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Characteristics and economic viability of milk production in the smallholder farming systems in The Gambia
AGRICULTURAL SYSTEMS Agricultural Systems 85 (2005) 42–58 www.elsevier.com/locate/agsy
Characteristics and economic viability of milk production in the smallholder farming systems in The Gambia Jacques Somda
a,b,*
, Mulumba Kamuanga c, Eric Tollens
b
a
b
International Trypanotolerance Centre (ITC), PMB 14, Banjul, The Gambia Department of Agricultural and Environmental Economics, Katholieke Universiteit Leuven, Belgium c International Livestock Research Institute (ILRI). C/o CIRDES, 01 B.P. 19 Bobo-Dioulasso 01, Burkina Faso Received 18 June 2003; received in revised form 23 June 2004; accepted 14 July 2004
Abstract Domestic milk production has been for a long time hindered by many factors including lack of interest from decision makers, distorted economic policy and biotechnical constraints. For the last 20 years, many developing countries have been attempting to develop the domestic milk production sector. However, research on the basic realities and the viability status of enterprises within this sector remain largely unproved in many developing countries. This study focuses on the characteristic of smallholder milk producers in The Gambia. Data were collected from 90 smallholder farm households to characterise milk producers and evaluate the profitability and viability status of this activity. Based on current typology of farms and gross margin analyses at farm level, the study identified two resource-based types of smallholder farms. The current milk production system is surely viable. Constraints to increased productivity include lack of improved technology at farm level and weak institutional support. Despite the low viability status, it is shown that milk production generates reliable incomes,
*
Corresponding author. Tel.: +00220 462928/+226 50340271; fax: +00220 462924. E-mail address: [email protected] (J. Somda).
0308-521X/$ - see front matter 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.agsy.2004.07.011
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which could be a departure for most farmers to intensify farming systems, particularly in areas where no loan schemes exist for purchasing agricultural inputs. 2004 Elsevier Ltd. All rights reserved. Keywords: The Gambia; Milk production; Farming systems; Discriminant analysis; Viability
1. Introduction Several farming systems and modes of production co-exist in developing countries. Their multiplicity has created the need for a system of classification that can help to group similar systems for the purpose of identifying opportunities and constraint to development. The most commonly found farming systems in sub-Saharan Africa are the mixed crop-livestock systems (Sere´ et al., 1995). Availability of labour is potentially high in these systems in contrast with land and capital that are less plentiful (Williams et al., 1999). These systems are also known to be socially, economically, and technologically diverse. This diversity stems partly from differences in agro-ecological conditions, population densities and economic opportunities. Milk production in West Africa takes place in these diverse mixed crop-livestock production systems where the trypanotolerant NÕDama cattle is an important breed (Aboagye, 1998). Indeed, this breed has enabled milk production (and beef as well) to be maintained in a region where tsetse-transmitted trypanosomosis inflicts severe productivity losses in local farming systems. NÕDama are milked to provide for human consumption and income in several parts of West Africa, but it is generally acknowledged that this breed has low milk-production potential (Ferguson, 1988), although it can be viewed as rather a good beef breed (Starkey, 1984). Its adaptation to local agro-ecological conditions in the region makes it attractive to smallholder farmers. Furthermore, the projected growth in the demand for dairy products in sub-Saharan Africa (Holloway et al., 2000) provides opportunities for smallholders to increase their income through milk production. According to Delgado et al. (1999), milk production and dairy product consumption are expected to grow in the region by 3.8–4% annually between 1993 and 2020. The gap between domestic supply and demand for livestock products, particularly milk, in West Africa is expected to widen, especially as incomes rise and rural–urban migration continues (Aboagye, 1998). The key questions are whether smallholders would be able to seize these opportunities and what policies are needed to boost domestic supplies of milk while saving on foreign exchange reserves used for importing dairy products. In the early 1980s, an adverse international environment created unsustainable external imbalances, and worsening patterns of growth in many developing countries (Scobie and Jacobsen, 1995). It is argued that difficulties in financing imports in order to feed the growing population originated from depressed agricultural export earnings (Tabor, 1992). As long as sub-Saharan Africa faces poor economic growth and growing external imbalances, it would be unrealistic to rely fully on imports of dairy products to meet
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the demand, as was the case before the economic crisis settled in most West African countries. One alternative is to increase domestic milk production, an option which most countries in Sub-Saharan Africa appear to have endorsed. The livestock sector in Sub-Saharan Africa has been recognised as significantly contributing to economic development (Winrock, 1992). Livestock production systems are an important component in local economies at both the national and farm household level (HanyaniMlambo et al., 1998). Specifically, the dairy sub-component has proved to be the most important in the smallholder sector where milk is a source of protein for young children and supplementary income to often cash-starved farm households (Hanyani-Mlambo et al., 1998). Despite existing opportunities for smallholders to develop dairy production, current output is well below the potential level of production that can be achieved even with the use of local breeds. One reason for this situation is that past and current field research and development policies have for long favoured crops over livestock development, and technical over socio-economic solutions. There appears to be a dearth of literature on the economic outlook of smallholder dairy enterprises in West Africa in general, and particularly in The Gambia. Only a few studies (e.g., Itty, 1992, 1996; Itty et al., 1993) have addressed the questions of profitability, efficiency and comparative advantage of African meat and milk production. Even then, most of the available studies failed to relate farm household resources to economic outcomes in their analysis framework. Consequently, the implications of the results were rather broad and limited in terms of recommendations for technology transfer. This paper builds on primary data to support on-going efforts by many governments in West Africa including The Gambia, to establish smallholder dairy schemes as a tool for poverty alleviation. Its aim is first to gain a better understanding of the existing potentials and constraints of the smallholder dairy industry. Based on the realities and the viability of milk production units taken as enterprises, the paper assesses the economic status and nature of constraints and opportunities that individual smallholder milk producers face in the context of farming systems. Thus the overall objective is to generate baseline information on the economic performance of milk production at the farmer level, and specifically, to: (1) analyse the extent of variation in productive resources among milk producers; (2) evaluate the economic performance and viability of small dairy farms and (3) identify paths for technology development.
2. The research context 2.1. Research hypotheses The diversity of patterns of development of dairy production in crop-livestock systems suggests that different prescriptions will be needed to improve productivity. Furthermore, the effectiveness of any prescription will depend on the ability of livestock farmers to adopt resulting recommendations. Appropriate strategies for
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improving domestic dairy production will be implemented against the background of differences in farm-level resource constraints. The following hypotheses are tested: 1. Smallholder farm households are endowed differently with productive resources that influence milk production; 2. Adoption of technologies for milk production is not scale-neutral; 3. Milk production in smallholder farming systems is cost-effective and economically viable.
3. Research framework Farmers keep livestock as a means of transforming resources into a wide range of outputs such as milk, meat, traction power, dung, hides, wool, fibre and animals (Rushton et al., 1999). They are also involved in crop production. Farmers can then smooth the flow of income to the household through making conservative production choices, combining production enterprises, and diversify economic activities. In this context, the limiting resources are not necessarily the same for all smallholder farms, and for one single farm household, they may even change over time. This research was carried out within the framework of a regional project – PROCORDEL – initiated by The International Trypanotolerance Centre (ITC) with the support of European Union and the governments of Senegal, Guinea, Guinea Bissau and The Gambia. The farming systems in these countries are a product of years of innovations and adaptation to the sub-humid and non-forested humid environments, under a uni-modal rainfall regime where wet periods are followed by rather long dry seasons from October to April. Dairy herds are mostly composed of NÕDama breeds, and some Zebu and crossbred cattle. The Gambia is a representative case of smallholder dairy farms where NÕDama cattle predominate.
4. Data collection and analysis The target population for the study is defined as consisting of all smallholder farm households owning at least three milking cows in three administrative divisions in The Gambia (Central River, Lower River and Western Divisions). This area accounts for 59% of the total land area estimated at 10,691 km2 and 48% of the population evaluated at around 1.4 million inhabitants. A baseline survey was conducted to collect data on cattle numbers, household and farm sizes, and other information including livestock and crop production and markets. A sample frame was constructed based on the list of 501 livestock owners enumerated in each of the selected divisions, from which 90 farm households were randomly selected. These represent the population of livestock owners involved in milk production in The Gambia.
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A detailed survey was then carried out from June to September 2001 using a structured questionnaire to collect data on the socio-demographic characteristics of smallholder farms, location of the production units with regard to markets, production unit assets, and associated costs (infrastructures and equipments). Data were also collected on livestock practices including feeding, health management, milk production, reproduction-genetic improvement, the herd structure, and off-take of animals. In all cases, the quantities used, costs and management practices were assessed. A subsample of owners was monitored to collect information on market access and constraints as well as opportunities for investment. Data were analysed using several methods. To test the first hypothesis, a classification function (Klecka, 1981) was adapted. This uses the theory of maximising group differences while minimising variation within groups through K-means analysis. The K-means analysis aims at assigning specific cases to the particular group most closely resembled. Given n farmers with p variables measured for each farmer, xij for i = 1, 2, . . ., n; j = 1, 2, . . ., p, K-means clustering consists of allocating each observation to one K group or cluster to minimise the within-cluster sum of squares shown as: p K X X X 2 xij xkj ; k¼1 i2S k
ð1Þ
j¼1
where Sk is the set of farmers in the kth cluster and xkj the mean for the variable j over cluster k. The number K was selected to ensure the recommendations drawn from the results were technically and practically feasible, rather than theoretical. We applied values of K from 2 to 5 and checked resultant groupings. In this study, we use K = 2. In order to judge the results of the K-means analysis, a discriminant analysis was derived to study the nature of group differences. We assessed the adequacy of the classification, given the group membership of the farmers under study. In discriminant analysis, classification into a given category (type) of farmers is a function of the predictor variables fkn ¼ u0 þ u1 X 1kn þ u2 X 2kn þ þ up X pkn ;
ð2Þ
where fkn is the value (score) on the canonical discriminant function for case n in the group k; Xikn represents the value of discriminating variable Xi for case n in group k; and ui are the coefficients or the discriminant weights. This allows testing of the p predictor variables as discriminators of the differences between the two groups of the single discrete dependent variable. Several test statistics, such as WilksÕs lambda, eigenvalue and canonical correlation, were used as measures of goodness-of-fit of the discriminant function. WilksÕs lambda is a multivariate measure of group differences over several variables (the discriminating variables). The test proceeds indirectly. Rather than testing the function itself, WilksÕs lambda examines the ability of the variables to discriminate among the groups beyond the information that has been extracted using the previously computed function. If the variability is too small, then it is meaningless to derive any more functions, even if they exist mathematically. In
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other words, values of lambda that are near zero denote high discrimination. The eigenvalues are a special set of scalars associated with a linear system of equations. They can be used to judge the number of important canonical discriminant functions. Eigenvalues that are greater than 1 are statistically significant. Correspondingly, a canonical correlation of greater than 0.6 is statistically significant. Computational details can be found in Klecka (1981). A stepwise discriminant analysis was applied to include many variables in order to determine those that discriminate between groups. The variables include the number of active members in the household, farm size, estimated cost of permanent investment for livestock production (buildings), estimated cost of intermediate assets (equipment), number of NÕDama cattle, number of exotic breeds, number of Zebu breed, number of crossed NÕDama and exotic breeds, number of Gabu and/or Futa sub-breed animals, and the number crossed with NÕDama. The stepwise procedure is guided by the respective F statistic. The F value for a variable indicates its statistical significance in the discrimination between groups. It is a measure of the extent to which a variable makes a unique contribution to the prediction of group membership. Economic analysis was performed to test the third hypothesis. The analysis is based on an assessment in which the gross margin serves as the unit of analysis in evaluating the economic performance of each resource-based group of farmers. Johnson (1985) defines the gross margin as the difference between the value of an enterpriseÕs gross output and the marginal cost of production. In the analysis, the value of the gross output included the value of sales of milk, as well as the value of milk consumed by the farm household. The marginal costs of production were the enterpriseÕs variable costs. Bought-in feed costs were calculated on the basis of financial prices, while non-priced items such as use of farm-produced feeds and labour were calculated using economic prices that are based on the opportunity cost principle, as adapted from Hill (1990). Financial and economic performances were obtained using either financial or economic prices, respectively. Gross margin ratios were calculated to evaluate the economic viability of the smallholder milk production. Finally, combining the discrimination and economic performance results drew implications for the second hypothesis.
5. Results 5.1. Dairy production and the farming systems Before discussing the characteristics of milk producers, it is worthwhile to understand the farming systems in The Gambia. This provides information that can help trace out patterns for developing dairy systems, with associated implications for the overall farming systems within which milk production takes place. The results from the baseline survey show that cattle, mainly the NÕDama breed, are kept under mixed crop-livestock production systems with a very limited utilisa-
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Table 1 Test statistics for the discriminant function of farm households, The Gambia Discriminating variables
Wilks
F
Family labour Land NÕDama cattle Zebu cattle Crossbreed cattle Intermediate assets DF
0.98 0.95 0.99 0.93 1.00 0.43 1
2.09 4.56 1.18 6.93 0.00 118.1 88
Notes. DF, degree of freedom (the first value corresponds to the df1 and the second the df2); F and Wilks are the Fisher statistic and the WilksÕ Lambda, respectively. Source. Survey results.
tion of external inputs. A variety of crops (cereals, legumes, cotton and trees) and livestock species (cattle, small ruminants and monogastrics) are grown for multiple objectives. Out of the 501 farmers enumerated during the baseline survey, 49% do not keep animals. Those with animals, and particularly cattle, aim to produce meat, milk or both. Hence, cattle are used for generating income through selling milk and live animals (69% of the sample) and for cultivation (draft power) in 19% of cases. Whether the farmer is milking the cows for human consumption or not, depends on a variety of factors, including the productivity per cow, which is related to the agro-ecological conditions, the resources at the farmerÕs disposal, and their allocation to milk production. 5.2. Typology of smallholder milk producers The resource-based typology of smallholder milk producers is built on selected structural variables, namely, the number of active members in the household (in man-equivalents), farm size, the estimated value of fixed agricultural assets (permanent and intermediary) and stock size (NÕDama, Zebu and/or crossbred). It is hypothesised that these variables are determinants in the decision of the farmer to engage in milk production. Moreover, if smallholders differ with regards to these resources, then the performance achieved in milk production and management will be affected. Results of the K-means and the discriminant analyses are given in Table 1. Regressions of the selected structural variables on the K-means group membership help to identify those significant variables that contribute to smallholder discrimination. The estimated discriminant functions suggest that in The Gambia, the statistically significant variables were: the number of Zebus (p < 0.01), farm size (p < 0.06) and the amount of investment in intermediate assets (p < 0.001). The test for goodness-of fit of the predicted discriminant function indicates an eigenvalue of 1.82, a WilksÕs lambda of 0.35 and a canonical correlation of 80%. The chi-square statistic is 88.17 with 6 degrees of freedom. All these statistics show that the predicted function is statistically significant at 1% level. The tests also suggest that the selected variables can be used to discriminate smallholder farm households. The smallholders
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Table 2 Resource-based groups statistics of the discriminant variables of smallholders Discriminating variables
Resource-poor farms
Medium-resource farms
Labour (Man-equivalent) Land (ha) NÕDama (head) Zebu (head) Crossbreed (head) Intermediate assets (GD) Sub-sample size
11 (0.84) 5.45 (0.78) 54 (4.41) 0.00 (0.00) 0.18 (0.09) 448.69 (63.18) 73
14 (1.83) 9.12** (1.19) 71 (12.79) 0.12** (0.12) 0.12 (0.12) 4094*** (369.42) 17
Notes. GD, Gambia Dalasi is the country currency (1 $US = 15.30 GD). Values in parentheses are standard errors. ANOVA test of equality of within country smallholder groups means: **, ***significant at 5% and 1% level. Type 1 farmersÕ resources are below the confidence interval of the sample average. Type 2 farmers are within the confidence interval. Source. Survey data.
can be qualified into resource-poor (below the confidence interval for the means of the discriminant variables) and medium groups (within the confidence interval). The former group (type 1) accounts for 81% of the sample and the latter for the remaining 19%. Table 2 summarises the group statistics for the discriminant variables. Very few Zebu and crossbreds are reported in the sample, and all belong to the second group with a medium level of resources. Crossbreds (including Zebu · NÕDama and Friesian · NÕDama) are unequally distributed among resource-poor and medium farmers. However, the difference is not statistically significant. Land ownership is significantly unequally distributed between the two types of farm households. The medium smallholders have also shown a capacity to invest (in farm equipment) more than the poor. On average, there are no significant differences in the number of active man-equivalents and the number of NÕDama cattle between the two types of smallholders. 5.3. Comparison of herd structures of smallholder milk producers The herd structures for the two types of smallholder are presented in Table 3. Irrespective of the resource-related type of farm household and the age categories of cattle, farmers keep more females than male animals in the herd, often in a proportion of over 60%. In particular, reproductive female cattle represent more than 80% of that category in both the resource-poor and medium farms. However, there exists high within-group variability of the sex-age category of cattle. The estimated coefficient of variation (C.V.) in the medium-resource group are: 94%, 79%, 71%, and 70% for female cattle of over 3 years, 2–3 years, less than 1 year, and 1–2 years, respectively. For male cattle, these values are 109%, 103%, 93%, and 84% for less than 1 year, 1–2 years, over 3 years, and 2–3 years old, respectively.
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Table 3 Comparative herd structure of resource-based farm households (head) Age categories
<1 year
1–2 years
2–3 years
Over 3 years
Female cattle Resource-poor farms Medium resource farms
7 (0.78) 11 (1.84)*
5 (0.66) 8 (1.32)
6 (0.91) 7 (1.33)
18 (1.57) 23 (5.14)
Male cattle Resource-poor farms Medium resource farms
5 (0.57) 7 (1.91)
5 (0.61) 7 (1.73)
3 (0.45) 4 (0.82)
4 (0.39) 5 (1.22)*
Values in parenthesis are standard errors. One-way ANOVA test of equality of groups means: *, significant at 10% level. Source. Survey data.
In resource-poor farm households, the highest within-group variation is found for females of 2–3 years (C.V. = 132%), followed by those of 1–2 years (C.V. = 103%). The coefficients of variation of the number of females less than 1 year and over 3 years are 90% and 76%, respectively. Male cattle are also diversely distributed among the resource-poor farms. Coefficients of variation of 115%, 114%, 96% and 91% were estimated for the number of animals of 2–3 years old, 1–2 years, over 3 years, and less than 1 year, respectively. 5.4. Herd structure and management of milk production The herd structure has implications for milk production, because dairy cows make up part of the reproductive herd. On average, 57% and 61% of the reproductive cows are milked in the medium-resource group during the dry and rainy season, respectively. On the other hand, resource-poor farm households extract milk from 48% of their reproductive cows in the dry season and 61% in the rainy season. Irrespective of the resource-related group, cows are manually milked. On average, the milking is done once a day in the dry season and twice in the rainy season. As a consequence, the quantity of milk extracted for human consumption varies between seasons. Likewise, the amounts allocated to family consumption and sales are different from dry to rainy season. In the rainy season, farm householdsÕ consumption averages two litres in both medium and resource-poor groups. In the dry season, medium-resource farm households consume slightly more milk than the resource-poor households (4 and 3 litres/household, respectively). However, there was no statistically significant difference. On the other hand, there was a significant difference (p < 0.05) in daily sales of milk between the resource-group of farm households. The medium-resource farm households sell on average 6 litres/day in the dry season and 17 litres in the rainy season; for the resource-poor farm households, this quantity averages 4 and 11 litres/ day during the dry and rainy season, respectively. At the same time, sales average 12 litres in the rainy season, decreasing to 4 litres in the dry season, for the whole sample.
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5.5. Feeding resources and utilisation Feeding strategies are not related to the identified resource-based groups of farm households. Feeding resources are mainly based on natural pasture, crop residues and agricultural by-products. The majority of farm households (79%) rely on natural pastures. The remaining (21%) combine natural pastures and stall feeding of some animals with crop residues and supplements. Animals benefiting from supplementation are mostly draft animals. Nearly 30% of the sample farms supplement draft animals, while only 1% supplement lactating cows as well as sick and draft animals. In practice, supplements fed to a particular category of cattle can also benefit other animals depending on availability. Because of the lack of infrastructure for feeding animals, cattle are invariably supplemented with crop residues and agricultural by-products. Among crop residues, groundnut hay is commonly used by 43% of the farm households, although this is own-produced in only 1% of cases. Some farm households (7%) make use of mineral licks. Farmers also use different agricultural byproducts to supplement their animals. Cereal (millet and rice) bran is the most important and is used in 9% of the farms, while groundnut cake is used in 8% of cases. Under these conditions, feed shortages, particularly during the dry season, remain a serious constraint to increased milk production. Rotating pastures and short transhumance during the dry season are responses that farmers have devised to address the feed constraint. Three types of movement are identified with relation to the distance covered. The majority of the sample farm householdsÕ herds (66%) move across the country, toward the southern part of neighbouring Senegal in the late dry season and return during the raining season. Some herds (22%) are moved round within the country; while 12% of the sample farms leave their animals to roam where natural pastures and crop residues are still available. During all these movements, draft animals, lactating cows and sick animals are kept near the village and fed with crop residues (groundnut hay and sorghum straw) and sometimes with agricultural byproducts (cereal bran and groundnut cake). 5.6. Supply of dairy products from smallholdersÕ households The above results suggest that various factors influence the supply of dairy products by farm households. The off-take of milk is shared between family consumption and sales. This is in turn influenced by the potential of the breed and the production techniques used, which are extensive. The quantity supplied to the markets depends on such factors as the periodicity of production, the location of the farm household with respect to markets, and the marketing strategies put in place by the farmers. Results from the survey indicate that the majority of farm households (66%) sell dairy products throughout the year, and 28% during the rainy season only. These products comprise fresh milk (56% of farm households), sour milk (6%) and both fresh and sour milk (31%).
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Table 4 Annual cost, gross income and gross margin of milk producers (Gambia Dalasi)
Sales (1) Self-consumption (2) Gross dairy income (3) = (1 + 2) Feeds (4) Health care (5) Herder (6)a Total variable costs (7) = (4 + 5 + 6) Gross margin (3–7) Net cash income (1–4–5)
Resource-poor farms
Medium resource farms
10230.41 (1117.49) 3713.43 (182.79) 13943.84 (1188.03) 46.78 (17.76) 390.23 (31.13) 2621.26 (74.13) 3058.27 (90.77) 10885.56 (1190.34) 9793.40 (1117.06)
16355.29** (4018.60) 4034.12 (497.29) 20389.41** (4133.34) 39.71 (21.74) 557.29** (74.51) 2786.82 (166.79) 3383.82 (196.54) 17005.59* (4193.58) 15758.29* (4011.93)
Values in parenthesis are standard errors. ANOVA test for equality of within country group means: * and ** significant at 10% and 5% level. US $1 = Gambia Dalasi 15.30. Source. Survey data. a Herder is member of the farm household.
Dairy products are sold at either the farm gate (74% of cases), or at the local markets (16%). Some farmers (2%) combine supplying to the local markets and selling their milk on a stand by the side of main roads. Several transport materials are used to take dairy products to the markets. Recycled oil or margarine cans of different capacities (5–20 litres) are filled with dairy products and transported to the markets by foot, bicycle, motorcycle or public transport. The distance that farmers walk to deliver their dairy products varies from 1 to 14 km, with an average of 6 km (SE = 1.07; SD = 4.16). Most farmers (54%) deliver their products directly to collectors, while 38% sell to consumers. The pricing of dairy products is based on bargaining, allowing prices to be set between sellers and buyers. Even though those prices are seasonally set, the relationship between sellers and the buyers greatly influences the level at which unit prices are negotiated. 5.7. Economic performance of smallholder milk producers Maximising production levels and profit are goals cherished by most smallholder dairy farms. It is thus important to understand the levels of performance that farmers achieve in the current milk production systems, and what the viability is of milk production and the farming system in general. Table 4 presents the partial budget of smallholder milk production units, based on the accrual method of accounting. The results indicate that smallholder dairy farms are economically profitable. The results indicate that cash investment for milk production among smallholder dairy farms is very low. Expenditure on feed (mainly crop residues) is not statistically different between types of farmers. On the other hand, resource-poor farmers (type 1) are spending significantly less money on animal health care than the medium farms. On average, because of low investment in feeds in particular, the economic performance of milk production depends essentially on the number of lactating cows owned by the household. The gross dairy income of the medium-resource farmers is 46%
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Table 5 Comparative tests for economic viability ratios Viability indicators
Resource-poor farms
Medium resource farms
Operating cost/gross margin Capital turnover Total fixed cost/gross margin Total expenses/gross margin Net cash/variable cost Net cash/lactating cow (GD)
0.37 (0.04) 0.28 (0.02) 0.05 (0.01) 0.42 (0.05) 3.39 (0.38) 502 (56.39)
0.38 (0.11) 0.24 (0.03) 0.51 (0.17)*** 0.89 (0.27)*** 5.34 (1.47)* 434 (74.31)
Values in parenthesis are standard errors. ANOVA test for equality of within country group means: * and ***, significant at 10% and 1% level, respectively. US $1 = Gambia Dalasi 15.30. GD = Gambia Dalasi. Source. Survey data.
higher than that of the resource-poor group. The estimated gross margin averaged US $911 and $1111 for the resource-poor and medium farmers, respectively. The estimates of the net cash income derived from milk are US $640 and $1030 for the poor and medium farmers. However, results indicate that those means are associated with a high variability. For the net cash income, the within-group coefficients of variation are evaluated to 105% and 97% for the medium and resource-poor farm households, respectively. In the former group, the minimum and maximum values of the net cash per year range from US $59 to US $3857, while in the latter group the corresponding values are $79 and $2361. Similar trends were observed for the within-group variation of the gross margin. 5.8. Economic viability of smallholder milk production Several indicators were calculated to assess the viability of smallholder dairy farms. The operating ratio (total operating costs divided by the gross margin) is a measure of the cost-effectiveness of the production unit. Capital turnover measures the comparative advantage of keeping cows for milk production as compared with selling them. The net cash ratio estimates the ratio of cash income to the variable costs. The fixed and gross ratios measure the viability of dairy production in mixed crop-livestock production systems. Table 5 summarises some viability indicators of the smallholder dairy farms. Assessment of the selected ratios cast no doubt on the economic viability of smallholder dairy production in the farming systems of The Gambia. The results indicate that investing in milk production under current farming practices is worthwhile. Although the averages indicators were positive, some results were somewhat disappointing. The total expenditure to gross margin ratio is rather low, particularly in the medium-resource group of farms (almost unity) as they incur high fixed costs. The capital turnover shows that milk production is not profitable compared with selling dairy cattle. In other words, farmers may be better off if they sell their dairy stock rather than keeping them for milk production.
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There is a strong indication from examining the selected financial ratios that it is profitable to engage and to invest in milk production. Each dollar invested in milk production earns an average of 3 and 5 dollars for the resource-poor and medium groups of farms, respectively. Hence, medium farmers are significantly more costeffective than the resource-poor farmers. On the other hand, the returns per dairy cow are estimated at US $33 for resource-poor farms and US $28for the medium. Although the difference is not statistically significant, this result suggests a scale effect on the net cash income to variable cost ratio.
6. Discussion Mixed crop-livestock production systems in sub-Saharan Africa are known to be socially, economically and technologically diverse (Sere´ et al., 1995). It is further argued that this diversity stems partly from the differences in the material conditions and the production relations, and that a diversified range of technical options are required to suit the needs of farmers with different resource endowments and management skills (Williams et al., 1999). However, few quantitative studies exit that document these conditions from the farm householdsÕ perspective. This study investigates the relation between resource endowments and the performance of dairy production, and the implications for the whole farming system. The results indicate that smallholder milk producers are heterogeneous in their resource endowment. Three resource variables were found to be significantly discriminating among farm households: farm size (ha), the amount of investment (Gambia Dalasi), and the number of Zebu breed owned. These variables are directly or indirectly related to milk production in the farming system. Farm size determines indirectly the amount of crop residue that will be available for feeding animals. The amount of investment on intermediate assets such as agricultural equipment is associated with the mechanisation level of the farm household, and is known as a determinant in intensifying crop production. The Zebu breed is known as a better milk producer than the local NÕDama, and its introduction in herds is expected to increase the amount of milk produced. The study established that smallholder dairying in The Gambia is indeed viable. The viability of current milk production is resource-scaled. In other words, viability is higher in the resource-medium group than in the resource-poor group. HanyaniMlambo et al. (1998) and Dube (1995) in Zimbabwe and Waters-Bayer (1988) in Nigeria also reported poor viability for smallholder dairy farms. The low level of economic viability in The Gambia can be attributed to various factors, including the lack of appropriate feed resources and the depletion of natural pastures. Dairying still involves few external inputs. Apart from veterinary products, farmers mainly rely on crop residues, which are known to be low in nutrients, to feed their animals. Consequently, productivity per cow remains low and is subject to seasonal variation related to feed availability. The diversity of farming situations and multi-purpose nature of raising cattle make it difficult to target agricultural investments for the sole objective of increasing
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milk production when it is perceived as one component of the mixed crop-livestock farming system that provides food for the ownerÕs family and feed for cattle. The marketing of milk contributes to generating income that can be used to purchase agricultural inputs, including dairying, and to satisfy other needs for cash. There is therefore no doubt that milk production in the current farming system will continue to be attractive because it is a source of relatively regular and reliable income for most farmers, compared with the traditional once-a-year return in crop production. It appears to be the only source of financing crop production in areas where there are no loan schemes to purchase agricultural inputs. The basic implication of the limited viability of smallholder dairy farms is that the expected higher incomes for poor rural households may not be forthcoming. The results show that viability problems are more acute for resource-poor farmers, who comprise the majority of smallholder dairy producers. Without any technological improvement, poor or smaller farmers need to accumulate capital, particularly cattle. However, a large stock of cattle does not guarantee increased viability, as smallholders may face management and resource allocation problems. The study also reveals that farmers are motivated to sell more milk because of improved access to markets at the farm level or at the side of main roads. Investment in feeds (groundnut hay and concentrates) during the dry season is increasing, in spite of the difficulty in accessing these inputs. Both medium and resource-poor farm households do supplement some milking cows with concentrate, roughage and mineral (salt) to stimulate milk production for calves and human consumption. However, the contribution of crop residues to milk production is low compared with draft animals that still receive more attention from both resource-related groups of farmers. Agyemang et al. (1997) reported similar results, indicating that 26% of cattle supplemented during the dry season are mainly oxen, weaners, older pregnant cows, or sick animals. Another important finding of this study is that improved breeds for milk production can be promoted at the smallholder farm level with respect to the scale. However, the resource-poor farmer could be reluctant to use such technology, because of recurrent costs. Such a technology requires that more feeds be available at the farm level or that farmers be willing to buy feed to complement their own production. High genetic-potential breeds for milk production are also known to be susceptible to diseases, which would increase expenditure on veterinary inputs. Under risky agro-climatic conditions, resource-poor farmers may be reluctant to adopt profitable new technologies because they require input investments that could be lost in an unfavourable year (Hazel and Haddad, 2001). Overall, technology that requires more resources for adoption is likely to be biased against resource-poor or smaller farmers (Waithaka, 1998; Batz, 1998). Despite these constraints, both resource-poor and medium smallholder milk producers can take advantage of the production and marketing environment. Total milk off-take increases because of the increased number of dairy cattle when agro-ecological conditions are favourable and access to markets is good. The proportion of marketed surplus in total milk off-take increases. This reveals another constraint that is not addressed in this study- milk processing and handling.
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In the absence of an organised marketing system (Somda et al., 2003), the impact of seasonal variation of milk prices on income generation is probably less important than that of market instability. Because of the inverse relationship between price and quantity, income variability arising from seasonal variation can be readily compensated for. On the other hand, market instability is difficult to overcome, as it may happen from one day to the next in the same season. Farmers are often constrained then to deliver milk at lower prices, or to consume it because of lack of facilities for product preservation.
7. Conclusions The results show that smallholder milk-oriented farms are established in extensive and non-specialised production systems. In these systems, crop and livestock are more or less integrated. Crop and livestock production aims at food security and/ or income generation for the farm household. SmallholdersÕ dairy projects have great potential because traditionally, farmers have always kept cattle, and the demand for milk in the rural community exists. However, the results suggest that a number of improvements need to be effected in order to raise the general productivity and viability in the smallholder dairy sector. First, there is a need to strengthen the milk production base through ensuring adequate feed for dairy animals throughout the production season. The results of this study have shown that little attention is given to cow supplementation compared with draft animals. In addition, the potential for crop residues that could be available is not yet well exploited. Only groundnut hay is of interest to farmers, the other crop residues being left for free grazing by animals. While it is apparent that grazing represents a significant source of feed, feeding strategies for milk production need to be enhanced. Second, the seasonality of milk production prevents farmers from earning a stable income throughout the year. The rainy season remains the best period for increased milk production, but it is also the period of low prices. In addition, the increased quantity of milk may lead to constraints related to product processing and preservation. In the absence of adequate milk processing and handling facilities, the risk of losing part of the milk offtake is high. Market access contributes to increase marketed surplus, and this will be more effective if processing and handling practices are improved, because dairy products are highly perishable. Third, the results point up the importance of well-designed interventions for increased milk production. Farmers are endowed differently with resources, and their attitudes towards new technologies may be influenced by their resource endowment. Agricultural research that leads to improved technologies needs to benefit resource-poor farmers as well as larger farmers. Improved feeding strategies may appear scale-neutral, and can be adopted by farmers regardless of the amount of endowed resources, but this is not the case for other technologies such as improved breeds. To invest in new technologies whose returns occur over a number of years, such as improved dairy breeds, resource-poor farmers need a secure income. Such
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technologies mean that poor farmers need access to credit for such long-term investments. On the other hand, larger farmers are more likely to assume some risks while poor-resource farmers faces limited alternatives. Research should therefore be reorientated toward producing a diversified range of technical options to suit the needs of farmers with different resource endowments, management skills, and ability to bear risk.
Acknowledgements We acknowledge the European Union through the Programme Concerte´ de Recherche Developpement sur lÕElevage en Afrique de lÕOuest-PROCORDEL REG/6157 for funding this research. In addition, we thank Binta Jallow, Sisawo Jarju and Ysupha Sama for the support to data collection. We record with gratitude the constructive criticisms of two anonymous reviewers and valuable editorial contribution from the editor. Standard disclaimers apply.
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Itty, P., 1996. Profitability, efficiency and comparative advantage of African cattle meat and milk production: the case of trypanotolerant village cattle production. Agricultural Economics 14, 33–44. Itty, P., Swallow, B.M., Rowlands, G.J., Agyemang, K., Dwinger, R.H., 1993. Economics of village production of NÕDama cattle in The Gambia. Quarterly Journal of International Agriculture 32, 293– 307. Johnson, D.T., 1985. The Business of Farming: A Guide to Farm Business Management in the Tropics. Macmillan Publishers Ltd, London and Babingstone. Klecka, W.R., 1981. Discriminant Analysis. Sage University Paper Series Quantitative Applications in the Social Sciences, Series No. 07-019. Sage Publications, Beverly Hills and London, p. 71. Rushton, J., Thonton, P.K., Otte, M.J., 1999. Methods of economic impact assessment: In: Perry, B.D. (Ed.), The Economics of Animal Disease Control, vol. 18 No. 2, pp. 315–338. Scobie, G.M., Jacobsen, V., 1995. Structural adjustment and agriculture. In: Tabor, S.R. (Ed.), Agricultural Research in an Era of Adjustment – Policies, Institutions, and Progress. Economic Development Institute of the World Bank, and International Service for National Agricultural Research. EDI Seminar Series. Washington, USA, pp. 1–21. Sere´, C., Steinfeld, H., Groenewold, J., 1995. World livestock systems: current status, issues and trends. In: Gardenier, P., Devendra (Eds.), Global Agenda for Livestock Research. Proceedings of a Consultation, 18–20 January 1995, International Livestock Research Institute (ILRI), Nairobi, Kenya, pp. 11–38. Somda, J., Kamuanga, M., Mu¨nstermann, S., Bittaye, A., 2003. Socio-economic characterisation of smallholder dairy systems in the Gambia: milk production, marketing and consumption. Socioeconomic Research Working Paper 1. ITC (International Trypanotolerance Centre), Banjul, The Gambia, 61 pp. Starkey, P.H., 1984. NÕDama cattle-a productive trypanotolerant breed. World Animal Review 50, 2–15. Tabor, S.R., 1992. Agriculture in transition: In Booth, A. (Ed.), The Oil Boom and After: The Indonesian Economy During the 1980s. Cambridge University Press, Cambridge, England. Waithaka, M., 1998. Intergration of a user perspective in research priority setting: the case of dairy technology adoption in Meru, Kenya. Disseration. Humboldt Universita¨t zu Berlin. Weikersheim, mararafverlag. Waters-Bayer, A., 1988. Dairying by settled Fulani agropastoralists in Central Nigeria. Farming Systems and Resource Economics in the Tropics, vol. 4. Wissenschaftsverlag Vauk Kiel. Williams, T.O., Hiernaux, P., Fernandez-Rivera, S., 1999. Crop-livestock systems in sub-Saharan Africa: Determinants and intensification pathways. In: McCarthy, N., Swallow, B., Kirk, M., Hazell, P. (Eds.), Property Rights, Risk, and Livestock Development in Africa. International Livestock Research Institute, Nairobi, Kenya, and International Food Policy Research Institute, Washington, DC, pp. 132–151. Winrock International, 1992. Assessment of animal agriculture in Sub-Saharan Africa. Winrock International Institute for Agriculture Development, Morrilton, Arkansas, USA, p. 162.
Characteristics and economic viability of milk production in the smallholder farming systems in The Gambia Jacques Somda
a,b,*
, Mulumba Kamuanga c, Eric Tollens
b
a
b
International Trypanotolerance Centre (ITC), PMB 14, Banjul, The Gambia Department of Agricultural and Environmental Economics, Katholieke Universiteit Leuven, Belgium c International Livestock Research Institute (ILRI). C/o CIRDES, 01 B.P. 19 Bobo-Dioulasso 01, Burkina Faso Received 18 June 2003; received in revised form 23 June 2004; accepted 14 July 2004
Abstract Domestic milk production has been for a long time hindered by many factors including lack of interest from decision makers, distorted economic policy and biotechnical constraints. For the last 20 years, many developing countries have been attempting to develop the domestic milk production sector. However, research on the basic realities and the viability status of enterprises within this sector remain largely unproved in many developing countries. This study focuses on the characteristic of smallholder milk producers in The Gambia. Data were collected from 90 smallholder farm households to characterise milk producers and evaluate the profitability and viability status of this activity. Based on current typology of farms and gross margin analyses at farm level, the study identified two resource-based types of smallholder farms. The current milk production system is surely viable. Constraints to increased productivity include lack of improved technology at farm level and weak institutional support. Despite the low viability status, it is shown that milk production generates reliable incomes,
*
Corresponding author. Tel.: +00220 462928/+226 50340271; fax: +00220 462924. E-mail address: [email protected] (J. Somda).
0308-521X/$ - see front matter 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.agsy.2004.07.011
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which could be a departure for most farmers to intensify farming systems, particularly in areas where no loan schemes exist for purchasing agricultural inputs. 2004 Elsevier Ltd. All rights reserved. Keywords: The Gambia; Milk production; Farming systems; Discriminant analysis; Viability
1. Introduction Several farming systems and modes of production co-exist in developing countries. Their multiplicity has created the need for a system of classification that can help to group similar systems for the purpose of identifying opportunities and constraint to development. The most commonly found farming systems in sub-Saharan Africa are the mixed crop-livestock systems (Sere´ et al., 1995). Availability of labour is potentially high in these systems in contrast with land and capital that are less plentiful (Williams et al., 1999). These systems are also known to be socially, economically, and technologically diverse. This diversity stems partly from differences in agro-ecological conditions, population densities and economic opportunities. Milk production in West Africa takes place in these diverse mixed crop-livestock production systems where the trypanotolerant NÕDama cattle is an important breed (Aboagye, 1998). Indeed, this breed has enabled milk production (and beef as well) to be maintained in a region where tsetse-transmitted trypanosomosis inflicts severe productivity losses in local farming systems. NÕDama are milked to provide for human consumption and income in several parts of West Africa, but it is generally acknowledged that this breed has low milk-production potential (Ferguson, 1988), although it can be viewed as rather a good beef breed (Starkey, 1984). Its adaptation to local agro-ecological conditions in the region makes it attractive to smallholder farmers. Furthermore, the projected growth in the demand for dairy products in sub-Saharan Africa (Holloway et al., 2000) provides opportunities for smallholders to increase their income through milk production. According to Delgado et al. (1999), milk production and dairy product consumption are expected to grow in the region by 3.8–4% annually between 1993 and 2020. The gap between domestic supply and demand for livestock products, particularly milk, in West Africa is expected to widen, especially as incomes rise and rural–urban migration continues (Aboagye, 1998). The key questions are whether smallholders would be able to seize these opportunities and what policies are needed to boost domestic supplies of milk while saving on foreign exchange reserves used for importing dairy products. In the early 1980s, an adverse international environment created unsustainable external imbalances, and worsening patterns of growth in many developing countries (Scobie and Jacobsen, 1995). It is argued that difficulties in financing imports in order to feed the growing population originated from depressed agricultural export earnings (Tabor, 1992). As long as sub-Saharan Africa faces poor economic growth and growing external imbalances, it would be unrealistic to rely fully on imports of dairy products to meet
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the demand, as was the case before the economic crisis settled in most West African countries. One alternative is to increase domestic milk production, an option which most countries in Sub-Saharan Africa appear to have endorsed. The livestock sector in Sub-Saharan Africa has been recognised as significantly contributing to economic development (Winrock, 1992). Livestock production systems are an important component in local economies at both the national and farm household level (HanyaniMlambo et al., 1998). Specifically, the dairy sub-component has proved to be the most important in the smallholder sector where milk is a source of protein for young children and supplementary income to often cash-starved farm households (Hanyani-Mlambo et al., 1998). Despite existing opportunities for smallholders to develop dairy production, current output is well below the potential level of production that can be achieved even with the use of local breeds. One reason for this situation is that past and current field research and development policies have for long favoured crops over livestock development, and technical over socio-economic solutions. There appears to be a dearth of literature on the economic outlook of smallholder dairy enterprises in West Africa in general, and particularly in The Gambia. Only a few studies (e.g., Itty, 1992, 1996; Itty et al., 1993) have addressed the questions of profitability, efficiency and comparative advantage of African meat and milk production. Even then, most of the available studies failed to relate farm household resources to economic outcomes in their analysis framework. Consequently, the implications of the results were rather broad and limited in terms of recommendations for technology transfer. This paper builds on primary data to support on-going efforts by many governments in West Africa including The Gambia, to establish smallholder dairy schemes as a tool for poverty alleviation. Its aim is first to gain a better understanding of the existing potentials and constraints of the smallholder dairy industry. Based on the realities and the viability of milk production units taken as enterprises, the paper assesses the economic status and nature of constraints and opportunities that individual smallholder milk producers face in the context of farming systems. Thus the overall objective is to generate baseline information on the economic performance of milk production at the farmer level, and specifically, to: (1) analyse the extent of variation in productive resources among milk producers; (2) evaluate the economic performance and viability of small dairy farms and (3) identify paths for technology development.
2. The research context 2.1. Research hypotheses The diversity of patterns of development of dairy production in crop-livestock systems suggests that different prescriptions will be needed to improve productivity. Furthermore, the effectiveness of any prescription will depend on the ability of livestock farmers to adopt resulting recommendations. Appropriate strategies for
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improving domestic dairy production will be implemented against the background of differences in farm-level resource constraints. The following hypotheses are tested: 1. Smallholder farm households are endowed differently with productive resources that influence milk production; 2. Adoption of technologies for milk production is not scale-neutral; 3. Milk production in smallholder farming systems is cost-effective and economically viable.
3. Research framework Farmers keep livestock as a means of transforming resources into a wide range of outputs such as milk, meat, traction power, dung, hides, wool, fibre and animals (Rushton et al., 1999). They are also involved in crop production. Farmers can then smooth the flow of income to the household through making conservative production choices, combining production enterprises, and diversify economic activities. In this context, the limiting resources are not necessarily the same for all smallholder farms, and for one single farm household, they may even change over time. This research was carried out within the framework of a regional project – PROCORDEL – initiated by The International Trypanotolerance Centre (ITC) with the support of European Union and the governments of Senegal, Guinea, Guinea Bissau and The Gambia. The farming systems in these countries are a product of years of innovations and adaptation to the sub-humid and non-forested humid environments, under a uni-modal rainfall regime where wet periods are followed by rather long dry seasons from October to April. Dairy herds are mostly composed of NÕDama breeds, and some Zebu and crossbred cattle. The Gambia is a representative case of smallholder dairy farms where NÕDama cattle predominate.
4. Data collection and analysis The target population for the study is defined as consisting of all smallholder farm households owning at least three milking cows in three administrative divisions in The Gambia (Central River, Lower River and Western Divisions). This area accounts for 59% of the total land area estimated at 10,691 km2 and 48% of the population evaluated at around 1.4 million inhabitants. A baseline survey was conducted to collect data on cattle numbers, household and farm sizes, and other information including livestock and crop production and markets. A sample frame was constructed based on the list of 501 livestock owners enumerated in each of the selected divisions, from which 90 farm households were randomly selected. These represent the population of livestock owners involved in milk production in The Gambia.
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A detailed survey was then carried out from June to September 2001 using a structured questionnaire to collect data on the socio-demographic characteristics of smallholder farms, location of the production units with regard to markets, production unit assets, and associated costs (infrastructures and equipments). Data were also collected on livestock practices including feeding, health management, milk production, reproduction-genetic improvement, the herd structure, and off-take of animals. In all cases, the quantities used, costs and management practices were assessed. A subsample of owners was monitored to collect information on market access and constraints as well as opportunities for investment. Data were analysed using several methods. To test the first hypothesis, a classification function (Klecka, 1981) was adapted. This uses the theory of maximising group differences while minimising variation within groups through K-means analysis. The K-means analysis aims at assigning specific cases to the particular group most closely resembled. Given n farmers with p variables measured for each farmer, xij for i = 1, 2, . . ., n; j = 1, 2, . . ., p, K-means clustering consists of allocating each observation to one K group or cluster to minimise the within-cluster sum of squares shown as: p K X X X 2 xij xkj ; k¼1 i2S k
ð1Þ
j¼1
where Sk is the set of farmers in the kth cluster and xkj the mean for the variable j over cluster k. The number K was selected to ensure the recommendations drawn from the results were technically and practically feasible, rather than theoretical. We applied values of K from 2 to 5 and checked resultant groupings. In this study, we use K = 2. In order to judge the results of the K-means analysis, a discriminant analysis was derived to study the nature of group differences. We assessed the adequacy of the classification, given the group membership of the farmers under study. In discriminant analysis, classification into a given category (type) of farmers is a function of the predictor variables fkn ¼ u0 þ u1 X 1kn þ u2 X 2kn þ þ up X pkn ;
ð2Þ
where fkn is the value (score) on the canonical discriminant function for case n in the group k; Xikn represents the value of discriminating variable Xi for case n in group k; and ui are the coefficients or the discriminant weights. This allows testing of the p predictor variables as discriminators of the differences between the two groups of the single discrete dependent variable. Several test statistics, such as WilksÕs lambda, eigenvalue and canonical correlation, were used as measures of goodness-of-fit of the discriminant function. WilksÕs lambda is a multivariate measure of group differences over several variables (the discriminating variables). The test proceeds indirectly. Rather than testing the function itself, WilksÕs lambda examines the ability of the variables to discriminate among the groups beyond the information that has been extracted using the previously computed function. If the variability is too small, then it is meaningless to derive any more functions, even if they exist mathematically. In
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other words, values of lambda that are near zero denote high discrimination. The eigenvalues are a special set of scalars associated with a linear system of equations. They can be used to judge the number of important canonical discriminant functions. Eigenvalues that are greater than 1 are statistically significant. Correspondingly, a canonical correlation of greater than 0.6 is statistically significant. Computational details can be found in Klecka (1981). A stepwise discriminant analysis was applied to include many variables in order to determine those that discriminate between groups. The variables include the number of active members in the household, farm size, estimated cost of permanent investment for livestock production (buildings), estimated cost of intermediate assets (equipment), number of NÕDama cattle, number of exotic breeds, number of Zebu breed, number of crossed NÕDama and exotic breeds, number of Gabu and/or Futa sub-breed animals, and the number crossed with NÕDama. The stepwise procedure is guided by the respective F statistic. The F value for a variable indicates its statistical significance in the discrimination between groups. It is a measure of the extent to which a variable makes a unique contribution to the prediction of group membership. Economic analysis was performed to test the third hypothesis. The analysis is based on an assessment in which the gross margin serves as the unit of analysis in evaluating the economic performance of each resource-based group of farmers. Johnson (1985) defines the gross margin as the difference between the value of an enterpriseÕs gross output and the marginal cost of production. In the analysis, the value of the gross output included the value of sales of milk, as well as the value of milk consumed by the farm household. The marginal costs of production were the enterpriseÕs variable costs. Bought-in feed costs were calculated on the basis of financial prices, while non-priced items such as use of farm-produced feeds and labour were calculated using economic prices that are based on the opportunity cost principle, as adapted from Hill (1990). Financial and economic performances were obtained using either financial or economic prices, respectively. Gross margin ratios were calculated to evaluate the economic viability of the smallholder milk production. Finally, combining the discrimination and economic performance results drew implications for the second hypothesis.
5. Results 5.1. Dairy production and the farming systems Before discussing the characteristics of milk producers, it is worthwhile to understand the farming systems in The Gambia. This provides information that can help trace out patterns for developing dairy systems, with associated implications for the overall farming systems within which milk production takes place. The results from the baseline survey show that cattle, mainly the NÕDama breed, are kept under mixed crop-livestock production systems with a very limited utilisa-
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Table 1 Test statistics for the discriminant function of farm households, The Gambia Discriminating variables
Wilks
F
Family labour Land NÕDama cattle Zebu cattle Crossbreed cattle Intermediate assets DF
0.98 0.95 0.99 0.93 1.00 0.43 1
2.09 4.56 1.18 6.93 0.00 118.1 88
Notes. DF, degree of freedom (the first value corresponds to the df1 and the second the df2); F and Wilks are the Fisher statistic and the WilksÕ Lambda, respectively. Source. Survey results.
tion of external inputs. A variety of crops (cereals, legumes, cotton and trees) and livestock species (cattle, small ruminants and monogastrics) are grown for multiple objectives. Out of the 501 farmers enumerated during the baseline survey, 49% do not keep animals. Those with animals, and particularly cattle, aim to produce meat, milk or both. Hence, cattle are used for generating income through selling milk and live animals (69% of the sample) and for cultivation (draft power) in 19% of cases. Whether the farmer is milking the cows for human consumption or not, depends on a variety of factors, including the productivity per cow, which is related to the agro-ecological conditions, the resources at the farmerÕs disposal, and their allocation to milk production. 5.2. Typology of smallholder milk producers The resource-based typology of smallholder milk producers is built on selected structural variables, namely, the number of active members in the household (in man-equivalents), farm size, the estimated value of fixed agricultural assets (permanent and intermediary) and stock size (NÕDama, Zebu and/or crossbred). It is hypothesised that these variables are determinants in the decision of the farmer to engage in milk production. Moreover, if smallholders differ with regards to these resources, then the performance achieved in milk production and management will be affected. Results of the K-means and the discriminant analyses are given in Table 1. Regressions of the selected structural variables on the K-means group membership help to identify those significant variables that contribute to smallholder discrimination. The estimated discriminant functions suggest that in The Gambia, the statistically significant variables were: the number of Zebus (p < 0.01), farm size (p < 0.06) and the amount of investment in intermediate assets (p < 0.001). The test for goodness-of fit of the predicted discriminant function indicates an eigenvalue of 1.82, a WilksÕs lambda of 0.35 and a canonical correlation of 80%. The chi-square statistic is 88.17 with 6 degrees of freedom. All these statistics show that the predicted function is statistically significant at 1% level. The tests also suggest that the selected variables can be used to discriminate smallholder farm households. The smallholders
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Table 2 Resource-based groups statistics of the discriminant variables of smallholders Discriminating variables
Resource-poor farms
Medium-resource farms
Labour (Man-equivalent) Land (ha) NÕDama (head) Zebu (head) Crossbreed (head) Intermediate assets (GD) Sub-sample size
11 (0.84) 5.45 (0.78) 54 (4.41) 0.00 (0.00) 0.18 (0.09) 448.69 (63.18) 73
14 (1.83) 9.12** (1.19) 71 (12.79) 0.12** (0.12) 0.12 (0.12) 4094*** (369.42) 17
Notes. GD, Gambia Dalasi is the country currency (1 $US = 15.30 GD). Values in parentheses are standard errors. ANOVA test of equality of within country smallholder groups means: **, ***significant at 5% and 1% level. Type 1 farmersÕ resources are below the confidence interval of the sample average. Type 2 farmers are within the confidence interval. Source. Survey data.
can be qualified into resource-poor (below the confidence interval for the means of the discriminant variables) and medium groups (within the confidence interval). The former group (type 1) accounts for 81% of the sample and the latter for the remaining 19%. Table 2 summarises the group statistics for the discriminant variables. Very few Zebu and crossbreds are reported in the sample, and all belong to the second group with a medium level of resources. Crossbreds (including Zebu · NÕDama and Friesian · NÕDama) are unequally distributed among resource-poor and medium farmers. However, the difference is not statistically significant. Land ownership is significantly unequally distributed between the two types of farm households. The medium smallholders have also shown a capacity to invest (in farm equipment) more than the poor. On average, there are no significant differences in the number of active man-equivalents and the number of NÕDama cattle between the two types of smallholders. 5.3. Comparison of herd structures of smallholder milk producers The herd structures for the two types of smallholder are presented in Table 3. Irrespective of the resource-related type of farm household and the age categories of cattle, farmers keep more females than male animals in the herd, often in a proportion of over 60%. In particular, reproductive female cattle represent more than 80% of that category in both the resource-poor and medium farms. However, there exists high within-group variability of the sex-age category of cattle. The estimated coefficient of variation (C.V.) in the medium-resource group are: 94%, 79%, 71%, and 70% for female cattle of over 3 years, 2–3 years, less than 1 year, and 1–2 years, respectively. For male cattle, these values are 109%, 103%, 93%, and 84% for less than 1 year, 1–2 years, over 3 years, and 2–3 years old, respectively.
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Table 3 Comparative herd structure of resource-based farm households (head) Age categories
<1 year
1–2 years
2–3 years
Over 3 years
Female cattle Resource-poor farms Medium resource farms
7 (0.78) 11 (1.84)*
5 (0.66) 8 (1.32)
6 (0.91) 7 (1.33)
18 (1.57) 23 (5.14)
Male cattle Resource-poor farms Medium resource farms
5 (0.57) 7 (1.91)
5 (0.61) 7 (1.73)
3 (0.45) 4 (0.82)
4 (0.39) 5 (1.22)*
Values in parenthesis are standard errors. One-way ANOVA test of equality of groups means: *, significant at 10% level. Source. Survey data.
In resource-poor farm households, the highest within-group variation is found for females of 2–3 years (C.V. = 132%), followed by those of 1–2 years (C.V. = 103%). The coefficients of variation of the number of females less than 1 year and over 3 years are 90% and 76%, respectively. Male cattle are also diversely distributed among the resource-poor farms. Coefficients of variation of 115%, 114%, 96% and 91% were estimated for the number of animals of 2–3 years old, 1–2 years, over 3 years, and less than 1 year, respectively. 5.4. Herd structure and management of milk production The herd structure has implications for milk production, because dairy cows make up part of the reproductive herd. On average, 57% and 61% of the reproductive cows are milked in the medium-resource group during the dry and rainy season, respectively. On the other hand, resource-poor farm households extract milk from 48% of their reproductive cows in the dry season and 61% in the rainy season. Irrespective of the resource-related group, cows are manually milked. On average, the milking is done once a day in the dry season and twice in the rainy season. As a consequence, the quantity of milk extracted for human consumption varies between seasons. Likewise, the amounts allocated to family consumption and sales are different from dry to rainy season. In the rainy season, farm householdsÕ consumption averages two litres in both medium and resource-poor groups. In the dry season, medium-resource farm households consume slightly more milk than the resource-poor households (4 and 3 litres/household, respectively). However, there was no statistically significant difference. On the other hand, there was a significant difference (p < 0.05) in daily sales of milk between the resource-group of farm households. The medium-resource farm households sell on average 6 litres/day in the dry season and 17 litres in the rainy season; for the resource-poor farm households, this quantity averages 4 and 11 litres/ day during the dry and rainy season, respectively. At the same time, sales average 12 litres in the rainy season, decreasing to 4 litres in the dry season, for the whole sample.
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5.5. Feeding resources and utilisation Feeding strategies are not related to the identified resource-based groups of farm households. Feeding resources are mainly based on natural pasture, crop residues and agricultural by-products. The majority of farm households (79%) rely on natural pastures. The remaining (21%) combine natural pastures and stall feeding of some animals with crop residues and supplements. Animals benefiting from supplementation are mostly draft animals. Nearly 30% of the sample farms supplement draft animals, while only 1% supplement lactating cows as well as sick and draft animals. In practice, supplements fed to a particular category of cattle can also benefit other animals depending on availability. Because of the lack of infrastructure for feeding animals, cattle are invariably supplemented with crop residues and agricultural by-products. Among crop residues, groundnut hay is commonly used by 43% of the farm households, although this is own-produced in only 1% of cases. Some farm households (7%) make use of mineral licks. Farmers also use different agricultural byproducts to supplement their animals. Cereal (millet and rice) bran is the most important and is used in 9% of the farms, while groundnut cake is used in 8% of cases. Under these conditions, feed shortages, particularly during the dry season, remain a serious constraint to increased milk production. Rotating pastures and short transhumance during the dry season are responses that farmers have devised to address the feed constraint. Three types of movement are identified with relation to the distance covered. The majority of the sample farm householdsÕ herds (66%) move across the country, toward the southern part of neighbouring Senegal in the late dry season and return during the raining season. Some herds (22%) are moved round within the country; while 12% of the sample farms leave their animals to roam where natural pastures and crop residues are still available. During all these movements, draft animals, lactating cows and sick animals are kept near the village and fed with crop residues (groundnut hay and sorghum straw) and sometimes with agricultural byproducts (cereal bran and groundnut cake). 5.6. Supply of dairy products from smallholdersÕ households The above results suggest that various factors influence the supply of dairy products by farm households. The off-take of milk is shared between family consumption and sales. This is in turn influenced by the potential of the breed and the production techniques used, which are extensive. The quantity supplied to the markets depends on such factors as the periodicity of production, the location of the farm household with respect to markets, and the marketing strategies put in place by the farmers. Results from the survey indicate that the majority of farm households (66%) sell dairy products throughout the year, and 28% during the rainy season only. These products comprise fresh milk (56% of farm households), sour milk (6%) and both fresh and sour milk (31%).
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Table 4 Annual cost, gross income and gross margin of milk producers (Gambia Dalasi)
Sales (1) Self-consumption (2) Gross dairy income (3) = (1 + 2) Feeds (4) Health care (5) Herder (6)a Total variable costs (7) = (4 + 5 + 6) Gross margin (3–7) Net cash income (1–4–5)
Resource-poor farms
Medium resource farms
10230.41 (1117.49) 3713.43 (182.79) 13943.84 (1188.03) 46.78 (17.76) 390.23 (31.13) 2621.26 (74.13) 3058.27 (90.77) 10885.56 (1190.34) 9793.40 (1117.06)
16355.29** (4018.60) 4034.12 (497.29) 20389.41** (4133.34) 39.71 (21.74) 557.29** (74.51) 2786.82 (166.79) 3383.82 (196.54) 17005.59* (4193.58) 15758.29* (4011.93)
Values in parenthesis are standard errors. ANOVA test for equality of within country group means: * and ** significant at 10% and 5% level. US $1 = Gambia Dalasi 15.30. Source. Survey data. a Herder is member of the farm household.
Dairy products are sold at either the farm gate (74% of cases), or at the local markets (16%). Some farmers (2%) combine supplying to the local markets and selling their milk on a stand by the side of main roads. Several transport materials are used to take dairy products to the markets. Recycled oil or margarine cans of different capacities (5–20 litres) are filled with dairy products and transported to the markets by foot, bicycle, motorcycle or public transport. The distance that farmers walk to deliver their dairy products varies from 1 to 14 km, with an average of 6 km (SE = 1.07; SD = 4.16). Most farmers (54%) deliver their products directly to collectors, while 38% sell to consumers. The pricing of dairy products is based on bargaining, allowing prices to be set between sellers and buyers. Even though those prices are seasonally set, the relationship between sellers and the buyers greatly influences the level at which unit prices are negotiated. 5.7. Economic performance of smallholder milk producers Maximising production levels and profit are goals cherished by most smallholder dairy farms. It is thus important to understand the levels of performance that farmers achieve in the current milk production systems, and what the viability is of milk production and the farming system in general. Table 4 presents the partial budget of smallholder milk production units, based on the accrual method of accounting. The results indicate that smallholder dairy farms are economically profitable. The results indicate that cash investment for milk production among smallholder dairy farms is very low. Expenditure on feed (mainly crop residues) is not statistically different between types of farmers. On the other hand, resource-poor farmers (type 1) are spending significantly less money on animal health care than the medium farms. On average, because of low investment in feeds in particular, the economic performance of milk production depends essentially on the number of lactating cows owned by the household. The gross dairy income of the medium-resource farmers is 46%
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Table 5 Comparative tests for economic viability ratios Viability indicators
Resource-poor farms
Medium resource farms
Operating cost/gross margin Capital turnover Total fixed cost/gross margin Total expenses/gross margin Net cash/variable cost Net cash/lactating cow (GD)
0.37 (0.04) 0.28 (0.02) 0.05 (0.01) 0.42 (0.05) 3.39 (0.38) 502 (56.39)
0.38 (0.11) 0.24 (0.03) 0.51 (0.17)*** 0.89 (0.27)*** 5.34 (1.47)* 434 (74.31)
Values in parenthesis are standard errors. ANOVA test for equality of within country group means: * and ***, significant at 10% and 1% level, respectively. US $1 = Gambia Dalasi 15.30. GD = Gambia Dalasi. Source. Survey data.
higher than that of the resource-poor group. The estimated gross margin averaged US $911 and $1111 for the resource-poor and medium farmers, respectively. The estimates of the net cash income derived from milk are US $640 and $1030 for the poor and medium farmers. However, results indicate that those means are associated with a high variability. For the net cash income, the within-group coefficients of variation are evaluated to 105% and 97% for the medium and resource-poor farm households, respectively. In the former group, the minimum and maximum values of the net cash per year range from US $59 to US $3857, while in the latter group the corresponding values are $79 and $2361. Similar trends were observed for the within-group variation of the gross margin. 5.8. Economic viability of smallholder milk production Several indicators were calculated to assess the viability of smallholder dairy farms. The operating ratio (total operating costs divided by the gross margin) is a measure of the cost-effectiveness of the production unit. Capital turnover measures the comparative advantage of keeping cows for milk production as compared with selling them. The net cash ratio estimates the ratio of cash income to the variable costs. The fixed and gross ratios measure the viability of dairy production in mixed crop-livestock production systems. Table 5 summarises some viability indicators of the smallholder dairy farms. Assessment of the selected ratios cast no doubt on the economic viability of smallholder dairy production in the farming systems of The Gambia. The results indicate that investing in milk production under current farming practices is worthwhile. Although the averages indicators were positive, some results were somewhat disappointing. The total expenditure to gross margin ratio is rather low, particularly in the medium-resource group of farms (almost unity) as they incur high fixed costs. The capital turnover shows that milk production is not profitable compared with selling dairy cattle. In other words, farmers may be better off if they sell their dairy stock rather than keeping them for milk production.
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There is a strong indication from examining the selected financial ratios that it is profitable to engage and to invest in milk production. Each dollar invested in milk production earns an average of 3 and 5 dollars for the resource-poor and medium groups of farms, respectively. Hence, medium farmers are significantly more costeffective than the resource-poor farmers. On the other hand, the returns per dairy cow are estimated at US $33 for resource-poor farms and US $28for the medium. Although the difference is not statistically significant, this result suggests a scale effect on the net cash income to variable cost ratio.
6. Discussion Mixed crop-livestock production systems in sub-Saharan Africa are known to be socially, economically and technologically diverse (Sere´ et al., 1995). It is further argued that this diversity stems partly from the differences in the material conditions and the production relations, and that a diversified range of technical options are required to suit the needs of farmers with different resource endowments and management skills (Williams et al., 1999). However, few quantitative studies exit that document these conditions from the farm householdsÕ perspective. This study investigates the relation between resource endowments and the performance of dairy production, and the implications for the whole farming system. The results indicate that smallholder milk producers are heterogeneous in their resource endowment. Three resource variables were found to be significantly discriminating among farm households: farm size (ha), the amount of investment (Gambia Dalasi), and the number of Zebu breed owned. These variables are directly or indirectly related to milk production in the farming system. Farm size determines indirectly the amount of crop residue that will be available for feeding animals. The amount of investment on intermediate assets such as agricultural equipment is associated with the mechanisation level of the farm household, and is known as a determinant in intensifying crop production. The Zebu breed is known as a better milk producer than the local NÕDama, and its introduction in herds is expected to increase the amount of milk produced. The study established that smallholder dairying in The Gambia is indeed viable. The viability of current milk production is resource-scaled. In other words, viability is higher in the resource-medium group than in the resource-poor group. HanyaniMlambo et al. (1998) and Dube (1995) in Zimbabwe and Waters-Bayer (1988) in Nigeria also reported poor viability for smallholder dairy farms. The low level of economic viability in The Gambia can be attributed to various factors, including the lack of appropriate feed resources and the depletion of natural pastures. Dairying still involves few external inputs. Apart from veterinary products, farmers mainly rely on crop residues, which are known to be low in nutrients, to feed their animals. Consequently, productivity per cow remains low and is subject to seasonal variation related to feed availability. The diversity of farming situations and multi-purpose nature of raising cattle make it difficult to target agricultural investments for the sole objective of increasing
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milk production when it is perceived as one component of the mixed crop-livestock farming system that provides food for the ownerÕs family and feed for cattle. The marketing of milk contributes to generating income that can be used to purchase agricultural inputs, including dairying, and to satisfy other needs for cash. There is therefore no doubt that milk production in the current farming system will continue to be attractive because it is a source of relatively regular and reliable income for most farmers, compared with the traditional once-a-year return in crop production. It appears to be the only source of financing crop production in areas where there are no loan schemes to purchase agricultural inputs. The basic implication of the limited viability of smallholder dairy farms is that the expected higher incomes for poor rural households may not be forthcoming. The results show that viability problems are more acute for resource-poor farmers, who comprise the majority of smallholder dairy producers. Without any technological improvement, poor or smaller farmers need to accumulate capital, particularly cattle. However, a large stock of cattle does not guarantee increased viability, as smallholders may face management and resource allocation problems. The study also reveals that farmers are motivated to sell more milk because of improved access to markets at the farm level or at the side of main roads. Investment in feeds (groundnut hay and concentrates) during the dry season is increasing, in spite of the difficulty in accessing these inputs. Both medium and resource-poor farm households do supplement some milking cows with concentrate, roughage and mineral (salt) to stimulate milk production for calves and human consumption. However, the contribution of crop residues to milk production is low compared with draft animals that still receive more attention from both resource-related groups of farmers. Agyemang et al. (1997) reported similar results, indicating that 26% of cattle supplemented during the dry season are mainly oxen, weaners, older pregnant cows, or sick animals. Another important finding of this study is that improved breeds for milk production can be promoted at the smallholder farm level with respect to the scale. However, the resource-poor farmer could be reluctant to use such technology, because of recurrent costs. Such a technology requires that more feeds be available at the farm level or that farmers be willing to buy feed to complement their own production. High genetic-potential breeds for milk production are also known to be susceptible to diseases, which would increase expenditure on veterinary inputs. Under risky agro-climatic conditions, resource-poor farmers may be reluctant to adopt profitable new technologies because they require input investments that could be lost in an unfavourable year (Hazel and Haddad, 2001). Overall, technology that requires more resources for adoption is likely to be biased against resource-poor or smaller farmers (Waithaka, 1998; Batz, 1998). Despite these constraints, both resource-poor and medium smallholder milk producers can take advantage of the production and marketing environment. Total milk off-take increases because of the increased number of dairy cattle when agro-ecological conditions are favourable and access to markets is good. The proportion of marketed surplus in total milk off-take increases. This reveals another constraint that is not addressed in this study- milk processing and handling.
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In the absence of an organised marketing system (Somda et al., 2003), the impact of seasonal variation of milk prices on income generation is probably less important than that of market instability. Because of the inverse relationship between price and quantity, income variability arising from seasonal variation can be readily compensated for. On the other hand, market instability is difficult to overcome, as it may happen from one day to the next in the same season. Farmers are often constrained then to deliver milk at lower prices, or to consume it because of lack of facilities for product preservation.
7. Conclusions The results show that smallholder milk-oriented farms are established in extensive and non-specialised production systems. In these systems, crop and livestock are more or less integrated. Crop and livestock production aims at food security and/ or income generation for the farm household. SmallholdersÕ dairy projects have great potential because traditionally, farmers have always kept cattle, and the demand for milk in the rural community exists. However, the results suggest that a number of improvements need to be effected in order to raise the general productivity and viability in the smallholder dairy sector. First, there is a need to strengthen the milk production base through ensuring adequate feed for dairy animals throughout the production season. The results of this study have shown that little attention is given to cow supplementation compared with draft animals. In addition, the potential for crop residues that could be available is not yet well exploited. Only groundnut hay is of interest to farmers, the other crop residues being left for free grazing by animals. While it is apparent that grazing represents a significant source of feed, feeding strategies for milk production need to be enhanced. Second, the seasonality of milk production prevents farmers from earning a stable income throughout the year. The rainy season remains the best period for increased milk production, but it is also the period of low prices. In addition, the increased quantity of milk may lead to constraints related to product processing and preservation. In the absence of adequate milk processing and handling facilities, the risk of losing part of the milk offtake is high. Market access contributes to increase marketed surplus, and this will be more effective if processing and handling practices are improved, because dairy products are highly perishable. Third, the results point up the importance of well-designed interventions for increased milk production. Farmers are endowed differently with resources, and their attitudes towards new technologies may be influenced by their resource endowment. Agricultural research that leads to improved technologies needs to benefit resource-poor farmers as well as larger farmers. Improved feeding strategies may appear scale-neutral, and can be adopted by farmers regardless of the amount of endowed resources, but this is not the case for other technologies such as improved breeds. To invest in new technologies whose returns occur over a number of years, such as improved dairy breeds, resource-poor farmers need a secure income. Such
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technologies mean that poor farmers need access to credit for such long-term investments. On the other hand, larger farmers are more likely to assume some risks while poor-resource farmers faces limited alternatives. Research should therefore be reorientated toward producing a diversified range of technical options to suit the needs of farmers with different resource endowments, management skills, and ability to bear risk.
Acknowledgements We acknowledge the European Union through the Programme Concerte´ de Recherche Developpement sur lÕElevage en Afrique de lÕOuest-PROCORDEL REG/6157 for funding this research. In addition, we thank Binta Jallow, Sisawo Jarju and Ysupha Sama for the support to data collection. We record with gratitude the constructive criticisms of two anonymous reviewers and valuable editorial contribution from the editor. Standard disclaimers apply.
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