Embed Size (px)
Citation preview
Improving Productivity Measurement:
Lessons from a Cassava Experiment
in Zanzibar, TanzaniaMinistry of Agriculture and Natural Resources, Zanzibar
The World Bank
Cassava Productivity in Zanzibar
Objectives• To identify the best means of estimating
productivity (yields per land area)
• To evaluate the data quality and costs vs. benefits for different measurement approaches
• Assess the feasibility of implementing each method in national household surveys
• Document best practices in data collection
Collaborating Partners• Ministry of Agriculture and Natural
Resources, Zanzibar
• Office of the Chief Government Statistician, Zanzibar
• The World Bank
Estimating Productivity
Identification of production per unit of land area requires:
1. Accurate estimate of production
2. Accurate estimate of land area
Methods
- Direct measurement
- Self-reporting
Trade-offs
- Cost: implementation challenges
- Benefit: improved accuracy
Methodologies Tested: Production
Cassava
production
• CC: Crop-cutting with balance scales
• D1: Crop diaries with BEO visits 2x a week
• D2: Crop diaries with telephone calls 2x a week
• R1: 6-month harvest recall survey
• R2: 12-month harvest recall survey
Group Method
CC Crop-cutting with balance scales
D1 Crop diaries with BEO visits 2x per week
D2 Crop diaries with telephone calls 2x per week
R1 6-month harvest recall survey
R2 12-month harvest recall survey
Methodologies Tested: Land Area
Land area • Traversing (i.e. compass-and-tape)
• GPS measurement
• Farmer self-reported area
Group Method
CT Traversing (i.e. compass-and-tape)
GPS GPS measurement
SR Farmer self-reported area
Sampling Strategy
PROJECT SAMPLE
1260households
864households in North B District
in Unguja
396households in Chake Chake
District in Pemba
2districts chosen based on large
numbers of cassava-producing
households:
North B and Chake Chake
24Block Extension Officers (BEOs)
from North B
11Block Extension Officers (BEOs)
from Chake Chake
4treatment arms for cassava
production
36households per BEO
9households per treatment arm per
BEO
Randomized Control Trial: Production
• 315 households: Crop diaries with BEO visits 2x per week (D1)
• 315 households: Crop diaries with telephone calls 2x per week (D2)
• 315 households: 6-month harvest recall survey (R1)
• 315 households: 12-month harvest recall survey (R2)
Randomized Control Trial: Production• Crop-cutting with
balance scales
Multiple Measures: Land AreaAll 1,260 households:
• Traversing
(compass-and-tape)
(CT)
• GPS measurement
(GPS)
• Farmer self-reported
area (SR)
Project Timeline
Timeline:
Training: North B and Chake Chake
April 2013
Pilot: North B and Chake Chake
May 2013
Cassava diaries:D1 – BEO visitD2 – CATI call
June 2013 – May 2014
Crop cutting August 2013 – May 2014
6-month recall survey (R1A)
November 2013
Area measurement:Compass-and-tapeGPS
August 2013 – January2014
12-month recall survey (R2) and 6-month recall survey (R1B)
May 2014
Data entry May 2013 – July 2014
• Comparison of production measurement methods
– Recall surveys vs. cassava diaries vs. crop-cutting
• Comparison of land area measurement methods
– GPS vs. compass-and-tape vs. farmer self-reporting
• Implications for yield measurement
Data Analysis Objectives
Cassava Production by Household Type
D1 D2 R1 R2 Crop Cut
June 209.74
715.45
223.32
854.1 734.38
1103.2 2074.8
July 112.4 138.61
August 118.93 120.16
September 97.69 148.41
October 82.18 110.49
November 94.51 113.11
December 107.25
522.95
111.55
719.73 573.0
January 67.63 125.62
February 98.46 101.95
March 80.01 130.29
April 83.28 115.27
May 86.32 135.05
TOTAL 1238.4 1573.83 1307.39 1103.2 2074.8
Cassava Production using Crop Cutting Estimates
0
1.0
e-0
42.0
e-0
43.0
e-0
44.0
e-0
45.0
e-0
4
De
nsity
0 1000 2000 3000 4000 5000hh_prod_kg_cc
0
2.0
e-0
44.0
e-0
46.0
e-0
48.0
e-0
4
De
nsity
0 1000 2000 3000 4000 5000hh_prod_kg_imp
0
2.0
e-0
44.0
e-0
46.0
e-0
4
De
nsity
0 1000 2000 3000 4000 5000hh_prod_kg_imp
0
2.0
e-0
44.0
e-0
46.0
e-0
4
De
nsity
0 1000 2000 3000 4000 5000hh_prod_kg_imp
Cassava Production by Household Type
0
2.0
e-0
44
.0e
-04
6.0
e-0
48
.0e
-04
De
nsity
0 1000 2000 3000 4000 5000hh_prod_kg_imp
D1 D2
R1 R2
T-Tests: Diary vs. Recall
Mean
D1
Mean
D2 T-stat P-Value
Diary-Visit vs. Diary-Phone 1231 1534 2.42 0.0156
Mean
D1
Mean
R1 T-stat P-Value
Diary-Visit vs. 6-mth Recall 1231 1308 0.73 0.4654
Mean
D2
Mean
R1 T-stat P-Value
Diary-Phone vs. 6-mth Recall 1534 1308 1.89 0.0596
Area Measurement, by method
1079 m²average plot size measured by compass-and-tape
1107 m²average plot size measured by GPS
2736 m²average plot size measured by farmer self-reporting
153.6%difference between farmers’ estimates & compass-and-tape
2.6%difference between GPS & compass-and-tape
0
2.0
e-0
44
.0e
-04
6.0
e-0
48
.0e
-04
.00
1
De
nsity
0 1000 2000 3000 4000 5000Area with GPS (square meters)
Area Measurement, compass vs. GPSGPS
0
2.0
e-0
44
.0e
-04
6.0
e-0
48
.0e
-04
.00
1
De
nsity
0 1000 2000 3000 4000 5000Area with compass (square meters)
Compass
0
5.0
e-0
4.0
01
.00
15
De
nsi
ty
0 2000 4000 6000 8000Area with farmer estimation (square meters)
Self-report
Area (m2) by quintiles (compass): compass vs. GPS
Compass GPS % Difference
Q1
(41.2 – 445 m2)
315 352 11.7%
Q2
(445 – 648m2)
542 549 1.3%
Q3
(648 – 920 m2)
782 821 5.0%
Q4
(921 – 1445m2)
1146 1180 3.0%
Q5
(1448 – 12499 m2)
2582 2614 1.2%
Total 1079 1107 2.6%
Area (m2) by quintiles (compass): compass, GPS and self-reporting
Compass GPS SR
Q1
(41.2 – 445 m2)
315 352 1758
Q2
(445 – 648m2)
542 549 1967
Q3
(648 – 920 m2)
782 821 2311
Q4
(921 – 1445m2)
1146 1180 2909
Q5
(1448 – 12499 m2)
2582 2614 4710
Total 1079 1107 2736
Mean yields (kg/m2), by method & type
Compass GPS SR Crop Cutting
D1 0.89 0.91 0.36 1.14
D2 1.04 1.16 0.42 1.22
R1 1.50 1.48 0.50 1.21
R2 0.97 0.91 0.36 1.13
• Capacity/experience– Agricultural extension officers; data entry staff
• Call center set-up– Zantel; airtime vs. phone
• Connectivity– Pemba
• Procurement delays
• Non-standard units
• Participation rates
• Incentive packages for staff
Lessons Learned from Implementation
Key Lessons on Productivity Measurement
• Land Area:– Self-reporting clearly subject to huge error
– GPS and compass-and-tape nearly identical
– GPS preferable based on time and cost implications
• Production:– 12-month recall clearly problematic
– Clear benefits to reducing recall period (6-mth recall)
– Production diary with phone monitoring looks to be promising
– Crop cutting for cassava: gold standard or upper bound?
Next Steps
• Establish cost implications for each method• Investigate lower reported production for D1
households– Check on regularity of visits
• Evaluate CATI data– Cross-check with harvests recorded in diaries– Check on regularity of phone calls during harvest
periods
• Investigate high crop cutting yields– Number of subplots– Number of plants in plots
Questions?
