CHAPTER 3

AGRICULTURAL METEOROLOGICAL DATA,

THEIR PRESENTATION AND STATISTICAL ANALYSIS

1. INTRODUCTION

Agricultural meteorology is an applied science that is the knowledge of weather

and climate applied to qualitative and quantitative improvement in agricultural
production. It is involved in meteorology, hydrology, agrology, and biology. It
requires a diverse multi-disciplined array of data for operational applications and
research. Basic agricultural meteorological data are largely the same as those used
in general meteorology. These data need to be supplemented with more specific data
relating to the biosphere, the environment of all living organisms, and biological data
relating to growth and development of these organisms. Agronomic, phenological,
and physiological data are necessary for dynamic modeling, operational evaluation,
and statistical analyses. Most data need to be processed for generating various
products that affect agricultural management decisions, such as cropping, irrigation
scheduling etc. The supports from other technologies, e.g. statistics, geographical
information and remote sensing are necessary for data processing. Geographical
information and remote sensing data such as images of vegetation status and crop
damaged by disasters, soil moisture, etc., also should be included in as supplementary
data. Derived agrometeorological parameters, such as photosynthetic active
radiation and potential evapotranspiration, are often used in agricultural meteorology
for both research and operational purposes. On the other hand, many
agrometeorological indexes such as drought index, critical point threshold of
temperature, and soil water for crop development are also important for agricultural
operations. Among the data, weather and climate data play a crucial role in many
agricultural decisions.

Agrometeorological information includes not only every stage of growth and

development of crops, floriculture, agroforestry, and livestock, but also the
technological factors, which impact on agriculture such as irrigation, plan protection,
fumigation, and dust spraying. Moreover, agricultural meteorological information
play a crucial role in the decision making process for sustainable agriculture and
natural disaster reduction with the aim toward preserving natural resources and
improving the quality of life.

2. DATA FOR AGRICULTURAL METEOROLOGY

Agrometeorological data are usually provided to Users in a transformed format; for

example, rainfall data are presented in pentads or in monthly amounts.

2.1. Nature of the data

Basic agricultural meteorological data may be divided into the following six
categories, which include data observed by instruments on the ground and observed
by remote sensing.

(a) Data related to the state of the atmospheric environment. These include
observations of rainfall, sunshine, solar radiation, air temperature, humidity,

1
 and wind speed and direction;

(b) Data related to the state of the soil environment. These include observations
of soil moisture, i.e., the soil water reservoir for plant growth and development.
The amount of water available depends on the effectiveness of precipitation or
irrigation, and on the soil’s physical properties and depth. The rate of water
loss from the soil depends on the climate, the soil’s physical properties, and
the root system of the plant community. Erosion by wind and water depend
on weather factors and vegetative cover;

(c) Data related to organism response to varying environments. These involve

agricultural crops and livestock, the variety and the state and stages of the
growth and development, as well as the pathogenic elements affecting them.
Biological data are associated with phenological growth stages and
physiological growth functions of living organisms;

(d) Information concerned with the agricultural practices employed. Planning

brings together the best available resources and applicable production
technologies into an operational farm unit. Each farm is a unique entity with
combinations of climate, soils, crops, livestock, and equipment to manage and
operate the farming system. The most efficient utilization of weather and
climate data for the unique soils on a farm unit will help conserve natural
resources, while at the same time, promote economical benefit to the farmer;

(e) Information related to weather disasters and their influence on agriculture;

and,

(f) Information related to the distribution of weather and agricultural crops, and
geographical information including the digital maps.

2.2. Data Collection

The collection of data is very important as it lays the foundation for agricultural
weather and climate data systems that are necessary to expedite generation of
products, analyses, and forecasts for agricultural cropping decisions, irrigation
management, fire weather management, and ecosystem conservation. The impact on
crops, livestock, water and soil resources, and forestry must be evaluated from the
best available spatial and temporal array of parameters. Agrometeorology is an
interdisciplinary branch of science requiring the combination of general
meteorological data observations and specific biological parameters. Meteorological
data can be considered as typically physical elements that can be measured with
relatively high accuracy while other types of observations (i.e., biological or
phenological) may be more subjective. In collecting, managing, and analyzing the
data for agrometeorological purposes, the source of data and the methods of
observation define their character and management criteria. However, a few useful
suggestions are listed below:

(a) Original data files, which may be used for reference purposes (the daily
register of observations, etc.), should be stored at the observation site; this
applies equally to atmospheric, biological, crop, or soil data;

2
(b) The most frequently used data should be collected at national or regional
agrometeorological centers and reside in host servers for network accessibility.
However, this may not always be practical since unique agrometeorological
data are often collected by stations or laboratories under the control of
different authorities (meteorological services, agricultural services,
universities, research institutes). Steps should, therefore, be taken to ensure
that possible users are aware of the existence of such data, either through some
form of data library or computerized documentation, and that appropriate data
exchange mechanisms are available to access and share these data;

(c) Data resulting from special studies should be stored at the place where the
research work is undertaken, but it would be advantageous to arrange for
exchanges of data between centers carrying out similar research work. At the
same time, the existence of these data should be publicized at the national
level and possibly at the international level, if appropriate, especially in the
case of longer series of special observations;

(d) All the usual data-storage media are recommended:

(i) The original data records, or agrometeorological summaries, are often
the most convenient format for the observing stations;

(ii) The format of data summaries intended for forwarding to regional or

national centers, or for dissemination to the user community, should be
designed so that the data may be easily transferred to a variety of
media for processing. The format should also facilitate either the
manual preparation or automated processing of statistical summaries
(computation of means, frequencies, etc.). At the same time, access
to and retrieval of data files should be simple, flexible, and
reproducible for assessment, modeling, or research purposes;

(iii) Rapid advances in electronic technology facilitate effective exchange

of data files, summaries, and charts of recording instruments
particularly at the national and international level; and,

(iv) Agrometeorological data should be transferred to electronic media in

the same way as conventional climatological data, with an emphasis on
automatic processing.

The availability of proper agricultural meteorological data bases is a major

prerequisite for studying and managing the processes of agricultural and forest
production. The agricultural meteorology community has great interest in
incorporating new information technologies into a systematic design for
agrometeorological management to ensure timely and reliable data from national
reporting networks for the benefit of the local farming community. While much
more information has become available to the agricultural user, it is essential that
appropriate standards be maintained for basic instrumentation, collection and
observations, quality control, and archive and dissemination. After recorded,
collected, and transferred to the data centers, all agricultural meteorological data need
to be standardized or treated by some techniques so that it can be used for different
purposes. In the data center, the special database is requisite. The database would

3
include meteorological, phenological, edaphic, and agronomic information. The
database management and processing, quality controlling, archiving, timely accessing,
and dissemination are all important components that will make the information
valuable and useful in agricultural research and operational programs.

Having been stored in a data center, the data are disseminated to users. Great
strides have been made in the automation age to make more data products available to
the user community. The introduction of electronic transfer of data files via Internet
using file transfer protocol (FTP) and the World Wide Web (WWW) has advanced this
information transfer process to a new level. The WWW allows users to access text,
images, and even sound files that can be linked together electronically. The WWW’s
attributes include the flexibility to handle a wide range of data presentation methods
and the capability to reach a large audience. Developing countries have some access
to this type of electronic information, but limitations still exist in the development of
their own electronically accessible databases. These limitations will diminish as the
cost of technology decreases and its availability increases.

2.3. Recording of data

Recording of basic data is the first step for agricultural meteorological data
collection. When the environmental factors and other agricultural meteorological
elements are measured or observed, they must be recorded in the same media, such
as agricultural meteorological registers, diskettes, etc., manually or automatically.

(a) The data, such as the daily register of observations and charts of recording
instruments, should be carefully preserved as permanent records. They should be
readily identifiable and include the place, date, time of each observation, and the units
used.

(b) These basic data should be sent to analysis centers for operational uses, e.g.
local agricultural weather forecasts, agricultural meteorological information service,
plant-protection treatment, irrigation, etc. The summaries (weekly, 10-day or
monthly) of these data should be made regularly from the daily register of
observations according to demand of users and then distribute to interested agencies
and users.

(c) The data should be recorded by a standard format so that they could be
readily transferred to data centers suitable for subsequent automatic processing, so the
observers should record all measurements complying with some rules. The data can
be transferred to data centers by many ways, such as mail, telephone, telegraph, and
fax or Internet, and comsat, in which Internet and comsat are a more efficient
approach. After reaching the data centers, data should be identified and processed
by special program for facilitating to other users.

2.4. Scrutiny of data

It is very important that all agricultural meteorological data be carefully
scrutinized, both at the observing station and at regional or national centers by
subsequent automatic processing of computers. All data should be identified
immediately. The code parameters should be specified, such as types, regions,
missing values, and possible ranges for different measurements. The quality control
should be done according to Wijngaard et al. (2003) and WMO-TD N° 1236 (2004)

4
and the current Climatological Guide. Every code of measurement must be checked
to make certain if the measurement is reasonable. If the value is unreasonable, it
should be corrected immediately. After being scrutinized, the data could be
processed further for different purposes.

2.5. Format of data

The basic data obtained from observing stations, whether specialized or not,
are of interest to both scientists and agricultural users. There are a number of
established formats and protocols to exchange data. A data format is a documented
set of rules for the coding of data in a form for both visual and computer recognition.
Its uses can be designed for either or both real-time use and historical or archival data
transfer. All the critical elements for identification of data should be covered in the
coding, including station identifies, parameter descriptors, time encoding conventions,
unit and scale conventions, and common fields.

Large amounts of data are typically required for processing, analysis, and
dissemination. It is extremely important that data are in a format being both easily
accessible and user friendly. This is particularly true as many data become available
in electronic format. Some software process data in a common form and disseminate
to more users, such as NetCDF (network common data form). It is software for
array-oriented data access and a library that provides an implementation of the
interface (Sivakumar, et al., 2000). The NetCDF software was developed at the
Unidata Program Center in Boulder, Colorado, USA. The freely available source
can be obtained by anonymous FTP from ftp://ftp.unidata.ucar.edu/pub/netcdf/ or
from other mirror sites.

NetCDF package supports the creation, access, and sharing of scientific data.
It is particularly useful at sites with a mixture of computers connected by a network.
Data stored on one computer may be read directly from another without explicit
conversion. NetCDF library generalizes access to scientific data so that the methods
for storing and accessing data are independent of the computer architecture and the
applications being used. Standardized data access facilitates the sharing of data.
Since the NetCDF package is quite general, a wide variety of analysis and display
applications can use it. The NetCDF software and documentation may be obtained
from the NetCDF website at http://www.unidata.ucar.edu/packages/netcdf/.

2.6. Catalogue of data

Very often, considerable amounts of agrometeorological data are collected by a
variety of services. These data sources are not readily publicized or accessible to
potential users. So, the users often have great difficulty in discovering whether such
data exist. Coordination should therefore be undertaken at the global, regional, and
national level to ensure that data catalogues are prepared periodically, giving enough
information to users. The data catalogues should include the following information:

(a) The geographical location of each observing site;

(b) The nature of the data obtained;
(c) The location where the data are stored;
(d) The types of file (manuscript, charts of recording instruments, automated weather
station, punched cards, magnetic tape, scanned data, computerized digital data); and,
(e) The methods of obtaining the data.

5
3. DISTRIBUTION OF DATA

3.1. Requirements for research

In order to highlight the salient features of the influence of climatic factors on
the growth and development of living things, scientists often have to process a large
volume of basic data. These data could be supplied to scientists in the following
forms:

(a) Reproductions of original documents (original records, charts of recording

instruments) or periodic summaries;
(b) Data sets on server or website ready for processing to different categories, which
can be read or viewed on a platform;
(c) Various kinds of satellite digital data and imagery on different regions and
different times;
(d) Various basic databases, which can be viewed as reference for research.

3.2. Special requirements of agriculturists

Two aspects of the periodic distribution of agrometeorological data to agricultural
users may be considered:

(a) Raw or partially processed operational data supplied after only a short delay
(rainfall, potential evapotranspiration, water balance, sums of temperature). These
may be distributed:

i. By periodic publications, twice weekly, weekly or at 10-day intervals;

ii. By telephone and note;
iii. By TV special program from regional television station;
iv. By regional radio broadcast; and,
v. By release on agricultural or weather websites.

(b) Agrometeorological or climatic summaries published weekly, 10-day, monthly or

annually, containing agrometeorological data (rainfall, temperatures above the ground,
soil temperature and moisture content, potential evapotranspiration, sums of rainfall
and temperature, abnormal rainfall and temperature, sunshine, global solar radiation,
etc.).

3.3. Determining the requirements of users

The agrometeorologist has a major responsibility to ensure that effective use of
this information offers an opportunity to enhance agricultural efficiency or to assist
agricultural decision making. The information must be accessible, clear, and
relevant. However, it is crucial for an agrometeorological service to know who the
specific users of information are. The user community ranges from global, national,
provincial organizations, and governments to agro-industries, farmers, agricultural
consultants, and the agricultural research and technology development communities
or private individuals. The variety of agrometeorological information requests
emanates from this broad community. So, the agrometeorological service must
distribute the appropriate information available at the right time.

6
 Researchers invariably know exactly what agrometeorologica1 data they require
for specific statistical analyses, modeling, or other analytical studies. Many
agricultural users are often not only unaware of the actual scope of the
agrometeorological services available, but have only a vague idea of the data they
really need. Frequent contact between agrometeorologists and professional
agriculturists, and enquiries through professional associations and among
agriculturists themselves or visiting professional websites, can help enormously to
improve the awareness of data needs. Sivakumar (1998) presents a broad overview
of user requirements for agrometeorological services. Better applications of the type
and amount of useful agrometeorological data available and the selection of the type
of data to be systematically distributed can be established on that bases. For
example, when both the climatic regions and the areas in which different crops are
grown are well defined, an agrometeorological analysis can illustrate which crops are
most suited to each climate zone. This type of analysis can also show which crops
can be adapted to changing climatic and agronomic conditions. These analyses are
required by the agricultural users, they can be distributed by either geographic region,
crop region, or climatic region.

3.4. Minimum distribution of agroclimatological documents

Since the large number of potential users of agrometeorological information is so
widely dispersed, it is not realistic to recommend a general distribution of data to all
users. In fact, the requests for raw agrometeorological data are rare. All of the raw
agrometeorological data available is not essential for those directly engaged in
agriculture (i.e., farmers, ranchers, foresters). Users generally require data to be
processed into an understandable format to facilitate their decision making process.
But, the complete data sets should be available and accessible to the technical services,
agricultural administrations, and professional organizations. These professionals are
responsible for providing practical technical advice concerning the treatment and
management of crops, preventive measures, adaptation strategies, etc., based on
collected agrometeorological information.

Agrometeorological information should be distributed to all users including:

(a) Agricultural administrations;
(b) Research institutions and laboratories;
(c) Professional organizations;
(d) Private crop and weather services;
(e) Government agencies; and,
(f) Farmers, ranchers, and foresters.

4. DATABASE MANAGEMENT

The management of agroclimatological data in the electronic age has become

more efficient. The management to be considered, and already reviewed in this
section, is data collection, data processing, quality control, archiving, data analysis
and product generation, and product delivery. A wide variety of database choices are
available to the agroclimatological user community. Accompanying the
agroclimatological databases created, the agrometeorologists and software engineers
develop the special software for agroclimatological database management. Thus, a
database management system for agricultural applications should be a comprehensive

7
system with the following considerations:

(a) Communication between climatologists, agrometeorologists and agricultural

extension personnel must be improved to establish an operational database;

(b) The outputs must be adapted for an operational database in order to support
specific agrometeorological applications at a national/regional/global level; and,

(c)Applications must be linked to the Climate Applications Referral System (CARS)

project, spatial interpolated databases, and GIS.

Personal computer (PC) is able to produce products formatted for easy reading
and presentation generated through simple processors, databases, or spreadsheet
applications. However, some careful thought needs to be given to what type of
product is needed, what the product looks like, and what it contains, before the
database delivery design is finalized. The greatest difficulty often encountered is
how to treat missing data or information (WMO-TD N° 1236, 2004). This process is
even more complicated when data from several different data sets such as climatic and
agricultural data are combined. Some software for database management, especially
the software for climatic database management, provide convenient tools for
agrometeorological database management.

4.1. CLICOM Database Management System

CLICOM (CLImate COMputing) refers to the WMO World Climate Data
Programme Project with the purpose of coordinating and assisting the implementation,
maintenance and upgrading of automated climate data management procedures and
systems in WMO Member countries (i.e., National Meteorological and Hydrological
Services). The goal of CLICOM is the transfer of three main components of modern
technology, i.e. desktop computer hardware; database management software, and
training in climate data management. CLICOM is a standardized automated
database management system (DBMS) software on a personal computer (PC) to
introduce a system in developing countries. At the May of 1996, CLICOM version
3.0 was installed in 127 WMO Member countries. Now CLICOM software is
available in English, French, Spanish, Czech, and Russian. CLICOM version 3.1
Release 2 was available in January 2000.

CLICOM provides tools to describe and manage the climatological network (i.e,
stations, observations, instruments, etc.). It offers procedures to key entry, check and
archive climate data, and compute and analyze the data. Typical standard outputs
include monthly or 10-day data from daily data; statistics such as means, maximums,
minimums, standard deviations; tables; and graphs. Other products, requiring more
elaborated data processing, include water balance monitoring, estimation of missing
precipitation data, calculation of the return period, and preparation of the CLIMAT
message.

The CLICOM is widely used in developing countries. The installation of

CLICOM as a data management system in many of these countries has successfully
transferred PC’s technology, but the resulting climate data management improvements
have not yet been fully realized. Station network density as recommended by WMO
has not been fully achieved and the collection of data in many countries remains

8
inadequate. However, CLICOM systems are beginning to yield positive results and
there is a growing recognition of the operational applications of CLICOM.

There are a number of constraints that have been identified over time and
recognized for possible improvement in future versions of the CLICOM system.
Among the technical limitations, the list includes (Motha, 2000):

(a)The lack of flexibility to implement specific applications in the agricultural

field and/or at a regional/global level;
(b)Lack of functionality in real-time operations;
(c)Few options for file import;
(d)Lack of transparent linkages to other applications;
(e)Risk of many datasets overlapping;
(f)Non-standard geo-referencing system;
(g)Climate data may be stored without the corresponding station information;
(h)The data entry module allows for easy modification, which may destroy
existing data.

4.2. Geographic Information System (GIS)

A geographic information system (GIS) is a computer-assisted system for
acquisition, storage, analysis, and display of observed data on spatial distribution.
GIS technology integrates common database operations such as query and statistical
analysis with the unique visualization and geographic analysis benefits offered by
mapping overlays. Maps have traditionally been used to explore the earth and its
resources. GIS technology takes advantage of computer science technologies,
enhancing the efficiency and analytical power of traditional methodologies.

GIS is becoming an essential tool in the effort to understand complex processes

at different scales: local, regional, and global. In GIS, the information coming from
different disciplines and sources, such as traditional point sources, digital maps,
databases, and remote sensing, can be combined in models that simulate the behavior
of complex systems.

The presentation of geographic elements is solved in two ways: using x, y

coordinates (vectors) or representing the object as variation of values in a geometric
array (raster). The possibility to transform the data from one format to the other
allows fast interaction between different informative layers. Typical operations
include overlaying different thermatic maps, contributing areas and distances,
acquiring statistical information about the attributes, changing the legend, scale and
projection of maps, and making three-dimensional perspective view plots using
elevation data.

The capability to manage this diverse information, analyzing and processing

together the informative layers, opens new possibilities for the simulation of complex
systems. GIS can be used to produce images, not only maps, but the cartographic
products, drawings, animations, or interactive instruments as well. These products
allow researchers to analyze their data in new ways, predicting the natural behaviors,
explaining events, and planning strategies.

For the agronomic and natural components in agrometeorology, these tools have

9
taken the name Land Information Systems (LIS) (Sivakumar et al., 2000). In both
GIS and LIS, the key components are the same: i.e., hardware, software, data,
techniques, and technicians. However, LIS requires detailed information on the
environmental elements such as meteorological parameters, vegetation, soil, and water.
The final product of LIS is often the result of a combination of numerous complex
informative layers, whose precision is fundamental for the reliability of the whole
system.

4.3. Weather generators (WG)

Weather generators are widely used to generate synthetic weather data, which
can be arbitrarily long for input into impact models, such as crop models and
hydrological models that are used for assessing agroclimatic long-term risk and
agrometeorological analysis. Weather generators are also the tool to develop future
climate scenarios based on GCM simulated or subjectively introduced climate
changes for climate change impact models. Weather generators project future
changes in means to the observed historical weather series incorporating changes in
variability, which is widely used for agricultural impact studies. Daily climate
scenarios can be used to study potential changes in agroclimatic resources. Weather
generators can calculate agroclimatic indices on the basis of historical climate data
and GCM outputs. Various agroclimatic indices can be used to assess crop
production potentials and to rate the climatic suitability of land for crops. A
methodologically more consistent approach is to use a stochastic weather generator,
instead of historical data, in conjunction with a crop simulation model. The
stochastic weather generator allows temporal extrapolation of observed weather data
for agricultural risk assessment as well as providing an expanded spatial source of
weather data by interpolation between the point-based parameters used to define the
weather generators. Interpolation procedures can create both spatial input data and
spatial output data. The density of meteorological stations is often low, especially in
developing countries, and reliable and complete long-term data are scarce. Daily
interpolated surfaces of meteorological variables rarely exist. More commonly,
weather generators can be used to generate the weather variables in grids that cover
large geographic regions and come from interpolated surfaces of weekly or monthly
climate variables. From these interpolated surfaces, daily weather data for crop
simulation models are then generated using statistical models that attempt to
reproduce series of daily data with means and variability similar to what would be
observed at a given location.

Weather generators have the capacity to simulate statistical properties of

observed weather data for agricultural applications, including a set of agroclimatic
indices. They are able to simulate temperature, precipitation, and related statistics.
Weather generators typically calculate daily precipitation risk and use this information
to guide the generation of other weather variables, such as daily solar radiation,
maximum and minimum temperature, and potential evapotranspiration. They also
can simulate statistical properties of daily weather series under a changing/changed
climate through modifications to the weather generator parameters with optimal use
of available information on climate change. For example, weather generators can
simulate the frequency distributions of the wet and dry spells fairly well by modifying
the four transition probabilities of the second-order Markov chain. Weather
generators are generally based on the statistics. For examples, to generate the
amount of precipitation on wet days, a two-parameter gamma distribution function is

10
commonly used. The two parameters, a and b, are directly related to the average
amount of precipitation per wet day. They can, therefore, be determined with the
monthly means for the number of rainy days per month and the amount of
precipitation per month, which are obtained either from compilations of climate
normal or from interpolated surfaces.

The popular weather generators are WGEN (Richardson, 1984, 1985),

SIMMETEO (Geng et al., 1986, 1988), and MARKSIM (Jones and Thornton, 1998;
2000), etc. They are including a first or high order Markov daily generator that
requires long-term, at least 5 to 10 years, daily weather data or climate clusters of
interpolated surfaces for estimation of theirs parameters. The software allows three
types of input to estimate parameters for the generator:

(1) Latitude and longitude;

(2) Latitude, longitude and elevation;
(3) Latitude, longitude, elevation and long-term monthly climate normals.

5. AGROMETEOROLOGICAL INFORMATION

The impacts of meteorological factors on crop growth and development are

consecutive, although sometimes they do not emerge over a short time. The weather
and climatological information should vary according to the kind of crop, its
sensitivity to the environment factors and water requirements, etc. Certain statistics
are important, such as sequences of consecutive days when maximum and minimum
temperatures or the amounts of precipitation exceed or are less than certain critical
threshold values and the average and extreme dates when these threshold values are
reached.

The following are some of the more frequent types of information which can be
derived from the basic data:
(a) Air temperature
(i) Temperature probabilities;
(ii) Chilling hours;
(iii) Degree days;
(iv) Hours or days above or below selected temperatures;
(v) Interdiurnal variability;
(vi) Maximum and minimum temperature statistics; and,
(vii) Growing season statistics. Dates when threshold values of temperature
for various kinds of crops growth begin and end.

(b) Precipitation
(i) Probability of specified amount during a period;
(ii) Number of days with specified amounts of precipitation;
(iii) Probabilities of thundershowers;
(iv) Duration and amount of snow cover;
(v) Date of beginning and ending of snow cover; and,
(vi) Probability of extreme precipitation amounts.

(c) Wind

11
 (i) Wind rose;
(ii) Maximum wind, average wind speed;
(iii) Diurnal variation; and,
(iv) Hours of wind less than selected speed.

(d) Sky cover, sunshine, radiation

(i) Percent possible sunshine;
(ii) Number of clear, partly cloudy, cloudy days; and,
(iii) Amounts of global and net radiation.

(e) Humidity
(i) Probability of specified relative humidity; and,
(ii) Duration of specified threshold of humidity with time.

(f) Free water evaporation

(i) Total amount;
(ii) Diurnal variation of evaporation;
(iii) Relative dryness of air; and,
(iv) Evapotranspiration.

(g) Dew
(i) Duration and amount of dew;
(ii) Diurnal variation of dew;
(iii) Association of dew with vegetative wetting; and,
(iv) Probability of dew formation with season.

(h) Soil temperature

(i) Mean and standard deviation at standard depth;
(ii) Depth of frost penetration;
(iii) Probability of occurrence of specified temperatures at standard depths;
and,
(iv) Dates when threshold values of temperature (germination, vegetation)
are reached.

(i) Weather hazards or extreme events

(i) Frost;
(ii) Cold Wave;
(iii) Hail;
(iv) Heat Wave;
(v) Drought;
(vi) Cyclones;
(vii) Flood;
(viii) Rare sunshine; and,
(ix) Waterlogging.

(j) Agrometeorological observations

(i) Soil moisture at regular depths;
(ii) Plant growth observations;
(iii) Plant population;
(iv) Phenological events;

12
 (v) Leaf area index;
(vi) Above ground biomass;
(vii) Crop canopy temperature;
(viii) Leaf temperature; and,
(ix) Crop root length.

5.1. Forecast information

Operational weather information is defined as real-time data which provide
conditions of past weather (over the previous few days), present weather, as well as
predicted weather. It is well known, however, that the forecast product deteriorates
with time, so that the longer the forecast period, the less reliable the forecast.
Forecasting of agriculturally important elements is discussed in Chapters 4 and 5.

6. STATISTICAL METHODS OF AGROMETEOROLOGICAL DATA

ANALYSIS

The remarks set out here are intended to be supplementary to Chapter 5, "The
use of statistics in climatology", of the WMO Guide to Climatological Practices and
to WMO Technical Note No. 8l, “Some methods of climatological analysis”, which
contain advice generally appropriate and applicable to agricultural climatology.

Statistical analyses play an important role in agrometeorology for they provide a

means of interrelating series of data from diverse sources, namely biological data, soil
and crop data, and atmospheric measurements. Because of the complexity and
multiplicity of the effects of environmental factors on the growth and development of
living organisms, and consequently on agricultural production, it is sometimes
necessary to use rather sophisticated statistical methods to detect the interactions of
these factors and their practical consequences.

It must not be forgotten that advice on the long-term agricultural planning, on the
selection of the most suitable farming enterprise, on the provision of proper
equipment, and on the introduction of protective measures against severe weather
conditions all depend to some extent on the quality of the climatological analyses of
the agroclimatic and related data, and, hence, on the statistical methods on which
these analyses are based. Another point which needs to be stressed is that one is
often obliged to compare measurements of the physical environment with biological
data, which are often difficult to quantify.

Once the agrometeorological data are stored in electronic form in a file or

database, it can be analyzed using a public domain number or commercial statistical
software. Some basic statistical analyses can be performed in widely available
commercial spreadsheets software. More comprehensive basic and advanced
statistical analyses generally require specialized statistical software. Basic statistical
analyses include simple descriptive statistics, distribution fitting, correlation analysis,
multiple linear regression, nonparametrics, and enhanced graphic capabilities.
Advanced software includes linear/non-linear models, time series and forecasting, and
multivariate exploratory techniques such as cluster analysis, factor analysis, principal
components and classification analysis, classification trees, canonical analysis, and
discriminant analysis. Commercial statistical software for PCs would be expected to

13
provide a user-friendly interface with self-prompting analysis selection dialogs.
Many software packages include electronic manuals which provide extensive
explanations of analysis options with examples and comprehensive statistical advice.

Some commercial packages are rather expensive, but there are some free
statistical analysis software which can be downloaded from the web or can be made
available upon request. One example of freely available software is INSTAT, which
was developed with applications in agrometeorology in mind. It is a general purpose
statistics package for PCs which was developed by the Statistical Service Centre of
the University of Reading, England. It uses a simple command language to process
and analyze data. The documentation and software can be downloaded from the web.
Data for analysis can be entered into a table or copied and pasted from the clipboard.
If CLICOM is used as the database management software, then INSTAT, which was
designed for use with CLICOM, can readily be used to extract the data and perform
statistical analyses. INSTAT can be used to calculate simple descriptive statistics
including: minimum and maximum values, range, mean, standard deviation, median,
lower quartile, upper quartile, skewness, and kurtosis. It can be used to calculate
probabilities and percentiles for standard distributions, normal scores, t-tests and
confidence intervals, chi-square tests, and non-parametric statistics. It can be used to
plot data, for regression and correlation analysis and analysis of time series.
INSTAT is designed to provide a range of climate analyses. It has commands for
10-day, monthly, and yearly statistics. It calculates water balance from rainfall and
evaporation, start of rains, degree days, wind direction frequencies, spell lengths,
potential ET according to Penman, and crop performance index according to FAO
methodology. The usefulness of INSTAT for agroclimatic analysis is illustrated in
the publication on the Agroclimatology of West Africa: Niger. The major part of the
analysis reported in the bulletin was carried out using INSTAT.

6.1. Series checks

Before selecting a series of values for statistical treatment, the series should be
carefully examined for validity. The same checks should be applied to series of
agrometeorological data as to conventional climatological data; in particular, the
series should be checked for homogeneity and, if necessary, gaps should be filled in.
It is assumed that beforehand, the individual values will have been carefully checked
(consistency and coherence) in accordance with section 4.3 of the WMO Guide to
Climatological Practices.

6.2. Climatic scales

In agriculture, perhaps more than in most economic activities, all scales of climate
need to be considered (see section 2.1.3):

(a) For the purpose of meeting national and regional requirements, studies on a
macroclimatic scale are useful, and may be based mainly on data from
synoptic stations. For some atmospheric parameters with little spatial
variation--e.g., duration of sunshine over a week or 10-day period--such an
analysis is found to be satisfactory;
(b) In order to plan the activities of an agricultural undertaking, or group of
undertakings, it is essential, however, to change over to the mesoclimatic or
topoclimatic scale, i.e., to take into account local geomorphological features
and to use data from an observational network with a finer mesh. These

14
 complementary climatological series of data may be for much shorter periods
than those used for macroclimatic analyses, provided they can be related to
some long reference series;
(c) For bioclimatic research, the physical environment should be studied at the
level of the plant or animal or the pathogenic colony itself. Obtaining
information about radiation energy, moisture, and chemical exchanges
involves handling measurements on the much finer scale of microclimatology.
(d) For research on impact of changing climate, past long-term historical and
future climate scenarios should be supposed and extrapolated.

6.2.1. Reference periods

The length of the reference period for which the statistics are defined should
be selected according to its suitability for each agricultural activity. Calendar
periods of a month or a year are not, in general, suitable. It is often best either to use
a reduced timescale or, alternatively, to combine several months in a way that will
slow the overall development of an agricultural activity. The following periods are
thus suggested for reference purposes:

(a) Ten-day or weekly periods, for operational statistical analyses, e.g.,

evapotranspiration, water balance, sums of temperature, frequency of
occasions when a value exceeds or falls below a critical threshold value, etc.
However, data for the weekly period, which has the advantage of being
universally adopted for all activities, are difficult to adjust for successive
years;

(b) For certain agricultural activities the periods should correspond to

phenological stages or to the periods when certain operations are undertaken
in crop cultivation. Thus, water balance, sums of temperature, sequences of
days with precipitation, or temperature below certain threshold values, etc.,
could be analyzed for:
(i) The mean growing season;
(ii) Periods corresponding to particularly critical phenological stages;
(iii) Periods during which crop cultivation, plant protection treatment, or
preventive measures are found to be necessary.

These suggestions, of course, imply a thorough knowledge of the normal

calendar of agricultural activities in an area.

6.2.2. The beginning of reference periods

In agricultural meteorology, it is best to choose starting points corresponding to
the biological rhythms, since the arbitrary calendar periods (year, month) do not
coincide with these. For example, in temperate zones, the starting point could be
autumn (sowing of winter cereals) or spring (resumption of growth). In regions
subject to monsoons or the seasonal movement of the intertropical convergence zone,
it could be the onset of the rainy season. It could also be based on the evolution of a
significant climatic factor considered to be representative of a biological cycle
difficult to assess directly, e.g., summation of temperatures exceeding a threshold
temperature necessary for growth.

6.2.3. Analysis of effects of weather

15
 The climatic elements do not act independently on the biological life-cycle of
living things: an analytical study of their individual effects is often illusory; handling
them all simultaneously, however, requires considerable data and complex statistical
treatment. It is often better to try to combine several factors into single agroclimatic
indices, considered as complex parameters, which can be compared more easily with
biological data.

6.3. Frequency Distributions

When dealing with a large set of measured data, it is usually necessary to arrange
it into a certain number of equal groupings, or classes, and to count the number of
observations that fall into each class. The number of observations falling into a
given class is called the frequency for that class. The number of classes chosen
depends on the number of observations. As a rough guide, the number of classes
should not exceed five times the logarithm (base 10) of the number of observations.
Thus, for 100 observations or more, there should be a maximum of ten classes. It is
also important that adjacent groups do not overlap. The result of doing this can be
displayed in a grouped frequency table, based on table 1, such as the one depicted in
table 2.

Table 1.Climatological series of annual rainfall (mm) for Mbabane (1930-1979)

Year 0 1 2 3 4 5 6 7 8 9
193- 1063 1237 1495 1160 1513 912 1495 1769 1319 2080
194- 1350 1033 1707 1570 1480 1067 1635 1627 1168 1336
195- 1102 1195 1307 1118 1262 1585 1199 1306 1220 1328
196- 1411 1351 1115 1256 1226 1062 1546 1545 1049 1830
197- 1018 1690 1800 1528 1285 1727 1704 1741 1667 1260

Table 2: Frequency Distribution of annual precipitation for Mbabane (1930-1979)

1 2 3 5 6 7
Group Group limits Mid-mark Frequency Cummulative Relative
boundaries or class xi fi frequency cumulative
interval Fi frequency(%
)
1 879.5-1029.5 880-1029 954.5 2 2 4
2 1029.5-1179.5 1030-1179 1104.5 8 10 20
3 1179.5-1329.5 1180-1329 1254.5 15 25 50
4 1329.5-1479.5 1330-1479 1404.5 4 29 58
5 1479.5-1629.5 1480-1629 1554.5 10 39 78
6 1629.5-1779.5 1630-1779 1704.5 8 47 94
7 1779.5-1929.5 1780-1929 1854.5 2 49 98
8 1929.5- 2079.5 1930-2079 2004.5 0 49 98
9 2079.5- 2229.5 2080-2229 2154.5 1 50 100

Total: 50 -

16
The table has columns showing limits defining classes and another column giving
lower and upper class boundaries that in turn give rise to class widths or class
intervals, yet another column gives the mid-marks of the classes, and another column
gives the totals of the tally known as the group or class frequencies.

Another column has entries that are known as the cumulative frequencies. They are
obtained from the frequency column by entering the number of observations with
values less than or equal to the value of the upper class boundary of that group.

The pattern of frequencies obtained by arranging data into classes is called the
frequency distribution of the sample. The probability of finding an observation in a
class can be obtained by dividing the frequency for the class by the total number of
observations. A frequency distribution can be represented graphically with a
two-dimensional histogram, where the heights of the columns in the graph are
proportional to the class frequencies.

Examples using frequency distribution

10
The probability of an observation falling in class number five is = 0.2 or 20%.
50
That is the same thing as saying that the probability of getting between 1480 mm and
1620 mm of rain in Mbabane is 20% or once in five years. The probability of
getting less than 1779 mm of rain in Mbabane as in class six is 0.94, found by
dividing the cumulative frequency up to this point by 50, the total number of
observations or frequencies. This kind of probability is also known as relative
cumulative frequency, given as percentage in column seven. From column seven,
the probability of getting between 1330 mm and 1929 mm is 98% minus 58%, which
is 40%.

Frequency distribution groupings have the disadvantage that when using them, certain
information is lost, such as the highest observation in the highest frequency class.

6.3.1 Probability Based On Normal Distributions

A normal distribution is a highly refined frequency distribution having an infinite
number of very narrow classes. The histogram from this distribution has smoothed
out tops that make a continuous smooth curve, known as a normal or bell curve. A
normal curve is symmetric about its centre having a horizontal axis that runs
indefinitely both to the left and to the right, with the tails of the curve tapering off
towards the axis in both directions. The vertical axis is chosen in such a way that the
total area under the curve is exactly 1 (one square unit). The central point on the
axis beneath the normal curve is the mean µ and the set of data that produced it has

a standard deviation σ . Any set of data that tends to give rise to a normal curve is
said to be normally distributed. The normal distribution is completely characterized

17
by its mean and standard deviation. Sample statistics are functions of observed
values that are used to infer something about the population from which the values are
drawn. The sample mean and sample variance, for instance, can be used as
estimates of population mean and population variance, respectively, provided the
relationship between these sample statistics and the populations from which the
samples are drawn is known. In general, the sampling distribution of means is less
spread out than the parent population. This fact is embodied in the central limit
theorem which states that if random samples of size n are drawn from a large
population( hypothetically infinite), which has mean µ and standard deviation σ ,

then the theoretical sampling distribution of X has mean µ and standard deviation

σ
. The theoretical sampling distribution of X can be closely approximated by
n
the corresponding Normal curve if n is large. Thus, for quite small samples,
particularly if we know that the parent population is itself approximately Normal, we
can confidently apply the theorem. If we are not sure that the parent population is
Normal, we should, as a rule, restrict ourselves to applying the theorem to samples of
size ≥ 30 .

The standard deviation of a sampling distribution is often called the standard error of
σ
the sample statistic concerned. Thus σ X = is the standard error of X .
n
In order to compare different distributions having different means and different
standard deviations with one another they need to be transformed. One way would
be to center them about the same mean by subtracting the mean from each observation
in each of the populations. This will move each of the distributions along the scale
until they are centered about zero, which is the mean of all transformed distributions.
However, each distribution will still maintain a different bell-shape.

The Z.score
A further transformation is done by subtracting the mean of the distribution from each
observation and dividing by the standard deviation of the distribution, a procedure
known as standardization. The result is a variable Z, known as a z -score and having
the standard normal form:

X −µ
z=
σ
This will give identical bell shaped curves with Normal Distribution around zero
mean and standard deviation equal to unit.

The z-scale is a horizontal scale set up for any given normal curve with some mean

18
µ and some standard deviation σ . On this scale, the mean is marked 0 and the unit
measure is taken to be σ , the particular standard deviation of the normal curve in
question. A raw score X can be converted into a z-score by the above formula:

For instance, with µ = 80 and σ = 4 , if we want formally to convert the X-score

85 into a z-score,

we write
X − µ 85 − 80 5
z= = = = 1.25.
σ 4 4

The meaning here is that the X-score lies one standard deviation to the right of the
mean. If we compute a z-score equivalent of X=74, we get
X − µ 74 − 80 − 6
z= = = = −1.5.
σ 4 4

The meaning of this negative z-score is that the original X-score 74 lies one and
one-half standard deviations (that is, six units) to the left of the mean. A z-score tells
how many standard deviations removed from the mean the original x-score is, to the
right (if z is positive) or to the left (if z is negative).

There are many different normal curves due to the different means and standard
deviations. However, for a fixed mean µ and a fixed standard deviation σ , there is

exactly one normal curve having that mean and that standard deviation.

Normal Distributions can be used to calculate probabilities. Since a normal curve is

symmetric having a total area of one square unit under it then the area to the right of
the mean is half a square unit and similarly to the left. The characteristics of the
standard normal distribution are extremely well known, and tables of areas under
specified segments of the curve are available in almost all statistical books. The
areas are directly expressed as probabilities. The probability of encountering a
sample, by random selection from a normal population, whose measurement falls
within a specified range, can be found with the use of these tables. The variance of
the population must, however, be known. The fundamental idea connected with the
area under a normal curve is that if a measurement X is normally distributed, then the
probability that X will lie in some range between a and b on any given occasion is
equal to the area under the normal curve between a and b.
To find the area under a normal curve between the mean µ and some x-value,

convert the x into a z-score. The number indicated is the desired area. If z turns
out to be negative, just look it up as if it were positive. If the data are normally

19
distributed, then it is probable that at least 68 percent of data in the series will fall
within ± 1σ of the mean, that is z = ±1 . Also, the probability is 95 percent that all
data fall within ± 2σ of the mean, or z = ±2 , and 99 percent within ± 3σ of the mean
or z = ±3 .

Example using the z-score

a) If the heights of all the rice stalks in a farm are thought to be normally
distributed with mean µ = 38 cm and standard deviation σ = 4.5 cm, find the

probability that the height of a stalk taken at random will be between 35 and 40 cm.
To solve this problem, we must find the area under a portion of the appropriate
normal curve, between x=35 and x=40. (See Figure below). It is necessary to
convert these x-values into z-scores as follows.

X −µ 35 − 38 5
For X=35: z = = = ≅ −0.67.
σ 4.5 4.5

X −µ 40 − 38 2
For X=40: z = = = ≅ 0.44.
σ 4.5 4.5

From the mean out to z=0.44, the area (on the right side of the region in Figure 12-15)
from Table 2 is 0.1700.
From the mean out to z=-0.67, the area (on the left side of the region in Figure 12-15)
from Table 2 is 0.2486.
It is clear from Figure 12-15 that the shaded area on both sides must be counted, and
so we add areas to obtain 0.1700+0.2486=0.4186.

Thus, the probability that a stalk chosen at random will have height X between 35 and
40 cm is 0.4186. In other words, we would expect 41.86% of the paddy field’s rice
stalks to have heights in that range.

Elements that are not normally distributed may easily be transformed mathematically
to the normal distribution, an operation known as normalization. Among the
moderate normalizing operators are the square root, the cube root, and the logarithm
for positively skewed data such as rainfall. The transformation reduces the higher

20
values by proportionally greater amounts than smaller values.

b) Suppose a population of pumpkins is known to have a Normal Distribution with a

mean and standard deviation of its length as 14.2 cm and 4.7 cm respectively. What
is the probability of finding, by chance, a specimen shorter than 3 cm? To find the
answer, we must convert 3 cm to units of standard deviation and use the Standard
Normal Distribution Table.

3.0 − 14.2
Z= ≈ −2.4 .
4.7

The probability of finding a variety smaller than -2.4 standard deviations is the
cumulative probability to this point; from our table, we can see that it is 0.0082, which
is very small indeed. Now, what is the probability of finding one longer than 20 mm?
Again, converting to standard normal form:

20.0 − 14.2
Z= ≈ 1.2
4.7

Because the total area under the normal distribution curve is 1.00, the probability of
obtaining a measurement of 1.2 standard deviations or greater than the mean is the
same as 1.0 minus the cumulative probability of obtaining anything smaller.
Standard Normal Distribution Table will give us the cumulative probability up to 1.2,
which is 0.8849. Therefore, the probability of finding a specimen longer than 20 cm
is 1.0000-0.8849=0.1151 or slightly greater than one chance out of ten. Now,
compute the probability of finding, at random, a stalk whose length falls in the size
range from 15 to 20 cm:

15.0 − 14.2
for 15 cm Z= ≈ 0.2
4.7

20.0 − 14.2
for 20 cm Z= ≈ 1.2
4.7

6.3.2. Extreme-value distributions

Certain crops may be exposed to lethal conditions (frost, excessive heat or

cold, drought, high winds, etc.), even in areas where they are commonly grown.
Extreme value analysis typically involves the collection and analysis of annual
maxima of parameters that are observed daily such as temperature, precipitation, and
wind speed. The process of extreme value analysis involves data gathering, the
identification of a suitable probability model, such as the Gumbel distribution or GEV
distribution; (Coles, 2001) to represent the distribution of the observed extremes, the
estimation of model parameters, and the estimation of the return values for periods of
fixed length.

21
The Gumbel double exponential distribution is the most used for describing extreme
values. An event which has occurred m times in a long series of n independent trials,
m
one per year say, has an estimated probability p = ; conversely the average
n
n
interval between recurrences of the event during a long period would be ; this is
m
defined as the return period T where:

1
T=
p

For example, if there is a 5 percent chance that an event will occur in any one year,
then its probability of occurrence is 0.05. This can be expressed as an event having a
return period of five times in 100 years or once in 20 years. This means that the
event is more likely that over a long period of say 200 years, ten events of equal or
greater magnitude would have occurred.

For a valid application of extreme value analysis, two conditions must be met:

First, the data must be independent, i.e., the occurrence of one extreme is not linked to
the next.

Second, the data series must be trend free and the quantity of data must be big, usually
not less than 15 values.

6.3.3. Probability and risk

Frequency distributions, which provide an indication of risk, are of particular
interest in agriculture due to the existence of ecological thresholds which, when
reached, may result either in a limited yield or in irreversible reactions within the
living tissue. Histograms can be fitted to the most appropriate distribution function
and used to make statements about probabilities or risk of critical climate conditions
such as freezing temperatures or dry spells of more than a specified number of days.
Cumulative frequencies are particularly suitable and convenient for operational use in
agrometeorology. Cumulative distributions can be used to prepare tables or graphs
showing the frequencies of occasions when the values of certain parameters exceed
(or fall below) given threshold values during a selected period. If a sufficiently long
series of observations (10 to 20 years) is available, it can be assumed to be
representative of the total population, so that mean durations of the periods when the
values exceed (or fall below) specified thresholds can be deduced. When calculating
these mean frequencies, it is often an advantage to extract information regarding the
extreme values observed during the period chosen such as the growing season, growth
stage, or period of particular sensitivity. Some examples are:

(a) Threshold values of daily maximum and minimum temperatures, which can be
used to estimate the risk of excessive heat or frost and the duration of this risk;
(b) Threshold values of ten-day water deficits, taking into account the reserves in

22
 the soil. The quantity of water required for irrigation can then be estimated.
(c) Threshold values of relative humidities from hour or 3-hour observations.

6.3.4. The distribution of sequences of consecutive days

The distribution of sequences of consecutive days in which certain climatic
events occur is of special interest to the agriculturist. From such data, one can, for
example, deduce the likelihood of being able to undertake cultural operations
requiring specific weather conditions and lasting for several days (haymaking,
gathering grapes, etc.). The choice of protective measures to be taken against frost
or drought may likewise be based on an examination of their occurrence and the
distribution of the corresponding sequences. For whatever purpose, the sequences
are to be used, it is important to specify clearly the periods to which they refer (also
whether or not they are for overlapping periods). Markov chain probability models
have frequently been used to estimate the probability of sequences of consecutive
days such as wet days or dry days. Under many climate conditions, the probability,
for example, of a day being dry is significantly larger if the previous day is known to
have been dry. Knowledge of the persistence of weather events such as wet days or
dry days can be used to estimate the distribution of consecutive days using a Markov
chain. INSTAT includes algorithms to calculate Markov chain models, to simulate
spell lengths and estimate probability using climatological data.

6.4. Measuring Central Tendency

One descriptive aspect of statistical analysis is the measurement of what is called
central tendency, which gives an idea of the average or middle value about which all
measurements coming from the process will cluster. To this group belong the mean,
the median, and the mode. Their symbols are as listed below:

X – arithmetic mean of a sample

µ – population mean

X w – weighted mean

X h – harmonic mean
Me – median

Mo – mode

6.4.1. The Mean

While frequency distributions are undoubtedly useful for operational purposes,
mean values of the main climatic elements (10-day, monthly, or seasonal) may be
used broadly to compare climatic regions. To show how the climatic elements are
distributed, these mean values, however, should be supplemented by other descriptive
statistics such as the standard deviation, coefficient of variation (variability), quintiles,
and extreme values. In agroclimatology, series of observations which have not been
made simultaneously may have to be compared. To obtain comparable means in

23
such cases, adjustments are applied to the series so as to fill in any gaps (see WMO
Technical Note No. 81). Sivakumar, et al., (1993) illustrate the application of
INSTAT in calculating descriptive statistics for climate data and discuss the usefulness
of the statistics for assessing agricultural potential. They produce tables for
available stations of monthly mean, standard deviation, maximum and minimum for
rainfall amounts, and for the number of rainy days. Descriptive statistics are also
presented for maximum and minimum air temperatures.

(a) The arithmetic mean is the most used measure of central tendency, defined as:
1 n
X = ∑ xi
n i =1
i = 1,2,...n (1)

This means adding all data in a series and dividing their sum by the number of data.
The mean of the annual precipitation series from table 1 is:

X =
∑ x i = 69449 / 50 = 1388.9 mm (2)
n

The arithmetic mean may be computed using other labour saving methods such as the
grouped data technique (WMO-No.100), which estimates the mean from the average
of the products of class frequencies and their mid-points.

Another version of the mean is the weighted mean, which takes into account the
relative importance of each variate by assigning it a weight. An example of the
weighted mean is when making areal averages such as yields, population densities or
areal rainfall over non-uniform surfaces. The value for each sub-division of the area
is multiplied by the sub-division area, and then the sum of the products is divided by
the total area. The formula for the weighted mean is expressed as:

∑n x i i
Xw = i ≡1
k
. (3)
∑n i ≡1
i

For example, the average yield of maize for the five districts in Ruvuma Region of
Tanzania was respectively, 1.5 tons, 2.0 tons, 1.8 tons, 1.3 tons, and 1.9 tons per
hectare. The respective areas under maize were, 3000, 7000, 2000, 5000, and 4000.
Substituting n1=3000, n2=7000, n3=2000, n4=5000, and n5=4000 into equation (3),
we have the over-all mean yield of maize for these 21,000 hectares of land.

24
 3000(1.5) + 7000(2.0) + 2000(1.8) + 5000(1.3) + 4000(1.9) 33800
Xw = = = 1.6
3000 + 7000 + 2000 + 5000 + 4000 21000

In operational agrometeorology, the mean is normally computed for ten-days, known

as dekads, as well as for the day, month, year, and longer periods. This is used in
agrometeorological bulletins and for describing current weather conditions. At
agrometeorological stations where the maximum and the minimum temperatures are
read, a useful approximation to the daily mean temperature is given by taking the
average of these two temperatures. Such averages should be used with caution when
comparing data from different stations as such averages may differ systematically
from each other.

Another measure of the mean is the harmonic mean defined as n divided by the sum
of the reciprocals or multiplicative inverses of the numbers

n
Xh = n
1
∑ Xi
i =1

If five sprinklers can individually water a garden in 4 hours, 5 hours, 2 hours, 6 hours,
and 3 hours, respectively, the time required for all pipes working together to water the
garden is given by

1
t = Xh = 46 minutes and 45 seconds.
n

Means of long-term periods are known as normals. A normal is defined as a period

average computed for a uniform and relatively long period comprising of at least three
consecutive 10-year periods. A climatological standard normal is the average of
climatological data computed for consecutive periods of 30 years as follows: 1
January 1901 to 31 December 1930, 1 January 1931 to 31 December 1960, etc.

6.4.2. The mode

The mode is the most frequent value in any array. Some series have even more
than one modal value. Mean annual rainfall patterns in some sub-equatorial
countries have bi-modal distributions, meaning they exhibit two peaks. Unlike the
mean, the mode is an actual value in the series. Its use is mainly in describing the
average.

6.4.3. The median

The median is obtained by selecting the middle value in an odd-numbered series
of variates or taking the average of the two middle-values of an even-numbered series.

25
6.5. Fractiles
Fractiles such as quartiles, quintals, and deciles are obtained by first ranking the
data in ascending order and then counting an appropriate fraction of the integers in the
series (n+1). For quartiles, we divide n+1 by four, for deciles by ten, and for
1
percentiles by a hundred. Thus if n = 50 , the first decile is the [n + 1] th or the
10
7
5.1th observation in the ascending order, the 7th decile is the [n + 1] th in the rank
10
th
or the 35.7 observation. Interpolation is required between observations. The
median is the 50th percentile. It is also the fifth decile and the second quartile. It
lies in the third quintile. In agrometeorology, the first decile means that value below
which one-tenth of the data falls and above which 9-tenths lie.

6.6. Measuring Dispersion

Other parameters give information about the spread or dispersion of the
measurements about the average. These include the range, the variance, and the
standard deviation.

6.6.1. The Range

This is the difference between the largest and the smallest values. For instance,
the annual range of mean temperature is the difference between the mean daily
temperatures of the hottest and coldest months.

6.6.2. The Variance and the Standard Deviation

The variance is the mean of the squares of the deviations from the arithmetic
mean. The standard deviation S is the square root of the variance and is defined as
the root-mean-square of the deviations from the arithmetic mean. To obtain the
standard deviation of a given sample, the mean X is computed first and then the
deviations from the mean ( X i − X ) :

S=
∑ (x i − x)2
n −1

It has the same units as the mean; together they may be used to make precise
probability statements about the occurrence of certain values of a climatological series.
The influence of the actual magnitude of the mean can be easily eliminated by
expressing S as a percentage of the mean to get a dimensionless quantity called the
coefficient of variation:

s
Cv = × 100
x

26
For comparing values of s between different places, this can be used to provide a
measure of relative variability, for such elements as total precipitation.

6.6.3. Measuring Skewness

Yet, others tell us if the population tends to have values straggling out in a tail on
one side, a property known as skewness, or asymmetry, i.e., there is a good chance of
finding an observation a long way from the middle value on one side but not on the
other.

7. DECISION MAKING

7.1. Statistical Inference and Decision Making

Statistical inference is a process of inferring information about a population from

the data of samples drawn from it. The purpose of statistical inference is to help a
decision-maker to be right more often than not or at least to give some idea of how
much danger there is of being wrong when a particular decision is made. It is also
meant to ensure that long-term costs through wrong decisions are kept to the
minimum.

Two main lines of attacking the problem of statistical inference are available. One is
to devise sample statistics which may be regarded as being suitable estimators of
corresponding population parameters. For example, we may use the sample mean
X as an estimator of the population mean µ , or else we may use the sample
median Me . Statistical estimation theory deals with the issue of selecting best
estimators.

The steps to be taken to arrive at a decision are as follows:

Step 1. Formulate the null and alternative hypotheses,

Once the null hypothesis has been clearly defined, we may calculate what kind of
samples to expect under the supposition that it is true. Then if we draw a random
sample, and if it differs markedly in some respect from what we expect, we say that
the observed difference is significant; and we are inclined to reject the null hypothesis
and accept the alternative hypothesis. If the difference observed is not too large, we
might accept the null hypothesis; or we might call for more statistical data before
coming to a decision. We can make the decision in a hypothesis test depending upon
a random variable known as a test statistic, such as z-score used in finding confidence
intervals, and we can specify critical values of this, which can be used to indicate not
only whether a sample difference is significant but also the strength of the
significance.

For instance in a coin experiment to determine if the coin is fair or loaded:

27
 Null Ho: p=0.5 (i.e. the coin is fair)

And alternative H1: p#0.5 (i.e. the coin is biased)

(Or equivalently H1: p<0.5 or p>0.5; this is called a two-sided alternative).

Step 2. Choose an appropriate level of significance

We call the probability of wrongly rejecting a null hypothesis the level of significance
( α ) of the test. We select the value for α first, before carrying out any
experiments; the values most commonly used by statisticians are 0.05, 0.01, and 0.001.
The level of significance α =0.5 means that our test procedure has only 5 chances in
100 of leading us to decide that the coin is biased if in fact it is not.

Step 3. Choose the sample size n.

It is fairly clear that if bias exists, a large sample will have more chance of
demonstrating its existence than a small one. And so, we should make n as large as
possible, especially if we are concerned with demonstrating a small amount of bias.
Cost of experimentation, time involved in sampling, necessity of maintaining
statistically constant conditions, amount of inherent random variation, and possible
consequences of making wrong decisions are among the considerations on which the
sizes of sample to be drawn depend.

Step 4. Decide upon the test statistic to be used.

We can make the decision in a hypothesis test depending upon a random variable
known as a test statistic such as z or t as used in finding confidence intervals. Its
sampling distribution, under the assumption that Ho is true, must be known. It can
be normal, binomial, or other sampling distributions.

Step 5. Calculate the acceptance and rejection regions

Assuming that the null hypothesis is true, and bearing in mind the chosen values of n
and alpha, we now calculate an acceptance region of values for the test statistic.
Values outside this region form the rejection region. The acceptance region is so
chosen that if a value of the test statistic, obtained from the data of a sample, fails to
fall inside it, then the assumption that Ho is true must be strongly doubted. In
general, we have a test statistic X, whose sampling distribution, defined by certain
parameters such as η and σ , is known. The values of the parameters are specified

in the null hypothesis Ho. From integral tables of the sampling distribution we
obtain critical values X1, X2 such that

P[X1<X<X2] =1- α .

28
These determine an acceptance region, which gives a test for the null hypothesis at the
appropriate level of significance ( α ).

Step 6. Formulate the decision rule.

The general decision rule, or test of hypothesis, may now be stated as follows:

(a) Reject Ho at the α significance if the sample value of X lies in the

rejection region (i.e. outside [X1, X2]). This is equivalent to saying that the observed
sample value is significant at the 100 α % level.

The alternative hypothesis H1 is then to be accepted.

(b) Accept Ho if the sample value of X lies in the acceptance region [X1, X2].
(Sometimes, especially if the sample size is small, or if X is close to one of the critical
values X1 and X2, the decision to accept Ho is deferred until more data is collected.)

Step 7. Carry out the experiment and make the test

The n trials of the experiment may now be carried out, and from the results, the value
of the chosen test statistic may be calculated. The decision rule described in Step 6
may then be applied. Note: All statistical test procedures should be carefully
formulated before experiments are carried out. The test statistic, the level of
significance, and whether a one-or two-tailed test is required, must be decided before
any sample data is looked at. To switch tests in mid-stream, as it were, leads to
invalid probability statements about the decisions made.

7.2. Two-Tailed and One-Tailed Test

If the critical region occupies both extremes of the test distribution, it is called a
two-tailed test. If the critical region occurs only at high or low values of the test
statistic, such a test is called one-tailed.

This leads to a two-tailed test. The critical region containing 5% of the area of the
normal distribution is split into two equal parts, each containing 2.5% of the total area.
If the computed value of Z falls into the left-hand region, the sample came from a
population having a smaller mean than our known population. Conversely, if it falls
into the right-hand region, the mean of the sample’s parent population is larger than
the mean of the known population. From the standardized normal distribution table
(Table.), we find that approximately 2.5% of the area of the curve is to the left of a Z
value of -1.9 and 97.5% of the area of the curve is to the left of +1.9.

Once the null hypothesis has been clearly defined, we may calculate what kind of
samples to expect under the supposition that it is true. Then, if we draw a random
sample, and if it differs markedly in some respect from what we expect, we say that

29
the observed difference is significant; and we are inclined to reject the null hypothesis
and accept the alternative hypothesis. If the difference observed is not too large, we
might accept the null hypothesis; or we might call for more statistical data before
coming to a decision. We can make the decision in a hypothesis test depending upon
a random variable known as a test statistic such as z or t as used in finding confidence
intervals, and we can specify critical values of this which can be used to indicate not
only whether a sample difference is significant but also the strength of the
significance.

7.3. Point Estimation

The two population characteristics µ and σ are called parameters of the

population, while each of the sample characteristics such as sample mean, X and
sample standard deviation S is called a sample statistic.

A sample statistic used to provide an estimate of a corresponding population

parameter is called a point estimator. For example, X may be used as an estimator

of µ , Me may be used as an estimator of µ , S2 may be used as an estimator of the

population variance σ2.

Any one of the statistics mean, median, mode, and mid-interquartile range would
seem to be suitable for use as estimators of the population mean µ . In order to pick

out the best estimator of a parameter out of a set of estimators, three important
desirable properties should be considered. These are unbiasedness, efficiency, and
consistency.

7.4. Interval Estimation

Confidence interval estimation is a technique of calculating intervals for
population parameters and measures of confidence placed upon them. If we have
chosen an unbiased sample statistic b as our point estimator of β , the estimator will

have a sampling distribution, with mean E(b) = β and standard deviation S.D.(b) =

σ b . Here the parameter β is the unknown and our purpose is to estimate it. Using
the remarkable fact that many sample statistics we use in practice have a Normal or
approximately Normal sampling distribution, we can obtain from the tables of the
Normal integral, the probability that a particular sample will provide a value of b
within a given interval ( β – d) to ( β + d).

30
This is indicated in the diagram below. Conversely, for a given amount of
probability, we can deduce the value d. For example, for 0.95 probability, we know
d
from standard Normal tables that = 1.96 . In other words, the probability that a
σb

sample will provide a value of b in the interval [ β -1.96 σ b , β +1.96 σ b ] is 0.95.

We write this as P[ β -1.96 σ b <=b<= β =1.96 σ b ]=0.95. After rearranging the

inequalities inside the brackets to the equivalent form [b − 1.96σ b ≤ b ≤ β + 1.96σ b ],

we get the 95% confidence interval for β , namely the interval [b-1.96 σ b , b+1.96 σ b ].

In general, we express confidence intervals in the form [b-z. σ b , b+z. σ b ], where z,

the z-score, is the number obtained from tables of the sampling distribution of b.
This z-score is chosen so that the desired percentage confidence may be assigned to
the interval; it is now called the confidence coefficient, or sometimes the critical value.
The end points of a confidence interval are known as the lower and upper confidence
limits. The probable error of estimate is half the interval length of the 50%
confidence interval, i.e., 0.674 σ .

The most commonly required point and interval estimates are for means, proportions,
differences between two means, and standard deviations. The following table gives
all the formulae needed for these estimates. The reader should note the standard
form of b ± z . σ b for each of the confidence interval estimators.

For the formulae to be valid, sampling must be random and the samples must be
independent. In some cases, σ b will be known from prior information. Then, the

sample estimator will not be used. In each of the confidence interval formulae, the
confidence coefficient z may be found from tables of the Normal integral for any
desired degree of confidence. This will give exact results if the population from
which the sampling is done are Normal; otherwise, the errors introduced will be small
if n is reasonably large ( n ≥ 30 ). A brief table of values of z is as follows:
31
Confidence 50% 60% 80% 86.8% 90% 92% 93.4% 94.2 95% 95.6% 96% 97.4% 98%
level
Confidence 0.674 0.84 1.28 1.50 1.645 1.75 1.84 1.90 1.96 2.01 2.05 2.23 2.33
coefficient z

What should we do when samples are small? It is clear that the smaller the sample,
the smaller amount of confidence we can place on a particular interval estimate.
Alternatively, for a given degree of confidence, the interval quoted must be wider than
for larger samples. To bring this about, we must have a confidence coefficient which
depends upon n. We shall use the letter t for this coefficient, and give confidence
interval formulae for the population mean µ , and for the difference of two population

means.

Confidence interval Degrees of

freedom ( υ )
n-1
Mean µ : x ± t.σ x
n1 + n2-2
Difference µ1 − µ 2 :
( x1 − x 2 ) ± t.σ ( x1 − x 2 )

The reader will note that these are the same as for large samples, except that t replaces
z. When the sample estimators for σ x and σ are used, the correct values
x1 − x 2
for t are obtained from what is called the Student t-distribution. For convenience,
they are related not directly to sample sizes, but to a number known as ‘degrees of
freedom’; we shall denote this by υ .

An abbreviated table of t-values is given below

υ: 3 4 5 7 9 10 15 20 25 30
90% 2.35 2.13 2.02 1.89 1.83 1.81 1.75 1.72 1.71 1.70
95% 3.18 2.78 2.57 2.36 2.26 2.23 2.13 2.09 2.06 2.04
99% 5.84 4.60 4.03 3.50 3.25 3.17 2.95 2.85 2.79 2.75
Degrees of freedom υ

7.5. The Z-Test.

The nature of the standard normal distribution allows us to test hypotheses about the
origin of certain samples. The test statistic, Z, has a normal frequency distribution
which is a standardized normal distribution defined as

32
 X − µ0
Z=
σ n

The observations in the sample were selected randomly from a normal population
whose variance is known.

7.6. Tests for Normal Population Means

A random sample size n is drawn from a Normal population having unknown mean
µ and known Standard Deviation σ . The objective is to test the hypothesis

Ho: µ = µ ′ ; i.e., the assumption that the population mean has value µ ′ .

X − µ′
The variate Z = has a standard Normal distribution if Ho is true. We may
σ n
use Z (or X ) as the test statistic.

Example 2.

Suppose the shelf life of 1 liter bottles of pasteurized milk is guaranteed to be at least
400 days, with standard deviation 60 days. If a sample of 25 bottles is randomly
chosen from a production batch, and after testing the sample mean shelf life of 375 is
calculated, should the batch be rejected as not meeting the guarantee?

Solution: Let µ be the batch mean.

Step 1. Null hypothesis Ho: η = 400 .

Alternative hypothesis H1: η < 400 (one-sided: we are only interested in whether or

not the mean is up to the guaranteed minimum value).

Steps 2 and 3, n=25(given); choose α = 0.05 .

Step 4. If X is the sample mean, the quantity

X − 400
Z=
60 25

is a standard normal variate (perhaps approximately) if Ho is true. We shall use Z as

33
 the test statistic.

Step 5. For a one-tailed test, standard normal tables give Z = -1.65 as the lowest value
to be allowed before Ho must be rejected, at the 5% significance level. The
acceptance region is therefore [-1.65, infinite].

Step 6. Decision rule:

(a) Reject the production batch if the value of z calculated from the sample is less
than -1.65.
(b) Accept the batch otherwise.

Step 7. Carry out the test:

From the sample data we find that

375 − 400
Z= = −2.083 .
60 25

Decision: the production batch must be rejected, since -2.083<-1.65. It is highly

unlikely that the mean shelf life of milk bottles in the batch will be 400 days or more.
The chance that this decision is wrong is smaller than 5%.

Example 2.

A sample of 66 seeds of a certain plant variety were planted on a plot using a

randomized block design. Before planting, 30 of the seeds were subjected to a
certain heat treatment. The times from planting to germination were observed. The
30 treated seeds took 52 days to germinate, while the 36 untreated seeds took 47 days.
If the common standard deviation to germination, for individual seeds, calculated
from several thousand seeds, may be taken as 12 days, can it be said that the heat
treatment significantly speeds up a seed’s germination rate?

From the data given, it is clear that the heat treated seeds had an earlier start in growth.
However, we may consider the wider question as to whether heat treated seeds are
significantly faster germinating generally than untreated seeds.

The test is as follows:

Step 1. Let µ A, µ B be the germination period population means for heat treated and

untreated seeds respectively.

Null hypothesis Ho: µ A = µ B (i.e. µ A − µ B = 0 )

34
Alternative hypothesis H1: µ A > µ B .

We were asked specifically whether the heat treated seeds were faster germinating
than the untreated seeds, so we use the one-sided alternative hypothesis.

Step 2 and 3, nA=30 and nB=36 (given).

We shall use α = 0.05 as the significant level.

Step 4. Test statistic:

We are not given any information other than the two sample means. Even if we were
told the individual students’ results, we could not use the paired comparison test –
there would be no possible reason for linking the results in pairs.

The difference in means ( x A − x B ) is approximately normally distributed, with mean

( µ A − µ B ) and standard deviation

σ2 σ2
σ′= +
nA nB

So we may use as test statistic the standard Normal variate

( x A − x B ) − (µ A − µ B )
z= .
σ′

And if Ho is true, µ A − µ B = 0 ; so the test statistic reduces to

x A − xB
z= .
σ′

Step 5. Acceptance region:

The critical value of z at 5% level of significance, for a one-tailed test, is 1.65.

Therefore, the acceptance region for the null hypothesis is the set of values z less than
or equal to 1.65.

Step 6. Decision rule:

35
(a) If the sample value of z>1.65, conclude that heat treated seeds germinate
significantly earlier (at the 5% level) than untreated seeds.
(b) If Z ≤ 1.65 , the germination rates of both heat treated and untreated seeds
may well be the same.

Step 7. Carry out the test.

The value of σ is given as 12.

1 1 1 1
Therefore σ ′ = σ + ) = 12 + ≅ 2.96
n A nB 30 36

And so the sample value of the test statistic is

x A − x B 52 − 47
z= = ≅ 1.69.
σ′ 2.96

Decision: The heat treated seed is just significantly earlier germinating at the 5% level
than the untreated seed.

7.7. The T-Test

The uncertainty introduced into estimates based on samples can be accounted for by
using a probability distribution which has a wider spread than the normal distribution.
One such distribution is the t-distribution, which is similar to the normal distribution,
but dependent on the size of sample taken. When the number of observations in the
sample is infinite, the t-distribution and the normal distribution are identical. Tables
of the t-distribution and other sample based distributions are used in exactly the same
manner as tables of the cumulative standard normal distribution, except that two
entries are necessary to find a probability in the table. The two entries are the
desired level of significance ( α ) and the degrees of freedom ( υ ) defined as the
number of observations in the sample minus the number of parameters estimated from
the sample.

X − µ0
Then for the test statistic we use t = which has Student-t distribution with
S n
n-1 degrees of freedom.

Examples using the Z-Test

Example 1

A farmer was found to be selling pumpkins that looked like ordinary pumpkins except
36
that these were very large, the average diameter for ten samples being 30.0 cm. The
mean and standard deviation of pumpkins is 14.2 cm and 4.7 cm, respectively. It is
intended to test whether the pumpkins that the farmer is selling are ordinary
pumpkins.

We hypothesize that the mean of the population from which the farmer’s pumpkins
was taken is the same as the mean of the ordinary pumpkins by the null hypothesis

H 0 : µ1 = µ 0

We also must give an alternative hypothesis

H 0 : µ1 ≠ µ 0 stating that the mean of the population from which the sample was

drawn does not equal the specified population mean. If the two parent populations
are not the same, we must conclude that the pumpkins that the farmer was selling
were not drawn from the ordinary pumpkin population, but from the population of
some other genus. We need to specify levels of probability of correctness, or level of
significance, denoted by α . Let us take a probability level of 5%; we are willing to
risk rejecting the hypothesis when it is correct 5 times out of 100 trials. We must
have the variance of the population against which we are checking. We may now set
up a formal statistical test in the following manner:

1. The hypothesis and alternative:

H 0 : µ1 = µ 0

H 0 : µ1 ≠ µ 0

2. The level of significance:

α = 0.05

3. The test statistic:

X − µ0
Z=
σ n

The test statistic, Z, has a frequency distribution which is a standardized normal

distribution, provided the observations in the sample were selected randomly from a
normal population whose variance is known. If we have specified that we are
willing to reject the hypothesis of the equality of means when they actually are equal
37
one time out of twenty: that is, we will accept a 5% risk of being wrong. On the
standardized normal distribution curve, therefore, we wish to determine the extreme
regions which contain 5% of the area of the curve. This part of the probability curve
is called the area of rejection or the critical region. If the computed value of the test
statistic falls into this area, we will reject the null hypothesis. The hypothesis will be
rejected if the test statistic is either too large or too small. T he critical region,
therefore, occupies the extremities of the probability distribution and each sub region
contains 2.5% of the total area of the curve.

Working through the grass example, the outline takes the following form:

1. H 0 : µ of grass = 14.2 mm

H 1 : µ of grass ≠ 14.2 mm

2. α level = 0.05

30 − 14.2
3. Z = = 10.6
4 .7 10

The computed test value of 10.6 exceeds 1.9, so we conclude that the means of the
two populations are not equal, and the grass must represent some genus other than
that of ordinary pumpkins.

7.8. Estimators Using Pooled Samples

Let two random samples of sizes n1, n2, respectively, be drawn from a large
population which has mean µ and variance σ2. Suppose that the samples yield

unbiased estimates, X 1 and X 2 of µ and S12 , S 22 of σ2. The problem arises of

combining these pairs of estimates, to obtain single unbiased estimates of µ and σ2.

The process of combining estimates from two or more samples is known as pooling.
The correct ways to pool unbiased estimates of means and variances, to yield single
unbiased estimates, are

n1 x1 + n 2 x 2
Means: µˆ =
n1 + n 2

38
 2 2
(n1 − 1) s1 + (n2 − 1) s 2
Variances: σ2 =
n1 + n2 − 2

Example:

A soil scientist made six determinations of the strength of dilute sulfuric acid. His
results showed a mean strength of 9.234 with standard deviation 0.12. Using acid
from another bottle, he made eleven determinations, which showed mean strength
8.86 with standard deviation 0.21. Obtain 95% confidence limits for the difference
in mean strengths of the acids in the two bottles. Could the bottles have been filled
from the same source?

Working:

The difference in mean strengths of the acids is estimated by x1 − x 2 =

9.234-8.86=0.374.

We first estimate σ ( x1 − x 2 ) , the standard deviation of the sampling distribution of

x1 − x 2 . We pool the data from the two samples, thus:

1 1 1 1
σˆ x1− x 2 = s. + = s. + ,
n1 n2 6 11

2 2
(n − 1) s1 + (n2 − 1) s 2
where s = 1 2

n1 + n2 − 2

5 x0.0144 + 10 x0.0441 0.72 + 0.441 1.161

= = = = 0.0774
6 + 11 − 2 15 15

and so s = 0.2782

Therefore

17
σˆ x1− x 2 = 0.2782 = 0.141196
66

With 15 degrees of freedom, the confidence coefficient is t=2.13 for 95% confidence.
Therefore the required limits for µ1 − µ 2 are

39
 ( x1 − x 2 ) ± t.σˆ X 1− X 2 = 0.374 ± 2.13x0.141196 = 0.374 ± 0.300748

Thus, the 95% confidence limits for the difference in mean strengths of the acids in
the two bottles are 0.0733 and 0.6747. That means that we are 95% confident that
the difference in mean strengths of the acids in the two bottles lies between 0.0733
and 0.6747.

7.9. The Paired Comparison Test and The Difference Between Two Means Test

Example: Paired comparison Test.

The yields from two varieties of wheat were compared. The wheat was planted on
25 test plots. Each plot was divided into two equal parts, one part was chosen
randomly and planted with the first variety, and the other part was planted with the
second variety of wheat. This process was repeated for all the 25 plots. When the
crop yields were measured, the difference in yields from each plot was recorded (2nd
variety minus first variety). The sample mean plot yield difference was found to be
3.5 ton/ha, and the variance of these differences was calculated to be 16 ton/ha.

(a) Does the 2nd variety produce significantly higher yields than the 1st variety?
(b) Test the hypothesis that the population mean plot yield difference is as high as
5 tons/ha.
(c) Obtain 95% confidence limits for the population mean plot yield difference.

It is clear that there is a good deal of variation in yields from plot to plot. This
variation tends to confound the main issue, which is to determine whether yields are
increased by using a second variety. This has been eliminated by considering only
the change in yields for each plot. If the second variety has no effect, the average
change will be zero.

These kind of data, where results are combined in pairs, each pair arising from one
experimental unit or having some clear reason for being linked in this way, are
analyzed by the paired comparison test. Each pair provides a single comparison as a
measure of the effect of the treatment applied (e.g., growing a different variety). Let
D denote the difference in a given pair of results. D will have Normal distribution
with mean µ and Standard Deviation σ (both the parameters are unknown in this

case).

Example using the t-Test

Step 1.
Null hypothesis Ho: µ = 0 ( i.e. the yield of the two wheat varieties are the same).

40
 Alternative hypothesis H1: µ > 0 i.e. Second variety yields are higher than first

variety yields).

H1 is one sided; we must apply a one-tailed test.

Steps 2 and 3. 25 plots were used, so n=25. We shall use α =0.05

D−0
Step 4. The quantity z = is a standard normal variate, and may be used as the
σ 25
test statistic if σ is known from previous experimentation.

The parameters of a population are rarely known. In our case, σ is not given, so
we must estimate it from the sample data.

Step 5. Acceptance region: the critical level of t at the 0.05 level of significance (one-
tailed test) is the same as the upper 90% confidence coefficient. As given in Table.
With 24 degrees of freedom, this value is 1.71. The acceptance region is, therefore,
all values of t from –infinity to 1.171.

Step 6. Decision rule:

(a) If the value of t calculated from the sample is greater than 1.71, we may
conclude that the second wheat variety gives higher yields than the first variety.
(b) If the value of t<1.71, we may not reject (at the 5% level) the hypothesis that
the observed increases in yield in the second wheat variety were due to chance
variation in the experiment.

Step 7. Carry out the test:

D−0 3.5 − 0
From the sample data t = = = 2.375
S n 6 2

Decision: since 2.375>1.71, we conclude at the 5% level that the second variety
significantly produces higher yields than the 1st variety.

7.10. The Difference Between Two Means

A sampling result, which is frequently used in inference tests, is one concerning the
distribution of the difference in means of independent samples drawn from two
different populations. Let a random sample of size n1 be drawn from a population
having mean µ1 and Standard Deviation σ X ; and let an independent sample of size

41
n2 be drawn from another population having mean µY and Standard Deviation σ Y .

Consider the random variable D = X − Y ; i.e., the difference in means of the two
samples. The theorem states that

D has a sampling distribution with mean µ D = µ X − µ Y and variance,

σX 2
σX2
Var (D) = +
n1 n1

7.11. The F-Test

It seems reasonable that the sample variances will range more from trial to trial if the
number of observations used in their calculation is small. Therefore, the shape of the
F-distribution would be expected to change with changes in sample size. The
degrees of freedom idea comes to mind, except in this situation the F-distribution is
dependant on two values of γ, one associated with each variance in the ratio. Since
the F- ratio is the ratio of two positive numbers, the F- distribution cannot be negative.
If the samples are large, the average of the ratios should be close to 1.0.

Because the F-distribution describes the probabilities of obtaining specified ratios of

sample variances drawn from the same population, it can be used to test the equality
of variances, which we obtain in statistical sampling.

We may hypothesize that two samples are drawn from populations having equal
variances. After computing the F- ratio, we then can ascertain the probability of
obtaining, by chance, that specific value from two samples from one normal
population.

If it is unlikely that such a ratio could be obtained, we regard this as indicating that the
samples come from different populations having different variances.

S1 S
For any pair of variances, two ratios can be computed ( and 2 ).
S2 S1

If we arbitrarily decide that the larger variance will always be placed in the numerator,
the ratio will always be greater than 1.0 and the statistical tests can be simplified.
Only one-tailed tests need be utilized, and the alternative hypothesis actually is a
statement that the absolute difference between the two sample variances is greater
than expected if the population variances are equal. This is shown in figure XX, a
typical F-distribution curve in which the critical region or area of rejection has been
shaded.

42
A typical F-distribution γ 1 = 10 and γ 2 = 25 degrees of freedom, with critical
region (shown by shading), which contains 5% of the area under the curve. Critical
value of F=2.24.

As an example of an elementary application of the F-distribution, consider a

comparison between the two sample sets of porosity measurements on soils of two
areas of a certain district. We are interested in determining if the variation in
porosity is the same in the two areas. For our purposes, we will be content with a
level of significance of 5%. That is, we are willing to run the risk of concluding that
the porosities are different when actually they are the same one time out of every
twenty trials.

The variances of the two samples may be computed by (3.8), when the F-ratio
between the two may be calculated by

2
S1
F= 2
S2

2 2
where S1 is the larger variance and S 2 is the smaller. We now are testing the
hypothesis

H 0 : σ 21 = σ 2 2

against H 1 : σ 21 ≠ σ 2 2

The null hypothesis states that the parent populations of the two samples have equal
variances: the alternative hypothesis states that they do not. Degrees of freedom
associated with this test are ( n1 − 1 ) for γ 1 and ( n 2 − 1 ) for γ 2 . The critical value

43
of F with γ 1 = 9 and γ 1 = 9 degree of freedom and a level of significance of 5%

(α=0.05) can be found from Table X; that value is 3.18.

The value of F calculated from (3.26) will fall into one of the two areas shown
on Fig 3.23. If the calculated value of F exceeds 3.18, the null hypothesis is rejected
and we conclude that the variation in porosity is not the same in the two groups. If
the calculated value is less than 3.18, we would have no evidence for concluding that
the variances are different (determine at α 0.05 if variances are the same).

In most practical situations, we ordinarily have no knowledge of the parameters of the

population except for estimates made from samples. In comparing two samples, it is
appropriate to first determine if their variances are statistically equivalent. If they
appear to be equal and the samples have been selected without bias from a naturally
occurring population, you probably are safe in proceeding to additional statistical
tests.

The next step in the procedure is to test equality of means. The appropriate test is
(3.23)

x1 − x 2
t=
Sp ( n11 ) + ( n12 )

where the quantity Sp, is the pooled estimate of the population standard deviation,
based on both samples. The estimate is found from the pooled estimated variance,
given by

(n1 − 1) S 21 + (n2 − 1) S 2 2
Sp =
2

n1 + n2 − 2

where the subscripts refer, respectively, to the sample from area A and area B of the
district.

7.12. RELATIONSHIP BETWEEN VARIABLES

7.12.1. Correlation methods

Correlation methods are used to discover objectively and quantitatively the

relationship that may exist between several variables. The correlation coefficient
determines the extent to which values of two variables are linearly related; that is, the
correlation is high if it can be approximated by a straight line (sloped upwards or
downwards). This line is called the regression line. Correlation analysis is
especially valuable in agrometeorology, because of the many factors that may be
involved, simultaneously or successively, during the development of a crop and also

44
 because for many of them--climatic factors in particular--it is impossible to design
accurate experiments, since their occurrence cannot be controlled. There are two
sets of circumstances in which, more particularly, the correlation and simple
regression method can be used:

(a) In completing climatological series having gaps. Comparisons of data for different
atmospheric elements (e.g. precipitation, evapotranspiration, duration of sunshine)
allow estimates of the missing data to be made from the other measured elements;

(b) In comparing climatological data and biological or agronomical data, e.g., yields,
quality of crops (sugar content, weight of dry matter, etc.).

Care should be exercised in interpreting these correlations. Graphs and scatter

plots should be used to give much more information about the nature of the
relationship between variables. The discovery of a significant correlation coefficient
should encourage the agrometeorologist, in most cases, to seek a physical or
biological explanation for the relationship and not just be content with the statistical
result.

Having discovered that there is a relationship between variables, one hopes to

establish the closeness of this relationship. This closeness of agreement between
two or more variables is called correlation. The closeness is expressed by a
correlation coefficient whose value lies between +1 (perfect, positive correlation) and
-1 (perfect, negative correlation). It is used to measure the linear relationship
between two random variables that are represented by pairs of numerical values.
The most commonly used formula is:

n∑ XiYi − (∑ Xi )(∑ Yi)

r=
[[n∑ Xi 2 − (∑ Xi ) 2 ][n∑ Yi 2 − (∑ Yi) 2 ]]

If the number of pairs is small, the sample correlation coefficient between the two
series is subject to large random errors, and in these cases numerically large
coefficients may not be significant.

The statistical significance of the correlation may be determined by seeing whether

the sample correlation r is significantly different from zero. The test statistic is

n−2
t=r
1− r2

and t is compared to the tabulated value of Student’s t with n-2 degrees of freedom.

7.12.2. Regression

After the strength of the relationship between two or more variables has been

45
quantified, the next logical step is to find out how to predict specific values of one
variable in terms of another. This is done by regression models. A single linear
regression model is of the form:

Y = a + bX

where Y is the dependant variable;

X is the independent variable;

a is the intercept on the Y axis;

b is the slope of the regression.

The least squares criterion requires that the line be chosen to fit the data so that the
sum of the squares of the vertical deviations separating the points from the line will be
a minimum.

The recommended formulae for estimating the two sample coefficients for least
squares are:

n∑ XiYi − (∑ Xi )(∑ Yi)

b= the slope of the line
n∑ Xi 2 − (∑ Xi ) 2

(∑ Yi)(∑ Xi 2 ) − (∑ Xi )(∑ XiYi)

a= the y-axis intercept
n∑ Xi 2 − (∑ Xi ) 2

Example

Compute a and b coefficients of the Angstrom formula

Angstrom’s formula:

R/RA = a + b n/N

is used to estimate the global radiation at surface level (R) from the radiation at the
upper limit of the atmosphere (RA), the actual hours of bright sunshine (n), and the
day length (N). RA and N are taken from appropriate tables or computed; n is an
observational value obtained from the Campbell–stokes sunshine recorder.

The data of the example are normals from Lyamungu, Tanzania

46
(latitude 3˚ 14S, longitude 27˚ 17E, elevation 1250m)

Table ΙΙ. 2.1

n/N R/RA
(X) (Y)

J .660 .620
F .647 .578
M .536 .504
A .366 .395
M .251 .368
J .319 .399
J .310 .395
A .409 .442
S .448 .515
O .542 .537
N .514 .503
D . 602 .582

N=12, X = •467, σx = •132, Y = .487, σy = .081, b = .603, a = .205, r

= .973

The regression explains r² = 95% of the variance of R/RA, and is significantly below
p =0.01.

There are cases where a scatter diagram suggests that the relationship between
variables is not linear. This can be turned into a linear regression by taking the
logarithms of the relationship if it is exponential or turning it into a reciprocal, if it is
square, etc. For example, when the saturation vapour pressure is plotted against
temperature, the curve suggests that a function like y = p.e bX could probably be
used to describe the function. This is turned into a linear regression ln
(y)=ln( p)+bX, where X is temperature function and y is the saturation vapour
pressure. An expression of the form y = aX 2 can be turned into a linear form by

1 X −2
taking the reciprocal = .
y a

47
 2000

1500 Actual yields

Tons/ha 1000
Linear trend
500 line

0
y = 35.263x + 979.04
19 1
19 4
19 7
19 0
19 3
19 6
79
6
6
6
7
7
7
19

R2 = 0.5344

7.12.3. Multiple regressions

The general purpose of multiple regression is to learn more about the
relationship between several independent or predictor variables and a dependent
variable. A linear combination of predictor factors is used to predict the outcomes or
response factor. For example, multiple regression has been successfully used to
estimate crop yield as a function of weather, or to estimate soil temperatures as a
function of air temperature, soil characteristic, and soil cover. It has been used to
perform a trend analysis of agrometeorological parameter using a polynomial
expansion of time. The general linear model is a generalization of the linear
regression model, such that effects can be tested for categorical predictor variables, as
well as for effects for continuous predictor variables. An objective in performing
multiple regression analysis is to specify a parsimonious model whose factors
contribute significantly to variation in response. Statistical software such as INSTAT,
provide tools to select independent factors for a regression model. They include
forward stepwise regression to individually add or delete the independent variables
from the model at each step of the regression until the "best" regression model is
obtained, or backward stepwise regression to remove the independent variables from
the regression equation one at a time until the "best" regression model is obtained. It
is generally recommended that one should have at least ten times as many observation
or cases as one has variables in a regression model.

Residual analysis is recommended as a tool to assess the multiple regression

models and to identify violations of assumptions that threaten the validity of results.
Residuals are the deviations of the observed values on the dependent variable from
the predicted values. Most statistical software provides extensive residuals analyses,
allowing one to use a variety of diagnostic tools in inspecting different residual and
predicted values, and thus, to examine the adequacy of the prediction model, the need
for transformations of the variables in the model, and the existence of outliers in the
data. Outliers (i.e., extreme cases) can seriously bias the results.

7.12.4. Stepwise Regression

This will be explained by using an example for yields. A combination of
variables may work together to produce the final yield. These variables could be the
annual precipitation, the temperature of a certain month, the precipitation of a certain

48
month, the potential evapotranspiration of a certain month, or the difference between
precipitation and potential evapotranspiration for a given month.

In stepwise regression, a simple linear regression for the yield is constructed on each
of the variables and their coefficients of determination found. The variable that
produces the largest r2 statistic is selected. Additional variables are then brought in
one by one and subjected to a multivariate regression with the best variable to see
how much that variable would contribute to the model if it were to be included. This
is done by calculating the F statistic for each variable. The variable with the largest
F statistic, that has a significance probability greater than the specified significance
level for entry, is included in the multivariate regression model. Other variables are
included in the model one by one. If the partial F statistic of a variable is not
significant at a specified level for staying in the regression model, it is left out. Only
those variables that have produced significant F statistics are included in the
regression. More deeply explanations could be found in Draper and Smith (1981).

7.12.5. Cluster Analysis

Cluster analysis is a technique for grouping individuals or objects into unknown
groups. In biology, cluster analysis has been used for taxonomy, to classify living
things into arbitrary groups on the basis of their characteristics. In agrometeorology,
cluster analysis can be used to analyze historical records of the spatial and temporal
variations in pest populations in order to classify regions based on population
densities and frequency and persistence of outbreaks. The analysis can be used to
improve regional monitoring and control of pest’s populations.

Clustering techniques require defining a measure of closeness or similarity of

two observations. Clustering algorithms may be hierarchical or nonhierarchical.
Hierarchical methods can be either agglomerative or divisive. Agglomerative
methods begin by assuming each observation is a cluster and then, through successive
steps, combine the closest clusters. Divisive methods begin with one cluster
containing all the observations and successively split off cases that are the most
dissimilar to the remaining ones. K-mean clustering is a popular nonhierarchical
clustering technique. It begins with user-specified clusters and then reassigns data
based on the distance from the centroid of each cluster. See von Storch and Zwiers
(2001) for more detailed explanations.

7.12.6. Classification trees

The goal of classification trees is to predict or explain responses on a categorical
dependent variable. They have much in common with discriminate analysis, cluster
analysis, nonparametric statistics, and nonlinear estimation. They are one of the
main techniques used in data mining. The ability of classification trees to perform
univariate splits, examining the effects of predictors one at a time contributes to their
flexibility. Classification trees can be computed for categorical predictors,
continuous predictors, or any mix of the two types of predictors when univariate splits
are used. They readily lend themselves to graphical display, which makes them
easier to interpret. Classification trees are used in medicine for diagnosis and
biology for classification. They have been used to predict levels of winter survival
of over wintering crops using weather, and categorical variables related to topography

49
and crop cultivars.

7.13 CLIMATIC PERIODICITIES AND TIME SERIES

Data are commonly collected as time series that is observations made on the same
variable at repeated points in time. INSTAT provides facilities for descriptive
analysis and display of such data. The goals of time series analysis include
identifying the nature of the phenomenon represented by the sequence of observations
and predicting future values of the times series. Moving averages are frequently
used to 'smooth' a time series so that trends and other patterns are seen more easily.
Sivakumar, et al., (1993) present a number of graphs showing the five-year moving
averages of monthly and annual rainfall at selected sites in Niger. Most time series
can be described in terms of trend and seasonality. When trends, seasonal or other
deterministic patterns, have been identified and removed from a series, the interest
focuses on the random component. Standard techniques can be used to look at its
distribution. The feature of special interest, resulting from the time series nature of
the data, is the extent to which consecutive observations are related. A useful
summary is provided by the sample autocorrelations at various lags, the
autocorrelation at lag m being the correlation between observations m time units apart.
In simple applications this is probably most useful for determining whether the
assumption of independence of successive observations used in many elementary
analyses is valid. The autocorrelations also give an indication of whether more
advanced modeling methods are likely to be helpful. The cross correlation function
provides a summary of the relationship between two series from which all trend and
seasonal patterns have been removed. The lag m cross correlation is defined as the
correlation between x and y lagged by m units.

More than any other user of climatic data, the agrometeorologist may be tempted
to search for climatic periodicities, which would provide a basis for the management
of agricultural production. It should be noted that the Guide to Climatological
Practices (section 5.3) is more than cautious with regard to such periodicities and that,
although they may be of theoretical interest, they have been found to be unreliable,
having amplitudes which are too small for any practical conclusions to be drawn.

8. PUBLICATION OF RESULTS

8.1. General methods

For statistical analyses to have practical value they must be distributed to users in
a readily understandable format which does not require an advanced knowledge of
statistics. Adequate details should be given in each publication to avoid any
ambiguity in interpreting the numerical tables or graphs.

8.2 Tables
Numerical tables of frequencies, averages, distribution parameters, return periods
of events, etc., should state clearly:

(a) The geographical location (including elevation of the observation site);

(b) The period on which the statistical analysis is based (necessary to estimate how
representative the data are);
(c) The number of data (enabling the continuity of the series to be assessed);
50
(d) The units;
(e) The meaning of any symbols.

For frequency tables, it is better to give relative (percentage) frequencies, to

facilitate the comparison of populations consisting of different numbers of
observations. In this case, it must be made quite clear whether the percentages refer
to the total population or to separate classes.

8.3. Contingency tables

Estimates of the simultaneous occurrence of given values of several elements or
events are often needed. The resulting contingency tables should be as simple as
possible.

8.4 Graphs
Graphs are used to show, in a concise format, the information contained in
numerical tables. They are a useful adjunct to the tables themselves and facilitate
the comparison of results. Cumulative frequency curves, histograms, and
climograms give a better overall picture than the multiplicity of numerical data
obtained by statistical analysis. The scales used on the graph must be specified and
their graduations should be shown. Pub1ications intended for wide distribution
among agricultural users should not have complicated scales (e.g. logarithmic,
Gaussian, etc.) with which the users may be unfamiliar, and which might lead to
serious errors in interpreting the data. Furthermore, giving too much information on
the same graph and using complicated conventional symbols should be avoided.

8.5 Maps
To present concisely the results of agroclimatological analysis covering an area or
region, it is often better to draw isopleths or color classification from the data plotted
at specific points. The interpolation between the various locations can be used in a
digital map plotted by special plotting tools such as Graph, Grids, Surfer, and GIS.
Many climatic parameters useful to agriculture can be shown in this way, for example:

(a) Mean values of climatic elements (temperature, precipitation, evapotranspiration,

water balance, radiation balance, etc.);
(b) Frequencies: number of consecutive days without frost, without thawing, without
rain, etc.; return periods of atmospheric events;
(c) Dispersion parameters: standard deviations, coefficients of variation;
(d) Agrometeorological indices.

Depending on the scale adopted, this type of supplementary chart can be drawn
more or less taking geomorphological factors into account. However, the users of
the charts should be made aware of their generalized nature and, to interpret them
usefully, should know that corrections for local conditions must be made. This is
particularly important for hilly regions.

8.6. The Agrometeorological Bulletin

Because of the diverse nature of the Users, the content of an Agrometeorological
Bulletin (Agmet Bulletin) cannot be standardized. But the basic objectives of all
successful Agmet Bulletins are the same: the provision of the right Agmet Information
(Agmet Info) to the right Users at the right time. To attain this objective, the

51
following guidelines are suggested. For a complete discussion on the matter, the
readers are referred to WMO/TD No. 1108 and to Paw U and Davis (2000).

First, it is essential to determine who the Users are. One category of Users may be
farmers who need daily information to assist them in day-to-day activities such as
sowing, spraying, and irrigating. Another category may be more interested in
long-term agricultural decisions such as crop adaptation to weather patterns, or
marketing decisions, or modelling.

Second, the Users’ requirements must be clearly established, so that the most
appropriate information is provided. This is possible only after discussing with them.
In most cases, they do not have a clear picture of the type of information which is best
suited for their purpose; here, the role of the Agrometeorologist is crucial.

Third, the methods of dissemination of information must be decided upon after

consultation with the Users. Some farmers may have full access to Internet, while
others have only limited access, and others have no such access to this technology.
Obviously, the presentation of data for these categories will not be the same.
Furthermore, some information must be provided as quickly as possible, while others
may be provided two or three weeks later.

Fourth, it is very important to consider the cost of the Agmet Bulletin that is proposed
to the Users, especially in developing countries where the financial burden is getting
worst.

8.6.1 Some Examples

Some examples of Agmet Info presentation are given below, to drive home the
points made above.

Data in Pentads

Table 1 shows part of an Agmet Bulletin issued by a Government Service in a tropical

country where agriculture is an important component of the economy.
AgMet Bulletin in Pentads

Rainfall Data Maximum Temperature

Dates Rainfall amounts Dates Maximum
in millimetres Temperature (deg. C)
November Observed Normal July Observed Normal
2003 values values 2003 values values
1-5 3.6 4.7 1-5 23.6 23.7
6-10 7.1 4.7 6-10 21.9 23.7
11-15 13.6 4.7 11-15 22.1 23.7
Total RR 24.3 14.2 Mean MxT 22.5 23.7
Table 1: Rainfall amounts (RR) and maximum temperature (MxT) are shown for a
given area of a tropical Country. Total rainfall amounts and the mean maximum
temperature observed during the three pentads, are compared to their respective
normal values.

The above Agmet Bulletin (Table 1) was developed to cater for all crops, ranging

52
from tomatoes to sugarcane. It is issued on a half-monthly basis and is sent to the
Users by post and is also available on the website. Bearing in mind the time taken to
collect the data, it would not be before, at least, the 20th that the Bulletin would reach
the Users. To provide farmers (tomato growers, for example) with data relevant to
their day-to-day activities, the Agmet Bulletin is supplemented by daily values of
rainfall and maximum and minimum temperatures, which are broadcast on radio and
television. Of course, data relevant to different geographical localities can be
included.

In AgMet Bulletins, extreme weather events, which are masked by the averaging
procedure involved in the calculation of the pentad, must be highlighted, probably in
the form of a footnote, to draw the attention of the Users. For example, from Table 1,
it can be seen that during the period 6-15 July, the maximum temperature was below
the normal by not more than 1.8°C. In fact, during the period 9-12 July, maximum
temperature was below the normal by 2.8 to 3.0 degrees Celsius; this can be of
importance to both animals and plants.

The presentation of data in this format, together with the broadcast of daily values on
the radio and on television, is very effective. It can be used by farmers interested in
day-to-day activities and by research workers and model builders. It is suitable for
all types of crops, ranging from tomatoes and lettuce to sugar cane and other deep root
crops.

Data in 10-day intervals

Based on agrometeorological requirements of a Mediterranean climate with two main
seasons and two transitional seasons, the main climatic parameters should be
published all the year around. The selection of agromet parameters/indices should
be published according to the season and the agricultural situation of the crops,
including data representing the various agricultural regions of the country.

The bulletin should include daily data, 10-daily means or totals, and deviation or
percent from average. In parameters, such as maximum and minimum temperature
and maximum and minimum relative humidity, absolute values of the decade based
on a long series of years are also recommended.

The list of the recommended data to be published in the agrometeorological bulletin is

as follows:
a) Daily data of maximum and minimum temperature and relative humidity;
b) Temperature near the ground;
c) Soil temperature;
d) Radiation and or Sunshine duration in hours;
e) Class A pan evaporation and/or Penman evapotranspiration;
f) Rainfall amount;
g) Accumulated rainfall from the beginning of the rainy season;
h) Number of rainy days;
i) Accumulated number of dry days since the last rainy day;
j) Number of hours below different temperature thresholds depending on the crop;

53
and,
k) Number of hours below 0οC.

Examples of agrometeorological parameters or indices that should be published are:

a) Accumulated number of dynamic model units since the beginning of the winter is
an indication of budbreak in deciduous trees;
b) Accumulated number of units above 13οC since the beginning of spring is an
indication of citrus growth; and,
c) Physiological days – the accumulated number of units above 12οC since the
beginning of spring is an indication of cotton growth.

Short Range Weather Outlook

With the availability of short range (5 to 10 days) weather forecast on the Internet,
provided by World Weather Centres (WWC), many Agrometeorological Services are
providing 5 to 10 days weather forecast to farmers. An example is given below.

December 2003 West North

Friday , 12 th < 1.0 < 1.0
th
Saturday, 13 1.1 - 5.0 < 1.0
Sunday, 14th 5.1 - 25.0 5.1 - 25.0
th
Monday, 15 1.1 - 5.0 1.1 - 5.0
Tuesday, 16th < 1.0 < 1.0
Table 2: Expected rainfall (millimetres) for two rainfed farming areas. The
information was released to the Users through e-mail and posted on the website.

This Weather Outlook, based on model output received early on Thursday the 11th
from WWC, was released in the afternoon on the 11th December and sent to the Users,
through the Farming Centres by e-mail and posted on the website. This Outlook was
neither broadcast on the radio nor on the television. The issue of such Weather
Outlook is important, but it must be carefully planned, otherwise, it can lead to
financial loses, as shown below.

Little rainfall was observed during the first two pentads of December 2003 and
farmers were starting to get worried. The indication that significant rain was
expected on Sunday the 14th (Table 2) had given great hope to the farmers and,
because it was a weekend, they made plans on Friday to do some field work on
Saturday and on Monday. Such plans are costly because it implies the booking of
manpower and of transportation, the buying of fertilizers, etc. But model output
received on Friday the 12th indicated that the probability of having rain during the
following five days was negligible, and in fact, it was not before the 31st of December
that significant rainfall was observed.

Here, it is not the validity of the Weather Outlook that is questioned. The point to be
noted is that no update of the Outlook could reach the farmers because the Farming
Centres were closed for the weekend. If, besides being sent by email and posted on
the website, the Outlook was broadcast on radio and television, the update version
would have reached the farmers and appropriate measures could have been taken.
To avoid similar incidents, it is advisable to decide on the methods of dissemination

54
of information.

Seasonal Forecast
An extract of a seasonal forecast issued, for a country situated in the southern
hemisphere, during the first half of October 2003 for summer 2003-2004 (Summer in
that country is from November to April) is shown: “The rainfall season may begin
by November. The summer cumulative rainfall amount is expected to reach the
long-term mean of 1400 millimeters. Heavy rainfall is expected in January and
February 2004.” This seasonal forecast was published in the newspapers and read
on the television.

The question is: who are qualified to interpret and use this forecast? Can it be
misleading to farmers? To show the problems which such forecast can create, real
data for the period October 2003 to January 2004 are presented in Table 3 for an
agricultural area.
Rainfall Amounts in millimeters (mm)
October November December January
2003 2003 2003 2004
First 1.8 4.1 5.2 176.4
Half
Second 12.8 35.7 12.8 154.1
Half
Table 3: Rainfall amounts recorded over an agricultural area during the period
October 2003 to January 2004. Out of the 35.7 mm of rainfall recorded during the
second half of November, 35.0 mm fell during the period 16-25

Given that October and the first half of November 2003 were relatively dry and that a
significant amount of rainfall was recorded during the second half of November, and
noting that the seasonal forecast opted for normal rainfall during summer and that the
rainfall season may start in November, the farmers thought that the rainy season was
on. Most of them started planting their crops during the last pentad of November.
Unfortunately, the rainfall during the second half of November was a false signal:
December was relatively dry. The rainy season started in January 2004.

To avoid seasonal forecasts to fall in the wrong hands, it is not advisable to have them
published in the newspapers; these seasonal forecast must be sent to specialists who
are trained to interpret them and should be supplemented by short-range weather
forecast.

8.6.2. How Costly Should the AgMet Bulletin Be?

Today, agricultural systems in some Small and Poor Countries (SPC) are in great
turmoil. Developed countries have dismantled the safety net, which has been giving
some protection to the agricultural products of these SPC. It is in relation to the
bleak future of agriculture in these SPC that the question of the cost of the issue of
AgMet Bulletin is raised.

Sooner or later, the financial situation in the SPC will not be able to sustain the issue
of costly AgMet Bulletin by local personnel. So in these SPC, the
agrometeorologists must think carefully about the cost-benefit of the AgMet Bulletin,
especially when developed countries are getting ready to propose their services for

55
free (for how long will these be free?).

Already, shipping bulletins, cyclone warnings, and aviation forecasts are being offered
for free on a global scale by a few developed countries. But, how long will these
services be free? Sooner or later, the SPC will have to pay for these services. It is
very important to keep the cost of the AgMet Bulletin to a minimum.

56
References

A.D. HARTKAMPA, J.W. WHITEA, G. HOOGENBOOMB, 2003. Comparison of

three weather generators for crop modeling: a case study for subtropical
environments, Agricultural Systems, 76, 539–560.

BESSEMOULIN, G., 1973: Sur la statistique es valeurs extremes. Monographie de

la Meteorologie Nationale No. 89, Paris, 24 pp.

CARRUTHERS, N. and BROOKS, C.E.P., 1953: Handbook of statistical methods in

meteorology. Her Majesty’s Stationery Office Mo. 538, 412 pp.

COLES, S., 2001: An Introduction to Statistical Modeling of Extreme Values. Springer

Series in Statistics.

DRAPER, N. R & SMITH, H., 1981. Applied Regression Analysis. Second

Edition, New York: John Wiley & Sons.

GUMBEL, E. J., 1959: Statistics of extremes. Columbia University Press, New York,
375 pp.

HUBBARD, K.G. and SIVAKUMAR, M.V.K. (Eds.) 2001. Automated Weather

Stations for Applications in Agriculture and Water Resources Management: Current
Use and Future Perspectives. Proceedings of an International Workshop held in
Lincoln, Nebraska, USA, 6-10 March 2000.

MOTHA, R.P. (Ed.), 2000. Agrometeorological Data Management. WMO / TD No.

1015, Geneva, Switzerland.

SIVAKUMAR, M.V.K., MAIDOUKIA, A. and STERN, R.D. 1993. Agroclimatology

of West Africa: Niger. Information Bulletin no. 5, ICRISAT, India.

SIVAKUMAR, M.V.K., STIGTER, C.J., RIJKS, D. (Eds.) 2000. Agrometeorology

in the 21st Century--Needs and Perspectives, Agricultural and Forest Meteorology 103,
227 pp.

SIVAKUMAR, M.V.K. U.S. DE, K.C. SIMHARAY, and M. RAJEEVAN (Eds.) 1998.
User Requirements for Agrometeorological Services. Proceedings of an
International Workshop held at Pune, India, 10-14 November 1997.

THOM, H.C.S. 1966. Some methods of climatological analysis. WMO Technical Note
No. 81, Geneva, Switzerland.

Von STORCH H and F ZWIERS, 1999. Statistical Analysis in Climate Research,

Cambridge University Press.

WIERINGA, J., and LOMAS, J. 2001. Lecture Notes for Training Agricultural
Meteorological Personnel. WMO-No. 551, Geneva, Switzerland.

WIJNGAARD, J.B., KLEIN TANK A.M.G., and KONNEN G.P. 2003. Homogeneity
57
of 20th century European daily temperature and precipitation series, International
Journal of Climatology, 23, 679-692.

WMO 1983. Guide to Climatological Practices. WMO-No. 100, Geneva, Switzerland

WMO 2004. Fourth Seminar for homogenization and quality control in

climatological databases. WMO-TD No. 1236, Geneva, Switzerland.

World Meteorological Organization. 2002. Improving Agrometeorological Bulletins,

WMO/TD No. 1108.

Paw U, K.T., Davis, C.A. (Eds. In Chief), 2000. Agricultural and Forest Meteorology.
Vol. 103, Elsevier.

58