Medium and Large-scale farmers and Agricultural

Medium and Large-scale farmers and Agricultural Mechanization in Ghana: Survey Results Conducted by The Ghana Strategy Support Program (IFPRI) in collaboration with The Savanna Agricultural Research Institute (CSIR)

Antony Chapoto, Nazaire Houssou, Athur Mabiso and Frances Cossar

June 2014

AKNOWLEDGMENTS

This report has been prepared by a team of researchers from the International Food Policy Research Institute (IFPRI) under the Ghana Strategy Support Program (GSSP). The findings presented in the report are based on a survey conducted on medium and large-scale farmers and tractor owners in Ghana between October and December 2013, in collaboration with the Savanna Agricultural Research Institute (SARI).

The authors would like to thank colleagues from IFPRI particularly Xinshen Diao and Shashi Kollavalli for significant contributions toward this work. The authors are grateful for the fruitful collaboration with colleagues from Savanna Agricultural Research Institute (SARI), especially SARI Director, Steven Nutsugah, and SARI Administrator, Gilbert Y. Nachim. We also thank the regional and district staff of the Ministry of Food and Agriculture for helping with listing and facilitating field interviews. We thank Margaret Beaver for excellent programming, enumerator training and data cleaning assistance, Sarah Asare and Mawuli Yevu for excellent supervision of survey implementation and assistance provided on the report.

We also acknowledge the field enumerators and the many farmers and tractor owners who participated in the survey.

Disclaimer

This report contains preliminary material and research results, and has not been peer reviewed. It is being circulated in order to stimulate discussion and critical comment; any opinions expressed are those of the author(s) and do not necessarily reflect the policies or opinions of IFPRI.

CONTENTS

Aknowledgments 0

1. Motivation and objective of the survey 1

2. Methodology and survey design 3

Stratification by districts 3

Stratification by farm size 4

Sampling of tractor owners 5

Design weights 6

Sampling weight 7

Analytical scope of the survey 7

3. Survey results 7

Main household questionnaire 7

Modules on Agricultural Mechanization 31

TABLES 1—Sample district characteristics 3

2—Sampling by farm size and district 5

2—Sampling of tractor owners by district 6

A- 1—Demographic characteristics of main landholder 8

A- 2—Main landholder farming history-decade when started farming and initial farm size 9

A- 3—Main landholder farming history-farm expansion and enterprise when started farming 9

A- 4—Settlement and employment history 10

A- 5—Assistance and inheritance received when farmer started farming 11

A- 6—Land acquisition, mode and source 11

A- 7—Land tenure security 12

Table A- 8—Perceptions about land availability in village 12

A- 9—Farmland and use by district: sample estimates 13

A- 10—Farm land and use by farmer category and tractor ownership 13

A- 11—Fertilizer use and fertilizer use intensity 14

Table A- 12—Fertilizer acquisition by source 15

A- 13—Source of seed 15

A- 14: Crop production by farmer category 16

A- 15—Crop yields by farmer category and tractor ownership 17

A- 16: Percentage of farmers selling maize and percentage sold by month 17

A- 17—Maize sales by buyer type 18

A- 18—Distance to maize market 18

A- 19—Percentage of farmers selling rice by farmer category 19

A- 20—Quantity of rice sold and percent by buyer type 19

A- 21—Distance to rice market 20

A- 22—Largest field: Hiring labor by activity (count and percent) 21

A- 23—Maize mono cropped largest plot: Family labor hours by activity and farm category 22

A- 24—Largest mono cropped maize plot: Cost-build up for maize production 22

A- 25—Largest rice field: Hiring labor by activity, count and percent 23

A- 26—Rice largest plot: Family labour hours by activity and farm category 24

A- 27—Largest maize plot: Cost-build up for maize production 24

A- 28—Farmers purchasing maize from other farmers 25

A- 29—Livestock production and marketing by farmer category 26

A- 30—Asset ownership by farmer category 27

A- 31—Social networks by farmer category and tractor ownership 27

A- 32—Budgeting and planning by farmer category and tractor ownership 28

A- 33—Loans and credit by farmer category and tractor ownership 30

A- 34—Savings by farmer category and tractor ownership 30

B- 1—Willingness to mechanize various farming operations 32

B- 2—Price farmers are willing to pay for mechanizing various operations 32

B- 3—Farmland cultivated when using hand hoe only to till 33

B- 4—Desire to own a machine by farmer category and tractor ownership 33

B- 5—Constraints to agricultural machines use –top factor mentioned by farmers (in percent) 34

B- 6—Ownership of agricultural machines and attachments (in percent) 34

B- 7—Characteristics of agricultural machines owned - tractor 35

B- 8—Motivation - decision criteria for acquiring a tractor (percent of machines owned) 35

B- 9—Source of tractor financing (in percent) 36

B- 10—Sources and places of agricultural machine acquisition –Tractors (in percent) 36

B- 11—Tractor use for various farming operations 37

B- 12—Agricultural mechanization service provision –Motivation for providing services 37

B- 13—Tractor service provision among owners - service rental 38

B- 14—Agricultural machine performance per working tractor utilization for first plowing 38

B- 15—Agricultural mechanization service provision - credit 39

B- 16—Cross regional migration - Characteristics 39

B- 17—Agricultural mechanization service provision – Top reasons for tractor owner being unable to meet demand for first plowing 40

B- 18—Agricultural machine - service charge by district (Ghana cedis) 40

B- 19—Agricultural mechanization service rental - Hiring for any farm operation 41

B- 20—Plowing service rental by farmer category and tractor ownership 41

B- 21—Crops mechanized – Plowing operation 41

B- 22—Average waiting time – plowing and shelling services 42

FIGURES 1 Map of Surveyed Districts 4

2: Distribution of main landholder's age by farmer category 8

3—Land use by farm-size category 14

SURVEY RESULTS | June 2014

1. MOTIVATION AND OBJECTIVE OF THE SURVEY IFPRI’s Ghana Strategy Support Program has tackled a diverse research agenda on Ghana’s agriculture and economic development. This survey was planned and executed in order to address key policy and research questions which have arisen as priorities for the research agenda, but for which there has been a lingering data gap.

Successful commercialization of farming enterprises and farmer entrepreneurship are thought to embody the key features of structural transformation and provide a pathway out of poverty and subsistence agriculture for the rural farm households. The past decade has seen several African countries increase their agricultural growth, largely driven by increases in land area cultivated instead of productivity increases. The debate on whether Africa should pursue a strategy of agricultural transformation through large-scale or smallholder farming rages, with little attention to a special group of smallholder farmers that have transitioned to become medium- and large-scale farmers.

In Ghana, we have seen the government and some development agencies begin to design agricultural development programs centered on homegrown large-scale farmers and other commercial entities as nucleus change agents to help create a vibrant and more commercialized smallholder sector. For example, the government has been implementing the block farm program as well as procurement and sale of machinery at subsidized prices to medium and large-scale farmers. On the other hand, through the financial support of USAID, the Agricultural Development and Value Chain Enhancement (ADVANCE) Project uses the value chain approach to help link smallholder farmers to markets, finance, inputs and equipment services and information through relatively larger nucleus (commercial) farmers and large traders (aggregators) who have the capacity to invest in the value chain. Essentially these models are based on the premise that there exist symbiotic relationships between the small-scale farmers and the larger farmers and that there are positive spillover effects from developing or channeling assistance to these nucleus farmers. If indeed smallholder farmers can benefit from the existence of large-scale and medium farmers in their communities then there is need to recast the debate and focus on finding ways to leverage these interactions in order to transform Ghana’s agricultural sector.

Also, the claim that smallholder agriculture is more productive than other forms of agriculture continue to be questioned and the empirical literature on determination of the optimal size of farms that ensure maximum productivity, efficiency and equity in Africa has remained inconclusive. This has given other respectable scholars to start questioning Africa’s smallholder growth strategy in favor of large-scale farmers’ growth strategy (for example, see Collier, 2008; Dercon, 2009; Collier and Dercon 2009; Foster and Rosenzweig, 2011 and Hedebrand, 2011).

On another front, African agriculture continues to rely predominantly on manual labor as the main power source. Past efforts to mechanize agriculture have failed and the continent continues to lag behind in terms of mechanization which has played a major role in the transformation of agriculture in many Asian countries. Recently, agricultural mechanization has been revived by the Government of Ghana in line with the country’s effort to attain middle income status through modernized agriculture (MoFA, 2007). Policymakers in the country have therefore expressed strong interest in understanding the role that mechanization can play in transforming the country's agriculture.

The standard approaches for nationally-representative surveys have failed to capture a large enough sample of medium-large scale farmers or of tractor owners for any meaningful analysis of their behavior to be done. For example, the Ghana Living Standards Survey Round 5 (2005/06) includes only 13 households which own a tractor, whereas a recent census of tractors by the ADVANCE Project in 2013 found 1,975 tractors in selected districts of the three Northern regions. Similarly, the Ghana Agricultural Production Survey uses a random sampling of enumeration areas and households which

1

led to the inclusion of only five households with more than five hectares of farmland. While GAPS is representative at the national level, the sample of medium-large scale farmers is too small for any meaningful analysis of their behavior and dynamics. As will be outlined in the subsequent section, the survey has aimed to complement the existing nationally-representative surveys covering agriculture in Ghana, whilst providing a rich dataset with which to analyze the non-trivial number of farmers who have a large farm size and those who own and operate agricultural machinery.

It is in this context that the International Food Policy Research Institute (IFPRI) in collaboration with the Savannah Agricultural research Institute (SARI) of Council for Scientific and Industrial Research and Ministry of Food and Agriculture designed a survey targeting mostly large- to medium-scale and tractor owners. The survey was aimed at characterizing the transition of smallholder farmers who have become medium- and large-scale commercial farmers in Ghana, assessing agricultural machinery ownership, and patterns of demand for agricultural mechanization among farmers in the country. The data generated from the survey will answer some of the critical questions pertaining to agricultural transformation in the country.

For large-and medium scale farming and commercialization, the survey design was guided by a number of key policy research questions as follows:

A. Characteristics of medium and large-scale farmers in Ghana. 1. Who are the medium and large scale farmers in Ghana? 2. What are the key traits or characteristics of successful farmers?

a. What do they farm? b. What technologies do they use? c. What farming practices to them use? d. Where do they sell or market their crops? e. What are their aspirations?

B. Farm productivity, technical efficiency and farm size 1. Does the inverse productivity farm size relationship hold in Ghana? What are the implications of results on

the current agricultural growth strategy debate? 2. What are the factors that weaken or strengthen the inverse relationship? 3. What is the optimal farm-size?

C. Spillover effects 1. Can large-scale and medium-scale farmers help solve the problems of missing markets?

a. Technology transfer: Do large-scale and medium-scale farmers offer farming advice to smallholder farmers? What kind of advice?

b. Mechanization: Do large-scale and medium-scale farmers provide mechanization services to smallholders (tractors, corn shellers, etc.)?

c. Access to markets (inputs and outputs): Do medium and large scale farmers buy grain from other farmers (aggregator services and improving access to markets).

d. Access to Credit: Do medium and large-scale provide credit to smallholder farmers? e. Labor markets dynamics: Do smallholders offer labor to large-scale and medium-scale farmers

(employment opportunities)? 2. Access to land: Do large-scale and medium-scale farmers take up land to the extent that the smallholders

no longer have access to land? 3. Which type of smallholder farmers is more likely to receive the positive spillovers and why?

D. Aspirations and farm Investments 1. Are there any differences in the levels of aspirations among farmers of different farm sizes in Ghana? 2. What are the determinants of the farmers’ aspirations levels and gaps? 3. What is the farm investment behaviors of farmers by farm size in Ghana? 4. What is the relationship between aspirations and farm investment behaviors among the farmers

Under mechanization, the key survey objectives were: =

1. To assess agricultural machinery ownership, use, and performances among owners; 2. To examine the motivations and the factors that drive traction acquisition, and the conditions under which

farmers decide to own a tractor; 3. To assess the patterns of demand for different farming operations and the determinants of use and intensity

of mechanization among farmers; 4. To assess whether and how state interventions in agricultural mechanization may have affected agricultural

machine performances;

2

5. To assess whether the ownership and use of agricultural machines, especially tractors, have played any

major role in the transition from small-scale to medium- and large-scale farming; 6. To assess whether tractor (and other agricultural machines) owners, most of whom are medium and large-

scale farmers can be effective in providing mechanization services to smallholder farmers; 7. To assess the characteristics, performance, and development of the tractor service rental market; 8. To examine the relationships between agricultural mechanization and intensification.

2. METHODOLOGY AND SURVEY DESIGN The IFPRI/SARI Survey was based on a two-stage, stratified cluster sampling design. The first step was to stratify the target population by district. This was followed by further stratification into 3 farm size categories (thereby creating 24 strata). The three cultivated farm size categories that were created are (i) Large-scale (greater than 50 acres); (ii) Medium-scale (greater than 12 acres and less than or equal to 50 acres) and (iii) Smallholder (less than or equal to 12 acres) where farm size is the area of land cultivated in the 2012 main agricultural season1.

After stratification we used simple random sampling of villages within each stratum (i.e. district and farm size category), thus designating the village as the Primary Sampling Unit (PSU). Initially we sampled 20 villages from each district but had to sample additional replacement villages due to the need to replace some farmers who could not be located during fieldwork.

Stratification by districts Eight districts were selected to conduct the Ghana Medium-scale and Large-scale Farmers and Mechanization Survey (IFPRI/SARI Survey). The selected districts were Kassena Nankana East, Sissala East, Bawku Municipal, Kintampo North, Ejura Sekye Dumasi, Techiman, Gushiegu and Yendi. These districts were purposively selected for two reasons. Firstly, they were identified through exploratory fieldwork as areas with relatively large numbers of large- and medium-scale farmers as well as tractor owners in Ghana – the target population of the survey. The exploratory fieldwork, which included qualitative key informant interviews and focus group discussions, was conducted by IFPRI research staff in 2012 and early 2013. Additionally, farm size information was obtained from regional and district-level officers of the Ministry of Food and Agriculture, and data on tractor ownership was used from a tractor census conducted in the Northern regions2 of Ghana by the USAID/ADVANCE project. Secondly, these districts were selected because they were randomly selected districts for the Ghana Agricultural Production Survey (GAPS).

The districts cover both the savannah and transitional agro-ecological zones in Ghana. Furthermore, there is considerable diversity in population density and proximity to urban markets. Although the survey is not nationally representative, the heterogeneity of agricultural systems across the non-cocoa dominated areas of Ghana has been captured well.

Table 1—Sample district characteristics

District Population density (persons/1000 sq km) Agro-ecological zone Length of Growing

Period (days/year) Ejura-Sekyedumasi 65.52 Central Transitional 210 Techiman 183.39 Main Transitional 225 Kintampo North 21.09 Main Transitional 181 Yendi 46.55 Open Guinea Savannah 160

Gushiegu 31.09 Open Guinea Savannah 143

Kassena Nankana 66.32 Eastern Sudan Savannah 115

Bawku Municipal 237.46 Eastern Sudan Savannah 103

Sisaala East 13.45 Central Sudan Savannah 127 Source: Authors’ calculations using Harvest Plus and GADM.

1 It is important to note that the farm size variable is land area actually cultivated in 2012 farming season and not total land area owned or under the control of the farmer.

2 Northern regions of Ghana include Upper East, Upper West, and Northern Region which covers the districts of Kassena Nankana, Sissala East, Bawku Municipal, Gushiegu and Yendi.

3

Figure 1 Map of Surveyed Districts

Source: HarvestChoice and GADM

Stratification by farm size

Sampling medium-large scale farmers

Prior to sampling, a listing exercise was conducted in September - October 2013 in each of the eight districts. The purpose was to list all farmers cultivating more than 5 hectares of land in 2012 in order to derive a sampling frame for the large- and medium-scale farmers. Due to the smaller number of large-scale farm holders we decided to include all of the large-scale farmers that were listed during the listing exercise.

For the medium-scale farmers we randomly sampled 10 farmers per village. However, in the event that the total number of medium-scale farmers was less than 10 we included all of the available medium-scale farmers then increased the number of villages sampled in the respective district in order to achieve the required sample size for medium-scale farmers. This means that some districts had slightly more villages than others where medium-scale farmers were sampled from.

4

Table 2—Sampling by farm size and district

Total

Ejura

Techiman

Kintam

po

Yendi

Gushiegu

Kassena

Nakana

Bakw

u

Sisaala E

ast Total in sample 1,843 253 250 247 289 314 191 230 254

Small sampled 946 99 139 121 128 82 132 153 92

frame 1,946 200 313 213 257 151 315 294 203

Medium sample 879 142 102 110 116 159 49 73 128

frame 5,455 1,619 398 545 520 919 208 232 1,014

Large** 230 39 9 16 45 73 10 4 34

Notes: *Farm-size category is derived from the amount of land cultivated by the main landholder in the household - small-scale, 5ha or less; Medium-scale, more than 5ha to 20ha; and large-scale farmers – greater than 20ha; **All large-scale farmers listed were initially included though not all responded.

Sampling small-scale farmers

In the case of small-scale farmers (those cultivating less than or equal to 5 hectares in 2012), the sampling frame was derived from the Ghana Agricultural Production Survey (GAPS) list of enumeration areas (EAs) since all the districts included in the IFPRI/SARI Survey are part of the GAPS districts. The main justification for sampling the small-scale farmers from the GAPS list was that it allowed us to obtain a representative3 sample of smallholder farmers without having to do a separate listing exercise, which would have been very costly given the large numbers of smallholder farmers compared to medium and large-scale farmers. In addition, this provides a unique opportunity to track the same farmers on a more regular basis since the GAPS is expected to be conducted regularly in the next few years.

For six of the eight districts (i.e. Kassena Nankana East, Sissala East, Bawku Municipal, Yendi, Gushiegu and Techiman) nine EAs were randomly sampled from the 40 EAs that were sampled in the GAPS survey in 2013. From these nine EAs, 11 farmers were then sampled randomly. In the other two districts (Kintampo North and Ejura Sekye Dumasi) a separate approach was followed because these were newly added to the GAPS and there was no existing list of sampled farmers in the selected EAs. Therefore, the approach used for these two EAs was to randomly sample five EAs, conduct a listing exercise to list all the small-scale farmers in the 5 EAs, then randomly sample 11 farmers from the list in each EA.

In all districts, additional sampling of small-scale farmers was done if replacements were needed or if the total numbers of large- and medium-scale farmers were relatively small (i.e. in Bawku, Kassena Nankana East and Techiman) allowing for additional random sampling among the small-scale farmers. The additional round of sampling was done by returning to the same EAs previously selected randomly then randomly sampling an additional 8-11 farmers per EA depending on the required sample size/replacements.

Sampling of tractor owners Tractor owners were sampled separately and using a different sampling design and sampling frame. The first step in executing the sampling design was to stratify by district, such that there were 8 strata for the tractor owners’ sample. For those tractor owners in the Northern districts (i.e. Bawku Municipal, Gushiegu, Kassena Nankana East, Sissala East and Yendi), the USAID/ADVANCE tractor census data were used as the sampling frame. This tractor census was conducted in 2013 and covered the whole tractor population in those districts. Before sampling the tractor owners in each district, tractors owned by organizations were removed from the census database, since these were not owned by individual farmers. In addition, data on tractors owned by the same owner were aggregated so that the odds of random selection from the database were at the tractor owner level and did not depend on the number of tractors owned. Having prepared the data,

3 There is recognition that the GAPS is in effect representative of all farmers, not just farmers with less than or equal to 5 hectares. However, the majority of GAPS farmers have less than or equal to 5 hectares, with only five farm households with more than 5 hectares. We restricted the sampling frame to those farmers in the GAPS lists that cultivated less than or equal to 12 acres.

5

simple random sampling proportional to size was performed within each district’s census database to obtain the sample of tractor owners.

In the case of Bawku Municipal, all the tractor owners were initially included in the sample since the population of tractor owners was small (only 13 in total). Ejura Sekye Dumasi, Kintampo North and Techiman districts were not covered by the tractor census, therefore we used data from the listing exercise, which includes farmer tractor ownership status. For Techiman and Kintampo North, the initial intent was to include all tractor owners that were listed as well, since they were few; however, not all tractor owners listed were included in the final sample due to nonresponse. Thus, we adjusted for these idiosyncrasies in computing the weights for the tractor ownership data. Table 2 shows the number of tractor owners sampled by district and farm size. It is important to note that three tractor owners sampled did not farm any land. These are treated as separate from the target of this study, i.e. different from tractor owners that farm.

Table 3—Sampling of tractor owners by district

Total

Ejura

Techiman

Kintam

po

Yendi

Gushiegu

Kassena

Nakana

Bakw

u

Sisaala East

Population 711 172 6 50 178 180 27 13 85 Survey sample 408 126 1 11 115 92 10 5 48

Source: IFPRI/SARI Survey on medium and large-scale famers and mechanization, 2013

Design weights This section describes the procedure used to calculate the weights. However, the results presented in this report have not been weighted. This survey report only presents the sample estimates.

Design weights were calculated based on the separate sampling probabilities for each sampling stage and for each cluster as follows:

𝑃𝑃1ℎ𝑖𝑖 = first-stage sampling probability of the i-th cluster in district h (all rural districts).

𝑃𝑃2ℎ𝑖𝑖 = second-stage sampling probability within the i-th cluster (farm holder selection).

The probability of selecting cluster i in the sample is:

𝑃𝑃1ℎ𝑖𝑖 =𝑚𝑚ℎ × 𝑁𝑁ℎ𝑖𝑖

𝑁𝑁ℎ

The second-stage probability of selecting household in cluster i is:

𝑃𝑃2ℎ𝑖𝑖 =𝑛𝑛ℎ𝑖𝑖𝐿𝐿ℎ𝑖𝑖

Where:

𝑚𝑚ℎ = number of sample clusters selected in district h.

𝑁𝑁ℎ𝑖𝑖= total population in the frame for the i-th sample cluster in district h.

𝑁𝑁ℎ = total population in the frame in district h.

𝑛𝑛ℎ𝑖𝑖 = number of sample farm holders selected for the i-th sample village cluster in district h.

𝐿𝐿ℎ𝑖𝑖 = number of farm holders listed in the farm holders listing for the i-th sample village cluster in

district h.

The overall selection probability of each household in cluster i of district h is the product of the selection probabilities of the two stages:

𝑃𝑃ℎ𝑖𝑖 = 𝑃𝑃1ℎ𝑖𝑖 × 𝑃𝑃2ℎ𝑖𝑖 =𝑚𝑚ℎ × 𝑁𝑁ℎ𝑖𝑖

𝑁𝑁ℎ×𝑛𝑛ℎ𝑖𝑖𝐿𝐿ℎ𝑖𝑖

6

The design weight for each household in cluster i of district h is the inverse of its overall selection probability:

𝑊𝑊ℎ𝑖𝑖 =1𝑝𝑝ℎ𝑖𝑖

=𝑁𝑁ℎ × 𝐿𝐿ℎ𝑖𝑖

𝑚𝑚ℎ × 𝑁𝑁ℎ𝑖𝑖 × 𝑛𝑛ℎ𝑖𝑖

Sampling weight Despite having randomly drawn replacements to address nonresponse, the total number of farmers in the final sample still suffered from nonresponse. Given this nonresponse by farm holders, we adjusted the design weight for nonresponse by using the method of weighting classes. The design weights for each respective districts and farm size category were multiplied by an adjustment factor which is the inverse of the response rate for that district and farm size category.

Therefore, the farm holder sampling weight was calculated using the formula:

𝑊𝑊𝑓𝑓ℎ𝑖𝑖 = 𝑊𝑊ℎ𝑖𝑖 × 𝑤𝑤𝑛𝑛𝑛𝑛

Where 𝑤𝑤𝑛𝑛𝑛𝑛 = 𝑛𝑛𝑠𝑠 is the ratio of holders that responded to the number of holders that were sampled for the survey interviews; r

is the number of holders that responded and s is the number of farm holders that were originally sampled.

For the tractor owner sample, different weight calculations were performed given the different sampling design which entailed stratification by district followed by simple random sampling within each district. Since the sampling of the tractor owners did not entail multiple sampling stages, the weights for tractor owner survey were calculated using the simpler formula:

𝑊𝑊𝑡𝑡𝑛𝑛𝑖𝑖 =𝑁𝑁𝑖𝑖𝑛𝑛𝑖𝑖𝑟𝑟𝑖𝑖

Where 𝑁𝑁𝑖𝑖 is the total number of tractor owners in district i, 𝑛𝑛𝑖𝑖 is the number of tractor owners sampled in district i, and 𝑟𝑟𝑖𝑖 is the response rate among tractors in district i. These weights are sufficient to extrapolate to the district level population parameters.

Analytical scope of the survey This survey was designed to provide valid estimates at selected district level and not regional or national level. Estimates generated from the survey data will be case study results for the sampled districts. Farm level and tractor ownership weight variables need to be used when generating these district estimates, otherwise any statistics without weights should be reported as sample statistics. Since small-scale farmers were not the primary focus of this survey, our random sample for this group is likely to have weak statistical power given that the sample was derived from the representative GAPS sample. Therefore any comparison by farm size group should be done with caution and would require ex post statistical power testing for the small-scale farmers’ group.

3. SURVEY RESULTS The key results from the survey are presented in this section. More detailed results are provided in the appendix tables. First, we discuss the results from the main household questionnaire in Part A which covers household and farm characteristics, land use, agricultural production and social networks. Part B focusses on the agricultural mechanization questionnaire which concentrates on machinery ownership, renting-in and renting-out services by farmers.

Main household questionnaire

Demographic characteristics

Table A-1 summarizes the demographic characteristics for the sample farmers. The average age of the main landholder was 45 years for small and medium scale farmers, with large-scale farmers being slightly older with 48 years on average. Figure A-1 shows the age distribution of the main land holders by farm size category. We find that there are more young farmers (aged between 20 and 35) in the small and medium-scale farmer category compared to those in the large-farm size category. Majority of the respondents were married, across farm size categories. Large-scale farmers were more likely to be married polygamously than other farmers with 63.6% of large-scale farmers in polygamous marriages.

7

Figure 2: Distribution of main landholder's age by farmer category


Table A- 1—Demographic characteristics of main landholder Small Medium Large Number of main holders 768 850 225 Main holder characteristics Age (A) 45 45 48 % Female farmers (B) 23.2 3.4 2.2 Marital status Married (C) 89.2 97.9 100.0 Polygamous marriage married (D) 25.3 45.6 63.6 Can read and write (E) 31.5 31.1 32.4 Mean years of education 3.5 3.4 3.7

% with no education (F) 60.0 61.4 65.3 % with primary (1-7 years) (G) 14.5 14.0 6.7 % with secondary school (8-16 years) (H) 20.7 19.6 19.1 % with tertiary education (> 13 years) (I) 4.8 4.9 8.9

% engaged in salary/wage employment (J) 9.5 7.8 8.0 % engaged in formal/informal business activity (K) 21.6 23.4 37.8 Household characteristics Average number of holders in household (L) 1.8 1.9 2.3 Household size (M) 8.1 10.0 13.0

Among polygamously married farmers (N) 10.6 12.0 15.2

Among single or monogamously married farmers (O) 7.2 8.3 9.1 Adult equivalents (P) 6.8 8.4 11.1 Average age of holders (Q) 45.1 43.8 44.1 Highest level of education in household (R) 4.3 4.3 4.9 % female holders in household (S) .5 .3 .2


In general, levels of education of the main holders was very low. Most farmers either had no education or had completed some level of primary education. Large-scale farmers were more likely to have achieved tertiary education but the percentage of farmers remained low (8.9%). Across farm sizes, 31-32% of farmers reported that they can read and write in any language. Less than 10% of farmers in each categories had engaged in salary/wage employment in the past 12 months. However, a higher proportion had engaged in some kind of formal or informal business activity, especially large-scale farmers (37.8%).

0.0

5.0

10.0

15.0

20.0

25.0

15-20 20-25 25-30 30-35 35-40 40-45 45-50 50-55 55-60 60-65 >65

Perc

ent

Age groups Small-scale Medium-scale Large-scale

8

Main landholder farming history

Table A-2 (rows B to H) shows that the majority of the farmers started farming without their parents between 1990 and 2009. The farming experience across all the farmer groups is around 20 years, with the large-scale farmers having the highest number of years of farming experience of 24 years on average. This follows from the fact that farmers with larger farmers are on average older than their other counterparts. The large scale farmers on average started with 9.3 hectare of land, 2.8 hectares for medium scale farmers, and 1.3 hectares for small-scale farmers. In general, initial farm sizes of the medium- and large-scale farmers are quite comparable and are significantly higher than the initial farm sizes of the smallholder farmers. Among the large-scale farmers, 78.5% of the farmers started as small-scale farmers, starting with less than 5 hectares. Interestingly, 9.3% of the large-scale farmers started with farms less than 0.5 ha and 27.5% if the land size is raised to 1 hectare. This suggests that it is be possible to grow smallholders into medium or large-scale farmer.

Table A- 2—Main landholder farming history-decade when started farming and initial farm size Small Medium Large Number of main holders 768 850 225 Years of farming experience (A) 19.0 20.2 23.2 Decade of first farming without parents 1959 or earlier (B) 2.6 1.6 2.7 1960 through 1969 (C) 3.1 2.9 2.7 1970 through 1979 (D) 8.6 8.2 11.1 1980 through 1989 (E) 16.7 19.5 24.9 1990 through 1999 (F) 27.0 34.0 40.0 2000 through 2009 (G) 36.3 31.1 15.6 2010 or later (H) 5.7 2.6 3.1 Initial farm size (ha)

Mean (I) 1.3 2.8 9.3 Median (J) .81 1.62 1.62 Minimum (K) .10 .10 .20 Maximum (L) 24.3 40.5 607.5

Proportion with initial farm size (%) 0.1-0.5 ha (M) 32.0 13.9 9.3 >.5 to 1 ha (N) 24.2 19.2 18.2 >1 to 1.5 ha (O) 13.9 13.6 12.4 >1.5 to 3 ha (P) 20.8 29.6 28.4 >3 to 5 ha (Q) 7.3 10.5 10.2 >5 to 10 ha (R) 1.3 7.4 8.0 >10 ha (S) .4 5.8 13.3


Table A-3 shows that 46-54% of farmers across categories started farming with growing crops only, a small percentage with livestock only and more than 45% with both crops and livestock.

Table A- 3—Main landholder farming history-farm expansion and enterprise when started farming Small Medium Large Number of main holders 768 850 225 Area cultivated after started farming Initial 1.3 2.8 9.3 5 years 2.2 4.7 12.3 10 years 3.0 6.9 21.0 15 years 3.5 8.9 31.4 Enterprise when started farming Crops only 53.0 46.2 54.2 Livestock only .4 .1 0.0 Crops and livestock 46.6 53.6 45.8 Number of crops 1.8 1.9 2.1


9

Settlement and employment history

Table A-4 shows that approximately 18% of the medium and large scale farmers and 22% of the small-scale farmers migrated to their current farming areas from elsewhere4. The main reason cited by the farmers why they migrated was because they followed a spouse or relatives to where they are currently farming. More than half (53.3%) of the small scale farmers migrated mainly because of this same reason. However, for the medium and large scale farmers, the main reason why they migrated was in search of land.

Table A- 4—Settlement and employment history Small Medium Large Number of main holders 768 850 225 Average number of years in settlement (A) 21.7 22.2 24.3 Migrants (%) (B) 21.5 17.8 18.2 For migrants: main reason for migration

Family/clan hails from this area (C) 6.7 7.3 12.5 In search of land (D) 18.2 28.5 37.5 Followed spouse/relative (E) 53.3 25.2 12.5 Improved farming opportunities (F) 11.5 23.2 17.5 Employment (G) 6.7 13.2 15.0 Other reasons (H) 3.6 2.6 5.0

Employment history Held a job before farming (I) 5.5 6.9 3.1 Worked along farming but no longer working (J) 3.8 4.1 4.9 Currently working alongside farming (K) 10.7 10.1 10.7 Never held a job other than farming (L) 80.1 78.8 81.3


The majority of farmers reported that they never held any off-farm employment before starting farming. These results are consistent across the different farm size categories, with 78.8% and 81.3% of the medium and large-scale farmers reporting that they never held any job other than farming respectively.

Family History

More than half of the farmers received assistance from parents when they started farming, with a greater proportion of large and medium-scale farmers receiving assistance from parents compared to small-scale farmers. The level of assistance irrespective of type of assistance (land, startup money or livestock is much higher for large-scale farmers followed by medium-scale farmers and lastly those currently farming 5 hectares or less.

About 24-29% of farmers, across farm size categories, reported receiving some kind of inheritance since they started farming. The results show a slightly higher percentage of medium-scale farmers (29.2%) reporting to have received inheritance since they started farming compared to 27.6% and 24.0% for the small-scale and large-scale farmers respectively. In terms of the level of inheritance, Table A-5 shows that the results follows a similar pattern to receiving assistance. As we would expect, the farm size received as inheritance increases with the current farm size, with larger farmers reporting to have inherited more land on average than smaller farmers. A similar pattern is observed for start-up money/inputs and number of livestock by farm size category.

4 Migrants are classified as those who responded negatively when asked if they grew up in the area they are currently farming.

10

Table A- 5—Assistance and inheritance received when farmer started farming

Small Medium Large Number of land holders 768 850 225 % Received assistance from parents (A) 53.1 63.2 65.3 % Received assistance from other relatives (B) 15.5 10.8 15.1 % Received Inheritance (C) 27.6 29.2 24.0 Type and amount of assistance received A. Assistance from parents and other relatives Farm land (ha) (mean) (D) 2.4 6.4 26.2 Startup money/inputs (Ghc) (E) 142.8 298.5 692.5 Livestock (Cattle , goats) (Ghc) (F) 39.7 196.3 206.0 Farm machinery, other machines (%) (G) .4 1.1 1.8 B. Inheritance Farm land (ha) -Mean (H) 6.9 13.8 66.8 Startup money/inputs (Ghc) (I) 363.7 603.2 1716.7 Livestock (Cattle , goats) (Number) (J) 6.7 12.7 28.6 Farm machinery, other machines (%) (K) .4 .5 .4


Land acquisition

Model of land acquisition: Table A-6 shows the total land acquired over time, the mode of acquisition and the proportion of land acquired by source. As expected, the large-scale farmers acquired much more land on average compared to the medium and small-scale farmers combined. When asked about the mode of land acquisition of various field acquired over the time period they started farming, three main modes of land acquisition emerged to be very important, first, ‘given free by relatives’; second, ‘inherited land’ and the third most important was ‘given free by chief/land owners’. The second and third main reasons are reversed for the large-scale farmers. Thus, the second main mode by which large scale farmers acquire land is from chiefs but not through inheritance like the small and medium-scale farmers.

Table A- 6—Land acquisition, mode and source

Small Medium Large Number of main holders 768 850 225 Total land acquired (ha) (A) 5.5 16.7 62.4 Mode of acquisition*

Given free by chief/land owners (B) 15.1 18.1 28.0 Given free by relative (C) 67.1 65.1 65.3 Given free by government (D) 1.0 1.1 .9 Purchased with title (E) 1.8 1.2 4.9 Purchased without title (F) 3.0 4.0 2.7 Inherited the land (G) 19.3 25.4 23.6 Just walked in (H) 7.2 5.9 4.4 Sharecropping-agreement with landowner (I) 1.2 2.2 3.1

Proportion of land acquired by source (%) Given free by chief (J) 14.7 15.9 22.0 Given free by relative (K) 50.5 51.1 47.4 Given free by government (L) 0.8 0.7 0.4 Purchased with title (M) 1.0 2.5 5.1 Purchased without title (N) 1.8 2.7 1.6 Inherited the land (O) 24.1 22.6 19.4 Just walked in (P) 6.1 3.3 2.7 Sharecropping-agreement with landowner (Q) 0.9 1.2 1.5

Source: IFPRI/SARI Survey on medium and large-scale famers and mechanization, 2013 *Can be more than 100% because of multiple acquisition sources by the farmer over his/her farming history

11

In terms of the proportion of land acquired from the different sources, table A-6 shows that about 50% of the total

proportion of land acquired by farmers was for free from their relatives. Other major sources of land were through inheritance and given free by the Chief. Large-scale farmers were more likely to acquire land for free from chiefs than through inheritance.

Table A-7 shows tenure security of the farmers interviewed. The results show that only a small proportion of farmers had title deeds to the land they were farming. The majority of the farmers have use rights under the customary tenure system. However, farmers across the spectrum feel very confident about the tenure security of the land under their control with 59.2% of small-scale farmers, 64% of medium-scale farmers and 62.7% of large-scale farmers reporting that they could leave their land indefinitely without worry of losing it. Less that 7% of farmers in each category reported that land may be lost after leaving it uncultivated for six months. When asked about what they were doing to secure land tenure, 32-37% of farmers in each farm size category said they stumped the land to avoid others taking that land, another 19-27% said they did not leave their lands fallow while less than 10% of farmers gave a share of harvest to chief/village authorities to secure land tenure.

Table A- 7—Land tenure security Small Medium Large Current tenure status of land (% reporting )* Customary (no title) (A) 95.1 91.8 89.3 Titled land (B) 5.9 9.5 14.7 Length of period land can be left without worry of losing it (% reporting)

Less than 6 months (C) 6.6 6.5 6.2 6 to 12 months (D) 10.7 8.6 10.7 12 months to 2 years (E) 9.9 11.8 11.1 More than 2 years (F) 21.0 20.0 21.8 Indefinitely (G) 59.2 64.0 62.7

Things being done to secure land tenure (% reporting)

Stumping the land (H) 35.8 37.3 32.0 Do not leave land fallow (I) 26.7 26.1 19.1 Give a share of harvest to chief/village authorities (J) 7.9 8.6 9.8

Source: FPRI/SARI Survey on medium and large-scale famers and mechanization, 2013 *The %ages do not add up to 100% because some farmers reported having multi plots with different tenure status

Table A-8, shows that the majority of farmers are of the perception that village authorities still have unallocated arable lands that could be allocated to households for cropping purposes. Results by district are presented in appendix A. When asked about whether it was possible to convert customary land into titled property, less than half of the farmers interviewed answered that it was possible.

Table A- 8—Perceptions about land availability in village

Small Medium Large Land availability perceptions (% answering yes)

Village authorities still have unallocated arable land that could be given to households to farm in this area

68.8 75.8 73.3

Unallocated land can be allocated to this household for cropping purposes 67.8 74.2 72.0

It is possible to convert customary land into titled property 38.3 45.4 41.3


Farmland and use

Table A-9, presents the average land holding size controlled and cultivated by the main landholders by district and farm size category and Table A-10 by farmer category. A closer look at the proportion of land owned that is cultivated, shows that farmers in Techiman and Bawku cultivated a higher proportion of their land compared to the other 6 districts.

12

Table A- 9—Farmland and use by district: sample estimates

Full Sample

Ejura

Techima

n Kintam

po Yendi

Gushieg

u Kassena

Nakana

Bakw

u

Sisaala East

(1) (2) (3) (4) (5) (6) (7) (8) (9) Land holding size (ha)

Small (A) 5.3 4.1 4.7 6.0 5.5 5.5 5.2 4.2 8.3 Medium (B) 16.5 14.7 11.1 18.6 19.0 18.0 13.9 11.4 20.4 Large (C) 61.6 65.6 49.9 86.5 49.8 54.4 53.7 75.0 80.1

Land cultivated Small (D) 2.9 2.6 3.1 3.0 3.1 3.2 2.4 3.0 3.1 Medium (E) 9.5 10.1 8.4 9.5 10.4 9.7 8.0 8.1 10.0 Large (F) 38.4 40.1 30.6 43.1 35.3 37.6 36.6 73.9 38.3

Length of fallow Small (G) 1.8 1.6 2.0 2.8 1.0 0.8 1.9 2.3 2.7 Medium (H) 1.7 1.3 2.0 2.5 1.2 1.0 1.7 2.3 2.7 Large (I) 2.0 1.3 3.8 1.7 1.8 1.5 0.6 8.7 1.4

Source: IFPRI/SARI Survey on medium and large-scale famers and mechanization 2013

Table A- 10—Farm land and use by farmer category and tractor ownership

Small Medium Large Number of main landholders 768 850 225

Average landholding size (ha) 5.3 16.5 61.6

Percent cultivated 54.7 57.6 62.3

Percent fallow 26.4 23.6 16.7

Percent virgin land 17.0 16.4 18.3

Percent rented/borrowed out 1.9 2.4 2.6

Average fallow land (ha) 1.4 3.9 10.3

Average virgin land (ha) .9 2.7 11.3

Average cultivated land (ha) 2.9 9.5 38.4

Area under field crops (%) 68.6 70.9 71.0

Garden (%) 2.46 1.33 .57

Tree crops (%) 3.06 4.06 3.69

Number of years fallow 1.8 1.7 2.0


Figure A-3, summarizes land utilization by farm size category. Three interesting patters are discernible from this figure. First, the results show that the of the total landholding that is cultivated increases by farm size category, 55% for small-scale farmers, 58% medium-scale and 62% among the large scale farmers. Second, Figure A-3 shows an inverse relationship between farm size category and the size of the land left fallow. Lastly, of the total landholding in 2012, small-scale farmers left 34% fallow, 10% among medium-scale farmers and with only 3% left fallow among the large-scale farmers. Therefore, if we consider cultivated plus fallow land, one can conclude that small-scale farmers are utilizing 89% of their land compared to 68% and 65% among medium-scale and large-scale farmers respectively.

13

Figure 3—Land use by farm-size category


Fertilizer and seed use and acquisition

Table A-11 below shows the fertilizer use among various categories of farmers. In general, fertilizer use was found to be intensive among sampled farmers revealing more than two-third of farmers, across all categories, used fertilizer on cultivated land. The percentage of cultivated area on which fertilizer was used varied from 45.8% for small-scale farmers, 52% for medium-scale farmers, to 59.5% for large-scale farmers. In terms of the type of crop fertilized, the results show that the majority of the respondents used fertilizer to their maize plot, with fertilizer also frequently used on rice plots.

In terms of fertilizer use intensity, Table A-11, shows that larger farmers used more fertilizer per hectare that than small and medium scale farmers, 130.4kg/ha compared to 99.8kg/ha and 121.2kg/ha for small and medium-scale farmers respectively. However, among users, the fertilizer use intensity advantage of large-scale farmers disappears as quantity of fertilizer used by farm size category is almost equal by farm size. When we consider fertilizer use intensity per area fertilized, the result show that on average, small-scale farmers applied about 20 kilograms more per hectare than the large-scale farmers.

Table A- 11—Fertilizer use and fertilizer use intensity

Small Medium Large Percent of area cultivated fertilized 45.8 52.0 59.5 % using fertilizer 68.4 81.4 89.8 % farmers using fertilizer on maize 62.9 76.9 86.7 % farmers using fertilizer on rice 17.8 20.7 34.7 Kg of fertilizer used/ha cultivated (among users) 144.81 148.30 143.33 Kg of fertilizer used/ha fertilized (among users) 227.34 229.63 204.09 Fertilizer quantity use/ha None 30.9 18.2 8.9 0-50kg 10.5 16.7 29.3 50-100kg 18.5 17.8 15.1 100-150kg 12.5 13.6 7.1 150-200kg 9.5 12.0 11.6 200-300kg 12.2 12.5 14.7 >300kg 5.1 8.8 12.0


Table A-12 shows the proportion of farmers (rows A to H) and fertilizer acquired by source (rows I to R). At least two thirds of farmers in each of the farm size categories acquired fertilizer through commercial sources. The majority of these commercial transactions were cash purchases from a private trader/retailer. Out-grower schemes and other commercial

14

sources were used by less than 5 % of farmers in each category. An interesting observation is that there is a greater proportion of small-scale farmers acquiring fertilizer from commercial sources, in particular cash purchase from private trader/retailer than medium and large-scale farmers, 75.1% compared to 65.9% and 71.1% respectively (row D). Also, even though the proportion of fertilizer acquired from commercial sources is much greater than the subsidized fertilizer, the results show that larger-and medium-scale farmers were more likely to acquire subsidized fertilizer compared to small-scale farmers.

Table A- 12—Fertilizer acquisition by source Small Medium Large % of farmers acquiring fertilizer from channel Subsidized fertilizer* (A) 28.4 39.2 35.5 Direct from MoFA office –Block Farm program (B) 5.9 10.8 7.4 Direct from MoFA office – Non -Block Farm program (C) 6.5 7.6 9.4 Subsidized fertilizer (from Agro dealer shop ) 17.0 22.2 20.3 Commercial sources (D) 75.1 65.9 71.1 Cash purchase from private trader/retailer (E) 72.5 62.8 69.1 Loan purchase from out-grower scheme (F) 2.1 3.6 3.0 Other commercial sources** (G) 1.5 2.3 2.0 Free/gift/grant (H) .6 1.0 .0 Average number of 50kg bags acquired (I) 6.8 23.0 93.8


*A small percentage of farmers receive subsidized fertilizer from more than one channel so sum of three sources do not add up to percent getting subsidized fertilizer. The same applies to commercial sources

** Other commercial sources include cash purchase from another household/farmers (12 cases only), loan purchase from private trader/retailer (5 cases) and direct commercial exchange or barter (12 cases).

Table A-13, presents the distribution of sources of seeds for maize and rice. The majority of respondents who grow maize and rice use recycled seed. These results cut across the farm size categories. Other sources of seed, including cash or loan purchases, are not as important as own harvest. These results may have significant implications on maize and rice yields and is an area for further research.

Table A- 13—Source of seed Small Medium Large

Number of farmers growing maize 598 794 217 % growing maize 77.9 93.4 96.4 Source of maize seed (%)

MOFA cash purchase 2.3 4.4 4.1 MOFA loan purchase .5 1.1 .5 Cash purchase private seller 8.2 4.3 5.5 Out-grower loan .8 2.1 2.3 Seed retailer loan .2 .1 .9 Barter 0.0 .1 .5 Grant / gift / free 1.7 .8 .5 From own harvest 86.3 87.0 85.7

Number of farmers growing rice 190 291 137 % growing rice 24.7 34.2 60.9 Source of rice seed (%)

MOFA cash purchase 4.2 3.1 4.4 MOFA loan purchase .5 1.7 0.0 Cash purchase private seller 13.2 5.8 6.6 Out-grower loan .5 1.0 .7 Seed retailer loan .5 .3 1.5 Barter 0.0 .3 0.0 Grant / gift / free .5 0.0 0.0 From own harvest 80.5 87.6 86.9


15

Crop production

Table A-14 presents the proportion of cultivated area which is devoted to various crops by farm size. In general, the results show that farmers surveyed across farm size categories devoted more than 50% of their cultivated area to cereals, with maize topping the list among cereals. The second most common crops are legumes followed by roots and tubers. The large-scale farmers allocated the greatest percentage of their farmland to cereals (70.8%) compared to 60.7% by medium-scale farmers and 53% by small-scale farmers. It also follows that the larger farmers allocated more land to maize compared to the smaller farmers. For other cereals, larger-scale farmers have a greater proportion of land cultivated devoted to rice compared to small-and medium-scale farmers combined. But on average small and medium-scale farmers have 2-3% more area devoted to millet than large-scale farmers. There are no differences for sorghum. Also, on average larger farmers devoted less of their cultivated land to legumes and roots and tubers, compared to the sampled small and medium scale farmers.

Table A- 14: Crop production by farmer category Small Medium Large

% area cultivated under: Tree crops 6.9 10.6 8.9 Cereals 53.0 60.7 70.8

Maize 35.0 43.8 47.7 Rice 8.7 8.4 17.7 Millet 6.6 5.2 3.2 Sorghum 2.7 3.3 2.3

Roots and tubers 18.6 14.6 7.2 Cassava 6.6 4.6 1.8 Yam 11.6 9.8 5.4

Legumes 23.9 20.0 17.1 Soybean 6.8 6.7 7.3 Groundnut 10.7 7.0 5.8 Cowpeas 5.5 5.6 3.8

Horticultural crops .9 .4 .2 All other crops .5 .5 .0


Table A-15 presents the yields for different crops. Here we define yield as the crop harvested divided by the area cropped for the particular crop. The highest yields among cereals were in rice in the range of 1188-1596 kg/ha, followed by maize in the range of 1317-1344 kg/ha and the other cereals crops were in the range of 600 to 900 kg/ha. The average maize yield from our sampled farmers is lower than the 1900kg/ha reported by MoFA/SRID in 2012, but are consistent with yield estimates from a national representative survey of maize farmers by CRI/SARI/IFPRI of about 1200kg/ha. A comparison of maize yields by fertilizer use, shows that, on average, fertilizer users produce 65.5% more maize per hectare than non-fertilizer users. Also, Table A-15 shows that irrespective of status of fertilizer use (users or non-users), small-scale farmers’ yields were lower than for either medium or large-scale farmers. These results are contrary to the famous productivity inverse farm size relationship. However, this may require further analysis.

For rice production, small-scale rice farmers rice yields (1596 kg/ha) were higher than those of the medium and large-scale farmers, 1490 kg/ha and 1188 kg/ha respectively, consistent with the inverse productivity - farm size relationship from the literature. The trend is the same among farmers who used fertilizer. A comparison of rise yields by fertilizer use, shows that, on average, fertilizer users produce 38-53% more rice per hectare than non-fertilizer users.

16

Table A- 15—Crop yields by farmer category and tractor ownership

Small Medium Large Land cultivated 2.9 9.5 38.4 Yield (kgs/ha)

Maize 1317 1368 1344 Rice 1596 1490 1188 Millet 657 686 670 Sorghum 839 699 604 Soybean 886 812 813 Cassava 3452 2988 3921 yam 6575 7558 6709

Maize yield with fertilizer 1362 1485 1404

Maize yield without fertilizer 759 913 952 Rice yield with fertilizer 1764 1689 1335 Rice yield without fertilizer 1146 1222 929 Gross value of crop production 4,195 10,875 31,648 Gross value of crop production per landholding (GHc/ha) 1,039 908 604

Gross value of crop production per cultivated land (Ghc/ha) 1,378 1,167 810


Maize and rice marketing and market access

Table A-16 shows maize market participation by farmer category. Over 80% of medium and large scale farmers sold maize in 2012, while 65.9% of small-scale farmers did. In terms of the timing of sales, the results show that farmers across the different categories sell their maize throughout the year, with about half of the sales happening between March and June. Suggesting that farmers, small, medium and large store their grain and sell at different times of the year.

Table A- 16: Percentage of farmers selling maize and percentage sold by month Small Medium Large

Grow maize (number) 593 790 217 % farmers selling maize 65.9 81.3 84.3 % farmers selling by month

January 2013 8.1 5.0 5.5 February 2013 7.1 8.7 11.5 March 2013 13.0 17.8 17.5 April 2013 13.0 12.5 10.9 May 2013 12.7 10.9 12.6 June 2013 10.2 12.3 9.3 July 2013 7.6 5.6 3.8 August 2012 5.3 6.1 7.1 September 2012 4.8 3.9 3.3 October 2012 3.6 3.4 4.9 November 2012 6.4 5.5 4.9 December 2012 6.6 7.3 8.2

Did not sell/not sold yet 1.5 .9 .5


Table A-17 shows that amongst farmers selling maize small private traders are the dominant private buyer of maize among the small and medium-scale farmers, whilst for large-scale farmers the highest quantity of their maize is sold to large private traders. However, combined, the proportion of maize sold to both small and large traders’ amounts to over 50%. Another important buyer of maize for the farmers are the marketer/maize retailers, with approximately 22-36% of the maize sold through this channel. NAFCO played a very minor role across all farmer categories. These results show the importance private buyers especially of small and large assembly traders in providing a market outlet for rural farmers.

17

Table A- 17—Maize sales by buyer type

-------Farmer category---------

Small Medium Large % of total sales by buyer type

Small-scale trader 39.2 52.9 15.1 Large-scale trader/wholesaler 16.5 19.3 35.4 Retailer/marketer 36.4 22.3 26.9 Other households for consumption .2 .1 0.0 Direct sale to NAFCO 1.7 .2 .5 Sale to NAFCO through an agent .3 .2 4.9 Out grower 4.7 3.1 11.3 Processors .1 .1 4.8 All other buyers 1.0 1.9 1.0


Table A-18 presents the distance to the maize market for the farmers at various percentiles of the distribution. Rows A to C show that the mean distance travelled from the farm to the nearest maize assembly buyer was 7.8km for small-scale farmers, 13.4km for medium-scale farmers and 13.1km for large-scale farmers. However these mean distances may be misleading because they suggest that on average all farmers are travelling more than 7 km to sell their maize. Rows E through I report the percentile distances traveled from the farm to the point of maize sale to a private trader for farmers selling maize. The results show that at least half of the farmers selling maize to a private trader reported that they traveled only 1km to sell their maize, with half of the small-scale farmers travelling 2.4 km, whilst the medium and large-scale farmers sold their maize on the farm/homestead. At the 75th percentile of the distribution, maize selling farmers were transporting their maize about 10km to the point of maize sale. For about 10 percent of the farmers – those at the 90th percentile of the distribution and higher – the distances traveled to a private buyer was much greater, 24km for all farmers with the exception of large-scale farmers who travelled 32km. These results generally show that there are market access variation but about half of the farmers either sell their maize directly on their farms or travel very short distances to sell their maize to private buyers.

Table A- 18—Distance to maize market

Small Medium Large Average distance of sales by buyer type (km) Small-scale trader (A) 7.86 8.50 22.11 Large-scale trader/wholesaler (B) 5.81 20.47 10.08 Retailer/marketer (C) 8.24 13.33 8.45 Distribution of distance to assembly buyers Mean (D) 7.8 13.4 13.1 Percentile 10 (E) 0.0 0.0 0.0 Percentile 25 (F) 0.0 0.0 0.0 Percentile 50 (G) 2.4 .3 0.0 Percentile 75 (H) 9.6 10.0 9.3 Percentile 90 (I) 24.0 24.0 32.0 Distribution of distance to maize market (general) Mean 7.5 14.8 13.4 Percentile 10 (J) 0.0 0.0 0.0 Percentile 25 (K) 0.0 0.0 0.0 Percentile 50 (L) 1.6 .3 0.0 Percentile 75 (M) 9.6 10.0 9.6 Percentile 90 (N) 20.8 25.6 32.0


Table A-19 shows rice market participation by farmer category. Almost all the large-scale farmers were selling their rice (98%). Market participation is also relatively high even for small-scale farmers (71%) and for medium-scale farmers (88%). Similar to maize sales, the results show that farmers across the different categories sell their rice throughout the year, with about half of the sales happening between March and June. Suggesting that farmers, small, medium and large store their rice to sell at different times of the year.

18

Table A- 19—Percentage of farmers selling rice by farmer category

Small Medium Large Grow maize (number) 187 285 134 % farmers selling rice 71 88 98 % farmers selling by month

January 2013 6.8 6.0 5.3 February 2013 6.1 9.2 8.4 March 2013 16.7 17.6 19.1 April 2013 12.1 16.8 18.3 May 2013 12.9 14.8 12.2 June 2013 12.1 12.0 8.4 July 2013 9.8 5.2 6.1 August 2012 3.8 2.0 5.3 September 2012 3.8 1.6 0.0 October 2012 .8 2.4 .8 November 2012 3.8 3.2 4.6 December 2012 11.4 9.2 11.5

Did not sell/not sold yet 0.0 0.0 0.0


Amongst farmers selling rice small private traders are the dominant private buyer of rice among the small and medium-scale farmers, whilst for large-scale farmers the highest quantity of their rice is sold to large private traders (Table A-20). However, combined, the proportion of rice sold to both small and large traders’ amounts to over 50%. Marketer/rice retailers are the next important buyer of rice from the farmers, with at least 30% of the maize sold through this channel. Similar to maize, NAFCO played a very minor role across all farmer categories.

Table A- 20—Quantity of rice sold and percent by buyer type Small Medium Large

% of total sales by buyer type Small-scale trader 33.9 35.9 19.2 Large-scale trader/wholesaler 6.4 35.6 41.5 Retailer/marketer 44.9 22.7 31.7 Other households for consumption 0.5 0 1.4 Direct sale to NAFCO 1.1 0 0 Sale to NAFCO through an agent 0 0.1 0 Out grower 4.1 0 0 Processors 9.2 4.4 5.6 Other (specify) 0 1.3 0.5


Table A-21 presents the distance to the rice market for the farmers at various percentiles of the distribution. Row D, shows that the mean distance travelled from the farm to the nearest rice private buyers was 6.8km for small-scale farmers and 8.5km and 8.6km for medium and large-scale farmers respectively. However, since the mean distances tend to ignore the distribution of the distance travelled by the farmers we also report the 10th, 25th 50th, 75th and 90th percentiles to show this distribution. Rows E through I report the percentile distances traveled from the farm to the point of maize sale to a private trader for farmers selling maize. The results show that at least half of the farmers selling rice to a private trader irrespective of farm size reported selling their rice on the farm/homestead. At the 75th percentile of the distribution, rice selling farmers were transporting their rice about 8km to the point of rice sale. For about 10 percent of the farmers – those at the 90th percentile of the distribution and higher – the distances traveled to a private buyer was on average 25.6km or greater. These results are very consistent to those for maize farmers where about half of the farmers either sell their grain directly on their farms or travel very short distances to private buyers.

19

Table A- 21—Distance to rice market

-----Farmer category---- Small Medium Large

Average distance of sales by buyer type (km) Small-scale trader (A) 6.75 8.65 7.77 Large-scale trader/wholesaler (B) 3.1 4.16 4.63 Retailer/marketer (C) 7.41 11.87 11.85 Distribution of distance to assembly buyers Mean (D) 6.8 8.5 8.6 Percentile 10 (E) 0 0 0 Percentile 25 (F) 0 0 0 Percentile 50 (G) 4 0 0 Percentile 75 (H) 8 11 6.4 Percentile 90 (I) 17.6 28 32 Distribution of distance to rice market (general) Mean 6.5 8.5 9.1 Percentile 10 (J) 0 0 0 Percentile 25 (K) 0 0 0 Percentile 50 (L) 3.5 0.2 0 Percentile 75 (M) 8 11 6.4 Percentile 90 (N) 14.4 28.4 32


Maize and rice labor use and production costs

In this section, we discuss labor use and production costs for the largest maize and rice plot. Due to the amount of time required to complete the labor use and cost modules, only detailed labor information and costs were collected for the largest maize and rice fields. Production costs associated with this plot collected in various sections of the questionnaire were combined to come up with the average production costs for both maize and rice crops.

In terms of family labor valuation, we followed a similar approach used by Norman, 1973, where female and children labor is converted into man equivalents by treating 1 men equal to 0.75 women and 0.5 children. Then we imputed family labor cost by multiplying the total family labors per activity by the median community farm wage rate derived from wages paid by farms that hired labor by farm operation.

Hiring labor for maize: Tables A-22 presents the number and percent of farmers hiring labor by activity for the largest maize plot. In general, the results show that the most common activities with a higher percentage of farmers hiring labor include manual hand hoeing, planting manual and chemical weeding and harvesting. Manual weeding tops the list followed by planting and hand hoeing. Another noteworthy point is that as expected the percent of farmers hiring labor across the different activities is highest for large-scale farmers compared to smaller the farm (small-scale and medium-scale farmers). This means smaller farmers are more likely to use family labor.

20

Table A- 22—Largest field: Hiring labor by activity (count and percent)

Count of farmers by activity Percent hiring labor by activity Small Medium Large Small Medium Large First plowing - own animals 39 34 4 10.3 2.9 50.0 Second plowing - own animals 15 12 4 33.3 8.3 0.0 First plowing - own-tractor 22 156 136 0.0 3.2 3.7 Hand hoeing 87 73 5 49.4 63.0 100.0 Harrowing 6 17 11 33.3 5.9 0.0 Planting 426 627 189 42.0 62.8 72.0 Fertilizer application 1 367 557 175 24.8 47.9 58.9 Fertilizer application 2 232 405 119 22.8 44.4 57.1 Fertilizer application 3 38 47 21 5.3 17.0 33.3 Chemical weeding 273 468 155 26.4 49.4 60.0 Manual weeding 1 338 448 128 51.8 64.3 68.0 Manual weeding 2 118 163 36 36.4 47.9 44.4 Pesticide application 47 59 20 17.0 28.8 20.0 Harvesting 420 629 187 32.9 56.8 63.6 Transport from plot to homestead 381 595 182 16.0 17.3 13.2 Manual shelling and packing 230 273 48 23.0 33.3 37.5 Mechanical shelling -own tractor 17 76 57 5.9 10.5 7.0


Family labor hours for maize: Table A-23 shows the mean and median family labor hours by activity and farmer category for the farmer largest maize plot. The results show that the activities requiring the most labor on average are manual land preparation, first and second weeding, harvesting and shelling. In all cases, the 50th percentile (median) is much lower than the group mean, indicating that for each production activity more than 50% of all the farmers in a particular group use less than the average amount of family labor hours. The differences in the mean and median figures suggest that the distribution of family labor input varies substantially with a small proportion of farmers in the distribution spending a lot of family labor hours hence pushing the mean higher. If the distribution was not highly skewed, the mean and the median would be almost equal.

Also, the results consistently show as expected that the larger the farm, the lower the amount of family labor hours used per activity. Thus, with increasing farm size, hired labor is required to complete the tasks. However, farmers seem to be comingling both family and hired labor. These results are consistent with what the team observed during the exploratory survey phase where large-scale farmers reported that they directly supervised or had their family members supervise the laborers for those farming activities considered to be critical for achieving high production levels, especially planting, fertilizer application and harvesting.

21

Table A- 23—Maize mono cropped largest plot: Family labor hours by activity and farm category

Small Medium Large

Mean Median Mean Median Mean Median 1st plowing-own-animals 144.4 73.5 44.7 31.6 - -

2nd plowing-own animals 124.0 83.3 20.4 16.0 - -

First plowing-own tractor 21.2 6.8 6.2 3.2 4.4 2.1

second plowing-own tractor - - - - 2.5 2.5

Hand hoeing 133.1 56.2 119.5 63.9 - -

Harrowing 76.9 76.9 45.3 45.3 - -

Planting 76.6 49.4 55.2 34.1 32.2 23.5 Fertilizer application 1 45.3 29.6 30.8 21.3 17.9 12.4 Fertilizer application 2 35.6 27.2 26.3 19.8 19.2 13.6

Fertilizer application 3 44.0 29.6 49.9 34.9 44.2 44.2

Chemical weeding 22.8 11.1 12.8 8.3 7.1 5.2

Manual weeding 1 115.9 76.7 92.3 56.6 45.3 34.0

Manual weeding 2 108.4 75.9 97.1 59.3 65.8 54.3

Pesticide application 56.7 12.0 144.4 10.3 6.9 6.9

Harvesting 152.0 108.3 101.8 69.3 76.4 47.6

Transport from plot to homestead 93.7 47.4 77.4 27.8 17.7 7.9

Shelling and packing 170.4 101.9 115.8 64.2 64.7 39.6

Mechanical shelling -own tractor 33.8 26.4 24.6 11.0 25.8 9.6


Maize production costs: Table A-24, presents the mean cost-buildup of the sample farmers by farm size and tractor ownership for their largest maize plot. It is important to note that mean production costs do not show that different farmers produce different amounts of maize. So the results presented in the Tables A-24 are for an average maize farmer. More detailed analysis will be required to examine the farmer production costs for different scenarios. The results in table A-24, show that on average the mean total costs of maize production excluding family labor, own animal and tractor use ranged from Ghc421.50 per ha for large-scale farmers to GHc525.30 per ha for the small-scale farmers. In general, the total cash expenditures decline with farm size. Hired labor and fertilizer costs were the two highest costs under the cash expenditures, averaging 31-33% for hired labor and 38-48% for fertilizer costs.

Table A- 24—Largest mono cropped maize plot: Cost-build up for maize production

Farmer category Own Tractor

Small Medium Large Yes No

Yield (kg/ha planted) 1317 1368 1344 1458 1309 Price (GHc/kg) 0.52 0.52 0.52 0.52 0.52 Gross revenue 686.5 713.1 700.6 760.0 682.3 Costs of production (GHc/ha)

Hired animal use 9.4 1.5 0.1 0.0 5.6 Hired tractor/machine use 84.7 76.5 45.2 32.7 90.9 Hired labor 167.0 171.3 136.2 146.4 171.6 NPK fertilizer 123.6 128.0 119.6 128.7 123.8 Urea 3.1 5.0 3.6 3.3 4.5 SOA 72.5 72.1 67.7 74.1 70.6 All other fertilizers 2.1 8.4 4.0 10.7 3.5 Seeds 2.5 5.2 1.3 1.8 4.5 Herbicides 59.7 51.4 43.4 47.0 55.3 Pesticides 0.7 0.5 0.2 0.2 0.6

22

Table A- 25—Continued.

Farmer category Own Tractor

Small Medium Large Yes No Total Cash expenditures 525.3 519.9 421.5 445.0 530.9

Family labor 436.5 252.2 93.5 98.4 366.5 Own animals use 9.4 6.3 2.8 1.0 9.1 Own machine 8.0 37.2 100.1 119.9 4.2

Family labor and use of assets owned by farmer 453.8 295.7 196.3 219.3 379.8 Total costs (incl. family labor and assets owned by farmer 979.1 815.6 617.8 664.2 910.7 Gross Margins (GHc/ha)

Without family labour 143.9 149.7 176.2 194.1 138.1 With family labor -292.6 -102.5 82.8 95.7 -228.4

Ratio of hired labor costs to total labor costs 27.7 40.4 59.3 59.8 31.9


Family labor costs are approximately 5 times higher for small-scale farmers compared to large-scale farmers and 2 times higher than medium-scale farmers. Smaller farmers tend to use more family labor than larger farmers. Also, Table A-24, show that at the current observed yields and excluding family labor costs, the gross margin per hectare for all the farmer categories is small but positive. However, the story changes when family labor costs are subtracted from the gross revenue, the gross margin for small and medium-scale farmers becomes negative. All factors constant, raising yields by 200 kilograms per hectare for these farmers would make their gross margin positive.

Hiring labor for rice: Tables A-25 presents the number and percent of farmers hiring labor by activity for the largest rice plot. Similar to the largest maize plot, the results show that the most common activities with a higher percentage of farmers hiring labor include manual hand hoeing, planting manual and chemical weeding and harvesting. However, manual harvesting tops the list followed by hand hoeing and then the first manual weeding. As expected the percent of farmers hiring labor across the different activities increases with farm size.

Table A- 26—Largest rice field: Hiring labor by activity, count and percent

Count of farmers by activity Percent hiring labor by activity Small Medium Large Small Medium Large

First plowing-own-animals 17 11 3 5.9 9.1 33.3 Second plowing-own animals 4 4 2 25.0 25.0 0.0 First plowing-own-tractor 2 66 91 0.0 0.0 2.2 Hand hoeing 49 34 6 51.0 52.9 83.3 Planting 175 267 130 49.1 39.7 42.3 Fertilizer application 1 139 187 79 21.6 29.9 32.9 Fertilizer application 2 90 108 41 25.6 36.1 36.6 Fertilizer application 3 15 19 10 6.7 5.3 30.0 Chemical weeding 106 185 112 28.3 37.8 47.3 Manual weeding 1 111 135 43 39.6 49.6 76.7 Manual weeding 2 46 45 14 26.1 48.9 50.0 Bird Scaring 56 75 28 28.6 29.3 28.6 Pesticide application 37 46 18 27.0 41.3 44.4 Manual harvesting 170 255 124 39.4 57.3 80.6 Mechanical harvesting 7 11 8 28.6 9.1 0.0 Transport from plot to homestead 167 249 127 18.6 13.7 7.9 Manual threshing and packing 144 163 47 27.8 38.7 48.9 Mechanical shelling -own tractor 17 79 70 17.6 7.6 12.9


23

Family labor hours for maize: Table A-26 shows the mean and median family labor hours by activity and farmer

category for the farmer largest rice plot. The results show that the activities requiring the most family labor on average are bird scaring, plowing with own animals, first and second weeding and harvesting. Similar to the largest maize plot, in most cases, the 50th percentile (median) is much lower than the group mean, indicating that for each production activity more than 50% of all the farmers in a particular group use less than the average amount of family labor hours. The differences in the mean and median figures suggest that the distribution of family labor input varies substantially and highly skewed. Therefore in order to gain a deeper understanding of the variations in family labor use by activity, there is need to look at the distribution of hours spent by the household on each activity rather than just looking at the mean. Also, the results consistently show as expected that the larger rice farmers have lower total family labor hours used per activity.

Table A- 27—Rice largest plot: Family labour hours by activity and farm category

Small Medium Large Mean Median Mean Median Mean Median

First plowing-own-animals 342.5 55.6 284.3 45.4 - - second plowing-own animals 31.6 26.3 796.3 796.3 - - First plowing-own-tractor 59.3 59.3 5.3 4.0 6.6 4.5 second plowing-own tractor - - 28.4 28.4 - - Hand hoeing 105.8 51.9 214.3 124.3 1.6 1.6 Harrowing - - 68.8 40.7 4.1 3.7 Planting 106.5 55.6 76.4 31.8 19.3 8.6 Fertilizer application 1 49.0 22.2 36.6 14.8 13.1 7.7 Fertilizer application 2 86.7 12.3 42.0 10.8 7.3 7.7 Fertilizer application 3 60.5 60.5 190.8 34.6 28.8 28.8 Chemical weeding 22.0 13.0 16.3 9.6 7.7 5.5 Manual weeding 1 202.6 148.1 175.3 86.4 27.0 21.7 Manual weeding 2 214.4 153.7 272.3 105.3 8.6 8.6 Bird Scaring 716.8 276.5 417.5 93.3 108.9 74.1 Pesticide application 31.1 9.1 120.1 6.3 36.7 4.2 Manual harvesting 241.9 154.7 188.2 108.3 104.4 48.3 Transport from plot to homestead 71.8 40.1 93.8 37.0 13.1 5.6 Threshing and packing 207.1 92.6 133.9 95.1 55.4 35.6 Mechanical Threshing-own tractor 17.9 17.9 124.4 16.5 30.4 13.3


Rice production costs: Table A-27, presents the mean cost-buildup of the sample farmers by farm size and tractor ownership for their largest rice plot. As for maize, it is important to note that the mean production costs does not capture the fact that farmers produce different amounts of rice instead the results are for an average rice farmer. Hence, more in depth rice costs of production analysis would reveal more details about the variations in the costs. It is beyond the scope of the survey report to go into details about production costs.

Table A- 28—Largest maize plot: Cost-build up for maize production

Farmer category Own Tractor Small Medium Large yes no

Yield (kg/ha planted) 1596 1490 1188 1375 1490 Price (GHc/kg) 0.73 0.73 0.73 0.73 0.73 Gross revenue 1159.2 1082.2 862.8 998.6 1082.2 Costs of production (GHc/ha)

Hired animal use 30.3 2.1 0.0 0.0 14.8 Hired tractor/machine use 94.2 79.0 34.2 23.8 95.4 Hired labor 232.5 176.0 102.1 126.4 198.5 NPK fertilizer 115.1 75.2 43.2 51.8 92.6 Urea 11.3 6.2 8.1 8.1 8.3 SOA 52.6 40.8 31.5 33.5 46.1 All other fertilizers 4.2 3.3 0.0 0.0 4.0 Seeds 9.2 40.4 78.5 47.6 36.0 Herbicides 32.4 26.3 23.5 22.2 29.8 Pesticides 28.8 15.6 11.0 12.4 20.8

24


Farmer category Own Tractor Small Medium Large yes no

Total Cash expenditures 610.5 464.7 332.1 325.8 546.5 Family labor 607.3 402.7 86.6 106.4 520.2 Own animals use 12.7 5.9 3.8 1.9 10.0 Own machine 2.6 43.4 126.9 152.6 4.5

Family labor and use of assets owned by farmer 622.6 452.0 217.4 260.9 534.7 Total costs (incl. family labor and assets owned by farmer 1233.1 916.7 549.5 586.7 1081.2 Gross Margins (GHc/ha)

Without family labour 533.4 568.1 400.0 518.3 521.2 With family labor -74.0 165.5 313.4 411.9 1.0

Ratio of hired labor costs to total labor costs 27.7 30.4 54.1 54.3 27.6

Ratio of hired labor to total cash expenditures 38.1 37.9 30.7 38.8 36.3

Ratio of fertilizer costs to total expenditures 30.0 27.0 24.9 28.7 27.6


The results in table A-27, show that on average the mean total costs of rice production excluding family labor, own animal and tractor use ranged from Ghc332.10 per ha for the large-scale farmers, to GHc610.50 per ha for the small-scale farmers. Hired labor and fertilizer costs were the two highest costs under the cash expenditures, average of 30-39% for hired labor and 24-30% for fertilizer costs. The ratio of total fertilizer costs to total cash expenditures is higher for maize than the ratio of hired labor costs to total cash expenditure, whilst the reverse is true for rice production.

Family labor costs are approximately 6 times higher for small-scale farmers compared to large-scale farmers and 1.5 times higher than medium-scale farmers. Thus, by a wide margin, smaller farmers use much more family labor per hectare than larger farmers who hire-in labor. The gross margin without family labor is positive for all times of farmers but becomes negative for the small-scale farmers when family labor is taken into account.

Maize purchases from other farmers

Results in Table A-28 show that only 110 farmers reported purchasing maize from other farmers, 35 small-scale, 51 medium scale and 24 large scale farmers. This is a very small proportion of the farmers interviewed, 4.6% of the small-scale farmers, 6.0% of the medium scale farmers and 10.7% of the large-scale farmers in the sample. Most of the purchases occurred between the month of March and June, 56-76%.

Table A- 30—Farmers purchasing maize from other farmers Small Medium Large Number of farmers purchasing maize from other farmers 35 51 24 Total quantity of maize purchased (Metric tons) 66 951 525 % Purchased by Month

January 5.9 4.0 4.3 February 11.8 4.0 8.7 March 26.5 22.0 30.4 April 11.8 8.0 13.0 May 20.6 16.0 .0 June 17.6 24.0 13.0 July .0 14.0 17.4 August 2.9 .0 4.3 September 2.9 4.0 .0 October .0 .0 4.3 November .0 2.0 4.3 December .0 2.0 .0


25

Livestock and poultry

Table A-29 presents the distribution of the livestock production and marketing by farm size categories. The average livestock holdings increased considerably with farm size. Large-scale farmers had 34.63 TLUs on average compared to 4.09 and 8.91 for small-scale and medium scale farmers. The most common livestock across the farmer categories were cattle, goats, sheep, village chicken and guinea fowls. However, as expected, a greater proportion of medium and large-scale farmers owned livestock compared to the small-scale farmers.

Table A- 31—Livestock production and marketing by farmer category Small Medium Large

Number of farmers Total livestock units (TLUs) (A) 4.09 8.91 34.63 Value of livestock owned (B) 4008 8976 33443 % owning Cattle (C) 26.2 42.0 66.7 Bullocks/oxen (D) 10.9 14.2 11.6 Donkeys (E) 9.2 9.6 9.8 Horses (F) .5 1.1 3.1 Goats (G) 46.9 63.1 62.2 Pigs (H) 6.8 9.9 10.7 Sheep (I) 32.3 52.9 65.3 Village Chickens (J) 54.3 62.7 62.2 Guinea Fowls (K) 22.7 29.4 38.7 Ducks & Geese (L) 1.3 2.1 6.2 Rabbits (M) .9 .5 .9 Livestock sales (GHc) (N) 469 2509 2167

Farmers selling (%) - among owners Cattle (O) 31.3 40.1 42.7 Goats (P) 38.1 34.0 37.1 Pigs (Q) 30.8 34.5 37.5 Sheep (R) 31.5 30.0 29.3

Percent raising broilers as a business (S) .3 .4 .9 Percent raising layers as a business (T) .4 .4 0.0

Source: IFPRI/SARI Survey on medium and large-scale famers and mechanization, 2013 The TLU conversion factors used are as follows: cattle = 0.70, sheep and goats = 0.10, pigs = 0.20 and chicken = 0.01 (horses 0.65 asses 0.5 others 0.05) see Jahnke et al., 1988

For cattle, goats, pigs, and sheep, only around one-third of farmers reported selling animals. In terms of livestock sales, the proportion of farmers participating in cattle and pigs market is higher for large-scale farmers compared medium-scale and small-scale farmers. Also, only a few farmers raised layer (0.3%) or broiler (0.4%) chickens as a business.

Household and farm assets

Table A-30 demonstrates that asset ownership is strongly related to farm size. On average, the value of assets for large-scale farmers is about 6 and 15 times more than that of medium and small-scale farmers respectively. This difference is explained by the variation in asset composition. Large farmers have a higher percentage of large farm assets 51.9% compared to 5.8% for small-scale farmers or 22.0% for medium-scale farmers. The same relationship is seen between farm size and value of the homestead.

In terms of the types of assets owned, Table A-30 shows that in general, a greater proportion of farmers owned cellphones, bicycles, and radios. However, a higher percentage of large-scale farmers owned farm machinery such as shellers and machine attachment such as harrows, plough and cart/trailer.

26

Table A- 32—Asset ownership by farmer category

Small Medium Large Value of assets 1,850 5,103 30,044 Small assets (%) 72 43 14 Large non-farm assets (%) 22.3 34.5 34.0 Large farm assets (%) 5.8 22.0 51.9 Value of homestead 5,078 10,422 25,864 Number of homesteads owned .5 .8 1.1 % farmers owning Car 2.3 5.5 17.8 Lorry or other vehicles .7 .2 .4 Motorbike 26.2 57.8 82.7 Boat or canoe .7 .2 .4 Bicycle 69.1 82.6 85.3 Water tank 2.1 5.1 10.7 Borehole 2.3 1.6 4.4 Protected well 3.5 5.5 8.9 Radio 67.6 80.4 88.0 Television 29.6 40.6 58.7 Cellphone 69.9 84.7 92.4 Tractor 4.0 25.3 74.2 Power Tiller .4 1.1 1.8 Plough 6.6 26.5 67.6 Harrow .8 4.0 24.9 Planting machine .1 .0 .4 Combine Harvester .0 .1 .9 Milling machine (maize/rice) .0 1.2 1.8 Shelling machine 1.2 5.4 19.1 Boom sprayer .5 .5 3.6 Cart/trailer 3.8 16.9 50.7 Pump for irrigation 1.6 2.1 3.1 Generator 1.3 2.1 6.2


Social networks

Table A-31 shows that, on average, the three main social groups farmers belong to are FBOs, Cooperative/Association, and Church/Faith based organizations. Farmers were asked about where they get most of their farming advice. MoFA is a primary source of farming advice for most of the farmers followed by fellow farmers, especially from large-scale farmers.

Table A- 33—Social networks by farmer category and tractor ownership

Small Medium Large Cooperative / Association 18.8 23.9 35.1 FBO 21.4 33.8 33.8 Women’s group or organization 7.3 1.1 0 Youth group or organization 9.1 8.4 8.9 Church-based or faith based organization 12.9 11.3 16.9 Block farm 3.3 6.8 10.7

27


Small Medium Large Main source of farming advice

MoFA 38.2 51.6 60.4 Small scale farmers 29 16 7.6 Large-scale farmers 15 21.8 22.7 FBO/Cooperative 0.9 1.2 2.2 NGO/Projects 1.2 1.6 0.9 Other sources 7.2 3.9 4.9

% Farmers with role model 67.1 67.1 56.0 Type of role model

Smallholder 27 14.2 11.1 Large-scale farmers 73 85.8 88.9

Main things farmer wants to emulate from role model (%) Farm size 36.7 23.4 19 Yields 38.8 39.9 42.9 Equipment 14.2 25 26.2 Type of seeds used 2.9 1.6 2.4 Fertilizer use 2.3 3.9 2.4 Marketing 1.7 2.3 4


Role models: More than half of the farmers interviewed said that they had someone in the community that they considered to be a role model and wanted to emulate some things about their farming (table A-31). In general, most farmers said their role model was a large-scale farmer. When asked about the things they wanted to emulate from these role models, the most common responses were farm size, yields and equipment.

Financing and budgeting

Table A-32 summarizes results about farmer’s commercialization plan, budgeting and record keeping by farmer category. Farmers were asked whether they had a plan to commercialize their farm and slightly more than a third of farmers in each category had a plan to commercialize their farm, with the highest percentage among the large-scale farmers (54.2%). For those who answered yes, that they had a plan, a follow up question on what this plan entailed was asked. Table A-32, rows B to F show that, among the small, medium and large-scale farmers, the most common plan entailed managing area cropped followed by savings and then farm expenditure (for small and medium-scale farmers) or investment (for large-scale farmers). This cuts across all farmer categories with large scale farmers being the majority.

Table A- 35—Budgeting and planning by farmer category and tractor ownership

Small Medium Large

Number of farmers 768 850 225 % farmers with plan to commercialize farm (A) 32.3 36.4 54.2 Plan involves (%):

Farm expenditure (B) 23.4 26.9 42.2 Savings (B) 24.6 27.9 46.7 Investment (C) 20.8 25.4 45.3 Labor needs (D) 17.1 22.1 38.7 Area cropped (E) 27 29.5 47.1 Type of crops to grow (F) 24 25.6 40.0

28


Small Medium Large Set budget targets for savings and investment

Never (%) (G) 19.9 11.2 4.9 Always (%) (H) 70.7 75.1 65.8 Sometimes (%) (I) 9.4 13.8 29.3 Stick to budget targets (J) Never (%) (K) 20.8 12.8 8.9 Always (%) (L) 73 78.8 76.9 Sometimes (%) (M) 6.1 8.4 14.2

Keep track of actual spending Never (%) (N) 24.3 16.4 10.2 Always (%) (O) 66.1 72.7 69.3 Sometimes (%) (P) 9.5 10.9 20.4

Keep farm records Never (%) (Q) 53.9 48.5 40.9 Always (%) (R) 38.8 43.1 44.0 Sometimes (%) (S) 7.3 8.5 15.1


When asked whether they set budget targets for savings and investment, the majority answered that they always did, with a lower percentage among large-scale farmers compared to medium and small-scale farmers. When asked whether they keep track of their actual spending, more than 65% of the farmers irrespective of farm category or tractor ownership status said they did track their expenditure always. Similar to setting budget targets the results show a higher percentage of small-scale farmers who do not track their expenditure when compared with the medium and large-scale farmers. Whilst, a higher percentage of medium-and large-scale farmers reported that they sometimes track their spending compared to small-scale farmers who said they do not track their spending. More than half (53.9%) of the small-scale farmers said they never keep farm records with 48.5% and 40.9% among the medium and larger-scale farmers respectively. A smaller percentage of the farmers sometimes keep farm records.

Table A-33 presents the distribution of farmers that access loan and credit sources across the farm categories. 4.2% of small-scale farmers applied for a loan in the last five years, while 8.4% and 8% of medium and large scale farmers applied for loans. Large-scale farmers were more successful in receiving loans than farmers in the other categories. The size of the loan increased by farmer size. For example, the average largest loan received by large-scale farmers was 141% large than medium-scale farmers and 342% than small scale farmers.

With the exception of large-scale farmers, more than 60% of the farmers reported than no collateral was required for the loans they received, whilst about 45% of the large-scale farmers said collateral was not required for the loans they received. One would conclude from these results that most loans received by farmers should be coming from non-commercial sources but the results show otherwise. The majority of farmers who reported getting loans obtained them from commercial sources, with about 22 to 35% getting their loans from non-commercial sources. More than 75% of the large-scale farmers reported getting their loans from commercial sources.

By farmer category, the results show that most of the small-scale farmers obtained a loan to buy fertilizer and pay for tractor service and or labor, 34.4% and 18.8% respectively. Whilst, a greater proportion of the large-scale farmers obtained loans to purchase a tractor (33.3%), followed by purchasing fertilizer (22.2%) and an equal percentage of farmers wanting to use the loan for purchasing other farm inputs (16.7%) and purchasing of other farm equipment (16.7%). Interestingly, a very small percentage of large scale farmers borrowed to pay for tractor service and/or labor.

29

Table A- 37—Loans and credit by farmer category and tractor ownership

Small Medium Large Number of farmers 768 850 225 Applied for a loan in past 5 years (%) 4.2 8.4 8.0 Farmers receiving loans (%) 3.4 6.9 7.6 Total value of loans (GHc) 2028 3860 12779 Largest loan GHc) 2488 4541 10988 Collateral required for largest loan (%) 34.4 31.0 55.6 Source of loan

Commercial sources 62.5 63.4 77.8 Other sources 37.5 36.6 22.2

Reason for loan (%) Purchase fertilizer 34.4 26.8 22.2 Purchase other farm inputs 3.1 19.7 16.7 Purchase tractor 3.1 8.5 33.3 Pay for tractor services and/or labor 18.8 14.1 5.6 Purchase of other farm equipment 6.3 5.6 16.7


Table A-24 presents the distribution of farmers’ savings across farm categories. A larger proportion of large-scale farmers reported to have savings (74.7%) compared to small-scale farmers (54.7%). Farmers have different types of savings with some farmers having more than one. However, the most common type of saving reported is in a bank with 69.9% reporting to have this type of saving, followed by savings at home (48.2%) and then savings through purchasing of livestock (17.2%). The results are consistent by farm size category, although a greater percentage of medium-and large-scale farmers reported saving money in a bank than at home. Other forms of savings are not very popular as with less than 15% reporting saving their money either with saving society, Susu collectors or another financial institutions.

When the farmers where asked why they were saving, the most important there reasons cited were for daily household expenses (over 40%) and for children’s education (16-27%). By farmer category, we see the same trend for small and medium-scale farmers but a different order for large-scale farmers. For large-scale farmers, the number main reason for saving is to meet daily household expenses, followed by conducting business and the third reason, to purchase agricultural equipment. Saving for children’s education for these farmers is ranked fourth.

Table A- 38—Savings by farmer category and tractor ownership

-----Farmer Category------- Small Medium Large

(2) (3) (4) Number of farmers 768 850 225 Farmers with savings Number (A) 420 582 168 Percent (B) 54.7 68.5 74.7

Value of savings (Ghc) Mean (C) 1659 4663 14125 Median (D) 500 1300 5000

Type of saving Deposit money into a bank (E) 58.1 75.7 79.3 Save with a savings society or group in the community (F) 4.4 3.5 1.8 Save with another financial institution (G) 3.7 3.9 3 Save with Susu collectors (H) 7.3 3 2.4 Purchase small animals (I) 11 9.3 16 Purchase large animals (J) 4 6.1 13 Keep money in the household (K) 54.3 44.9 44.4

30


-----Farmer Category-------

Small Medium Large (2) (3) (4)

Reasons for saving (%) Day-to-day household expenses (L) 54.1 45.6 44.4 Conducting business (M) 9.4 16.6 24.9 Purchasing, constructing or improving housing (N) 7 5.6 4.1 Education (O) 25.3 26.7 16 Health or medical expenses (P) 5.4 3.9 2.4 Purchasing livestock (Q) 4 2.9 3 Purchasing or upgrading farm land (R) 4.4 3.2 4.7 Purchasing agricultural equipment (S) 2.6 9.6 20.1 Purchasing agricultural inputs (T) 7 9.8 12.4 Ceremonies (e.g. naming ceremonies, weddings or f l )

(U) 2.83 0.2 1.2 Purchasing vehicles (V) 0.5 0.8 1.2 Other consumer goods (W) 4.7 5.2 3 For dealing with emergencies (X) 8.4 10.6 12.4


Modules on Agricultural Mechanization The questions regarding mechanization were organized into three modules. The first was administered to all farmers and focuses on constraints and perceptions regarding agricultural machinery use. Module 2 was administered to those farmers who owned any type of agricultural machinery in 2012. Module 3 was administered to farmers who hired-in agricultural machinery services in 2012. We present some highlights from each of these modules in this section. The data will be presented by farm size category and by tractor ownership.

Module 1 - Mechanization demand among surveyed farmers

Table B-1 presents farmers’ willingness to mechanize various farming operations. When asked if there were operations which farmers would like to mechanize but have not, over 70% of the respondents responded ‘yes’. Expectedly, more non-tractor owners (81%) were willing to mechanize their farming operations than the tractor-owners (72%), reflecting the fact that tractor owners already have some access to mechanized operations, but the current level of mechanization on their farms is incomplete. The first operation that farmers would like to mechanize is plowing/tilling/rotovating with 60% of non-tractor owners and 65% of small-scale farmers expressing their will to mechanize that operation. Planting, harvesting, shelling, and carting are the next operations which farmers would like to mechanize. The percentages of farmers willing to mechanize these operations are generally higher among medium and large-scale farmers and tractor owners compared with small-scale farmers and non-tractor owners. These results may indicate differences in demand for mechanization across farming households, with most small-scale farmers and non-tractor owners willing to mechanize plowing and most medium and large-scale farmers willing to mechanize control intensive operations.

31

Table B- 1—Willingness to mechanize various farming operations

Farmer category Tractor ownership Small Medium Large Owner Non-owner Number of main landholders 768 850 225 402 1441 Percent of farmers willing to mechanize 79.3 77.5 81.8 71.9 80.7 Percent of farmers willing to mechanize by operation

Land clearing/De-stumping 22.0 19.3 17.9 14.9 21.6 Plowing/Tilling /Rotovating 64.9 46.3 29.9 19.7 60.0 Harrowing 11.8 14.9 18.5 17.3 13.2 Planting 28.9 34.9 41.8 48.4 29.5 Irrigation 11.7 10.9 8.2 5.9 12.1 Weeding 15.3 17.1 18.5 19.7 15.7 Fertilizer application 4.9 9.9 15.8 16.6 6.5 Harvesting 28.7 35.1 51.6 48.8 31.0 Shelling 33.8 36.6 31.5 38.1 34.0 Threshing 6.2 6.4 7.6 8.3 6.0 Carting farm products and inputs 19.2 20.9 13.6 8.3 22.0

Source: IFPRI/SARI Survey on medium and large-scale famers and mechanization, 2013 Notes: *Farm-size category is derived from the amount of land cultivated by the main landholder in the household - small-scale, 5ha or less;

Medium-scale, more than 5ha to 20ha; and large-scale farmers – greater than 20ha.

Table B-2 presents the prices or service charges that farmers are willing to pay for the four major operations that they are willing to mechanize. In general, these prices are higher among medium and large-scale farmers and tractor owners compared with small-scale farmers and non-tractor owners with exceptions. These results may indicate that the need to mechanize varies depending on farm size and tractor ownership. Interestingly, the prices that farmers are willing to pay differ only slighlty compared with actual service fees charged by tractor owners for plowing and shelling, but they are higher than hired labor costs with respect to planting and harvesting operations which are currently not mechanized.

Table B- 2—Price farmers are willing to pay for mechanizing various operations

Farmer category Tractor ownership Actual service price/hired labor

cost per acre Small Medium Large Owner Non-owner Plowing/Tilling/

Number of farmers 395 305 55 57 698 313 Mean price 37.1 37 39 39.3 37.0 37.4

Planting Number of farmers 176 230 77 140 343 709 Mean price 26.2 30.2 29.3 29.9 28.0 20.6*

Harvesting Number of farmers 154 210 86 130 320 614 Mean price 37.8 38.1 48.1 43.8 38.3 29.9*

Shelling Number of farmers 203 240 58 110 391 61 Mean price (GHC/maxibag) 3.1 3 3.4 3.4 3.0 4.1

Source: IFPRI/SARI Survey on medium and large-scale famers and mechanization, 2013 Ghana cedis per acre, unless otherwise indicated * Indicates hired/manual labor costs incurred to perform the operation on largest maize field.

Table B-3 shows the distribution of farmland cultivated by the main landholders at the time when they were using hand hoe only for land preparation. The results show that over 65% of the respondents had at one time used only hand hoe to till their farmlands. However, more non-tractor owners (82%) had used only hand hoe for tilling compared with tractor-owners (67%). Likewise, the average farmland size cultivated when using hand hoe only to till increases with farm size and tractor ownership. It ranges from 1.1 to 2.1 ha across farm categories which indicates that both tractor owners and non-owners were all small-scale farmers (by the national standard) when they were using only hand hoe to till their farmlands.

32

Table B- 3—Farmland cultivated when using hand hoe only to till

Farmer category Tractor ownership Small Medium Large Owner Non-owner

Percent of farmers who used hand hoe only to till (in the past) 82.2 78.4 69.8 66.9 82.2 Total area cultivated when using only hand hoe (ha) 631 666 157 269 1185

Mean 1.1 1.7 2.1 1.3 1.6 Median 0.8 1.2 0.8 .81 .81 Minimum 0.1 0.2 0.1 .10 .10 Maximum 10.9 25 50.6 6.07 50.63


Table B-4 presents the types of machines and attachments which farmers would like to own. Over 60% of the farmers were willing to own a machine or an attachment, irrespective of their farm category. The majority of non-tractor owners (89%) would like to own a tractor, and the second most popular machine among this group was a combine-harvester with only 8% expressing their desire to own such a machine. Among tractor owners, 27% were willing to own a combine-harvester. With regard to attachments, the most popular were ploughs, shellers, and trailers. It is interesting to note that the willingness to own a combine-harvester or a sheller-attachment is higher among medium and large –scale farmers and tractor owners compared with small-scale farmers and non-tractor owners. Conversely, fewer farmers among the medium and large-scale farmers and tractor owners were willing to own a tractor compared with small-scale farmers and non-tractor owners. With the exception of tractor plowing operation, it is worth mentioning that many farmers are willing to mechanize various farming operations (Table B-1), but fewer farmers are willing to own a machine/attachment. Appendix B provides more details on preferences for machinery brands, prices, and horse-power.

Table B- 4—Desire to own a machine by farmer category and tractor ownership

Farmer category Tractor ownership Small Medium Large Owner Non-owner

Number of farmers 768 850 225 402 1441 Percentage willing to own a machine/attachment 62.5 76.2 87.1 89.6 66.9 Machines Tractor 86.5 84.4 75.5 70.3 88.9 Power tiller 6.5 2.9 2.6 1.4 5.2 Stand-alone planter 2.3 2.6 3.6 4.2 2.1 Generator 1.0 0.8 0.0 .3 .9 Combine-harvester 6.7 12.5 29.6 26.4 7.9 Standalone Sheller 1.9 4.8 6.1 5.8 3.2 Thresher 0.6 0.8 0.0 .0 .8 Boom sprayer 2.1 2.2 1.5 .8 2.5 Mechanical weeder 1.3 1.9 2.0 2.5 1.3 Water pump 5.8 5.2 3.1 1.4 6.5 Attachment Plough (attachment) 34.0 26.7 18.9 16.9 32.4 Harrow (attachment) 3.8 6.8 8.7 11.1 4.0 Sheller (attachment) 24.4 31.3 33.2 42.5 24.1 Planter (attachment) 4.4 6.5 12.2 13.9 3.8 Boom sprayer (attachment) 2.5 2.9 3.6 2.8 2.9 Cart/trailer (attachment) 17.1 17.3 12.8 16.1 16.7 Fertilizer dispenser 0.6 0.8 2.0 1.1 .8 Water pump (attachment) 6.3 5.2 2.6 3.1 6.0 Thresher (attachment) 2.9 2.9 3.1 3.1 2.9 Harvester (attachment) 4.6 5.4 9.7 9.2 4.5


With regard to the limitations to the use of agricultural machines (Table B-5), farmers identified four major constraints which in their view have affected their use of agricultural machinery: lack of access to credit, high cost of

33

machine/attachment, high service charges, and high input price across farm categories and tractor ownership with one exception. High cost of machine/attachment is the most pressing constraint among tractor owners. Furthermore, the proportions of farmers indicating lack of access to credit and high input price as constraints decrease with farm size and tractor ownership, whereas the proportion of farmers indicating high cost of machine/attachment as a constraint increases. Further details on the constraints are presented in appendix B.

Table B- 5—Constraints to agricultural machines use –top factor mentioned by farmers (in percent) Farmer category Tractor ownership Small Medium Large Owner Non-owner Number of farmers 768 850 225 402 1441 Constraints to agricultural machine use

None 0.3 0.5 1.3 1.0 0.3 Unavailability of complimentary inputs 6.4 6.2 5.8 8.2 5.7 High prices of inputs 11.7 12.4 16.0 15.9 11.6 Lack of access to credit 36.8 32.9 28.9 28.9 35.5 Lack of land 3.8 1.5 0.4 1.0 2.7 Limited implements 2.0 2.7 4.4 3.0 2.5 High service charge 13.8 14.1 8.0 6.7 15.1 Low access to mechanized service providers 4.3 4.2 2.2 2.5 4.4 High labor requirements 1.6 1.1 0.9 0.5 1.5 Lack of knowledge on know-how 2.7 1.3 3.1 1.7 2.2 Lack of advice/ guidance 0.9 0.9 1.3 1.0 1.0 High cost of machine / attachment 15.4 20.8 27.6 29.4 16.6 Unsuitable farmland 0.4 1.2 0 0.2 0.8 Lack of market 0 0.1 0 0 0.1


Module 2 – Machinery ownership and use among tractor owners

Table B-6 shows that on average, 22% of surveyed farmers own tractors, and less than 1% owning other types of machines. It should be noted for the following discussions that these statistics overestimate the percentage of tractor owners in the population. As mentioned earlier, the tractor owners sample includes about 60% of the tractor owners in the population, whereas the farmer sample is a sample of farmers (see full discussion in Section II). Most tractors are owned by medium and large-scale farmers. In our sample, 71% of large-scale farmers and 25% of medium-scale farmers own tractors versus 4% of small-scale farmers. The most common attachments owned include ploughs, carts/trailers, and shellers.

Table B- 6—Ownership of agricultural machines and attachments (in percent)

Total

Farmer category Farmer category

Small Medium Large Number of farmers 1,843 768 850 225 Machines

Tractor 21.8 3.8 25.1 71.1 Power tiller 0.8 0.3 0.9 2.2 Planter (stand-alone) 0.1 0 0 0.4 Combine-harvester 0.1 0 0.1 0.4 Sheller (stand-alone) 0.5 0.4 0.4 1.8 Thresher (stand-alone) 0.2 0 0.2 0.4 Boom sprayer 0.3 0 0.1 1.8 Water pump 0.9 0.3 0.6 4.0

Attachments Plough 19.3 3.0 22.0 64.4 Harrow 4.6 0.5 3.4 22.7 Sheller 4.9 0.9 5.1 18.2 Cart/trailer 12.2 1.6 13.3 44.0 Thresher 0.2 0 0.1 1.3 Miller 0.1 0 0.1 0 Water pump 0.2 0 0.2 0.4


34

Table B-7 shows that there are 402 tractor owners in the sample5, out of which the majority are medium and large-scale

farmers, 53% (213) and 40% (160), respectively. Each tractor owner owns one tractor on average, but large-scale farmers are more likely to own more than one tractor.

Furthermore, most tractors are owned by medium-scale farmers (239 tractors out of 487) and small-scale farmers own the least number of tractors (33 tractors out of 487). Out of the 487 tractors owned, 462 are in working condition, 477 were imported, and 383 are used tractors. On average, these tractors were acquired about five years ago. The results also indicate that group ownership is higher among small and medium-scale farmers compared with large-scale farmers.

Table B- 7—Characteristics of agricultural machines owned - tractor

Total Farmer category

Small Medium Large Number of owners 402 29 213 160 Percentage of owners (%) 100 7.2 53.0 39.8 Number of equipment owned per owner 1.2 1.1 1.1 1.3 Total number of equipment owned 487 33 239 215 Total of number of equipment in working condition 462 32 223 207 Total number of equipment imported 477 32 231 214 Total number of used equipment 383 28 199 156 Number of years since equipment was acquired 4.6 4.4 4.4 4.8 Group ownership Average number of farmers in a group 6.0 9.0 5.7 2.3 Total number of equipment owned through group ownership 10 4 3 3

Total number of farmers owning through group ownership 60 36 17 7

Registration and Insurance Total number of equipment registered 291 17 142 132 Total number of equipment insured 83 8 34 41


With regard to investments in agricultural machines, the results indicate that the primary motivation for acquiring a tractor among owners is to expand/scale up own farm (Table B-8). Timeliness and service provision also seem to play a role in the acquisition of tractors among owners in the sample. Across farm categories, expansion of own farms is the key motivation among medium and large-scale farmers, whereas, service provision is the main motivation among small-scale farmers.

The main source of farm power before acquiring tractor among owners is hired mechanization services (84.8%), indicating that these owners had accumulated some experiences from the rental market before investing in a tractor. About 50% of the tractor owners used fellow tractor owners as their source of information in the choice of their tractors, whereas 30% of these owners relied on tractor dealers as their source of information.

Table B- 8—Motivation - decision criteria for acquiring a tractor (percent of machines owned)


Small Medium Large Number of tractors 486 33 238 215 Motivation

To expand/scale up own farm 50.4 27.3 50.0 54.4 To farm timely/timeliness 22.4 15.2 20.6 25.6 To provide hiring services/make money 22.2 54.5 23.5 15.8 Replace older machine 3.7 0 4.6 3.3 Received as a gift 1.2 3.0 1.3 0.9

Main power source before acquiring tractor Hired mechanization service 84.8 90.9 87.8 80.5 Draft animals 6.8 3.0 5.5 8.8 Manual labor 8.4 6.1 6.7 10.7

5 Originally, the sample includes 408 tractor owners, but only 402 owners have complete information in the survey.

35

Table B- 9—Continued.

Total

Farmer category Small Medium Large

Main source of information used for tractor acquisition

Government/Ministry of Food and Agriculture 12.6 12.1 10.5 14.9 Fellow farmers/owners 49.0 30.3 55.0 45.1 Used tractor dealers 29.4 39.4 27.7 29.8 Local manufacturers 6.0 12.1 4.2 7.0 Non-Governmental Organization 0.4 0 0 0.9 Received as gift 1.4 6.1 1.3 0.9 Own knowledge 1.2 0 1.3 1.4


With regard to financing, Table B-9 shows that the majority of farmers bought their tractors with own funds without subsidy or loans. Most of the tractors (87%) were acquired with farmers’ personal savings only. Only 2% of the tractors were financed partly with some loans, whereas 4% were financed partly with some remittances. With regard to government support, about 13% of the tractors were subsidized. These patterns apply across farm size with minor exceptions. It seems fair to say that under all of sort of market failures, farmers have raised own funds to purchase tractors with no Government support.

Table B- 10—Source of tractor financing (in percent) Total Farm category Small Medium Large

Number of tractors 487 33 239 215 Subsidy involved (%) 13.4 18.2 10.5 15.8 Personal savings only (%) 87.5 87.9 84.1 91.2 Loans involved (%) 2.5 0 3.4 1.9 Remittances involved (%) 3.5 3.0 3.8 3.3

Source: IFPRI/SARI Survey on Medium and large-scale farmers and mechanization, 2013

Table B-10 shows that on average, about 90% of the tractors were purchased from the private sector (private and commercial dealers), and only 9% of those came from the Government/ Ministry of Food and Agriculture. Most of the tractors were purchased from the major cities, such as Accra, Kumasi, Tamale, or from owner’s district capitals. Further details on the sources and places of acquisition of farm machines are presented in appendix B.

Table B- 11—Sources and places of agricultural machine acquisition –Tractors (in percent)

Total

Farmer category Small Medium Large Number of tractors 487 33 237 215 Sources of tractor purchase

Government/ Ministry of Food and Agriculture 8.6 15.2 5.9 10.7 Commercial dealer 34.7 33.3 35.4 34.4 Non-Governmental Organization / Programs 1.4 0 1.7 1.4 Gifts / Prize .8 0 1.3 0.5 Private individual sale 54.4 51.5 55.7 53.0

Places of acquisition Accra 32.6 39.4 27.0 37.9 Kumasi 22.7 12.1 23.2 23.8 Tamale 10.3 9.1 10.5 10.3 Wa 1.2 6.1 0.4 1.4 Bolgatanga 0.2 0 0 0.5 Koforidua 0.4 0 0.4 0.5 Sunyani 3.3 0 3.4 3.7 Ho 0.2 0 0.4 0 Sekondi 0.6 0 1.3 0 Abroad 1.9 0 3.0 0.9 District capital 26.4 33.3 30.4 21.0


36

Farmers used their tractors for various farming activities. First plowing is the most common operation for which tractors

are used with 90% of owners using their machines for this purpose (Table B-11) Tractors are also commonly used for carting farm products and inputs (58%), second plowing (30%), maize shelling (24%), harrowing (16%), and for transporting laborers (16%). Across farmer categories, medium and large-scale farmers use their tractor the most for these farming operations. Apart from the operations mentioned above, tractor use for other farming activities (on-farm and off-farm) appears marginal.

Table B- 12—Tractor use for various farming operations

Total Farmer category Small Medium Large Number of owners who used their tractors in 2012 375 27 194 154 Percentage of tractor users, by operation First plowing 89.8 86.2 86.4 95.0 Second plowing 29.6 6.9 30.0 33.1 Harrowing 16.2 3.4 12.7 23.1 Planting 0 0 0 0 Fertilizer application 0 0 0 0 Weeding 0.7 0 0.9 0.6 Irrigation 0.5 3.4 0 0.6 Harvesting/combine - harvester rice 0 0 0 0 Shelling maize 24.1 20.7 22.5 26.9 Threshing 1.0 0 0.5 1.9 Milling 2.0 0 2.3 1.9 Carting farm products and inputs 57.7 48.3 58.2 58.8 Transporting laborers 15.7 13.8 15.5 16.3 Local transportation 2.2 3.4 2.3 1.9 Fetching water or not 1.7 0 1.4 2.5 Firewood transportation 6.2 6.9 8.0 3.8 Timber transportation 0.2 0 0 0.6 Building material transportation 1.5 3.4 2.3 0 Road maintenance 2.0 0 3.3 0.6 Electric power generation 0 0 0 0


In addition to using the machines on their own farms, tractor owners provide services to non-owners (Table B-12). On average, service providers indicated that they are motivated by three main reasons: main business (34%), sourcing of operating capital (33%), and quest for extra-revenue (32%). Across farm categories, main business is the main motivation among small-scale farmers, whereas sourcing of operating capital is the dominant reason among medium-scale-farmers, and the quest for extra-revenue is the key reason for providing services among large-scale farmers.

Table B- 13—Agricultural mechanization service provision –Motivation for providing services


Small Medium Large Number of providers 327 25 164 138 Main business 34.3 60.0 34.1 29.7 To source operating capital for own farm 32.4 28.0 37.8 26.8 To make extra revenue 33.3 12.0 28.0 43.5


With regard to service provision, 88% of owners rented out their tractors out of the 375 tractor owners who used their tractors in 2012 (Table B-13). Furthermore, tractor service rental is concentrated on first plowing operation with 83% of owners providing this service. As with own use, owners rented out services for second plowing, maize shelling, and carting farm products and inputs. Harrowing services are provided by only 9% of owners. Across farm categories, there is little variation in the percentages of owners who provided different services in 2012, except for second plowing operation.

37

Table B- 14—Tractor service provision among owners - service rental

Total Farmer category Small Medium Large Number of farmers who used their tractors in 2012 375 27 194 154

Percentage of farmers who rented out their machines 88.3 88.9 87.6 89.0

First plowing 83.2 85.2 82.0 84.4 Second plowing 20.0 3.7 21.6 20.8 Harrowing 9.6 3.7 9.3 11.0 Planting 0.3 3.7 0 0 Weeding 0.3 0 0 0.6 Irrigation 0.3 3.7 0 0 Shelling maize 19.5 18.5 18.0 21.4 Threshing 0.3 0 0.5 0 Milling 1.6 0 2.1 1.3 Carting farm products and inputs 35.5 40.7 37.6 31.8 Transporting laborers 3.2 7.4 4.1 1.3 Local transportation 1.1 3.7 1.5 0 Fetching water 0.5 0 0.5 0.6 Firewood transportation 4.5 7.4 6.2 1.9 Timber transportation 0 0 0 0 Building material transportation 1.3 3.7 2.1 0 Road maintenance 0.3 0 0.5 0 Electric power generation 0.3 0 0 0.6


As concerns the intensity or scale of tractor use, Table B-14 presents the hectares plowed by owners. The results show that tractor owners plowed (first plowing) on average, about 20 hectares (ha) of farmland on their own farms, and 163 ha in terms of plowing (first) services provided to other farmers. Considering both own farm and the services provided, owners plowed on average 182 ha per working tractor in 2012. Across farm categories, the number of hectares plowed on own farm increases with farm size. However, small-scale tractor owners provided more services in terms of number of hectares plowed compared with medium and large-scale farmers. In total, large-scale farmers plowed the highest number of hectares, and medium-scale farmers plowed the lowest number of hectares per working tractor in 2012. Further results on tractor use are presented in appendix B.

Table B- 15—Agricultural machine performance per working tractor utilization for first plowing

Total Farmer category Small Medium Large Use on own farm

Number of hectares plowed (1st plowing) Number of owners plowing 358 25 183 150 Mean number of hectares plowed 19.9 4.1 10.8 33.6 Median number of hectares plowed 13.8 3.6 10.1 26.3

Service rental Number of hectares plowed (1st plowing) Number of owners plowing 309 22 157 130 Mean number of hectares plowed 163.2 184.0 156.6 167.6 Median number of hectares plowed 101.2 131.6 89.1 109.3

Total number of hectares plowed (1st plowing) Number of owners plowing 309 22 157 130 Mean number of hectares plowed 182.4 188.2 167.4 199.6 Median number of hectares plowed 123.9 134.2 104.6 147.8


38

Farm machine service providers offer different payment schemes to their service users/recipients during the farming

season. The survey results indicate that tractor owners provide rental services on credit. Forty one percent of the owners accept partial payment of mechanized services provided up front, 35% demand full payments up front, whereas 24% take full payments of their services after harvest (Table B-15). Small and large-scale farmers are more likely to provide services on credit than medium-scale farmers. With regard to repayment rates, about 60% of service providers indicated that more than 70% of the credit recipients had fully repaid their services.

Table B- 16—Agricultural mechanization service provision - credit


Small Medium Large Credit schemes provided by types (%) Number of service providers 327 25 164 138 Full credit (full payment after harvest) 24.2 16.0 23.2 26.8

Partial credit (partial payment up front) 40.7 56.0 38.4 40.6

Does not provide credit (full payment up front) 35.2 28.0 38.4 32.6

Repayment rates among credit recipients Number of credit providers 212 18 101 93 Less than 50 percent 10.4 11.1 11.9 8.6 Between 50-70 percent 31.6 33.3 33.7 29.0 More than 70 percent 58.0 55.6 54.5 62.4


The survey also captured the movement of tractors across regions to provide hiring services (table B-15). Cross- regional movement of tractor owners is made possible by the two distinct rainfall zones of the country and the quest to achieve profitability of tractor investment. Table B-16 shows that out of 336 tractor owners who provided services, 22% (73 owners) migrated to the other rainfall zone to render services. Small and medium-scale farmers were more likely to migrate compared with large-scale farmers. Most of these migrant owners (65 of 73) come from one district only: Ejura district in the transitional zone of the country. On average, owners have been migrating for five years. Usually, the length of stay of migrant owners outside of their rainfall zone is 60 days which is in range with the plowing season in the North (45 days). First plowing operation is the primary service provided by migrant owners. On average, these providers plowed about 128 ha, but small-scale farmers provide the most services (See Appendix B for further details on cross regional migration).

Table B- 17—Cross regional migration - Characteristics Total Farmer category Small Medium Large

Cross regional migration among service providers

Number of providers 336 25 173 138 Number of migrant providers 73 6 42 25 Migration rate (%) 21.7 24.0 24.3 18.1 Experience with migration (years) 4.5 4.5 4.0 5.4 Median number of days spent in migration zone 60 75 60 60


With regard to the demand for services, 45% (137 out of 305 providers) reported that they are unable to meet farmer’s demand for services (Table B-17). About 50% of these service providers indicated that the limited tractor number is the main reason behind their inability to meet farmer demand for mechanization, whereas 22% of them reported that they were unable to meet the demand because their machines/attachments were broken. Farm distance/remoteness also plays a role in providers’ inability to meet farmer’s demand for mechanization: 19% of the providers indicated they did not respond to customer requests because their farms are too far away (See Appendix B for further details).

39

Table B- 18—Agricultural mechanization service provision – Top reasons for tractor owner being unable to meet demand for first plowing

Total

Farmer category Small Medium Large

Number of valid service providers 305 23 155 127 Number of providers unable to meet demand 137 8 69 60 Percentage of providers unable to meet demand 44.9 34.8 44.5 47.2

Reasons Full capacity / limited tractor number 50.4 62.5 55.1 43.3 Farms are too far away 19.0 12.5 17.4 21.7 Low acreage/fragmented farms 1.5 12.5 1.4 0.0 Can't effectively oversee my operators 1.5 0.0 1.4 1.7 Field badly prepared (stumps and stones) 5.8 0.0 4.3 8.3 Broken machine / attachment 21.9 12.5 20.3 25.0


With regard to service payment, plowing service charges are usually paid in cash, whereas maize shelling service charges are either paid or in kind. Average service charge for first plowing is estimated at GHC 37 (Table B-18). However, this charge varies across districts and regions. For example, service charges are higher in Kassena Nankana, Bawku, and Sissala East districts which lie in the Upper East and Upper West regions, and in Ejura and Techiman districts in the Ashanti region compared with districts in the Northern region (Yendi and Gushiegu districts). As concerns maize shelling, there are little variations of service charges across districts. The same trend applies to service charges paid in kind (See appendix B for further details).

Table B- 19—Agricultural machine - service charge by district (Ghana cedis)

Total

Owner district

Ejura

Techiman

Kintam

po

Yendi

Gushiegu

Kassena

Nankana

Baw

ku

Sissala East

Average service charge (GHC/per acre) First plowing Number of providers 309 89 1 0 97 77 9 4 32 Service charge 37.3 41.5 50.0 33.7 31.8 47.8 43.8 46.4 Maize shelling

Number of providers 60 26 0 1 11 2 0 2 18 Service charge (GHC/maxi bag) 4.1

4.4 - 4.0 3.6 4.0 - 4.0 3.9


Module 3 – Hiring of Machinery Services

This final module of the survey was administered to those farmers who hired-in machinery services.

Table B-19 presents the distribution of agricultural mechanization service hired-in by farm category and tractor ownership. The results indicate that 57% of non-tractor owners hired-in agricultural machines for three major activities: first plowing (52%), shelling of maize (24%), and carting of farm products and inputs (26%). It is also interesting to note that 40% of the tractor owners also hired-in some services. Medium-scale farmers and non-tractor owners were more likely to hire-in machinery services than small and large-scale farmers and tractor owners. Large-scale farmers and tractor owners were least likely to hire-in plowing services since these farmers are most likely to own machinery. Across farm categories, shelling service hiring rates are higher among medium and large-scale farmers and tractor owners compared with small-scale farmers and non-tractor owners. Conversely, service hiring rates for carting and transportation are higher among small and

40

medium-scale farms and non-tractor owners compared with large-scale farmers and tractor owners. The fact that over a third of the tractor owners hired in services indicates that the current level of mechanization on their farms is incomplete.

Table B- 20—Agricultural mechanization service rental - Hiring for any farm operation Farmer category Tractor ownership Small Medium Large Owner Non-owner

Number of farmers 768 850 225 402 1441 Farmers who hired in any agricultural machines (%) 48.6 59.4 43.6 39.8 56.6 Farmers hiring mechanization services (%) Land clearing 0 0 0 0 0 First plowing 44.3 48.2 22.2 12.4 52.0 Second plowing 2.5 2.5 2.7 0.7 3.0 Harrowing 1.4 1.5 1.8 0.7 1.7 Planting 0.4 0.2 0 0 0.3 Fertilizer application 0.4 0.1 0 0 0.3 Weeding 0.3 0.1 0 0 0.2 Irrigation 0.4 0.1 0 0 0.3 Harvesting/combine 1.3 2.1 2.2 2.2 1.7 Maize shelling 17.2 30.5 28.9 29.4 23.5 Threshing 0.8 1.9 2.7 1.2 1.6 Milling 0.3 0.9 0.4 0.5 0.6 Carting farm products and inputs 19.7 27.3 15.1 10.4 26.0


Table B-20 presents the main findings with respect to experience with the service rental market. The results show that medium and large-scale farmers have been hiring-in plowing services for over nine years, whereas small-scale farmers have been hiring services for about eight years. Conversely, tractor owners and non-owners have the same level of experience on the service rental market. Furthermore, the number of hectares plowed among farmers who hired in services has been increasing steadily across farm categories and tractor ownership between 2010 and 2012.

Table B- 21—Plowing service rental by farmer category and tractor ownership Farmer category Tractor ownership Small Medium Large Owner Non-owner

Number of farmers hiring service 340 410 50 50 750 Experience with plowing service rental (years) 7.7 9.5 11.6 9.1 8.8 Number of hectares serviced

2010 2.4 6.7 23.8 12.1 5.5 2011 2.5 7.2 25.6 12.9 5.9 2012 2.6 7.7 27.0 13.5 6.3


Table B-21 presents the types of crops for which farmers mechanize plowing operations. The two major crops for which farmers hired-in mechanized plowing services are maize and rice. These two crops are the major cereal crops in Ghana. The plowing of groundnuts and soybean fields are also mechanized, but by very few farmers in the sample.

Table B- 22—Crops mechanized – Plowing operation Farmer category Tractor ownership Small Medium Large Owner Non-owner Number of farmers 340 410 50 50 750 Crops mechanized (%)

Groundnuts 5.9 2.4 2.0 0 4.1 Maize 67.4 82.2 82.0 82.0 75.5 Rice 13.2 8.5 12.0 8.0 10.9 Soya bean 5.3 2.2 2.0 2.0 3.6


41

Table B-22 presents the distribution of service charge and waiting time for plowing. On average, over 90% of farmers accessed their first plowing hiring services within their home districts, irrespective of the farm category and tractor ownership status. Even though the current tractor population is limited, these results may indicate that farmers were able to access services close to their home locations. On average, farmers wait for about four to five days to access first plowing services. There are little variations across farm categories in terms of waiting time, but waiting times are shorter among tractor owners (three days) compared with non-tractor owners (five days). The trends regarding access to plowing services apply to maize shelling service with exceptions: waiting times are shorter among medium and large-scale farmers and tractor-owners compared with small-scale farmers and non-tractor owners.

Table B- 23—Average waiting time – plowing and shelling services

Farmer category Tractor ownership Smal

l Medium Larg

Owner Non-owner

Number of farmers hiring plowing services 340 410 50 50 750 Percent of farmers accessing first plowing service within home district 95.3 93.9 98.0 96.0 94.7

Average waiting time to access first plowing services (days) 4.5 4.4 5.0 3.2 4.6

Number of farmers hiring maize shelling service 132 259 65 118 338

Percent of farmers accessing maize shelling within home district 93.2 96.1 100.0 100.0 94.4

Average waiting time to access maize shelling services (days) 4.5 4.2 3.5 3.5 4.4


42

REFERENCES

Collier, P., and S. Dercon. 2009. “African Agriculture in 50 Years: Smallholders in a Rapidly Changing World.” Paper presented at the Food and Agriculture Organization of the United Nations Expert Meeting on How to Feed the World in 2050, Rome, June 24–26.

Collier, P. 2008. “The Politics of Hunger.” Foreign Affairs, November Dercon, S., Hill, R. V., & Zeitin, A. 2009. In Search of a Strategy: Rethinking Agriculture-led Growth in Ethiopia. Synthesis

Paper prepared as part of a study on Agriculture and Growth in Ethiopia, University of Oxford, UK. Foster, A. D., and Rosenzweig, M. R. 2011. Are Indian farms too small? Mechanization, agency costs, and farm efficiency.

Manuscript, Yale University. Hedebrand, C. 2011. Leveraging Private Sector Investment in Developing Country Agrifood Systems. The Chicago Council on

Global Affairs Policy Paper (PDF). Chicago, Illinois. The Chicago Council on Global Affairs. Ghana, MoFA (Ministry of Food and Agriculture). 2007. Food and Agriculture Sector Development Policy

(FASDEP II). Accra, Ghana. Jahnke, H.E., G. Tacher, P. Keil, and D. Rojat. 1988. Livestock production in tropical Africa with special reference to the

tsetse-affected zone. In Livestock production in tsetse-affected areas of Africa. Proceedings of a meeting held by the International Livestock Centre for Africa (ILCA) and the International Laboratory for Research on Animal Diseases (ILRAD), November 23–27, 1987, in Nairobi, Kenya. Nairobi: ILCA/ILRAD.

Norman, David W. 1973. "Methodology and Problems of Farm Management Investigations: Experiences from Northern Nigeria," AfricanRural Employment Paper No. 8. East Lansing: Michigan State University, Department of Agricultural Economics.

INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

2033 K Street, NW | Washington, DC 20006-1002 USA | T+1.202.862.5600 | F+1.202.457.4439 | Skype: ifprihomeoffice | [email protected] | www.ifpri.org

IFPRI-ACCRA

c/o International Water Management Institute (IWMI) | PMB CT 112, Cantonments, Accra, Ghana | CSIR Campus (Opposite Chinese Embassy) | Airport Residential Area | T: +233-(0)21-7807163333 | F: +233-(0)21-784752 | gsss.ifpri.info

This Working Paper has been prepared as an output for the Ethiopia Strategy Support Program, funded by USAID, and has not been peer reviewed. Any opinions stated herein are those of the author(s) and do not necessarily reflect the policies or opinions of IFPRI.

Copyright © 2014, International Food Policy Research Institute. All rights reserved. To obtain permission to republish, contact [email protected].

ABOUT THE AUTHORS

Antony Chapoto is Research Fellow, Nazaire Houssou and Athur Mabiso are Associate Research Fellows, and Frances Cossar is Research Analyst, in the Development Strategy and Governance Division of the International Food Policy Research Institute (IFPRI).

43

mailto:[email protected]

Documents

Medium and Large-scale farmers and Agricultural