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7/28/2019 Results from the Strategic Assessment of International Rice Research Priorities: comparing the potential of rice technologies to make a difference for the poor, the food insecur
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Results from the Strategic Assessment ofInternational Rice Research Priorities:
comparing the potential of rice technologiesto make a difference for the poor, the food
insecure and the environment in Asia
David A. Raitzer, IRRI Strategic Planning and Impact Assessment Specialist
On behalf of IRRI Strategic Assessment Taskforce and IRRI colleagues
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Key unresolved questions
Does IRRIs greatest potential to benefit the poor
Rest in irrigated or rainfed environments?
Arise in South Asia or Southeast Asia?
Stem from genetic improvement or enhanced management?
Result from upstream science or downstream adaptationand delivery?
Approach: compile and integrate state of the artunderstanding, data and tools
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Goals
Key intended findings impact potential:
estimates of economic, poverty, food security and environmentalbenefits in developing Asian rice producing countries in differentpotential international research areas through 2035
Evidence based, inclusive scientist-participatory approachdrawing tools and expertise from multiple disciplines
Better appreciation of
Our (often unstated) assumptions
The magnitude and distribution of target constraints
Tradeoffs in the achievement of mission levels goals
Modular approach that can be updated when new informationbecomes available to support learning
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Approach & stages1. Background
analysis (baselinescenarios, etc.)
2. Analysis of dataon problem
prevalence
3. Characterizationof scientificsolutions
(assumptions,timeframes,
effectiveness)
4. Estimation ofoutcomes and effects
at scale (adoption,productivity, supply)
5. Partialequilibriummodeling
Output:Expectedimpacts
quantified
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Stage1. Background analyses
Understanding the adoption context now Definition of new rice agro-ecologies
Quantification of baseline input use and production costs
Understanding the implications of mega-trends
Projection of future spatial distribution of rice production
Projection of attainable yields, actual yields and yield gaps
Projection of consumption and self consumption
Projection of wages and labor use Projection of spatial distribution of poverty
Projection of caloric insufficiency and associated disease burden
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1 - IR 2 - IR / other 3 - IR / IR 4 - IR / IR / other 5 - RF 6 - RF / RF 7 - RF / RF other 8 - RF Dry/Upland
New rice agro-ecologies for Asia (ca. 2005)
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Projected change in harvested area 2012-2035 (ARIMA, proportion)
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Yield gap projections
Defines additive scope for most technologies other than
yield potential Yield modeled for irrigated (potential) and rainfed
(rainfall constrained) areas of Asia under A1B IPCCclimate scenario to 2035
PRECIS downscaling of Hadley GCM, daily data
ORYZA2000 crop growth model
Actual yield is based on adjusted ARIMA forecasts:
Adjusted to remove IRRI genetic improvement contribution(0.4% average annual growth, pro-rated according to yield
growth) Adjusted to reflect parallel shift to changes in yield potential
caused by climate change for each spatial unit
Gap = attainable yield actual yield
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Yield gaps in2035 (no
IRRI)
Average gaps -2035 (t/ha)
Irrigated Rainfed
South Asia 2.58 1.29
SoutheastAsia 2.09 0.93
East Asia 1.54 1.15
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ARIMA projections of annual per capita rice consumption (kg)[multiplied by population projections for total consumption]
0
20
40
60
80
100
120
140
160
180
200
1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035
1 Bangladesh
2 Cambodia
3 China
4 North Korea
5 India
6 Indonesia
7 Laos
8 Malaysia
9 Myanmar
10 Nepal
11 Pakistan
12 Philippines
13 Sri Lanka
14 Thailand
15 East Timor
16 Vietnam
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Projected 2035 baseline spatial distribution of PPP$2/day poverty
or no data
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Stage 2: Interdisciplinary working groups
Define key problem/opportunities within category For each, characterize distribution, magnitude and
frequency of problem by geography
Draws on remote sensing (drought, salinity in SouthAsia), national data (submergence & drought),climate data (heat, cold), soil maps (problem soils),RICPEST outputs (biotic syndromes), national dataon damages
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Example of constraintcharacterization process - biotic
Detailed loss surveys of456 farms in 6
Production Situations
RICEPEST and Epiricemodeling
Data collected by3000 Indonesian stafffor 22 years on ricedamage, reconciled
to AEs
Expertknowledge
Results of on farmexperiments in 6
Production Situations
Estimates
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Example of constraint characterization output average areaaffected by flooding (proportion harvested area)
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Stage 3: Scientist assessment of scientific solutions Unit of analysis is constraint by technology (i.e. drought tolerance QTLs)
For each solution appraise: Investment required (IRRI + partners)
Years of research to product
Probability of success, key factors affecting success
Alternative suppliers Course of research progress without international effort
For each solution in each ecology and subregion
Likely adoption profiles
Expected on farm costs and benefits of adoption by operation Expected on farm environmental effects
Delivery and extension requirements
63 solutions * 31 spatial units * 23 parameters = 45000 scientist estimates!
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Adoption
Expected adoption is elicited by season, ecology and subregionas a product of several variables
Probability of success
Percentage of season/ecology/subregion relevant to solution(e.g. portion with problem that solution addresses)
Expected adoption within relevant portion ofseason/ecology/subregion in 2020 (near term products) and
2035 (all), considering :
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Stage 4: Modeling - logistic diffusion
Adoption over time is modeled against a symmetric logisticdiffusion curve by solving for the inflection point usingprovided points on the curve by season/ecology/subregion
International researchattributable adoptionidentified viadifference betweenadoption curve and
delayed availability
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Estimation of shifts in production costs and output
Data from 20 household surveys
used to estimate input and laboruse by operation
For each agro-ecology &season
In each major Asian country
Each relative change is multipliedagainst each current average cost
Gives conditional percentage effecton cost and output by season,ecology and subregion for adopters
Subset of IRRI household surveydatabase
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Calculating output and cost shifts over timeand space
Agroecological cost and output shifts are interpolated over timeaccording to the diffusion curve for eachseason/agroecology/subregion
Each of those shifts is interpolated to 210 Asian provinces
based on production in each season and ecology
National/subnational adjustments are performed as necessary
Constraints that vary geographically
Policy barriers (e.g. transgenic approval)
Supply shocks are used to model price effects, with adjustmentof price responses to reflect effects of trade
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Economic surplus framework Each price effect is used with a proportionally shifted
supply function to calculate producer surplus bysolution, spatial unit and year under two functionalforms
2 functional forms Constant elasticity
Observed elasticity near the equilibrium and a
positive shutdown price Consumer surplus estimated nationally
consumer benefits are reallocated to producers on self-consumed output
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Constant elasticity pivotal shift framework
A
BC
E
F
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Supply curve with positive shut down price andobserved elasticities near the initial equilibrium
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Poverty
Poor producers benefits approximated as producersurplus * the poverty rate in the unit-year * averagepoor rice area/average rice area.
The effect on poor consumers is calculated based on
their share of consumption and consumer surplus.
Using income-disaggregated expenditure data onrice from national income and expenditure surveys
With projected poverty rates and gaps
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Effects on hired labor Each technology has quantified effects on hired
labor demand, under projected labor market
baseline equilibria
Modeled as a proportional shift in a demand foreach geographic unit in each year for eachtechnology.
This is translated into equilibrium price (wage)effects.
Effects on labor suppliers are modeled as theproducer surplus changes resulting from
changes in the wage. Effects on poverty are quantified assuming that
the poverty rate for unskilled agricultural labor istwice the prevailing poverty rate in the area and
year
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Hunger
Historical trends in the national proportionof hungry , population and average caloricgaps for the hungry are used to projectfuture hunger.
Absolute trends in hunger and caloric gaps
are used to project WHO hunger risk factorassociated Disability Affected Life Years(DALYs)
Increases in consumption by the deficientpopulation from reductions in equilibrium
price are used to estimate reductions inDALYs
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Incorporating environmental impacts
Area responses to the price effects are estimated Translated into forest cover effects using land use data
Translated in greenhouse gas emission effects
Translated into reductions in water use
Translated into reductions in methane emissions
Direct environmental effects are calculated
Water use reduction based on adoption, soil texture class,ET and rainfall
Methane reductions based on IPCC coefficients
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Modeling impact
Some facts about the model 300,000 estimates per indicator (e.g.
adoption, producer surplus)
>15 million cells
6 gigs of RAM
Can be run historically or for the future Can be updated easily with new
information
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Overall results
Gains do not exceed yield gaps
Total new technology attributable increases in Asianproduction by 2035: 6.3% 10%
Total international research attributable increases inAsian rice production by 2035: 3.9% 6.3%
Gains consistent with research contributions assessed
historically in Asia
Aggregate impact potential (2005 PPP$ discounted at 5%) low
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Aggregate impact potential (2005 PPP$, discounted at 5%), lowattributable scenario 2013-2035
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Total technology impacts from 2013 to 2035 constant elasticityform in low scenario (values in billions of discounted [5%] 2005 PPP$,
DALYs in millions)
Host plant
resistance
Abiotic stress
tolerance Inbred yield C4 rice
Hyrbrid
rice
Other
traits Management Mechanization
Total 3.27 9.94 5.85 1.73 2.32 1.54 6.88 1.82
Consumers 2.21 7.19 4.39 1.20 1.68 1.22 5.86 8.29
Producers 0.78 1.64 0.77 0.38 0.31 0.39 1.79 3.19
Hired labor 0.28 1.12 0.69 0.15 0.32 -0.07 -0.78 -9.66
Poor consumers
(PPP2/day) 0.59 2.56 1.29 0.32 0.58 0.33 1.75 2.21
Poor producers
(PPP2/day) 0.19 0.84 0.28 0.10 0.12 0.11 0.35 0.23
Poor hired labor
(PPP2/day) 0.15 0.72 0.35 0.08 0.16 -0.04 -0.46 -4.46
Total poor (PPP2/day) 0.92 4.13 1.91 0.50 0.87 0.40 1.64 -2.02
DALYs reduced 0.91 4.29 2.51 0.72 1.24 0.63 2.96 3.82
GHG & water 0.04 0.13 0.07 0.02 0.03 0.02 1.15 0.15
Germplasm Management related
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Total technology impacts from 2013 to 2035constantelasticity with positive shutdown price in low scenario
(values in billions of discounted [5%] 2005 PPP$, DALYs in millions)
Host plant
resistance
Abiotic stress
tolerance Inbred yield C4 rice
Hyrbrid
rice
Other
traits Management Mechanization
Total 5.75 17.46 9.10 2.23 3.49 2.57 13.91 10.38
Consumers 2.06 6.74 3.63 0.86 1.44 1.05 5.44 7.77
Producers 3.36 9.48 4.71 1.20 1.70 1.57 8.08 12.11
Hired labor 0.28 1.12 0.69 0.15 0.32 -0.07 -0.78 -9.66
Poor consumers
(PPP2/day) 0.55 2.42 1.07 0.51 0.27 0.28 1.63 2.09
Poor producers
(PPP2/day) 0.77 3.52 1.36 0.31 0.54 0.37 1.89 1.95
Poor hired labor(PPP2/day) 0.15 0.72 0.35 0.08 0.16 -0.04 -0.46 -4.46
Total poor (PPP2/day) 1.47 6.67 2.77 0.62 1.21 0.62 3.06 -0.42
DALYs reduced 0.83 3.96 2.05 0.51 1.04 0.53 2.68 3.52
GHG & water 0.04 0.13 0.07 0.02 0.03 0.02 1.15 0.15
Germplasm Management related
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Potential international research impacts from 2013 to 2035constant elasticity with positive shutdown price in low scenario
(values in billions of discounted [5%] 2005 PPP$, DALYs in millions)
Host plant
resistance
Abiotic stress
tolerance Inbred yield C4 rice
Hyrbrid
rice
Other
traits Management Mechanization
Total 3.59 12.20 8.93 2.23 2.47 2.38 7.84 7.58
Consumers 1.32 4.64 3.57 0.86 1.03 0.97 2.80 5.61
Producers 2.06 6.71 4.62 1.20 1.20 1.46 4.15 8.98
Hired labor 0.18 0.76 0.68 0.15 0.23 -0.07 -0.22 -7.12
Poor consumers
(PPP2/day) 0.37 1.68 1.05 0.36 0.25 0.26 0.85 1.48
Poor producers
(PPP2/day) 0.51 2.46 1.33 0.31 0.39 0.35 1.03 1.48Poor hired labor
(PPP2/day) 0.10 0.49 0.34 0.08 0.11 -0.04 -0.14 -3.31
Total poor (PPP2/day) 0.98 4.63 2.72 0.62 0.86 0.57 1.74 -0.36
DALYs reduced 0.52 2.45 2.01 0.51 0.72 0.49 1.23 2.16
GHG & water0.03 0.09 0.06 0.02 0.02 0.02 1.11 0.11
Germplasm Management related
T 15 i di id l i t ti l i h l ti
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Top 15 individual international rice research solutions (lowscenario, positive shutdown price, 2013-2035 surplus effects in million 2005
PPP$ discounted [5%])
Total
benefits
Total
benefits to
1.25 poor(including
labor)
Total benefits
to 2 poor(including
labor)DALYs
reducedGHG &
waterVarieties with increased attainable
yield 5942.42 751.01 1863.89 1.38 43.62
Mechanically transplanted
puddled price 5032.62 3.92 111.36 1.73 64.09
Inbred yield potential 2989.70 337.50 860.67 0.63 20.99
Submergent tolerant varieties 2575.14 425.03 927.74 0.49 18.09
Combine harvesters 2549.77 -199.50 -472.33 0.44 44.72
Hybrid yield potential 2474.87 356.56 862.20 0.72 17.94
Introgression of major drought
QTLs 2447.50 520.51 1126.48 0.65 19.20
SSNM for NPK 2314.69 244.10 604.40 0.34 16.84
C4 rice 2229.92 242.62 619.24 0.51 17.14
Salt tolerant varieties for coastalareas 2211.13 391.32 867.94 0.39 12.81
Tolerant varieties for saline and
alkaline inland soils 2012.98 328.99 741.85 0.40 11.68
Safe AWD 1435.61 87.01 186.85 0.06 979.11
Heat tolerant rice 1390.06 102.43 257.46 0.13 7.34
Blast HPR 1315.71 184.86 420.14 0.20 9.31
Conventional drought tolerance1232.62 257.44 548.29 0.28 9.58
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Results by ecology & subregion totalbenefits (2005 PPP$ billions)
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Results by ecology & subregion benefitsto the PPP$2 poor(2005 PPP$ billions)
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Results by
upstream/downstream
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Implications Suggests emphasis on applied & upstream research
Suggests many common technologies to irrigatedand rainfed environments and continued importanceof irrigated technologies
Reinforces emphasis on abiotic stress tolerance
Reinforces emphasis on South Asia
Suggests additional emphasis on increasing
attainable yield Exposes mechanization tradeoffs regarding efficiency
and equity goals
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Reflections Longer to compile the underlying data than expected
Limitations of scientist knowledge beyond project sites
There are known knowns; there are things we know
we know.
We also know there are known unknowns; that is tosay, we know there are some things we do not know.
But there are also unknown unknowns the ones
we dont know we dont know.
Former United States Secretary of Defense,Donald Rumsfeld (on WMDs in Iraq)
C l i
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Conclusions
Synthesizes best currently available information into theories of
change - hypotheses to be tested further
Learned what we needed to know
Illustrates potential of a multi-disciplinary, evidence-based approachto defining outcomes and impacts
Confirms many existing choices (emphasis on abiotic tolerance,focus on South Asia)
Exposes many clear tradeoffs
Can be updated with new information, so offers potential forevaluation follow up for learning
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With special thanks to all whocontributed!Extensive support was provided by :
Andy Nelson & GIS
Rio MaligaligZeny Huelgas
Strategic Assessment Taskforce Members
Reactions and feedback are welcome
Can still be incorporated!