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What Drives Annual Agricultural Growth Rates in India?

M Dinesh Kumar (,Arijit Ganguly (, and M V K Sivamohan ( are researchers at the Institute for Resource Analysis and Policy, Hyderabad.

In India, the agricultural growth rate is linked to rainfall, and medium-term growth to technology adoption, policy frameworks, and institutional interventions. But, growth in a year may be poor as much due to the good monsoon or abnormally wet conditions in the previous year as the poor monsoon during that year, or it may be high due as much to the poor monsoon in the previous year as to a good monsoon of the year or to policy reforms. As the rainfall fluctuates annually, medium-term growth rates should be assessed; the annual rainfall in the base year should be close to normal.

The authors are extremely grateful to the anonymous reviewer, whose review comments on the original version of the manuscript immensely helped sharpen the arguments made in this article.

In India, the practice of predicting the annual agricultural growth rate dates back to independence in 1947. Predictions are made before the onset of the monsoon, once the forecasts of the India Meteorological Department (IMD) of the country’s rainfall are made available. The prediction of agricultural growth for the ensuing crop year considers mainly whether the rainfall would be normal, above normal, or below normal. It is still widely believed that agriculture in India is a “gamble with the monsoon”; the short-term (mainly annual) agricultural growth rate is linked with annual rainfall, and it is held that a good monsoon would ensure high agricultural growth rate during that year. By extension of this logic, a low annual growth rate in the agriculture sector is attributed to the failure of the monsoon during that year. More strangely, an impressive growth rate in agriculture in the medium term is attributed to high performance of the sector in terms of technology adoption, policy frameworks, and institutional interventions. The two questions that arise are discussed.

What Drives Agricultural Growth?

Is it appropriate to link the performance of the agriculture sector in terms of growth rate to the aggregate predictions of monsoon rainfall in a country like India? The underlying argument is that India has several rainfall zones—the IMD itself has defined 36 rainfall zones—and the mean annual rainfall varies widely spatially from as low as 200 mm in Jaisalmer to as high as 11,000 mm in Cherrapunji. A large downward deviation of rainfall in one low rainfall region (like the north-western part of India) can be made up by a small upward deviation of rainfall in a high rainfall zone (like the North East or the Western Ghats).

And even if rainfall does influence agricultural growth rates, how can the average rainfall of a single year alone cause wide year-to-year fluctuations in the agricultural growth rate?

Earlier, long-term data on (spatial) average annual rainfall were not available, which hindered a nuanced, empirical understanding of this vexed issue. Now, such data are available for a considerably long-time duration from the IMD. Herein, the historical agricultural growth trends and trends in (spatial) average annual rainfall in India are analysed to see whether any useful inferences on annual agricultural growth rate during a year can be drawn on the basis of the annual rainfall data. Key factors that drive short-term agricultural growth rates are explored in order to draw implications for the analysis of the impact of national agricultural policies.

The analysis begins with historical growth rates in the agricultural gross domestic product (GDP) of India since 1955–56. The value of agricultural output grew in real terms from ₹ 1,68,361 crore in 1955–56 to ₹ 7,64,510 crore in 2012–13 (Figure 1). The variation in annual agricultural growth rates is large—from –12.77% during 1979–80 to the highest of 15.24% during 1988–89. Long-term growth in agricultural output has been considerable in value terms, but output dropped drastically in some years compared to the previous years. Such drops are considered to be due to drought, and any major jump in agricultural output is attributed to a bountiful monsoon received during the year. There has been no attempt yet to analyse the influence of rainfall on the agricultural growth rate. One reason is that long-term data on average spatial rainfall at the country level is not available on an annual basis.

For several years, data have been available on spatial average rainfall at the level of both country and division. These data have enabled the analysis of the linkage between rainfall and annual agricultural output growth. As per the IMD estimates, between 1955–56 and 2013–14, the (spatial) average annual rainfall ranged from 925 mm (the lowest, during 1965–66) to 1,380.5 mm (the highest, during 1956–57) (Figure 2). The mean annual rainfall of the country was estimated at 1,175 mm for the 58-year period considered. Regression analysis of annual average rainfall in different years, expressed as a percentage departure from the mean value, shows a somewhat weak relationship with the annual agricultural growth rates of the country for the corresponding years. The R2 value was only 0.31, although at the 5% significance level. In terms of output growth, agricultural performance was dismal in many years of excessive average rainfall (at least 1,300 mm) in comparison to the immediately preceding years, while some such wet years experienced two-digit growth rates. The annual agricultural growth rate was in the range of 5%–10% in many years when rainfall was less than normal (1,175 mm); it touched 15% in one instance. It can be inferred that obtaining a good monsoon is neither a necessary condition nor a sufficient condition for securing high agricultural growth during a year in relation to the previous year. Wide variations in agricultural growth rates (in percentage terms) between years (–5.78% and 14.75%) that experienced more or less the same quantum of average rainfall (around 1,150 mm) suggest that factors other than annual rainfall influence the agricultural growth rate.

Types of Crops

In value terms, the agricultural output is a function of the types of crops grown in terms of their value in the market; total area under each crop; the number of different types of livestock and their productivity; price of livestock products; crop productivity (yield per hectare); and produce prices. Crop productivity depends on whether the crop is irrigated—irrigated crops generally yield higher than rain-fed crops—and on the level of inputs such as fertiliser and pesticides, seed varieties, and labour inputs for agronomic practices. Agricultural output would increase if a greater proportion of the area is irrigated even if the total cropped area is the same. A shift towards high-value crops would help enhance agricultural output in value terms. Total factor productivity growth, resulting from the advent of high-yielding crop varieties, would ensure high agricultural growth even if the area under irrigation, or input use, does not increase (Kumar et al 2010). Agricultural outputs have increased substantially over the past decades in value terms because adoption of high-yielding crop varieties has grown, high-value crops have been sown over a greater proportion of the cultivated area, the cropped area has been expanded, and a greater proportion of the area has been irrigated.

Kannan and Sundaram (2011) estimate a regression model for predicting the value of crop outputs of the country and find that rainfall and gross irrigated area are two key parameters that explain the growth performance of major crops at the national level to an extent of 70%. Their study deals with the long-term growth performance of the crop sector. But, for many crops, the area cropped and yield fluctuate between years in the short run depending on the monsoon. The gross cropped area in a particular year can be less than in the previous year, as can be the yield and total production, if the rainfall during the year was less than that in the previous year, as the effect of crop technology, crop shift, and the like will not be significant during such short time spans. The reverse can happen if a particular year happens to be wetter than the previous year. For certain high-value crops, the prices can fluctuate between years depending on market conditions, influenced by the supply of produce and the demand situation. Hence, short-term trends can be influenced by the situation in the current year—vis-à-vis rainfall, market conditions, and other parameters in relation to the previous year—and can differ from long-term trends.

A univariate regression analysis is carried out to assess the influence of rainfall of the previous year and the current year on agricultural output growth rates. The increase/decrease in rainfall in a particular year over the previous year is estimated as a percentage of the previous year’s rainfall for all the years from 1956–57 onwards. The change in rainfall varied widely—from (–)26.83% to (+)28.60%—as did the annual agricultural growth rate, from (–)12.77% to (+)15.24%. The analysis shows that the rate of growth in annual agricultural output in a particular year is heavily influenced by the percentage difference in rainfall of that year over the previous year, which explains the change in annual agricultural growth rate to an extent of 51% (Figure 3, p 35). The regression model is Y = 0.328*X + 2.727, where Y is the annual growth in the agricultural output (%) and X is the percentage change in rainfall.

A high percentage of increase in rainfall over the previous year results in high agricultural growth; a high percentage of decline results in very low growth, often resulting in negative growth. This is quite comprehensible, as good monsoon rainfall ensures sufficient soil moisture for the production of kharif crops without irrigation, as well as good recharge of aquifers and sufficient inflows into reservoirs that can be used for intensifying cropping during the subsequent seasons. A positive change in rainfall never led to negative growth in annual agricultural output, but positive growth in output was sometimes observed despite a decline in rainfall. The agricultural growth rate was impressive in many years that did not experience high rainfall, because the preceding years had very low rainfall or were drought years, and many wet years did not experience high agricultural growth rates as they were preceded by equally wet years or normal years. The agricultural growth rate was positive in some years despite a negative percentage change in rainfall over the previous year, in line with the model prediction. As per the model, when the decline in rainfall becomes very large (above 8.29%), the growth becomes negative.

The remaining 49% should be explained by many complex factors—if put together—one of them is the spatial variation in rainfall. If the country receives very good monsoon at the aggregate level, but some regions contribute significantly to agricultural outputs despite experiencing below-normal rainfall, this “anomaly” can reduce the effect of increase in aggregate rainfall. What matters is in which region the rainfall departure actually takes place and the contribution of that region to the overall agricultural output of the country in value terms. A 100–200 mm downward deviation in annual rainfall from the normal value in north-western India, which receives low to medium rainfall (450–600 mm) but has high agricultural productivity, will have a much larger impact on overall agricultural output of the country than a 100–200 mm downward deviation in rainfall in eastern India, which receives very high rainfall but has low agricultural productivity. The regions that experience rainfall departure in India can keep changing every year, making the relationship between average annual rainfall and agricultural output more complex. These nuances are not captured in the weighted average of rainfall available for the country as a whole from the IMD. Nevertheless, it is unlikely that the trend in the spatial average rainfall at the country level does not conform to the rainfall trend in the majority of the geographical area. That being the case, how an increase in rainfall over the previous year may contribute to higher agricultural output is explained theoretically.

Other factors that can drive agricultural growth are agricultural inputs (seeds, irrigation water, labour/machinery, fertilisers, and pesticides), crop diversification, especially adoption of high-value crops, and adoption of new crop technologies (with high-yielding varieties). A positive change in these factors help maintain a low annual agricultural growth rate even when the change in rainfall is negative. In the presence of good monsoon, or with improved access to irrigation water, farmers are encouraged to apply the optimum dosage of fertilisers and pesticides, use good seed varieties, and put in sufficient labour, because without water from precipitation or irrigation to crops, none of these inputs will have an effect on agricultural outputs. The effect of many of these independent variables gets subsumed in statistical analysis as the effect of change in rainfall, which is the primary driver of agricultural growth. Their “differential effects” on agricultural output growth would be pronounced only if, previously, farmers did not know the importance of using the optimum level of inputs, such as fertilisers and agronomic practices, and now start accruing and using such knowledge, or when they introduce some new high-yielding varieties.

Influence of Irrigation

The influence of irrigation on agricultural growth rate was assessed; there was a statistically significant influence of irrigation growth (estimated as the percentage increase in gross irrigated area in a particular year over the previous year) (Table 1). The R2 value was 0.17 (Figure 4). Subsequently, a multivariate analysis was performed using rainfall departure (in percentage terms) and irrigation growth (in percentage terms) as independent variables against agricultural growth rates as a dependent variable. The R2 value increased to 0.55, and both parameters were found to be significant at 5% level. The model was found to be quite significant statistically. The estimated regression equation is Annual agricultural growth rate = 1.6245 + [0.476 × percentage of irrigation growth] + [0.3075 × percentage difference in annual rainfall over the previous year].


The variation in rainfall explains a large part of the possible variation in irrigated area between two consecutive years and, in turn, the only marginal improvement in R2 value. The effect of change in irrigated area on agricultural output is largely subsumed in the model as the effect of rainfall. Even the irrigation potential already created does not become utilisable in the absence of good monsoon precipitation, as the aquifers remain depleted and the reservoirs do not get sufficient inflows. The effect of irrigation shown by the model is probably only the effect of additional irrigation potential created, due to the effect of rainfall, and not the additional area irrigated. Only in a few situations is there an increase in irrigated area over a short period (one year, in this case), which is not explained by an increase in rainfall. This increase in irrigated area is due mainly to increased investment in building infrastructure, which taps the available water resources for irrigation expansion, or for water distribution from the existing irrigation system.

The analysis suggests that agricultural growth rates can be significant or high if there is a high percentage increase over the previous year in either rainfall or irrigated area. Under the current conditions, the model suggests, a 20% year-on-year increase in rainfall would raise annual agricultural output by 6%, and a 5% increase in irrigated area would raise it by 2.35%. The beta coefficient for irrigation growth is higher than that of rainfall difference (positive) but, historically, the maximum values ever obtained are 8.6%, for the annual irrigation growth rate, in 1988–89, and 28.6%, for rainfall. Fluctuations in agricultural growth rates are driven much more by fluctuations in rainfall than changes in irrigated area.

The multivariate analysis was carried out with average annual rainfall (expressed as a percentage departure from the mean value) and the actual gross irrigated area. All the four variables were found significant at 5% level. However, this improved the R2 value only marginally to 0.573. The percentage departure from normal rainfall had a much lower effect on agricultural growth rate, with a beta coefficient of 0.10, than the percentage increase in rainfall over the previous year, with a beta coefficient of 0.25. Since the rainfall can depart less from the normal value than from the previous year’s value—the departure of annual rainfall from the mean value ranges from –20.25% to 17.50%, it would have less effect on the agricultural growth rate. The coefficient for irrigation growth in the new model was 0.48. The least significant variable was gross irrigated area of the year, with a beta coefficient of 0.000040.


In India, agricultural economists and planners have long believed that the annual agricultural growth rate is determined mainly by the quantum of monsoon rainfall in a particular year (or by its departure from the mean value); they attributed poor agricultural growth in a year to the inadequate rainfall in that year. This belief stems from a logical extension of the concept that agricultural output in a particular year can change with the monsoon rainfall, which ensures sufficient moisture for kharif crops and adequate inflows in reservoirs and replenishment of aquifers for irrigation. But this view ignores that rainfall can fluctuate widely in two consecutive years, and cause large variations in agricultural outputs, and the value of outputs can be less than that of the previous year; and that the preceding year’s rainfall determines agricultural growth in a year. Economists and planners also attributed high growth rates to effective policy interventions.

The absence of long-term data on average annual rainfall on the country’s land mass made it difficult to test this hypothesis. The data are available now; the current analysis uses that data and suggests that obtaining a good monsoon is neither a necessary nor a sufficient condition for securing high agricultural growth during a year in relation to the previous year. Wide variations in agricultural growth rates between years that experienced more or less the same quantum of rainfall suggest that the growth rate of the year under consideration is explained by factors other than annual rainfall, the percentage difference in average rainfall from the previous year and the percentage increase in gross irrigated area. These two variables explain the growth performance to an extent of 55%. The remaining should be explained by factors such as the level of agricultural inputs, crop diversification, introduction of new high-yielding varieties, and the spatial variation in rainfall. The chance of obtaining a high growth rate during a year rises with the percentage increase in average annual rainfall and gross irrigated area in that year over the previous year. The average rainfall of the year (expressed as percentage departure from the mean value) has a much smaller effect on annual agricultural growth rates in reality.

Based on this analysis, this article argues that too much is made out of the annual agricultural growth rates in planning and policy circles. Poor agricultural growth performance in a year may not be because of poor performance of the monsoon during that year. Instead, it can as well be due to the very good performance of the monsoon or abnormally wet conditions during the previous year. Likewise, very high agricultural growth performance (agricultural growth rate) during a year may neither be because of good monsoon performance nor because of any policy reforms; it may be due to poor performance of monsoon during the previous year.

Given the high fluctuations in average annual rainfall in the country, the estimates of annual agricultural growth rates can often be misleading in terms of drawing inferences on agricultural sector
performance. Future focus should be on assessing medium-term growth rates, carefully picking up the base year in which the magnitude of annual rainfall is quite close to that of normal rainfall.


Kannan, E and S Sundaram (2011): “Analysis of Trends in India’s Agricultural Growth,” Working Paper No 276, Institute for Social and Economic Change, Bangalore.

Kumar, M Dinesh, A Narayanamoorthy, O P Singh, M V K Sivamohan, M Sharma and Nitin Bassi (2010): “Gujarat’s Agricultural Growth Story: Exploding Some Myths,” Occasional Paper No 2, Institute for Resource Analysis and Policy, Hyderabad, March.

Updated On : 7th Jan, 2019


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