Influence of milk yield on profitability—A quantile regression analysis

Abstract

This paper analyzes the factors that influence the economic success of Swiss dairy farms, as measured by the annual income per family work unit, using panel data regression techniques. Based on more than 5,400 farm-year observations, the main focus of the analysis concerns the milk yield per cow and year as the key explanatory variable, which can be adjusted by the farm manager in the medium term. We apply both a random effects model and a quantile regression based on deciles, which allows us to study the heterogeneity of the sample in greater detail. Consistent with the current literature, the random effects model shows the positive contribution of the milk yield, namely an additional 1,000 kg/cow results in an increase of CHF 2,660; that is, 6% of the annual income. The quantile regression reveals that the effect of the milk yield differs between deciles, with a high milk yield being most beneficial for the best-performing farms, accounting for up to CHF 7,210 per 1,000 kg (where CHF1 = €0.86 = $1.01). Our analysis further shows the influence of the milk yield on profitability to be highly heterogeneous among Swiss dairy farms, indicating business-specific extension services and not suggesting the requirement for an increased milk yield at each level of economic success.

Heat risks in Swiss milk production

“…The main reasons for the current robustness must be found within farms because a natural hedge between Swiss milk prices and milk production at farm‐level is, if there is any, small (see Supporting Information: Section ), and we do not expect heat stress to reduce prices for veterinary services and feed (see also Schaub & Finger, 2020). There exist various sources for robustness to heat stress on the farm and Mayer et al (1999) find that robust milk production systems can sustain THI‐values of up to 87 without a loss in well‐managed systems.…”

“…The Swiss Confederation's center of excellence for agricultural research (Agroscope) provides farm-level accountancy data from milk producers from the official Swiss Farm Accountancy Data Network (FADN) (Hoop & Schmid, 2015). This data set consists of representative farms, aims to monitor the economic development of Swiss agriculture, and has been used in previous publications (e.g., de Mey et al, 2016;Renner et al, 2021;Schorr & Lips, 2018). Our subsample from the Swiss FADN consists of the 1314 milk producers shown in Figure 1.…”

Production intensification and animal health expenditure on dairy farms in New Zealand

“…The crucial distinction between the 2 models is whether the unobserved individual effect embodies elements that are correlated with regressors in the model. Both specifications have been applied in recent studies (Borchardt et al, 2018;Hadrich et al, 2018;Schorr and Lips, 2018). For example, using a fixed-effects model, Hadrich et al (2018) have examined how SCC affects milk production.…”

“…For example, using a fixed-effects model, Hadrich et al (2018) have examined how SCC affects milk production. Using a random-effects model and data obtained from the Swiss Farm Accountancy Data Network, Schorr and Lips (2018) analyzed the factors that influence the annual income per family work unit for Swiss dairy farms.…”

The skin-deep beauty of dairy cows : Investigation of metabolic disorders by using morphological traits quantified with 3-Dimensional vision

“…yield to assess the effects of their on-farm feeding practice change (Schorr and Lips, 2018). In addition, some farms are equipped with automated body condition scoring systems.…”