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2014 » Papers » Volume 3 » Importance of using graphical and statistical computing programs in agricultural research 1. IMPORTANCE OF USING GRAPHICAL AND STATISTICAL COMPUTING PROGRAMS IN AGRICULTURAL RESEARCH Authors: Trifan Daniela, Bularda Marcel Volume 3 | DOI: 10.12753/2066-026X-14-204 | Pages: 411-416 | Download PDF | Abstract
Determinations and analysis are needed in agricultural research to verify soil quality, irrigation water quality, yield quantity and quality also. All these data resulted of these measurements can not practicality, if not achieved statistical calculation. In the past, statistical calculation was done with pencil on paper, requiring more time for processing. With the advent of computers and software, this has become much easier time processing statistics becoming increasingly shorter, which greatly increased the efficiency of obtaining results and conclusions in agricultural research.
This paper presents examples of statistical calculation like analysis of variance with ANOVA and correlation of experimental data from agricultural production using MS Excel, and creating of maps for physical and chemical indicators of soil or groundwater, using MATLAB.
To calculate the variance analysis and correlation are presented the steps to follow in their performance. The paper contain explains in three case studies: first - the analysis of variance in production of wheat in a farm experience with several varieties; the second study - the correlation between productivity elements and sowing density for winter wheat; and the third study - about carrying out maps for physicochemical indices of soil and groundwater from a farm in Big Island of Braila.
For a quick understanding of the calculation were carried print screens of each step, with explanations and the modality of creating graphics and how to interpret the final significances. Also, in creating maps is explained everything, step by step, including how to make legends and how can interpret the results. These examples of statistical calculation can be useful to researchers, engineers and students of agricultural sciences. | Keywords
programs, agriculture research, variance analyze, correlation, maps |
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