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