scholarly journals Soil and Climate Characterization to Define Environments for Summer Crops in Senegal

2021 ◽  
Vol 13 (21) ◽  
pp. 11739
Author(s):  
Carlos Manuel Hernández ◽  
Aliou Faye ◽  
Mamadou Ousseynou Ly ◽  
Zachary P. Stewart ◽  
P. V. Vara Prasad ◽  
...  
Keyword(s):  
Principal Component ◽  
Exchange Capacity ◽  
Total Carbon ◽  
Limiting Factors ◽  
Climate Data ◽  
Soil Database ◽  
Field Crops ◽  
Soil Climate ◽  
Large Soil

Investigating soil and climate variability is critical to defining environments for field crops, understanding yield-limiting factors, and contributing to the sustainability and resilience of agro-ecosystems. Following this rationale, the aim of this study was to develop a soil–climate characterization to describe environmental constraints in the Senegal summer-crops region. For the soil database, 825 soil samples were collected characterizing pH, electrical conductivity (EC), phosphorus (P), potassium (K), cation exchange capacity (CEC), and total carbon (C) and nitrogen (N). For the climate, monthly temperature, precipitation, and evapotranspiration layers were retrieved from WorldClim 2.1, CHIRPS and TERRACLIMATE. The same analysis was applied individually to both databases. Briefly, a principal component analysis (PCA) was executed to summarize the spatial variability. The outcomes from the PCA were subjected to a spatial fuzzy c-means algorithm, delineating five soil and three climate homogeneous areas, accounting for 73% of the soil and 88% of the climate variation. To our knowledge, no previous studies were done with large soil databases since availability field data is often limited. The use of soil and climate data allowed the characterization of different areas and their main drivers. The use of this classification will assist in developing strategic planning for future land use and capability classifications.

Generic monitoring tools for water characteristics assessment

2003 ◽  
Vol 3 (1-2) ◽  
pp. 351-357
Author(s):  
S. Le Bonté ◽  
M.-N. Pons ◽  
O. Potier ◽  
S. Chanel ◽  
M. Baklouti
Keyword(s):  
Municipal Wastewater ◽  
Principal Component ◽  
Toxic Substances ◽  
Capacity Curves ◽  
Water Characteristics ◽  
Monitoring Tools ◽  
Visible Spectra ◽  
On Line ◽  
Mining Procedure

An adaptive principal component analysis applied to sets of data provided by global analytical methods (UV-visible spectra, buffer capacity curves, respirometric tests) is proposed as a generic procedure for on-line and fast characterization of wastewater. The data-mining procedure is able to deal with a large amount of information, takes into account the normal variations of wastewater composition related to human activity, and enables a rapid detection of abnormal situations such as the presence of toxic substances by comparison of the actual wastewater state with a continuously updated reference. The procedure has been validated on municipal wastewater.

Characterization of putative salinity-responsive biomarkers in olive (Olea europaea L.)

2021 ◽  
pp. 1-11
Author(s):  
Monther T. Sadder ◽  
Ahmad F. Ateyyeh ◽  
Hodayfah Alswalmah ◽  
Adel M. Zakri ◽  
Abdullah A. Alsadon ◽  
...  
Keyword(s):  
Food Production ◽  
Stress Protein ◽  
Adaptor Protein ◽  
Structural Features ◽  
Nacl Stress ◽  
Soil Salinization ◽  
Limiting Factors ◽  
Stress Levels ◽  
Quality Water

Abstract Low-quality water and soil salinization are increasingly becoming limiting factors for food production, including olive – a major fruit crop in several parts of the world. Identifying putative salinity-stress tolerance in olive would be helpful in the future development of new tolerant varieties. In this study, novel salinity-responsive biomarkers (SRBs) were characterized in the species, namely, monooxygenase 1 (OeMO1), cation calcium exchanger 1 (OeCCX1), salt tolerance protein (OeSTO), proteolipid membrane potential modulator (OePMP3), universal stress protein (OeUSP2), adaptor protein complex 4 medium mu4 subunit (OeAP-4), WRKY1 transcription factor (OeWRKY1) and potassium transporter 2 (OeKT2). Unique structural features were highlighted for encoded proteins as compared with other plant homologues. The expression of olive SRBs was investigated in leaves of young plantlets of two cultivars, ‘Nabali’ (moderately tolerant) and ‘Picual’ (tolerant). At 60 mM NaCl stress level, OeMO1, OeSTO, OePMP3, OeUSP2, OeAP-4 and OeWRKY1 were up-regulated in ‘Nabali’ as compared with ‘Picual’. On the other hand, OeCCX1 and OeKT2 were up-regulated at three stress levels (30, 60 and 90 mM NaCl) in ‘Picual’ as compared to ‘Nabali’. Salinity tolerance in olive presumably engages multiple sets of responsive genes triggered by different stress levels.

scholarly journals A Methodology Based on FT-IR Data Combined with Random Forest Model to Generate Spectralprints for the Characterization of High-Quality Vinegars

Foods ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 1411
Author(s):  
José Luis P. Calle ◽  
Marta Ferreiro-González ◽  
Ana Ruiz-Rodríguez ◽  
Gerardo F. Barbero ◽  
José Á. Álvarez ◽  
...  
Keyword(s):  
Random Forest ◽  
Raw Materials ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Raw Material ◽  
Support Vector ◽  
Protected Designation Of Origin ◽  
Ft Ir

Sherry wine vinegar is a Spanish gourmet product under Protected Designation of Origin (PDO). Before a vinegar can be labeled as Sherry vinegar, the product must meet certain requirements as established by its PDO, which, in this case, means that it has been produced following the traditional solera and criadera ageing system. The quality of the vinegar is determined by many factors such as the raw material, the acetification process or the aging system. For this reason, mainly producers, but also consumers, would benefit from the employment of effective analytical tools that allow precisely determining the origin and quality of vinegar. In the present study, a total of 48 Sherry vinegar samples manufactured from three different starting wines (Palomino Fino, Moscatel, and Pedro Ximénez wine) were analyzed by Fourier-transform infrared (FT-IR) spectroscopy. The spectroscopic data were combined with unsupervised exploratory techniques such as hierarchical cluster analysis (HCA) and principal component analysis (PCA), as well as other nonparametric supervised techniques, namely, support vector machine (SVM) and random forest (RF), for the characterization of the samples. The HCA and PCA results present a clear grouping trend of the vinegar samples according to their raw materials. SVM in combination with leave-one-out cross-validation (LOOCV) successfully classified 100% of the samples, according to the type of wine used for their production. The RF method allowed selecting the most important variables to develop the characteristic fingerprint (“spectralprint”) of the vinegar samples according to their starting wine. Furthermore, the RF model reached 100% accuracy for both LOOCV and out-of-bag (OOB) sets.

scholarly journals Semi-automated regional classification of the style of activity of slow rock-slope deformations using PS InSAR and SqueeSAR velocity data

Landslides ◽  
2021 ◽  
Author(s):  
Chiara Crippa ◽  
Elena Valbuzzi ◽  
Paolo Frattini ◽  
Giovanni B. Crosta ◽  
Margherita C. Spreafico ◽  
...  
Keyword(s):  
Rock Slope ◽  
Progressive Failure ◽  
Principal Component ◽  
Cost Effective ◽  
Displacement Rate ◽  
Machine Learning Classification ◽  
Management Perspective ◽  
Alpine Environments

AbstractLarge slow rock-slope deformations, including deep-seated gravitational slope deformations and large landslides, are widespread in alpine environments. They develop over thousands of years by progressive failure, resulting in slow movements that impact infrastructures and can eventually evolve into catastrophic rockslides. A robust characterization of their style of activity is thus required in a risk management perspective. We combine an original inventory of slow rock-slope deformations with different PS-InSAR and SqueeSAR datasets to develop a novel, semi-automated approach to characterize and classify 208 slow rock-slope deformations in Lombardia (Italian Central Alps) based on their displacement rate, kinematics, heterogeneity and morphometric expression. Through a peak analysis of displacement rate distributions, we characterize the segmentation of mapped landslides and highlight the occurrence of nested sectors with differential activity and displacement rates. Combining 2D decomposition of InSAR velocity vectors and machine learning classification, we develop an automatic approach to characterize the kinematics of each landslide. Then, we sequentially combine principal component and K-medoids cluster analyses to identify groups of slow rock-slope deformations with consistent styles of activity. Our methodology is readily applicable to different landslide datasets and provides an objective and cost-effective support to land planning and the prioritization of local-scale studies aimed at granting safety and infrastructure integrity.

scholarly journals Characterization of Basil Volatile Fraction and Study of Its Agronomic Variation by ASCA

Molecules ◽  
2021 ◽  
Vol 26 (13) ◽  
pp. 3842
Author(s):  
Alessandro D’Alessandro ◽  
Daniele Ballestrieri ◽  
Lorenzo Strani ◽  
Marina Cocchi ◽  
Caterina Durante
Keyword(s):  
Food Industry ◽  
Principal Component ◽  
Component Analysis ◽  
Gas Chromatography Mass Spectrometry ◽  
Volatile Fraction ◽  
Combined Effects ◽  
A Posteriori ◽  
Aroma Profile ◽  
Simultaneous Component Analysis

Basil is a plant known worldwide for its culinary and health attributes. It counts more than a hundred and fifty species and many more chemo-types due to its easy cross-breeds. Each species and each chemo-type have a typical aroma pattern and selecting the proper one is crucial for the food industry. Twelve basil varieties have been studied over three years (2018–2020), as have four different cuts. To characterize the aroma profile, nine typical basil flavour molecules have been selected using a gas chromatography–mass spectrometry coupled with an olfactometer (GC–MS/O). The concentrations of the nine selected molecules were measured by an ultra-fast CG e-nose and Principal Component Analysis (PCA) was applied to detect possible differences among the samples. The PCA results highlighted differences between harvesting years, mainly for 2018, whereas no observable clusters were found concerning varieties and cuts, probably due to the combined effects of the investigated factors. For this reason, the ANOVA Simultaneous Component Analysis (ASCA) methodology was applied on a balanced a posteriori designed dataset. All the considered factors and interactions were statistically significant (p < 0.05) in explaining differences between the basil aroma profiles, with more relevant effects of variety and year.

scholarly journals Simultaneous determination of various platycosides in Four Platycodon grandiflorum cultivars by UPLC-QTOF/MS

2019 ◽  
Vol 62 (1) ◽  
Author(s):  
Dae Young Lee ◽  
Bo-Ram Choi ◽  
Jae Won Lee ◽  
Yurry Um ◽  
Dahye Yoon ◽  
...  
Keyword(s):  
Principal Component ◽  
Ultra Performance Liquid Chromatography ◽  
Platycodon Grandiflorum ◽  
Pentacyclic Triterpenoid ◽  
Flight Mass Spectrometry ◽  
Platycodi Radix ◽  
Isomeric Pairs ◽  
Different Levels

Abstract In Platycodi Radix (root of Platycodon grandiflorum), there are a number of platycosides that consist of a pentacyclic triterpenoid aglycone and two sugar moieties. Due to the pharmacological activities of platycosides, it is critical to assess their contents in PR, and develop an effective method to profile various platycosides is required. In this study, an analytical method based on ultra performance liquid chromatography coupled with quadrupole time-of-flight/mass spectrometry (UPLC-QTOF/MS) with an in-house library was developed and applied to profile various platycosides from four different Platycodi Radix cultivars. As a result, platycosides, including six isomeric pairs, were successfully analyzed in the PRs. In the principal component analysis, several platycosides were represented as main variables to differentiate the four Platycodi Radix cultivars. Their different levels of platycosides were also represented by relative quantification. Finally, this study indicated the proposed method based on the UPLC-QTOF/MS can be an effective tool for identifying the detail characterization of various platycosides in the Platycodi Radix.

scholarly journals STATISTICAL MULTIVARIATE ANALYSIS APPLIED TO ENVIRONMENTAL CHARACTERIZATION OF SOIL IN SEMIARID REGION1

2019 ◽  
Vol 32 (1) ◽  
pp. 200-210
Author(s):  
Antônio Italcy de Oliveira Júnior ◽  
Luiz Alberto Ribeiro Mendonça ◽  
Sávio de Brito Fontenele ◽  
Adriana Oliveira Araújo ◽  
Maria Gorethe de Sousa Lima Brito
Keyword(s):  
Organic Matter Content ◽  
Principal Component ◽  
Matter Content ◽  
Multivariate Statistical ◽  
Statistical Tool ◽  
Environmental Characterization ◽  
Hierarchical Grouping ◽  
Two Factors ◽  
Total Data

ABSTRACT Soil is a dynamic and complex system that requires a considerable number of samples for analysis and research purposes. Using multivariate statistical methods, favorable conditions can be created by analyzing the samples, i.e., structural reduction and simplification of the data. The objective of this study was to use multivariate statistical analysis, including factorial analysis (FA) and hierarchical groupings, for the environmental characterization of soils in semiarid regions, considering anthropic (land use and occupation) and topographic aspects (altitude, moisture, granulometry, PR, and organic-matter content). As a case study, the São José Hydrographic Microbasin, which is located in the Cariri region of Ceará, was considered. An FA was performed using the principal component method, with normalized varimax rotation. In hierarchical grouping analysis, the “farthest neighbor” method was used as the hierarchical criterion for grouping, with the measure of dissimilarity given by the “square Euclidean distance.” The FA indicated that two factors explain 75.76% of the total data variance. In the analysis of hierarchical groupings, the samples were agglomerated in three groups with similar characteristics: one with samples collected in an area of the preserved forest and two with samples collected in areas with more anthropized soils. This indicates that the statistical tool used showed sensitivity to distinguish the most conserved soils and soils with different levels of anthropization.

scholarly journals Detection, Identification, and Molecular Characterization of the 16SrII-D Phytoplasmas Infecting Vegetable and Field Crops in Oman

Plant Disease ◽  
2018 ◽  
Vol 102 (3) ◽  
pp. 576-588 ◽  
Author(s):  
Ali M. Al-Subhi ◽  
Saskia A. Hogenhout ◽  
Rashid A. Al-Yahyai ◽  
Abdullah M. Al-Sadi
Keyword(s):  
Molecular Characterization ◽  
Ribosomal Rna ◽  
Faba Bean ◽  
Sequence Variation ◽  
Field Pea ◽  
16S Ribosomal Rna ◽  
Diverse Range ◽  
Field Crops ◽  
Phytoplasma Infection

Typical symptoms of phytoplasma infection were observed on 11 important crops in Oman that included alfalfa, sesame, chickpea, eggplant, tomato, spinach, rocket, carrot, squash, field pea, and faba bean. To identify the phytoplasmas in these crops, samples from infected and asymptomatic plants were collected, followed by amplifying and sequencing of the 16S ribosomal RNA, secA, tuf, imp, and SAP11 genes. We found that these sequences share >99% similarity with the peanut witches’ broom subgroup (16SrII-D). Whereas some sequence variation was found in the five genes among 11 phytoplasma isolates of different crops, all sequences grouped into one clade along with those of other phytoplasmas belonging to the 16SrII-D group. Thus, 16SrII-D phytoplasmas infect a diverse range of crops in Oman. Phytoplasmas in this group have not been reported to occur in carrot, spinach, rocket, and field pea previously. Within Oman, this is the first report of the presence of 16SrII-D phytoplasmas in tomato, spinach, rocket, carrot, squash, field pea, and faba bean. Sequences of the five genes enabled for better distinction of the 16SrII-D phytoplasmas that occur in Oman.

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