Estimating Overnight Weight Loss of Corralled Yearling Steers in Semiarid Rangeland

Derner, Justin D.; Reeves, Justin L.; Mortenson, Matthew C.; West, Mark; Irisarri, J. Gonzalo N.; Durante, Martí­n

doi:10.1016/j.rala.2016.02.004

Cited by 7 publications

(2 citation statements)

References 20 publications

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“…Vegetation height-density was measured at 64 locations per plot at the conclusion of the grazing season (early October) using the vegetation visual obstruction method (Robel et al 1970), modified to use increments of centimeters instead of decimeters. Cattle were weighed at the beginning and end of each grazing season, with weight gains per animal (kg/steer) determined using shrunk weights (Derner et al 2016).…”

Section: Ecological Datamentioning

confidence: 99%

Complexity fosters learning in collaborative adaptive management

Fernández‐Giménez¹,

Augustine²,

Porensky³

et al. 2019

E&S

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Learning is recognized as central to collaborative adaptive management (CAM), yet few longitudinal studies examine how learning occurs in CAM or apply the science of learning to interpret this process. We present an analysis of decision-making processes within the collaborative adaptive rangeland management (CARM) experiment, in which 11 stakeholders use a structured CAM process to make decisions about livestock grazing and vegetation management for beef, vegetation, and wildlife objectives. We analyzed four years of meeting transcripts, stakeholder communications, and biophysical monitoring data to ask what facilitated and challenged stakeholder decision making, how challenges affected stakeholder learning, and whether CARM met theorized criteria for effective CAM. Despite thorough monitoring and natural resource agency commitment to implementing collaborative decisions, CARM participants encountered multiple decision-making challenges born of ecological and social complexity. CARM was effective in achieving several of its management objectives, including reduced ecological uncertainty, knowledge coproduction, and multiple-loop social learning. CARM revealed limitations of the idealized CAM cycle and challenged conceptions of adaptive management that separate reduction of scientific uncertainty from participatory and management dimensions. We present a revised, empirically grounded CAM framework that depicts CAM as a spiral rather than a circle, where feedback loops between monitoring data and management decisions are never fully closed. Instead, complexities including time-lags, trade-offs, path-dependency, and tensions among stakeholders' differing types of knowledge and social worlds both constrain decision making and foster learning by creating disorienting dilemmas that challenge participants' pre-existing mental models and relationships. Based on these findings, we share recommendations for accelerating learning in CAM processes.

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Section: Ecological Datamentioning

confidence: 99%

Complexity fosters learning in collaborative adaptive management

Fernández‐Giménez¹,

Augustine²,

Porensky³

et al. 2019

E&S

Self Cite

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“…Steers were mixed-European breeds provided by local ranchers, and were weighed individually at the beginning and end of the grazing season. Steers were weighed without overnight restriction of water or food; however, weights were corrected for estimated overnight shrinkage following Derner et al (2016). Steers were additionally weighed 53 days before the end of the 2019 grazing season, 59 days after the start of the 2020 grazing season, and 60 days prior to the end of the 2020 season, to obtain seasonal estimates of average daily weight gain (ADG) that corresponded to the time periods when cattle foraging behaviour was measured (see below and Table 1).…”

Section: Cattle Stocking Rates and Measurements Of Liveweight And For...mentioning

confidence: 99%

Can measurements of foraging behaviour predict variation in weight gains of free-ranging cattle?

Augustine

Raynor²,

Kearney³

et al. 2022

Anim. Prod. Sci.

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Context Technologies are now available to continuously monitor livestock foraging behaviours, but it remains unclear whether such measurements can meaningfully inform livestock grazing management decisions. Empirical studies in extensive rangelands are needed to quantify relationships between short-term foraging behaviours (e.g. minutes to days) and longer-term measures of animal performance. Aims The objective of this study was to examine whether four different ways of measuring daily foraging behaviour (grazing-bout duration, grazing time per day, velocity while grazing, and turn angle while grazing) were related to weight gain by free-ranging yearling steers grazing semiarid rangeland. Methods Yearling steers were fitted with neck collars supporting a solar-powered device that measured GPS locations at 5 min intervals and used an accelerometer to predict grazing activity at 4 s intervals. These devices were used to monitor steers in four different paddocks that varied in forage biomass, and across two grazing seasons encompassing a wide range of forage conditions. Steer weight gain (kg/steer.day) was measured in each paddock during each of three ∼60 day time intervals, and daily foraging behaviours were measured during 15–21 days in each interval. Results A model based on only two daily measurements of foraging behaviour, mean grazing bout duration (calculated at a 5 min resolution) and mean velocity while grazing explained 62% of the variation in animal weight gain. Conclusions Daily measurements of foraging behaviour vary substantially in response to varying foraging conditions in space and time, and can effectively serve as indicators of variation in cattle weight gain. Implications On-animal sensors that monitor foraging behaviour have the potential to transmit indicators to livestock managers in real time (e.g. daily) to help inform decisions such as when to move animals among paddocks, or when to sell or transition animals from rangeland to confined feeding operations.

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