Seed storage studies in Mesua ferrea L. a medicinal tree of Indo-Malayan region

This paper, deals with testing the storage and viability of the seeds of Mesua ferrea L. in 5 different storage conditions. Seeds of M. ferrea are recalcitrant in nature and lose viability with a short span 8-15 days. Of the different conventional methods tried using the polycarbonate bottle and bags, M. ferrea seeds retained viability for 150 days with a slow moisture loss from the seeds stored in closed polycarbonate bottles at 10 °C. Here, we have standardised a conventional technique whereby the viability of the seeds can be extended to 150-180 days by storing the seeds in polycarbonate bottles at 10 °C

Engineered exciton diffusion length enhances device efficiency in small molecule photovoltaics

n organic photovoltaic blends, there is a trade-off between exciton harvesting and charge extraction because of the short exciton diffusion length. Developing a way of increasing exciton diffusion length would overcome this trade-off by enabling efficient light harvesting from large domains. In this work, we engineered (enhanced) both exciton diffusion length and domain size using solvent vapour annealing (SVA). We show that SVA can give a three-fold enhancement in exciton diffusion coefficient (D) and nearly a doubling of exciton diffusion length. It also increases the domain size, leading to enhancement of charge extraction efficiency from 63 to 89%. Usually larger domains would reduce exciton harvesting but this is overcome by the large increase in exciton diffusion, leading to a 20% enhancement in device efficiency

Biases in model-simulated surface energy fluxes during the Indian monsoon onset period

We use eddy-covariance measurements over a semi-natural grassland in the central Indo-Gangetic Basin to investigate biases in energy fluxes simulated by the Noah land-surface model for two monsoon onset periods: one with rain (2016) and one completely dry (2017). In the preliminary run with default parameters, the offline Noah LSM overestimates the midday (1000–1400 local time) sensible heat flux (H) by 279% (in 2016) and 108% (in 2017) and underestimates the midday latent heat flux (LE) by 56% (in 2016) and 67% (in 2017). These discrepancies in simulated energy fluxes propagate to and are amplified in coupled Weather Research and Forecasting model simulations, as seen from the High Asia Reanalysis dataset. One-dimensional Noah simulations with modified site-specific vegetation parameters not only improve the partitioning of the energy fluxes (Bowen ratio of 0.9 in modified run versus 3.1 in the default run), but also reduce the overestimation of the model-simulated soil and skin temperature. Thus, use of ambient site parameters in future studies is warranted to reduce uncertainties in short-term and long-term simulations over this region. Finally, we examine how biases in the model simulations can be attributed to lack of closure in the measured surface energy budget. The bias is smallest when the sensible heat flux post-closure method is used (5.2 W m −2 for H and 16 W m −2 for LE in 2016; 0.17 W m −2 for H and 2.8 W m −2 for LE in 2017), showing the importance of taking into account the surface energy imbalance at eddy-covariance sites when evaluating land-surface models

Observations of aerosol–vapor pressure deficit–evaporative fraction coupling over India

Northern India is a densely populated subtropical region with heavy aerosol loading (mean aerosol optical depth or AOD is ∼0.7), frequent heat waves, and strong atmosphere–biosphere coupling, making it ideal for studying the impacts of aerosols and the temperature variation in latent heat flux (LH) and evaporative fraction (EF). Here, using in situ observations during the onset of the summer monsoon over a semi-natural grassland site in this region, we confirm that strong co-variability exists among aerosols, LH, air temperature (Tair), and the vapor pressure deficit (VPD). Since the surface evapotranspiration is strongly controlled by both physical (available energy and moisture demand) and physiological (canopy and aerodynamic resistance) factors, we separately analyze our data for different combinations of aerosols and Tair/VPD changes. We find that aerosol loading and warmer conditions both reduce sensible heat (SH). Furthermore, we find that an increase in atmospheric VPD tends to decrease the gross primary production (GPP) and, thus, LH, most likely as a response to stomatal closure of the dominant grasses at this location. In contrast, under heavy aerosol loading, LH is enhanced partly due to the physiological control exerted by the diffuse radiation fertilization effect (thus increasing EF). Moreover, LH and EF increases with aerosol loading even under heat wave conditions, indicating a decoupling of the plant's response to the VPD enhancement (stomatal closure) in the presence of high aerosol conditions. Our results encourage detailed in situ experiments and mechanistic modeling of AOD–VPD–EF coupling for a better understanding of Indian monsoon dynamics and crop vulnerability in a heat stressed and heavily polluted future India

Eddy covariance flux observations at a semi-natural grassland on the Indo-Gangetic Plain

A new network of eddy covariance (EC) stations was established at a variety of semi-natural and managed ecosystems located across India during the INCOMPASS project. These new stations were installed to monitor surface-atmosphere fluxes of water, energy and carbon dioxide (CO2) and to provide supporting micrometeorological and soil physics observations. In this presentation, EC flux observations obtained at a semi-natural Phragmites-Saccharum-Imperata grassland on the Indo-Gangetic Plain are presented. The poster presents flux observations captured over two complete annual cycles. The grassland was characterised by a distinct seasonality. Latent heat dominated the turbulent energy flux during Monsoon, whereas sensible heat was the dominant turbulent flux during winter. The site experienced periodic flooding by waters from an adjacent irrigation canal as well as the removal of aboveground biomass during a wildfire in May 2017. Additional flood waters did not have a large influence on turbulent energy fluxes during inundation periods. Wildfire influenced fluxes in the period after the burn. Latent heat and net carbon gain recovered to pre-disturbance levels within a month of the wildfire

Monitoring dryland energy and water dynamics in India: an analysis of COSMOS-India and flux tower observations

Small changes in precipitation and temperature can dramatically influence surface energy and water budgets in semi-arid regions. Quantifying land-atmosphere interactions and feedbacks in these areas is crucial to understanding global water and carbon cycles, for the development and testing of land surface, weather prediction and climate models, as well as for monitoring local water resources and agricultural output. We report the results of co-located observations of land surface water and energy fluxes and large-area soil moisture dynamics obtained at three study sites located across India. These sites were instrumented as part of the INCOMPASS (INteraction of Convective Organisation with Monsoon Precipitation, Atmosphere, Surface and Sea) and COSMOS-India projects. Two sites are located on contrasting red (Alfisols) and black (Vertisols) soils on the Deccan Plateau. A third site is installed on alluvial soils (Fluvisols) on the Indo-Gangetic Plain. Each site consists of an eddy covariance flux tower providing measurements of sensible (H) and latent heat (LE) fluxes, micrometeorology and soil physics, in combination with a COSMOS (COsmic-ray Soil Moisture Observing System) sensor that provides spatially-integrated measurements of soil water content at field scale. In this presentation, we report on feedbacks between the land surface and the atmosphere, with a specific focus on the evaporative fraction (EF=LE/LE+H), precipitation and time varying soil moisture dynamics. CEH: Ross Morrison, Jonathan Evans, Chris Taylor, Lucy Ball, Alan Jenkins, Hollie Cooper, Jenna Thornton. IISc (Indian Institute of Science): Sekhar Muddu. University of Agricultural Sciences, Dharwad: S.S. Angadi. Indian Institute of Technology, Kanpur: Sachi Tripathi, Mithun Krishnan, Geet George. University of Reading: Andrew G. Turner

Engineered exciton diffusion length enhances device efficiency in small molecule photovoltaics

Funding: European Research Council (grant 321305). IDWS acknowledges a Royal Society Wolfson Research Merit Award. We are grateful to EPSRC for equipment grant (EP/L017008/1) and for support of OB (EP/M508214/1).In organic photovoltaic blends, there is a trade-off between exciton harvesting and charge extraction because of the short exciton diffusion length. Developing a way of increasing exciton diffusion length would overcome this trade-off by enabling efficient light harvesting from large domains. In this work, we engineered (enhanced) both exciton diffusion length and domain size using solvent vapour annealing (SVA). We show that SVA can give a three-fold enhancement in exciton diffusion coefficient (D) and nearly a doubling of exciton diffusion length. It also increases the domain size, leading to enhancement of charge extraction efficiency from 63 to 89%. Usually larger domains would reduce exciton harvesting but this is overcome by the large increase in exciton diffusion, leading to a 20% enhancement in device efficiency.PostprintPeer reviewe

Cosmic-ray soil water monitoring: the development, status & potential of the COSMOS-India network

Soil moisture (SM) plays a central role in the hydrological cycle and surface energy balance and represents an important control on a range of land surface processes. Knowledge of the spatial and temporal dynamics of SM is important for applications ranging from numerical weather and climate predictions, the calibration and validation of remotely sensed data products, as well as water resources, flood and drought forecasting, agronomy and predictions of greenhouse gas fluxes. Since 2015, the Centre for Ecology and Ecology has been working in partnership with several Indian Research Institutes to develop COSMOS-India, a new network of SM monitoring stations that employ cosmic-ray soil moisture sensors (CRS) to deliver high temporal frequency, near-real time observations of SM at field scale. CRS provide continuous observations of near-surface (top 0.1 to 0.2 m) soil volumetric water content (VWC; m3 m-3) that are representative of a large footprint area (approximately 200 m in radius). To date, seven COSMOS-India sites have been installed and are operational at a range of locations that are characterised by differences in climate, soil type and land management. In this presentation, the development, current status and future potential of the COSMOS-India network will be discussed. Key results from the COSMOS-India network will be presented and analysed

Fast Adaptation of Manipulator Trajectories to Task Perturbation by Differentiating through the Optimal Solution

Joint space trajectory optimization under end-effector task constraints leads to a challenging non-convex problem. Thus, a real-time adaptation of prior computed trajectories to perturbation in task constraints often becomes intractable. Existing works use the so-called warm-starting of trajectory optimization to improve computational performance. We present a fundamentally different approach that relies on deriving analytical gradients of the optimal solution with respect to the task constraint parameters. This gradient map characterizes the direction in which the prior computed joint trajectories need to be deformed to comply with the new task constraints. Subsequently, we develop an iterative line-search algorithm for computing the scale of deformation. Our algorithm provides near real-time adaptation of joint trajectories for a diverse class of task perturbations, such as (i) changes in initial and final joint configurations of end-effector orientation-constrained trajectories and (ii) changes in end-effector goal or way-points under end-effector orientation constraints. We relate each of these examples to real-world applications ranging from learning from demonstration to obstacle avoidance. We also show that our algorithm produces trajectories with quality similar to what one would obtain by solving the trajectory optimization from scratch with warm-start initialization. Most importantly, however, our algorithm achieves a worst-case speed-up of 160x over the latter approach