PROFITABILITY ANALYSIS OF SMALLHOLDER COFFEE PRODUCERS
Wageningen University
Borja Cardeñoso Gutierrez
05/08/2013
Supervisors IITA: Ghislaine Bongers
John Herbert Ainembabazi
Piet Van Asten
Supervisor Wageningen University:
Kees Burger
Contenido
1. INTRODUCTION 3
1.2 Research objective 3
1.3. Contents of the report 4
2. STUDY AREA AND DATA COLLECTION 4
3. HOUSEHOLD CHARACTERISTICS 4
4. GROSS MARGIN ANALYSIS AND HOUSEHOLD INCOME 5
4.1 Methodology 5
4.2 Results 6
4.2.1 Crops 6
4.2.2 Inputs 11
4.2.3 Total Household income 12
5. FARMERS CONSTRAINTS AND OPPORTUNITIES IN COFFEE PRODUCTION 13
5.1 Poor agronomic practices 13
5.2 Lack of adoption of inputs 14
5.3 Marketing Chain 14
5.4 Lack of access to credit 15
5.5 Opportunities available to farmers 15
6. SENSITIVITY ANALYSIS 16
6.2 Better marketing position 17
6.3 Improve management and agronomic practices 17
7. RECOMMENDATIONS FOR FARMERS 18
8. CONCLUSION 19
References 21
Appendix . report of meeting with stakeholders 23
1. INTRODUCTION
The agricultural sector in Uganda has major relevance for the Ugandan economy. The agricultural sector accounts for 23.7% of GDP; it generates 90% of exports earnings, and employs 80% of the population (Kraybill & Kidoido, 2009). One of the most important cash crops is coffee. It is a vital sector for the Ugandan economy generating government revenues, foreign exchange earnings, and provides income for all stakeholders throughout the coffee chain. It creates 20% of export revenue in the Ugandan economy, and constitutes a major source of income for smallholder farmers which produce 90% of the total coffee production (Bongers et al. 2012). Therefore, improving the profitability of the coffee sector has a major relevance for all stakeholders involved. It will contribute substantially to poverty reduction among farmers (Deininger & Okidi, 2003), and generate a considerable spillover effect into the overall economic performance of the country.
Economic and profitability analysis have become more relevant among development agencies to evaluate and assess the current methods of production, and quantify smallholders income obtained from the different economic activities in which they are engaged. An economic evaluation of smallholder activities would also be useful to analyse strengths and weakness of the current production systems, identifying and evaluating opportunities available to increase household income and subsequently the standards of living.
Most of the studies on profitability of smallholder farms are motivated by a specific development project, which generally based their foundations and strategy on previous research work. For instance, in Uganda the governmental plan for the modernization of agriculture (PMA) was assisted by IFPRI, Kraybill, & Kidoido, (2009) who conducted a profitability study of the main agricultural enterprises by region. Additionally, profitability analyses have been conducted to evaluate the performance of development projects and the impact that different organizations have on the income of the farmers who participated in the project (see for instance Fowler (2007)).
1.2 Research objective
The main research objective is to study the income of smallholder farmers engaged in the production of coffee, and analyse the profitability of the different economic activities that constitute the income of the household. The focus of this research aims to identify the main obstacles to higher profitability in coffee farming households, and examine the opportunities available to increase income. We aim to provide a thorough insight on the effect that agronomic management practices, farm size, input application, the allocation of crops on the land, and related factors, have on the profitability of the farm.
Budgeting techniques were employed to compute the income of the households of the study, and to analyse the profitability of the different economic activities in which households are engaged.
A comparison of the relative profitability of crops will be performed in terms of income and land allocation.
1.3. Contents of the report
The organization of the report goes as follows: In section 2 the characteristics of the study area and the data collection methods are explained. In section 3 we explain the characteristics of the households in our study. In section 4 we conduct the gross margin analysis, explaining the methodology employed and results obtained. In section 5 we discuss the constraints and opportunities that farmers face. In section 6 we conduct a sensitivity analysis to indicate how improvements in the management of coffee, including value addition and improved agronomic practices, would influence household income. In section 7 we provide some recommendations to farmers based on the results of the study. In section 8 we conclude the report by summarizing the most important findings.
2. STUDY AREA AND DATA COLLECTION
The study was conducted in several villages of 7 sub counties in the districts of Luwero (75 km north of Kampala) and Bukomansimbi (157 km south-west of Kampala). Primary data was collected using a questionnaire from 24 farmers randomly selected in each district. Additional information was gathered through the assistance of facilitators, key farmers of a village, who conducted a plot evaluation, and provided data regarding the farm gate prices and the agronomic practices currently employed by the farmers in the study.
The soil in Luwero is generally sandy loam and especially in the southern part of the district is relatively fertile (Luwero district profile).The rainfall is distributed throughout the year with an average of 1300 mm, the dry seasons are between December and March and June to July . The soil in Bukomansimbi ranges from red laterite, sandy loam and loam. The average rainfall of the district is 1200 mm, and the dry seasons are between January to March and July and August (Uganda government, 2013).
3. HOUSEHOLD CHARACTERISTICS
The household characteristics of the farmers interviewed for this study are provided in table 1. The households of the study own 2.22 hectares on average. The average land ownership in Luwero is considerably higher than in Bukomansimbi, 2.42 and 2 hectares respectively. Household size averaged 6.7 members, the mean in Luwero is 7.6 members while in Bukomansimbi is of 6 members. 78 % of the household heads are males, in Luwero 24% of the household head are females, this percentage is slightly lower in Bukomansimbi in where 20 % of the household heads are females. The age of the household head is 46 years old on average, with the average age of the household head in Luwero being 9 years older (51 years old) than in Bukomansimbi (42 years old). The number of household members at school is 3.8 on average, in Luwero the number of household members at school is 4.7, while in Bukomansimbi 3.1 members of the household are going to school. All but one household head in Bukomamsimbi had received education, 75 % of them have completed primary education and 24.5 % have completed secondary education. None of the household heads in the area of the study have higher education than secondary. The number of years farming for the head of the household is 23.2 on average, the mean in Luwero is 5 years more than in Bukomansimbi.
Table 1. Household characteristics
Characteristics / Both regions / Luwero / BukomansimbiAverage land ownership ( Ha) / 2.22 / 2.43 / 2.02
Average household members / 6.72 / 7.6 / 6
% female head of the household / 22 / 24 / 20
Average number of household members at school / 3.8 / 4.7 / 3.1
Average age of the household head / 46 / 51 / 42
Average number of years farming / 23.23 / 25.6 / 20.3
% household head with no education / 0.02 / 0 / 0.04
% household head with primary education / 75 / 74 / 76
% household head with secondary education / 24.5 / 26 / 23
4. GROSS MARGIN ANALYSIS AND HOUSEHOLD INCOME
To study the profitability of the farms in the sample we calculated the gross margins for each farm. The gross margins are the gross income obtained from an enterprise less the monetary costs of the variable inputs incurred in it (tech-talk international, 2013). We use the gross margins because they are a relatively accurate indicator of the performance of an individual farm and it allows a comparison of the performance of different farms (Nemes, 2009) as used by Kraybil & Kidoido (2009) to calculate the profitability of Ugandan agricultural enterprises.
4.1 Methodology
Following the methodology described by tech-talk international gross margin training notes (2013) and CIMMYT (1988), the information needed to calculate the gross margins are the yield for each crop, the farm gates prices and the variable cost of production. We excluded fixed costs, labour cost and the depreciation of assets from the calculation as Kraybil & Kidoido (2009). We calculated the income that each individual crop generated to the farm, including those for self-consumption, add them together and subtract the monetary costs of the inputs employed in the farm. The inputs taken into account were fertilizer, herbicide and pesticide. The use of farm manure, and mulch were excluded as they do not have a direct monetary cost, they are labour intensive inputs, and their cost should be computed considering the hours needed for their application. The formula used for the calculation of the gross margins is as follows:
GM=∑ Pi∙Yi-∑ pi∙xi
Where GM is gross margin per farm in USD, Pi is the farm gate prices of product ith and Yi is total production of crop ith . pi is the price of the input ith and xi is the amount of input ith use.
4.2 Results
The results of the gross margin for all crops for each area of the study are illustrated in table 2.
Table 2. Gross margins of crops in USD per year
Income crops / Input costs / Gross margin cropsMean for both regions / 1803 / 59 / 1744
Luwero / 2082 / 17.8 / 2064
Bukomansimbi / 1535 / 97 / 1438
The results of the calculations indicate that the average gross margin among the farms in the study is 1744 USD. There is a considerable difference between regions, in Luwero farmers on average obtain 626 dollars more than in Bukomansimbi. The total market value of all crops produced by the average farm is 1803 USD, including those crops that are consumed in the household, those that are given away freely and those sold in the market. Surprisingly, farmers in Bukomansimbi spend 80 dollars more on inputs than farmers in Luwero.
4.2.1 Crops
Table 3 shows the detailed information for each crop, the average production per farmer and per hectare, the total market value of the product, the percentage of total household income that is given by each crop, the returns to land, and the percentage of the product that is sold in the market.
Table 3. Crop description for both regions. Standard deviation in brackets
Crop / Total value of product ( average USD)* / Production in kg ( average per farmer) / % of crop over total income % / Area of the crop ** ( Ha) / % land allocated to the cropIn % / Production per Ha in kg / Returns per land
( USD) / % of product sold in the market
Coffee / 860 / 441*** / 39 / 0.6 / 30 / 773
(452)
n=35 / 1433
(706)
n= 33 / 100
Banana / 652 / 228**** / 28 / 0.33 / 16 / 692
(710)
n=29 / 1597
(1002)
n=26 / 22
Maize / 85.5 / 354 / 6 / 0.27 / 13 / 1527
(1206)
n=20 / 643
(494)
n=21 / 42
Cassava / 69 / 360 / 4 / 0.13 / 6.8 / 3954
(2551)
n=14 / 848
(839)
n=20 / 15
Beans / 57 / 98 / 4 / 0.07 / 3.5 / 1380
(780)
n=12 / 919
(1053)
n=17 / 40
Sweet potato / 44 / 277 / 2 / 0.1 / 5 / 3320
(2956)
n=12 / 786
(1000)
n=11 / 7
*the conversion rate between UGX and USD is 2500
**in % of the plot, based on the plot evaluation done by the facilitators
*** Converted to FAQ( Fair average quality) at the rates of 0.17 from red cherry to FAQ and 0.54 from kiboko to FAQ
****Production in bunches
(The outliers in the data base that we have eliminated, based on literature review and/or a scatter plot, from the calculations of the production of kg per hectare and returns per hectare respectively are as follow for each crop:
Coffee: 200-2100 kg, 450-3000 USD. Banana: 100-2500 Bunches, 250-5000 USD. Maize: 200-4000 kg, 100-2000 USD. Cassava: 1200-8500 kg, 100-4000 USD. Beans: 400-3500 kg, 100-5000 USD. Sweet potato: 1000-8000 kg, 200-4000.)
As indicated in table 3 the most important crop for the households in the area of the study is coffee, it generates 39 % of total household income, which in monetary terms accounts for 860 dollars on average for each farm. However, there is a wide variation among the farmers in the sample, as there are households for which coffee represents nearly 70 % of their total income, while for others it can be as low as 15 %. Coffee also constitutes the most important crop in regard to the amount of land that is allocated to it, 30 % of their productive plot area is planted with coffee. The production per hectare is 773 kg of FAQ , which is slightly above the average production of Robusta for Uganda 648 kg/ ha (USAID, 2010) but is still very far away from the production levels of Vietnam, which produce 2.2 tons of coffee/ha (USAID, 2010). The monetary returns to the land that is allocated to coffee production in USD is 1433 dollars, which makes coffee the second more profitable crop in relation to the land that is allocated to it after banana.
Banana (both matooke and sweet banana) is also a vital crop for the farmers as it constitutes 28 % of farmers’ total income and it’s the main food of the household, which explains why only 22% of its total production is sold in the market. The average production per farm is 228 bunches and the average value of the product generated from banana production is 652 dollars. The land allocated to banana production is 16% of the productive plot area, which makes banana the crop with the highest returns to land with 1688 dollars per hectare of the cultivated area with banana.