Scaling up climate smart agriculture via the Climate Smart Village Approach for Telangana State
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Given the high climatic variability in Telangana state in India, stakeholders came together to discuss context specific climate smart agriculture (CSA) practices and identify synergies to design and promote local level CSA implementation plans.
Scaling up climate smart agriculture via the Climate Smart Village Approach for Telangana State
CCAFS- ICRISAT
Scaling 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, ANGRAU
Dept. 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 best
practices that are relevant to target districts/mandals (Inventory of CSA practices/ technical
coefficients)
Participatory prioritization of location specific climate smart agricultural practices (districts/region
specific CSAPs)
Participatory identification of barriers and incentives, and convergence opportunities for promoting
CSA (Targeting on incentives, harmonization of governance)
Ex-ante impact analysis of potential adoption of selected CSA practices in different districts of
Telangana 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 climate
forecasts help develop district specific scaling up strategy
Approach to develop a strategy for scaling up climate smart agriculture in Telangana
state: 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
1. Broad bed and furrow (BBF) for moisture conservation and drainage
2. Ridge and furrow for in-situ water conservation
3. Residue incorporation (paddy and cotton) in high potential districts
4. Farm pond for critical/supplemental irrigation in relatively high
value crops
5. 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 practices
Rainfall
Situation
Sorgh
um
Maiz
e
Green
gram
Redgra
m
Groun
dnut Cotton
Soyab
ean Paddy Mango
Mosa
mbi
Sapot
a
Tomat
o
Ridges and
Furrows Drought
15 20 18 18 18 15
Mild
Drought
8 10 9 9 9 8
Normal 4 5 4 4 5 4
Excess 8 10 9 4 9 8
Broad Bed
Furrow Drought
22 28
Mild
Drought
12 15
Normal 5 5
Excess 12 15
Farm Pond Drought 20 20 25 37 24 25 42
Mild
Drought
10 10 15 25 15 15 21
Normal 10 10 10 15 15 15 10
Excess 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
19
Broad bed and Furrow: Major concerns
Concerns Options
Lack of
awareness on
potential benefits
and technical
skills
Trainings and
demonstrations/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
24
District
Infrastructure
/ implement
(Million Rs)
Capacity
Developmen
t (Million Rs)
Total Addl
Net
Returns*/
year
(Million Rs)
Adilabad 74 4 236
Nizamabad 9 3 133
Karimnagar 38 4 208
Medak 26 3 326
Rangareddy 12 3 48
Mahabubnagar 48 5 265
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)
Additional Operational Cost per hectare - Rs. 1500
* 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
Trainings and
demonstrations/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
ridgers
Power drawn as well as
bullock drawn ridgers
through 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
26
Potential Additional Net Returns due to Crop Residue Incorporation
in Cotton in TS (INR/year, Current Prices)
Additional Operational Cost per hectare - Rs. 3500
Benefits estimated as value of nutrient added into soil
District
Infrastructure
(Million Rs)
Capacity
Development
(Million Rs)
Total
Additional
Net Returns
(Million Rs)
Adilabad 425 4 795
Nizamabad 25 3 47
Karimnagar 215 4 401
Medak 150 3 273
Rangareddy 70 3 126
Mahabubnagar 272 5 495
Nalgonda 356 4 643
Warangal 180 4 333
Khammam 199 3 369
AgMIP Sentinel
Sites
27
Residue incorporation (Rotavator):
Major concerns
Concerns Options
Lack of
awareness
on potential
benefits and
technical
skills
Trainings and
demonstrations/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
• 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 Adilabad
Nizamaba
d
Karimnag
ar Medak
Rangaredd
y
Mahabu
bnagar Nalgonda
Waranga
l
Khamma
m
Potential No of
Farm Ponds
42 15 22 31 18 58 34 22 23
Potential area of suitable crops for supplemental irrigation, 000 ha
Potential 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 Rangareddy
Mahabub
nagar 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/vegeta
ble
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
29
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
Adilabad Nizamabad Karimnagar
Medak Rangareddy Mahabubnagar
Nalgonda Warangal Khammam
-1000
0
1000
2000
3000
4000
5000
6000
7000
2010-11 2011-12 2012-13 2013-14 2014-15
Maize
Adilabad Nizamabad Karimnagar
Medak Rangareddy Mahabubnagar
Nalgonda Warangal Khammam
0
10000
20000
30000
40000
50000
60000
70000
2010-11 2011-12 2012-13 2013-14 2014-15
Mango
Adilabad Nizamabad Karimnagar
Medak Rangareddy Mahabubnagar
Nalgonda Warangal Khammam
Despite the instability
average returns are
attractive for suitable
crops
AgMIP Sentinel
Sites
32
District
Infrastructure
with Drip
System
(Million Rs)
Infrastructure
with Sprinkler
System (Million
Rs)
Capacity
Development
(Million Rs)
Total
Additional Net
Returns
(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 appropriate
20mx20mx3m
• 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
Reliability
Inability 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 mandal
level 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