Statistics and agricultural

Introduction
 In the modern world of computers and
information technology, the importance of
statistics is very well recogonised by all the
disciplines. Statistics has originated as a
science of statehood and found applications
slowly and steadily in Agriculture,
Economics, Commerce, Biology, Medicine,
Industry, planning, education and so on. As
on date there is no other human walk of life,
where statistics cannot be applied.

Origin and growth of statistics
The word ‘ Statistics’ and ‘ Statistical’ are all
derived from the Latin word Status, means a
political state. The theory of statistics as a
distinct branch of scientific method is of
comparatively recent growth. Research
particularly into the mathematical theory of
statistics is rapidly proceeding and fresh
discoveries are being made all over the
world.

Meaning of statistics
Statistics is concerned with scientific
methods for collecting, organising,
summarising, presenting and analysing
data as well as deriving valid conclusions
and making reasonable decisions on the
basis of this analysis. Statistics is
concerned with the systematic collection
of numerical data and its
interpretation.The word ‘ statistic’ is used
to refer to Numerical facts, such as the
number of people living in particular area.
The study of ways of collecting, analysing
and interpreting the facts.

Definition
Statistics are numerical statement of facts in
any department of enquiry placed in
relation to each other. - A.L. Bowley
Statistics may be called the science of
counting in one of the departments due to
Bowley, obviously this is an incomplete
definition as it takes into account only the
aspect of collection and ignores other
aspects such as analysis, presentation and
interpretation.

Statistics and agricultural
 In agricultural research, for example, there are
different statistical techniques for crop and animal
research, for laboratory and field experiments, for
genevic and physiological research, and so on.
Although this diversit" indicates the ability of
appropriate statistical techniques for most research
problems, it also indicates the difficulty of matching
the best technique to a specific experiment. Obviously,
this difficulty increases as more procedures develop.
 Choosing the correct statistical procedure for a given
experiment must be based on expertise in statistics
and in the subject matter of the experiment. Thorough
knowledge of only one of the two is not enough.

 For most agricultural research institutions in the
developing countries, the presence of trained statisticians
is a luxury. Of the already small number of such
statisticians, only a small fraction have the interest and
experience agricultural research necessary for effective
consultation. Thus, we feel the best alternative is to give
agricultural researchers a statistical background so that
they can correctly choose the statistical technique most
appropriate for their experiment.
 For research institutions in the developed countries, the
shortage of trained statisticians may not be as acute as in
the developing countries. Nevertheless, the subject matter
specialist must be able to communicate with the consulting
statistician. Thus, for the developed-country researcher,
this volume should help forge a closer researcher-
statistician relationship.

Example
In the early 1950s, a Filipino journalist,
disappointed with the chronic shortage of rice
in his country, decided to test the yield
potential of existing rice cultivars and the
opportunity for substantially increasing low
yields in farmers' fields. He planted a single rice
seed-from an ordinary farm-on a well-prepared
plot and carefully nurtured the developing
seedling to maturity. At harvest, he counted
more than 1000 seeds produced by the single plant.
The journalist concluded that Filipino farmers
who normally use 50 kg of grains to plant a hectare,
could harvest 50 tons (0.05 x 1000) from a
hectare of land instead of the disappointingly
low national average of 1.2 t/ha.

In agricultural research, the key questions to be
answered are generally expressed as a statement of
hypothesis that has to be verified or disproved through
experimentation.These hypotheses are usually
suggested by past experiences, observations, and, at times, by
theoretical considerations. For example, in the case of
the Filipino journalist, visits to selected farms may
have impressed him as he saw the high yield of some
selected rice plants and visualized the potential for
duplicating that high yield uniformly on a farm and
even over many farms. He therefore hypothesized that
rice yields in farmers' fields were way below their
potential and that, with better husbandry, rice yields
could be substantially increased.

What do we mean by agricultural statistics
 The terms data, statistics and information are often used
interchangeably but there are important distinctions. Data, statistics
and information • What are they? • Why are they important? • Where do
they come from? • What is the scope of agriculture stats and
information?
 Data are the basic part of a broader information system. When
statisticians produce data, they are trying to measure or count
phenomena (things or activities) that are part of the real world. Data
may be viewed as a lowest level of abstraction from which information
and knowledge are derived.
 Examples of data: Number of cows on a farm ,Number of people in a
household Number of children in a family In these cases, the data are
derived by counting.
 If the question were: “How many dollars did you spend last year on
improved seed?” the answer must be provided by a respondent who
would look at records, or simply cite the number from memory. This is
another example of measurement.

Statistics and data
 Statistics is also a mathematical science that focuses
on the collection, analysis, interpretation or
explanation, and presentation of data. 1We often think
of statistics as being produced by National Statistical
Organizations (NSOs) but in fact they can be
generated by any number of people. They can come
from
 • Opinion polls
 • Surveys
 • Censuses
 • Administrative data (e.g., imports and exports)

General Data Dissemination System,

Agricultural data and information are required to support the
following types of processes:
 • underpinning the planning processes;
 • compiling national accounts;
 • informing public policy analysis, debate and
advice;
 • observing sector performance;
 • monitoring and evaluating the impact of
policies and programmes and
 • enlightening the decision-making processes.

Examples of agriculture development objectives
 • Improving food supply (cereals, cashew nut, sugar,
cotton)
 • Improving seeds
 • Providing access to fertilizer
 • Monitoring and controlling pests of basic crops and
reducing animal mortality

Purpose of statistics
 statistics are produced and valued because they help
decision makers and program managers make
decisions and evaluate progress. It is these needs that
must be kept in mind when planning and designing
agriculture surveys.

STATISTISICAL COORDINATION
 • Legislation
 • Statistical priorities
 • Surveys and census must work together
 • Surveys, early warning systems and market information
 • Coordination improves the efficiency and usefulness of statistics
 o Classifications and definitions
 o Software tools
 o Statistical websites/portals
 • Sampling frames (The census of population is a key national resource)
 • Response burden
 • Specialized staff (survey design and sampling expertise)
 • Coordination with provincial bodies

The Stages of the Survey Process
 The statistical survey can be considered to fall into
three parts all of which will be discussed in this paper
 • Planning and Design Phase
 • Implementation and Analysis
 • Dissemination and Archiving and Evaluation

 Quality Control Survey Implementation
Quality Evaluation
 Data collection
 Data capture and coding
 Correction and Cleaning
 Editing and Imputation
 Estimation, documentation
 Data Analysis

Statistics and agricultural

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