Imke J.M. de Boer

Environmental impact assessment of conventional and organic milk production

Abstract Organic agriculture addresses the public demand to diminish environmental pollution of agricultural production. Until now, however, only few studies tried to determine the integrated environmental impact of conventional versus organic production using life cycle assessment (LCA). The aim of this article was to review prospects and constraints of LCA as a tool to assess the integrated environmental impact of conventional and organic animal production. This aim was illustrated using results from LCAs in the literature and from a pilot study comparing conventional and organic milk production. This review shows that LCAs of different case studies currently cannot be compared directly. Such a comparison requires further international standardisation of the LCA method. A within-case-study comparison of LCAs of conventional and organic production, however, appeared suitable to gain knowledge and to track down main differences in potential environmental impact. Acidification potential of milk production, for example, is for 78–97% due to volatilisation of ammonia, which is not reduced necessarily by changing from conventional to organic milk production. Eutrophication potential per tonne of milk or per ha of farmland was lower for organic than for conventional milk production due to lower fertiliser application rates. Global warming potential of milk production is for 48–65% due to emission of methane. Organic milk production inherently increases methane emission and, therefore, can reduce global warming potential only by reducing emission of carbon dioxide and nitrous oxide considerably. Organic milk production reduces pesticide use, whereas it increases land use per tonne of milk. Conclusions regarding potential environmental impact of organic versus conventional milk production, however, are based largely on comparison of experimental farms. To show differences in potential environmental impact among various production systems, however, LCAs should be performed at a large number of practical farms for each production system of interest. Application of LCA on practical farms, however, requires in-depth research to understand underlying processes, and to predict, or measure, variation in emissions realised in practice.

Environmental Impact of the Production of Mealworms as a Protein Source for Humans – A Life Cycle Assessment

The demand for animal protein is expected to rise by 70–80% between 2012 and 2050, while the current animal production sector already causes major environmental degradation. Edible insects are suggested as a more sustainable source of animal protein. However, few experimental data regarding environmental impact of insect production are available. Therefore, a lifecycle assessment for mealworm production was conducted, in which greenhouse gas production, energy use and land use were quantified and compared to conventional sources of animal protein. Production of one kg of edible protein from milk, chicken, pork or beef result in higher greenhouse gas emissions, require similar amounts of energy and require much more land. This study demonstrates that mealworms should be considered a more sustainable source of edible protein.

The Need and Potential of Biosensors to Detect Dioxins and Dioxin-Like Polychlorinated Biphenyls along the Milk, Eggs and Meat Food Chain

Dioxins and dioxin-like polychlorinated biphenyls (DL-PCBs) are hazardous toxic, ubiquitous and persistent chemical compounds, which can enter the food chain and accumulate up to higher trophic levels. Their determination requires sophisticated methods, expensive facilities and instruments, well-trained personnel and expensive chemical reagents. Ideally, real-time monitoring using rapid detection methods should be applied to detect possible contamination along the food chain in order to prevent human exposure. Sensor technology may be promising in this respect. This review gives the state of the art for detecting possible contamination with dioxins and DL-PCBs along the food chain of animal-source foods. The main detection methods applied (i.e., high resolution gas-chromatography combined with high resolution mass-spectrometry (HRGC/HRMS) and the chemical activated luciferase gene expression method (CALUX bioassay)), each have their limitations. Biosensors for detecting dioxins and related compounds, although still under development, show potential to overcome these limitations. Immunosensors and biomimetic-based biosensors potentially offer increased selectivity and sensitivity for dioxin and DL-PCB detection, while whole cell-based biosensors present interpretable biological results. The main shortcoming of current biosensors, however, is their detection level: this may be insufficient as limits for dioxins and DL-PCBs for food and feedstuffs are in pg per gram level. In addition, these contaminants are normally present in fat, a difficult matrix for biosensor detection. Therefore, simple and efficient extraction and clean-up procedures are required which may enable biosensors to detect dioxins and DL-PCBs contamination along the food chain.

When experts disagree: the need to rethink indicator selection for assessing sustainability of agriculture

Sustainability indicators are well recognized for their potential to assess and monitor sustainable development of agricultural systems. A large number of indicators are proposed in various sustainability assessment frameworks, which raises concerns regarding the validity of approaches, usefulness and trust in such frameworks. Selecting indicators requires transparent and well-defined procedures to ensure the relevance and validity of sustainability assessments. The objective of this study, therefore, was to determine whether experts agree on which criteria are most important in the selection of indicators and indicator sets for robust sustainability assessments. Two groups of experts (Temperate Agriculture Research Network and New Zealand Sustainability Dashboard) were asked to rank the relative importance of eleven criteria for selecting individual indicators and of nine criteria for balancing a collective set of indicators. Both ranking surveys reveal a startling lack of consensus amongst experts about how best to measure agricultural sustainability and call for a radical rethink about how complementary approaches to sustainability assessments are used alongside each other to ensure a plurality of views and maximum collaboration and trust amongst stakeholders. To improve the transparency, relevance and robustness of sustainable assessments, the context of the sustainability assessment, including prioritizations of selection criteria for indicator selection, must be accounted for. A collaborative design process will enhance the acceptance of diverse values and prioritizations embedded in sustainability assessments. The process by which indicators and sustainability frameworks are established may be a much more important determinant of their success than the final shape of the assessment tools. Such an emphasis on process would make assessments more transparent, transformative and enduring.

Methods for global sensitivity analysis in life cycle assessment

PurposeInput parameters required to quantify environmental impact in life cycle assessment (LCA) can be uncertain due to e.g. temporal variability or unknowns about the true value of emission factors. Uncertainty of environmental impact can be analysed by means of a global sensitivity analysis to gain more insight into output variance. This study aimed to (1) give insight into and (2) compare methods for global sensitivity analysis in life cycle assessment, with a focus on the inventory stage.MethodsFive methods that quantify the contribution to output variance were evaluated: squared standardized regression coefficient, squared Spearman correlation coefficient, key issue analysis, Sobol’ indices and random balance design. To be able to compare the performance of global sensitivity methods, two case studies were constructed: one small hypothetical case study describing electricity production that is sensitive to a small change in the input parameters and a large case study describing a production system of a northeast Atlantic fishery. Input parameters with relative small and large input uncertainties were constructed. The comparison of the sensitivity methods was based on four aspects: (I) sampling design, (II) output variance, (III) explained variance and (IV) contribution to output variance of individual input parameters.Results and discussionThe evaluation of the sampling design (I) relates to the computational effort of a sensitivity method. Key issue analysis does not make use of sampling and was fastest, whereas the Sobol’ method had to generate two sampling matrices and, therefore, was slowest. The total output variance (II) resulted in approximately the same output variance for each method, except for key issue analysis, which underestimated the variance especially for high input uncertainties. The explained variance (III) and contribution to variance (IV) for small input uncertainties were optimally quantified by the squared standardized regression coefficients and the main Sobol’ index. For large input uncertainties, Spearman correlation coefficients and the Sobol’ indices performed best. The comparison, however, was based on two case studies only.ConclusionsMost methods for global sensitivity analysis performed equally well, especially for relatively small input uncertainties. When restricted to the assumptions that quantification of environmental impact in LCAs behaves linearly, squared standardized regression coefficients, squared Spearman correlation coefficients, Sobol’ indices or key issue analysis can be used for global sensitivity analysis. The choice for one of the methods depends on the available data, the magnitude of the uncertainties of data and the aim of the study.

Assessing broad life cycle impacts of daily onboard decision-making, annual strategic planning, and fisheries management in a northeast Atlantic trawl fishery

PurposeCapture fisheries are the only industrial-scale harvesting of a wild resource for food. Temporal variability in environmental performance of fisheries has only recently begun to be explored, but only between years, not within a year. Our aim was to better understand the causes of temporal variability within and between years and to identify improvement options through management at a company level and in fisheries management.MethodsWe analyzed the variability in broad environmental impacts of a demersal freeze trawler targeting cod, haddock, saithe, and shrimp, mainly in the Norwegian Sea and in the Barents Sea. The analysis was based on daily data for fishing activities between 2011 and 2014 and the functional unit was a kilo of landing from one fishing trip. We used biological indicators in a novel hierarchic approach, depending on data availability, to quantify biotic impacts. Landings were categorized as target (having defined target reference points) or bycatch species (classified as threatened or as data-limited). Indicators for target and bycatch impacts were quantified for each fishing trip, as was the seafloor area swept.Results and discussionNo significant difference in fuel use was found between years, but variability was considerable within a year, i.e., between fishing trips. Trips targeting shrimp were more fuel intensive than those targeting fish, due to a lower catch rate. Steaming to and from port was less important for fuel efficiency than steaming between fishing locations. A tradeoff was identified between biotic and abiotic impacts. Landings classified as main target species generally followed the maximum sustainable yield (MSY) framework, and proportions of threatened species were low, while proportions of data-limited bycatch were larger. This improved considerably when reference points were defined for saithe in 2014.ConclusionsThe variability between fishing trips shows that there is room for improvement through management. Fuel use per landing was strongly influenced by target species, fishing pattern, and fisheries management. Increased awareness about the importance of onboard decision-making can lead to improved performance. This approach could serve to document performance over time helping fishing companies to better understand the effect of their daily and more long-term decision-making on the environmental performance of their products.RecommendationsFishing companies should document their resource use and production on a detailed level. Fuel use should be monitored as part of the management system. Managing authorities should ensure that sufficient data is available to evaluate the sustainability of exploitation levels of all harvested species.

The importance of hormonal circadian rhythms in daily feeding patterns: An illustration with simulated pigs

Abstract The interaction between hormonal circadian rhythms and feeding behaviour is not well understood. This study aimed to deepen our understanding of mechanisms underlying circadian feeding behaviour in animals, using pigs, Sus scrofa, as a case study. Pigs show an alternans feeding pattern, that is, a small peak of feed intake at the beginning of the day and a larger peak at the end of the day. We simulated the feeding behaviour of pigs over a 24 h period. The simulation model contained mechanisms that regulate feeding behaviour of animals, including: processing of feed in the gastrointestinal tract, fluctuation in energy balance, circadian rhythms of melatonin and cortisol and motivational decision‐making. From the interactions between these various processes, feeding patterns (e.g. feed intake, meal frequency, feeding rate) emerge. These feeding patterns, as well as patterns for the underlying mechanisms (e.g. energy expenditure), fitted empirical data well, indicating that our model contains relevant mechanisms. The circadian rhythms of cortisol and melatonin explained the alternans pattern of feeding in pigs. Additionally, the timing and amplitude of cortisol peaks affected the diurnal and nocturnal peaks in feed intake. Furthermore, our results suggest that circadian rhythms of other hormones, such as leptin and ghrelin, are less important in circadian regulation of feeding behaviour than previously thought. These results are relevant to animal species with a metabolic and endocrine system similar to that of pigs, such as humans. Moreover, the modelling approach to understand feeding behaviour can be applied to other animal species. HighlightsMechanisms underlying circadian feeding patterns in pigs are modelled.Interaction between energy balance and circadian rhythms explains feeding patterns.Rhythms in melatonin and cortisol are important in the regulation of feeding.Timing and amplitude of cortisol peaks affects diurnal and nocturnal feeding.

How social factors and behavioural strategies affect feeding and social interaction patterns in pigs

Animals living in groups compete for food resources and face food conflicts. These conflicts are affected by social factors (e.g. competition level) and behavioural strategies (e.g. avoidance). This study aimed to deepen our understanding of the complex interactions between social factors and behavioural strategies affecting feeding and social interaction patterns in animals. We focused on group-housed growing pigs, Sus scrofa, which typically face conflicts around the feeder, and of which patterns in various competitive environments (i.e. pig:feeder ratio) have been documented soundly. An agent-based model was developed to explore how interactions among social factors and behavioural strategies can affect various feeding and social interaction patterns differently under competitive situations. Model results show that pig and diet characteristics interact with group size and affect daily feeding patterns (e.g. feed intake and feeding time) and conflicts around the feeder. The level of competition can cause a turning point in feeding and social interaction patterns. Beyond a certain point of competition, meal-based (e.g. meal frequency) and social interaction patterns (e.g. displacements) are determined mainly by behavioural strategies. The average daily feeding time can be used to predict the group size at which this turning point occurs. Under the models assumptions, social facilitation was relatively unimportant in the causation of behavioural patterns in pigs. To validate our model, simulated patterns were compared with empirical patterns in conventionally housed pigs. Similarities between empirical and model patterns support the model results. Our model can be used as a tool in further research for studying the effects of social factors and group dynamics on individual variation in feeding and social interaction patterns in pigs, as well as in other animal species.

Pastoralists in a changing environment : The competition for grazing land in and around the W Biosphere Reserve, Benin Republic

Pastoralists face increasing competition for land with crop farmers and nature in and around the W Biosphere Reserve (WBR) in Benin. Our aim was to describe and analyse land use changes in order to understand their drivers, and to describe and analyse the viewpoints of relevant stakeholders in order to understand the competition for land. To this end, remote sensing data, regional statistics, and survey data were collected. We found that crop land expansion around the WBR was the direct driver of decrease of the grazing land area. Population growth and rising demand for food crops, and government support to the cotton sector were indirect drivers of grazing land reduction. Furthermore, competing claims on land among users arose from the complex interaction of crop expansion, presence of WBR and the way it is governed, the lack of support to pastoralists, and the increasing shift of pastoralists’ lifestyle into one of settled crop farmers. Pastoralism is under threat and its survival depends on the successful implementation of policies to support pastoralists and protect grazing lands.

Effects of dry period length on production, cash flows and greenhouse gas emissions of the dairy herd: A dynamic stochastic simulation model

Shortening or omitting the dry period of dairy cows improves metabolic health in early lactation and reduces management transitions for dairy cows. The success of implementation of these strategies depends on their impact on milk yield and farm profitability. Insight in these impacts is valuable for informed decision-making by farmers. The aim of this study was to investigate how shortening or omitting the dry period of dairy cows affects production and cash flows at the herd level, and greenhouse gas emissions per unit of milk, using a dynamic stochastic simulation model. The effects of dry period length on milk yield and calving interval assumed in this model were derived from actual performance of commercial dairy cows over multiple lactations. The model simulated lactations, and calving and culling events of individual cows for herds of 100 cows. Herds were simulated for 5 years with a dry period of 56 (conventional), 28 or 0 days (n = 50 herds each). Partial cash flows were computed from revenues from sold milk, calves, and culled cows, and costs from feed and rearing youngstock. Greenhouse gas emissions were computed using a life cycle approach. A dry period of 28 days reduced milk production of the herd by 3.0% in years 2 through 5, compared with a dry period of 56 days. A dry period of 0 days reduced milk production by 3.5% in years 3 through 5, after a dip in milk production of 6.9% in year 2. On average, dry periods of 28 and 0 days reduced partial cash flows by €1,249 and €1,632 per herd per year, and increased greenhouse gas emissions by 0.7% and 0.5%, respectively. Considering the potential for enhancing cow welfare, these negative impacts of shortening or omitting the dry period seem justifiable, and they might even be offset by improved health.