Markov Decision Processes to Model Livestock Systems

Nielsen, Lars Relund; Kristensen, Anders Ringgaard

doi:10.1007/978-1-4939-2483-7_19

Cited by 6 publications

(8 citation statements)

References 99 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Livestock systems are considered complex and subject to considerable variation, cyclic behavior, and a finite supply of animals (Nielsen and Kristensen, 2015;Jaurena and Cantet, 2016).…”

Section: Modelling Farm Resources Herd Dynamics and Animal Footprintsmentioning

confidence: 99%

“…Although the model deals with a limited number of possible alternatives, it illustrates the most noticeable trade-offs (de Souza Filho et al, 2019), trends, and expectations despite avoiding a larger number of spatial and temporal dimensions. Caballero et al (2009) and Nielsen and Kristensen (2015) pointed out that sophisticated models require very large amounts of data and demand the estimation of numerous parameters. This further suggests that relatively parsimonious models such as the present one may generally be easier to use, adapt and understand when applied to relatively constrained environmental situations.…”

Section: Modelling Farm Resources Herd Dynamics and Animal Footprintsmentioning

confidence: 99%

See 1 more Smart Citation

A dynamic simulation model to assess farm-level effects of pasture intensification strategies on beef herd outputs and carbon footprints in acid soil savannas of Eastern Colombia

Vera-Infanzón

Rao²,

Ramírez-Restrepo³

2023

ALPA

View full text Add to dashboard Cite

The neotropical savannas of Eastern Colombia (Llanos) are subjected to changes in land use associated with intensification of beef production and there is limited knowledge on the long-term impacts of these change processes. Furthermore, the effects of spatial and temporal intensification at farm level via the introduction of sown pastures on beef herd outputs, their greenhouse gas (GHG) emissions and the resulting carbon (C) footprints in contrasting savanna landscapes of of the Llanos are unknown. This study was aimed to assess the consequences of gradual system intensification via introduction of sown tropical pastures in two contrasting landscapes, the tillable, flat savannas and the highly dissected rangelands, of the well-drained Eastern savannas of Colombia, in terms of cow-calf production and GHG emissions and the resulting C footprints. A dynamic model was developed to simulate the gradual introduction of two types of tropical pastures in a region with dissected and steep slopes and limited tillable areas where cattle would also have access to Serrania savanna and sown pastures, versus one located in the Plains that are fully transformed over time with the exclusion of native rangelands. Marked changes in herd demography, animal outputs, and emissions were found over time. The C footprint of all systems varied over time depending upon the length of time that pastures contributed to soil organic carbon accumulation and the balance between savanna and sown pasture areas at a whole farm level. In conclusion, the dynamics of the systems subject to intensification were marked and were dependent on the temporal and spatial deployment of sown forage resources. Therefore, generalizing the trends for the region as a whole result in uncertainty. Nevertheless, examination of simulated prototypes may shed light on the expected trends and provides guidance for decision-making

show abstract

“…Livestock systems are considered complex and subject to considerable variation, cyclic behavior, and a finite supply of animals (Nielsen and Kristensen, 2015;Jaurena and Cantet, 2016).…”

Section: Modelling Farm Resources Herd Dynamics and Animal Footprintsmentioning

confidence: 99%

Section: Modelling Farm Resources Herd Dynamics and Animal Footprintsmentioning

confidence: 99%

A dynamic simulation model to assess farm-level effects of pasture intensification strategies on beef herd outputs and carbon footprints in acid soil savannas of Eastern Colombia

Vera-Infanzón

Rao²,

Ramírez-Restrepo³

2023

ALPA

View full text Add to dashboard Cite

show abstract

“…Hierarchical MDPs (HMDPs) [10] are a special case of BMDPs where there are no cycles in the offspring graph (equivalently, no cyclic dependency between types). BMDPs and HMDPs have found applications in manpower planning [29], controlled queuing networks [2,15], management of livestock [20], and epidemic control [1,25], among others. The focus of these works was on optimising the expected average, or the discounted reward over a run of the process, or optimising the population growth rate.…”

Section: Related Workmentioning

confidence: 99%

Model-Free Reinforcement Learning for Branching Markov Decision Processes

Hahn

Perez

Schewe

et al. 2021

Computer Aided Verification

View full text Add to dashboard Cite

We study reinforcement learning for the optimal control of Branching Markov Decision Processes (BMDPs), a natural extension of (multitype) Branching Markov Chains (BMCs). The state of a (discrete-time) BMCs is a collection of entities of various types that, while spawning other entities, generate a payoff. In comparison with BMCs, where the evolution of a each entity of the same type follows the same probabilistic pattern, BMDPs allow an external controller to pick from a range of options. This permits us to study the best/worst behaviour of the system. We generalise model-free reinforcement learning techniques to compute an optimal control strategy of an unknown BMDP in the limit. We present results of an implementation that demonstrate the practicality of the approach.

show abstract

“…In the past, many studies have attempted to investigate and subsequently optimize the replacement decisions of individual cows for better economic gains (reviews by De Vries and Marcondes, 2020; Lehenbauer and Oltjen, 1998;Nielsen and Kristensen, 2015). Van Arendonk (1984) identified two approaches in 1960s and 1970s to optimize replacement decisions mathematically, namely, Marginal Net Revenue (MNR) approach and Dynamic Programming (DP) approach.…”

Section: Optimal Culling and Replacement Of Dairy Cowsmentioning

confidence: 99%

“…For example, models by , DeLorenzo et al (1992), and Cabrera (2010) were developed as tools for making optimal replacement decisions, whereas replacement decision models by Bar et al (2008), and Demeter et al (2011) were developed to study the cost of clinical mastitis and economic impact of breeding policies on dairy farms respectively. Nielsen and Kristensen (2015) gave detailed comments on the two applications of the decision optimizers as decision support tools and as research models.…”

Section: Optimal Culling and Replacement Of Dairy Cowsmentioning

confidence: 99%

Dairy cattle replacement under changing agri-environmental policies

Kulkarni

View full text Add to dashboard Cite

Culling and replacement of dairy cowsCulling and replacement of dairy cows is a routine but important aspect of dairy farm management. Culling is defined by removal of a cow from the herd for slaughter, salvage, disposal (due to death) or for dairy sale with intent to continue production (Fetrow et al., 2006).In theory, culled cows are replaced by younger, suitable heifers. In the Netherlands, the average annual replacement rate has been varying between 20% and 30% in the first two decades of the 21 st century (CRV, 2019;Nor et al., 2014a). This means that dairy cattle longevity in the Netherlands of 5.8 years is far below the biological potential lifespan (Han et al., 2022). These replacements can be either due to economic reasons (voluntary) or due to health considerations, hence involuntary . Fetrow et al. (2006) defined two types of culling namely, economic, and forced or biological. A large part of the replacements is due to economic reasons with the intent of improving production, reproduction, and the health of the producing herd. On the other hand, and in minority, biological or forced culling occurs when cows are incurable, permanently unable to produce, reproduce or recover or when cows die suddenly or are euthanized. Since most replacements of dairy cows are

show abstract

Markov Decision Processes to Model Livestock Systems

Cited by 6 publications

References 99 publications

A dynamic simulation model to assess farm-level effects of pasture intensification strategies on beef herd outputs and carbon footprints in acid soil savannas of Eastern Colombia

A dynamic simulation model to assess farm-level effects of pasture intensification strategies on beef herd outputs and carbon footprints in acid soil savannas of Eastern Colombia

Model-Free Reinforcement Learning for Branching Markov Decision Processes

Dairy cattle replacement under changing agri-environmental policies

Contact Info

Product

Resources

About