Using milksolids production and stocking rate as indicators of a

of a low profitability problem

The next set of indirect indicators of low profitability used by the consultant are milksolids production per cow and per hectare along with stocking rate. Again, the work of Ho et al. (2013) reported that these partial indicators had a low correlation with operating profit per hectare for New Zealand farms (R² between 0.1 and 0.3). Miller and Savage (2016) reported correlations between operating profit and milksolids per cow (R² = 0.06) for a group of South Island farms in 2015/16. Savage and Lewis (2005) however reported that data from South Island farmers in their Dairy Systems Monitoring programme showed a high correlation (R² = 0.75) between stocking rate and gross margin per hectare. An important aspect of his use of milksolids production data for benchmarking purposes and the identification of low profitability problems is his knowledge of what represents good, average and poor levels of milksolids production for each district (location/climate), soil type, and system type within his region. This three-dimensional matrix has been built up as a result of years of experience and through a range of data sources. It provides an interesting contrast to some of the limitations the consultant has with financial benchmarking information. For example, if a client is producing low levels of milksolids per cow and per hectare at a low stocking rate for the system type and soil/climate combination, this may indicate a low profitability problem. For example, a farm that is system 3 producing 320 kg MS/cow or less and around 700 – 800 kg MS/ha with a below average stocking rate for the district and farming system type, may indicate a potential problem in relation to profitability.

The consultant stressed that low profitability problems are all relative in terms of what is good and what is bad and what indicators he uses to identify such problems. He stated that milksolids production per hectare, per cow and stocking rate by themselves are not useful indicators because they need to be “placed in context”. This is where the consultant’s benchmarking matrix is useful because he knows what levels of milksolids production are: good, average, and poor for a given soil type, location and system type. However, there is other contextual data that must be taken into account such as the nature of the bought-in feeds or supplements used on a farm. For example, there is a farmer in the Manawatu who is achieving 2000 kg MS/ha at a stocking rate of 4.0 cows/ha producing 500 kg MS/cow. His production figures looked good given his soil type, location and system type, but when the consultant explored what he was feeding the herd to achieve such levels, he found that the farmer was feeding very high cost feeds. This explained the low levels of profitability the farmer was achieving.

The consultant provided examples of his benchmarks for different locations in his region. For example, farms on river silts in the Manawatu should be producing 1000 – 1100 kg MS/ha on average. A good farmer will be producing more

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producing 700 – 800 kg MS/ha on this country. In contrast, for sand country, a farmer should be producing 800 – 900 kg MS/ha on average. A good farmer

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farmer as poor if he was producing 600 – 700 kg MS/ha on this country. In terms of variation in milksolids production per hectare, the consultant stated that in his region, there is not a lot of variation in terms of climate. The biggest variation is in relation to soil type. Altitude has some effect in terms of the length of the growing season and the level of variation in pasture production. A district such as Apiti which is at a higher altitude than most other dairying districts will have this problem. The consultant stated that there is a rain gradient across the Manawatu, but it is not the critical driver of milksolids production per hectare, whereas soil type is (sand, clay, silt, peat). He has a quite detailed knowledge of the soil types within his different districts along with a good knowledge of the contour in different areas, another driver of net pasture dry matter harvested.

The consultant’s assessment of productivity data is also dependent upon the type of system the client is running. The consultant has rules of thumb that adjust his basic benchmarks for soil type and location (climate), for the amount of feed a farmer brings into his system or his system type (System 1 – 5). In effect, the consultant has a three dimensional matrix for soil type, location (climate) and system types in which he has instantiated good, average and poor levels of his various physical productivity measures (e.g. MS/ha, MS/cow, stocking rate) (Figure 3).

Figure 3. Representation of the consultant’s physical productivity matrix for milksolids production

The problem identification process the consultant uses in relation to milksolids production and stocking rate is shown in Figure 4. First he classifies the farm in terms of soil type, location and system type. Second he calculates the milksolids production per hectare, stocking rate and milksolids production per cow for the farm. Drawing on his benchmarking data from his physical productivity matrix (Figure 3) he benchmarks the farm’s physical performance and then classifies it as poor, average or good. If the farm’s physical performance is classified as poor, then this identifies a potential low profitability problem. If the farm is not classified as poor, then the consultant moves on to look at other indicators. The consultant stressed that production per cow only accounts for about 10 – 20% of the variation in profitability and as such it is a very poor indicator. Therefore the indicator by itself does not mean too much to the consultant. As such it is important to be careful when interpreting what such measures indicate for a client. The consultant does however assess milksolids production per cow and stocking rate when evaluating the productivity of a client’s farm.

Generally, low productivity does not result in high levels of profitability unless the client has very good cost control. Similarly, a client could have moderate to good levels of productivity (e.g. 400 kg MS/cow and 1000 kg MS/ha), but their profitability could be low because they are buying in a lot of feed to achieve these levels of productivity. This shows that the consultant is again using a benchmarking and classification process reported in other studies to identify potential problems (Rogers et al., 1996a,b; Gray et al., 1999a,b; 2000; Bruce, 2013).

Soil type

Sand Silt Clay Peat

Himatang Tokomaru Halcombe Rongote Apiti Kairanga

Figure 4. The problem identification process using milksolids production and stocking rate indicators.