Scaling up climate smart agriculture via the Climate Smart Village Approach for Telangana State

CCAFS- ICRISATScaling up climate smart agriculture in

Telangana state

Shalander Kumar, K DakshinaMurthy, Murali Gumma, S Nedumaran, Anthony Whitbread, ICRISAT

Arun Khatri, CCAFS

Partners from EPTRI, PJTSAU, ANGRAUDept. of Agriculture/others, Telangana

Major hindrances/constraints in scaling up climate smart

agriculture in Telangana state

Lack of information on climatic stresses/hot spots at disaggregate (mandal level)

• Which area to target for what CSAPs and priority Lack of awareness and integration of stakeholders perspective in

identifying CSA practices for different sub-regions• No mechanism to integrate stakeholders perspective in prioritization

of CSAPs SAPs impacts consider incremental yields but not income, market,

even soil types Lack of information on region/district wise & CSA practice specific

potential benefits and investment requirements• High instability in incremental returns across years e.i good / drought

years (esp. NRM based CSA options) Poor access to farm machinery and reliable climate information (SCF) Hindrances due to compartmentalization/ lack of convergence among

deptts Severe gaps in capacities of farmers, extension staff, bankers as well

as input dealers on CSA

Poor Targeting, lack of capacity at different levels& low adoption of CSAPs

Climate Risk mapping to identify and prioritize mandals vulnerable to climate stresses(Prioritization of areas- Targeting)

Review the success stories of climate smart villages from different stakeholders and identify bestpractices that are relevant to target districts/mandals (Inventory of CSA practices/ technicalcoefficients)

Participatory prioritization of location specific climate smart agricultural practices (districts/regionspecific CSAPs)

Participatory identification of barriers and incentives, and convergence opportunities for promotingCSA (Targeting on incentives, harmonization of governance)

Ex-ante impact analysis of potential adoption of selected CSA practices in different districts ofTelangana state (potential benefits for better targeting)

Assessment of investment and infrastructure need to support CSA in TS (identifying gaps)

Stakeholders consultations and sharing prioritization outcomes (realistic & ownership)

Integrating climate risk analysis, CSAPs prioritization, Ex-ante analysis and seasonal climateforecasts help develop district specific scaling up strategy

Approach to develop a strategy for scaling up climate smart agriculture in Telanganastate: Framework & information for decision support

Aim is to provide tools and information wrt above to help policy makers and development actors take informed decisions to scale up CSA

i. Mandal wise climate risk analysis– understanding current climate risk and identifying the factors that render some mandals more vulnerable than others to climate

Mandal wise climate exposure index for baseline climate

Mandal wise climate exposure index with major crop type and command areas (2015-16)

Climate risk analysis helps prioritizing and better targeting of different districts / Mandals to address different climate stresses

There are areas having high risk of droughts, have concentration of cotton and maize

Need for location specific diversification of cropping systems/cultivars/enhancing water access for most attractive crops in vulnerable areas

ii. Participatory prioritization of climate smart agriculture (CSA)

practices

Participants representing all TS districts and other NARS & project partners, PJTSAU, EPTRI, NABARD

i. Climate smartness index based on potential contribution of each CSA option to

productivity, climate risk reduction (resilience), mitigation (emission & sequestration) and

Resource sustainability;

ii. Index for ‘Ease of adoption’ was estimated based on the technical feasibility of the CSA

option; cost of technology; inclusivity (smallholder, gender) and synergy with state

plans/development programs.

Workshop participants arrived at a weight for each criterion & score for each CSA practice

against each criterion.

I/ J = C1w1+………………+cn wn

I = ∑ ci wi

J = ∑ cj wj

Multi criteria analysis (MCA) prioritization of CSAPs

Participatory prioritization of CSA practice in Telangana

Southern zone of Telangana

Central zone of Telangana

0 1 2 3 4 5

19. Integrated Pest Management

18. Grading of local cows

17. AWD/SRI in Rice

16. Shelter for animal

15. Conservation of fodder resource…

14. Drought tolerant cultivars

13. Integrated Nutrient Management

12. Mechanization-small scale

11. Cotton + Pigeonpea/Soybean

10. Seed bank - Soybean/Pigeonpea

9. Rainwater Harvesting-…

8. Micro Irrigation

7. Crop Insurance

6. Paddy crop residue managemnet…

5. CLIC- Climate Information Service

4. Agro-Horti/IFS

3. Contingent Crop Planning

2. In-situ moisture conservation -…

1. Rainwater Harvesting- Farm…

Finance/Capital

0 1 2 3 4 5

Machinery

Incentives needed to promote CSA

Incentives needed to promote CSA

0 1 2 3

Capacity Building

0 1 2 3

Market Linking

0 1 2 3

Infrastructure

1. Broad bed and furrow (BBF) for moisture conservation and drainage2. Ridge and furrow for in-situ water conservation3. Residue incorporation (paddy and cotton) in high potential districts4. Farm pond for critical/supplemental irrigation in relatively high

value crops5. Provision of seasonal and mid-season climate forecast based

cropping systems options

iii. Ex-ante impact analysis of potential adoption of selected CSA practices in different districts of Telangana state

Considered actual area and yields of major crops and rainfall level for 5 years from 2010-11 top 2014-15 to estimate net additional returns due to potential adoption of CSA practice per ha and at district level

The coefficient of yield increment due to CSA practices are considered based on the empirical studies and results of various on-farm research in different locations in TS/SAT.

We selected five high priority CSA practices for ex-ante impact assessment:

Assumptions

Ridge and furrow system: 50% of rainfed area under cotton, maize, pigeon pea, groundnut, sorghum, green gram

Farm pond: 5% of rainfed land holdings <2 ha 20-25 % of rainfed land holdings >2 ha

Residue incorporation: Crops: cotton 50% of rainfed area

Broad bed & furrow (BB&F): All black soil area under cotton and Soybean

Machinery & implements: Rotavator (rainfed), Happy seeder (irrigated), BBF maker, ridge maker 70% at village level- Machine used for 15 days (individual/custom hiring) 30% at cluster of villages/mandal level- Machine used for 30 days (FPOs/business models)

Capacity development (cost): Training program and demonstration/field school at village level consecutively at least for

2 years

Technical coefficients- Rate of yield increments in different crops due to different CSA interventions

CSA practicesRainfall Situation

Sorghum

Maize

Greengram

Redgram

Groundnut Cotton

Soyabean Paddy Mango

Mosambi

Sapota

Tomato

Ridges and Furrows Drought

15 20 18 18 18 15

Mild Drought

8 10 9 9 9 8

Normal 4 5 4 4 5 4Excess 8 10 9 4 9 8

Broad Bed Furrow Drought

22 28

Mild Drought

12 15

Normal 5 5Excess 12 15

Farm Pond Drought 20 20 25 37 24 25 42Mild Drought

10 10 15 25 15 15 21

Normal 10 10 10 15 15 15 10Excess 5 5 5 10 10 5 10

Crop Residue incorporation Impact assessed in terms of value of nutrient added to the soil

Based on various published sources

16

Potential Additional Net Returns due to Broad Bed and Furrow in cotton &

soybean (INR/year, Current Prices)

Black soil area suitable for BBF

Soybean

Cotton

Drought 22%

Mild Drought

12%

Normal 5%Excess 12%

AgMIP Sentinel

Sites

17

High instability in the additional annual net returns from BBF depending on

the rainfall and price

For example: Cotton

Seasonal climate forecast could prevent additional costs during normal years-reducing the investment risk

-2000

0

2000

4000

6000

8000

10000

12000

14000

2010-11 2011-12 2012-13 2013-14 2014-15

Adilabad Nizamabad Karimnagar Medak Rangareddy

Mahabubnagar Nalgonda Warangal Khammam

AgMIP Sentinel

Sites

18

District

Infrastructure/implements(Million Rs)

Capacity Development (Million Rs)

Total Additional Net Returns*/ year (Million Rs)

Adilabad 97 4 205

Nizamabad 6 3 132

Karimnagar 57 4 40

Medak 34 3 106

Rangareddy 16 3 27

Mahabubnagar 63 5 32

Nalgonda 80 4 14

Warangal 54 4 19

Khammam 47 3 2

Initial Investment Needs and Potential Total Additional Net Returns by

adopting BB&F in cotton & soybean (INR, Current Prices)

Additional Operational Cost per hectare - Rs. 2000

* varies depending on the amount of rainfall and price

AgMIP Sentinel

Sites

19

Broad bed and Furrow: Major concerns

Concerns Options

Lack of awareness on potential benefits and technical skills

Trainings anddemonstrations/farm field schools at village level at least for 2-3 consecutive years

This is not in the agenda of extension staff

Extension staff also lack awareness and skills

Poor access to BBF makers

Promote BBF makers through custom hiring centers or individual entrepreneurs

Individual tractor farmers has low interest to buy as it has utility for few days in a year.

Targeting Need to target most promising crops cotton and soybean in black soil regions

Need to consider net additional economic returns

AgMIP Sentinel

Sites

20

Major crops under soils other than black, suitable for ridge

and furrow systems (targeting rainfed areas)

AgMIP Sentinel

Sites

21

District

Average Additional Net Returns/ha

Total Additional Net Returns (Million Rs)

Adilabad 1751 214

Nizamabad 2603 14

Karimnagar 2101 153

Medak 3082 87

Rangareddy 1307 4

Mahabubnagar 1549 119

Nalgonda 2157 268

Warangal 2221 140

Khammam 1725 128

Potential Additional Net Returns due to Ridge and Furrow in

Cotton in TS (INR/year, Current Prices)

Drought 15%

Mild Drought

8%

Normal 4%Excess 8%

AgMIP Sentinel

Sites

22

High instability in the annual net additional returns from ridge & furrow

system depending on the rainfall and prices (INR, Current Prices)

Seasonal climate forecast could prevent additional costs during normal years- reducing the investment risk

-2000

0

2000

4000

6000

8000

10000

2010-11 2011-12 2012-13 2013-14 2014-15

Cotton

Adilabad Nizamabad Karimnagar

Medak Rangareddy Mahabubnagar

Nalgonda Warangal Khammam

-5000

0

5000

10000

15000

2010-11 2011-12 2012-13 2013-14 2014-15

Maize




23

Similar estimates for other crops (Ridge and furrow)

Pigeon peaGreen gram

Sorghum

Maize

AgMIP Sentinel

Sites

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District

Infrastructure/ implement(Million Rs)


Total AddlNet Returns*/ year (Million Rs)

Adilabad 74 4 236

Nizamabad 9 3 133

Karimnagar 38 4 208

Medak 26 3 326

Rangareddy 12 3 48


Nalgonda 62 4 313

Warangal 31 4 228

Khammam 35 3 154

Initial Investment Needs and Potential Total Additional Net Returns

by adopting Ridge and Furrow in Major Crops (INR, Current Prices)


* varies depending on the amount of rainfall and price

AgMIP Sentinel

Sites

25

Ridge and Furrow: Major concerns

Concerns Options

Lack of awareness on potential benefits and technical skills


This is not in the agenda of extension staff

Extension staff also lack awareness and skills

Poor access to ridgers

Power drawn as well as bullock drawn ridgersthrough custom hiring centers or individual entrepreneurs

Though there is poor access to power drawn ridgers, buck drawn machines are hardly available

Targeting Need to target most promising crops and regions

Need to consider economic returns, not only productivity.

AgMIP Sentinel

Sites

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Potential Additional Net Returns due to Crop Residue Incorporation

in Cotton in TS (INR/year, Current Prices)


Benefits estimated as value of nutrient added into soil

DistrictInfrastructure(Million Rs)



Adilabad 425 4 795

Nizamabad 25 3 47

Karimnagar 215 4 401

Medak 150 3 273

Rangareddy 70 3 126


Nalgonda 356 4 643

Warangal 180 4 333

Khammam 199 3 369

AgMIP Sentinel

Sites

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Residue incorporation (Rotavator):

Major concerns

Concerns Options

Lack of awareness on potentialbenefits and technical skills


• This is not in the agenda of extension staff

• Extension staff also lack awareness and skills

• Farmers are not able to visualize the benefits in the form of adding nutrients to soil (support!!)

Poor access to machine

Promote rotvator & happy seeder through custom hiring centers or individual entrepreneurs

• Individual tractor farmers has low interest to buy as it has utility for few days

• Local skills building for repair and maintenance is also critical

Targeting Targeting most promising crops cotton and soybean and black soil regions

• Need to consider net additional economic returns

28

Potential No of Farm Ponds (000) & area (000 ha) under suitable crops for supplemental irrigation in TS (5% of <2ha and 20-25% of >2 ha land holdings)

Particulars AdilabadNizamabad

Karimnagar Medak

Rangareddy

Mahabubnagar Nalgonda

Warangal

Khammam

Potential No of Farm Ponds

42 15 22 31 18 58 34 22 23

Potential area of suitable crops for supplemental irrigation, 000 haPotential Total area: Cotton,Groundnut, maize, mango, Mosambi, Vegetable/tomato

84 30 44 62 37 116 67 45 45

Farm ponds

Crops Adilabad Nizamabad Karimnagar Medak RangareddyMahabubnagar Nalgonda Warangal Khammam

Cotton 2736 3391 3250 3549 1839 2121 2474 3998 3988

Groundnut 4149 6337 3817 3097 4803 4602 3295 4902 4060

Maize 1960 4143 3464 2307 1740 1396 1227 3315 3829

Mango 15019 23859 28672 27246 20746 24327 40706 26535 25393

Batavia 18601 22164 18601 22164 18601 18601 22164 18601 18601

Tomato/vegetable

17600 15800 17600 19100 14300 14300 19100 17600 17600

Potential Additional Net Returns due to Farm Pond in Major Crops in TS (INR/ha)

AgMIP Sentinel

Sites

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Instability in additional Net Returns due to Farm Pond across years (INR/ha, Current Prices)

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

2010-11 2011-12 2012-13 2013-14 2014-15

Cotton




-1000

0

1000

2000

3000

4000

5000

6000

7000

2010-11 2011-12 2012-13 2013-14 2014-15

Maize




0

10000

20000

30000

40000

50000

60000

70000

2010-11 2011-12 2012-13 2013-14 2014-15

Mango




Despite the instability average returns are attractive for suitable crops

AgMIP Sentinel

Sites

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Proposed used of harvested water for different crops (%)

District Cotton 2.0Groundnut

2.0Maize 2.0 Mango 2.0

(Mosambi) 2.0

Cotton 1.6 + Tomato 0.4

Adilabad 50 0 10 10 0 30

Nizamabad 20 0 50 5 0 25

Karimnagar 25 10 20 10 5 30

Medak 20 0 40 10 0 30

Rangareddy 20 8 25 10 2 35

Mahabubnagar 25 10 30 10 5 20

Nalgonda 40 10 0 10 20 20

Warangal 25 15 25 10 0 25

Khammam 45 5 20 30 0 0

AgMIP Sentinel

Sites

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Potential Total Additional Net Returns due to Farm Pond in

Major Crops in TS (INR/ha, Current Prices)

AgMIP Sentinel

Sites

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District

Infrastructure with Drip System(Million Rs)

Infrastructure with Sprinkler System (Million Rs)



Adilabad 7439 6419 4 387

Nizamabad 2647 2284 3 171

Karimnagar 3869 3339 4 326

Medak 5480 4729 3 436

Rangareddy 3235 2791 3 220

Mahabubnagar 10303 8890 5 428

Nalgonda 5947 5131 4 678

Warangal 3952 3410 4 262

Khammam 4006 3456 3 460

Initial Investment Needs and Potential Total Additional Net Returns

through Major Crops due to Farm Pond in TS (INR, Current Prices)

Farm Ponds: Concerns to address

MNREGS Smaller size- 10x10x3 • In farmers perception not useful• No lifting device• No training on efficient & economic use

of water

NHM

(Major program)

Size is appropriate20mx20mx3m

• Farmers with drip has preference• Available mostly for horticulture crops,

not for other crops• Number of slots available not as per the

potential

There has to be a common platform to access farm pond support based on farmers preference for crops and size

The farm pond support has to be a customized package (pond, water lifting device, MIS) based on farmers preference and resources

Capacity building of farmers and extension staff

Climate information- Seasonal and short & medium term forecast

Farmers to some extent have access and faith on the short and medium term forecasts

But the seasonal forecasts Poor access ReliabilityInability to understand to make their use in decision making

Need to build capacity on scientists and extension system on delivering crop management options based on credible downscaled SCFs (RARS & KVKs could play an important role)

Excel based tools or an app could be developed to prioritize cropping options based on probabilistic SCFs

Need to have understanding/ MOU with IITM for timely availability of mandallevel SCFs

Remote sensing based Tank water resource monitoring systems created for enabling

efficient allocations using relative depth of water tanks through ‘topo sheets’

accessible from the Geological survey of India

Monitoring of common minor irrigation tanks

Based on prioritization and ex-ante analysis, we undertake these activities:

District specific gaps on targeting of CSAPs and infrastructure

Validation of results with farmers and field level extension functionaries

Stakeholders workshop with senior government functionaries, NARS & private sector

Working paper

Where we have reached

Climate Risk mapping for mandal specific prioritization

Inventory of CSA practices and associated technical coefficients

Participatory prioritization of location specific CSA practices (MCA)

Participatory identification of barriers and incentives for promoting CSA

Shared climate risk analysis and prioritization outputs with state govt and NABARD

Ex-ante impact analysis on potential benefits for better targeting of CSA practices

Assessment of investment and infrastructure need (identifying gaps)

Steps completed

Next steps

Thank you!

Government & Nonprofit

Scaling up climate smart agriculture via the Climate Smart Village Approach for Telangana State