Farming smarter with big data: Insights from the case of Australia's national dairy herd milk recording scheme

Newton, Joanna E.; Nettle, Ruth; Pryce, J.E.

doi:10.1016/j.agsy.2020.102811

Cited by 57 publications

(49 citation statements)

References 34 publications

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“…The DGV for the 379 animals used in this study were estimated as part of the routine genetic evaluation service of DataGene Ltd. (Bundoora, Victoria, Australia) using a data repository that contained 200 million herd test records at the time of this study, and that continues to increase by 2.9 million herd test records annually (Newton et al, 2020). As DataGene does not calculate DGV for ECM as part of the national service, DGV for ECM were calculated using Equation 2, where phenotypic measurements for milk, fat, and protein in Equation 1 were replaced for milk, fat, and protein DGV that were readjusted for the baseline trait means.…”

Section: Phenotypesmentioning

confidence: 99%

Genetic parameters for methane emission traits in Australian dairy cows

Richardson

Nguyen

Abdelsayed

et al. 2021

Journal of Dairy Science

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Methane is a greenhouse gas of high interest to the dairy industry, with 57% of Australia's dairy emissions attributed to enteric methane. Enteric methane emissions also constitute a loss of approximately 6.5% of ingested energy. Genetic selection offers a unique mitigation strategy to decrease the methane emissions of dairy cattle, while simultaneously improving their energy efficiency. Breeding objectives should focus on improving the overall sustainability of dairy cattle by reducing methane emissions without negatively affecting important economic traits. Common definitions for methane production, methane yield, and methane intensity are widely accepted, but there is not yet consensus for the most appropriate method to calculate residual methane production, as the different methods have not been compared. In this study, we examined 9 definitions of residual methane production. Records of individual cow methane, dry matter intake (DMI), and energy corrected milk (ECM) were obtained from 379 animals and measured over a 5-d period from 12 batches across 5 yr using the SF 6 tracer method and an electronic feed recording system, respectively. The 9 methods of calculating residual methane involved genetic and phenotypic regression of methane production on a combination of DMI and ECM corrected for days in milk, parity, and experimental batch using phenotypes or direct genomic values. As direct genomic values (DGV) for DMI are not routinely evaluated in Australia at this time, DGV for FeedSaved, which is derived from DGV for residual feed intake and estimated breeding value for bodyweight, were used. Heritability estimates were calculated using univariate models, and correla-tions were estimated using bivariate models corrected for the fixed effects of year-batch, days in milk, and lactation number, and fitted using a genomic relationship matrix. Residual methane production candidate traits had low to moderate heritability (0.10 ± 0.09 to 0.21 ± 0.10), with residual methane production corrected for ECM being the highest. All definitions of residual methane were highly correlated phenotypically (>0.87) and genetically (>0.79) with one another and moderately to highly with other methane candidate traits (>0.59), with high standard errors. The results suggest that direct selection for a residual methane production trait would result in indirect, favorable improvement in all other methane traits. The high standard errors highlight the importance of expanding data sets by measuring more animals for their methane emissions and DMI, or through exploration of proxy traits and combining data via international collaboration.

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Section: Phenotypesmentioning

confidence: 99%

Genetic parameters for methane emission traits in Australian dairy cows

Richardson

Nguyen

Abdelsayed

et al. 2021

Journal of Dairy Science

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“…The new duties that they are expected to perform urge these organizations to alter themselves [44]. New practices, work routines, structures, and organizational arrangements can, and already do, emerge [8,40,45], whereas changes in core organizational attributes (purposes, values) are also possible [46].…”

Section: Introductionmentioning

confidence: 99%

Extension and Advisory Organizations on the Road to the Digitalization of Animal Farming: An Organizational Learning Perspective

Charatsari

Lioutas

Rosa

et al. 2020

Animals

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Agricultural digitalization emerged as a radical innovation, punctuating the gradual evolution of the agrifood sector and having the potential to fundamentally restructure the context within which extension and advisory organizations operate. Digital technologies are expected to alter the practice and culture of animal farming in the future. To suit the changing environmental conditions, organizations can make minor adjustments or can call into question their purposes, belief systems, and operating paradigms. Each pattern of change is associated with different types of organizational learning. In this conceptual article, adopting an organizational learning perspective and building upon organizational change models, we present two potential change and learning pathways that extension and advisory organizations can follow to cope with digitalization: morphostasis and morphogenesis. Morphostatic change has a transitional nature and helps organizations survive by adapting to the new environmental conditions. Organizations that follow this pathway learn by recognizing and correcting errors. This way, they increase their competence in specific services and activities. Morphogenetic change, on the other hand, occurs when organizations acknowledge the need to move beyond existing operating paradigms, redefine their purposes, and explore new possibilities. By transforming themselves, organizations learn new ways to understand and interpret contextual cues. We conclude by presenting some factors that explain extension and advisory organizations’ tendency to morphostasis.

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“…Our results estimate gross emissions per breeding cow of 4,297.86 kg and an emissions intensity of 12.67 kg of CH 4 /kg of protein-eq, and consistently showed the importance of selection for survival, production, and efficiency through the estimated coefficients. Using the gross methane per breeding cow value estimated in our study, we estimated a national inventory due to dairy cattle methane of 6.02 Mt of CO 2 e, assuming that the Australian dairy cattle inventory was 1.4 million milking and dry cows (Newton et al, 2020). These values are reasonably consistent with the current Australian national emissions inventory for the dairy industry of 8.6224 Mt of CO 2 e (UNFCCC, 2018), of which methane constitutes approximately 57% at 4.914 Mt of CO 2 e.…”

Section: Discussionmentioning

confidence: 99%

Estimating methane coefficients to predict the environmental impact of traits in the Australian dairy breeding program

Richardson

Amer²,

Fs³

et al. 2021

Journal of Dairy Science

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The dairy industry has been scrutinized for the environmental impact associated with rearing and maintaining cattle for dairy production. There are 3 possible opportunities to reduce emissions through genetic selection: (1) a direct methane trait, (2) a reduction in replacements, and (3) an increase in productivity. Our aim was to estimate the independent effects of traits in the Australian National Breeding Objective on the gross methane production and methane intensity (EI) of the Australian dairy herd of average genetic potential. Based on similar published research, the traits determined to have an effect on emissions include production, fertility, survival, health, and feed efficiency. The independent effect of each trait on the gross emissions produced per animal due to genetic improvement and change in EI due to genetic improvement (intensity value, IV) were estimated and compared. Based on an average Australian dairy herd, the gross emissions emitted per cow per year were 4,297.86 kg of carbon dioxide equivalents (CO 2 -eq). The annual product output, expressed in protein equivalents (protein-eq), and EI per cow were 339.39 kg of protein-eq and 12.67 kg of CO 2eq/kg of protein-eq, respectively. Of the traits included in the National Breeding Objective, genetic progress in survival and feed saved were consistently shown to result in a favorable environmental impact. Conversely, production traits had an unfavorable environmental impact when considering gross emissions, and favorable when considering EI. Fertility had minimal impact as its effects were primarily accounted for through survival. Mastitis resistance only affected IV coefficients and to a very limited extent. These coefficients may be used in selection indexes to apply emphasis on traits based on their environmental impact, as well as applied by governments and stakeholders to track trends in industry emissions. Although initiatives are underway to develop breeding values to reduce methane by combining small methane data sets internationally, alternative options to reduce emissions by utilizing selection indexes should be further explored.

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Farming smarter with big data: Insights from the case of Australia's national dairy herd milk recording scheme

Cited by 57 publications

References 34 publications

Genetic parameters for methane emission traits in Australian dairy cows

Genetic parameters for methane emission traits in Australian dairy cows

Extension and Advisory Organizations on the Road to the Digitalization of Animal Farming: An Organizational Learning Perspective

Estimating methane coefficients to predict the environmental impact of traits in the Australian dairy breeding program

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