Climate Smart Villages in India
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Transcript of Climate Smart Villages in India
A Tour to Climate Smart Villages
For and on behalf of all the actors of CSA
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
CSA Research 4 Development Sites
• Irrigated intensive systems
• Rainfed mixed systems
• Rainfed unfavorable systems
• Flood prone ecologies
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
CSV CSV
CSV
Participatory platform
Participatory platform
Participatory platform
Participatory platform
Research Platform
PCA of the performance of wheat genotypes under different environments
Jat et al (CIMMYT-BISA)
Performance of wheat genotypes under 21 different environments during 2011-14
Jat et al (CIMMYT-BISA)
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)
Sensor
Wireless
Transmitter
Rec
eiv
er
Timer &
Relay
Pump
Starter
How much we should save? Crop should guide tubewell
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
Welcome to Climate Smart Village-Noorpur Bet
Participatory strategic research for developing portfolio of CSAPs • 9 Sites across IGP • Developing new generation scientists • Scenario analysis-Modelling
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)
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
Weather related risks are increasing in winter, Example of 2014-15
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
Karnal, Haryana
Vaishali, Bihar
Maize and Wheat Nutrient Expert for South Asia
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)
Smallholder Precision: Android phone App for GreenSeeker N calculator
Smart Nutrient Management by young farmers
Adapted Agroforestry Based Systems
Adaptability of crops/cultivars
Sustainable intensification/diversification with resilient systems
Integration of livestock
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.
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
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)
Targeting genotypes: The Systems’ Approach is important
Source: Santiago L Ridaura,
CIMMYT
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)
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
Linkages among NAPCC, SAPCC, LAPA and climate smart villages (CSVs)
Local Planning with Stakeholders is a Must
• 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
Climate Smart Agriculture: Bihar on the Move Agriculture Minister of Bihar in CSVs
• 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
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
Haryana is on Fast Track for Scaling CSA
Thanks