Lowland Areas Research Articles

Empirical Bayesian Kriging, a Robust Method for Spatial Data Interpolation of a Large Groundwater Quality Dataset from the Western Netherlands

No single spatial interpolation method reigns supreme for modelling the precise spatial distribution of groundwater quality data. This study addresses this challenge by evaluating and comparing several commonly used geostatistical methods: Local Polynomial Interpolation (LPI), Ordinary Kriging (OK), Simple Kriging (SK), Universal Kriging (UK), and Empirical Bayesian Kriging (EBK). We applied these methods to a vast dataset of 3033 groundwater records encompassing a substantial area (11,100 km2) in the coastal lowlands of the western Netherlands. To our knowledge, no prior research has investigated these interpolation methods in this specific hydrogeological setting, exhibiting a range of groundwater qualities, from fresh to saline, often anoxic, with high natural concentrations of PO4 and NH4. The prediction performance of the interpolation methods was assessed through statistical indicators such as root means square error. The findings indicated that EBK outperforms the other geostatistical methods in forecasting groundwater quality for the five variables considered: Cl, SO4, Fe, PO4, and NH4. In contrast, SK performed worst for the species except for SO4. We recommend not using SK to interpolate groundwater quality species unless the data exhibit low spatial variation, high sample density, or evenly distributed sampling.

Use of Phenomics in the Selection of UAV-Based Vegetation Indices and Prediction of Agronomic Traits in Soybean Subjected to Flooding

Flooding is a frequent environmental stress that reduces soybean growth and grain yield in many producing areas in the world, such as the United States, Southeast Asia, and Southern Brazil. In these regions, soybean is frequently cultivated in lowland areas in crop rotation with rice, which provides numerous technical, economic, and environmental benefits. In this context, the identification of the most important spectral variables for the selection of more flooding-tolerant soybean genotypes is a primary demand within plant phenomics, with faster and more reliable results enabled using multispectral sensors mounted on unmanned aerial vehicles (UAVs). Accordingly, this research aimed to identify the optimal UAV-based multispectral vegetation indices for characterizing the response of soybean genotypes subjected to flooding and to test the best linear model fit in predicting tolerance scores, relative maturity group, biomass, and grain yield based on phenomics analysis. Forty-eight soybean cultivars were sown in two environments (flooded and non-flooded). Ground evaluations and UAV-image acquisition were conducted at 13, 38, and 69 days after flooding and at grain harvest, corresponding to the phenological stages V8, R1, R3, and R8, respectively. Data were subjected to variance component analysis and genetic parameters were estimated, with stepwise regression applied for each agronomic variable of interest. Our results showed that vegetation indices behave differently in their suitability for more tolerant genotype selection. Using this approach, phenomics analysis efficiently identified indices with high heritability, accuracy, and genetic variation (>80%), as observed for MSAVI, NDVI, OSAVI, SAVI, VEG, MGRVI, EVI2, NDRE, GRVI, BNDVI, and RGB index. Additionally, variables predicted based on estimated genetic data via phenomics had determination coefficients above 0.90, enabling the reduction in the number of important variables within the linear model.

Field Study and Numerical Modeling to Assess the Impact of On-Site Septic Systems on Groundwater Quality of Jeju Island, South Korea

Septic-derived nitrogen (N) sources have harmful effects on water resources, humans, and ecosystems in several countries. On Jeju Island, South Korea, the rapid increase in personal sewage treatment facilities (PSTFs, also known as on-site septic systems) raises concerns regarding the deterioration of groundwater quality, as groundwater is the sole water resource on the island. Therefore, this study employed a field study and numerical modeling to assess the impact of PSTF effluents on groundwater quality in the Jocheon area of northeastern Jeju. Water quality analysis revealed that the total nitrogen (T-N) concentrations in the effluent exceeded the effluent standards (75–92% PSTFs). The numerical model simulated the transport of N species, showing limited NH4+ and NO2− plume migration near the surface due to nitrification and adsorption. However, NO3− concentrations increased and stabilized over time, leaching on the water table with higher levels in lowland areas and clustered PSTFs. The predictive model estimated a 79% reduction in NO3− leaching when the effluents followed standards, indicating the necessity of effective PSTF management. This study highlights the importance of managing improperly operated septic systems to mitigate groundwater contamination based on an understanding of the behavior of N species in subsurface hydrologic systems.

No deceleration signs in the permafrost ground subsidence four years after the 2019 fire in Northwest Territories, Canada

Abstract The circum-arctic permafrost environment is often disturbed by wildfires but could also show resilience to these disturbances. However, the increased frequency and extent of wildfires, coupled with unprecedented hot weather, have introduced greater uncertainties in the post-fire permafrost dynamics. We need to address emerging questions, e.g., How will permafrost respond to the joint effect of hot anomalies and wildfires? To what extent will post-wildfire deformation evolve? How will permafrost resilience to wildfires vary? Utilizing Interferometric Synthetic Aperture Radar (InSAR) time series analysis, we investigated the post-wildfire ground deformation around a 2019 fire scar in the lower Mackenzie Valley, Northwest Territories, Canada, where dramatic heat anomalies and severe wildfires have been recorded in recent years. The resilience of permafrost to wildfires appears to be weakened by the continuous and rapid warming after the fire, as evidenced by the year-on-year acceleration in subsidence rates. Such acceleration was never reported by previous findings that typically observed deceleration in subsidence rates four to five years after wildfires. The deformation along the line of sight (LOS) of the satellite demonstrates significant permafrost degradation induced by wildfires and exacerbated by climate warming, and the cumulative subsidence was detected up to 25 cm in the LOS direction in the upland areas and up to 10 cm in the lowland areas four years after the fire. The difference in deformation magnitude could be attributed to local factors, including ground ice, topography, and vegetation. Our study highlights the increasingly severe threat to circum-arctic permafrost due to the combined effects of wildfires and extreme heat anomalies.

Geoelectrical and seismoelectric mapping of subsurface pollution in a closed landfill near the Tongo Bassa and Ngongue river, Douala Cameroon

Located in Douala V (Cameroon), Makepé Missoké is a lowland area that is influenced by the Tongo Bassa and Ngongué rivers. The site has a closed dumpsite, that operated from the 1975’s to 2003 for the disposal and storage of domestic waste produced in Douala. Geoelectric profiling (ERT) coupled with seismo-electric imaging was used to characterize the shallow aquifer of the Douala subbasin and map subsurface pollution. Fourteen geoelectrical profiles were deployed between June and August 2018 and March 2019. Along these lines, 49 sets of seismo-electric point data were collected in December 2021. A total of 118 wells drilled (2018) were used to obtain water level and topographic data. The aquifer geometry, hydraulic characteristics, permeability, lithology and leachate plume extent were determined. This survey enables us to visualize waste infiltration and migration within Makepé. The drilled wells indicate that the main lithologies observed are clay, sandy clay, sand, clayey sand and gravel. The leachate plume observed after processing the electric profiles had a resistivity signature of ≤ 10 Ωm, and high electrical conductivity are observed in some wells. The leachate migrates within the subsurface along a northwest‒southwest trend, where ground water pollution is observed due to leachate infiltration. This infiltration resulted in poor water quality indices in some collected samples. Such pollution is common in unconfined aquifers (< 50 m) due to the absence of a confining layer at the landfill. An increase in resistivity values with depth toward the northeast direction indicates progressive vertical dilution during leachate mineralization. This study integrated geoelectric and seismo-electric tomography with basic water chemistry analysis to effectively characterize the groundwater within the phreatic Quaternary/Mio-Pliocene aquifers of the Douala basin.

Cultivo de soja em área de arroz irrigado para reduzir a infestação de plantas daninhas

ABSTRACT Lowland rice in Santa Catarina (SC), Brazil, is grown as a sole crop, which has aggravated problems with weeds. In this context, the objective of this study was to evaluate the efficacy of weed management systems in soybean crops to decrease weed infestation in lowland rice production areas, using pre- and post-emergence herbicides. The experiment was conducted in Turvo, SC, from August 2021 to April 2022. A randomized block design with four replications was used, in a 6 × 5 factorial arrangement. The factors consisted of six pre-emergence herbicides treatments (S-metolachlor, diclosulam, sulfentrazone, sulfentrazone + diuron, pyroxasulfone + flumioxazin, and a control without herbicide), and five post-emergence herbicide treatments: control without herbicide, glyphosate at two rates (930 and 1,550 g a.e. ha-1), applied alone or in combination with clethodim. The herbicide sulfentrazone caused persistent phytotoxicity and resulted in the lowest soybean grain yield. The herbicide S-metolachlor was ineffective in controlling weed species, except barnyard grass, even when combined with post-emergence application. The poor control of these weed species does not result in significant interference, causing no significant decreases in grain yield of soybean crops grown in floodplain soils. The use of S-metolachlor, diclosulam, and pyroxasulfone + flumioxazin, combined with post-emergence application of glyphosate (930 g a.e. ha-1), resulted in highly effective control of barnyard grass, inhibiting weed seed production. Only the combination of glyphosate (1,550 g a.e. ha-1) with clethodim can suppress barnyard grass seed production without using pre-emergence herbicides.

UPAYA PENCEGAHAN DEMAM BERDARAH MELALUI LINTAS PROGRAM BERTERNAK IKAN CUPANG (BETTA SPLENDENS) DI DUSUN MERTOSANAN DESA POTORONO BANTUL

The area of Banguntapan Sub-district is a lowland area with a high density, every change of season the incidence of Dengue Fever is quite high, efforts are needed to suppress the development of mosquitoes. One way to break the chain of mosquito breeding is to raise Betta fish, which aims to increase economic income from fish farming. Counseling on fish farming activities in order to prevent mosquito breeding. A total of 23 participants in the Pre-test and Post-test Evaluation of Health Counseling Activities on DHF and its prevention were in the good range. While the results of the evaluation of counseling activities by training participants were in the satisfactory range. Efforts are needed to prevent DHF using simple technology not only to have an impact on reducing incidence and increasing family economy through Betta farming.

Analisi Prediksi Tingkat Kesejahteraan Masyarakat Nelayan Lombok Timur Dengan Algoritma Naïve Bayes

The water area in Indonesia is wider than the land area, therefore Indonesia is known as a maritime country. Indonesia also has many natural resources that come from the ocean, for example fish. Coastal communities use fish as their livelihood, where most of them become fishermen. Fishing is a daily job to meet the family's economic needs. The Fishermen's Community can be called a group of people who mostly work as fishermen. This group of people is different from people who live in lowland and highland areas, they have different characteristics, natural factors have changed their lifestyle and also their economy. It is because of the uniqueness of their lifestyle that makes the author interested in conducting research. This research uses existing data to obtain new information from that data. Researchers will predict the level of welfare of fishing communities in East Lombok using the Naive Bayes algorithm. To support the results of the decisions that will be evaluated, researchers use five variables, namely: Education, family members, wells, employment and housing. From these five variables, researchers hope to get good final results that can be used to predict the level of welfare of the fishing community in East Lombok. Evaluation is carried out using the Naive Bayes algorithm, this algorithm was chosen because it suits the characteristics of the data we have. Naive Bayes is a simple algorithm. The final results of this research were 99% for accuracy value, 81% for precision value and 100% for recall

Seasonal movements of small-island birds along habitat and elevation gradients highlights the conservation value of small-scale agroforests

Knowledge on species distributions and seasonal movement patterns within landscapes is important for understanding their ecology and roles in agroecosystems, and is critical for enabling their effective conservation and monitoring, particularly in small-island states where conservation resources are limited. We undertook bird surveys to examine species distributions across a range of agricultural land-use intensities and elevations on the Caribbean island of Grenada by collecting data over 6 repeated monthly surveys (3 wet season, 3 dry season) at sites located in a variety of habitats spread over 6–300 m elevation. We observed a gradual upslope increase in bird species diversity and abundance from the mid-wet season to the late-dry season, as well as a movement from reduced-canopy habitats to closed-canopy habitats in the dry season. The mid-elevation hillsides of Grenada consist of a diverse mosaic of closed canopy secondary forest patches, interspersed among more open agroforests and small-scale cropping and grazed areas. These agroecosystems appear to provide important habitats to birds throughout the year, especially open canopy agroforests in the wet season. We also found that open mixed agroforests contained a greater diversity of fruit, flower, and seed resources than closed canopy habitats such as secondary forest, cacao agroforests, young secondary forests, or dry forest scrub; and that plant food diversity was highest in the wet season. However, bird abundance significantly increased in closed canopy forest habitats during the dry season, suggesting that intact canopy forests provide important dry season habitat for many Grenadian birds, which may be particularly important for species in drier lowland areas experiencing more intensive agricultural land use and urban and resort development. Overall, this research demonstrates the importance of maintaining the functional connectivity of treed habitats at local and landscape scales for species conservation, as well as the contribution of diverse, small-scale agriculture to maintaining small-island bird communities.

Pengembangan Potensi Petani Jagung Melalui Edukasi Budidaya Jagung Hibrida Di Desa Kesiut, Kabupaten Tabanan, Bali

Kesiut Village is located in the lowlands area of 266 hectares, with a population of 2,203 people. Most people in the village of Kesiut are corn farmer. The results of the conversation with the chief of the village of Kesiut on May 25, 2022, counseling and outreach were needed as well as direct demonstrations of how to cultivate hybrid corn to farmers in Kesiut Village. The activity was conducted for 3 months, from August 15 to November 17, 2022. The method of integrated community service activities is counseling. Activities carried out to educate how to grow hybrid corn, discuss problems and provide solutions related to hybrid corn cultivation. Initial interviews revealed that 60% of farmers rarely participated in agricultural training activities. Regarding crop failure experienced, 40% often experienced crop failure. The problem often faced by corn farmers in Kesiut Village is the management of downy mildew caused by Peronosclerospora spp. Based on questions by farmers in the activity, farmer in the village of Kesiut already have a basic understanding of hybrid corn cultivation. However, it is necessary to enrich their knowledge about good and correct cultivation techniques, especially seeds requirements, spacing, pest and disease control techniques. Farmers' response to this activity was very positive.

Linking terrestrial biogeochemical processes and water ages to catchment water quality: A new Damköhler analysis based on coupled modeling of isotope tracers and nitrate dynamics

Catchment-scale nitrate dynamics involve complex coupling of hydrological transport and biogeochemical transformations, imposing challenges for source control of diffuse pollution. The Damköhler number (Da) offers a dimensionless dual-lens concept that integrates the timescales of exposure and processing, but quantifying both timescales in heterogeneous catchments remains methodologically challenging. Here, we propose a novel spatio-temporal framework for catchment-scale quantification of Da based on the ecohydrological modeling platform EcH2O-iso that coupled isotope-aided water age tracking and nitrate modeling. We examined Da variability of soil denitrification in the heterogeneous Selke catchment (456 km2, central Germany). Results showed that warm-season soil denitrification was of catchment-wide significance (Da >1), while its high spatial variations were co-determined by varying exposure times and removal efficiencies (e.g., channel-connected lowland areas are hotspots). Moreover, Da seasonally shifted from processing-dominance to transport-dominance during the wet-spring season (from >1 to <1), implying important linkages between summer terrestrial denitrification and subsequent winter river water quality. Under the prolonged 2018–2019 droughts, denitrification removal generally reduced, resulting in further accumulation in agricultural soils. Moreover, the space-time responses of Da variability indicated important implications for catchment water quality. The older water in lowland areas exhibited extra risks of groundwater contamination, whilst agricultural areas in the hydrologically responsive uplands became sensitive hotspots for export and river water pollution. Importantly, the lowland pixels intersecting river channels exhibited high removal efficiencies, as well as high resilience to the disturbances (wet-spring Da shifted to >1 under drought conditions). The proposed catchment-wide Da framework is implied by mechanistic modeling, which is transferable across various environmental conditions. This could shed light on understanding of catchment N processes, and thus providing site-specific implications of non-point source pollution controls.

Broadscale deep learning model for archaeological feature detection across the Maya area

Many Maya archaeological areas are not comprehensively or systematically mapped because ruins, often hidden under tropical forest canopy in rugged terrain, can take decades to locate, identify, and map. Recent years have seen an explosion of lidar data collection, and machine learning provides a way to exploit these lidar data, making feature analyses more efficient and consistently executed. At present, there are a limited number of small, area-specific models that exist for the Maya area, the largest of which covers 230 km2. Here we present the foundation for a broadscale, multi-area-based convolutional neural network (CNN) object detection model that uses airborne laser scanning data, or lidar, for archaeological feature detection across 615 km2 of the Maya area, as well as preliminary results from an additional 885 km2 test area. This sets the path for a model that will enable researchers to map archaeological areas across the entire Maya Lowland area in weeks or months instead of decades. Notably, we find that a model trained on multiple areas with significantly different topographies produces better results for all areas as compared to a model trained on a single area. The broadscale model here presented produced an F1 score of 0.80. Results also include many potential new structure detections, including detections on lidar at an archaeological area that has not yet been comprehensively ground-surveyed and is located in an entirely different country in the Maya Lowlands from where the model was trained on. This model represents an attempt at a broadscale machine learning approach for archaeological feature mapping in the Maya area and demonstrates how big data can be integrated into traditional archaeological workflows. Lidar has already shown much greater ancient Maya infrastructure throughout the Maya world and elsewhere in the tropics, and this study using machine learning with lidar is showing even greater Maya infrastructure through vast areas of the Maya tropical forest.

Seroprevalence and associated risk factors of contagious caprine pleuropneumonia in selected districts of South Wollo Zone Northeast Ethiopia

Contagious caprine pleuropneumonia (CCPP) is a severe and devastating respiratory disease of goats, which is characterized by severe serofibrinous pleuropneumonia accompanied by high morbidity and mortality. A cross-sectional study was conducted from July 2022 to January 2023 to determine the seroprevalence of CCPP and identify risk factors associated with the occurrence of CCPP in goats in five selected districts of the South Wollo Zone of the Eastern Amhara region. A total of 384 sera samples were collected from goats and examined for antibodies specific to Mycoplasma capricolum subspecies capripneumoniae (Mccp) using Competitive Enzyme-Linked ImmunoSorbent Assay (cELISA) test. Out of the total examined sera, 26 samples were positive for CCPP, giving an overall seroprevalence of 6.7% (95% CI = 6.64–9.77). A seroprevalence of 5.05%, 4.65%, 2.78%, 12.90%, and 10.77% were recorded in Ambasel, Tehuledere, Kalu, Dessie Zuria and Kutaber districts, respectively. However, there was no statistically significant difference among these five districts (p > 0.05). The seroprevalence of CCPP varies significantly between age groups and agroecology (p < 0.05). However, the seroprevalence did not vary with sex, body condition score (BCS), and flock size (p > 0.05). Old-aged goats (OR = 4.10) and goats found in the lowlands (OR = 5.09) were at higher risk of infection with CCPP than young-aged goats and goats found in the highlands, respectively. In conclusion, the present seroprevalence investigation indicated the occurrence of CCPP in those selected study districts of the South Wollo Zone. Therefore, appropriate control measures, including avoiding the mixing of flocks and vaccination should be designed and implemented especially in the lowland areas and older goats to reduce the further spread and magnitude of the disease.

Assessment of sustainability of natural complexes of the Minyarsky physiographic region of the Western slope of the Southern Urals

The article considers the evaluation of sustainability of natural complexes of low-mountain sections of the western slope of the Southern Urals (Minyarsk physical and geographical area) on the basis of analysis of landscape profiles on the indicator of sustainability of natural complexes. Peculiarities of the study area are highlighted: specific climatic conditions, complex geological and geomorphological conditions with ongoing industrial development, as well as a variety of soil and hydrological conditions combined with anthropogenic transformation of the territory. In the course of landscape profiling, as well as the application of methods of assessment of anthropogenic transformation of landscapes (at the level of the terrain), a decrease of diversity in natural complexes on the studied slopes was established, Replacement of typical plant communities by secondary successional communities in lowland areas dominated by horsetail. It has also been established that the aggregate of conditions in natural complexes indicates anthropogenic transformation of slope landscapes, the aggregate leading factor of which is the geochemical position on the element of the slope, Low resistance to external influence of the soil with a rinsing type of water regime defining plant associations. The obtained materials can be used as a basis for analysis of ecological condition of landscapes of the western slope of the Southern Urals, as well as characteristics of slope types of locations. Materials allow to further assess the recreational attractiveness of landscapes and the choice of ways of natural use of the studied area.

Public support for flood adaptation policy in Tokyo lowland areas

ABSTRACT Sea level rise induced flooding is projected to cause significant damage to the low-lying coastal areas of many countries around the planet. Many households located in vulnerable areas have already started to adapt to flooding, although some empirical studies indicate that informal adaptation is reaching its limit and that more formal countermeasures by governments will be necessary to cope with the increased flood risks. In such cases, public support for flood adaptation policy is crucial to ensure a successful and timely adaptation, as these strategies can directly affect coastal residents. However, there is limited understanding on the mechanisms and factors that determine public support for flood adaptation policy. Thus, this study proposes a new model – the Foundation of Adaptation Policy Support (FAPS) – which integrates the Protection Motivation Theory and the Risk Information Seeking and Processing models. FAPS was tested using structural equation modelling with latent variables and data that was collected from a household questionnaire survey conducted in the Tokyo lowland areas (n = 388), with the super levee representing a policy that could be applied to sea level rise adaptation. The proposed FAPS model showed a good fit with the collected data, indicating that the strongest predictors of policy support include the perceived effectiveness of the policy and the importance of the issue of flooding to individuals. Other important predictors of policy support include knowledge about flooding, the opinions of surrounding people (i.e. family and friends), and the systematic processing by respondents of information related to floods. The FAPS model sets the foundation for understanding the mechanisms of public support for adaptation policy and provides new insights into designing and planning such policies in practice. Key policy insights Perceived policy effectiveness and the importance of addressing floods are the strongest predictors of public support for adaptation policy. Increasing people’s knowledge about flooding is essential to communicate the importance of adaptation policy. Investigating people’s knowledge and the perceived importance of the issue of flooding prior to designing policies can predict public preferences for certain countermeasures.

A Wide Survey To Assess Population Of Long-Tailed Macaque Macaca Fascicularis (Primates: Cercopithecidae) In Lowland Region Of South Sumatra

The Long-tailed Macaque Macaca fascicularis is an Endagered primate because species' population decline very rapidly due to high levels of hunting for trade. A survey to collect data on the M. fascicularis population was carried out in the lowland areas of South Sumatra Province on 7 September-16 October 2023. This survey was carried out in 17 sites in four main districts/cities, including Palembang, Musi Banyuasin, Banyuasin and Ogan Ilir. This survey was recorded 4,316 individuals of M. fascicularis. This number suggest that M. fascicularis has good population number and still commonly found at the survey sites. Based on the survey coverage of the survey area and the total number of ??the lowlands in South Sumatra, we estimate the population of M. fascicularis in the lowlands in this province to be around 300,000 individuals. Further surveys are needed to determine the exact number of M. fascicularis populations in South Sumatra Province.

Urbanisation on the coastline of the most populous and developed state of Brazil: the extent of coastal hardening and occupations in low-elevation zones

Worldwide, coastlines have been replaced and altered by hard infrastructures to protect cities and accommodate human activities. In addition, human settlements are common and increasing in lowland areas threatened by coastal risk hazards. These urbanisation processes cause severe socioeconomic and ecological losses which demand policy reforms towards better coastal management and climate resilience. A first step in that direction is to comprehend the status of coastal hardening and occupation of vulnerable areas. Here, we mapped the coastline of the most populous and developed state of Brazil: São Paulo (SP). Our goal was to quantify the linear extent of natural habitats, artificial structures (AS), and occupations in low-elevation coastal zones (≤ 5 m) within 100 m from marine environments (LECZ100m) along the coastline and within estuaries. SP coast has a total extent of 244 km of AS, of which 125 and 119 km correspond to AS running along the coastline (e.g., seawalls, breakwaters) and extending from the shoreline into adjacent waters (e.g., jetties, pontoons, groynes), respectively. 63% of the total extent of AS is located in the most urbanised region. Breakwalls were the most common infrastructure (108 km), followed by jetties and wharves (~40 km each), and aquaculture and fishing apparatus (~24 km). Over 300 km of the SP coastline has inland occupations in LECZ100m: 235 and 67 km are adjacent to sandy beaches and estuarine/river margins, respectively. Coastal hardening is advanced in the central region of SP resulting from intense port activities and armoured shorelines. In other regions, much of coastal urbanisation seems to be driven by secondary usage of the cities, such as real estate development for beach houses and tourism. Our findings suggest that coastal urbanisation poses a major but often neglected source of environmental impact and risk hazards in SP and Brazil.

Flood Susceptibility Mapping for Kedah State, Malaysia: Geographics Information System-Based Machine Learning Approach

ABSTRACT Background: The world economy is significantly impacted by floods. Identifying flood risk is essential to flood mitigation techniques. Aim: The primary goal of this study is to create a geographic information system (GIS)-based flood susceptibility map for the study area. Methods: Ten flood-influencing factors from a geospatial database were taken into account when mapping the flood-prone areas. Every element demonstrated a robust relationship with the probability of flooding. Results: The highest contributing elements for the flood disaster in the study region were drainage density, distance, and the curvature. Flood susceptibility models’ performance was validated using standard statistical measures and AUC. The ROC curves demonstrated that all ensemble models had good performance on the validation data sets (AUC = &gt;0.97) with high accuracy scores of 0.80. Based on the flood susceptibility maps, most of the northwest regions of the study area are more likely to flood because of low land areas, areas with a lower gradient slope, linear and concave shape curvature, high drainage density with high rainfall, more “water bodies,” “crops land,” and “built areas,” abundance on sea and surface water, and Quaternary types of soil feature and so on. The very high flood susceptibility class accounts for 18.2% of the study area, according to the RF-embedding model, whereas the high, moderate, low, and very low susceptibility classes were found at about 20.0%, 24.6%, 24.3%, and 12.9%, respectively. Conclusion: In comparison with other commonly used applied approaches, this research presents a novel modeling approach for flood susceptibility that integrates machine learning and geospatial data. It has been found to be stronger and more efficient, highly accurate, has good prediction performance, and is less biased. Overall, our research into machine learning-based solutions points in a positive path technologically and can serve as a reference manual for future research and applications for academic specialists and decision-makers.

Evaluation of Impact of Biofertilizer and Mulch Types on Growth and Production of Tomato Cultivar Gustavi F1 in Lowland Areas

Tomato plants are expected to have an ideal growing environment to optimize their growth and production. Therefore, this study aimed to investigate the effect of various concentrations of biofertilizer, mulch types, and their interactions on growth and production of the Gustavi F1 tomato cultivar. A randomized group design (RGD) arranged factorially was used for the analysis, with the first factor consisting of no mulch (M0), straw mulch (M1), and plastic mulch (M2). The second factor was five biofertilizer concentrations, including 0 ml.l-1 (L0), 5 ml.l-1 (L1), 10 ml.l-1 (L2) 15 ml.l-1 (L3), and 20 ml.l-1 (L4). Data were analyzed using the F test (analysis of variance), and in cases of significant differences, the analysis proceeded with Duncan Multiple Range Test (DMRT) at an α level of 5%. Growth parameters observed in the experiments included plant height at 1 and 3 weeks after planting, while production parameters comprised fruit diameter, the number of fruits per plant, the percentage of fruits experiencing Blossom End-Rot (BER), and fruit weight per plot. The results showed that the use of plastic mulch significantly increased the number of fruits per plant, while the 0 ml.l-1 biofertilizer concentration showed the most effective reduction in the percentage of fruits experiencing BER. Moreover, an interaction between mulch and biofertilizer treatments was observed, particularly in relation to plant height 1 week after planting, the number of fruits per plant, and the percentage of fruits experiencing BER.

Shifts of the pond area ratio for lowland polders: Implication for nutrient control

Shifts for natural ecosystems were increasingly concerned due to its profound impacts on ecosystem services. Ponds within lowland artificial watersheds (polders) play a critical role in nitrogen (N) and phosphorus (P) cycling. From the perspective of N & P control in management practices, it is needed to determine an optimal pond area ratio for polders. For this purpose, our study proposed a process-based modelling framework to investigate the response of polder N & P loss to pond area, and thus to determine the threshold value of pond area ratio to achieve maximum N & P reduction for polders. The proposed framework included two process-based models (NDP and PDP) specially developed to describe N & P dynamics in lowland polders. To evaluate the proposed performance of the framework, it was applied to 171 polders in Zhong River Watershed in Lake Taihu Basin, eastern China. Our investigation results revealed that the correlation between polder N & P reduction rate and pond area ratio had an abrupt shift of 13.6 %, 14.7 % for N & P, respectively. Therefore, polders with a pond area ratio of 13.6–14.7 % had the largest N & P reduction (5.27 and 0.19 kg/ha). Polder size affected P reduction rate, with smaller polders (<200 ha) showing a higher P reduction rate, while it did not affect N reduction rate. Compared with annual precipitation, rainy-season precipitation more significantly (P<0.01) determined polder N & P reduction. This study demonstrated the use of our process-based framework in characterizing the shifts for the pond area ratio for polders, and thus provided technical support for N & P control of lowland areas in water management practices.