AGRICULTURAL METEOROLOGICAL DATA, THEIR PRESENTATION AND STATISTICAL ANALYSIS
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CHAPTER 3
AGRICULTURAL METEOROLOGICAL DATA,
THEIR PRESENTATION AND STATISTICAL ANALYSIS
3.1 INTRODUCTION 3.2 DATA FOR AGRICULTURAL
METEOROLOGY
Agricultural meteorology is the science that applies
knowledge in weather and climate to qualitative Agrometeorological data are usually provided to
and quantitative improvement in agricultural users in a transformed format; for example, rainfall
production. Agricultural meteorology involves data are presented in pentads or in monthly
meteorology, hydrology, agrology and biology, amounts.
and it requires a diverse, multidisciplinary array of
data for operational applications and research.
3.2.1 Nature of the data
Basic agricultural meteorological data are largely
the same as those used in general meteorology. Basic agricultural meteorological data may be
These data need to be supplemented with more divided into the following six categories, which
specific data relating to the biosphere, the envi- include data observed by instruments on the ground
ronment of all living organisms, and biological and by remote-sensing.
data relating to the growth and development of (a) Data relating to the state of the atmospheric
these organisms. Agronomic, phenological and environment. These include observations
physiological data are necessary for dynamic of rainfall, sunshine, solar radiation, air
modelling, operational evaluation and statistical temperature, humidity, and wind speed and
analyses. Most data need to be processed for gener- direction;
ating various products that affect agricultural (b) Data relating to the state of the soil envi-
management decisions in matters such as crop- ronment. These include observations of soil
ping, the scheduling of irrigation, and so forth. moisture, that is, the soil water reservoir for
Additional support from other technologies, such plant growth and development. The amount
as geographical information and remote-sensing, of water available depends on the effective-
as well as statistics, is necessary for data process- ness of precipitation or irrigation, and on the
ing. Geographical information and remote-sensing soil’s physical properties and depth. The rate
data, such as images of the status of vegetation of water loss from the soil depends on the
and crops damaged by disasters, soil moisture, and climate, the soil’s physical properties, and the
the like, should also be included as supplementary root system of the plant community. Erosion
data. Derived agrometeorological parameters, such by wind and water depends on weather factors
as photosynthetically active radiation and poten- and vegetative cover;
tial evapotranspiration, are often used in (c) Data relating to organism response to vary-
agricultural meteorology for both research and ing environments. These involve agricultural
operational purposes. On the other hand, many crops and livestock, their variety, and the state
agrometeorological indices, such as the drought and stages of their growth and development,
index, the critical point threshold of temperature as well as the pathogenic elements affect-
and soil water for crop development, are also ing them. Biological data are associated with
important for agricultural operations. Weather phenological growth stages and physiological
and climate data play a crucial role in many agri- growth functions of living organisms;
cultural decisions. (d) Information concerned with the agricultural
practices employed. Planning brings the best
Agrometeorological information includes not only available resources and applicable production
every stage of growth and development of crops, technologies together into an operational farm
floriculture, agroforestry and livestock, but also the unit. Each farm is a unique entity with combi-
technological factors that affect agriculture, such as nations of climate, soils, crops, livestock and
irrigation, plant protection, fumigation and dust equipment to manage and operate within the
spraying. Moreover, agricultural meteorological farming system. The most efficient utilization
information plays a crucial role in the decision- of weather and climate data for the unique
making process for sustainable agriculture and soils on a farm unit will help conserve natural
natural disaster reduction, with a view to preserving resources, while at the same time promoting
natural resources and improving the quality of life. economic benefit to the farmer;3–2 GUIDE TO AGRICULTURAL METEOROLOGICAL PRACTICES
(e) Information relating to weather disasters and carrying out similar research work. At the
their influence on agriculture; same time, the existence of these data should
(f) Information relating to the distribution of be publicized at the national level and possi-
weather and agricultural crops, and geograph- bly at the international level, if appropriate,
ical information, including digital maps; especially in the case of longer series of special
(g) Metadata that describe the observation tech- observations;
niques and procedures used. (d) All the usual data storage media are recom-
mended:
(i) The original data records, or agromete-
3.2.2 Data collection
orological summaries, are often the most
The collection of data is very important as it lays convenient format for the observing
the foundation for agricultural weather and climate stations;
data systems that are necessary to expedite the (ii) The format of data summaries intended
generation of products, analyses and forecasts for for forwarding to regional or national
agricultural cropping decisions, irrigation manage- centres, or for dissemination to the user
ment, fire weather management, and ecosystem community, should be designed so that
conservation. The impact on crops, livestock, water the data may be easily transferred to a vari-
and soil resources, and forestry must be evaluated ety of media for processing. The format
from the best available spatial and temporal array should also facilitate either the manual
of parameters. Agrometeorology is an interdiscipli- preparation or automated processing
nary branch of science requiring the combination of statistical summaries (computation
of general meteorological data observations and of means, frequencies, and the like). At
specific biological parameters. Meteorological data the same time, access to and retrieval of
can be viewed as typically physical elements that data files should be simple, flexible and
may be measured with relatively high accuracy, reproducible for assessment, modelling or
while other types of observations (namely, biologi- research purposes;
cal or phenological) may be more subjective. In (iii) Rapid advances in electronic technology
collecting, managing and analysing the data for facilitate effective exchange of data files,
agrometeorological purposes, the source of data summaries and charts of recording instru-
and the methods of observation define their char- ments, particularly at the national and
acter and management criteria. Some useful international levels;
suggestions with regard to the storage and process- (iv) Agrometeorological data should be trans-
ing of data can be offered, however: ferred to electronic media in the same way
(a) Original data files, which may be used for as conventional climatological data, with
reference purposes (the daily register of obser- an emphasis on automatic processing.
vations, and so on), should be stored at the
observation site; this applies equally to atmos- The availability of proper agricultural meteorological
pheric, biological, crop and soil data; databases is a major prerequisite for studying and
(b) The most frequently used data should be managing the processes of agricultural and forest
collected at national or regional agrometeoro- production. The agricultural meteorology
logical centres and reside in host servers for community has great interest in incorporating new
network accessibility. This may not always be information technologies into a systematic design
practical, however, since stations or laborato- for agrometeorological management to ensure
ries under the control of different authorities timely and reliable data from national reporting
(meteorological services, agricultural services, networks for the benefit of the local farming
universities, research institutes) often collect community. While much more information has
unique agrometeorological data. Steps should become available to the agricultural user, it is
therefore be taken to ensure that possible users essential that appropriate standards be maintained
are aware of the existence of such data, either for basic instrumentation, collection and
through some form of data library or compu- observations, quality control, and archiving and
terized documentation, and that appropriate dissemination. After they have been recorded,
data exchange mechanisms are available to collected and transferred to the data centres, all
access and share these data; agricultural meteorological data need to be
(c) Data resulting from special studies should be standardized or technically treated so that they can
stored at the place where the research work is be used for various purposes. The data centres need
undertaken, but it would be advantageous to to maintain special databases. These databases
arrange for exchanges of data among centres should include meteorological, phenological,CHAPTER 3. AGRICULTURAL METEOROLOGICAL DATA, THEIR PRESENTATION AND STATISTICAL ANALYSIS 3–3
edaphic and agronomic information. Database ways, including by mail, telephone, telegraph,
management and processing and the quality fax and Internet, and via Comsat; transmission
control, archiving, timely accessing and via the Internet and Comsat is more efficient.
dissemination of data are all important components After reaching the data centres, data should
that render the information valuable and useful in be identified and processed by means of a
agricultural research and operational programmes. special program in order to facilitate their
dissemination to other users.
After they have been stored in a data centre, the data
are disseminated to users. There have been major
3.2.4 Scrutiny of data and acquisition of
advancements in making more data products availa-
metadata
ble to the user community through automation. The
introduction of electronic transfer of data files via the It is very important that all agricultural meteorologi-
Internet using the file transfer protocol (FTP) and the cal data be carefully scrutinized, both at the observing
World Wide Web (WWW) has brought this informa- station and at regional or national centres, by means
tion transfer process up to a new level. The Web allows of subsequent automatic computer processing. All
users to access text, images and even sound files that data should be identified immediately. The code
can be linked together electronically. The Web’s parameters should be specified, such as types, regions,
attributes include the flexibility to handle a wide missing values and possible ranges for different meas-
range of data presentation methods and the capabil- urements. The quality control should be done
ity to reach a large audience. Developing countries according to Wijngaard et al. (2003), WMO-TD
have some access to this type of electronic informa- No. 1236 (WMO, 2004a) and the current Guide to
tion, but limitations still exist in the development of Climatological Practices (WMO, 1983). Every measure-
their own electronically accessible databases. These ment code must be checked to make certain that the
limitations will diminish as the cost of technology measurement is reasonable. If the value is unreasona-
decreases and its availability increases. ble, it should be corrected immediately. After being
scrutinized, the data can be processed further for
different purposes. In order to ascertain the quality of
3.2.3 Recording of data
observation data and determine whether to correct or
Recording of basic data is the first step for agricul- normalize them before analysis, metadata are needed.
tural meteorological data collection. When the These are the details and history of local conditions,
environmental factors and other agricultural mete- and instrumentation, operational, data-processing
orological elements are measured or observed, they and other factors relevant to the observation process.
must be recorded on the same media, such as agri- Such metadata should be documented and treated
cultural meteorological registers, diskettes, and the with the same care as the data themselves (see WMO
like, manually or automatically. 2003a, 2003b). Unfortunately, observation metadata
(a) The data, such as the daily register of obser- are often incomplete and poorly organized.
vations and charts of recording instruments,
should be carefully preserved as permanent In Chapter 2 of this Guide, essential metadata are
records. They should be readily identifiable specified for individual parameters and the
and include the place, date and time of each organization of their acquisition is reviewed in
observation, and the units used. 2.2.5. Many kinds of metadata can be recorded as
(b) These basic data should be sent to analysis simple numbers, as is the case with observation
centres for operational uses, such as local heights, for example; but more complex aspects,
agricultural weather forecasts, agricultural such as instrument exposure, must also be
meteorological information services, plant recorded in a manner that is practicable for the
protection treatment and irrigation guidance. observers and station managers. Acquiring
Summaries (weekly, 10-day or monthly) of metadata on present observations and inquiring
these data should be made regularly from the about metadata on past observations are now a
daily register of observations according to the major responsibility of data managers. Omission
user demand and then distributed to inter- of metadata acquisition implies that the data will
ested agencies and users. have low quality for applications. The optimal
(c) Observers need to record all measurements set-up of a database for metadata is at present still
in compliance with rules for harmonization. in development, because metadata characteristics
This will ensure that the data are recorded are so variable. To be manageable, the optimal
in a standard format so that they can readily database should not only be efficient for archiving,
be transferred to data centres for automatic but also easily accessible for those who are
processing. Data can be transferred in several recording the metadata. To allow for future3–4 GUIDE TO AGRICULTURAL METEOROLOGICAL PRACTICES
improvement and continuing accessibility, good 3.2.6 Catalogue of data
metadata database formats are ASCII, SQL and
XML, because they are independent of any Very often, considerable amounts of agrometeoro-
presently available computing set-up. logical data are collected by a variety of services.
These data sources are not readily publicized or
accessible to potential users, which means that
3.2.5 Format of data
users often have great difficulty in discovering
The basic data obtained from observing stations, whether such data exist. Coordination should
whether specialized or not, are of interest to both therefore be undertaken at the global, regional and
scientists and agricultural users. A number of national levels to ensure that data catalogues are
established formats and protocols are available for prepared periodically, while giving enough back-
the exchange of data. A data format is a docu- ground information to users. The data catalogues
mented set of rules for the coding of data in a form should include the following information:
for both visual and computer recognition. Its uses (a) The geographical location of each observing site;
can be designed for either or both real-time use (b) The nature of the data obtained;
and historical or archival data transfer. All the crit- (c) The location where the data are stored;
ical elements for identification of data should be (d) The file types (for instance, manuscript,
covered in the coding, including station identifi- charts of recording instruments, auto-
ers, parameter descriptors, time encoding mated weather station data, punched cards,
conventions, unit and scale conventions, and magnetic tape, scanned data, computerized
common fields. digital data);
(e) The methods of obtaining the data.
Large amounts of data are typically required for
processing, analysis and dissemination. It is For a more extensive specification of these aspects,
extremely important that data are in a format that see Chapter 2, section 2.2.5.
is both easily accessible and user-friendly. This is
particularly pertinent as more and more data
become available in electronic format. Some types
of software, such as NetCDF (network common 3.3 DISTRIBUTION OF DATA
data form), process data in a common form and
disseminate them to more users. NetCDF consists
3.3.1 Requirements for research
of software for array-oriented data access and a
library that provides for implementation of the In order to highlight the salient features of the influ-
interface (Sivakumar et al., 2000). The NetCDF ence of climatic factors on the growth and
software was developed at the Unidata Program development of living things, scientists often have
Center in Boulder, Colorado, United States. This is to process a large volume of basic data. These data
an open-source collection of tools that can be might be supplied to scientists in the following
obtained by anonymous FTP from ftp://ftp. forms:
unidata.ucar.edu/pub/netcdf/ or from other mirror (a) Reproductions of original documents (origi-
sites. nal records, charts of recording instruments)
or periodic summaries;
The NetCDF software package supports the crea- (b) Datasets on a server or Website that is ready
tion, access and sharing of scientific data. It is for processing into different categories, which
particularly useful at sites with a mixture of can be read or viewed on a platform;
computers connected by a network. Data stored (c) Various kinds of satellite digital data and imagery
on one computer may be read directly from on different regions and different times;
another without explicit conversion. The NetCDF (d) Various basic databases, which can be viewed
library generalizes access to scientific data so that as reference for research.
the methods for storing and accessing data are
independent of the computer architecture and
3.3.2 Special requirements for
the applications being used. Standardized data
agriculturists
access facilitates the sharing of data. Since the
NetCDF package is quite general, a wide variety Two aspects of the periodic distribution of agro
of analysis and display applications can use it. meteorological data to agricultural users may be
The NetCDF software and documentation may be considered:
obtained from the NetCDF Website at http:// (a) Raw or partially processed operational data
www.unidata.ucar.edu/packages/netcdf/. supplied after only a short delay (rainfall,CHAPTER 3. AGRICULTURAL METEOROLOGICAL DATA, THEIR PRESENTATION AND STATISTICAL ANALYSIS 3–5
potential evapotranspiration, water balance or of the type of data to be systematically distributed
sums of temperature). These may be distributed can be established on that basis. For example, when
by means of: both the climatic regions and the areas in which
i. Periodic publications, twice weekly, different crops are grown are well defined, an agrom-
weekly or at 10-day intervals; eteorological analysis can illustrate which crops are
ii. Telephone and note; most suited to each climate zone. This type of analy-
iii. Special television programmes from a sis can also show which crops can be adapted to
regional television station; changing climatic and agronomic conditions.
iv. Regional radio broadcasts; Agricultural users require these analyses; they can be
v. Release on agricultural or weather distributed by geographic, crop or climatic region.
Websites.
(b) Agrometeorological or climatic summaries
3.3.4 Minimum distribution of
published weekly, every 10 days, monthly or
agroclimatological documents
annually, which contain agrometeorological
data (rainfall, temperatures above the ground, Since the large number of potential users of agro
soil temperature and moisture content, poten- meteorological information is so widely dispersed,
tial evapotranspiration, sums of rainfall and it is not realistic to recommend a general distribu-
temperature, abnormal rainfall and temperature, tion of data to all users. In fact, the requests for raw
sunshine, global solar radiation, and so on). agrometeorological data are rare. Not all of the raw
agrometeorological data available are essential for
those persons who are directly engaged in agricul-
3.3.3 Determining the requirements
ture – farmers, ranchers and foresters. Users
of users
generally require data to be processed into an
The agrometeorologist has a major responsibility to understandable format to facilitate their decision-
ensure that effective use of this information offers making process. But the complete datasets should
an opportunity to enhance agricultural efficiency be available and accessible to the technical services,
or to assist agricultural decision-making. The infor- agricultural administrations and professional organ-
mation must be accessible, clear and relevant. It is izations. These professionals are responsible for
crucial, however, for an agrometeorological service providing practical technical advice concerning the
to know who the specific users of information are. treatment and management of crops, preventive
The user community ranges from global, national measures, adaptation strategies, and so forth, based
and provincial organizations and governments to on collected agrometeorological information.
agro-industries, farmers, agricultural consultants,
and the agricultural research and technology devel- Agrometeorological information should be distrib-
opment communities or private individuals. The uted to all users, including:
variety of agrometeorological information requests (a) Agricultural administrations;
emanates from this broad community. Therefore, (b) Research institutions and laboratories;
the agrometeorological service must distribute the (c) Professional organizations;
information that is available and appropriate at the (d) Private crop and weather services;
right time. (e) Government agencies;
(f) Farmers, ranchers and foresters.
Researchers invariably know exactly which agro
meteorological data they require for specific
statistical analyses, modelling or other analytical
studies. Often, many agricultural users are not just 3.4 DATABASE MANAGEMENT
unaware of the actual scope of the agrometeorologi-
cal services available, but also have only a vague idea The management of weather and climate data for
of the data they really need. Frequent contact agricultural applications in the electronic age has
between agrometeorologists and professional agri- become more efficient. This section will provide an
culturists, and enquiries through professional overview of agrometeorological data collection, data
associations and among agriculturists themselves, or processing, quality control, archiving, data analysis
visiting professional Websites, can help enormously and product generation, and product delivery. A
to improve the awareness of data needs. Sivakumar wide variety of database choices are available to the
(1998) presents a broad overview of user require- agroclimatological user community. To accompany
ments for agrometeorological services. Better the agroclimatological databases that are created,
applications of the type and quantity of useful agrometeorologists and software engineers develop
agrometeorological data available and the selection the special software for agroclimatological database3–6 GUIDE TO AGRICULTURAL METEOROLOGICAL PRACTICES
management. Thus, a database management system CLICOM provides tools (such as stations, observa-
for agricultural applications should be comprehen- tions and instruments) to describe and manage the
sive, bearing in mind the following considerations: climatological network. It offers procedures for the
(a) Communication among climatologists, key entry, checking and archiving of climate data,
agrometeorologists and agricultural extension and for computing and analysing the data. Typical
personnel must be improved to establish an standard outputs include monthly or 10-day data
operational database; from daily data; statistics such as means, maxi-
(b) The outputs must be adapted for an opera- mums, minimums and standard deviations; and
tional database in order to support specific tables and graphs. Other products requiring more
agrometeorological applications at a national/ elaborate data processing include water balance
regional/global level; monitoring, estimation of missing precipitation
(c) Applications must be linked to the Climate data, calculation of the return period and prepara-
Applications Referral System (CARS) tion of the CLIMAT message.
project, spatial interpolated databases and a
Geographical Information System (GIS). The CLICOM software is widely used in developing
countries. The installation of CLICOM as a data
Personal computers (PCs) are able to provide prod- management system in many of these countries has
ucts formatted for easy reading and presentation, successfully transferred the technology for use with
which are generated through simple processors, PCs, but the resulting climate data management
databases or spreadsheet applications. Some careful improvements have not yet been fully realized.
thought needs to be given, however, to what type of Station network density as recommended by WMO
product is needed, what the product looks like and has not been fully achieved and the collection of
what it contains, before the database delivery design data in many countries remains inadequate.
is finalized. The greatest difficulty often encountered CLICOM systems are beginning to yield positive
is how to treat missing data or information (WMO, results, however, and there is a growing recognition
2004a). This process is even more complicated when of the operational applications of CLICOM.
data from several different datasets, such as climatic
and agricultural data, are combined. Some software There are a number of constraints that have been
programs for database management, especially the identified over time and recognized for possible
software for climatic database management, provide improvement in future versions of the CLICOM
convenient tools for agrometeorological database system. Among the technical limitations, the list
management. includes (WMO, 2000):
(a) The lack of flexibility to implement specific
applications in the agricultural field and/or at
3.4.1 CLICOM Database Management
a regional/global level;
System
(b) The lack of functionality in real-time operations;
CLICOM (CLImate COMputing) refers to the (c) Few options for file import;
WMO World Climate Data Programme Project, (d) The lack of transparent linkages to other appli-
which is aimed at coordinating and assisting the cations;
implementation, maintenance and upgrading of (e) The risk of overlapping of many datasets;
automated climate data management procedures (f) A non-standard georeferencing system;
and systems in WMO Member countries (that is, (g) Storage of climate data without the corre-
the National Meteorological and Hydrological sponding station information;
Services in these countries). The goal of CLICOM (h) The possibility of easy modification of the data
is the transfer of three main components of entry module, which may destroy existing data.
modern technology, namely, desktop computer
hardware, database management software and 3.4.2 Geographical Information System
training in climate data management. CLICOM is (GIS)
a standardized, automated database management
system software for use on a personal computer A Geographical Information System (GIS) is a
and it is targeted at introduction of a system in computer-assisted system for the acquisition, storage,
developing countries. As of May 1996, CLICOM analysis and display of observed data on spatial
version 3.0 was installed in 127 WMO Member distribution. GIS technology integrates common
countries. Now CLICOM software is available in database operations such as query and statistical
Czech, English, French, Spanish and Russian. analysis with the unique visualization and geographic
CLICOM Version 3.1 Release 2 became available in analysis benefits offered by mapping overlays. Maps
January 2000. have traditionally been used to explore the Earth andCHAPTER 3. AGRICULTURAL METEOROLOGICAL DATA, THEIR PRESENTATION AND STATISTICAL ANALYSIS 3–7
its resources. GIS technology takes advantage of developing future climate scenarios based on global
computer science technologies, enhancing the climate model (GCM) simulations or subjectively
efficiency and analytical power of traditional introduced climate changes for climate change impact
methodologies. models. Weather generators project future changes in
means (averages) onto the observed historical weather
GIS is becoming an essential tool in the effort to series by incorporating changes in variability; these
understand complex processes at different scales: projections are widely used for agricultural impact
local, regional and global. In GIS, the information studies. Daily climate scenarios can be used to study
coming from different disciplines and sources, such potential changes in agroclimatic resources. Weather
as traditional point sources, digital maps, databases generators can calculate agroclimatic indices on the
and remote‑sensing, can be combined in models basis of historical climate data and GCM outputs.
that simulate the behaviour of complex systems. Various agroclimatic indices can be used to assess crop
production potentials and to rate the climatic suita-
The presentation of geographic elements is solved in bility of land for crops. A methodologically more
two ways: using x, y coordinates (vectors), or repre- consistent approach is to use a stochastic weather
senting the object as a variation of values in a generator, instead of historical data, in conjunction
geometric array (raster). The possibility of transform- with a crop simulation model. The stochastic weather
ing the data from one format to the other allows fast generator allows temporal extrapolation of observed
interaction between different informative layers. weather data for agricultural risk assessment and
Typical operations include overlaying different provides an expanded spatial source of weather data
thematic maps; acquiring statistical information by interpolation between the point-based parameters
about the attributes; changing the legend, scale and used to define the weather generators. Interpolation
projection of maps; and making three-dimensional procedures can create both spatial input data and
perspective view plots using elevation data. spatial output data. The density of meteorological
stations is often low, especially in developing coun-
The capability to manage this diverse information, tries, and reliable and complete long-term data are
by analysing and processing the informative layers scarce. Daily interpolated surfaces of meteorological
together, opens up new possibilities for the simula- variables rarely exist. More commonly, weather gener-
tion of complex systems. GIS can be used to produce ators can be used to generate the weather variables in
images – not only maps, but cartographic products, grids that cover large geographic regions and come
drawings, animations or interactive instruments as from interpolated surfaces of weekly or monthly
well. These products allow researchers to analyse climate variables. On the basis of these interpolated
their data in new ways, predicting the natural behav- surfaces, daily weather data for crop simulation
iours, explaining events and planning strategies. models are generated using statistical models that
attempt to reproduce series of daily data with means
For the agronomic and natural components in and a variability similar to those that would be
agrometeorology, these tools have taken the name observed at a given location.
Land Information Systems (LIS) (Sivakumar et al.,
2000). In both GIS and LIS, the key components are Weather generators have the capacity to simulate
the same, namely, hardware, software, data, tech- statistical properties of observed weather data for agri-
niques and technicians. LIS, however, requires cultural applications, including a set of agroclimatic
detailed information on environmental elements, indices. They are able to simulate temperature, precip-
such as meteorological parameters, vegetation, soil itation and related statistics. Weather generators
and water. The final product of LIS is often the result typically calculate daily precipitation risk and use this
of a combination of a large number of complex information to guide the generation of other weather
informative layers, whose precision is fundamental variables, such as daily solar radiation, maximum and
for the reliability of the whole system. Chapter 4 of minimum temperature, and potential evapotranspi-
this Guide contains an extensive overview of GIS. ration. They can also simulate statistical properties of
daily weather series under a changing/changed
climate through modifications to the weather genera-
3.4.3 Weather generators (WG)
tor parameters with optimal use of available
Weather generators are widely used to generate information on climate change. For example, weather
synthetic weather data, which can be arbitrarily long generators can simulate the frequency distributions of
for input into impact models, such as crop models the wet and dry spells fairly well by modifying the
and hydrological models that are used for assessing four transition probabilities of the second-order
agroclimatic long-term risk and agrometeorological Markov chain. Weather generators are generally based
analysis. Weather generators are also the tool used for on the statistics. For example, to generate the amount3–8 GUIDE TO AGRICULTURAL METEOROLOGICAL PRACTICES
of precipitation on wet days, a two-parameter gamma (b) Precipitation
distribution function is commonly used. The two i. Probability of a specified amount during a
parameters, a and b, are directly related to the average period;
amount of precipitation per wet day. They can, there- ii. Number of days with specified amounts
fore, be determined with the monthly means for the of precipitation;
number of rainy days per month and the amount of iii. Probabilities of thundershowers;
precipitation per month, which are obtained either iv. Duration and amount of snow cover;
from compilations of climate normals or from inter- v. Dates on which snow cover begins and
polated surfaces. ends;
vi. Probability of extreme precipitation
The popular weather generators are, inter alia, WGEN amounts.
(Richardson, 1984, 1985), SIMMETEO (Geng et al., (c) Wind
1986, 1988), and MARKSIM (Jones and Thornton, i. Windrose;
1998, 2000). They include a first- or high-order ii. Maximum wind, average wind speed;
Markov daily generator that requires long-term (at iii. Diurnal variation;
least 5 to 10 years) daily weather data or climate clus- iv. Hours of wind less than selected speed.
ters of interpolated surfaces for estimation of their (d) Sky cover, sunshine, radiation
parameters. The software allows for three types of i. Per cent possible sunshine;
input to estimate parameters for the generator: ii. Number of clear, partly cloudy, cloudy
(a) Latitude and longitude; days;
(b) Latitude, longitude and elevation; iii. Amounts of global and net radiation.
(c) Latitude, longitude, elevation and long-term (e) Humidity
monthly climate normals. i. Probability of a specified relative humid-
ity;
ii. Duration of a specified threshold of
humidity.
3.5 AGROMETEOROLOGICAL (f) Free water evaporation
INFORMATION i. Total amount;
ii. Diurnal variation of evaporation;
The impacts of meteorological factors on crop iii. Relative dryness of air;
growth and development are consecutive, although iv. Evapotranspiration.
sometimes they do not emerge over a short time. (g) Dew
The weather and climatological information should i. Duration and amount of dew;
vary according to the kind of crop, its sensitivity to ii. Diurnal variation of dew;
environmental factors, water requirements, and so iii. Association of dew with vegetative
on. Certain statistics are important, such as wetting;
sequences of consecutive days when maximum and iv. Probability of dew formation based on
minimum temperatures or the amount of precipita- the season.
tion exceed or are less than certain critical threshold (h) Soil temperature
values, and the average and extreme dates when i. Mean and standard deviation at standard
these threshold values are reached. depth;
ii. Depth of frost penetration;
The following are some of the more frequent types of iii. Probability of occurrence of specified
information that can be derived from the basic data: temperatures at standard depths;
(a) Air temperature iv. Dates when threshold values of temper-
i. Temperature probabilities; ature (germination, vegetation) are
ii. Chilling hours; reached.
iii. Degree-days; (i) Weather hazards or extreme events
iv. Hours or days above or below selected i. Frost;
temperatures; ii. Cold wave;
v. Interdiurnal variability; iii. Hail;
vi. Maximum and minimum temperature iv. Heatwave;
statistics; v. Drought;
vii. Growing season statistics, that is, dates vi. Cyclones;
when threshold temperature values for vii. Flood;
the growth of various kinds of crops begin viii. Rare sunshine;
and end. ix. Waterlogging.CHAPTER 3. AGRICULTURAL METEOROLOGICAL DATA, THEIR PRESENTATION AND STATISTICAL ANALYSIS 3–9
(j) Agrometeorological observations statistical methods on which these analyses are based.
i. Soil moisture at regular depths; Another point that needs to be stressed is that one is
ii. Plant growth observations; often obliged to compare measurements of the physi-
iii. Plant population; cal environment with biological data, which are often
iv. Phenological events; difficult to quantify.
v. Leaf area index;
vi. Above-ground biomass; Once the agrometeorological data are stored in
vii. Crop canopy temperature; electronic form in a file or database, they can be
viii. Leaf temperature; analysed using a public domain or commercial
ix. Crop root length. statistical software. Some basic statistical analyses
can be performed in widely available commercial
spreadsheet software. More comprehensive basic
3.5.1 Forecast information
and advanced statistical analyses generally require
Operational weather information is defined as real- specialized statistical software. Basic statistical
time data that provide conditions of past weather analyses include simple descriptive statistics,
(over the previous few days), present weather, as distribution fitting, correlation analysis, multiple
well as predicted weather. It is well known, however, linear regression, non-parametrics and enhanced
that the forecast product deteriorates with time, so graphic capabilities. Advanced software includes
that the longer the forecast period, the less reliable linear/non-linear models, time series and forecast-
the forecast. Forecasting of agriculturally important ing, and multivariate exploratory techniques such
elements is discussed in Chapters 4 and 5. as cluster analysis, factor analysis, principal
components and classification analysis, classifica-
tion trees, canonical analysis and discriminant
analysis. Commercial statistical software for PCs
3.6 STATISTICAL METHODS OF would be expected to provide a user-friendly inter-
AGROMETEOROLOGICAL DATA face with self-prompting analysis selection
ANALYSIS dialogues. Many software packages include elec-
tronic manuals that provide extensive explanations
The remarks set out here are intended to be of analysis options with examples and compre-
supplementary to WMO-No. 100, Guide to hensive statistical advice.
Climatological Practices, Chapter 5, “The use of
statistics in climatology”, and to WMO-No. 199, Some commercial packages are rather expensive, but
Some Methods of Climatological Analysis (WMO some free statistical analysis software can be down-
Technical Note No. 81), which contain advice loaded from the Web or made available upon request.
generally appropriate and applicable to agricul- One example of freely available software is INSTAT,
tural climatology. which was developed with applications in agromete-
orology in mind. It is a general-purpose statistics
Statistical analyses play an important role in agro package for PCs that was developed by the Statistical
meteorology, as they provide a means of Service Centre of the University of Reading in the
interrelating series of data from diverse sources, United Kingdom. It uses a simple command language
namely biological data, soil and crop data, and to process and analyse data. The documentation and
atmospheric measurements. Because of the software can be downloaded from the Web. Data for
complexity and multiplicity of the effects of envi- analysis can be entered into a table or copied and
ronmental factors on the growth and development pasted from the clipboard. If CLICOM is used as the
of living organisms, and consequently on agricul- database management software, then INSTAT, which
tural production, it is sometimes necessary to use was designed for use with CLICOM, can readily be
rather sophisticated statistical methods to detect used to extract the data and perform statistical analy-
the interactions of these factors and their practical ses. INSTAT can be used to calculate simple descriptive
consequences. statistics, including minimum and maximum values,
range, mean, standard deviation, median, lower quar-
It must not be forgotten that advice on long-term tile, upper quartile, skewness and kurtosis. It can be
agricultural planning, selection of the most suitable used to calculate probabilities and percentiles for
farming enterprise, the provision of proper equip- standard distributions, normal scores, t-tests and
ment and the introduction of protective measures confidence intervals, chi-square tests, and non-para-
against severe weather conditions all depend to some metric statistics. It can be used to plot data for
extent on the quality of the climatological analyses of regression and correlation analysis and analysis of
the agroclimatic and related data, and hence, on the time series. INSTAT is designed to provide a range of3–10 GUIDE TO AGRICULTURAL METEOROLOGICAL PRACTICES
climate analyses. It has commands for 10-day, for much shorter periods than those used for
monthly and yearly statistics. It calculates water macroclimatic analyses, provided that they can
balance from rainfall and evaporation, start of rains, be related to some long reference series;
degree-days, wind direction frequencies, spell lengths, (c) For bioclimatic research, the physical envi-
potential evapotranspiration according to Penman, ronment should be studied at the level of the
and the crop performance index according to meth- plant or animal, or the pathogenic colony
odology used by the Food and Agriculture itself. Obtaining information about radiation
Organization of the United Nations (FAO). The useful- energy, moisture and chemical exchanges
ness of INSTAT for agroclimatic analysis is illustrated involves handling measurements on the
in Sivakumar et al. (1993): the major part of the analy- much finer scale of microclimatology;
sis reported here was carried out using INSTAT. (d) For research on the impacts of a changing
climate, past long-term historical and future
climate scenarios should be used.
3.6.1 Series checks
Before selecting a series of values for statistical treat-
3.6.2.1 Reference periods
ment, the series should be carefully examined for
validity. The same checks should be applied to series of The length of the reference period for which the
agrometeorological data as to conventional climato- statistics are defined should be selected according to
logical data; in particular, the series should be checked its suitability for each agricultural activity. Calendar
for homogeneity and, if necessary, gaps should be filled periods of a month or a year are not, in general, suit-
in. It is assumed that the individual values will have able. It is often best either to use a reduced timescale
been carefully checked beforehand (for consistency or, alternatively, to combine several months in a way
and coherence) in accordance with section 4.3 of the that will show the overall development of an agricul-
Guide to Climatological Practices (WMO-No. 100). tural activity. The following periods are thus
suggested for reference purposes:
Availability of good metadata is essential during (a) Ten-day or weekly periods for operational
analysis of the homogeneity of a data series. For statistical analyses, for instance, evapotran-
example, a large number of temperature and precipi- spiration, water balance, sums of temperature,
tation series were analysed for homogeneity (WMO, frequency of occasions when a value exceeds
2004b). Because some metadata are archived in the or falls below a critical threshold value, and so
country where those observations were made, the forth. Data for the weekly period, which has
research could show that at least two thirds of the the advantage of being universally adopted
homogeneity breaks in those series were not due to for all activities, are difficult to adjust for
climate change, but rather to instrument relocations, successive years, however;
including changes in observation height. (b) For certain agricultural activities, the periods
should correspond to phenological stages or
to the periods when certain operations are
3.6.2 Climatic scales
undertaken in crop cultivation. Thus, water
In agriculture, perhaps more than in most economic balance, sums of temperature, sequences of
activities, all scales of climate need to be considered days with precipitation or temperature below
(see 3.2.1): certain threshold values, and the like, could
(a) For the purpose of meeting national be analysed for:
and regional requirements, studies on a i. The mean growing season;
macroclimatic scale are useful and may be ii. Periods corresponding to particularly crit-
based mainly on data from synoptic stations. ical phenological stages;
For some atmospheric parameters with little iii. Periods during which crop cultivation,
spatial variation, for example, duration of plant protection treatment or preventive
sunshine over a week or 10-day period, such measures are found to be necessary.
an analysis is found to be satisfactory;
(b) In order to plan the activities of an agricultural These suggestions, of course, imply a thorough
undertaking, or group of undertakings, it is knowledge of the normal calendar of agricultural
essential, however, to change over to the meso- activities in an area.
climatic or topoclimatic scale, in other words,
to take into account local geomorphological
3.6.2.2 The beginning of reference periods
features and to use data from an observational
network with a finer mesh. These comple- In agricultural meteorology, it is best to choose
mentary climatological series of data may be starting points corresponding to the biologicalCHAPTER 3. AGRICULTURAL METEOROLOGICAL DATA, THEIR PRESENTATION AND STATISTICAL ANALYSIS 3–11
rhythms, since the arbitrary calendar periods Any one of the statistics mean, median, mode and
(month, year) do not coincide with these. For mid-interquartile range would seem to be suitable
example, in temperate zones, the starting point for use as an estimator of the population mean m. In
could be autumn (sowing of winter cereals) or order to choose the best estimator of a parameter
spring (resumption of growth). In regions subject to from a set of estimators, three important desirable
monsoons or the seasonal movement of the properties should be considered. These are unbias-
intertropical convergence zone, it could be the edness, efficiency and consistency.
onset of the rainy season. It could also be based on
the evolution of a significant climatic factor
3.6.4 Frequency distributions
considered to be representative of a biological cycle
that is difficult to assess directly, for example, the When dealing with a large set of measured data, it
summation of temperatures exceeding a threshold is usually necessary to arrange it into a certain
temperature necessary for growth. 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
3.6.2.3 Analysis of the effects of weather
class is called the frequency for that class. The
The climatic elements do not act independently on number of classes chosen depends on the number
the biological life cycle of living things: an analyti- of observations. As a rough guide, the number of
cal study of their individual effects is often illusory. classes should not exceed five times the logarithm
Handling them all simultaneously, however, (base 10) of the number of observations. Thus, for
requires considerable data and complex statistical 100 observations or more, there should be a maxi-
treatment. It is often better to try to combine several mum of 10 classes. It is also important that adjacent
factors into single agroclimatic indices, considered groups do not overlap. Table 3.1 serves as the basis
as complex parameters, which can be compared for Table 3.2, which displays the result of this oper-
more easily with biological data. ation as a grouped frequency table.
3.6.3 Population parameters and The table has columns showing limits that define
sample statistics classes and another column giving lower and upper
class boundaries, which in turn give rise to class widths
The two population characteristics m and s are or class intervals. Another column gives the mid-marks
called parameters of the population, while each of of the classes, and yet another column gives the totals
the sample characteristics, such as sample mean –x of the tally known as the group or class frequencies.
and sample standard deviation s, is called a sample
statistic. Another column contains entries that are known as
the cumulative frequencies. They are obtained from
A sample statistic used to provide an estimate of a the frequency column by entering the number of
corresponding population parameter is called a observations with values less than or equal to the
point estimator. For example, x– may be used as an value of the upper class boundary of that group.
estimator of m, the median may be used as an esti-
mator of m and s2 may be used as an estimator of the The pattern of frequencies obtained by arranging
population variance s2. data into classes is called the frequency
Table 3.1. Climatological series of annual rainfall (mm) for Mbabane, Swaziland (1930–1979)
Year 0 1 2 3 4 5 6 7 8 9
193- 1 063 1 237 1 495 1 160 1 513 912 1 495 1 769 1 319 2 080
194- 1 350 1 033 1 707 1 570 1 480 1 067 1 635 1 627 1 168 1 336
195- 1 102 1 195 1 307 1 118 1 262 1 585 1 199 1 306 1 220 1 328
196- 1 411 1 351 1 115 1 256 1 226 1 062 1 546 1 545 1 049 1 830
197- 1 018 1 690 1 800 1 528 1 285 1 727 1 704 1 741 1 667 1 2603–12 GUIDE TO AGRICULTURAL METEOROLOGICAL PRACTICES
distribution of the sample. The probability of 3.6.4.1.1 Probability based on normal
finding an observation in a class can be obtained distributions
by dividing the frequency for the class by the
total number of observations. A frequency A normal distribution is a highly refined frequency
distribution can be represented graphically with a distribution with an infinite number of very
two-dimensional histogram, where the heights of narrow classes. The histogram from this
the columns in the graph are proportional to the distribution has smoothed-out tops that make a
class frequencies. continuous smooth curve, known as a normal or
bell curve. A normal curve is symmetric about its
3.6.4.1 Examples using frequency centre, having a horizontal axis that runs
distribution indefinitely both to the left and to the right, with
the tails of the curve tapering off towards the axis
The probability of an observation’s falling in class in both directions. The vertical axis is chosen in
10
number five is 50 = 0.2 or 20 per cent. That is the such a way that the total area under the curve is
same as saying that the probability of getting exactly 1 (one square unit). The central point on
between 1 480 mm and 1 620 mm of rain in the axis beneath the normal curve is the mean m
Mbabane is 20 per cent, or once in five years. The and the set of data that produced it has a standard
probability of getting less than 1 779 mm of rain in deviation s. Any set of data that tends to give rise
Mbabane as in class six is 0.94, which is arrived at to a normal curve is said to be normally distributed.
by dividing the cumulative frequency up to this The normal distribution is completely characterized
point by 50, the total number of observations or by its mean and standard deviation. Sample
frequencies. This kind of probability is also known statistics are functions of observed values that are
as relative cumulative frequency, which is given as used to infer something about the population
a percentage in column seven. From column seven, from which the values are drawn. The sample
one can see that the probability of getting between mean –x and sample variance s2, for instance, can
1 330 mm and 1 929 mm of rain is 98 per cent be used as estimates of population mean and
minus 58 per cent, or 40 per cent. Frequency population variance, respectively, provided the
distribution groupings have the disadvantage that relationship between these sample statistics and
certain information is lost when they are used, such the populations from which the samples are drawn
as the highest observation in the highest frequency is known. In general, the sampling distribution of
class. means is less spread out than the parent population.
Table 3.2. Frequency distribution of annual precipitation for Mbabane, Swaziland (1930–1979)
1 2 3 5 6 7
Group boundaries Group limits or Mid-mark xi Frequency fi Cumulative Relative
class interval frequency Fi cumulative
frequency (%)
1 879.5–1 029.5 880–1 029 954.5 2 2 4
2 1 029.5–1 179.5 1 030–1 179 1 104.5 8 10 20
3 1 179.5–1 329.5 1 180–1 329 1 254.5 15 25 50
4 1 329.5–1 479.5 1 330–1 479 1 404.5 4 29 58
5 1 479.5–1 629.5 1 480–1 629 1 554.5 10 39 78
6 1 629.5–1 779.5 1 630–1 779 1 704.5 8 47 94
7 1 779.5–1 929.5 1 780–1 929 1 854.5 2 49 98
8 1 929.5–2 079.5 1 930–2 079 2 004.5 0 49 98
9 2 079.5–2 229.5 2 080–2 229 2 154.5 1 50 100
Total: 50 – –CHAPTER 3. AGRICULTURAL METEOROLOGICAL DATA, THEIR PRESENTATION AND STATISTICAL ANALYSIS 3–13
This fact is embodied in the central limit theorem; The meaning here is that the X-score lies one stand-
it states that if random samples of size n are drawn ard deviation to the right of the mean. If a z-score
from a large population (hypothetically infinite), equivalent of X=74 is computed, one obtains:
which has mean m and standard deviation s, then
– has X − μ 74 − 80 −6
the theoretical sampling distribution of x Z= = = = −1.5 (3.3)
σ σ 4 4
mean m and standard deviation . The
n
theoretical sampling distribution of .–xcan be closely The meaning of this negative z-score is that the
approximated by the corresponding normal curve original X-score of 74 lies 1.5 standard devia-
if n is large. Thus, for quite small samples, tions (that is, six units) to the left of the mean.
particularly if one knows that the parent A z-score tells how many standard deviations
population is itself approximately normal, the removed from the mean the original x-score is,
theorem can be confidently applied. If one is not to the right (if Z is positive) or to the left (if Z is
sure that the parent population is normal, negative).
application of the theorem should, as a rule, be
restricted to samples of size ≥30. The standard There are many different normal curves due to the
deviation of a sampling distribution is often called different means and standard deviations. For a fixed
the standard error of the sample statistic concerned. mean m and a fixed standard deviation s, however,
Thus σ X = σ is the standard error of .–x there is exactly one normal curve having that mean
n
and that standard deviation.
A comparison among different distributions with
different means and different standard deviations Normal distributions can be used to calculate prob-
requires that they be transformed. One way would be abilities. Since a normal curve is symmetrical, having
to centre them about the same mean by subtracting a total area of one square unit under it, the area to
the mean from each observation in each of the popula- the right of the mean is half a square unit, and the
tions. This will move each of the distributions along same is true for the area to the left of the mean. The
the scale until they are centred about zero, which is the characteristics of the standard normal distribution
mean of all transformed distributions. Each distribu- are extremely well known, and tables of areas under
tion will still maintain a different bell shape, however. specified segments of the curve are available in
almost all statistical textbooks. The areas are directly
expressed as probabilities. The probability of encoun-
3.6.4.1.2 The z-score
tering a sample, by random selection from a normal
A further transformation is done by subtracting the population, whose measurement falls within a speci-
mean of the distribution from each observation fied range can be found with the use of these tables.
and dividing by the standard deviation of the distri- The variance of the population must, however, be
bution, a procedure known as standardization. The known. The fundamental idea connected with the
result is a variable Z, known as a z-score and having area under a normal curve is that if a measurement X
the standard normal form: is normally distributed, then the probability that X
X−μ will lie in some range between a and b on any given
Z=
σ (3.1) occasion is equal to the area under the normal curve
between a and b.
This will give identical bell-shaped curves with
normal distribution around zero mean and stand- To find the area under a normal curve between
ard deviation equal to unit. the mean m and some x-value, convert the x into
a z‑score. The number indicated is the desired
The z-scale is a horizontal scale set up for any given area. If z turns out to be negative, just look it up
normal curve with some mean m and some standard as if it were positive. If the data are normally
deviation s. On this scale, the mean is marked 0 distributed, then it is probable that at least 68 per
and the unit measure is taken to be s, the particular cent of data in the series will fall within ±1s of
standard deviation of the normal curve in question. the mean, that is, z = ±1. Also, the probability is
A raw score X can be converted into a z-score by the 95 per cent that all data fall within ±2s of the
above formula. mean, or z = ±2, and 99 per cent within ±3s of the
mean, or z = ±3.
For instance, with m = 80 and s = 4, in order to
formally convert the X-score 85 into a z-score, the
3.6.4.1.3 Examples using the z-score
following equation is used:
X − μ 85 − 80 5 Suppose a population of pumpkins is known to
Z= = = = 1.25
σ 4 4 (3.2) have a normal distribution with a mean andYou can also read
