Economic Role of Prices an Approaches to Market Analysis

EFFICIENCY OF RESOURCE USE IN SMALLHOLDER DAIRY FARMS IN WESTERN KENYA

J. I. MOSE1, G. AMUSALA1, P. NYANGWESO1 , & C. O. KENYANITO2

1. Moi University Department of Economics and Agricultural Resource Management, P.O. Box 3900-30100 Eldoret, Kenya. 2. Bukura Agricultural College, P.O. Box 23, Bukura, Kenya

Corresponding author: [e-mail]

ABSTRACT

This study was motivated by the need to find out whether farmers could increase their milk production through efficient allocation of input use. The specific objectives of the study were to characterize the dairy production system in terms of management practices, input use and the costs and returns, to determine whether farmers earn profits in dairy production and to determine whether farmers allocate their resources efficiently. The data used was collected from 48 farmers in Kakamega East District. The data collected included the resources used in milk production, yields obtained, Prices of inputs and output and the problems faced by farmers in dairy production. Gross margin analysis was done for milk production. Quadratic function was fitted using the inputs used in dairy production and marginal products equated to inverse price ratios. The results showed that it was possible to increase milk yield and profits through increased use of forage and farm by-products. The important conclusion which can be drawn from this study is that there is unexploited potential in the dairy production. The study recommends that farmers should be encouraged to use more forage by addressing problems which lead to limited use of forage by farmers.

Key words: Dairy production systems, intensification, profits.

1.0 INTRODUCTION

Agriculture plays an important role in the economic development of Kenya. It provides food for the growing population, employment to over 70% of the population, raw materials for industries and foreign exchange earning (CBS, 2007). It therefore has potential to improve the Kenyan economy, if well harnessed. This however, will depend, to a large extent, on the efficiency in agricultural production. Dairy accounts for about 14% of agricultural GDP and contributes to the livelihoods of many small-scale farmers in Kenya through income, employment and food. The Kenyan dairy industry has a potential for spurring substantial growth in the economy. Some of the investment opportunities available include artificial insemination services, dipping services, clinical services, rearing of livestock for dairy products and milk processing for local and regional markets.

Dairy farming is one of the second largest economic activities in Kakamega East District after sugar production. It has been established that dairy production in the entire district is predominantly a smallholder undertaking. More than 200 smallholder dairy farmers, with between one and five lactating cows are said to be currently producing 80 per cent of the milk. The importance of milk production, marketing and processing to the wealth and health of the Kakamega district and its population cannot therefore be overstated.

1.1 Problem Statement

An increase in income, population growth and urbanization is expected to substantially increase the demand for dairy products in the region. The increased demand is expected to stimulate the growth of the dairy sector in the entire Kakamega County. Although the growth of dairy production can be pictured as a success story, further gains in production and marketing continue to be constrained by a wide range of problems. The most prominent of these include reducing sizes of land due to increasing population which generally decreases the amount of land available for dairy farming, poor animal husbandry practices, poor quality feeds, increasing prices of these services, slow development and utilization of modern breeding services and poor access to credit and milk markets. Most of these problems can be solved by efficient allocation of resources by farmers in production.

The available statistics show that milk production has been decreasing from year to year since 2005 (MOLD 2005-2009). This study was carried out to try and find out whether the smallholder dairy farmers in Kakamega County allocate their resources efficiently in milk production.

1.2 Objectives

The general objective of the study was to determine the allocative efficiency of resources and inputs in production of milk by smallholder dairy farmers in Kakamega East District.

Specific objectives

1. To determine whether farmers are making profits in dairy farming in Kakamega East District.

2. To determine whether smallholder dairy farmers allocate resources efficiently.

2.0 methodology

Study Area

This study was carried out in Kakamega East District, situated in Western Province (Kenya). The majority of its inhabitants work in agricultural sector (GOK, 2002), with most of them being small scale farmers. In fact, 80% of the population lives in rural areas, and 62% of all households generate their income from agriculture. At the same time the district suffers from extreme demographic pressure with an annual population growth rate of 2.12%. Therefore, with 76% of the district’s area being under agricultural cultivation and with the District’s poverty rate of 52% shows the importance of exploring ways to facilitate secure income for households living on small scale dairy farming.

Data collection

Primary data collection was done using structured questionnaires which were administered to the small scale farmers who practice dairy farming in Kakamega East District. An interview schedule was also used on key informants such as extension service providers, mainly the staff of the Ministry of Livestock and buyers of dairy milk.

Secondary data was collected from research stations; both government and private research institutions like Kenya Dairy Board (KBD) and Ministry of Livestock.

Sampling procedures

This research employed simple random sampling to identify small scale dairy farmers to be included for the purposes of this study. This method is preferred since it is probabilistic and gives every small scale dairy farmers equal chance to be chosen for the study. To complement information that was gathered from questionnaires, an interview schedule was administered to key informants such as extension services providers, mainly the staff of the Ministry of Livestock (MoLD). A sample size of 48 smallholder dairy farmers was selected.

Data Analysis

Two methods of analysis were used namely;

i. Gross margin analysis and ii. Regression analysis
These are briefly outlined below

Gross Margin Analysis

The second method of analysis included gross margin analysis. Gross margin is calculated as:

GM = T.I – VC

Where:

GM = Gross margin

T.I = Total Income

VC = Variable Costs

The total income here refers to the value of the milk produced. The average price (Ksh per litre) realized by farmers in the study area was multiplied by the milk produced by each cow to get the total income per cow.

The variable costs included expenditures on farm forages, concentrate, by-products, labour and veterinary fees all expressed in Ksh per cow per year. These were obtained by asking the farmers how much be spent on each item (e.g. on minerals) per year. The year’s expenditure was divided by the number of cows involved to find the amount spent on each cow. The price of farm produced forage was obtained by asking farmers what they could have charged someone for cutting and carrying away a given amount of forage per Kg.

Gross margin was expressed in Ksh per cow per year.

Production Function Analysis

A production function is a quantitative relationship between inputs and outputs. A production function defines the production possibilities available to the farmer. With such production function together with information on prices and opportunity costs, one can judge and also study the effect on production of alternative government policies influencing prices and a quality of resources available to the farmer.

Functional forms

The Quadratic form was adopted in this study. The quadratic function was generally represented as;

M = βO+β1 X1 + β2 X2+ β3 X3+ β4 X4+ β5X12 + β6 X22 + β7X32+ β8X42+

Β9X1X2+ β10X1X3 + β11X1X4+ β12X2X3+ β13X2X4 + β14X3X4

Where:

M = milk yield in kg/cow/year

B1…… β14 = Regression coefficients

βO = Constant

X1 = forage in kg/cow/year

X2 = Concentrates in kg/cow/year

X3 = By-products in kg/cow/year

X4 = Labour in man-hours/cow/year

Description of data used for regression:

The dependent variable

The dependent variable was milk yield per cow per year in kilogrammes. This was obtained by asking farmers the number of animals that were in milk and how much milk each animal produced during that lactation period. The total amount of milk produced in the farm in the year was divided by the number of animals that were in milk to get the average yield per animal per year. The price of milk was a mean of the price received by the farm local sales and from the society.

The independent variables

The independent variables considered important in explaining milk yield were feeds (concentrates, forage and farm by-products), and labor. All these are expressed on per animal per year per farm basis.

Labor

This was expressed as man-hours per cow per year. Farmers were asked how long each farm activity took to accomplish. The duration of each activity was then multiplied by its frequency in the year to obtain the total man-hours. The man-hours for all the activities were then summed up to give total labor used. This was divided by the number of cows per farm to get man-hours/cow/year. The mean wage rate was obtained by dividing the mean total wage paid to each worker in the dairy enterprise by the mean number of hours worked. This was taken to be the price family labor would have received elsewhere were it not used in production.

Farm forages

This consisted of pasture produced by farmers and fodder crops. This was expressed in kg/cow/year. It was obtained by asking farmers the amount of forage dry matter in Kg they fed their animals per day; this was then multiplied by the number of days in a year. The total amount of forage per year was divided by the number of animals which fed on the forage. Forage value per Kg per year was obtained by asking farmers what they could have charged someone for cutting forage in their farm per year.

Farm by-products

This was mainly maize stalk. It was also expressed in Kg per cow per year. No farmer bought or sold maize stalk. Its price was estimated on the basis of its dry matter yield (3750kg/Ha/Year) which was then related to the price and dry matter yield of Napier (13750kg/Ha/Year). Thus the price was estimated to be ksh 1190 per

Concentrates

This consists of the commercial feeds purchased by farmers which are high in energy and the grains grown and fed to animals by farmers. This was expressed in kg/cow/year.

The price for this was obtained by asking farmers what they paid per 70 kg bag

OPTIMAL LEVEL OF INPUT USE
To determine the optimal level of input use, assume M= f(X1,X2,………, Xn)is the milk response function where.

M= Milk output Kg/Cow/Year

Xi…….Xn = input levels all expressed on per cow basis.

The levels of inputs that maximize gross margins may be given by the combination at which the value of the additional produce obtained from a small increment of each input just balances the cost of the added input. In doing this considerations are given to the economic circumstances of the farmers and to the alternative demands on the limited resources other for the production of milk. Profit will be maximized when:

δπ = 0

δXi

Where π is the profit function given by π = Pm M - Pxi Xi

Where Pm = milk price

M = milk produced

Xi = Factor Input

Pxi = Factor Cost

The function being unconstrained, the best operating conditions are obtained by setting marginal productivities equal to their inverse price ratio i.e.

δM = Pxi

δxi Pm

From which can be solve for the optimal level of inputs Xi.

3.0 RESULTS AND DISCUSSION

3.1 GROSS MARGIN RESULTS

Table 1: Gross Margin results

|Item |Ksh |
|Milk yield (Kg/cow/year) |2480.50 |
|Price (Ksh/cow/year) |43.54 |
|Value (Ksh/cow/year) |107979.2 |
|Forage (Ksh/cow/year) |24350 |
|Concentrate (Ksh/cow/year) |21742 |
|By-products (ksh/cow/year) |6250 |
|Treatment cost (Ksh/cow/year) |2750 |
|Labor (Ksh/cow/year) |28980 |
|Gross Margin |23907.2 |

Source: Author’s computation 2011

Positive Gross margin indicated that small scale dairy farming in Kakamega East District is profitable.

3.2 REGRESSION RESULTS

The results of OLS estimation of the Quadratic model are given are given in table 2

Table 2: Regression results

|Variable |B |SE B |t |
|Intercept |601.926 |284.56 |2.112 |
|Farm forage (X1) |3714.45 |1.885 |2.210 |
|Concentrates (X2) |162.91 |260.93 |0.624 |
|By-products (X3) |0.839 |1.812 |0.463 |
|Labour (X4) |0.1222 |0.1710 |0.715 |
|Square terms |
|X12 1.630 1.42 1.148 |
|X22 - 0.015 0.006 -2.320 |
|X32 3.65 7.82 2.783 |
| |
|X42 - 0.191 0.372 -0.514 |
|Interaction | | | |
|Terms | | | |
|X1X3 |-3.2 |1.198 |2.67 |
|X1X2 |0.382 |0.510 |0.749 |
|X1X4 |4.11 |3.72 |1.10 |
|X2X4 |2.5 |1.147 |2.185 |
|X2X3 |3.344 |3.279 |1.020 |
|X3X4 |-0.362 |0.185 |-1.957 |

R2 = 0.680; Adj.R2 = 0.570; n=48

The quadratic specification described 68.0 percent of the variation in milk yield. These measures are given by the R2 statistic which indicates the goodness of fit of a model specification. The respective adjusted R2 statistic was 57%. The F-statistics was 17.79 significant at 95% level of confidence.

Function attention needs to be drawn to the variable concentrate, by-products and labour. The t-statistic shows that the coefficients are not significant at 5%. This means that no significant role was being played by concentrate, by-products and labor in explaining the variability in milk yield among the smallholder farmers in the sample. Therefore these variables were left out in deriving optimal levels of inputs use. Similarly the squared terms of concentrates (X12), labor (X42) and the interaction of forage with concentrates (X1X2), forage with labor (X1X4), By-products and labor (X3X4), concentrate with by-products (X2X3) were not statistically significant in influencing milk yield. Their coefficients were not significant at 5% level. They were also not used in deriving optimal levels of input use. Only the interaction of forage with by-product (X1X3) and concentrate with labor (X2X4) was significant.

From the general form of the function

M = βO+β1 X1 + β2 X2+ β3 X3+ β4 X4+ β5X12 + β6 X22 + β7X32+ β8X42+

Β9X1X2+ β10X1X3 + β11X1X4+ β12X2X3+ β13X2X4 + β14X3X4

The optimal milk yield is obtained by substituting for the values of the variables whose coefficients were statistically significant at 5% level in the model.

M = 601.92 +3714.45 X1 -3.2 X1X3+ 3.65X32 -0.015X22+0.5X2X4

Economic optimal gives the best operating conditions. The best operating conditions are obtained by setting the marginal productivities equal to their inverse price ratios. The results of this process are given in table 3.

Table 3: Comparison of farm average and economic optimum

| |Economic Optimum |Farm average |
|Forage (Kg/cow/yr) |2203.2 |2019.5 |
|Concentrate (Kg/cow/yr) |1331 |1480 |
|By-product (Kg/cow/yr) |986 |858 |
|Labor (Manhrs/cow/yr) |938 |945 |

Source: Author’s Calculation.

The results of the economic optimization are presented in the table above. From the results it can be seen that it is possible to increase milk yield through increasing usage of forage from 2019.5 Kg/cow/yr to 2203 Kg/cow/yr and by-product from 858 Kg/cow/yr to 986 Kg/cow/yr.

4.0 SUMMARY, CONCLUSION and recommendation

This study aimed at analyzing the allocative efficiency of small scale dairy production in Kakamega East District and to assess the impact of smallholder dairy farming to farm incomes. Data was collected from 48 dairy farmers between the months of May and August 2011. Farmers were personally interviewed using a pre-designed and protested questionnaire. The data collected included the resources used in Dairy production, yields obtained, prices of inputs and output and problems encountered in dairy farming.

Gross margin and regression analyses were used to analyze the data. The results of gross margin analysis suggest that small scale dairy farming is profitable in Kakamega East District. A quadratic function fitted the data well on the basis of R square, adjusted R square and t-statistics. Use of regression results and economic optimization procedures indicated that farmers could increase their milk yield to reach the economic optimum by using more by-products, farm forages and capital. The major conclusion is that there is unexploited potential in dairy production in Kakamega East District.

The findings from this study show that the problem in milk production seems to lie with the feeding management of the dairy cattle. The main efforts to increase milk yield should be directed at encouraging farmers to use more forage and by-products. The lack of proper feeding of the animals during the dry season was also identified to be constraints among some farmers. Farmers indicated that they have excess feeds during the harvest and rain season but none preserved any. Farmers should be educated on how to preserve excess feeds available to them for use during the dry season. Cost effective methods of feed preservation need to be devised for small scale farmers like these in the study area.

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