status of the fast-track on rice sri lanka...mr. chamila perera, crop modeling, fcrdi, doa...
TRANSCRIPT
Status of the Fast-Track on Rice – Sri Lanka
Fast-Track Report On:
Modeling the impacts of a variable and changing climate on
rice agricultural systems in one of the major rice
producing district (Kurunegala) in Sri Lanka.
Name of Investigator(s) & Institutions
Dr. S.P. Nissanka, Crop Modeling, UoP
Dr. W.M.W. Weerakoon, Agronomist, FCRDI, DoA (Co-PI)
Dr. B.V.R. Punyawardhene, Agro-Meteorologist, DoA (Co-PI)
Dr. R.M. Herath, Senior Agricultural Economist, SEPC, DoA (Investigator)
Dr. Asha S. Karunaratne, Crop Modeling, Sabaragamuwa University (Investigator)
Ms. Punya Delpitiya, Crop Modeling, DoA, (Investigator)
Mr.Samal Dharmarathne, Crop Modeling, KIT (Investigator)
Mr. Chamila Perera, Crop Modeling, FCRDI, DoA (Investigator)
Climate-team of the FECT
Team directly involve in the fast-tract on rice. Other members identified in the
project also assisted the fast-tract activities on rice.
District wise Performance of the rice Ssctor of Sri Lanka
Zone and Districts Gross Extent Production (mt)
Harvested (hectares)
Maha Yala Maha Yala Total
2009/2010 2010
Wet Zone
Colombo 4763 2656 14190 7244 21434
Gampaha 12464 7810 36560 24475 61035
Kalutara 15289 11935 45245 29527 74772
Galle 17142 11685 44078 26912 70990
Matara 15811 15467 45455 41703 87158
Kegalle 8331 7209 29589 19222 48811
Ratnapura 13485 11449 38034 31922 69956
Kandy 13826 10103 37943 28502 66445
Nuwara Eliya 5583 2960 13007 7373 20380
Badulla 26988 12665 99203 48224 147427
Dry /Intermediate Zones
Kurunegala 68023 56386 274343 225159 499502
Puttalam 14236 9436 48075 28608 76683
Matale 20088 10103 81175 41370 122545
Moneragala 27360 13884 118331 57277 175608
Jaffna (b) 8703 - 20117 - 20117
Kilinochchi(c) - 1468 - 3360 3360
Mannar 8093 600 41755 2240 43995
Mullaitivu 2144 1150 10753 4207 14960
Vavuniya 4830 827 24457 3685 28142
Anuradhapura 81063 22808 320937 89625 410562
Polonnaruwa 60301 58300 284628 262738 547366
Ampara 69840 59196 358274 275913 634187
Batticaloa 54329 20894 193274 73906 267180
Trincomalee 26521 13059 106727 58879 165606
Hambantota 26012 24244 117793 108332 226125
Mahaweli 'H' Area 25012 15824 145263 79097 224360
Udawalawe 13068 14317 80361 91555 171916
Sri Lanka 643305 416435 2629567 1671055 4300622
(a) The cultivation year comprise Maha ( September/October- march/april)
and Yala (April/May - August/ September)
(b) No Cultivation during Yala season
( c) No cultivation during Maha season
Kurunegala district was selected
for the fast tract since it is one of
the leading rice producing district
and where the Rice Research and
Development Institute is located
CLIMATE:
BASELINE AND
FUTURE SCENARIO
Time Series plots- Maximum/ Minimum Temperature
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Daily Maximum Temperature time series for 30 years- Batalagoda
Baseline 2040- 2070
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Daily Minimum Temperature time series for 30 years- Batalagoda
Baseline 2040- 2070
Time Series plots- Rainfall
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Pre
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(m
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Daily Rainfall time series for 30 years (2040- 2070) - Batalagoda
Comments
Data Acquisition:
Datasets for Sunshine hours, Minimum temperature , maximum temperature and precipitation observed at the
Meteorological station in Batalagoda (lat- 7.51N, lon- 80.28E) in the Kurunegala District were obtained.
Quality Control of Data:
All four datasets were checked for data gaps and the percentage of missing observations from 1980/1/1 –
2010/12/31 was found to be less than 1% in precipitation, minimum/ maximum temperature datasets and less than
3% in sunshine hours dataset. All four datasets were then checked for any unusual/ implausible observations (over
3 x rms anomalies) and such identified observations were removed from the dataset. Other particularly high
outliers were checked with a neighboring station (Kurunegala) and retained if consistent. After that gaps were
filled as instructed in the AgMIP handbook (page 10 of Draft: November 5, 2012).
Obtaining Solar Radiation:
Since only sunshine hours were available for the duration, using FAO , 1998 (#56: irrigation and drainage paper
page 50) it was converted into energy from (MJ/m2 ).
Putting it into AgMIP formats:
Then a .AgMIP file for Batalagoda was created using available data and the file was run through the R script
provided by the AgMIP climate team. Baseline (1980- 2010) and future (2040- 2070) datasets were used in
plotting graphs for precipitation & Maximum/ Minimum temperatures.
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Tmax Tmin Average
1980-2010
2040-20701.57
1.65
1.61
1500
1550
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1980-2010 2040-2070
Temperature Rainfall
CROP MODELING : Rice
Progressive improvement of Model Calibration and
Validation
Stage 1: (March 11)
Varieties : Bg 300, Bg 357, Bg 358, At 308
Experimental date: One season -2010 Yala
Genetic coefficient: Manually calculated
Stage 2: After ICRISAT Training in March
Varieties : Bg 300, Bg 357, Bg 358, At 308
Experimental date: One season -2010
Genetic coefficient: GLUE - Auto run for 6000 times
Management changes (irrigation)
Stage 3: After Dubai Meeting and STICS (in May) Training
Explore for more research information on variety trial for calibration
Varieties : Bg 300, Bg 357, Bg 358, At 308
Variety Calibration Experiments
RRDI- athalagoda
Coordinated Variety Trials RRDI FCRDI Bathalagoda Mahailluppallama
Bg 300 2001 M (m), 2001 M (h) 2000 Y, 2001 Y, 2001 M 2000Y, 2000 M, 2002 Y
(3 months) 2007 Y (m), 2001 Y (h) 2003 Y, 2003 M, 2004 M 2002 M, 2003 Y, 2003 M
2010 Y 2005 Y, 2006 Y, 2006 M 2004 M, 2004 Y, 2005 Y
2007 Y, 2007 M 2005 M, 2006 Y, 2008 Y
(5) (11) (12)
Bg 357 2006 Y (m), 2006 (h) 2000 Y, 2000 M, 2001 Y 2000 Y, 2000 M, 2002 Y
(3.5 months) 2010 Y 2001 M, 2002 Y, 2003 Y 2002 M, 2003 Y, 2003 M
2003 M, 2004 M, 2005 Y 2005 Y, 2005 M, 2006 Y
2005 M, 2006 Y, 2008 Y 2008 Y
(3) (12) (10)
Stage 3: After Dubai and STICS Training
Variety Calibration Experiments
RRDI- athalagoda
Coordinated Variety Trials RRDI FCRDI Bathalagoda Mahailluppallama
Bg 358 2007 Y (m) , 2007 Y (h) 2004 M, 2005 M, 2006 Y 2004 M, 2005 Y, 2005 M,
(3.5 months) 2010 Y 2006 M, 2007 Y, 2007 M 2006 Y, 2006 M, 2007 Y
2008 Y 2007 M, 2008 Y
(3) (7) (8)
At 308 2010 Y (h)
(3 months)
Calibration Experiments
BG 300- 5 Experiments 2010 (Y) 440.4 146.2 381.6 12.9 53.7 .0260 0.85 1.17
2007 high fertiliser (Y) 377.9 168.9 390.5 12.5 50.6 .0260 0.96 1.24
2007 medium fertiliser (Y) 335.1 89.0 425.6 11.9 51.7 .0260 0.66 1.24
1997 medium fertiliser (M) 515.4 92.1 559.4 11.5 55.8 .0260 0.84 1.23
1997 high fertiliser (M) 531.7 116.7 568.5 11.9 57.9 .0260 0.81 1.23
BG 357- 3 Experiments: 2006 medium fertiliser (Y) 405.7 133.7 421.6 12.1 58.0 .0250 0.90 1.23
2006 high fertiliser (Y) 512.1 133.5 331.8 12.1 57.4 .0250 0.85 1.23
2010 477.3 140.0 368.3 12.7 50.5 .0250 0.85 1.24
BG 358- 3 Experiments: 2010 489.5 115.0 400.9 12.2 60.2 .0190 1.00 1.20
2007 medium fertiliser 435.6 72.5 500.1 10.7 50.3 .0190 0.69 1.10
2007 high fertiliser 516.5 35.4 496.9 10.4 50.8 .0190 0.97 1.24
2007 medium fertiliser (M) 500.2 152.3 480.3 12.9 60.6 .0190 0.95 1.00
P1 P2R P5 P2O G1 G2 G3 G4
BG 300: CRVT EXPERIMENTS
RMSE = 1583 kg/ha
BG 357: CRVT EXPERIMENTS
RMSE = 1824 kg/ha
BG 358: CRVT EXPERIMENTS
RMSE=1334 kg/ha
Genetic coefficients for respective varieties are selected based
on the lowest RMSE for the yield .
However, time taken to heading stage and physiological
maturity, and also the past experiences are also considered.
Farmers Data: Only Rain-fed paddy farming – 2010 for FT
Improvement with respect to soil status of farmer fields
Identify the soil variation of different farmer fields. Instead of
one soil group we initially used (Bathalagoda series), farmer
fields were categorized in to 3 soil groups and respective
SBuilds were used for yield simulation.
Maha Season
Batalagoda
Major soil groups of Kurunegala District of Sri Lanka
Farmers were categorized into three (Yala) and two (Maha) major soil groups
Yala Season
DOME YEAR 2010
Note:
Yield simulation for individual farmer for the base 30 years (1980-2010) and
future scenario (2040-2070); fertilizer management (N, P, K), varieties and
other managements and Solar Radiation were considered the same as the
historical year of 2010.
[Observed historical farmer information for the study was taken form the
Department of Agriculture which is the authorized Institute that collect
information on cost of cultivation of crops for annual crop statistics
publications of Sri Lanka]
3500
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7000
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YIE
LD (
kg/h
a)
FARMER
MAHA SEASON
HISTORICAL
FUTURE NOADOPTATION
2500
3000
3500
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4500
5000
5500
6000
6500
7000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
YIE
LD (
kg/h
a)
FARMER
YALA SEASON
HISTORICAL
FUTURE NOADOPTATION
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4000
4500
5000
5500
6000
6500
7000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
YIE
LD (
kg/h
a)
FARMER
TOTAL
HISTORICAL
FUTURE NOADOPTATION
Farm ID No Season Farm size(ha) Inorganic Fertilizer
(kg/farm)
N
Historical Observed
Yield
Kg/ha
Historical Simulated
Yield
Kg/kg
Simulated
Base 30y
mean
Kg/ha
Simulated
futurecc
mean
Kg/farm
1 09/10maha 0.61 36.1 2386 6863 6030 4926 2 09/10maha 0.82 103.5 7030 8139 6517 5818 3 09/10maha 0.61 76.5 6408 8567 6625 5816 4 09/10maha 0.20 22.0 3579 7347 6454 5464 5 09/10maha 0.61 87.4 5283 6994 6667 6014 6 09/10maha 0.10 17.5 6135 6861 6852 6245 7 09/10maha 0.61 36.9 2181 7995 6052 4946 8 09/10maha 0.20 23.0 4908 8372 6539 5537 9 09/10maha 0.10 25.3 8589 7254 6966 6666 10 09/10maha 0.41 48.3 6135 6863 6578 5710 11 09/10maha 2.04 185.0 5113 6874 5637 4826 12 09/10maha 0.82 47.7 4857 5506 5147 5167 13 09/10maha 1.02 124.3 6135 6956 6445 5763 14 09/10maha 0.82 53.9 4089 5760 6105 5013 15 09/10maha 0.41 43.7 6135 7925 6351 5583 16 09/10maha 0.41 28.8 3732 6966 6158 5044 17 09/10maha 0.82 103.1 6135 6841 6165 5247 18 09/10maha 0.61 79.6 6817 8374 6630 5847 19 09/10maha 0.41 39.1 5113 7268 6400 5435 20 09/10maha 0.41 39.1 4395 6780 6367 5336 21 09/10maha 0.20 15.5 4601 6595 6197 5093 22 09/10maha 0.51 55.2 6135 6776 5583 5673 23 09/10maha 0.61 75.9 5624 7402 5900 5267 24 09/10maha 0.10 7.8 4090 6441 5368 5320 25 09/10maha 0.61 73.5 6408 8324 6465 5752 26 09/10maha 0.10 14.8 7158 6859 5884 6186 27 09/10maha 0.61 62.1 7158 6430 5541 5669 28 09/10maha 0.71 39.4 3798 6332 5084 5113 29 09/10maha 0.61 83.6 7328 6644 5794 5985 30 09/10maha 0.10 6.1 4090 6893 6057 5076
Continue…..
31 10yala 0.41 48.3 5113 6813 7079 5085 32 10yala 1.02 137.5 6135 7094 7094 5250 33 10yala 0.82 98.9 5624 6982 7115 5123 34 10yala 0.10 11.5 4908 7292 7038 4910 35 10yala 0.61 34.3 2215 6787 6239 4021 36 10yala 0.41 46.0 6391 6791 6941 4984 37 10yala 0.61 64.9 4772 8264 6491 6905 38 10yala 0.20 22.4 5062 7924 6750 7221 39 10yala 0.41 45.7 6391 7257 7092 5112 40 10yala 1.02 68.1 4908 6533 5785 5894 41 10yala 0.61 61.8 6817 7120 6965 4935 42 10yala 0.41 48.3 5113 7411 7216 5228 43 10yala 0.41 44.5 4601 6713 5978 6311 44 10yala 0.41 48.3 6646 6915 6984 5060 45 10yala 1.22 148.6 8180 7070 7083 5136 45 10yala 0.20 23.0 5113 7440 7038 4910 47 10yala 0.82 50.7 5266 6715 5995 6003 48 10yala 0.20 25.3 4601 6360 6165 6631 49 10yala 0.20 21.7 3579 8018 6477 6552 50 10yala 0.20 24.4 3068 7322 7123 5019 51 10yala 0.41 41.8 4857 7902 6752 7025 52 10yala 1.02 124.7 6340 6917 6082 6666 53 10yala 0.41 44.6 4704 8005 6775 7024 54 10yala 2.45 262.2 5965 6745 6921 4886 55 10yala 1.63 184.0 6071 8782 6966 4982 56 10yala 0.61 69.0 6135 7199 6842 4976 57 10yala 0.82 43.0 2556 5863 5752 3495 58 10yala 0.61 67.8 7839 7114 6921 4952 59 10yala 1.02 120.7 6646 6663 5883 4847 60 10yala 0.82 96.6 6135 7421 7167 5235
Mean yield over farms/ha 5388 7144 6421 5499
Bias= 5388/7144 0.75
Climate effect is (5499-6421)/6421 -0.14
Simulated yield data revealed that :
Physical reduction of famer future rice productivity – overall
14%
For both seasons of Yala (May-August) [minor rice cultivation
season] and the Maha (Oct-Feb) [major rice cultivation season]
for irrigated farming (no moisture stress)
For irrigated Reduction is : (not yet completed)
Mean yield over farms/ha
5388
7144
6421
5499
Farm ID No Season Farm
size(ha)
Inorganic
Fertilizer
(kg/farm)
N
Historical
Observed
Yield
Kg/ha
Historical
Simulated
Yield
Kg/kg
Simulated
Base 30y
mean
Kg/ha
Simulated
futurecc
mean
Kg/farm
Bias= 5388/7144 0.75
Climate effect is (5499-6421)/6421 -0.14 (14%)
Irrigated condition – no moisture stress situation
Stakeholder/consultative meetings for future expectations
and possible adaptation views
1.Several Rice modeling team meetings
2.Leading farmers from dry-zone of Sri Lanka with staff of the Faculty of
Agriculture, University of Peradeniya and Research Officers of the Field Crop
Research and Development Centre of Department of Agriculture At
Mahailluppallama sub campus
3.Special session on Adaptation strategies for paddy farming in Sri Lanka at
the Workshop on Research progress review where Directors, Research
Officers, Agricultural Instructors , University Academics and other Public and
Private sector participated at the Mahailluppalama FCRDI
4.Meeting with the Director, Research Officers and Agricultural Instructors at
the Rice Research and Development Institute (RRDI), Bathalagoda.
Mahailluppallama - Sub Campus Unit
Mahailluppallama - Sub Campus Unit
Mahailluppallama - Sub Campus Unit
Mahailluppallama - Sub Campus Unit
Special session at the Annual Research Progress meeting at DOA, MOA
Special meeting with the Director and the research officers at the RRDI, DOA,
MOA
CATEGORY VARIABLE /
INDICATOR
Direction of
change Magnitude of change
Rationale for direction and
magnitude of change
Percent change
over the period
Rationale for
percent change
over period
Agreement? Confidence
?
Bio-Physical*
Rice Land degradation
increase Small to medium
High temperature/ET, rainfall variability, poor water quality, over exploitation of resource. Rice ecosystem fairly stable
2% Built in adaptation responses and slow
rate of change high
Water availability
decrease medium
High temperature/ET, rainfall variability, poor water quality, over exploitation of resource, rapid economic development. Decrease availability and Increased demand.
15
High competion among stake
holders and limited resources
High
Land use
decrease Small to medium
Low water availability, high cost of production, alternative high remunerative crops
15% Decrese return to investment
medium
Insitutional/Policy*
Input subsidies (fertilizer)
decrease medium Budgetary constraint and enviornment concern, Budgetory restrictions
50% heavy burdern to economy, cost to the envionment
high
Infrastructure/rural/agriculture roads/irrigation
increase small
High demand and to assure the sustainability of system, Budgetary constraint and priority issues
5% Sectoral competion high
trade policy/taxes on import and export
increase medium
protection of producer and consumer, ensure food security, balance of payment
20% Stable government revenue
high
Investment on R and D
increase small
Address national needs, Counteract environment stress Budgetary constraints
1%
Indebt situation and state sector financila constraints
Medium
Socio-Economic* labor (availability)
decrease High
demand from other sectors Poor wages, uncertainty in continuation, common problem for all regions, high cost, poor performances
40%
Increased demand from other sectors, blue color jobs
high
Synchronization of demand at a given time in a given region, Less payments
Technology*
Improved rice cultivar productivity trends
increase Medium
Meet production demands under changing climatic conditions, Limited resources and infrastructure, long term investment,
25% Established breeding programs at the country
high
Improved management prctises
Increased medium
Labour shortage, increase resource use efficiency, Lack of resource and infrastructure facilities
20% Financial and human resource restrictions
high
CATEGORY
VARIABLE
/
INDICATO
R
Direction of
change Magnitude of change
Rationale for direction and
magnitude of change
Percent change
over the period
Rationale for
percent change
over period
Agreement
?
Confidenc
e?
Proposed adaptation measures
Short –term
Adjusting growing season
Shifting of seasons (Advanced / delayed planting dates)
Screening existing varieties for heat, drought tolerance and recommend
those
Management improvements
Organic matter manipulation
Increase efficiency of resource use – Fertilizer /water use
efficiency
Capacity building of stake holders
Medium to Long-term
Develop more tolerant varieties to heat and drought stress
Rehabilitate tank systems (20000 odds minor and mega scale tanks to
increase storage capacity) to ensure water availability
Technology advancement
Establish reliable weather/climate forecasting where farmers can get instant
advice
Capacity building for farmers
Adaptation strategies
Short term – Adjusting planting dates
Introduce tolerant varieties - Potential rice varieties to heat tolerance are
being screened
Bg 300
Adaptation Scenarios for future yield simulation
1. Advanced planting date – one week (varieties as in base year)
2. Delay planting date – one week (varieties as in base year)
3. Replace with the heat tolerant variety - Bg 300
4. Adjusted planting date with the heat tolerant variety – Bg 300
Adaptation Scenarios
1. Advanced planting date – one weed
2.Delayed planting date – week
3.Replace the varieties with more tolerant among
the cultivated (BG 300)
4.Adjusted planting date and tolerant cultiva
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6500
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AV
ERA
GE
YIE
LD (
kg /
ha)
FARMER
MAHA SEASON
BASELINE
LATE PLANTING
BG 300
INTERACTION
EARLY PLANTING
2800
3300
3800
4300
4800
5300
5800
6300
6800
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AV
ERA
GE
YIE
LD (
kg /
ha)
FARMER
YALA SEASON
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4500
5000
5500
6000
6500
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AV
ERA
GE
YIE
LD (
kg /
ha)
FARMER
TOTAL
BASELINE
LATE PLANTING
BG 300
INTERACTION
EARLY PLANTING
2. FAST TRACK
2.1. DESCRIPTION
Project Country Location Climate Scenario
Crop Modeled
Number of Strata
Number of Farms (number
of observatio
ns per strata)
Analysis units (Per ha
or per farm)?
Modeling the impact of a
variable and changing
climate on rice agricultural
system in Sri Lanka
Sri Lanka Kurunegala
AgMIP Climate Scripts with Access1-0 scenario
simulation from BCCR
DSSAT 1 60 Per ha
2. FAST TRACK…Cont
2.2. YIELDS SUMMARY: CC IMPACTS ON YIELDS, RELATIVE YIELDS, HISTORICAL AND FUTURE TIME/NOT TIME AVERAGED YIELDS
Stratum Climate Scenario
Base Period Yield
(kg/ha)
Base Period
Simulated Yield
(kg/ha)
Future Simulated
Yield (Kg/ha)
Time Averaged Relative
Yield
Relative Yield (Not
Time Averaged)
Predicted Future Yield
(Time Averaged)
(kg/ha)
Predicted Future Yield (Not Time Averaged)
(kg/ha)
Type data (simulated data from
crop models or pseudo
data)
1
AgMIP Climate
Scripts with Access1-0 scenario
simulation from BCCR Base(1980-
2010) future (2040-2070)
5388 6421 5498 0.86 6798 Simulated
data
2. FAST TRACK…Cont
2.3. RESULTS: GAINS AND LOSSES
Stratum Climate Scenario
Gainers (%) Gains (%) Loss (%) Net Loss
(%)
1 28.95 3.57 9.76 6.19
S1: Base Climate – Base Technology
S2: Changed Climate – Base Technology
Gain and Loss Diagram
-200000
-150000
-100000
-50000
0
50000
100000
150000
200000
0 10 20 30 40 50 60 70 80 90 100
Lo
sses(R
s/f
arm
)
Percent gainers
RAP • Sri Lanka is expected to continue current trend of
economic growth with a lower population growth rate.
• Population growth rate remain low and urban population increase and rural house hold size decrease and farm size declines.
• Transportation infrastructure increase and trade policy encourage export and imposes high taxes on import of food that can be grown locally.
• Rice productivity would decline due to deterioration of soil fertility, extreme weather events, climate change and phasing out of fertilizer subsidy (with similar varieties and technology)
4. ADAPTATION 4.1. DESCRIBE THE ADAPTATION SCENARIO. INCLUDE: Adoption of drought tolerance, heat tolerance varieties shifting of planting dates Adoption of agronomic package
-Description of variables affected by adaptation following the table below:
Variable name Base
Technology value
Adapted Technology
value Units
Yield 6421 6798 Kg/ha
S1: Changed Climate – Base Technology
S2: Changed Climate – Changed Technology
4. ADAPTATION
4.2. ADAPTATION RESULTS:
Strata 1
Predicted Adoption rate = 89.6%
Economic Indicators Base Adopters Non
Adopters All Farms
Ave. farm income (Rs/year) 65269 157 -11 139
Per capita income (Rs/person/year) 15132 157 -11 139
Poverty rate (%) (Pov.line $1.25/day/person) 8.32 3.8 61.09 9.79
S1: Changed Climate – Base Technology
S2: Changed Climate – Changed Technology
-200000
-150000
-100000
-50000
0
50000
100000
150000
0 10 20 30 40 50 60 70 80 90 100
OP
PC
OS
T
ADOPT_A
-300000
-200000
-100000
0
100000
200000
300000
400000
500000
600000
0 10 20 30 40 50 60 70 80 90 100
NE
T R
ET
UR
NS
PE
R F
AR
M
ADOPT_A
NRFM_A NRMF1_A NRMF2_A
-80000
-60000
-40000
-20000
0
20000
40000
60000
80000
100000
120000
140000
0 10 20 30 40 50 60 70 80 90 100
PE
R C
AP
ITA
IN
CO
ME
ADOPT_A PCINC_A PCINC1_A PCINC2_A
-20
0
20
40
60
80
100
120
0 10 20 30 40 50 60 70 80 90 100
PO
VE
RT
Y
ADOPT_A
POVERTY_A POVERTY1_A POVERTY2_A
Other major rice growing season
On going/future activities repeat the same for other rice growing region
Capacity building activities
- Mainly stake holder institutes
(Research officers in DOA, Research Institutes,
Universities etc.)
Faculty of Agriculture, UOP
Faculty of Agriculture, UOP
Thanking you