Agroforestry—the practice of growing trees and crops in interacting combinations—is recognized worldwide as an integrated approach to sustainable land-use. It is estimated to be practiced over 1 billion hectares in developing countries, and to a lesser extent in the industrialized countries. Agroforestry systems (AFSs) are believed to have a higher potential to sequester carbon (C) because of their perceived ability for greater capture and utilization of growth resources (light, nutrients, and water) than single-species crop or pasture systems. The estimates of C stored in AFSs range from 0.29 to 15.21 Mg ha − 1  yr − 1 aboveground, and 30 to 300 Mg C ha − 1 up to 1-m depth in the soil. Recent studies under various AFSs in diverse ecological conditions showed that tree-based agricultural systems, compared to treeless systems, stored more C in deeper soil layers near the tree than away from the tree; higher soil organic carbon content was associated with higher species richness and tree density; and C3 plants (trees) contributed to more C in the silt- + clay-sized (

Carbon Sequestration in Agroforestry Systems

Forests play a key role in the carbon cycle as they store huge quantities of organic carbon, most of which is stored in soils, with a smaller part being held in vegetation. While the carbon storage capacity of forests is influenced by forestry, the long-term impacts of forest managers' decisions on soil organic carbon (SOC) remain unclear. Using a meta-analysis approach, we showed that conventional biomass harvests preserved the SOC of forests, unlike intensive harvests where logging residues were harvested to produce fuelwood. Conventional harvests caused a decrease in carbon storage in the forest floor, but when the whole soil profile was taken into account, we found that this loss in the forest floor was compensated by an accumulation of SOC in deeper soil layers. Conversely, we found that intensive harvests led to SOC losses in all layers of forest soils. We assessed the potential impact of intensive harvests on the carbon budget, focusing on managed European forests. Estimated carbon losses from forest soils suggested that intensive biomass harvests could constitute an important source of carbon transfer from forests to the atmosphere (142-497 Tg-C), partly neutralizing the role of a carbon sink played by forest soils.

/pdf/forest-soil-carbon-is-threatened-by-intensive-biomass-81vjxf7c36.pdf

Forest soil carbon is threatened by intensive biomass harvesting.

Large mammalian herbivores manifest a strong top-down control on ecosystems that can transform entire landscapes, but their impacts have not been reviewed in the context of terrestrial carbon storage. Here, we evaluate the effects of plant biomass consumption by large mammalian herbivores (>10 kg adult biomass), and the responses of ecosystems to these herbivores, on carbon stocks in temperate and tropical regions, and the Arctic. We calculate the difference in carbon stocks resulting from herbivore exclusion using the results of 108 studies from 52 vegetation types. Our estimates suggest that herbivores can reduce terrestrial above- and below-ground carbon stocks across vegetation types but reductions in carbon stocks may approach zero given sufficient periods of time for systems to respond to herbivory (i.e. decades). We estimate that if all large herbivores were removed from the vegetation types sampled in our review, increases in terrestrial carbon stocks would be up to three orders of magnitude less than many of the natural and human-influenced sources of carbon emissions. However, we lack estimates for the effects of herbivores on below-ground biomass and soil carbon levels in many regions, including those with high herbivore densities, and upwards revisions of our estimates may be necessary. Our results provide a starting point for a discussion on the magnitude of the effects of herbivory on the global carbon cycle, particularly given that large herbivores are common in many ecosystems. We suggest that herbivore removal might represent an important strategy towards increasing terrestrial carbon stocks at local and regional scales within specific vegetation types, since humans influence populations of most large mammals.

Carbon storage in terrestrial ecosystems: do browsing and grazing herbivores matter?

The effects of forest thinning on soil carbon stocks and dynamics: A meta-analysis

Aim of the study: To review and acknowledge the value of carbon sequestration by forest management in the Mediterranean area.Material and methods: We review the main effects of forest management by comparing the effects of silvicultural systems (even-aged vs. uneven-aged stands, coppice systems, agroforestry systems), silvicultural options (thinning, rotation period, species composition), afforestation, harvesting, fire impact or effects of shrub layer on carbon sequestration in the Mediterranean area.Main results: We illustrate as forest management can clearly improve forest carbon sequestration amounts. We conclude that forest management is an effective way to maintain and enhance high carbon sequestration rates in order to cope with climate change and provision of ecosystem services. We also think that although much effort has been put into this topic research, there are still certain gaps that must be dealt with to increase our scientific knowledge and in turn transfer this knowledge to forest practitioners in order to achieve sustainable management aimed at mitigating climate change.Research highlights: It is important to underline the importance of forests in the carbon cycle as this role can be enhanced by forest managers through sustainable forest management. The effects of different management options or disturbances can be critical as regards mitigating climate change. Understanding the effects of forest management is even more important in the Mediterranean area, given that the current high climatic variability together with historical human exploitation and disturbance events make this area more vulnerable to the effects of climate change

https://dialnet.unirioja.es/descarga/articulo/6240150.pdf

Forest management and carbon sequestration in the Mediterranean region: A review

Food is an essential need for human survival. Throughout history, food has been recognised as a crucial need for people in order to maintain good health as well as to treat illness. As with all living things, it is one of the most basic necessities that man has as well as those of all other living creatures. In a recent publication, it was said that an extremely affordable, robust, and biocompatible impedance sensor that serves as a fractional-order element has been created and may be used to distinguish milk and tainted milk. A complete study on milk adulteration includes more than 160 academic articles on the topic. A comprehensive study on milk adulteration is available online. Specifically, the goal of this research is to discover various types of milk adulterants, different approaches for detecting each kind of adulterant, as well as the health hazards associated with milk product adulteration. In the proposed project, the fractional-order element would be investigated for its potential use in the detection of milk adulteration. With this fractional-order element-based impedance sensor, you can distinguish between different types of contaminated milk and different types of faking it, which is quite useful in the detection and differentiation of fake and real milk. According to the researchers, they have created a low-cost, user-friendly instrumentation system for detecting milk adulteration. They hope to commercialise it soon. An automated sensing system for the detection of synthetic milk, based on a microcontroller, has been created in order to reduce the reliance on specialised labour and to improve efficiency. In order to model the sensor, the dipole layer capacitance at the interface of the impedance sensor immersed in milk and the contaminated milk must be taken into account throughout the modelling process. In this study, an electrical equivalent circuit is built, and the correctness of the circuit is shown by both theoretical and experimental investigation. The detection of milk adulteration is classified with the use of Recurrent Neural Networks, and the status is updated in the cloud server with the help of the Internet of Things and Recurrent Neural Networks. It is estimated that the proposed work will have an accuracy rate of 92.31 percent, a sensitivity rate of 75.23 percent, and a specificity rate of 90.12 percent, all of which are higher than the present rate.

/pdf/detection-of-adulteration-in-food-using-recurrent-neural-bsyi3g3q.pdf

Detection of Adulteration in Food Using Recurrent Neural Network with Internet of Things

In today’s scenario, mental stress is common among people. Stress can be defined as tension arising due to daily life’s emotional, psychological, and physical pressures. It has become a serious issue in the modern world as it affects not only our physical health but also our work performance, passion for work, and general attitude in daily life. Chronic exposure to stress exhibits physical symptoms such as aches, muscle tension, and dizziness. If not treated well, it paves the way toward severe health consequences which results in depression, cardiovascular diseases, sleep disorders, degradation in the immune system, and cancer. Stress can be detected at an early stage by measuring the variability in a few parameters. The virtual reality (VR) based IoT embedded system is designed to transfer data through the cloud platform. Electromyography (EMG), galvanic skin response, oxygen saturation level, and heart rate are used to detect stress. The statistical analysis has performed on the acquired VR sensor data and MATLAB software was used for the statistical analysis of EMG signals. The VR technology was utilized in this research for stress alleviation by revealing the human subjects to happy video and prior to that stress was induced in the human subjects by using illusion video. The convolution neural network was also employed in this research work for the classification of stress and non-stress states. The results reveal that VR technology with the aid of pleasant music or video clipping minimizes the stress level in human beings. 

Virtual Reality Sensor Based IoT Embedded System for Stress Diagnosis

Medical science in recent times has witnessed the large implications of AI-based IoT approaches that made the clinical process easier than before. However, effective IoT technologies can connect as well as exchange necessary clinical data with other healthcare systems and devices conducted across the vast Internet facilities. With the help of IoT-enabled big data processing technologies, physicians can measure accurate weight, blood pressure, and daily symptoms related to spreading breast cancer cases across the globe. Utilizing IoT is essential for providing proper medical assistance, treatment, and detection at the initial stages within the healthcare environment regulated by the facilities of the Internet of Things. The implementation of IoT-based big data processes food products for supporting the detection and prevention of breast cancer. The study supports in making a critical analysis on the role of IoT in the big data mainly in cancer detection and increasing the quality of food products. The study’s main scope is to employ IoT-enabled big data processing to aid in the identification of breast cancer. However, the research is mainly focused on studying the assistance offered to healthcare professionals and others in identifying the disease effectively. The overall research study is going to investigate the role of IoT in the early detection of breast cancer symptoms. A total of 20 women were studied and certain factors have been identified which are the early symptoms of breast cancer and can potentially cause breast cancer. These include age, family history, breast density, and breast temperature (independent variables). A dependent variable has been selected: probability of breast cancer occurrence. After that, linear regression analysis has been carried out to understand how the independent variables impact the dependent variable. Findings showed that age, family history of cancer, breast density, and breast temperature are some measurable factors for breast cancer detection. The work contributes to a critical investigation of the function of IoT in big data, specifically in cancer detection and improving food product quality. Age acceleration increases the risk of breast cancer development; breast temperature increases slightly during cancer formation, and breast density has a positive impact on cancer development. Lastly, this study has provided a future scope of using IoT in cancer detection and prevention.

/pdf/apprehending-the-effect-of-internet-of-things-iot-enables-2v6ekh7a.pdf

Apprehending the Effect of Internet of Things (IoT) Enables Big Data Processing through Multinetwork in Supporting High-Quality Food Products to Reduce Breast Cancer

Litter plays a vital role in the nutrient cycling of plantations and agroforests. Silvicultural interventions can alter litter production and decay rates, thereby varying nutrient fluxes. We evalua...

Does thinning affect litterfall, litter decomposition, and associated nutrient release in Acacia mangium stands of Kerala in peninsular India?

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Detection of Adulteration in Food Using Recurrent Neural Network with Internet of Things

Virtual Reality Sensor Based IoT Embedded System for Stress Diagnosis

Apprehending the Effect of Internet of Things (IoT) Enables Big Data Processing through Multinetwork in Supporting High-Quality Food Products to Reduce Breast Cancer

Does thinning affect litterfall, litter decomposition, and associated nutrient release in Acacia mangium stands of Kerala in peninsular India?