Plastic residues have become a serious environmental problem in the regions with intensive use of plastic mulching. Even though plastic mulch is widely used, the effects of macro- and micro- plastic residues on the soil-plant system and the agroecosystem are largely unknown. In this study, low density polyethylene and one type of starch-based biodegradable plastic mulch film were selected and used as examples of macro- and micro- sized plastic residues. A pot experiment was performed in a climate chamber to determine what effect mixing 1% concentration of residues of these plastics with sandy soil would have on wheat growth in the presence and absence of earthworms. The results showed that macro- and micro- plastic residues affected both above-ground and below-ground parts of the wheat plant during both vegetative and reproductive growth. The type of plastic mulch films used had a strong effect on wheat growth with the biodegradable plastic mulch showing stronger negative effects as compared to polyethylene. The presence of earthworms had an overall positive effect on the wheat growth and chiefly alleviated the impairments made by plastic residues.
Whereas domestication of farm animals has primarily focused on desired productivity traits, the intensification of livestock farming has highlighted the need for improving animal resilience, too. Animal resilience is a complex concept that encompasses the ability for an animal to recover from a particular disturbance. The concept includes resilience to disease, environmental resilience such as extreme and fluctuating climates, but also psychological resilience including stress resilience. Sensor-based data models enable prediction of livestock farming outcomes in response to varying behavioral, physiological, stress and affective states. The quantification of resilience post-disturbance, as well as estimating and predicting resilience pre-disturbance, is challenging. We present a review-based approach in exploring the sensor-data enabled indicators in the investigation of livestock resilience. We assess the intricacies of resilience of farm animals using conceptual, comprehensive, and integrated systems framework. We analyze progress in sensor methods and its possible use to assess various dynamic indicators of livestock resilience. With the rise of sensor-based technologies for livestock farming systems, accurate and sophisticated monitoring systems of animal resilience become more readily available. Wearable sensors, tracking systems, as well as automatic milking systems, provide a way to continuously collect large amounts of quantitative and qualitative data that aid the monitoring of not only health, productivity, and welfare aspects, but also resilience. Sensor-based technologies help breeding goals by contributing to the understanding of the complex, multidimensional framework of livestock resistance. Animal resilience is an essential trait that should be promoted to improve the sustainability of intensive livestock farming. Through digitalization of data collection, farmers can make better livestock management decisions by enhanced understanding of the indicators of environmental, health and psychological resilience, and will be able to predict degrading resilience.
Whereas domestication of farm animals has primarily focused on desired productivity traits, the intensification of livestock farming has highlighted the need for improving animal resilience, too. Animal resilience is a complex concept that encompasses the ability for an animal to recover from a particular disturbance. The concept includes resilience to disease, environmental resilience such as extreme and fluctuating climates, but also psychological resilience including stress resilience. Sensor-based data models enable prediction of livestock farming outcomes in response to varying behavioral, physiological, stress and affective states. The quantification of resilience post-disturbance, as well as estimating and predicting resilience pre-disturbance, is challenging. We present a review-based approach in exploring the sensor-data enabled indicators in the investigation of livestock resilience. We assess the intricacies of resilience of farm animals using conceptual, comprehensive, and integrated systems framework. We analyze progress in sensor methods and its possible use to assess various dynamic indicators of livestock resilience. With the rise of sensor-based technologies for livestock farming systems, accurate and sophisticated monitoring systems of animal resilience become more readily available. Wearable sensors, tracking systems, as well as automatic milking systems, provide a way to continuously collect large amounts of quantitative and qualitative data that aid the monitoring of not only health, productivity, and welfare aspects, but also resilience. Sensor-based technologies help breeding goals by contributing to the understanding of the complex, multidimensional framework of livestock resistance. Animal resilience is an essential trait that should be promoted to improve the sustainability of intensive livestock farming. Through digitalization of data collection, farmers can make better livestock management decisions by enhanced understanding of the indicators of environmental, health and psychological resilience, and will be able to predict degrading resilience.
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