Multivariate Analysis Applied to Forestry Agricultural Sciences: The Model-Directed Study

This is a literature review that aimed to find articles that exemplify and describe the use of multivariate analysis in different fields of Forest Agricultural Sciences, considering effective practices using multivariate statistical techniques for the simultaneous processing of data. For data collection were selected for the meta-analysis of 70 technical articles of which 54 were employed in the study directed to the use of multivariate techniques applied in the areas of agricultural sciences. The results showed thatstudies directed to certain areas within the Forest Agricultural Sciences exhibit some regularity in the use of multivariate analysis, and most application analyzes were more usual as the Cluster Analysis (AA) and Principal Component Analysis (PCA). Thus the use of multivariate analysis studies and evaluations of experiments in Agricultural Sciences proved to great value to allow greater clarity and better interpretation of dealing with complex phenomena

Understanding gender differences in agricultural productivity in Uganda and Nigeria

We investigate gender differences in agricultural productivity using data collected in 2005 from Nigeria and in 2003 from Uganda. Results indicate that lower productivity is persistent from female-owned plots and female-headed households, accounting for a range of socioeconomic variables, agricultural inputs, and crop choices using multivariate Tobit models. These results are robust to the inclusion of household-level unobservables. However, productivity differences depend on the type of gender indicator used, crop-specific samples, agroecological region, and inclusion of biophysical characteristics. More nuanced gender data collection and analysis in agricultural research spanning diverse regions are encouraged to identify interventions that will increase productivity and program effectiveness for male and female farmers.Agricultural productivity, Gender,

INVESTIGATING PREFERENCES FOR ENVIRONMENT FRIENDLY PRODUCTION

This paper reports some preliminary results on a mixed logit random utility analysis of conjoint data from costumers' preferences over agricultural products. The data are collected via a telematic sample representative of Italian households. The survey instrument was implemented via a computer supported system. A multivariate normal full correlation structure is imposed in the mixed logit estimation and the implications of such a taste structure are examined.

Hydro-climatic variability and agricultural production on the shores of Lake Chad

The purpose of this study is to present and analyze previously unpublished quantitative agricultural data for the area on the shores of Lake Chad in Chad, and explore its relations to hydro-climatic factors (lake levels, rainfall and temperature). This is a rural area with livelihoods based on agropastoral and fishing activities, which are directly dependent on the region's high-varying hydro-climate. By using regression analysis on data from 1988-2012 this study was able to establish correlations between the latter and agricultural output. These correlations were used to build multivariate models to explore the predictive capacities of hydro-climatic factors with regards to the agricultural data. The selected models were able to account for considerable proportions of the agricultural dynamics. Some 5 of the 10 multivariate models tested had cross-validated R2s of 0.50 or more. Thus, there were still noteworthy unexplained variations in the agricultural data, which likely stem from technological, behavioral, economic and pest factors that were not explored in this study due do data limitations. Additional studies are called for to build on results presented here and further examine these relationships

Evaluation of metals that are potentially toxic to agricultural surface soils, using statistical analysis, in northwestern Saudi Arabia

© 2015, Springer-Verlag Berlin Heidelberg. Heavy metals in agricultural soils enter the food chain when taken up by plants. The main purpose of this work is to determine metal contamination in agricultural farms in northwestern Saudi Arabia. Fifty surface soil samples were collected from agricultural areas. The study focuses on the geochemical behavior of As, Cd, Co, Cr, Cu, Hg, Pb and Zn, and determines the enrichment factor and geoaccumulation index. Multivariate statistical analysis, including principle component analysis and cluster analysis, is also applied to the acquired data. The study shows considerable variation in the concentrations of the analyzed metals in the studied soil samples. This variation in concentration is attributed to the intensity of agricultural activities and, possibly, to nearby fossil fuel combustion activities, as well as to traffic flows from highways and local roads. Multivariate analysis suggests that As, Cd, Hg and Pb are associated with anthropogenic activities, whereas Co, Cr, Cu and Zn are mainly controlled by geogenic activities. Hg and Pb show the maximum concentration in the analyzed samples as compared to the background concentration

Terms of Trade and Supply Response of Indian Agriculture: Analysis in Cointegration Framework.

In this paper, we examine the presence of stochastic trend (unit root) and structural break in various agriculture-industry terms of trade series in India. The results suggest that underlying data generating process of terms of trade are most likely non-stationary. We subsequently re-examine the aggregate supply response of Indian agriculture in this light. We investigate the presence of long-run functional relationship(s) underlying the supply response model through cointegration analysis and error correction framework. The multivariate results indicate presence of a cointegrating relationship in the supply response model. The vector error correction estimates suggest that short-run output adjustments are not related to changes in agricultural terms of trade in a temporal causal relationship. However, the short-run deviations in terms of trade from its long-term level create error-correction in the long-term output adjustments through changes in technology (irrigation). This may imply that agricultural growth can respond better if price incentives are combined with investments in irrigation.domestic terms of trade, agricultural supply response, unit root, cointegration

Analysis and comparison of three Coleoptera families in organic and conventional orchards within the Etna Regional Park (Catania, Sicily)

The objective of this work was to compare two agricultural management systems, organic and conventional, in the Etna Regional Park. The influence in both spatial and temporal dimensions, that natural habitats adjacent to these two different managed agricultural systems have on Coleoptera biocoenosis in orchards was also studied. This comparison was done through standardised data and multivariate analysis using a multi-taxa approach. Specifically, three families of the Order Coleoptera, namely Carabidae, Tenebrionidae and Staphylinidae (excluding Aleocharinae and Scydmaeninae) were studied.peer-reviewe

Analysis and comparison of three Coleoptera families in organic and conventional orchards within the Etna Regional Park (Catania, Sicily)

The objective of this work was to compare two agricultural management systems, organic and conventional, in the Etna Regional Park. The influence in both spatial and temporal dimensions, that natural habitats adjacent to these two different managed agricultural systems have on Coleoptera biocoenosis in orchards was also studied. This comparison was done through standardised data and multivariate analysis using a multi-taxa approach. Specifically, three families of the Order Coleoptera, namely Carabidae, Tenebrionidae and Staphylinidae (excluding Aleocharinae and Scydmaeninae) were studied.peer-reviewe

Distinguishing Carrot's Characteristics by Near Infrared (NIR) Reflectance and Multivariate Data Analysis

Rosana G. Moreira, Editor-in-Chief; Texas A&M UniversityThis is a paper from International Commission of Agricultural Engineering (CIGR, Commission Internationale du Genie Rural) E-Journal Volume 6 (2004): N. Abu-Khalaf, B. Bennedsen and G. Bjorn. Distinguishing Carrot's Characteristics by Near Infrared (NIR) Reflectance and Multivariate Data Analysis. (March 2004