Plant age, crop stage and surrounding habitats: their impact on sucking pests and predators complex in cotton (Gossypium hirsutum L.) field plots in arid climate at district Layyah, Punjab, Pakistan

Sucking pests are major threat to cotton field crop which cause unbearable losses to the crop yield. Aim of the current study was to record seasonal dynamics of major sucking insect pests including whitefly, jassid, thrips and their natural arthropod predators i.e. green lacewings and spiders in cotton field plots. The effects of surrounding field crops on pests’ density and predatory efficiency of predators were also recorded. For sampling and survey of insects, the visual counting was found to be the most efficient method for recording the abundance of insects, trailed by net sweeping and tapping. Whitefly was the most dominant sucking pest found on the vegetative stage of cotton, followed by jassid and thrips. Fluctuated populations of predatory arthropods, spiders and green lacewings were also recorded during whole cropping season however, the densities of pests and predators varied with crop phenology. Spiders’ population was encouraging at both vegetative and flowering stage and also the same trend of jassid and whitefly were observed at both stages of the crop. Surrounding habitats showed non-significant effect on population densities of insect pests and predators. For abiotic factors, the spiders showed strong positive correlation with humidity and temperature. However, green lacewing was only positively correlated with humidity. On the other hand, the populations of whitefly, jassid and thrips showed non-significant correlation with both temperature and humidity. Overall densities of sucking insect pests were found above economic threshold level. The plant age, crop stage and surrounding habitats effect on the population fluctuation of pests as well as the predators’ abundance. The future studies are also warranted to investigate the altered habitats and multiple trap cropping to find out their impact on unattended insect predators and parasitoids in cotton crop.

Characterization of Dry-Season Phenology in Tropical Forests by Reconstructing Cloud-Free Landsat Time Series

Fine-resolution satellite imagery is needed for characterizing dry-season phenology in tropical forests since many tropical forests are very spatially heterogeneous due to their diverse species and environmental background. However, fine-resolution satellite imagery, such as Landsat, has a 16-day revisit cycle that makes it hard to obtain a high-quality vegetation index time series due to persistent clouds in tropical regions. To solve this challenge, this study explored the feasibility of employing a series of advanced technologies for reconstructing a high-quality Landsat time series from 2005 to 2009 for detecting dry-season phenology in tropical forests; Puerto Rico was selected as a testbed. We combined bidirectional reflectance distribution function (BRDF) correction, cloud and shadow screening, and contaminated pixel interpolation to process the raw Landsat time series and developed a thresholding method to extract 15 phenology metrics. The cloud-masked and gap-filled reconstructed images were tested with simulated clouds. In addition, the derived phenology metrics for grassland and forest in the tropical dry forest zone of Puerto Rico were evaluated with ground observations from PhenoCam data and field plots. Results show that clouds and cloud shadows are more accurately detected than the Landsat cloud quality assessment (QA) band, and that data gaps resulting from those clouds and shadows can be accurately reconstructed (R2 = 0.89). In the tropical dry forest zone, the detected phenology dates (such as greenup, browndown, and dry-season length) generally agree with the PhenoCam observations (R2 = 0.69), and Landsat-based phenology is better than MODIS-based phenology for modeling aboveground biomass and leaf area index collected in field plots (plot size is roughly equivalent to a 3 × 3 Landsat pixels). This study suggests that the Landsat time series can be used to characterize the dry-season phenology of tropical forests after careful processing, which will help to improve our understanding of vegetation–climate interactions at fine scales in tropical forests.

Study of species associations in native habitats of the genus Dacrydium Lamb. (Podocarpaceae) in Central Kalimantan, Indonesia

The tropical coniferous genus Dacrydium Lamb. is occurring with some species in various habitat types in Kalimantan, Indonesia. So far four species are recorded for that area. Even though the species are considered globally as Endangered and Least Threatened species by the IUCN, in Central Kalimantan the genus is under threat from pressures related to logging, fire and land conversion. Locally known as Alau, this genus prospers in a range of habitats from heath to deep-peat swamp forests in Central Kalimantan. Data from field plots across four sites in Central Kalimantan are used to compare variations in habitat and species composition where Alau trees are present. The results of the analysis show the wide range of habitat structure as well as species diversity where Alau tends to thrive. The characterization of these sites may be helpful in protecting and conserving Alau forest areas under social forestry and local forest management resource use plans.

The Role of Time-Series L-Band SAR and GEDI in Mapping Sub-Tropical Above-Ground Biomass

Physics-based algorithms estimating large-scale forest above-ground biomass (AGB) from synthetic aperture radar (SAR) data generally use airborne laser scanning (ALS) or grid of national forest inventory (NFI) to reduce uncertainties in the model calibration. This study assesses the potential of multitemporal L-band ALOS-2/PALSAR-2 data to improve forest AGB estimation using the three-parameter water cloud model (WCM) trained with field data from relatively small (0.1 ha) plots. The major objective is to assess the impact of the high uncertainties in field inventory data due to relatively smaller plot size and temporal gap between acquisitions and ground truth on the AGB estimation. This study analyzes a time series of twenty-three ALOS-2 dual-polarized images spanning 5 years acquired under different weather and soil moisture conditions over a subtropical forest test site in India. The WCM model is trained and validated on individual acquisitions to retrieve forest AGB. The accuracy of the generated AGB products is quantified using the root mean square error (RMSE). Further, we use a multitemporal AGB retrieval approach to improve the accuracy of the estimated AGB. Changes in precipitation and soil moisture affect the AGB retrieval accuracy from individual acquisitions; however, using multitemporal data, these effects are mitigated. Using a multitemporal AGB retrieval strategy, the accuracy improves by 15% (55 Mg/ha RMSE) for all field plots and by 21% (39 Mg/ha RMSE) for forests with AGB less than 100 Mg/ha. The analysis shows that any ten multitemporal acquisitions spanning 5 years are sufficient for improving AGB retrieval accuracy over the considered test site. Furthermore, we use allometry from colocated field plots and Global Ecosystem Dynamics Investigation (GEDI) L2A height metrics to produce GEDI-derived AGB estimates. Despite the limited co-location of GEDI and field data over our study area, within the period of interest, the preliminary analysis shows the potential of jointly using the GEDI-derived AGB and multi-temporal ALOS-2 data for large-scale AGB retrieval.

A Comparative Study of Groundwater between Geological Groups of Southern Benue Trough, Nigeria Using Modelling Approach

Groundwater studies were carried out between two geological groups to evaluate factors that influences groundwater geochemistry. To achieve this, 30 groundwater samples were collected. Parameters such as pH, Electrical Conductivity (Ec), Total Dissolved Solids (TDS), Total Hardness (TH), and hydrochemical characteristics (Na2+, K+ , Ca2+, Mg2+, HCO3¯, NO3¯, Cl¯, CO23¯, and SO42¯) of groundwater were determined. Findings revealed that the pH value for Asu River Group ranges from 5.3 to 7.5, and that of Eze Aku Group ranges from 4.1 to 7.9. It was observed that areas around the mines had low pH values. Analyzed results that were obtained were interpreted using various hydrogeochemical models. Parson plots reflected that groundwater within the two geological groups fell within Ca˗Mg˗SO4 and Ca˗Mg˗Cl. Results from End˗member plots showed that 96% of groundwater samples analyzed were categorized under carbonate weathering, 4% fell silicate weathering. Gibbs plots revealed that interactions between groundwater and surrounding host rocks are mostly the main processes responsible for chemical characteristics of groundwater, Diamond field plots suggested that groundwater within the study were categorized to be high in Ca + Mg & SO4 + Cl, the plot of Ca2+/(HCO3¯+SO42¯) against Na+/Cl¯ revealed that groundwater was considered to be within the natural state for the two group. The plot of TDS against TH showed that groundwater is classified as soft freshwater. The study revealed there was no significant difference between factors that influence groundwater within the two geological.

Plot size effects on airborne LiDAR-derived metrics and predicted model performance of subtropical planted forest stand attributes

Background Field plot measurement is an essential task for forest inventory and monitoring and ecological applications based on airborne LiDAR. To optimize the field plot size and reduce cost, it is necessary to investigate the influence of field plot size on LiDAR-derived metrics and the accuracy of forest parameter estimation models. Methods A subtropical planted forest with an area of 4,770 ha was used as the study site, and 104 square plot of 900 m2 (30 m×30 m, subdivided into nine quadrats, each with an area of 100 m2 (10 m×10 m)) was divided into field plots with six different areas (100 m2, 200 m2, 300 m2, 400 m2, 600 m2 and 900 m2) by grouping quadrats. The differences in the LiDAR-derived metrics and stand attributes of different sized plots with four forest types (Chinese fir, pine, eucalyptus and broadleaf) were investigated. Through multivariate power models with stable structures, the differences in forest parameter (BA, VOL) estimation accuracies for plots with different sizes were compared. Results (1) The mean differences in LiDAR-derived metrics related to height, density and vertical structure between the plots with different sizes and the 900 m2 plot containing all forest types were very small, and when the plot size changed, these differences changed irregularly; however, the standard deviations of the differences increased rapidly with decreasing plot size. (2) There were significant differences in the mean of the maximal height of the point cloud (Hmax), density of the 75th percentile of the point cloud (dh75) and mean leaf area density (LADmean) (except for Chinese fir and eucalyptus) between the plots with different sizes and the 900 m2 plot containing all forest types; other LiDAR-derived metrics had significant differences in only some or a certain size of plots, but there was no regularity. (3) Except for the maximal tree height of the plot (Hm), the forest stand attributes, including the mean tree height (H), diameter at breast height (DBH), basal area (BA), and stand volume (VOL), of all forest types showed either no significant differences or minimal differences between plots with different sizes and the 900 m2 plot. (4) With increasing plot size, the coefficient of determination (R2) of the estimation models for VOL and BA of all forest types increased gradually, while the relative root mean square error (rRMSE) and mean prediction error (MPE) decreased gradually, and the estimation accuracy of the models improved. Conclusion Due to the heterogeneity of the vertical and horizontal forest structures, some LiDAR-derived metrics and stand parameters for field plots with different sizes varied. As the plot size increased, the variations in the independent variables (LiDAR-derived metrics) and dependent variables (stand parameters) of the estimation models decreased gradually. These changes improved the robustness and accuracy of the models. In the application of airborne LiDAR in forest inventory and monitoring, both prediction accuracy and cost should be considered. For subtropical planted forests, we preliminarily suggest the following appropriate sizes for field plots: 900 m2 for Chinese fir and pine forests, 400 m2 for eucalyptus forests and 600 m2 for broadleaf forests. However, this protocol still needs to be tested in further studies.

Evaluation of Unmanned Multicopters’ Performance Indicators for Pesticide and Agrochemical Application

The authors considered the use of unmanned aerial vehicles as one of the promising innovative directions for the development of economic and social sectors. The authors touched upon the prospects for their use in agriculture, especially for pesticide and agrochemical application, where accuracy, quality and timeliness are important. The relevance of multicopter performance assessment was noted. (Research purpose) The authors aim to develop and test a methodology for the evaluation of multicopters’ performance indicators for pesticide and agrochemical application in the agricultural industry. (Materials and methods) The authors used scientific and technical information and experimental materials, applied methods of system, statistical and functional-cost analysis, mathematical modeling, object and process parameter optimization, as well as previously developed methodological approaches to studying the aerial distribution of substances. (Results and discussion) The authors presented a general description and content of the developed methodology and means for assessing multicopter performance when applying working solutions that provide for an estimation error of up to 7 percent.The typical options for field plots and their treatment were specified. The authors analyzed the results of testing the methodology and software for a typical hexacopter with the payload of up to 10 kilograms. The authors analyzed the impact of working speed of up to 10 meters per second, application rates of 2-30 liters per hectare, the size and characteristics of the field plot up to 200 hectares, traffic patterns and other factors on productivity and multicopter treatment cost. (Conclusions) The authors confirmed the efficiency of implementing complex multi-factor assessment of multicopter performance indicators for working fluids application in agricultural production. The authors determined the appropriate area of applying multicopters with a payload of up to 10 kilograms in the field plots up to 50-60 hectares with a rut length of up to 800-900 meters with different treatment performance: flight – up to 10.5 hectares per flight hour, working – up to 7.5 hectares per hour, daytime – up to 55 hectares. Proposals and recommendations for the provision, organization and implementation of this work were formulated.

Ecological land survey and soils inventory for Katmai National Park and Preserve, 2016–2017

This study was conducted to inventory, classify, and map soils and vegetation within the ecosystems of Katmai National Park and Preserve (KATM) using an ecological land survey (ELS) approach. The ecosystem classes identified in the ELS effort were mapped across the park, using an archive of Geo-graphic Information System (GIS) and Remote Sensing (RS) datasets pertaining to land cover, topography, surficial geology, and glacial history. The description and mapping of the landform-vegetation-soil relationships identified in the ELS work provides tools to support the design and implementation of future field- and RS-based studies, facilitates further analysis and contextualization of existing data, and will help inform natural resource management decisions. We collected information on the geomorphic, topographic, hydrologic, pedologic, and vegetation characteristics of ecosystems using a dataset of 724 field plots, of which 407 were sampled by ABR, Inc.—Environmental Research and Services (ABR) staff in 2016–2017, and 317 were from existing, ancillary datasets. ABR field plots were located along transects that were selected using a gradient-direct sampling scheme (Austin and Heligers 1989) to collect data for the range of ecological conditions present within KATM, and to provide the data needed to interpret ecosystem and soils development. The field plot dataset encompassed all of the major environmental gradients and landscape histories present in KATM. Individual state-factors (e.g., soil pH, slope aspect) and other ecosystem components (e.g., geomorphic unit, vegetation species composition and structure) were measured or categorized using standard classification systems developed for Alaska. We described and analyzed the hierarchical relationships among the ecosystem components to classify 92 Plot Ecotypes (local-scale ecosystems) that best partitioned the variation in soils, vegetation, and disturbance properties observed at the field plots. From the 92 Plot Ecotypes, we developed classifications of Map Ecotypes and Disturbance Landscapes that could be mapped across the park. Additionally, using an existing surficial geology map for KATM, we developed a map of Generalized Soil Texture by aggregating similar surficial geology classes into a reduced set of classes representing the predominant soil textures in each. We then intersected the Ecotype map with the General-ized Soil Texture Map in a GIS and aggregated combinations of Map Ecotypes with similar soils to derive and map Soil Landscapes and Soil Great Groups. The classification of Great Groups captures information on the soil as a whole, as opposed to the subgroup classification which focuses on the properties of specific horizons (Soil Survey Staff 1999). Of the 724 plots included in the Ecotype analysis, sufficient soils data for classifying soil subgroups was available for 467 plots. Soils from 8 orders of soil taxonomy were encountered during the field sampling: Alfisols (<1% of the mapped area), Andisols (3%), Entisols (45%), Gelisols (<1%), Histosols (12%), Inceptisols (22%), Mollisols (<1%), and Spodosols (16%). Within these 8 Soil Orders, field plots corresponded to a total of 74 Soil Subgroups, the most common of which were Typic Cryaquents, Typic Cryorthents, Histic Cryaquepts, Vitrandic Cryorthents, and Typic Cryofluvents.

Inventory of refugia plants potentially medicinal in the agricultural area of Plandaan Subdistrict, Jombang Regency, East Java Province

Agricultural and plantation areas dominate the Plandaan subdistrict, so the majority of the livelihoods of its residents are engaged in agriculture. In practice, in addition to using pesticides, crop pest control in the Plandaan subdistrict is to make use of refugia plants. The refugia technique is economical and environmentally friendly because it does not use synthetic chemicals that damage the environment. In addition to acting as natural pest control, refugia plants are also known to have potential as medicines. This study aimed to inventory or collect medicinal plants refugia in the agricultural area of ​​the Plandaan subdistrict. This study used descriptive explorative methods. Data collection techniques were carried out through observation and directly documenting species of refugia plants with potential drugs found in the area of observation plots. In this study, the subject was a potentially medicinal refugia plant found on an observational plot with a plot size of 10 × 10 meters. The observations were three plots of agriculture fields and three plots of telajakan (open space). The frequency of refugia plants found from the study on the plot of agriculture fields as many as 1035 plants, while in the plot of telajakan as many as 1007 plants consisting of 37 types of species that each has different medicinal potentials, such as heat and fever lowering, treating cough, flu, skin diseases, and wounds. Agriculture field plots had more frequency of potentially medicinal refugia plants than telajakan plots.