Reducing uncertainty in life cycle assessment of livestock production systems

Life cycle assessment (LCA) has been increasingly applied to livestock production systems to estimate their environmental footprints, but the degree of uncertainties associated with these values is known to be generally high. This thesis explores novel methods of LCA modelling to reduce uncertainty associated with environmental footprints of meat production systems, with the view to contribute to objective and transparent debates about the role of livestock in global food security. Three innovative approaches are proposed in this thesis. First, as information on individual animals is often unavailable, livestock data are often aggregated at the time of inventory analysis. To investigate the level of bias caused by this aggregation, Chapter 3 uses primary data collected at the North Wyke Farm Platform in Southwest England and calculates emission intensities for individual animals and their intra-farm distributions, providing a step towards deriving optimal animal selection strategies based on livestock LCA. Second, the severity of greenhouse gas emissions from agricultural production is known to vary spatially and temporally, yet available LCA frameworks often fail to sufficiently consider these differences due to data constraints. To evaluate the degree of avoidable uncertainties attributable to this practice, Chapter 4 conducts an original field experiment to derive site-specific nitrous oxide emission factors, which are subsequently used in Chapter 5 to compare LCA results derived under these localised values and generic alternatives intended for the widest possible users. Finally, while LCA results are typically communicated in the form of environmental burdens per output of mass, it is gradually becoming recognised that product quality also needs to be accounted for to truly understand the value of each farming system to society. Using data from seven livestock production systems encompassing cattle, sheep, pigs, and poultry, Chapter 6 develops new methods to incorporate nutritional values of meat products into livestock LCA

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Purpose: The nutritional quality of final products is attracting an increased level of attention within life cycle assessment (LCA) literature of agri-food systems. The majority of these studies, however, are based on comparisons at the dietary level and, therefore, are unable to offer immediate implications for farmers as to how best to produce food. This article evaluates recent literature examining the nutrition environment nexus at the commodity-level, with the aim to identify potential pathways towards sustainability analysis that can inform both consumers and producers. Methods: A systematic search of literature was carried out to produce a shortlist of studies, and strict exclusion criteria were applied to them afterwards to eliminate irrelevant material. The studies thus selected were classified into one of three tiers based on the level of complexity with regards to their functional units: (1) based on single nutrients; (2) based on composite indicators derived from multiple nutrients, and; (3) based on commodity-level analysis in a dietary context. Results and discussion: Sixteen papers were identified for inclusion in the review. All of them accounted for climate change either directly or indirectly, whilst only five addressed different impact categories at the same time. Nine studies estimated environmental impacts under functional units associated with nutrient density scores, and the others utilised alternative approaches to account for nutritional value such as linear programming and end-point modelling combined with epidemiological data. A recently developed method to calculate the marginal contribution of a commodity to the overall nutritional value of a specific diet was considered to be a successful first step in bridging the aforementioned knowledge gap. Conclusions: The LCA community should continue the ongoing effort to link farm management decisions to diet-level environmental impacts through an enhanced focus on human nutrition across the entire value chain. Future research comparing environmental performances of multiple food groups or multiple production systems should acknowledge differences in nutritional composition and bioavailability between the final products and, ideally, the effects of these nutrients on overall dietary quality

Near-infrared monitoring of roller compacted ribbon density: investigating sources of variation contributing to noisy spectral data

The aim of this study was to highlight how variability in roller compacted ribbon quality can impact on NIR spectral measurement and to propose a simple method of data selection to remove erroneous spectra. The use of NIR spectroscopy for monitoring ribbon envelope density has been previously demonstrated, however to date there has been limited discussion as to how spectral data sets can contain erroneous outliers due to poor sample presentation to the NIR probes. In this study compacted ribbon of variable quality was produced from three separate blends of microcrystalline cellulose (MCC)/lactose/magnesium stearate at 8 Roll Force settings (2–16 kN/cm). The three blends differed only in the storage conditions of MCC prior to blending and compaction. MCC sublots were stored at ambient (41% RH/20 °C), low humidity (11% RH/20 °C) and high humidity (75% RH/40 °C) conditions prior to blending. Ribbon envelope density was measured and ribbon NIR spectral data was acquired at line using a multi-probe spectrometer (MultiEye™ NIR). Initial inspection of the at-line NIR spectral data set showed a large degree of variability which indicated that some form of data cleaning was required. The source of variability in spectral measurements was investigated by subjective visual examination and by statistical analysis. Spectral variability was noted due to the storage conditions of MCC prior to compaction, Roll Force settings and between individual ribbon samples sampled at a set Roll Force/Blend combination. Variability was also caused by ribbon presentation to probes, such as differences in the presentation of broken, curved and flat intact ribbons. Based on the subjective visual examination of data, a Visual Discard method was applied and was found to be particularly successful for blends containing MCC stored at ambient and low humidity. However the Visual Discard method of spectra cleaning is subjective and therefore a non-subjective method capable of screening for erroneous probe readings was developed. For this data set a Trimmed Mean method was applied to set a limit on how data is cleaned from the data set allowing for the removal of a faulty probe reading (25% of data) or a poor sample (33% of data). The 33% Trimmed Mean reduced the impact of spectral variation or misreads between samples or probes and was found to be as successful as the Visual Discard method at cleaning the data set prior to development of the calibration equation

The impact of training non-physician clinicians in Malawi on maternal and perinatal mortality : a cluster randomised controlled evaluation of the enhancing training and appropriate technologies for mothers and babies in Africa (ETATMBA) project

Background: Maternal mortality in much of sub-Saharan Africa is very high whereas there has been a steady decline in over the past 60 years in Europe. Perinatal mortality is 12 times higher than maternal mortality accounting for about 7 million neonatal deaths; many of these in sub-Saharan countries. Many of these deaths are preventable. Countries, like Malawi, do not have the resources nor highly trained medical specialists using complex technologies within their healthcare system. Much of the burden falls on healthcare staff other than doctors including non-physician clinicians (NPCs) such as clinical officers, midwives and community health-workers. The aim of this trial is to evaluate a project which is training NPCs as advanced leaders by providing them with skills and knowledge in advanced neonatal and obstetric care. Training that will hopefully be cascaded to their colleagues (other NPCs, midwives, nurses). Methods/design: This is a cluster randomised controlled trial with the unit of randomisation being the 14 districts of central and northern Malawi (one large district was divided into two giving an overall total of 15). Eight districts will be randomly allocated the intervention. Within these eight districts 50 NPCs will be selected and will be enrolled on the training programme (the intervention). Primary outcome will be maternal and perinatal (defined as until discharge from health facility) mortality. Data will be harvested from all facilities in both intervention and control districts for the lifetime of the project (3–4 years) and comparisons made. In addition a process evaluation using both quantitative and qualitative (e.g. interviews) will be undertaken to evaluate the intervention implementation. Discussion: Education and training of NPCs is a key to improving healthcare for mothers and babies in countries like Malawi. Some of the challenges faced are discussed as are the potential limitations. It is hoped that the findings from this trial will lead to a sustainable improvement in healthcare and workforce development and training. Trial registration: ISRCTN6329415

Rethinking efficiency: Growth curves as a proxy for inputs and impacts in finishing beef systems

Quantifying and improving efficiency within beef systems is essential for economic and environmental sustainability. The industry standard for assessing efficiency is liveweight gain per day, however, this metric is limited in that it values each day of a growing animal's life as equally costly, despite the increasing maintenance requirements, inputs, and emissions associated with increasing liveweight. Quantifying the area under the growth curve (AUC) considers both time and liveweight as a cost and therefore may hold potential as a better estimate of cost, impact, and efficiency in beef systems. Liveweight data was taken from 439 finishing beef cattle split across three herds grazing on different pastures, known as ‘farmlets’. Analysis was conducted in three parts: [1] Validation of AUC as a proxy for costs using data from a sub-set of 87 animals that had been part of a previous life cycle assessment (LCA) study in which dry matter intake (DMI), methane emissions (CH4), and nitrous oxide emissions (N2O) were calculated. [2] Calculation of AUC relative to liveweight gain (LWG AUC−1) and comparison of that metric against the industry standard of liveweight gain per day (LWG day−1). [3] Assessment of how LWG AUC−1 varied with breed, sex, and management. When comparing to LCA results, AUC correlated significantly with DMI (r = 0.886), CH4 (r = 0.788) and N2O (r = 0.575) emissions. Over the full dataset, there was a negative non-linear relationship between LWG AUC−1 and slaughter age (r = −0.809). There was a significant difference in LWG AUC−1 between breeds (p = 0.046) and farmlets (p = 0.028), but not sex (p = 0.388). LWG AUC−1 has the potential to act as a proxy for feed intake and emissions. In that regard it is superior to LWG day−1, whilst requiring no additional data. Results highlighted the decreasing efficiency of beef cattle over time and the potential benefits of earlier slaughter. The use of LWG AUC−1 could allow farmers to improve their understanding of efficiency within their herds, aiding informed management decision making

Willingness to adopt green house gas mitigation measures: Agricultural land managers in the United Kingdom

Rapid uptake of greenhouse gas (GHG) mitigation measures is central to reducing agricultural and land use emissions and meeting the UK Net Zero policy. The socioeconomic challenges and barriers to uptake are poorly understood, with yet unclear structural pathways to the uptake of GHG mitigation measures. Using an online survey of 201 agricultural land managers across the UK, and applying multiple linear regression and stepwise regression analysis, this research established farm and farmers’ factors influencing perceptions and willingness to adopt GHG mitigation measures. The results consistently show that farm sector, farmers’ business perception, and labour availability influence willingness to adopt GHG mitigation measures. Based on the farmers’ qualitative feedback, other barriers to adoption include costs and concerns for profitability, lack of flexibility in land tenancy contracts, poor awareness and knowledge of the application of some GHG mitigation measures, perception about market demand e.g bioenergy crops, and scepticism about the future impacts of adopting varying GHG mitigation measures. In the midst of the ongoing net zero transition, this study identifies existing barriers to the uptake of GHG mitigation measures, and specifically, a substantial gap between farmers and the science of GHG mitigation measures and the need to incentivise a farm and farming community-led policy interventions to promote adoption of GHG mitigation measures

The impact of using novel equations to predict nitrogen excretion and associated emissions from pasture-based beef production systems

Excretion of nitrogen (N) in faeces and urine from beef cattle contributes to atmospheric pollution through greenhouse gas and ammonia emissions and eutrophication of land and aquatic habitats through excessive N deposition and nitrate leaching to groundwater. As N excretion by beef cattle is rarely measured directly, it is important to accurately predict losses utilising a combined knowledge of diet and production parameters so that the effect of dietary changes on the potential environmental impact of beef production systems can be estimated. This study aimed to identify differences between IPCC and more detailed country-specific models in the prediction of N excretion and N losses at a system level and determine how the choice of model influences the interpretation of differences in diet at the system scale. The data used in this study were derived from a farm-scale experimental system consisting of three individual grazing farms, each with a different sward type: permanent pasture, a high sugar ryegrass monoculture, and a high sugar ryegrass with white clover (~30% groundcover). Data were analysed using a mixed linear model (residual maximum likelihood analysis). The IPCC methods demonstrated significantly lower estimates of N excretion than country-specific models for the first housing period and significantly greater losses for the grazing and second housing periods. The country-specific models enabled prediction of N partitioning to urine and faeces, important for estimation of subsequent N losses through the production system, although the models differed in their estimates. Overall, predicted N losses were greater using the IPCC approaches compared to using more detailed country-specific approaches. The outcomes of the present study have highlighted that different models can have a substantial impact on the predicted N outputs and subsequent losses to the environment for pasture-based beef finishing systems, and the importance, therefore, of using appropriate models and parameters

The impact of using novel equations to predict nitrogen excretion and associated emissions from pasture-based beef production systems.

Publication history: Accepted - 6 June 2022; Published online - 14 June 2022The excretion of nitrogen (N) in faeces and urine from beef cattle contributes to atmospheric pollution through greenhouse gas and ammonia emissions and eutrophication of land and aquatic habitats through excessive N deposition and nitrate leaching to groundwater. As N excretion by beef cattle is rarely measured directly, it is important to accurately predict losses by utilising a combined knowledge of diet and production parameters so that the effect of dietary changes on the potential environmental impact of beef production systems can be estimated. This study aimed to identify differences between IPCC and more detailed country-specific models in the prediction of N excretion and N losses at a system level and determine how the choice of model influences the interpretation of differences in diet at the system scale. The data used in this study were derived from a farm-scale experimental system consisting of three individual grazing farms, each with a different sward type: a permanent pasture, a high sugar ryegrass monoculture, and a high sugar ryegrass with white clover (~30% groundcover). Data were analysed using a mixed linear model (residual maximum likelihood analysis). The IPCC methods demonstrated significantly lower estimates of N excretion than country-specific models for the first housing period and significantly greater losses for the grazing and second housing periods. The country-specific models enabled prediction of N partitioning to urine and faeces, which is important for estimation of subsequent N losses through the production system, although the models differed in their estimates. Overall, predicted N losses were greater using the IPCC approaches compared to using more detailed country-specific approaches. The outcomes of the present study have highlighted that different models can have a substantial impact on the predicted N outputs and subsequent losses to the environment for pasture-based beef finishing systems, and the importance, therefore, of using appropriate models and parametersThe authors would like to acknowledge funding support from the University of Reading, Rothamsted Research, and UK Biotechnology and Biological Sciences Research Council (BBS/E/C/000I0320). The NWFP is a UK National Capability, also supported by the Biotechnology and Biological Sciences Research Council (BBS/E/C/000J0100)

Working with the National Framework for Inclusion: a guide for teacher educators

This companion resource accompanies the National Framework for Inclusion 3rd edition and was developed by the Scottish Universities Inclusion Group (SUIG) and edited by Di Cantali (SUIG Chair). SUIG is a working group of the Scottish Council of Deans of Education

Working with the National Framework for Inclusion: a guide for teacher educators

This companion resource accompanies the National Framework for Inclusion 3rd edition and was developed by the Scottish Universities Inclusion Group (SUIG) and edited by Di Cantali (SUIG Chair). SUIG is a working group of the Scottish Council of Deans of Education