expert opinion: an alternative method of estimating varietal adoption
DESCRIPTION
Expert opinion: an alternative method of estimating varietal adoption, Ms. Ma. Lourdes Velasco, Associate Scientist, Wednesday, 24 April 2013, 10:30-11:30pm, SSD Conference Room, Drilon Hall, Social Sciences Division, IRRITRANSCRIPT
Tracking Improved Varieties in South Asia
Expert opinion: an alternative method of estimating varietal adoption
TRIVSA
Development of improved varieties (MVs)
Green revolution in the 70s in Asia
Rapid increase in food production
Decline in poverty• Directly, through increases in farmers’ incomes• Through a long-term decline in the prices of foodgrains
The decreasing growth in research funds resulted to a stagnation in crop genetic improvement.
• The magnitude of adopted area is an important determinant in the size of economic benefits in ex-post impact assessments of agricultural technologies.
• However, information on recent vintages of improved varieties replacing earlier vintages, the sources of seeds and varietal information, and rate of seed replacement are important but are limited if not unavailable.
• There is a need to know what is happening to varietal change as a result of crop improvement.
Need for information
• Information on the uptake and impact of new varieties is valuable for donors in maintaining and even increasing investment in genetic research.
• Information on the nature of dynamism in varietal adoption and turn-over are important for allocating scarce research resources to aspects that are likely to increase impact.
• Information on the adoption and cultivar use helps in deciding on relative resource allocation for commodities and specific lines of research.
Value of information
Aim
Lay the groundwork for tracking the successes and failures of crop improvement investments and for understanding the impact of those investments on
poverty, nutrition and food security
Construction of a routine system of monitoring varietal adoption
Objective 1: Attain a wider understanding of key aspects of theperformance of crop genetic improvement in South Asia
Activity 1: Compile varietal releases upto 2010
Objectives
Activity 2: Conduct survey on rice scientists’ time allocation in genetic improvement
Activity 3: Piloting a method of obtaining “quick and clean” estimates of varietal adoption through expert elicitation
Objective 2: Gain a deeper understanding of the adoption anddiffusion of new varieties
Activity 4: Conduct focus group discussions on varietal adoption at community level
Activity 5: Conduct farm household survey on varietal adoption
IRRI ICRISAT
Rice Sorghum, Pearl millet, Chickpea, Pigeon pea,
GroundnutIndia
Bangladesh India
Nepal
Sri Lanka
Bhutan
Chhattisgarh, West Bengal, Odisha
NARES
India
Chhattisgarh- Indira Gandhi Agricultural University
West Bengal - NZFDO – NGO
Odisha - Orissa University of Agriculture and Technology
Bangladesh - Bangladesh Rice Research Institute
Nepal
Western - Institute of Agriculture and Animal Science
Eastern - Nepal Agricultural Research Council
Sri Lanka - Department of Agriculture
Bhutan - Ministry of Agriculture
Country/State Area(000 ha)
Prod(000 t)
Yield(t/ha)
India 42862 143970 3.36
Chhattisgarh 3703 9239 2.50
West Bengal 4944 19569 3.96
Odisha 4226 10242 2.42
Bangladesh 11700 47555 4.06
Nepal 1560 4354 2.79
Sri Lanka 1117 3662 3.28
Bhutan 23 72 3.14
Source: USDA, FAO for Bhutan, Indiastat.com for India
Rice area, production and yield, 2010
Varietal releases
Country/State Period No. of varieties No. of varieties/year
India-DRR 1933-2010 1004 12.9
Chhattisgarh 1996-2010 15 1.0
West Bengal 1969-2007 120 2.9
Odisha 1968-2010 144 3.3
Bangladesh 1966-2010 72 1.6
Nepal 1966-2010 62 1.4
Sri Lanka 1958-2010 69 1.3
Bhutan 1988-2010 24 1.0
Source of varieties released(Percentage of varieties)
Source Bangladesh Nepal Bhutan Sri Lanka
Developed locally 74 27 29 100
South Asia 1 24 29
India (1) (18) (8)
Bangladesh (2) (4)
Nepal (13)
Sri Lanka (5) (4)
Southeast Asia 1 7 4
Indonesia (3) (4)
Malaysia (1) (2)
Philippines (2)
East Asia 1 9 12
China (1) (3) (4)
Japan (4)
RDA, Korea (4)
Taiwan (6)
IRRI 22 32 25
Total no. of varieties 72 62 24 69
IRRI materials in varietal releases
No. of releases
Percentage of varieties
With IRRI material
IRRI release
With 1IRRI
parent
With both IRRI
parents
India-DRR 1004 33 4 26 3
Bangladesh 72 54 22 33 15
Nepal 62 47 32 31 6
Sri Lanka 69 19 0 19 0
Bhutan 24 50 25 38 8
Varietal type % Area
Traditional/Local
Modern/Improved/Hybrid
Total100%
Top 10 modern varieties (List varieties based on sown area in descending order)
% Area
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11. Other modern varieties
Total100%
Expert Estimates of Varietal Adoption
Name:___________________Affiliation:___________________Perceptions of the relative importance of modern rice varieties in 2010.
1. Individual estimates from each expert
2. Revised individual estimates after a listof varietal releases is provided
3. Initial group estimates after experts are grouped into heterogeneous groups
4. Estimates by agro-ecology from each group
5. Revised group estimates after considering Step 4
6. Consensus group estimate
6-Step process of elicitation
A. The facilitator• Balance participation among panel members especially
when senior experts begin to dominate the discussion.• Assist the group in resolving differences among panel
members in the event that a consensus cannot be reached.
B. Composition of the expert panel• Breeders• Socio-economists• Agricultural extension officers• Seed/Grain traders• Seed producers• Farmer group representatives
Success of the elicitation process
National/State
LevelDistrictLevel
Chhattisgarh
West Bengal
Odisha
Bangladesh
Nepal
Sri Lanka
Bhutan
Expert elicitation
Validation of expert estimates
Community survey: To obtain background information on varietal adoption and disadoption and to counter check results of household survey
Household survey: To obtain information on varietal adoption for use in validating results of expert elicitation and collect information on adoption patterns at the farm level
No. ofdistricts
No. ofblocks
No. ofvillages
Chhattisgarh 8 19 78
West Bengal 17 34 126
Odisha 29 158 302
Bangladesh 18 53 53
Nepal 29 68 116
No. of
districtsNo. ofblocks
No. ofvillages
No. offarmers
Chhattisgarh 8 19 120 902
West Bengal 17 34 126 1262
Odisha 29 159 307 3139
Bangladesh 18 53 61 522
Nepal 29 174 265 1160
Bhutan 8 40 154 301
Community survey
Household survey
Survey sample
Sampling scheme
2-6 blocks/district
1-6 villages/block
2-10 HHs/village
675
7286
Results of the expert elicitation process
Comparison of area grown to all MVs
Overall, estimates correspond well within 10percentage-ptsover- and under-estimation.
Chhattisgarh
West
Bengal
Odish
a
Bangladesh
Nepal
Sri Lanka
Bhutan
0
20
40
60
80
100
Expert elicitation Household survey
MAE – Mean Absolute Error (percentage points)
Good correspondence is observed in the estimates between the 2 methods.
Comparison of cultivar-specific adoption estimatesExpert elicitation vs Household survey
MAE(percentage-pts)
Correlationcoefficient
Chhattisgarh 2.34 0.98
West Bengal 5.39 0.80
Orissa 1.33 0.97
Bangladesh 2.37 0.98
Nepal 3.87 0.77
Sri Lanka 0.76 0.99
Bhutan 3.64 0.90
VarietyShare in MV area (%) SMAPE (%)
EE HH (EE vs HH)
Swarna 32 31 10
Pooja 10 14 25
MTU 1001 9 10 18
Lalat 12 8 23
Pratikshya 4 4 36
Khandagiri 5 4 52
MTU 1010 3 4 71
Gayatri 2 2 66
Savithri 4 1 57
Naveen 2 1 64
Moti 1 1 70
CR 1030 0.3 1 94
Parijat 1 1 90
Samba Mahsuri 1 0.5 82
Swarna Sub1 0.1 0.3 94
T 141 2 0.1 99
Kalinga III 0.4 0.01 99
Other MVs 12 17 22
Comparison of estimates by variety across experts - Odisha
Widely-grown MVs have lesser margin of error than MVs grown in smaller areas.
Variety State of releaseCultivar-specific adoption estimates
EE HH
MTU 1001 Andhra Pradesh 23 29
Swarna Andhra Pradesh 16 20
MTU 1010 Andhra Pradesh 0 6
Samba Mahsuri Andhra Pradesh 0 1
Sona Mahsuri Andhra Pradesh 0 2
IR 64 CVRC 9 5
IR 36 CVRC 4 8
PA 6444 CVRC 0 3
PKV HMT Maharashtra 1 3
Mahamaya Chhattisgarh 15 10
Karma Mahsuri Chhattisgarh 4 1
Poornima Chhattisgarh 3 0
Danteshwari Chhattisgarh 2 0
Adoption estimates by state of release in Chhattisgarh
Experts tend to be biased upwards in favor of MVs developed locally.
MV adoption by agro-ecologyShare of all MVs in total rice area (%)
Upland MediumlandMedium lowland
Lowland
Chhattisgarh 83 97 98 98
West Bengal 100 95 81 45
Odisha 62 93 85 18
UplandRainfed lowland
Irrigated lowland
Nepal 87 77 81
Aman Boro Aus
Bangladesh 83 99 91
High-altitude Mid-altitude Low-altitude
Bhutan 84 19 46
Popular MVs by agro-ecologyUpland Mediumland Medium lowland Lowland
Chhattisgarh MTU 1010 MTU 1010 MTU 1010 MTU 1010 Swarna Swarna Swarna SwarnaWest Bengal Lalat Swarna Swarna Jaya Shankar Pooja Sabita Odisha Khandagiri Swarna Swarna Varshadhan Pooja Hanseswari Durga
Upland Rainfed lowland Irrigated lowlandNepal Kanchhi Masuli Masuli Sona Mahsuri
Masuli Radha 4 MasuliHardinath 1 Sabitri
Ghaiya 2
Aman Boro Aus Bangladesh Swarna BRRI dhan 28 BRRI dhan 28 BR 11 BRRI dhan 29 BR 26
High altitude Mid altitude Low altitude Bhutan Khangma Maap IR 64 BR 153 Yusi Ray Maap 1 Sorbang IR 8 No 11 Bhur Raykaap 1
These complexities
must be considered
when conducting
expert elicitation.
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(%
)Spatial variability in MV adoption across districts in Odisha
Spatial variability can be captured by conducting elicitations at the sub-national level.
%MV area(EE – HH)
Cultivar-specific adoption
MAE(percentage-
pts)
SMAPE(%)
Correlation coefficient
Odisha
State-level -2 1.2 17 0.99
District-level 3 1.3 6 0.97
Chhattisgarh
State-level -9 3.3 37 0.93
District-level -8 2.2 25 0.96
Comparison of expert elicitationState-level vs District-level
Minimal gains in correspondence is observed in district-level expert elicitation.
Results of elicitation process
• There is close correspondence between expert estimates and household survey estimates on area grown to all MVs.
• Cultivar-specific estimates provided by experts closely match those from the household survey.
• Experts were able to provide reliable estimates of area under dominant varieties.
• Panel composed of experts with diverse backgrounds is essential for reducing systematic bias in the estimates.
• In areas with high environment variability, it is important to conduct expert elicitation at the district level.
• Estimates obtained through national-level elicitations are consistent with district-level elicitations provided resource persons with adequate expertise are selected in the panel.
Patterns in varietal adoption
Country/State Average
varietal age(years)
Averageadoption lag
(years)
Chhattisgarh 19 14
West Bengal 22 15
Odisha 20 12
Bangladesh 19 12
Nepal 23 16
Sri Lanka 14
Bhutan 14 7
Varietal turnover
Adoption lag is the age of the variety when it was first adopted by farmers.
Is the low varietal turnover a result of problems with the seed systemand information dissemination?
Chhattisgarh
West
Bengal
Odish
a
Bangladesh
Nepal
Sri Lanka
Bhutan
0
20
40
60
80
100
unknowns
2000-2010
1980-1999
Before 1980
%M
V a
rea
Proportion of MV area by year of release
Less than 20% of MV area is grown to MVs released in 2000 and after.
2-3 MVs account for a large area under MVs most of which are more than 10 years old.
Year of release
% of MV area
Year of release
% of MV area
Chhattisgarh 67 Bangladesh 55
MTU 1010 2000 29 BRRI dhan 28 1994 20
Swarna 1979 20 BRRI dhan 29 1994 14
Mahamaya 1996 10 BR 11 1980 13
IR 36 1981 8 Swarna 1979 8
West Bengal 52 Nepal 52
Swarna 1979 34 Sona Mahsuri 1982 13
Gontra Bidhan-1 2008 7 Radha 4 1994 12
Lalat 1989 6 Masuli 1973 11
MTU 1010 2000 5 Kanchhi Masuli 9
Odisha 63 Hardinath 1 2004 7
Swarna 1979 31 Sri Lanka 57
Pooja 1999 14 Bg 352 1992 18
MTU 1001 1995 10 Bg 300 1987 15
Lalat 1989 8 Bg 358 1999 14
Bg 94-1 1975 10
Bhutan 588
BR 153 1989 27
Khangma Maap 1999 14
Yusi Ray Maap 1 2002 9
IR 64 1988 8
No 11 1989 8
Top 4 popular MVs
Disadopted MVs and Replacement varieties
27 17 29 18 27 19 34 19 26 24 Varietal age
CH WB OD Bang Nepal
Dis M
Vs
Rep
var
Dis M
Vs
Rep
var
Dis M
Vs
Rep
var
Dis M
Vs
Rep
var
Dis M
Vs
Rep
var0
20
40
60
80
100
UnknownFallowTV2000-20101980-1999Before 1980
%Fr
eque
ncy
of v
arie
ties
% of farmers 58
%Area grown to Swarna 42
No. of varieties grown 3
Average varietal age (years)
Swarna 31
Other MVs 17Average adoption lag (years)
Swarna 20
Other MVs 9
Varietal adoption of Swarna growers in Odisha
Mega varieties are not grown in large areas by few farmers butare grown in a substantial portion of the farm by many farmers with
other varieties in between fields.
Tracking varietal change: the case of Odisha
HarvestPlus survey in 2008
Farm practices in 2007
6447 households
TRIVSA survey in 2011
Farm practices in 2010
3139 households
Year of release
Varietalage
Share in MV area (%)
2007 2010
Swarna 1979 31 41 31
Lalat 1988 22 10 8
Khandagiri 1992 18 4 4
Gayatri 1988 22 4 2
Savithri 1982 28 2 1
24 60 47
Pooja 1999 11 9 14
MTU 1010 1995 15 6 10
MTU 1001 2000 10 2 4
Sarala 2000 10 1 2
Pratikshya 2005 5 0 4
Naveen 2005 5 0 1
9 18 35
Hybrids 2000s 0.5 0.8
Other MVs 21 18
Varietal change, 2007 and 2010 in Odisha
• 2-3 dominant varieties cover large areas under MVs.
• Old MVs dominate with <20% of MV area grown to MVs released in 2000-2010.
• Farmers adopt new MVs more than 10years after its official release.
• Mega varieties are not grown in large areas by few farmers but are grown in a substantial portion of the farm by many farmers.
Characteristics of varietal adoption
Summary and Conclusions
• Expert elicitation is an effective tool for a quick assessment of cultivar-specific adoption.
• Community interviews is a cost-efficient method for specific case studies of adoption/disadoption and is useful in providing information needed for cross-checking of household survey.
• Widely-adopted MVs are generally those released in 1980-1999 with limited adoption of newly-released varieties.
• Regularly assessing varietal adoption every 3-5 years through expert elicitation will be useful in tracking varietal change.