Selection of input variables

Milk production was selected as the output variable. Gross farm revenue can be used to aggregate the multiple outputs produced (milk, beef, excess forage sold, equipment hire) by a dairy farm, at the cost of capturing allocative and technical efficiency effects in the inefficiency term (Jaforullah and Devlin, 1996). Furthermore, in order to convert gross farm revenue into a “quantity,” it has to be deflated by the Consumer Price Index (CPI). This poses another problem. The milk price index and the CPI moved closely together between seasons 1996/97 to 1999/00. However, milk payout increased by 32% in nominal terms for season 2000/01, remained at the same level for two more seasons, and finally declined to the levels of 1996/97 in 2003/04. Hence, deflating gross farm revenue by CPI would overestimate milk production. Given that the share of milk revenue in total farm revenue was greater than 78% for all farms in any given year, milk production per farm in physical units was preferred as the output variable. Ahmad and Bravo-Ureta (1995, 1996), Bravo-Ureta (1986), Fraser and Cordina (1999), Mbaga et al. (2003), Tauer (1998) and Piesse, Thirtle and Turk (1996) followed a similar approach.

The number of cows in milk and effective farm area were chosen as variables. Output is measured in physical units, i.e., kgs of milksolids. Therefore, only cows in milk (following Ahmad and Bravo-Ureta, 1995, 1996; Bravo-Ureta, 1986 and Kumbhakar, Gosh and McGuckin, 1991) and effective farm area can be considered as inputs in the production process. As was explained in Chapter 2, cow numbers and farm area has increased at faster rates in Canterbury-Southland than in Waikato-Taranaki. Using “cows” and “area” as input

variables would allow the different pattern of expansion between regions to be shown. Furthermore, these two inputs are readily accessible.

Labour input is measured as the total yearly hours worked by family and hired labour on the farm. As mentioned above, all the studies reviewed use labour as an input. The discussion, if any, was centred on how was it measured, physical or monetary. The database provides information on total wages but does not include the owners’ wages. In order to transform total wages into a physical unit (total hours worked), total wages was divided by the average hourly earnings reported by the Reserve Bank of New Zealand. Furthermore, given that the database precluded identifying farms that were owner-operated, and that most, if not all, owners are somehow involved in farming activities, it was decided to add the hours worked by the owner to all farms in the sample. The owners’ hours worked per year was assumed to be 58 hours per week as reported in the Economic Survey of New Zealand Dairy Farmers by Dexcel.

Given the increasing use of feed supplements in NZ dairy farming, “feed expenditure” was considered separately and so was “fertiliser expenditure” to allow more technical details to be modelled (Ahmad and Bravo-Ureta, 1995, 1996). Regrettably, the dataset had no information on the type of supplementary feed purchased. Having this information would have enabled different strategies in feeding practices to be taken into account. For example, given the same feed expenditure for two different farms, the cost of supplementary feed purchased may be different. Therefore effective quantity of supplements purchased may be different, e.g., between all concentrate or all hay. Similarly, the database had no information about the amount of fertilizer used as feed supplement. An aggregate measure of “intermediate inputs” comprised of health, breeding, shed, feed and fertilizer expenditure was created à la Brümmer, Glauben and Thijssen (2002), albeit a slightly different approach to aggregate was taken. Expenditure on each input was deflated by the corresponding price index taken from the Farm Expenses Price Index for Dairy Farms (Statistics New Zealand). Aggregating inputs comes at the cost of sacrificing potentially useful information. Capital input (K2) was measured as the user cost, defined as the sum of depreciation and interest on the stock of capital (Ahmad and Bravo-Ureta, 1995; Heshmati and Kumbhakar, 1994; Kumbhakar, Biswas and Bailey, 1989; Kumbhakar and Heshmati, 1995 and Kumbhakar and Hjalmarsson, 1993). The database included a stock measure of capital for

“land and buildings” and “vehicles and machinery.” However, an aggregate measure of capital for “land and buildings” does not allow different rates of depreciation to be applied depending on the intensity at which capital is used. Hence, the value of the “buildings” was set at 12% of the stock value of “land and buildings.” Depreciation for “buildings” was set at 4% and for “vehicles and machinery” at 10%. The average interest rate of the government bond for the period, at 7%, as reported by the Reserve Bank of New Zealand, was chosen to proxy the opportunity cost of employing capital elsewhere. Depreciation on “buildings” was deflated by the average price of dairy farm land as estimated by Quotable Value New Zealand (Situation and Outlook for New Zealand Agriculture and Forestry, 2006). “Vehicles and machinery” was deflated by the price index on repairs and maintenance. Meanwhile, interest was corrected by the corresponding price index from the Farm Expenses Price Index for Dairy Farms (Statistics New Zealand).

Farm surveys do not usually include information about the capital stock on land, buildings and machinery. Conversely, expenditure on different items is always reported. Therefore, a different measure of capital input (K9) was estimated. It is comprised of the expenditure on electricity, freight, fuel, rates and insurance, repairs and maintenance on buildings, vehicles and machinery, administration and miscellaneous (à la Ahmad and Bravo-Ureta, 1996), all deflated by the corresponding price index taken from the Farm Expenses Price Index for Dairy Farms (Statistics New Zealand).

As mentioned in Chapter 2, the industrial organisation of the dairy industry changed dramatically in 2001 with the demise of the NZDB and the creation of Fonterra. Therefore, a dummy variable for policy change (DPch) was included to capture the impact of this change on the production frontier, if any.

The models defined had alternative combination of factor inputs, and the same output. Model J7 was defined following Brümmer, Glauben and Thijssen (2002) and Kumbhakar and Hjalmarsson (1993). Model L8 resembles input selection made by Cuesta (2000). The difference is that Cuesta (2000) used “cows” as a proxy of capital, while model L8 includes a measure of “capital.” Model Y5 followed Bravo-Ureta and Rieger (1990) and Tauer (1998). Model K9 was a variation of Model L8, where originally effective area was substituted by fertilizer and K2 by K9. The time trend and the dummy for policy change were included in all models (Table 4.9).

Table 4.9 - Models estimated and variables used; X shows the variables that were included in each of the models

Variables Code _{J7 L8} Model _{Y5 K9}

Output (Milk Production) Y X X X X

Factor inputs

Cows in Milk (number) CW X X

Area (milking platform, ha) A X X

Labour (total hrs per year) L X X X X

Feed (all purchased feed, NZ$ 92/93) FD X

Fertilizer (expenditure, NZ$ 92/93) FT X X

Intermediate inputs (health, breeding,

shed, feed and fertilizer expenses) II X Depreciation and interest on: buildings

and vehicles and machinery plus expenditure on repairs and maintenance (NZ$ 92/93)

K2 X X X

Expenditure on: repairs and maintenance on buildings and machinery, fuel, electricity, rates and insurance, administration and miscellaneous (NZ$ 92/93)

K9 X

Year Y X X X X

Dummy for policy change DPch X X X X

Other aggregation of inputs and combinations of variables were tested. Some of them followed studies reviewed before, some not. The four models reported were those economically meaningful. For example, negative labour input elasticity implies excess labour. Common sense indicates that this cannot be the case in NZ dairy farming, where labour shortages are a huge problem. However, it can be logical in Africa for small family- operated dairy farms, where excess labour at home is masking unemployment at a national level. So for the case of NZ, we might expect high labour elasticity.