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Citation of this paper
The study was carried out in Andonosari village, East Java, Indonesia. The objectives were to: (a) investigate the pattern of the household labour allocation; (b) examine factors that influencing household labour in dairy farming activities. Time use patterns for household labour were computed as the time spent on work (dairy farming and non-dairy farming, including on- and off-farm activities). Interviews were conducted separately with the husband, wife, and family members aged 15-64 years for 50 households. Households were classified into three strata based on the number of dairy cattle farmed: strata 1 (owned < 3 animal units (AUs)), n=16; strata 2 (raised 3 to 5 AUs, n=18); and strata 3 (controlled > 5 AUs, n=16). Descriptive, univariate, bivariate and multivariate analyses were performed using SAS package.
Household labour for income generating activities was allocated more to dairy farming compared to the farm and non-farm work. The household labour requirement in dairy farming per animal unit decreased as herd size increased, thereby allowing more time for non-dairy activities. Female participation was most evident in feed preparation and feeding, whereas the predominant male activity was forage collection for the dairy cattle. The size of the landholding had no impact on household labour allocation to dairy farming activities. An increase in household income and dependency ratio had a minor impact on household labour requirement in dairy farming.
Key words: gender, on and off-farm activities, small scale dairy farming
As a part of a subsistence farming system, dairy farming in Indonesia provides milk for consumers and to rural households. Domestic supply alone cannot meet consumption demand, and thus Indonesia imports from overseas countries. The government of Indonesia has taken new initiatives to improve the dairy production system, and boost milk-production by improving the quality of dairy cattle, which in turn will provide higher incomes for dairy farmers.
East Java is an important province for dairy farm development. The region accounts for about one-third of the total dairy cows in Indonesia, and dairying constitutes an important source of income for rural households. Dairy farming in East Java commonly involves family labour, including women. Women are largely involved in milking, rearing calves and other activities contributing to the raising of dairy cattle.
Household labour (including both male and female) was assumed to play an important role in income generating activities. Both male and female members of households devote considerable hours to dairy farming and non-dairy activities, including on-farm and non-farm. The gender division of labour was used to explain gender roles in dairy farming. Utami (1992) examined the household labour allocation on East Java dairy farms, however the gender role, and contribution of time allocation and income generated from dairy farming and its role in alleviating poverty have not been studied. This study examined the household labour allocation and gender roles in dairy farming.
The overall objective of the research was to investigate the pattern of the household labour allocation; and (c) quantify the role of gender in dairy farming activities.
The number of family labourers may affect the net-income of dairy farming. As the number of household labourers increase, a greater amount of time may be devoted in dairy farming to acquiring more income from dairy farming. The difference in number of household labour hours can represent the variation in net income of dairy farming per AU. Therefore, the household labour requirement in dairy farming per animal unit is hypothesised to decrease as herd size increased.
The theory of Becker (1965) confirms that the utility of households depends on their income, and time allocation pattern. Gronau (1977) elaborated the theory by allocating household time (labour) to three activities: (i) labour market; (ii) home activities; and (iii) leisure time. However, Sumner (1982) had a slightly different perspective from Gronau’s model. Household utility is subject to both time, income and farm production constraints.
Other researchers have extended Gronau’s model, and such approaches assume that family utility function involves the constraint of time allocation between various members of the household, a specification of farm income sources, and household characteristics. Studies of household time allocation do not concentrate on the farm operator only (Huffman, 1980; Robinson, et al 1982; Simpson and Kapitany, 1983; Van Kooten and Arthur, 1985; Gunter and McNamara, 1990), but examine both the farm operator and his/her spouse (Furtan, et al 1985). Other studies have included other members of the household (Schmitt, 1989) as well as emphasising on gender roles (Olfert, 1993).
Ehrenberg and Smith (1994), for instance, classified three alternative uses of time – namely, working for pay, working at home, and consuming leisure time. Subsequently, Fratkin (1989) provided a starting point with which to determine the time allocation of household activities into (i) household task; (ii) livestock task; (iii) manufacturing task; (iv) the performance of essential social activities; (v) and rest and leisure.
Similarly, Malathy (1994) examined the determinants of work activities differentiating between paid work and household tasks. Traditionally, female activities are regarded as non-labour force activities. This ignoring of the contribution of activities undertaken by women can seriously distort the measurement of their productive roles. This view is supported by Rosenfield and Tigges (1988) who point out that, in reality, women’s contributions to farm productivity are likely to be underestimated by conventional measures. Skoufias’s (1994) study is consistent with the concept suggested by Tomoda (1985) that productive time includes working on the farm, cooking and cleaning, and other housework. However, Tomoda fails to provide methods of valuing household chores, such as cooking, cleaning and childcare.
The theoretical framework for the research is based on the Becker Model (1965). The utility of the household is derived from home-produced goods, market goods, the leisure time of both female and male, and household characteristics. Dairy farm households allocate time to three daily activities: (a) in the labour market; (b) in home activities; and (c) leisure time. The labour market activities comprise all productive activities, on- and non- dairy farming yielding income in cash or in kind. The relationship of time constraint can be stated as Gronau’s model:
T : total time available to the ith dairy farmer household,
Twi : time allocated to market work in the ith household,
Thi : time allocated to home production in the ith household and
Tli : time allocated to leisure in the ith household.
The work activities include time-use by all household members: male and female in income-generating activities. The household’s time spent on three separate activities can be viewed as:
Twi: total time allocated to market work,
i : ith member of the household (n-members),
j : jth activity (1= market work; 2= household activities; and 3= leisure time),
k : gender of the household labour (1=male; 2=female).
The time allocation in dairy farming is a function of the postulated exogenous variables (household characteristics) of the model:
N : time allocated by household labour in dairy farming (hours/year),
X1 : the number of lactating cows (animal units),
X2 : the income of dairy farming (Rp/year),
X3 : non-labour income (Rp /year),
X4 : income from outside dairy farming (including both farm and non-farm) (Rp/year),
X5 : the number of family members (persons),
X6 : dependency ratio,
X7 : the number of dairy cattle (animal units),
X8 : the size of land holding (ha),
ε : the error or residual,
αo : intercept, and
α1, α2, .......,α8 : regression coefficients associated with X1, X2, …….., X8 respectively.
Andonosari village was selected for the research based on a greater number of dairy cows as well as dairy farmers in its areas compared to other regionsA multistage sampling technique was used to determine 150 respondents according to two criteria (a) land holdings and (b) ownership of at least one lactating cow (Dane, 1990; Miah, 1993).Households were classified into three strata based on the number of dairy cattle farmed: strata 1 (owned fewer than 3 animal units (AUs)), n=16; strata 2 (raised 3 to 5 AUs, n=18); and strata 3 (controlled more than 5 AUs, n=16). Interviews were conducted separately with the husband, wife, and family members aged 15-64 years for 50 households to obtain data. Descriptive, univariate, bivariate and multivariate analyses were performed using SAS package for analysing the data.
Most dairy farmers (36%) were aged 41-50 years old, while only 4% of farmers were aged 60-70 years. About 26% and 23% of farmers were aged 31-40 years and 51-60 years respectively. A few of the farmers (11%) were aged 27-30 years. This indicated that the majority of farmer was aged between 27-60 years. The level of farmer education in Andonosari village was mostly that they had completed primary school (77%), whereas only 13% had not completed. 11% of the farmers were illiterate. The majority of dairy farmers (66%) in Andonosari village have more than 10 years’ experience in operating dairy farms. About 28% and 6% of farmers have had experience in operating dairy cattle for 6-10 years, and fewer than 5 years respectively.
Household labour requirements in dairy farming were classified into nine tasks namely, collecting water for cows, forage collection, feed preparation and feeding, cleaning the barn, cleaning milking and feeding equipment, washing cattle, milking cows, marketing milk to “KUD Setia Kawan” (milk co-operatives), and other activities (including calf rearing, taking care of unhealthy cows or calving cows) (See Figure 1, 2, and 3). Overall, household labour has more devoted their time on dairying activities and tend to decrease along with an increase the number of dairy cattle owned by farmer. There was about 3.83 hours/AU for stratum I, 3.18 hours for stratum II, and 1.7 hours for stratum III. This may have reflected the location of stratum 1 dairy farms. Location can determine the number of hours spent in operating dairy farming. For example, more time is required if the site of forage collection is located far from the house. The forage collection activity in stratum I has a higher percentage (42%) compared to stratum II (30%) and stratum III (24%). Also, the water sources for cows is not available near the house, or the place to market milk is not close to the farmer’s house. This leads to more hours being allocated to dairy farming tasks.
Another interesting fact which emerges from household labour in stratum II, in which the amount of family labour time spent in animal husbandry activities appeared lowest in comparison with both stratum I and stratum II. The higher labour requirement was concentrated in non-dairy either farm or non-farm activities, rather than in dairy activities. Furthermore, some of the dairy farmers in stratum III use hired labour for dairy farming activities, such as collection of forage, milking cows and marketing milk. On the other hand, non-dairy tasks are performed only by family labour.
On the basis of gender, females have allocated consistently higher numbers of hours to giving feed to animals, and washing feeding and milking equipment activities, while male labour dominated in giving feed to animals and grazing activities. Irrespective of strata, men were an important source of labour in the forage collection activity (29-42%), while women (34-55%) dominated feed preparation and feeding to animals. Generally, males spent more time in animal husbandry activities, while the reverse was true for female labour. Men’s hours were more strongly tied to the type of work which was more time consuming, such as forage collection. On average, up to three hours per day were required to collect forage for the cows.
Figure 1. Household Labour Allocation in Dairy Farming Activities for Stratum I
Figure 2. Household Labour Allocation in Dairy Farming Activities for Stratum II
Figure 3. Household Labour Allocation in Dairy Farming Activities for Stratum III
This time could be longer during the dry season when feed supply was limited and the dairy farmers needed to travel greater distances to the forestry areas to find sufficient fodder. In contrast, some of the women’s tasks such as feed preparation and feeding, cleaning the barn and cleaning milking and feeding equipment activities required fewer hours per day, or were relatively small in their demand for time.This time could be longer during the dry season when feed supply was limited and the dairy farmers needed to travel greater distances to the forestry areas to find sufficient fodder. In contrast, some of the women’s tasks such as feed preparation and feeding, cleaning the barn and cleaning milking and feeding equipment activities required fewer hours per day, or were relatively small in their demand for time.
The regression analysis was applied to obtain insight into how household labour allocation in dairy farming differs according to household characteristics: the number of dairy cattle owned by the farmer, the number of family members and the total household income. Regression analysis reveals that the number of dairy cattle owned by the farmer and the total household income are the strongest predictors of the household labour allocation in dairy farming (R² = 0.54 and F=10.394). The model explained 54% of the variation in the household labour requirement per AU basis. The variation of household labour allocation in dairy farming activities can be explained by (i) number of lactating cows; (ii) income from dairy farming; (iii) non-labour income; (iv) income from outside dairy farming (including both farm and non-farm); (v) number of family members (persons); (vi) dependency ratio; (vii) number of dairy cattle owned; (viii) and the size of land holding.
The regression model indicated that both the numbers of dairy cattle owned by farmers and the household income variables were negatively associated with the total number of hours allocated per AU in dairy farming (P<0.01 and P<0.05 respectively). The household size, although was positively associated with labour requirement, was not statistically significant.
Y : the number of household labour allocation on dairy farming per animal unit (hours/year),
CATLSIZE : the number of dairy cattle owned by farmers (animal units),
FAMEMB : the number of family members (persons),
FAMINCO : total household income (Rp/year).
The result of regression analysis of household labour requirement per animal units is consistent to the hypothesis. An increase in the number in dairy cattle owned would decrease hours of household labour per animal unit spent on animal husbandry tasks - whereas family labour per animal unit in a dairy enterprise shows the reverse trend with the accruing total household income.
Doubling the number of dairy cows would have little impact on the additional labour requirement on a per farm basis. As herd size increases by 50%, about 14% of household labour would be required. On the other hand, the change in total household income showed a small impact in household labour allocation to dairy farming. This indicated that, although dairy farming is a second income source, it was the reasonable alternative to allocate household labour. This is because not all of potential household labour is allocated to farm or non-farm activities, so the surplus labour can be devoted to dairy farming. Moreover, it can absorb female labour. In addition, an attraction which dairy farming provides is a steady source of income on a regular basis – which is an incentive to continuing dairying.
On the other hand, doubling dairy cattle numbers would reduce the labour requirement by 49% per AU. As the household income increased by 100%, the household labour requirement per AU would decrease by only 8%. This means that higher household income has only a marginal influence on the labour requirement in dairy farming.
An investigation of “Household Labour Allocation in Small Scale Dairy Farming” in Indonesia indicated the following conclusions.
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Received 21 August 2013; Accepted 10 September 2013; Published 1 October 2013
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