A closer look at the Beed syndrome

New indicators and measures are needed if we are to better understand how villages allocate and use land, and whether their households survive or thrive through such use.

There is a great diversity of practices concerning the environment and land within the administrative unit we call a district. A typical district of India is often more than 10,000 square kilometres and will be divided into a number of talukas or tehsils – it could be eight or less, it could be 15 or more.

As a district like Beed has many hundreds of gramas – it has 1,368 gramas (11 uninhabited) by the count of Census 2011 – the local practices of land management, cultivation, maintaining micro watersheds, administering pastures and grazing lands, following the traditions of handicrafts, hand weaves and village industries, are many and only cursorily documented if at all they are.

The Beed syndrome – of the rapid change in crop choice and its impact on land use – is a sum of its parts. While those parts have as much to do with the physical characteristics, they have also to do with behaviours, perceptions and choices. But for the latter kind of factors there is hardly any data. For physical uses and changes, there is data (as I showed in the linked post).

Just as districts are the sum of diverse talukas (and towns) so too talukas are the sum of villages. With 176 gramas, the taluka of Ashti has a diversity of knowledge systems enough to occupy a bus-load of social scientists for a decade, if only they would be interested enough to visit what sounds like a humdrum taluka in a hot and dusty zilla of Maharashtra.

The land use and crop choice changes in Beed are the result of a widespread change. But with a district of this complexity – 1,368 gramas, 11 talukas, 9 towns, 534,278 households with a population of 2,585,049 – how feasible is it to identify the major factors among several that have caused such change?

My attempt in these posts is to show, through the available data at taluka and grama levels, that tracing such changes is possible, and that a new, quite different, set of measures should be adopted if district administrations and other planning bodies are to look ahead, two to three generations ahead, and provide guidance.

Turning more locally to Ashti, one of Beed’s 11 talukas, I found using the Census 2011 data (the District Census Handbook and its detailed tables) that it is in terms of area the second largest taluka (after Beed taluka). Its population count of 243,607 places it as 7th among Beed’s 11 talukas (it was at this rank within the district by Census 2001 data too).

What has changed in Ashti is that whereas in 2001 the entire population of the taluka was rural, Census 2011 had Ashti town as home to 11,972 urban residents (just under 5% of the taluka population).

Through a first extraction of the District Census Handbook data I found that Ashti’s villages are by no means homogenous. They vary widely by population, land use and sown area.

To better illustrate how the changes in The Beed syndrome came about, for the examination of taluka-level data I am creating a new ratios and indicator types, a few of which I have applied to Ashti (and will extend the application to the other 10 talukas of Beed).

The grama level data is extensive and for my purposes I selected population, spatial area, number of households and net area sown. How varied the gramas are for each of these can be seen in the adjoining table.

Variations apart, since Census 2011 allows us to see the ways in which collections of even 200 households use land, decide labour and secure their food, I calculated the following: (1) percentage of sown area (hectares under cultivation) to total village area, (2) number of households per hectare of sown area (hectare under cultivation). This let me see at the grama level how critical cultivated land was to the household and grama economy through the percentage of total, and how well each hectare was being utilised by very broadly finding out how many household ‘units’ the hectare was supporting.

The main chart I drew therefore plots the gramas using both these – a ratio and an indicator. These is in the chart a density of gramas in the south-eastern quadrant. More pertinently, the densest concentration of the gramas of Ashti taluka occur within and near the grid square that reads 2 to 3 households per cultivated hectare and 75% to 80% of the grama land being under cultivation. (There are a few other zones of concentration but this is the heaviest.)

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It’s time to rid India of the GDP disease

A woman in the Aravalli hills of Rajasthan carries home a headload of field straw. India’s National Accounts Statistics is completely ignorant of the biophysical economy.

On 5 January 2017 the Central Statistics Office of the Ministry of Statistics and Programme Implementation, Government of India, issued a note titled “First advance estimates of national income, 2017-18”. The contents of this note immediately caused great consternation among the ranks of those in business and industry, trading, banking anf finance, and government who hold that the growth of India’s gross domestic product is supremely important as it is this growth which describes what India is and should be.

In its usual bland way, the Central Statistics Office said that this was “the First Advance estimates of national income at constant (2011-12) and current prices, for the financial year 2017-18” and then proeeded, after a short boilerplate explanation about the compilation of estimates, delivered the bombshell to the GDP standard-bearers: “The growth in GDP during 2017-18 is estimated at 6.5% as compared to the growth rate of 7.1% in 2016-17.” [pdf file here]

To me, this is good news of a kind not heard in the last several years.

But India’s business and financial press were thrown into a caterwauling discord which within minutes was all over the internet.

An example of one out of the many messages in a daily barrage delivered by the Government of India’s ‘GDP First’ corps. This is from what is called the Make in India ‘initiative’ of the Department of Industrial Policy and Promotion, Ministry of Commerce. “Make in India is much more than an inspiring slogan,” the DIPP says. “It represents a comprehensive and unprecedented overhaul of out-dated processes and policies.” For this childish GDP rah-rah club, environmental protection, natural reserves, watershed conservation, handloom and handicrafts are all outdated practices and ideas.

‘GDP growth seen at four-year low of 6.5% in 2017-18: CSO’ said the Economic Times: “Most private economists have pared the growth forecast to 6.2 to 6.5 percent for this fiscal year, citing the teething troubles faced by businesses during the roll out of a goods and services tax (GST).”

‘7 reasons why FY18 GDP growth forecast should be viewed with caution’ advised Business Standard: “The fact that growth will be 6.5% is significant as it is even lower than the Economic Survey assumption of 6.75-7.5% for the year. Hence, it is not expected to be higher than the base mark which means that it would be lowest in the past three years. The effects of demonetisation and GST have played some role here.”

‘CSO pegs FY18 growth at 6.5%; why forecast is an eye-opener for Narendra Modi govt’ said Firstpost: “The healthy uptick in volumes displayed by many sectors in November 2017, is expected to strengthen in the remainder of FY2018, benefiting from a favourable base effect and a ‘catch up’ following the subdued first half. Accordingly, manufacturing is likely to display healthy expansion in volumes in H2 FY2018, which should result in a substantial improvement in capacity utilisation on a YoY basis.”

‘GST disruptions eat FY18 economic growth; GDP seen growing at 6.5%, lowest under Modi government’ huffed the Financial Express: “For a broad-based recovery the rural economy needs to recover and we can expect the upcoming budget to focus on alleviating some of the stress in the rural economy and concentrating on measures to augment the flow of credit in the economy. Overall growth is likely to improve in the coming year and possibly move up beyond the 7% mark in FY19.”

‘India’s GDP growth seen decelerating to 6.5% in 2017-18 from 7.1% in 2016-17’ said the Mint: “The nominal GDP, or gross domestic product at market prices, is expected to grow at 9.5% against 11.75% assumed in the 2017-18 budget presented last year. This may make it difficult for the government to achieve the fiscal deficit target of 3.2% of GDP in a fiscally tight year.”

‘India Sees FY18 GDP Growth At 6.5%’ observed Bloomberg Quint: “Growth in gross value added terms, which strips out the impact of indirect taxes and subsidies, is pegged at 6.1 this year, versus a revised 6.6 percent last fiscal. Both GDP and GVA growth were marginally below expectations. A Bloomberg poll had pegged GDP growth at 6.7 percent. The RBI had forecast GVA growth at 6.7 percent at the time of its last policy review in December.”

‘India’s FY18 GDP growth estimated at 6.5%, says CSO data’ said Zee Business: “Real GVA, i.e, GVA at basic constant prices (2011-12) is anticipated to increase from Rs 111.85 lakh crore in 2016-17 to Rs 118.71 lakh crore in 2017-18. Anticipated growth of real GVA at basic prices in 2017-18 is 6.1 percent as against 6.6 percent in 2016-17.”

So great is the power of the School of GDP and of its regents, who are as priests of the Sect of GDP Growth, that the meaninglessness of GDP is a subject practically invisible in India today. Just as it has no meaning at all to the woman in my photograph above, so too GDP has no meaning for all, including the 2.7% (or thereabouts) who pay income tax.

This tweet shows us the scale of the problem. An article by Klaus Schwab of the World Economic Forum (a club of powerful globalists) is posted on the website of Prime Minister Narendra Modi ! The head of the ruling BJP’s information unit broadcasts it.

India’s National Accounts Statistics presents every quarter and annually, estimates of the size of the country’s GDP, of the rate of GDP growth, of the size of ‘gross value added’, to which GDP is bound in ways as complicated as they are misleading. There are wages, interests, salaries, profits, factor costs, net indirect taxes, product taxes, product subsidies, market prices, industry-wise estimates and producer prices to juggle.

For the most part, these are prices and costs alone, upon which various kinds of taxes are levied and whose materials and processes may qualify for subsidies. All these are added and deducted, or deducted and added, and finally totalled show a GVA which then leads to a GDP. The prices are arbitrary and speculative, as all prices are, the arbitrariness and speculative nature being attributed to something called market demand, itself a creation of policy and advertising – policy to choke choices and advertising to spur greed. On this putrid basis does the School of GDP stand.

The GDP and GDP-growth frenzy in India spares not a minute for a questioning of its fundamental ideas, which in certain quarters had begun to shown as hollow and destructive in the early 1970s, when the effects of the material and consumption boom in Europe, North American (USA and Canada) and some of the OECD countries after the end of the Second World War became visible as environmental degradation.

Over 30 years later, sections of those societies inhabit and practice what are called ‘steady state’ economics, ‘transition’ economics (that is, transition to low energy, low consumption, recycling and sharing based ways of collective living) and ‘de-growth’, which is a scaling down of economic production and consumption done equitably and to ensure that a society (or groups of settlement and their industries) strictly observe the bio-physical limits of their environment (pollution and pollutants, land, water, biodiversity, etc).

But the Central Statistics Office of the Ministry of Statistics and Programme Implementation, Government of India, is ignorant of such critical thinking. It is just as ignorant of the many efforts at swadeshi living, production, cultivation (agro-ecological) and education (informal learning environments instead of reformatted syllabi lifted wholsesale from countries whose exploitative economies installed globalisation as the default economics mode) that are visible all over India today. The CSO and MoSPI are not entirely to blame for this abysmal blindness, because the Ministry of Finance (like every other major line ministry of the Government of India, and like every state government) has decided to be even more blind.

To read the insensate paragraphs disgorged every quarter from the CSO (and Ministry of Finance, likewise the Niti Aayog, the chambers of commerce and industry, the many economy and trade think-tanks) is to find evidence to pile upon earlier evidence that here is an administration of a very large, extremely populous country which cares not the slightest about the indubitably strong correlations between ‘GDP growth’ and more forms of environmental damage than have been reckoned.

The GDP-GVA-growth fantasy cares not the slightest about energy over-use and CO2 emissions, about the effects of widespread atmospheric and chemical pollution on the health of the 185 million rural households and 88 million urban households (my estimates for 2018) of India, and about the terrible stresses that the urban households in more than 4,000 towns, district headquarters and metros are subject to as a result of their lives – through mobile phone apps, banks, the food industry, the automobile industry and the building industry – being micro-regulated so that an additional thousandth of a per cent of GDP growth can be squeezed out of them.

The GDP asura has brought ruin to India’s environment, cities, farms, households, forests, rivers, coasts and hills. Let 2018 be the year we burn the monster once and for all.

Masses of cotton but mere scraps of vegetables

The sizes of the coloured crop rectangles are relative to each other based on thousand hectare measures. The four pie charts describe the distribution of the main crops amongst the main farm sizes.

For a cultivating household, do the profits – if there are any – from the sale of a commercial crop both enable the household to buy food to fit a well-balanced vegetarian diet, and have enough left over to bear the costs of its commercial crop, apart from saving? Is this possible for smallholder and marginal kisans? Are there districts and talukas in which crop cultivation choices are made by first considering household, panchayat and taluka food needs?

Considering the district of Yavatmal, in the cotton-growing region of Maharashtra, helps point to the answers for some of these questions. Yavatmal has 838,000 hectares of cultivated land distributed over 378,000 holdings and of this total cultivable area, the 2010-11 Agriculture Census showed that 787,000 hectares were sown with crops.

Small holdings, between 1 and 2 hectares, account for the largest number of farm holdings and this category also has the most cultivated area: 260,000 hectares. Next is farms of 2 to 3 hectares which occupy 178,000 hectares, followed by those of 3 to 4 hectares which occupy 92,000 hectares.

The district’s kisans allocate their cultivable land to food and non-food crops both, with cereals and pulses being the most common food crops, and cotton (fibre crop) and oilseeds being the non-food (or commercial) crops.

How do they make their crop choices? From the agriculture census data, a few matters immediately stand out, which are illustrated by the graphic provided. First, a unit of land is sown 1.5 times in the district or, put another way, is sown with one-and-a-half crops. This means crop rotation during the agricultural year (July to June) is practiced but – with Yavatmal being in the hot semi-arid agri-ecoregion of the Deccan plateau with moderately deep black soil – water is scarce and drought-like conditions constrain rotation.

Second, land given to the cultivation of non-food crops is 1.6 times the area of land given to the cultivation of food crops (including the crop rotation factor), a ratio that is made abundantly clear by the graphic. This tells us that the food required by the district’s households (about 647,000 of which about 516,000 are rural) cannot be supplied by Yavatmal’s own kisans.

The vegetables required by the populations of Yavatmal’s 16 talukas (Ner, Babulgaon, Kalamb, Yavatmal, Darwha, Digras, Pusad, Umarkhed, Mahagaon, Arni, Ghatanji, Kelapur, Ralegaon, Maregaon, Zari-Jamani, Wani) can in no way be supplied by the surprisingly tiny acreage of land allocated to their cultivation. Nor do they fare better for fruit, which has even less land (although this is a more complex calculation for fruit trees, less so for vine fruits).

Third, 125,000 hectares to wheat and 71,000 hectares to jowar makes up almost the entire cereals cultivation. Likewise 126,000 hectares to tur (or arhar) and 94,000 hectares to gram accounts for most of the land allocated to pulses. Thus while Yavatmal’s talukas are well supplied with wheat, jowar, gram and tur dal, its households must depend on neighbouring (or not so neighbouring) districts for vegetables, as a minimum of 280,000 tons per year is to be supplied to meet each household’s recommended dietary needs.

What the graphic helps us ask is the size of the costs associated with crop cultivation choices in Yavatmal. The cultivation of hybrid cotton in India’s major cotton growing regions (several districts each in Maharashtra, Andhra Pradesh and Gujarat) is associated with heavy chemical fertiliser and pesticides use. Whether the soil on which cotton has grown can be sown again with a food crop is not clear from the available data but if so such a crop would be saturated with a vicious mix of chemicals that include nitrates and phosphates.

The health of the soil in Yavatmal’s 16 talukas is probably amongst the most fragile in Deccan Maharashtra, and after years of coaxing a false ‘productivity’ out of the ground for cotton, it would be best for the district’s 516,000 rural households to take a cotton ‘holiday’ for three to four years and revert to the mixed and integrated cropping of their forefathers (small millets). But the grip of the financiers and the textiles intermediaries is strong.

Villages in their splendid talukas

As part of my continuing and long term study on the relation between populations both rural and urban, the land base upon which they depend for the growing of food, and the socio-economic changes taking place in our districts, I have begin an examination of how households are distributed in administrative regions, that is, districts and talukas. This graphed plot describes one kind of finding. (Click here for a full size plot that lets you explore each data point.)

States are administratively divided into districts (earlier the concept of a ‘division’, which was a group of districts, was more common – the ‘division’ is still used, for revenue determination but also for home affairs) and these are divided into talukas. How many talukas does the typical district have? Some have four, others as many as 12. There are talukas whose households are entirely rural as there is not a single census town, let alone a municipal council, within its precincts. The taluka contains villages and these can be numerous. Some talukas may have 50-60 villages whereas others may have 200 and more.

It is always an interesting matter to ponder. How did households in a small sub-region – at the confluence of a stream and a river for example or at the edge of a plain and at the margins of hills – become villages and what determined the distribution of such hamlets in a very local habitat? The factors were always environmental and there was often a strong cultural reason, such as proximity to a sacred site, a mandir or a venerated shrine, historical sites (such as those mentioned in the Ramayana and documented in detail thereafter in numerous local commentaries).

From the set of districts analysed so far a few guiding figures have emerged. The number of rural households in a taluka varies from 7,200 to 96,800; the number of villages in a taluka varies from 28 to 338; the average number of households in a village is 330; there is one urban household for every 3 rural households.

Where the agro-ecological conditions are favourable, there is to be found a denser gathering of villages and these will have larger populations. This can easily be understood. It is less clear how the toil of the households accommodated in a large number of villages are required to maintain, in many ways, urban households which are now clustered in a town or two of the same taluka. This dependence is what a study of not only the rural-urban population, but also how it is distributed within agro-ecological boundaries, can help uncover. The graphed plot included here is one step towards that understanding.

Debt and kisan households

For the 21 larger states, this is the picture of how much average debt a cultivator household has incurred, and how many amongst cultivator households are in debt.

The data I have used for this chart are taken from the report, ‘Household Indebtedness in India’, which is based on the 70th Round of the National Sample Survey Office (NSSO), Ministry of Statistics and Programme Implementation, Government of India, and collected during January to December 2013.

The states are plotted on average debt of such households, in lakh rupees on the left scale, and the proportion of such households, as a percentage of all such households, on the horizontal scale. The size of the circles are relative and based on the amount of average debt.

The average amount of debt per cultivator household seen in this chart is for most of the 21 larger states, lower than the overall rural household average debt of Rs 1,03,457. However the cultivator household is one of six types of rural household (the categories are: self-employed in agriculture, self-employed in non-agriculture, regular wage/salary earning, casual labour in agriculture, casual labour in non-agriculture, others). About 31.4% of all rural households are in debt.

The chart shows that cultivator households in Punjab and Kerala carry the highest amounts of debt, and that a highest percentage of cultivator households carrying debt are in Telengana and Andhra Pradesh. There are two distinct groups. One group of nine states occupies the right and most of the vertical area of the chart. The second group is of 12 states clustered towards the lower left corner of the chart panel.

This difference describes regional variation. In the first group are all the southern states. In the second are all the eastern and central states. Madhya Pradesh, Gujarat, Uttar Pradesh and Uttarakhand cultivator households exhibit the greatest similarity, with average debt of under Rs 40,000 and with less than 35% of cultivator households carrying debt.

Bihar, West Bengal and Odisha are likewise similar, their average debt being less than Rs 20,000. Assam, Jammu and Kashmir, Chhattisgarh and Jharkhand cultivator households bear the lowest average debt and less than one in five of such households is in debt.

These are the broad brush strokes of debt amongst cultivator households in the 21 major states painted by the NSSO’s ‘Household Indebtedness in India’ report. A more detailed examination of such debt, and also the debt of the other kinds of rural households, will give us a deeper understanding of the subject.

Sizing up rural and urban settlements in Maharashtra

The districts of Jalna, Osmanabad, Hingoli, Satara, Ratnagiri, Washim, Nandurbar, Gondiya, Gadchiroli and Sindhudurg in Maharashtra all enjoy a rural built-up to urban built-up ratio of more than 2 (where the built-up area of the district’s rural settlements are at least twice the area of its urban settlements).

In the chart, the light green bars show a district’s rural built-up area, the light maroon its urban built-up area. The number associated with the name of the district is the ratio between the two kinds of built-up area.

Such a comparison helps us understand the dependency of the two kinds of populations in a district, rural and urban, upon the natural resources (as classified by land types). The chart shows us that some districts (see Jalgaon, Sholapur, Satara and Ratnagiri) have total rural built-up areas of 150 square kilometres and above. But whereas the urban built-up areas of Jalgaon and Sholapur are more than 100 sq km each this is not so for the other two districts.

Districts may have similar ratios between rural and urban built-up areas – see Ahmednagar, Akola and Dhule – but whereas the built-up areas of both types are more than 100 sq km in Ahmednagar they are smaller in the other two districts. There are only three districts for which the total rural built-up area is less than 50 sq km: Parbhani, Hingoli ad Washim.

There are 15 districts in which there is at least 1.5 sq km of rural built-up area for 1 sq km of urban built-up and this indicates that in these districts the base of agricultural and allied activities is still strong and therefore needs continuous encouragement. There are 7 districts for which this ratio is between 1.5 and 1 and these therefore must be watched for signs of quickening urbanisation which will need to be curbed in the interests of sustainability and indeed of the provision of food.

I have taken the data from the land use and land change information for 2011-12 collected by the Resourcesat-2 satellite with land classification and calculation carried out by the National Remote Sensing Centre (NRSC), Indian Space Research Organisation (ISRO), Department of Space, under the Natural Resources Census Project of the National Natural Resources Repository Programme. It is available through Bhuvan, the geo-platform of ISRO.

Urban areas are non-linear built-up areas covered by impervious structures adjacent to or connected by streets. This class includes residential areas, mixed built-up, recreational places, public and private utilities, communications, commercial areas, reclaimed areas, vegetated areas within urban zones, transportation infrastructure, industrial areas and their dumps, and ash/cooling ponds. Rural built-up areas are the lands used for human settlement in which the majority of the population is involved in agriculture. These are built-up areas, small in size, mainly associated with agriculture and allied sectors and non-commercial activities. They can be seen in clusters both non-contiguous and scattered.

The last 4 districts – Nagpur, Nashik, Thane and Pune – have their urban built-up bars coloured differently to indicate that their scales are beyond, and very much above, the 150 sq km of the chart. Mumbai city and suburban is omitted entirely.

Bharat at 1.3 billion

In July 2016, the population of Bharat will cross one billion three hundred million. In 1937, the population of what was then British India was 300 million. Seventy-nine years later, there are a billion more.

This numerical landmark is based on the 2011 Census of India total population (which was 1.21 billion) and the growth rate of the population, or what demographers refer to as the rate of natural increase.

For a country of the size of Bharat – and for that matter, even for the states with large populations – any ‘total’ or ‘final’ is no more than an estimate that is subject to variability. The population count of any administrative unit (such as a state or district) can be estimated with census data modified by health data (birth rate, death rate) and by seasonal changes (migration).

There are several extenuating reasons why this exercise needs to be done automatically at least every month by the states and the central government ministries and departments. Perhaps the 1.3 billion landmark can goad them into doing so. The carrying capacities of our river basins, the watersheds, the valleys and floodplains, the ghats both Western and Eastern, the plateaus and grasslands, the deltas and the hill tracts cannot be ignored.

Equations that govern these are simpler than they are typically made out to be by science. There is only so much water, land, forest, and vegetation (or biomass) available to support us. The 2001 Census found that the population of Bharat had crossed a thousand million. At that point at least the consequences of a steadily growing population (182 million had been added since 1991, and 345 million – which was the population at the time of Independence – since 1981) needed to have become the subject of monthly reflection and policy.

With Bharat at 1.3 billion being barely three months away, the new state population counts (in the chart) show why such monthly reflection and policy is vital, indeed a matter of urgency. We now have ten states whose population is more than 50 million – the comparisons of the sizes of our state populations with those of various countries around the world are now well-known.

West Bengal in May 2016 has a population of 97.7 million and will cross 100 million by the same time in 2017. In May 2016 the population of Bihar is 111.4 million, Maharashtra is 120.3 million and Uttar Pradesh is 214 million. These are gigantic numbers and it is because they are gigantic that they seem to escape planning notice – but the population of these four states is very much more than the population of the European Union of 28 countries.

The table shows the current estimated population (2016 May) for the age bands (from Census 2011 and adjusted for simple growth), which helps us understand the populations of infants, children, adolescents, youth, early adults, mature adults, the middle-aged and the elderly. About 257 million are under nine years old (19%), about 271 million are between 10 and 19 years old (20%) and about 111 million (8%) are 60 years old and older.

These are aspects that require as much study, comprehension and policy measures as we demand on subjects such as governance, corruption, the price of food, the extent of our forests, the supply of water, and the adequacy of monthly incomes. At the 1.3 billion mark, Bharat’s population is starkly in the foreground.

How a district employs land and crop

A plotting of the cropland size categories with the number of holdings for the district of Hingoli in Maharashtra. The central group of rectangles displays the distribution, relative to each other, of the size categories of holdings (in hectares, ha.). The blue squares, also relative to each other, displays the number of holdings for each farm size category. The data source is the Agricultural Census 2010-11.

In the district of Hingoli, Maharashtra, the allocation of cultivated land between food crops and non-food crops is somewhat in favour of non-food crops. That is, for every hectare planted with a food crop 1.3 hectares is planted with a non-food crop. The broad categories we have under food crops are: cereals, pulses, vegetables and fruit. Under non-food crops there are: oilseeds, sugarcane, fibres, spices and fodder.

Where Hingoli district is in Maharashtra state.

The Agricultural Census 2010-11 detailed data for Hingoli shows that at the time of the survey 493,927 hectares were under cultivation for all kinds of crops, both food and non-food. As this is a count of how much land was under cultivation by crop, the total land under cultivation for all crops taken together is more than the total land under cultivation when measured according to land use. This is so because of crop rotation during the same agricultural year, inter-cropping and mixed cropping – for a plot, the same land may raise two kinds of crops in a year.

Size categories of farm holdings, with number of holdings and total area under each category for Hingoli district, Maharashtra.

The 493,927 hectares under cultivation in Hingoli are divided under 213,286 hectares for food crops and 280,640 hectares under non-food crops. This gives us the overall picture that the farming households of Hingoli choose to give more land for crop types under the ‘non-food’ category. As the settlement pattern of Hingoli is very largely rural – that means, few towns and these are the district headquarters and two more taluka centres – do the farming households of Hingoli grow enough to feed themselves comfortably? Do the farming households have the labour needed to continue cultivating so that they can feed themselves and their village communities? How are choices relating to land use and crop made?

Using the publicly available information from a variety of government sources, we are able to find parts of answers. The Agricultural Census 2010-11 is one such source, the Census of India 2011 is another, so are the tables provided by the Department of Economics and Statistics of the Ministry of Agriculture. The graphical representation I have prepared here helps provide the land use basis upon which to layer the district information from other sources.

Where the farmers are in Bharat

The Census 2011 helps us understand where the great farming populations are: Nashik, Paschim Medinipur, Ahmadnagar, Guntur, Mahbubnagar, Purba Champaran, Belgaum, Kurnool, Madhubani, Jalgaon and 90 other districts are found in this chart, which shows the relationship between the populations of farmers and the total working populations of these districts.

Many of the districts in this chart, represented by the circles (click for full resolution version), lie between the population markers of 750,000 and 1.1 million. They also lie within the percentage band of 60% to about 85%. This shows how important agriculture is – and will continue to be as long as annual budgets and five-year plans support it – for the districts that give us our staple foods.

Ten years of India’s great rural guarantee

Ten years of a rural employment guarantee programme in India is well worth marking for the transformations it has brought about in rural districts and urban towns both, for the two kinds of Indias are so closely interlinked. The ten year mark has been surrounded by opportunistic political posturing of the Bharatiya Janata Party (BJP) of the ruling National Democratic Alliance and by churlish accusations from the Indian National Congress (or Congress party, now in the opposition).

When the National Rural Employment Guarantee Act came about (it is now prefixed by MG, which is Mahatma Gandhi) ten years ago, it was only the newest in a long line of rural poverty alleviation programmes whose beginnings stretch past the Integrated Rural Development Programme (still a touchstone during the Ninth Five Year Plan) whose early period dates from the 1970s as a more coherent manifestation of the ‘Food For Work’ programme. Democratic decentralisation, which is casually dropped into central government communications nowadays as if it was invented only last week, was explained at length as early as the Sixth Five Year Plan. And in the Fourth Five Year Plan, in the guidance section it was stated that measures were needed for “widening opportunities of productive work and employment to the common man and particularly the less privileged sections of society” which “have to be thought out in a number of different contexts and coordinated in to effective, integrated programmes”.

Work demand patterns in four districts (all in Maharashtra) from 2012 April to 2016 February. The cyclical nature of work demanded usually coincides with crop calendar activities in districts and sub-districts. This aspect of the MGNREGA information system can be used as a good indicator for planning by other line ministries, not only rural development. We can see the difference between the set of two districts of Akola and Gondiya, and the districts of Washim and Hingoli: the cyclical nature in the first two is more pronounced. The April to June demand is seen common, and increasing over the three years recorded by the charts.

This is only the barest glimpse of the historical precursors to the MGNREGA. The size of our rural population in the decade of the 2010s transforms any national (central government) programme into a study of gigantism over a number of dimensions, and so it is with the (MG)NREGA whose procedural demands for organising information over time and place became a discipline by itself, leading to the creation of a management information system whose levels of detail are probably unmatched anywhere in the world.

For its administrators, every week that the MGNREGA delivers money to households in a hamlet for work sanctioned by that small panchayat is one more successful week. There have over this last decade been considerably more successful weeks than unsuccessful ones. This has happened not because of politicians of whichever party of persuasion, but because of the decision made by many households to participate in the shape that NREGA (and later MGNREGA) took in their particular village. The politicians, like the parties they belong to, are incidental and transitory. At this stage of the programme’s life, it is to be hoped that it continues to run as a participatory pillar of the economy of Bharat, and assimilates in the years to come new concerns from the domains of organic (or zero budget) agriculture, sustainable development and ecological conservation.

At this stage the commentaries look back at the last year or perhaps two of the programme. “It is unclear, however, what the present NDA government thinks about the performance of the scheme,” commented the periodical Down To Earth. “Last year, Prime Minister Narendra Modi called MGNREGA a ‘monument of failure’. Now, the rural development ministry has termed it as ‘a cause of national pride’.” The magazine went on to add that MGNREGA “started losing steam when wages were kept pending, leading to the liability being carried forward to the following year”.

“What is relatively less known is the impact of MGNREGA on several other aspects of the rural economy, such as wages, agricultural productivity and gender empowerment,” a commentary in the financial daily Mint has pointed out. “While most critics lament the quality of assets created under MGNREGA, there is now increasing evidence based on rigorous studies, which suggest that not only has the asset quality been better than comparable government programmes, they are also used more by the community.”

The finance minister has been quoted by the daily Indian Express as follows: “A kind of indifference towards it (MGNREGA) was growing by 2013-14, when the scheme entered its seventh and eighth years. When there was a change of government in 2014-15, there was talk on whether the scheme will be discontinued, or its fund allocation curtailed,” Minister Arun Jaitley is reported to have said at the MGNREGA ‘Sammelan’ in New Delhi. “The new government [the BJP] not only took forward the scheme but also increased its fund.”

In a Press Information Bureau release, the Minister for Rural Development, Birender Singh, said that 2015-16 has seen a revival of the MGNREGA programme. He also said that more than 64% of total expenditure was on agriculture and allied activities and 57% of all workers were women (well above the statutory requirement of 33%), and that among the measures responsible for the “revival of MGNREGA are the timely release of funds to states to provide work on demand, an electronic fund management system, consistent coordination between banks and post offices besides monitoring of pendency of payments”.

So far so good. What MGNREGA administrators need to mind now is for managerial technology and methods to not get ahead (or around) the objectives of the programme because these tend to keep the poor and vulnerable out instead of the other way around. The evaluations and studies on NREGA – and there have been a number of good ones – have shown that the more new financial and administrative measures there are, the greater the decline in participation in the programme. Administrative complexity also provides fodder to those, like this pompous commentator, who try to find in data ‘evidence’ that NREGA does “not help the poor”.

The MGNREGA’s usefulness and relevance is not only about creating employment when it is needed and its generally positive impact on wages. For all its shortcomings the MGNREGA programme has also helped revitalise the need to understand labour dynamics in rural areas particularly as it pertains to agriculture and cultivation. At a time when the flashier sections of the modern economy have lost their shine (if ever there was a shine) and when the need for panchayat-led, village-centric development that is self-reliant in deed and spirit is growing in Bharat, a programme like the MGNREGA has all the potential to serve the country well for another generation.