Principal Component Analysis Biplot Research Articles

Principal Component Analysis in rice (Oryza sativa L.) varieties for three seasons in Annamalai Nagar, an east coast region of Tamil Nadu

Principal component analysis (PCA) was used to assess genetic variability and identify key yield-contributing traits among 50 rice genotypes evaluated over three seasons under saline conditions. The study aimed to select promising donor lines for developing salt-tolerant, high-yielding cultivars suitable for coastal regions. PCA identified three principal components (PCs) that explained 73.19%, 70.34%, and 64.81% of the total genetic variation in seasons 1, 2 and 3, respectively. Key plant attributes, such as grain yield per plant, panicle grain density, total tiller number, and productive tiller count, showed significant positive associations with PC1 across various growth stages, underscoring their crucial role in explaining the genetic diversity among the genotypes. Plant height, panicle length, and hundred-grain weight also emerged as major contributors to variation. The PCA biplots consistently demonstrated positive correlations between grain yield and traits like panicle length, hundred-grain weight, and tillering ability. In contrast, days to 50% flowering exhibited a negative association with yield. Genotypes G10, G49, G36, G41, G9, G4, G22, and G21 displayed favourable combinations of grain yield per plant, identifying them as potential donor lines for breeding programs aimed at improving rice yield and associated agronomic traits. This comprehensive PCA analysis highlights the effectiveness of the approach in capturing genotypic diversity and guiding the selection of promising germplasm for targeted trait improvement in rice.

Taxonomic insights into medicinal plants pollen, using advanced microscopy techniques, from the Western Ghats, India

Pollen morphological study on 18 medicinal plants from the Western Ghats (India) was carried out using light microscopy (LM), confocal laser scanning microscopy (CLSM) and field emission scanning electron microscopy (FESEM) techniques. The principal aim was to obtain high-resolution three-dimensional images and ultra-structure of the pollen grains of the studied taxa. The palyno-morphological characters, such as polar axis, equatorial diameter, P/E ratio, pollen shape, aperture type and number, exine thickness and surface ornamentation will be useful in the identification and taxonomic classification of the studied medicinal plants. Statistical analysis reveals grouping of the taxa in three different clusters and also isolated presence of taxa on the principal component analysis (PCA) biplot, irrespective of their families, showing similarities and/or variations among the studied taxa, belonging to the same and/or different families. The present study has taxonomic significance, providing high taxonomic resolution (identification of the taxa up to species level), and further aid in the identification and delimitation of the 18 studied plants.

Planting pattern and nitrogen management strategies: positive effect on yield and quality attributes of Triticum aestivum L. crop

Wheat (Triticum aestivum L.) is a staple food crop that plays a crucial role in global food security. A suitable planting pattern and optimum nitrogen (N) split management are efficient practices for improving wheat production. Therefore, an experiment was performed to explore the effect of N split management and sowing patterns on wheat at the Agronomy Research Farm, The University of Agriculture Peshawar, during rabi season 2020-21 and 2021-22. The treatments consisted of different nitrogen rates of 0, 80, 120, and 160 kg ha− 1 and planting patterns of W, M, broadcast and line sowing. The pooled analysis of both cropping seasons showed that application of 120 kg N ha− 1 increased spikelets spike− 1, grains spike− 1, 1000 grains weight, grain yield, grain N content, evapotranspiration and water use efficiency by 21.9, 16.7, 21.8, 70, 13, 19.9 and 40% as compared to control, respectively. In addition, W and M were observed the best management practices among all planting patterns. The M planting pattern enhanced chlorophyll a, b, carotenoids and evapotranspiration while W plating pattern improved yield components and yield of wheat as compared to broadcast planting patterns. The principal component analysis biplot showed a close association of M and W planting patterns with 120 kg N ha− 1 in most of the studied traits. Hence, it is concluded that split application of 120 kg N ha− 1 in W and M sowing patterns enhanced growth, biochemical traits and water use efficiency, reducing N fertilization from 160 to 120 kg ha− 1 while increasing grain yield of wheat. Hence, it is recommended that application of 120 kg N ha⁻¹ in combination with W and M planting patterns offer a sustainable approach to enhancing wheat production in the alkaline soil conditions of the Peshawar valley.

Assessment of Genetic Variability for Yield and Yield Related Traits in Safflower Core Subset Germplasm

One hundred and fifty safflower genotypes of Core subset germplasm provided by ICAR-IIOR, were evaluated to study genetic variability, correlation and principal component analysis (PCA). A wide range of phenotypic data with near normal distribution was observed for most of the traits analysed. High variability was observed for all the traits studied indicating diverse nature of the germplasm. Significant positive correlation was observed for seed yield with test weight and oil content. Genotype GMU 6869 has been identified as potential genotype surpassing the best check A1 for seed yield and earliness. Twenty three genotypes significantly superior to the 2nd best check PBNS-12 were identified. High heritability along with GAM was recorded for the traits number of effective capitula/plant, test weight and single plant yield indicating that these characters may be considered as selection criteria for genetic improvement in safflower. Principal component analysis revealed that first four principal components contributed 65.2% of total variance. PCA-variables plot and PCA Bi-plots revealed that two traits viz., LPB and HPB contributed maximum variations in PC1, while two traits viz., DAF & DAM contributed to the maximum variability in PC-2. Safflower accessions displaying highest performance for the traits studied were identified. These genotypes can be used as parental lines for improvement of specific traits in safflower breeding program.

Data Analytics in Agriculture: Enhancing Decision-Making for Crop Yield Optimization and Sustainable Practices

Collaboration across the agriculture supply chain is essential to address the high-yield demand and sustainable practices amid global overpopulation. Limited resources, such as soil and water, are compromised by excessive chemical agents and nutrient use. The Internet of Things (IoT) and smart farming offer solutions by optimizing agent applications, data analysis, and farm monitoring. Evidence from numerous studies indicates that collaboration in the supply chain, including farmers, can improve efficiency and productivity, reduce costs, and enhance crop quality. This paper investigates the transformation of traditional agriculture into smart farming through the integration of IoT technology and community partnerships. It presents a case study focused on educating farm owners about advanced technologies to enhance decision-making, improve crop yields, and promote sustainability. Additionally, the paper highlights the role of data analytics in agriculture. Farmers in the southern region of Zagreb, Croatia, were trained on the use of sensors and yield monitoring. Small farms in that region face challenges in improving yields due to limited capacity and lack of entrepreneurial experience. The DMAIC methodology was employed to address these issues and measure relevant parameters. The paper also discusses consistent patterns between electrical conductivity (EC) measurements and potassium levels in soil. It explains the potential of estimating potassium concentrations based on EC readings, or vice versa. Leveraging EC as a proxy for potassium levels could offer a cost-effective means of assessing soil fertility and nutrient dynamics. Additionally, Principal Component Analysis (PCA) biplot analysis is presented, showing that pH values behaved independently. Understanding these dynamics enhances knowledge of soil variability and informs sustainable soil management practices.

Genetic variability for salinity tolerance of tomato (Solanum lycopersicon MILL.) genotypes determined by stress tolerance indices

BackgroundTomato is an important vegetable crop; however, salinity hampers its growth and yield-related traits. Different tomato genotypes significantly differ in their salinity tolerance; thus, selecting the tolerant genotypes could improve yield and productivity under saline environments. MethodsThis study investigated salinity tolerance of five tomato genotypes, i.e., ‘Pakit’, ‘Riogrande’, ‘Roma’, ‘Continental’, and ‘Nagina’ under 0-, 100-, 150- and 200-mM salinity levels. Data relating yield-related traits (plant height, shoot fresh and dry weights, number of fruits per plant, single fruit weight and fruit yield per plant) and nine different stress tolerance indices were employed to infer salinity-tolerance of the genotypes. ResultsIncreasing salinity levels negatively affected the yield-related traits of genotypes; however, genotypes exhibited varied tolerance to salinity. The better and the poor yield-related traits were noted under 0- and 200-mM salinity levels, respectively. Similarly, genotypes ‘Pakit’ and ‘Nagina’ recorded the highest and the lowest values of yield-related traits. The genotype ‘Pakit’ proved the most tolerant to tested salinity levels compared to the rest of the genotypes. The ‘Pakit’ genotypes had the highest stress tolerance index (0.19, 1.06 and 1.79) and the lowest relative change in yield (6.00, 32.59, and 55.75%) under all salinity levels. Principal component analysis biplot revealed that ‘Pakit’ genotypes had superior yield traits and tolerance indices. ConclusionThe genotype ‘Pakit’ exhibited a higher level of salinity tolerance (6 % yield reduction under 100 mM salinity compared to 27.67 % reduction in ‘Nagina’ genotype. Therefore, genotype ‘Pakit’ can be recommended for cultivation on low to moderate saline soils without significant yield losses. On the other hand, ‘Nagina’ genotype proved the most sensitive to salinity (27.67 %, 56.79 %, and 68.90 % yield reduction under 100-, 150-, and 200-mM salinity, respectively). Therefore, this genotype should not be cultivated on saline soils.

Identification of Geochemical Anomalies Based on RPCA and Multifractal Theory: A Case Study of the Sidaowanzi Area, Chifeng, Inner Mongolia.

The focus of exploration geochemistry is an accurate interpretation of geochemical data and the precise extraction of anomaly information related to mineralization from complex geological information. However, geochemical data are component data and exhibit a closure effect. Thus, traditional statistical methods cannot adequately reveal and identify the distribution of deep-seated anomaly information. This paper focuses on the Sidaowanzi area in Inner Mongolia and uses multivariate component data analysis methods to process 1:50 000 soil geochemical data. Using the Exploratory Data Analysis (EDA) method, the spatial distribution and internal structure characteristics of raw, logarithmic, and isometric logarithmic ratio (ILR) transformed data were compared and, coupled with robust principal component analysis (RPCA) and elemental component biplots, the association between element combinations and mineralization indicated by these three types of data was revealed. The S-A method was used to decompose composite anomalies of the ILR transformed RPCA score data to extract the characteristics of elemental combination anomalies and background distribution, and the Fry analysis method was utilized to analyze the dominant mineralization direction within the area. The results show that (1) data transformed using the ILR eliminated the influence of the closure effect, making the data more uniform on a spatial scale and exhibiting characteristics of an approximately normal distribution. (2) The S-A method was further used to decompose the composite anomaly of the PC1 and PC2 principal component combinations. The screened-out anomaly and background fields can essentially reflect the ore-causing anomalies dominated by Au and Cu-Mo mineralization. Moreover, the extracted anomalies and background information closely align with known mineral deposits (prospects) and can effectively identify weakly retarded geochemical anomaly information. (3) Fry analysis based on geochemical anomalies indicates that the dominant mineralization directions, by an assemblage dominated by Au and Cu-Mo, predominantly occur in the NE, NW, and proximate EW orientations. The combined application of the aforementioned three methods for the quantitative analysis of geochemical data aims to explore a transferable methodological system, providing new insights and approaches for further prediction of mineralization potential.

Characterization of the Non-Volatiles and Volatiles in Correlation with Flavor Development of Cooked Goat Meat as Affected by Different Cooking Methods.

Thai-Native×Anglo-Nubian goat meat cooked by grilling (GR), sous vide (SV), and microwave (MW), was compared to fresh meat (Raw) in terms of flavor development. Non-volatile [i.e., free amino acids, nucleotide-related compounds, taste active values (TAVs) and umami equivalency, sugars, lipid oxidation, Maillard reaction products] and volatile compounds, were investigated. Notably, inosine monophosphate and Glu/Gln were the major compounds contributing to umami taste, as indicated by the highest TAVs in all samples. Raw had higher TAVs than cooked ones, indicating that heat-cooking removes these desirable flavor and taste compounds. This could be proportionally associated with the increase in aldehyde, ketone, and nitrogen-containing volatiles in all cooked samples. GR showed the highest thiobarbituric acid reactive substances (1.46 mg malonaldehyde/kg sample) and browning intensity (0.73), indicating the greatest lipid oxidation and Maillard reaction due to the higher temperature among all cooked samples (p<0.05). In contrast, SV and Raw exhibited similar profiles, indicating that low cooking temperatures preserved natural goat meat flavor, particularly the goaty odor. The principal component analysis biplot linked volatiles and non-volatiles dominant for each cooked sample to their unique flavor and taste. Therefore, these findings shed light on cooking method selection based on desirable flavor and preferences.

Parametric and Non-Parametric Measures to Identify Stable and Adaptable Cotton (Gossypium Hirsutum L.) Genotypes

ABSTRACT The study, comprised seventeen genotypes, was conducted during 2016 to 2019 growing seasons. The design was Randomized Complete Block Design (RCBD) with three replications to identify adaptable cotton genotypes. Parametric and non-parametric measures, principal component analysis (PCA) and correlation among the ranks of the parameters computed using R software. The environment, genotype and genotype-environment interaction contributed 79.58, 3.2 and 15.46% of the variation, respectively, and were significant (p < 0.001). The highest (1510.7 kg/ha) and lowest (1080.1 kg/ha) seed cotton yield was recorded from G12 and G8 genotypes respectively. Coefficients of determination (R2) and superiority index (Pi) recognized G13, G11, G10 and G11, G12, G7 as the top three stable genotypes correspondingly. G10 was selected three times as superior by stability variance (σ2), wricke’s ecovalence (Wi) and deviation from regression (S2di). Seed cotton yield positively correlated with GAI, Kang’s rank-sum (KRS), Huhn and Nassar S(1), S(6) S(3) while negatively correlated with Thennarasu stability measures NP(2), NP(3) and NP(4 . The first, second and third clusters of the PCA biplot comprised, 4, 4 and 10 parameters respectively. G12, with highest seed cotton yield and good stability, was the best genotype and recommended for variety verification.

Multivariate analysis of yield and quality traits in sweet potato genotypes (Ipomoea batatas L.)

Improving sweet potato traits and genotype selection based on specific traits of interest is crucial to motivate farmers and rural households to enhance their livelihoods by increasing income and improving nutritional intake. This research offers a thorough analysis of genotype selection guidelines considering multiple traits of sweet potatoes, including vine length (VL), average root length (ARL), average root diameter (ARD), marketable storage root number (MRN), marketable storage root yield (MRY), non-marketable root number (NMRN), non-marketable root weight (NMRW), dry weight of root (DW), beta-carotene content (BC), vitamin C content (VC), starch content (ST), total sugar content (TS), total soluble solid (TSS), and titratable acidity (ACD). The findings showed that the genotype Moz1.15 achieved the highest MRY at 46.46 t/ha, H5.ej.10 displayed the top BC level at 48.94 mg/100 g, and Moz1.9 had the highest VC (23.89 mg/100 g). Furthermore, there was a significant positive correlation between VL and DW, TSS, ST, TS, and VC. Additionally, ARL, ARD, and MRN showed significant positive correlations with MRY. The principal component analysis (PCA) biplot further confirmed how traits were connected with each other. There were four separate clusters of genotypes found, each having its specific significant trait representation. The distance between these clusters ranged from 21.79 to 54.13, while within each cluster, the distances varied from 0 to 17.45. The factor analysis using the multi-trait genotype-ideotype distance index (MGIDI) showed that sweet potato traits played a substantial role in three distinct factors during both growing seasons (2020-21 and 2021-22). This allowed for the selection of genotypes capable of achieving favorable genetic gains ranging from 1.86% to 75.4%, alongside a broad-sense heritability (h2b) spanning from 64.9% to 99.8%. Through the application of the MGIDI index, a number of potential genotypes were identified, among which BARI Mistialu-12 and H9.7.12 stood out as consistently chosen across both years. These promising genotypes exhibited high performance while displaying their strengths and weaknesses in terms of their contributions to the identified factors. This thorough research offers valuable insights into the complex relationship between different traits in sweet potatoes and how genotype selection based on a multi-trait index can significantly assist in shaping upcoming breeding and cultivation strategies.

Uncovering Key Factors in Graphene Aerogel-Based Electrocatalysts for Sustainable Hydrogen Production: An Unsupervised Machine Learning Approach.

The application of principal component analysis (PCA) as an unsupervised learning method has been used in uncovering correlations among diverse features of aerogel-based electrocatalysts. This analytical approach facilitates a comprehensive exploration of catalytic activity, revealing intricate relationships with various physical and electrochemical properties. The first two principal components (PCs), collectively capturing nearly 70% of the total variance, attested the reliability and efficacy of PCA in unveiling meaningful patterns. This study challenges the conventional understanding that a material's reactivity is solely dictated by the quantity of catalyst loaded. Instead, it unveils a complex perspective, highlighting that reactivity is intricately influenced by the material's overall design and structure. The PCA bi-plot uncovers correlations between pH and Tafel slope, suggesting an interdependence between these variables and providing valuable insights into the complex interactions among physical and electrochemical properties. Tafel slope stands to be positively correlated with PC1 and PC2, showing an evident positive correlation with the pH. These findings showed that the pH can have a positive correlation with the Tafel slope, however, it does not necessarily reflect a direct positive correlation with the overpotential. The impact of pH on current density (j)and Tafel slope underscores the importance of adjusting pH to lower overpotential effectively, enhancing catalytic activity. Surface area (from 30 to 533 m2 g-1) emerges as a key physical property, inclusively inverse correlation with overpotential, indicating its direct role in lowering overpotential and increasing catalytic activity. The introduction of PC3, in conjunction with PC1, enriches the analysis by revealing consistent trends despite a slightly lower variance (60%). This reinforces the robustness of PCA in delineating distinct characteristics of graphene aerogels, affirming their potential implications in diverse electrocatalytic applications. In summary, PCA proves to be a valuable tool for unraveling complex relationships within aerogel-based electrocatalysts, extending insights beyond catalytic sites to emphasize the broader spectrum of material properties. This approach enhances comprehension of dataset intricacies and holds promise for guiding the development of more effective and versatile electrocatalytic materials.

Protein Quality of African Locust Bean-A High-Value Gathered Tree Food Contributing Protein and Palatability to Plant-Based Diets.

The African locust bean tree Parkia biglobosa (Jacq.) R.Br. ex G. Don is a leguminous species native to the Sudanian parkland of western Africa. The seed obtained from pods collected from trees by rural women is fermented into a dense and aromatic paste known as soumbala, dawadawa, or iru-a protein-rich condiment underlying much of West African cuisine, its rich umami base lending a depth of flavor as a "meat substitute" in plant-based diets for which animal protein is a limiting component. Here, we assess the protein quality of P. biglobosa seed and its fermented product (soumbala) from three locations of southern Mali comprising three different eco-climatic zones, to determine whether variation in nutritional composition and protein quality could be correlated with the geographic variables of latitude and elevation. Proximate composition was determined, and amino acid profiles were compiled for 19 amino acids, with results compared by location and eco-climatic zone. A protein quality test was conducted in the aggregate and for each zone using the WHO/FAO Protein Digestibility-Corrected Amino Acid Score (PDCAAS) method. Principal component analysis (PCA) was used to assess patterns of amino acid compositional variation between the three origins. The results underline the nutritional significance of African locust bean as a source of dietary protein and of a depth of flavor providing enhanced palatability to plant-based diets. Although the PCA biplot for the amino acid profiles does indicate geographic clustering, the variation in nutritional composition and protein quality is insignificant for the raw seed, but highly significant for the fermented product (soumbala). The results indicate no correlation between phytochemical parameters and geographic variables of latitude and elevation, suggesting that management and processing may contribute more to nutritional quality than product provenance. Further studies should assess specific processing methods and the ambient microbiome as factors contributing to protein quality.

Advanced Mutant Line Developed from Fatemadhan Shows Salinity Tolerance at both Seedling and Reproductive Stages

The generation of high-yielding rice mutants and their assessment under salt stress offers a great possibility to isolate salt tolerant line(s) with desired trait of interest. Two separate experiments were conducted at the seedling and reproductive stages of rice to assess the level of salinity tolerance of few advanced high-yielding rice mutants. In the first experiment, rice seedlings were grown under hydroponic conditions and 14-day-old seedlings were subjected to salt stress (EC=10 dS/m; 7 days). Salt stress caused significant reduction in root and shoot length and biomass and leaf chlorophyll content; however, a little reduction was found in the mutant Line-1. In contrast, a sharp increase in shoot Na+/K+ ratio was found in all the genotypes except, Binadhan-10, FL-478 and the mutant Line-1, which exhibited little increased ratio. The second experiment involved exposure of plant to salt stress (EC=10 dS/m) for three weeks at the late booting stage in a sizable plastic tub filled with field soil. Salt stress resulted in a significant decrease in yield and yield attributing traits in all the genotypes except Binadhan-10. Grain yield per panicle was found significantly positive correlation with panicle length, the number of filled grains per panicle, and 100-seed weight under both control and salt stress conditions. Based on the studied traits and stress tolerance indices, Binadhan-10 and mutant Line-1 categorized as salt tolerant and rest of the genotypes were categorized as susceptible, which is also evident from the biplot of principal component analysis. Considering the results from both of the experiments, mutant Line-1 was found tolerant genotype at both seedling and reproductive stage. However, further studies are required to determine the genetic issues controlling the salinity tolerance in mutant Line-1 and the high-yield potential of mutant Line-65 under control condition in a way to develop salt tolerant and high-yielding rice varieties, respectively.

Legume Fingerprinting through Lipid Composition: Utilizing GC/MS with Multivariate Statistics.

This study presents a tentative analysis of the lipid composition of 47 legume samples, encompassing species such as Phaseolus spp., Vicia spp., Pisum spp., and Lathyrus spp. Lipid extraction and GC/MS (gas chromatography with mass spectrometric detection) analysis were conducted, followed by multivariate statistical methods for data interpretation. Hierarchical Cluster Analysis (HCA) revealed two major clusters, distinguishing beans and snap beans (Phaseolus spp.) from faba beans (Vicia faba), peas (Pisum sativum), and grass peas (Lathyrus sativus). Principal Component Analysis (PCA) yielded 2D and 3D score plots, effectively discriminating legume species. Linear Discriminant Analysis (LDA) achieved a 100% accurate classification of the training set and a 90% accuracy of the test set. The lipid-based fingerprinting elucidated compounds crucial for discrimination. Both PCA and LDA biplots highlighted squalene and fatty acid methyl esters (FAMEs) of 9,12,15-octadecatrienoic acid (C18:3) and 5,11,14,17-eicosatetraenoic acid (C20:4) as influential in the clustering of beans and snap beans. Unique compounds, including 13-docosenoic acid (C22:1) and γ-tocopherol, O-methyl-, characterized grass pea samples. Faba bean samples were discriminated by FAMEs of heneicosanoic acid (C21:0) and oxiraneoctanoic acid, 3-octyl- (C18-ox). However, C18-ox was also found in pea samples, but in significantly lower amounts. This research demonstrates the efficacy of lipid analysis coupled with multivariate statistics for accurate differentiation and classification of legumes, according to their botanical origins.

Physicochemical homogeneity of stingless bee honey (Heterotrigona itama) produced in the west coast, east coast and inland area of Peninsular Malaysia.

The nutritional composition of stingless bee honey (SBH) can be affected by different climates and soil composition across different geographical areas. However, the range of attributes set for a honey quality standard should be inclusive. This study analysed the sugar profile's physiochemical properties, including quantifying the rare sugar trehalulose, organic acid and mineral composition of SBH collected from inland, and west and east coasts of Peninsular Malaysia. Forty-three SBH (Heterotrigona itama) samples were collected and labelled as <20 and <40 West Coast (<20WC, <40WC), <20 and <40 East Coast (<20EC, <40EC) and Inland, according to their distance from the coasts. The moisture, pH and sugar composition of all SBH samples adhered to the Malaysian Kelulut Honey Standard (MS2683:2017) but not to the International Codex Standard (CODEX) for honey. Trehalulose presence in all samples, regardless of geographical area, was predominant alongside fructose and glucose. Only hydroxymethylfurfural (HMF) content and electrical conductivity (EC) results complied with both standards. The principal component analysis biplot showed that the discrimination of SBH according to the five different areas was not feasible, indicating sample homogeneity. The physicochemical evaluation of SBH from Peninsular Malaysia shows mainly homogeneous attributes of samples across geographical locations. These findings demonstrated that the current MS2683:2017 is relevant and accommodates all SBH of H. itama species produced in Peninsular Malaysia. Furthermore, the trehalulose range calculated in this study can be implemented as a new benchmark for the indicator of SBH honey quality standard by national and international food standard committees. © 2023 Society of Chemical Industry.

Cardiovascular risk and systemic inflammation in male professional rugby: a cross-sectional study

ObjectiveTo investigate cardiovascular risk factors’ prevalence and association with systemic inflammation in professional male rugby players (RP).MethodsA cross-sectional investigation of 46 professional male RP (26.1±4.1 years) cardiovascular risk factors were...

Phytochemical composition, anti-amylase and anti-acetylcholinesterase activities of leaf extracts and herbal tea of Dolichandrone serrulata (DC.) seem

The number of elderly population is continually growing in Thailand. Major health problems mostly occurred in the elder are non-communicable diseases namely Alzheimer's disease and type 2 diabetes mellitus. In current study, phytochemical composition, anti-amylase and anti-acetylcholinesterase activities of leaf extracts and herbal tea from Dolichandrone serrulata (DC.) Seem were determined. The result showed that extracts and herbal tea samples of D. serrulata leaves had effective in inhibition of amylase (an effective concentration at half maximum (EC50) = 1.21 to 23.80 mg/ml) and acetylcholinesterase activities (EC50 = 2.63 to 49.55 mg/ml). The effect of infusion time on the inhibition of amylase indicated that the best infusion time for preparing herbal tea beverage was 3 min, while the inhibition of acetylcholinesterase was 5 min. The fourier transform infrared spectroscopy (FTIR) and gas chromatograph - mass spectrometer (GC–MS) data indicated the presence of phytochemicals in the extracts and herbal tea samples related to phenolic compounds and flavonoids. The principal component analysis (PCA) result from FTIR data could discriminate herbal tea samples from the ethanol and aqueous extracts better than the PCA biplot of anti-amylase activity and anti-acetylcholinesterase activity.

A system for concurrent on-the-go soil apparent electrical conductivity and gamma-ray sensing in micro-irrigated orchards

On-the-go soil apparent electrical conductivity (ECa) sensors are great tools for mapping and monitoring soil properties such as water content, texture, and salinity. ECa maps and surveys are most useful and reliable when obtained in uniformly wet fields. However, soil moisture in micro-irrigated (e.g., drip or micro-sprinklers) orchards is typically non-uniform, with moist soil along tree and irrigation lines, and dry soil between tree rows. We developed a mobile platform and data post-processing algorithm to facilitate geospatial ECa measurements along or near driplines. Gamma-ray (γ-ray) spectrometry is commonly used for clay content and type mapping. Fusion between ECa and γ-ray is often reported to increase the accuracy of field-scale soil maps. However, contrarily to ECa, γ-ray spectrometry is best suited for sensing soils in dry conditions. Micro-irrigated orchards are ideal environments for the combined application of these two sensor technologies. The fusion of topsoil (top 0.5 m) ECa (measured along the driplines) and γ-ray total counts (TC) (measured between the tree rows) data was tested at a 0.4-ha sandy loam citrus orchard in Southern California. Here, we discuss sensor data acquisition, data processing, sensor-directed sampling scheme delineation, and characterization of field-scale soil particle size fraction (0–0.4 m soil profile) spatial variability. Pearson correlation coefficients between sand and silt content with both ECa and TC were significant (p < 0.05). A principal component analysis biplot suggested strong positive relationship with TC and clay content. Backwards stepwise multiple linear regression predicted sand content using TC, elevation, and spatial coordinates as explanatory variables with mean absolute error (MAE) of 3.06 %. Silt content was predicted (MAE=1.55 %) using ECa, elevation, and spatial coordinates. The development of this platform enables better characterization of soil properties in micro-irrigated orchard systems using on-the-go sensing technology.

Quantification of hormones and profiling of metabolites from vegetative to reproductive growth phase shift in Lagenaria siceraria (Molina) Standl

In the world of agricultural research, there has been a lot of emphasis on increasing insight into the decline of crop yield gap levels. Metabolomics is a relatively recent approach that aims to better understand the metabolic networks and is extensively being applied to agricultural research for its divergent growth and productivity by enhancing the behavior of plants under various stress. Lagenaria siceraria (Bottle gourd – Salad variety) has commonly been used because of its enormous nutritional, commercial, and medicinal benefits. The purpose of this investigation was to quantify the level of endogenous hormones and metabolites profiling at the vegetative and reproductive phase from various tissues of L. siceraria by liquid chromatography-tandem mass spectrometry (LC-MS/MS) and gas chromatography-mass spectrometry (GC–MS). The field-grown plant samples such as vegetative, male, and female flower-initiated parts including leaf, stem, tendril, and shoot apex were used for this study. The vegetative tissues had the highest level of trans zeatin and indole-3-acetic acid whereas indole-3-propanoic acid, trans zeatin, and gibberellic acid-3 were seen highest in the male flower-initiated tissues, and the level of IPA alone was found highest in the female flower-initiated tissues quantified by LC-MS/MS method. Additionally, N-Methyl-N-trimethylsilyl-trifluoroacetamide derivatized tissues showed the presence of astaxanthin and 1H-Indole, 2,3,5-trimethyl-1-(trimethylsilyl)- in vegetative tissues. On the contrary, kaur-16-ene-(8β,13β), an intermediate of GAs synthesis, gibberellic acid, and fumaric acid were recorded in reproductive tissues by GC–MS analysis. The first principal component analysis (PCA) biplot revealed two principal components that together accounted for 34.59 % and 22.96 % of the variance in the tissues of vegetative and reproductive phases respectively. In the second PCA biplot, the male and female flower-initiated tissues showed principal component levels of 42.94 % and 26.48 %, respectively. Also, the heat map analysis showed the fluctuation of phytohormone levels during both phases.

GENETIC ARCHITECTURE OF NEW PLANT TYPE RICE (ORYZA SATIVA L.) LINES BASED ON A 12-YEARS MULTI-EXPERIMENT

New plant-type (NPT) rice can increase grain yield globally. Although many studies have shown the association between harvest and yield-related traits in rice, it remains obscure in the case of NPT rice. This study aimed to elucidate the relationship between agronomic and yield features in new plant-type rice based on a 12-years multi-experiment. A large dataset of 704 NPT rice lines of various generations (F4 to F10), derived from 24 different populations, underwent study. The analysis of variance showed a highly significant population effect (P &lt; 0.01) on all traits studied. The NPT rice populations had groupings into four clusters, with a highly substantial cluster effect (P &lt; 0.01) on the number of total tillers (NTT), number of total grains (NTG), number of filled grains (NFG), and thousand-grain weight (TGW). Standard best NPT lines from each experiment achieved an average of 1.67 t ha-1 or 26% higher yield than the Ciherang variety. Determining the yield advantage of NPT lines was by higher plant height (PH) (r = 0.37), panicle length (PL) (r = 0.15), NTG (r = 0.28), NFG (r = 0.28), TGW (r = 0.10), and lower days to harvest (DTH) (r = -0.10). The principal component analysis biplot revealed that NFG could better serve as a selection characteristic for enhancing grain yield in developing NPT rice adapted to irrigated tropical ecosystems.