Susceptibility Index Research Articles

Identification Of Shear Strain On The Surface Ground Of Wangi-Wangi Island, Southeast Sulawesi, Indonesia, Using Nakamura’s Technique and The Possibility Of Its Impacts

This research was conducted to determine the possible impact of an earthquake on the mainland of Wangi-Wangi Island based on the presence of shear strain on the surface ground (γ). The size of γ is obtained by multiplying the ground susceptibility index and the acceleration of basement ground or PGA using Nakamura’s technique. The data used are microtremor data and earthquake data from 1920 to 2020 sourced from the USGS. Microtremor data are obtained from the results of filtering ground vibration signals using a Band Pass Filter in the frequency range between 0.5 to 25 Hz. Ground vibration signals were recorded at 47 measurement points spread over the surface of Wangi-Wangi Island within 29.25 to 48.16 minutes. Furthermore, the microtremor data were processed using the HVSR (Horizontal to Vertical Spectral Ratio) method. The use of earthquake data must meet the requirements for a surface magnitude (MS) ≥ 5.0 SR and an earthquake epicenter depth (h) ≤ 45 km. The results obtained are the γ sizes of Wangi-Wangi Island in the order of 10-06 to 10-03. Based on the size distribution, it is known that the majority of the Wangi-Wangi Island area has the potential to experience cracks and land subsidence due to settlements if an earthquake occurs, and only a portion of the area is vibrating. In addition, it is also known that the mainland of Wangi-Wangi Island is not prone to landslides and liquefaction because γ<10-2.

Application of HVSR Method on Microtremor Data for Analysis of Earthquake Potential in Candipuro District, Lumajang, Indonesia

The Earthquake of 6.1 magnitude occurred on April 10, 2021, in Lumajang district. One affected area suffered building losses, road damage, and casualties in Candipuro District, Lumajang Regency, East Java. Then the research was conducted to determine the strength of the soil against the potential earthquake by micro zoning the area in Candipuro District. This research uses the microtremor method with HVSR (Horizontal to Vertical Spectral Ratio) analysis. Analysis of earthquake potential includes parameters of Ground Amplification (A0), Dominant Frequency (f0), Ground Vulnerability Index (Kg), and Ground Shear Strain (GSS). The acquisition was completed with 16 measurement points in Supiturang Village with 500 meters between points using the Portable Seismograph TDS - 303. The results obtained in Supiturang Village have the results of the amplification value factor which is classified as medium-high (2.7 - 8.7), low dominant frequency value (0.5 - 1.4 Hz), medium-high soil susceptibility index (10 - 90), VS30 value which includes medium - hard soil (187.81 - 548.38) m/s, and GSS value which shows the response of cracks and soil subsidence to shaking (2 x 10-5 - 2 x 10-4).

Drought resistance and index screening of domestic selection oat varieties

The determination of drought-resistant oat varieties is based on the selection of identification indicators and the creation of a comprehensive assessment system that effectively identifies the most promising genotypes for breeding work. During three-year model experiments, 40 varieties of oats of Russian breeding were studied by measuring morphophysiological indicators and calculating the corresponding indices under stress caused by soil drought during tillering and flowering. The research was carried out in an unheated summer-type greenhouse located on the territory of the Research Institute of Agriculture of the Northern Trans-Urals. As a control, the same varieties that grew with regular watering and did not experience moisture deficiency were used. Plant height and crop structure elements were studied. The values of the protein content in the leaves, the chlorophyll index, the relative water content in the leaves and the membrane stability index were determined. It is established that the use of each indicator separately as a criterion of drought resistance does not make it possible to fully assess the drought resistance of oats. Assessment of genotypes by indices of susceptibility to drought (DSI) and tolerance to it (DTI) allows us to identify groups of drought-resistant varieties of oats and assess the degree of influence of soil drought on the elements of the crop structure. In the course of research, it was found that the varieties Privet, Peredovik, Sig, Faust, Novosibirsk 5, Barguzin, Assol are resistant to soil droughts during tillering and flowering. Their DSI was less than 0.83 and 0.78 units. accordingly. Such varieties as CROSS, Vilensky and Horizont also had high resistance to drought during tillering. The drought in the later period led to very severe stress, which negatively affected their productivity. The group of genotypes most susceptible to drought was: Dedal, Narymsky 943, Pesec, Lgovsky, Tigrovy, Express, Tayoschnik, Bulany, Borec and Desant. Their drought susceptibility index was more than 1.2 units.

A Novel Approach to Assess the Influence of Rockfall Source Areas: The Case Study of Bardonecchia (Italy)

In this research article, we propose a practical methodology for evaluating the affecting potential of detachment areas in rockfalls. Our innovative approach combines an assessment of the visibility of rockfall source areas, with reference to specific rockfall scenarios and elements at risk, considering the rockfall Susceptibility Index to Failure (SIF) of these areas. The result is the characterization of source areas through a rockfall Source Affecting Index (SAI), which considers both the morphology of the slope and the geostructural conditions of the rock walls. This information can be very useful since it aids in optimizing more in-depth analyses, as well as the placement of monitoring instruments or stabilization systems. The proposed methodology has been implemented in the open-source software QGIS through the development of an easy-to-use plugin named Ranking of the Affecting Potential of Detachment Areas in Rockfalls, or “RADAR”. RADAR is designed to be used in conjunction with QPROTO, a well-known QGIS plugin for preliminary rockfall susceptibility/hazard analyses based on a visibility analysis and a simplified mechanical method. To demonstrate the effectiveness of the proposed approach, an application to a case study located in the Western Alps (Bardonecchia, Italy) is presented and discussed in the paper.

Morpho-physiological diversity of Indian mustard (Brassica juncea L.) germplasm for terminal heat tolerance

Two hundred genotypes with seven checks of Indian mustard were sown at two different interval (timely and late) in augmented block design for trait association and comparative phenotyping against heat stress at terminal stage. Correlation studies revealed that seed yield per plant had significant positive association with plant height, number of primary branches per plant, number of secondary branches per plant, leaf area index, total number of siliqua per plant, siliqua on main shoot and 1000- seed weight under both normal and late sown conditions. Based on results of heat screening parameters like heat susceptibility index, yield stability index and percent yield change, genotypes viz. NDRE-4, EC-399300, DRMR-13, DRMR-9, DRMR-7, DRMR-357, DRMR-6, EC-766423 and IM76 were found promising for terminal heat tolerance.

Screening of Wild and Cultivated Vigna Accessions for Resistance to Bruchid (Callosobruchus maculatus F.)

The bruchid beetles (Callosobruchus maculatus) is one of the primary causes for the loss in pulses due to the seed damages during storage. The wild relatives of crop species are essential sources of new genes for crop improvement. A set of 40 wild Vigna accessions and cultivated Vigna species were screened for C. maculatus resistance. Based on the results, the accessions viz., V. radiata var. sublobata, V. trilobata and V. umbellata were found resistant as it recorded less adult emergence, lesser seed damage, low index of susceptibility and less seed weight loss among bruchid resistance traits. These traits are more influential principal components and have a high correlation. The identified accessions can be involved in the resistance breeding program as a donor to introgress resistance against bruchid beetles towards developing superior cultivars. One of the identified key traits, viz.,seed weight loss or seed damage or index of susceptibility, should be given more importance while framing the breeding program for reliable and stable bruchid resistance among the cultivar.

Screening Potato Landraces to Cope with Climate Change in the Central Andes of Peru

Agriculture in the Andean region is mainly small-scale and rainfed, especially in Peru where almost 80% of its population depends on agriculture-related activities. Climate change in addition to social factors threatens the food security of this region. The forecast of more frequent dry spells would especially affect potato crops, domesticated centuries ago in the Andes, where there remains a great genetic diversity. This study aimed to characterize the response to drought stress of 79 potato landraces traditionally grown in the Central Andes of Peru (Pasco region) as a first selection for future breeding evaluations. The used indices were mean productivity (MP), geometric mean productivity (GMP), stress tolerance (STI), stress susceptibility (SSI), and tolerance index (TOL), and a scoring methodology that integrates all of them into a single descriptor in a simple and fast way. The varied responses showed a wide genetic diversity within the assessed landraces, where at least nine of them own high resilience and productivity qualities, and many others are highly vulnerable to drought. It is recommended to complement these studies with physiological and molecular evaluations in stress situations, especially in those with tolerance qualities highlighted in this study, and thus promote the conservation of the biodiversity of this region.

Durum wheat heat tolerance loci defined via a north-south gradient.

The global production of durum wheat (Triticum durum Desf.) is hindered by a constant rise in the frequency of severe heat stress events. To identify heat-tolerant germplasm, three different germplasm panels ("discovery," "investigation," and "validation") were studied under a range of heat-stressed conditions. Grain yield (GY) and its components were recorded at each site and a heat stress susceptibility index was calculated, confirming that each 1°C temperature rise corresponds to a GY reduction in durum wheat of 4.6%-6.3%. A total of 2552 polymorphic single nucleotide polymorphisms (SNPs) defined the diversity of the first panel, while 5642 SNPs were polymorphic in the "investigation panel." The use of genome-wide association studies revealed that 36 quantitative trait loci were associated with the target traits in the discovery panel, of which five were confirmed in a "subset" tested imposing heat stress by plastic tunnels, and in the investigation panel. A study of allelic combinations confirmed that Q.icd.Heat.003-1A, Q.icd.Heat.007-1B, and Q.icd.Heat.016-3B are additive in nature and the positive alleles at all three loci resulted in a 16% higher GY under heat stress. The underlying SNPs were converted into kompetitive allele specific PCR markers and tested on the validation panel, confirming that each explained up to 9% of the phenotypic variation for GY under heat stress. These markers can now be used for breeding to improve resilience to climate change and increase productivity in heat-stressed areas.

Frequency ratio analysis to determine the landslide susceptibility in East Sikkim district of Indian Himalayan region

Landslides are a significant natural disaster causing damage to many mountainous regions worldwide including the Indian Himalayan region. In the East Sikkim district of the Eastern Himalayas, the most used bivariate frequency ratio (FR) model was utilized with high-resolution satellite imagery to understand the susceptibility of the region to landslides. Conditioning factors such as slope aspect, slope angle, slope curvature, drainage density, land use and land cover (LULC), normalized difference vegetation index (NDVI), lithology, and geomorphology were considered in the analysis. LULC is the most crucial factor contributing to landslide susceptibility with a normalized FR value of 14.1. Slope and geomorphology followed closely with values of 12.5 and 11.8 respectively. In contrast, the least important factors were slope aspect and lithology with values of 8.7 and 9.3 respectively. These results can be used to prioritize landslide conditioning factors (LCF) and generate a final landslide susceptibility map (LSM). By adding the values of all LCFs, a landslide susceptibility index was obtained, and the LSM was zoned into high, medium, and low susceptibility classes covering 23.4%, 44.4%, and 32.2% of the study area respectively. The validity of the method used was confirmed using a receiver operating characteristic curve which yielded an accuracy of 78%. The findings highlight the importance of LULC, slope, and geomorphology as critical factors in landslide susceptibility in the East Sikkim district of the Eastern Himalayas.

Laser powder bed fusion of immiscible steel and bronze: A compositional gradient approach for optimum constituent combination

The microstructures and the mechanical properties of a laser powder bed fusion (LPBF) manufactured alloy coupons that were compositionally graded with austenitic stainless steel 316 L and bronze (Cu10Sn), which are immiscible, were investigated. While the microstructure of the pure 316 L contains only columnar γ-Fe grains, the formation of the equiaxed α-Cu, with α-Fe particles embedded in them, at the grain boundaries of the γ-Fe grains was observed upon alloying with Cu10Sn, for up to 50 wt.%. In the graded alloys with > 50 wt.% Cu10Sn, the microstructure inverts and contains α-Fe particle embedded α-Cu matrix along with some scattered γ-Fe grains. The two-dimensional phase-field simulations were employed to reproduced the phase transition and microstructure evolution in different compositions, revealing that the nucleation of γ-Fe occurs synchronously with the spinodal decomposition of liquid and that with further cooling, Cu-rich liquid solidifies and nanoscale spherical γ-Fe precipitates appear. Cross-sections with 10–40 wt.% Cu10Sn were found structurally unreliable owing to the formation of liquation cracks and shrinkage pores, whereas the rest of the build contained few pores and was crack-free. The Scheil solidification simulations together with the crack susceptibility index calculations over the entire compositions range of the CGA, reveal that when with < 50 wt.% Cu10Sn, liquid feeding to interdendritic regions of γ-Fe is limited, which is otherwise necessary to mitigate shrinkage-induced stresses. The hardness, yield and tensile strengths of the compositionally graded alloy in the cross-section with 50 wt.% Cu10Sn are higher than those of the parent constituents and the one with 80 wt.% Cu10Sn has the best combination of strength and ductility. The variations in strength and ductility are attributed to the microstructural and compositional changes in α-Cu matrix that were influenced by the formation of α-Fe particles. Finally, the appropriate proportions and the influence of the LPBF method in successfully mixing Cu10Sn and SS316L was discussed.

Modelling Uncertainties and Sensitivity Analysis of Landslide Susceptibility Prediction under Different Environmental Factor Connection Methods and Machine Learning Models

The utilization of different connection methods between landslides and environmental factors introduces uncertainty in landslide susceptibility prediction (LSP). Investigating and identifying the characteristics of this uncertainty and determining more suitable connection methods are of significant importance for LSP. This study uses original 12 environmental factors data as well as calculated data from five connection methods, namely, probability statistics (PS), frequency ratio (FR), information volume (IV), index of entropy (IOE), and weight of evidence (WOE), as model input variables. Then, four machine learnings logistic regression (LR), Bayesian networks (BN), support vector machines (SVM) and C5.0 Decision Trees (C5.0 DT) are combined with the calculated data and the original data to create 24 unique combinations of connection methods and models. Under these 24 combinations, the uncertainty analysis of LSP modeling is conducted, using Huichang County of China as an example. The analysis entails accuracy assessment, statistical analysis of landslide susceptibility indexes (LSIs), distribution patterns of LSIs and sensitivity analysis of the two uncertainty issues. The results show that: 1) LSP accuracies of the FR-, IV- and IOE-based models are comparable, but are lower than those of the WOE-based models, with those of the PS-based models being the worst. WOE has better nonlinear connection performance than the other methods. 2) LSP accuracies of individual models are slightly lower than those of coupled models, but their modeling efficiencies are higher than those of coupled models. 3) The machine learning is more sensitive than the connection method for LSP. In short, WOE-C5.0 DT has the lowest LSP uncertainty while a single machine learning can produce satisfied LSP results with high modelling efficiency.

Drought resistance, quality characteristics and water‐yield relationships of some wheat (Triticum aestivum L.) lines and varieties

AbstractThe aim of this study was to determine the drought tolerance of four different genotypes, including two wheat varieties (Ceyhan 99 and Sagittario) and two wheat lines (Zdeb101 and Zdeb102), and create irrigation schedules. This study was performed with treatments applied at three irrigation levels (S100: treatment where the entire water need of the plant was met, S50: 50% of the water provided in the S100 treatment, S0: Rainfed), with four genotypes and four replications in a split‐plot design in Kahramanmaras East Mediterranean Transitional Zone Agricultural Research Institute in 2019 and 2020. The Ceyhan 99 and Sagittario varieties, which are widely cultivated in the region and the Zeb101 and Zdeb102 lines that are candidates for varieties were used in the study. In the study, 217.1 and 213.77 mm of water was given to S100, 108.54 and 106.88 mm of water was given to S50, and no irrigation was provided to S0 in 2019 and 2020, respectively. The effects of different irrigation levels on yield, water use efficiency (WUE), irrigation water use efficiency (IWUE), protein content and wet gluten content were investigated. At the end of this study, seasonal evapotranspiration (ET), irrigation water and yield decreased from S100 to S0. WUE, IWUE and yield response factor (Ky) values showed different trends in both years. There was no significant difference in yield between the varieties. However, in the stress susceptibility index (SSI) analyses of the genotypes, it was found that the most drought‐tolerant genotypes were Zdeb102 (0.50) and Ceyhan 99 (0.54). The highest protein content and wet gluten content were found as 11.38% and 22.24% in the Sagittario variety, respectively. It is recommended that Zdeb102 and Ceyhan 99 be cultivated in regions such as Kahramanmaras which have semi‐arid climate characteristics in the Mediterranean.

EMS-induced mutagenesis in Choy sum (Brassica chinensis var. parachinensis) and selection for low light tolerance using abiotic stress indices

BackgroundChoy Sum (Brassica rapa ssp. chinensis var. parachinensis), grown in a controlled environment, is vulnerable to changes in indoor light quality and displays distinct photo-morphogenesis responses. The scarcity of Choy Sum germplasm for indoor cultivation necessitates the development of new cultivars. Hence, this study attempted to develop mutants through chemical mutagenesis and select low-light-tolerant mutants by using abiotic stress tolerance indices.ResultsA mutant population of Choy Sum created using 1.5% ethyl methane sulfonate (EMS) at 4 h was manually pollinated to obtain the M2 generation. 154 mutants with reduced hypocotyl length were initially isolated from 3600 M2 seedlings screened under low light (R: FR = 0.5). Five mutants that showed reduced plant height at mature stages were selected and screened directly for shade tolerance in the M3 generation. Principal component analysis based on phenotypic data distinguished the M3 mutants from the wild type. Abiotic stress tolerance indices such as relative stress index (RSI), stress tolerance index (STI), geometric mean productivity (GMP), yield stability index (YSI), and stress resistance index (SRI) showed significant (P < 0.05), and positive associations with leaf yield under shade. M3-12–2 was selected as a shade-tolerant mutant based on high values of STI, YSI, and SRI with low values for tolerance (TOL) and stress susceptibility index (SSI).ConclusionsThe results demonstrate that mutation breeding can be used to create dominant mutants in Choy Sum. Furthermore, we show that screening for low light and selection based on abiotic tolerance indices allowed the identification of mutants with high resilience under shade. This method should apply to developing new cultivars in other crop plants that can be suitable for controlled environments with stable yield performance.

A novel evolutionary combination of artificial intelligence algorithm and machine learning for landslide susceptibility mapping in the west of Iran.

Detecting and mapping landslides are crucial for effective risk management and planning. With the great progress achieved in applying optimized and hybrid methods, it is necessary to use them to increase the accuracy of landslide susceptibility maps. Therefore, this research aims to compare the accuracy of the novel evolutionary methods of landslide susceptibility mapping. To achieve this, a unique method that integrates two techniques from Machine Learning and Neural Networks with novel geomorphological indices is used to calculate the landslide susceptibility index (LSI). The study was conducted in western Azerbaijan, Iran, where landslides are frequent. Sixteen geology, environment, and geomorphology factors were evaluated, and 160 landslide events were analyzed, with a 30:70 ratio of testing to training data. Four Support Vector Machine (SVM) algorithms and Artificial Neural Network (ANN)-MLP were tested. The study outcomes reveal that utilizing the algorithms mentioned above results in over 80% of the study area being highly sensitive to large-scale movement events. Our analysis shows that the geological parameters, slope, elevation, and rainfall all play a significant role in the occurrence of landslides in this study area. These factors obtained 100%, 75.7%, 68%, and 66.3%, respectively. The predictive performance accuracy of the models, including SVM, ANN, and ROC algorithms, was evaluated using the test and train data. The AUC for ANN and each machine learning algorithm (Simple, Kernel, Kernel Gaussian, and Kernel Sigmoid) was 0.87% and 1, respectively. The Classification Matrix algorithm and Sensitivity, Accuracy, and Specificity variables were used to assess the models' efficacy for prediction purposes. Results indicate that machine learning algorithms are more effective than other methods for evaluating areas' sensitivity to landslide hazards. The Simple SVM and Kernel Sigmoid algorithms performed well, with a performance score of one, indicating high accuracy in predicting landslide-prone areas.

Potentiostatic preparation and in vitro characterization of functional hazenite conversion coatings on AZ31 magnesium alloy

A functional hazenite coating was deposited on AZ31 Mg alloy by polarization at −0.8 V in a deaerated phosphate solution containing 0.1 M K2HPO4 and 0.1 M Na2HPO4 with pH 9. The growth process of the coating was determined by analyzing its morphological and composition evolution. The roughness, hydrophilicity, long-term protectiveness, bioactivity and bio-compatibility of the coating were characterized. The stress corrosion cracking (SCC) susceptibility of the coated AZ31 Mg alloy was also investigated. The results showed that the coating consisted of crystallized Mg(OH)2, MgHPO4·3H2O, KMgPO4·6H2O and KNaMg2(PO4)2·14H2O. The latter three compounds were transformed into hydroxyapatite, improving the bioactivity of AZ31 Mg alloy. The coating increased the roughness and hydrophilicity and significantly stimulated the proliferation of rabbit bone marrow stem cells, enhancing the biocompatibility of AZ31 Mg alloy. Additionally, the coating reduced the degradation rate of AZ31 Mg alloy by more than two orders of magnitude and the hydrogen volume by 50.9 % and showed good long-term protectiveness. Moreover, the coating decreased the SCC susceptibility indexes IUTS and Iε of AZ31 Mg alloy by 41.9 % and 44.9 %, respectively.

Spatio-temporal analysis of seismic potential in the Himalayas and its nearby region: an insight from seismicity and earthquake susceptibility index

Spatio-temporal analysis of seismic potential in the Himalayas and its nearby region: an insight from seismicity and earthquake susceptibility index

Multi-Criteria Evaluation (MCE) of Groundwater Prospect and Vulnerability Index Mapping from Second-Order Geo-Electric Indices: A Case Study of Coastal Environments

Exploration, management, and conservation of groundwater resources are critical stages toward potable water supply, driven by an expanding populace and the threat of a new norm posed by the distinctive coronavirus (COVID-19) pandemic. An in-depth assessment of the potential of groundwater reserves and susceptibility, using a multi-criteria evaluation, is required to aid in the planning of exploration programs for groundwater well location. Thirty (30) vertical electrical soundings (VES) were collected in Okerenkoko, Warri-Southwest, Delta State, to assess groundwater potential and vulnerability indicators. The VES data were used to obtain the first-order geoelectric variables, which were further exploited to calculate the geo-hydraulic parameters (hydraulic conductivity and transmissivity) and the vulnerability indices of the aquifer. For aquifer vulnerability appraisal, the AVI (aquifer vulnerability index), GOD (groundwater occurrence, overlying lithology, and depth to the aquifer), and GLSI (geoelectric layer susceptibility index) models were used. The groundwater characteristics in the area were evaluated using the aquifer resistivity, thickness, transmissivity and coefficient of anisotropy values of the aquifer layers defined from VES 1-30. The results show that aquifer layers with low resistivity favor more saturation due to immense porosity and therefore have greater groundwater potential than aquifers with high resistivity. The geoelectric structures defined by VES 1, 2 and 4 were consistent in their groundwater potential and yield judging from the multi-criteria assessments. The estimation of AVI, GOD, and GLSI models for aquifer threat assessment was facilitated by the multi-criteria evaluation of vulnerability indices utilizing hydro-geophysical parameters and index-based approaches. The models depend on the symbiotic effects of geologic array and thickness as the basis for the magnitude of conservation imparted to any particular aquifer involved. The AVI model map depicts that most of the VES locations were rated high (C between 1 and 2) to extremely high (C &lt; 1), indicating that the aquifers at these locations are vulnerable to pollution. However, the extent of vulnerability observed in the GOD model is less than in the AVI model, as GOD accords much more inclination to the inherent properties of geologic entities. The GOD model map categorized the vulnerability index ratings in the area as negligible (0.0-0.1), low (0.1-0.3) and moderate (0.3-0.5), with most VES locations ranked low to moderate, which indicates that these locations are susceptible to vulnerability. In the GLSI model, individual overlying layer thicknesses were prioritized. The GLSI model map shows that the vulnerability index ratings in the area are ranked as moderate (2.00-2.99), high (3.00-3.99) and extremely high (≥ 4.00) with most of the VES locations ranked moderate to high with the exception of VES 27, which ranked extremely high in both AVI and GLSI indices. By correlating the results of vulnerability index valuation for the AVI, GOD and GLSI models, more correlation was observed between the AVI and GLSI models. These findings validate the adoption of a multi-criteria evaluation methodology for groundwater potential and aquifer vulnerability studies and are strongly recommended as practical criteria for locating subsurface aquifers and their protective measures for groundwater prospect development planning and management.

Landslide susceptibility mapping in the Adwa Volcanic Mountain Plugs, Northern Ethiopia: a comparative analysis of frequency ratio and analytical hierarchy process methods

Landslides are a widespread occurrence in various regions across the globe, and Ethiopia serves as a typical example. The objective of this research was to develop and evaluate a landslide susceptibility map (LSM) using two different approaches: the frequency ratio (FR) and analytical hierarchy process (AHP). The suitability and effectiveness of both approaches were assessed for the specific study area. In the initial phase, a total of 175 past landslides, covering an area of 13.09 km2, were identified and mapped for further analysis. Eight causative factors, including elevation, slope steepness, slope aspect, lithology, distance from fault and stream, precipitation, and land use/land cover, were selected and mapped accordingly. Subsequently, a landslide susceptibility index (LSI) was calculated based on the relative influence of these factors on the recorded past landslides. The resulting LSMs were then categorized into five zones representing varying degrees of susceptibility: very low, low, moderate, high, and very high. For the LSM derived from the FR approach, the very high and high susceptibility zones accounted for 16.65 km2 (7.58%) and 79.96 km2 (36.4%) of the total area, respectively. Conversely, for the LSM derived from the AHP approach, these values were 21.9 km2 (9.99%) and 68.9 km2 (31.37%). Generally, both the FR and AHP approaches yielded comparable results. The LSMs were validated using the area under the curve (AUC) method. The FR model exhibited higher accuracy and prediction rates, with values of 0.807 and 80.7%, respectively, compared to the AHP model with values of 0.756 and 75.6%, respectively. These generated LSMs offer valuable insights for identifying and addressing the requirements of landslide hazard mitigation and adaptation.

Calcium-Dependent Protein Kinase 5 (OsCPK5) Overexpression in Upland Rice (Oryza sativa L.) under Water Deficit.

Water deficit significantly affects global crop growth and productivity, particularly in water-limited environments, such as upland rice cultivation, reducing grain yield. Plants activate various defense mechanisms during water deficit, involving numerous genes and complex metabolic pathways. Exploring homologous genes that are linked to enhanced drought tolerance through the use of genomic data from model organisms can aid in the functional validation of target species. We evaluated the upland rice OsCPK5 gene, an A. thaliana AtCPK6 homolog, by overexpressing it in the BRSMG Curinga cultivar. Transformants were assessed using a semi-automated phenotyping platform under two irrigation conditions: regular watering, and water deficit applied 79 days after seeding, lasting 14 days, followed by irrigation at 80% field capacity. The physiological data and leaf samples were collected at reproductive stages R3, R6, and R8. The genetically modified (GM) plants consistently exhibited higher OsCPK5 gene expression levels across stages, peaking during grain filling, and displayed reduced stomatal conductance and photosynthetic rate and increased water-use efficiency compared to non-GM (NGM) plants under drought. The GM plants also exhibited a higher filled grain percentage under both irrigation conditions. Their drought susceptibility index was 0.9 times lower than that of NGM plants, and they maintained a higher chlorophyll a/b index, indicating sustained photosynthesis. The NGM plants under water deficit exhibited more leaf senescence, while the OsCPK5-overexpressing plants retained their green leaves. Overall, OsCPK5 overexpression induced diverse drought tolerance mechanisms, indicating the potential for future development of more drought-tolerant rice cultivars.

Studies on Genetic Diversity and Stress Indices for Salinity Tolerance in Triticum dicoccum

Salinity stands out as a crucial abiotic stress factor that significantly impacts both crop yield and quality. Wheat, considered a moderately salt-tolerant crop, offers a wealth of variability and diversity within its species, presenting an accessible avenue for enhancing wheat's salt tolerance. Consequently, this study investigated the extent of genetic diversity for salt tolerance among sixty Triticum dicoccum germplasm accessions. In both saline and control condition, the genotypes were divided into three and nine separate clusters respectively, showing that there was a significant level of genetic variability among the genotypes by multivariate analysis. It was observed that biomass was the largest contributor (85.19 %) to the divergence in the saline situation. Among the yield parameters under saline condition, tillers per meter followed by thousand-grain weight and spike length were observed to be important. Thirteen genotypes were salt-tolerant, according to the Stress susceptibility index (SSI). The stress tolerance index (STI) was defined as a useful tool for determining the high yield and potential stress tolerance of genotypes. Based on the stress tolerance index, only eight genotypes were said to be tolerant.