Farm-specific carbon footprinting to the farm gate for agricultural co-products using the OVERSEER® model

Wheeler, D. M.; Ledgard, S. F.; Boyes, M.

doi:10.1017/s1751731113000797

Cited by 7 publications

(8 citation statements)

References 5 publications

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“…Inclusion of the forestry blocks of the farm in the analysis reduced the overall N and P losses from the Base Farm and Fecund farm by 40 and 34%, respectively (Table 5). At this point in time, OVERSEER ® does not estimate GHG emissions from the livestock carried on each LMU [38]. Estimated methane production dropped from 1805 to 1679 kg CO 2 -e/ha/yr (7%), nitrous oxide emissions fell from 657 to 631 kg CO 2 -e/ha/yr (4%), with an overall decline from 2627 to 2471 kg CO 2 -e/ha/yr, a 6% decline, with the shift from the Base Farm to the Fecund farm.…”

Section: Utility Of the Aginform ® Output For Inclusion In Other Modelsmentioning

confidence: 99%

Optimisation of the Resource of Land-Based Livestock Systems to Advance Sustainable Agriculture: A Farm-Level Analysis

Rendel¹,

Mackay

Smale

et al. 2020

Agriculture

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Land dedicated to livestock contributes at least 40% of the global agricultural output. While advances in the application of geospatial information systems and remote sensing technologies offer much to agriculture, capturing and using that rich spatial biophysical information is not a feature available in most farm systems models. In this paper, we tackle this gap describing a land-based integrated grazing farm optimisation and resource allocation model (AgInform®) that departs from the use of whole farm and average data, to the integration of biological data obtained directly from each of the land units within the farm. The model allows the exploration of the dynamics of biophysical and financial performance of the farm in a steady-state, single-year approach, where the opening and closing values of the biological elements of the farm system conditions must remain the same (e.g., animal numbers, herbage mass), unless otherwise specified. The user supplies pasture growth rates, minimum and maximum acceptable pasture masses for each land management unit (LMU), differential boundary conditions to deliver defined environmental outcomes, animal performance (sheep, beef and deer), farm costs and market prices. The linear programming (LP) equations formed by AgInform® can be divided into a single objective and constraints (which accommodate the boundaries), including those placed on individual LMUs. The optimization routine uses this information to identify the mix of livestock production enterprises that maximises profit for the business. The model in maintaining the link between available pasture mass and livestock requirements for each LMU throughout all calculations, enables the livestock type and number carried, along with the pasture mass required on each LMU throughout the year to achieve the required animal performance levels to be included as model outputs. A hill land sheep and beef farm consisting of seven distinct LMUs was used as a farm-level case to assess if AgInform® 1) has sufficient flexibility to integrate biological information from each LMU; 2) could use the specified livestock performance targets to derive a feasible livestock policy that optimised resource use and farm returns; 3) can assign each fortnight animal type and number and herbage mass to each LMU; and 4) can mimic reality to produce credible solutions.

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Section: Utility Of the Aginform ® Output For Inclusion In Other Modelsmentioning

confidence: 99%

Optimisation of the Resource of Land-Based Livestock Systems to Advance Sustainable Agriculture: A Farm-Level Analysis

Rendel¹,

Mackay

Smale

et al. 2020

Agriculture

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“…A modification of the SU, the revised stock unit (RSU), is used within Overseer ® (Wheeler 2018a), which is the primary software tool used to estimate nutrient management at farm level for regulatory purposes in New Zealand. The RSU is the same as SU in that it represents annual feed requirement for a 54 kg ewe to rear a single lamb to weaning.…”

Section: Introductionmentioning

confidence: 99%

“…The RSU is the same as SU in that it represents annual feed requirement for a 54 kg ewe to rear a single lamb to weaning. However, with the RSU, this requirement is expressed in terms of megajoules (MJ) of metabolisable energy (ME) (6000 MJ ME/year) rather than on a dry-matter (DM) basis as the ME of pasture varies among regions, season and farm systems (Wheeler 2018a). This approach theoretically provides a more robust ability to compare among livestock classes and species.…”

Section: Introductionmentioning

confidence: 99%

“…This approach theoretically provides a more robust ability to compare among livestock classes and species. Within Overseer ® , this value, along with the nutrient and energy content of the feed, contributes to the estimation of nutrients consumed and excreted by the animals, which influences the nutrient leaching estimation on a farm (Wheeler 2018a(Wheeler , 2018b.…”

Section: Introductionmentioning

confidence: 99%

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Quantification of relative stock units for horses to permit correct application within pasture-based production systems

et al. 2023

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Context Overseer® is the primary software tool used to estimate farm-level nutrient cycle and management for regulatory purposes in New Zealand. The model compares feed demand among different livestock by using ‘revised stock units’ (RSUs, the annual energy requirement of a mature ewe to raise a single lamb to weaning; 6000 MJ metabolisable energy). The RSUs for several common equine stock classes are not yet available, while those currently available within the model are based on the linear scaling of feed demand to liveweight, which does not consider allometric scaling of metabolism to liveweight or the differences in digestive physiology and nutrient metabolism between ruminants and monogastric hindgut fermenters (horses). Aim To compare the current RSU values used in Overseer® for different equine stock classes, with the equineRSU values calculated using equine-specific models. Methods Weighted average estimates of the bodyweight for the different equine livestock classes were calculated from the published literature. These weighted average estimates of bodyweight were used to estimate the energy requirements on the basis of data published by National Research Council. The resulting dry-matter intake and N intake from the equineRSU values and the current RSU values in use within Overseer® were modelled using published data on diet composition, crude protein content and the digestibility of the different feeds offered. Results The current RSUs in Overseer were 2.5–6.8 units higher than the equineRSU values obtained from the equine-specific models. This overestimation in feed demand resulted in N-intake estimates at an animal level being 52–108% higher than values derived using the equine-specific estimates. Conclusion The use of RSUs based on linear scaling of feed demand from ruminants on the basis of liveweight overestimates feed demand and N intake in horses. If horses are to be included within nutrient management models, feed demand must be based on published equine data for energy requirements to avoid over-inflation of N excretion. The equineRSUs calculated in this study reduce the risk of over-inflation of N intake and excretion, and subsequently the N leaching estimations. Implication Failure to accurately model feed demand of horses within nutrient management software would unfairly compromise stocking density and horse management on large commercial breeding farms. The implication for these errors on economic impact and restricted livestock number is greatest for the Thoroughbred breeding industry due to the scale of the operations.

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“…del Prado et al (2013) provided an overview of modelling in the GHG area and provided new insights by making comparisons between models and considering potential to make links between different GHG models and other physical and economic models. Wheeler et al (2013) described the use of the OVERSEER model to produce farmspecific estimates of emissions per unit product for New Zealand individual farms, while Murphy et al (2013) described the development of the CARBON NAVIGATOR as a knowledge transfer tool to guide farmers and advisers in reducing emissions from Irish dairy and beef herds.…”

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confidence: 99%

Editorial: Greenhouse Gases and Animal Agriculture Conference, Dublin, 2013

Dewhurst

2013

Animal

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Farm-specific carbon footprinting to the farm gate for agricultural co-products using the OVERSEER® model

Cited by 7 publications

References 5 publications

Optimisation of the Resource of Land-Based Livestock Systems to Advance Sustainable Agriculture: A Farm-Level Analysis

Optimisation of the Resource of Land-Based Livestock Systems to Advance Sustainable Agriculture: A Farm-Level Analysis

Quantification of relative stock units for horses to permit correct application within pasture-based production systems

Editorial: Greenhouse Gases and Animal Agriculture Conference, Dublin, 2013

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