agricultural production in ethiopia: key results of the ... · agricultural production in ethiopia:...

Agricultural production in Ethiopia:

Results of the 2012 ATA Baseline Survey

21 April 2013

Nicholas Minot

Bradley Sawyer

International Food Policy Research Institute

Washington, DC

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INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

The International Food Policy Research Institute (IFPRI) was established in 1975. IFPRI is one of 15 agricultural research centers that receive principal funding from governments, private foundations, and international and regional organizations, most of which are members of the Consultative Group on International Agricultural Research (CGIAR).

RESEARCH FOR ETHIOPIA’S AGRICULTURE POLICY (REAP): ANALYTICAL SUPPORT FOR THE AGRICULTURAL TRANSFORMATION AGENCY (ATA)

IFPRI gratefully acknowledges the generous financial support from the Bill and Melinda Gates Foundation (BMGF) for IFPRI REAP, a five-year project to support the Ethiopian ATA. The ATA is an innovative quasi-governmental agency with the mandate to test and evaluate various technological and institutional interventions to raise agricultural productivity, enhance market efficiency, and improve food security. REAP will support the ATA by providing research-based analysis, tracking progress, supporting strategic decision making, and documenting best practices as a global public good.

DISCLAIMER

This report has been prepared as an output for REAP and has not been reviewed by IFPRI’s Publication Review Committee. Any views expressed herein are those of the authors and do not necessarily reflect the policies or views of IFPRI.

AUTHORS

Nicholas Minot, International Food Policy Research Institute

Senior Research Fellow, Markets, Trade and Institutions Division

[email protected]

Bradley Sawyer, International Food Policy Research Institute

Senior Research Assistant, Markets, Trade and Institutions Division

[email protected]

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Table of Contents

1. Introduction ...................................................................................................................................... 1

2. Crop production and yield ................................................................................................................ 1

3. Agricultural inputs ............................................................................................................................ 6

3.1. Fertilizer .................................................................................................................................... 6

3.2. Seed ........................................................................................................................................ 10

4. Agronomic Practices ....................................................................................................................... 14

4.1. Soil conservation practices ..................................................................................................... 14

4.2. Mechanization ........................................................................................................................ 15

5. Irrigation ......................................................................................................................................... 16

6. Crop marketing ............................................................................................................................... 17

7. Cooperatives ................................................................................................................................... 22

8. Access to Services .......................................................................................................................... 23

9. Characteristics of the heads of household ..................................................................................... 23

Appendix 1: Methods of the 2012 ATA Baseline Survey....................................................................... 27

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List of tables

Table 1. Crop production per household growing each crop ................................................................ 2

Table 2. Crop yield per household (kg/ha) ............................................................................................. 3

Table 3. Crop yield in 2011-12 compared to normal by crop ................................................................ 4

Table 4. Reason for increase in yield by crop......................................................................................... 5

Table 5. Reason for decrease in yield by crop ....................................................................................... 6

Table 6. Percentage of plots of each crop receiving inorganic fertilizer................................................ 7

Table 7. Average fertilizer application rate by crop ............................................................................... 8

Table 8. Percentage of plots receiving manure by crop ........................................................................ 9

Table 9. Frequency of manure use by region (percent of plots)............................................................ 9

Table 10. Use of other organic inputs applied this season by region (percentage of plots) ............... 10

Table 11. Types of other organic inputs by region among those using ............................................... 10

Table 12. Source of purchased seed by region .................................................................................... 10

Table 13. Source of purchased seed by crop ....................................................................................... 11

Table 14. Percentage of farmers using improved seed by crop .......................................................... 11

Table 15. Average seed application rate by crop ................................................................................. 12

Table 16. Main problems with variety for selected grains .................................................................. 13

Table 17. Main problem with variety for selected grains (cont.) ........................................................ 13

Table 18. Main problem with varieties for selected grains (cont.) ...................................................... 14

Table 19. Tree planting by region (percentage of plots with trees) .................................................... 14

Table 20. Use of erosion control by region (percentage of plots) ....................................................... 14

Table 21. Use of specific erosion control methods by region (percentage of plots) ........................... 15

Table 22. Type of cultivation by region (percentage of plots) ............................................................. 15

Table 23. Type of cultivation by farm size category (percentage of plots) .......................................... 16

Table 24. Importance of irrigation by region ....................................................................................... 16

Table 25. Price farmers received for their crop sales .......................................................................... 17

Table 26. Total revenue from sales among growers by crop ............................................................... 18

Table 27. Main place where farmers sell produce ............................................................................... 19

Table 28. Main buyers of crop ............................................................................................................. 20

Table 29. Percentage of farmers under contract sale agreement by crop .......................................... 21

Table 30. Percentage of post-harvest storage loss per household by crop ......................................... 22

Table 31. Percentage of farmers that are members of primary cooperatives .................................... 22

Table 32. Satisfaction with cooperative services among members ..................................................... 23

Table 33. Travel time from house to roads and markets ..................................................................... 23

Table 34. Sex of head of household by region ..................................................................................... 23

Table 35. Schooling and literacy of head by region ............................................................................. 24

Table 36. Schooling and literacy of head by sex .................................................................................. 24

Table 37. Age of head by region .......................................................................................................... 25

Table 38. Age of head by sex of head .................................................................................................. 25

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Table 39. Marital status of head by sex of head .................................................................................. 25

Table 40. Main activity of head by sex of head.................................................................................... 26

Table A1. Characteristics of the sample ............................................................................................... 28

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1. Introduction

To support the activities of the Ethiopian Agricultural Transformation Agency (ATA), the International Food Policy Research Institute (IFPRI) carried out a baseline survey in 2012. One goal of the survey was to provide background information about agricultural production, marketing, and related topics. This information will help ATA program directors make informed decisions on how to design interventions to boost agricultural productivity and market efficiency. The second goal of the survey was to serve as a baseline to assess the impact of certain interventions implemented by the ATA.

The survey was based on a stratified random sample of 3,000 households in the four main regions of Ethiopia: Tigray, Amhara, Oromiya, and the Southern Nations, Nationalities, and People’s (SNNP) region. The selected households were dispersed across 100 woredas and 200 kebeles. In order to gather more in-depth information on the 83 woredas, initially identified as the ATA zone, these woredas were oversampled. In other words, 67 of the 83 ATA woredas were included in the sample.

The analysis uses sampling weights to offset the over-sampling of the ATA woredas. All means and percentages presented in this report are weighted estimates using the sampling weights. In addition, the standard errors are calculated by accounting for the sampling design, which includes stratification and clustering. The standard error indicates the amount of sampling error in the estimate, given the variability in the variable itself across households and the sampling design. There is a 95% chance that the true mean is within 1.96 standard errors of the estimated mean.

This report describes the main results of the survey on a range of key indicators identified by the ATA. It covers crop production and yields, agricultural inputs, agronomic practices, crop marketing, cooperatives, irrigation, access to services, and household characteristics. Future reports will address each topic listed above in more detail.

2. Crop production and yield

Table 1 shows the average production of each crop per household growing the crop. It does not show, however, the overall importance of the crop because some crops (such as maize) are grown by a large share of farmers, while other crops (such as red peppers) are grown by a few. The average production of each cereal was 400-800 kilograms (kg) per household growing the crop. Since the average production of cereals per household is less than the average yield of cereals (discussed below), this indicates that, on average, farmers grow less than one hectare of each cereal crop. With pulses, most farmers grew 100-250 kg of each crop, except for grass peas, which are grown in larger quantities. The standard error is an estimate of the sampling error and can be used to construct confidence intervals around the estimates. For example, the 95% confidence interval is roughly twice the standard error, so production of white teff per farmer is 435 kg ± 92 kg.

Yield is the volume of the crop harvested divided by the area of cultivated land. The highest yields among cereals were in maize at nearly 2500 kg/ha, while wheat, barley, and sorghum were in the range of 1200 to 1500 kg/ha. The two types of teff had yields below 900 kg/ha. These yield estimates are lower than official estimates of cereal yields, although they are in the range of yield estimates from the 2013 IFPRI survey of teff growers and the 2008 Ethiopian Agricultural Marketing Household Survey.

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Table 1. Crop production per household growing each crop

Mean production

(kg/household)

Standard Error

White teff 435.0 46.8

Black/mixed teff 295.0 23.1

Barley 454.4 60.0

Wheat 742.9 100.4

Maize 758.2 151.0

Sorghum 579.9 56.8

Finger millet 251.6 22.8

Other cereals 680.0 119.7

Faba/horse bean 238.7 25.4

Field peas 143.2 21.0

Haricot beans 198.1 34.9

Chick-peas 223.4 23.8

Grass peas/vetch 474.7 106.7

Other pulses 102.9 24.5

Neug 91.6 12.0

Sesame 741.1 126.0

Other oilseeds 166.5 42.7

Cabbage 464.3 155.4

Red peppers 297.6 75.6

Other vegetables 468.2 158.3

Onion 783.8 406.3

Potato 640.6 86.0

Taro/godere 434.4 59.9

Other root crops 613.5 169.3

Banana 557.3 240.9

Other fruit 367.4 74.8

Chat 163.0 43.1

Coffee 277.7 43.7

Hops 289.0 73.4

Enset 595.6 91.7

Other permanent 2,280.7 978.6

Source: 2012 Ethiopia ATA Baseline Survey

In pulses, the highest yields were above 1300 kg/ha for haricot beans and grass peas. The oilseeds had relatively low yields of less than 800 kg/ha. On the other hand, vegetables, fruit, and root crops had high yields, but the effect on profitability is partially offset by the higher costs of production (particularly labor) and higher risks in production and marketing.

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Table 2. Crop yield per household (kg/ha)

Mean yield (kg/ha)

Standard Error

White teff 850 67

Black/mixed teff 660 45

Barley 1,247 64

Wheat 1,368 102

Maize 2,496 569

Sorghum 1,129 108

Finger millet 784 104

Other cereals 1,400 151

Faba/horse bean 1,123 98

Field peas 735 82

Haricot beans 1,384 411

Chick-peas 965 157

Grass peas/vetch 1,328 243

Other pulses 590 99

Neug 357 48

Sesame 478 52

Other oilseeds 752 92

Cabbage 9,466 3,755

Red peppers 1,975 331

Other vegetables 33,171 29,951

Onion 3,832 768

Potato 4,886 654

Taro/godere 8,471 1,454

Other root crops 6,112 928

Banana 8,759 1,503

Other fruit 125,333 88,369

Chat 2,174 620

Coffee 2,378 793

Hops 7,667 2,941

Enset 6,146 1,216

Other permanent 18,914 3,642


The survey team asked farmers to compare their yields in 2011-2012 to a typical harvest in past years. The survey respondents reported that their 2012 harvest was close to their normal returns in prior years. A majority (78%) reported little to no change in yield from the last few years. However, 18% of farmers said their yield was much lower than normal, compared to just 4% who said their yield was higher than normal. Table 3 shows the results by crop. There were only two crops where more than 10% of growers reported that yields were much higher than normal (grass peas and other root crops), but three crops for which more than 30% of farmers reported much lower yields (neug, sesame, and red peppers). For another dozen crops, more than 20% of growers reported that yields were much lower than normal.

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Table 3. Crop yield in 2011-12 compared to normal by crop

Yield compared to normal

Crop Much higher

A little higher

Roughly the

same

A little lower

Much lower

Total

White teff 4 21 27 26 22 100

Black/mixed teff 3 20 24 27 27 100

Barley 5 16 32 28 20 100

Wheat 6 20 28 31 15 100

Maize grain 4 16 28 30 22 100

Sorghum 3 15 24 32 26 100

Finger millet 3 17 32 25 24 100

Other cereals 9 21 31 21 18 100

Faba/horse bean 3 16 32 27 21 100

Field peas 4 16 30 23 27 100

Haricot beans 0 7 46 32 15 100

Chick-peas 5 10 43 23 19 100

Grass peas/vetch 12 15 44 14 15 100

Other pulses 6 13 34 26 22 100

Neug 2 12 32 17 38 100

Sesame 5 13 24 26 32 100

Other oilseeds 4 11 56 19 11 100

Cabbage 1 16 73 7 2 100

Red peppers 3 14 28 22 33 100

Other vegetables 2 26 32 26 14 100

Onion 6 15 26 24 29 100

Potato 9 14 30 27 20 100

Taro/godere 2 9 69 8 12 100

Other root crops 11 17 50 14 8 100

Banana 2 12 61 17 8 100

Other fruit 2 6 66 20 6 100

Chat 1 12 40 33 14 100

Coffee 1 14 35 27 23 100

Hops 6 15 69 9 1 100

Enset 2 8 72 11 7 100

Other permanent 1 8 74 9 7 100

Total 4 15 39 24 18 100

N= 659 2,475 7,069 3,865 2,683 16,751


The survey also asked farmers for the reasons why their crop yields varied. Among farmers reporting an increase in crop yields, 64% attributed the increase to good rainfall, 23% to fertilizer use, and 5% to better seeds. Table 4 shows the reasons for above-normal yields, listed by crop. Although rainfall is the main explanation for most crops, fertilizer is commonly given as a reason in the case of several cereals (particularly black/mixed teff and wheat), vegetables, onions, and potatoes. This is not surprising since these crops are more likely to be fertilized, as discussed below. Because few farmers apply fertilizer to sorghum, taro, or coffee, high yields could not be associated with its use.

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Table 4. Reason for increase in yield by crop

Reason for increase

Crop Rainfall Fertilizer use

Seed use

Other chemicals

Soil degrad-

ation

Pests or

disease

Other Total

White teff 50 34 13 3 0 0 0 100

Black/mixed teff 48 50 1 0 0 0 0 100

Barley 72 20 1 0 2 0 4 100

Wheat 46 45 8 0 0 0 1 100

Maize grain 58 28 7 3 0 0 3 100

Sorghum 89 2 0 2 1 2 4 100

Finger millet 53 26 0 0 0 11 10 100

Other cereals 75 20 4 2 0 0 0 100

Faba/horse bean 67 14 0 1 0 16 1 100

Field peas 62 17 3 0 0 17 0 100

Haricot beans 60 13 10 8 0 0 9 100

Chick-peas 63 0 22 0 0 14 0 100

Grass peas/vetch 89 0 2 0 1 9 0 100

Other pulses 86 2 12 0 0 0 0 100

Neug 64 0 4 0 21 0 12 100

Sesame 73 17 1 0 1 3 5 100

Other oilseeds 96 3 1 0 0 0 0 100

Cabbage 22 64 2 1 1 0 9 100

Red peppers 54 39 2 3 0 0 3 100

Other vegetables 16 2 39 2 0 26 15 100

Onion 42 45 4 0 0 4 5 100

Potato 59 23 5 0 0 0 12 100

Taro/godere 100 0 0 0 0 0 0 100

Other root crops 72 12 11 0 3 0 3 100

Banana 77 0 0 0 8 0 16 100

Other fruit 69 9 9 1 0 0 12 100

Chat 87 0 0 0 6 0 7 100

Coffee 79 1 0 0 11 1 7 100

Hops 87 0 0 5 0 0 9 100

Enset 87 0 1 0 0 0 12 100

Other permanent 90 6 2 0 0 0 2 100

Pasture/grazing 99 0 0 0 0 0 0 100

Planted trees 76 24 0 0 0 0 0 100

Others 76 16 7 0 0 0 1 100

Total 64 23 5 1 1 2 3 100

N= 1,813 877 187 40 39 33 134 3,123


Farmers who reported a below-normal yield were also asked for a list of reasons. The most common response was poor rainfall, given by 62% of the farmers. The second-most common response was pest and disease, cited by 16% of the farmers. Soil degradation was given by 9% of the respondents. About 9% of farmers said that the cause of low yields was fertilizer use, which could refer to insufficient fertilizer or poor quality fertilizer. Table 5 shows the reasons by crop. A few farmers growing cereals cited pests and diseases as a problem (9-20%), but a higher percentage was seen with farmers growing vegetables and onions (one-third) and those growing pulses and oilseeds (over one-quarter).

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Table 5. Reason for decrease in yield by crop

Reason for decrease

Crop Rainfall Fertilizer Seed use

Other chemicals

Soil degrad-

ation

Pests or

disease

Other Total

White teff 61 11 3 2 16 5 3 100

Black/mixed teff 69 6 1 0 10 11 2 100

Barley 75 5 1 0 8 6 4 100

Wheat 60 8 3 0 8 20 0 100

Maize grain 62 11 5 1 8 9 6 100

Sorghum 70 1 3 0 10 11 4 100

Finger millet 58 6 0 0 22 11 3 100

Other cereals 64 6 0 0 8 16 5 100

Faba/horse bean 51 2 3 0 4 31 9 100

Field peas 52 1 1 0 2 31 14 100

Haricot beans 55 16 3 2 5 14 5 100

Chick-peas 58 0 3 1 6 31 1 100

Grass peas/vetch

59 3 1 0 6 29 2 100

Other pulses 55 0 14 0 6 24 0 100

Neug 42 5 8 1 22 20 2 100

Sesame 59 1 4 0 5 28 3 100

Other oilseeds 66 4 0 0 2 23 4 100

Cabbage 58 0 0 2 3 36 0 100

Red peppers 51 3 1 0 7 35 3 100

Other vegetables 21 0 24 0 3 50 1 100

Onion 38 1 2 1 5 43 10 100

Potato 79 1 3 0 3 13 2 100

Taro/godere 87 0 0 0 11 2 0 100

Other root crops 59 6 10 0 12 13 0 100

Banana 63 0 0 0 19 18 0 100

Other fruit 43 0 0 0 4 21 32 100

Chat 88 0 4 0 1 6 1 100

Coffee 43 1 1 2 13 37 4 100

Hops 62 0 0 0 1 15 22 100

Enset 41 0 0 0 12 44 3 100

Other permanent 85 0 1 1 8 2 3 100

Total 62 5 3 1 9 16 4 100

N= 3,607 418 234 55 584 1,266 341 6,505


3. Agricultural inputs

3.1. Fertilizer

Table 6 shows the percentage of plots for each crop that received inorganic (chemical) fertilizer during the 2001-2012 growing season. Over 60% of the plots dedicated to white teff, black/mixed teff, and wheat were fertilized, and over one-third of the barley, maize, onion, and potato plots were fertilized. On the other hand, pulses, oilseed, fruit crops, and other perennial crops were rarely fertilized.

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Table 6. Percentage of plots of each crop receiving inorganic fertilizer

Percentage of farmers

Standard Error

White teff 65.0 9.0


Barley 42.6 6.3

Wheat 64.6 7.0

Maize grain 39.9 4.4

Sorghum 9.5 3.6




Field peas 14.3 4.3


Chick-peas 1.0 0.8



Neug 1.5 0.7

Sesame 13.9 3.2


Cabbage 15.7 5.3



Onion 39.9 9.0

Potato 36.5 8.0

Taro/godere 6.8 5.1


Banana 0.0

Other fruit 0.0

Chat 1.3 0.7

Coffee 0.0 0.0

Hops 0.0

Enset 0.1 0.1

Other permanent 5.0 3.8


The application rate refers to the amount of input use, measured in kilograms of inputs per hectare of land. The ATA Baseline Survey collected information on the amount of seed and fertilizer used, but not the quantity of pesticides used. This is because pesticides are sold in different concentrations— a liter of pesticide is not a standard quantity. Estimates of the application rates should be interpreted with some caution because farmers do not always know the quantities used or the size of their plots. Or they may use local units that are difficult to convert to standard units.

Table 7 provides the average fertilizer application rate including those plots that are not fertilized. The fertilizer application rates for teff, wheat, maize, and barley are generally in the range of 50 to 100 kg/ha. The average application rate for sorghum is much lower, 6 kg/ha, partly because just 10% of the plots have any fertilizer. The fertilizer application rate for vegetables is also high, which is not surprising given that they are high-value crops that respond well to fertilizer. Red peppers have the highest rate of fertilizer application (140 kg/ha), although it is important to recognize that red peppers and other vegetables are typically grown on very small plots. Potatoes, onions, and other vegetables have fertilizer application rates of more than 45 kg/ha. The fertilizer application rates are very low

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(below 10 kg/ha) for chickpeas, grass peas, oilseeds, banana, other fruit trees, coffee, chat, and other perennial crops.

Table 7. Average fertilizer application rate by crop

Mean fertilizer

application rate (kg/ha)

Standard Error



Barley 46.7 9.3

Wheat 94.9 17.0


Sorghum 6.5 2.8




Field peas 13.5 4.8


Chick-peas 0.8 0.8



Neug 0.6 0.4

Sesame 6.9 1.9


Cabbage 13.6 5.7



Onion 55.9 14.1

Potato 46.1 11.8

Taro/godere 9.8 6.8


Banana 0.0

Other fruit 0.0

Chat 0.9 0.5

Coffee 0.1 0.1

Hops 0.0

Enset 0.0 0.0


Source: 2012 ATA Baseline Survey

Table 8 shows the percentage of plots receiving manure (organic fertilizer). An important question is whether farmers substitute chemical fertilizer with manure or use both together. Looking at all 36 crops, there is no clear relationship, but when focusing on cereals, there is a negative relationship. The two types of teff, wheat, and other cereals are often fertilized (more than 50% of the plots) but less commonly receive manure (less than 15% of the plots). In contrast, maize, barley, sorghum, and millet are less commonly fertilized (less than 50% of the plots), but receive manure more often (more than 15% of the plots).

Looking at the regional patterns, manure use is most common in Tigray, followed by the SNNP region (see Table 9. In Tigray, almost half of the plots receive manure, and 32% of plots receive manure every year or most years; this may be related to more widespread production of livestock, particularly cattle.

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Table 8. Percentage of plots receiving manure by crop

Percentage of plots

Standard Error

White teff 9.8 2.7


Barley 24.5 3.1

Wheat 13.1 2.7


Sorghum 20.9 5.1




Field peas 13.5 4.9


Chick-peas 8.3 4.9



Neug 1.3 0.7

Sesame 6.2 3.7


Cabbage 60.0 11.8



Onion 36.4 12.6

Potato 53.1 6.4



Banana 23.5 5.8


Chat 27.1 5.8

Coffee 23.9 6.3

Hops 39.2 7.8

Enset 57.7 3.8



Table 9. Frequency of manure use by region (percent of plots)

Region

Manure frequency Tigray Amhara Oromiya SNNP Total

Every year 23 13 16 20 16

Most years 8 2 4 5 4

Some years 17 4 6 8 6

Rarely 2 1 1 2 1

Never 51 81 72 65 74

Total 100 100 100 100 100

N= 1,740 5,508 5,613 5,228 18,089


The percentage of plots that receive other organic inputs, such as mulch, crop residues, and so on, is relatively small. As shown in Table 10, only 8% of plots receive other organic inputs, ranging from 4% to 12% across regions. Among the small number of households using other organic inputs, household

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refuse is the most common in Tigray and the SNNP region, while mulch is the most common type in Amhara and crop residue is most common in Oromiya (see Table 11).

Table 10. Use of other organic inputs applied this season by region (percentage of plots)

Region

Other organic inputs Tigray Amhara Oromiya SNNP Total

Yes 12 10 7 4 8

No 88 90 93 96 92

Total 100 100 100 100 100

N= 1,740 5,455 5,316 4,994 17,505


Table 11. Types of other organic inputs by region among those using

Region

Type of organic input Tigray Amhara Oromiya SNNP Total

Household refuse 59 15 25 73 26

Mulch/compost 38 84 16 3 52

Crop residue 3 1 58 24 21

Other/mixed 0 0 0 0 0

Total 100 100 100 100 100

N= 180 511 339 186 1,216


3.2. Seed

Overall, about 56% of farmers bought seed for at least one crop during 2011-12. It is important to note that purchased seed is not necessarily certified seed or even an improved variety, since it includes seed purchased from other farmers and grain traders. The share of farmers using purchased seed was highest in the SNNP region (70%) and lowest in Tigray (34%).

The ATA Baseline Survey did not ask farmers about their use of “improved” seed because of the difficulties defining it. The source of purchased seed, however, is a strong indicator of improved seed varieties. Table 12 shows the distribution of purchased seed according to the place where it was purchased. In general, over half the seed purchases are from other farmers, while another 16% are from grain traders. Cooperatives and the Bureau of Agriculture (BoA) represent about 31% of seed purchases, but these percentages vary by region. Cooperatives are an important source in Tigray and Amhara, while the BoA plays a larger role in the SNNP region. Other sources, such as agro-input dealers, research institutes, and non-governmental organizations are very minor sources of seed.

Table 12. Source of purchased seed by region

Source of purchased seed

Region Farmer Grain trader

Coop Bur of Ag

Other Total

Tigray 63 13 22 1 1 100

Amhara 56 6 35 0 3 100

Oromiya 64 15 8 11 2 100

SNNP 21 34 9 35 1 100

Total 52 16 19 12 2 100

N= 926 381 528 212 40 2,087


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Table 13 shows the source of purchased seed by crop instead of by region. Cooperatives play a relatively important role in distributing maize, wheat, and white teff seed, and account for more than a quarter of the purchases of each crop. The Bureau of Agriculture was cited as the source for 26% of the maize seeds and 15% of the haricot bean seed. Farmers are the most common source of purchased seed for all cereals and pulses except maize and haricot beans.

Table 13. Source of purchased seed by crop

Source of purchased seed

Grain Farmer Grain trader

Coop Bur of Ag

Other Total

White Teff 58 8 27 7 0 100

Black/mixed Teff 89 10 1 0 0 100

Barley 61 29 5 3 2 100

Wheat 48 10 29 9 4 100

Maize 27 13 32 26 2 100

Sorghum 88 12 0 0 0 100

Finger millet 84 16 0 0 0 100

Faba/horse beans 76 23 1 0 0 100

Field beans 63 31 0 0 6 100


Chick-peas 67 19 5 0 10 100

Lentils 85 9 0 0 6 100

Total 52 16 19 12 2 100

N= 926 381 528 212 40 2,087


Table 14 shows the percentage of farmers that used improved cereal and pulse seeds, based on purchases from an official source such as a cooperative, agro-input dealer, or the BoA. For the major cereal crops, improved seed was purchased by more than 15% of the maize growers, 4% of wheat growers, and about 3% of white teff growers. For the other cereals and pulses, less than 1% of farmers purchased improved seed. The standard error is blank in a few cases because if the households in the sample did not purchase improved seed, it is not possible to calculate the standard error.

Table 14. Percentage of farmers using improved seed by crop

Percentage of farmers

Standard Error

White Teff 2.7 1.1

Black/mixed Teff 0.0 0.0

Barley 0.6 0.3

Wheat 3.9 0.8

Maize 15.3 3.2

Sorghum 0.0 0.0

Finger millet 0.0

Faba/horse beans 0.0 0.0

Field beans 0.0


Chick-peas 0.1 0.1

Lentils 0.0


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The seed application rate is the quantity of seed (or planting material) used per hectare of cultivated land. As shown in Table 15, wheat and barley have high seed application rates, over 150 kilograms/hectare (kg/ha). Faba beans and grass peas also have seeding rates above 100 kg/ha. Maize and sorghum have seeding rates of 37 and 29 kg/ha, respectively. The seed application rate for both white teff and black/mixed teff is about 45 kg/ha. Root crops require planting materials rather than seeds per se, which explains the high quantities used for onions, potatoes, taro, and other root crops.

Table 15. Average seed application rate by crop

Mean application

rate (kg/ha)

Standard Error

White teff 43.9 3.7


Barley 177.4 12.2

Wheat 160.2 6.4


Sorghum 29.1 4.4






Chick-peas 92.3 7.9



Neug 16.2 2.5

Sesame 9.1 0.7


Cabbage 268.0 107.3



Onion 647.6 177.2

Potato 1,165.6 94.3

Taro/godere 1,073.0 211.2


Banana 251.4 138.0


Chat 205.5 117.4

Coffee 207.1 45.3

Hops 212.5 145.0

Enset 575.4 212.2



As Table 16 through Table 18 demonstrates, a large majority of respondents (84% to 98%) did not report any problems with the variety of seed they obtained for most crops. Maize was the only exception, where only 75% of farmers did not report a problem. The most common problem reported was that the variety needed additional inputs to perform well. Other problems cited by at least 5% of growers included vulnerability to drought (black/mixed teff), low prices (faba beans), and poor-quality straw (field beans).

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Table 16. Main problems with variety for selected grains

Grain

Main problem with variety White Teff

Black/mix Teff

Barley Wheat

No problems 84 86 94 93

Needs inputs 5 4 2 3

Low price 0 0 3 0

Poor taste 0 0 0 0

Poor feed 0 2 0 0

Too costly 1 0 0 0

Can't save 0 0 0 0

Vulnerable to drought 0 5 0 0

Vulnerable to flooding 3 0 0 1

Poor straw 3 0 0 0

Others 3 2 1 2

Total 100 100 100 100

N= 172 99 220 268


Table 17. Main problem with the variety for selected grains (cont.)

Grain

Main problem with variety Maize Sorghum Finger millet

No problems 75 85 92

Needs inputs 9 0 7

Low price 1 1 1

Poor taste 1 6 0

Poor feed 1 0 0

Too costly 1 2 0

Can't save 1 2 0

Vulnerable to drought 1 0 0

Vulnerable to flooding 0 4 0

Poor straw 4 0 0

Shelf life 1 0 0

Others 3 0 0

Total 100 100 100

N= 806 115 25


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Table 18. Main problem with varieties for selected grains (cont.)

Grain

Main problem with variety Faba/horse beans

Field beans

Haricot Chick-peas

Lentils

No problems 89 89 99 95 98

Needs inputs 4 0 0 0 0

Low price 6 0 0 0 0

Poor taste 0 0 0 0 0

Poor feed 0 2 0 1 0

Too costly 0 0 0 0 2

Can't save 0 0 0 0 0

Vulnerable to drought 0 1 0 4 0

Vulnerable to flooding 0 1 0 0 0

Poor straw 0 7 0 0 0

Others 0 0 0 0 0

Total 100 100 100 100 100

N= 136 68 95 40 42


4. Agronomic Practices

This section describes some of the agronomic practices of Ethiopian farmers. It focuses on soil conservation methods (Section 5.1) and mechanization (Section 5.2).

4.1. Soil conservation practices

Soil conservation practices, including tree planting, terracing, drainage, and trash lines, were reported with noticeable variation across regions. Overall, 11% of the plots were planted with trees, but this was over seven times more common among farms in Oromiya than it was in Tigray (see Table 19).

Table 19. Tree planting by region (percentage of plots with trees)

Region

Tree on plot Tigray Amhara Oromiya SNNP Total

Yes 2 9 15 7 11

No 98 91 85 93 89

Total 100 100 100 100 100

N= 1,740 5,509 5,614 5,229 18,092


Overall, other erosion control techniques were used on about one-third of plots. They were widely used in Tigray (71% of plots), but much less common in Oromiya (25%) and the SNNP region (6%) (see Table 20).

Table 20. Use of erosion control by region (percentage of plots)

Region

Erosion control Tigray Amhara Oromiya SNNP Total

Yes 71 53 25 6 34

No 29 47 75 94 66

Total 100 100 100 100 100

N= 1,740 5,508 5,614 5,229 18,091


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Table 21 shows the proportion of plots in each region that use selected types of erosion control. About 60% of the plots in Tigray and 36% of those in Amhara use stone terraces. About 10% of the plots in Amhara had some form of drainage. Soil bunds, which reduce water runoff, were used in 21% of the plots in Tigray and 13% of the plots in Amhara. Other erosion control techniques were used on less than 10% of the plots in each region.

Table 21. Use of specific erosion control methods by region (percentage of plots)

Region Stone terrace

Fanya juu

Other terrace

Drainage Trash lines

Soil bund

Tigray 60 0 0 1 0 21

Amhara 36 1 1 10 3 13

Oromiya 9 1 3 9 4 6

SNNP 2 0 0 1 1 2

Total 20 1 1 7 3 9

N= 18,644 18,644 18,644 18,644 18,644 18,644


4.2. Mechanization

The survey asked about the methods used to prepare fields for planting. The three main methods used in Ethiopia are cultivation by hoe, animal traction, and tractors. As shown in Table 22, 84% of the plots in the four main regions of Ethiopia are prepared using animal traction. Another 15% are prepared with hoes. Less than 0.5% of the plots are cultivated with a tractor. These proportions vary substantially by region. In Tigray and Amhara, over 90% of the plots are prepared with animal traction. In Oromiya, the percentage is somewhat less, 87%, with greater use of hoes (11%). In the SNNP region, however, hoe cultivation is the most common method used for plots (56%). Animal traction is used on most of the remaining plots (42%).

Table 22. Type of cultivation by region (percentage of plots)

Region

Type of ploughing Tigray Amhara Oromiya SNNP Total

Hoe 4 5 11 56 15

Animal 93 94 87 42 84

Tractor 2 0 0 0 0

Mixed 1 0 1 1 1

Other 0 0 1 0 0

Total 100 100 100 100 100

N= 1,682 4,936 4,441 3,896 14,955


The method of cultivation is also correlated with farm size. Table 23 shows that the percentage of plots cultivated with a hoe declines as a farm size increases: 46% of the farms with 0.5 hectares or less use a hoe, while just 7% of farms with more than 2 hectares use a hoe. Conversely, the use of animal traction rises from 53% in the smallest farm-size category to 92% in the largest farm-size category. The use of tractors for most farm sizes is rare, even among the large farms.

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Table 23. Type of cultivation by farm size category (percentage of plots)

Farm size category

Type of ploughing <= 0.5 h 0.5-1.0 1.0-2.0 > 2.0 ha Total

Hoe 46 20 10 7 15

Animal 53 78 88 92 84

Tractor 0 0 0 0 0

Mixed 0 2 1 0 1

Other 0 1 1 0 0

Total 100 100 100 100 100

N= 1,666 3,008 4,446 5,835 14,955


5. Irrigation

How widespread is irrigation? Almost 14% of the farms in the four main regions of Ethiopia have irrigation on at least one plot of land for at least one of the two planting seasons (see Table 24). The percentage of farms with irrigated land is highest in Oromiya (23%) and less than 10% in the other three regions.

Table 24. Importance of irrigation by region

Percentage of

households with

irrigation

Standard Error

Percentage of crop land

that is irrigated

Standard Error

Tigray 9.5 4.1 1.7 0.7

Amhara 8.2 2.4 1.4 0.5

Oromiya 22.8 4.4 5.8 1.1

SNNP 6.6 2.3 1.8 0.8

Total 13.7 2.1 3.2 0.6

Source: 2012 ATA Baseline Survey.

The survey data also allows us to calculate the percentage of agricultural land that is irrigated. Overall, about 3% of cropland is irrigated. Again, the percentage is highest in Oromiya (almost 6%) and less than 2% in the other three regions (see Table 24).

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6. Crop marketing

This section provides a few key indicators related to crop marketing in the four main regions of Ethiopia. The average price farmers received for each crop varies substantially across crops, as shown in Table 25. On a per-kg basis, the most valuable crop is chat, which is worth 140 Birr/kg on average. Chat is followed by “other vegetables,” hops, coffee, and sesame, where the average price ranges from 20 to 50 Birr/kg. Among the cereals, the average prices for the two types of teff are just over 10 Birr/kg, followed by wheat at 5 Birr/kg and maize and barley at around 2 Birr/kg.

Table 25. Price farmers received for their crop sales

Crop Mean price (Birr/kg).

Standard Error

White teff 11.2 3.5


Barley 1.9 0.7

Wheat 5.1 1.6

Maize grain 2.0 0.4

Sorghum 1.7 0.2




Field peas 7.6 2.3


Chick-peas 4.9 1.2



Neug 6.8 0.8

Sesame 21.5 6.8


Cabbage 12.9 9.2



Onion 15.7 8.7

Potato 2.1 0.5

Taro/godere 3.4 2.4


Banana 2.4 0.4

Other fruit 3.9 1.1

Chat 140.5 45.3

Coffee 26.1 3.2

Hops 38.6 19.2

Enset 1.0 0.2



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The average revenue earned per household for each crop (excluding non-growers) is shown in Table 26. The figures include cash sales of crops, excluding the value of crops that households consume. As expected, revenue for smallholder farmers varies by crop depending on price and average volume sold. Overall, chat, haricot beans, and other vegetables generated the most revenue for farmers; however, the standard error of these estimates is sizeable, implying a large variation in revenue across farmers. Within the cereals, the average revenue for white and black/mixed teff is substantially higher than the average revenue for any other cereals, followed by wheat. The average maize revenue is fairly modest. These figures should not be interpreted as measuring the overall importance of each crop in the rural economy for two reasons. First, these figures are averages among growers, so they exclude non-growers. Second, these values exclude the value of home consumption, which is important for other cereals such as maize.

Table 26. Total revenue from sales among growers by crop

Revenue per

household growing the

crop (Birr/year)

Standard Error


Black/mixed teff 1,041.4 535.1

Barley 101.2 16.3

Wheat 488.5 169.0


Sorghum 146.0 20.6





Haricot beans 1,860.9 1,483.2

Chick-peas 450.0 99.8


Other pulses 1,164.5 420.7

Neug 474.3 105.0

Sesame 1,824.8 691.8


Cabbage 623.4 453.2


Other vegetables 2,079.5 1,546.1

Onion 1,520.6 867.7

Potato 154.0 26.5



Banana 21.3 5.6


Chat 3,249.7 1,487.7

Coffee 1,042.4 246.2

Hops 1,262.1 612.4

Enset 52.9 23.1



19

Overall, 90% of the crop sales transactions took place at local markets (see Table 27). For most of the cereals, pulses, oilseeds, vegetables, and root crops, over 90% of the sales were at local markets. In the case of millet, potato, taro, and fruit, the percentage sold at local markets is somewhat lower but still over 70%. The only crops for which sales at the farm account for more than a quarter of the total are bananas and chat. Cooperatives play a negligible role in crop marketing except for coffee, with 14% marketed through cooperatives.

Table 27. Main place where farmers sell produce

Place of sale

Crop On farm Local market

Cooperative Other Total

White teff 6 92 0 2 100

Black/mixed teff 2 93 0 5 100

Barley 1 93 0 6 100

Wheat 3 91 0 5 100

Maize grain 2 95 0 2 100

Sorghum 1 96 0 3 100

Finger millet 0 85 0 15 100

Other cereals 1 93 0 6 100

Faba/horse bean 1 96 0 4 100

Field peas 1 92 0 7 100

Haricot beans 3 97 0 0 100

Chick-peas 0 97 0 3 100

Grass peas/vetch 2 95 0 3 100

Other pulses 1 95 0 4 100

Neug 0 95 0 5 100

Sesame 5 71 3 21 100

Other oilseeds 0 97 0 3 100

Cabbage 1 96 1 1 100

Red peppers 3 96 0 2 100

Other vegetables 1 97 0 2 100

Onion 5 93 0 2 100

Potato 16 83 0 1 100

Taro/godere 16 81 0 3 100

Other root crops 2 98 0 0 100

Banana 31 67 0 2 100

Other fruit 21 77 0 2 100

Chat 29 68 0 3 100

Coffee 3 83 14 0 100

Hops 7 87 0 6 100

Enset 4 95 0 2 100

Other permanent crops 15 82 0 2 100

Total 6 90 1 3 100

N= 331 4,752 52 329 5,464


The survey also collected information on the type of buyer. As shown in Table 28, nearly three out of four crop sales were made to a trader. The second most common type of buyer was a consumer, accounting for 16% of all sales transactions. A common example of this is when a farmer goes to a local market and sells his or her own crop directly to consumers. In the case of enset, 53% of the sales were made directly to consumers, but for many crops the percentage was just 10-30%. Again, the

20

role of cooperatives is minor, with the possible exception of coffee. About 8% of coffee sales were made to a cooperative (the difference between this figure and the 14% in the previous table may be cases where the farmer considered the cooperative to be facilitating the sale rather than actually buying the crop).

Table 28. Main buyers of crop

Main buyer

Crop Farmer Trader Cooperative Consumer Other Total

White teff 16 64 0 17 2 100

Black/mixed teff 8 59 0 32 0 100

Barley 12 71 0 17 0 100

Wheat 7 76 0 15 1 100

Maize grain 7 80 1 13 0 100

Sorghum 16 63 0 21 0 100

Finger millet 21 44 0 35 0 100

Other cereals 2 72 0 26 0 100

Faba/horse bean 5 77 0 18 0 100

Field peas 16 60 0 24 0 100


Chick-peas 4 87 0 9 0 100

Grass peas/vetch 4 73 0 23 0 100

Other pulses 14 78 0 8 0 100

Neug 6 84 0 10 0 100

Sesame 0 87 3 0 10 100

Other oilseeds 6 80 0 14 0 100

Cabbage 3 61 1 34 0 100

Red peppers 3 89 0 8 1 100

Other vegetables 2 73 0 25 0 100

Onion 1 83 0 16 0 100

Potato 22 61 0 15 2 100

Taro/godere 27 47 0 26 0 100

Other root crops 3 81 0 16 0 100

Banana 2 80 0 18 0 100

Other fruit 3 82 0 15 0 100

Chat 8 79 0 13 0 100

Coffee 3 86 8 2 0 100

Hops 28 51 0 13 7 100

Enset 7 40 0 53 0 100

Other permanent 21 63 0 16 0 100

Total 9 74 1 16 1 100

N= 365 4,114 49 876 47 5,451


21

For each crop sold, the questionnaire asked whether the farmer had a contract to sell to the buyer— either written or oral and including both pre-planting and pre-harvest contracts. Even with this broad definition of contract farming, only a small fraction of sales transactions (1.7%) were under contract. And of the contract sales, most were contracts negotiated after planting but before harvest. Less than 0.5% of sales transactions involved a pre-planting contract. The crops with the largest percentage of sales under contract (of any type) were sesame (8.4), bananas (6.1%), and chat (6.1%).

Table 29. Percentage of farmers under contract sale agreement by crop

Avg. Standard Error

White teff 0.1 0.0


Barley 0.6 0.4

Wheat 1.2 0.7

Maize grain 0.1 0.1

Sorghum 0.4 0.2

Finger millet 0.0



Field peas 0.2 0.2


Chick-peas 0.3 0.2

Grass peas/vetch 0.0


Neug 0.0

Sesame 8.4 2.2


Cabbage 0.2 0.2

Red peppers 0.0


Onion 2.2 1.6

Potato 0.2 0.1

Taro/godere 0.0


Banana 6.1 3.3

Other fruit 3.7 3.1

Chat 6.1 3.2

Coffee 3.3 1.9

Hops 0.5 0.5

Enset 0.0

Other permanent 0.0


22

The survey did not ask about post-harvest losses, because it is difficult for farmers to determine their losses during harvesting and threshing. The survey did ask about the loss of cereals and pulses during storage. Respondents reported extremely low storage losses. Less than 0.5% of crop production was lost due to any reason when stored after the harvest in either the Belg or Meher seasons of 2011-12.

Table 30. Percentage of post-harvest storage loss per household by crop

Avg. Standard Error

White teff 0.2 0.1


Barley 0.3 0.1

Wheat 0.4 0.1

Maize grain 0.4 0.1

Maize cob 0.3 0.1

Sorghum 0.4 0.1



7. Cooperatives

The household and cooperative surveys of the 2012 ATA Baseline Survey provided considerable information about cooperative membership, services provided by cooperatives, and reasons for using or not using cooperative services. This section provides a brief introduction of some results related to cooperative membership and satisfaction with cooperative services. Overall, 36% of farm households, in the four main regions of Ethiopia, have at least one person who is a member of an agricultural cooperative. The percentage varies by region, being over 50% in Tigray and Amhara, but less than one-quarter in Oromiya, and just 11% in the SNNP region.

Table 31. Percentage of farmers that are members of primary cooperatives

Percentage of farm

households

Standard Error

Tigray 51.7 3.4

Amhara 59.5 5.9

Oromiya 23.5 4.6

SNNP 11.4 3.1

Total 36.1 3.5


The level of satisfaction with cooperative membership depends partly on how the question is asked. Table 32 presents two measures of satisfaction with cooperative membership. One question asked whether farmers think they have benefited from cooperative membership. Overall, two-thirds of members say they have benefited. Over 70% respond positively in Tigray and Amhara, but only 62% in Oromiya and about half in the SNNP region. If asked whether the cooperative has problems providing services, close to one-third said yes. Those who said cooperatives do have problems providing services, varies from 19% in the SNNP to almost 40% in Amhara.

23

Table 32. Satisfaction with cooperative services among members

Percentage who say

they have benefited

from membership

Standard Error

Percentage who say

coop has problems providing services

Standard

error

Tigray 71.2 3.0 21.7 2.7

Amhara 74.4 2.0 39.6 2.2

Oromiya 61.6 2.8 30.4 2.7

SNNP 49.1 4.8 19.0 3.9

Total 67.9 1.4 31.5 1.4


8. Access to Services

The 2012 ATA Baseline Survey asks about access to roads and markets. Because access depends on both distance and means of transportation, the survey includes questions about travel time to different types of services when using normal modes of transportation. Farm households reported that it takes an average of 47 minutes to get to a dry-season road, almost two hours to get to an all-weather road, and close to four hours to get to the nearest asphalt road (see Table 33). On average, it takes more than an hour to get to a market or an agricultural cooperative, roughly two hours to travel to an agro-input dealer, and about three hours to get to the nearest woreda center.

Table 33. Travel time from house to roads and markets

Avg. time (minutes)

Standard Error

Dry season road 47 8.5

All weather road 110 23.7

Asphalt road 234 NA

Market place 85 6.1

Woreda center 196 31.0

Ag. cooperative 73 8.0

Agro-input deal 118 NA


9. Characteristics of the heads of household

This section describes some basic characteristics of the household heads in the four main regions of Ethiopia. The results are based on Section A of the household questionnaire of the 2012 ATA Baseline Survey. As shown in Table 34, about 14% of the households are female-headed, though the share is higher in Tigray and lower in Amhara.

Table 34. Sex of head of household by region

Region

Sex Tigray Amhara Oromiya SNNP Total

Male 78 89 84 86 86

Female 22 11 16 14 14

Total 100 100 100 100 100

N= 480 840 870 810 3,000


24

Table 35 shows that household heads have on average 2.4 years of schooling. The average is somewhat higher in the SNNP region, but even in this region the average is just 3.4 years. Slightly less than half of the heads of household can read, and a similar percentage can write. The literacy levels are slightly higher among heads of household in the SNNP region. Although not shown in these tables, the levels of education and literacy are significantly higher among young people, which reflect improvements in school enrolment and literacy.

Table 35. Schooling and literacy of head by region

Mean Standard Error

Years of schooling

Tigray 1.8 0.1

Amhara 1.8 0.2

Oromiya 2.6 0.5

SNNP 3.4 0.3

Total 2.4 0.2

Reading literacy

Tigray 43.3 2.6

Amhara 41.4 4.0

Oromiya 51.8 5.7

SNNP 53.3 3.9

Total 47.8 2.7

Writing literacy

Tigray 40.9 2.5

Amhara 39.2 3.4

Oromiya 49.8 5.8

SNNP 53.6 3.8

Total 46.1 2.7


Table 36 provides the same three variables by gender of the head of household. These tables show a sharp gender gap in terms of schooling, reading, and writing. For example, over half of the male-headed households can read, but barely 10% of the female-headed households can do so. A similar pattern exists for writing literacy. Analysis in later reports will show that this gender gap has become markedly smaller in the younger cohorts, reflecting more widespread acceptance of the value of girls’ education over time.

Table 36. Schooling and literacy of head by sex

Avg. Standard Error

Years of schooling

Male 2.7 0.2

Female 0.5 0.1

Total 2.4 0.2

Reading literacy

Male 54.0 2.6

Female 10.5 2.6

Total 47.8 2.7

Writing literacy

Male 52.0 2.6

Female 10.7 2.6

Total 46.1 2.7


As seen in Table 37, the average age of heads of household is about 46 years. Household heads in the SNNP region seem to be somewhat younger, while those in Tigray tend to be older.

25

Table 37. Age of head by region

Avg. Standard Error

Tigray 49.8 1.5

Amhara 47.1 1.3

Oromiya 45.5 0.8

SNNP 42.8 1.4

Total 45.9 0.6


Table 38 shows that female heads of household tend to be older (50 years old) than male heads of household (45 years old). This reflects the fact that a significant proportion of female-heads are widows, as discussed below.

Table 38. Age of head by sex of head

Avg. Standard Error

Male 45.2 0.7

Female 50.1 1.4

Total 45.9 0.6


Table 39 reveals that almost all male heads (96%) are married, while the majority of female heads (58%) are widowed. Another quarter of female heads are either divorced or separated, whereas just 1% of male heads fall into these categories. This imbalance implies that divorced men are remarrying in greater numbers and/or moving out of the rural areas.

Table 39. Marital status of head by sex of head

Sex

Marital status Male Female Total

Single 1 1 1

Married 96 16 84

Separated 0 6 1

Divorced 1 19 4

Widowed 1 58 10

Total 100 100 100

N= 2,568 432 3,000


Almost all (96%) of the male household heads cite crop production as their main activity. Among female heads, however, 48% say crop production and 42% say unpaid household work consists of their main activity. Female heads are also more likely to list commerce (trading) and other businesses as their main activity.

26

Table 40. Main activity of head by sex of head

Sex

Main activity Male Female Total

Crop production 96 48 89

Livestock 0 0 0

Commerce 0 3 1

Other business 1 3 1

Employee 1 1 1

Student 0 0 0

Unpaid housework 0 42 6

Retired 1 1 1

Disabled or Others 1 1 1

Total 100 100 100

N= 2,568 432 3,000


27

Appendix 1: Methods of the 2012 ATA Baseline Survey

Objectives

The 2012 ATA Baseline Survey was designed to serve two objectives. First, it was to provide up-to-date information on a range of topics related to agricultural production and marketing, as well as rural livelihoods in general. The topics of interest include household composition, housing, assets, land use, crop production, crop marketing, storage, livestock production, non-farm income, credit, and perceptions about markets and government services. This information was intended to assist the ATA in designing interventions to help raise agricultural productivity and improve marketing efficiency.

The second objective was to serve as a baseline for the interventions to be carried out by the ATA and their partners. The baseline would serve as a pre-project benchmark to compare outcomes that the ATA interventions are designed to influence. Without a baseline survey, an evaluation would have to rely on differences in outcomes between beneficiaries and non-beneficiaries. But, it would be difficult to attribute any differences between the two groups to the project, since the differences may have existed before the program was launched.

Questionnaires

The ATA Baseline Survey is composed of three questionnaires: a household questionnaire, a kebele questionnaire, and a cooperative questionnaire. The household questionnaire is the largest of the three, encompassing 23 pages of questions and tables. It was administered to 3,000 farm households in the main four regions of Ethiopia: Tigray, Amhara, Oromiya, and the Southern Nations, Nationalities, and People’s (SNNP) region. The questionnaire is divided into 13 modules on the following topics:

A. Household member characteristics B. Housing and assets C. Land ownership and use D. Crop production E. Crop inputs F. Crop sales G. Crop storage H. Livestock income I. Non-farm income J. Changes in sources of income over time K. Credit L. Food and non-food consumption M. Attitudes and perceptions

The community questionnaire consists of four pages and was administered to kebele leaders in each of the 200 kebeles visited for the household survey. The community questionnaire covered the following topics:

A. Infrastructure and market access B. Transportation services for agricultural productions C. Inputs and output markets D. Cooperatives E. Credit F. Post-harvest facilities G. Access to information H. Other

28

Finally, the ATA Baseline Survey included a short two-page questionnaire for cooperatives that were based in the kebeles visited by the survey team. The questionnaire was designed to complement a parallel phone survey of cooperatives and a larger cooperative survey to be carried out by the project later. This questionnaire included questions about the size and age of the cooperative, services offered by the cooperative, and storage capacity.

Sampling

The sample design reflects a compromise between the goal of providing general information on the agricultural sector in Ethiopia and the goal of serving as a baseline for ATA interventions, which would be concentrated in 83 woredas. These are known as the AGP woredas because they are the targets of the Agricultural Growth and Productivity (AGP) project. To meet the first objective, some households were selected from the four main regions of Ethiopia: Tigray, Amhara, Oromiya, and SNNP. To meet the second objective, households in the AGP woredas were over-sampled.

More specifically, the households were selected using a three-stage stratified random sample. In the first stage, 100 woredas were selected. In order to over-sample AGP areas, the woredas were stratified into AGP and non-AGP categories and two-thirds of the 100 woredas were selected from the AGP area and one-third from non-AGP areas. As a result, the sample covers more than 80% of the 83 AGP woredas but just 7% of the non-AGP woredas.

In the second stage, two kebele were randomly selected from each of the 100 woredas. In the third stage, 15 agricultural households were selected randomly from each of the 200 kebele, using household lists maintained by kebele officials. As a result, the sample includes 3,000 households, about two-thirds of which are in the AGP woredas.

Table A1. Characteristics of the sample

AGP Non-AGP Total

Total number of woreda 83 467 550

Selected woreda 67 33 100

Number of selected kebele (2 per woreda) 134 66 200

Number of selected households (15 per kebele) 2,100 990 3,000

The community survey was administered to kebele leaders in each of the 200 kebeles included in the household sample. The cooperative survey was administered to cooperative leaders whenever there was a cooperative in the kebele that was included in the sample. Because only about half the kebeles have a cooperative based there, the sample in the cooperative survey was 102.

Implementation

The ATA Baseline Survey was carried out by seven teams, each consisting of a supervisor, 4-5 enumerators, and a driver. The work of the seven teams was coordinated and supervised by a survey manager. The supervisors and enumerators were provided with a seven-day training course in how to administer the interview and fill in the questionnaire. The training also included three days of field-testing. The data collection was carried out using Mirus Schoolmate tablets equipped with data entry program prepared using CSPro software. A tablet was provided to each supervisor and enumerator for the duration of the survey. The teams were given three days of training on how to use and maintain the tablets, as well as protocols to ensure data integrity and security. The training took place in July 2012, and the data collection was carried out from the end of July into September 2012.

29

When the data collection phase was completed, the CSPro files were converted to Stata for cleaning and analysis. Because the CSPro data entry program was written to include range checks for every variable and skip patterns, the data quality was higher than usual for surveys of this type.

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