A stochastic dynamic model of a dairy farm to evaluate the technical and economic performance under different scenarios

Abstract

Dairy farms need to improve their competitiveness through decisions that are often difficult to evaluate because they are highly dependent on many economic and technical factors. The objective of this project was to develop a stochastic and dynamic mathematical model to simulate the functioning of a dairy farm to evaluate the effect of changes in technical or economic factors on performance and profitability. Submodels were developed for reproduction, feeding, diseases, heifers, environmental factors, facilities, management, and economics. All these submodels were simulated on an animal-by-animal and day-by-day basis. Default values for all variables are provided, but the user can change them. The outcome provides a list of technical and economic indicators essential for the decision-making process. Performance of the program was verified by evaluating the effects and sensitivity analysis of different scenarios in 20 different dairy farms. As an example, a case study of a dairy farm with 300 cows producing 40 L/d and a 12% pregnancy rate (PR) was used. The effect of using a time-fixed artificial insemination (TFAI) protocol in the first insemination at 77 d in milk, with 45 and 40% conception rates for first-lactation and older cows, respectively, and a cost of €13 was explored. During the 5-yr simulation, the TFAI increased PR (12 to 17%) and milk yield per milking cow (39.8 to 41.2 L/d) and reduced days to first AI (93 to 74), days open (143 to 116), and the proportion of problem cows (24.3 to 15.9%). In the TFAI, cows were dried 30 d earlier, resulting in more dry cows, and a smaller difference in milk yield by present cows (35.5 vs 36.0 L/d for control and TFAI, respectively). A longer productive life (2.56 vs. 2.79 yr) with shorter lactations in TFIA resulted in less first-lactation cows (42 vs 36%), 32 more calvings per year, and, therefore, more cases of postpartum diseases. Total (32.5 to 29.9%) and reproductive (10.5 vs 6.8%) culling rates decreased in TFIA. Overall, the net margin was €245 and €309/cow per year in control and TFIA, respectively. The net margin of applying TFAI decreased as PR of the farm increased, with limited benefit of TFAI at a PR of 30%. The model provides a powerful web-based tool to explore the short- and medium-term consequences of technical and economic decisions on the economic sustainability of dairy farms.

Understanding the adoption of smartphone apps in dairy herd management

“…As dairy herd sizes increase, herd management becomes increasingly difficult and time consuming for a dairy farmer (Gargiulo et al, 2018). Insufficient herd management can result in reduced animal welfare and health, which can lower cow performance and harm the economic status of the dairy farmer (Calsamiglia et al, 2018). Furthermore, record keeping and evaluation at the cow level are considered to be essential for monitoring herd performance and making effective herd management adjustments if necessary (Barragan et al, 2016).…”

Modelling of Livestock Systems to Enhance Efficiency

“…The life cycle submodule simulates animal states and herd dynamics, which can then be used for ration formulation, milk and manure production, and economics. Future performance is uncertain and dependent on probabilistic outcomes, which is better captured by stochastic probabilistic models (Calsamiglia et al, 2018) when compared with deterministic or conventional models. Stochastic models simulate probabilistic distribution of events and also reports a distribution of possible outcomes, which better represent reality.…”

“…Different than dynamic programming (De Vries, 2006) or Markov-chain-based (Cabrera, 2012) models, which have been widely used to simulate dairy herds, stochastic Monte-Carlo models have the advantage of simulating each animal individually and still accommodate the interactions of the herd dynamics. With improved computational power, big continuous data for parameterization, and opportunity for individual-and herd-level permanent operational and strategic decision-making, stochastic Monte-Carlo models seem to be the most adequate framework (Kalantari et al, 2016;Calsamiglia et al, 2018). A simulation example is as follows: a calf is generated with a selected breed (e.g., Holstein), semen type (e.g., sexed semen), and birth weight (e.g., 40.8 kg).…”

A participatory ecodesign framework to address both environmental and economic dimensions in viticulture at farm scale

“…As well, evaluating economic performance mobilizes several methods and indicators and often considers only the financial indicators. For example, some studies mobilize indicators such as gross and net margins, resources costs, and farm income to evaluate the economic performance (Calsamiglia et al 2018;Blasi et al 2016). Others mobilize the technical-economic efficiency defined by the difference between the maximum output determined by the production frontier and the necessary production factors (minimum inputs) (Boussemart and Dervaux 1994;Alem et al 2018).…”