PRINCIPAL COMPONENTS ANALYSIS FOR CHARACTERIZATION OF FARM PRODUCTION CORN ((Zea mays )) IN GUANARE MUNICIPALITY, PORTUGUESA STATE, VENEZUELA (original) (raw)

Usefulness of principal components analysis in agriculture

Bulletin of University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca: Horticulture, 2013

Factor analysis (principal components analysis is a factor method) of downsizing the complexity of data and fixing the number of principal components to be retained in the final model. In this paper we present the usefulness of Principal Components Analysis in the agriculture. Data we have used in this paper were taken from a public database, namely the National Institute of Statistics of Romania.

Agrometeorological conditions in western Pará compared to agricultural production variables using principal component analysis

Biotechnology, Agronomy, Society and Environment, 2023

Description of the subject. Since the beginning of the twenty-first century, soybean cultivation areas have been expanding in the Legal Amazon. Among the factors that contribute to the process of expansion of cultivation of grains in the Amazon, favorable climate conditions are one of the most important, and these include high rainfall indices and good thermal regimes during the cultivation period. Objectives. In this context, the objective of this study was to describe responses of soybean production as a function of climate variables, considering data from an important grain production center in the Amazon. Method. Principal Component Analysis (PCA) was used including the following response variables: rainfall (R), air temperature (T), real evapotranspiration, water stress (WST) and surplus, soybean area harvested (HVA), quantity produced (PD), occurrence of El Niño and La Niña (LAN). Results. Production variables were negatively correlated with precipitation and water surplus. There was also a negative correlation between El Niño, temperature and water stress. The variables that had greater weight in component 1 were R, T and WST. Considering component 2, the variables that most contributed to the variation were HVA, PD and LAN. Conclusions. The strong or severe occurrence of La Niña influences the soybean production in the region of Belterra-PA, due to the high rainfall index causing excess water in the soil, leading the plants to stress. The moderate occurrence of La Niña positively influences soybean production in the region to maintain the water supply at adequate levels for the plants. These results show the importance of monitoring climate variables for agriculture in the region.

Agri-systems variations determined through principal component analysis

The present study tested the capability of Principal Component Analysis (PCA) in determining variations of the agri-systems landscape using ten Qualitative Characters viz. levels of nitrogen, phosphorus, potassium and pH, as well topography, vegetation type, presence of rocks, and characters of color such as hue, chroma and value. The 60 hectare farm in Manresa was used as model where assessment of variations using PCA was done. Exactly 103 random samples were collected from 11 sites within the model farm, and soils were analyzed and characterized using numerical coding technique. There were 1030 coded data generated and PCA was implemented on these data using PAST (Paleontological Statistics) software version 1.78. The similarity of sites was tested using cluster analysis and validation of results was made using Discriminant Function Analysis using SPSS ver. 17. Here, we found that PCA effectively deciphered variation existing in the agri-systems landscape, and dominant factors contributing to such variations identified. Practical applications of the method in agriculture is discussed. Key Words: Agriculture, agri-system, landscape variation, principal component analysis, cluster analysis.

Definição de Unidades de Gestão Agrícola em um Cambissolo do departamento Casanare (Colômbia)

2013

Sixty-four representative samples of the 20 cm of shallow soil were taken in an Oxic Dystrudept of the Eastern Mountain Ridge foothills (Casanare, Colombia), on a 58-hectare farm using a nested sampling of four levels. The measured properties correspond to those that determine crop yields. The principal components technique was used for data analysis. Thus, we generated a variable to classify soil with a comprehensive approach called First Principal Component (PC1), which explained 78% of the variation found in the data of the properties that affect specifically crop production. PC1 proved to be a regionalized variable and interpolated via Kriging on the map of the farm. The positive and negative values of this new variable (PC1) determined the UMH for the establishment of commercial crops in the farm.

Characterization of corn producers and sustainability indicators in Chiapas

2020

Currently, the information on the maize agroecosystem in the Frailesca region of Chiapas, Mexico, and its forms of management, is insufficient to address it with sustainable development strategies. In the present investigation it was characterized; through a typification of corn producers and their relationship with energy efficiency and its management methods. It is a descriptive and exploratory research from a socio-agronomic approach in 300 cases of farmers, with the support of descriptive statistical techniques, as well as exploratory multivariate of main components and clusters. Six producer groups or typologies were identified based on 11 main components that explain 73% of the total variability. All producer groups are energy efficient, which is associated with the productive and economic efficiency of the agroecosystem. Regarding grain yield, in all producer groups, it ranges between 2.8 and 4 t ha. In addition, three large groups of management systems were identified (conve...

A Study of Selected Crops in the Agricultural Sector of the Cordillera Administrative Region for the period 1997-2014

Agricultural productivity is important for the economy, as it plays a major role in growth and progress. This study aims to assess the productivity of selected crops in the agricultural sector of the Cordillera Administrative Region from the year 1997 to 2 014. Cabbage Production in the region has the highest average yield and this has been increasing, while Palay and Corn production have almost the same average yield which have been slightly increasing from 2010 up to the present. The CobbDouglas producti variables on function was adopted for the study with four explanatory labor employed in agriculture of CAR, total quantity of postharvest and processing facilities or agricultural capital, irrigated land area, and number of tropical cyclones as an indic ator of the environmental barrier. Using Enyedi’s Productivity Index, the study found Palay as very highly productive; Corn and Cabbage Production as Highly productive from 1997 to 2014. The regression results showed that there is a significant positive relationship between the labor employed in agriculture and the irrigation system and agricultural productivity. Hence, an increase in people entering the agricultural labor force in Cabbage , Palay and Corn production and developing the irrigation systems in the CAR are both crucial determinants for the level of its agricultural productivity. Also, the regression result revealed that tropical storms have a significant negative relationship w ith agricultural productivity. This confirms that storms cause physical damages and financial costs for agricultural production which result to an overall decreasing agricultural productivity in CAR.

Definition of Agricultural Management Units in an Inceptisol of the Casanare Department (Colombia)

ORINOQUIA, 2013

Sixty-four representative samples of the 20 cm of shallow soil were taken in an Oxic Dystrudept of the Eastern Mountain Ridge foothills (Casanare, Colombia), on a 58-hectare farm using a nested sampling of four levels. The measured properties correspond to those that determine crop yields. The principal components technique was used for data analysis. Thus, we generated a variable to classify soil with a comprehensive approach called First Principal Component (PC1), which explained 78% of the variation found in the data of the properties that affect specifically crop production. PC1 proved to be a regionalized variable and interpolated via Kriging on the map of the farm. The positive and negative values of this new variable (PC1) determined the UMH for the establishment of commercial crops in the farm.

Analysis Of Factors Affecting Corn Farmers 'Revenue In Gorontalo District

Jambura Equilibrium Journal

This study aims to determine what factors influence the income of corn farmers in Gorontalo district (case study of Poor Corn Farmers in Gorontalo District). The data in this study are primary data by distributing questionnaires to poor corn farmers in Gorontalo Regency. The data analysis technique in this research is multiple regression. The results of this study indicate that the factors that influence the income of poor corn farmers in Gorontalo District (1) simultaneously the area of farmer Education, number of dependents in the family, land ownership status and the use of technology in corn farming significantly influence the income of poor corn farmers) in Gorontalo Regency. (2) partially only land area and land ownership status have a significant effect on the income of poor corn farmers in Gorontalo Regency. Keywords: Income, Corn Farming, Poor Farmers