Field Plots Research Articles

Resistance and resilience: Tidal freshwater marsh response and recovery to acute and chronic saltwater intrusion

The ability to both resist and recover from disturbances like storm surge and saltwater intrusion plays a key role in shaping the structure and function of tidal marshes. In this study, porewater chemistry, vegetation, and soil elevation change were measured in field plots of a tidal freshwater marsh exposed to four years of experimental press (chronic) and pulse (acute) brackish water additions followed by five years of recovery to assess their resistance and resilience to saltwater intrusion. Press additions produced significant, widespread changes in marsh structure and function including increased porewater N and P, reduced macrophyte cover and species richness, and loss of soil surface elevation whereas pulse additions had little effect. Once dosing ceased, porewater chemistry, vegetation and soils in press plots recovered at differing rates, with porewater N and P declining to background levels after one year, plant cover and species richness increasing within two to four years, and soil surface elevation increasing to similar levels found in control plots after five years. The plant community in the press treatment converged with the other treatments after 3–4 years, though macrophyte species exhibited varying rates of recovery. Ground cover (Ludwigia repens) and soft stem species (Persicaria) that declined first, recovered faster than Zizaniopsis miliacea that was more resistant but less resilient to brackish water intrusion. While tidal freshwater marshes are resistant and resilient to pulses such as those that stem from hurricanes and storm surges, continued long-term intrusion events like sea level rise (SLR) will likely lead to conversion into brackish marsh. Understanding long-term responses and tradeoffs in resistance and recovery as shown in this experiment offers insight into the future trajectory of tidal freshwater marshes as well as broader ecosystem responses to disturbance and recovery crucial to management and restoration.

Model-assisted estimation of domain totals, areas, and densities in two-stage sample survey designs

Model-assisted, two-stage forest survey sampling designs provide a means to combine airborne remote sensing data, collected in a sampling mode, with field plot data to increase the precision of national forest inventory estimates, while maintaining important properties of design-based inventories, such as unbiased estimation and quantification of uncertainty. In this study, we present a comprehensive set of model-assisted estimators for domain-level attributes in a two-stage sampling design, including new estimators for densities, and compare the performance of these estimators with standard poststratified estimators. Simulation was used to assess the statistical properties (bias, variability) of these estimators, with both simple random and systematic sampling configurations, and indicated that 1) all estimators were generally unbiased. and 2) the use of lidar in a sampling mode increased the precision of the estimators at all assessed field sampling intensities, with particularly marked increases in precision at lower field sampling intensities. Variance estimators are generally unbiased for model-assisted estimators without poststratification, while model-assisted estimators with poststratification were increasingly biased as field sampling intensity decreased. In general, these results indicate that airborne remote sensing data, collected as an intermediate level of sampling, can be used to increase the efficiency of national forest inventories in remote regions.

Non-similar analysis of two-phase hybrid nano-fluid flow with Cattaneo-Christov heat flux model: a computational study

The purpose of this research elaborates the behaviour of the two-phase hybrid nano-fluid flow of viscous fluid through the stretching sheet. Impacts of heat transfer, for MHD two-dimensional steady flow over stretching surface with Cattaneo-Christov (CC) model are discussed. Bio-convective, thermal radiation and convective conditions are considered. Porous surface and velocity slip effects are also considered. Darcy-Forchheimer relation characterizes porous medium. The governing equations are solved through MATLAB. Plots of temperature and velocity fields are discussed via various estimations of emerging variables. Results findings shows the velocity profile decreases in case of both simple and hybrid nano-fluids for higher estimations of magnetic parameter and local inertia coefficient parameter. Fluid temperature reduces for maximum thermal relaxation parameter and possess higher values against radiation parameter. Microorganism profile reduces bio-convection Lewis number but increases for the magnetic parameter. Prominent results from this study include determining the effective parameters to enhance heat transfer efficiency and defining the circumstances in which the nano-fluid performs better than traditional fluids. These discoveries offer significant perspectives on the utilization of Cattaneo-Christov model with hybrid nano-fluid flow in comprehensive thermal management systems and establish a basis for subsequent practical and theoretical investigations within the domain.

Strategic tillage of no-till decreased surface and subsurface losses of dissolved phosphorus.

Enrichment of soluble P on the surface layer of long-term no-till (NT) soils, and consequent increase in dissolved P losses, is a concern for which occasional plowing has been suggested as a remedy. We measured the effect of such strategic tillage (ST) on surface and subsurface P losses from 0.5-ha field plots on clay soil for 4years. Two NT plots had discharged threefold dissolved molybdate-reactive P (DRP) losses compared to annually plowed soil conventional tillage (CT). ST by plowing to 20-cm depth was applied on one of the NT plots, whereas the other remained under NT. ST done in July was sown with canola (Brassica napus ssp. oleifera) to establish plant cover before winter. Summed 4-year DRP loss from ST treatment was 60% lower compared to NT (0.78vs. 1.96kg ha-1), accompanied with 11% higher particulate P (PP) loss (4.39vs. 3.97kg ha-1). CT plots produced slightly lower DRP losses (0.53-0.76kg ha-1) than ST, but higher PP losses (6.02-7.96kg ha-1). Bioavailable P (BAP) losses from ST were lower than from the other treatments if >7% of PP turns bioavailable. After ST, soil P stratification first vanished, but started to develop again when NT was resumed. Occasional tillage of NT soils mitigates DRP losses over several years, and it was at the study site the preferred mitigation option in reducing BAP losses.

Comparative Analysis of 24-2C, 24-2, and 10-2 Visual Field Tests for Detecting Mild-Stage Glaucoma With Central Visual field Defects

This study sought to identify the most effective testing program for detecting visual-field defects in mild-stage glaucoma with central visual-field defects. A multicenter, retrospective diagnostic testing evaluation. The study involved 93 eyes (83 patients) with mild-stage glaucoma (median mean deviation [interquartile range]: -1.79 [2.16] dB) with central visual-field defects and 69 eyes (63 patients; median mean deviation, -1.38 [2.31] dB) with mild-stage glaucoma without central visual-field defects, from Jikei University School of Medicine and Tajimi Iwase Eye Clinic. Patients underwent 10-2 Swedish Interactive Thresholding Algorithm (SITA) Standard, 24-2 SITA Standard, and 24-2C SITA Faster tests. Central visual-field defects were defined using 10-2 SITA Standard and optical coherence tomography (OCT). A detection power of 4 points in the 24-2 that coincided with 10-2 (Center4), 12 points that lie within 10° (24-2-12), and 22 points that lie within 10° of 24-2C (24-2C-22) were analyzed using receiver operating characteristic (ROC) curves based on logistic regression analysis, using total deviation (TD) and pattern deviation (PD) probability plots. Area under the receiver operating characteristic curve (AUC) of the Center4, 24-2-12, and 24-2C-22 tests. In the upper-central visual field, AUCs of the TD plot were 0.50 (0.40-0.58) for the Center4, 0.75 (0.67-0.83) for 24-2-12, and 0.85 (0.78-0.91) for 24-2C-22, with 24-2C-22 AUC significantly exceeding 24-2-12 AUC. For the PD plot, AUCs were 0.53 (0.44-0.63), 0.81 (0.74-0.89), and 0.84 (0.77-0.90), respectively. In the lower-central visual field, using a total plot, AUCs were 0.27 (0.18-0.36), 0.57 (0.47-0.69), and 0.57 (0.46-0.68) for the Center4, 24-2-12, and 24-2C-22, respectively. Using the PD plot in the upper field, AUCs were 0.27 (0.19-0.36), 0.64 (0.53-0.75), and 0.81 (0.72-0.90), respectively, with the AUC of the 24-2C-22 significantly exceeding that of 24-2-12. The 24-2C test was significantly faster than both the 24-2 and 10-2 tests, reducing testing duration by 46% and 52%, respectively. The 24-2C SITA Faster test is highly effective and efficient for detecting mild-stage glaucoma with central visual-field defects. This, and its reduced duration, makes it a valuable tool in clinical settings.

Variation in Phenology and Morphological Traits of Seed-Propagated Laggera alata and Laggera crispata Forms

The phenological and morphological variation studies among forms of Laggera Sch. Bip. Ex. Benth. and Hook species are limited, despite the medicinal use of the genus. Therefore, this study aimed to document phenology and morphological variation in cultivated populations of Laggera alata and Laggera crispata forms from seedling to maturity. The forms were categorized as Laggera alata with a small capitulum (LA-SC), Laggera alata with a large capitulum (LA-BC), Laggera crispata from South Africa (LC-SA), and Laggera crispata from Zimbabwe (LC-ZIM). Seeds were germinated in Petri dishes, transplanted to plug trays, and later to field plots at 60 days. Phenological events were recorded when observed in at least one plant. Twelve qualitative and four morphometric traits were measured monthly on five plants per Laggera form. Analysis of variance and Tukey’s Honestly Significant Difference test (p < 0.05) were used for data analysis. Results indicated significant variation in phenology, qualitative traits, leaf traits, plant height, and stem diameter both within and between L. crispata and L. alata forms. Morphometric traits, such as leaf size and the number of leaves per plant, were identified as key descriptors for differentiating L. alata forms. These findings provide a foundation for the introduction of Laggera forms into farming systems for medicinal and commercial purposes.

Growth and Fecundity of Nonnative Blue Honeysuckle Cultivars: Comparisons with Native and Invasive Congeners in a Maine Field Trial

Blue honeysuckle (Lonicera caerulea) is a circumpolar species complex with representatives in Europe, Asia, and North America. Although honeysuckles (Lonicera spp.) from Eurasia have a history of invasiveness in North America, farmers and homeowners are interested in growing nonnative blue honeysuckle hybrids because of their edible blue fruits. To assess whether these cultivars and closely related native blue honeysuckles (Lonicera caerulea subsp. villosa) might have similar growth and fecundity, we planted five nonnative cultivars of blue honeysuckle and five native genotypes in a common garden in Orono, ME, USA, along with invasive red-fruited honeysuckles [Tatarian honeysuckle (Lonicera tatarica) and European fly honeysuckle (Lonicera xylosteum)] for comparison. Rooted cuttings were planted into a field plot in Jun 2016 and fully maintained during the first season; thereafter, maintenance consisted of weeding once annually. Seventy-three percent of native blue honeysuckle plants survived to the end of the study, whereas survival and growth of nonnative cultivars were more robust. In 2021, nonnative cultivars had an average height of 81 cm and width of 86 cm, which were 2.8 times the height and 2.9 times the width of surviving native plants. The estimated canopy volumes of nonnative blue honeysuckles were an average of 20 times those of their native counterparts. The bloom periods of native and nonnative blue honeysuckles overlapped considerably. However, only seven of the 22 living native plants produced fruits in 2021, with an average of three fruits per plant among them. In contrast, nearly all plants of the nonnative cultivars produced fruits, with an average of 616 fruits per plant. In comparison, the red-fruited invasives had an average of 9739 fruits per plant. Native blue honeysuckles produced very few seeds, whereas nonnative cultivars had an average of 13,918 seeds per plant, which was approximately one-fourth the number produced by invasive red-fruited honeysuckles. We concluded that native and nonnative genotypes of blue honeysuckle differ strikingly in survival, growth, and production of fruits and seeds. However, invasive red-fruited honeysuckles grew faster with higher fecundity than nonnative blue honeysuckles in our full-sun landscape. Because bloom times overlapped substantially between native and nonnative blue honeysuckles, the potential for gene flow to occur from planted cultivars into native populations merits consideration. Several possible explanations of differences in performance among blue honeysuckles include hybrid vigor of cultivars or shallow rooting or poor adaptability of native genotypes to the environment of the common-garden trial. Our results, which demonstrated that nonnative blue honeysuckles are likely to be distinct from their native relatives in terms of competitiveness and fecundity, suggest that caution is warranted during the introduction and cultivation of agricultural genotypes.

Unraveling the forage productivity puzzle: Comparing fast and slow-growing grasses.

Functional traits are powerful tools for distinguishing between plants with different resource acquisition strategies. Fast-growing plants normally dominate resource-rich habitats and present trait values associated with high productivity, such as high specific leaf area (SLA), short leaf lifespan, and rapid leaf elongation rate (LER). In contrast, slow-growing species have a higher leaf weight ratio (LWR), leaf lifespan (LLS), and phyllochron, which are useful traits for survival in stressful and unfertile environments, but are normally thought to be incompatible with high productivity, even under fertile conditions. We tested the hypothesis that slow-growing forage grasses have demographic parameters (tiller population density and canopy density) that offset their slow individual traits, making them as productive as fast-growing species when grown in fertile soil. Species with contrasting growth strategies (Arrhenatherum elatius L. and Festuca arundinacea Schreb cv. Quantum II, fast and slow-growing species, respectively) were cultivated in 45 m2 field plots and subjected to the same cutting regime and nitrogen supply level. Functional traits and canopy attributes were continuously measured during 8 growing cycles after the establishment of the swards. A. elatius had higher SLA, LER, leaf senescence, and leaf appearance rates, whereas F. arundinacea had higher LLS and LWR values. Conversely, there were no differences in relative growth rate or forage accumulation. F. arundinacea was able to offset their plant functional traits, typically associated with slow-growing grasses, with some demographic parameter like higher tiller population density, allowing it to be as productive as the fast-growing A. elatius when both were grown in fertile soil. Therefore, we suggest cautionary use of traditional plant functional traits to explain and predict the annual productivity of slow-growing grasses.

Manipulation of sward diversity is a more effective management strategy than addition of microbial inoculants in intensively managed grassland

Abstract Background and aims Diversifying grassland sward composition and application of microbial inoculants are potential alternative routes to facilitate enhanced nutrient acquisition by plants, but their relative effects have been rarely tested in grasslands. In a two-year field experiment, we investigated the impact of inoculants and sward types on forage yield, nutrient uptake, and the soil microbiome. Methods We implemented a fully factorial experiment with inoculants (eight levels; arbuscular mycorrhizal fungi (AMF), Paraburkholderia phytofirmans PsJN and Bacillus sp. P5 (P5), applied singly and in combination), and sward types (three levels: grass-only, grass and legume, mixture of grass, legume, and herb) as the two factors. Results In both years, there was a very strong effect of sward type, with the grass + legume and grass + legume + herb swards having much higher yields and nutrient uptake (N, P, K, S, Ca, Mg, Zn, Cu) than grass-only. Generally, there was no effect of the inoculants on yield and nutrient uptake, with limited exceptions involving AMF + P5. The best-performing microbial inoculant increased forage yield by 597 kg/ha/year, while switching from grass-only to one of the other sward types increased forage yield by 3932 kg/ha/year (grass + legume) and 4693 kg/ha/year (grass + legume + herb). The inoculants persisted in plots for > 1 year after application. Inoculants and sward type significantly affected the overall prokaryotic and fungal community structures. Conclusion Overall, under controlled field plot conditions, including legumes and herbs in a grass sward proved to be a far better farm-scale management strategy for increasing grassland forage yield and nutrient uptake than the application of microbial inoculants.

Effect of Application of Elemental Sulphur Incubated with Different Organic Amendments on Yield and Quality of Groundnut in Sulphur Deficient Inceptisol Soil

Aim: Under intensive farming system, use of high analysis fertilizers with rare use of organic manures, depleted the organic carbon status in the soil leads to decrease in productivity though applying improved technologies. After N, P, K fourth major nutrient now days considered as S (Sulphur) which play important role in oilseed and pulse crop productivity and its application with organics to soil found beneficial. Considering this, the proposed research as “Effect of Application of Elemental Sulphur Incubated with Different Organic Amendments on Yield and Quality of Groundnut in Sulphur Deficient Inceptisol Soil.” was carried out with five organic amendments having different quality and maturity parameters which showed the significant results with yield and quality of Kharif groundnut. Study Design: Randomized Block design with seven treatments and four replications. Place and Duration of the Study: The field research trial was conducted during Kharif 2019 at Post Graduate Farm, College of Agriculture, Kolhapur, Maharashtra, India. Methodology: As per the treatments details, the five organic amendments viz: FYM, Vermicompost, Press mud compost, Poultry manure, and City waste compost were incubated with 40 kg ha-1 elemental Sulphur for 30 days prior to application in field plots. The treatment wise incubated organic amendments and RDF applied to groundnut crop. The initial soil analysis, initial and after incubation, the characteristics and quality and maturity parameters of five organic amendments were analyzed by using standard methods. The growth observations and post-harvest observation of groundnut crop were recorded and data were analyzed statistically. Results: The application of different organic amendments incubated with elemental Sulphur @ 40 kg ha-1 namely FYM, Vermicompost, Press mud compost, Poultry manure, and City waste compost along with RDF to Kharif groundnut in Sulphur Deficient Inceptisol soil, significantly improved yield contributing characters, dry pod, haulm yields, protein and oil content as compared to absolute control and RDF. Amongst the different organic amendments, application of good quality and maturity poultry manure and vermicompost found significantly superior over rest of organic amendments incubated with Sulphur for improving the growth, dry pod, haulm yields, protein and oil content as compared rest of organic amendments.

Identification and characterization of the causal agent of powdery mildew on native salmonberry and thimbleberry plants in British Columbia

Salmonberry (Rubus spectabilis) and thimbleberry (Rubus nutkanus) are important native berry plants for Indigenous communities and restoration sites in the coastal region of British Columbia (BC). Compared to cultivated berry plants, information on the diseases impacting native berry plant species is limited. In late August 2022, powdery mildew symptoms and signs were observed on field plots of salmonberry and thimbleberry plants at the Agassiz Research and Development Centre, BC. The disease symptoms progressed until the end of September. The morphology of conidiophores and conidia when examined under a compound and scanning electron microscope showed they matched with those of Podosphaera aphanis. DNA from diseased foliage was subjected to PCR amplification with primers representing the internal transcribed spacer (ITS) region of rDNA followed by sequencing. The corresponding sequences from both hosts showed >99% similarity to P. aphanis in GenBank and grouped with several accessions of P. aphanis following phylogenetic analysis. In artificially inoculated trials conducted in the greenhouse, powdery mildew symptoms developed within 3−4 weeks post-inoculation and P. aphanis isolates from salmonberry and thimbleberry were cross-pathogenic to each host. To our knowledge, this is the first study characterizing the powdery mildew pathogen infecting salmonberry and thimbleberry and demonstrating cross-pathogenicity to both hosts. The outcomes from this study provide insight into the biology of the powdery mildew pathogen infecting native Rubus spp.

Estimating the dynamics of ecosystem functions under climate change in a temperate forest region

Understanding the dynamics and spatial patterns of forest biodiversity and functioning is vital for effective conservation and management. This study introduces a new framework for estimating the spatio-temporal dynamics of biodiversity and ecosystem functions in northeastern China. Our novel approach evaluates 96 future scenarios, including four points in time, four socio-economic pathways, and six global climate models based on an extensive network of permanent field plots with specific bioclimatic, topographic, and soil data. Our findings indicate a future trend of diminishing biodiversity and forest biomass productivity and increasing biomass density and carbon storage. The relationship between biodiversity and ecosystem functionality indicates a non-linear response to changes in temperature and precipitation, and a stable correlation between biodiversity and ecosystem stability across most of the socio-economic pathways. Unravelling the complex relationship between biodiversity, ecosystem functions, and stability of production, in the face of global change, requires new methods of analysis that were used in this study, including machine learning and other advanced methods.

Estimating Sugarcane Maturity Using High Spatial Resolution Remote Sensing Images

Sugarcane suffers from the increased frequency and severity of droughts and floods, negatively affecting growing conditions. Climate change has affected cultivation, and the growth dynamics have changed over the years. The identification of the development stages of sugarcane is necessary to reduce its vulnerability. Traditional methods are inefficient when detecting those changes, especially when estimating sugarcane maturity—a critical step in sugarcane production. Hence, the study aimed to develop a cost- and time-effective method to estimate sugarcane maturity using high spatial-resolution remote sensing data. Images were acquired using a drone. Field samples were collected and measured in the laboratory for brix and pol values. Normalized Difference Water Index, Green Normalized Difference Vegetation Index and green band were chosen (highest correlation with field samples) for further analysis. Random forest (RF), Support Vector Machine (SVM), and multi-linear regression models were used to predict sugarcane maturity using the brix and pol variables. The best performance was obtained from the RF model. Hence, the maturity index of the study area was calculated based on the RF model results. It was found that the field plot has not yet reached maturity for harvesting. The developed cost- and time-effective method allows temporal crop monitoring and optimizes the harvest time.

Role of Artificial Intelligence in the crime prediction and pattern analysis studies published over the last decade: a scientometric analysis

Crime is the intentional commission of an act usually suspected as socially detrimental and specifically defined, forbidden, and punishable under criminal law. Developing a society that is less susceptible to criminal acts makes crime prediction and pattern analysis (CPPA) a paramount topic for academic research interest. With the innovation in technology and rapid expansion of Artificial Intelligence (AI), the research in the field of CPPA has evolved radically to predict crime efficiently. While the number of publications is expanding substantially, we believe there is a dearth of thorough scientometric analysis for this topic. This work intends to analyze research conducted in the last decade using Scopus data and a scientometric technique, emphasizing citation trends and intriguing journals, nations, institutions, their collaborations, authors, and co-authorship networks in CPPA research. Furthermore, three field plots have been staged to visualize numerous associations between country, journal, keyword, and author. Besides, a comprehensive keyword analysis is carried out to visualize the CPPA research carried out with AI amalgamation. A total of five clusters have been identified depicting several AI methods used by the researchers in CPPA and the evolution of research trends over time from various perspectives.

Using Multi-Source National Forest Inventory Data for the Prediction of Tree Lists of Individual Stands for Long-Term Simulation

Forest resource maps and small area estimates have been produced by combining national forest inventory (NFI) field plot data, multispectral satellite images and numerical map data. We evaluated k-nearest neighbors (k-NN) method-based predictions of forest variables for pixels in predicting tree lists of individual stands, including tree diameters at breast height and tree heights and then calculated stem volumes and tree species proportions. We compared alternative parameters (k-NN) using k of either 1 or 5 according to preliminary plot-level study and applying either measured trees (1-NN_trees) or mean stand characteristics (k-NN_stand). In the 1-NN_trees method, a tree list was generated based on the measured trees of the NFI plots, whereas in the 1-NN_stand and 5-NN_stand methods, a Weibull-based diameter distribution was recovered from the stand characteristics of the same inventory plots. In both methods, tree lists were predicted for each 16 m × 16 m pixel included in the stand compartment. Both methods performed well and resulted in 8–14% differences in the total volume compared with the field inventory of the 27 stands used for the evaluation. Moreover, the main tree species was correctly predicted for 74% of cases. The RMSE in total volume ranged from 25% (5-NN_stand) to 31% (1-NN_stand), while the smallest RMSEs in volume by tree species were 61% for broadleaves and 65% for pine and spruce using the 5-NN_stand. When comparing input data for a long-term growth simulation, the choice of the method was less influential as the effect of the error in the initial stand characteristics decreased over time during the simulation period. After 30-year simulation of the inventoried stands, the respective RMSEs were 9.4% for total volume and 39%, 50% and 59% for tree species, respectively. The satellite-based data with NFI plots were useful for predicting tree lists for pixels of a stand. However, the accuracy for operational forest management was still questionable. For a larger area’s strategic information, the accuracy is considered adequate.

Spatial heterogeneity of soil moisture caused by drainage and its effects on cadmium variation in rice grain within individual fields

Paddy drainage is the critical period for rice grain to accumulate cadmium (Cd), however, its roles on spatial heterogeneity of grain Cd within individual fields are still unknown. Herein, field plot experiments were conducted to study the spatial variations of rice Cd under continuous and intermittent (drainage at the tillering or grain-filling or both stages) flooding conditions. The spatial heterogeneity of soil moisture and key factors involved in Cd mobilization during drainages were further investigated to explain grain Cd variation. Rice grain Cd levels under continuous flooding ranged from 0.16 to 0.22 mg kg−1 among nine sampling sites within an individual field. Tillering drainage slightly increased grain Cd levels (0.19–0.31 mg kg−1) with little change in spatial variation. However, grain-filling drainage greatly increased grain Cd range to 0.33–0.95 mg kg−1, with a huge spatial variation observed among replicated sites. During two drainage periods, soil moisture decreased variously in different monitoring sites; greater variation (mean values ranged from 0.14 to 0.27 m3 m−3) was observed during grain-filling drainage. Accordingly, 2.9–3.3-fold variation in soil Eh and 0.55–0.67-unit variation in soil pH were observed among those sites. In the soil with low moisture, ferrous fractions such as ferrous sulfide (FeS) were prone to be oxidized to ferric fractions; meanwhile, the followed generation of hydroxyl radicals involved in Cd remobilization was enhanced. Consequently, soil dissolved Cd changed from 2.97 to 8.92 μg L−1 among different sampling sites during grain-filling drainage; thus, large variation was observed in grain Cd levels. The findings suggest that grain-filling drainage is the main process controlling spatial variation of grain Cd, which should be paid more attention in paddy Cd evaluation.

Prediction of the rainfall-runoff process using field plots and artificial neural network (ANN)

Prediction of the rainfall-runoff process using field plots and artificial neural network (ANN)

Assessing the Utility of Uncrewed Aerial System Photogrammetrically Derived Point Clouds for Land Cover Classification in the Alaska North Slope

Uncrewed aerial systems (UASs) have been used to collect “pseudo field plot” data in the form of large-scale stereo imagery to supplement and bolster direct field observations to monitor areas in Alaska. These data supplement field data that is difficult to collect in such a vast landscape with a relatively short field season. Dense photogrammetrically derived point clouds are created and are facilitated to extract land cover data using a support vector machine (SVM) classifier in this study. We test our approach using point clouds derived from 1-cm stereo imagery of plots in the Alaska North Slope region and compare the results to field observations. The results show that the overall accuracy of six land cover classes (bare soil, shrub, grass, forb/herb, rock, and litter) is 96.8% from classified patches. Shrub had the highest accuracy (>99%) and forb/herb achieved the lowest (<48%). This study reveals that the approach could be used as reference data to check field observations in remote areas.

Крупноплодный заразихоустойчивый сорт подсолнечника кондитерского типа Казак

A sunflower variety of confectionary type was developed at the V.S. Pustovoit All-Russian Research Institute of Oil Crops in 2019–2023. The purpose of the work was to develop lager-seeded forms of sunflower resistant to the exiting virulent broomrape race G. The research was conducted both on infected plots in field and in greenhouses and artificial climate chambers. A line RG was a donor of a resistance gen to the broomrape race G. The large-seeded sunflower variety SPK plus was a recipient. Consequent usage a complex of some methods: hybridization, back crossing, group repollination of resistant biotypes, allowed developing a population of large-seeded sunflower resistant to the broomrape race G (Orobanche cumana Wallr.). This variety is middle maturing. A growing season is 96–98 days. Seeds are large, fully formed, 1000 seeds weight at recommended plant population (30 thou-sand plants/ha) – 150–155 g. It is responsive to fertilizers, highly resistant to lodging. In zones of unstable and insufficient moistening, it is able to realize a productivity potential at the high level of agricultural techniques.

Dark energy and dark matter interaction: A nonlinear dynamical system study

This paper presents a methodical dynamical analysis of the Big Bang model, taking into account dark matter and an arbitrary form of dark energy. Nevertheless, why dynamic analysis? The primary advantage of the dynamical analysis approach is that it obviates the necessity to precisely solve the system for every individual dependent variable. Instead, it enables the prediction of the overall evolutions. In this regard, there are some powerful tools of non-dynamical systems (theorems, perturbation, etc.) which can help us a lot. The model under consideration is a nonlinear system with two dimensions (2D). In order to investigate this, we employ a sophisticated form of nonlinear dynamical systems theory in our research. We have delved into the full potential of nonlinear dynamical system theory known until now in our work. This analysis has produced very interesting solutions, which are in quite a contrast with the linear analysis approach. The stability of the system is considered at length, and the corresponding evolutions of the universe are also discussed consequently. A handful number of figures are given to visualize the behavior of the evolutions. The plots that are presented here are vector field plots and a new plotting technique initiated by us and used in our earlier papers. Interestingly, our work indicates the universe might resemble “phantom” evolution. Also, among the fixed points of our model, one is able to escape the singular situation called “Big Rip”.