Monday, January 25, 2010

NABARD and microFinance

Time (still needed) for greater viewership - NABARD's mF initiative

Sunday, January 24, 2010

Bacillus in the Brinjal.......Yes or NO ?

The animated public debate (including the ministerial spat) on permitting Bt Brinjal has prompted me for this posting.

While its important to get a view of the major stakeholders and people with thinking hats !!. There is one Minister who is going around doin a great consultative meet….for my humble self all I can do is posting in my employers intranet to entice reactions from fellow employees and get an official measured version !!...

Readers would be aware that GEAC has given its approval for environmental release of Bt Brinjal, but about 7 state governments have opposed this. Is Bt Brinjal’s biosafety questionable?? Is it Bt per se a question ????….can we paint all results of genetic engineering as very unpredictable, questionable and with unintended hazardous consequence!! I fear, perhaps not ….while some have gone overboard blaming all that is got to do with the Bacillus …..Bt has been a near success story in Gujarat. The state has outshined the rest of the state with its hefty agri growth (close to double digits) partly driven by Bt Cotton. The Monsanto ghost ….has been kept aside with ingenuous farmers of Gujarat making their own Bt seeds for cotton which is cheaper and farmer friendly….. For me, the issue is about Bt (Food crops) ?? whether its is baigan today or bhindi tomorrow or makki day after !! ….time to care about health and keenly participate in a healthy debate….not the acrimonious, commercial thoughts only, debate.


Sunday, January 10, 2010

Leveraging existing database for UID project – a case of Gujarat

Leveraging existing database for UID project – a case of Gujarat

Providing all citizens with an identity card in three or four years is a part of the government's ambitious Unique Identity (UID) programme. For long, national identity cards have been advocated to enhance national security, prevent potential terrorist attacks and stop illegal immigration. This measure is expected to curtail frauds related to the identity of a person, enhance national security etc. Once the project is rolled out, each Indian citizen will have one unique identification number that will identify him/her. While the challenges in UID project are tremendous in terms of technology and scale of implementation, the social and cultural impact can be much larger.
The new agency / authority set up for the purpose will be responsible for maintaining the core database and laying down the procedure of issuance of smart cards, carrying the unique identification number (UID). Initially, the UID number will be assigned to all voters by building on current electoral roll data.

The UID will not only help the government track down individuals ( illegal immigrants), but will make life far easier for citizens as they will not have to submit so many documents each time they want to avail a new service — private or government. The scheme is designed to leverage intensive usage of the UID for multiple purposes. Photographs and biometric data will be added progressively to make the identification foolproof. Easy registration and information change procedures are envisaged for the benefit of the people, especially the poor, who often struggle with issues of identification, house address etc. However, it needs to be cautioned that this project is definitely no panacea to all problems, it will remove ONE major hurdle (problem of identification) if implemented successfully. The UID project is also expected to create 1, 00,000 new jobs in the country and business opportunities worth Rs 6500 crore in the first phase itself. The first phase of the project is likely to cover nine states and 4 Union territories in the country. The following paras suggest a few options that could be endeavoured to ensure smoother implementation of the UID project.

2. Where do they expect to start from?

These are some directions which the Government could plan / is planning on:

1. Using mobile number as UID. The government is planning to rework the current numbering plan along with C-DOT, and is already debating with the telecom regulator on the feasibility of offering 11- and 12-digit numbers can be offered. The only hitch being lack of transparency for the mobile number. The same logic also applies for landline telephony as well.
2. Synchronising unique ID numbers with mobile numbers, which could also serve, as a driving licence number is another option under consideration of the Government. This will entail collating data from different state Governments.
3. Another possibility is to use the databases (42 million) that are available with Employee Provident Fund, besides the Ministry could also offer the database of over 12 million of the Employee State Insurance Corporation (ESIC). The database could cover one segment ie; the salaried class in government jobs. The public Provident Fund could also an option worthy of consideration.
4. NREGS enrolments at the panchayats stated to be close to 65 million people could be option for the agency could effectively leveraged
5. Another state level database with Government is the details of the births registered with Registrars. However, these databases are often stated to be not comprehensive and reliable.
6. Similarly the village level information of land records (with talatis) and details of house tax with corporations / village offices could be data worthy of consideration.
7. The other available options could also suggest accessing Bank Deposit accounts of individuals with the banking system in India, which in many cases could be electronically stored in commercial banks and in RRBs. The database of post office savings account holders could also be accessed.
8. Another option is to leverage the Voter Identification Card, however, this is saddled with issues of many fake / non-genuine Voter IDs
9. Ration Cards and Pension cards issues by employers and Government.
10. The passports issued could be a reliable database that the UID could leverage, which is normally verified and could be digital form.
11. The PAN Cards issued could also be a reliable database that the UID could leverage; however, these are exclusive and very incomplete.
12. Gas connections and Public distribution cards (Ration cards) issued to households could be substantial and could be accessed.
13. Credit Cards issued by banks to its clients could be a sizeable database in digital form, which however, could be predominantly in the urban segment.
14. A similar analogy could be that of KCC issued to farmers by the banking system.

Once the system is in place, a whole lot of the financial and non-financial services could be converged, as listed below:

Finance related Non-financial or service related
1. Social Security
2. Pensions
3. Banking services / savings/ credit products including KCC
4. Insurance
5. Subsidy targeting – food / fertilizers etc (BPL)
6. Remittances
7. Investments – MF / shares / commodities / overseas investments/ bullion
8. Payments – online purchases
9. Payments - NREGS
10. Credit cards / Debit cards /ATM
11. Online trading 1. PAN – income and wealth tax assessment
2. Election / Voter card
3. Driving licence
4. Passport / VISA
5. PDS
6. Child health/ vaccination
7. Mobile/ telephone connections
8. Water connections
9. Electricity connections
10. Internet connections / email ID
11. Gas connections
12. Housing tax / registration
13. Admissions in courses
14. Job applications
15. Entrance / competitive Exams
16. Vehicle registration
17. Land revenue
18. land registrations
19. Death and Birth Registration
20. Asset base / wealth tax, assessments
21. Travel – overseas, ticketing
22. Migration data
23. Company / business/ enterprise registrations

3 How could the Government entice people to obtain an UID?

The biggest enticement as suggested by the Chairman of UIDA Shri Nandan Nilekani is ” enrol once and get an identity for life – this is the biggest USP”. UID being a one-shot hassle free system, you don’t need anything for getting a passport, a licence or exercising your franchise. Well all this sounds good for an urbanite, however, the rural masses and particularly the illiterate poor will may have to be enticed, the option on hand perhaps is to tempt them with what they generally look for / what they seek for …for eg kerosene (fuel), or a smokeless chula, a gas connection food, government subventions, pensions etc. One way of enticing rural masses to enrol for UID is ensure those who register (Take a UID) gets a special treatment
1. Ensuring quick release of payments due to them if they have a UID, ie; like NREGA/ pension / subsidy etc.
2. Subsidy related schemes (SGSY), make it mandatory to get an UID
3. Schemes like smokeless Chula – for registered candidates only.
4. Tree Patta / land patta to landless - only for registered candidates
5. Additional kerosene ( 1 lt) for registered candidates only.
6. Additional ration of wheat / rice ( 2 kgs ) or such commodities at less than normal rates in a PDS system(subsidised rates).
7. Fertilizers (NPK) at discounted rates – discounted coupons
8. Another option to entice could be to incentive the individual who normally could be responsible for delivery of services to a group of people in a limited geographical hinterland or a village. This could interalia involve to three major functionaries whom the government generally involve in many governmental tasks ie; 1) anganwadi worker for women and child related tasks in a village 2) post man who normally has a significant understanding of the local area and its people with the regular contacts 3) this could include the teachers in rural governmental schools, which has regular in its operations and significant enrolments.
9. Yet another option could be to entice a entire community or say a integrated unit like a village. Ie; Villages where all members are registered – gets an approach road completed in 6 months or a telephone connection or a farm pond is de-silted with government support.
10. Other option could be to ensure that, early registration of all villagers in a village fetches a quick release of funds to the panchayat from Zilla authorities without any hassles.
11. Yet another option could be to entice with non-monetary incentives- early registration of all villagers in a village fetches a merit certificate from the Tehsildar or a monetary reward like Rs 10,000/ - ( or say 50 % cost to be met by GOI for any development work initiated and completed in one year by the villagers in their village).

4 Gujarat initiatives:

Besides the option listed above, Gujarat has two unique efforts which also throw-up some lessons and also possibilities for leveraging an existing a data base that is comprehensive and reliable. The initiatives are Viz; the socio-economic survey of rural households which the government has attempted as an all inclusive data base 2) A few coastal villages (like valsad District) have initiated an effort at issuing ID cards to all villagers as an effort at Gram surekasha. While the latter was a limited initiative of select gram panachayats, to stop incursions and know your co-villagers concept, the former is a comprehensive list of all households and its socio-economic characteristics in the rural hinterlands of Gujarat state. A few details of the same is given in the following paras:

The socio-economic survey done by GoG in 2002 was initiated at the behest of the Ministry of Rural Development, Government of India, to prepare the list of all households in the rural hinterlands and categorised based on 13 socio-economic parameters. However, the Gujarat Government at its command added a three more parameters for undertaking the socio-economic survey. The parameters were:
1. Size group of operational holding of land
2. Type of house
3. Average availability of normal wear clothing (per person in pieces)
4. Food Security
5. Sanitation
6. Ownership of consumer * durables : Do you own
7. Literacy status of the highest literate adult
8. Status of the Household Labour Force
9. Means of livelihood
10. Status of children (5-14 years)
11. Type of indebtedness
12. Reason for migration from household
13. Preference of Assistance
14. Head lady a widow
15. Family lady dependent
16. Family member handicapped

Thus, the uniqueness of the initiative of GOG (Commissioner of Rural Development, Government of Gujarat) has been that a) Though the survey was to be conducted based on 13 different parameters as specified by GoI, it was further expanded to cover more crucial elements in the list ( Sl no 14-16 listed above) 2) the entire exercise has been done with the help of enumerators ( school teachers ) in a short period as a socio-economic survey and not as a BPL survey which is normally the approach adopted in other states 3) the data has been captured at the field level in a codified 1-page alpha-numerical format 4) the entire data sheets were scanned so to capture the same in soft form using Intelligent Character Recognition technology, thus obviating the issues of data re-entry errors and ensuring achieve maximum accuracy 5) the data has been uploaded on website of the department allowing people to report mistakes if any in data collection and compilation , thus ensuring transparency.

Department had a series of training programmes for the associated staff to explain about the filling up the forms to have maximum accuracy. Various parameters were also coded to have minimum errors while filling up the forms. All (68 lakh) rural households data collected at the field level were captured by scanning of field schedules and data was converted into soft form. For each parameter the households were further categorised as follows:

Sr. No. Characteristic Scores: 0 to 4 (Code)
0 (A1) 1 (B2) 2 (C3) 3 (D4) 4 (E5)
1 Size group of operational holding of land Nil Less than 1 ha. of un-irrigated land (or less than 0.5 ha. of irrigated land) 1 ha.-2 ha. of un-irrigated land (or 0.5- ha. of irrigated land) 2 ha. -5 ha. of un-irrigated land (or 1.0-2.5 ha. of irrigated land) More than 5 ha. of un-irrigated land (or 2.5-ha. of irrigated land)
2 Type of house Houseless Kutcha Semi-pucca pucca Urban type
3 Average availability of normal wear clothing (per person in pieces) Less than 2 2 or more, but less than 4 4 or more, but less than 6 6 or more, but less than 10 10 or more
4 Food Security Less than one square meal per day for major part of the year Normally, one square meal per day, but less than one square meal occasionally One square meal per day throughout the year Two square meals per day, with occasional shortage Enough food throughout the year
5 Sanitation Open defecation Group latrine with irregular water supply Group latrine with regular water supply clean group latrine with regular water supply and regular sweeper Private latrine
6 Ownership of consumer * durables : Do you own Nil Any One Two items only Any three or all items All items and/or Ownership of any one **
7 Literacy status of the highest literate adult Illiterate Upto primary (Class V) Completed secondary (Passed Class X) Graduate / Professional Diploma Post Graduate/Professional Graduate
8 Status of the Household Labour Force Bonded Labour Female & Child Labour Only adult females & no child labour Adults males only Others
9 Means of livelihood Casual labour Subsistence Cultivation Artisan Salary Others
10 Status of children (5-14 years) [any child] Not going to School@ and working Going to School@ and working Going to School@ and NOT working
11 Type of indebtedness For daily consumption purposes from informal sources For production purpose from informal sources For other purpose from informal sources Borrowing only from Institutional Agencies No indebtedness and possess assets
12 Reason for migration from household Casual work Seasonal Employment Other forms of livelihood Non-migrant Other purposes
13 Preference of Assistance Wages Employment Self Employment Training and Skill Upgradation Housing Loan / Subsidy more than Rs. one lakhs or no assistance

14 Head lady a widow

Three parameters added by the GoG
15 Family lady dependent
16 Family member handicapped

10 % of the data so collected were subjected to checks by DRDA staff to ensure its correctness. Based on the scoring patterns (the scores could vary between 0-52 for each household) the households were grouped into separate segments. Households with a scoring pattern 0-16 where classified as very poor household and those with 17-20 were classified as poor households. The entire set of data has been uploaded / stored on the website and also displayed at government offices. The data could be accessed from the webpage Another uniqueness of the data storage has been the query mode, which enables greater transparency and facilitates anyone to check the correctness of the data complied. Grievances if any addressed to ensure that the information is not static one. The state Government had envisaged to use this set of rural household data for targeting households for all its social sector programmes, be it for housing, poverty related or microFinance or any other programme of the Government.

While its unlikely that the UIDA will access the existing data even though it’s a comprehensive one, as in the instant case, however, the agency could use such comprehensive information for any crosscheck exercise that it may endeavour.

Wednesday, January 6, 2010

Drought coding : Cannot the Apex enable it ?????

1. Introduction
Farmers face floods, drought, pests, disease, and a plethora of other natural disasters. The weather is stated to be their greatest adversary, something that can never be controlled. The most prominent of all disruptive of weather patterns is drought. However, the term drought is often used rather casually; it perhaps means different things to different people. There are several kinds of droughts, such as meteorological drought, agricultural drought and hydrological drought. While a layman judges a drought by the level of human discomfort due to the lack of rain, meteorologists measure it as the negative departure from the long-period average rainfall for a region. An agriculturist, on the other hand, is least bothered about these criteria, for him, it is drought if the amount and distribution of precipitation is incapable of supporting a normal crop. In short drought is a temporary negative deviation in environmental moisture status that adversely affects living beings, the economy and ecology.

Rainfall data of the past 100 years indicates that this arid tract experiences drought of varying intensity in over 40 per cent of the years. Under these circumstances, drought preparedness ought to be a routine affair and not a contingency-driven reaction as it is at present. The possible aberrations in the monsoon are already known. These include belated starts, early withdrawals and prolonged dry spells in between. Keeping the strategies to deal with such contingencies - called a “drought code” .
The more recent drought caused largely by an inordinate delay in onset and advancement of the monsoon followed by its erratic patterns viz; regions of higher rainfall areas like NER , receiving very low rainfall and parts like Saurastra and inner deccan areas receiving very high rainfall suggests the need for region-specific strategy to mitigate such effects. However, the declaration of drought is the prerogative of State Governments and in the current year (2009) 12 state governments have declared 300 districts as drought hit . While there is often a discussion in other circles of many state governments not declaring droughts despite the rainfall deficits? The subjectivity in such announcements of droughts by state governments was particularly felt after proclamation of diesel subsidy of Rs 1000 per ha by the GOI. The recent disaster management congress suggests drought is not a temporary phenomenon, but a long lasting one that cripples and affects the rural poor the most. Drought impacts all agro-climatic regions, but more severally in arid and semiarid regions” Thus a deficiency in rainfall causes depletion of both surface and ground water levels, and adversely affects the water supply and agricultural operations.

While as a drought mitigation measure shorter-duration crops, such as pulses can be planted, fodder crops are also considered as an option when rains are delayed till mid-August. The damage to sown crops are often mitigated by increasing the fertiliser dose and gap-filling or resowing, depending upon the time of revival of the monsoon. However, more important than that is the need to evolve a “good weather code”, outlining the measures that can, and should, be taken during years of good monsoons, to conserve water for minimising the stress during the monsoon failure. Ideally, one-third of the water falling on the ground as rainfall should be stored in reservoirs, big or small, and the bulk of the rest should be guided to seep down into the vast natural underground reservoir. The watershed development approach is ideal for that purpose.

The U.S., for instance, has 900,000 farming families, while the figure is 105 million in India. The U.S. government last year set aside $18 billion just for insurance cover for the farming families, whose average farm size is 200 hectares; delivered $3.7 billion as Loan Deficiency Payment and Marketing Loan Gains; gave an emergency lending of $329 million; and provided $1.4 billion as direct loans and $2.5 billion as guaranteed loans. Compare this with India, where 78 per cent of the farm families own less than two hectares. The total subsidy given by the government for these people, who are directly affected by such calamities, is about Rs. 2,000-3,000 crore every year. So our investment in drought proofing is poor, and it is getting poorer. Thus the coping mechanism of the farm families is extremely poor and the people are severely affected during calamities.
.....Source: Frontline, Volume 17, issue 11, 2009

2. How a proper weather mapping can help farmer’s plight?Prime Minister in his Independence Day speech has rightly emphasised the need to help farmers in their hour of distress, so that they can help the country to produce as much food as possible under the prevailing meteorological conditions. He has announced that the repayment of loans taken from banks will be rescheduled. In this connection, it will be useful to find a long-term solution to the problems faced by farmers in rain-fed areas by adopting the recommendation of the National Commission on Farmers (NCF) that the repayment period for loans in drought-prone areas should be four to five years. This is particularly important, since we do not have an effective crop insurance policy for farmers in drought-prone areas.
Highlighting on the need for a long term solution due to climate change in one of the recent articles by Prof Swaminathan suggests the need for classifying agriculture land into Most seriously affected areas (MSA) and Most Favourable Areas (MFA)and action as follows :
MSA- where the monsoon irregularity has multiple adverse effects on crops, farm animals and human food, and livelihood security. Also, hydropower generation is affected, leading to energy shortage. The power shortage, in turn, makes it difficult to give a crop life-saving irrigation, wherever opportunities for this exist. There is urgent needs of saving farm animals from distress sale through Farm Animal Camps near a water source or near a groundwater sanctuary. …….The need for short duration crops like saathi maize (60 days maize), sweet potato, pulses, oilseeds, fodder crops, and other less water-requiring but high-value crops, according to scientifically prepared contingency plans. Another urgent need is the launch of “A Pond in Every Farm” movement. This can be done by permitting NREGA workers to build Jat Kunds in the farms of small and marginal farmers. The revised NREGA guidelines permit this. At least five cents in every acre should be reserved for the construction of ponds to store rainwater. Where there is adequate ground water in MSA areas, subsidised electricity and diesel should be made available on a priority basis. Energy is the key limiting factor in taking advantage of ground water.
MFA: can be identified where there is enough moisture for a good crop. A compensatory production programme can be launched in such MFA farms by taking steps to increase the productivity of the crops already sown. This can be achieved by undertaking top-dressing with urea or other needed fertilizers, including micro-nutrients, with government support. Wherever there are opportunities for launching such compensatory production programmes because of adequate rainfall, the faculty and scholars of the agricultural university in the area can be requested to move from class rooms to farmers’ fields to help ensure the proper administration of the nutrient top–dressing programme. This will help to increase crop productivity significantly.
During the next few months, detailed drought, flood, and good weather codes should be prepared for every agro-climatic zone in the country. These codes should indicate the pro-active measures such as building Seed Banks of alternative crops needed for minimising the adverse impact of rainfall abnormalities. The Good Weather Code should provide guidelines for maximising the benefits of good soil moisture. Another step urgently needed is the identification and training of two members of every panchayat – one woman, one man – as Climate Risk Managers. It is best that they are identified by the Gram Sabha.

The Climate Risk Managers can be trained in the science and art of managing uncertain rainfall patterns leading to drought or flood. They could also operate a Weather Information for All programme based on village level agro-met. stations. A mini agro-met. station can be built in every block with basic instruments to measure temperature, rainfall, wind speed, and relative humidity. The Climate Risk Managers can be trained in data collection and interpretation, so that the right decisions are taken at the right time and place. Such a technological upgrading of agricultural infrastructure will also help to attract youth in farming.

While the Meteorological Departments of various States put out detailed maps on the onset of monsoons, which could help farmers, their preparedness to commence agriculture operations, however, there are hardly any information of inter-spell duration or the distribution of rainfall during the crop period, which is critical for crop production. High rainfall spells in a short duration may cause more harm than poor rainfall. IMD study conducted have shown that a long inter-spell duration of even 20 days did not matter much in the Vidarbha region, which had heavy black soil, but central Maharashtra, which has shallow red soil, was badly affected. Thus, the information on soil's moisture-holding capacity needs to be integrated with the rainfall and temperature data in the area to serve as useful information for the farmers crop planning and crop protocol systems. As rainfall distribution, its frequency and soil texture and structure etc vary between short distances, its important that a weather coding based on these parameters is made available to farming community as a better crop planning and risk mitigation measure. Such information will do yeomen service to the farming community and could enable better grounding of the weather based insurance product in the country.

3. The need for weather coding

While, many argue that the farmer knows the weather better than anyone—it is their greatest foe and their greatest friend, but they still deserve the chance to farm on their own with better access to information. A weather coding exercise could better enable this process. However,

a) Presently, there is no long-term government policy on managing disruptive weather patterns. Most of the present approaches are adhoc and reactive. There is a firm need for a risk mitigation initiative, which could start with weather mapping and drought forecasting at all possible locations in the country especially in the rural hinterlands.

b) Over 66 per cent of our farmlands are rainfed with varying degree of rainfall patterns and distribution. While, many states depend on the southwest monsoon for its cropping initiatives, many states depend on the more unpredictable northeast monsoon for it’s cropping.

c) The existing and available data on rainfall is from IMD, which furnishes data from its 36 meteorological stations and predicts data at the district level only . The farming community has little access of this information.

d) While, different kinds of droughts exist in agriculture, soil drought, is one where there is no moisture in the soil is the most critical one. The atmospheric drought is one in which the temperature is very high. A combination of the two is the worst kind for cropping. There could be significant drop in crop production or even total failure when the drought occurs very early in a crop season, preventing sowing. There are empirical methods of assessing the impact of drought, provided the information on the same is available. However, the utility of the data of IMD to the farmers is very limited at present.

e) Droughts if predicted in advance, it could help in minimizing the disastrous consequences of it on agriculture ie; crop to be sown or standing crop. Its therefore important that the data pertaining to drought is made available to the farming community thru appropriate channels or help farmers to effectively manage drought or take measures to effectively de-risk by participating in insurance based risk mitigation system.

f) However, the crop insurance schemes of NAIS do have great difficulty in implementation and the coverage continues to be low. Further, the schemes do face high transaction costs and also issues of moral hazard.

g) The better substitute to crop insurance ie; the weather based insurance scheme however needs very specific data in a limited area (specific data in the 25 Kms radius) for its enrolment and claim settlement. Presently these companies collect the weather data on a real time basis from weather forecasters on payment of fees.

h) Increasing unpredictability of weather is another reason which facilitates the greater role for weather forecasters. Assessment made by experts in the field suggests that more farmers are availing of insurance because of frequent crop loss due to frequent changes in weather.

Thus, there is an increased need for customized forecasts for localized areas, which the Automatic Weather Stations can provide. This suggests the need for collecting weather data on a real time basis from as many locations across the country; perhaps say 4000 (rural) development bocks, commencing with blocks where agriculture is mainly rainfed.

4 Crop insurance and its coverage

Making farm insurance universal and effective has been one of the persistent challenges being faced by the financial service providers. Making crop insurance compulsory has not really served much purpose especially when the client systems cannot be convinced of the benefits of the scheme. Studies by researchers have shown that, replacing CCIS with National Agriculture Insurance Scheme has served little purpose.

As a study indicates “Government crop insurance has proved to be a failure worldwide, but India seems to have ignored both its own failure and the failure of other countries. The main flaws of the NAIS are the goal of financial viability, its mandatory nature, its failure to address adverse selection, arbitrary premiums, and the area approach” . The researcher goes on to suggest that even if India withdrew from crop insurance schemes, it could still support farmers through an income guarantee or investment in infrastructure. While this would be an alternate option in the longer term, there are other insurance options being attempted in the country, which could deserve a closer look.

As per the AIC’s data 3.56 crore farmers in the country have benefited during the 19 cropping seasons since the rabi 1999 to rabi 2008-09 ie; ever since the NAIS came into operation. The total claims paid and payable amounted to Rs 14,772.70 crore against the premium collected of Rs 4,426.48 crore. Thus the aggregate claims / premium ratio was 3.33 ie, claims preferred were 3.3 times higher than the premium received. The overall coverage of the farmers in the area has been around 9-16 % while the operation area covered did not exceed about 4 %.
Crop insurance Coverage in India
2004-05 2005-06 2006-07
Farmers covered (Lakh) 162 167 180
Acreage (lakh ha) 296 278 273
Sum assured (Rs crore) 16844 18588 21351
Premium (Rs crore) 535 554 600
Claims paid (Rs crore) 1199 1398 2245
(Source: IFMR website)

Further with high premium rates ranging between 1.5 % - 15 % p.a depending on the crop and season, insuring the crop serves as a costly endeavor for the farmer, given the fact that Government has been making efforts to reduce the cost of credit to the farmer. This high premium rates clubbed with high claims: premium ratio for crop insurance suggests the need for a perpetual subvention system from the Government for the scheme to be run continually. The available data on crop insurance coverage in the last three years indicates the following:

Thus, to cover all farmers the insurance coverage needs to expand by five times to say the least. There has been a huge mismatch between the premium collected and the claims paid in crop insurance schemes. There are also reports of large claims payouts being made in regions where the climatic conditions has been favourable for crop growth, suggestive of the issue of moral hazards and the unresolved issues in implementing the crop insurance scheme. Further, the issue of moral hazard has been a long debated one. Many studies on crop insurance have also suggested that the best way to de-risk the farmer is to make investments on the agriculture sector or government meeting a part of the insurance premium especially for all small farmers. However, the weather insurance product is considered as a better risk mitigation measure for farmers.

5 Advantages of the weather insurance
The product has several important advantages compared to most pre-existing
crop insurance schemes. Weather insurance does not suffer from the same moral hazard and adverse selection problems. A comparison has been made between the existing crop insurance and weather insurance has been made in the following table:

SI No National Agricultural Insurance Scheme (NAIS) Weather Based Crop Insurance Scheme (WBCIS)
1 Practically all risks covered(drought, excess rainfall,flood, hail, pest infestation, etc.) Parametric weather related risks like rainfall, frost, heat (temperature), humidity etc.) are covered. However, these parametric weather parameters appear to account for majority of crop losses
2 Easy-to-design if historicalyield data up to 10 years’ isavailable Technical challenges in designingweather indices and also correlatingweather indices with yield losses.Needs up to 25 years’ historicalweather data
3 High basis risk [differencebetween the yield of the Area(Block / Tehsil) and theindividual farmers] Basis risk with regard to weather could be high for rainfall and moderate for others like frost, heat, humidity etc.
4 Objectivity and transparencyis relatively less Objectivity and transparency isrelatively high
5 Quality losses are beyondconsideration Quality losses to some extent getsreflected through weather index
6 High loss assessment costs(crop cutting experiments) No loss assessment costs
7 Delays in claims settlement Faster claims settlement
8 Government’s financialliabilities are open ended, as itsupports the claims subsidy Government’s financial liabilities could be budgeted up-front and close ended, as it supports the premium subsidy
9 Highly prone to manipulation possibilities Very well isolated from manipulation possibilities
10 High relevance with crop management practices (Appropriate/correct use of good quality seeds, pesticides, fertilizers etc) No relevance with crop management practices.
(Source: NCML)

6 Findings from Research studies

Drought is said to have very serious human implications as it affects the livelihood security of a majority of the population. That is why it is important to have a pro-active policy relating to monsoon management by farmers. The majority of the population, which depends on the monsoon for livelihood, has only become more vulnerable with poor coping mechanisms. The government subsidies and other measure taken presently are short term in nature. While much has been talked about the crop insurance appear as inadequate
· Research studies undertaken in two states of India; states that 89% of farmers reported that variation in rainfall was the most important risk they faced. A poor monsoon for example —drought or flood—may cause farmers’ crops to fail. To deal with such crop failures, rural households may sell productive assets, borrow from moneylenders who charge high rates of interest, or choose to work more hours at the neglect of other pursuits.

· A poor monsoon could make it difficult for agricultural labourers to find work and earn an income.

· Weather insurance represents a novel approach in formal risk management for farmers.
· Even in weather insurance maintaining farmer interest in the product is difficult as a case in Gujarat, has shown that three consecutive years, rainfall stayed within ‘normal’ levels and clients did not receive any payouts.
· Insurance demand is highly sensitive to price. Thus, minimizing transaction costs, and boosting competition amongst suppliers of insurance, leading to lower premia, would significantly boost takeup.

· Evidence suggests that liquidity constraints are an important barrier to household risk management. One design change that would potentially
help to ameliorate these credit constraints would be to provide the insurance contract alongside a loan

7 Some issues in weather forecasting

· India Meteorological Department does predict that monsoons and its variance from normal by taking inputs from all sources, thus in qualitative terms the forecast is said to be near accurate. But, the dynamical models do not give reasonably accurate forecasts at all times and for all regions.

· At present the forecast models predict weather for areas measuring 50 to 100 sq km (mesoscale models), perhaps the reason why IMD gives region-wise forecasts. IMD is still in the process of aiming for district-level forecasts in the next five to seven years. However, for farmers to benefit, the forecasts will have to be at the village level. For insurance agencies of WBCIS, data of a 25 Km radius is sought for its implementation, most of which is handled by private players like NCMSL which charges for the data being collected.

· IMD is understood to have earmarked over Rs 70 crore for activities related to climate change. The money is expected to be used for: (a) developing a climate change research centre to study the behaviour of weather (including monsoons) in different climate conditions and if weather predictability will change (b) introducing high speed computing for climate change predictions and (c) studying cloud-aerosol interaction and how it is influencing warming of the globe. Thus, the availability of microscale data for use by agrarian community is yet to be met.

8 How NABARD can help?

It’s often stated that agriculture is not just a food producing machine but the backbone of the livelihood of 60 per cent of Indians. The extensive drought spotlights a situation of mass rural deprivation. The GOI is understood to be setting up a Crisis Management Committee not only to look into the immediate short-term solutions, but will also develop a medium- and long-term plan that can enable the challenges of drought, flood, high temperature, and sea level rise, which in future will be the recurrent consequences of global warming and climate change.
NABARD as an apex bank in the rural and agri-development sector could take a pro-active measure besides its efforts at credit enhancement and other farmer supportive ventures. While many of its supportive measures are ventured through intermediaries like bankers or NGOs for eg like farmer education through FCs, watershed development, grain banks etc have yielded tangible results, it could consider taking up supportive measure more directly by establishing “weather stations” through a subsidiary “ NABARD crop weather watch”. This could enable or facilitate

1. Greater farmer participation in weather based crop insurance (WBCIS) product.

2. With the advantages in the WBCIS like better objectivity, transparency and efficiency in insurance settlement compared to the present NAIS, it could entice a greater farmer participation in WBCIS. This could in the long run enable reduction in insurance premium. While the cost of credit for crop loaning has been reduced substantially ( 7 % pa), insurance premium at 2-10 % serves as a deterrent. This could be countered through better farmer enrolments, acceptability and consequent reduction in insurance premium.

3. Enable drought / weather coding systems by farmers. As these “weather stations” would enable capturing critical weather forecast data and enable area-specific farming advisories. There are generally three scales at which climate is described and these are related to differences in the scales of area (or surface) and time viz; macro, meso and micro. This approach would enable the capturing localised information (micro) for the farming community, permitting better weather-sensitive farming for better productivity.

4. Timely dissemination of farm advisories up to the village-level and farmers. This could be possible with tie up with extension machinery of the State Government, State Agricultural University, Krishi Vigyan Kendras (KVKs), Agriculture Technology Management Agencies (ATMA) and also the electronic and print media could be used. This approach could interalia involve training farmers to conserve and manage water, develop contingency plans to suit different rainfall patterns and work out a compensatory production or water deficit cropping plans.

5. Increasing unpredictability of weather is another reason which facilitates the greater role for weather forecasters. Assessment made by experts in the field suggests that more farmers are availing of insurance because of frequent crop loss due to frequent changes in weather. There is an increased need for customized forecasts for localized areas, which the AWS can provide

6. Tie up of NREGS with drought proofing activities in chronically drought hit districts or undertaking drought preparatory works, especially when low rainfall patterns and distribution is predicted.

Needless to mention that such a proactive investment on the part of NABARD could get tremendous mileage and acceptability among all related stakeholders including Government. While gaining a reputational boost may not be a priority for NABARD itself, it would increase its visibility further as a serious player in addressing concerns of farmers. While as of now the concerns are addressed through credit provision or development actions and farmer education efforts which are performed through other partners, but, this effort of NABARD would seen as a direct contributor for enabling risk mitigation for the hapless agrarian community.

9 Some features of “NABARD Crop weather Watch” (NCW)

9.1 Organisation structure

There area various options for establishing the NCW. It could be established as a “Not for Profit “subsidiary of NABARD. With appropriate approvals from CBDT, contributions to it by NABARD could also be exempt from income tax (u/s 36 for the weather forecasting infrastructure being created). If the “NABARD Crop weather Watch” is registered as a Trust / an education society with ground level scientific research and tracking unit for farmers it could be possible for it to seek exemptions under Sec 35 of the IT Act. It could also set up a no-profit Company including registering its Memorandum and Articles of Association with the Registrar of Companies of the state, followed by giving the details of the Articles of Association for achieving its specified objects and purposes, paying registration fees etc.

9.2 What it will do?

The subsidiary could establish remote “Automatic weather stations” (AWS) across the country. Track and monitor the weather data on a daily basis. The reporting will be restricted to minimal factors like temperature (Min and Max), wind velocity, relative humidity, rainfall etc. With appropriate alliance with support institutions and access to past data, it could use the available information to facilitate weather forecasting on a weekly basis. Tie ups with agriculture research stations/ Universities / KVKs etc to facilitate development of crop advisories in Most Severe Area (MSA) of areas were drought has been forecasted.

Some facts about Automatic weather stations (AWS)
The automatic weather station consists of 5 sensors and tipping bucket rain guage. The sensors measure pressure, temperature, humidity, wind speed and wind direction. Except the pressure sensor and the rain gauge, the other sensors are mounted on a 3-m tower. Temperature compensated piezo-resistive pressure sensor is used to measure the pressure with a resolution of 0.1 mb. For humidity measurement, a thin film capacitance sensor is used which provides an accuracy of ± 3%. RTD type sensor is used to measure temperature with a resolution of 0.1o C. 3-cup rotor type sensor is used to measure wind speed with an accuracy of ± 1%. Potentiometer type sensor is used to measure the wind direction with a resolution of < 1o. A tipping bucket rain gauge provides rain rate information with a resolution of 0.5 mm . AWS is fully computerized; digital and self contained power source system, fitted with Data Logger and battery charging systems (with or without solar panel or rechargeable, maintenance free batteries). AWS would have its sensors mounted on a standalone tripod stand system, with tamper-proofed mechanisms.

The setting up of AWS could be done in a phased manner and over a period cover most of the development blocks in the country. An rough estimate suggests that such investments would be needed for about 5000 development blocks in the country, leaving aside very low agri-intensity blocks with larger forest cover, poor land capability classification, very saline – alkaline areas, desert and alpine areas. Given the spread areas needing commencing from preparation of the list of blocks where Automatic Weather Stations

9.3 AWS : Coverage
The establishment of weather stations could be done in a phased manner, starting with about 1000 in the first year. The AWS will be established at the block level, in order to capture the micro-level and location specific weather patterns, which has real time use for the local farming community.

Some facts about AWS coverage
NO Particulars Unit
1 India Geographical area Sq Kms 3287263
2 No. Of districts Nos 611
3 No. of Blocks Nos 5564
4 Total cultivated area Sq Kms 1428190

5 Average Area / Block Sq Kms 590
6 Average Cultivated area /Block Sq Kms 257

7 Radius likely to be covered by AWS Kms 10-12
8 Area covered by one AWS (estimate) Sq Kms 315-450

Presuming from the above computations, that an AWS can cover a Block , the AWS could be set up in phased manner based on certain defined criteria viz;

Ø Arid or Semiarid region with annual rainfall less than 1250 mm per annum
Ø Areas / Blocks which have coverage of commercial crops will have to be taken on a priority basis
Ø Areas having cropping intensity of around 80 %

The ultimate aim will be to cover all agriculturally important blocks with an AWS over a period of 3-5 years. Perhaps, only development blocks with limited agriculture ( less than 50 % cropping intensity ), forest cover, uncultivable lands, wastelands and non arable lands etc will have to be left out of the coverage of the Weather subsidiary.

9.4 AWS :Location

The AWS will have to be located in rural hinterlands to better capture the micro-climatic data prevailing in the area. As AWS will be located as stand alone basis, it’s important that it is set up in areas which are safe and free of theft. Such pilferage - proof sites which could be ideal for the stand-alone AWS could be on roof –tops of Government offices viz; Post offices, KVKs, Agri Departments, panchayat office, or PACS etc. While the AWS receivers and sensor will be tamper proof, it’s important that the instrument is located initially in such locations to ensure that there is no theft, till local people realise the importance of such unit.

While the data is expected to be collected using remote sensing mechanisms, it is ideal that as a standby system for transmitting the automatic recorded data in local servers and messaged through email or computer based systems. Therefore, positioning these AWS in government offices like Agri Department or Post offices would be a thoughtful measure.

9.5 Staff

The staff requirements for such an initiative would be limited at the central office where the data collation is being done, while it would be need at least 2 staff at each of its state HQ, with support engineers for every state. Each support engineer could cover close to 100 AWS ie; close to 10 districts.

9.6 Data transmission & revenues

The data collected on a real time basis using the AWS will be transmitted to the state level / control units for further processing and dissemination. While it’s expected that a part of the information could be sold to potential buyers like corporates involved in agriculture inputs / machinery etc, insurance companies and other institutes in need of the same. This is expected to generate adequate revenue to meet the operating costs of such units over a period of time. It is expected that weather patterns / forecasts and crop advisories could be shared with “Active Farmer Clubs”. This should serve as a mechanism for enticement of greater membership for Farmers Clubs and could facilitate the process of keeping these Farmer Clubs active. It is expected that a over a period of time, nominal charges could be levied from farmer clubs to ensure adequate cost cover. This information could be reached out to farmer clubs volunteers through appropriate tie ups with mobile companies – which could SMS information on weather patterns and forecasts.

As a promotional and (image) development initiative of NABARD, the NCW could consider tie up with mass media (TV, Radio) , community radio and other channels to provide weather forecasts and crop advisories to farmers in MSA – Most seriously affected areas on a weekly basis.

Met department is not the only one predicting weather in India. Forecasts are now sold by businesses to businesses
…….the weather mart by Bhatta & Rangarajan( 2009)

9.7 How is the data now being made available?

Over the past few years a no. of companies in India have entered the business of predicting and recording the weather every day, or every hour. It depends on how frequently the client wants the data. These companies cater to the specialized needs of increasingly competitive sectors like power companies, weather turbines etc.
Companies like National Collateral Management Services Limited (NCMSL) specialize in setting up automatic weather stations. The company was launched in 2004 with the support of several banks like HDFC, Bank of India and Canara Bank; IFFCO and the National Commodities and Derivatives Exchange to provide farmers with warehousing and certificate of crop quality so that they can get low-interest credit from banks. In 2004, weather-based crop insurance was introduced in India. The next year NCMSL diversified and started setting up automatic weather stations. The company has set up 400 weather stations across 16 states in India. It charges between Rs 1,000 and Rs 10,000 per month for the data. For data to be supplied at frequent intervals , the price is the highest: Rs 10,000 per month.
As per the information given by the NCMSL, each automatic weather station costs between Rs 1 lakh and Rs 1.5 lakh, depending on the type of data required. Another entity INGEN, which is also a Weather Risk Management and Services provider, prefers to build its own stations, which is stated to cost only Rs 25,000 each.
ICICI Bank’s weather-based crop insurance scheme works around weather stations. The required weather data like temperature, rainfall, wind speed and wind direction are bought from IMD and private companies like NCMSL and INGEN.
However , the growing demand for such data suggests the increased need for customized forecasts for localized areas, which the AWS can provide. A comparison of various aspects of weather forecasting data provided by IMD and Private Weather forecasters indicates the following
A comparison
IMD Private forecasters
Ground observation stations 525 1,200
Weather balloons 39 None
Doppler radars (for studying clouds and wind) 5 None
Microwave sounders (for temperature, humidity) None, but planned None
Satellite data Get in real time Get at intervals
Historical data Of 100 years No data
Research support Can approach government institutes No such system
Forecast models Statistical and dynamical models Dynamical models
Packaging Poor Good
Customized service Do not provide Available
Information update Observations every hour. Forecasts thrice a day for the next day Observations every 10 minutes. Forecasts every 15 minutes for next day
Source : The weather mart ( Bhatta and Rangarajan, 2009)

While IMD continues to have more advanced technology, research support for weather mapping and forecasting, still there is reliance on private forecasters for whole gamut of services. This has resulted in number of private weather forecasters especially meeting the requirements of industrial units and power generation units, windmills and power supply and distribution companies. The forecast data fees collected by the private weather providers range between Rs 14 lakh - Rs 18 lakh per year; while the current weather data is charged anywhere between Rs 12,000 - Rs 1,20,000 per year. It is also understood that Daily forecasts requirements for power distribution companies though also has a penalty clause. (Penalty for each inaccurate forecast beyond a certain percentage is Rs 2-3. There are 400 such forecasts per day.)

A compilation made by the some researchers have shown that while the weather data availability is cheaper , there is limited agencies serving the cause of agriculture compared to other industrial uses. The reports also mention of most of the farmers using their traditional knowledge for predicting weather and climatic changes.
Tribals read weather clues in birds, insects, wind and sky
· “If the nest is in the centre of the tree, close to the trunk, it means heavy rains. But if birds build their nests on the tips of the branches, it means scanty rains.”
· Tribal-dominated eastern part of Vidarbha farmers : The most important sign is the perti wha bird. “It is a small brown bird with blue streaks,” explained Dhole. “It must be a migratory because it is seen in this region only just before the monsoons.” The call of the bird, interpreted by villagers as perti wha, meaning ‘let the sowing begin’ in Marathi, is believed to be a sign that rains are on their way.
· Red velvet mite, a small fuzzy insect known as miragya kitak in the area, that shows up in the soil after the first rains, is a sign of more rains. In early July the insect was seen crawling in the farms there.
………………………………………..Natures forecasters, Pallavi, A (2009)

The available information on the subject does mention of the scope for commencing such facilities for the farming communities. NABARD could consider commencing a weather subsidiary “ NCW “ as its “ Climate Change” initiative.

9.8 Proposed Phasing for the project

Presuming that all agriculturally active developments blocks are covered with an AWS, it is expected that about 4000 odd AWS will have to be set up in a phased manner. As discussed earlier, the priority for selection for AWS will have to be made based the criteria already spelt out at Sl No : 9.3 above The coverage could be phased out as following :

Particulars Year 1 Year -2 Year-3 Year-4 Total
No. of AWS 1000 1000 1000 1000 4000
Coverage of states 10 15 20 23 23
Likely capital expenditure for AWS 2000 Lakhs 2000 Lakhs 2000 Lakhs 2000 Lakhs 8000 Lakhs

Diagrammatic representation of what can be done

( sorry for this distorted picture : i did the best possible with my limited knowledge of blogging)

9.9 Project outlay and benefits

The country has around 5546 development blocks spread across 611 Districts, does provide ample opportunities for weather data collection and forecasting. Even with a laissez-faire coverage; a maximum of about 4000 blocks would need AWS coverage for weather tracking- these blocks would be agriculturally active with the agrarian community dependent on farming for their livelihood. Thus, the project is expected to incur a capital expenditure of about Rs 80 crore to set up 4000 AWS across various development blocks. The selection and prioritisation for setting up AWS will have to be guided by norms / criteria stated in the earlier paras.
(Rs in lakh)
Particulars Year 1 Year -2 Year-3 Year-4 Total
No. of AWS 1000 1000 1000 1000 4000
Capital cost
Likely capital expenditure for AWS @ Rs 2 lakh / AWS. Includes installation cost and training needs. 2000 2000 2000 2000 8000
Other capital Exp – office related 40 40 40 40 160
Operating Expenses
Service Engineers: 100 AWS /Engineer 20 40 60 80 200
AMC others 0.5 % 10 20 30 40 100
Staff cost / Data collection, analysis & dissemination 200 300 400 460 1360
Insurance @ 200 AWS 20 30 40 60 150
TOTAL Expenses 2290 2430 2570 2680 9970
Weather Reports @ Rs 15000 pa /AWS 112.5 225 337.5 600 1275
Crop advisory & crop alerts / weekly basis @ 50,000 pa for 500 centres 150 200 250 250 850
SMS farming alerts 5 15 20 35 75

TOTAL Revenue Expected 267.5 440 607.5 885 2200
Operational surplus 17.5 50 77.5 245 390

With minimal staffing, AWS being serviced by service engineers and office space being accessed or met through NABARD, it is expected that the overheads are nominal. With appropriate tie-ups with mobile service providers (on revenue sharing basis) and agri universities and research stations proper (localised) crop advisories could be reached to farmers through mass media, community radios/ SMS . The capital investment in “Weather Subsidiary” is expected to serve as a revenue model atleast to cover its recurring costs by selling information on weather data (on real time basis) to insurance companies, commodity exchanges, corporate in the agri related sector or others in need of it (kindly see pictorial presentation in the Annexure 2 ).

9.10 Sensitivity analysis

a) With 10 % in crease in operating expenses (Rs in lakh)

Particulars Year 1 Year -2 Year-3 Year-4 Total
10 % increase in Operational Expenses 275 429 583 704 1991
TOTAL Revenue Expected 267.5 440 607.5 885 2200
Operation Def/ surpluses -7.5 11 24.5 181 209

b) With 20 % reduction in revenues (Rs in lakh)
Particulars Year 1 Year -2 Year-3 Year-4 Total
Operating Expenses 250 390 530 640 1810
20 % Revenue reduction Expected 214 352 486 708 1760
Operational deficits -36 -38 -44 68 -50

10 Project Risk Factors

Likely risks Ways to overcome
1 Technology failures in AWS · AWS to be purchased from established and certified AWS manufacturers · Service engineers to be contracted to on-site help · Arrangements for proper maintenance and calibration of sensors to be ensured by engineers and AMC to be ensured from AWS provider · Back up arrangements for data transmission through govt offices, where AWS located
2 Theft or tampering of standalone AWS · AWS will be located / positioned in reliable locations like Govt offices, minimising the likelihood of theft or pilferage · Insurance of AWS to be ensured · Tamper-proof AWS – available in the market would be purchased for installation. Data is normally recorded on a real time basis in the server – any tampering done can be detected.
3 Inability to sell weather data or information on forecast and crop advisory – dip in projected revenues · There could be a likely dip in the projected revenues from the subsidiary as indicated in the sensitivity analysis. · The information being furnished initially at free of cost for farmer clubs, if proven useful, could generate interest among the farmers for such regular inputs in the subject matter · NABARD to assist to bridge operational deficits in the start-up years
4 Cost overruns – due to delay in implementation · The project approvals could be made upfront , drawing appropriate PERT charts for project implementation.· Concurrence of other support institutions like Agri Universities / research centres / mobile service providers and offices where AWS will be needs to be pre-planned and taken without delays.
5 Technology service provider- fails · Promoter organization & subsidiary advocates mitigating technical and process related risks while selecting low cost supplier. · On-site services and AMC to be ensured for the initial 3 years· Performance guarantees to be taken to cover 25 % of the cost of AWS being installed for 1 year.
6 Forecasting errors and weather modelling errors · Only certified and quality sensors will be embedded in AWS· Regular calibration and maintenance of AWS to be ensured · Besides the service engineers, 1-2 qualified staff (in meteorological science ) to be recruited for the purpose · Adequate capacity building programme to be planned to cover this. · Tie –ups with research institutions to be ensured

11 Expected Indirect Benefits
The real benefit of the project is expected through cost saving. Though these savings are not expected to accrue to the investor / promoter in the instant case, it is expected to directly accrue to farmers and Government. Farmers are likely to derive the benefit by switchover to WBCIS scheme which would entail lesser premium payments by farmers and easier / quicker compliance and claim settlements. Further, farmer enticements into WBCIS would enable them with better risk mitigation mechanisms as well. The GOI is expected to a superior beneficiary with limited payouts for crop insurance settlements. Presently, only 10 % of the farmers and about 20 % of the cropped area (Annexure 1 for details) is covered under the NAIS, creating a proper weather mapping / tracking insurance could entice more sale of WBCIS and better coverage of farmers under the scheme.

Ø A 20 % switchover to WBIC could reduce premium payable by farmers by close to Rs 100 crore
Ø As studies have indicated that lower insurance premium can result in better off take in insurance; therefore a 5 % increased coverage of farmers under WBCIS would entail better risk coverage and mitigation mechanisms in close to 10 million farmers.
Ø The GoI’s present (2008-09) insurance payouts is indicated as Rs 2,200 crore , a wilful switchover by farmers to a better administered and cost effective WBICS could reduce payouts by close to 10 % per year (Rs 220 crore). However, for this to be achieved, it would entail proper marketing of the WBCIS, capacity building of farmers and making the insurance product accessible to willing farmers.

Annexure 1
Crop Insurance Coverage (NAIS) season wise since inception
Season No. of Farmers Covered Area (In Hec) Sum Insured (Rs. Crore) Premium (Rs.Crore) Claims Reported (Rs Crore)
Rabi 1999-00 579940 780569.36 35640.71 542.48 769.26
Kharif 2000 8409374 13219828.68 690338.38 20673.55 122248.15
Rabi 2000-01 2091733 3111423.25 160268.46 2778.76 5948.63
Kharif 2001 8696587 12887710.38 750246.11 26161.82 49353.55
Rabi 2001-02 1955431 3145872.65 149751.11 3014.79 6465.80
Kharif 2002 9768711 15532348.53 943169.37 32546.68 182431.26
Rabi 2002-03 2326811 4037824.35 183754.52 3850.43 18854.83
Kharif 2003 7970830 12355513.83 811412.55 28333.19 65202.67
Rabi 2003-04 4421287 6468662.75 304949.21 6405.87 49692.25
Kharif 2004 12687046 24273241.97 1317049.22 45893.97 103787.25
Rabi 2004-05 3531045 5343243.62 377420.53 7584.93 16058.60
Kharif 2005 12674080 20530776.70 1351725.43 44986.19 105976.06
Rabi 2005-06 4048524 7218420.95 507166.12 10482.40 33830.20
Kharif 2006 12934035 19674159.96 1475911.74 46727.43 126800.13
Rabi 2006-
07@ 4967280 7758975.31 615358.12 14209.12 549.29
Total 97073461 156211397.40 9718111.85 294271.77 985692.44
Source: Credit Division, Ministry of Agriculture, New Delhi
Annexure 2 A typical weather advisory

Bharuch District
Regional Cotton Research StationNavsari Agricultural UniversityMaktampur, Bharuch – 392 012
Email : rcrs@rediffmail. com Phone: ( 02642 ) 245253
Agromet Advisory Service Bulletin for the District Bharuch
(Period 18/11/09 to 22/11/09)
(Issued jointly by RCRS, Maktampur, Bharuch Navsari Agricultural University and India Meteorological Department, Ahmedabad)
Significant past weather for the preceding week (13/11/09 to 17/11/09)
Rainfall(mm): 0.0
Maximum temperature (0 C) 22.8-33.2
Minimum temperature (0 C) 19.2-20.6
Maximum RH (%) 73-96
Minimum RH (%) 54-59
Wind speed (kmph) -

Weather forecast until 0830 hrs of 22.11.09
Parameters DAY-1 DAY-2 DAY-3 DAY-4 DAY-5
Date 18/11 19/11 20/11 21/11 22/11
Rainfall(mm) 0 0 0 0 0
Max temp trend (0 c) 32 32 32 32 33
Min temp trend (0 c) 20 20 21 19 19
Cloud condition Clear sky Mainly clear sky Clear sky Clear sky Clear sky
Max relative humidity (%) 72 61 59 56 63
Min relative humidity (%) 34 40 35 31 33
Wind speed (kmph) 009 007 009 009 011
Wind direction Easterly Easterly Easterly Easterly Easterly

Agrometeorological Advisories
Pigeon Pea In pigeon pea crop Endosulphan 35 EC @20ml or Monocrotophos 36 SL 20ml/10 litre water should be sprayed for effective control of Heliothis and pod fly at the stage of initiation of flowering and 50% of pod formation.
cotton Monocrotophos 30SL 25ml or Methy-o-Dimeton 25EC 10ml with 10gm detergent powder in 10liter of water should be sprayed in case of Mealybug infestation in cotton crop.
Banana To apply urea @ 75 gm per plant and to make ring around plant for irrigation.

Different users have different requirements:
· Some AWS are installed for Short-term projects (e.g. animal health emergency monitoring or near wild fires), some are installed for long-term projects (e.g. studying climate change)
· Some AWS are required to provide data in real-time (e.g. for irrigation), some provide delayed reports (e.g. for climate monitoring)
· Some AWS are required to perform in all weather (e.g. including cyclone forecasting), some do not (e.g. crop disease monitoring).
One common set of conditions for all the above users is that the data must be representative of the area and time period under investigation, and that the data must continually meet the accuracy required. In addition, the data collection and storage systems must be cost effective and must also be considered before AWS purchase.
The issues of representativeness, accuracy, collection, and storage may be conveniently broken down to the following topics:
· siting
· sensors
· algorithms
· maintenance
· documentation
· data formats and communications
· archiving and retrieval
· cost
These issues are discussed in more detail in the following sections.
Spatial Representativeness
The AWS should be sited so the variables measured are representative of the area of interest. Subtle variations in exposure may mean that the data are not representative.
Three examples will suffice:
· rainfall collection efficiency varies with height, due to high wind turbulence effects in select areas. (e.g. rain measured at 1m above ground level is only 97% of rain measured at 300mm
· temperatures measured over a bitumen surface are significantly different to those measured over a grass surface
· wind speed measured at 3m is significantly less than wind speed measured at 10m (the wind directions are also different)
To ensure consistency between sites, an overseas Governments set of standards for the physical siting of the instruments, Bureau Specification 2013, has been developed using the World Meteorological Organization's (WMO) guidelines.
Temporal Representativeness
In addition to difficulties with the correct exposure of instruments, thought has to be given to changes in the long-term exposure of the site. Buildings in close proximity to the instrument enclosure will result in the area of representativeness being reduced.
(For example, in Australia when the instrument enclosure at Sydney was installed in 1788, the instruments were representative of a relatively wide area around Sydney. With subsequent construction of high-rise buildings and freeways, climatic and meteorological conditions only 50m from the site are now significantly different to those at the site).
It is important that the station be inspected regularly and any changes in the siting are properly documented.
The sensors used on an AWS are the heart and soul of the system. Therefore a great deal of care should be taken when choosing sensors appropriate to the user's requirements.
The Bureau's standard AWSs use sensors to monitor temeprature, humidity, wind speed and direction, pressure and rainfall. Various advanced sensors are available for specialised applications. These sensors can monitor cloud height (ceilometer), visibility, present weather, thunderstorms, soil temperature (at a range of depths) and terrestrial temperature.
The quality of the final data received by the researcher or farmer can only be as good as the quality of the sensors used. No post analysis of the data can improve the accuracy or reliability of the information obtained.
Many AWS manufacturers use sensors which have poor accuracy, and whose calibration may drift significantly over a short time. Some sensors, particularily cheap ones, are also prone to premature failure.
The manufacturer's sensor specifications should be read very carefully as they can be misleading in some situations and manufacturer's claims can often not be replicated in the laboratory. For example, a manufacturer may quote the response time for a humidity sensing element but not the combined response time of the sensing element, electronics and filter which can be orders of magnitude longer; also, the manufacturer may quote an accuracy for a device such as a pressure sensor but give no indication as to confidence limits of the specification. These omissions can make a large difference as to the suitability of the device.
There are a number of fundamental characteristics, which make up the accuracy and precision of a sensor.
· Resolution - the smallest change the device can detect (this is not the same as the accuracy of the device).
· Repeatability - the ability of the sensor to measure a parameter more than once and produce the same result in identical circumstances.
· Response time - normally defined as the time the sensor takes to measure 63% of the change.
· Drift - the stability of the sensor's calibration with time.
· Hysteresis - the ability of the sensor to produce the same measurement whether the phenomenon is increasing or decreasing.
· Linearity - the deviation of the sensor from ideal straight line behaviour.
All of these factors go into defining the accuracy and precision of a sensor but some are more important in particular situations than others. For example, for monitoring climatic temperature changes a significant amount of data is collected over a long period therefore a sensor is required which has very little drift. However if you want to measure short term wind gusts then the repeatability of the device and the response time become more important.
Another factor to consider is the robustness of the device. As a general rule, these devices are installed in harsh environments. This requires the sensors to be well designed and constructed, have strong waterproof housings for the electronics and be able the withstand extremes of climate variability. It is counterproductive to install a lightweight wind vane that will break the first time a sparrow sits on it or to use a sensing device which is designed for laboratory use (e.g. many humidity probes) in a dusty environment. Frequent replacement of lightweight or unreliable instruments can end up costing more than their more costly counterparts. The swapping of sensors can also have a significant effect on the quality of data, frequently introducing discontinuities into a data series.
The usefulness of the data obtained from a sensor is heavily dependent on the calibration of the sensor. For data to be comparable with other sites and networks, the calibration of sensors needs to be traceable back to common standards. This is often difficult to establish, particularly with cheaper sensors, but is of equal importance regardless of the quality of the sensor.
The easiest way to ensure that the calibration is reliable is to buy sensors from a certified supplier or to have the purchased sensor independently calibrated by a certified laboratory. The other way is to spend some time establishing with the manufacturer the traceability of the standards used by the company. One must not assume a company certified to calibrate rain gauges is certified to calibrate temperature probes as well.
Integral to the sensor and its calibration is sensor maintenance. There is no sensor designed which does not need to be cleaned and checked to verify its calibration. It is important that a maintenance program periodically reassesses the calibration of all sensors, otherwise the data quality will degrade.
The algorithms used to derive meteorological variables should be meaningful, documented, and comparable between networks.
For example, the maximum temperature derived from one second readings can be quite different to a maximum temperature derived from hourly readings, wind gusts based on one second readings will be significantly greater than gusts based on three second readings, and scalar averaging of wind direction generally produces meaningless results.
Documenting the algorithms used, and all changes to those algorithms, is necessary for future users of the data.
It should be noted that many AWS manufacturers are unaware of the subtleties involved with the algorithms and with the meaning of the meteorological variables derived.
Bureau Specification A2669 details the algorithms used in the Bureau's AWSs. These algorithms are compatible with those recommended by the WMO, and as used by other National Meteorological Services.
The Bureau's AWS Co-ordinator, the Instrument Engineering Section and the Regional Instrument Centre are available to provide advice on processing algorithms.
AWS should be chosen for their ease of maintenance.
Maintenance should be able to be performed on an AWS without affecting the climatological record. For example, the temperature and humidity sensors should be able to be disabled before the instrument shelter is washed.
Many of the cheaper AWS cannot be adjusted in the field and need to be returned to the manufacturer for periodic calibration. In addition, many of these AWS lack robustness and require frequent maintenance visits to replace electronics and/or sensors.
It is important to consider the lifetime costs of an AWS rather than simply the initial cost. Generally, the lower the initial cost, the higher the ongoing cost to maintain acceptable data. In the end, this may result in either a higher total cost or long periods with no useful data.
The Bureau's Engineering Maintenance Section (and the Regional Engineering Services Sections in each State) can provide advice regarding the inspection and maintenance of AWS.
One area of observational networks which is frequently overlooked is proper ongoing documentation of equipment and siting (metadata).
Many station-years of data have been rendered useless for climate-related research due to lack of metadata showing changes in the station's immediate surroundings or instrumentation.
The initial siting of the AWS should be documented with maps and photographs. In addition, all inspection and maintenance visits should be fully documented to record any changes in representativeness and changes or errors detected in the instrumentation.
The Field Operations Group can provide information concerning inspection procedures. Most of the station metadata is stored in SitesDb, a comprehensive database of information concerning sites, systems, equipment and history for 12 000 sites around the country and offshore. The Networks Operations Group is able to provide information on these metadata.
Output Format
Careful thought must be given to the output data format. Ideally, the format used should be:
· Flexible - so new sensors can be added without having to re-process all the stations records into the new format
· Simple - such that only simple programming is required to decode the data
· Preferably human-readable without reformatting - to assist in the quality monitoring of the data
· Independent of AWS manufacturer - to allow data to be easily exchanged between agencies and to encourage cost competitiveness between manufacturers
· Unambiguous - the use of features such as check-sums minimise the possibility of data corruption
The use of standard data formats permits easy exchange of data between agencies and for their processing with a minimum of reformatting.
Most AWS manufacturers use their own proprietary data formats. Their use reduces the user's ability to exchange data to/from other agencies and reduces the AWS owner's flexibility to add AWS of another manufacturer.
The Bureau of Meteorology AWS generate five standard data formats. They are the one second format (for maintenance and real-time read-outs), one minute format (data logging, display), ten minute format (data logging), thirty minute format (forecasting), and three hourly format (international exchange and archiving).
Bureau Specification A2669 details the five data formats. These data formats are compatible with WMO standards and are used by a number of other National Meteorological Services. The Bureau's Networks & Codes Unit can provide advice on data formatting protocols.
The communications between the AWS and the collection agency should be:
· Reliable
· Inexpensive
· Follow standard protocols
AWSs report observations by a variety of formats, including telephone lines, radio modems, mobile phone networks and satellite networks. Consideration must be given to the frequency of messages, cost (satellite telephone can be expensive) and availability of services.
Bureau Specification A2670 details the communication protocols used by the Bureau's AWS. This specification includes the command set whereby the user can remotely configure the AWS. The Bureau's Instrument Engineering and Communications Engineering Sections can provide advice on AWS communications protocols.
The archival and retrieval of AWS data must be considered.
Apart from, possibly, short term projects AWS data should be kept permanently. This will require balancing the need to store high temporal resolution data against the large volumes generated.
When deciding on a data storage system, consideration should be given to the ease of quality control and retrieval of the data. This applies as equally to data stored on hard copy as to data held in electronic form.
The Bureau of Meteorology's National Climate Centre maintains the Bureau's data archives and, under certain conditions, stores data from other agencies. Before any data can be accepted into the Bureau's climatological database, the foregoing issues must be addressed.
The Bureau's Data Management Section (and the Climate and Consultancy Services Sections in each State) can provide advice regarding the requirements for data to be archived in the Bureau's climatological database. Generally, the AWS installation and operation should follow the procedures outlined in this document.
No definitive AWS costs can be given as each user has different requirements, as noted above. Each AWS purchase needs to be considered in the context of these requirements.

Works at NABARD for poor HH / was Research Affiliate at CDS, Tvm / was Visiting Faculty on microFinance for MBA students NMIMS, Mumbai.