Resource use assessment of an agricultural system from a life cycle perspective – a dairy farm as case study

Huysveld, Sophie; linden, Veerle Van; Meester, Steven De; Peiren, Nico; Muylle, Hilde; Lauwers, Ludwig; Dewulf, Jo

doi:10.1016/j.agsy.2014.12.008

Cited by 28 publications

(16 citation statements)

References 37 publications

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“…Models such as GLEAM that integrate simulation modeling and Life Cycle analysis (LCA) approaches, offer modeling solutions that make environmental sense at the global as well as the local scale (de Boer et al, 2011). The development of more holistic LCA methodologies (Bruckner et al, 2015;Huysveld et al, 2015) and the exploration of new LCA applications, for example as a farm decision support (DSS) tool (Meul et al, 2014) may present further opportunities to combine farm-scale modeling and LCA approaches. Farm-scale modelers share many of the challenges recognised in LCA, such as the need to increase standards and consistency of data and assumptions (Eshel et al, 2015) and to ensure that users correctly interpret the outcomes of studies (Cederberg et al, 2013;Meul et al, 2014).…”

Section: Environmental Impacts Beyond the Farmmentioning

confidence: 99%

Modeling European ruminant production systems: Facing the challenges of climate change

Kipling

Bannink

Bellocchi³

et al. 2016

Agricultural Systems

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This is the author accepted manuscript. The final version is available from Elsevier via http://dx.doi.org/10.1016/j.agsy.2016.05.007Ruminant production systems are important producers of food, support rural communities and culture, and help to maintain a range of ecosystem services including the sequestering of carbon in grassland soils. However, these systems also contribute significantly to climate change through greenhouse gas (GHG) emissions, while intensification of production has driven biodiversity and nutrient loss, and soil degradation. Modeling can offer insights into the complexity underlying the relationships between climate change, management and policy choices, food production, and the maintenance of ecosystem services. This paper 1) provides an overview of how ruminant systems modeling supports the efforts of stakeholders and policymakers to predict, mitigate and adapt to climate change and 2) provides ideas for enhancing modeling to fulfil this role. Many grassland models can predict plant growth, yield and GHG emissions from mono-specific swards, but modeling multi-species swards, grassland quality and the impact of management changes requires further development. Current livestock models provide a good basis for predicting animal production; linking these with models of animal health and disease is a priority. Farm-scale modeling provides tools for policymakers to predict the emissions of GHG and other pollutants from livestock farms, and to support the management decisions of farmers from environmental and economic standpoints. Other models focus on how policy and associated management changes affect a range of economic and environmental variables at regional, national and European scales. Models at larger scales generally utilise more empirical approaches than those applied at animal, field and farm-scales and include assumptions which may not be valid under climate change conditions. It is therefore important to continue to develop more realistic representations of processes in regional and global models, using the understanding gained from finer-scale modeling. An iterative process of model development, in which lessons learnt from mechanistic models are applied to develop ?smart? empirical modeling, may overcome the trade-off between complexity and usability. Developing the modeling capacity to tackle the complex challenges related to climate change, is reliant on closer links between modelers and experimental researchers, and also requires knowledge-sharing and increasing technical compatibility across modeling disciplines. Stakeholder engagement throughout the process of model development and application is vital for the creation of relevant models, and important in reducing problems related to the interpretation of modeling outcomes. Enabling modeling to meet the demands of policymakers and other stakeholders under climate change will require collaboration within adequately-resourced, long-term inter-disciplinary research networks.authorsversionPeer reviewe

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Section: Environmental Impacts Beyond the Farmmentioning

confidence: 99%

Modeling European ruminant production systems: Facing the challenges of climate change

Kipling

Bannink

Bellocchi³

et al. 2016

Agricultural Systems

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“…In order to perform a fair allocation, occupation of the occupied land should take into account the lower production potential of the soil during winter. In Huysveld et al (2015) this was done by taking into account the seasonal variation of the surface solar radiation. Although the catch crop is harvested, its function can still be mainly the reduction of adverse effects on the soil instead of productivity.…”

Section: Choosing the Temporal Boundary Of The Primary Biomass Producmentioning

confidence: 99%

“…These analyses account fully or partially for the input of solar radiation on occupied land. When the entire amount of solar radiation is taken into account, crops achieve dramatically low efficiencies (Huysveld et al, 2015). Because the photosynthetic process can inherently utilize only a portion of the solar spectrum, i.e., the photosynthetically active radiation (PAR), a distinction is often made between the total solar radiation and its PAR fraction (Bisio and Bisio, 1998;Petela, 2008;Reis and Miguel, 2006).…”

Section: Indicators For Resource Efficiencymentioning

confidence: 99%

“…more efficient the land surface is used, the higher the resource efficiency of the cropping system. However, when optimizing land use efficiency, it is extremely important that the effects on other environmental aspects, such as soil fertility, and nutrient and water availability are simultaneously evaluated (Huysveld et al, 2015).…”

Section: The Temporal System Boundary Of the Primary Biomass Productimentioning

confidence: 99%

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Cumulative Overall Resource Efficiency Assessment (COREA) for comparing bio-based products with their fossil-derived counterparts

Huysveld

Meester

linden³

et al. 2015

Resources, Conservation and Recycling

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a b s t r a c tBio-based products potentially decrease consumption of non-renewable fossil resources compared to their fossil-derived counterparts, but are more demanding for bio-productive land use. Although thermodynamics-based resource accounting methods are available for calculating overall resource efficiency from a life cycle perspective, their accounting for bio-productive land resources as an input during quantification of efficiencies is unclear. This paper aims to fill the gap in scientific literature about how to calculate a cumulative overall resource efficiency indicator by developing a framework, called Cumulative Overall Resource Efficiency Assessment (COREA). COREA (i) takes into account bio-productive land resources and (ii) addresses the non-renewable character of fossil resources. To account for bioproductive land resources, two methodological questions need to be addressed: (1) "how to define the system boundary of the solar energy input in the primary biomass production system?" and (2) "how to choose the temporal system boundary of this system?". Resource efficiencies are calculated for three cases at crop level and two cases at bio-based product level. To account for the non-renewable character of fossil resources, we propose an accounting approach that includes the ancient solar energy consumption of fossil resources. This methodological choice is illustrated through comparing the resource efficiencies of the two bio-based products with their fossil-based counterparts. The results showed that the bio-based products only had a higher resource efficiency than their fossil-derived counterparts if fossil resources were considered as ancient consumers of solar energy.

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“…Participating in all organisms' essential reaction, protein is an important component of the human body's cells. In general, the proportion of protein in the total dry mass of the human body is approximately 18%, which is an element close to human life [2] . Soy protein isolate (SPI) is a kind of protein food additive that can be obtained from low-temperature desolventizing soybean meal as raw materials [3] .…”

Section: Introductionmentioning

confidence: 99%

Effects of xanthan gum on the rheological properties of soy protein dispersion

Bi¹,

Gao²,

Zhu³

et al. 2018

International Journal of Agricultural and Biological Engineering

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This study focused on the effects of addition of xanthan gum (XG) on the rheological properties of soy protein isolate (SPI) solution. Three types of tests (steady shear test, strain sweep test, and frequency sweep test) were performed to figure out the influences of shear rate on the viscosity of the SPI-XG hybrid system, the effects of strain variable on the storage modulus of the hybrid system, and the effects of frequency on both the storage modulus and the loss modulus of the hybrid system, respectively. SPI-XG hybrid system showed more obvious shear-thinning properties compared to pure SPI and pure XG solution. Meanwhile, it was found that the critical point of hybrid system was highly related to the XG concentration. XG can postpone the critical point strain amplitude to a higher value, and the addition of XG can enhance the strain resistance of hybrid system. The concentration of XG influenced the viscoelastic frequency dependence of the hybrid system significantly and complicatedly. After the addition of XG, the correlation between G′ and frequency changed from negative to positive.

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Resource use assessment of an agricultural system from a life cycle perspective – a dairy farm as case study

Cited by 28 publications

References 37 publications

Modeling European ruminant production systems: Facing the challenges of climate change

Modeling European ruminant production systems: Facing the challenges of climate change

Cumulative Overall Resource Efficiency Assessment (COREA) for comparing bio-based products with their fossil-derived counterparts

Effects of xanthan gum on the rheological properties of soy protein dispersion

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