Measuring and modelling carbon stocks in rubber (Hevea brasiliensis) dominated landscapes in Subtropical China

Rubber plantation has been rapidly expanded in Montane Mainland South East Asia in past decades. Limited by long-term monitoring data availability, the impacts of environmental change on rubber trees carbon stock development still not fully understood. Against global warming background, in order to better facilitate regional forest management, we applied synergetic approach combining field survey and modelling tools to improve predictions of dynamic carbon stock changes. The trade-off analysis regarding to rubber carbon stock and latex production optimization was further discussed in view of sustainable rubber cultivation. The first study explored the impact of regional land-use changes on landscape carbon balances. The Naban River Watershed National Nature Reserve (NRWNNR), Xishuangbanna, China, was selected as a case study location. Carbon stocks were evaluated using the Rapid Carbon Stock Appraisal (RaCSA) method based on tree, plot, land use and landscape level assessments of carbon stocks, integrating field sampling with remote sensing and GIS technology. The results showed that rubber plantations had larger time-averaged carbon stocks than non-forest land use types (agricultural crops, bush and grassland) but much lower than natural forest. During 23 years (1989-2012), the whole landscape of the nature reserve (26574 ha) gained 0.644 Tg C. Despite rubber expansion, the reforestation activities conducted in NRWNNR were able to enhance the carbon stocks. Regional evaluation of the carbon sequestration potential of rubber trees depends largely on the selection of suitable allometric equations and the biomass-to-carbon conversion factor. The second study developed generic allometric equations for rubber trees, covering rotation lengths of 4-35 years, within elevation gradient of 621-1,127 m, and locally used rubber tree clones (GT1, PRIM600, Yunyan77-4) in mountainous South Western China. Allometric equations for aboveground biomass (AGB) estimations considering diameter at breast height (DBH), tree height, and wood density were superior to other equations. We also tested goodness of fit for the recently proposed pan-tropical forest model. The results displayed that prediction of AGB by the model calibrated with the harvested rubber tree biomass and wood density was more accurate than the results produced by the pan-tropical forest model adjusted to local conditions. The relationships between DBH and height and between DBH and biomass were influenced by tapping, therefore biomass and C stock calculations for rubber have to be done using species-specific allometric equations. Based on the analysis of environmental factors acting at the landscape level, we noticed that above- and belowground carbon stocks were mostly affected by stand age, soil clay content, aspect, and planting density. The results of this study provide reference for reliable carbon accounting in other rubber-cultivated regions. In the last study, we explored how rubber trees growth and production response to climate change and regional management strategies (cultivation elevation, planting density). We applied the process-based Land Use Change Impact Assessment tool (LUCIA) calibrated with detailed ground survey data to model tree biomass development and latex yield in rubber plantations at the tree, plot and landscape level. Model simulation showed that during a 40-year rotation, lowland rubber plantations (< 900m) grew quicker and had larger latex yield than highland rubber (&#8807;900m). High planting density rubber plantations showed 5% higher above ground biomass than those at low- and medium-planting density. The mean total biomass and cumulative latex yield per tree over 40 years increased by 28% and 48%, respectively, when climate change scenarios were modelled from baseline to highest CO2 emission scenario (RCP 8.5). The same trend of biomass and latex yield increase with climate change was observed at plot level. Denser plantations had larger biomass, but the cumulative latex production decreased dramatically. The spatially explicit output maps produced during modelling could help maximize carbon stock and latex production of regional rubber plantations. Overall, rubber-based system required for appropriate monitoring scale in both temporal aspect (daily-, monthly-, and yearly-level) and in spatial aspect (pixel-, land use-, watershed-, and landscape- level). The findings from present study highlighted the important application of ecological modelling tools in nature resources management. The lessons learned here could be applicable for other rubber-cultivated regions, by updating with site-specific environmental variables. The significant role of rubber tree not limited in its nature latex production, it also lies in its great carbon sequestration potential. Our results here provided entry point for future developing comprehensive climate change adaption and mitigation strategies in South East Asia. By making use of interdisplinary cooperation, the sustainable rubber cultivation in Great Mekong Regions could be well realized.In den vergangenen Jahrzehnten wurde der Kautschukanbau in den Bergregionen des südostasiatischen Festlandes rasch ausgebaut. Die Auswirkungen von Umweltveränderungen auf die Entwicklung des Kohlenstoffbestandes von Kautschukbäumen sind durch die eingeschränkte Verfügbarkeit von Langzeit-Monitoring-Daten noch nicht vollständig geklärt. Vor dem Hintergrund der globalen Erwärmung und um die regionale Waldbewirtschaftung zu unterstützen, haben wir einen synergetischen Ansatz angewandt, der Feldmessungen und Modellierungswerkzeuge kombiniert, um die Vorhersage dynamischer Veränderungen der Kohlenstoffbestände zu verbessern. Die Kosten-Nutzen Abwägung für einen nachhaltigen Kautschukanbau bezüglich der Kautschuk-Kohlenstoffvorräte und der Optimierung der Latexproduktion wird im Weiteren diskutiert. Die erste Studie untersuchte die Auswirkungen regionaler Landnutzungsänderungen auf die Kohlenstoffbilanz der Landschaft. Das Naban River Watershed National Nature Reserve (NRWNNNR), Xishuangbanna, China, wurde als Fallstudienstandort ausgewählt. Die Bewertung der Kohlenstoffvorräte erfolgte mit der Rapid Carbon Stock Appraisal (RaCSA)-Methode. Diese basiert auf der Bewertung von Kohlenstoffvorräten auf dem Niveau von Bäumen, Grundstücken, Landnutzung und Landschaft, mit Einbindung von Feldprobennahme verbunden mit Fernerkundung und GIS-Technologie. Die Ergebnisse zeigten, dass Kautschukplantagen einen größeren zeitgemittelten Kohlenstoffvorrat hatten als nicht-forstliche Landnutzungsarten (Ackerland, Busch- und Grünland), aber viel weniger als natürliche Wälder. Während 23 Jahren (1989-2012) gewann das gesamte Gebiet des Naturschutzgebietes (26574 ha) 0,644 Tg C hinzu. Trotz Ausdehnung der Kautschukanbauflächen konnten die Aufforstungsaktivitäten in NRWNNR die Kohlenstoffvorräte erhöhen. Die regionale Bewertung des Kohlenstoffsequestrierungspotenzials von Kautschukbäumen hängt wesentlich von der Auswahl geeigneter allometrischer Gleichungen und des Biomasse-Kohlenstoff-Umwandlungsfaktors ab. Die zweite Studie entwickelte allgemeine allometrische Gleichungen für Kautschukbäume, basierend auf Daten aus Kautschukplantagen mit Umtriebszeiten von 4-35 Jahren, Höhenlagen von 621-1.127 m und lokal verwendeten Kautschukbaumklonen (GT1, PRIM600, Yunyan77-4) im bergigen Südwesten Chinas. Allometrische Gleichungen zur Berechnung der oberirdischen Biomasse (AGB), welche den Durchmesser in Brusthöhe (DBH), Baumhöhe und Holzdichte berücksichtigten, waren anderen Gleichungen überlegen. Wir haben auch die Anpassungsgüte des kürzlich vorgeschlagene pan-tropische Waldmodell getestet. Die Ergebnisse zeigten, dass die Vorhersage der AGB durch das mit der destruktiv bestimmten Biomasse und der Holzdichte kalibrierte Modell genauer war als die Ergebnisse des pan-tropischen Waldmodells, das an die lokalen Bedingungen angepasst wurde. Die Beziehungen zwischen DBH und Höhe, und DBH und Biomasse wurden durch die Anzapfung der Bäume beeinflusst. Aufgrund dessen müssen Biomasse- und C-Bestandsberechnungen für Kautschuk mit artspezifischen allometrischen Gleichungen durchgeführt werden. Basierend auf der Analyse von Umweltfaktoren, die auf Landschaftsebene wirken, stellten wir fest, dass die ober- und unterirdischen Kohlenstoffvorräte vor allem durch das Bestandsalter, den Tongehalt des Bodens, die Hanglage und die Pflanzdichte beeinflusst wurden. Die Ergebnisse dieser Studie liefern Anhaltspunkte für eine zuverlässige Kohlenstoffbilanzierung in anderen Kautschukanbaugebieten. In der letzten Studie haben wir untersucht, wie Kautschukbäume auf den Klimawandel und regionalen Managementstrategien (Anbauhöhe, Pflanzdichte) reagieren. Wir setzten das prozessbasierte Land Use Change Impact Assessment Tool (LUCIA) ein, das mit detaillierten Bodenuntersuchungsdaten kalibriert wurde, um die Entwicklung der Baumbiomasse und den Latexertrag in Kautschukplantagen auf Baum-, Parzelle- und Landschaftsebene zu modellieren. Die Modellsimulation zeigte, dass während einer 40-jährigen Rotationzeit die Flachland-Kautschukplantagen (< 900m) schneller wuchsen und eine höhere Latexausbeute hatten als die Hochland-Kautschukplantagen (&#8807;900m). Kautschukplantagen mit hoher Pflanzdichte zeigten eine um 5% höhere oberirdische Biomasse als solche mit niedriger und mittlerer Pflanzdichte. Der durchschnittliche Gesamtertrag an Biomasse und der kumulative Latexertrag pro Baum stieg in 40 Jahren um 28% bzw. 48%, wenn die Klimaszenarien vom Basisszenario bis zum höchsten CO2-Emissionsszenario (RCP 8. 5) durchsimuliert wurden. Dieser Trend der Zunahme der Biomasse- und Latexausbeute mit verstärktem Klimawandel wurde auch auf der Ebene der Parzelle beobachtet. Dichtere Plantagen hatten eine größere Biomasse, aber die kumulative Latexproduktion ging drastisch zurück. Die während der Modellierung erstellten räumlich expliziten Output-Karten könnten helfen, die Kohlenstoffvorräte und die Latexproduktion regionaler Kautschukplantagen zu maximieren. Allgemein ist für ein angemessenes Monitoring ein Kautschuk-basiertes System erforderlich, das sowohl in zeitlicher Hinsicht (Tages-, Monats- und Jahresebene) als auch in räumlicher Hinsicht (Pixel-, Landnutzungs-, Wassereinzugs- und Landschaftsebene) geeignet ist. Die Ergebnisse der vorliegenden Studie verdeutlichen die Bedeutung ökologischer Modellierungswerkzeuge im Naturressourcenmanagement. Die hier gemachten Erfahrungen könnten auch auf andere Kautschukanbaugebiete übertragen werden, indem sie mit standortspezifischen Umweltvariablen aktualisiert werden. Die bedeutende Rolle des Kautschukbaums ist nicht nur auf dieHerstellung von Naturlatex beschränkt, sondern liegt auch in seinem großen Potenzial zur Kohlenstoffbindung. Unsere Ergebnisse lieferen den Ausgangspunkt für die künftige Entwicklung umfassender Strategien zur Anpassung an den Klimawandel und zur Eindämmung des Klimawandels in Südostasien. Durch interdisziplinäre Zusammenarbeit könnte der nachhaltige Kautschukanbau in den Großen Mekong-Regionen realisiert werden

Agricultural carbon emission efficiency and agricultural practices: implications for balancing carbon emissions reduction and agricultural productivity increment

The current Ukraine War underlines the importance of grain self-sufficiency. After the adoption of the Paris Agreement, two major challenges developing countries are facing in the coming decades are increasing agricultural production to ensure food security and reducing carbon emissions (CE). The key to such an “environment-development dilemma” is to improve agricultural carbon emission efficiency (CEE). Using China as the study site, we systematically analyze the impacts of agricultural management activities on agricultural CEE from 1997 to 2019. Global and local Moran's I index tests provide evidence of a positive spatial dependence of agricultural CEE. Using the LISA cluster map, we observe that high CEE regions tend to be distributed together, dominated by environmental conditions. However, with the promotion of agricultural management activities, such a clustering pattern vanished. Our spatial Durbin model (SDM) estimation results indicate that there are significant nonlinear relationships between agricultural practices and agricultural CEE. While the consumption of fertilizers and pesticides has economies of scale effects, the deployment of agricultural machinery and irrigation have diseconomies of scale effects on local CEE. Based on the SDM results, the direct and indirect effect estimation results suggest that the significant direct and spillover effects of many practices on agricultural CEE have opposite nonlinear shapes, implying a more complicated situation in promoting these activities, as the positive regional effect of an agricultural activity might have a negative impact on adjacent regions. All the results indicate that local policymakers should carefully tailor agricultural development policies based on local environmental conditions

Almost Sure Asymptotical Adaptive Synchronization for Neutral-Type Neural Networks with Stochastic Perturbation and Markovian Switching

The problem of almost sure (a.s.) asymptotic adaptive synchronization for neutral-type neural networks with stochastic perturbation and Markovian switching is researched. Firstly, we proposed a new criterion of a.s. asymptotic stability for a general neutral-type stochastic differential equation which extends the existing results. Secondly, based upon this stability criterion, by making use of Lyapunov functional method and designing an adaptive controller, we obtained a condition of a.s. asymptotic adaptive synchronization for neutral-type neural networks with stochastic perturbation and Markovian switching. The synchronization condition is expressed as linear matrix inequality which can be easily solved by Matlab. Finally, we introduced a numerical example to illustrate the effectiveness of the method and result obtained in this paper

Testing Hateful Speeches against Policies

In the recent years, many software systems have adopted AI techniques, especially deep learning techniques. Due to their black-box nature, AI-based systems brought challenges to traceability, because AI system behaviors are based on models and data, whereas the requirements or policies are rules in the form of natural or programming language. To the best of our knowledge, there is a limited amount of studies on how AI and deep neural network-based systems behave against rule-based requirements/policies. This experience paper examines deep neural network behaviors against rule-based requirements described in natural language policies. In particular, we focus on a case study to check AI-based content moderation software against content moderation policies. First, using crowdsourcing, we collect natural language test cases which match each moderation policy, we name this dataset HateModerate; second, using the test cases in HateModerate, we test the failure rates of state-of-the-art hate speech detection software, and we find that these models have high failure rates for certain policies; finally, since manual labeling is costly, we further proposed an automated approach to augument HateModerate by finetuning OpenAI's large language models to automatically match new examples to policies. The dataset and code of this work can be found on our anonymous website: \url{https://sites.google.com/view/content-moderation-project}

Stochastic Synchronization of Neutral-Type Neural Networks with Multidelays Based on M

The problem of stochastic synchronization of neutral-type neural networks with multidelays based on M-matrix is researched. Firstly, we designed a control law of stochastic synchronization of the neural-type and multiple time-delays neural network. Secondly, by making use of Lyapunov functional and M-matrix method, we obtained a criterion under which the drive and response neutral-type multiple time-delays neural networks with stochastic disturbance and Markovian switching are stochastic synchronization. The synchronization condition is expressed as linear matrix inequality which can be easily solved by MATLAB. Finally, we introduced a numerical example to illustrate the effectiveness of the method and result obtained in this paper

Improved performance of the rechargeable hybrid aqueous battery at near full state-of-charge

The final publication is available at Elsevier via https://dx.doi.org/10.1016/j.electacta.2018.03.152 © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/For the first time, a green lignin/silica nanocomposite (LSC) is introduced to the rechargeable hybrid aqueous Zn/LiMn2O4 battery (ReHAB) as additive in the cathode formulation. Lignin acts as a key role to regulate and control the structure of LSC, intending to enhance the stability of the ReHAB by improving the float charge performance while maintaining other electrochemical performances of the battery. The lignin/silica nanocomposites (LSCs) are characterized by X-ray diffraction, scanning electron microscopy, surface area and porosimetry analyzer, and transmission electron microscopy. The results show that amorphous, uniform and mesoporous LSC-1 is prepared at the mass ratio of 1:2 of lignin to silica. LSC-1 used as the cathode additive improves the float charge performance of ReHAB via decreasing the float charge capacity by 57%. To compensate the loss of conductivity caused by LSC-1 and increase the capacity of the battery, graphene (G) is added. Compared to the reference battery, battery using the cathode containing 3 wt% combined additive of LSC-1 and G at mass ratio of 1:1, has 50% lower float charge capacity, higher rate performance and better cyclability. Up to a discharge capacity of 95 mAh g−1 is still obtained after 300 cycles of 100% depth-of-discharge.National Natural Science Foundation of China [21436004]Natural Science Foundation of Guangdong Province [2017A030308012]Positec Canada Ltd.Chinese Scholarship Council (CSC

Almost Sure Asymptotical Adaptive Synchronization for Neutral-Type Neural Networks with Stochastic Perturbation and Markovian Switching

The problem of almost sure (a.s.) asymptotic adaptive synchronization for neutral-type neural networks with stochastic perturbation and Markovian switching is researched. Firstly, we proposed a new criterion of a.s. asymptotic stability for a general neutral-type stochastic differential equation which extends the existing results. Secondly, based upon this stability criterion, by making use of Lyapunov functional method and designing an adaptive controller, we obtained a condition of a.s. asymptotic adaptive synchronization for neutral-type neural networks with stochastic perturbation and Markovian switching. The synchronization condition is expressed as linear matrix inequality which can be easily solved by Matlab. Finally, we introduced a numerical example to illustrate the effectiveness of the method and result obtained in this paper

A Quick and Parallel Analytical Method Based on Quantum Dots Labeling for ToRCH-Related Antibodies

Quantum dot is a special kind of nanomaterial composed of periodic groups of II–VI, III–V or IV–VI materials. Their high quantum yield, broad absorption with narrow photoluminescence spectra and high resistance to photobleaching, make them become a promising labeling substance in biological analysis. Here, we report a quick and parallel analytical method based on quantum dots for ToRCH-related antibodies including Toxoplasma gondii, Rubella virus, Cytomegalovirus and Herpes simplex virus type 1 (HSV1) and 2 (HSV2). Firstly, we fabricated the microarrays with the five kinds of ToRCH-related antigens and used CdTe quantum dots to label secondary antibody and then analyzed 100 specimens of randomly selected clinical sera from obstetric outpatients. The currently prevalent enzyme-linked immunosorbent assay (ELISA) kits were considered as “golden standard” for comparison. The results show that the quantum dots labeling-based ToRCH microarrays have comparable sensitivity and specificity with ELISA. Besides, the microarrays hold distinct advantages over ELISA test format in detection time, cost, operation and signal stability. Validated by the clinical assay, our quantum dots-based ToRCH microarrays have great potential in the detection of ToRCH-related pathogens