The objective of this case study was to investigate if respiratory ratios derived from non-invasive exhaled breath measurements could be used as a tool to detect dairy cows at risk for impaired postpartum health. Measurements of exhaled breath from individual cows took place during visits to a concentrate feeder from 2 wk prepartum until 6 wk postpartum (Veterinary Metabolism System, Relitech). Per measurement, concentrations of oxygen (O2), carbon dioxide (CO2), and methane (CH4) (vol%) were measured. Subsequently, daily average respiratory exchange ratio (RER; CO2/O2), CH4 exchange ratio (MER; CH4/O2) and CH4-to-CO2 ratio (MCR; CH4/CO2) were calculated per cow. Cows were scored clinically twice weekly from 2 wk prepartum until 6 wk postpartum on 19 clinical signs. Blood β-hydroxybutyric acid was determined twice weekly, and additional blood samples were taken in week 1 and week 5 after calving to determine clinical-chemical parameters. A total deficit score (TDS) was assigned to each cow based on blood values and clinical scores. Per health score (e.g., decrease in body condition score (BCS) after calving, β-hydroxybutyric acid values, and TDS values), cows were divided into two categories (high or low). Differences in exhaled breath composition between these categories were evaluated using mixed models. The RER was lower in cows with a high reduction in BCS during the first 6 wk after calving, which can result in ketosis and fatty liver caused by the increased body fat mobilization. For TDS Locomotion, TDS Metabolic, TDS Liver, and TDS Macro-minerals, MER was lower in cows with a higher TDS compared with cows with a lower TDS, which may be related to decreased feed intake in cows at risk for disease leading to a decreased methane production. Prepartum MER was lower in cows with a high decrease in BCS, high TDS Total, or high TDS inflammation compared with cows with low values in these categories. The MCR was inconsistent for different disease categories. Based on the results of this experiment, respiratory ratios derived from non-invasive exhaled breath measurements seem to be promising indicators to detect cows at risk for disease around calving, but further validation of breath measurements is required. Variables such as individual daily milk production, dry matter intake, and time lag between breath measurement and feeding time should be taken into account in future research to improve the interpretation of results.

This chapter focuses on the emissions of greenhouse gases (GHGs), ammonia, particulate matter (PM), odours and volatile organic compounds from layer production systems. It discusses the types of emissions and the factors affecting them. The chapter then provides an overview of the options to mitigate emissions that are currently available for layer production, while clearly defining shortcomings, knowledge gaps, and barriers associated with each of them.

The global food system is under significant pressure from challenges like resource depletion, climate change, and population growth, which drive a heightened demand for food production in conflict with unsustainable methods. Circular agriculture aims to mitigate these challenges by minimizing waste. A promising approach involves utilizing waste as biofertilizers, but this raises concerns about the safety of contaminants such as chemicals, plastics, and pathogens present in waste streams. Soil health, vital for agriculture, faces potential risks from these contaminants. This review examines the accumulation and hazards of waste stream contaminants on soil biota, addressing knowledge gaps and advancing strategies for robust risk assessment based on European studies. Findings reveal a notable disparity between emerging contaminants detected in waste streams and their limited presence in soil organisms. Correlations between bioaccumulation and sub-lethal effects across species, contaminants, and waste streams are sparse, reflecting critical gaps in understanding. In particular, behavioural changes in soil organisms, which significantly influence soil functionality and ecosystem processes, remain underexplored. To address these gaps, an ecological perspective that considers the roles of diverse soil species is crucial. Earthworms, with their sensitivity to toxic stress (avoidance behaviour) and ecological functions (bioturbation), are highlighted as essential bioindicators. Evaluating their responses to waste stream applications offers insights into soil toxicity and fertility. By balancing toxic stress with the potential benefits of waste utilization under specific environmental conditions, this approach aims to enhance soil health while supporting sustainable agricultural practices.

Monitoring methane production from individual cows is necessary to evaluate the success of greenhouse gas reduction strategies. However, monitoring methane production rates (MPR) under practical conditions remains challenging. In this paper, we investigate the performance of a potential solution to this challenge. The cubicle hood sampler (CHS) is an on-barn monitoring device placed in cubicles that collects the air exhaled by the animals while they lie down. The MPR of 28 dairy cows were measured by four CHS devices and compared to the levels measured by climate respiration chambers (CRC). A linear regression showed no strong correlation between the two sets of estimates (r = 0.24). The estimates made by the CHS appeared to be inaccurate due to a sampling bias (insufficient breath recovery), which could not be corrected for. Using Bayesian modelling, information was pooled across individuals to model complete methane production curves and potentially improve the accuracy of the MPR estimates. However, the model was unable to compensate for the biased observations used for fitting, and accuracy levels did not improve. An under-recovery of the breath samples by the hood is suspected. These issues must be resolved. Nevertheless, the CHS ranked cows satisfactorily, with Kendall W values of 0.625 (p = 0.201) in the original dataset, and 0.659 (p = 0.214) after using the model. Resolving the bias issue is expected to have a simultaneous positive effect on the agreement between the two MPR rankings. We recommend to keep using the model to convert discrete measurements into methane production curves.

Monitoring methane production from individual cows is crucial for the implementation of greenhouse gas reduction strategies. However, monitoring methane production rates (MPR) under practical conditions and with acceptable levels of accuracy, intrusiveness, and throughput remains challenging. In this study, we present a renewed design of the Cubicle Hood Sampler (CHS) as a potential solution to this challenge. Placed in the cubicles, the CHS collects and analyses the methane content of the air exhaled by cows when lying down. The ability of four CHS units to recover known MPR was assessed in three series of recovery tests using a reference method (artificial reference cow (ARC)). For the fluxes tested, there were no significant difference in recovery rates (mean 110.5 +/- 8.7%) between CHS units (p = 0.207), production rates (p = 0.080), and repetitions (p = 0.148). Recovery rates appeared not to be significantly different from 100% (p = 0.154), and root mean square error equated 35.0g, which is considered acceptable for MPR levels of 200 and 400g/day. Repeatability equated 0.94, showing the high repeatability and reliability across replicates. These results place the CHS as a promising tool for on-barn methane measurements. However, the radio frequency identification used to link measurements to specific cows did not yield sufficient levels of correct identification. Similarly, the monitoring of head poses was not satisfactory, and the CHS still requires further improvements to be made.

Managing dairy excreta as slurry can result in significant emissions of ammonia (NH3) and greenhouse gases (GHGs) during storage and thereafter. Additionally, slurry often has an imbalanced nitrogen (N) to phosphorus (P) ratio for crop fertilization. While various treatments exist to address emissions and nutrient imbalances, each has trade-offs that can result in pollution swapping. An integrated management system, starting with source segregation (SS) in-house to separate faeces and urine into two manageable streams followed by step-wise complementary treatments has been designed to manage nutrients and reduce emissions in the whole chain, but its effect on emissions in storage remains untested. This study investigated NH3, nitrous oxide (N2O), and methane (CH4) emissions and total N losses from integrated storage systems combining SS, mesophilic or thermophilic anaerobic digestion (AD), acidification, drying and zeolite addition and an impermeable cover. These systems were compared to two reference slurry storage systems: in-house uncovered (US) and outside covered (CS). A 30-day lab-scale experiment was conducted at 10 °C, monitoring emissions using an INNOVA1412 gas analyser, while total N losses were assessed using mass balance. Results indicated that the SS fractions treated before covered storage exhibited significantly lower emissions (NH3 or CH4 or both) compared to both reference slurry storage systems (US and CS). Source segregation combined with acidification of urine and AD of faeces at 35 °C and an impermeable cover allowed for a 99% reduction in NH3 emissions, a 45% reduction in CH4 emissions and had no effect on N2O emissions as compared to US. When AD of faeces was conducted at 55 °C instead of 35 °C, the CH4 emission was reduced by 77% compared to US. This study concludes that SS combined with urine and faeces treatment allows a more effective and simultaneous reduction of all emissions in storage as compared to slurry storage systems, while also effectively separating nutrients allowing more precise N and P fertilization with dairy excreta. Further research is necessary to assess emissions and fertilizer value of treated fractions after field application, in addition to the associated costs.

Monitoring methane production from individual cows is required for evaluating the success of greenhouse gas reduction strategies. However, converting non-continuous measurements of methane production into daily methane production rates (MPR) remains challenging due to the general non-linearity of the methane production curve. In this paper, we propose a Bayesian hierarchical stochastic kinetic equation approach to address this challenge, enabling the sharing of information across cows for improved modelling. We fit a non-linear curve on climate respiration chamber (CRC) data of 28 dairy cows before computing an area under the curve, thereby providing an estimate of MPR from individual cows, yielding a monitored and predicted population mean of 416.7 +/- 36.2 g/d and 407.2 +/- 35.0 g/d respectively. The shape parameters of this model were pooled across cows (population-level), while the scale parameter varied between individuals. This allowed for the characterization of variation in MPR within and between cows. Model fit was thoroughly investigated through posterior predictive checking, which showed that the model could reproduce this CRC data accurately. Comparison with a fully pooled model (all parameters constant across cows) was evaluated through cross-validation, where the Hierarchical Methane Rate (HMR) model performed better (difference in expected log predictive density of 1653). Concordance between the values observed in the CRC and those predicted by HMR was assessed with R2 (0.995), root mean square error (10.0 g/d), and Lin's concordance correlation coefficient (0.961). Overall, the predictions made by the HMR model appeared to reflect individual MPR levels and variation between cows as well as the standard analytical approach taken by scientists with CRC data.

The use of a robot cleaner for manure removal improves housing conditions for dairy cows in the face of labor shortages. However, current robot cleaners follow programmed fixed routes without considering the dynamic behaviors of cows. This cleaning approach is less efficient and leads to more cow-robot encounters or collisions, thus affecting animal welfare. To address these issues, this paper (1) developed heatmap models for cow locations and defecation behaviors; (2) proposed a dynamic path planning approach for the manure robot cleaner using Grid-based Reinforcement Learning; (3) incorporated cow location information and defecation behavior into the path planning process; (4) compared the performance of the proposed approach with two different cleaning methods: the current fixed programmed cleaning in practice and the ideal path produced by simulated annealing for traveling salesman problem. The simulations mimic the situation in a barn at Dairy Campus of Wageningen Livestock Research located in Leeuwarden (the Netherlands). Obviously, the best performance was achieved when the route was executed without cows present, resulting in no cow-robot collision. However, with cows present, the proposed dynamic path planning strategy achieved a 67.6% reduction in cow-robot encounters while maintaining 85.4% of the cleaning performance compared to the current programmed fixed routes. Compared to the ideal path produced by simulated annealing for traveling salesman problem, the proposed dynamic path planning approach achieved 5% better cleaning performance, at the cost of 25% more cow-robot encounters due to its longer working path. We conclude the proposed grid-based Reinforcement Learning solution for manure robots in barns cleaned most efficient with the least interference with cow traffic.

This paper addresses the challenge of safe stabilization, ensuring the system state reach the origin while avoiding unsafe regions. Existing approaches relying on smooth Lyapunov barrier functions often fail to guarantee a feasible controller. To overcome this limitation, we introduce the nonsmooth Control Lyapunov Barrier Function (NCLBF), which ensures the existence of a safe and stabilizing controller. We provide a systematic framework for designing NCLBF and feedback control strategies to achieve safe stabilization in the presence of multiple bounded unsafe regions. Theoretical analysis and simulations of both linear and nonlinear systems demonstrate the effectiveness and superiority of our approach compared to the existing smooth functions method.

Source Segregation (SS) is a novel strategy in dairy housing that can reduce emissions and separate organic matter and nutrients more efficiently than traditional slurry solid-liquid separation. The anaerobic digestion (AD) yield of the SS fractions, however, is unknown. We aimed at unveiling the biomethane yield of these fractions by conducting AD experiments under different configurations: batch, continuous feeding, and fed-batch. In the batch test, the solid (SF) and liquid fraction (LF) from the SS system, a slurry collected from the pit (CS), and a self-made slurry (MF) were used as substrates. The results showed that the specific CH4 yields of the SF and MF were in same range and both higher than the CS. We concluded that SS can increase the CH4 yield of dairy excreta mainly by reducing losses in the animal house. The SF and MF were then compared in a continuously-fed thermophilic test, where SF had a higher specific (174 compared to 105 NL kg-1 VS) and volumetric (12.2 compared to 9.9 NL CH4 kg-1 excreta) yields. We concluded that the SF can effectively substitute slurry in AD without compromising the yield, possibly increasing economic viability by reducing transport costs and reactor size. Further, SF produced 356 NL CH4 kg-1 VS and a digestate with 1.8% lower dry matter in the fed-batch as compared to continuous feeding. Continuously stirred fed-batch can thus increase the CH4 yield of the SF and reduce the DM of its digestate potentially contributing to lower emissions in storage and field application.

Addressing heat stress in dairy farming is a substantial challenge, and there is an increasing need for efficient cooling systems, even in regions with moderate climates. Accurately predicting the efficacy of diverse cooling options under different climatic conditions is crucial for reducing heat stress in modern high-producing dairy cows, aligning with sustainability goals. This study assessed the effectiveness and feasibility of different cooling measures, including fans, sprinklers with fans, and evaporative air cooling, using a dynamic thermoregulatory model. This 3-node dynamic model was developed based on recent animal data simulating the processes of dairy cows' physiological regulation and heat dissipation under various environmental conditions. The cooling methods were based on two principles: enhancing heat loss from cows using fans with/without sprinklers; lowering the ambient temperature by evaporative air cooling. The predicted results were discussed and partly validated using the experimental data from the literature. The predictions indicated that fan cooling alone was effective in ambient temperatures below 26 degrees C, while higher temperatures required a combination of fans and sprinklers for effective heat stress alleviation. Consideration of individual cow characteristics and environmental factors, including fan speed and wetting area, is crucial for optimal cooling. In regions with high relative humidity, evaporative air cooling could be counterproductive to some extent. The model's predictions largely aligned with experimental data, demonstrating its capability to forecast cooling effects under various climatic conditions. Future model improvements included refining calculations for water holding capacity, wetted skin area, and dry time, depending on the influence of spraying time and rate.

Laying hen activities in modern intensive housing systems can dramatically influence the policies needed for the optimal management of such systems. Intermittent monitoring of different behaviors during daytime cannot provide a good overview, since daily behaviors are not equally distributed over the day. This paper investigates the application of deep learning technology in the automatic recognition of laying hen behaviors equipped with body-worn inertial measurement unit (IMU) modules in poultry systems. Motivated by the human activity recognition literature, a sophisticated preprocessing method is tailored on the time-series data of IMU, transforming it into the form of so-called activity images to be recognized by the deep learning models. The diverse range of behaviors a laying hen can exhibit are categorized into three classes: low-, medium-, and high-intensity activities, and various recognition models are trained to recognize these behaviors in real-time. Several ablation studies are conducted to assess the efficacy and robustness of the developed models against variations and limitations common for an in situ practical implementation. Overall, the best trained model on the full-feature acquired data achieves a mean accuracy of almost 100%, where the whole process of inference by the model takes less than 30 milliseconds. The results suggest that the application of deep learning technology for activity recognition of individual hens has the potential to accurately aid successful management of modern poultry systems.

Using portable accumulation chambers (PAC) is an attractive approach to recording methane (CH4) production of small ruminants. Mass flow controllers (MFC), for their part, are an effective way of validating PAC measurements, as they allow to simulate methane production rates freely of extraneous factors. The present study describes a series of tests carried out to evaluate the accuracy and precision of methane mass recordings of eight PAC against known CH4 masses released by a MFC. Across the tested range, the PAC were able to recover between 67.6 and 74.5% of the true CH4 released. No significant differences were detected between the different PAC, but a statistically significant linear shift was detected over the mass range. Therefore, PAC as currently used are not well suited for applications looking at absolute production levels requiring high absolute accuracy. If recovery tests are conducted regularly across the measurement range, a calibration factor could be generated to correct for the inaccuracy. These PAC were however well suited for investigating relative differences between animals, e.g. ranking animals to compare differences in CH4 production between breeds. The methane recordings made by these chambers were highly precise, with low coefficients of variation between replicates (0.0-2.4%) and high repeatability (>0.99). Furthermore, the correlation between released and recorded CH4 masses was very strong and positive (R > 0.99). Overall, the mass recovery test presented here provides a feasible method for harmonizing methane monitoring procedures using PAC between research groups, thereby improving joint efforts aimed at mitigating greenhouse gas production in small ruminants.

CONTEXT: Pests and pathogens can have great impacts on agricultural production. The use of plant protection products (PPPs) is a main preventive and curative management method in conventional farming today worldwide. However, increasing evidence shows the negative effects of PPPs on the environment and human health. New, especially preventive, approaches are needed and a promising option for developing these is the redesign of current agricultural systems, in which knowledge about the system and biological processes is integrated. Yet frameworks that can guide this process are not widely used.OBJECTIVE: This study aims to demonstrate the use of an innovative and systematic approach to the development of design concepts for pest and disease management without the use of PPPs, using Phytophthora infestans in potatoes as a case study. METHODS: The Reflexive Interactive Design method was applied to define design goals, requirements and key functions of the innovative design. These definitions were based on the needs of relevant stakeholders and the biology of the crop and disease. A range of strategies were researched that could effectively limit pathogen transmission and disease severity by interrupting the pathogen's life cycle.RESULTS AND CONCLUSIONS: The proposed design concept focuses on a combination of more traditional aspects like providing farmers with uninfected starting material and resistant varieties with novel approaches like increasing canopy porosity through intercropping with onions and preventing the spread of inoculum both into and within the field with a combination of host dilution and barriers. This research demonstrates how the Reflexive Interactive Design method can be used to develop innovative solutions for crop protection that go beyond current solution sets.SIGNIFICANCE: The systematic approach backed by literature provided the compass for the search for new and innovative solutions with strong focus on prevention. The design concept acts as a promising starting point to be further elaborated with stakeholders while effectiveness of the final design concept needs to be tested under real life conditions.

The main objectives of this review were to: (1) review different methods/techniques to assess gaseous N-losses from manure (2) review N-gaps, attributed to dinitrogen loss as the difference between directly measured N compounds summed as total N loss and indirectly measured N loss through a mass balance in livestock manure systems, and (3) provide approaches to close the N-gap. In literature, N-gaps run up to 80% of total N loss, this undermines N emission assessments and leaves a huge part of the emission unexplained. However, studies that measure N-gaps are scarcely available or are limited in their evaluation, hence more study is needed. Three approaches are introduced to research N-gaps: (1) measure N2 through a suggested Gas Flow Soil Core (GFSC) technique and compare the sum of all measured N losses with the indirect method, (2) assume N2 loss as being the N-gap and (3) include N2 as an estimate based on ratios from literature. In a hypothetical example for poultry manure, assumed values for measurement error of 50% and variance due to physical differences between the experimental units of 50% led to a total standard deviation of 131% in the N-gap. Variance of N-gap was reduced with 80% point when assuming 16 vessels compared to single vessel. Using literature-based-ratios to estimate losses of N compounds led to variation of N-gap from 0.06% initial N overestimation to 26% of initial N underestimation. Future research should address this variance and apply methods to measure N2 to close N-gaps.

The effects of ambient temperature (AT) on total evaporative water loss from dairy cows at different relative humidity (RH) and air velocity (AV) levels were studied. Twenty Holstein dairy cows with an average parity of 2.0 ± 0.7 and body weight of 687 ± 46 kg participated in the study. Two climate-controlled respiration chambers were used. The experimental indoor climate was programmed to follow a diurnal pattern with AT at night being 9°C lower than during the day. Night AT was gradually increased from 7 to 21°C and day AT was increased from 16°C to 30°C within an 8-d period, both with an incremental change of 2°C/d. The effect of 3 RH levels with a diurnal pattern were studied as well, with low values during the day and high values during the night: low (day, 30%; night, 50%), medium (day, 45%; night, 70%), and high (day, 60%; night, 90%). The effects of AV were studied during the daytime at 3 levels: no fan (0.1 m/s), fan at medium speed (1.0 m/s), and fan at high speed (1.5 m/s). The medium and high AV levels were only combined with medium RH. In total, there were 5 treatments with 4 replicates each. The animals had free access to feed and water. Based on the water balance principle inside the respiration chambers, the total evaporative water loss from dairy cows at a daily level was quantified by measuring the mass of water in the incoming and outgoing air, condensed water, added water from a humidifier, and evaporative water from a wet floor, drinking bowl, manure reservoir, and water bucket. Water evaporation from a sample skin area was measured with a ventilated skin box, and water evaporation, through respiration with a face mask. The results show that RH/AV levels had no significant effect on total evaporative water loss, whereas the interaction effect between RH/AV with AT was significant. Cows at a high RH had a tendency for a lower increasing rate of evaporative water loss compared with cows at a low RH (0.61 vs. 0.79 kg/d per 1°C increase of AT). Cows at medium and high AV levels had a greater increasing rate than cows at low AV (0.91 and 0.95 vs. 0.71 kg/d per 1°C increase of AT, respectively). The increase of evaporative heat loss from dairy cows was mainly a result of the increase in evaporation (of sweat) from the skin. The skin water evaporation determined with the water balance method (less evaporation from respiration) and the ventilated skin box method showed no significant difference. The implication of this study is that cows at a high AT depend mainly on evaporative cooling from the skin. The ventilated skin box method, measuring only a small part of the skin during a short period during the day, can be a convenient and accurate way to determine the total cutaneous evaporative water loss from cows.

Sustainability transitions research increasingly engages with agency and individual actor perspectives to explain complex system change. This paper introduces the spheres of transformation framework to study how and why 21 Dutch farmers, interviewed in the winter of 2020/2021, transform their business models towards sustainability. This framework is composed of three spheres: the personal (values and worldviews), the political (institutions), and the practical (everyday outcomes). Our results show that the interactions between spheres harbour the greatest potential for transformation as well as the greatest barriers, especially when all three spheres intersect. We furthermore identify individual actors' personal characteristics that are significant in transformations. We conclude that the spheres of transformation framework is a suitable middle-range framework for the study of agency and behaviour in sustainability transitions that bridges between local and global transition models, and that policymakers and researchers should consider all three spheres when engaging individual actors in efforts to make sociotechnical systems more sustainable.

[This corrects the article DOI: 10.3168/jdsc.2021-0165.].

Inaccessibility of veterinary and livestock extension services, and shortages of labour and forage could potentially impact the welfare of yaks (Bos grunniens) in Bhutan. The objective of this study was to assess practices relating to the welfare and management of free-ranging yaks in Bhutan and explore variations between different yak-farming regions. We interviewed herders and observed the behaviour and health status of their animals, using an adaptation of the Welfare Quality® protocol, in three yak-farming regions (east, central and west) of Bhutan between October 2018 and January 2019. In total, for 567 cows and 549 calves, integumentary condition, body cleanliness, ocular and nasal discharge, diarrhoea, signs of damage, and gait were scored. In addition, we assessed 324 cows and 272 calves for avoidance distance and examined 324 cows for subclinical mastitis. The behaviour of the herds was observed in six consecutive 20-min blocks with each block divided into two stages. The first stage (5 min) consisted of counting the number of animals eating, lying down, standing idle and walking. The second stage (15 min) consisted of counting the number of events of agonistic, allogrooming, flehming, self-licking, rubbing/scratching and playing behaviour. Avoidance distance differed between regions for calves, but not for lactating cows. Integumentary lesions, dirty body areas, nasal discharge, ocular discharge, signs of diarrhoea, subclinical mastitis and lameness were virtually absent. A few instances of agonistic behaviour (6% of all counted behavioural events) and flehming behaviour (5% of all counted behavioural events) were observed. Yaks in the central and western regions exhibited more scratching and rubbing behaviour than those in the eastern region. Herders perform a variety of painful management practices (castration, ear tagging, nasal septum piercing) without analgesia, which is a prominent welfare issue. Furthermore, mortality among yaks is relatively high and water sources often dirty, creating a health risk. Nevertheless, the welfare status of yaks living in various regions of Bhutan was assessed as good at the time of visit.