Soil native microbes and multi-species planting for restoring soil function in dryland rehabilitation

2020 ◽  
Author(s):  
Miriam Muñoz-Rojas ◽  
Todd E. Erickson ◽  
Amber Bateman ◽  
Angela M. Chilton ◽  
David J. Merritt
Keyword(s):  
Soil Carbon ◽  
Land Surface ◽  
Large Scale ◽  
Environmental Changes ◽  
Anthropogenic Impacts ◽  
Organic Amendments ◽  
Cost Effective ◽  
Field Trials ◽  
Design Strategies ◽  
Soil Function

<p>Global environmental changes and other anthropogenic impacts are rapidly transforming the structure and functioning of ecosystems worldwide. These changes are leading to soil degradation with an estimated 25 % of the global land surface being affected. The need to develop cost-effective large-scale solutions to restore disturbed landscapes becomes imperative to preserve biodiversity and achieve ecosystem functionality and sustainability. As part of a large-scale industry-academia partnership, we have developed a soil research program that aims to build knowledge and design strategies to restore degraded landscapes in Western Australia and other dryland regions worldwide. Within this program, a series of laboratory experiments, glasshouse studies, and field trials, have been conducted over the past six years to advance our knowledge on soil limitations and to provide solutions to enhance soil carbon levels and restore above and belowground biodiversity in restoration programs. These studies include (i) the analysis of the influence of multi-species planting on soil organic carbon and microbial activity and diversity (ii) the evaluation of soil physicochemical and microbiological indicators to assess functionality of restored soils in degraded semiarid ecosystems and (ii) the development of nature-based strategies based on bio-tools (e.g. inoculation of soil biocrust cyanobacteria) to increase soil carbon and enhance overall soil function. In this presentation we will highlight some key findings of these studies that include the benefits of combining diverse plant species and using native microbes and organic amendments for increasing soil carbon and promote soil function in reconstructed soil substrates. We will also discuss the potential applicability of these bio-technological approaches in landscape-scale restoration programs.</p>

Effective zinc mobilization to rice grains using rhizobacterial consortium

2020 ◽  
pp. 1-13
Author(s):  
Sachin Kumar Vaid ◽  
Prakash Chandra Srivastava ◽  
Satya Pratap Pachauri ◽  
Anita Sharma ◽  
Deepa Rawat ◽  
...  
Keyword(s):  
Phytic Acid ◽  
Low Income ◽  
Large Scale ◽  
Cost Effective ◽  
Field Trials ◽  
Rice Crop ◽  
Microbial Inoculation ◽  
Rice Grains ◽  
Food Grains ◽  
Set Up

Low Zn in staple food grains like rice is closely related to large scale Zn malnutrition in many countries of the World. Zinc biofortification of rice grains by some cost effective agronomic method is important for low income farmers. To explore the possibility of enhancing the bioavailability of Zn in rice grains besides higher yields of two cultivars, the combinations of varying Zn fertilizer doses with or without inoculation of rhizobacteria consortium under split plot design set up were evaluated in two years field trials. Microbial inoculation + 5 kg Zn ha-1 to I year rice crop resulted in the highest number of effective tillers, grain yields, Zn concentration and uptake in grains and straw and total Zn uptake in both years. Grain yield of rice during two years increased by 19.7-27.9 and 17.1-20.4 percent over control under treatments receiving microbial inoculation + 5 kg Zn ha-1 to I year rice and 5 kg Zn ha-1 alone to I year rice crop, respectively. The highest concentration of Zn (10.9-19.1 mg kg-1) and the lowest concentration of phytic acid (18.5-25.3 g kg-1) in dehulled rice grains were recorded with soil application of 5 kg Zn ha-1; however, the values were at par with those observed under microbial inoculation + 5 kg Zn ha-1 (12.0-17.0 mg Zn kg-1 and 19.2-26.9 g phytic acid kg-1). The percent utilization of soil applied Zn increased with microbial inoculation in both the years and it was relatively higher in NDR 359 as compared to PD 16.

scholarly journals A vertical representation of soil carbon in the JULES land surface scheme (vn4.3_permafrost) with a focus on permafrost regions

2017 ◽  
Vol 10 (2) ◽  
pp. 959-975 ◽  
Author(s):  
Eleanor J. Burke ◽  
Sarah E. Chadburn ◽  
Altug Ekici
Keyword(s):  
Soil Respiration ◽  
Soil Carbon ◽  
Land Surface ◽  
Large Scale ◽  
Soil Layer ◽  
Surface Model ◽  
Permafrost Region ◽  
Standard Version ◽  
Discretized Model ◽  
Permafrost Regions

Abstract. An improved representation of the carbon cycle in permafrost regions will enable more realistic projections of the future climate–carbon system. Currently JULES (the Joint UK Land Environment Simulator) – the land surface model of the UK Earth System Model (UKESM) – uses the standard four-pool RothC soil carbon model. This paper describes a new version of JULES (vn4.3_permafrost) in which the soil vertical dimension is added to the soil carbon model, with a set of four pools in every soil layer. The respiration rate in each soil layer depends on the temperature and moisture conditions in that layer. Cryoturbation/bioturbation processes, which transfer soil carbon between layers, are represented by diffusive mixing. The litter inputs and the soil respiration are both parametrized to decrease with increasing depth. The model now includes a tracer so that selected soil carbon can be labelled and tracked through a simulation. Simulations show an improvement in the large-scale horizontal and vertical distribution of soil carbon over the standard version of JULES (vn4.3). Like the standard version of JULES, the vertically discretized model is still unable to simulate enough soil carbon in the tundra regions. This is in part because JULES underestimates the plant productivity over the tundra, but also because not all of the processes relevant for the accumulation of permafrost carbon, such as peat development, are included in the model. In comparison with the standard model, the vertically discretized model shows a delay in the onset of soil respiration in the spring, resulting in an increased net uptake of carbon during this time. In order to provide a more suitable representation of permafrost carbon for quantifying the permafrost carbon feedback within UKESM, the deep soil carbon in the permafrost region (below 1 m) was initialized using the observed soil carbon. There is now a slight drift in the soil carbon ( <  0.018 % decade−1), but the change in simulated soil carbon over the 20th century, when there is little climate change, is comparable to the original vertically discretized model and significantly larger than the drift.

scholarly journals A vertical representation of soil carbon in the JULES land surface scheme with a focus on permafrost regions

2016 ◽  
Author(s):  
Eleanor J. Burke ◽  
Sarah E. Chadburn ◽  
Altug Ekici
Keyword(s):  
Soil Respiration ◽  
Soil Carbon ◽  
Land Surface ◽  
Large Scale ◽  
Soil Layer ◽  
Surface Model ◽  
Permafrost Region ◽  
Deep Soil ◽  
Standard Version ◽  
Permafrost Regions

Abstract. An improved representation of the carbon cycle in permafrost regions will enable more realistic projections of the future climate-carbon system. Currently JULES (the Joint UK Land Environment Simulator) – the land surface model of the UK Earth System Model (UKESM) – uses the standard 4-pool RothC soil carbon model. This paper describes a new version of JULES (vn4.3_permafrost) in which the soil vertical dimension is added to the soil carbon model, with a set of four pools in every soil layer. The respiration rate in each soil layer depends on the temperature and moisture conditions in that layer. Cryoturbation/bioturbation processes, which transfer soil carbon between layers, are represented by diffusive mixing. The litter inputs and the soil respiration are both parameterised to decrease with increasing depth. The model now includes a tracer so that selected soil carbon can be labelled and tracked through a simulation. Simulations show an improvement in the large-scale horizontal and vertical distribution of soil carbon over the standard version of JULES (vn4.3). Like the standard version of JULES, the vertically discretised model is still unable to simulate enough soil carbon in the tundra regions. This is in part because JULES underestimates the plant productivity over the tundra, but also because not all of the processes relevant for the accumulation of permafrost carbon are included in the model. In comparison with the standard model, the vertically discretised model shows a delay in the onset of soil respiration in the spring, resulting in an increased net uptake of carbon during this time. In order to provide a more suitable representation of permafrost carbon for quantifying the permafrost carbon feedback within UKESM, the deep soil carbon in the permafrost region (below 1 m) was initialised using the observed soil carbon. There is now a slight drift in the soil carbon (

scholarly journals The use of radiocarbon <sup>14</sup>C to constrain carbon dynamics in the soil module of the land surface model ORCHIDEE (SVN r5165)

2018 ◽  
Vol 11 (12) ◽  
pp. 4711-4726 ◽  
Author(s):  
Marwa Tifafi ◽  
Marta Camino-Serrano ◽  
Christine Hatté ◽  
Hector Morras ◽  
Lucas Moretti ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Soil Carbon ◽  
Land Surface ◽  
Carbon Stock ◽  
Environmental Changes ◽  
Carbon Dynamics ◽  
Total Carbon ◽  
Soil Parameters ◽  
Surface Model

Abstract. Despite the importance of soil as a large component of the terrestrial ecosystem, the soil compartments are not well represented in land surface models (LSMs). Indeed, soils in current LSMs are generally represented based on a very simplified schema that can induce a misrepresentation of the deep dynamics of soil carbon. Here, we present a new version of the Institut Pierre Simon Laplace (IPSL) LSM called ORCHIDEE-SOM (ORganizing Carbon and Hydrology in Dynamic EcosystEms-Soil Organic Matter), incorporating the 14C dynamics into the soil. ORCHIDEE-SOM first simulates soil carbon dynamics for different layers, down to 2 m depth. Second, concentration of dissolved organic carbon and its transport are modelled. Finally, soil organic carbon decomposition is considered taking into account the priming effect. After implementing 14C in the soil module of the model, we evaluated model outputs against observations of soil organic carbon and modern 14C fraction (F14C) for different sites with different characteristics. The model managed to reproduce the soil organic carbon stocks and the F14C along the vertical profiles for the sites examined. However, an overestimation of the total carbon stock was noted, primarily on the surface layer. Due to 14C, it is possible to probe carbon age in the soil, which was found to be underestimated. Thereafter, two different tests on this new version have been established. The first was to increase carbon residence time of the passive pool and decrease the flux from the slow pool to the passive pool. The second was to establish an equation of diffusion, initially constant throughout the profile, making it vary exponentially as a function of depth. The first modifications did not improve the capacity of the model to reproduce observations, whereas the second test improved both estimation of surface soil carbon stock as well as soil carbon age. This demonstrates that we should focus more on vertical variation in soil parameters as a function of depth, in order to upgrade the representation of the global carbon cycle in LSMs, thereby helping to improve predictions of the of soil organic carbon to environmental changes.

Efficient Schedule of Energy-Constrained UAV Using Crowdsourced Buses in Last-Mile Parcel Delivery

2021 ◽  
Vol 5 (1) ◽  
pp. 1-23
Author(s):  
Yan Pan ◽  
Shining Li ◽  
Qianwu Chen ◽  
Nan Zhang ◽  
Tao Cheng ◽  
...  
Keyword(s):  
Traffic Congestion ◽  
Large Scale ◽  
Load Capacity ◽  
Scheduling Algorithm ◽  
Cost Effective ◽  
Promising Alternative ◽  
Logistics Industry ◽  
Last Mile ◽  
Delivery Scheduling ◽  
Schedule Approach

Stimulated by the dramatical service demand in the logistics industry, logistics trucks employed in last-mile parcel delivery bring critical public concerns, such as heavy cost burden, traffic congestion and air pollution. Unmanned Aerial Vehicles (UAVs) are a promising alternative tool in last-mile delivery, which is however limited by insufficient flight range and load capacity. This paper presents an innovative energy-limited logistics UAV schedule approach using crowdsourced buses. Specifically, when one UAV delivers a parcel, it first lands on a crowdsourced social bus to parcel destination, gets recharged by the wireless recharger deployed on the bus, and then flies from the bus to the parcel destination. This novel approach not only increases the delivery range and load capacity of battery-limited UAVs, but is also much more cost-effective and environment-friendly than traditional methods. New challenges therefore emerge as the buses with spatiotemporal mobility become the bottleneck during delivery. By landing on buses, an Energy-Neutral Flight Principle and a delivery scheduling algorithm are proposed for the UAVs. Using the Energy-Neutral Flight Principle, each UAV can plan a flying path without depleting energy given buses with uncertain velocities. Besides, the delivery scheduling algorithm optimizes the delivery time and number of delivered parcels given warehouse location, logistics UAVs, parcel locations and buses. Comprehensive evaluations using a large-scale bus dataset demonstrate the superiority of the innovative logistics UAV schedule approach.

scholarly journals Design Strategies for Electrochemical Aptasensors for Cancer Diagnostic Devices

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 736
Author(s):  
Kamila Malecka ◽  
Edyta Mikuła ◽  
Elena E. Ferapontova
Keyword(s):  
Cancer Diagnosis ◽  
Cost Effective ◽  
Quality Of Healthcare ◽  
Cancer Diagnostics ◽  
Screening Tests ◽  
Design Strategies ◽  
Excellent Thermal Stability ◽  
Electrochemical Biosensing ◽  
Equipment Size

Improved outcomes for many types of cancer achieved during recent years is due, among other factors, to the earlier detection of tumours and the greater availability of screening tests. With this, non-invasive, fast and accurate diagnostic devices for cancer diagnosis strongly improve the quality of healthcare by delivering screening results in the most cost-effective and safe way. Biosensors for cancer diagnostics exploiting aptamers offer several important advantages over traditional antibodies-based assays, such as the in-vitro aptamer production, their inexpensive and easy chemical synthesis and modification, and excellent thermal stability. On the other hand, electrochemical biosensing approaches allow sensitive, accurate and inexpensive way of sensing, due to the rapid detection with lower costs, smaller equipment size and lower power requirements. This review presents an up-to-date assessment of the recent design strategies and analytical performance of the electrochemical aptamer-based biosensors for cancer diagnosis and their future perspectives in cancer diagnostics.

scholarly journals Multi-Country Scale Assessment of Available Energy Recovery Potential Using Micro-Hydropower in Drinking, Pressurised Irrigation and Wastewater Networks, Covering Part of the EU

Water ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 899
Author(s):  
Djordje Mitrovic ◽  
Miguel Crespo Chacón ◽  
Aida Mérida García ◽  
Jorge García Morillo ◽  
Juan Antonio Rodríguez Diaz ◽  
...  
Keyword(s):  
Drinking Water ◽  
Northern Ireland ◽  
Energy Recovery ◽  
Large Scale ◽  
Significant Proportion ◽  
Value Added ◽  
Cost Effective ◽  
Net Energy ◽  
Water Networks ◽  
Scale Assessment

Studies have shown micro-hydropower (MHP) opportunities for energy recovery and CO2 reductions in the water sector. This paper conducts a large-scale assessment of this potential using a dataset amassed across six EU countries (Ireland, Northern Ireland, Scotland, Wales, Spain, and Portugal) for the drinking water, irrigation, and wastewater sectors. Extrapolating the collected data, the total annual MHP potential was estimated between 482.3 and 821.6 GWh, depending on the assumptions, divided among Ireland (15.5–32.2 GWh), Scotland (17.8–139.7 GWh), Northern Ireland (5.9–8.2 GWh), Wales (10.2–8.1 GWh), Spain (375.3–539.9 GWh), and Portugal (57.6–93.5 GWh) and distributed across the drinking water (43–67%), irrigation (51–30%), and wastewater (6–3%) sectors. The findings demonstrated reductions in energy consumption in water networks between 1.7 and 13.0%. Forty-five percent of the energy estimated from the analysed sites was associated with just 3% of their number, having a power output capacity >15 kW. This demonstrated that a significant proportion of energy could be exploited at a small number of sites, with a valuable contribution to net energy efficiency gains and CO2 emission reductions. This also demonstrates cost-effective, value-added, multi-country benefits to policy makers, establishing the case to incentivise MHP in water networks to help achieve the desired CO2 emissions reductions targets.

scholarly journals Modeling Fused Filament Fabrication using Artificial Neural Networks

2021 ◽  
Author(s):  
Paul Oehlmann ◽  
Paul Osswald ◽  
Juan Camilo Blanco ◽  
Martin Friedrich ◽  
Dominik Rietzel ◽  
...  
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Cost Effective ◽  
Print Quality ◽  
Ann Model ◽  
Production Environment ◽  
Fused Filament Fabrication ◽  
Artificial Neural ◽  
Using Data ◽  
Artificial Neural Network Ann

AbstractWith industries pushing towards digitalized production, adaption to expectations and increasing requirements for modern applications, has brought additive manufacturing (AM) to the forefront of Industry 4.0. In fact, AM is a main accelerator for digital production with its possibilities in structural design, such as topology optimization, production flexibility, customization, product development, to name a few. Fused Filament Fabrication (FFF) is a widespread and practical tool for rapid prototyping that also demonstrates the importance of AM technologies through its accessibility to the general public by creating cost effective desktop solutions. An increasing integration of systems in an intelligent production environment also enables the generation of large-scale data to be used for process monitoring and process control. Deep learning as a form of artificial intelligence (AI) and more specifically, a method of machine learning (ML) is ideal for handling big data. This study uses a trained artificial neural network (ANN) model as a digital shadow to predict the force within the nozzle of an FFF printer using filament speed and nozzle temperatures as input data. After the ANN model was tested using data from a theoretical model it was implemented to predict the behavior using real-time printer data. For this purpose, an FFF printer was equipped with sensors that collect real time printer data during the printing process. The ANN model reflected the kinematics of melting and flow predicted by models currently available for various speeds of printing. The model allows for a deeper understanding of the influencing process parameters which ultimately results in the determination of the optimum combination of process speed and print quality.

scholarly journals Anti-meningococcal B vaccination in Italian adolescents: a cost-effective health opportunity

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
D Panatto ◽  
P Landa ◽  
D Amicizia ◽  
P L Lai ◽  
E Lecini ◽  
...  
Keyword(s):  
Large Scale ◽  
Public Health Problem ◽  
Vaccination Strategy ◽  
Cost Effective ◽  
Dose Schedule ◽  
Developed Countries ◽  
Invasive Disease ◽  
International Setting ◽  
The Impact

Abstract Background Invasive disease due to Neisseria meningitidis (Nm) is a serious public health problem even in developed countries, owing to its high lethality rate (8-15%) and the invalidating sequelae suffered by many (up to 60%) survivors. As the microorganism is transmitted via the airborne route, the only available weapon in the fight against Nm invasive disease is vaccination. Our aim was to carry out an HTA to evaluate the costs and benefits of anti-meningococcal B (MenB) vaccination with Trumenba® in adolescents in Italy, while also considering the impact of this new vaccination strategy on organizational and ethics aspects. Methods A lifetime Markov model was developed. MenB vaccination with the two-dose schedule of Trumenba® in adolescents was compared with 'non-vaccination'. Two perspectives were considered: the National Health Service (NHS) and society. Three disease phases were defined: acute, post-acute and long-term. Epidemiological, economic and health utilities data were taken from Italian and international literature. The analysis was conducted by means of Microsoft Excel 2010®. Results Our study indicated that vaccinating adolescents (11th year of life) with Trumenba® was cost-effective with an ICER = € 7,912/QALY from the NHS perspective and € 7,758/QALY from the perspective of society. Vaccinating adolescents reduces the number of cases of disease due to meningococcus B in one of the periods of highest incidence of the disease, resulting in significant economic and health savings. Conclusions This is the first study to evaluate the overall impact of free MenB vaccination in adolescents both in Italy and in the international setting. Although cases of invasive disease due to meningococcus B are few, if the overall impact of the disease is adequately considered, it becomes clear that including anti-meningococcal B vaccination into the immunization program for adolescents is strongly recommended from the health and economic standpoints. Key messages Free, large-scale MenB vaccination is key to strengthening the global fight against invasive meningococcal disease. Anti-meningococcal B vaccination in adolescents is a cost-effective health opportunity.

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