Monday, 12 December 2016

PMFBY has provided coverage to 366.64 lakh farmers (26.50%) and at this rate it is likely to exceed the target of 30% coverage for both Kharif and Rabi seasons in 2016-17

PMFBY has provided coverage to 366.64 lakh farmers (26.50%) and at this rate it is likely to exceed the target of 30% coverage for both Kharif and Rabi seasons in 2016-17 


The Pradhan Mantri Fasal Bima Yojana (PMFBY) launched in the country from Kharif 2016 has made impressive progress in the first season itself. As on date the scheme has provided coverage to 366.64 lakh farmers (26.50%) and at this rate it is likely to exceeding the target of 30% coverage for both Kharif and Rabi seasons in 2016-17.

In terms of total area covered the achievement has been significant amounting to a total area of 388.62 lakh ha. and sum insured of Rs. 141339 crore. The Pradhan Mantri Fasal Bima Yojana was recast as a new scheme by the Government as the earlier existing insurance schemes were not meeting the full requirements of the farmers for insurance coverage.

The performance this season has improved by 18.50% in terms of farmers coverage, 15% in terms of area coverage and 104% in terms of sum insured in comparison to Kharif 2015, which happened to be one of the worst drought affected seasons when the number of farmers covered was 309 lakh (22.33%), total area coverage was 339 lakh ha. and sum insured was Rs. 69307 crore. The performance in Kharif 2016 is better despite the fact that there were teething issues to begin with. For instance, many States did the bidding process for selection of the insurance companies for concerned clusters for the first time and consequently, the notification of the scheme was delayed in a number of States.

The achievements in Kharif 2016 as compared to Kharif 2015 are notable specially as in Kharif 2015 in most of the States the cut-off date for availing insurance for loanee farmers was 30th September, 2016 and the enrolment under crop insurance shot up after prolonged spell of drought, whereas this year has been one of normal monsoons and window for availing insurance was much smaller with the cut-off date for availing insurance being 31st July, 2016, which was later extended to 10th of August, 2016.

Furthermore there has been a quantum jump of more than 6 times in the coverage of non-loanee farmers from 14.88 lakh in Kharif 2015 to 102.6 lakh in Kharif 2016, which shows that the scheme has been well received by the non-loanee segment. Another significant achievement in this season has been 104% enhancement in sum insured. This was made possible as PMFBY mandates that the sum insured must be equal to the Scale of Finance and therefore, reflects better risk coverage of farmers in comparison to earlier schemes.

courtesy: PIB

Wednesday, 19 October 2016

Govt to use satellite-based tech to assess rice yield

Good Step by NRSC & IRRI

HYDERABAD: If everything goes well, Telangana government may soon adopt latest satellite-based technology to assess rice production and crop damage accurately. The technology developed by International Rice Research Institute (IRRI) is being implemented by some states like Tamil Nadu and Odisha now.

The main advantage of this new technology is that reports on crop condition can be prepared with ease every 12 days and during natural calamities such as cyclones and floods, the government can get accurate information on the extent of crop damage. The other advantage of the ease of data availability is that crop insurance amounts can be released without any delay by getting yield information. Principal scientist and director of Economic Division and Programme Leader of IRRI Samarendu Mohanty and Economist of IRRI-India programme's Delhi representative Aldas Janaiah, met Telangana Agriculture minister Pocharam Srinivas Reddy on Monday. Mohanty explained the advantages of the satellite-based technology to the minister.


"IRRI has developed a software where satellite maps can be quickly analysed. Under the existing National Remote Sensing Agency (NRSA), rice production report assessment is not accurate and the maps can give only information about areas. Countries such as Philippines, Cambodia and Vietnam have been using the IRRI technology for crop assessment," Mohanty told TOI.

He said by knowing the crop condition, yield assessment and weather conditions every 12 days, it is easy for releasing crop insurance to farmers. The state government or insurance companies can make part payment of insurance to farmers without waiting till the end of season. The agriculture minister directed the secretary of agriculture Parthasarathi to send a team of officials to Tamil Nadu to study IRRI's satellite-based technology that is being used there. Agriculture university vice-chancellor Praveen Rao was also present at the meeting.

courtesy: TOI 

Monday, 26 September 2016

Village Level Yield - Historical Data

Village level crop yield drawn from Remote Sensing Technology - Current yield and Historical Yield


District: Aurangabad

State: Maharashtra

Crop: Gram


Tuesday, 13 September 2016

All Over India Crop Health at a Glance - State-wise - Kharif-2016

- Below Table derived based on completely Satellite Image Analysis

- Depends on NDVI trend

- Normal means No major Change in NDVI

- Below Normal means - Negative NDVI trend

- Field Inputs not incorporated, if incorporated better accurate results can be expected

- Analysis was till end of August 2016.


District-wise, Crop wise analysis is available (contact "arun.balla@gmail.com")

Friday, 9 September 2016

Crop Health Status - Haryana (Kharif-2016 season) - Paddy, Bajra & Jowar

1. Satellite derived NDVI showing negative trend

2. Negative trend indicates stress on different crops existing in the field

3. The below results were derived and crop status till end of August 2016

4. This is completely satellite based results, the ultimate conclusion has to be drawn after field checks

5. Below table indicates the crop health status:

6. Health status below normal indicates may impact yield also.


Block-wise analysis available on demand basis.

Tuesday, 30 August 2016

Processed Microwave Satellite Data

In India generally there was cloud cover in Optical Satellite Images during Monsoon Period. Generally July, August, September images fully covered with cloud cover.

Here is the processed data of Micro wave Satellite data, completely cloud free and the results were amazing!!!


FCC was combination of August (2 images) & September.

Different colors denotes difference in sowing dates, for example: 



Tuesday, 9 August 2016

Ideal Location Identification of Weather Station

Below Picture depicts:

1. Temperature Map - Isotherms
2. Rainfall Map        - Isohyets

Overlaying the both maps and look for homogeneity and heterogeneity.  In an area of same Homogeneity one Weather station is sufficient and  below district is sample district.




Remote Sensing Agriculture Flow Chart and Broad Methodology


Friday, 22 July 2016

NDVI - comparison - Sub-district (taluk) - Punjab

NDVI - Historical Comparison - Sub-district (taluk) - Punjab



2016 - Showing clear up-trend compared to 2014 & 2015
2016 - Uptrend - because of good rainfall in current monsoon
This trend comparison till end of June to corresponding year
2015 some areas showing uptrend because June rains were in good in 2015

The above analysis is completely based on Satellite images.


Punjab - Kharif Crops Sowing Status

Punjab - Kharif Crops Sowing Status - Till End of 1st Week of July


1. Around 90% sowing was completed in Punjab till end of 1st week of July.

2. The Major crops were: Paddy & Cotton

3. The spatial distribution of Kharif Crops was shown below.

4. The below map was prepared using Satellite images

5. Using Remote Sensing & GIS Techniques.

Green Colour Indicates Kharif Crops (2016)
Red Indicates: Cloud cover areas
Megenta is district boundaries






Wednesday, 1 June 2016

Availability of Sub-district Crop Yields

Reliable and timely information on crop area, crop production and land use is of great importance to planners and policy makers for efficient agricultural development and for taking decisions on procurement, storage, public distribution, export, import and many other related issues including risk assemment and crop insurnace support. With an increasingly evident trend of decentralised planning and administration, these statistics are needed with as much disaggregation as possible down to the level of village panchayats. India possesses an excellent infrastructure and it has a long-standing tradition of generating a comprehensive series of crop and land use statistics though, of late, there has been a disturbing deterioration in their quality. With most parts of the country having detailed cadastral survey maps, frequently updated land records and the institution of a permanent village reporting agency, the country has all the necessary means to produce reliable and timely statistics.  The performance of the system was quite satisfactory until 2 – 3 decades ago but it has since become dysfunctional essentially due to administrative apathy and inaction. 

Current status on crop area statistics
As the total production of crop is the product of the area under the crop and average yield per hectare, the Crop Production has two major components viz., area sown and average yield. The primary responsibility for collection of statistics on these two aspects rests with the State and Union Territory Governments.
From the point of view of crop area statistics, the States and Union Territories can be classified into three broad groups:
a)      The first category comprises what is called as temporary settled states. The temporary settled states include Andhra Pradesh, Assam (excluding hill districts), Bihar, Chatisgarh, Goa, Gujarat, Haryana, Himachal Pradesh, Jammu and Kashmir, Jharkhand, Karnataka, Madhya Pradesh, Maharashtra, Punjab, Rajasthan, Tamil Nadu, Telangana, Uttaranchal and Uttar Pradesh, and the five Union Territories of Chandigarh, Dadra and Nagar Haveli, Daman and Diu, Delhi and Pondicherry. These states are cadastrally surveyed and having a primary reporting agency for collecting the statistics of crop area. In these states, crop area statistics are being collected by complete enumuration method. The primary worker called Patwari is responsible for collection of Agriculture Statistics in the state. The Agriculture Statistics is coillected through field inspection during each of the agriculture aseason. This exercise is kown as “Girdawari”. The register in which area is recorded is known as “Khasra Register”.
b)      The second category includes states like West Bengal, Prissa and parts of Kerala. These three states are called permanantly settled states. These states are cadastrally surveyed but they do not have primary reporting agency. Area statistics in these states are compiled by sample survey approach through a scheme entitled “Establishment for an Agency for Reporting of Agriculture Statistics” (EARAS) by the regular reporting agency. Every year a sample of 20% villages is selected and the selected villages are completely enumerated for the purpose of reporting crop area statistics. Next year a fresh sample of 20% villages is selected and data collected. Thus, all te villages in the respective state are covered in five years.
c)      The area estimates in the remaining areas of the country i.e., NEH regiona (except Assam) are not based on any systematic approach. Here, the statistics of land records are collected on a sample basis. The revenue/agriculture officer collects the information on the basis of his personal belief and knowledge.
The crop yield estimation in the country is carried out on the basis of sample survey approach. The estimates of yield rates are obtained on the basis of scientifically designed Crop Cutting Experiments (CCE) conducted under a scheme of the Directorate of Economics and Statistics, Ministry of Agriculture (DESMOA) entitled “General Crop Estimation Surveys” (GCES).
Crop area statistics of the temporarily settled areas are comprehensive, being based on the complete enumeration method. They are considered fairly reliable because of the patwari's intimate knowledge of local agriculture and his ready availability in the village.  However, due to an increasing range of functions assigned to the patwari, the girdawari tended to receive low priority. Hence, it was observed that a high degree of negligence in carrying out the girdawari, thereby casting doubt on the reliability of crop area statistics. It is a matter of concern that this has continued on for many years evidently with the knowledge and indulgence of the higher-level officials of the State departments of revenue and land records.
Another deficiency of crop area statistics is that with the development and modernisation of agriculture, several new short duration crops are grown. Although the patwari is required to undertake intermediate crop inspection between the two major kharif and rabi seasons, this does not appear to be done regularly. Even if short duration crops like vegetables, flowers, mushroom, etc. are covered during the crop inspection, they are not listed separately in the final crop abstract but clubbed together under “other crops”.
Current status in crop production
Estimates of crop production are obtained by multiplying the area under crop and the yield rate. The yield rate estimates are based on scientifically designed crop cutting experiments conducted under the General Crop Estimation Survey  (GCES).  The GCES covers around 68 crops (52 food and 16 non-food) in 22 States and 4 Union Territories.  Around 5,00,000 experiments are conducted every year with the help of State revenue and agricultural staff of a rank higher than the primary field staff of the departments.  The survey design adopted is that of a stratified three stage random sampling with tehsil or taluka as the stratum, a village as the first stage unit, a field growing the specified crop as the second stage unit and a plot, usually 5m x 5m, as the ultimate unit. The experiment consists of marking the plot and harvesting and weighing the produce from the plot. These weights form the basic data for yield estimation. The number of experiments and their distribution over the strata are made in a manner to be able to obtain the yield rate estimates with a fair degree of precision at the level of the State and each major crop-growing district.

The method of crop cutting experiments is objective and unbiased and if properly followed provides reliable estimates of yield rates.  In practice, however, the field staff do not strictly adhere to the prescribed procedures and thereby the survey estimates are subject to a variety of non-sampling errors. GCES carries out around 5,00,000 experiments every year; but these are not still adequate to provide usable estimates below the district level.  With the introduction of National Agricultural Insurance Scheme (NAIS) in several States a need is felt for assessment of yields of insured crops at the level of tehsil or C.D. Block and even at the panchayat level. NAIS has, therefore, prescribed additional crop cutting experiments for this purpose at the rate of 16 per block or 8 per panchayat for each insured crop. This imposes an enormous burden on the field agency, increases considerably the non-sampling errors and results in further deterioration of the quality of work. Apart from non-feasibility of carrying out such a huge number of experiments, the recent decision of Government of India that the States should combine GCES and NAIS series of experiments and use them together for framing crop production estimates is fraught with serious consequences.  The objectives of the two series are different and the NAIS series is likely to underestimate yield rates because of local pressure from insured farmers whose interest lies in depressing the crop output. Yet another deficiency in the production statistics is the divergence between the production figures available from different sources especially in respect of cash crops like cotton, oilseeds and horticultural crops.
As yield data is very critical for insurance claim settlement, we tried to improve the deficiencies through use of technology and modeling techniques with the objective of generating sub-district crop yield surfaces for all major crops in the country. So that the insurance companies can calculate the risk involved in underwriting the business. The statistical model that is being used for creating surfaces is Linear Rubber Sheeting with the help of image processing software ERDAS and Global Mapper. Sept-wise procedure for creating paddy yield surfaces, as an example, for the year 2000 kharif is discussed here under following sub sections.


Thursday, 12 May 2016

NDVI based Site Selection for Crop Cutting Experiments

Satellite Derived NDVI based Site Selection for Crop Cutting Experiments
Introduction:

Agricultural statistics has great importance for the planners for planning of most important economic policies for betterment of the people of the country.  Crop area and crop production forms the backbone of any agricultural statistics system.  In India, crop area figures are compiled on the basis of complete enumeration while the crop yield is estimated on the basis of sample survey approach.  The estimates of yields are obtained on the basis of scientifically designed Crop Cutting Experiments (CCE) conducted under a scheme of the Directorate of Economics and Statistics, Ministry of Agriculture (DESMOA entitled “General Crop Estimation Surveys” (GCES).

Yield:

The yield estimates of major crops are obtained through analysis of Crop Cutting Experiments (CCE) conducted under scientifically designed General Crop Estimation Surveys (GCES). At present over 95% of the production of food grains in India is estimated on the basis of yield rates obtained from the CCEs. The CCEs consist of identification and marking of experimental plots of a specified size and shape in a selected field on the principle of random sampling, harvesting and threshing the produce and recording of the harvested produce for determining the percentage recovery of dry grains or the marketable form of the produce.

CCE:

Initially 1,70,000 CCE were planned at all India level to generate district-wise estimates of yield for major crops with less than 5% of margin of errors. For a few crops like Paddy, Wheat etc, the size of the sample was sufficient enough to generate estimate at block/tehsil level. Gradually, the number of CCEs increased to accommodate more number of crops under GCES and recently for the purpose of crop insurance to generate yield estimate at Gram Panchayat level. At present at all India level approximately 8,88,000 CCEs are conducted.

Limitations of to conduct these many CCEs spends much time and effort in scrutinizing the data for gaps in coverage and inconsistencies, seeking clarifications from different organizations and first hand impressions from state officials and field visits, and auxiliary information on trends in input use (for yields). This exercise, and adjustments to estimates of area and yield reported by states based on them, is focused mainly on rice, wheat and a few other major crops. The Directorate needs to be commended for the care and seriousness with which they try to do as best as they can in presenting estimates.

·        But inevitably informed, but necessarily subjective, judgment plays an important role in this process. Most assessments of trends in agricultural production and policy discussions use the estimates as the best available and broadly consistent with other macro economic trends. But the margins of error remain uncertain. The need for improvements in the quality of the basic data and for more transparent and objective ways of making adjustments to imperfect data using cross checks with independent sources and consistency with the behavior of consumption, prices and wages is indisputable.

·         The deficiencies in the current system of both area and yield estimation and are not due to deficiencies in its design. The selection of sample villages for collecting data on land use and crop area, as well as the selection of sample villages and plots for crop cutting experiments are based on rigorous and statistically sound principles.

WITH THE GROWING TECHNOLOGY AND TECHNIQUES MOST OF THE ABOVE SAID ISSUES OR LIMITATIONS CAN BE SOLVED BY USING REMOTE SENSING AND GIS TECHNIQUES.


The identification of Plot numbers on the ground is an issue. Farmers also interfere in deciding the experimental plot. To overcome all these problems, the satellite-based index values (NDVI) come in handy.

The choice of representative NDVI range was made by identifying the concentration of adjacent 60% - 80% of the values from the median (30% or 40% on either side). The median is an ideal choice of averaging the yield in such cases as some large values might affect the mean. Mode that depends on frequency has been ruled out.


Low NDVI represents low yielding areas
High NDVI represents high yielding areas
Median NDVI represents medium yielding areas




Thursday, 5 May 2016

Remote sensing helps farmers and policy makers

Nice Article and it is true

Remote sensing has helped the national crop estimates committee (CEC) achieve a 0.13% error margin in 2015 from 9.77% in 2002 and will allow policy makers to assess drought impact down to municipal level in the Western Cape.
When the agricultural control boards such as the Maize Board and Wheat Board were abolished in 1997 there was a need to have an independent body to provide crop estimates to guide farmers, processors, futures dealers and policy makers. This led to the establishment of the Crop Estimates Committee (CEC) within the Department of Agriculture. To prevent conflict of interest no member of the CEC could trade in the commodities being forecast.
The initial crop estimates were collected by means of a telephonic survey of industry participants, be they farmers, processers or agricultural cooperatives. As the graph below shows, there was an inherent bias to under-estimate the national maize crop in part because low supply would push up prices.
1
To help reduce the bias, the producer independent crop estimate system (PICES) was developed which uses remote sensing using satellite images and aerial surveys using low-flying aircraft and helicopters to estimate acreage planted to the various crops. The remote sensing is so sophisticated that it can distinguish between the various plant types and asses how well they are growing. This helps with the yield estimates, which are also compiled by on-site surveys conducted by the state-run Agricultural Research Centres (ARC) Grain Crops Institute.
From 2009 to 2015, the CEC had three underestimates and four overestimates for the size of the crop so there was no prevalent bias. In addition, the error margin has moved from 9.77% in 2002 to only 0.13% in 2015, a feat better than most national crop estimates conducted in the world, where the error margin is “satisfactory” if it is less than 5%.
In particular, the CEC was justifiably proud of its record in the last three years when despite wide fluctuation in the maize crop from 11.69 million tonnes in 2013 to 14.25 million tonnes in 2014 to 9.94 million in 2015, the error margins were respectively -.13%, +0.4% and -0.75%.
“The fact that we had less than one percent error in the past three seasons shows how robust the system is as during that time the maize crop slipped by 3.6% in 2013 from 2012, then expanded by 21.9% to the largest crop since 1981 before plunging by 30.2% as the drought cut yields,” said Eugene du Preez, director of privately-held SiQ, which provides the committee with satellite and aerial data, which helps it determine the size of the area planted.
The expertise and equipment built up to service the CEC has allowed SiQ to expand its services so that it can help individual farmers and provincial departments of agriculture. Recently they have conducted surveys to see how much arable land in the former homelands of Ciskei and Transkei could be used for maize, while also conducting census for other provincial departments.
One such census was recently completed in the Western Cape and that showed that there 97 crop types cultivated and that there was a massive 12,000 facilities in terms of agricultural infrastructure such as abattoirs. The remote sensing could answer questions such as how many pear orchards are there and what is the amount of hectares devoted to pears. What is the value of crops per municipality and how does this fluctuate in good and poor weather conditions? How many farms are there devoted to game and how much to sheep or cattle? What agri-tourism facilities are there and what do they offer?
This kind of detail means that SiQ can say that the top five districts in terms of grain production potential in the Western Cape are the Swartland at R757m, Cape Agulhas at R585m, Thewaterskloof at R547m, Hessequa at R465m and Swellendam at R463m. This will help policy makers with assessing how much revenue is likely to decrease due to drought given that Western Cape dams are at critical levels with Voelvlei dam for instance at only 19% of capacity.

Courtesy: CAPE Business news

Friday, 18 March 2016

Crop Loss Wheat, Mistard & Gram - Rabi 2015-16

Before Hail storm Crop Condition and yield situation of Major Rabi Crops

Small update of my research and continuous monitoring on Indian Agricultural crops: Wheat, Mustard and Gram there could be loss in yield in this year, the approximate losses were given at region level, here are the results:

1. Northern Punjab region - approximately 10-20% less yield
2. Southern Punjab region - approximately 5% less yield
3. Delhi Surrounded area of Haryana - approximately 10-20% less yield
4. Remaining Haryana - approximately 5% less yield
5. Western UP - approximately 10-20% less yield
6. Eastern UP - Morethan 25% yield losses
7. Bihar - Morethan 25% yield losses
8. Rajasthan - More-or-less 5 to 10% yield loss.

The reason of losses could be higher temperatures during winter season and less soil moisture.

The above losses identified mainly based on Satellite Images, this update was till end of Feb. that means before hail storm condition. Harvesting is in progress and moving harvesting from Central India to North India i.e. Madhya Pradesh & Maharashtra harvesting is in-process and North India crop is ready for harvest.

The above losses could higher because of Hail storm during March 1st Week.

Wednesday, 16 March 2016

Updates on PMFBY - New Crop Insurance Scheme (INDIA)

Stricter norms for new crop insurance scheme
The Pradhan Mantri Fasal Bima Yojana (PMFBY) will be stricter to insurers, states who do not adhere to its norms. Ashish Kumar Bhutani, Joint Secretary, Ministry of Agriculture & Farmers' Welfare, Government of India said that a policy of 'one season one rate' will be followed.
PMFBY will have actuarial yield-based scheme with provision for upfront premium subsidy to be released to insurers. The sum insured will be same for both loanee and non-loanee farmers. Also, there would be no capping and there will be full claim amount paid against the sum insured.
This scheme will also cover localised risks like inundation and post harvest losses. A detailed protocol for assessment of losses and payment of claims for post harvest losses, prevented sowing and localised risks will be followed. Speaking at an agriculture insurance seminar, Bhutani said that they will insist on use of technology like remote sensing so that fudging of data can be avoided.
He added that no extensions would be allowed. "There will be strict adherence to seasonality discipline and no extensions can be allowed. If a state decides to give extensions, it will have to bear the entire cost of the subsidy," he added.
The government is planning to spend Rs 5500 crore for the crop insurance scheme that was announced earlier. In his budget speech finance minister Arun Jaitley said that the farmers will pay a nominal premium for the coverage.

The Pradhan Mantri Fasal Bima Yojana that has approved by the cabinet in January. Here, there will be a uniform premium of 2% to be paid by farmers for all Kharif crops and 1.5% for all Rabi crops. In case of annual commercial and horticultural crops, the premium to be paid by farmers will be only 5%. The balance premium will be paid by the government.
Here, there is no upper limit on government subsidy and even if balance premium is 90%, it will be borne by the government. Insurance executives said that the Modified National Agricultural Insurance Scheme (MNAIS) had high premium rate due to which farmers could not afford it.


It is anticipated that there would be clusters that would be formed of districts to implement the scheme. Senior insurance officials said that that how the clusters are classified will define how the premiums will be fixed.

Here's why the new crop insurance scheme's success pivots around supporting infrastructure


Sometime in the next few weeks, farmers in Madhya Pradesh or Rajasthan may find drones circling their fields. These drones would likely be flown by insurance companies as part of surveying standing crops. 

"The litmus test of effective crop insurance is how quickly assessment is done and claims settled," says Ashok Gulati, Infosys chair professor at the Indian Council for Research on International Economic Relations. Government funded crop insurance has been around in the country for more than 30 years but despite it changing avatars twice, has hardly been effective. Majority of Indian farmers are small and marginal and cannot afford expensive insurance. They also do not get reliable information on new technologies, weather and prices, making it difficult for them to plan production. 


In March-April 2015, rogue weather destroyed crops across large swathes of northern and western India. The government had to raise the compensation, which was capped at Rs 4,500 per hectare for dry land and Rs 9,000 per hectare for irrigated land, to Rs 6,750 for dry and Rs 13,500 per hectare for irrigated land respectively That was hardly enough. In Haryana, for instance, the average wheat yield is about 4.7 tonnes per hectare, the value of which had then worked out to about Rs 60,000. A farmer would have typically spent Rs 30,000 per hectare but got a compensation of only Rs 13,500 per hectare. 


Besides, assessment and claim settlement is a cumbersome, time-consuming process. Minister Jaitley has now set aside Rs 5,500 crore in the Budget for crop insurance. In January, the government had announced the new insurance scheme Pradhan Mantri Fasal Bima Yojana with ultra low premiums. Farmers have to pay just 2% of the actuarial premium during Kharif season and 1.5% during Rabi season. Commercial and horticultural crop farmers will pay 5%. 


Gulati says for the crop insurance to work well the government will need to quickly set up supporting infrastructure. It needs to have weather stations in every block, use drones to assess damage and low-earth orbit satellites, or LEOs, to geo-tag plots to identify farmers. He says India needs LEOs that more frequently monitor a point on earth compared to the remote-sensing satellites the country owns now. China launched over a hundred LEOs in 2014. Drones and LEOs are a globally mushrooming industry, Gulati says.

With all this in place, if, say, a hailstorm flattens crop in a district in Madhya Pradesh, the satellites could pin-point the plots and the geo-tags could identify the owner of the land. The weather stations would have already reported the hailstorm and drones would assess the damage more closely. The data is enough to make a quick assessment and the insurer can identify the farmer with the geo-tag and pay the claim directly into her Aadhar-linked bank account. 

Gulati says if the government puts its might behind implementing the scheme, the infrastructure could be in place in six months. But with business as usual, it could take at least two years. 



Courtesy: business-standard & economictimes

Wednesday, 9 March 2016

Tuesday, 1 March 2016

Weather Forecast - Current Monsoon 2016

Here are High lights of Current Monsoon - 2016


el nino temperature decreasing - Good Sign for Good/Normal Monsoon

Expecting Normal or Above Normal Rainfall

Already several Research agencies announced this year hottest year intermes of temperature in summer.

This may reflect on monsoon, that means delayed monsoon.  Rainfall less in early monsoon period. June and July less rainfall.  But it will pick in August and September.

Generally rainfall distribution is: June - 17%, July 33%, August - 33% & September 17%

June and July less rainfall - means delay in sowing of monsoon crops will be good for better yields.

Below is the El Nino temperature..


White Paper TEA plant Management using Remote Sensing

Introduction:
India continues to be the world’s largest producer and consumer of tea. Domestic productions as well as exports have been on the rise. However, the country is facing stiff competition from countries like Sri Lanka, Kenya, China, Bangladesh and Indonesia, and issues of quality and realizations on Indian teas have been witnessing a downward trend. Statistics indicate that tea plants start yielding from the third year onwards, maintain a steady increasing trend upto a certain age and reach a peak followed by a decline, thus questioning the further commercial viability of the section. The economic life of the bush has been estimated to be 40 years. After this, the amount of non-productive tissues of tea plants becomes so great that its maintenance adversely affects the production of new shoots. Even with best management practices, tea bushes still get infested with pests and diseases. This causes gradual decay and death, thus creating vacant patches in the field which increase with time, resulting in the loss of productive tea area. 


Being a mono-cultured crop, tea has to stand in fields in situations with very less inter-culture operation and no crop rotation. Such conditions ultimately lead to degradation of soil environment and health of the bushes. The mono-culture of tea is said to cause a condition of improper soil functioning known as soil sickness. Because of these conditions, when a tea area reaches the economic life age, the section is uprooted and a new generation of young tea is planted. However, immediate replanting after uprooting has universally experienced serious establishment problems of young tea. Its stand and health remain far behind an average healthy young tea plant despite using best known growing techniques and inputs. To combat such situations after uprooting, Guatemala grass should be planted for 18-24 months which is said to rejuvenate the soil for replanting. Following rejuvenation, a desirable growth of young tea is usually noticed. However, a gestation period of 18-24 months has always been playing an important role in replanting as the break-even period of young tea including these two years becomes quite longer.

Soil fertility status has to be kept at an optimum level to achieve desirable yields. Soil health deterioration is a prime concern for tea gardens in India. In order to achieve high yields and quality, exact parameters on soil physics, soil biology and soil chemistry in relation to two years of rehabilitation/crop rotation period have to be stressed upon. It is also necessary to know the various inputs to soil which are being applied to increase the fertility and availability of organic carbon/potash/sulphur in soil so that effective soil management techniques can be put into action. Pest and diseases are very important factors in the decline of yield and quality of tea. According to these researchers, use of remote sensing could prove to be an important tool in monitoring the health of tea bush and also delineation of affected areas by pests and diseases. Water logging and floods have caused serious threats to low-lying gardens. As a result of water logging, yield and quality have considerably declined. The whole facet of water logging is on an upward trend and continues to include more and more gardens with the passage of time. It has been observed that the causes of waterlogging include eutrophication, man-made bunds and diversion of natural drainage channels by garden authorities. All these aspects form a very important area of research that will immensely help the tea industry in India. 


Space technology which largely includes remote sensing and satellite communication systems, offers an efficient and reliable means of collecting the data/information required to map the tea types and acreage. Remote sensing through its satellite imageries provides the structure information on the health of the vegetation. The spectral reflectance of a tea field always varies with respect to phenology, stage type and crop health and these could be well monitored and measured using the multispectral sensors. Information from remotely sensed data can be fed into GIS which when combined with ancillary data can provide deep insight into the cultural practice being implied in the cropping system. Stresses associated with moisture deficiencies, insects, fungal and weed infestations must be detected early enough to provide an opportunity to the planters for undertaking mitigation measures. Remote sensing would allow the planters to identify areas within a field which are experiencing difficulties, so that they can apply, for instance, the correct type and amount of fertilizer, pesticide or herbicide. Using this approach, planters can not only improve the productivity of their land, but also reduce farm input costs and minimizes environmental impacts. Remote sensing has a number of attributes that lend themselves to monitoring the health of tea plants. Satellite imageries also give the required spatial overview of a large catchment or land which can aid in identifying the tea crops affected by too dry or wet conditions; by insect, weed or fungal infestations or weather related damage.


Examining the ratio of reflected infrared to red wavelengths is an excellent measure of vegetation health. This is the premise behind some vegetation indices such as the normalised differential vegetation index (NDVI). Healthy plants have a high NDVI value because of their high reflectance of infrared light, and relatively low reflectance of red light. Phenology and vigour are the main factors affecting NDVI. It is possible to examine variations in tea crop growth within one field is possible. Areas of consistently healthy and vigorous crop would appear uniformly bright. Stressed vegetation would appear dark amongst the brighter, healthier crops. To achieve timely and accurate information on the status of crops, there is need to have an up-to-date crop monitoring system that provides accurate information. Remotely sensed data has the potential and capacity to achieve this. The use of remotely sensed data in crop acreage estimation has been demonstrated by various researchers across the world. Remote sensing and crop growth simulation models are becoming increasingly recognised as potential tools for growth monitoring and yield estimation. To harvest an everlasting benefit, the tea industry will have to take up uprooting and replanting in large areas at a time, while looking into the real scientific cause of the problem immediately after uprooting to reduce/remove the gestation period. To monitor the activities effectively and in real time, the use of space technology which may include remote sensing and a satellite communication and monitoring system is inevitable. Keeping in mind the various problems being faced by the tea industry (as outlined above), the proposed methodology is designed with the following long term and short term objectives. 


Long term objective

The long term objective is to develop an interactive monitoring and decision support system framework using a systematic analysis of scaled (spatial/temporal) and geo-referenced data/information for tea plantations (with special emphasis on replanted crop) and conditioning variables (policy, infrastructure, markets, ethno-demographics) relevant for planning sustainable tea production.


Short term objective 

Short term objectives are the following:
·         To develop a geo-database of tea growing areas of study area
·         To study and monitor the activity of tea gardens using satellite imagery
·         To monitor the extent of damage to tea plantations caused by pests, diseases and other biotic and abiotic stresses
·         To develop a systematic decision support system framework for planning sustainable tea plantations.



The study will comprise of satellite data procured from NRSC, Hyderabad for monitoring and carrying out required analysis for the individual gardens. Time series data should be considered in order to monitor the different stages of replantation. The basic objective of taking different images is to monitor and assess the entire process of uprooting and replanting in tea gardens of specific areas. Subsequent soil surveys should be carried out at regular intervals and mapping of the areas should be done based on the available datasets and ground truths. In addition to monitoring of pests and disease infestations, drainage aspects should also needs to be considered. This entire approach would then give us a better understanding of the patterns observed in a tea ecosystem. This would then enable the planters to modify their current decision support system for effective management and strategic planning of the gardens.


Methodology
Generation of base maps: The base maps will include the study area boundaries, latitude and longitude of the area, road maps, contour maps, cadastral maps and attribute data. Individual garden maps should be collected for each zone at cadastral level. Every garden has garden maps with well demarcated sections and their coordinates. These garden maps could provide information like identifying the sections undergoing replantation with their section numbers and also the sections showing variations due to soil conditions and cultural practices. 


Generation of thematic maps: Generation of DEM to observe the elevation of the area and its role is important in this study. Based on the existing contour maps and/or stereo satellite image, a DEM of the entire terrain could be generated, followed by the generation of slope and aspect maps since these factors (slope, elevation and aspect) have enormous importance to tea plants and also to soil conditions. These would then help the managers in effective decision-making. High resolution DEM at 90 m could be integrated and used for further analysis.


Satellite images: Space-based remote sensing due to its advantages of synoptic and repetitive coverage and providing data in a quantifiable manner has enabled the monitoring and assessment of natural resources and environment periodically and thus help decision makers to appropriately integrate the same with the other conventional inputs. IRS data has been extensively used in crop acreage estimation, ground water prospecting, cropping system analysis, precision farming,  biodiversity characterization at landscape level, desertification monitoring, wet land information generation, watershed development, etc.
Resourcesat-1/2 (IRS P6) with its varied sensors will add to our understanding in the above application areas. The data to be used for this study are the LISS IV (5.8m) and LISS III (23.5m). The ASTER (15m) data could also be used for monitoring purposes. ASTER products have characteristics such as classification of vegetables, grains, trees and pastures. It could be effectively used in monitoring tea areas due to its following characteristics such as measurement of planting and forest areas, crop yield estimation, monitoring of forest growth, investigation of soil health, investigation of human influence on the environment, mapping and monitoring and influence on environment due to natural disasters.
CARTOSAT-2 (1m) could provide the capability to update the large scale maps to the levels of 1:4000 to 1:2500 scales. With these satellites, several applications like mapping the individual settlements, morphometric analysis of urban features, declination of water sheds and individual fields are possible. The image could be effectively used for delineation and characterisation of tea areas and their changes in the land use pattern.
RADAR (30m) data can be used since it has the ability to penetrate through clouds and crop canopy, thereby avoiding shade tree interference. It could be fused with optical data and information could be extracted regarding the activities going on under shade trees. The above mentioned datasets could be effectively used for delineation of tea patches into different categories by applying various classification techniques like the sub-pixel classifier, segmentation-based classifier and fuzzy classifier. Large scale variations in the terrain properties that are relevant to tea bush could be studied through the use of remote sensing techniques. 

Image processing, reconnaissance and survey: Once the images are procured, they would be geometrically, radiometrically and atmospherically corrected and the noise in the images would be removed, followed by the generation of false colour composites (FCC) and its interpretation. The image interpretation will involve the identification of tea patches using shape, size, colour, tone, texture, etc. It will also involve monitoring of uprooted and replanted tea garden sections. The identified patches in the images require verification through visual interpretation and field visits which would involve visiting the area, identifying the patches, ground truthing and collecting relevant garden data for carrying out both image processing and statistical modelling. 

Analysis: Different classification techniques would be involved (sub-pixel, fuzzy and segmentation based classifier) to delineate the tea patches into different categories. For the classification techniques, different datasets at different time periods should be used to classify the health status of tea patches in the area. The sub-pixel classification would help identify the mix classes within a single pixel. The fuzzy classification would help to characterise the complexities in the tea plantations. The analysis will divide the image data into various segments and then classify the segments by means of fuzzy-based approach. The fuzzy supervised classification used for mapping the tea areas with high resolution images like LISS IV or CARTOSAT-1 may provide high spectral seperability among different classes. By applying the fuzzy classification, vegetation heterogeneity and variability can be modelled if a relationship between fuzzy membership and percent cover can be reliably established. Improved classification accuracy and the potential to model vegetation structure and density will prove useful to the managers. This is especially true in areas where existing classifications do not adequately portray the complexity of vegetation found in the region. Field data can be used to build a reference dataset. The soil map and yield map generated using soil and yield data can be used to detect and analyse diseases, thereby assessing the crop damage. Further, the use of fuzzy technique would help in better understanding of the influence of various parameters like NDVI, textural data, etc. A soil moisture retrieval algorithm can be developed by combining parametric and non-parametric tools like maximum likelihood, fuzzy logic, etc. Qualitative and quantitative maps can be generated with different levels of accuracy. Crop and disease specific signatures can be used to observe and assess the damage to crops. Once the crop assessment is done, wavelets can be applied to detect changes in the time series data. The patterns analysis carried out can then help to identify, detect and assess changes in the datasets using various statistical techniques. Therefore, using the space-time analysis one can consider the effects of pests and diseases on tea bush health and its prediction of occurrence. This would then enable analysis of the interactions between pest and diseases in space and time using multivariate statistical modelling.

Leaf selection: The collected spectra will contain a large number of soil spectra that will be recorded together with the plant spectra. In order to consider the entire spectral signature of the canopy, it is very important to select only plant spectra and separate them from the soil spectra. The whole canopy has to be taken into consideration because the disease may occur in any direction in the tea plant. NDVI is a widely used parameter for leaf detection in presence of soil. It is defined as follows: 
Description: http://geospatialworld.net/images/application/application-agriculture/agriculture-overview/rishiraj3.jpg 

where, NIR is the near-infrared reflectance (740–760 nm) and R the red reflectance (620–640 nm). The spread of the NDVI over a plant (or an entire plot) characterizes the state of the plant (age, leaf area index and health to some extent). 

Tea Model: The unique Tea Model will be developed by the Ag Risk to this study area.  Using this model we can synthesise the large quantity of existing data on the yield responses of tea to climate and management factors, and to make the results accessible to advisers, managers and planners in the smallholder sector through the development of a model to predict yield potential and distribution in tea. 

One of the uses of our proposed Tea model is to estimate the potential yield for a particular area or region. Potential yield is defined as the yield that can be obtained when factors such as water shortage, nutrient limitations, and pests and diseases are not restricting the growth of the crop in any way. Only factors such as temperature, sunlight, day length, and clonal characteristics affect the potential yield. It is important for managers to know what the potential yield of a particular site is and its scope for improving the existing yields. In this so-called ‘yield-gap analysis’, if there is only a small difference between the predicted potential yield and what tea growers are achieving at present, then any resources spent in trying to improve yields are likely to be wasted. On the other hand, if there are large differences between potential and actual yields, then it is likely to be worthwhile to improve management in some way, provided that the factor(s) limiting the yields are correctly identified. 

MODIS-NDVI analysis: The MODIS-based NDVI and LAI values can be used for yield estimation. Past studies, confirms that NDVI and tea LAI have a strong relationship. The same method should be applied to all tea growing areas to see the relationship between NDVI and LAI which would further help in effective yield monitoring of individual gardens.


Conclusion 
Monitoring of tea plantations from time to time has become a pressing need. Statistical modelling and image mining could play an important role in monitoring the tea gardens from time to time. This proposed study will lead to improving the existing decision support system of tea management wherein all the information will help the management in making effective strategies for improving their tea gardens and the industry as a whole through constant monitoring thereby preventing the yield loss through quality production and increase in profitability. Scheduling of fertilizer, pesticide application and plucking will be generated. This will result in the development of a customised GIS package that will help users have all their spatial and non-spatial information related to the estate and that in turn help the management to take decisions easily.

Courtesy by Dutta 

Popular Posts