Electronic Solutions against Agricultural Pests (e-sap)

Transcription

1 Electronic Solutions against Agricultural Pests (e-sap)

2 Present versus Future in Pest management The platform, players and real-time interactions E-SAP the solution for Pest Management Realized benefits A success story Implementation Future path

3 Current approaches Direct human approach the KVKs, RSKs, etc Mass communication approach print, radio, TV, internet, mobile phones PIGGY-BACK APPROACH TIMELY DELIVERY OF TECHNOLOGIES ARE DIFFICULT THROUGH CURRENT APPROACHES

4 With respect to information With respect to communication FARMER SHOULD KNOW WHAT & HOW TO SEARCH BIG PROBLEM Creating a digitized text book on technologies SMS services Voice message services Internet access to digitized info Setting up of info-kiosks Questions and answers prompted through internet Linking farmers with experts using mobile apps Community radio, CD distribution, etc DO NOT COVER FEEDBACKS AND DATA GENERATION

5 A How new do beginning we make it possible? The concept is to move away from using the existing media of mass communication, and move towards creating an entirely dedicated system which will bring all players of agriculture to play on the same platform

6 Platform, players and real-time interactions

7 The unique platform facilitates storage/access and capture/transfer of information between all players of agriculture It enables real-time forward flow of multimedia-based agriculture content to individual field workers; and return flow of data to the other players like policymakers/researchers

8 It enables rapid and effective dissemination of technologies to farmlands, and delivery of farm data in various forms, including multimedia, to researchers, policymakers and other users in realtime Device empowers the EXTENSION WORKER Decision flow Forward flow to extension worker Device Processing and broadcastin g data Backward flow from extension worker Technology flow Data flow for policy and research Assists policy making and publishes policy decisions Connects farmers with technologists Figure 1 Generalized model depicting forward (broken arrows) and backward (solid arrows) flow of information mediated by empowered extension workers

9 Handheld field device: it contains the application and content; content is accessible online/offline; has ability for multimedia data capture; can send and receive data, including multimedia, in GPRS/3G/Wi-Fi modes Web-based application: it would enable retrieval and presentation of data generated from field devices; forms the entry point for agricultural content to be disseminated to the field devices; provides data in raw and synthesized forms; enable device management

10 empowers users with information they need in the field contains elegantly metamorphosed information that transcends language barriers; it can be appreciated and used even by illiterate users provides information irrespective of remoteness of the farm comes with substantial inbuilt intelligence for on-farm decision supports allows surveys and data collection with ingeniously integrated protocols offers remote, real-time management of field devices thus enabling remote IT infrastructure in true sense bridges field users with experts in real-time to handle extraordinary situations has inbuilt automated mechanism for real-time exchange of information, including multimedia, between farms and labs

11 For those at the other end of the spectrum, like policymakers and researchers, the platform delivers field information in real-time Field data that streams-in can be viewed over the GIS Data across space and time are automatically synthesized in the form of graphs and tables along with decision support intelligence Provides opportunity to disseminate knowledge to the field users in real-time

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14 FIELD DEVICE Four attributes A B C D A ADVISORY Image & voice assisted pest identification Detailed pest information Management schedules Transcends language barriers Appreciated and used even by illiterate users

15 FIELD DEVICE Four attributes AB B C D B SURVEILLANCE Pest-specific sampling plans Image capture GPS tagging Simplified built-in data entry

16 FIELD DEVICE Four attributes AB C B C D C UNKNOWN PEST Voice assisted feedback Image assisted feedback GPS tagging Real-time information transfer

17 FIELD DEVICE Four attributes AB C B D C D D FARMER DETAILS Individual farmer details Farmer image capture Farmer-crop-pest matrix

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19 WEB APPLICATION Four attributes A B C D DATA ARCHIVAL/RETRIEVAL

20 WEB APPLICATION Four attributes AB B C D GIS MAPS

21 WEB APPLICATION Four attributes AB C B C D GRAPHS

22 Undiagnosed reports to enable expert connect...

23 Tree anatomy enables better content management...

24 WEB APPLICATION Four attributes AB C B D C D RAW DATA

25 Farmer Extension worker Administrator/Policymaker Researcher

26 Seeing is believing: There is greater conviction about pest diagnosis and pest management recommendations Increased adoption of recommendations Translated to higher penetration of technology Increased connect with University Farmers want the device for themselves

27 Higher confidence to tackle field situations Able to reach greater number of farmers Increased efficiency in handling extraordinary situations with real-time expert connect Real-time connect with administrators Greater sophistication and a boost to the relationship with farmers

28 Overall perception of farm facilitators towards e-sap Sl. No. Categories Range interval Frequency Percentage 1 Low (Mean *SD) < Medium (Mean *SD) High (Mean *SD) to >

29 Overall perception of farmers towards e-sap (District wise) Categories Districts Raichur (n1=97) Gulbarga (n2=126) Yadgir (n3=82) Bellary & others (n4=79) Total (N=384) Low (<38.97) Medium (38.97 to 42.91) High (>42.91) Freq % Freq % Freq %

30 Discovery of White Tip disease in Raichur region A few undiagnosed reports started appearing early in the paddy season

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32 Experts soon found that this problem was not usual to Raichur region The issue was immediately investigated and found that the cause was the nematode and the disease was WHITE TIP of Paddy

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34 A success story of e-sap Cotton leafhopper

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37 The problem was quite rampant on all stages of the cotton crop, which corroborated the e-sap reports Administrative decision was taken

38 It was found that Farmer feedbacks revealed current management practices were not proving to be of use as the pest appeared to have developed strong resistance to all existing chemicals At the same time, a novel molecule currently being evaluated in research plots, was found to be extremely effective This molecule was advocated for use in the problematic areas. The pest was managed

39 Administration was convinced easily with factual data streaming live to them The process of decision-making was easy and timely New technologies developed were immediately translated to field actions

40 Complete adoption in UAS Raichur Jurisdiction for the last three years Covered over 55,000 farmers in six Districts for crop advisory and pest surveillance Crop advisory in 19 crops in relation to insect, diseases, nutrient disorder and weeds was provided Adopted by other SAUs of the state

41 Sl. University Date of No. adoption 1. UAS, Raichur June, 2012 Districts Crops Farms visited No. of field devices , UAS, Bangalore , UAS, Dharwad July , UAHS, Shimoga Jan , TOTAL ,

42 Any Package of Practice developed will reach the field directly, in real-time The prediction models that we have started building will be continuously strengthened Human Resources will be better utilized

43 Nation wide implementation will draw a clear picture of pest situation Integration of all the players in agriculture (SAU, State Dept., ICAR, DPPQS etc.) on a single platform on real time Developing accurate prediction models for key pests in relation to abiotic and biotic stress

44 T H A N K Y O U