The Reykjanes Ridge is a major topographic feature that lies south of Iceland in the North- Atlantic Ocean and strongly influences the Subpolar Gyre (SPG) circulation. Based on velocity and hydrographic measurements carried out along the crest of the Ridge from the Icelandic continental shelf to 50°N during the RREX cruise in June-July 2015, we derived the first direct estimates of volume and water masses transports over the Ridge. The circulation was mainly westward north of 53.35°N and eastward south of it. The westward transport was estimated at 21.9 ± 2.5 Sv (Sv = 10 6 m 3 s -1 ) and represents the SPG intensity. The westward flows followed two main pathways at 57°N near Bight Fracture Zone and at 59 – 62°N. We argue that those pathways were respectively connected to the northern branch of the North Atlantic Current and to the Sub-Arctic Front that were intersected by the southern part of the section. In addition to this horizontal circulation, mixing and bathymetry shaped the water mass distribution. Water mass transformations in the Iceland Basin lead to the formation of weakly stratified SubPolar Mode Water (SPMW). We explain why SPMW, which was the main contributor in terms of water mass to the westward flow, was denser at 57°N than at 59–62°N along the Ridge. At higher densities, both Intermediate Water, defined by a dissolved oxygen minimum, and Icelandic Slope Water contributed as much to the westward transport across the Ridge as the sum of Labrador Sea Water and Iceland-Scotland Overflow Water.

A new Arts/Lab Student Residence program was developed that brings artists into a research lab. Science and Engineering undergraduate and graduate students working in the lab describe their research and allow the artists to shadow them to learn more about the work. The Arts/Lab Student Residencies are designed to be unique and fun, while encouraging interdisciplinary learning and creative production by exposing students to life and work in an alternate discipline’s maker space - i.e. the artist in the engineering lab, the engineer in the artist’s studio or performance space. Each residency comes with a cash prize and the expectation that a work of some kind will be produced as a response to experience. The Moldwin Prize is designed for an undergraduate student currently enrolled in the Penny W. Stamps School of Art & Design, the Taubman School of Architecture and Urban Planning or the School of Music, Theatre and Dance who is interested in exchange and collaboration with students engaged in research practice in an engineering lab. No previous science or engineering experience is required, although curiosity and a willingness to explore are essential! Students receiving the residency spend 20 hours over 8 weeks (February-April) participating with the undergraduate research team in the lab of Professor Mark Moldwin, which is currently doing work in the areas of space weather (how the Sun influences the space environment of Earth and society) and magnetic sensor development. The resident student artist will gain a greater understanding of research methodologies in the space and climate fields, data visualization and communication techniques, and how the collision of disciplinary knowledge in the arts, engineering and sciences deepens the creative practice and production of each discipline. The student is expected to produce a final work of some kind within their discipline that reflects, builds on, explores, integrates or traces their experience in the residency. This talk will describe the program, the inaugural year’s outcomes, and plans to expand the program to other research labs.

Lossless compression can reduce climate data storage by 30-40%. In general, further reductions require lossy compression that also reduces precision. Fortunately, geoscientific models and measurements generate false precision (scientifically meaningless data bits) that can be eliminated without sacrificing scientifically meaningful data. We introduce Bit Grooming, a lossy compression algorithm that removes the bloat due to false- precision, those bits and bytes beyond the meaningful precision of the data. We evaluated Bit Grooming against competitors Linear Packing, Layer Packing, and GRIB2/JPEG2000.

High performance liquid chromatography (HPLC) remains one of the most widely applied methods currently available for estimation of phytoplankton taxonomy from ocean samples. This method measures the concentrations of phytoplankton pigments, some of which are useful chemotaxonomic markers that can be used to diagnose the relative abundance of phytoplankton groups. Here, we use HPLC phytoplankton pigment concentrations measured on surface water samples from 38 field surveys for a total of over 3,000 distinct samples that cover every major ocean basin and represent a wide range of ecological regimes. The data compilation has been quality controlled to remove measurements below pigment detection limits and outliers from the linear regression of total chlorophyll-a concentration with total accessory pigment concentrations and only samples from labs that have participated in round-robin quality assurance experiments (e.g. NASA SeaHARRE) have been included. We assess the environmental and spatial drivers controlling the global distribution and co-variability of individual phytoplankton pigments. Preliminary results of hierarchical clustering show strong differentiation in phytoplankton pigments following known relationships between phytoplankton size class and relative pigment concentration, partitioning their contributions by micro-, nano-, and pico-phytoplankton size classes. However, the exact clusters relationships change when the data are divided by ocean basin or latitude. We also use statistical techniques, including EOFs and network-based exploration, to examine the associations between groups of pigments over a range of environmental conditions on local to global scales and diagnose the main controls on these associations.

The Ganga plains represent the abode of more than 400 million people and a region of severe anthropogenic disturbance to natural processes. Changing agricultural practices, inefficient use of water, contamination of groundwater systems, and decrease in soil fertility are some of the issues that have affected the long-term resilience of hydrological processes. The quantification of these processes demands a network of hydro-meteorological instrumentation, low-cost sensors, continuous engagement of stakeholders and real time data transmission at a fine interval. We have therefore set up a Critical Zone Observatory (CZO) in a small watershed (21 square Km ) that forms an intensively managed rural landscape consisting of 92% of agricultural land in the Pandu River Basin (a small tributary of the Ganga River). Apart from setting up a hydrometeorological observatory, the major science questions we want to address relate to development of water balance model, understanding the soil-water interaction and estimation of nutrient fluxes in the watershed. This observatory currently has various types of sensors that are divided into three categories: (a) spatially not dense but temporally fine data, (b) spatially dense but temporally not fine data and(c) spatially dense and temporally fine data. The first category represent high cost sensors namely automatic weather stations that are deployed at two locations and provide data at 15 minute interval. The second category includes portable soil moisture, discharge and groundwater level at weekly/ biweekly interval. The third category comprises low-cost sensors including automatic surface and groundwater level sensors installed on open wells to monitor the continuous fluctuation of water level at every 15 minutes. In addition to involving the local communities in data collection (e.g. manual rainfall measurement, water and soil sampling), this CZO also aims to provide relevant information to them for improving their sustainability. The preliminary results show significant heterogeneity in soil type, cropping system, fertilizer application, water quality, irrigation source etc. within a small catchment.

Natural methane gas release from the seafloor is a widespread phenomenon that occurs at cold seeps along most continental margins. Since their discovery in the early 1980s, seeps have been the focus of intensive research, partly aimed to refine the global carbon budget. However, deep-sea research is challenging and expensive and, to date, few works have successfully monitored the variability of methane gas release over long time periods (> 1 yr). Long-term monitoring is necessary to study the mechanisms that control seabed gas release. The M³ project, funded by the German Ministry of Education and Research, aims to study the temporal and spatial variability of gas emissions at the Southern Hydrate Ridge (SHR) by acoustically monitoring and quantifying gas effluxes over several years. Located 850 m deep on the Cascadia accretionary prism offshore Oregon, the SHR is one of the most studied seep sites and persistent but variable gas release has been observed for more than 20 years. Since 2015, the Ocean Observatories Initiative’s (OOI) Cabled Array observatory, provides power supply and two-way communication to the SHR, making it an ideal site for continuous long-term monitoring work. In this work, we present how we will take advantage of the OOI infrastructure and deploy several instruments on the seabed for at least 1.5 year. A multi-beam “overview” sonar mounted on a rotor will identify every gas bubble stream located within 200 m from the sonar location. A scanning “quantification” sonar will be used to estimate the amount of gas that is released from discrete gas streams. A camera system and a CTD probe will help process and analyze the hydro-acoustic data. All instruments will be powered and controlled from land through the OOI infrastructure. We present the instrument design, the operation protocol, as well as the data processing steps and expected results.

The net surface water flux (evaporation minus precipitation minus runoff, E-P-R) of the Atlantic Ocean is approximately 0.4 – 0.6 Sv (1 Sv = 10^9 kg s-1) larger than that of the Pacific Ocean, as shown in atmospheric and oceanic reanalyses and by oceanographic estimates. This asymmetry is linked to the asymmetry in sea surface salinity and the existence of the Atlantic Meridional Overturning Circulation. It is shown that the reason for the asymmetry in E-P-R is greater precipitation per unit area over the Pacific south of 30N, while evaporation rates are similar over both basins. It is further argued that the Pacific Ocean is anomalous compared to the Atlantic and Indian Oceans in terms of atmospheric moisture flux convergence and precipitation across the tropics and subtropics. To clarify the mechanism by which water vapour is exported out of the Atlantic basin and imported into the Pacific, we use an air mass trajectory model driven by ERA-Interim reanalysis. Using 12-hourly releases of 14-day back trajectories on the boundaries of ocean drainage basins over the period 2010-2014, we are able to partition the atmospheric moisture fluxes between basins according to their origins (i.e. last contact with the boundary layer). We show that at most a quarter of the E-P-R asymmetry is explained by higher moisture export to the Arctic and Southern basins from the Atlantic than from the Pacific. The main contributions come from differences in the longitudinal atmospheric transport of moisture between the Atlantic, Indian and Pacific basins. In particular, during the Asian summer monsoon the recurvature of the low level flow in the Somali Jet results in a much weaker westward moisture transport from the Indian into the Atlantic basin than across Central America (where it is similar to the zonal average) while there is stronger eastward transport from the Indian to Pacific basins. The net effect is stronger moisture convergence into the Pacific, but weaker into the Atlantic. In contrast to previous thinking, the role of the moisture flux across Central America in the asymmetry, albeit significant, is not dominant.

Studies of intraseasonal and annual cycles of meteorological variables, using projections of climate change, are nowadays extremely important to improve regional socio-economic planning for countries. This is particularly true in Costa Rica, as Central America has been identified as a climate change hot spot. Today many of the economic activities in the region, especially those related to agriculture, tourism and hydroelectric power generation are linked to the seasonal cycle of precipitation. Changes in rainfall (mm/day) and in the diurnal temperature range ($^{\circ}$C) for the periods 1970-1999 and 2070-2099 were investigated using the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) constructed using the CMIP5 (Coupled Model Intercomparison Project version 5) data. Differences between the multi-model ensembles of the two prospective scenarios (RCP 4.5 and RCP 8.5) and the retrospective baseline scenario were computed. This study highlights Costa Rica as an inflexion point of the climate change in the region and also suggests an early onset of the rainy season and future drying conditions.

AbstractFeature interaction is a crucial concept when it comes to machine learning interpretability. These interactions make it easier for the person using a machine learning tool to know how the model made its prediction. Intrinsic models such as the Generalized linear models pull their interpretability aspects from detecting feature interactions on the data. However, these models do not search the whole sample space for interactions and assume all interactions to the target feature are identical. A hybrid model combining Rough Set and Generalized linear models is proposed. Rough Set uses the concept of information granulation and approximations of regions with meaningful information to find feature interactions in the whole sample space. The detected features will then be modeled onto a Generalized linear model for prediction purposes. The dataset used in the experiment was drawn from wunderground.com online weather site, specifically the Kariki Farm online weather station. The proposed methodology for the research is the CRISP-DM method.Keywords: Machine interpretability, Rough Set, Generalized linear model, weather prediction.

Access to groundwater leaves riparian plants in drylands resistant to atmospheric drought but vulnerable to changes in climate or water use that reduce streamflow and groundwater tables. Despite the vulnerability of riparian vegetation to water balance changes few extensible methods have been developed to automatically map riparian plants at the scale of individual stands or stream reaches, to assess their response to changes in moisture due to drought and climate change, and to contrast those responses across plant functional types. We used LiDAR and a sub-annual timeseries of NDVI to map vegetation and then assessed drought response by comparing a drought index to variation in a remotely sensed metric of plant health. First, a random forest model was built to classify vegetation communities based on phenological changes in Sentinel-2 NDVI. This model produced community classes with an overall accuracy of 97.9%; accuracy for the riparian vegetation class was 98.9%. Following this initial classification, LiDAR measurements of vegetation height were used to split the riparian class into structural subclasses. Multiple Endmember Spectral Mixture Analysis was applied to a timeseries of Landsat imagery from 1984 to 2018, producing annual sub-pixel fractions of green vegetation, non-photosynthetic vegetation, and soil. Relationships were assessed within structural subclasses between mid-summer green vegetation fraction (GV) and the Standardized Precipitation-Evapotranspiration Index (SPEI), a measure of soil moisture drought. Among riparian vegetation subclasses, all groups showed significant positive correlations between SPEI and GV, indicating an increase in healthy plant material during wetter years. However, the relationship was strongest for herbaceous plants (R^2=0.509, m=0.0278), intermediate for shrubs (R^2=0.339, m=0.0262), and weakest for the largest trees (R^2=0.1373, m=0.0145). This implies decoupling of larger riparian plants from the impacts of atmospheric drought due to subsidies provided by groundwater resources. Our method was extended successfully to multiple climatically-dissimilar dryland systems in the American Southwest, and the results provide a basis for ongoing studies on the fine-scale drought response and climatic vulnerability of riparian woodlands.

The architecture and structure of cereal spikes are key indicators of quality and yield and are determined by both genetic and environmental factors. Traditional phenotyping methods for cereal spike trait measurements rely largely on manual work and are often qualitative or destructive, which leads to loss of spatial information. In this study, by using the newly installed X-ray computed tomography (X-ray CT) system at the Plant Accelerator (Australian Plant Phenomics Facility), we developed a high-throughput and non-destructive method to analyse cereal spikes. The fully automated algorithm generates detailed grain traits measurement, including count, weight, size, surface area, sphericity and spatial position. The average scan time required per sample cassette is under 7 minutes for 30 cereal spikes. Various cultivars of wheat, barley, oat and sorghum have been tested, and the results have confirmed the high accuracy and efficiency of using the X-ray CT system in grain studies. To further utilise the obtained 3D information, a pipeline for analyse spike and spikelet morphological traits has been developed for wheat spikes and oat panicles. This method allows to study the spikelet morphological traits by categorising the spikelets base on grain count, volume and weight, which can potentially bring the insights of the relationship between the development of single or multiflorous spikelets and grain quality and yield. The innovative X-ray CT system can efficiently generate grain and spike traits in high-throughput and non-destructive way, which provides a new method to contribute to a better understanding of cereal screening and breeding directions.

Chenopodium quinoa is an important crop that is known for its salt tolerance. Salinity is an osmotic stress and accumulation of ions in the root zone causes a reduction in soil water availability, which negatively affects the uptake of essential nutrients, changes seed composition, and reduces biomass. Therefore, there is a need to develop crops that are highly productive under poor soils. This research examines the effect of salt stress on quinoa photosynthetic efficiency and chloroplast development. Sensitive and tolerant quinoa lines were examined under salt stress conditions when a concentration of 155mM NaCl was applied. Electrical soil conductivity was monitored during the experiment to assure salt stress and at approximately two months old, CropReporter images, which are used to estimate photosystem II efficiency, non-photochemical quenching (NPQ), chlorophyll content, and anthocyanin content were captured and analyzed using PlantCV. The analysis showed that the salt treatment did not negatively affect the plant photosynthetic efficiency (no changes in Fv/Fm, NPQ, Fq'/Fm') but leaf area and chlorophyll content was statistically negatively affected by the treatment when comparing genotypes. Leaf tissue was sampled and chloroplasts were imaged using super-resolution microscopy. Salt-sensitive lines showed swelling of the chloroplast lamellae, starch accumulation, and unrecognizable grana structure. A comprehensive understanding of the quinoa salt tolerant mechanisms by employing multidisciplinary approaches is necessary for their effective incorporation into salt-sensitive crops for better crop yields under stressful environments.

The majority of domesticated plant species are herbaceous annuals and woody perennials, yet many herbaceous perennial species hold potential for future agricultural systems. In addition to multiyear harvests, herbaceous perennials provide many ecosystem services, including erosion control as a result of their large and persistent root systems. However, the multiyear lifespan of perennial species has been a barrier to rapid domestication as breeding cycles require phenotyping over multiple growing seasons. Using phenomic selection, high-dimensional secondary traits measured on seedlings could be used to develop relationship matrices among individuals which are then used to predict field traits. Additionally, these models can serve as the selection criteria to identify individuals to advance to the next (pre)breeding generation, thus shortening the breeding cycle. This project substitutes costly genomics data with high-dimensional phenomics data asking: Can elite individuals of perennial species be predicted by phenomic relatedness models based on high-dimensional traits recorded on seedlings? To date, we have imaged 2280 seedlings from each of the following three perennial crop candidate species: intermediate wheatgrass (Thinopyrum intermedium), sainfoin (Onobrychis viciifolia), and silphium (Silphium integrifolium) on the Bellwether Foundation Phenotyping Facility housed at the Danforth Center. The images were processed using PlantCV to generate high-dimensional color and near-infrared profiles for each plant on each image day. Additionally, profiles were generated with handheld spectrometers. This work re-imagines innovations in plant traits, kinship matrices, genomic selection, phenotyping centers, and ultimately domestication, in order to expedite the development of an emerging generation of climate resilient, ecologically sustainable crops.

Automated monitoring and evaluation systems for plant phenotyping are one of the keys to advance and strengthen crop breeding programs. In this study, the improvements of the camera-based sensor system and a weather station from a previous study-assembled mainly from Raspberry Pi products-board with dual cameras (RGB and NoIR) providing high spatial and temporal resolution data-is outlined. Hardware for the internet connection and the power supply system of the sensor were upgraded. Previously, the sensor could automatically capture plant images following user-defined time points; thus, an image processing algorithm (edge computing) was developed and installed to extract digital phenotypic traits from the images after capturing process. With the development, the new sensor system could be integrated with the internet, and a cloud server was configured to store data online (digital traits and raw images). A real-time monitoring system was created to visualize the time series data of a trait development and plant images throughout the season. With such a system, plant breeders will be able to monitor multiple trials for timely crop management and decision-making process, which is also resources efficiency.

Joelle Claussen1, Antje Wolff2, Markus Rehak1, Johannes Wolff2, Bas van Eerdt3, Stefan Gerth1

The application of herbicides in agriculture has significantly increased in recent decades. While many herbicides improve the efficiency and efficacy of weed control, their excessive use at the wrong growth stage can cause crop foliar damage, higher input cost and negative environmental footprints. There are limited techniques to accurately monitor herbicide effects. Visual ratings are highly subjective and require extensive training or experience. High-throughput digital imaging is a promising tool to measure plant herbicide interaction in field crops. In this study, proximal and aerial based advanced sensors have been utilized to evaluate different herbicide modes-of-action in two model species, tame oat [Avena sativa; model for wild oat (Avena fatua)] and oriental mustard [Brassica juncea; model for wild mustard (Sinapis arvensis)]. The experimental trials were performed at three agro-climatic locations in Canada (Lethbridge (AB), Saskatoon (SK), and Lacombe (AB)). The proximal and UAV multispectral imagery data were collected for baseline (before treatment) and 1, 3, 7, 10, 14 and 21 days after treatments (DAT), alongside visual ratings. The Normalized Difference Vegetation Index (NDVI), Photochemical Reflectance Index, Chlorophyll Vegetation Index, and Optimized Soil Adjusted Vegetation Index were used to assess variation of different DAT pigment content (photosynthetic rate) and chlorosis (damage %) in plot vegetation. The variation in obtained temporal indices (NDVI) suggest that the developed technology has potential to replace visual ratings (R2 ≈0.65-0.94) and can be used as a rapid screening tool for herbicide activity. Therefore, remote sensing tools could improve the precision and consistency of future herbicide assessments.

The automatic, and accurate plant phenotyping plays important role to improve the crop yield through enabling efficient plant analysis and plant breeding studies. The 3d deep learning allows automatic segmentation of plant parts from point cloud data. However, the network architecture is designed manually and performance is limited to prior experience. The aim of this study is to search for optimal 3d deep networks to perform the plant part segmentation. We perform the 3d neural architecture search by training a super network composed of candidate networks. Using the trained super network, the evolutionary searching is used to search for top performing architecture. The results demonstrate the searched architecture outperforms manually designed architectures by attaining mean IoU and accuracy of more than 90% and 96%, respectively. The searched architecture achieves more than 83% class-wise IoU for all main stem, branches, and boll class. This plant part segmentation method shows promising results and holds potential to be utilized by plant breeders for enhancing the production quality.

Crop improvement over the last few decades, especially after the Green Revolution, is partially driven by the intensive application of less expensive inorganic nitrogen (N) fertilizer. However, the unsustainable use of inorganic N fertilizer in crop production decreases farming profitability and creates a series of ecological burdens. One of the long-standing goals of crop breeding is to increase crops' nitrogen use efficiency (NUE). Studies have shown a number of phenotypic variations of sorghums grown in different N conditions, including root architecture, leaf parameters, growth parameters, yield, and biochemistry traits. Additionally, previous studies showed that the demand for N varies during the sorghum developmental stages, indicating a dynamic genetic control. In our study, taking advantage of the CRISPR-based gene editing and UNL's automatic high throughput phenotyping platform, we generated five edited sorghum lines under TX430 background and phenotyped them in two N conditions from 30 days after planting to full maturity. We extracted time-series plant growth traits from these edited lines as well as the wild type (i.e., TX430), such as plant height, plant width, and pixel counts, along with vegetation indices. Statistical analyses suggested the distinct N responses between some of the edited lines and the wild type. These N-responsive edited lines will be tested in replicated field trials and potentially be incorporated into the breeding protocol for N-resilient sorghum development.