In-field Observations Research Articles

Shedding light on predator detections: evaluating the impact of camera-trap flash type for feral cat monitoring through in-field observations

Shedding light on predator detections: evaluating the impact of camera-trap flash type for feral cat monitoring through in-field observations

Predictive models for grape downy mildew (Plasmopara viticola) as a decision support system in Mediterranean conditions

Plasmopara viticola (Peronosporales: Peronosporaceae) is a highly destructive disease that can cause very serious damage under favorable conditions. Cultural practices, disease-tolerant varieties, and phytosanitary treatments have been effective tools to control this disease. However, predictive models such as decision support systems (DSS) could help reduce the need for phytosanitary treatments. In this study, we aimed to develop, adjust, and validate a customized predictive model at the plot level, comparing it with the other three existing models (Milvit, VitiMeteo-Plasmopara, and Goidanich) in Mediterranean conditions. Downy mildew symptoms were observed in the field and predicted using the four mentioned models in eight plots during the 2018 and 2019 campaigns. All tested models predicted possible daily infection cycles, which were grouped into infection blocks every 5–7 days to validate predicted data with in-field observations. In Mediterranean conditions, Milvit and VitiMeteo-Plasmopara models provided both over-prediction and under-prediction infection blocks compared to field observations. However, the Goidanich model only provided over-prediction infection blocks with very few matches to field observations. The UR-model was successfully improved to adjust it to Mediterranean conditions, and it matched observed and predicted infection blocks in 56,25% of the plots and years. However, the UR-model was more accurate (75% matching) in humid years like 2018 than in dryer years like 2019 (37,5% matching), although in both cases it was the most accurate model to be used as a decision support system.

Empirical estimation of size-resolved scavenging coefficients derived from in-situ measurements at background sites in Korea during 2013–2020

Owing to insufficient detailed in-field observations for explaining and quantifying the wet-scavenging coefficients, especially those below-cloud (Λbelow), prediction of accurate aerosol concentrations and/or behaviors in the atmosphere remains challenging. As a pioneering effort, this study focused on establishing an empirical equation for size-resolved Λbelow, which represent the characteristics in Korea, using long-term aerosol number size distribution and meteorological variable measurements at two background sites in Korea (Baengnyeong and Jeju). The median Λbelow values derived from the total number concentrations were 8.06 × 10−6 s−1 for Baengnyeong and 1.04 × 10−5 s−1 for Jeju, which are similar to or slightly lower than the Λbelow reported by previous in-situ studies. These may be caused by differences in the precipitation rates, precipitation drop sizes, aerosol chemical/hygroscopic properties, and aerosol size distributions at the measurement sites. Although the derived parameterization can cover a wide range (10 nm to 10 μm), the derived equation for the fourth-degree polynomial shows a high correlation coefficient of 0.81, indicating that the measured and fitted Λbelow converge within a narrow range. Moreover, the variation in Λbelow is relatively constant for aerosol diameters larger than ∼1 μm, similar to those derived from theoretical equations rather than empirical equations in previous studies. This study is thus expected to contribute to improving the below-cloud scavenging module implemented in a chemical transport model by covering a wide range of aerosol diameters to avoid overestimation by extrapolation of Λbelow.

Pre-service teachers’ experiences of affective nature connection through intentional pedagogies on an extended expedition

AbstractEmotion plays a significant role in the human experience. Nevertheless, emotion (as an attribute of the affective domain), is often side-lined in formal learning environments (including Higher Education) in favour of a focus on the cognitive. This paper shares findings of a research project involving pre-service outdoor education teachers as they affectively experienced connections with nature on an extended expedition, while exposed to intentional nature connection pedagogies. It achieves this through the lens of the current shifts in outdoor education practice and pedagogy specifically, and curriculum and educational policies more broadly. The research was conducted as a case study, within a constructivist paradigm. The collection of data involved in-field observations on a six-day expedition, participant interviews, and researcher reflective journals. The data was analysed inductively which revealed a novel framework: the Affective Nature Connection Matrix. Through this matrix, the affective nature connection wave and heartbeat emerged. This theoretically derived and practice informed model highlighted the collective similarity of the expedition experiences, while acknowledging that these experiences were individually subjective. All participants experienced a rise and fall of affective nature connection, the wave, within individual and collective critical moments, the heartbeats, in response to intentional learning experiences which directly impacted and resulted in a deepened connection to self, others and nature.

Assessing the severity of cotton Verticillium wilt disease from in situ canopy images and spectra using convolutional neural networks

Verticillium wilt (VW) is a common soilborne disease of cotton. It occurs mainly in the seedling and boll-opening stages and severely impairs the yield and quality of the fiber. Rapid and accurate identification and evaluation of VW severity (VWS) forms the basis of field cotton VW control, which has great significance to cotton production. Cotton VWS values are conventionally measured using in-field observations and laboratory test diagnoses, which require abundant time and professional expertise. Remote and proximal sensing using imagery and spectrometry have great potential for this purpose. In this study, we performed in situ investigations at three experimental sites in 2019 and 2021 and collected VWS values, in situ images, and spectra of 361 cotton canopies. To estimate cotton VWS values at the canopy scale, we developed two deep learning approaches that use in situ images and spectra, respectively. For the imagery-based method, given the high complexity of the in situ environment, we first transformed the task of healthy and diseased leaf recognition to the task of cotton field scene classification and then built a cotton field scenes (CFS) dataset with over 1000 images for each scene-unit type. We performed pretrained convolutional neural networks (CNNs) training and validation using the CFS dataset and then used the networks after training to classify scene units for each canopy. The results showed that the DarkNet-19 model achieved satisfactory performance in CFS classification and VWS values estimation (R2 = 0.91, root-mean-square error (RMSE) = 6.35%). For the spectroscopy-based method, we first designed a one-dimensional regression network (1D CNN) with four convolutional layers. After dimensionality reduction by sensitive-band selection and principal component analysis, we fitted the 1D CNN with varying numbers of principal components (PCs). The 1D CNN model with the top 20 PCs performed best (R2 = 0.93, RMSE = 5.77%). These deep learning-driven approaches offer the potential of assessing crop disease severity from spatial and spectral perspectives.

Student Satisfaction with Hostel facilities: A Case Study of Njala University, Njala Campus, Sierra Leone

Aim and Scope: The importance of students' social environments in their day-to-day lives cannot be overstated. The extent to which students are happy in their hostel depends on its facilities' availability, adequacy, and functionality. A case study of Njala University, Njala campus, was used to determine the amenities provided and the level of satisfaction the students derived from those amenities. Methods: A cross-sectional research design was used to conduct the research through in-field observations and a standardized questionnaire. The study's sample size was estimated at 482 total students. SPSS version 26.0 was used for the data analysis. Result: The study revealed that 82.0% of the respondents were within the age group 20-29 years, and more than two-thirds (70.7%) of the respondents were living in overcrowded rooms with a population of more than the required number of persons per room; this is attributed to the squatting phenomenon among the hostel occupants with the attendant implications of increasing pressure on the available facilities. Equally, students were reasonably satisfied with hostel facilities such as recreational grounds, security status, Library/ICT, waste management and electricity. Results showed respondents were unhappy with the adequacy and performance of certain facilities such as the drainage system, foam, toilet, water supply, kitchen, room size, laundry, and firefighting device. Conclusion: For student convenience, as enrollment continues to rise, the paper suggests that public-private partnerships be established to fund the construction of other hostels with updated designs and amenities.

Aligning work-as-imagined and work-as-done using FRAM on a hospital ward: a roadmap

IntroductionModern safety approaches in healthcare differentiate between daily practice (work-as-done) and the written rules and guidelines (work-as-imagined) as a means to further develop patient safety. Research in this area has...

FEW SHOT CROP MAPPING USING TRANSFORMERS AND TRANSFER LEARNING WITH SENTINEL-2 TIME SERIES: CASE OF KAIROUAN TUNISIA

Abstract. In this paper, we present an approach to land cover mapping from Sentinel-2 (S-2) satellite image time series using deep learning methods in the context of few shots in agricultural areas which aims to learn a classifier to recognize unseen classes during training with limited labelled examples. In many countries, there is a lack of Land Parcel Information Systems (LPIS) and thus of agricultural crop type annotations. Annotations are still based on fastidious digitization of parcels and in-field observations that are available in few numbers. Our idea is to transfer learning from pre-trained models on existing LPIS in France and apply them to a different geographical area in Kairouan in Central Tunisia. We build on work employing multi-headed self-attention mechanisms that have contributed to results that outperform other deep learning algorithms such as convolutional neural networks (CNNs), recurrent neural networks (RNNs) in agricultural context using S-2 Time series. We used two transformer-based deep learning models PSE-TAE (Pixel-Set Encoders + Temporal Self-Attention) and PSE-LTAE (Pixel-Set Encoders + Lightweight Temporal Self-Attention). We first studied their generalisation capacity in a few shot context and on different geographical study site. Then, by transferring the knowledge of these models and adapting them to the Tunisian context with the transfer learning techniques we have demonstrated experimentally that the adaptation of these methods is efficient for land cover mapping in agricultural areas with few in-field observations in terms of accuracy with an overall accuracy for both models reaching almost 93% for a detailed classification level with 17 classes.

Development and investigative studies on solar hot case food warmer for food safety at offices/institutes.

The paper presents the design, development and investigative studies into the use of a solar hot case/food warmer. The solar hot case can prove to be highly useful to the persons who carry hot food to their workplaces for lunch and eat it 4-5 h later. It can help keep food warm and infection free, by maintaining the food at an elevated temperature. The solar hot case has been developed with a total aperture area of 0.673 m2. The energy requirements for food warming have been calculated and found to be around 62 kJ in summer and 90 kJ in winter for an average lunch box of mass 0.25 kg containing 0.4 kg of food. The design of the solar hot case has been prepared on the basis of the food warming calculations to allow around ten lunch boxes to be warmed simultaneously. A computer program based on the solar radiation geometry and an ASHRAE model has been prepared to study the energy available in various months and to estimate the solar energy interception by the solar hot case. The results show that the energy absorbed by the system at an instant varies from around 300 to 1400 kJ/m2h in winter and 1000-2000 kJ/m2h in summer. Experimental in-field observations related to the temperature profiles of the developed system are also reported.

Impacts of Soil Information on Process-Based Hydrological Modelling in the Upper Goukou Catchment, South Africa

Although soils form an integral part of landscape hydrological processes, the importance of soil information in hydrological modelling is often neglected. This study investigated the impact of soil information on streamflow modelling accuracy and hydrological process representation. Two different levels of soil information were compared to long-term streamflow in the upper Goukou catchment (230 km2), South Africa, over a period of 23 years using the Soil Water Assessment Tool (SWAT+). The land-type soil map (LTSM) dataset was less detailed and derived from the best, readily available soil dataset for South Africa currently. The hydrological soil map (HSM) dataset was more detailed and was created using infield hydropedological soil observations combined with digital soil-mapping techniques. Monthly streamflow simulation was similar for both soil datasets, with Nash–Sutcliffe efficiency and Kling–Gupta efficiency values of 0.57 and 0.59 (HSM) and 0.56 and 0.60 (LTSM), respectively. It is, however, important to assess through which hydrological processes were these streamflow values generated as well as their spatial distribution within the catchment. Upon further assessment, the representation of hydrological processes within the catchment differed greatly between the two datasets, with the HSM more accurately representing the internal hydrological processes, as it was based on infield observations. It was concluded that hydropedological information could be of great value in effective catchment management strategies since it improves representation of internal catchment processes.

Sulfate formation is dominated by manganese-catalyzed oxidation of SO2 on aerosol surfaces during haze events

The formation mechanism of aerosol sulfate during wintertime haze events in China is still largely unknown. As companions, SO2 and transition metals are mainly emitted from coal combustion. Here, we argue that the transition metal-catalyzed oxidation of SO2 on aerosol surfaces could be the dominant sulfate formation pathway and investigate this hypothesis by integrating chamber experiments, numerical simulations and in-field observations. Our analysis shows that the contribution of the manganese-catalyzed oxidation of SO2 on aerosol surfaces is approximately one to two orders of magnitude larger than previously known routes, and contributes 69.2% ± 5.0% of the particulate sulfur production during haze events. This formation pathway could explain the missing source of sulfate and improve the understanding of atmospheric chemistry and climate change.

Urban Fire Severity and Vegetation Dynamics in Southern California

A combination of satellite image indices and in-field observations was used to investigate the impact of fuel conditions, fire behavior, and vegetation regrowth patterns, altered by invasive riparian vegetation. Satellite image metrics, differenced normalized burn severity (dNBR) and differenced normalized difference vegetation index (dNDVI), were approximated for non-native, riparian, or upland vegetation for traditional timeframes (0-, 1-, and 3-years) after eleven urban fires across a spectrum of invasive vegetation cover. Larger burn severity and loss of green canopy (NDVI) was detected for riparian areas compared to the uplands. The presence of invasive vegetation affected the distribution of burn severity and canopy loss detected within each fire. Fires with native vegetation cover had a higher severity and resulted in larger immediate loss of canopy than fires with substantial amounts of non-native vegetation. The lower burn severity observed 1–3 years after the fires with non-native vegetation suggests a rapid regrowth of non-native grasses, resulting in a smaller measured canopy loss relative to native vegetation immediately after fire. This observed fire pattern favors the life cycle and perpetuation of many opportunistic grasses within urban riparian areas. This research builds upon our current knowledge of wildfire recovery processes and highlights the unique challenges of remotely assessing vegetation biophysical status within urban Mediterranean riverine systems.

New range records and life history observations of insects (Diptera: Dryomyzidae, Chironomidae; Coleoptera: Staphylinidae) associated with barnacles (Balanomorpha: Balanidae, Chthamalidae) on the Pacific coasts of North America and Japan

AbstractInsects are usually considered to be excluded from the marine environment. A small number of species, however, are considered to be marine, due to spending some portion of their life cycle in salt water. We use natural history collection specimens, in-field observations, and molecular analysis to generate new locale records and natural history data for seven insect species. All seven species are associated with barnacles (Balanomorpha: Balanidae, Chthamalidae) along the Pacific coast of Canada, the United States of America, or Japan. Use of DNA barcode analysis confirms the monophyly of three species of Oedoparena (Diptera: Dryomyzidae). Natural history collection specimens expand the geographical range and illuminate the phenology of Oedoparena spp. In-field observations record direct associations between three species of Thalassosmittia (Diptera: Chironomidae), Diaulota densissima (Casey) (Coleoptera: Staphylinidae), and intertidal barnacles from various locations in British Columbia, Canada. Barnacle host associations and microhabitat preferences are proposed for all species. A new definition of what constitutes a marine insect is offered.

First records of Dermacentor albipictus larvae collected by flagging in Yukon, Canada

BackgroundThe winter tick (Dermacentor albipictus) has garnered significant attention throughout North America for its impact on wildlife health, and especially for moose (Alces alces), where high tick burdens may result in host hair loss, anemia, and can prove fatal. The environmental transmission of D. albipictus larvae to a host is a critical event that has direct impact on infestation success, yet in-field observations of this life stage are lacking. In Yukon, Canada, D. albipictus had previously been found on hosts, but its larval life stage had not been detected in the field, despite previous sampling attempts.MethodsWe sampled for D. albipictus larvae using traditional flagging methods in Ibex Valley and Braeburn, Yukon. Sites were sampled repeatedly for D. albipictus larvae by flagging from late August to end of October in 2018 and late August to end of November 2019.ResultsLarvae of D. albipictus were collected throughout Ibex Valley, at approximate densities ranging from 0.04 to 4236 larvae/100 m2. Larvae were present primarily on grassy vegetation on south-facing slopes in the Ibex Valley region and in Braeburn. Highest average larval numbers suggest peak questing activity was towards the end of September and beginning of October, as elsewhere in North America.ConclusionsTo the best of our knowledge, we report the first successful collection of the off-host, larval life stage of D. albipictus by flagging, north of 60° latitude in Yukon, Canada. These new observations provide critical information on the spatial distribution of the host-seeking life stage of D. albipictus and confirm that this species is completing its whole life cycle in southern Yukon. Understanding the environmental conditions where larvae spend their vulnerable period off-host in this northern location can inform both management strategies and projections of future range expansion which may occur with a changing climate.

Application of dust mitigation strategies to single‐axis‐tracking photovoltaic modules in the semi‐arid areas of South Africa

Field generated data for single-axis-tracker (SAT) photovoltaic (PV) modules, subjected to the semi-arid Northern Cape region of South Africa, is presented. Experimental dust mitigation methods and the effects of dust soiling on PV module performance are explored. In particular, the use of a hydrophobic anti-soiling coating and a unique SAT self-cleaning manoeuvre was investigated. The SAT results are further compared to six coated and uncoated stationary PV modules. For the performance comparisons, maximum PV module power output was determined from current-voltage (I–V) curves extracted from each individual PV module. This work further presents a novel I–V curve fitting algorithm, which improves measured I–V curve data resolution for precise maximum power point extraction. The PV module performance comparisons were executed with a performance ratio metric, which is defined as the irradiance and temperature corrected performance factor of a PV module. Contrary to the initial research hypothesis, the hydrophobic anti-soiling coating was found to promote dust soiling. The SAT self-cleaning manoeuvre further delivered unique insights regarding the effects of dew water on dust soiling and displacement. Finally, also unique to the research presented here, is the formal description of infield observations made regarding dust deposition.

Using functional traits to model annual plant community dynamics.

Predicting the response of biological communities to changes in the environment or management is a fundamental pursuit of community ecology. Meeting this challenge requires the integration of multiple processes: habitat filtering, niche differentiation, biotic interactions, competitive exclusion, and stochastic demographic events. Most approaches to this long-standing problem focus either on the role of the environment, using trait-based filtering approaches, or on quantifying biotic interactions with process-based community dynamics models. We introduce a novel approach that uses functional traits to parameterize a process-based model. By combining the two approaches we make use of the extensive literature on traits and community filtering as a convenient means of reducing the parameterization requirements of a complex population dynamics model whilst retaining the power to capture the processes underlying community assembly. Using arable weed communities as a case study, we demonstrate that this approach results in predictions that show realistic distributions of traits and that trait selection predicted by our simulations is consistent with in-field observations. We demonstrate that trait-based filtering approaches can be combined with process-based models to derive the emergent distribution of traits. While initially developed to predict the impact of crop management on functional shifts in weed communities, our approach has the potential to be applied to other annual plant communities if the generality of relationships between traits and model parameters can be confirmed.

Effects of herbicide management practices on the weed density and richness in dicamba-resistant cropping systems in Indiana

AbstractThe addition of dicamba as a weed control option in soybean [Glycine max (L.) Merr.] is a valuable tool. However, this technology must be utilized with other herbicide sites of action (SOAs) to reduce selection pressure on weed communities and ensure its prolonged usefulness. A long-term trial was conducted for 7 yr in Indiana to evaluate weed community densities and species richness with four levels of dicamba selection pressure in a corn (Zea mays L.)–soybean rotation. Monocot densities and richness increased over time in the dicamba-reliant treatment. Dicot densities in the dicamba-reliant treatment declined over time, but dicot richness increased. The soil weed seedbank was affected by the varying herbicide strategies. The dicamba-reliant strategy had greater than 43% higher total weed density than all other treatments, primarily due to having a monocot density that was at least 71% higher than the other treatments. The fully diversified strategy with eight SOAs and residual herbicides used every year had the lowest total weed species richness in the soil seedbank, which supported the in-field observations.

A systematic assessment of uncertainties in large-scale soil loss estimation from different representations of USLE input factors – a case study for Kenya and Uganda

Abstract. The Universal Soil Loss Equation (USLE) is the most commonly used model to assess soil erosion by water. The model equation quantifies long-term average annual soil loss as a product of the rainfall erosivity R, soil erodibility K, slope length and steepness LS, soil cover C, and support measures P. A large variety of methods exist to derive these model inputs from readily available data. However, the estimated values of a respective model input can strongly differ when employing different methods and can eventually introduce large uncertainties in the estimated soil loss. The potential to evaluate soil loss estimates at a large scale is very limited due to scarce in-field observations and their comparability to long-term soil estimates. In this work we addressed (i) the uncertainties in the soil loss estimates that can potentially be introduced by different representations of the USLE input factors and (ii) challenges that can arise in the evaluation of uncertain soil loss estimates with observed data. In a systematic analysis we developed different representations of USLE inputs for the study domain of Kenya and Uganda. All combinations of the generated USLE inputs resulted in 972 USLE model setups. We assessed the resulting distributions in soil loss, both spatially distributed and on the administrative level for Kenya and Uganda. In a sensitivity analysis we analyzed the contributions of the USLE model inputs to the ranges in soil loss and analyzed their spatial patterns. We compared the calculated USLE ensemble soil estimates to available in-field data and other study results and addressed possibilities and limitations of the USLE model evaluation. The USLE model ensemble resulted in wide ranges of estimated soil loss, exceeding the mean soil loss by over an order of magnitude, particularly in hilly topographies. The study implies that a soil loss assessment with the USLE is highly uncertain and strongly depends on the realizations of the model input factors. The employed sensitivity analysis enabled us to identify spatial patterns in the importance of the USLE input factors. The C and K factors showed large-scale patterns of importance in the densely vegetated part of Uganda and the dry north of Kenya, respectively, while LS was relevant in small-scale heterogeneous patterns. Major challenges for the evaluation of the estimated soil losses with in-field data were due to spatial and temporal limitations of the observation data but also due to measured soil losses describing processes that are different to the ones that are represented by the USLE.

Comparison of SWMM evaporation and discharge to in-field observations from lined permeable pavements

ABSTRACT Limited documentation exists regarding parameterization of SWMM’s permeable pavement module and whether the output is realistic, particularly for long-term simulations. In this paper, we evaluated SWMM’s ability to replicate discharge and evaporation from three lined permeable pavement stalls. An assessment of parameter sensitivity identified the permeable pavement module’s input parameters with the highest relative sensitivity. Agreement between observations over a 1-year period and output from the calibrated model was inconsistent for 18 modelled rainfall events. While event volumes and shapes were matched relatively well for two concrete paver stalls for simple single-peak events, more complex multi-peak events showed poor agreement for all stalls. The porous asphalt stall was modelled least satisfactorily, due to its retention capacity that cannot be represented in the model. Based on the findings of this study, an evaporation coefficient (between 0.2 and 0.9), should be applied for long-term simulations of permeable pavement for best results.

Collapse and damage to vernacular buildings induced by 2012 Emilia earthquakes

In May 2012, two seismic events hit the Emilia-Romagna Region, in Northern Italy. The earthquakes caused collapses and damage to traditional vernacular building heritage, highlighting its seismic high vulnerability. The paper presents a wide collection of damage and collapses observed in the aftermath field inspections on 22 historical vernacular buildings. It was noted that few recurring structural typologies are present in the area hit by earthquake and buildings of the same typology show similar damage mechanisms. Then, with the main goal to identify the most typical damage affecting similar structures, the building stock has been subdivided in different categories based on building plan distribution and intended use. The principal collapse causes, based both on in-field observations and analyses, performed on local and global finite element models, can be ascribed to poor connections between orthogonal walls, lack of effective connections between floor elements and walls, excessive flexibility of floor diaphragms and high slenderness of vertical elements. The outcomes in the present paper allow identifying the vernacular building typologies most vulnerable to earthquake and can help to plan the future retrofitting strategies for vernacular heritage.