mis for indian agriculture

MIS In Agriculture

Introduction

Strengths

ThreatsOpportunity

Weakness

•Every day need of people.•Subsidy on fertilizers .•Government’s initiation to support kiosk for every 10 kms.

•Poor storage facility•Poor market for selling farmer’s products.•Farmers unaware of the suitable crop for their land and fertilizers required.

•Increasing population growth fuelling the need for agricultural products.•Government’s proposal to come up with soil card enables farmers know the right fertilizers required for their land.•Government’s support for fertilizer industries in India.

•Unpredictable weather condition.•Excessive use of chemical fertilizers and pesticides degrading the soil quantity.

Assumptions about the client

• We assume the farmer to be of upper middle class . •Like many farmers he is unaware of the right quantity of fertilizers required for the crop.•Like in many part of India, we assume rain is uncertain even at his location.•Like many farmers in India, we assume our farmer is facing issues in selling his agricultural products at a right market at an appropriate price. •The farmer can access the kiosks to get weather and other information.

Assumptions & Stakeholders Analytics

Seeding, Watering &

Fertililization

Harvesting & Returns

FinanceRe-

engineering & Technology

SWOT Analysis


Seeding, Watering &

Fertililization


FinanceRe-


• Equipment manufacturers• Government / dealers purchasing our farm products.• Technology facilitators. • Bank and other micro financial bodies.

Stake holders :

Climatic condition

Crop decision

Agricultural Model

Water availability

Soil

Pest and fertilizer utilization

Forecasted with the aid of Analytics

Government profiling of soil (soil card)

Scientific farming via social

Sales of agricultural product Warehousing

Mobile based devices

MIS

How MIS can help

•Using MIS technology, farmer can get to know the on going price in the market. Accordingly he can assess the selling price.•Farmer can also get to know as where he can get a better price. So he can sell at the particular dealer/ location.•He can get to know the forecast of the weather and plan cultivation accordingly.•Use mobile call centre facility provided by government on the usage of fertilizers using MIS facilities.•Government’s plan of having kiosks at a distance of 10kms from one another can be tapped to get information of weather forecast/ rain fall expected, possible pesticides for fungal/ bacterial infections for crops.


Seeding, Watering &

Fertililization


FinanceRe-


Weather forecasting : Predictive Analytics

Present data

Previous forecast

Time

Quality control

Assimilation of Data

Forecast run

Post Processing

Forecast weather

Forecasting Model

•Collect the data for the present which has been forecasted before .

•Quality control eliminates the measurements of the observed which are lying significantly higher / lower than that of the observed value. These if included affects our future forecast because they are outliers.

•We then properly format the data required for forecasting.

•Setting the boundary conditions in which our forecast will be consistent.

•Running the forecast.


Seeding, Watering &

Fertililization


FinanceRe-


Content Management System

Agriculture KiosksInfrastructure support

Cloud service

Bank/ micro financial agencies

Tractor/ machine providers

Pesticide / fertiliser provider

Government/ purchasing dealers

Other farmers

•The key entity in CMS model for agriculture is interaction of farmers with kiosk. Here farmers can come and access the computer placed in kiosk and get information on weather forecast, new technology in agriculture , updates on general agriculture practices.

•Farmers purchase fertilisers/ pesticides and other chemicals required for agriculture. They also purchase tractors or take it for lease for a stipulated period of time.

•Short- term loans for operational activities and long-term loans for fixed asset / equipment purchase are financed by banks and other micro economic institutes.

•Government purchase a good percentage of agricultural price at a reasonable price . In addition to this, even many private dealers/ procurers also purchase agricultural products from the farmers.

DBMS

Assumptions & Stakeholders

AnalyticsSeeding, Watering

Fertililization


Marketing FinanceRe-


Decision Support System : Seeding

Database Management System ()

Analysis for decision making

Meteorological data

Market demand and variation in price

Dealer’s status

Soil Test outcome

Ploughing of land Seeding

•They abstract data and information to a higher level to enable decision making.

•Government provides soil cards to each farmers to assess their soil quality, fertilisers suitable for a particular crop in their farm land. With this farmers would have a list of suitable crops cultivable in their plot.

•Using Social model of SMAC technology, farmers can interact with one another and their by can get to know the changing demands and cultivate crops accordingly to maximize their profits.

•Farmers can access the weather conditions by accessing the service offered in kiosks. By this , they can plan cultivation accordingly.

•Farmers can also have a co-operative understanding with the dealers. As and when the dealer’s stock is about to get depleted, farmers can be messaged the need for the product. This reduces the overall overhead involved (eg : Increased warehousing can be minimised.)

Water availability



Fertililization

Harvesting & Renture



Decision Support System : Harvesting time

Database Management System (DBMS)

Analysis for decision making

Meteorological data

Market demand and variation in price

Dealer’s status

Pathogenic break breakthrough

Plucking scheduling Replenishment scheduling

•The availability of storage facility for the agricultural produce is a major influencing factor for appropriate harvesting period.•Mobile communication between dealers and farmers to dynamically inform requirement / scarcity. •Market’s demand for the product at the earliest. •If their is a pathogenic outbreak , then their is a high probability of the crops getting infected. So if their is an infection outbreak, then harvesting at the earliest is very essential.

•Based on the above mentioned considerations, we can predict the appropriate harvesting time.

Warehouse availability

Resource availability

Market



Fertililization




CLOUD

BIG DATA

Warehouse Availability

Current Market Requirement

Population and food requirement

forecastAnalytics

Market needs for produce

Warehouse planning

• Sharing of warehouses – Farmers having partly empty or empty warehouses can utilize the space by renting it to farmers having excess farm produce.

• Current Market requirements can put into the cloud by the government for optimum farm produce.

• Population forecast by the government and other agencies will lead to not having unnecessary farm produce.

• All these will be stored in the bigdatawhich will be in the cloud.

• Analytics will be done on them to provide the end producers – farmers in an optimum level. For any excess produce which is due to wrong production can be put in the warehouse for future use or for export.

Current problems:-• Excess produce• No warehouse for excess capacities• Distribution of farm produce


AnalyticsSeeding,

Watering & Fertililization




SCM in agriculture

• Using the kiosks for farmers the farm produce can be put in the cloud.

• The processors can accordingly pick up the farm produce and put them to distributors and distributors to retailers .

• The penultimate customers-retailers can order the farm produce after checking the data

from the cloud.• Processors will collect from

multiple farmers and distribute to multiple distributors .

Farmer

Village TraderCommission

AgentFarmer’s market

Wholesaler

Exporter Retailer Consumer

Marketing channels for onions in Tamil Nadu



Fertililization




Currency exchange rate

Current market price

Present food stock in domestic &foreign

countries

CLOUD

BIGDATA

Analytics

Time to Market Price to pitch in with

• Rupee to dollar exchange rate is included in system.

• The current market price in the foreign markets is also feed in to have a holistic view.

• The current food stock in the world is feed in to make the farmers get a right time to market and also the right time to pitch in to sell their produce.

SMAC in ERP – SAP-HANA

• SAP- HANA will be implemented for the same.

- Real time business- Smarter and faster service- Single platform

• Data will be cascaded to the end users through mobile phones and Kiosks

• Kiosks will be used as an input for the queries and concerns from the farmers

• The outer layer will be the user driven experience which will encompass the SAP Business suit.

• The entire Analytics will be done in the SAP Business suit.

• The entire data will be stored in the cloud for real time processing.

Banking

Information system.

• For a quicker approach for harvesting

• Buying the right pesticide for the crop

• Pre approved micro finance available from the bank.

• Data exchange is enabled between the two systems.

AIS

CLOUD

Time to harvest

Types of Fertilizers needed

Time to market

Analytics on fund management

BIS and AIS Connection

AIS and EIS connection

AIS

Analytics in data

EIS(Education Information System)

CLOUD

DARE ICAR

CLOUD

• DARE- Department Of Agriculture and Education will conduct education sessions for the farmers .

• Members of DARE can have live projects enabling a symbiotic environment with the farmers.

• ICAR- Indian Council for Agricultural Research will provide innovative ways for farming.

• Data from AIS about the farmers will be sent to EIS for more data analytics.

Quality Issues

Assurance Issues High level of automation from HANA drives cost savings , staff efficiency and round the clock quality assurance

Testing Issues Validity of data is being checked . Mainly done by predictive analysis from the past data.

Business Process Model

Government bodies

Private Dealers

BPM Suite

Final agriculture product

Interfaces

Interfaces

Fertilizer provider

Pesticides provider

Agricultural equipments provider

Cloud Service providers

Information from KaoiskAgricultural updates/

information

Farmers

Internal / External users

Work flow application services

Harvesting

Weeding

Irrigation

SeedingPloughing

Enterprise Architecture

Work flow model

Yield

Seed

Fertilizers

Pesticides

MIS input (From Kiosks / SMAC )

Sell

Pests Loss

Post-harvest Loss

Business Location System

MIS input from other farmers

Seeds

Pesticides

Fertilizers

Kiosks inputs (MIS)

Farmer / cultivation Warehouse

Other farmers (MIS)

Wholesaler/ government

/ village market

Retailers/ local

shops

Export

Collect trends (MIS)

Enterprise Architecture

References

•http://www.intechopen.com/books/climate-change-and-regional-local-responses/forecasting-weather-in-croatia-using-aladin-numerical-weather-prediction-model#F1•http://tnau.ac.in/eagri/eagri50/HORT381/pdf/lec05.pdf•http://www.imd.gov.in/section/nhac/dynamic/endofseasonreport.pdf•http://www.imdpune.gov.in/endofseasonreport2013.pdf•http://www.imd.gov.in/section/nhac/dynamic/monsoon_report_2011.pdf•http://www.tropmet.res.in/~kolli/MOL/Monsoon/year2010/Monsoon-2010.pdf•http://www.imd.gov.in/section/nhac/dynamic/endseasonreport09.pdf•http://www.tropmet.res.in/~kolli/MOL/Monsoon/year2008/Monsoon-2008.pdf•http://www.tropmet.res.in/~kolli/MOL/Monsoon/year2007/Monsoon-2007.pdf•http://reliefweb.int/report/india/india-meteorological-department-southwest-monsoon-2005-end-season-report•http://www.imd.gov.in/section/nhac/dynamic/endofmonsoon.htm

http://www.intechopen.com/books/climate-change-and-regional-local-responses/forecasting-weather-in-croatia-using-aladin-numerical-weather-prediction-model

http://tnau.ac.in/eagri/eagri50/HORT381/pdf/lec05.pdf

http://www.imd.gov.in/section/nhac/dynamic/endofseasonreport.pdf

http://www.imdpune.gov.in/endofseasonreport2013.pdf

http://www.imd.gov.in/section/nhac/dynamic/monsoon_report_2011.pdf

http://www.tropmet.res.in/~kolli/MOL/Monsoon/year2010/Monsoon-2010.pdf

http://www.imd.gov.in/section/nhac/dynamic/endseasonreport09.pdf



http://reliefweb.int/report/india/india-meteorological-department-southwest-monsoon-2005-end-season-report

http://www.imd.gov.in/section/nhac/dynamic/endofmonsoon.htm

Thank you