Digital extension pilots in India provided empirical evidence that digital advisories can improve farm productivity and profitability. Randomized control trials showed treatments that combined critical inputs, demonstrations and video/Whatsapp services led to higher yields than other treatments. Farmers perceived weather forecasts, market prices access and diagnostics as most useful digital services, though realized utility was lower. Industry stakeholders saw potential for disruptive technologies but barriers to adoption. Direct market interventions helped farmers realize better prices. Smartphone apps for irrigation pump control provided benefits like time savings and flexibility. Desk research found many digital startups in areas like market access, predictive analytics and financial inclusion that could integrate with extension systems. The studies highlighted tipping points for digital disruption in agriculture through

1. Tipping Point in Digital Extension
Advisory Systems:
Empirical Evidences from Indian
Digital Pilots
SHAIK N MEERA*, S. Arun Kumar, R. Praveen, Ch. Gayathri & SR Voleti
* PRINCIPAL SCIENTIST
ICAR Indian Institute of Rice Research
Hyderabad, India
16 October

2. Tipping Point Pilots
• Experiment 1: Randomised Control trials – Digital Extension &
Productivity, Profitability
• Experiment 2: Farmers’ Perception about Data Driven Services
through Mobiles n=160
• Experiment 3: Industry perception about the use of disruptive
technologies n=80
• Experiment 4: Direct Market Interventions – Impacts on
Profitability
• Experiment 5: Smart Phone Applications in irrigation– Pump
Starters
• Experiment 6: Desk Study – Digital Start Ups in India

3. Can Digital Extension lead to
Farm productivity and Profitability?
• Experiment 1: Randomised Control trials – Digital Extension & Productivity,
Profitability
Rice Farmers
Small and Marginal
160
T1
n=40
T3
n=40
Randomize
T2
n=40
C
n=40

4. Can Digital Extension lead to
Farm productivity and Profitability?
• Experiment 1: Randomised Control trials – Digital Extension & Productivity,
Profitability Multiple Comparisons
Dependent Variable: data LSD
(I)
treatment
(J)
treatment
Mean
Difference
(I-J)
Std.
Error
Sig. 95% Confidence
Interval
Lower
Bound
Upper
Bound
T1
T2 .782* .314 .014 .16 1.40
T3 1.122* .314 .000 .50 1.74
C 1.696* .314 .000 1.08 2.32
T2
T1 -.782* .314 .014 -1.40 -.16
T3 .340 .314 .281 -.28 .96
C .914* .314 .004 .29 1.53
T3
T1 -1.122* .314 .000 -1.74 -.50
T2 -.340 .314 .281 -.96 .28
C .574 .314 .069 -.05 1.19
C
T1 -1.696* .314 .000 -2.32 -1.08
T2 -.914* .314 .004 -1.53 -.29
T3 -.574 .314 .069 -1.19 .05
*. The mean difference is significant at the 0.05 level.
Descriptive
data
N Mean Std.
Deviation
Std.
Error
95% Confidence
Interval for Mean
Minimu
m
Maximu
m
Lower
Bound
Upper
Bound
1 40 6.00 1.671 .264 5.47 6.54 3 9
2 40 5.22 1.406 .222 4.77 5.67 2 8
3 40 4.88 1.347 .213 4.45 5.31 2 9
4 40 4.31 1.145 .181 3.94 4.68 2 8
Total 160 5.11 1.521 .120 4.87 5.34 2 9
T1=
Critical Inputs + Services (Demonstrations) + Video
/ Whatsapp
T2=
Critical Inputs + Services (Demonstrations) - Video
/ Whatsapp
T3=
Farmers Practice + Undirected Video, Whatsapp
Control= 2016-17 Benchmark Yield
40 farmers each
One way ANOVA

5. Can Digital Extension lead to
Farm productivity and Profitability?
• Experiment 1: Randomised Control trials
** Significant at 0.01 level
*Significant at 0.05 level
Correlation with Profitability
Correlation coefficients (‘r‘ value)
TS NLG TS MBNR TN Total
farmers
(n = 40) (n = 40) (n = 40) (n = 120)
Access and Use of Digital advisories0.685** 0.610** -0.18 0.500**
Financial Inclusion includes mkt 0.588** 0.521** 0.132 0.375**
Gender 0.277 0.163 0.414 0.121
Access to Time critical services 0.734** 0.325** 0.712** 0.579**
Socio-economic factors 0.508** 0.373** 0.703** 0.448**

6. What data driven services matter?
Experiment 2: Farmers’ Perception about Data Driven Services
through Mobiles n=160
Data Driven Services
through Mobiles
(n=160)
% Perceived
Utility
%Realized
Utility
Diagnostics- Advices 95 78.12
Weather forecast -
Advices
76.25 35
Management advisory 93.75 71.87
Harvest Management 78.75 53.12
Market prices access 96.87 28.12
Access to customers 87.5 16.25
Certification 37.5 7.5
Demand for seed, rice 81.25 40.62
Miller perception Info 78.12 53.75
Access to payment
gateways
54.37 6.25
Access to Credit 57.5 7.5
Access to insurance 70 15.62
Micro finance,
Cooperative
56.25 6.25
Government policies 41.87 35

7. Whether rice industry is ready?
• Experiment 3: Industry perception about the use of disruptive
technologies n=80

8. Whether rice industry is ready?
• Experiment 3: Industry perception about the use of disruptive
technologies n=80

9. Whether rice industry is ready?
• Experiment 3: Industry perception about the use of disruptive
technologies n=80

10. How digital help realizing better markets?
• Experiment 4: Direct Market Interventions – Impacts on
Profitability
Market prices Kharif
Rs/Q
MTU 1010 IR 64
DRR Dhan 44 MTU 1001, JGL BPT, HMT, JS RNR
Grade 1 Govt 1590
Grade 1 Millers 1550 1850 1800
Grade 2 Govt 1540
Grade 2 Millers 1540
Market prices Rabi
Rs/Q
MTU 1010 IR 64
DRR Dhan 44 MTU 1001, JGL BPT RNR
Grade 1 Govt 1590
Grade 1 Millers 1550 1450 1500
Grade 2 Govt
Grade 2 Millers
Hyderabad Direct Mkt
Rs/Q Buying Selling
Jai Sriram 5500 5800
HMT 4500 4800
BPT 3500 3800
RNR 3400 3600
Seed Direct Marketing
Rs /Kg
25 20 30 28-30

11. Smart phones..beyond advisory Apps?
• Experiment 5: Smart Phone Applications in irrigation– Pump
Starters n=111
Yes No Can’t say
Direct benefits P F P F P F
Easy to operate / irrigate compared to earlier 100 111
Time saving 100 111
Safety (Personal safety, Pump set safety) 100 111
Flexibility of irrigating 100 111
Remote Accessibility of pump sets (vehicle can’t
get there!)
74.7 83 26 23.4 2 1.8
Can be operated by any family member (unlike
only family head has to go)
73.8 82 29 26.1
Gender / Literacy no barrier 93.6 104 7 6.3
Updates about status of irrigation 96.3 107 3 2.7 1 0.9
Ease in planning irrigation cycles 94.5 105 5 4.5 1 0.9
Bypass (Manual mode) option (in case auto starter
doesn’t work)
100 111 0.0
Update about the status of water in a tank/ well 92.7 103 7 6.3 1 0.9
After sales service support 79.2 88 23 20.7 0

12. Smart phones..beyond advisory Apps?
• Experiment 5: Smart Phone Applications in irrigation– Pump
Starters n=111 – “Retail like Extension”
0 10 20 30 40 50 60 70 80 90 100
Indirect benefits
Reduced water wastage
Reduced electricity wastage
Reduced labour wastage
Reduced fuel wastage (POL)
Cost effectiveness
Adding to awareness about resource conservation
Local capacity – business opportunities
Daily / weekly reports – decision making
Increased crop productivity
100
100
100
100
97.3
84.6
98.2
88.2
78.3
60.3

13. Digital Start ups – Integrating with Extension
Advisory Systems
1
3
8
4
1
5
1
7
4
2
5 6
4
2
5
6
4
5
4
1
1
5
643
5
8
2
1
6
2
1
5
22
5
1
22
5
2
5
1
3
1
3
4
2
5
45
2
1
6
8
16
8 2 1
2
6
4
2
4
5
7
1
8
4
5
4
8
4
3
0
1
2
3
4
5
6
7
8
Aarav Unmanned Systems
Aarav Unmanned SystemsAarav Unmanned SystemsGold FarmGold Farm
Gold Farm
Farms2Fork
Farms2Fork
Agribolo
Agribolo
Agribolo
Agribolo
eFresh Pvt Ltd
eFresh Pvt Ltd
eFresh Pvt Ltd
eFresh Pvt Ltd
Agrostar
Agrostar
TQ Pump starters
Kisan Raja
Skymet
Skymet
Skymet
EM3 Agri Services
EM3 Agri Services
EM3 Agri Services
EM3 Agri Services
Reuters Market Light (RML)
Reuters Market Light (RML)
Reuters Market Light (RML)
AgroWave
AgroWave
AgroWave
Earthy Tales
TruceTruceFarm AgainFarm AgainShanMukha InnovationsShanMukha InnovationsCrofarmCrofarm
Aibono
Aibono
Fasal
Fasal
ONganic Foods
ONganic Foods
ONganic Foods
Oxen Farm Solutions
Oxen Farm Solutions
Farmizen
Harvesting
Harvesting
Harvesting
SatSure
SatSure
SatSure
Triton Foodworks
vDrone
Ninjacart
Ninjacart
BigHaat
BigHaat
Ravgo
Ravgo
Ravgo
FlyBird Innovations
FlyBird Innovations
farMart
farMart
AgroStarAgroStarCropInCropIn
• Experiment 6: Desk Study – Digital Start Ups in India
1: PA
2: Mkt
3:KT
4: Access
5: Aggregation
6: Financial Inclusion
7: Networking
8: Aes – Unique ID

14. Digital Start ups – Integrating with Extension
Advisory Systems
• Experiment 6: Desk Study – Digital Start Ups in India
*DEAS Data driven extension advisories – data types (N.Meera Shaik 2018 and Maru Ajit 2018)
(LD: Localized, ID: Imported, ED: Exported and AD: Ancillary data)
Agriculture
related
information,
access to agri
inputs
Buying &
selling of
agri
outputs
Predictiv
e
analytics
Big data
Process
automation
LBM
IOT
Mobile /
cloud
Social media
/
networking
Ai others
Agrowbook Yes Yes No No No Yes No
Scope for DEAS LD, ID ID, ED LD, ID,
ED, AD
LD,ED ED, AD ID, ED AD
Agmart No Yes Yes No No Yes No
Scope for DEAS ED ED AD ID, ED,
AD
ED, AD ED, AD ID, ED AD
Bharatrohan No No Yes No Yes Yes No
Scope for DEAS LD ED - LD, ID,
ED, AD
LD ID ED LD, ID, ED,
AD
LD, ID, ED LD, ID, ED,
AD
StampIT No No No Yes Yes Yes No
Scope for DEAS LD, ID, ED, LD, ED, AD LD, ID,
ED, AD
LD, ID, ED LD, ID, ED,
AD
LD, ED ID, AD

15. Tipping Points to Digital Disruption
• Experiment 1: Randomised Control trials – Digital Extension &
Productivity, Profitability
• Experiment 2: Farmers’ Perception about Data Driven Services
through Mobiles n=160
• Experiment 3: Industry perception about the use of disruptive
technologies n=80
• Experiment 4: Direct Market Interventions – Impacts on
Profitability
• Experiment 5: Smart Phone Applications in irrigation– Pump
Starters
• Experiment 6: Desk Study – Digital Start Ups in India

16. Tipping Points to Digital Disruption - Why
0
10
20
30
40
Sub-
Saharan
Africa
North Afria
South- West
Central Asia
Latin
America
Japan,
Korea,
China
Europe
incl.Russia
North
America,
Oceania
13%
32%
2%
3%
4%
11% 11%
3%
4%
2%
11%
31%
10%
33%
4%
3%
7%
9%
13%
8%
5%
4%
4%
34%
3%
28%
5%
3%
10%
9%
11%
8%
6%
6%
6%
36%
5%
36%
5%
13%
13%
1%
ø22%
ø33%
South and
East Asia
Consumption-levellosses
Distributionlosses
Processingand packaging losses
Post-harvest losses
Harvestlosses
Up to 22%yield
losses could be
savedwith more
efficientsupply
chains
In order to compensate
33%of valuechain losses,
an unfeasible50%1
yield
increase would be
necessary,
while increasingsupply
chain efficiency byonly5%
points hasthesame
effectasa10%yield
improvement.
1
The production increase needed to compensate 33%losses is 50%,since
losses need to be deducted from any potential yield by dividing it by the
effectiveyield.
Source: United Nations, FAO, IWMI 2007,Monitor DeloitteResearch

17. Tipping Points to Digital Disruption - How
1. Give Farmers what they want
2. Start up Digital disruption - where do we stand?
2.1. Current avenues for digital disruption
2.2. Start-up based digital disruptions models
2.3. Strategies to redesign practices in delivery
3. Winning the game of disruption – digital way
4. Navigating through Digital Disruption
Please download: N.Meera, Shaik (2018).
http://www.aesanetwork.org/a-treatise-on-navigating-
extension-and-advisory-services-through-digital-disruption/

18. Tipping Points to Digital Disruption - How
Please download: N.Meera, Shaik (2018).
http://www.aesanetwork.org/a-treatise-on-navigating-
extension-and-advisory-services-through-digital-disruption/
eNAM = Amazon – (LBM+ Retailers + Aggregation + Delivery +Payment gateway)
Knowledge Portals = Google - (SEO + Crowd Content + Scale + Personalization)
eParwana / AeFDS = Uberization – (Fertimeter + Diagnostics+ Crop Profiling + Workflows)
Soil Health Cards = SMART farming – (Internet of Things + LBM + GPS + Google Maps)
Input supply app / Market App = UberApp – (aggregated demand+ LBM+3D Printing + Block
chain)

19. Take home Points..
• Can Digital Extension lead to Farm productivity and Profitability?
• What data driven services matter?
• How digital help realizing better markets?
• Smart phones..beyond advisory Apps?
• Digital Start ups – Integrating with Extension Advisory Systems
• Digital Start ups – Integrating with Extension Advisory Systems
• Digital Disruption & Social Inclusion
• Tipping Points to Digital Disruption
• Tipping Points to Digital Disruption – Why
• Tipping Points to Digital Disruption - How

20. Thank you.
meera.shaik@icar.gov.in
shaiknmeera@gmail.com