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Several issues must be sorted out first before the ambitious river linking plan is taken up 

The National Democratic Alliance government is all set to begin work on an estimated $87 billion plan to connect around 60 of India’s largest rivers; this includes the Ganga. Once complete it is expected to help end farmers’ dependence on fickle monsoon rains bring millions of hectares of cultivable land under irrigation and help generate thousands of megawatts of electricity.

Water management

The river-linking plan was first proposed in 2002 by the Atal Bihari Vajpayee-led NDA government. However it was stalled as States failed to end differences over water sharing contracts and clearances. This government has been able push through clearances for the first phase of the project. Work is now set to link the Betwa and Ken rivers which pass through Uttar Pradesh and Madhya Pradesh States ruled by the Bharatiya Janata Party.

Several issues should be sorted out first before billions of rupees are spent on a project like this. Water is listed as entry 17 in List II of the Seventh Schedule of the Constitution. While the government has initiated discussions to bring the subject under the concurrent list it may not be an easy task to achieve. Also if there are changes in the political dispensation in various States the government in a State that is upstream for example may refuse to share water with downstream States. When there has been a deficient monsoon we have seen conflicts arise among States over water access. Thus without having a full-fledged architecture to solve disputes it would not be prudent to embark on a mammoth project like this.

Second India is technically poor with respect to data related to the water sector. Unlike other countries the Central Statistics Office has neither attempted nor funded studies to gather data on water tables at an all-India or State level. Many water stressed countries produce these on a regular basis at a regional level and link them to national accounts statistics. Basically ‘water resource accounts provide an accounting framework that enables the integration of specialised physical resource sector data with other information on the economics of water supply and use in a structure that is consistent with the way data on economic activities are organised in the system of national accounts. In addition to facilitating the integration and sharing of a more comprehensive knowledge base the natural resource accounting framework provides the basis for evaluating the consistency between the objectives and priorities of water resource management and broader goals of economic development planning and policy at a national and local scale. This in turn improves communication between various agencies generating and using information about water for various purposes and contributes to better coordination packaging and analyses of such information that are more relevant to the needs of water managers and policy-makers’. The advantage of such an account is that it makes it possible to capture direct indirect and induced water demand in the process of economic production. Since indirect and induced water demands are typically higher/closer to direct demand it is essential to include them in combination with water supply table data while estimating the water balance situation in a region.

Context of agriculture

Fourth the government should pay more attention to its ‘more crop per drop’ mission to what extent Indian agriculture follows this practice and whether water-stressed regions are water exporters due to the crops they cultivate. However there is a dearth of studies in the Indian context — unlike other countries — addressing the gap by first analysing water flows embodied (virtual/hidden) in agriculture products moving between the States to create knowledge on the flows. The absence of a well-informed water policy reflects a knowledge governance gap. A recent study (Katyaini and Barua 2016) on virtual water (VW) flow assessment in respect of foodgrains indicates that though the north zone is highly water scarce it is a net VW exporter to the highly water scarce west and south which are net VW importers. Among the north zone States Punjab has the highest water losses while Maharashtra (west) and Tamil Nadu (south) the highest water savings in 1996–2005 and 2005–2014 respectively. Therefore at a subnational scale VW flows are not consistent with relative water scarcity. This finding is also crucial as it emphasises the need to carry out a subnational VW flow assessment. Such analysis for all the major crops at subnational levels is a must for efficient planning of a scarce resource such as water.

There is much to be done before embarking on a gigantic project of river linking.

The total foodgrain production in India has more than doubled from 108.42 million tonnes in 1970-71 to 252.22 million tonnes in 2015-16. More importantly India is now the largest producer in the world of a host of farm products be it jute or dairy or total pulses buffalo meat and chicken products.

This is nothing short of a revolution for a country that not so long ago had to approach the global market cap in hand to feed its millions and it is widely acknowledged not only in India but globally as well. What is less known however is the shift in the seasonal cropping pattern that has accompanied this dramatic change viz. the growing contribution of rabi crop vis-à-vis kharif. Despite the fact that the area under rabi crops is 22.4% less than under kharif the share of rabi foodgrain production in total foodgrain production of the country has increased from 36.4% in 1970-71 to 50.83% in 2015-16. In contrast the share of kharif foodgrain production has declined from 63.6% to 49.16% during the same period (see table).

The higher contribution of rabi crops has been driven primarily by higher productivity. Consider this: The yield per hectare of rabi foodgrain crops more than doubled from 941 kg per hectare in 1970-71 to 2379 kg per hectare in 2015-16. During the same period the yield per hectare of kharif foodgrain crops increased by just 1.72%—from 837 kg per hectare to 1804 kg per hectare during the same period

This is borne out by the 70th round of the National Sample Survey Organisation (NSSO) that collected information on the area under each crop quantity produced value of production and yield rates for both seasons (rabi was January-June 2013 and kharif was July-December 2012) for 10 common crops cultivated in both seasons—paddy potato jowar maize arhar (tur) moong sugarcane groundnut coconut and cotton. Except for two crops viz. jowar and cotton the productivity in terms of kg per hectare is much higher in the rabi season than in the kharif season. Specifically it is estimated that 57.85% of paddy 68.35% of maize 51% each of arhar (tur) moong and sugarcane 65% of groundnut and 51% of coconut output comes from crops grown during the rabi season.

Income of farmers by seasons

As per NSSO’s 70th round of the 90.2 million estimated agricultural households in the country 86.5% households were engaged in crop production during the kharif season whereas only 71.1% of agricultural households in the country were engaged in cultivation in the rabi season. Despite the higher share of households engaged in kharif cultivation the average gross cropped area was higher at 0.937 hectare per agricultural household during the kharif season as compared to 0.782 hectare per household during the rabi season. The average value of production of 10 common crops (which included value of harvested crop pre-harvested sale and value of by-products) per household during the kharif period was marginally higher at Rs 26258 as against Rs 25491 during rabi crops. However the income received from five products viz. paddy jowar maize potato and coconut is higher for the rabi season than the income received from these same crops grown in the kharif season because of seasonal price variations.

Using unit level data of NSSO’s 70th round—Situation Assessment Survey of Agricultural Households—to assess the efficiency of inputs used in agricultural production by the farmers with varying holdings in terms of land size both in rabi and kharif seasons it is clear that there are decreasing returns to scale on investment on agricultural inputs among all categories of farmers in both rabi and kharif cultivation. However here too data suggests the return on investment is higher for investment in rabi than in kharif crops. One rupee worth of investment in agriculture gives a return of 51 and 43 paise for large land holding categories of farmers in rabi and kharif season respectively. Clearly farmers benefit more from rabi than from kharif crops. In a nutshell the rabi foodgrain production contributes more to total foodgrain production despite lower land use and lower participation by agricultural households. At the same time individual farmers benefit more from rabi than from kharif crops. At a time when the government is seized with the issue of how to double farmers’ income in five years greater focus on rabi cultivation could be a win-win at both the micro and macro levels.

Sub-categorisation of OBCs provides an opening to ensure social justice works better

Regardless of the political impulse that led the government to announce creation of a committee to look into sub-categorisation of Other Backward Classes (OBC) it provides an opening to ensure social justice in an efficient manner. The biggest challenge India faces is that the groups perceived to be disadvantaged consist of a very large segment of Indian society while public policies are highly limited in scope.

The jobs-claimants mismatch

Some illustrative statistics are eye-opening. The National Sample Survey (NSS) data from 2011-12 show that about 19% of the sample claims to be Dalit 9% Adivasi and 44% OBC. While some of these claims may be aspirational rather than real this totals a whopping 72%. Among the population aged 25-49 less than 7% have a college degree. By most estimates less than 3% of the whole population is employed in government and public-sector jobs. Since reservations cover only half the college seats and public-sector jobs the mismatch is obvious. A vast proportion of the population eligible for reservations must still compete for a tiny number of reserved and non-reserved category jobs. It is not surprising that there is tremendous internal competition within groups.

If we want reservations to make a significant difference in the lives of the marginalised groups there are only two options. Either the government must drastically increase availability of government jobs and college seats or it must reduce the size of the population eligible for these benefits. While the Supreme Court would not allow reservations to exceed 50% frankly it does not matter. Whether available public sector jobs cover 1.5% of the population or 3% these will only offer opportunities to a minuscule fraction of individuals in reserved categories. Hence the only viable option is to reduce the size of the eligible population possibly along the lines of sub-categorisation proposed by the government.

However while the media and claimants to the coveted OBC status such as Jats Kapus and Patels are busy arguing over the merits of this proposal very little attention is paid to the practical challenges facing sub-categorisation. How will we know which castes are the most disadvantaged? At the moment the only reputable nationwide data on caste comes from the 1931 colonial Census and some of the ad hoc surveys conducted for specific castes.

Lack of credible data

The Socio-Economic Caste Census (SECC) of 2011 was supposed to provide up-to-date comprehensive data. However the results remain shrouded in mystery. When releasing poverty and deprivation data from the SECC in 2015 it was found that about 4.6 million distinct caste names including names of gotra surname and phonetic variations were returned making the results almost impossible to interpret. For nearly 80 million individuals caste data were believed to be erroneous. Since then we have heard little about the quality of caste data in SECC and even less about its results. In 2015 the then NITI Aayog Vice Chairperson Arvind Panagariya was asked to head a committee to chair the caste classification using SECC data. Little seems to have come of it.