Predicting forage intake in extensive grazing systems1

Galyean, M. L.; Gunter, S. A.

doi:10.2527/jas.2016-0523

Cited by 23 publications

(15 citation statements)

References 115 publications

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“…While a grazing-day for the whole herd was defined as mean consumption of 2.5% of body weight (12.5 kg), the mean 99.9% daily range of consumption for all model runs was 2.34–2.66% of body weight. This range of consumption aligns well with common “rules of thumb” and predictive formulae [ 43 , 57 , 58 ].…”

Section: Resultssupporting

confidence: 81%

An agent-based model of cattle grazing toxic Geyer's larkspur

2018

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By killing cattle and otherwise complicating management, the many species of larkspur (Delphinium spp.) present a serious, intractable, and complex challenge to livestock grazing management in the western United States. Among the many obstacles to improving our understanding of cattle-larkspur dynamics has been the difficulty of testing different grazing management strategies in the field, as the risk of dead animals is too great. Agent-based models (ABMs) provide an effective method of testing alternate management strategies without risk to livestock. ABMs are especially useful for modeling complex systems such as livestock grazing management, and allow for realistic bottom-up encoding of cattle behavior. Here, we introduce a spatially-explicit, behavior-based ABM of cattle grazing in a pasture with a dangerous amount of Geyer’s larkspur (D. geyeri). This model tests the role of herd cohesion and stocking density in larkspur intake, finds that both are key drivers of larkspur-induced toxicosis, and indicates that alteration of these factors within realistic bounds can mitigate risk. Crucially, the model points to herd cohesion, which has received little attention in the discipline, as playing an important role in lethal acute toxicosis. As the first ABM to model grazing behavior at realistic scales, this study also demonstrates the tremendous potential of ABMs to illuminate grazing management dynamics, including fundamental aspects of livestock behavior amidst ecological heterogeneity.

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Section: Resultssupporting

confidence: 81%

An agent-based model of cattle grazing toxic Geyer's larkspur

2018

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“…Nevertheless, this threshold is hard to achieve, particularly for grazing dairy cows, as the available reference methods for development and validation of the HDMI estimation models are indirect methods. Furthermore, Galyean (2016) argued that daily intake of cattle is naturally variable, and Gruber et al (2005) stated that a biologically determined natural scattering of about 10% might also impede the development of reliable intake estimation models.…”

Section: Precision Of the Hdmi Estimation Modelsmentioning

confidence: 99%

Herbage dry matter intake estimation of grazing dairy cows based on animal, behavioral, environmental, and feed variables

Rombach

Südekum

Münger

et al. 2019

Journal of Dairy Science

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Information about the individual herbage DMI (HDMI) of grazing dairy cows is important for an efficient use of pasture herbage as an animal feed with a range of benefits. Estimating HDMI, with its multifaceted influencing variables, is difficult but may be attempted using animal, performance, behavior, and feed variables. In our study, 2 types of approaches were explored: 1 for HDMI estimation under a global approach (GA), where all variables measured in the 4 underlying experiments were used for model development, and 1 for HDMI estimation in an approach without information about the amount of supplements fed in the barn (WSB). The accuracy of these models was assessed. The underlying data set was developed from 4 experiments with 52 GA and 50 WSB variables and one hundred thirty 7-d measurements. The experiments differed in pasture size, herbage allowance, pregrazing herbage mass, supplements fed in the barn, and sward composition. In all the experiments, cow behavioral characteristics were recorded using the RumiWatch system (Itin and Hoch GmbH, Liestal, Switzerland). Herbage intake was estimated by applying the n-alkane method. Finally, HDMI estimation models with a minimal relative prediction error of 11.1% for use under GA and 13.2% for use under WSB were developed. The variables retained for the GA model with the highest accuracy, determined through various selection steps, were herbage crude protein, chopped whole-plant corn silage intake in the barn, protein supplement or concentrate intake in the barn, body weight, milk yield, milk protein, milk lactose, lactation number, postgrazing herbage mass, and bite rate performed at pasture. Instead of the omitted amounts of feed intake in the barn and, due to the statistical procedure for model reduction, the unconsidered variables postgrazing herbage mass and bite rate performed at pasture, the WSB model with the highest accuracy retained additional variables. The additional variables were total eating chews performed at pasture and in the barn, total eating time performed at pasture, number of total prehension bites, number of prehension bites performed at pasture, and herbage ash concentration. Even though behavioral characteristics alone did not allow a sufficiently accurate individual HDMI estimation, their inclusion under WSB improved estimation accuracy and represented the most valid variables for the HDMI estimation under WSB. Under GA, the inclusion of behavioral characteristics in the HDMI estimation models did not reduce the root mean squared prediction error. Finally, further adaptation, as well as validation on a more comprehensive data set and the inclusion of variables excluded in this study such as body condition score or gestation, should be considered in the development of HDMI estimation models.

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“…The other potential factors such as forage quality and environmental constraints on the intake rate are designed in some models such as IOM and the SAVANNA model (Coughenour 1993, Illius andO'Connor 2000). However, they are difficult to be used at large scales due to the lack of enough observations and useable datasets (Galyean 2016, Senft et al 1985. Another process-based modeling gap is how to consider the feedback of vegetation to grazing activity.…”

Section: Discussionmentioning

confidence: 99%

Great uncertainties in modeling grazing impact on carbon sequestration: a multi-model inter-comparison in temperate Eurasian Steppe

Chen

Tao

Cheng

et al. 2018

Environ. Res. Lett.

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The impact of grazing activity on terrestrial carbon (C) sequestration has been noticed and studied worldwide. Recent efforts have been made to incorporate the disturbance into process-based land models. However, the performance of grazing models has not been well investigated at large scales. In this study, we performed a spatially explicit model uncertainty assessment in the world's largest pasture ecosystem, the temperate Eurasian Steppe. Five grazing models were explicitly incorporated into a single terrestrial biogeochemical model to simulate regional C consumption from grazing activity (C graze ). First, we summarized the underlying mechanisms and explicitly compared the general functions used to describe the processes in different models. Then, the models (five models with 12 simulations) were run in parallel using the same forcing data and livestock distribution map in 2006. Results indicated that the modeled regional C graze varied from 0.1-16.1 gC m −2 for the year. The corresponding ratios of C graze to aboveground net primary productivity ANPP and net primary productivity (NPP) ranged from 0.08%-24.6% and 0.028%-11.2%, respectively. Parameter sensitivity was further analyzed. Model outputs are highly sensitive to the intake rate (i.e. feeding rate of livestock per day), half maximum intake rate, and initial livestock weight. Our results indicate that great uncertainty exists in simulating C graze . We ascribed the major uncertainty to the different process description and poor parameterization. This study calls for more efforts to the comprehensive synthesis of usable dataset, the foundation of a standard observation system and the observe-based inter-comparison to evaluate models, which would facilitate more accurate assessment of C sequestration by pasture ecosystems and lead to better representation in earth system models.

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Predicting forage intake in extensive grazing systems1

Cited by 23 publications

References 115 publications

An agent-based model of cattle grazing toxic Geyer's larkspur

An agent-based model of cattle grazing toxic Geyer's larkspur

Herbage dry matter intake estimation of grazing dairy cows based on animal, behavioral, environmental, and feed variables

Great uncertainties in modeling grazing impact on carbon sequestration: a multi-model inter-comparison in temperate Eurasian Steppe

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