International institute of tropical agriculture
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
Contenido1. 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
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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 objectiveThe 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.
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1.3. Contents of the reportThe 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
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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
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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 ∙ Y i−∑ pi ∙ x i
Where GM is gross margin per farm in USD, Pi is the farm gate prices of product ith and Y i is total production of crop ith . pi is the price of the input ith and x i 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 crops
Mean 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.
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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
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*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
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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.
The less profitable crop for the farmers with respect to land allocation is Maize. Of one hectare that is cultivated with maize the average farmer makes 643 dollars. The average market value of the production of coffee is 85 dollars, which is quite low is relation to the land that is allocated to the production of maize, which is 13 % of the productive plot area. This constitutes an inefficient allocation of land, as the land could yield more economic benefits if is allocated to other crops like coffee or banana.
The less marketed crop is sweet potato, only 7 % of total production is sold in the market. The low price of sweet potato and the fact that it is a highly labour demanding crop (Bagamba et, al. 1998) could explain why sweet potato is mostly for household consumption.
The most profitable of the annual crops is beans. Out of each hectare of beans farmers can get 991 dollars, however the land allocated to it is relatively low, only 3.5 % of the productive plots are planted with beans. The reason behind this decision could be the physically demanding labour requirements needed to produce the crop (Bagamba et, al. 1998).
Table 4 and 5 shows the different production characteristics for each region.
Table 4. Crop description for Luwero.
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 crop
Production
per Ha in kg
Returns per land
( USD)
% of product
sold in the market
Coffee 925 438 35 0.69 32 628(296)n=16
1250(694)n=13
100
Banana 736 246 30 0.26 12 771(499)n=16
2014(1120)n=15
24
Maize 111 440 6 0.33 15 1749(1403)
n=7
859(445)
48
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n=8Cassava 89 495 5 0.17 8 4232
(2815)n=8
908(996)n=10
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Beans 45 80 3 0.075 3.4 1394(688)n=6
547(437)n=10
34
Sweet potato
80 478 4 0.14 6.7 2291(1333)
n=8
996(1111)
n=8
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Table 5. Crop description in Bukomansimbi.
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 crop
Production
per Ha in kg
Returns per land
( USD)
% of product
sold in the market
Coffee 798 452 42 0.51 28 927(412)n=19
1605(711)n=20
100
Banana 568 209 27 0.37 20 601(547)n=13
1324(788)n=11
20
Maize 67 270 5 0.22 12 1458(1137)n=13
510(491)n=13
36
Cassava 49 235 3 0.10 5.7 3666(2369)
n=6
776(650)n=10
20
Beans 68 114 5 0.065 3.3 1370(864)n=8
1200(1311)
n=8
44
Sweet potato
9 68 1 0.05 3.5 3377(3544)
n=4
552(466)n=3
0
There are some interesting differences between both regions. For coffee the average production per hectare in Bukomansimbi is substantially higher than in Luwero (927 kg/ha and 628 kg/ha, respectively). The explanation behind this fact could be that, as it will be indicated below, the application of inputs in Bukomansimbi is much higher than in Luwero and the pest are more prevalent in Luwero. In addition, the hectares cultivated with coffee in Luwero are higher than in Bukomansimbi, and as land endowments are smaller they compensated with more intensive management (see for instance R. Helbert, 1998). The production of FAQ for the average farmers is slightly higher in Bukomanismbi 452 kg than in Luwero 432 kg. However, the average income that farmers obtained from coffee is much higher in Luwero ,925 USD, than in Bukomansimbi 798 USD. The explanation behind this
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fact is, as it will be indicated in table 6, that farmers in Luwero invest more time in primary processing, rarely selling red cherries, while in Bukomansimbi a considerable proportion of their coffee harvest is sold as red cherries. It is interesting to point out that despite the fact that coffee constitutes 42 % of total household income in Bukomansimbi, the amount of land allocated to it is only 28 % while in Luwero the land allocated to coffee is 32 %. For all the other crops, with the exception of sweet potato, the average production per hectare is considerably lower in Bukomansimbi than in Luwero. It is particulary striking in the case of banana, for which the proportion of land allocated to it in Bukomansimbi is much higher than is Luwero, 20 % and 12 % respectively. However, the production per hectare in Luwero is substantially higher than in Bukomansimbi, 771 and 601 bunches respectively.
The differences in the area cultivated for crops between both regions are 2.16 in Luwero and 1. 84. As was indicated in the section of households characteristics farmers in Luwero have higher land endowments than in Bukomansimbi.
Table 6 shows the different forms of coffee sold in the market, the proportion of each form sold in relation to the total coffee production after being converted in FAQ for each area. Table 6 also shows the income obtained from selling each form in each region of the study.
Table 6. coffee marketing
% production sold in red
cherries
Dollars obtained from selling
red cherries
% production sold in kiboko
dollars obtained from selling kiboko
% production sold in
FAQ
dollarsobtained from selling FAQ
Average both regions
12 107 70 568 18 163
Luwero 6 55 60 540 34 323Bukomansimbi 19 152 79 594 2 16
The most commonly form of coffee sold is kiboko
, 38 farmers in the sample sold their coffee in this form, which accounts for 70 % of the total production, obtaining an average of 568 USD, the average price of kg of kiboko is around 0.88 dollars. Farmers in Bukomansimbi sell 20 % more coffee in kiboko form than in Luwero.14 farmers sell their coffee when there are red cherries, making on average 107 USD out of it, the approximate price per kilo is 0.36 USD. The proportion of coffee sold in red cherries is much higher in Bukomansimbi, in where nearly 20 % of their total harvest is sold in this unprocessed form. The average income for the famer obtained from selling FAQ is 163 USD, however, only 6 farmers sell FAQ earning an average income of 1.369 dollars and its price per kilo is around 1.8 dollars. The farmers that sell FAQ are from Luwero, this fact explains the considerable higher income obtained from coffee in Luwero despite the lower production per farmer and per hectare.
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4.2.2 Inputs
The information of the inputs use in the crop production is illustrated in the following table 4.
Table 7. Inputs
Inputs Fertilizer ( in kg) Herbicide ( in litres)
Pesticide ( in litres)
Average amount 28 3.2 0.7 Average total costs (USD)
25.7 15.7 4
Luwero number of users ( in farms)
6 11 12
Average amount (luwero)
2.5 1.8 1
Average total cost (Luwero)
3.5 8.7 5.8
Bukomansimbi users ( in farms)
11 19 11
Average amount in Bukomansimbi
52 4.6 0.4
Average cost Bukomansimbi ( in USD)
47.5 22.5 2.1
As indicated in table 4 the average amount of fertilizer that each farmer applies on his farm is 28 kg. This gives an average application level of fertilizer per hectare of 14 kg, which is higher than the average usage in Uganda which is 1 kg per hectare of arable land. Although is still much lower than other east African countries like Kenya 35 kg (S. Bayite, 2009). However, we see wide differences between both regions in the application of fertilizer, surprisingly the application of fertilizer is much higher in the villages of Bukomansimbi than in Luwero, each farmer in Bukomansimbi on average applies 50 kg more of fertilizer than in Luwero. This difference in the application of fertilizer could partly explain the different returns to land for coffee in both regions. For the farmers that use fertilizer the average expenditure on it is 74 dollars. Herbicide is widely used by farmers, 31 farmers in the sample use it, and the farmers that apply herbicide spend on average 25 dollars on it.Farmers in Luwero use more pesticide than in Bukomansimbi. Considering the higher willingness of farmers in Bukomansimbi to invest on inputs, this fact could indicate that pests are more prevalent in Luwero, especially the coffee berry borer (CBD).
The most interesting issue related to the use of inputs is the fact that In Bukomansimbi farmers use much more inputs than in Luwero. This finding is a surprise because Bukomansimbi is further away from Kampala and more is a more remote region with worse roads than Luwero.
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4.2.3 Total Household income
Table 5 illustrates the total household income in USD and the sources of it.
Table 5. Household income
Both regions Luwero Bukomansimbi
Total household income ( USD)*
2191 2562 1834
% income generated by crops
79 81.5 76
Degree of commercialization of
the farm in %
59 58 59
% income generated by livestock
7.22 7.9 6.5
% income generated by businesses
5.58 7.5 4.3
% income generated by employment
3.5 2.5 4.5
% income generated by coffee trading
2.3 0.41 4
In section 4.2.1 and 4.2.2 we have described the results of the gross margin analysis for the agricultural crops. However, as indicated in table 5, agricultural crops constitute on average 79 % of total household income. In this section we will study the other economic activities that the household of our study are engage in.
We calculated the total household income based on the farmer estimation that each economic activity generates to the household. Once we calculated the cash income that the agricultural crops generated to the household, through simple operations we calculated the cash income that other economic activities generate to the household.
The average total income of the household in our study is 2191 dollars. There is a substantial difference in the income between the two regions of our study, the average household in Luwero have 728 dollars more than the average household in Bukomansimbi. The different endowments in terms of physical capital could explain this difference. As indicated in table 1 farmers in Luwero have higher land endowments than in the Bukomansimbi.
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Agricultural crops constitute the most important source of income from the households of the study, it generates nearly 80 % of total household income. This percentage is higher in Luwero, where they rely more on agricultural crops than in Bukomansimbi. The second most important source of income for the household is livestock, as it constitutes 7.22 % of total income mainly from selling goats and piglets. For the households in Luwero nearly 8 % of total income is generated through livestock which is slightly higher than in Bukomansimbi. Petty businesses represent 5. 58 % of total household income. Farmers in Luwero are more in engaged in petty businesses than in Bukomansimbi, it represents 7.5 % and 4.3 % of total income respectively. It is possible that better communications and its proximity to Kampala explain this difference. The most common businesses among the households are small shops, brewing liquor out of banana, or occasional repairing of electronic devices, like radios. Income from employment represents a small proportion of total household income (3. 5 %). Coffee trading also generates 2.3 % of total income to the household. Unexpectedly, households from Bukomansimbi are more engaged in coffee trading than in Luwero. We expected that the proximity to Kampala would give incentives to households in Luwero to be more engage in Coffee trading, however this is not the case.
A note of caution: Farmers do not keep any written records during their activities like harvesting, input application or selling of the product. Subsequently, we have to rely on the memory of the household head to gather the data that has been used in the calculations. Additionally, when farmers were asked how much they produce of each crop, they share the information for the main crops. However, when the inspection of the plots was done by the facilitators, they reported more crops that the ones that we have in the figures of production after the interview with farmers. Nonetheless, we consider that the information provided constitutes a relatively adequate data base to make the calculation explained above.
5. FARMERS CONSTRAINTS AND OPPORTUNITIES IN COFFEE PRODUCTION
5.1 Poor agronomic practices
As indicated in the gross margin analysis, the production levels of coffee for the farmers in the study (773 kg/ha) are considerably lower than the world leaders like Vietnam (2.2 tons of Robusta per hectare) (USAID, 2010). Based on the interviews with key stakeholders that have been conducted, our visits to the field and the literature review, we consider that some key management practices that are not properly employed are the main reason that explain the low yield that coffee farmers achieve (Cognigni, 2010). In the farmer interview we found a considerable gap between the current agronomic practices employed and the recommended practices as explain by (Carr, 1993) or in the manual of Ibero for sustainable production of coffee (Ibero Uganda (2005)). According to the manual of Ibero, good pruning and stumping can increase yields by 30 %, other studies such as Van Asten et al. (2012) confirm the relation between frequent pruning and higher yields. Farmers, when interviewed, they reported that they generally pruning, however, according to our field visits the quality of the
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pruning is low and its frequency is erratic. Stumping coffee trees is also very important to avoid over competition for nutrients and to rejuvenate the coffee tree (Carr, 1993). According to the interviews 78 % of the farmers maintain that they do stumping at least once a year, and that they keep three to four stems in the coffee tree. However, according to our observation in the field, stumping is generally not conducted properly or uniformly across the different trees or plots.
Furthermore, mulching is also very beneficial for the coffee trees as it preserves moisture, releases nutrients into the soil and reduces weeds growth (Carr, 1993); however among the farmers interviewed only 15 % of them do mulching on coffee. Most of the farmers acknowledge the benefits that mulching has, but they said that insufficient mulching material deprives them of the possibility to mulch.
5.2 Lack of adoption of inputs
The amount of fertilizer use in Uganda is far from the recommended levels of application. In Sub-Sahara Africa the application of fertilizer is 9 kg per hectare, much lower than elsewhere in the world 96 kg/ha in Latin America and 104 kg/ha in south Asia (Crawford et al. 2005). The farmers of our study applied 14 kg of fertilizer per hectare on average, which while it is slightly higher than the average in Uganda of 1 kg per hectare (Kasule, 2009), is still much lower than the countries in east Africa. Several studies indicate the potential effects that fertilizer could have on yield, such as alleviating nutrient deficiencies, especially when combined with adequate agronomic practices, like mulching. Cannell (1973) showed that mulching and fertilizer increases yield by 66 % on Arabica coffee in Kenya. Declining soil fertility also constitutes another reason that further supports the advice to apply fertilizer (Van Asten et al. (2010)).
The main constrains that hinder the application of fertilizers are:
High monetary cost involved in the purchase of fertilizer and difficulties with financing the investment (kelly et al. 2003). Financing issues constitute a major problem in adoption of inputs throughout Africa. The inefficiencies in the financial market and the difficulties for farmers to access credit in fair conditions, propagates a significant credit constraint, that hinders the application of fertilizers and increases the effective price that farmers face, in case they required finance to purchase fertilizers.
Lack of knowledge and skills regarding the procedure of applying fertilizer on crops (Kelly et al. 2003). If a farmer does not have precise and accurate knowledge on how to apply fertilizer, the investment of using fertilizer becomes more uncertain and risky, therefore lowering the expected benefits of it use and reducing the application of it. Raising awareness of the benefits of its application combined with training on how to use them could motivate a higher application rate, as it was successfully probed by the sustainable community- oriented development programme ( SCODP) that took place in Nyanza province in Kenya (kelly et al. 2003).
Low and unreliable availability of inputs in the stores of the villages (Kasule, 2009), which increases the difficulties to access inputs and the transaction costs, lowering the application of fertilizer.
5.3 Marketing Chain
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Uganda coffee chain is relatively efficient. Significant improvements have been made after the liberalization process (Baffes, 2006). Ugandan coffee farmers obtained around 80 % of the exporting value of the product, which is above African average, though smaller than in Vietnam where farmers obtained 95 % of the exporting value (Baffes, 2006). However, there are still gains that can be realized through a more efficient organization of the marketing chain, particularly connecting farmers with exporters could generate better prices that farmers face. Better prices could provide the required incentives to allocate more labour to coffee production, improving the management and agronomic practices applied (Markelova (2009).
5.4 Lack of access to credit
Farmers constant need for cash, especially in the presence of adverse shocks, can precipitate an instant demand for money. Since farmers lack access to formal credit, they generally rely on coffee traders, who advance them cash in return for significantly lower prices when the harvest takes place (Paulus, 2013). The vulnerable position in which farmers find themselves creates a disadvantageous bargaining position for the farmers, who are force to accept extremely high interest rates, depriving them of the full value that their coffee could generate. Therefore, providing farmers with a reliable and fair access to credit could improve substantially their capacity to add value to their coffee.
5.5 Opportunities available to farmers
Summarizing the previous section, the main constraints that farmers face are: inefficient management and agronomic practices, lack of access to credit and a vulnerable position in the marketing chain.
Different organizations have been trying to address these constraints either separately or together. According to the interviews that have been conducted, the organization that is most capable to deal with all constraints mentioned above, and being able to create an impact on the livelihood of the farmers, is the Uganda Coffee Farmers Alliance (UCFA). The UCFA is a farmer organisation created to support coffee farmers with the objective of improving their livelihoods. In order to do so they provide agronomic training through demonstration plots managed by one farmer, marketing services to directly connect farmers to exporters and financial services for both input acquisition and household needs, such as medical expenses or school fees. The UCFA is supported by Hans Neumann Stiftung and the Bill & Melinda Gates Foundation.
The accomplishments achieved by the UCFA are as follow (Cognigni, 2010):
56.000 farmers have joined the organization since its beginning Due to primary processing and an improved marketing chain, farmers have achieved
25 % higher prices Farmer organizations have been able to attract credit from formal financial institutions
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Adoption levels of good agronomic practices, like pruning or mulching have reach levels of 60 %, while the adoption of application of fertilizer is around 40 %.
In the demonstrations plots production have quadrupled while the average farmer of organization have reach levels of 1.3 MT of green bean.
Farmers have planted more coffee trees The availability of inputs has increased in the villages where UCFA have farmers
organizations.
The quantify benefits of joining the UCFA, according to sources from the organization itself go as follows (Cognigni, 2010): increases in production generated from the application of improved management and agronomic practices, enabling the average farmer in the organization to reach 1.3 MT of green beans, applying around 60 % of the recommended practices. The increases in costs associated with the application of improve management practices are 105 %. In the demonstration plots the increases in production is much higher, doubling the production of the average farmer of the UCFA. The value chain organization of the UCFA has been able to increase prices that farmers obtained by 22 %. The interest rate applied to the farmers that obtained finance is around 20 %, although it might seem high, it is much lower than the rates offered by middlemen. In addition the management of financing smallholder farmers is costly and full of challenges.
6. SENSITIVITY ANALYSIS
Now we will proceed to analyse how sensitive the total household income is in relation to coffee management practices, and an improved marketing position. We will indicate how production, costs and income could change if farmers obtained better prices and adequate agronomic training. More specifically, we will indicate the monetary benefits of achieving a better position in the marketing chain, obtaining significantly higher prices. Moreover, we will show the effect that receiving adequate training, to improve management and agronomic practices, will have on the production level of the farmers and how this influences household income. To analyse the effect that these improvements will have on the farmers, we conducted a sensitivity analysis, considering the benefits and costs that are involved in this process, indicating how total household income would change accordingly as Fowler (2005).
We will consider different scenarios, introducing sequentially the improvements that would take place. Firstly, we will indicate how the average total income for the average farmer would change in relation to an increase in prices of 22 %. This increase is generated through enhancing the marketing position and organizational capacity of the farmers. If farmers of the organization bulk their coffee together, and transport and sell it to the exporters in Kampala directly, they will be able to increase their net prices obtained by 22 %, taking into account the operational cost (Cognigni, 2010). Secondly, we will indicate how improved management and agronomic training would influence production and subsequently income. This improvement in agronomic practices can be achieved through adequate training by experience and motivated extension services and application of recommended fertilizers. These trainings, in combination with the incentives that better prices provide, generated an average production level of 1.3 MT,
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(Cognigni, 2010). However, these figures of production cannot be achieved without a considerable investment on inputs. Subsequently, we will take into account the increase in production cost associated with the application of improved management techniques.
6.2 Better marketing position
Table 6. Sensitivity analysis, scenario 1: access to better prices
Simulation Income from coffee in USD
Total income in USD Returns to coffee area in USD
Current prices860 2191 1433
Better prices (↑ 22 % )*1024 2313 1906
*The increase in prices is given for every form of coffee, red cherry, Kiboko and FAQ
As indicated in Table 6, increases in the prices by 22%, for all forms of coffee sold will increase income generated from coffee from 860 dollars to 1024 dollars, raising total income in the average household by 164 dollars. This benefit exemplifies the potential gains that smallholders farmers can gain when properly organized and well informed, they seek better market (Markelova (2009)). By finding a better market their land becomes more valuable, increasing the returns to land, in this case the returns from the land that is allocated to coffee will be raise by 339 dollars.
6.3 Improve management and agronomic practices
Table 7. Sensitivity analysis, scenario 2 : improve management and agronomic practices and access to better prices.
simulation Production per
hectare( in kg FAQ)
Input cost (USD)
Gross margins
from coffee
Total income Returns to coffee per
hectare
Better prices 773 59 965 2313 1906
Better prices and
agronomic training
1300 121 1721 3131 2879
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When adequate training have been given, suitable inputs have been applied, and with the incentives that higher prices provide, farmers would be able to increase their production to reach levels of 1.3 MT per hectare. For the farmers in our sample it would represent an increase in production of 75 %. In order to meet this production level, it is required to increase the investment in inputs by 105%, which will raise the cost of production to 121 dollars. This increase in production by 79 % with better prices would generate an extra 756 dollars of gross margin for coffee. These improvements in coffee management techniques and a better marketing position will increase the returns per hectare to 2879 dollars, figure much higher than any other annual crop and even banana.
From the sensitivity analysis we can conclude that the potential to increase household income and the standards of living are enormous. Household income can increase from the current 2191 dollars to around 3131 dollars for the average farmer due to organizational marketing improvements, adequate management of coffee, application of inputs and good agronomic practices.
A note of caution: The information provided above regarding the UCFA, has been obtained from internal reports and from interviews with the representatives of the organization. No independent studies have been conducted to assess the veracity of the report and information provided by the inner sources of the UCFA. Informal conversations with other stakeholders, from NUCAFE from instance, doubt the viability of what has been reported by UCFA. However, the information provided could indicate potential achievements for the famers in our study and based on that information we will conduct the following section of our report, the sensitivity analysis.
7. RECOMMENDATIONS FOR FARMERS
Based on the gross margin and sensitivity analysis we can recommend that farmers try to obtained better prices through collective organization and selling their coffee directly to exporters in Kampala. Based on the sensitivity analysis and the literature review the potential gains attainable through increases in production due to adoption of improved agronomic practices are very substantial. From the current 773 kg/ha of coffee, field experiments for a regular farmers that adopts 60 % of the recommended agronomic practices, shows that the production could increase to 1.3 MT (Cognigni, 2010). It is more likely that farmers will make the desirable improvements in management and agronomic practices when they work cooperatively with motivated extension agents (Seyum et al. 1998). Additionally, the introduction of credit could provide the financial assistance required to cover household needs and financing the purchase of the advisable inputs, enabling the farmers to move from a farm subsistence approach towards a more proactive business mentality (Paulus, 2013).
The opportunities to improve the standards of living through an increase in income are very substantial. According to the sensitivity analysis farmers on average can increase their total household income by 40 %. Farmers should acknowledge the wealth that lies within their land, and the opportunities available to improve their economic situation. Farmers also have
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to be willing to make changes in the way they regard farming, acquiring a proactive business approach to farming, that would give incentives to invest their time on learning how to manage properly their coffee (Collett & Gale, 2009).
The main obstacles that we have identified in this study are the lack of value addition for farmers in Bukomansimbi, since the sell 20 % of their coffee production in red cherries. While in Luwero the main constraint is the low productivity per hectare. Therefore, the recommendation for farmers in Bukomansimbi would be to add value to their coffee and dry the red cherries before they are sold. While for the farmers in Luwero they should try to improve their production per hectare, through the application of inputs or the adoption of good agronomic practices. In doing so, farmers from both areas would be dealing with the main obstacle for achieving a higher profitability in their farms.
The recommendations according to the management of the spacing and the area that is allocated to each crop would be as follow: Since maize is the less profitable crop in relation to the amount of land that is allocated to it (0.27 Ha on average), and 42 % of the production is sold in the market, an efficient management of their farm would suggest a decrease in the land that is allocated to maize by 42 % and stop selling maize in the market. In the new space made available from this decrease coffee or banana should be planted, based on its highest return to land. In doing this 0.126 hectares would be available to plant coffee instead. From this 0.126 hectares of coffee the benefits that the production of coffee will generate, with the current agronomic practices and prices, would be 313 dollars while the opportunity cost amount to 124 dollars, which gives a profit of 189 dollars. However, gender considerations have not been taking into account.
Other recommendations from an agronomic perspective would be to promote a fallow land practice, since only 9 % of the farmers interviewed said that they practice fallow land techniques. The benefits of this practice range from being able to regenerate the nutrients of the soil, increased organic matter and a chance to break the circle of pest and diseases (see for instace, Sanchez et, al, (1997)).
8. CONCLUSION
In this report we have analysed the household income, and the profitability of the economic activities of 50 farmers randomly selected from villages in the districts of Luwero and Bukomansimbi. Budgeting techniques were employed to calculate the gross margins for the production of crops that farmers are engaged in. We found that the average income for the household in the study is 2191 dollars pero year and that in Luwero it is 728 dollars higher than in Bukomansimbi . The main reason that could explain this finding is the higher land endowments in Luwero. We also found that the livelihoods of the households relied heavily on coffee and banana, these crops constitute nearly 65 % of total household income and are the most profitable in relation to the land that is allocated to it.
Considering the low levels of production that Ugandan coffee farmers achieve in general and for the farmers of our study in particular (773 kg/ha) in relation to the production of Vietnam 2.2 MT. We identified the main constraints that hinders the achievement of more desirable
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levels of production. These constraints range from weak marketing position and vulnerability in relation to middlemen, lack of access to finance, and most importantly a lack of adoption of good agronomic practices and inefficient management of the farm.
Considering the main constraints that farmers face, we conducted a sensitivity analysis to illustrate the monetary benefits that dealing successfully with the main obstacles would generate to the economy of the households. We used data provided by Hanns Neumann Stiftung and the Uganda Coffee Farmers Alliance (UCFA), to identify the potential gains that a better marketing organization, and the adoption of good agronomic and management practices could generate. We found that there are lucrative opportunities available for the farmers to increase substantially their total household income. We estimated that for the average household the potential benefits, of obtaining better prices and adopting good agronomic practices, are of 940 dollars.
This potential gain exemplifies the significant improvements that smallholder farmers in Uganda can obtained when farms are managed according to efficient agronomic and economic practices. This report has focused on coffee, although as illustrated by other studies there are also substantial opportunities in other crops, like banana (Van Asten et al.,2010) and also maize, cassava or other farming enterprises like selling goats or milk (Kraybil & Kidoido, 2009).
We conclude by stressing the huge potential that African smallholders have to substantially increase their livelihoods through feasible improvements in their farm management. Given the high dependency of African economies on their agricultural sector, achieving these improvements could generate a huge leap in their development process.
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Cognigni, S(2010): Kaweri coffee farmers alliance support project. Establishment of the “uganda coffee farmers alliance”. NKG Coffee Alliance Trust.
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Fowler, Martin. (2007) Kaweri Coffee Farmers’ Alliance Support Project: analysis of coffee gross margins. Final report.
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Helen Markelova, Ruth Meinzen-Dick, Jon Hellin, Stephan Dohrn, Collective action for smallholder market access, Food Policy, Volume 34, Issue 1, February 2009, Pages 1-7, ISSN 0306-9192
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Van Asten, P. J. A. Van, Wairegi, L. W. I., Bagamba, F., & Drew, C. (2010). Factors Driving Fertilizer Adoption in Banana ( Musa spp .) Systems in Uganda. Acta Horticulturae 879: 465-478
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Willen Paulus (2013): Strengthening member commitment at NUCAFE. An overview of the factors influencing member commitment at NUCAFE and proposed measures to strengthen it. Technical report .
Appendix . Report of meeting with stakeholders
Interview with Anneke Fermont 22/5/ 2013
Anneke started the interview by informing me on what she does and how Kyagalany and Volcafe operate. She talk about the certification scheme and she told me that it has limited scope in the strategy of Volcafe and that it wasnt profitable but the reason behind their support was motivated to achieve sustainable supply of coffee over a long period of time. The requirements of traceability made necessary a high number of staff to monitor farmers activities and does not allow the use of middle men for the supply of certified coffee, which raises costs significantly.
She emphasize the current suboptimal management techniques use by the farmers, in particular stumping ( for Arabica though), lack of knowledge for pruning, very low use of fertilizers. Kyagalany tries to promote the use of fertilizers and give incentives to farmers to improve their management techniques, so far with little success.
Related to the study she suggested to analyse the effects of price volatility on the profitability of the farm, in comparison with other crops. Her other main suggestion was to create 3 categories on degree of input use by the farm: Low, average and intense and evaluate the effect of this systems on the profitability of the farm and more specifically on coffee. She also mention the difficulty the isolate the effect of fertilizer on coffee as its likely to affect the yield of other crops.
She considers the study as relevant and she requested me to keep her update on the progress.
In kyagalany the have not done a study on this issue, but she recommend me other people who are position to provide better information.
Interview with Toni Mugoya 7 / 6/ 2013
At the end of the coffee breakfast on the 7th of June, Anneke Fermont introduce me to Toni. Our talk started with him talking about the Uganda coffee farmers alliance. The most interesting and useful aspect of their organization for our study was the fact that well knowledgeable farmers of their organization have a demonstration plot for other farmers within their section of the organization (P.O.). In this demonstration plots UCFA provides initial training and the advisable inputs so the owner can follow the optimal, or suboptimal agronomic practices and show how to do it to other farmers. He showed me the figures of production for production for the farmers that belong to the organization and compare with the average in Uganda, the difference in income for those who belong to UCFA are more than double the average Uganda coffee farmer. We can use this figures during the partial budget analysis to compute the benefits that the improve practices will have on
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the farmers of our project. Toni also mention that Hanns stiftung and COREC have more accurate figures on the estimates of the use of fertilizer and, pruning …
Tony thinks that is a relevant study and he is particularly interested on the methodology to calculate the gross margin as they want to do it themselves for their organizations. He would be also interested on a cost benefit analysis and he requested to be updated on the study.
His main suggestion was to check the data of the field so we are sure that we have accurate figures.
Interview with Martin Fowler, 24/06/2013
I went to Emin Pasha Hotel to meet with Martin, our talked lasted around 50 minutes as he had a meeting with the minister of agriculture. He talked to me about his career and about the work he has done related to study the profitability of coffee on what factors influence it, he did their work when he was working as a consultant he did the report to evaluate the effect that a coffee farmers alliance could have on the income of the farmers. He also indicated me how to conduct the sensitivity analysis.
The main constrains that he considers are the limited labour , the distance to the road and the middle man issue which reduces the incentives to improve the quality of coffee. The main suggestion to improve the position of the farmers in relation to middle man is to distribute prices among farmers so they would be aware of what their product is really worth, increasing their bargain power. He thinks that working together with middle man, especially with the organization called coffee Quality Association can be fruitful.
He told me that the main constrains that hinders the adoption of improve management practices are the lack of labour combined with the fact that the returns to agronomic practices takes place after the second year and specially after the third year.
He gave me some contacts of people that I should interview, Peter Wathum chief of the lead project who calculated the gross margins for all crops and Dr Godfrey Bashaasha professor of agricultural economics at Makerere University.
He will also send me the documents that he work on, or he knows about them related to the topic of our study.
He is interesting on our study and wants to receive the results of our study.
Interview with Stefan Cognigni, 24 th of June
I went to Hanns Neumann Stiftung office to interview with Stefan, our conversation lasted around 1 hour and 15 minutes. First I presented the research that we are conducting, when I talked about the constrains that we are looking at, he has the opinion that the most relevant ones are the lack of use
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of good agricultural practices, lack of training due to absent of good organizations or extension services. He also stressed the pernicious effect that lack of credit have on the farmers not only to finance their agricultural inputs but mainly to satisfy their household expenditures, like schooling fees or to cope with adverse shocks like medical issues. These necessities put the farmers in hands of the middle man who advance their money in return for the coffee beans once they are harvested discounting an approximate of 40-50% of the value of the coffee. The process of getting finance to smallholder farmers is complicated, the newer interventions aim at providing credit to the farmer organizations with formal banks like Centenary bank involving the exporters as well. To test the effects that access to credit will have on the farmers, they conducted an experiment in a trial field in which the farmers had access to credit, the results showed a considerable increase in income.
They also conducted another trial in which improve agronomic practices were applied by the farmers, like pruning mulching and land water conservation techniques (basically mulching) and also fertilizer was used. In the ideal conditions the production can soar to nearly 2 tons per hectare, the feasible attainable levels of production , in which 60- to 70% of the improve management techniques were applied the production can reach 1.2 tons per hectare in contrast with current 600 kg per hectare. Stefan is convinced that those levels can be achieved. Stefan told me that he would send me the information on this trial.
The main aspects that hinders adoption of improve agricultural practices are the low farm gate prices that generally farmers obtain. For Stefan the key would be to create value addition so farmers will have incentives to allocate more labour towards coffee production. He also said that middle man do not have enough market power to decrease the price in case that farmers increase production.
I ask if we could try to convince the farmers in our study to join the UCFA where better prices are obtained and also get some training and he said that yes there could create a producer organization and integrate the depot committee of their respective areas.
Stefan Considers that the volatility of prices is not a mayor determinant of farmers decisions, he thinks that the prices of coffee will remain good for the next years.
Finally he shows interest on or study and requested to be updated on our progress.
Interview with Harris Luzinda. 17/06/2013
Harris Luzinda From COREC came to IITA office in Naguru Hill. I talked about the research we are conducting, he was interesting on it particulary on the analysis of the factors that influence farms profitability, according to him that where we can get a paper out of our study, it would also be particularly interesting for policy makers. He suggested to perform a time series study of 3 years to compare the profitability between different years. He stressed the importance of taking into account the labour costs and also the post-harvest handling and marketing costs. He also suggested to analyse the effect that the age of coffee, the variety of the coffee trees and the management practices employed have on the profitability of the farm. He also pointed out that normally the work with larger sample sizes, but if the regression analysis is not feasible we can use a more qualitative approach. Finally, he suggested to use a cobb-Douglas model to find the level of inputs required to achieve the break-even level of output.
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In COREC they have not done a strictly related study on profitability of coffee farms, nor he does not know of any organizations that have done anything related, but he suggested to get in touch with UCDA.
COREC and Harris are interesting on the study, in fact the want to collaborate with it, he is available and very willing to do so. I asked him what he could bring to the study, he said that they have a report on the effect that the improve management practices have on the yield of coffee. They also have done a study in Mount Elgon in which the obtained the revenue and costs of production of Arabica coffee.
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