Presented in September 2015 at CSV Workshop, India by ML Jat (CIMMYT)

1. A Tour to Climate
Smart Villages
For and on behalf of all the
actors of CSA

2. CCAFS Flagship 1 Projects
CCAFS Flagship Project 1.1
Developing, adapting and targeting
portfolios of climate smart agricultural
practices for sustainable intensification
of smallholder and vulnerable farming
systems in South Asia
• Science-based, scalable evidences for
CSAPs identified and implemented
through CSVs
• Framework for targeting large-scale
adoption of CSAP portfolios by a diverse
range of farm household types within
CSVs of different agro-ecologies
• Mechanism for verification/certification
of CSVs as indicators of improved
income, food security, livelihoods over
non-CSVs across diverse agro-
ecosystems2
CCAFS Flagship Project 1.2
Recommendation domains, incentives
and institutions for equitable local
adaptation planning at sub national level
and scaling up climate smart agricultural
practices in wheat and maize systems
• Guidelines, capacity, governance,
recommendation domains and synergies
for Local Adaptation Plan for Action (LAPA)
and CSVs for scaling Climate Smart
Agriculture Practices (CSAPs)
• Developing and defining innovative
business models and open innovation
platforms for scaling Climate Smart
Agriculture Practices (CSAPs)
• Develop incentive based policy instruments
that influence the trajectories of farmer
households towards better adaptation to
climate change

3. CSA Research 4 Development Sites
• Irrigated
intensive
systems
• Rainfed
mixed
systems
• Rainfed
unfavorabl
e systems
• Flood
prone
ecologies

4. CSAR4D Approach
• System focused
• Participatory approach
• Continuum of ‘strategic-
applied research-capacity
development-delivery’
• Innovation systems
• Convergence and synergy
with networks, and
project, investments
• Consortium of active and
complementing
stakeholders with Farmer
in center of it
Strategic
Research
Platforms
CSV
CSVCSV
CSV
Participatory
platform
Participatory
platform
Participatory
platform
Participatory
platform

5. Research Platform

6. PCA of the performance of wheat genotypes
under different environments
Jat et al (CIMMYT-BISA)

7. Performance of wheat genotypes under 21
different environments during 2011-14
Jat et al (CIMMYT-BISA)

8. Rice-wheat System: How smart we are on
irrigation water use?
Tillage, crop
establishment
Irrigation
system
RW system
grain yield
(t/ha)
RW system
irrigation
water use
(cm)
RW system
irrigation water
productivity
(kg/m3)
CTTPR-CTW§ Flood 09.64a
143c 0.67a
ZTDSR-ZTW Flood 10.03b (4.0)
122b (14.7) 0.82b (22.4)
ZTDSR-ZTW Surface drip 10.20b (5.8)
71a (50.3) 1.44c (114.9)
ZTDSR-ZTW
Sub-surface
drip 10.47c (8.6)
72a (49.7) 1.45c (116.4)
Source: (CIMMYT-BISA-CCAFS, 2015)

9. Sensor
Wireless
Transmitter
Receiver
Timer &
Relay
Pump
Starter
How much we should save?
Crop should guide tubewell

10. Cropping Systems
CT
system
CA
system
Rice-Wheat-Mungbean 37.30 36.90
Maize-Wheat-Mungbean 55.33 52.69
Maize-Maize-Sesbania 50.17 43.25
Maize-Mustard-Mungbean 48.21 45.35
GWP (Mg CO2-eq/ha/year) of various crop
rotations under CT and CA systems

11. Welcome to Climate Smart Village-
Noorpur Bet

12. Participatory strategic research for developing
portfolio of CSAPs
• 9 Sites across IGP
• Developing new generation scientists
• Scenario analysis-Modelling

13. Some results: grain yield of rice-wheat system
with differential investments
Punjab Haryana Bihar
Rice Wheat System Rice Wheat System Rice Wheat System
Scenario-1
6.4
(0)*
4.9
(0)
11.3
(0)
6.6
(0)
4.7
(0)
11.3
(0)
5.5
(0)
3.2
(0)
8.7
(0)
Scenario-2
6.2
(0)
4.8
(0)
11
(0)
6.6
(0)
4.7
(0)
11.2
(0)
5.4
(0)
3.2
(1964)
8.6
(1964)
Scenario-3
6.4
(-162)
4.9
(551)
11.3
(389)
6.5
(-6510)
5.3
(-51.2)
11.8
(-6561)
5.6
(-6442)
4.0
(-2709)
9.6
(-9151)
Scenario-4
6.7
(-2483)
5.1
(-2439)
11.9
(-4922)
6.5
(-6949)
5.4
(-871)
12
(-7820)
6.6
(-6792)
5.0
(-8752)
11.6
(-15544)
Scenario-5
7.0
(-4679)
5.2
(-2493)
12.2
(-7172)
6.6
(-7968)
5.5
(-899)
12.1
(-8867)
6.4
(-7075)
4.6
(-8936)
11
(-16011)
Scenario-6
7.1
(-941)
5.3
(-2077)
12.5
(-3018)
6.6
(-8854)
5.7
(-1081)
12.3
(-9935)
6.5
(-8331)
4.4
(-9333)
10.9
(-17664)

14. Gains in Net Profit (USD) over BAU
(Scenario 1)
Punjab Haryana Bihar
Rice Wheat System Rice Wheat System Rice Wheat System
Scenario-2 -47 -28 -75 -1 -13 -13 -26 -31 -57
Scenario-3 1 -25 -24 91 183 274 129 112 240
Scenario-4 127 65 192 105 234 340 424 443 867
Scenario-5 -16 86 303 134 256 390 373 354 727
Scenario-6 193 109 302 158 308 466 433 376 809

15. Weather related risks are increasing in
winter, Example of 2014-15

16. Wheat Adapting to untimely excess rain
Evidence from farmers fields: Wheat 2014-15 in
Karnal villages (n=207)
Source: Sakshi Balyan* and ML Jat (2015)
*CIMMYT-CCAFS Graduate Student

17. Karnal,
Haryana
Vaishali,
Bihar

18. Maize and Wheat Nutrient Expert for South Asia

19. States Rice 2014 Wheat 2014-15
Grain yield (Mg ha-1)
SSNM-NE FFP SSNM-NE FFP
Punjab 7.10 (18) +0.35 6.75 (18) 4.82 (47) + 0.22 4.60 (47)
Haryana 5.22 (243) +0.14 5.08 (243) 4.94 (222) + 0.45 4.49 (222)
Net Returns (US$ ha-1
)
SSNM-NE FFP SSNM-NE FFP
Punjab 1233 (+ 92) 1141 976 (+43) 933
Haryana 1506 (+ 33) 1473 1254 (+119) 1135
Field Scale Evidence for Smallholder
Precision Nutrient Management (SSNM-NE)
in Rice and Wheat
Source: CIMMYT-CCAFS (unpublished)

20. Smallholder Precision:
Android phone App for GreenSeeker N calculator

21. Smart Nutrient Management by young farmers

22. Adapted Agroforestry Based Systems

23. Adaptability of crops/cultivars

24. Sustainable intensification/diversification with
resilient systems

25. Integration of livestock

26. CSAPs Powered with ICTs
• More awareness and thus informed
decisions - Better impact to manage
risk
• Better weather information,
adoption of LLL- waters saving,
improved application of
fertilizers- safeguarded during
rainfall uncertainty in 20014-15
• Expected multiplier effects of CSV’s
• Increased exposure to CSAP’s,
Collective decision, market
surplus of communities
Comparative
analysis before
and after
N=180 ZT LLL Residue
Burning
%
area
% of
HH
%
area
% of
HH
%
area
% of
HH
Adoption
survey
(2013-14)
9.6 16.1 45.9 57.8 6.0 12.2
CCAFS
baseline
(2011-12)
4.5 12.4 44.6 56.8 - -
Evidences of improved adoption- interviews of subset
of farmers enrolled in M(obile) solutions
Our ICT initiative is now being directly run
by the project partners through a Climate
smart corpus funds they have generated.

27. Mainstreaming Gender in CSV’s
• Understanding gender gap through baseline surveys
– Exist gender differences in access to resources,
education
– Although male and female both are involved in
agriculture, however decision making is largely
controlled by male
– gender differences makes it difficult for female
counterparts to adapt to various climate coping
strategies as effectively as done by the males.
• Thus need to integrate gender in household decision
making to adopt of CSAP’s
– Reducing information asymmetry and empowering them
with information- women in the male headed
households feel that their participation in family
agriculture has improved with increased information flow
– Making women in the households accountable for Book
keeping the expenses and savings -Lekha Jokha-. 141
HH in 33 villages (2014-15) with 18% women directly
recording the information.
– Training- Service windows, DSR, Green seeker,
weather related inf. etc.
• Creating gender empowerment index to measure the
change between CSVs and non CSVs
– Measuring access, control and giving appropriate
weights to indicators- Social, economic, political
and contribution to agriculture and livestock

28. Innovative business models for scaling CSA
• Enhancing the role of private sector in scaling up CSAP
• Three types of business models: back end (service
providers), front end (markets /export purpose) , end-to
end (value chain approach)
Source: Annemarie Groot (WUR)

30. Targeting genotypes: The Systems’ Approach is
important
Source: Santiago L Ridaura,
CIMMYT

31. Typologies of Farming Systems
Vaishali district (CCAFS)
51 variables were used to classify 140 farm households
5 groups:
G0 Landless farmers (12)
G4. Resource poor mixed crop-livestock farmers (.3 Ha)(off farm)(32)
G1. Average mixed crop-livestock farmers (.65 Ha)(rent in)(38)
G3. Wealthy mixed crop-livestock farmers (1.3 Ha)(hire lab)(24)
G2. Non-livestock farmers (rent out)(34)
Santiago et al (2014)

32. 1.1.1: CSAPs
in CSVs
1.1.2:
Framework/
HH typology
1.1.3:
Certification
of CSVs
1.2.1:
LAPA
1.2.2:
Business
Model
1.2.3:
Policy
linkage
4.1
Policy and
institutions
(IFPRI
FP3:
CC mitigation
in Ag
Developing adapting
and targeting CSAPs
Domain, inceptive and
institutions for scaling up CSAPs
Linkages of different activities of CCAFS-FP 1 with FP 3 and 4

33. Linkages among NAPCC, SAPCC, LAPA and
climate smart villages (CSVs)

34. Local Planning with
Stakeholders is a
Must

35. • Haryana and Punjab: State Agricultural Department, NICRA, SAPCC,
National mission on sustainable agriculture, WHEAT, BMZ-wheat,
Diversification program
• Bihar: State Agricultural Department, NICRA, SAPCC, National mission on
sustainable agriculture, CSISA, Bioversity International, SRFSI, STRASSA,
HTMA, Monsanto CA program, Climate Resilient Maize for Asia (CRMA),
• Karnataka: DoA, CRP 1.1 (dryland systems), Krishi Bhagya (GoK
initiatives), Bhuchetana plus, International maize improvement consortium
(IMIC) Asia, NICRA,
• Andhra Pradesh: DoA, CRP 1.1 (dryland systems), International maize
improvement consortium (IMIC) Asia, NICRA, Primary sector mission (new
project in ICRISAT),
• Odisha: DoA, CSISA, STRASSA, AICRP on IFS,
• Bangladesh: Costal Saline: STRASSA salinity, GSR (Green Super Rice),
CSISA-MI
• Nepal: DoA CSISA, STRASSA, SRFSI, Sustainable Agriculture Kits
Convergence opportunities within and between CCAFS-FPs
and with other similar initiatives

36. Climate Smart Agriculture: Bihar on the Move
Agriculture Minister of Bihar in CSVs

37. • Agriculture Minister of Karnataka at CSA
learning sites in Punjab
• Chief Minister’s Budget speech of Karnataka
Policy planners reaching at CSVs
to understand the process for
scaling CSAPs

38. Policy level sensitivity for Climate Vulnerability:
Chief Minister of Punjab taking stock of losses due to
untimely rains in winter 2014-15 and plans for winter
2015-16

39. Haryana is on Fast Track for Scaling CSA

40. Thanks