2. Introduction
Modern agricultural production is characterized by some
particularities and many different activities. Тhe agricultural
investigations are based on the application of statistical
methods and procedures which are helpful in testing
hypotheses using observed data, in making estimations of
parameters and in predictions. The application of statistical
principles and methods is necessary for effective practice in
resolving the different problems that arise in the many
branches of agricultural activity. Because of the variability
inherent in biological and agricultural data, knowledge of
statistics is necessary for their understanding and interpretation.
3. The importance of statistical science in agriculture is
obvious, where the collection, analysis and interpretation
of numerical data are concerned. Statistical principles
apply in all areas of experimental work and they have a
very important role in agricultural experiments. Statistics
plays an important role in experimentation while many
scientific problems could be solved by different
statistical procedures.
4. Scientists use statistics as a tool, which, when
correctly applied, is of enormous assistance in
the study of the laws of science. It is important to
emphasize that there are no statistical procedures
which are applicable only to specific fields of
study. There are general statistical procedures
which are applicable to any branch of knowledge
in which observations are made
5. Some of examples of the use of Statistics in
Agricultural activities.
Crop farming (wheat, sugar beet, sunflower, soy, fodder crops, other
industrial crops etc),
Vegetable crops (potatoes, tomatoes, beans, onions, peppers etc),
Fruit growing (apples, pears, plums, cherries, sour cherries, apricots,
peaches, walnuts etc),
Horticulture plants,
Livestock breeding (cattle breeding, sheep breeding, poultry breeding),
Exploitation of agricultural machines and transport means,
Utilization and protection of waters,
Consumption of mineral fertilizers,
6. Problems related to agricultural
economics
Agricultural population,
Cultivable area,
Agricultural enterprises and cooperatives,
Individual (private) holdings,
Workers in agricultural enterprises and
cooperatives,
Costs,
Sources of income etc.
7. Some examples of the application of statistical
methods in problems through research
processes in Agriculture
Genetics and plant breeding,
Crop production concerning different conditions of
agrotechnics and plant protection,
Type of soils,
Localities,
Varieties,
Hybrids,
Conditions of irrigation,
12. Sampling
Sampling is a process used in statistical analysis in which
a predetermined number of observations are taken from a
larger population. The methodology is used to pick a
sample from a larger population depends on the type of
analysis being performed.
The advantages of sampling are the saving of money, time
and effort.
To check the quality of crop, soil, seeds, fertilizer,
pesticides and food and medicine for the cattles.
13. Times Series
Time series analysis is a statistical technique that deals
with time series data, or trend analysis. Time series data
means that data is in a series of particular time periods
or intervals. In this branch of statistics we forecast the
long term trends about the future on the basis of past
data.
Agricultural Production of a country, region or a farmer.
14. Regression
Regression is a statistical method used in finance,
investing, and other disciplines that attempts to
determine the strength and character of the relationship
between one dependent variable (usually denoted by Y)
and a series of other variables (known as independent
variables).
In the research, Regression Analysis (RA) is used to
analyze the environmental factors and their effect on
crop yield. This helps to recognize the positive or negative
effect of a factor on the crop yield and also help to
predict the yield in future if the factors were changed or
remained same.
15. Experimental design
Experimental design is a way to carefully plan
experiments in advance so that your results are both
objective and valid. It tells how to plan, initiate and
conduct an experiment to get required results. It also
describe how to deal with the problems occur during the
experiment.
Conducting an experiment to check the performance of
different types of seeds or fertilizer.
16. Categorical Data analysis
Categorical data analysis is the analysis of data where
the study variable has been grouped into a set of mutually
exclusive ordered (such as age group) or unordered (such
as eye color) categories.
The quality of crop, fertilizer or pesticides could be
categorized as:
(Very Good, Good, Normal, Bad, very bad)
