CHAPTER 7: MODELLING THE ECONOMIC PERFORMANCE OF DAIRY FARMS
7.2 Variables used in the analysis of economic performance
(i) Herd size, indicated by the number of milking cows (Herdit)
Herd size, indicated by the number of milking cows, has commonly been used as a proxy for farm size (Bragg & Dalton, 2004; Tauer & Mishra, 2006; Abdulai & Tietje, 2007) and has the benefit of intrinsically accounting for differences in the quality of farm land (Gentner & Tanaka, 2002).
Furthermore, by incorporating herd size into the production frontier, residual information not captured by the incorporated variables, which may be correlated with farm size, can be accounted for (Tauer & Mishra, 2006). In other words, efficiency related to farm size, not captured by the variables in the production function, is likely to be captured by the herd size variable. Considering the above, herd size appears to be the most appropriate measure of farm size for use in this study.
Herd size is hypothesized to have a positive relationship with economic performance since increased herd size is expected to result in increased output, ceteris paribus.
(ii) Average milk production per cow (Milkit)
Gloy et al. (2002) noted several studies which have reported a positive relationship between milk production per cow and various measures of farm financial success. Von Bailey et al. (1989), Huirne et al. (1997) and Gloy et al. (2002) all included average milk production per cow as a measure of productivity. Average milk production per cow captures latent characteristics such as a producer’s knowledge and ability to apply efficient production, feeding and breeding practices
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as well as the ability to benefit from new technologies (Gloy et al., 2002). Tauer & Mishra (2006) suggest that milk production per cow is an indicator of poor or good management, capturing the effects of genetics, feeding, disease control and other managerial factors. Following Gloy et al.
(2002), milk production per cow is hypothesized to have a positive relationship with economic performance, ceteris paribus.
(iii) Level of specialization in dairy (Specit)
The level of specialization provides a measure of diversity on-farm. Bragg & Dalton (2004) measured the diversity of on-farm income using the Herfindahl index, which is calculated as the sum of the squared income shares. Gillespie et al. (2009) simply expressed the level of specialization as the percentage of farm income derived from milk, while Du Toit (2009) calculated the ratio of total milk income to gross farm income to represent the level of specialization in milk production. This study adopted the approach highlighted by Du Toit (2009), expressing specialization as a ratio of milk income to total farm income. The range is thus bound between 0 and 1, with values approaching unity indicating increased specialization in milk production.
Since all farms in the sample may be considered specialized, deriving more than 80% of total income from the dairy enterprise, the Spec variable is intended to capture differences between the most and least specialized farms in the sampled data set, thus revealing if a greater degree of economic performance can be derived from further specialization into dairy. Since relatively more specialized farms are more likely to dedicate a greater portion of their management efforts and farm resources towards dairy production, the level of specialization is hypothesized to have a positive relationship with economic performance, ceteris paribus.
(iv) Trading income (Tradeit)
Trading income is included to determine whether income through the sale of dairy livestock (cull cows and calves, for example) is a potential means of improving farm performance. Trading income is calculated as follows, and is expressed as a ratio of trading income of total milk income.
Trading income = livestock sales + closing value – livestock purchases - opening value (7.1)
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Du Toit (2009) found trading income to have a positive effect on the competitiveness of East Griqualand milk producers. In line with this finding, additional income through the sale of dairy livestock is expected to improve economic performance. Trade is, therefore, hypothesized to have a positive relationship with economic performance, ceteris paribus.
(v) Breeding management (BREED)
Genetic improvement through breeding is an important source of technological change (Babcock
& Foster, 1991, as cited by Atsbeha et al., 2012). Dairy production involves a continuous transfer of genetic material, either naturally or by AI, and therefore managerial decisions such as choice of insemination method, selection of semen and breed of cows will determine the genetic performance of cows to some degree. This genetic variation, due to managerial influence, may result in productivity differences among farms, even if only in the short term (Atsbeha et al., 2012).
Following Richards & Jeffrey (2000), breeding expense per cow and breeding expense per unit output were selected as indicators of the breeding management variable (BREED). Breeding expense was calculated as total rand value expenditure on artificial insemination (AI) divided by herd size and quantity of milk produced in litres, respectively. Farms using bulls, rather than AI, were assigned values of zero (0). To ensure identification, it is generally desirable to specify three indicators when constructing an index; however, due to data limitations this was not possible.
Furthermore, it is important to note that a high degree of correlation may exist between breeding expense per cow and breeding expense per unit output since the denominators, herd size and milk output, are likely to exhibit a high degree of correlation. This should be borne in mind when interpreting the results. Since BREED is expected to capture the genetic progress due to managerial influence between farms, it is expected to exhibit a positive relationship with economic performance, ceteris paribus.
(vi) Feeding management (FEED)
Total expenditure on feed is generally one of the largest costs associated with milk production (Buza et al., 2014) and is expected to be a significant determinant of economic performance. Total feed cost per unit output, cost of purchased feed per unit output and the ratio of purchased feed to home grown feed were all included as indicators of FEED. All costs are expressed in aggregate value terms, deflated by the CPI to account for inflationary effects. Total feed cost is represented
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by the aggregation of total rand value expenditure on purchased feed, including supplementary feed, concentrates, and calf meal, as well as total expenditure on homegrown feeds. Expenditure on homegrown feeds is a function of several costs, including but not limited to, seed, fertilizer, planting, and harvesting costs. The ratio of purchased feed to homegrown feed is included in an attempt to assess a manager’s ability to meet the nutrient requirements of the herd using pasture and home grown feeds. Since the ability to manage purchased and home grown feeds effectively is expected to improve economic performance, FEED is hypothesized to have a positive relationship with economic performance, ceteris paribus.
(vii) Labour management (LABOUR).
A latent index of labour management was constructed in an effort to assess the ability of producers to effectively manage labour and implement labour saving technologies where possible. Following Richards & Jeffrey (2000), latent labour management indicators consist of labour per cow and labour per unit output. In this study, labour is expressed in terms of the aggregate wage bill in rand value and includes all labour of various quality. As mentioned for BREED, it is generally desirable to specify three indicators when constructing an index, however, due to data limitations this was not possible. Furthermore, a high degree of correlation may exist between labour cost per cow and labour cost per unit output since the denominators, herd size and milk output, are likely to exhibit a high degree of correlation. This should be borne in mind when interpreting the results for LABOUR. The results presented by Richards & Jeffery (2000) suggest that labour management (quality) is positively related to economic performance. Therefore, LABOUR is hypothesized to have a positive relationship with economic performance, ceteris paribus.
7.2.2 Measurement equation variables 1. Technical efficiency = β1PERF + u1
2. Scale efficiency = β2PERF + u2
3. Breeding expense per cow = β5BREED + u5
4. Breeding expense per unit output = β6BREED + u6
5. Concentrate/forage ratio = β7FEED + u7
6. Feed cost per unit output = β8FEED + u8
117 7. Concentrates per unit output = β9FEED + u9
8. Labour cost per cow = β10LABOUR + u10
9. Labour cost per unit output = β11LABOUR + u11