Canopy Cover Research Articles

The aesthetic value of natural vegetation remnants, city parks and vacant lots: The role of ecosystem features and observer characteristics

Physical and mental well-being are linked to exposure to ecosystems perceived as aesthetically pleasing. However, the perceived aesthetic value is shaped by both ecosystem features and observer characteristics. This study explores the relationships between these two influential factors, and perceptions across three Canadian urban greenspace types: natural vegetation remnants (forest stands, marshes and peatlands), city parks, and vacant lots. We administered an online photo-questionnaire, completed by 514 respondents, who assessed the beauty of 45 greenspaces on a five-point Likert scale. Respondents were university students and staff, members of the general public, and specialists in ecosystem services. They also answered questions on socio-demographic factors, pro-environmental attitudes, nature-related travel habits, and ecological knowledge. We calculated five variables from each greenspace photo: vegetation structural complexity, visual complexity, percentage of green pixels, percentage of canopy cover, and presence/absence of artificial structures. On average, natural vegetation remnants and city parks were preferred over vacant lots. Among natural remnants, forests were preferred over semi-open peatlands. Different ecosystem features were associated with perceived beauty across the three greenspace types. For natural remnants and city parks, visual complexity was positively related to appreciation scores, whereas for vacant lots, the presence of artificial structures was associated with lower appreciation scores. Respondents who self-identified as environmental defenders, had greater knowledge of flora, and travelled to observe fauna gave higher appreciation scores to vacant lots compared to other respondents. Younger respondents, and those who engaged in frequent nature-related travelling attributed higher scores to natural remnants. We conclude that aesthetic preferences for natural vegetation remnants, city parks, and vacant lots are influenced by a complex interplay of ecosystem features and observer characteristics. Understanding these intricate relationships can guide the management of vacant lots and promote more inclusive engagement with greenspaces, by considering the varying preferences of diverse segments of the population.

Customizing pyfao56 for evapotranspiration estimation and irrigation scheduling at the Limited Irrigation Research Farm (LIRF), Greeley, Colorado

Estimation of evapotranspiration (ET, the water used by soil evaporation and plant transpiration) and soil water depletion (Dr, the amount of water to bring the soil water in the root zone to field capacity) are critical in irrigation water management. The open-source Python-based crop ET and water balance modeling package named “pyfao56” was originally developed based on the dual crop coefficient approach as described in the Food and Agricultural Organization of the United Nations, Irrigation and Drainage Paper No. 56 (FAO-56). The package later expanded on the seminal FAO-56 document to consider various options relevant to ET and water balance modeling, including both short (grass) and tall (alfalfa) reference crops, optional discretization of variable soil layers based on field capacity, and interpretation of readily available water (RAW) and Dr based on both a dynamic (growing) root zone and maximum root zone. The package requires two input data objects; the “Parameters” class defines variables affecting soil water balance and ET and the “Weather” class specifies relevant meteorological data. Other optional input data objects include the “Irrigation” class for irrigation events, the “Soil_Profile: class for defining stratified soil layer data, and the “Update” class for assimilation of measured data. Additional tools are available for estimating and forecasting standardized reference ET (ETref), providing seasonal water balance summaries, computing goodness-of-fit statistics between measured and modeled values, and visualizing time series plots of daily Dr, ET, and crop coefficients. The current pyfao56 release (v1.2.1) was incorporated into a customized workflow for specific use at the USDA-ARS Limited Irrigation Research Farm (LIRF) in Greeley, Colorado, and named the LIRF Implementation of Pyfao56 (LIRFIP). Field data from 2023 full and limited irrigation field trials were used to demonstrate the functionality of LIRFIP and its customizations and integration of pyfao56 into the LIRF workflow. Specific customizations included use of pyfao56 “customload” functions to input data from various sources, including weather from an on-site micrometerological station (using the application programming interface (API) developed by the maintainers of the Colorado Agricultural Meteorological (CoAgMet) weather station network), irrigation events (via a shared Google docs sheet), field capacity values by plot and soil layer (via an Excel spreadsheet), and measured soil water content as well as basal crop coefficient (Kcb) calculated from fractional canopy cover (fc) (via an SQLite database). To evaluate multiple research plots, LIRFIP was designed to iterate simulations from multiple instances of pyfao56 and to produce customized output summary files and interactive hypertext markup language (html) graphs as an aid for irrigation management decisions for LIRF field trials. This study demonstrated pyfao56 as a useful, flexible, customizable, and repeatable ET-based water balance model by showcasing its integration within a specific computational workflow for irrigation management field research at LIRF. The approach serves as an example for pyfao56 integration in other water management tools as conceived by water managers, researchers, and practitioners worldwide.

Performance Evaluation of AquaCrop Model of Tomato under Stage Wise Deficit Drip Irrigation at Southern Ethiopia

Crop modeling is a powerful tool for predicting yield and water productivity. The aim of the study was to calibrate and validate the AquaCrop model for tomato under staged deficit drip irrigation in Ethiopia. The AquaCrop model was calibrated and validated by using the observed data of canopy cover, biomass, dry yield, and soil water content. The results showed that the model was accurate in predicting canopy cover, biomass, and dry yield under different water levels. The overall performance in simulating canopy cover of AquaCrop, biomass, and soil water content showed a good match between measured and simulated data. The calibration results indicated good performance on canopy cover with 0.96 ≤ r ≤ 1.00, 2.9% ≤ RMSE ≤ 8.0%, 7.5% ≤ CV (RMSE) ≤ 21.1%, 0.91 ≤ EF ≤ 0.99, and 0.98 ≤ d ≤ 1.00. On biomass, the model calibration values were 0.99 ≤ r ≤ 1.00, 0.3 t/ha ≤ RMSE ≤ 0.8 t/ha, 8.3% ≤ CV (RMSE) ≤ 25.2%, 0.92 ≤ EF ≤ 0.99, and 0.98 ≤ d ≤ 1.00. Soil water content displayed poor performance on calibration, with performance values of 0.54 ≤ r ≤ 0.82, 3.6 mm ≤ RMSE ≤ 24.4 mm, 1.30% ≤ CV (RMSE) ≤ 9.00%, −4.18 ≤ EF ≤ 0.00, and 0.66 ≤ d ≤ 0.81. The validation results demonstrated that the model performed well on canopy cover with 0.96 ≤ r ≤ 1.00, 3.1% ≤ RMSE ≤ 7.7%, 8.2% ≤ CV (RMSE) ≤ 20.8%, EF ≥ 0.91, and d ≥ 0.9. The model validation correctly predicted biomass with r ≥ 0.98, 0.3 t/ha ≤ RMSE ≤ 0.7 t/ha, 8.9% ≤ CV (RMSE) ≤ 21.8%, EF ≥ 0.94, and d ≥ 0.98. The model validation poorly performed in forecasting soil water content, 0.20 ≤ r ≤ 0.80, 10.2 t/ha ≤ RMSE ≤ 24.0 t/ha, 3.5% ≤ CV (RMSE) ≤ 9.0%, −22.76 ≤ EF ≤ 0.63, and 0.40 ≤ d ≤ 0.87. The AquaCrop model is easy to use, requires less input data, and has high simulation accuracy, making it a useful tool for predicting crop yields and water productivity under staged deficit irrigation in areas with limited data.

Stream Algal Biomass Associations with Environmental Variables in a Temperate Rainforest

Benthic algae and autotrophic seston are important bases of stream food webs, and several different environmental factors may influence their biomass. We explored how benthic algae and autotrophic seston biomass (using chlorophyll-a as a proxy for algal biomass) were associated with stream temperature, channel width, canopy cover, stream cardinal orientation, benthic macroinvertebrate functional feeding group abundance, salmonid biomass, and water velocity in 16 small, fish-bearing streams in the temperate rainforest of the Olympic Peninsula in Washington State, USA, in the summer of 2020. We performed a mixed-effects regression analysis of extracted chlorophyll-a (chl-a) and then used model averaging to determine significant (α = 0.05) algal–environmental associations for benthic algae and autotrophic seston separately. We found that benthic algae chl-a concentration increased significantly with stream temperature (p = 0.0085) and decreased significantly with water velocity (p = 0.0053). For autotrophic seston, we found that chl-a concentration increased significantly with benthic macroinvertebrate predator abundance (p = 0.0007) and stream temperature (p = 0.0160). This study underscores the need to consider a broad range of environmental variables when making research and management decisions concerning stream ecology.

Testing the Performance of LSTM and ARIMA Models for In-Season Forecasting of Canopy Cover (CC) in Cotton Crops

Cotton (Gossypium spp.), a crucial cash crop in the United States, requires the constant monitoring of growth parameters for informed decision-making. Recently, forecasting models have gained prominence for predicting canopy indicators, aiding in-season planning and management decisions to optimize cotton production. This study employed unmanned aerial system (UAS) technology to collect canopy cover (CC) data from a 40-hectare cotton field in Driscoll, Texas, in 2020 and 2021. Long short-term memory (LSTM) models, trained using 2020 data, were subsequently applied to forecast the CC values for 2021. These models were compared with real-time auto-regressive integrated moving average (ARIMA) models to assess their effectiveness in predicting the CC values up to 14 days in advance, starting from the 28th day after crop emergence. The results showed that multiple-input multi-step output LSTM models achieved higher accuracy in predicting the in-season CC values during the early growth stages (up to the 56th day), with an average testing RMSE of 3.86, significantly lower than other single-input LSTM models. Conversely, when sufficient testing data are available, single-input stacked-LSTM models demonstrated precision in CC predictions for later stages, achieving an average RMSE of 3.06. These findings highlight the potential of LSTM models for in-season CC forecasting, facilitating effective management strategies in cotton production.

Forest canopies as nature‐based solutions to mitigate global change effects on people and nature

Abstract Via sheltering, decoupling and buffering mechanisms, tree canopies have the capacity to mitigate impacts of multiple global‐change drivers on below‐canopy processes and organisms in forests. As a result, canopies have an important potential as nature‐based solution. The optimal combinations of forest canopy structural attributes to jointly mitigate the impacts of multiple global‐change drivers on below‐canopy organisms and processes have received little attention to date. To help solving this research gap, here we review how forest canopies modulate the effects of four important global‐change drivers—climate warming, drought, air pollution and biological invasions—on below‐canopy conditions. Particular attention is paid to mitigating canopy attributes that can be influenced by forest management, including canopy cover, tree species composition and vertical and horizontal structure. Synthesis. We show that the potential of forest canopies to mitigate global‐change effects is highly context‐dependent and that optimal canopy‐based solutions strongly depend on the environmental context and the targeted subcanopy organisms. Hence, holistic approaches, which maximize synergies and minimize trade‐offs, are needed to optimize the solution potential of forest canopies.

Participatory mapping of tree equity, preferences, and environmental justice in Portland, Oregon

In this study, we examine community lived experiences, preferences, and narratives about tree equity in Portland, Oregon. Using an environmental justice framework combined with participatory mapping, focus group discussions, and semi-structured interviews, we examine values, perceptions of canopy cover, and everyday relationships with trees across different racial and socio-economic backgrounds. Results show that participants from neighborhoods with histories of investment and privilege had different lived experiences compared to those with histories of disinvestment and marginalization. While participants overall valued trees, concerns about tree-related risk and safety varied in ways that reflected racial and class identities. Furthermore, memories and historic knowledge shaped perceived tree equity but contemporary problems such as tree removal, maintenance policies, and gentrification were viewed as barriers to achieving more equitable outcomes. Our study underscores the importance of centering community experiences, values, and needs along with tree programs that can alleviate barriers to tree care and maintenance.

Variation in fine-scale water table depth drives abundance of a unique semi-terrestrial crayfish species.

With anthropogenic changes altering the environment and the subsequent decline of natural habitats, it can be challenging to predict essential habitats for elusive and difficult to study taxa. Primary burrowing crayfish are one such group due to the complexity in sampling their semi-terrestrial, subterranean habitat. Sampling burrows usually requires a labor-intensive, time-consuming excavation or trapping process. However, limited information on burrowing crayfish suggests that fine-scale habitat variation may drive burrowing crayfish habitat choice. This project aimed to evaluate the fine-scale habitat characteristics that influence burrowing crayfish presence and abundance at a large, restored-remnant grassland preserve in north-central Illinois. We documented burrow abundance and quadrat-specific habitat variables such as root biomass, canopy cover, apparent seasonal high-water table (water table) depth and dominant vegetation at sites with and without burrowing crayfish populations. Data was recorded at every quadrat and analyzed using generalized linear mixed models. A total of 21 models were created to determine what habitat variables affected burrow presence and abundance. We found that the water table depth was a significant driver of burrow presence and abundance. Root biomass and vegetation cover were not significant drivers, although they did show up in the final models, explaining the data. These findings demonstrate empirical support for previous observations from other burrowing crayfish research and demonstrate the influence of fine-scale habitat when modeling elusive taxa requirements.

Evaluation of row width and nozzle selection on spray coverage and weed control in flooded rice

Abstract Barnyardgrass and other troublesome weeds have become a major problem for producers in a flooded rice system. Cultural control options and more efficient herbicide applications have become a priority to increase efficiency and weed control in rice. This study aimed to determine the effects of row width and nozzle selection on spray coverage and weed control in a flooded rice system. A field experiment was conducted at 7 site-years (Lonoke, AR, in 2021 and 2022; Pine Tree, AR, in 2021 and 2022; Rohwer, AR, in 2022; and Stoneville, MS, in 2021 and 2022) as a randomized complete block split-plot design. Five nozzles (XR, AIXR, TTI, TTI60, and AITTJ60) (subplot factor) were used for herbicide applications, and plots were drill-seeded in four row widths (whole plot factor) (13, 19, 25, and 38 cm). A droplet size experiment was conducted to evaluate the droplet size and velocity of each nozzle type used in the field experiment. Overall, as row width increased, barnyardgrass density increased. The rice grown in a wider width took longer to generate canopy closure, allowing weed escapes in the crop. For example, the 13-cm width had a 12 percentage point canopy coverage increase compared to the 38-cm row width at the preflood timing resulting in a reduction of six barnyardgrass plants per square meter. The smallest droplet size-producing nozzle (XR) provided greater weed control throughout the study but is more prone to drift. The dual-fan nozzles (AITTJ60 and TTI60) had variable weed control impacts, and it was difficult to predict when this might occur; however, they did have increased deposits on water-sensitive cards compared to single-fan counterparts (AIXR and TTI). In conclusion, a narrower row width (e.g., 19-cm or less) and a smaller droplet size producing nozzle (XR) are optimal for barnyardgrass control in a flooded rice system.

An Advanced Terrain Vegetation Signal Detection Approach for Forest Structural Parameters Estimation Using ICESat-2 Data

Accurate forest structural parameters (such as forest height and canopy cover) support forest carbon monitoring, sustainable forest management, and the implementation of silvicultural practices. The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2), which is a spaceborne Light Detection and Ranging (LiDAR) satellite, offers significant potential for acquiring precise and extensive information on forest structural parameters. However, the ICESat-2 ATL08 product is significantly influenced by the geographical environment and forest characteristics, maintaining considerable potential for enhancing the accuracy of forest height estimation. Meanwhile, it does not focus on providing canopy cover data. To acquire accurate forest structural parameters, the Terrain Signal Neural Network (TSNN) framework was proposed, integrating Computer Vision (CV), Ordering Points to Identify the Clustering Structure (OPTICS), and deep learning. It encompassed an advanced approach for detecting terrain vegetation signals and constructing deep learning models for estimating forest structural parameters using ICESat-2 ATL03 raw data. First, the ATL03 footprints were visualized as Profile Raster Images of Footprints (PRIF), implementing image binarization through adaptive thresholding and median filtering denoising to detect the terrain. Second, the rough denoising buffers were created based on the terrain, combining with the OPTICS clustering and Gaussian denoising algorithms to recognize the terrain vegetation signal footprints. Finally, deep learning models (convolutional neural network (CNN), ResNet50, and EfficientNetB3) were constructed, training standardized PRIF to estimate forest structural parameters (including forest height and canopy cover). The results indicated that the TSNN achieved high accuracy in terrain detection (coefficient of determination (R2) = 0.97) and terrain vegetation signal recognition (F-score = 0.72). The EfficientNetB3 model achieved the highest accuracy in forest height estimation (R2 = 0.88, relative Root Mean Squared Error (rRMSE) = 13.5%), while the CNN model achieved the highest accuracy in canopy cover estimation (R2 = 0.80, rRMSE = 18.5%). Our results have significantly enhanced the accuracy of acquiring ICESat-2 forest structural parameters, while also proposing an original approach combining CV and deep learning for utilizing spaceborne LiDAR data.

Evaluating Urban Green Space Inequity to Promote Distributional Justice in Portland, Oregon

Access and exposure to urban green space—the combination of parks and vegetative cover in cities—are associated with various health benefits. As urban green space is often unequally distributed throughout cities, understanding how it is allocated across socio-demographic populations can help city planners and policy makers identify and address urban environmental justice and health equity issues. To our knowledge, no studies have yet combined assessments of park quality, park availability, and green cover to inform equitable urban green space planning. To this end, we developed a comprehensive methodology to identify urban green space inequities at the city scale and applied it in Portland, OR, USA. After auditing all public parks in Portland and gathering green cover data from publicly accessible repositories, we used a suite of statistical tests to evaluate distribution of parks and green cover across Census block groups, comprising race, ethnicity, income, and educational attainment characteristics. Right-of-way tree canopy cover was the most significant urban green space inequity identified in bivariate analysis (rs = −0.73). Spatial autoregressive models identified that right-of-way, private, and overall tree canopy cover (Nagelkerke pseudo-R2 = 0.66, 0.77, and 0.67, respectively) significantly decreased with the proportion of minoritized racial population and increased with median income. The results were then used to identify priority locations for specific urban green space investments. This research establishes a process to assess intra-urban green space inequities, as well as identify data-informed and spatially explicit planning priorities to promote health equity and environmental justice.

Research on Assimilation of Unmanned Aerial Vehicle Remote Sensing Data and AquaCrop Model.

Taking the AquaCrop crop model as the research object, considering the complexity and uncertainty of the crop growth process, the crop model can only achieve more accurate simulation on a single point scale. In order to improve the application scale of the crop model, this study inverted the canopy coverage of a tea garden based on UAV multispectral technology, adopted the particle swarm optimization algorithm to assimilate the canopy coverage and crop model, constructed the AquaCrop-PSO assimilation model, and compared the canopy coverage and yield simulation results with the localized model simulation results. It is found that there is a significant regression relationship between all vegetation indices and canopy coverage. Among the single vegetation index regression models, the logarithmic model constructed by OSAVI has the highest inversion accuracy, with an R2 of 0.855 and RMSE of 5.75. The tea yield was simulated by the AquaCrop-PSO model and the measured values of R2 and RMSE were 0.927 and 0.12, respectively. The canopy coverage R2 of each simulated growth period basically exceeded 0.9, and the accuracy of the simulation results was improved by about 19.8% compared with that of the localized model. The results show that the accuracy of crop model simulation can be improved effectively by retrieving crop parameters and assimilating crop models through UAV remote sensing.

Physiological Age of Potato Seed Tubers of Contrasting Cultivars Hardly Affects Crop Performance in a Temperate Climate

AbstractThe physiological age of potato seed tubers influences the subsequent field performance of the crop, conditioning its growth and development. This study investigates the effects of the physiological age of seed tubers on the field performance of four contrasting, currently widely grown cultivars (Agria, Festien, Innovator, Lady Claire) across three sites in the Netherlands over 3 years (Cycles 1—3). Seed tubers were produced annually at a single site under uniform conditions, with variations in physiological age induced by applying different storage temperatures (4, 7, 10, and/or 17 °C). Our previous study revealed varying effects of storage temperature on sprouting behaviour among cultivars, suggesting different rates of ageing. After completing the storage season, in the current study these seed tubers were planted in the field and subsequent crop performance was thoroughly phenotyped. Cultivar-specific effects of storage temperature were evident during early growth stages and when seed tubers were stored at 17 °C in Cycle 3. However, within the storage temperature range of 4 to 10 °C, the influence of storage temperatures was small or inconsistent. Overall, effects on canopy cover, stem and tuber number, yield and quality, and tuber yield explanatory variables were limited. The outcomes suggest that while very high storage temperatures should be avoided in regions with long crop cycles, stringent cold storage regimes are unnecessary to achieve optimal growth vigour in potato seed tubers of the studied cultivars in a temperate climate.

Sudden openings and gradual closures in canopy cover modulate acclimation, survival, and growth of a shade-tolerant rainforest tree species

Sudden openings and gradual closures in canopy cover modulate acclimation, survival, and growth of a shade-tolerant rainforest tree species

Effects of bed width and crop row spacing on barnyardgrass (Echinochloa crus-galli) emergence and seed production in furrow-irrigated rice

Abstract Furrow-irrigated rice (Oryza sativa L.) has become a popular option for rice production in Arkansas. Highly troublesome weeds like barnyardgrass [Echinochloa crus-galli (L.) P. Beauv.] are a major problem for producers in all rice production systems. Cultural tactics should become a priority to enhance crop growth and competitiveness. This research aimed to determine the effects of bed width (irrigation furrow spacing) and crop row spacing manipulation on E. crus-galli emergence and seed production in a furrow-irrigated rice system. Three bed widths (76, 97, and 152 cm) (whole-plot factor) were used, and plots were drill seeded in four crop row spacings (13, 19, 25, and 38 cm) (subplot factor). The widest width of 152 cm had a slight increase in E. crus-galli density in the early rice life cycle but, by the end of the season, did not differ from the narrower bed widths. Conversely, a decrease in E. crus-galli seed production was observed as the bed width increased. Similar rice canopy coverage and yields occurred among all three bed widths. As for crop row spacing, as the width increased, E. crus-galli density also increased. The 13-cm crop row spacing had the lowest preflood E. crus-galli density, preharvest panicle count, and seed production. No effect of crop row spacing was observed on rice canopy coverage; however, the 13-cm crop row spacing produced the greatest rice yield. The 13-cm crop row spacing paired with the 152-cm bed width may be the optimum combination of ecological strategies in furrow-irrigated rice to reduce E. crus-galli seed production while maintaining rice growth and yield.

Examining the Impacts of Pre-Fire Forest Conditions on Burn Severity Using Multiple Remote Sensing Platforms

Pre-fire environmental conditions play a critical role in wildfire severity. This study investigated the impact of pre-fire forest conditions on burn severity as a result of the 2020 Bighorn Fire in the Santa Catalina Mountains in Arizona. Using a stepwise regression model and remotely sensed data from Landsat 8 and LiDAR, we analyzed the effects of structural and functional vegetation traits and environmental factors on burn severity. This analysis revealed that the difference normalized burn ratio (dNBR) was a more reliable indicator of burn severity compared to the relative dNBR (RdNBR). Stepwise regression identified pre-fire normalized difference vegetation index (NDVI), canopy cover, and tree density as significant variables across all land cover types that explained burn severity, suggesting that denser areas with higher vegetation greenness experienced more severe burns. Interestingly, residuals between the actual and estimated dNBR were lower in herbaceous zones compared to denser forested areas at similar elevations, suggesting potentially more predictable burn severity in open areas. Spatial analysis using Geary’s C statistics further revealed a strong negative autocorrelation: areas with high burn severity tended to be clustered, with lower severity areas interspersed. Overall, this study demonstrates the potential of readily available remote sensing data to predict potential burn severity values before a fire event, providing valuable information for forest managers to develop strategies for mitigating future wildfire damage.

Endangered giant kelp forests support similar fish and macroinvertebrate communities to sympatric stipitate kelp forests

Kelp forests provide habitat for many species, but it remains uncertain whether specific kelp forest types support distinct biodiversity. Surface canopy-forming giant kelp (Macrocystis pyrifera) forests in Tasmania, Australia, have declined significantly due to climate change, and have been widely replaced by forests dominated by smaller stipitate kelps like Ecklonia radiata. However, there is limited knowledge of the community composition of Macrocystis forests and how this may differ from the stipitate kelp forest community. Underwater visual census surveys were conducted of the fishes and macroinvertebrates (> 2.5 cm length) in remnant Macrocystis forests in south-eastern Tasmania, and in adjacent stipitate kelp forests. 18 sites (9 of each forest type) were surveyed across two regions during the period of peak growth and canopy cover (i.e. winter-spring). Faunal community composition varied little between forest types, although Macrocystis forests supported more than double the abundance and biomass of mobile fishes, while cryptic fishes differed by forest type depending on region. Macroinvertebrate assemblages did not differ between forest types nor regions. Thus, for the taxa and time period examined, Tasmanian Macrocystis and stipitate kelp forests supported mostly similar faunal communities. Kelp forest communities showed spatial variation and were also likely influenced by a variety of other habitat characteristics, such as the relatively small patch sizes and/or ephemeral state of the remnant Macrocystis forests. Quantifying the community structure of these endangered communities informs the ecological changes that have occurred and will serve as an important reference for ongoing conservation and restoration activities.

Water temperature and microenvironmental factors predict the presence and detection of the snail host of Fasciola hepatica in Andean Patagonia

The transmission of Fasciola hepatica occurs only where there are -or recently were- aquatic or amphibious snails of the Lymnaeidae family, the intermediate host of this parasite. Direct detection of these snails is time-consuming and imprecise, hindering accurate and detailed mapping of transmission risk. To identify which microenvironmental factors could be used as proxies for the occurrence of the lymnaeid snail Galba viator, a major intermediate host in South America, a total of 183 1-m2 quadrants across diverse water bodies in an endemic area in Andean Patagonia were manually timed-searched for snails and microenvironmental variables were registered. Data was analyzed using a Bayesian hierarchical occupancy model that assessed the effects of the microenvironmental variables on the presence of snails while considering imperfect snail detection. The model estimated that G. viator predominantly inhabits shallow aquatic environments, in the presence of grasses, where snails of the genus Biomphalaria are also detected, and with scarce tree canopy cover. Physical factors affecting occupancy presumably act as proxies for the average water temperature, while the temperature at the time of sampling was found to affect snail detectability. The identified variables are easy, fast, and inexpensive to measure, and can complement management decisions and risk maps based on coarser remote-sensing data, particularly relevant in a context of growing resistance to anthelminthic drugs.

Assessment of Growth and Carbon Stock of 6-Year-Old Dryobalanops lanceolata

Dipterocarp species are the mainstay of forest products in the form of woodworking. Dryobalanops lanceolata is one of the dipterocarp species. The study aimed to analyze the effect of canopy cover on the growth and carbon storage of 6-year-old D. lanceolata. Data were collected by measuring diameter, height, canopy cover, soil samples, and carbon storage. The research method was carried out by regression between canopy cover and tree diameter and height growth, analyzing soil fertility and carbon content. The 6-year-old D. lanceolata has grown well with a canopy cover of less than 34% (canopy openness of more than 66%) and could grow on clay soil with acidic pH. D. lanceolata at the age of 6 years has been able to improve the nutrient content in the soil. Under 6-year-old D. lanceolata stands, it has provided significant changes to increase the availability of soil nutrients for C-organic, total N, and K-available content. Carbon storage in D. lanceolata has increased in 1 year by 41.91%. Keywords: biomass, canopy cover, carbon growth, Dryobalanops lanceolata

Examining the Percent Canopy Cover and Health of Winter Wheat in No-Till and Conventional Tillage Plots Using a Drone

The percent canopy cover and health of winter wheat are important crop performance indicators. Thus, understanding how tillage management practices affect these indicators is beneficial for improving crop performance and consequently yield. The availability of high-resolution drone data with spectral characteristics provides an opportunity to examine the percent canopy cover and health of winter wheat in different tillage systems. This is because the use of drones provides real-time high spatial resolution and temporal images to effectively monitor winter wheat conditions throughout the growing season. Nonetheless, very limited studies have utilized drone data for assessing the percent canopy cover and health conditions of winter wheat for different tillage practices. This study aimed to examine the percent canopy cover and health of winter wheat in no-till and conventional tillage plots using a drone. We used the mean Normalized Difference Vegetation Index (NDVI) ± Standard Deviation (SD) (0.89 ± 0.04) of winter wheat for the growth stages of tillering, jointing, and boot/heading to generate the percent wheat canopy cover. The Normalized Difference Red-Edge (NDRE) produced for winter wheat at the middle and late growth stages was used as a proxy for wheat health condition. We found that the mean percentage canopy cover of winter wheat was about 4% higher in no-till compared to conventional tillage plots in most of the growing season. The mean NDRE ± standard error (SE) of winter wheat was about 0.44 ± 0.01 and 0.43 ± 0.01 for no-till and conventional tillage plots, respectively, during the mid- and late growth stages. There was no significant difference in either the percent canopy cover or health of winter wheat between no-till and conventional tillage plots. The results generated in this study could be used to support farmers’ decision-making process regarding tillage practices and wheat crop performance.