1 / 14

F A S A L

F A S A L. ( F orecasting A gricultural Output Using S pace, A grometeorology And L and - Based Observations ). FASAL SCHEME. Background India has a comprehensive system for collection of Agricultural Statistics.

erica
Download Presentation

F A S A L

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. F A S A L ( Forecasting Agricultural Output Using Space, Agrometeorology And Land - Based Observations )

  2. FASAL SCHEME Background • India has a comprehensive system for collection of Agricultural Statistics. • Most of the Indian States have an official agency (Revenue administration) which collects and compiles crop area and production estimates at the village level and transmits it for aggregation at higher levels (national/state/district). • These estimates suffer from large time lag and non sampling errors. • Ministry of Agriculture required timely and accurate estimates for taking various policy decisions.

  3. Background (Contd.) • The Ministry also required regular updates during a crop season. • To meet these requirements, the Ministry explored the use of alternative mechanisms and latest technological tools. • Ministry visualized the use of Remote Sensing (R.S) technology as early as 1987 and sponsored a project called “Crop Acreage and Production Estimates (CAPE)”.

  4. Background (Contd.) • Under CAPE, SAC Ahmedabad developed and standardised Remote Sensing methodologies for specific crops in selected States. • In 1995-96, CAPE project was reviewed with a view to enlarge its scope and coverage. • These methodologies were discussed in different fora and were found to have attained a reasonable degree of standardization for integration into the main system.

  5. Background (Contd..) • It was felt that crop forecasts should also take into account such economic and weather variables which play a vital role in influencing farmers decisions on sowing of different crops. • These include both exogenous (weather) and endogenous variables (Prices, fertilizers, seeds, irrigation facilities etc.) • Accordingly. a comprehensive project titled FASAL was prepared which aims at giving multiple crop production forecasts using an integrated approach involving RS data, meteorological data and field data.

  6. FASAL CONCEPT • To Forecast Area & Yield of Major crops. • Use of econometric and agro-met models at initial stages of crop growth. • Use of Remote Sensing methodology at mid-season and pre-harvesting stage. • Use of Land Based Observation to develop above models and for sample validation of forecasts.

  7. F4 F3 F2 F1 F0

  8. Forecasting Agricultural output usingSpace, Agrometeorology and Land basedobservations (FASAL) Land Observations Conventional Remote Sensing Agro Meteorology RS, Mod. Re. Temporal RS, High Re. Single date Econometry Cropped area Crop condition Crop acreage Crop yield MULTIPLE IN-SEASON FORECAST Pre- Harvest District Pre- Season Early- Season Mid- Season State Pre- Harvest State Revised Incorporating damage

  9. FASAL Project System Network Database Remote Sensing Weather Land Observations IN-SEASON DATA ANALYSIS & MODELING Data Flow DOS-RS INDI. RES. NSSO IMD-MET NCFC Procedure Dev. &Upgradation Forecast Evaluation R&D for New Approach FASAL TECH. FASAL Forecast IEG-ECONO.

  10. Core Activities The functions to be carried out during the operation of FASAL have been broadly grouped into five core activities as follows: • Agricultural Information Group (AIG) • Statistical Analysis Group (SAG) • Image Analysis and Pattern Recognition Group (IAPRG) • Ground Observation and Analysis Group (GOAG) • Crop Growth and Yield Modeling Group (CYMG)

  11. Agencies involved in FASAL • Following agencies are involved in FASAL : • Institute of Economic Growth: for Econometric Modeling • India Meteorological Department: for Agro-met modelling • SAC Ahmedabad: for Remote Sensing methodology • All other functions, including co-ordination with various agencies performed by National Crop Forecasting Centre (NCFC) in the Ministry.

  12. Crops to be Covered under FASAL Eleven crops: Rice (Kharif=K and Rabi=R), Jowar (K & R) , Maize, Bajra (K), Jute, Ragi , Cotton, Sugarcane, Groundnut(K & R), Rapeseed & Mustard and Wheat.

  13. FASAL versus Conventional System • Integration of various approaches to provide information related to crop condition and crop production at any time in the season from sowing to harvest. • Multiple in season crop forecasts till the pre-harvest stage. • Alternative options of National Forecasts (NF), National and State Forecasts (NSF) and National, State and District Forecasts (NSDF). • Quick response on forecasting the effect of episodic events. • However, FASAL is meant to provide an independent mechanism for early forecasts and validation of conventional estimates AND NOT as a replacement of conventional system.

  14. Thank You

More Related