Abstract:Beef cow-calf farming systems are assumed to be resilient to biological disturbances that induce variations in herd demography; however, this hypothesis has not been fully investigated to date. Modeling is an interesting approach to study farming system resilience and to evaluate the impact of biological disturbances, taking into account interactions between system components, including biological variability and management practices. Our objective was to evaluate the resilience of beef cow-calf farming system… Show more
“…Although biological studies may provide an in-depth description of biological processes, they fall behind to include other aspects of livestock systems. Similarly, population studies [ 61 , 62 , 63 ] focus on population dynamics and provide less emphasis on the other important aspects of livestock production such as productivity or economic outcomes. Livestock production represents a complex phenomenon, and to understand how complex systems function, combining biological, physical, and economic aspects of the system is useful [ 64 ].…”
Section: Discussionmentioning
confidence: 99%
“…Stochastic approaches, however, incorporate the probability distribution of input parameters or the desired outcome representing the variability that is present in the system or might occur as a result of simulation. For instance, studies such as [ 21 , 63 , 75 ] used the stochastic nature of various parameters in the simulation model. Lancaster and Larson [ 21 ] used the energy required for maintenance, reproductive efficacy, nutritive value of forage, and forage yield as stochastic parameters, whereas Viet et al [ 63 ] and Merico et al [ 75 ] used the probability of occurrence of various reproductive and management events as model parameters.…”
Section: Discussionmentioning
confidence: 99%
“…For instance, studies such as [ 21 , 63 , 75 ] used the stochastic nature of various parameters in the simulation model. Lancaster and Larson [ 21 ] used the energy required for maintenance, reproductive efficacy, nutritive value of forage, and forage yield as stochastic parameters, whereas Viet et al [ 63 ] and Merico et al [ 75 ] used the probability of occurrence of various reproductive and management events as model parameters. In contrast to studies employing either deterministic or stochastic approaches, some studies [ 22 , 48 , 76 ] use both approaches.…”
Simulation models are used in various areas of agriculture to better understand the system and assist in decision making. In the beef production sector, a variety of simulation research focusing on various dimensions of the system is available. However, an overview of the available research is lacking. Therefore, a systematic review was conducted to provide an overview of simulation studies of beef production and create an understanding of the simulation approaches used. Scopus, Web of Science, and ProQuest Central research databases were used to search the relevant articles, with the last search conducted in June 2023. Studies that developed or used simulation strategies and used beef cattle as a primary focus of the study were included. The 105 studies included in this review were examined thoroughly to record the authors, year of publication, country of study, type of study, focus area of the study, simulated scenarios, validation methods, and software programs used. There has been growing research interest in simulating beef production systems worldwide, with most studies conducted in North America and Europe. Among these studies, the majority (84.76%, n = 89) are biophysical or bioeconomic study types and use deterministic approaches (n = 42). Additionally, most studies have a whole-farm scope (38.09%, n = 40) and focus on productivity (51.43%, n = 54). Since only less than half of the studies mentioned the validation techniques and software programs used, there is a need to improve the availability of this information to ensure that the models are adopted effectively in decision making.
“…Although biological studies may provide an in-depth description of biological processes, they fall behind to include other aspects of livestock systems. Similarly, population studies [ 61 , 62 , 63 ] focus on population dynamics and provide less emphasis on the other important aspects of livestock production such as productivity or economic outcomes. Livestock production represents a complex phenomenon, and to understand how complex systems function, combining biological, physical, and economic aspects of the system is useful [ 64 ].…”
Section: Discussionmentioning
confidence: 99%
“…Stochastic approaches, however, incorporate the probability distribution of input parameters or the desired outcome representing the variability that is present in the system or might occur as a result of simulation. For instance, studies such as [ 21 , 63 , 75 ] used the stochastic nature of various parameters in the simulation model. Lancaster and Larson [ 21 ] used the energy required for maintenance, reproductive efficacy, nutritive value of forage, and forage yield as stochastic parameters, whereas Viet et al [ 63 ] and Merico et al [ 75 ] used the probability of occurrence of various reproductive and management events as model parameters.…”
Section: Discussionmentioning
confidence: 99%
“…For instance, studies such as [ 21 , 63 , 75 ] used the stochastic nature of various parameters in the simulation model. Lancaster and Larson [ 21 ] used the energy required for maintenance, reproductive efficacy, nutritive value of forage, and forage yield as stochastic parameters, whereas Viet et al [ 63 ] and Merico et al [ 75 ] used the probability of occurrence of various reproductive and management events as model parameters. In contrast to studies employing either deterministic or stochastic approaches, some studies [ 22 , 48 , 76 ] use both approaches.…”
Simulation models are used in various areas of agriculture to better understand the system and assist in decision making. In the beef production sector, a variety of simulation research focusing on various dimensions of the system is available. However, an overview of the available research is lacking. Therefore, a systematic review was conducted to provide an overview of simulation studies of beef production and create an understanding of the simulation approaches used. Scopus, Web of Science, and ProQuest Central research databases were used to search the relevant articles, with the last search conducted in June 2023. Studies that developed or used simulation strategies and used beef cattle as a primary focus of the study were included. The 105 studies included in this review were examined thoroughly to record the authors, year of publication, country of study, type of study, focus area of the study, simulated scenarios, validation methods, and software programs used. There has been growing research interest in simulating beef production systems worldwide, with most studies conducted in North America and Europe. Among these studies, the majority (84.76%, n = 89) are biophysical or bioeconomic study types and use deterministic approaches (n = 42). Additionally, most studies have a whole-farm scope (38.09%, n = 40) and focus on productivity (51.43%, n = 54). Since only less than half of the studies mentioned the validation techniques and software programs used, there is a need to improve the availability of this information to ensure that the models are adopted effectively in decision making.
“…Purchases are limited over time and consist of replacement calves, pregnant females, and bulls for replacement. The herd dynamics presented here was based on that presented in [ 19 ]. Figure 1 Diagram of the beef cow - calf herd dynamics over a year.…”
Section: Methodsmentioning
confidence: 99%
“…During the breeding period, the delay between the moment when a cow was available for breeding and the start of a new pregnancy was determined by a gamma distribution with parameters a and b . The values of these parameters (Table 1 ) were chosen to reproduce the observed calving-to-calving intervals as presented in [ 19 ]. The same gamma distribution with the same parameter values was used regardless of whether breeding females were indoors or at pasture.…”
Bovine viral diarrhea virus (BVDV) is a common pathogen of cattle herds that causes economic losses due to reproductive disorders in breeding cattle and increased morbidity and mortality amongst infected calves. Our objective was to evaluate the impact of BVDV spread on the productivity of a beef cow-calf herd using a stochastic model in discrete time that accounted for (1) the difference in transmission rates when animals are housed indoors versus grazing on pasture, (2) the external risk of disease introductions through fenceline contact with neighboring herds and the purchase of infected cattle, and (3) the risk of individual pregnant cattle generating persistently infected (PI) calves based on their stage in gestation. The model predicted the highest losses from BVDV during the first 3 years after disease was introduced into a naive herd. During the endemic phase, the impact of BVDV on the yearly herd productivity was much lower due to herd immunity. However, cumulative losses over 10 years in an endemic situation greatly surpassed the losses that occurred during the acute phase. A sensitivity analysis of key model parameters revealed that herd size, the duration of breeding, grazing, and selling periods, renewal rate of breeding females, and the level of numerical productivity expected by the farmer had a significant influence on the predicted losses. This model provides a valuable framework for evaluating the impact of BVDV and the efficacy of different control strategies in beef cow-calf herds.
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