SlideShare uma empresa Scribd logo
1 de 26
2012 FTCs and farmers tef technology
demonstration
Overview of results
May 2013
Agenda
A. Overview of results at federal level
B. Breakdown of results by region
C. Supplemental analyses
1
2
Newly-scaled up tef technologies, particularly row planting and transplanting, demonstrate
yield improvements, with an average yield increase of ~70% over the national average
Average yield by planting method
Quintals/hectare • Data was collected from
~15,800 validating farmers
(and some control farmer
groups) to determine the
results of new tef
technologies
• For the 14,605 farmers,
average yields for row
planting and transplanting
increased 70% from
national average (20.9
versus 12.6 qtls/ha)
• As the chart shows, there
is still much work to be
done in properly managing
transplanting to realize
potential yield gains
17
12
16
21222122
18
20
2323
Amhara
n = 4,637
SNNP
n = 3,480
Oromia
n = 6,002
Tigray
n = 486
N/A
Broadcasting
Row planting
Transplanting
Source: 2012 Data from Regional, Zonal and Woreda administration staff (collected Feb-April 2013)
Note: Includes data from 14,605 farmers (omitted error/outlier data from 15,790 total collected)
3
Distribution of yield data shows that 30% of all validating farmers surveyed experienced
yield increase between 20 and 80% over the national average
Source: 2012 Data from Regional, Zonal and Woreda administration staff (collected Feb-April 2013)
Note: Includes data from 14,605 farmers (omitted error/outlier data from 15,790 total collected)
Distribution of Validating Farmers’ yields
Frequency of yield increase (as % of total data set)
4
7
6
8
10
8
13
11
7
150 -
200%
125 -
150%
Over
200%
100 -
125%
80 -
100%
60 - 80%40 - 60%20 - 40%10 - 20%Less than
10%
27
~30% of farmers saw a
20 – 80% yield increase
Farmers who broadcasted, used high seed
rates, or may have experienced challenges
with new technologies
~20% of farmers saw a 100 –
200% yield increase (~60% of
this group row planted)
4
Top-performing woredas across the four regions have demonstrated success in adopting
the new technologies, reaching maximum yields higher than a 400% yield increase
Average and maximum yields for row planting and transplanting farmers
Quintal/hectare
36
36
35
35
35
35
33
33
32
31H/Abote 53
Tahaty-
Maichew
56
47
Lume 68
Maraka
Kuxha
68
65
Alefa 42
Dabat
Enebsie
Sar Midir
58
Chilga 53
67
Libo Kemkem
AMHARA
SNNP
OROMIA
SNNP
TIGRAY
OROMIA
68 quintals/hectare
versus the national
average of 12.5
quintals/hectare
Source: 2012 Data from Regional, Zonal and Woreda administration staff (collected Feb-April 2013)
Note: Includes data from 14,605 farmers (omitted error/outlier data from 15,790 total collected)
5
Data indicates that validating farmers’ seed rate usage varies by planting method, with the
highest rates used in broadcasting to the least amount of seed used in transplanting
Average seed rate and yields by planting type across regions
Yield (quintal/hectare), Seed rate (kg/hectare)
4
1011
30
2222
15
13
25
20
15
10
5
0
30
25
20
15
10
5
0
TransplantingRow plantingBroadcastingNational
average
• While this does not imply causation, there appears to be an inverse relationship between planting type and seed
rate practices of the validating farmers
• Overall, the average seed rate has dropped significantly from traditional practices of 30-50 kg/ha to an average
of 8.8 kg/ha across all planting types (includes broadcasting farmers as well)
• Use of Quncho variety appears to be on the rise, with 92% of validating farmers choosing it over other options
(local varieties, Cross-37, Yedega, etc.)
Seed rate
Avg yield
Source: 2012 Data from Regional, Zonal and Woreda administration staff (collected Feb-April 2013)
Note: Includes data from 14,605 farmers (omitted error/outlier data from 15,790 total collected)
6
Fertilizer application rates appear to match recommendations for DAP (average application
rate of 99.5 kg/ha), yet fall slightly below recommendations on Urea (88.4 kg/ha)
Average DAP and Urea application rates by region
Kg/hectare
Key takeaways on DAP and Urea
utilization by farmers:
• Urea use is highly variable,
much more than DAP: the
standard deviation for Urea is
24.6 kg/hectare versus 3.8 for
DAP, indicating the that farmer
use of DAP is more consistent
• Application rates are fairly
agnostic of planting method:
for each planting method, the
average application rates
remain similar
9910099
91
868890
Amhara
n = 4,037
SNNP
n = 3,051
Oromia
n = 4,943
Tigray
n = 304
N/A
DAP
Urea
4.1 21.9 4.4 25.4 1.3 26.5 6.5 21.5
X Standard deviation
Source: 2012 Data from Regional, Zonal and Woreda administration staff (collected Feb-April 2013)
Note: Includes data from 12,335 farmers (omitted error/outlier fertilizer data from 15,790 total collected)
7
A high-level view of planting time indicates that most farmers observed ‘traditional’
planting times, though farmers that planted earlier saw significant yield increases
69616261626272
86
66
8089
0
50
100
June
Week 2
Sept
Week 1
July
Week 4
August
Week 1
August
Week 2
August
Week 3
August
Week 4
59
July
Week 3
July
Week 2
July
Week 1
June
Week 4
June
Week 3
Average productivity increase by planting time
% productivity increase (compared to national average)
0.3%0.1% 2%0.8% 16%5% 19%24% 4%7% 1%4%
Percentof
farmers
Key takeaways
 Planting time of validating farmers followed
standard practices, with ~60% of farmers
planting in mid-July to early August
 Though a small sample size, there seems to be a
strong productivity increase for farmers who
planted earlier, in the 3 and 4th weeks of June
Standard
planting
period
Next steps for exploration
 Early planting time, when is coincides with
rainfall, is a topic to be further explored with
farmers and on FTC plots
 EIAR and RARIs, with support from ATA, will
conduct formal research projects to determine
the impact of planting 2, 3 or 4 weeks early
Source: 2012 Data from Regional, Zonal and Woreda administration staff (collected Feb-April 2013)
Note: Includes data from 12,335 farmers (omitted error/outlier data from 15,790 total collected)
Largely driven by
transplanting
8
20.1
18.3
15.5
8.4
12.6
+60%
Average yields by experimental plot across regions
Quintal/hectare
X.X Standard
deviation
Avg yield qt/ha
Planting type
National
Average for
2012
Broadcast
by hand
Broadcast
by machine
Row plant Transplant
Seed rate (kg/ha) 30-50 5-10 5-10 0.5-0.7
Fertilizer type none DAP +
Urea
DAP +
Urea
DAP +
Urea
Seed type Local Quncho Quncho Quncho
8.16.9 9.4 11.4
In ~1,100 FTC trials, it can be observed that factors such as reduced seed rate, planting in
rows, and use of DAP and Urea can each contribute to increasing yields
• Findings indicate that
significant yield
improvement results
from new technologies
such as row planting,
transplanting, and
reduced seed rate
• Across the 1,100 FTCs,
yields were recorded for
each experimental plot
that was designed to
test either planting
method, seed rate, seed
variety, or fertilizer use
Source: 2012 Data from Regional, Zonal and Woreda administration staff (collected Feb-April 2013); CSA 2012
9
20.1
18.3
15.514.9
13.9
12.012.6
+4%+7%
+18%
+10%
+16%
Average yields by experimental plot across regions
Quintal/hectare
Planting
type
National
Average for
2012
Broadcast by
hand
Broadcast by
hand
Broadcast by
hand
Broadcast by
machine
Row plant Transplant
Seed rate
(kg/ha)
30-50 30-50 5-10 5-10 5-10 0.5-0.7
Fertilizer
type
DAP + Urea DAP +
Urea
DAP +
Urea
DAP +
Urea
DAP +
Urea
DAP +
Urea
Seed type Local Quncho Quncho Quncho Quncho Quncho
Taking a closer look at each variable, it appears that switching to Quncho and row planting
are the largest drivers of productivity increase
Source: 2012 Data from Regional, Zonal and Woreda administration staff (collected Feb-April 2013); CSA 2012
Agenda
A. Overview of results at federal level
B. Breakdown of results by region
C. Supplemental analyses
10
11
AMHARA: overview of data collected
Source: 2012 Data from Regional, Zonal and Woreda administration staff (collected Feb-April 2013)
Farmer data overview
Average yield 20.47
Standard deviation 9.65
Average productivity increase 62%
Count of farmers Raw Cleaned
Farmers 4,958 4,637
Gender split
Male 4,373 95%
Female 685 4%
Geographic scope within region
(as reported)
Count of zones 10
Count of woredas 85
31%
Broadcasting (machine)
1%
Broadcasting
(hand)
Transplanting
Row planting48%
19%
Distribution of planting type
% of total farmers (from raw data)
12
AMHARA: input use, yields achieved, and planting time
Source: 2012 Data from Regional, Zonal and Woreda administration staff (collected Feb-April 2013)
Inputs use of Validating Farmers
Planting time of Validating Farmers
Highest-producing woredas (based on Validating Farmers)
Average input use by planting method
Kg/hectare
10
88
9994
9
99 96100
2
Row planting
n = 2,238
Broadcasting
n = 1,446
Transplanting
n = 889
Seed
DAP
Urea
Average and maximum farmer yields
Kg/hectare (average and max by woreda)
36
35
32
31
30
Enebsie Sar midir 58
Alefa
Dabat 67
Libo Kemkem 68
53Chilga
42
Average
Max
Average yield based on planting time
% productivity increase (compared to national average)
68
194136455671947285105104
100
August
Week 3
200
0
Sept
Week 1
August
Week 4
August
Week 2
August
Week 1
July
Week 4
July
Week 3
July
Week 2
July
Week 1
June
Week 4
June
Week 3
June
Week 2
13
18.0
16.3
14.5
8.0
12.6
+124%
Average yields by experimental plots across Amhara FTCs
Quintal/hectare
X.X Standard
deviation
Avg yield qt/ha
Planting type
National
Average for
2012
Broadcast
by hand
Broadcast
by machine
Row plant Transplant
Seed rate (kg/ha) 30-40 5-10 5-10 0.5-0.7
Fertilizer type none DAP +
Urea
DAP +
Urea
DAP +
Urea
Seed type Local Quncho Quncho Quncho
8.15.0 8.9 10.4
AMHARA: FTC demonstration plot results
Source: 2012 Data from Regional, Zonal and Woreda administration staff (collected Feb-April 2013); CSA 2012
14
OROMIA: overview of data collected
Source: 2012 Data from Regional, Zonal and Woreda administration staff (collected Feb-April 2013)
Farmer data overview
Average yield 21.26
Standard deviation 8.56
Average productivity increase 69%
Count of farmers and FTCs Raw Cleaned
Farmers 6,515 6,002
Gender split
Male 6,198 95%
Female 292 5%
Geographic scope within region
(as reported)
Count of zones N/A
Count of woredas 48
15%
Broadcasting
57%
Transplanting
Row planting
16%
Distribution of planting type
% of total farmers (from raw data)
15
OROMIA: input use, yields achieved, and planting time
Source: 2012 Data from Regional, Zonal and Woreda administration staff (collected Feb-April 2013)
Inputs use of Validating Farmers
Planting time of Validating Farmers
Highest-producing woredas (based on Validating Farmers)
Average input use by planting method
Kg/hectare
4810
1009999
859085
Transplanting
n = 889
Row planting
n = 2,238
Broadcasting
n = 1,446
Urea
DAP
Seed
Average and maximum farmer yields
Kg/hectare (average and max by woreda)
32
30
30
28
26Dawoo 38
A/Nagale 65
Gimbichuu 61
H/Abote 53
Lume 68
Average
Max
Average yield based on planting time
% productivity increase (compared to national average)
92
626164676772
90
6675
51
2050
August
Week 3
August
Week 2
August
Week 1
July
Week 4
July
Week 3
July
Week 2
100
0
Sept
Week 1
August
Week 4
July
Week 1
June
Week 4
June
Week 3
June
Week 2
16
20.1
18.1
15.2
8.1
12.6
+148%
Average yields by experimental plots across Oromia FTCs
Quintal/hectare
X.X Standard
deviation
Avg yield qt/ha
Planting type
National
Average for
2012
Broadcast
by hand
Broadcast
by machine
Row plant Transplant
Seed rate (kg/ha) 30-40 5-10 5-10 0.5-0.7
Fertilizer type none DAP +
Urea
DAP +
Urea
DAP +
Urea
Seed type Local Quncho Quncho Quncho
8.46.4 9.5 11.1
OROMIA: FTC demonstration plot results
Source: 2012 Data from Regional, Zonal and Woreda administration staff (collected Feb-April 2013); CSA 2012
17
SNNP: overview of data collected
Source: 2012 Data from Regional, Zonal and Woreda administration staff (collected Feb-April 2013)
Farmer data overview
Average yield 20.72
Standard deviation 9.06
Average productivity increase 64%
Count of farmers and FTCs Raw Cleaned
Farmers 3,766 3,480
Gender split
Male 3,061 81%
Female 124 3%
Geographic scope within region
(as reported)
Count of zones N/A
Count of woredas 38
11%
65%
Row planting
1%
Broadcasting
(hand) Broadcasting (machine)
13%
Transplanting
Distribution of planting type
% of total farmers (from raw data)
18
SNNP: input use, yields achieved, and planting time
Source: 2012 Data from Regional, Zonal and Woreda administration staff (collected Feb-April 2013)
Inputs use of Validating Farmers
Planting time of Validating Farmers
Highest-producing woredas (based on Validating Farmers)
Average input use by planting method
Kg/hectare
41211
100100100
838783
Transplanting
n = 889
Row planting
n = 2,238
Broadcasting
n = 1,446
Urea
DAP
Seed
Average and maximum farmer yields
Kg/hectare (average and max by woreda)
33
33
27
27
27Bona Zuria 33
Maraka 47
Gombera 30
Kuxha 70
Dalocha 69
Average
Max
Average yield based on planting time
% productivity increase (compared to national average)
686367666972725234
178
-37
144
-200
200
0
Sept
Week 1
August
Week 4
August
Week 3
August
Week 2
August
Week 1
July
Week 4
July
Week 3
July
Week 2
July
Week 1
June
Week 4
June
Week 3
June
Week 2
June planting consists of only 5 farmers
19
22.7
21.0
17.0
8.2
12.6
+177%
Average yields by experimental plots across SNNP FTCs
Quintal/hectare
X.X Standard
deviation
Avg yield qt/ha
Planting type
National
Average for
2012
Broadcast
by hand
Broadcast
by machine
Row plant Transplant
Seed rate (kg/ha) 30-40 5-10 5-10 0.5-0.7
Fertilizer type none DAP +
Urea
DAP +
Urea
DAP +
Urea
Seed type Local Quncho Quncho Quncho
7.27.8 8.9 12.5
SNNP: FTC demonstration plot results
Source: 2012 Data from Regional, Zonal and Woreda administration staff (collected Feb-April 2013); CSA 2012
20
TIGRAY: overview of data collected
Source: 2012 Data from Regional, Zonal and Woreda administration staff (collected Feb-April 2013)
Farmer data overview
Average yield 20.55
Standard deviation 9.09
Average productivity increase 63%
Count of farmers and FTCs Raw Cleaned
Farmers 551 486
Gender split
Male 410 74%
Female 54 10%
Geographic scope within region
(as reported)
Count of zones N/A
Count of woredas 11
4%
62%
Row planting
5%
Transplanting 4%
Broadcasting
(hand) Broadcasting (machine)
Distribution of planting type
% of total farmers (from raw data)
21
TIGRAY: input use, yields achieved, and planting time
Source: 2012 Data from Regional, Zonal and Woreda administration staff (collected Feb-April 2013)
Inputs use of Validating Farmers
Planting time of Validating Farmers
Highest-producing woredas (based on Validating Farmers)
Average input use by planting method
Kg/hectare
7813
10098100 9192100
Transplanting
n = 889
Row planting
n = 2,238
Broadcasting
n = 1,446
Urea
DAP
Seed
Average and maximum farmer yields
Kg/hectare (average and max by woreda)
32
30
30
28
26Tahtay Koraro 38
Weri-Leke 65
Medebay Zana 61
Adwa 53
Tahaty- Maichew 68
Average
Max
Average yield based on planting time
% productivity increase (compared to national average)
36
7449
-25
29100
0
Sept
Week 1
August
Week 4
August
Week 3
-100
August
Week 2
August
Week 1
July
Week 4
July
Week 3
July
Week 2
July
Week 1
75
June
Week 4
-17
June
Week 3
67
June
Week 2
76
36N/A N/A
Includes only 2
farmers
Includes only 2
farmers
22
18.918.7
16.5
11.812.6
+60%
Average yields by experimental plots across Tigray FTCs
Quintal/hectare
X.X Standard
deviation
Avg yield qt/ha
Planting type
National
Average for
2012
Broadcast
by hand
Broadcast
by machine
Row plant Transplant
Seed rate (kg/ha) 30-40 5-10 5-10 0.5-0.7
Fertilizer type none DAP +
Urea
DAP +
Urea
DAP +
Urea
Seed type Local Quncho Quncho Quncho
9.511.0 10.5 11.8
TIGRAY: FTC demonstration plot results
Source: 2012 Data from Regional, Zonal and Woreda administration staff (collected Feb-April 2013); CSA 2012
Takeaways from 2012 farmer and FTC data analysis that should inform ATA and RBoA
planning and execution for 2013 and 2013 tef planting seasons
23
Takeaways to inform 2013 and 2014 intervention planning and execution
• The benefits of optimal fertilizer application rates should be better
disseminated to farmers via trainings and materials, particularly for Urea
• More intensive training on transplanting management practices should be
provided to farmers given the volatility in yields observed in 2012
• FTCs should be encouraged to conduct demonstrations to test for specific
variables’ impact on yield (e.g., planting 2-3 weeks earlier, reduced seed
rates of 5/10/15/ kg per ha, pelleted Urea)
• FTC yields indicate significant yield increases due to use of Quncho with
90% of farmers in these trials using Quncho; should explore further
support for Quncho while also considering other improved varieties
24
 Includes 15,790 validating
farmers’ data
 8% of data was omitted as
inaccurate or incomplete
 45% (33% + 12%) of
validating farmers’ yields
were between 20 and 40
quintals per hectare
Overview of data collected: yield distribution for 15,790 validating farmers and 1,107 FTCs
Source: 2012 Data from Regional, Zonal and Woreda administration staff (collected Feb-April 2013)
40 to 60
40
0
20
1%
60+
3%
30 to 40
12%
20 to 30
33%
10 to 20
40%
Less than 10
8%
Distribution of Validating Farmers’ yields
Frequency of yield increase (as % of total data set)
 Covers 1,107 FTCs with an
average of 9.6 experimental
data plots per FTC
 4% of the data was omitted
as inaccurate or incomplete
 Distribution of yields is
wider given trials included
many variables
40
20
0
40+
2%
30 to 40
7%
20 to 30
20%
10 to 20
38%
5 to 10
18%
Less than 5
11%
Distribution of FTC yield per experimental plot
Frequency of yield increase (as % of total data set)
Quintals / hectare
Quintals / hectare
25
Overview of data collected: basics
Source: 2012 Data from Regional, Zonal and Woreda administration staff (collected Feb-April 2013)
Farmer data overview
average yield 20.86
std dev 12.38
bell curve upper bound 58.00
average productivity increase 66%
Count of farmers
Amhara 4,958 31%
Oromia 6,515 41%
SNNP 3,766 24%
Tigray 551 3%
Total farmers 15,790
Gender split
Male 14346 95%
Female 685 5%
20%
1%
17% 61%
Broadcasting (machine)
Row plantingTransplanting
Broadcasting (hand)
Distribution of planting type
% of total farmers

Mais conteúdo relacionado

Mais procurados

Mais procurados (20)

0865 System of Rice Intensification (SRI): Experiences of Nepal
0865 System of Rice Intensification (SRI): Experiences of Nepal0865 System of Rice Intensification (SRI): Experiences of Nepal
0865 System of Rice Intensification (SRI): Experiences of Nepal
 
0715 Preliminary Evaluation of SRI in Fiji for Enhancing Rice Production
0715 Preliminary Evaluation of SRI in Fiji for Enhancing Rice Production0715 Preliminary Evaluation of SRI in Fiji for Enhancing Rice Production
0715 Preliminary Evaluation of SRI in Fiji for Enhancing Rice Production
 
0412 Development of System of Rice Intensification for Rice Production in China
0412 Development of System of Rice Intensification for Rice Production in China0412 Development of System of Rice Intensification for Rice Production in China
0412 Development of System of Rice Intensification for Rice Production in China
 
1312- System of Wheat Intensification
1312- System of Wheat Intensification1312- System of Wheat Intensification
1312- System of Wheat Intensification
 
1309 - The System of Rice Intensification (SRI) in the Context of ‘Sustainabl...
1309 - The System of Rice Intensification (SRI) in the Context of ‘Sustainabl...1309 - The System of Rice Intensification (SRI) in the Context of ‘Sustainabl...
1309 - The System of Rice Intensification (SRI) in the Context of ‘Sustainabl...
 
1051 Modified SRI and super-high yield of hybrid rice in Sichuan Basin
1051 Modified SRI and super-high yield of hybrid rice in Sichuan Basin1051 Modified SRI and super-high yield of hybrid rice in Sichuan Basin
1051 Modified SRI and super-high yield of hybrid rice in Sichuan Basin
 
1042 System of Rice Intensification (SRI) -Producing more rice with less inpu...
1042 System of Rice Intensification (SRI) -Producing more rice with less inpu...1042 System of Rice Intensification (SRI) -Producing more rice with less inpu...
1042 System of Rice Intensification (SRI) -Producing more rice with less inpu...
 
1436 - Participation and Impact of SRI Training in Madagascar
1436 -  Participation and Impact of SRI Training in Madagascar1436 -  Participation and Impact of SRI Training in Madagascar
1436 - Participation and Impact of SRI Training in Madagascar
 
1414 - Development of SRI Transplanter - TN-IAMWARM Experiences
1414 - Development of SRI Transplanter - TN-IAMWARM Experiences1414 - Development of SRI Transplanter - TN-IAMWARM Experiences
1414 - Development of SRI Transplanter - TN-IAMWARM Experiences
 
0733 System of Rice Intensification (SRI) and Integrated Crop Management (ICM...
0733 System of Rice Intensification (SRI) and Integrated Crop Management (ICM...0733 System of Rice Intensification (SRI) and Integrated Crop Management (ICM...
0733 System of Rice Intensification (SRI) and Integrated Crop Management (ICM...
 
1056 Meshing mechanization with SRI methods for rice cultivation in Nepal
1056 Meshing mechanization with SRI methods for rice cultivation in Nepal1056 Meshing mechanization with SRI methods for rice cultivation in Nepal
1056 Meshing mechanization with SRI methods for rice cultivation in Nepal
 
0716 The System of Rice Intensification in Jharkhand and Bihar Bringing New P...
0716 The System of Rice Intensification in Jharkhand and Bihar Bringing New P...0716 The System of Rice Intensification in Jharkhand and Bihar Bringing New P...
0716 The System of Rice Intensification in Jharkhand and Bihar Bringing New P...
 
1170 System of Rice Intensification SRI - A Global Overview
1170 System of Rice Intensification SRI - A Global Overview1170 System of Rice Intensification SRI - A Global Overview
1170 System of Rice Intensification SRI - A Global Overview
 
0703 Survey of SRI and Other Rice Management Practices on Acid Soils in Prey ...
0703 Survey of SRI and Other Rice Management Practices on Acid Soils in Prey ...0703 Survey of SRI and Other Rice Management Practices on Acid Soils in Prey ...
0703 Survey of SRI and Other Rice Management Practices on Acid Soils in Prey ...
 
0739 Status of SRI Cultivation and its Future Prospects in India
0739 Status of SRI Cultivation and its Future Prospects in India0739 Status of SRI Cultivation and its Future Prospects in India
0739 Status of SRI Cultivation and its Future Prospects in India
 
Igkvv raipur
Igkvv raipurIgkvv raipur
Igkvv raipur
 
0916 From Madagascar to the Rice Terraces of Ifugao: SRI Validation and Prom...
0916 From Madagascar to the Rice Terraces of Ifugao:  SRI Validation and Prom...0916 From Madagascar to the Rice Terraces of Ifugao:  SRI Validation and Prom...
0916 From Madagascar to the Rice Terraces of Ifugao: SRI Validation and Prom...
 
0423 SRI & TQPM Trial Demo Farms in the Visayas: Negros Occidental
0423 SRI & TQPM Trial Demo Farms in the Visayas: Negros Occidental0423 SRI & TQPM Trial Demo Farms in the Visayas: Negros Occidental
0423 SRI & TQPM Trial Demo Farms in the Visayas: Negros Occidental
 
Sas presentation
Sas presentationSas presentation
Sas presentation
 
1514 - The Concept of the TIRR Package
1514 -  The Concept of the TIRR Package1514 -  The Concept of the TIRR Package
1514 - The Concept of the TIRR Package
 

Destaque

Destaque (20)

Marco nicoli greenback
Marco nicoli   greenbackMarco nicoli   greenback
Marco nicoli greenback
 
Josephine cervero
Josephine cerveroJosephine cervero
Josephine cervero
 
1433 - Farmer Evaluation of the SRI and Conventional Rice Cultivation Method...
1433 -  Farmer Evaluation of the SRI and Conventional Rice Cultivation Method...1433 -  Farmer Evaluation of the SRI and Conventional Rice Cultivation Method...
1433 - Farmer Evaluation of the SRI and Conventional Rice Cultivation Method...
 
Dilip ratha
Dilip rathaDilip ratha
Dilip ratha
 
Bela hovy
Bela hovy Bela hovy
Bela hovy
 
David khoudour
David khoudourDavid khoudour
David khoudour
 
1606 - The System of Rice Intensification (SRI) in Iran
1606 - The System of Rice Intensification (SRI) in Iran1606 - The System of Rice Intensification (SRI) in Iran
1606 - The System of Rice Intensification (SRI) in Iran
 
1603 - Improving Food Production for Health in a Water-constrained World - Ag...
1603 - Improving Food Production for Health in a Water-constrained World - Ag...1603 - Improving Food Production for Health in a Water-constrained World - Ag...
1603 - Improving Food Production for Health in a Water-constrained World - Ag...
 
1601- SRI monitoring - Overview and preliminary results - Samar, Philippines
1601- SRI monitoring - Overview and preliminary results - Samar, Philippines1601- SRI monitoring - Overview and preliminary results - Samar, Philippines
1601- SRI monitoring - Overview and preliminary results - Samar, Philippines
 
1438 - Development of Small-Scale Equipment for the System of Rice Intensific...
1438 - Development of Small-Scale Equipment for the System of Rice Intensific...1438 - Development of Small-Scale Equipment for the System of Rice Intensific...
1438 - Development of Small-Scale Equipment for the System of Rice Intensific...
 
1437 - Water Management of Yield Record Holding SRI Farmer in Indonesia; A Ca...
1437 - Water Management of Yield Record Holding SRI Farmer in Indonesia; A Ca...1437 - Water Management of Yield Record Holding SRI Farmer in Indonesia; A Ca...
1437 - Water Management of Yield Record Holding SRI Farmer in Indonesia; A Ca...
 
1605 - Community of Hope Agricultural Project - SRI in Liberia
1605 - Community of Hope Agricultural Project - SRI in Liberia1605 - Community of Hope Agricultural Project - SRI in Liberia
1605 - Community of Hope Agricultural Project - SRI in Liberia
 
1430 - Application of SRI Principles in Sustainable Rice Production in Bhutan
1430 - Application of SRI Principles in Sustainable Rice Production in Bhutan1430 - Application of SRI Principles in Sustainable Rice Production in Bhutan
1430 - Application of SRI Principles in Sustainable Rice Production in Bhutan
 
1434 - Improving and Scaling Up the System of Rice Intensification in West Af...
1434 - Improving and Scaling Up the System of Rice Intensification in West Af...1434 - Improving and Scaling Up the System of Rice Intensification in West Af...
1434 - Improving and Scaling Up the System of Rice Intensification in West Af...
 
Tomas miller fomin
Tomas miller fominTomas miller fomin
Tomas miller fomin
 
Pedro de vasconcelos
Pedro de vasconcelosPedro de vasconcelos
Pedro de vasconcelos
 
3 bsp awards-gfrd2015
3 bsp    awards-gfrd20153 bsp    awards-gfrd2015
3 bsp awards-gfrd2015
 
1610 - Carbon offsetting to sustainably finance the System of Rice Intensifi...
1610 -  Carbon offsetting to sustainably finance the System of Rice Intensifi...1610 -  Carbon offsetting to sustainably finance the System of Rice Intensifi...
1610 - Carbon offsetting to sustainably finance the System of Rice Intensifi...
 
1602 - Scaling Up Climate Smart Rice Production in West Africa
1602  - Scaling Up Climate Smart Rice Production in West Africa1602  - Scaling Up Climate Smart Rice Production in West Africa
1602 - Scaling Up Climate Smart Rice Production in West Africa
 
1510 - Farmer Adaptation of System of Rice Intensification (SRI) Methods in t...
1510 - Farmer Adaptation of System of Rice Intensification (SRI) Methods in t...1510 - Farmer Adaptation of System of Rice Intensification (SRI) Methods in t...
1510 - Farmer Adaptation of System of Rice Intensification (SRI) Methods in t...
 

Semelhante a 1327 - FTCs and Farmer Tef Demonstration and Results 2012

DYSON LIGOMBA POWERPOINT
DYSON LIGOMBA POWERPOINTDYSON LIGOMBA POWERPOINT
DYSON LIGOMBA POWERPOINT
Francio Ligomba
 
Jianqiang Ren_Simulation of regional winter wheat yield by EPIC model.ppt
Jianqiang Ren_Simulation of regional winter wheat yield by EPIC model.pptJianqiang Ren_Simulation of regional winter wheat yield by EPIC model.ppt
Jianqiang Ren_Simulation of regional winter wheat yield by EPIC model.ppt
grssieee
 

Semelhante a 1327 - FTCs and Farmer Tef Demonstration and Results 2012 (20)

Row planting in tef
Row planting in tefRow planting in tef
Row planting in tef
 
An economic analysis of teff productivity, efficiency, and supply response in...
An economic analysis of teff productivity, efficiency, and supply response in...An economic analysis of teff productivity, efficiency, and supply response in...
An economic analysis of teff productivity, efficiency, and supply response in...
 
The Impact of the Promotion of Row Planting on Farmers’ Teff Yield in Ethiopia
The Impact of the Promotion of Row Planting on Farmers’ Teff Yield in EthiopiaThe Impact of the Promotion of Row Planting on Farmers’ Teff Yield in Ethiopia
The Impact of the Promotion of Row Planting on Farmers’ Teff Yield in Ethiopia
 
Agricultural Productivity in Ethiopia - Challenges for Future Growth
Agricultural Productivity in Ethiopia - Challenges for Future GrowthAgricultural Productivity in Ethiopia - Challenges for Future Growth
Agricultural Productivity in Ethiopia - Challenges for Future Growth
 
Sources of Inefficiency and Growth in Agricultual Output in Subsistence Agric...
Sources of Inefficiency and Growth in Agricultual Output in Subsistence Agric...Sources of Inefficiency and Growth in Agricultual Output in Subsistence Agric...
Sources of Inefficiency and Growth in Agricultual Output in Subsistence Agric...
 
Agricultural growth in Ethiopia (2004-2014): Evidence and drivers
Agricultural growth in Ethiopia (2004-2014): Evidence and driversAgricultural growth in Ethiopia (2004-2014): Evidence and drivers
Agricultural growth in Ethiopia (2004-2014): Evidence and drivers
 
0619 The System of Rice Intensification (SRI)
0619 The System of Rice Intensification (SRI)0619 The System of Rice Intensification (SRI)
0619 The System of Rice Intensification (SRI)
 
Perceptions on the impcat of improved teff technologies by exposed farmers
Perceptions on the impcat of improved teff technologies by exposed farmersPerceptions on the impcat of improved teff technologies by exposed farmers
Perceptions on the impcat of improved teff technologies by exposed farmers
 
DNA Finger Printing of Maize and Wheat in Ethiopia
DNA Finger Printing of Maize and Wheat in EthiopiaDNA Finger Printing of Maize and Wheat in Ethiopia
DNA Finger Printing of Maize and Wheat in Ethiopia
 
DYSON LIGOMBA POWERPOINT
DYSON LIGOMBA POWERPOINTDYSON LIGOMBA POWERPOINT
DYSON LIGOMBA POWERPOINT
 
Adoption of production technologies and post harvest management in papaya
Adoption of production technologies and post harvest management in papayaAdoption of production technologies and post harvest management in papaya
Adoption of production technologies and post harvest management in papaya
 
precision farming.ppt
precision farming.pptprecision farming.ppt
precision farming.ppt
 
Agricultural transformation in Africa? Assessing the evidence in Ethiopia
Agricultural transformation in Africa? Assessing the evidence in Ethiopia Agricultural transformation in Africa? Assessing the evidence in Ethiopia
Agricultural transformation in Africa? Assessing the evidence in Ethiopia
 
Returns to fertilizer and program efficiency: Estimation techniques & result...
Returns to fertilizer and program efficiency: Estimation techniques & result...Returns to fertilizer and program efficiency: Estimation techniques & result...
Returns to fertilizer and program efficiency: Estimation techniques & result...
 
Farmers’ uptake of improved feed practices and reasons for adoption/ non adop...
Farmers’ uptake of improved feed practices and reasons for adoption/ non adop...Farmers’ uptake of improved feed practices and reasons for adoption/ non adop...
Farmers’ uptake of improved feed practices and reasons for adoption/ non adop...
 
Jianqiang Ren_Simulation of regional winter wheat yield by EPIC model.ppt
Jianqiang Ren_Simulation of regional winter wheat yield by EPIC model.pptJianqiang Ren_Simulation of regional winter wheat yield by EPIC model.ppt
Jianqiang Ren_Simulation of regional winter wheat yield by EPIC model.ppt
 
0702 SRI: Report of PRADAN Staff Working in Eastern India
0702 SRI: Report of PRADAN Staff Working in Eastern India0702 SRI: Report of PRADAN Staff Working in Eastern India
0702 SRI: Report of PRADAN Staff Working in Eastern India
 
Overview of the ifad funded clca project
Overview of the ifad funded clca project Overview of the ifad funded clca project
Overview of the ifad funded clca project
 
Key learning from the SRI-LMB within the context of food security, water mana...
Key learning from the SRI-LMB within the context of food security, water mana...Key learning from the SRI-LMB within the context of food security, water mana...
Key learning from the SRI-LMB within the context of food security, water mana...
 
Technical Achievements KVK Sangaria
Technical Achievements KVK SangariaTechnical Achievements KVK Sangaria
Technical Achievements KVK Sangaria
 

Mais de SRI-Rice, Dept. of Global Development, CALS, Cornell University

Mais de SRI-Rice, Dept. of Global Development, CALS, Cornell University (20)

2205 - System of Rice Intensification in Indonesia - Research, Adoption, and ...
2205 - System of Rice Intensification in Indonesia - Research, Adoption, and ...2205 - System of Rice Intensification in Indonesia - Research, Adoption, and ...
2205 - System of Rice Intensification in Indonesia - Research, Adoption, and ...
 
2204 -System of Rice Intensification - Improving Rice Production and Saving W...
2204 -System of Rice Intensification - Improving Rice Production and Saving W...2204 -System of Rice Intensification - Improving Rice Production and Saving W...
2204 -System of Rice Intensification - Improving Rice Production and Saving W...
 
2203 - Overview of System of Rice Intensification SRI Around the World
2203 - Overview of System of Rice Intensification SRI Around the World2203 - Overview of System of Rice Intensification SRI Around the World
2203 - Overview of System of Rice Intensification SRI Around the World
 
2202 - Water Savings, Yield, and Income Benefits with SRI in Iraq.ppt
2202 - Water Savings, Yield, and Income Benefits with SRI in Iraq.ppt2202 - Water Savings, Yield, and Income Benefits with SRI in Iraq.ppt
2202 - Water Savings, Yield, and Income Benefits with SRI in Iraq.ppt
 
2201 - El Sistema Intensivo del Cultivo de Arroz
2201 - El Sistema Intensivo del Cultivo de Arroz2201 - El Sistema Intensivo del Cultivo de Arroz
2201 - El Sistema Intensivo del Cultivo de Arroz
 
2104 - El Sector Agropecuario Panameno Contribuyendo a la Lucha Frente al Cam...
2104 - El Sector Agropecuario Panameno Contribuyendo a la Lucha Frente al Cam...2104 - El Sector Agropecuario Panameno Contribuyendo a la Lucha Frente al Cam...
2104 - El Sector Agropecuario Panameno Contribuyendo a la Lucha Frente al Cam...
 
2103 - Reduced Methane Emissions Rice Production Project in Northern Nigerian...
2103 - Reduced Methane Emissions Rice Production Project in Northern Nigerian...2103 - Reduced Methane Emissions Rice Production Project in Northern Nigerian...
2103 - Reduced Methane Emissions Rice Production Project in Northern Nigerian...
 
1711 - Sistema Intensivo del Cultivo del Arroz para la Producción y Sustentab...
1711 - Sistema Intensivo del Cultivo del Arroz para la Producción y Sustentab...1711 - Sistema Intensivo del Cultivo del Arroz para la Producción y Sustentab...
1711 - Sistema Intensivo del Cultivo del Arroz para la Producción y Sustentab...
 
1615 Ecological Intensification - Lessons from SRI from Green Revolution to...
1615   Ecological Intensification - Lessons from SRI from Green Revolution to...1615   Ecological Intensification - Lessons from SRI from Green Revolution to...
1615 Ecological Intensification - Lessons from SRI from Green Revolution to...
 
2102 - Establishing an equitable SRI value chain in the Philippines
2102 - Establishing an equitable SRI value chain in the Philippines2102 - Establishing an equitable SRI value chain in the Philippines
2102 - Establishing an equitable SRI value chain in the Philippines
 
2101 - Agroecological Opportunities with SRI and SCI
2101 - Agroecological Opportunities with SRI and SCI2101 - Agroecological Opportunities with SRI and SCI
2101 - Agroecological Opportunities with SRI and SCI
 
Farmers' Handbook on System of Rice Intensification - SRI (Burmese)
Farmers' Handbook on System of Rice Intensification - SRI (Burmese)Farmers' Handbook on System of Rice Intensification - SRI (Burmese)
Farmers' Handbook on System of Rice Intensification - SRI (Burmese)
 
2001 - System of Rice Intensification SRI in Iraq
2001 - System of Rice Intensification SRI in Iraq2001 - System of Rice Intensification SRI in Iraq
2001 - System of Rice Intensification SRI in Iraq
 
1914 Towards a More Sustainable Rice Crop: System of Rice Intensification (SR...
1914 Towards a More Sustainable Rice Crop: System of Rice Intensification (SR...1914 Towards a More Sustainable Rice Crop: System of Rice Intensification (SR...
1914 Towards a More Sustainable Rice Crop: System of Rice Intensification (SR...
 
1913 Resuitados SRI MIDA-IICA Panama 2019
1913   Resuitados SRI MIDA-IICA Panama 2019 1913   Resuitados SRI MIDA-IICA Panama 2019
1913 Resuitados SRI MIDA-IICA Panama 2019
 
1912 - Agroecological Management of Soil Systems for Food, Water, Climate Res...
1912 - Agroecological Management of Soil Systems for Food, Water, Climate Res...1912 - Agroecological Management of Soil Systems for Food, Water, Climate Res...
1912 - Agroecological Management of Soil Systems for Food, Water, Climate Res...
 
1910 - Integrating Climate Smart Rice Agriculture in Supply Networks - Lotus ...
1910 - Integrating Climate Smart Rice Agriculture in Supply Networks - Lotus ...1910 - Integrating Climate Smart Rice Agriculture in Supply Networks - Lotus ...
1910 - Integrating Climate Smart Rice Agriculture in Supply Networks - Lotus ...
 
1911- Gender Responsive Smallholder Rice Production Practices and equipment
1911- Gender Responsive Smallholder Rice Production Practices and equipment1911- Gender Responsive Smallholder Rice Production Practices and equipment
1911- Gender Responsive Smallholder Rice Production Practices and equipment
 
1908 Rice cultivation in Africa: How traditional practices relate to modern o...
1908 Rice cultivation in Africa: How traditional practices relate to modern o...1908 Rice cultivation in Africa: How traditional practices relate to modern o...
1908 Rice cultivation in Africa: How traditional practices relate to modern o...
 
1907 - The Effects of Exposure Intensity on Technology Adoption and Gains: Ex...
1907 - The Effects of Exposure Intensity on Technology Adoption and Gains: Ex...1907 - The Effects of Exposure Intensity on Technology Adoption and Gains: Ex...
1907 - The Effects of Exposure Intensity on Technology Adoption and Gains: Ex...
 

Último

Último (20)

Developing An App To Navigate The Roads of Brazil
Developing An App To Navigate The Roads of BrazilDeveloping An App To Navigate The Roads of Brazil
Developing An App To Navigate The Roads of Brazil
 
GenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day PresentationGenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day Presentation
 
🐬 The future of MySQL is Postgres 🐘
🐬  The future of MySQL is Postgres   🐘🐬  The future of MySQL is Postgres   🐘
🐬 The future of MySQL is Postgres 🐘
 
Powerful Google developer tools for immediate impact! (2023-24 C)
Powerful Google developer tools for immediate impact! (2023-24 C)Powerful Google developer tools for immediate impact! (2023-24 C)
Powerful Google developer tools for immediate impact! (2023-24 C)
 
AWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of TerraformAWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of Terraform
 
What Are The Drone Anti-jamming Systems Technology?
What Are The Drone Anti-jamming Systems Technology?What Are The Drone Anti-jamming Systems Technology?
What Are The Drone Anti-jamming Systems Technology?
 
presentation ICT roal in 21st century education
presentation ICT roal in 21st century educationpresentation ICT roal in 21st century education
presentation ICT roal in 21st century education
 
Strategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a FresherStrategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a Fresher
 
HTML Injection Attacks: Impact and Mitigation Strategies
HTML Injection Attacks: Impact and Mitigation StrategiesHTML Injection Attacks: Impact and Mitigation Strategies
HTML Injection Attacks: Impact and Mitigation Strategies
 
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
Workshop - Best of Both Worlds_ Combine  KG and Vector search for  enhanced R...Workshop - Best of Both Worlds_ Combine  KG and Vector search for  enhanced R...
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
 
Boost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivityBoost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivity
 
Tata AIG General Insurance Company - Insurer Innovation Award 2024
Tata AIG General Insurance Company - Insurer Innovation Award 2024Tata AIG General Insurance Company - Insurer Innovation Award 2024
Tata AIG General Insurance Company - Insurer Innovation Award 2024
 
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, AdobeApidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
 
Advantages of Hiring UIUX Design Service Providers for Your Business
Advantages of Hiring UIUX Design Service Providers for Your BusinessAdvantages of Hiring UIUX Design Service Providers for Your Business
Advantages of Hiring UIUX Design Service Providers for Your Business
 
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
 
Real Time Object Detection Using Open CV
Real Time Object Detection Using Open CVReal Time Object Detection Using Open CV
Real Time Object Detection Using Open CV
 
Scaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organizationScaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organization
 
Exploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone ProcessorsExploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone Processors
 
Understanding Discord NSFW Servers A Guide for Responsible Users.pdf
Understanding Discord NSFW Servers A Guide for Responsible Users.pdfUnderstanding Discord NSFW Servers A Guide for Responsible Users.pdf
Understanding Discord NSFW Servers A Guide for Responsible Users.pdf
 
Handwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed textsHandwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed texts
 

1327 - FTCs and Farmer Tef Demonstration and Results 2012

  • 1. 2012 FTCs and farmers tef technology demonstration Overview of results May 2013
  • 2. Agenda A. Overview of results at federal level B. Breakdown of results by region C. Supplemental analyses 1
  • 3. 2 Newly-scaled up tef technologies, particularly row planting and transplanting, demonstrate yield improvements, with an average yield increase of ~70% over the national average Average yield by planting method Quintals/hectare • Data was collected from ~15,800 validating farmers (and some control farmer groups) to determine the results of new tef technologies • For the 14,605 farmers, average yields for row planting and transplanting increased 70% from national average (20.9 versus 12.6 qtls/ha) • As the chart shows, there is still much work to be done in properly managing transplanting to realize potential yield gains 17 12 16 21222122 18 20 2323 Amhara n = 4,637 SNNP n = 3,480 Oromia n = 6,002 Tigray n = 486 N/A Broadcasting Row planting Transplanting Source: 2012 Data from Regional, Zonal and Woreda administration staff (collected Feb-April 2013) Note: Includes data from 14,605 farmers (omitted error/outlier data from 15,790 total collected)
  • 4. 3 Distribution of yield data shows that 30% of all validating farmers surveyed experienced yield increase between 20 and 80% over the national average Source: 2012 Data from Regional, Zonal and Woreda administration staff (collected Feb-April 2013) Note: Includes data from 14,605 farmers (omitted error/outlier data from 15,790 total collected) Distribution of Validating Farmers’ yields Frequency of yield increase (as % of total data set) 4 7 6 8 10 8 13 11 7 150 - 200% 125 - 150% Over 200% 100 - 125% 80 - 100% 60 - 80%40 - 60%20 - 40%10 - 20%Less than 10% 27 ~30% of farmers saw a 20 – 80% yield increase Farmers who broadcasted, used high seed rates, or may have experienced challenges with new technologies ~20% of farmers saw a 100 – 200% yield increase (~60% of this group row planted)
  • 5. 4 Top-performing woredas across the four regions have demonstrated success in adopting the new technologies, reaching maximum yields higher than a 400% yield increase Average and maximum yields for row planting and transplanting farmers Quintal/hectare 36 36 35 35 35 35 33 33 32 31H/Abote 53 Tahaty- Maichew 56 47 Lume 68 Maraka Kuxha 68 65 Alefa 42 Dabat Enebsie Sar Midir 58 Chilga 53 67 Libo Kemkem AMHARA SNNP OROMIA SNNP TIGRAY OROMIA 68 quintals/hectare versus the national average of 12.5 quintals/hectare Source: 2012 Data from Regional, Zonal and Woreda administration staff (collected Feb-April 2013) Note: Includes data from 14,605 farmers (omitted error/outlier data from 15,790 total collected)
  • 6. 5 Data indicates that validating farmers’ seed rate usage varies by planting method, with the highest rates used in broadcasting to the least amount of seed used in transplanting Average seed rate and yields by planting type across regions Yield (quintal/hectare), Seed rate (kg/hectare) 4 1011 30 2222 15 13 25 20 15 10 5 0 30 25 20 15 10 5 0 TransplantingRow plantingBroadcastingNational average • While this does not imply causation, there appears to be an inverse relationship between planting type and seed rate practices of the validating farmers • Overall, the average seed rate has dropped significantly from traditional practices of 30-50 kg/ha to an average of 8.8 kg/ha across all planting types (includes broadcasting farmers as well) • Use of Quncho variety appears to be on the rise, with 92% of validating farmers choosing it over other options (local varieties, Cross-37, Yedega, etc.) Seed rate Avg yield Source: 2012 Data from Regional, Zonal and Woreda administration staff (collected Feb-April 2013) Note: Includes data from 14,605 farmers (omitted error/outlier data from 15,790 total collected)
  • 7. 6 Fertilizer application rates appear to match recommendations for DAP (average application rate of 99.5 kg/ha), yet fall slightly below recommendations on Urea (88.4 kg/ha) Average DAP and Urea application rates by region Kg/hectare Key takeaways on DAP and Urea utilization by farmers: • Urea use is highly variable, much more than DAP: the standard deviation for Urea is 24.6 kg/hectare versus 3.8 for DAP, indicating the that farmer use of DAP is more consistent • Application rates are fairly agnostic of planting method: for each planting method, the average application rates remain similar 9910099 91 868890 Amhara n = 4,037 SNNP n = 3,051 Oromia n = 4,943 Tigray n = 304 N/A DAP Urea 4.1 21.9 4.4 25.4 1.3 26.5 6.5 21.5 X Standard deviation Source: 2012 Data from Regional, Zonal and Woreda administration staff (collected Feb-April 2013) Note: Includes data from 12,335 farmers (omitted error/outlier fertilizer data from 15,790 total collected)
  • 8. 7 A high-level view of planting time indicates that most farmers observed ‘traditional’ planting times, though farmers that planted earlier saw significant yield increases 69616261626272 86 66 8089 0 50 100 June Week 2 Sept Week 1 July Week 4 August Week 1 August Week 2 August Week 3 August Week 4 59 July Week 3 July Week 2 July Week 1 June Week 4 June Week 3 Average productivity increase by planting time % productivity increase (compared to national average) 0.3%0.1% 2%0.8% 16%5% 19%24% 4%7% 1%4% Percentof farmers Key takeaways  Planting time of validating farmers followed standard practices, with ~60% of farmers planting in mid-July to early August  Though a small sample size, there seems to be a strong productivity increase for farmers who planted earlier, in the 3 and 4th weeks of June Standard planting period Next steps for exploration  Early planting time, when is coincides with rainfall, is a topic to be further explored with farmers and on FTC plots  EIAR and RARIs, with support from ATA, will conduct formal research projects to determine the impact of planting 2, 3 or 4 weeks early Source: 2012 Data from Regional, Zonal and Woreda administration staff (collected Feb-April 2013) Note: Includes data from 12,335 farmers (omitted error/outlier data from 15,790 total collected) Largely driven by transplanting
  • 9. 8 20.1 18.3 15.5 8.4 12.6 +60% Average yields by experimental plot across regions Quintal/hectare X.X Standard deviation Avg yield qt/ha Planting type National Average for 2012 Broadcast by hand Broadcast by machine Row plant Transplant Seed rate (kg/ha) 30-50 5-10 5-10 0.5-0.7 Fertilizer type none DAP + Urea DAP + Urea DAP + Urea Seed type Local Quncho Quncho Quncho 8.16.9 9.4 11.4 In ~1,100 FTC trials, it can be observed that factors such as reduced seed rate, planting in rows, and use of DAP and Urea can each contribute to increasing yields • Findings indicate that significant yield improvement results from new technologies such as row planting, transplanting, and reduced seed rate • Across the 1,100 FTCs, yields were recorded for each experimental plot that was designed to test either planting method, seed rate, seed variety, or fertilizer use Source: 2012 Data from Regional, Zonal and Woreda administration staff (collected Feb-April 2013); CSA 2012
  • 10. 9 20.1 18.3 15.514.9 13.9 12.012.6 +4%+7% +18% +10% +16% Average yields by experimental plot across regions Quintal/hectare Planting type National Average for 2012 Broadcast by hand Broadcast by hand Broadcast by hand Broadcast by machine Row plant Transplant Seed rate (kg/ha) 30-50 30-50 5-10 5-10 5-10 0.5-0.7 Fertilizer type DAP + Urea DAP + Urea DAP + Urea DAP + Urea DAP + Urea DAP + Urea Seed type Local Quncho Quncho Quncho Quncho Quncho Taking a closer look at each variable, it appears that switching to Quncho and row planting are the largest drivers of productivity increase Source: 2012 Data from Regional, Zonal and Woreda administration staff (collected Feb-April 2013); CSA 2012
  • 11. Agenda A. Overview of results at federal level B. Breakdown of results by region C. Supplemental analyses 10
  • 12. 11 AMHARA: overview of data collected Source: 2012 Data from Regional, Zonal and Woreda administration staff (collected Feb-April 2013) Farmer data overview Average yield 20.47 Standard deviation 9.65 Average productivity increase 62% Count of farmers Raw Cleaned Farmers 4,958 4,637 Gender split Male 4,373 95% Female 685 4% Geographic scope within region (as reported) Count of zones 10 Count of woredas 85 31% Broadcasting (machine) 1% Broadcasting (hand) Transplanting Row planting48% 19% Distribution of planting type % of total farmers (from raw data)
  • 13. 12 AMHARA: input use, yields achieved, and planting time Source: 2012 Data from Regional, Zonal and Woreda administration staff (collected Feb-April 2013) Inputs use of Validating Farmers Planting time of Validating Farmers Highest-producing woredas (based on Validating Farmers) Average input use by planting method Kg/hectare 10 88 9994 9 99 96100 2 Row planting n = 2,238 Broadcasting n = 1,446 Transplanting n = 889 Seed DAP Urea Average and maximum farmer yields Kg/hectare (average and max by woreda) 36 35 32 31 30 Enebsie Sar midir 58 Alefa Dabat 67 Libo Kemkem 68 53Chilga 42 Average Max Average yield based on planting time % productivity increase (compared to national average) 68 194136455671947285105104 100 August Week 3 200 0 Sept Week 1 August Week 4 August Week 2 August Week 1 July Week 4 July Week 3 July Week 2 July Week 1 June Week 4 June Week 3 June Week 2
  • 14. 13 18.0 16.3 14.5 8.0 12.6 +124% Average yields by experimental plots across Amhara FTCs Quintal/hectare X.X Standard deviation Avg yield qt/ha Planting type National Average for 2012 Broadcast by hand Broadcast by machine Row plant Transplant Seed rate (kg/ha) 30-40 5-10 5-10 0.5-0.7 Fertilizer type none DAP + Urea DAP + Urea DAP + Urea Seed type Local Quncho Quncho Quncho 8.15.0 8.9 10.4 AMHARA: FTC demonstration plot results Source: 2012 Data from Regional, Zonal and Woreda administration staff (collected Feb-April 2013); CSA 2012
  • 15. 14 OROMIA: overview of data collected Source: 2012 Data from Regional, Zonal and Woreda administration staff (collected Feb-April 2013) Farmer data overview Average yield 21.26 Standard deviation 8.56 Average productivity increase 69% Count of farmers and FTCs Raw Cleaned Farmers 6,515 6,002 Gender split Male 6,198 95% Female 292 5% Geographic scope within region (as reported) Count of zones N/A Count of woredas 48 15% Broadcasting 57% Transplanting Row planting 16% Distribution of planting type % of total farmers (from raw data)
  • 16. 15 OROMIA: input use, yields achieved, and planting time Source: 2012 Data from Regional, Zonal and Woreda administration staff (collected Feb-April 2013) Inputs use of Validating Farmers Planting time of Validating Farmers Highest-producing woredas (based on Validating Farmers) Average input use by planting method Kg/hectare 4810 1009999 859085 Transplanting n = 889 Row planting n = 2,238 Broadcasting n = 1,446 Urea DAP Seed Average and maximum farmer yields Kg/hectare (average and max by woreda) 32 30 30 28 26Dawoo 38 A/Nagale 65 Gimbichuu 61 H/Abote 53 Lume 68 Average Max Average yield based on planting time % productivity increase (compared to national average) 92 626164676772 90 6675 51 2050 August Week 3 August Week 2 August Week 1 July Week 4 July Week 3 July Week 2 100 0 Sept Week 1 August Week 4 July Week 1 June Week 4 June Week 3 June Week 2
  • 17. 16 20.1 18.1 15.2 8.1 12.6 +148% Average yields by experimental plots across Oromia FTCs Quintal/hectare X.X Standard deviation Avg yield qt/ha Planting type National Average for 2012 Broadcast by hand Broadcast by machine Row plant Transplant Seed rate (kg/ha) 30-40 5-10 5-10 0.5-0.7 Fertilizer type none DAP + Urea DAP + Urea DAP + Urea Seed type Local Quncho Quncho Quncho 8.46.4 9.5 11.1 OROMIA: FTC demonstration plot results Source: 2012 Data from Regional, Zonal and Woreda administration staff (collected Feb-April 2013); CSA 2012
  • 18. 17 SNNP: overview of data collected Source: 2012 Data from Regional, Zonal and Woreda administration staff (collected Feb-April 2013) Farmer data overview Average yield 20.72 Standard deviation 9.06 Average productivity increase 64% Count of farmers and FTCs Raw Cleaned Farmers 3,766 3,480 Gender split Male 3,061 81% Female 124 3% Geographic scope within region (as reported) Count of zones N/A Count of woredas 38 11% 65% Row planting 1% Broadcasting (hand) Broadcasting (machine) 13% Transplanting Distribution of planting type % of total farmers (from raw data)
  • 19. 18 SNNP: input use, yields achieved, and planting time Source: 2012 Data from Regional, Zonal and Woreda administration staff (collected Feb-April 2013) Inputs use of Validating Farmers Planting time of Validating Farmers Highest-producing woredas (based on Validating Farmers) Average input use by planting method Kg/hectare 41211 100100100 838783 Transplanting n = 889 Row planting n = 2,238 Broadcasting n = 1,446 Urea DAP Seed Average and maximum farmer yields Kg/hectare (average and max by woreda) 33 33 27 27 27Bona Zuria 33 Maraka 47 Gombera 30 Kuxha 70 Dalocha 69 Average Max Average yield based on planting time % productivity increase (compared to national average) 686367666972725234 178 -37 144 -200 200 0 Sept Week 1 August Week 4 August Week 3 August Week 2 August Week 1 July Week 4 July Week 3 July Week 2 July Week 1 June Week 4 June Week 3 June Week 2 June planting consists of only 5 farmers
  • 20. 19 22.7 21.0 17.0 8.2 12.6 +177% Average yields by experimental plots across SNNP FTCs Quintal/hectare X.X Standard deviation Avg yield qt/ha Planting type National Average for 2012 Broadcast by hand Broadcast by machine Row plant Transplant Seed rate (kg/ha) 30-40 5-10 5-10 0.5-0.7 Fertilizer type none DAP + Urea DAP + Urea DAP + Urea Seed type Local Quncho Quncho Quncho 7.27.8 8.9 12.5 SNNP: FTC demonstration plot results Source: 2012 Data from Regional, Zonal and Woreda administration staff (collected Feb-April 2013); CSA 2012
  • 21. 20 TIGRAY: overview of data collected Source: 2012 Data from Regional, Zonal and Woreda administration staff (collected Feb-April 2013) Farmer data overview Average yield 20.55 Standard deviation 9.09 Average productivity increase 63% Count of farmers and FTCs Raw Cleaned Farmers 551 486 Gender split Male 410 74% Female 54 10% Geographic scope within region (as reported) Count of zones N/A Count of woredas 11 4% 62% Row planting 5% Transplanting 4% Broadcasting (hand) Broadcasting (machine) Distribution of planting type % of total farmers (from raw data)
  • 22. 21 TIGRAY: input use, yields achieved, and planting time Source: 2012 Data from Regional, Zonal and Woreda administration staff (collected Feb-April 2013) Inputs use of Validating Farmers Planting time of Validating Farmers Highest-producing woredas (based on Validating Farmers) Average input use by planting method Kg/hectare 7813 10098100 9192100 Transplanting n = 889 Row planting n = 2,238 Broadcasting n = 1,446 Urea DAP Seed Average and maximum farmer yields Kg/hectare (average and max by woreda) 32 30 30 28 26Tahtay Koraro 38 Weri-Leke 65 Medebay Zana 61 Adwa 53 Tahaty- Maichew 68 Average Max Average yield based on planting time % productivity increase (compared to national average) 36 7449 -25 29100 0 Sept Week 1 August Week 4 August Week 3 -100 August Week 2 August Week 1 July Week 4 July Week 3 July Week 2 July Week 1 75 June Week 4 -17 June Week 3 67 June Week 2 76 36N/A N/A Includes only 2 farmers Includes only 2 farmers
  • 23. 22 18.918.7 16.5 11.812.6 +60% Average yields by experimental plots across Tigray FTCs Quintal/hectare X.X Standard deviation Avg yield qt/ha Planting type National Average for 2012 Broadcast by hand Broadcast by machine Row plant Transplant Seed rate (kg/ha) 30-40 5-10 5-10 0.5-0.7 Fertilizer type none DAP + Urea DAP + Urea DAP + Urea Seed type Local Quncho Quncho Quncho 9.511.0 10.5 11.8 TIGRAY: FTC demonstration plot results Source: 2012 Data from Regional, Zonal and Woreda administration staff (collected Feb-April 2013); CSA 2012
  • 24. Takeaways from 2012 farmer and FTC data analysis that should inform ATA and RBoA planning and execution for 2013 and 2013 tef planting seasons 23 Takeaways to inform 2013 and 2014 intervention planning and execution • The benefits of optimal fertilizer application rates should be better disseminated to farmers via trainings and materials, particularly for Urea • More intensive training on transplanting management practices should be provided to farmers given the volatility in yields observed in 2012 • FTCs should be encouraged to conduct demonstrations to test for specific variables’ impact on yield (e.g., planting 2-3 weeks earlier, reduced seed rates of 5/10/15/ kg per ha, pelleted Urea) • FTC yields indicate significant yield increases due to use of Quncho with 90% of farmers in these trials using Quncho; should explore further support for Quncho while also considering other improved varieties
  • 25. 24  Includes 15,790 validating farmers’ data  8% of data was omitted as inaccurate or incomplete  45% (33% + 12%) of validating farmers’ yields were between 20 and 40 quintals per hectare Overview of data collected: yield distribution for 15,790 validating farmers and 1,107 FTCs Source: 2012 Data from Regional, Zonal and Woreda administration staff (collected Feb-April 2013) 40 to 60 40 0 20 1% 60+ 3% 30 to 40 12% 20 to 30 33% 10 to 20 40% Less than 10 8% Distribution of Validating Farmers’ yields Frequency of yield increase (as % of total data set)  Covers 1,107 FTCs with an average of 9.6 experimental data plots per FTC  4% of the data was omitted as inaccurate or incomplete  Distribution of yields is wider given trials included many variables 40 20 0 40+ 2% 30 to 40 7% 20 to 30 20% 10 to 20 38% 5 to 10 18% Less than 5 11% Distribution of FTC yield per experimental plot Frequency of yield increase (as % of total data set) Quintals / hectare Quintals / hectare
  • 26. 25 Overview of data collected: basics Source: 2012 Data from Regional, Zonal and Woreda administration staff (collected Feb-April 2013) Farmer data overview average yield 20.86 std dev 12.38 bell curve upper bound 58.00 average productivity increase 66% Count of farmers Amhara 4,958 31% Oromia 6,515 41% SNNP 3,766 24% Tigray 551 3% Total farmers 15,790 Gender split Male 14346 95% Female 685 5% 20% 1% 17% 61% Broadcasting (machine) Row plantingTransplanting Broadcasting (hand) Distribution of planting type % of total farmers