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