12 prices. Growth rates are computed for the 14 years from 2011-12 to 2024-25 using the State Domestic Product (SDP) series published by the Ministry of Statistics and Programme Implementation (MoSPI) and represent compound annual growth rates over the 14 years.

Figure 2 shows that there is substantial variation in the growth rates of agriculture and allied activities across states. While the all-India average is around 4 percent, states like Andhra Pradesh (7.7 percent), Madhya Pradesh (6.5 percent), Telangana (5.6 percent), and Assam (5.5 percent) turn out to be top performers with a relatively high growth in the range of about 5 to 8 percent per annum. Another group of states, such as Chhattisgarh, Karnataka, and Tamil Nadu, has experienced good growth of around 5 to 5.2 percent. A larger set of states has recorded moderate growth in the range of 3 to 5 per cent, including Gujarat, Odisha, Rajasthan, Uttar Pradesh, Maharashtra, Bihar, West Bengal, Jammu and Kashmir, Haryana, and Bihar. At the lower end, some states like Kerala, Uttarakhand, and the Northeastern states have exhibited negligible or low growth over the period, while Punjab (2.1 per cent) and a few other states have seen relatively muted growth.
III. Sectoral composition of growth

Table 1: Sectoral contribution to agriculture and allied growth by states

Notes: The table presents the percentage contribution of individual sub-sectors to growth in Agriculture and Allied Gross State Domestic Product (GSDP) across states, along with the average growth rate of the Agriculture and Allied sector. All estimates are measured at constant 2011–12 prices. Growth rates are computed as compound annual growth rates over the 14 years from 2011–12 to 2024–25, using the State Domestic Product (SDP) series published by the Ministry of Statistics and Programme Implementation (MoSPI). Sub-sectoral contributions are calculated using a baseline-share weighted decomposition, wherein each sub-sector’s contribution is obtained as the product of its baseline share (2013) and the estimated growth rate. Northeastern states, i.e., Arunachal Pradesh, Assam, Manipur, Meghalaya, Mizoram, Nagaland, Sikkim, and Tripura, are grouped into a single composite category.

Table 1 reports the contributions of different sub-sectors to overall growth in the agriculture and allied sector. A key finding is that allied activities like livestock and fisheries have been an important contributor to growth in many states, and their contribution often exceeds the contribution from the crop sector. This aligns with the broader picture that diversification and high-value agriculture will become central to agricultural growth in the future (Birthal et al., 2014; Balaji et al., 2025). It is also consistent with the well documented shift in demand patterns where future growth in demand for agricultural commodities would be driven by high-value commodities such as milk and dairy products, meat, fish, eggs, fruits, and vegetables, rather than by cereals (Singh, 2025). Rising incomes, urbanization, and dietary diversification are therefore likely to amplify the role of livestock and fisheries as engines of agricultural growth, while the relative importance of food crops will continue to decline (Balaji et al, 2025).

A decomposition of growth shows that states with higher overall growth in agriculture and allied activities are also those where livestock and fisheries sectors have made a larger contribution to growth. Livestock has emerged as a particularly important driver of growth in top growing states like Andhra Pradesh, Telangana, Madhya Pradesh, and Rajasthan. The expansion of dairy and animal husbandry has provided a relatively stable source of income, contributing significantly to overall agricultural growth.

Fisheries and aquaculture, while geographically concentrated, have played an important role in some states. States like Andhra Pradesh, Assam, and Odisha, in particular, have recorded strong growth in agriculture and allied activities alongside a rapidly expanding fisheries sector. Despite employing a smaller share of the workforce, fisheries have contributed substantially to growth due to higher productivity and greater domestic and foreign demand.

Forestry has played an important role in some hill and north-eastern states. Forestry has contributed substantially in the Northeastern states, Himachal Pradesh, and Uttarakhand. This partly reflects the fact that these states have relatively large forest resources and therefore have a larger baseline share of forestry value-added in the total agriculture and allied sector.

Figure 2: Relationship between agriculture and allied growth and share of area under cereals

Notes: The figure presents the annual growth in Agriculture and Allied Gross State Domestic Product (GSDP) across states on the vertical axis, along with the proportion of cropped area under cereals in 2013 on the horizontal axis. The state of Kerala is excluded in this figure as an outlier because of its low area under cereals and negligible growth in Agriculture and Allied Gross State Domestic Product over the selected period. Growth rates are computed as compound annual growth rates over the 14-year period from 2011–12 to 2024–25, using the State Domestic Product (SDP) series published by the Ministry of Statistics and Programme Implementation (MoSPI).

In general, the contribution of the crop sector has been relatively modest across many states. Figure 2 reinforces this pattern, showing a negative association between agriculture and allied sector growth and the baseline (2013) share of cropped area under cereals. States with a higher degree of specialization in cereal cultivation tend to exhibit lower overall growth in agriculture and allied activities. This pattern is particularly evident in states with cereal-dominated systems, such as Punjab and Haryana, where crop-sector growth has remained low over the period. The persistence of cereal-centric production in these states is also associated with a relatively high dependence on public procurement mechanisms (Negi et al., 2021; Negi, 2025).

The exception is Madhya Pradesh, where relatively high growth in agriculture and allied activities (6.5 per cent) is largely accounted for by the crop sector. The pattern observed for Madhya Pradesh is consistent with evidence from the literature, which attributes this crop sector-led growth to factors such as expanded irrigation coverage (including canals and tube wells), improved availability of power, and relatively supportive procurement and market conditions (Gulati et al., 2017).

Taken together, these patterns suggest that states with a high concentration in cereal-centric cropping systems have experienced relatively slower growth, while diversification towards non-cereal crops and allied activities has been associated with stronger overall growth in agriculture and allied activities.

 IV. Discussion and implications

India’s agricultural growth has increasingly been associated with diversification away from staple cereals toward high-value sectors such as livestock, fisheries, and horticulture. This structural shift has been driven by rising incomes, rapid urbanization, and dietary change, along with supply-side investments and technological improvements in production, marketing, and value chains (Birthal et al., 2014; Birthal et al., 2020; Negi et al., 2021). The key observation is that states that have been able to expand allied sectors in line with their natural and geographical advantages have experienced faster growth. Prominent examples include forestry-led growth in hilly and northeastern states, as well as the expansion of fisheries and aquaculture in coastal states and those endowed with favorable water and marine resources.

 In contrast, states where agricultural growth has historically been driven by the cereal sector have generally observed moderate or low growth and now face an increasing need to diversify into high-value crops and greater investment in the livestock sector. Livestock often complements the crop sector through the use of crop residues as feed and manure for soil fertility, while providing income flows. Therefore, livestock and animal husbandry are not merely a supplementary activity to agriculture but are central to enhancing rural incomes and reducing vulnerability, particularly for smallholders (Birthal & Negi, 2012).

States where fisheries have emerged as a major contributor tend to be those that have successfully scaled aquaculture, developed marketing and logistics infrastructure, and integrated with domestic and export markets. Evidence from Andhra Pradesh illustrates how aquaculture commercialization can add to growth in agriculture and allied value-added.

Finally, lower agriculture and allied growth in major agricultural states like Punjab and Haryana is consistent with the view that the rice-wheat production system faces severe constraints, particularly groundwater depletion and rising costs. The literature documents how procurement-linked cereal systems in north-west India have contributed to environmental stress, reinforcing the need for crop and activity diversification (Negi, 2025).

This stock-taking paper assesses the current state of India’s agriculture and rural economy across multiple dimensions.

It begins by documenting shifts in household consumption patterns and nutritional outcomes, then takes stock of major policy instruments including the Public Distribution System, minimum support prices, and input subsidies—examining their scale, functioning, and impacts. The paper reviews the evidence on agricultural diversification, noting the gap between consumption patterns and cropping choices. It surveys critical challenges including groundwater extraction levels, climate vulnerability, environmental impacts of current farming practices, constraints in supply chains and farmer organizations, and India’s agricultural trade performance. The assessment also considers the fundamental structural issue: agriculture’s disproportionate share of employment relative to its contribution to GDP, and what this means for farm incomes and rural livelihoods.

This comprehensive stocktaking provides a foundation for understanding where Indian agriculture stands today and what questions merit deeper investigation going forward.

Introduction

Agriculture and agriculture-based livelihoods in developing countries are highly prone to weather shocks. Although there exist various informal mechanisms in rural communities that allow farmers to pool their idiosyncratic risks, they provide little or no insurance when entire communities are affected.  

This is typically the case with extreme climate events such as droughts, floods and heat waves.  These shocks are correlated across regions and are called covariate risks.  There is substantial evidence that rural households in high risk environments stick to low return subsistence agriculture and cope with a correlated shock by liquidating productive assets to maintain consumption and thus remain trapped in poverty¹. 

Formal insurance that protects crops and livestock from climate risks may, therefore, have large private and social benefits.  Notwithstanding these benefits, private provided unsubsidized agricultural insurance is the exception rather than rule.  This is so even in the wealthy developed countries².  Information on past yields of individual producers (or animal mortality in the case of livestock insurance), necessary for actuarial computations and necessary to avoid adverse selection, are rarely complete.  Insurers fear moral hazard given the influence of producer actions on output.   Insurers also fear that they may not have complete information to detect and prevent adverse selection.  

These difficulties have led to index insurance products where payouts are triggered by an index such as rainfall, temperature or local average yields.  Premium setting is relatively easier because past data on indices of weather and average yield are more readily available than on individual production histories. As individual farmers have little or no influence on payouts, index-based insurance products are also less likely to fail due to asymmetry in information between the insurer and the insured. 

While index insurance is practical, insurance companies can expect claims to be very large in some years because of covariate risks.  This may quickly exhaust a company’s capital unless they have access to reinsurance.  The most common response is for the government to offer reinsurance³. To keep insurance affordable, governments typically offer subsidies.  Indeed, without such subsidies, uptake of agricultural insurance generally remains low⁴.  Past research has highlighted many reasons for the low uptake. These include the unfamiliarity among farmers of formal insurance, the lack of trust in the insurance provider, the difficulties of communication resulting in poor understanding of the insurance product. Poor farmers also face liquidity constraints and insurance demand is highly sensitive to price⁵. 

However, even if the above factors were absent, research has highlighted the fundamental constraint of basis risk which occurs because of imperfect correlation between the index and farmer losses. If the association is weak, then index insurance might not be reliable. Research has shown, both theoretically and empirically, that basis risk reduces the demand for insurance⁶.  The worst case scenario is when substantial subsidies are incurred on crop insurance and yet the basis risk is so high that it does not offer meaningful utility to farmers.  The minimization of basis risk is a topic of current research.  

Basis Risk

India has had a long experience with index crop insurance and is globally one of the pioneers in this field.  Indeed, it was here that rainfall insurance was proposed more than a hundred years ago⁷. Index (linked to area-yield) insurance was finally introduced in the country in the late 1970s as a subsidised scheme of the government.  The take-up and the subsidy was, however, limited.  In 2016, the government launched the Pradhan Mantri Fasal Bima Yojana (PMFBY) – a program of area-yield insurance.  The program and the rate of subsidy were substantially scaled up.  The Central government spends close to Rs. 15,000 crores annually.  The program also involves equivalent expenditures by state governments.  The program introduced several novel elements – in particular, the program implementation involved, for the first time, private insurance companies.  The premiums are pegged to be very low – below the cost of providing insurance (actuarial cost plus administrative costs).  The difference is the subsidy incurred by the central and state governments.  

While the program coverage is much greater than the coverage in the earlier insurance programs, it is fair to say that the program has not expanded beyond the initial success.  The budget allocations have stagnated.  Why have farmers not responded more enthusiastically to the program?  As noted earlier, such disappointing outcomes are not unique to India.  Could it be because of basis risk?  

In a recent work, we analyzed the all-India district-level relationship between crop yields and rainfall indices for 9 kharif season crops⁸. The empirical association between their cumulative densities is shown in three-dimensions in Figure 1.  It can be seen that the association is strong for extreme shortfalls in rainfall.  Statistically, such an association is called lower tail dependence.  This means that the associations between yield losses and index losses are stronger for large deviations than for small deviations. The major implication is that the value (to farmers) of index-based insurance relative to actuarial cost is highest for insurance against extreme or catastrophic losses (of the index) than for insurance against all losses. Or in simpler words, basis risk is least for large deviations of the index. 

Figure 1:  Estimated Joint Distribution

Source:  Negi and Ramaswami (2024)

These results are not surprising.  When there is an extreme shortfall of rainfall at one location, it is likely that the same situation obtains elsewhere.  Such spatial correlation is what characterizes drought.  When that happens, aggregate yields are low and so are individual yields.  In other words, basis risk will be low and the index insurance will be valuable in drought situations.  

This proposition is illustrated in Figure 2 below for a hypothetical rainfall contract for paddy crop calibrated to data from two districts in India: Mahabubnagar and Anantpur.  Figure 2 plots the insurance performance measured by the ratio of expected claims to commercial premium (computed as 1.56 times the actuarial cost) at every level of output.  The performance ratio is plotted for three different insurance designs.  In the first insurance design, the farmer is compensated for all losses below mean output.  In the second insurance design, the farmer receives compensation whenever output is less than half of a standard deviation away from the mean.  The third insurance design corresponding to drought insurance pays off only for severe losses – whenever output is less than a full standard deviation away from the mean.  It can be seen that the performance ratio rises steeply above 1 for the drought insurance design.  In the other insurance designs, basis risk is higher and so is the premium cost.  

Figure 2:  The ratio of expected claims to commercial premium

Source: Negi and Ramaswami (2024)

The important implication of our findings is that, for farmers, the utility of index-based insurance relative to actuarial cost is greater for insurance limited to catastrophic losses than for insurance against all losses. For this reason, tail dependence boosts the demand for catastrophic insurance. This may, therefore, be one route for rainfall index insurance to receive greater uptake and for it to be an effective, if limited, risk management strategy. 

Policy Implications

Index insurance is the most practical form of crop insurance.  To encourage uptake, countries frequently subsidize index insurance.  If basis risk is the reason for low uptake, it is unlikely that the subsidy is socially valuable.  Farmers do not receive protection when they need it.

Our findings show that basis risk is least for catastrophic insurance.  These are low probability events.  In subsidising crop insurance, the extreme layer of risk should, therefore, receive priority.  What about higher probability events that involve moderate losses?  This should receive lower weight in subsidy allocation.  The basis risk in index insurance that covers these events is large.  Hence, its value to farmers is low even though it will be actuarially expensive. A policy that fully subsidizes insurance against severe losses may well be revenue neutral compared to an insurance that partially subsidises all shortfalls from an index.  

An example of an insurance against extreme events is drought insurance.  Besides farmers, drought insurance is likely to be useful to local aggregators of risk such as banks, producer companies, cooperatives, agri-business firms and local governments.  Index insurance to such entities may be socially valuable but they may not require a subsidy.  There is a very established protocol for drought relief expenditures by the government. However, its timeliness is often questioned because of many layers of permissions required for such expenditures. On the other hand, an extreme loss insurance program offers the benefits of drought relief but in a timely manner.  

The PMFBY is a complex partnership between the Centre, the State and the insurance companies and breaks new ground in shared governance.  The program is operationally difficult because of the extensive demands it makes for coordination among stakeholders.  States have to notify the program and declare the crops to be insured well in advance.  Past data on yields have to be supplied to the prospective insurers by them as well.  States have to float tenders for clusters of districts and select the lowest cost bidder.  Finally, the average yields for the `notified’ area have to be collected by crop cutting experiments and accordingly the claims have to be adjudicated.  Scientific yield assessment is the stiffest constraint to scaling up the program.  

While greater experience has smoothened some of these operational glitches, the program lacks an in-built evaluation design to assess basis risk.  By their very nature, the utility of insurance programs to its beneficiaries cannot be judged within a year or two but may be apparent over a crop cycle of 4-5 years.  However, for this to happen, it is vital that a sample of sufficient size be tracked over time.  The costs of such evaluation are trivial given the scale of the program but such data will be invaluable in understanding how these programs benefit farmers and in working tweaks or design changes to reduce basis risk and make them more effective.