Posts Tagged ‘rural’
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.
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.
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.
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.
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.
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.
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.
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 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”.
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.
Being unorganised, rural and particularly agricultural labour constitutes a relatively vulnerable segment of the work force. Rural and agricultural labour is generally deprived of the benefits of collective bargaining and lacks the protection of labour enactments which their counterparts in the organised sectors of the economy can fall back upon during times of work uncertainty, or calculated mismanagement. Agricultural labourers however have to live with casual employment, frequent changes of employers as well as places and wide fluctuations in the pay.
Farming remains at the centre of rural Indian life, even as more men and women today seek out non-farm work. Using data from the MGNREGA records, the proportion of men aged 15–59 working solely in agriculture fell from 41% in 2004–05 to 31% in 2011–12. The decline for women was smaller, from 40% to 35%. Many men and women combine farm work with non-farm labour, whether or not they participate in MGNREGA.
The labour scenario in a rural area is influenced by a number of factors such as its topography, natural resources, population growth, pressure on land, level of economic development, level of utilisation of resources and the institutional factors, namely, land tenure systems and inheritance laws.
Rural wages are considered to have risen steadily between 2004–05 and 2011–12, but the increase has been greater at higher wage levels compared with lower levels. MGNREGA records show that men’s daily wages for agricultural work grew by 50% between 2004–05 and 2011–12, women’s by 47%. Overall, growth in rural wages is higher in states and districts whose populations have greater participation in MGNREGA but it is important to note that MGNREGA plays only a modest role in wage increases.
Taking national averages, about a quarter of rural households participate in the programme, about 60% of these would like to work more days but are can’t get MGNREGA work. This widespread ‘rationing’ of work affects about 29% of all rural households, but percentages vary between regions. Households in the lowest income quintile worked only 23 days a year when they were allocated work.
The information base on the working and living conditions of this segment of labour market is scanty. The only major source of reliable information on socio-economic conditions of the rural labour is the Rural Labour Enquiry conducted by the National Sample Survey Organisation (NSSO) every five years. Consumer Price Index Numbers for Agricultural and Rural Labourers, released by the Bureau every month, provides a basis for minimum wages in agriculture under the Minimum Wages Act,1948.
The below average June to September monsoon season will lead to lower foodgrains production. What is the likely impact and how can society cope?
Context – For the last four years the numbers that describe India’s essential food security have become a common code: 105 million tons (mt) of rice, 95 mt of wheat, 41 to 43 mt of coarse cereals, 19 to 20 mt of pulses, 165 to 170 mt of vegetables and 80 to 90 mt of fruit.
With these quantities assured, our households feed themselves, army and factory canteens are supplied, the public distribution system is kept stocked and the processed and retail food industry secures its raw material.
Only provided there is such assurance, and that the allowance for plus or minus is as small as possible. Monsoon 2015 has removed that assurance for the agricultural year 2015-16. Our 36 states and union territories – and the 63 cities whose populations are more than a million – must begin to deal with the possible scenarios immediately.
Stock scenarios – In September 2015 the Department of Agriculture, Cooperation and Farmers Welfare, of the Ministry of Agriculture, Government of India, released the first of its usual four ‘advance estimates’ for the 2015-16 agricultural year. Each estimate sets the targets for the year for the foodgrain (and also commercial) crops, and provides with every estimate how likely it is that the annual target will be met.
This first advance estimate has issued a direct warning: rice production is estimated at 90.6 mt against a target of 106.1 mt. The wheat target is just under 95 mt but there is no estimate provided as yet. The target for coarse cereals is 43.2 mt whereas the advance estimate is just under 28 mt. The target for pulses is 20mt and the first estimate is 5.5mt.
What are the implications? The responsibility of the Department is to provide a provisional reading of the conditions that affect the production of our staple crops, and to inform and prepare state and central governments of the likelihood of shortfalls in foodgrain. The signal it has given for rice, estimated at 85% of the target, must be taken as a flashing red beacon which demands that our food stocks return to the foreground of the national agenda.
It is likely that the second and third advance estimates will see quantities revised upwards, but our planning must be based on this first estimate so that even the most adverse of natural contingencies can be met with suitable measures.
Using the first advance estimate as the basis, here are the likely annual production quantities, at 90% of the target and at 95% of the target: rice, between 95 and 101 mt; wheat, between 85 and 91 mt; coarse cereals, between 39 and 42 mt; pulses, between 16 and 17 mt; total foodgrains, between 236 and 250 mt of which cereals are between 220 and 232 mt.
To help answer this question, two sets of deductions must be accounted for. To begin with, for each main category of foodgrain, there are production quantities, imports, stock variations and exports. When these are added or subtracted, a gross domestic supply quantity remains.
It is worth also noting that this gross quantity is still no more than a best assessment that is synthesised from the information provided by state governments. The first set of deductions is by way of feed, seed and waste (foodgrain that is used in animal feed, is harvested to use as seed for sowing, and which is damaged after harvest or rendered unusable because of pests and infection). Allowing for the lowest likely level of deductions, the combined deduction is about 7% for rice, 10.5% for wheat, 17% for coarse cereals, 15% for pulses, 5% for vegetables and 10% for fruits.
The available quantities are now revised further. Under a 95% of target scenario, we will have 93.5 mt of rice, 81 mt of wheat, 34.5 mt of coarse cereals and 14.5 mt of pulses. In the same way, a 95% of target scenario for vegetables is 153.5 mt and for fruits it is 72.5 mt. On the consumption side we have the households – in 2016 we will have 175 million rural and 83 million urban households.
These households will require a baseline minimum of 181 mt of cereals, 136 mt of vegetables, 45 mt of fruits and 41 mt of pulses. Under a 95% of production target scenario therefore, there will be enough cereals, enough vegetables and enough fruits. We have been falling short in pulses for several years.
But this apparent comfort is still without the second set of deductions. And these are: (1) buffer stocks of rice and wheat to be maintained, with 5-8 mt of rice during the year and 10-18 mt of wheat during the year (to fulfil the demands on the public distribution system and to fulfil the allocations for the food-based welfare programmes), and in addition the strategic reserve of 2 mt of rice and 3 mt of wheat to be maintained; (2) the use of foodgrains by the food processing and retail food industry; (3) exports of primary crops (such as rice and in particular basmati) and processed crops (vegetables and fruits); (4) the industrial use of foodgrain (including for biodiesel); (5) the diversion of cereals to alcohol distilleries.
Some amongst the second set of deductions are known – such as the withdrawals for buffer stocks and the food reserves, and the export quantities – but the others are either hidden, concealed or misreported. In a food production scenario that is less than 95% of targets (in the way that rice has already been estimated for 2015-16), the deductions from gross crop production will decrease available foodgrains, vegetables and fruits to levels that will compromise household food security, especially those households in the lower income brackets.
Recommendations – The climate variations that have led the Department of Agriculture to raise a red flag warning are no longer uncommon. The 2015 monsoon was affected by El Nino conditions, which are expected to continue into the first quarter of 2016. These changes in the pattern of the Indian summer monsoon are amplified by land use change in our districts, by deforestation, by rapid urbanisation, by inequitous water use, and by consumption behaviour. Some of these can be addressed through policy, education and incentives over the long term. What is needed immediately however are:
a) A review of the drivers of crop cultivation choice in our watersheds and agro-ecological zones so that, as far as possible, these settlements units begin the transition towards local food security in sustainable ways. This means that the income-led arguments which favour the cultivation of commercial crops for farming households must be critically re-examined – in a situation of primary crop scarcity an income buffer alone will not help these households.
b) The demands placed by export arrangements (including the export of meat, which represents fodder and feed) and by the food processing and retail food industry must be quantified and made public. Especially at the level of district administrations, the need to rationally incentivise land use towards the cultivation of food crop staples that suit agro-ecological conditions has become an urgent one. The decentralisation of planning that can make such an approach possible can take place only when hitherto hidden and concealed foodgrains use becomes public.
c) To reach self-reliance at the level of panchayat or block (tahsil, taluka), cooperative farming must be vigorously encouraged, villages must become self-reliant in the provisioning of their food staples (a consideration that must balance that of the ‘national market’), the bio-physical limits of the major food producing districts (the top 250 by quantity) have already been reached and this necessarily limits the demand urban India can exert upon rural districts, in terms not only of food quantities but also in terms of the population that must be fully engaged in foodgrains cultivation.
With two weeks of the June to September monsoon remaining in 2015, one of the end-of-season conclusions that the India Meteorological Department (IMD) has spoken of is that four out of ten districts in the country has had less rainfall than normal.
This overview is by itself alarming, but does not aid state governments and especially line ministries plan for coming months, particularly for agriculture and cultivation needs, water use, the mobilisation of resources for contingency measures, and to review the short- and medium-term objectives of development programmes. [See ‘A method for a post-carbon monsoon’ for a recent discussion.]
The detailed tabulation (done for 15 weeks) is meant to provide guidance of where this may be done immediately – in the next two to four weeks – and how this can be done in future. The districts are chosen on the basis of the size of their rural populations (calculated for 2015). Thus Purba Champaran in Bihar, Bhiwani in Haryana, Rewa in Madhya Pradesh and Viluppuram in Tamil Nadu are the districts in those states with the largest rural populations.
In this way, the effect of rainfall variability, from Week 1 (which ended on 3 June) to Week 15 (which ended on 9 September), in the districts with the largest rural populations can be analysed. Because a large rural population is also a large agricultural population, the overall seasonal impact on that district’s agricultural output can also be inferred.
The distribution of the districts is: six from Uttar Pradesh; five each from Andhra Pradesh, Bihar, Chhattisgarh, Gujarat, Haryana, Jharkhand, Karnataka, Maharashtra, Madhya Pradesh, Odisha, Punjab, Rajasthan, Tamil Nadu and West Bengal; four each from Assam, Jammu and Kashmir, and Kerala; three from Uttarakhand; two from Himachal Pradesh; one each from Arunachal Pradesh, Manipur, Meghalaya, Mizoram, Nagaland, Sikkim and Tripura.
Using the new 11-grade rainfall categorisation, a normal rainweek is one in which the rainfall is between +10% more and -10% less for that week. The overview for this group of 100 districts, only 11 have had five or more normal weeks of rain out of 15 weeks. In alarming contrast, there are 77 districts which have had three or fewer normal weeks of rain – that is, more than three-fourths of these most populous districts. Half the number (51 districts) have had two, one or no normal weeks of rain. And 22 of these districts have had only one or no normal weeks of rain.
From this group of 100 most populous (rural population) districts Gorakhpur in Uttar Pradesh and Nagaon in Assam have had the most deficit rainweeks, tallying 13, out of the 15 tabulated so far. There are ten districts which have had 12 deficit rainweeks out of 15 and they are (in decreasing order of rural population): Muzaffarpur (Bihar), Pune and Jalgaon (Maharashtra), Surguja (Chhattisgarh), Panch Mahals and Vadodara (Gujarat), Firozpur (Punjab), Thiruvananthapuram (Kerala), Hoshiarpur (Punjab) and Mewat (Haryana).