scholarly journals Variability in soil properties influencing pigeonpea (Cajanus cajana L.) yield: a multivariate statistical analysis

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 944
Author(s):  
Rajesh N L ◽  
Narayana Rao K ◽  
Sathishkumar U ◽  
Wali V B ◽  
Basavaraj K ◽  
...  
Keyword(s):  
Soil Properties ◽  
Seed Yield ◽  
Surface Soil ◽  
Growth Parameters ◽  
Linear Regression Analysis ◽  
Principal Component ◽  
Varimax Rotation ◽  
Multivariate Statistical ◽  
Rainfed Condition ◽  
Available Water Content

Aims: The aim of the study was to reveal the variability in soil properties influencing pigeonpea (Cajanus cajana L.) seed yield under semi-arid rainfed condition. Methods: Soils were initially classified into series level and further these series were divided into soil-phase units. For two site years viz., 2018-19 and 2019-20, surface soil samples from each soil-phase unit were collected before sowing of pigeonpea and subsequently crop growth parameters at critical stages were recorded. Results: The principal component analysis with varimax rotation resulted in seven components for both the site years, having eigenvalues greater than one, explained more than 80% of the variability. The step wise linear regression analysis showed that the pigeonpea seed yield was linearly correlated with PC3 (p<0.01), PC4 (p<0.01) and PC7 (p<0.05) of soil properties with R2 = 0.679, during 2018-19. Whereas, during 2019-20, the seed yield was linearly correlated with PC1 (p<0.01), PC3 (p<0.01) and PC6 (p<0.05) with R2 = 0.677. In site year 1, the available P2O5, Fe, Zn, S, Cu, number of pods, surface soil moisture determined the yield. In site year 2, the available K2O, P2O5, Fe, Zn, S, clay, CEC and available water content determined the yield. All these variables together explain variability in yield.

↵Genetic Diversity and Principal Component Analysis in Mungbean [Vigna radiata (L.) Wilczek] under Rainfed Condition

2021 ◽  
Author(s):  
Deepak Gupta ◽  
Suresh Muralia ◽  
N.K. Gupta ◽  
Sunita Gupta ◽  
M.L. Jakhar ◽  
...  
Keyword(s):  
Genetic Diversity ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Seed Yield ◽  
Agricultural Research ◽  
Principal Component ◽  
Component Analysis ◽  
Grain Legume ◽  
Research Station ◽  
Rainfed Condition

Background: Mungbean is a short duration grain legume widely grown in south and Southeast Asia. The extent of variability through Principal Component Analysis (PCA) and cluster analysis in promising mungbean genotypes should be known for possible yield improvement. A study was undertaken to work out the extent of variability among twenty four mungbean genotypes through cluster analysis and Principal Component Analysis (PCA). Methods: The experiment was laid out in a randomized block design with three replications during kharif 2018 and 2019 at the experimental field of Agricultural Research Station, Navgaon (Alwar) under rainfed condition. Result: Principal component analysis revealed that the first three main PCAs amounted 78.80% of the total variation among genotypes for different traits. Out of total principal components, PC1 accounts for maximum variability in the data with respect to succeeding components. Number of branches per plant (28.62%), number of clusters per plant (23.55%) and seed yield (15.58%) showed maximum per cent contribution towards total genetic divergence on pooled basis. Cluster analysis showed that genotypes fall into seven different clusters and their inter and intra cluster distance showed genetic diversity between different genotypes. The maximum number of genotypes i.e., 8 was found in cluster II followed by cluster III comprising of 6 genotypes. Genotypes RMG-1138 and IPM-02-03 representing the mono genotypic cluster signifies that it can be the most diverse variety and it would be the appropriate genotype for hybridization with ones present in other clusters to tailor the agriculturally important traits and ultimately to boost the seed yield in mungbean under rainfed conditions.

Evaluation of Sunflower (Helianthus annuus L.) Hybrids Using Multivariate Statistical Analysis

Helia ◽  
2015 ◽  
Vol 38 (63) ◽  
pp. 175-187
Author(s):  
Natalija Markova Ruzdik ◽  
Ilija Karov ◽  
Sasa Mitrev ◽  
Biljana Gjorgjieva ◽  
Biljana Kovacevik ◽  
...  
Keyword(s):  
Seed Yield ◽  
Environmental Conditions ◽  
Agronomic Traits ◽  
Block Design ◽  
Principal Component ◽  
Original Data ◽  
Research Field ◽  
Multivariate Statistical ◽  
Main Components

AbstractEvaluation and characterization of sunflower hybrids using morphological, physiological and biochemical data are necessary and essential in sunflower breeding programs. The aim of this paper was to evaluate the productive possibilities of some sunflower hybrids grown in Macedonian environmental conditions. The experiment was carried out during the period 2013 and 2014 on the research field of the Faculty of Agriculture, “Goce Delchev” University – Shtip, in Ovche Pole locality, Republic of Macedonia. Total 20 sunflower hybrids were used as an experimental material. The trial was arranged as randomized complete block design (RCBD) with three replications. The average content of oil and oleic acid were 45.6 and 65.2%, respectively. The highest seed yield from all sunflower hybrids was obtained for the hybrid NLK12M144 (3,344 kg ha–1) and the lowest for hybrid NLK12S126 (2,244 kg ha–1). Cluster analysis classified the sunflower hybrids into four groups based on agronomic traits and seed yield. Most of the hybrids were included in cluster I and III (7 hybrids) followed by cluster IV. Using principal component analysis two main components have been extracted, with eigenvalue greater than one. They account for 72.99% of the variability in the original data. Only four hybrids had positive values for both main components (NLK12M144, NLK12S070, NLK12S125 and NLN12N011 DMR). The results from this study can be used to identify the best sunflower hybrids grown under Macedonian environmental conditions and to select the superior hybrids for future exploitation.

scholarly journals Iron oxides and quality of organic matter in sugarcane harvesting systems

2014 ◽  
Vol 38 (4) ◽  
pp. 1143-1152 ◽  
Author(s):  
Diogo Mazza Barbieri ◽  
José Marques Júnior ◽  
Diego Silva Siqueira ◽  
Daniel De Bortoli Teixeira ◽  
Alan Rodrigo Panosso ◽  
...  
Keyword(s):  
Organic Matter ◽  
Soil Properties ◽  
Iron Oxides ◽  
Soil Layer ◽  
Chemical Properties ◽  
Principal Component ◽  
Multivariate Statistical ◽  
Phosphorus Adsorption ◽  
Harvesting Systems

Improvements in working conditions, sustainable production, and competitiveness have led to substantial changes in sugarcane harvesting systems. Such changes have altered a number of soil properties, including iron oxides and organic matter, as well as some chemical properties, such as the maximum P adsorption capacity of the soil. The aim of this study was to characterize the relationship between iron oxides and the quality of organic matter in sugarcane harvesting systems. For that purpose, two 1 ha plots in mechanically and manually harvested fields were used to obtain soil samples from the 0.00-0.25 m soil layer at 126 different points. The mineralogical, chemical, and physical results were subjected to descriptive statistical analyses, such as the mean comparison test, as well as to multivariate statistical and principal component analyses. Multivariate tests allowed soil properties to be classified in two different groups according to the harvesting method: manual harvest with the burning of residual cane, and mechanical harvest without burning. The mechanical harvesting system was found to enhance pedoenvironmental conditions, leading to changes in the crystallinity of iron oxides, an increase in the humification of organic matter, and a relative decrease in phosphorus adsorption in this area compared to the manual harvesting system.

A synoptic approach for crop loss assessment used to study wheat. I. An appraisal of the physical and chemical soil properties in the study area

Soil Research ◽  
1979 ◽  
Vol 17 (2) ◽  
pp. 217 ◽  
Author(s):  
BA Stynes ◽  
HR Wallace ◽  
LG Veitch
Keyword(s):  
Soil Properties ◽  
South Australia ◽  
Principal Component ◽  
Growth And Yield ◽  
Crop Loss ◽  
Loss Assessment ◽  
Multivariate Statistical ◽  
Crop Loss Assessment ◽  
Selection Of Variables ◽  
Synoptic Approach

A synoptic approach to crop loss assessment is proposed whereby the relative importance of various factors in the environment can be assessed in relation to the growth and yield of plants. A degree of simplification of complex sets of data, collected in field surveys, can be achieved by analysing natural subsets of the data using multivariate statistical procedures. The concepts that emerge form a basis for constructing regression models to predict growth and yield by influencing the selection of variables for inclusion that ensure statistical stability and biological realism. Wheat crops on Yorke Peninsula, South Australia, were studied to evaluate this approach. This paper, comprising part of the study, is a principal component analysis of the soil properties which form a natural subset of the variables measured. It shows that the variation of soils in the study area can be characterized by the variation of relatively few soil properties.

scholarly journals Multivariate statistical approach to comparison of the nutrient status of Norway spruce (Picea abies [L.] Karst.) and top-soil properties in differently managed forest stands

2008 ◽  
Vol 53 (No. 3) ◽  
pp. 101-112 ◽  
Author(s):  
P. Samec ◽  
D. Vavříček ◽  
P. Šimková ◽  
J. Pňáček
Keyword(s):  
Picea Abies ◽  
Soil Properties ◽  
Norway Spruce ◽  
Principal Component ◽  
Nutrient Status ◽  
Element Cycling ◽  
Multivariate Statistical ◽  
Plant Soil ◽  
Multivariate Statistical Approach ◽  
Altitudinal Zone

The soil is an irreplaceable component of forest ecosystems. Soil-forming processes directly influence element cycling (EC). Plant-soil interaction is a specific part of EC. Plant-soil interactions were observed on an example of natural spruce stand (NSS), semi-natural spruce stand (SNSS) and allochthonous spruce stand (ASS) in conditions of the spruce forest altitudinal zone (1,140&minus;1,260 m a.s.l.; +3.0&deg;C; 1,200 mm) of the Hrubý Jeseník Mts. (Czech Republic, Central Europe), where Norway spruce (<i>Picea abies</i> [L.] Karst.) is the main edificator and stand-forming tree species. We evaluated the soil properties of H- and Ep-horizons at selected sites with Haplic and Skeletic Podzols and they were compared with the nutrient status of spruce. A method of the principal component analysis was used for definition of the basic hypotheses: (1) each forest stand is in specific and topically individual interactions with soil and these interactions influence its state, (2) the influence of forest management reflects in humification and in the nutrient status in plant assimilatory tissues. Cluster analysis calculated results comparable with the multivariate analysis of variance. The results show that the continuity of linear and multivariate statistical methods gives the approach to detection of the forest stage based on soil and plant tissue data.

scholarly journals Evaluation by Multivariate Statistical Methods of Relationship with Physicochemical Properties of Humic Acid and Some Soil

2020 ◽  
Vol 4 (4) ◽  
pp. 843-852
Author(s):  
Mehmet DÜZGÜN ◽  
Uğur BİLGE ◽  
M. Zahir DÜZ
Keyword(s):  
Humic Acid ◽  
Linear Regression ◽  
Correlation Analysis ◽  
Linear Regression Analysis ◽  
Principal Component ◽  
Soil Samples ◽  
Acid Fraction ◽  
Simple Linear Regression ◽  
Total Change ◽  
Multivariate Statistical

Humic acid is a very important component for soil. In particular, It is an important factor for regulation of plant nutrients. In this study, HA (humic acid) contents of soil samples taken from 12 different locations were determined by appropriate method. After extracting the humic acid from the soil samples, the remaining soil elements concentrations were determined by ICP-OES method. In addition, the element concentrations in the humic acid fraction were determined by the same method. In soil samples were determined organic matter (OM), pH, EC, clay, silt and sand. The relationships between all variables were evaluated by multivariate statistical analysis such as correlation, simple linear regression, PCA (Principal Component Analysis). Spearman rho coefficient was taken into consideration in the correlation analysis. Simple linear regression analysis showed significant regression model between HA and OM, pH, HAfMn (HAf: humic acid fraction), HAfSe, HAfP, TfCd (Tf:soil fraction), TfMn, TfPb, TfSe. In the PCA analysis, 4 factors were found to explain 88.23% of the total change. P1:OM, TPb, TSe, HA  P2:TAs, TSb, TBa, TMn, TFe  P3: TCd, TP, pH, TSn, Silt, TAl  P4: Kil, sand, TBe, EC. In the correlation analysis (N: 12), HA was significant positive with TfSe (r= 0.615, P <0.05), and significant negative with HAfBe (r= -0.786, P <0.01) and significant positive with HAfMn (r= 0.918, P <0.01) and significant negative with HAfSn (r= -0.700, P <0.05) and significant positive with HAfSe (r= 0.704, P <0.05) and significant positive with HAfP (r= 0.700, P <0.05) correlation was found.

Multivariate analysis in some genotypes of mungbean [Vigna radiata (L.) Wilczek] on the basis of agronomic traits of two consecutive growing cycles

2016 ◽  
Author(s):  
Moushree Sarkar ◽  
Sabyasachi Kundagrami
Keyword(s):  
Plant Height ◽  
Seed Yield ◽  
Vigna Radiata ◽  
Morphological Traits ◽  
Agronomic Traits ◽  
Principal Component ◽  
Mean Value ◽  
Multivariate Statistical ◽  
Plant Seed ◽  
Wide Range

An experiment to evaluate eleven agro morphological traits in twenty three genotypes of mungbean [Vigna radiata (L.) Wilczek] in the field experiment was conducted in two consecutive years 2013 and 2014. Data were recorded on quantitative as well as visually assayed agro morphological traits including days to 50 per cent flowering, days to 1st picking of pods, days to final picking, plant height (cm), number of secondary branches per plant, number of pods per plant, pod length (cm), number of seeds per pod, 100 seed weight (g), seed yield/plant (g) and harvest index (%). All the traits were analysed using multivariate statistical analysis. Most of the important evaluated traits showed a wide range of variation. Cluster analysis using UPGMA method grouped the genotypes into five clusters. The component of mungbean genotypes among different clusters was varied from two to nine genotypes. The maximum number of genotypes i.e., 9 is found in cluster III followed by cluster IV comprising of 6 genotypes. Cluster V showed the maximum mean value for plant height, branch/plant, pods/plant, seed/pod, seed yield/plant and lowest values for days to 50% maturity, 1st picking and days to maturity. Principal component analysis revealed that the first five main PCAs amounted 71.11% of the total variation among genotypes. PC1 accounts for maximum variability in the data with respect to succeeding components.

scholarly journals Remediation of Soil Polluted with Cd in a Postmining Area Using Thiourea-Modified Biochar

2020 ◽  
Vol 17 (20) ◽  
pp. 7654
Author(s):  
Yanfeng Zhu ◽  
Jing Ma ◽  
Fu Chen ◽  
Ruilian Yu ◽  
Gongren Hu ◽  
...  
Keyword(s):  
Soil Properties ◽  
Soil Ph ◽  
Pearson Correlation ◽  
Linear Regression Analysis ◽  
Principal Component ◽  
Ammonium Nitrogen ◽  
Available Phosphorus ◽  
Multivariate Linear Regression Analysis ◽  
Soil System ◽  
Remediation Of Soil

Cadmium presence in soil is considered a significant threat to human health. Biochar is recognized as an effective method to immobilize Cd ions in different soils. However, obtaining effective and viable biochar to remove elevated Cd from postmining soil remains a challenge. More modifiers need to be explored to improve biochar remediation capacity. In this investigation, pot experiments were conducted to study the effects of poplar-bark biochar (PBC600) and thiourea-modified poplar-bark biochar (TPBC600) on Cd speciation and availability, as well as on soil properties. Our results showed that the addition of biochar had a significant influence on soil properties. In the presence of TPBC600, the acid-soluble and reducible Cd fractions were transformed into oxidizable and residual Cd fractions. This process effectively reduced Cd bioavailability in the soil system. Compared to PBC600, TPBC600 was more effective in improving soil pH, electrical conductivity (EC), organic matter (SOM), total nitrogen (TN), ammonium nitrogen (NH4+-N), nitrate nitrogen (NO3−-N), available potassium (AK), available phosphorus (AP), and available sulfur (AS). However, this improvement diminished as incubation time increased. Results of Pearson correlation analysis, multivariate linear regression analysis, and principal component analysis showed that soil pH and available phosphorus played key roles in reducing the available cadmium in soil. Therefore, TPBC600 was shown to be an effective modifier that could be used in the remediation of soil polluted with Cd.

PERFORMANCE OF TORIA (BRASSICA COMPESTRIS SYN. RAPA L.) VARIETY: RAJ VIJAY TORIA-2 IN CENTRAL, EASTERN AND NORTH-EASTERN STATES OF INDIA UNDER RAINFED CONDITION

2018 ◽  
Vol 24 (2) ◽  
Author(s):  
VIMLESH KUMAR TIWARI
Keyword(s):  
Seed Yield ◽  
Rainfed Condition ◽  
Significant Difference ◽  
North Eastern ◽  
Finger Printing ◽  
Quantitative And Qualitative Traits ◽  
Central Eastern ◽  
Dna Finger Printing ◽  
Mature Seeds ◽  
Weight Trait

Performance of developed genotype RMT 08-2 was evaluated in central, eastern and north-eastern states of India under rain-fed condition for quantitative and qualitative traits. It gave highest seed yield over checks in zone III and V. Morphologically plants were erect, medium spreading in nature and primary branches with dichotomous habit. Plants height ranged from 107124 (cm) which matured in 82-112 days. Mature seeds were round in shape and blackish brown in colour. No significant difference between RVT-2 and checks were observed for test weight trait. An average oil yield 485 (kg/h) was recorded over 7 places which was 10% higher than both checks i.e. 14.12% and 11.24% under AICRP trials. Maximum seed yield was obtained on farmers field during 2013-14 and 2014-15 which was 1500 (kg/h) and 1215 (kg/h) that is 33.42% and 26.30% respectively over farmers own seeds. At Morena center, highest seed yield (1753 kg/h) over Bhawani (1512 kg/h) was 15.94% higher than check whereas RVT-2 gave 2245 (kg/h) against Bhawani (1975 kg/h) which was 13.67% higher. DNA finger printing indicated that primers PUT-19, PUT-96, PUT-149, PUT-169, PUT-181 and PUT-271 are useful in generating unique profile of RVT-2 containing 27 bands for its discrimination from other varieties.

Statistical Analsysis to Evaluate Heavy Metal Pollution in the Air Obatained by Moss Technique in Hanoi and its Surrounding Region

2019 ◽  
Vol 29 (3SI) ◽  
pp. 411
Author(s):  
N. H. Quyet ◽  
Le Hong Khiem ◽  
V. D. Quan ◽  
T. T. T. My ◽  
M. V. Frontasieva ◽  
...  
Keyword(s):  
Heavy Metal ◽  
Statistical Analysis ◽  
Metal Pollution ◽  
Heavy Metal Pollution ◽  
Principal Component ◽  
Multivariate Statistical ◽  
Five Factors ◽  
Heavy Metal Elements ◽  
Surrounding Areas ◽  
Potential Pollution

The aim of this paper was the application of statistical analysis including principal component analysis to evaluate heavy metal pollution obtained by moss technique in the air of Ha Noi and its surrounding areas and to evaluate potential pollution sources. The concentrations of 33 heavy metal elements in 27 samples of Barbula Indica moss in the investigated region collected in December of 2016 in the investigated area have been examined using multivariate statistical analysis. Five factors explaining 80% of the total variance were identified and their potential sources have been discussed.

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