Statistics is widely used in agriculture for research, planning, and decision making. It involves collecting, organizing, analyzing, and interpreting numerical data to draw valid conclusions. In agricultural research, statistical techniques are used for experiments on crops, animals, and other areas. Researchers must have expertise in both statistics and their subject area to select the appropriate statistical procedure. Statistics helps farmers and organizations by informing decisions, evaluating policies, and underpinning agricultural planning processes.

1. Statistics and agricultural
 In the modern world of computers and information technology, the importance of statistics is very well
recognized by all the disciplines. Statistics has originated as a science of statehood and found applications
slowlyandsteadilyinAgriculture,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.
 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
particularlyintothe mathematical theory of statistics is rapidly proceeding and fresh discoveries are being
made all over the world.
 Statistics is concerned with scientific methods for collecting, organizing, summarizing, presenting and
analyzing data as well as deriving valid conclusions and making reasonable decisions on the basis of this
analysis.Statisticsisconcernedwiththe systematic collection of numerical data and its interpretation. The
word‘statistic’ isusedtoreferto Numerical facts,suchas the numberof people livinginparticular area. The
study of ways of collecting, analyzing and interpreting the facts.
 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,
obviouslythis is an incomplete definition as it takes into account only the aspect of collection and ignores
other aspects such as analysis, presentation and interpretation.
 In agricultural research, forexample,thereare differentstatistical techniques for crop and animal research,
for laboratory and field experiments, for genevic and physiological research, and so on. Although this
diversity" 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
mustbe basedonexpertise instatisticsandinthe subjectmatterof the experiment.Thoroughknowledge of
only one of the two is not enough. For most agricultural research institutions in the developing countries,
the presence of trainedstatisticiansisaluxury.Of the alreadysmall numberof suchstatisticians,onlyasmall
fractionhave the interestandexperience agricultural researchnecessaryforeffective consultation.Thus,we
feel the bestalternative istogive agricultural researchers a statistical background so that they can correctly
choose the statistical technique most appropriate for their experiment. 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
selectedfarmsmay 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
hypothesizedthatrice yieldsinfarmers' fields
were waybelow theirpotential andthat,withbetterhusbandry;
rice yields could be substantially increased.
 For researchinstitutions inthe developedcountries,the shortage of trainedstatisticiansmaynotbe as acute
as in the developing countries. Nevertheless, the subject matter specialist must be able to communicate
withthe consultingstatistician.Thus,forthe developed-countryresearcher,thisvolume should help forge a
closer researcher-statistician relationship.
 In the early 1950s, a Filipinojournalist,disappointedwiththe chronicshortage of rice inhiscountry,decided
to testthe yieldpotentialof existing rice cultivars andthe opportunityforsubstantiallyincreasing low yields
in farmers’ fields. He planted
a single rice seed-from an ordinary farm-on a well-prepared plot and carefully
nurturedthe developing seedling to maturity. At harvest, he counted more than 1000 seeds produced by the

2. 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.
 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
(thingsoractivities) thatare part of the real world.Datamay be viewedasa lowestlevel of abstraction from
which information and knowledge are derived. Examples of data: Number of cows on a farm, Number of
people inahouseholdNumberof childrenina familyIn these cases,the data are derived by counting. If the
questionwere:“Howmanydollarsdidyouspendlastyearonimprovedseed?” the answermustbe provided
by a respondent who would look at records, or simply cite the number from memory. This is another
example of measurement.
 Statistics is also a mathematical science that focuses on the collection, analysis, interpretation or
explanation,andpresentationof data. 1
We oftenthinkof statistics asbeingproduced 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)
 Agricultural data and types of processes
1. Improvement of food supply
2. Improving seeds
3. Providing access to fertilizer
4. Monitoring and controlling pests of basic crops
5. Reducing animal mortality
6. Counting time period
7. Underpinning the planning processes
8. Observing performance
9. Monitoring and evaluating the impact of policies
10. Decision making
Purpose of statistics in agricultural
The value of statistics is because it helps in decision making , evaluating progress, help in planning and
designing agricultural surveys , helps in classifications , sampling , estimation , implementation , quality control
quality evaluation , data collection , data capture and coding.