Evidence from the USAID Strengthening Agricultural and Nutrition Extension (SANE) Activity. Presentation by Paul McNamara, AgReach (March 5, 2020). For more details, visit http://bit.ly/FutureAgExt

1. Strengthening Agricultural & Nutrition Extension (SANE)
SANE: https://AgReach.illinois.edu/sane Resources: http://bit.do/SANEAgNutrition
Strengthening Agriculture and Nutrition Extension in Malawi:
Theory of Change and Evidence
The
Does Strengthening Extension at the Meso Level Improve Quality at the
Village Level? Evidence from the USAID Strengthening Agricultural and
Nutrition Extension (SANE) Activity
March 5, 2020 Future Extension Conference, IFPRI, Washington DC
Paul E. McNamara, AgReach, Dept of Agric. & Consumer Econ, University of Illinois
Support from SANE team: Alvarez-Mingote, Moore, Chowa, Nordin, Mandula, Mzumura, Amadu, Hounnou, Snider, Schreiber

2. Strengthening Agricultural & Nutrition
Extension
2
Improve the policy environment to promote
knowledge sharing across government, donor, NGOs
and private sector
•Mechanism: Implement a pluralistic and demand-driven extension
policy
Strengthen coordination and capacity of extension
service providers
•Mechanism: Build networks of decision-makers, implementers, and
extension experts.
Increase connections between research institutions
and extension service providers
•Mechanism: Address communication and knowledge gaps and
connect research institutions and extension providers.

3. Theory of Change
If...
• DAESSstructures
are strengthened,
• Capacityis built
around DAESS
and extension
service delivery
skills, and
• Policyis clarified,
disseminated,
and better
understood,
then...
• Coordination
and
collaboration,
and
• Farmers'
voicewill
improve,
leading to
improved...
• Access to
and
• Quality of
extension
services in
Malawi.
Key Strengthening Mechanism in SANE: Iterative
Extension Strengthening Process Applied to District and
SubDistrict Levels – Participatory Assessment, Design,
Implementation, Reflection & Learning, Repeat

4. SANE Approach and USAID Malawi
CDCS Results Framework
4

5. Impacts of SANE
• SANE’s system strengthening approach has far-
reaching results
– Engaging 150 DAESS platforms benefits the 5,407,334
rural people they represent
– Significant quantitative and qualitative evidence of
increased functionality and performance of extension
platforms
• Changes towards empowerment and ownership of
services can occur
– Communities demanding better services and holding
providers accountable
– Platforms actively raising own funds to promote
sustainability
– Districts leveraging other resources to expand service
access and quality
• Investments in extension can produce multiplier
effects
– Strong systems underpin effective and needs-based
services
– Tangible agricultural and nutritional outcomesoccur,
which improves quality of life

6. Indicator 1:
• Average functionality of all
DAESS platforms in SANE
districts improves over time;
• Non-SANE DAECC
Functionality also improves
over time;
• DSPs and ASPs Functionality
in Non-SANE districts
decreases over time;
• SANE platforms performs
better than Non-SANE
platforms
65%
69%
72%
31%
52%
61%
82%
86%
95%
49% 47%
18%
11%
82%
92%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2017 2018 2019
DAESS Functionality
SANE ASP
SANE DSP
SANE DAECC
Non-SANE ASP
Non-SANE DSP
Non-SANE DAECC

7. Indicator 2:
• Average research score of
all DAESS platforms in SANE
districts improves from
2017 to 2018 and remains
almost the same in 2019;
• It slightly improves for ASPs
and DSPs in Non-SANE
districts;
• DAECC research in Non-
SANE districts improves in
2018;
• SANE platforms performs
better than Non-SANE
platforms
27%
39%
44%
13%
43%
40%
74%
81%
76%
16%
18%
0%
5%
44%
70%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2017 2018 2019
Research related topics
SANE ASP
SANE DSP
SANE DAECC
Non-SANE ASP
Non-SANE DSP
Non-SANE DAECC

8. Indicator 3:
• Average 3-Cs indicators of
all DAESS platforms in SANE
districts improves from
2017 to 2018 and remains
almost the same in 2019;
• It slightly improves for ASPs
in Non-SANE districts but
decreases for DSPs;
• DAECC score is Non-SANE
districts improves in 2019;
• SANE platforms performs
better than Non-SANE
platforms
46%
67%
62%
10%
46%
62%
82%
91% 91%
20%
26%
15%
7%
70%
82%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2017 2018 2019
DAESS 3-Cs
SANE ASP
SANE DSP
SANE DAECC
Non-SANE ASP
Non-SANE DSP
Non-SANE DAECC

9. DAESS overall
Performance:
• DAESS platforms overall
performance improves in
SANE districts over time;
• DAECC overall performance
improves in Non-SANE
districts;
• ASPs’ overall performance
remains the same and that
of DSPs decreases;
• SANE platforms performs
better than Non-SANE
platforms
46%
59% 59%
18%
47%
54%
79%
86% 87%
29%
31%
11% 8%
65%
82%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2017 2018 2019
DAESS overall Performance
SANE ASP
SANE DSP
SANE DAECC
Non-SANE ASP
Non-SANE DSP
Non-SANE DAECC

10. Policy
• District Development Plans now include agriculture and nutrition more frequently
– District Agricultural Development Officer: ‘SANE has strengthened structures which have resulted in
inclusion of agricultural projects in the District Development Plans.’
Rank
SANE Districts Non-SANE Districts
Blantyre Dedza Lilongwe Mangochi Mulanje Ntchisi Salima Zomba
1
2
3
4
5
6
7
8
9
10

11. Farmers’ Voice
• Farmers are increasingly advocating for prioritized needs and holding
extension providers accountable
–“Farmers say what their problems are and they actually have demands.It is
not business as usual. Now extension is required to find solutions.” Extension
Worker in SANE District
–VAC member: ‘Yes, we have a voice. We communicatewith service providers
on various issues that are of concern.’
–VAC member: ‘Previously (NGOs) would bring activities to us without our
input. Currently, they depend on the reports that we as farmers present.’
• Different sentiments in non-SANE districts:
–ASP member in Salima: ‘It’s a top-down approach.We are receiving the
projects that have been planned at a higher level, not what the community is
in need.’

12. Variable Definition Obs Mean SD. Min Max
Lead farmer
ratings
If lead farmer rating is excellent
(1/0) 1998 0.13 0.33 0.00 1.00
VAC functionality
If Village Ag Committee has a
strong performance (1/0) 2629 0.19 0.39 0.00 1.00
Face-to-face-
extension visits
If famers received excellent
extension contacts (1/0) 1236 0.23 0.42 0.00 1.00
Overall ASP
functionality
The overall performance of ASPs
(an index) 1998 3.19 0.79 0.00 4.28
Average ASP
functionality
Average platform functionality
(an index) 1998 0.69 0.15 0.00 0.94
Average inclusion
rate
Average inclusion rate is high
(1/0) 1998 0.64 0.23 0.00 1.00
Note, ASP isArea StakeholderPanel,VAC is Village Agriculture Committee;for dummy variables,yes=1;
Descriptive statistics of the main variables
2 Data Sets: IFPRI/Illinois (Ragasa et al) farmer level
data; Platform level data (Alvarez Mingote et al); both
2018 data

13. Weak performing VACs High performing VACs
Variables
No.
obs
Mea
n SD Median
No.
obs
Mea
n SD
Media
n MeanDiff
Lead farmer ratings 1629 0.12 0.32 0.00 369 0.16 0.37 0.00 -0.043**
Face-to-face-extension
visits 932 0.18 0.38 0.00 304 0.38 0.49 0.00 -0.201***
Overall ASP
functionality 1629 3.18 0.79 3.40 369 3.23 0.80 3.41 -0.05
Average ASP
functionality 1629 0.69 0.15 0.71 369 0.70 0.15 0.71 0.00
Average inclusion rate 1629 0.64 0.23 0.62 369 0.64 0.22 0.69 0.00
Note, ASP isArea StakeholderPanel,VAC is Village Agriculture Committee;for dummy variables,yes=1; : Asteriks***, **, * indicate
significance at 1% and 5% respectively
Summary Statisticsconditional on the
functionality of Village Agriculture Committees

14. Probit estimateof the effect of platform functionality on lead
farmer ratings
Note: Asteriks***, **, * indicate significance at 1%, 5%, and 10% respectively; Log Likelihood = -377.40;
N=1235;
LR Chi Square=130.57***; Pseudo R-square = 0.15;
Variables Coef. Std. Err. z stat dy/dx Std. Err.
VAC functionality 0.25687 ** 0.118 2.17 0.0429 0.01975
Face-to-face extension visits -0.082 0.126 -0.65 -0.0137 0.02102
Overall ASP functionality 0.54445 *** 0.147 3.7 0.0909 0.02445
Average ASP functionality 3.05109 ** 1.333 2.29 0.5095 0.22261
Average inclusion rate -0.8859 * 0.536 -1.65 -0.148 0.08958
Male headed household 0.10556 0.212 0.5 0.0176 0.03539
Age of respondent -0.0023 0.005 -0.44 -0.0004 0.00087
Education level 0.01485 0.014 1.03 0.0025 0.0024
Male under 35 -0.3134 * 0.175 -1.79 -0.0523 0.02923
Female under 35 0.12588 0.309 0.41 0.021 0.05164
No. of adults in household 0.04933 0.059 0.84 0.0082 0.00982
Household size -0.0278 0.03 -0.94 -0.0046 0.00493
Marital status 0.1108 *** 0.038 2.95 0.0185 0.00624
(Dummy variables for district effects)
Constant -5.2002 0.9684 -5.37
Lead farmer rating Marginal effects

15. • Quantitative and qualitative evidence supports the approach of
focusing on the middle level of extension in Malawi to improve quality
ratings of lead farmers at the village level
• Sustainability – we have some evidence on sustainability, but will
need to track this post-SANE
• Key to sustainability – building value to all DAESS platform participants
• From implementing SANE what have we learned?
–Government units, funders, and donors need to advocate for and
support actively the DAESS system – expect projects and activities to
engage with it
–DAESS benefits farmers and promotes efficiency and coordination –
it is worth the effort
– Obtaining buy in and commitment of various extension actors in a
pluralistic extension system takes significant effort and time – build
on successes
SANE Lessons and Challenges
15

16. Thank you!