Mapping management zones in a sandy pasture soil using an objective model and multivariate techniques

2020 ◽  
Author(s):  
F. J. Moral ◽  
F. J. Rebollo ◽  
J. M. Serrano ◽  
F. Carvajal
Keyword(s):  
Multivariate Techniques ◽  
Management Zones ◽  
Pasture Soil ◽  
Objective Model

scholarly journals Management zones in agriculture acording to the soil and landscape variables

2012 ◽  
Vol 32 (6) ◽  
pp. 1197-1204 ◽  
Author(s):  
Hevandro C. Delalibera ◽  
Pedro H. Weirich Neto ◽  
Noemi Nagata
Keyword(s):  
Precision Agriculture ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Multivariate Techniques ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical ◽  
Management Zones ◽  
Independent Variables ◽  
Chemical Indicators ◽  
Scientific Standards

The study of spatial variability of soil and plants attributes, or precision agriculture, a technique that aims the rational use of natural resources, is expanding commercially in Brazil. Nevertheless, there is a lack of mathematical analysis that supports the correlation of these independent variables and their interactions with the productivity, identifying scientific standards technologically applicable. The aim of this study was to identify patterns of soil variability according to the eleven physical and seven chemical indicators in an agricultural area. It was used two multivariate techniques: the hierarchical cluster analysis (HCA) and the principal component analysis (PCA). According to the HCA, the area was divided into five management zones: zone 1 with 2.87ha, zone 2 with 0.8ha, zone 3 with 1.84ha, zone 4 with 1.33ha and zone 5 with 2.76ha. By the PCA, it was identified the most important variables within each zone: V% for the zone 1, CTC in the zone 2, levels of H+Al in the zone 4 and sand content and altitude in the zone 5. The zone 3 was classified as an intermediate zone with characteristics of all others. According to the results it is concluded that it is possible to separate into groups (management zones) samples with the same patterns of variability by the multivariate statistical techniques.

scholarly journals Management zones based on corn yield and soil physical attributes

2015 ◽  
Vol 33 (3) ◽  
pp. 373-382 ◽  
Author(s):  
Milton F. Alarcón-Jiménez ◽  
Jesús H. Camacho-Tamayo ◽  
Jaime H. Bernal
Keyword(s):  
Agricultural Production ◽  
Particle Density ◽  
Total Porosity ◽  
Descriptive Statistics ◽  
Multivariate Techniques ◽  
Corn Yield ◽  
Regular Grid ◽  
Management Zones ◽  
Physical Attributes ◽  
Results Management

The need to increase the yield and thus the income of farmers and provide food for the growing population requires the search for more efficient and innovative ways for growing, such as management by zones or site-specific practices. This knowledge improves the process of decision making in agricultural production for better crop management. The aim of this study was to determine zones of agricultural management based on corn yield and its relationship with some physical attributes in an Oxisol on the Eastern plains of Colombia. For this, the soil sampling was done in a regular grid whose sampling points were spaced every 70 m, in an area of 37 ha. The studied soil attributes were penetration resistance (PR), texture, total porosity (TP), macropores, mesopores, micropores, saturated hydraulic conductivity (KS), bulk density (BD), particle density (PD), soil water content (SW) and yield. The results were analyzed using descriptive statistics, geostatistics and multivariate techniques. From these results, management zones were defined. The soil physical attributes presented high variability in the different regions of the sampled area. The methods used for characterizing the management zones, allowed for identifying which area presented the best physical characteristics, an area that also showed the highest production of maize, similar in the different methods that were studied.

A Short Review on Structural Equation Modeling: Applications and Future Research Directions

2015 ◽  
Vol 4 (3) ◽  
Author(s):  
Surajit Bag
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
First Generation ◽  
Analytical Techniques ◽  
Short Review ◽  
Equation Modeling ◽  
Future Research ◽  
Multivariate Techniques ◽  
Statistical Efficiency ◽  
Future Research Directions

The application of multivariate techniques is mainly to expand the researchers explanatory ability and statistical efficiency. The first generation analytical techniques share a common limitation i.e. each technique can examine only a single relationship at a time. Structural Equation Modeling, an extension of several multivariate techniques is the technique popularly used today can examine a series of dependence relationships simultaneously. The purpose of this study is to provide a short review on Structural Equation Modeling (SEM) being used in social sciences research. A comprehensive literature review of article appearing in top journals is conducted in order to identify how often SEM theory is used. Also the key SEM steps have been provided offering potential researchers with a theoretical supported systematic approach that simplify the multiple options with performing SEM.

scholarly journals Economical Speed for Optimizing the Travel Time and Energy Consumption in Train Scheduling using a Fuzzy Multi-Objective Model

2021 ◽  
Author(s):  
Ahmad Reza Jafarian-Moghaddam
Keyword(s):  
Energy Consumption ◽  
Travel Time ◽  
Pollutant Emissions ◽  
Scheduling Problems ◽  
Train Scheduling ◽  
Multi Objective ◽  
Consumption Ratio ◽  
Proposed Model ◽  
Objective Model ◽  
Time And Energy

AbstractSpeed is one of the most influential variables in both energy consumption and train scheduling problems. Increasing speed guarantees punctuality, thereby improving railroad capacity and railway stakeholders’ satisfaction and revenues. However, a rise in speed leads to more energy consumption, costs, and thus, more pollutant emissions. Therefore, determining an economic speed, which requires a trade-off between the user’s expectations and the capabilities of the railway system in providing tractive forces to overcome the running resistance due to rail route and moving conditions, is a critical challenge in railway studies. This paper proposes a new fuzzy multi-objective model, which, by integrating micro and macro levels and determining the economical speed for trains in block sections, can optimize train travel time and energy consumption. Implementing the proposed model in a real case with different scenarios for train scheduling reveals that this model can enhance the total travel time by 19% without changing the energy consumption ratio. The proposed model has little need for input from experts’ opinions to determine the rates and parameters.

scholarly journals Visualizing Profiles of Large Datasets of Weighted and Mixed Data

Mathematics ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 891
Author(s):  
Aurea Grané ◽  
Alpha A. Sow-Barry
Keyword(s):  
Multidimensional Scaling ◽  
Random Sample ◽  
Simulation Study ◽  
Clustering Algorithm ◽  
Computational Cost ◽  
Interpolation Formula ◽  
Large Datasets ◽  
Mixed Data ◽  
Multivariate Techniques ◽  
High Computational Cost

This work provides a procedure with which to construct and visualize profiles, i.e., groups of individuals with similar characteristics, for weighted and mixed data by combining two classical multivariate techniques, multidimensional scaling (MDS) and the k-prototypes clustering algorithm. The well-known drawback of classical MDS in large datasets is circumvented by selecting a small random sample of the dataset, whose individuals are clustered by means of an adapted version of the k-prototypes algorithm and mapped via classical MDS. Gower’s interpolation formula is used to project remaining individuals onto the previous configuration. In all the process, Gower’s distance is used to measure the proximity between individuals. The methodology is illustrated on a real dataset, obtained from the Survey of Health, Ageing and Retirement in Europe (SHARE), which was carried out in 19 countries and represents over 124 million aged individuals in Europe. The performance of the method was evaluated through a simulation study, whose results point out that the new proposal solves the high computational cost of the classical MDS with low error.

scholarly journals The Relationship between Soil Electrical Parameters and Compaction of Sandy Clay Loam Soil

Agriculture ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 114
Author(s):  
Katarzyna Pentoś ◽  
Krzysztof Pieczarka ◽  
Kamil Serwata
Keyword(s):  
Electrical Conductivity ◽  
Magnetic Susceptibility ◽  
Precision Agriculture ◽  
Soil Compaction ◽  
Soil Layer ◽  
Soil Parameters ◽  
Soil Electrical Conductivity ◽  
Electrical Parameters ◽  
Management Zones ◽  
Additional Information

Soil spatial variability mapping allows the delimitation of the number of soil samples investigated to describe agricultural areas; it is crucial in precision agriculture. Electrical soil parameters are promising factors for the delimitation of management zones. One of the soil parameters that affects yield is soil compaction. The objective of this work was to indicate electrical parameters useful for the delimitation of management zones connected with soil compaction. For this purpose, the measurement of apparent soil electrical conductivity and magnetic susceptibility was conducted at two depths: 0.5 and 1 m. Soil compaction was measured for a soil layer at 0–0.5 m. Relationships between electrical soil parameters and soil compaction were modelled with the use of two types of neural networks—multilayer perceptron (MLP) and radial basis function (RBF). Better prediction quality was observed for RBF models. It can be stated that in the mathematical model, the apparent soil electrical conductivity affects soil compaction significantly more than magnetic susceptibility. However, magnetic susceptibility gives additional information about soil properties, and therefore, both electrical parameters should be used simultaneously for the delimitation of management zones.

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