Sustainability transitions in consumption-production systems.

The need for faster and deeper transitions toward more sustainable development pathways is now widely recognized. How to meet that need has been at the center of a growing body of academic research and real-world policy implementation. This paper presents our perspective on some of the most powerful insights that have emerged from this ongoing work. In particular, we highlight insights on how sustainability transitions can be usefully conceptualized, how they come about and evolve, and how they can be shaped and guided through deliberate policy interventions. Throughout the paper, we also highlight some of the many how questions that remain unresolved and on which progress would be especially helpful for the pursuit of sustainable development. Our approach to these "how" questions on sustainability transitions draws on two strands of solution-driven research and policy advice: one emerging from studies of how human societies interact with nature and the other emerging from studies of how those societies interact with their technologies. Consumption-production systems have been a focus of extensive work in both strands. To help build bridges between them, we recently brought together a cross-section of relevant scholars for a PNAS Special Feature on "Sustainability transitions in consumption-production systems." Their contributions are summarized in a companion paper we have written to introduce the Special Feature [F. W. Geels, F. Kern, W. C. Clark, Proc. Natl. Acad. Sci. U.S.A. (2023)]. We draw on that work in the Perspective we present here as well as our reading of the relevant literatures.

Recent Progress in Modular Electrochemical Synthesis of Hydrogen and High-Value-added Chemicals based on Solid Redox Mediator.

Electrochemical synthesis of H2 and high-value-added chemicals is an efficient and cost-effective approach that can be powered using renewable electricity. Compared to a conventional electrochemical production system, the modular electrochemical production system (MEPS) based on a solid redox mediator (SRM) can separate the anodic and cathodic reactions in time and space. The MEPS can avoid the use of membranes and formation of useless products, as well as eliminate the mutual dependence of production rates at anode and cathode. The SRM can temporarily store or release electrons and ions to pair with cathodic and anodic reactions, respectively, in MEPS. Designing of SRMs with large charge capacity and good cyclability is of great significance for constructing a high-performance MEPS. This work summarizes the design principles, recent advances in MEPS based on SRM, and application in redox flow cells. Moreover, structure design strategies as well as in situ characterization techniques and theoretical calculations for SRM is also proposed. It is expected to promote the vigorous development of MEPS based on SRM. Finally, the challenges and perspectives of MEPS based on SRM are discussed.

Addressing the safety of new food sources and production systems.

New food sources and production systems (NFPS) are garnering much attention, driven by international trade, changing consumer preferences, potential sustainability benefits, and innovations in climate-resilient food production systems. However, NFPS can introduce new challenges for food safety agencies and food manufacturers. Most food safety hazards linked to new foods have been identified in traditional foods. However, there can be some food safety challenges that are unique to new foods. New food ingredients, inputs, and processes can introduce unexpected contaminants. To realize the full potential of NFPS, there is a need for stakeholders from governments, the food industry, and the research community to collectively work to address and communicate the safety of NFPS products. This review outlines known food safety hazards associated with select NFPS products on the market, namely, plant-derived proteins, seaweeds, jellyfish, insects, microbial proteins, as well as foods derived from cell-based food production, precision fermentation, vertical farming, and 3D food printing. We identify common elements in emerging NFPS regulatory frameworks in various countries/regions. Furthermore, we highlight current efforts in harmonization of terminologies, use of recent scientific tools to fill in food safety knowledge gaps, and international multi-stakeholder collaborations to tackle safety challenges. Although there cannot be a one-size-fits-all approach when it comes to the regulatory oversight for ensuring the safety of NFPS, there is a need to develop consensus-based structured protocols or workflows among stakeholders to facilitate comprehensive, robust, and internationally harmonized approaches. These efforts increase consumers' confidence in the safety of new foods and contribute toward fair practices in the international trade of such foods.

Partial budget analysis of laying hens' transition from cages to production systems of improved welfare: a case study in Greece.

1. In recent years, public concern regarding animal welfare has increased while, at the same time, cage systems for animal production have been greatly criticised by EU citizens. In addition, large food retailers promote food products that are made with eggs produced in cage-free systems.2. The objective of this study was to evaluate the economic viability of the transition of laying hens' to production systems; from conventional to alternative systems with improved welfare. Three independent scenarios were assumed as follows: transition from enriched colony cages to barn (S1), transition from barn to free-range (S2), and transition from free-range to organic (S3). Economic assessments of each transition was applied to a model farm in Greece with 12 000 hens, through partial budget analysis and net benefits and costs were estimated.3. The results showed a positive economic impact in all transitions to a production system of improved animal welfare (€12,044 in S1, €18,957 in S2 and €7,977 in S3) which indicated that they are economically sustainable. In all scenarios, unit cost increased by 19% in S1, 12% in S2, and 85% in S3.4. In conclusion, transitioning towards improved animal welfare production systems in laying hen farms could be an economically viable option for egg producers in compliance with societal demands and market trends.

Surveillance and risk assessment for early detection of emerging infectious diseases in livestock.

Those who work in the area of surveillance and prevention of emerging infectious diseases (EIDs) face a challenge in accurately predicting where infection will occur and who (or what) it will affect. Establishing surveillance and control programmes for EIDs requires substantial and long-term commitment of resources that are limited in nature. This contrasts with the unquantifiable number of possible zoonotic and non-zoonotic infectious diseases that may emerge, even when the focus is restricted to diseases involving livestock. Such diseases may emerge from many combinations of, and changes in, host species, production systems, environments/habitats and pathogen types. Given these multiple elements, risk prioritisation frameworks should be used more widely to support decision-making and resource allocation for surveillance. In this paper, the authors use recent examples of EID events in livestock to review surveillance approaches for the early detection of EIDs, and highlight the need for surveillance programmes to be informed and prioritised by regularly updated risk assessment frameworks. They conclude by discussing some unmet needs in risk assessment practices for EIDs, and the need for improved coordination in global infectious disease surveillance.

Les personnes travaillant dans le domaine de la surveillance et de la prévention des maladies infectieuses émergentes (MIE) sont confrontées à la difficulté de prédire avec exactitude le lieu d'émergence d'une maladie, ainsi que l'espèce, le système ou le site affectés. La mise en place de programmes de surveillance et de lutte contre les MIE exige une mobilisation conséquente et durable de ressources nécessairement limitées. Par contraste, le nombre des maladies infectieuses zoonotiques et non zoonotiques pouvant se déclarer est impossible à quantifier, même si l'on s'en tient aux seules maladies affectant les animaux d'élevage. Ces maladies surviennent à la faveur des nombreuses et diverses configurations, associations ou modifications qui peuvent se produire parmi les espèces hôtes, les systèmes de production, les environnements ou habitats et les types d'agents pathogènes. Compte tenu de la multiplicité de ces éléments, il devrait être fait plus largement appel à des cadres de priorisation du risque afin de soutenir les processus de prise de décision et d'allocation des ressources en matière de surveillance. Les auteurs s'appuient sur des exemples récents d'événements liés à des MIE pour faire le point sur les méthodes de surveillance appliquées pour la détection précoce de ces maladies et soulignent l'importance de documenter et de prioriser les programmes de surveillance en procédant à des mises à jour régulières des cadres utilisés pour l'évaluation du risque. Ils concluent en évoquant certains aspects importants que les pratiques actuelles d'évaluation du risque ne permettent pas de couvrir lorsqu'il s'agit de MIE, ainsi que l'importance d'améliorer la coordination de la surveillance des maladies infectieuses au niveau mondial.

Cuantos trabajan en el ámbito de la vigilancia y la prevención de enfermedades infecciosas emergentes (EIE) tienen dificultades para predecir con precisión dónde va a surgir y a quién (o qué) afectará una infección. La instauración de programas de vigilancia y control de EIE exige una inversión sustancial y duradera de recursos que por definición son escasos, sobre todo teniendo en cuenta el número incalculable de enfermedades infecciosas zoonóticas y no zoonóticas que pueden aparecer, aun considerando solo aquellas que afectan al ganado. Este tipo de enfermedades pueden surgir como resultado de muchas combinaciones distintas de especie hospedadora, sistema productivo, medio/hábitat y tipo de patógeno o por efecto de cambios que se den en cualquiera de estos elementos. En vista de la multiplicidad de factores que concurren, convendría emplear de modo más generalizado un sistema de jerarquización de los riesgos en el cual fundamentar las decisiones de vigilancia y la distribución de los recursos destinados a ella. Los autores, valiéndose de ejemplos recientes de episodios infecciosos emergentes que afectaron al ganado, pasan revista a distintos métodos de vigilancia para la detección temprana de EIE y recalcan que los programas de vigilancia deben reposar en procedimientos de determinación del riesgo periódicamente actualizados y en las prioridades fijadas a partir de estos procedimientos. Por último, los autores se detienen en algunas necesidades desatendidas en la praxis de la determinación del riesgo de EIE y en la necesidad de una mejor coordinación de la vigilancia mundial de las enfermedades infecciosas.

Decentralized Real-Time Anomaly Detection in Cyber-Physical Production Systems under Industry Constraints.

Anomaly detection is essential for realizing modern and secure cyber-physical production systems. By detecting anomalies, there is the possibility to recognize, react early, and in the best case, fix the anomaly to prevent the rise or the carryover of a failure throughout the entire manufacture. While current centralized methods demonstrate good detection abilities, they do not consider the limitations of industrial setups. To address all these constraints, in this study, we introduce an unsupervised, decentralized, and real-time process anomaly detection concept for cyber-physical production systems. We employ several 1D convolutional autoencoders in a sliding window approach to achieve adequate prediction performance and fulfill real-time requirements. To increase the flexibility and meet communication interface and processing constraints in typical cyber-physical production systems, we decentralize the execution of the anomaly detection into each separate cyber-physical system. The installation is fully automated, and no expert knowledge is needed to tackle data-driven limitations. The concept is evaluated in a real industrial cyber-physical production system. The test result confirms that the presented concept can be successfully applied to detect anomalies in all separate processes of each cyber-physical system. Therefore, the concept is promising for decentralized anomaly detection in cyber-physical production systems.

Estimating nutrient concentrations and uptake in rice grain in sub-Saharan Africa using linear mixed-effects regression.

Context or problem: Quantification of nutrient concentrations in rice grain is essential for evaluating nutrient uptake, use efficiency, and balance to develop fertilizer recommendation guidelines. Accurate estimation of nutrient concentrations without relying on plant laboratory analysis is needed in sub-Saharan Africa (SSA), where farmers do not generally have access to laboratories. Objective or research question: The objectives are to 1) examine if the concentrations of macro- (N, P, K, Ca, Mg, S) and micronutrients (Fe, Mn, B, Cu) in rice grain can be estimated using agro-ecological zones (AEZ), production systems, soil properties, and mineral fertilizer application (N, P, and K) rates as predictor variables, and 2) to identify if nutrient uptakes estimated by best-fitted models with above variables provide improved prediction of actual nutrient uptakes (predicted nutrient concentrations x grain yield) compared to average-based uptakes (average nutrient concentrations in SSA x grain yield). Methods: Cross-sectional data from 998 farmers' fields across 20 countries across 4 AEZs (arid/semi-arid, humid, sub-humid, and highlands) in SSA and 3 different production systems: irrigated lowland, rainfed lowland, and rainfed upland were used to test hypotheses of nutrient concentration being estimable with a set of predictor variables among above-cited factors using linear mixed-effects regression models. Results: All 10 nutrients were reasonably predicted [Nakagawa's R2 ranging from 0.27 (Ca) to 0.79 (B), and modeling efficiency ranging from 0.178 (Ca) to 0.584 (B)]. However, only the estimation of K and B concentrations was satisfactory with a modeling efficiency superior to 0.5. The country variable contributed more to the variation of concentrations of these nutrients than AEZ and production systems in our best predictive models. There were greater positive relationships (up to 0.18 of difference in correlation coefficient R) between actual nutrient uptakes and model estimation-based uptakes than those between actual nutrient uptakes and average-based uptakes. Nevertheless, only the estimation of B uptake had significant improvement among all nutrients investigated. Conclusions: Our findings suggest that with the exception of B associated with high model EF and an improved uptake over the average-based uptake, estimates of the macronutrient and micronutrient uptakes in rice grain can be obtained simply by using average concentrations of each nutrient at the regional scale for SSA. Implications: Further investigation of other factors such as the timing of fertilizer applications, rice variety, occurrence of drought periods, and atmospheric CO2 concentration is warranted for improved prediction accuracy of nutrient concentrations.

Amalgamation of Farmers' Bio-priming Knowledge in Integrated Nutrient Management for Sustainable Management of Red Cabbage Soil Under Middle Gangetic Plains, India.

Biotic stress management through bio-priming is a common practice among the farmers of the Indo-Gangetic Plains. However, this indigenous technology is less explored for the sustainable management of soil resources. Therefore, field-based experiments (2016-17 and 2017-18) were conducted in Varanasi to evaluate the combined effect of seedling bio-priming and fertilization on biochemical properties, microbiological properties, and fertility of red cabbage soil at harvest. Based on the farmers' fertilization practice, the recommended dose of fertilizers (RDF) of N:P2O5:K2O were applied @ 120:60:60 kg ha-1. Three compatible bio-agents, viz., Trichoderma harzianum, Pseudomonas fluorescens, and Bacillus subtilis were applied alone and in combination with 75% RDF. The effect of treatment combinations was also analyzed for carbon (C) mineralization by conducting an incubation experiment for 90 days. Bio-priming treatments recorded a higher richness of soil microflora and soil fertility than control and sole application of chemical fertilizers. Application of 75% RDF + T. harzianum + P. fluorescens resulted in highest urease and cellulase activities and soil organic C. Inclusion of dual-species bacterial consortium (P. fluorescens and B. subtilis) in integrated system resulted in highest dehydrogenase activity and available P. These priming agents also exhibited significantly higher CO2 fluxes and C mineralization in our incubation study. A microbial consortium of T. harzianum and B. subtilis increased the microbial biomass C and available K. Although application of triple-species consortium improved C mineralization in laboratory conditions, the positive effects lowered down in field conditions. As a bottom-up approach, customization of bio-priming technology among farmers will help in attaining the UN-Sustainable Development Goals.

Subsistence swine farming: seroprevalence and risk factors associated with Lawsonia intracellularis infection in the state of Minas Gerais Brazil in 2016.

Although it is considered an economically relevant and prevalent disease, little information is available on the epidemiology and risk factors of porcine proliferative enteropathy (PPE) in commercial pigs, and no publication is available on subsistence pig farming. The objectives of this study were to estimate the seroprevalence of L. intracellularis and identify associated risk factors in backyard pigs in the 12 mesoregions of the state of Minas Gerais, Brazil. Blood from pigs between 2 months and 6 years of age were sampled; an epidemiological questionnaire was applied to 288 properties investigated in 2016. Serum samples were tested for the presence of anti-L. intracellularis antibodies using an immunoperoxidase monolayer assay. The seroprevalence of L. intracellularis was 97.7% (CI 95%: 96.7-98.4), and there was no statistical difference among the prevalence of the sampled mesoregions. Only 3 of the 12 risk factors were significant when samples were analyzed from strongly seropositive animals (≥ 1:120) in a Poisson multivariate regression model. There was an interaction between properties in peri-urban areas and extensive production systems. This interaction demonstrated an increase in prevalence rates by 3.7 times (95%CI: 2.4-5.8). Properties close to dumps demonstrated an increase in prevalence rates by 2.2 times (95%CI: 0.99-4.8). In conclusion, anti-L. intracellularis antibodies were widely dispersed in subsistence pig farming's in Minas Gerais, indicating a wide circulation of the agent in this type of production system. The interactions of animals raised close to peri-urban areas, extensively, and close to landfills are risk factors for spread of PPE.

A Novel Analytical Modeling Approach for Quality Propagation of Transient Analysis of Serial Production Systems.

Production system modeling (PSM) for quality propagation involves mapping the principles between components and systems. While most existing studies focus on the steady-state analysis, the transient quality analysis remains largely unexplored. It is of significance to fully understand quality propagation, especially during transients, to shorten product changeover time, decrease quality loss, and improve quality. In this paper, a novel analytical PSM approach is established based on the Markov model, to explore product quality propagation for transient analysis of serial multi-stage production systems. The cascade property for quality propagation among correlated sequential stages was investigated, taking into account both the status of the current stage and the quality of the outputs from upstream stages. Closed-form formulae to evaluate transient quality performances of multi-stage systems were formulated, including the dynamics of system quality, settling time, and quality loss. An iterative procedure utilizing the aggregation technique is presented to approximate transient quality performance with computational efficiency and high accuracy. Moreover, system theoretic properties of quality measures were analyzed and the quality bottleneck identification method was investigated. In the case study, the modeling error was 0.36% and the calculation could clearly track system dynamics; quality bottleneck was identified to decrease the quality loss and facilitate continuous improvement. The experimental results illustrate the applicability of the proposed PSM approach.

Fault Handling in Industry 4.0: Definition, Process and Applications.

The increase of productivity and decrease of production loss is an important goal for modern industry to stay economically competitive. For that, efficient fault management and quick amendment of faults in production lines are needed. The prioritization of faults accelerates the fault amendment process but depends on preceding fault detection and classification. Data-driven methods can support fault management. The increasing usage of sensors to monitor machine health status in production lines leads to large amounts of data and high complexity. Machine Learning methods exploit this data to support fault management. This paper reviews literature that presents methods for several steps of fault management and provides an overview of requirements for fault handling and methods for fault detection, fault classification, and fault prioritization, as well as their prerequisites. The paper shows that fault prioritization lacks research about available learning methods and underlines that expert opinions are needed.

Variation of miRNA Content in Cow Raw Milk Depending on the Dairy Production System.

Pasture-based milk presents several advantages over milk from intensive industrial farming in terms of human health, the environment, animal welfare, and social aspects. This highlights the need for reliable methods to differentiate milk according to its origin on the market. Here, we explored whether miRNA profiles could serve as a marker of milk production systems. We compared levels of previously described miRNAs in milk from four production systems (altogether 112 milk samples): grazing, zero grazing, grass silage or corn silage. Total RNA was extracted from the fat phase, and miRNAs levels were quantified by real-time quantitative PCR. The levels of the miRNAs bta-miR-155 and bta-miR-103 were higher in the grazing system than in corn silage farms. The levels of bta-miR-532, bta-miR-103 and bta-miR-7863 showed differences between different farm managements. The miRNAs bta-miR-155 and bta-miR-103 were predicted to participate in common functions related to fat metabolism and fatty acid elongation. All four differentially expressed miRNAs were predicted to participate in transport, cell differentiation, and metabolism. These results suggest that the dairy production system influences the levels of some miRNAs in milk fat, and that bta-miR-155 and bta-miR-103 may be potential biomarkers to identify milk from pasture-managed systems.

Characterization of production systems and management practices of the cattle population in Zambia.

This study was an attempt at the analysis of the Zambia cattle population, its production systems and management practices using data collected during the 2017/2018 livestock and aquaculture census. The Public User Microdata Sample dataset provided by the Central Statistics Organization were analyzed using both qualitative and quantitative methods. Traditional system and free range grazing were found to be the main production system and feeding practices (97.2%). Despite large expanse of arable land, crop and fodder production, there was poor integration with cattle production system thus predisposing the animal to poor productivity due to inadequate nutrition. The management practices were found to be limiting and a hindrance to improved performance. The study revealed diverse cattle genetic resources comprising of local and exotic breeds, and their crosses at different genetic proportions. The local breed crosses were mainly directed at exotic beef breeds (and evidence of crosses with exotic dairy breeds) as smallholder farmers tend to improve on the production performances and productivity. Disease prevalence was high and had been an impediment to the growth of the cattle industry. It was clear that cattle production development must be anchored on a value chain system approach. Efforts aimed at capacity building should be targeted at the smallholder farmers with the bulk (93.5%) of the cattle population. This should include impacting farmers with husbandry skills through provision of elaborate livestock extension services aimed at integrating crops and fodder production in feeding practices, communal grazing management and adequate access to veterinary services to control disease prevalence. Value addition and market development would be helpful in unlocking the potential of the beef meat and milk products industry.

Potential for combining semantics and data analysis in the context of digital twins.

Modern production systems can benefit greatly from integrated and up-to-date digital representations. Their applications range from consistency checks during the design phase to smart manufacturing to maintenance support. Such digital twins not only require data, information and knowledge as inputs but can also be considered integrated models themselves. This paper provides an overview of data, information and knowledge typically available throughout the lifecycle of production systems and the variety of applications driven by data analysis, expert knowledge and knowledge-based systems. On this basis, we describe the potential for combining data analysis and knowledge-based systems in the context of production systems and describe two feasibility studies that demonstrate how knowledge-based systems can be created using data analysis. This article is part of the theme issue 'Towards symbiotic autonomous systems'.

Evolution of the sheep industry and genetic research in the United States: opportunities for convergence in the twenty-first century.

The continuous development and application of technology for genetic improvement is a key element for advancing sheep production in the United States. The US sheep industry has contracted over time but appears to be at a juncture where a greater utilization of technology can facilitate industry expansion to new markets and address inefficiencies in traditional production practices. Significant transformations include the increased value of lamb in relation to wool, and a downtrend in large-scale operations but a simultaneous rise in small flocks. Additionally, popularity of hair breeds not requiring shearing has surged, particularly in semi-arid and subtropical US environments. A variety of domestically developed composite breeds and newly established technological approaches are now widely available for the sheep industry to use as it navigates these ongoing transformations. These genetic resources can also address long-targeted areas of improvement such as growth, reproduction and parasite resistance. Moderate progress in production efficiency has been achieved by producers who have employed estimated breeding values, but widespread adoption of this technology has been limited. Genomic marker panels have recently shown promise for reducing disease susceptibility, identifying parentage and providing a foundation for marker-assisted selection. As the ovine genome is further explored and genomic assemblies are improved, the sheep research community in the USA can capitalize on new-found information to develop and apply genetic technologies to improve the production efficiency and profitability of the sheep industry.

Continuous Productivity Improvement Using IoE Data for Fault Monitoring: An Automotive Parts Production Line Case Study.

This paper presents a case study of continuous productivity improvement of an automotive parts production line using Internet of Everything (IoE) data for fault monitoring. Continuous productivity improvement denotes an iterative process of analyzing and updating the production line configuration for productivity improvement based on measured data. Analysis for continuous improvement of a production system requires a set of data (machine uptime, downtime, cycle-time) that are not typically monitored by a conventional fault monitoring system. Although productivity improvement is a critical aspect for a manufacturing site, not many production systems are equipped with a dedicated data recording system towards continuous improvement. In this paper, we study the problem of how to derive the dataset required for continuous improvement from the measurement by a conventional fault monitoring system. In particular, we provide a case study of an automotive parts production line. Based on the data measured by the existing fault monitoring system, we model the production system and derive the dataset required for continuous improvement. Our approach provides the expected amount of improvement to operation managers in a numerical manner to help them make a decision on whether they should modify the line configuration or not.

A Deep Learning Model for Predictive Maintenance in Cyber-Physical Production Systems Using LSTM Autoencoders.

Condition monitoring of industrial equipment, combined with machine learning algorithms, may significantly improve maintenance activities on modern cyber-physical production systems. However, data of proper quality and of adequate quantity, modeling both good operational conditions as well as abnormal situations throughout the operational lifecycle, are required. Nevertheless, this is difficult to acquire in a non-destructive approach. In this context, this study investigates an approach to enable a transition from preventive maintenance activities, that are scheduled at predetermined time intervals, into predictive ones. In order to enable such approaches in a cyber-physical production system, a deep learning algorithm is used, allowing for maintenance activities to be planned according to the actual operational status of the machine and not in advance. An autoencoder-based methodology is employed for classifying real-world machine and sensor data, into a set of condition-related labels. Real-world data collected from manufacturing operations are used for training and testing a prototype implementation of Long Short-Term Memory autoencoders for estimating the remaining useful life of the monitored equipment. Finally, the proposed approach is evaluated in a use case related to a steel industry production process.

Effect of production system and inhibitory potential of aroma volatiles on polyphenol oxidase and peroxidase activity in tomatoes.

BACKGROUND: Polyphenol oxidase (PPO) and peroxidase (POD) are key enzymes associated with shelf life and defense responses. Thus, the activity of PPO and POD enzymes is usually assessed to check the quality of food samples and to understand the physiological responses of plants to different stresses. However, the outcomes of PPO and POD activity assessment studies are highly dependent on assay conditions. Hence, in this study, we initially optimized PPO and POD extraction and high-throughput 96-well plates-based enzymatic activity assessment methods to evaluate the inhibitory potential of tomato volatile compounds. Later, we explored the effects of net-house and open-field growing on the PPO and POD activity in tomato fruits of eight cultivars. RESULTS: We found 150 mM of catechol and pH 7.0 were the optimal conditions for the maximum activity for the PPO assay. Conversely, 24 mM guaiacol with 12 mM H2 O2 and pH 6.0 was the best condition for the POD assay. Thermal inactivation studies confirmed that tomato POD is more resistant to heat than PPO. We found that the production systems had a considerable genotype-specific impact on tomato PPO and POD activity. Moreover, amongst the volatiles that were studied, ß-damascenone and d-limonene showed 50% PPO inhibition at 40 and 80 mM, respectively. CONCLUSION: The results of this study can be used to improve the shelf-life of fresh tomato fruit and its products. The findings also underscore the significance of PPO and POD enzymes as physiological trait markers in the tomato crop and fruit quality improvement programs. © 2020 Society of Chemical Industry.

Physiological response to heat stress and ingestive behavior of lactating Jersey cows in silvopasture and conventional pasture grazing systems in a Brazilian subtropical climate zone.

The aim of this study was to evaluate the physiological response to heat stress and ingestive behavior of Jersey cows in silvopasture and conventional pasture grazing systems. The experiment was carried out during the warm season, spanning spring, summer, and fall seasons in the Brazilian subtropical climate zone. Twelve lactating Jersey cows were observed in rotational grazing on Cynodon nlemfuensis Vanderyst and Panicum maximum Jacq. Treatments were composed of different grazing systems (silvopasture and conventional pasture). The silvopasture grazing system had eucalyptus trees (Eucalyptus grandis Hill ex Maiden) with an average height of ≈ 10 m and row spacing of 20 m. In the conventional pasture grazing system, there were no rows of eucalyptus and no other type of tree or structure to provide shade to the animals. During the summer and fall periods of evaluation, the silvopasture animals presented a lower respiratory rate, whereas during the spring and fall evaluation periods, these animals presented a lower rectal temperature. Cattle in the silvopasture showed longer grazing times at night (+21.65 min) and overall (+36.00 min) and remained lying down (ruminating and resting) for longer (+ 73.07 min) than conventional pasture grazing system animals. In addition, the animals in the silvopasture had a lower water intake (3.12 L/100 kg BW). The silvopasture grazing system improved the welfare of the grazing Jersey cows, as evidenced by the improvement in physiological response to heat stress, increased grazing time and decreased standing time (resting + ruminanting) when compared to cows in the conventional pasture grazing system.

Genetic parameters for test-day milk yield, lactation persistency, and fertility in low-, medium-, and high-production systems in Kenya.

Genetic parameters for test-day milk yield, lactation persistency, and age at first calving (as a fertility trait) were estimated for the first 4 lactations in multiple-breed dairy cows in low-, medium-, and high-production systems in Kenya. Data included 223,285 test-day milk yield records from 11,450 cows calving from 1990 to 2015 in 148 herds. A multivariate random regression model was used to estimate variance and covariance components. The fixed effects in the model included herd, year, and test month, and age as a covariate. The lactation profile over days in milk (DIM) was fitted as a cubic smoothing spline. Random effects included herd, year, and test month interaction effects, genetic group effects, and additive genetic and permanent environmental effects modeled with a cubic Legendre polynomial function. The residual variance was heterogeneous with 11 classes. Consequently, the variance components were varied over the lactation and with the production system. The estimated heritability for milk yield was lower in the low-production system (0.04-0.48) than in the medium- (0.22-0.59) and high-production (0.21-0 60) systems. The genetic correlations estimated between different DIM within lactations decreased as the time interval increased, becoming negative between the ends of the lactations in the low- and medium-production systems. Low (0.05) to medium (0.60) genetic correlations were estimated among first lactation test-day milk yields across the 3 production systems. Genetic correlations between the first lactation test-day milk yield and age at first calving ranged from 0.27 to 0.49, 0 to 0.81, and -0.08 to 0.27 in the low-, medium-, and high-production systems, respectively. Medium to high heritabilities (0.17-0.44) were estimated for persistency, with moderate to high (0.30-0.87) genetic correlations between 305-d milk yield and persistency. This indicates that genetic improvement in persistency would lead to increased milk yield. The low to medium genetic correlations between test-day milk yield between production systems indicate that sires may be re-ranked between production systems. Therefore, we conclude that sires should be selected based on a genetic evaluation within the target production system.