PURPOSE: Livestock already use most global agricultural land, whereas the demand for animal-source food (ASF) is expected to increase. To address the contribution of livestock to global food supply, ...we need a measure for land use efficiency of livestock systems. METHODS: Existing measures capture different aspects of the debate about land use efficiency of livestock systems, such as plant productivity and the efficiency of converting feed, especially human-inedible feed, into animal products. So far, the suitability of land for cultivation of food crops has not been accounted for. Our land use ratio (LUR) includes all above-mentioned aspects and yields a realistic insight into land use efficiency of livestock systems. LUR is defined as the maximum amount of human-digestible protein (HDP) derived from food crops on all land used to cultivate feed required to produce 1Â kg ASF over the amount of HDP in that 1Â kg ASF. We illustrated our concept for three case systems. RESULTS AND DISCUSSION: The LUR for the case of laying hens equaled 2.08, implying that land required to produce 1Â kg HDP from laying hens could directly yield 2.08Â kg HDP from human food crops. For dairy cows, the LUR was 2.10 when kept on sandy soils and 0.67 when kept on peat soils. The LUR for dairy cows on peat soils was lower compared to cows on sandy soils because land used to grow grass and grass silage for cows on peats was unsuitable for direct production of food crops. A LUR <1.0 is considered efficient in terms of global food supply and implies that animals produce more HDP per square metre than crops. CONCLUSIONS: Values <1.0 demonstrate that livestock produce HDP more efficiently than crops. Such livestock systems (with a LURâ
When cows on dairy farms are milked with an automatic milking system or in high capacity milking parlors, clinical mastitis (CM) cannot be adequately detected without sensors. The objective of this ...paper is to describe the performance demands of sensor systems to detect CM and evaluats the current performance of these sensor systems. Several detection models based on different sensors were studied in the past. When evaluating these models, three factors are important: performance (in terms of sensitivity and specificity), the time window and the similarity of the study data with real farm data. A CM detection system should offer at least a sensitivity of 80% and a specificity of 99%. The time window should not be longer than 48 hours and study circumstances should be as similar to practical farm circumstances as possible. The study design should comprise more than one farm for data collection. Since 1992, 16 peer-reviewed papers have been published with a description and evaluation of CM detection models. There is a large variation in the use of sensors and algorithms. All this makes these results not very comparable. There is a also large difference in performance between the detection models and also a large variation in time windows used and little similarity between study data. Therefore, it is difficult to compare the overall performance of the different CM detection models. The sensitivity and specificity found in the different studies could, for a large part, be explained in differences in the used time window. None of the described studies satisfied the demands for CM detection models.
The livestock sector is in urgent need for more sustainable feed sources, because of the increased demand for animal-source food and the already high environmental costs associated with it. Recent ...developments indicate environmental benefits of rearing insects for livestock feed, suggesting that insect-based feed might become an important alternative feed source in the coming years. So far, however, this potential environmental benefit of waste-fed insects is unknown. This study, therefore, explores the environmental impact of using larvae of the common housefly grown on poultry manure and food waste as livestock feed. Data were provided by a laboratory plant in the Netherlands aiming to design an industrial plant for rearing housefly larvae. Production of 1 ton dry matter of larvae meal directly resulted in a global warming potential of 770 kg CO2 equivalents, an energy use of 9329 MJ and a land use of 32 m2, caused by use of water, electricity, and feed for flies, eggs and larvae. Production of larvae meal, however, also has indirect environmental consequences. Food waste, for example, was originally used for production of bio-energy. Accounting for these indirect consequences implies, e.g., including the environmental impact of production of energy needed to replace the original bio-energy function of food waste. Assuming, furthermore, that 1 ton of larvae meal replaced 0.5 ton of fishmeal and 0.5 ton of soybean meal, the production of 1 ton larvae meal reduced land use (1713 m2), but increased energy use (21,342 MJ) and consequently global warming potential (1959 kg CO2-eq). Results of this study will enhance a transparent societal and political debate about future options and limitations of larvae meal as livestock feed. Results of the indirect environmental impact, however, are situation specific, e.g. in this study food waste was used for anaerobic digestion. In case food waste would have been used for, e.g., composting, the energy use and related emission of greenhouse gases might decrease. Furthermore, the industrial process to acquire housefly larvae meal is still advancing, which also offers potential to reduce energy use and related emissions. Eventually, land scarcity will increase further, whereas opportunities exist to reduce energy use by, e.g., technical innovations or an increased use of solar or wind energy. Larvae meal production, therefore, has potential to reduce the environmental impact of the livestock sector.
•One of the first studies that assesses environmental impact of waste fed larvae as livestock feed.•Using waste fed larvae as livestock feed reduced land use significantly.•Energy use increases when using waste as larvae feed instead of producing bio-energy.•Using larvae as livestock feed increased global warming potential due to energy use.
Abstract
In pig production, efficiency is benefiting from uniform growth in pens resulting in single deliveries from a pen of possibly all animals in the targeted weight range. Abnormalities, like ...pneumonia or aberrant growth, reduce production efficiency as it reduces the uniformity and might cause multiple deliveries per batch and pigs delivered with a low meat yield or outside the targeted weight range. Early identification of pigs prone to develop these abnormalities, for example, at the onset of the growing-finishing phase, would help to prevent heterogeneous pens through management interventions. Data about previous production cycles at the farm combined with data from the piglet’s own history may help in identifying these abnormalities. The aim of this study, therefore, was to predict at the onset of the growing-finishing phase, that is, at 3 mo in advance, deviant pigs at slaughter with a machine-learning technique called boosted trees. The dataset used was extracted from the farm management system of a research center. It contained over 70,000 records of individual pigs born between 2004 and 2016, including information on, for example, offspring, litter size, transfer dates between production stages, their respective locations within the barns, and individual live-weights at several production stages. Results obtained on an independent test set showed that at a 90% specificity rate, the sensitivity was 16% for low meat percentage, 20% for pneumonia and 36% for low lifetime growth rate. For low lifetime growth rate, this meant an almost three times increase in positive predictive value compared to the current situation. From these results, it was concluded that routine performance information available at the onset of the growing-finishing phase combined with data about previous production cycles formed a moderate base to identify pigs prone to develop pneumonia (AUC > 0.60) and a good base to identify pigs prone to develop growth aberrations (AUC > 0.70) during the growing-finishing phase. The mentioned information, however, was not a sufficient base to identify pigs prone to develop low meat percentage (AUC < 0.60). The shown ability to identify growth aberrations and pneumonia can be considered a good first step towards the development of an early warning system for pigs in the growing-finishing phase.
In pig production, efficiency is benefiting from uniform growth in pens resulting in single deliveries from a pen of possibly all animals in the targeted weight range. Abnormalities, like pneumonia ...or aberrant growth, reduce production efficiency as it reduces the uniformity and might cause multiple deliveries per batch and pigs delivered with a low meat yield or outside the targeted weight range. Early identification of pigs prone to develop these abnormalities, for example, at the onset of the growing-finishing phase, would help to prevent heterogeneous pens through management interventions. Data about previous production cycles at the farm combined with data from the piglet's own history may help in identifying these abnormalities. The aim of this study, therefore, was to predict at the onset of the growing-finishing phase, that is, at 3 mo in advance, deviant pigs at slaughter with a machine-learning technique called boosted trees. The dataset used was extracted from the farm management system of a research center. It contained over 70,000 records of individual pigs born between 2004 and 2016, including information on, for example, offspring, litter size, transfer dates between production stages, their respective locations within the barns, and individual live-weights at several production stages. Results obtained on an independent test set showed that at a 90% specificity rate, the sensitivity was 16% for low meat percentage, 20% for pneumonia and 36% for low lifetime growth rate. For low lifetime growth rate, this meant an almost three times increase in positive predictive value compared to the current situation. From these results, it was concluded that routine performance information available at the onset of the growing-finishing phase combined with data about previous production cycles formed a moderate base to identify pigs prone to develop pneumonia (AUC > 0.60) and a good base to identify pigs prone to develop growth aberrations (AUC > 0.70) during the growing-finishing phase. The mentioned information, however, was not a sufficient base to identify pigs prone to develop low meat percentage (AUC < 0.60). The shown ability to identify growth aberrations and pneumonia can be considered a good first step towards the development of an early warning system for pigs in the growing-finishing phase.
•Predicted P yields have potential to support decisions on applied quantity of manure.•Yields were predicted with machine learning on historical field and weather data.•Yields of individual fields ...were predicted before the first manure application.•This is helpful to promote circular agriculture and reduce environmental impacts.
An important factor in a circular agricultural system is the efficient use of animal manure. Until now, the applied quantity of manure is regulated by law at farm level, based on fixed phosphorus (P) application norms. However, a first step towards more efficient manure application is to better balance P input and output at field level by predicting future P yields. Machine learning techniques can be useful in this respect, because they can be trained with many variables without prior knowledge regarding their interrelationship. This study’s objective, therefore, was to predict P yields based on detailed records of on-farm data as recorded on an experimental farm combined with open source weather data. The dataset contained 657 records of annual crop yields per field between 1993 and 2016, and the boosted regression model was used for model development. Validation on the final five years of the dataset resulted in an RMSE of 7.3 kg P per ha per year, an R-squared of 0.46 and a correlation between observed and predicted values of 0.68, outperforming legal norms. We conclude that with the limited but detailed data available, prediction of P yield, and therewith, defining flexible P application norms before first manure application, is already feasible. This conclusion, together with the expected increasing availability of data through proximal and remote sensing technologies, opens the way to further improve nutrient management and move towards circular agriculture in the future.
The ambition of the Dutch Ministry of Agriculture is to stimulate the transition to circular agriculture. The objective of this paper is to develop and apply a farm level model toolbox for ...circular-agriculture policy assessment. Transition to circular agriculture affects farm management practices and outcome in the field of finance and economics, soil quality, use of finite resources, emissions, and biodiversity. Based on this, there is a need for an integrated assessment at farm level. Therefore, Bio Economic Farm Models should be at the core of the model toolbox. Model collaboration enables answering more complex questions and enlarges the scope of the analysis. Challenges of model collaboration are among others overlapping modules, different approaches (optimisation versus simulation), and existence of different networks of model developers and users. It is argued that a governance structure and networking will foster model collaboration. To stimulate transition to more circular agriculture practices and as a demonstration, the model toolbox was applied to assess the economic and environmental impacts of a tax on N from mineral fertiliser on a representative dairy and arable farm in a region in the Netherlands. It was found that a tax on N from mineral fertiliser has relatively large income effects, while the impacts on various environmental indicators are relatively limited.
PURPOSE: The livestock sector has a major impact on the environment. This environmental impact may be reduced by feeding agricultural co-products (e.g. beet tails) to livestock, as this transforms ...inedible products for humans into edible products, e.g. pork or beef. Nevertheless, co-products have different applications such as bioenergy production. Based on a framework we developed, we assessed environmental consequences of using co-products in diets of livestock, including the alternative application of that co-product. METHODS: We performed a consequential life cycle assessment, regarding greenhouse gas emissions (including emissions related to land use change) and land use, for two case studies. Case 1 includes increasing the use of wheat middlings in diets of dairy cattle at the expense of using it in diets of pigs. The decreased use of wheat middlings in diets of pigs was substituted with barley, the marginal product. Case 2 includes increasing the use of beet tails in diets of dairy cattle at the expense of using it to produce bioenergy. During the production of biogas, electricity, heat and digestate (that is used as organic fertilizer) were produced. The decrease of electricity and heat was substituted with fossil fuel, and digestate was substituted with artificial fertilizer. RESULTS AND DISCUSSION: Using wheat middlings in diets of dairy cattle instead of using it in diets of pigs resulted in a reduction of 329 kg CO₂ eq per ton wheat middlings and a decrease of 169 m² land. Using beet tails in diets of dairy cattle instead of using it as a substrate for anaerobic digestion resulted in a decrease of 239 kg CO₂ eq per ton beet tails and a decrease of 154 m² land. Emissions regarding land use change contributed significantly in both cases but had a high uncertainty factor, ±170 ton CO₂ ha⁻¹. Excluding emissions from land use change resulted in a decrease of 9 kg CO₂ eq for case 1 ‘wheat middlings’ and an increase of 50 kg CO₂ eq for case 2 ‘beet tails’. CONCLUSIONS: Assessing the use of co-products in the livestock sector is of importance because shifting its application can reduce the environmental impact of the livestock sector. A correct assessment of the environmental consequences of using co-products in animal feed should also include potential changes in impacts outside the livestock sector, such as the impact in the bioenergy sector.
Sustainability or sustainable development (SusD) is stated as a core element of many government policies, research projects, and corporate strategies. Due to the widespread use and the different ...meanings given to the term 'sustainability', it is discarded as a buzz-word sometimes. There is, however, a common essence in using the term, which originates from the most often quoted definition from the World Commission on Environment and Development (WCED): "Sustainable development meets the needs of current generations without compromising the ability of future generations to meet their needs and aspirations". Subsequently, in scientific literature many authors gave their own definition, with the result that there is no generally agreed definition. Therefore, it is necessary to first define SusD in broad terms, and, subsequently, come to a more precise and context specific definition.In order to define SusD, two core elements have been identified. The first element is that SusD is not a fixed state of harmony, but rather a process of change, consistent with future as well as with present needs. This means that we cannot define a sustainable system or product, but that it is necessary to monitor SusD of a process. The second element is that SusD relates to economic, ecological, and societal (EES) issues. Within the domain defined by these core elements, researchers have to find out what people think about SusD in a specific situation. A participatory approach can help to obtain people's opinions, which means that all relevant stakeholders of a certain system are involved.The objective of this study was to further develop and apply a methodology to assess the contribution of animal production systems to SusD. Here, a methodology means not a fixed method, which always will lead to a certain type of outcome. It is a general approach, in which different methods are applied. The practical use of the methodology is tested in a case study on egg production systems, because the upcoming ban on the battery-cage system in 2012 forces farmers to change to an alternative, more animal-friendly production system in the near future. A decision to introduce a new production system should not be based on one single issue, e.g., animal welfare, as was the case with the ban on the battery cage. In order to prevent future shortcomings on other aspects, the selection of new production systems must consider all EES issues, which together determine the contribution to SusD. Assessment of the contribution of animal production systems to SusD implies four steps: (1) description of the situation; (2) identification and definition of relevant EES issues; (3) selection and quantification of suitable sustainability indicators (SI); and (4) final assessment of the contribution to SusD. The first step requires that you have to define and describe the system that is subject of study, in our case the egg production system. Stakeholders, who represent the internal and surrounding components (i.e., the context) of a system, were identified. These stakeholders were involved in the second step, the identification and definition of relevant EES issues. This step was supplemented with information from literature. During the third step, issues were made measurable by selecting SI, which were quantified. For the case study, this meant that data had to be collected in the field, i.e., on the farm, from different egg production systems, characterized by differences in housing system. The fourth step encompassed the final assessment, which meant that all information was combined to determine the final contribution of the system to SusD.Chapter 2 describes the first two steps of the methodology, the description of the situation, and the identification and definition of relevant EES issues. It demonstrates how participatory strengths, weaknesses, opportunities and threats (SWOT) analysis can be used to identify relevant EES issues for the assessment of SusD. Participatory methods were used to facilitate the exchange of ideas, experiences and knowledge of all relevant stakeholders and to create a basis for implementation of the final results. We concluded that the combination of a brainstorming session and SWOT analysis with a heterogeneous group of stakeholders constituted a useful tool to order and structure these listed aspects and to identify relevant issues for SusD. Final selection of EES issues from the SWOT analysis, however, required additional reviewing of the literature and consultation with experts from specific fields. Final EES issues selected in the case study of Dutch egg production included animal health and welfare, environment, egg quality, ergonomics, economics, consumer concerns, and knowledge and innovation.Chapter 3 describes an example of step three of the methodology, i.e., the selection of appropriate SI. Before selection can take place, a clear set of criteria for indicators must be defined. SI have to be a) relevant, i.e., they have to express something about the issue, b) simple, i.e., they have to be understandable for users, and c) sensitive and reliable, i.e., they have to react to changes in the system, and different measurements must lead to the same outcome. Furthermore, d) it must be possible to determine a target value or trend, and e) data have to be accessible. This chapter describes the selection of SI for one aspect of the issue animal welfare, i.e., the opportunity to express normal behaviour, because this aspect cannot be assessed easily on a large number of commercial farms.Methods available to assess opportunities to express normal behaviour at farm level are based on a range of welfare parameters, which can be divided into two categories, environment-based and animal-based parameters. The first category describes features of the environment (dimensions of the house and facilities) and management, which can be considered prerequisites for welfare. The second category records animals' responses to that particular environment and management more directly. The objective was to validate a mainly environment-based method, i.e., the animal needs index (ANI), with animal-based methods, i.e., behavioural observations and feather condition scores. The study was conducted on 20 commercial laying hen farms; 10 farms with battery cages and 10 farms with deep-litter systems. The results showed that ANI is valid and sensitive enough to show differences in animalwelfare between housing systems, whereas differences in welfare within housing systems cannot be shown. For the other criteria, simplicity, data accessibility, and possibility to set a target value, the animal needs index performed better than the animal-based methods. We concluded that ANI is an appropriate method for assessment of this aspect of laying hen welfare on a large number of farms with different housing systems.Because of the outbreak of Avian Influenza in the Netherlands during spring and summer 2003, it was not allowed to visit farms to quantify all SI. Therefore, we resorted to the use of an existing data set. From the screening of Salmonella enteritidis (SE) infections in laying hens, a data set of regularly collected blood samples was available for analysis. Chapter 4 describes an in-depth study on the incidence of SE infections in laying hens, which is the main threat regarding food safety in egg production. Various risk factors exist for infection with and spreading of SE on a farm. The objective of the analysis was to identify risk factors associated with SE infection in laying hens. Results showed that bigger flocks increased the chance of infection with SE in all housing systems. The system with the lowest chance of infection was the cage system with wet manure. An outdoor run increased the chance of infection only at farms with all hens of the same age. The presence of hens of different ages on a farm was a risk factor for deep-litter systems only. This resulted in the highest chance of infection for a deep-litter system on a farm with hens of different ages. On a farm with all hens of the same age, however, a deep-litter system did not increase the chance of infection with SE compared with a cage system. The main risk factors associated with SE infection, therefore, were flock size, housing system, and farm with hens of different ages.Chapter 5 describes the third step of the methodology, the selection and quantification of suitable SI, for all issues. The objective was to select SI for all EES issues, and to analyze the performance of different production systems on the selected SI. We compared four egg production systems, characterized by different housing systems, which were most common in the Netherlands: the battery-cage system, the deep-litter system with and without outdoor run, and the aviary system with outdoor run. We showed that on-farm quantification of SI was an appropriate method to identify strengths and weaknesses of different systems, and the variation within these systems as well. From this analysis it appeared that, within the boundaries of this study, the aviary system with outdoor run was the best alternative for the battery-cage system. The aviary system performed better on animal welfare and economics, however, worse on environmental impact. No significant differences were found for other SI. Deep litter with and without outdoor run performed equally or worse than aviary with outdoor run on all SI.Chapter 6 presents an ethical reflection on the whole methodology, with special emphasis on the fourth step of the methodology, the final assessment of the contribution to SusD. Many decisions during the four-step methodology, e.g., decisions on which stakeholders to involve, which reference values to choose, or how to aggregate information, are based on implicit value judgements. These value judgements influence the final results of the assessment, which makes it important to elucidate them when applying this methodology. Therefore,
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