|Livestock Research for Rural Development 25 (8) 2013||Guide for preparation of papers||LRRD Newsletter||
Citation of this paper
Dependence on natural pastures with little or no supplementation makes the dairy production systems among small scale farmers in the East African region vulnerable to seasonal weather variations, characterized by low dry–season milk production and high wet-season production, sometimes exceeding the consumption capacity of the market. There is therefore a need to need to develop a method that can help farmers and policy makers estimate future milk production for purposes of planning, in order to avoid losses brought about by the excessive wet season production.
This paper examines the rainfall patterns and milk production trends over a period of three years between 2008 and 2010 in an attempt to establish a relationship between the two variables in Mweiga Location, Nyeri County, Kenya. It used monthly milk collection and rainfall data from Mweiga Location. The data were analyzed in order to establish the regression relationship between the two variables.
Results of the study show a very slight immediate influence of precipitation on milk production (r2 = 0.089). However the influence after one month is considerable (r2 = 0.415). The corresponding regression equation shows that the quantity of milk (kg) produced in the Mweiga Cooperative catchment area after one month is equated to the amount of rainfall received multiplied by a factor of 580 then add a constant 83300.
Keywords: livestock performance, precipitation
Livestock in Mweiga Location are reared in an open grazing system sometimes with little or no grazing rotation. They rely on rainfed pastures that receive no supplementary irrigation throughout the year. Dependence on seasonal weather variations therefore, becomes a major influencing factor on their productivity, manifested in low milk production and loss of livestock body condition during the dry season and high production coupled with good body condition during the wet season. Sometimes the wet season is accompanied by such a high level of milk production that the capacity of the market to consume it is overwhelmed bringing about a milk glut like the one experienced in the months of March and April 2010, in which the surplus milk went to waste.
Pastures consisting of associations of perennial grasses form the most important source of feed for Dairy Cattle in Mweiga Location (MLD 2009). A preliminary survey observed that the predominant grass species in Mweiga location include Themeda triandra, Setaria sphacelata, Chloris gayana and Borthriocloa insculpta, while pockets of Pennisetum clandestinum dominate the higher altitude sections of the location. Njarui et al (2011) confirms that animals in semi-arid tropical Kenya depend on natural pastures and home-grown fodders, mainly Napier grass and crop residues.
Studies indicate that supplementary feeding in the central highlands of Kenya in which the study area falls is done with little regard to the quantity and quality of the supplements or the animal’s requirements. Methu et al (2001) observed that smallholder farmers in Kenya Highlands feed small amounts of concentrates daily to lactating cows regardless of milk yield. Indications were that the concentrate supply was not adjusted as the basal diet fluctuates except in extreme cases such as drought when feeds such as cereal bran may be given. This suggests that supplementary feeding would not have a significant influence on the relationship between pasture availability and milk production.
Among the developing countries, Kenya has one of the most rapidly expanding dairy sub-sectors (ILRI 2000). Smallholder farmers using exotic dairy cattle breeds, mainly in the highland areas, dominate the dairy sub-sector (Omore et al 1999). Smallholder dairy farmers in Kenya produce 56% of the total milk produced and 80% of the total milk marketed (Omore 1997). However, the major constraint to improving production and reproductive performance of dairy cattle is scarcity of feed resources and their poor quality (Methu et al 2001).
Reynolds et al (1996) have found that forage production is related to rainfall. In the study area, there has been an erratic supply of feed which is the result of erratic rainfall. Pasture quality also declines significantly during the dry season and is insufficient to meet animal potential (Thairu et al 1987). Dry weather results in the cessation of growth of pastures and consequently limiting fodder production. The converse is also true where a high amount of precipitation leads to increased pasture growth and a subsequent improved livestock performance.
It is common practice in the Kenyan Highlands to measure dairy animal performance in terms of milk yield, growth rate and mature weight. Mdoe et al (1990), in a study conducted in the Kilimanjaro Highlands, found out that improved feed supply and utilization in dairy enterprise had a positive impact on the performance of dairy cattle. This was reflected in the improvement in growth rate of young animals, better body condition of mature animals, better health status and increased milk production. Various studies conducted on smallholder dairy farms concurred that nutrition which includes feed availability and utilization was a major factor limiting animal performance (Omore et al 1996; Omore 1997; Staal et al 1997; Methu 1998).
Milk production in cattle is known to be influenced by the amount of feed intake when other factors are held constant. In an experiment carried out in typical Dutch farms with high merit dairy cows producing 8000 – 9000 kg milk/cow/lactation, Kohnen (2008) calculated a regression equation exhibiting the linear relationship between relative pasture intake contribution to total Dry Matter Intake (DMI) and Fat Corrected Milk (FCM) following the simple linear function of y= a+ bx with Y = FCM and x = DMI from pasture. Further, he found that “b” is nearly constant for all studies and is independent from level of FCM, environment, experimental conditions and amount of supplementation whereas “a” is constant for a specific study and so expresses a physiological limit for daily milk production. These limits are defined by animal factors (mostly genetic merit, days in milk) and pasture factors (pasture quality and conditions).
Dragovich (2006) found that rainfall-milk production associations were present in areas where economic incentives to reduce production fluctuations were least. Kenya and most other countries in East and Central Africa do not offer any incentives to reduce production during high rainfall seasons. The seasonal availability of moisture in these countries, therefore have a sufficiently strong influence on pasture growth and the associated changes in milk output.
This study was carried out in Mweiga Location, Nyeri County, Kenya, which covers an area of 13.1km2. It has a human population of 17300 people distributed in 5260 households (Opendata, 2009). This population is homogenous such that members are exposed to similar climatic conditions and practice similar crop and livestock production systems.
The study targeted small scale dairy farmers in Mweiga Location, who are served by Mweiga Dairy Cooperative Society. The Cooperative Society is the main milk collection and marketing agent in the study area handling 85% of the milk produced in the location (MLD, 2011). Its geolocation coordinates are (0.501S, 37.020E). No other milk marketing agent exists in the study area hence the amount of milk collected by the cooperative can be used to provide a good estimate of the total amount of milk produced within its catchment area.
This was a correlational study which involved the collection and analysis of unprocessed secondary data on monthly rainfall and monthly milk collection in Mweiga Location for the three year period between January, 2008 and December, 2010. Milk collection data was obtained from monthly milk collection ledgers in Mweiga Dairy Cooperative Society. Data on rainfall was collected from records at the Mweiga Estates Weather Station, which is the major rainfall recording station for the Kenya Meteorological Department in Mweiga Location. The data were analyzed using the Microsoft Excel and Statistical Package for Social Sciences (SPSS).
An analysis of the relationship between precipitation and milk deliveries indicated that only 8.9% of the milk production could be attributed to the amount of rainfall received (Figures 1 and 2). On further examination of the data, the weak correlation was attributed to the fact that it takes some time for the rain to cause an increase in amount of forage obtained through vegetation growth, which would then be available for conversion into milk. Based on this argument, data were then compared between rainfall and milk production one month later (Figures 3 and 4). In this case the correlation was much stronger (r=0.64) indicating that 42% of the variation in milk deliveries could be accounted for by the rainfall during thje previous month.
|Figure 1: Monthly rainfall and milk collection.|
|Figure. 2: Relationship between monthly rainfall and milk collection the same month.|
|Figure 3: Monthly rainfall and milk production one month later.|
|Figure 4: Relationship between rainfall and milk collection one month later.|
It was found from this study that rainfall does not have an immediate effect on milk production but has a significant effect on production one month later. This would be explained by the fact that it takes some time for the moisture to be converted into pasture growth through the process of photosynthesis, which is in turn converted into milk after consumption by the cattle. The finding is in agreement with the findings of Methu et al (2001); Reynolds et al (1996) and Thairu et al (1987) that increased rainfall brings about improved pastures in both quantity and quality.
The findings further confirm the findings of Omore et al (1996); Omore (1997); Staal et al (1997); Methu (1998) and Mdoe et al (1990) that improved pastures lead to increased milk production.
There was a moderate relationship (R2 = 0.42) between monthly milk deliveries and rainfall in the previous month, which could be used to plan the activities of the cooperative.
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Received 13 June 2013; Accepted 13 July 2013; Published 1 August 2013
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