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What can modern agricultural meteorology do for the subsistence farmers?


Abstract and Figures Paper conclusions: Modern agrometeorology can resort to a number of sources of data and techniques of analysis, such crop models, Geographic Information Systems, Random Weather Generators and spatial interpolation techniques. In addition, the transmission of crop and weather data from the rural areas to the national agrometeorological services is now easier than in the past. National meteorological services should focus on two aspects to reduce the wasteful use of climate resources: • develop decision tools that are calibrated against local (village) data, for use by farmers in combination with the observation of the local weather; • based on the local information and the use of models, evaluate the best tactical management decisions for farmers, and broadcast the information in real time together with the weather forecasts.
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What can modern agricultural meteorology do for the
subsistence farmers?
By René Gommes
Senior agrometeorologist
Environment and Natural Resources Service (SDRN)
FAO, Rome
1. Introduction
The factors, which determine the availability of agricultural products at the
local level (farm, village), are environmental conditions and management. The
environment includes biophysical factors (climate, soil, pests, land
available…), while management encompasses the decisions taken by farmers
Management decisions are determined by the knowledge of the interactions
between the environment, the characteristics of crops and animals,
technology, economic factors and the institutional context (including customs,
government rules, etc). By definition, economic factors play a relatively minor
role for subsistence farmers.
Among the listed factors, weather remains the largest source of variability of
farm outputs, directly and indirectly (pests). Depending on the level of
development, roughly 20 to 80% of the inter-annual variability of yields stems
for the variability of weather. Losses due to pests, diseases and weeds are
normally around 15% (Oerke et al., 1994). Post-harvest losses are also of the
same order of magnitude.
Extreme agrometeorological events are factors, which often are at the same
time rare (low statistical frequency) and characterised by high intensities.
They include for instance large pest outbreaks, fire, torrential rains, tropical
cyclones etc. They can provoke massive destruction of infrastructure, crops,
livestock, fishing gear, etc. and the loss of human life (Gommes, 1999a,
Extreme events are not dealt with in this paper for methodological reasons
and because far more losses are associated with the chronic and
inconspicuous effects of climate variability, like droughts, local pest attacks,
biodegradation of agricultural materials, hail etc.
In spite of their variability, rainfall, sunshine etc. constitute basic
environmental resources. Agrometeorology has the threefold objective of
studying the agroclimatic resources, to assess their impact (positive and
negative) on agriculture, and to use the knowledge to improve yields.
The effects of extreme factors on agriculture are difficult to model because of their sporadic
nature. Preventive measures and warning systems require investments that exceed by orders
of magnitude those used for the “normal” meteoroloical factors. In addition, extreme factors
are normally dealt with by the National Meteoroloical Services in the Ministry of Transport,
Public Works etc., and only rarely in Ministries of Agriculture
2. The scope of “modern” agrometeorology
Agrometeorology deals with all the weather-sensitive elements of agriculture
production, as illustrated below in figure 1. The spectrum of subjects is thus
rather wide. It includes pollination, animal migration, trafficability, transport of
pathogens by wind, irrigation, climate manipulation and artificial climates,
weather risk assessments, the use of weather forecasts in farming, crop yield
and phenology forecasts and particularly advice to farmers… as well as the
required data and methods.
Figure 1: Agrometeorology deals with all the weather-sensitive
components of the chain leading from production to consumption of all
agricultural products, specifically including animals and plants (after
Gommes, 1998a).
Managed production
“Wild” production
Agrometeorology now relies on a package of new tools, which define
« modern agrometeorology». They include data acquisition techniques
(ground observation, aircraft and satellite), data transmission techniques
(including the Internet) and data analysis (models and other software).
3. Models
Most text borrowed from Gommes 1999b, figure original.
A model is a programme (and the science behind it) that simulates (predicts)
the behaviour (output) of a plant (e.g. yield) from environmental conditions
(inputs, incl. management) and variables describing the plant’s ecophysiology
(parameters). There is in fact a large variety of models (as well as associated
problems) which cannot be described here (refer to Gommes 1999b). Figure 2
below schematically illustrates the operation of a typical model
Crop modelling, and most numerical applications in natural sciences became
actually possible because of the development of computers, and it is
interesting to compare the sophistication of current work with the methods and
approaches prevailing 15 to 20 years ago (Weiss, 1981; Gommes 1983 and
The volume by Albert Weiss is interesting in that it shows that most problems
being dealt with today in crop-climate relations were already on the agenda
twenty years ago: problems of scale, real-time collection of data and
modelling etc. There are, however, three major new issues in the field of crop-
climate simulation: geographic information systems (GIS), the spatial
interpolation of agroclimatic data, and random weather generators (RWG).
Figure 2: schematic representation of a crop simulation model showing the
interaction between environmental factors, crop growth and development,
and management.
Daily biomass
Partitioning of biomass between plant
organs (phenology)
Most popular models now have an “associated” Random Weather Generator,
a programme that generates synthetic weather series for a given location
They are extremely useful for many applications, from risk evaluation to crop
Of the associated models and tools, Geographic Information Systems have
become ubiquitous. GIS techniques, normally in conjunction with
geostatistical software, are used to prepare the spatial input data for the
regional applications; they are used after model runs to format and present
the output and analyses.
Finally, weather conditions are normally not knows at the exact location where
a model is run. Geostatistical tools are now widely used to estimate weather
condition at the location where a model is being run, although there is no
meteorological station.
There is clear complementary nature of simulation models and satellite data,
not only because remote sensing can contribute to estimating surface
agrometeorological variables
, but also because it can provide some model
inputs, such as phenology (planting dates) and Leaf Area Index, an essential
model variable.
4 FAO and agrometeorology
In an organisation like FAO, it is difficult to exactly delimit the “mandate” of
Agrometeorology. The “Agrometeorology Group”, formerly in the Agriculture
Department, was aggregated in 1994 with Remote Sensing and Geographic
Information Systems -GIS-, the “Rural Energy Group”, to create the new
Environment and Natural Resources Service under the Sustainable
Development Department. This constitutes a logical decision insofar as
climate encompasses some of the main renewable natural resources.
Unlike other organisations and institutions (such as WMO and
Agrometeorology Departments in various universities), FAO adopts a rather
restrictive definition of agrometeorology. Services and products with a marked
agroclimatic component
are provided by other Departments of the
Organization. For instance, irrigation scheduling is dealt with by the Land and
Water Development Division, Water Resources Development and
Management Service. Microclimate manipulation is an essential component of
many of the projects of the Plant Production Service, in particular for
vegetables. The same could be said about plant protection and production
(including forestry), aquaculture development, etc.
The only section of FAO with an explicit mandate to cover agricultural
climatology is the Agricultural Meteorology Group. Its main activities include
agroclimatic databases, agrometeorology and remote-sensing based crop
monitoring and forecasting, international collaboration in the field of climate
RWG outputs are obviously based on the statistical properties of real world weather data
from the same location.
They include rainfall, in combination with ground observations, actual evapotranspiration,
leaf temperature.
Adapted from Gommes, 1998c
Agroclimatology is assumed to include agrometeorology.
and weather, including field projects. Recently Climate-change has become a
major activity of the Group.
5 Agrometeorological advice to farmers
While FAO has a long and good record in agricultural extension at all levels,
agrometeorological advisory services to farmers have so far received little
attention. It is well recognised that this is an important and potentially very
useful field (Gommes, 1992). The Agrometeorology Group has repeatedly
tried to develop some activities in this area, but was never able to attract
donor attention.
5.1 Weather forecasts
Weather forecasts play an essential part in many farming operations. For
instance, weeding is best done in a rainless period, planting requires regular
but not too heavy rain, spraying pesticides cannot be done in windy weather,
etc. The main difficulty is often to present the forecasts in such as way that
they are understandable to farmers, thus avoiding jargon and ensuring that
the uncertainty inherent to all forecasts is duly understood. The general issue
issue of communicating information to the farming community was recently
discussed by Weiss et al. (2000)
5.2 Response farming applications
“Response farming” is a methodology developed by Stewart (1988), based on
the observation that farmers can improve their return by closely monitoring
on-farm weather and by using this information in their day to day management
Figure 3: Flag diagram for Niamey (Niger, based on data from 1953 to
1984), showing the dependence of the total seasonal rain on the date of
the beginning of the season (simplified from Gommes, 1992)
110 120 130 140 150 160 170 180 190 200 210
Number of day(1-365) when season starts
Total seasonal rainfall
Stewart’s original work relied a lot on “flag diagrams”, like the one illustrated in
figure 3. Such a simple diagram allowed farmers to determine the likely
amount of rainfall to be expected based on the starting date of the since, and
hence to plan the choice of varieties and soil types accordingly. Operational
flag diagrams must be based on recent years to take into account short-lived
climate fluctuations.
Response farming emphasises the use of quantitative current data that are
then compared with historical information and other local
reference data
(information on soils etc.). This is a simple variant of the what-if approach.
What about planting now if only 25 mm of rainfall was received from the
beginning of the season? What about using 50 Kg N-fertiliser if rainfall so far
has been scarce and the fertiliser will increase crop water requirement and
the risk of a water stress?
The method implies that, using the long-term weather series, decision tools
(usually in tabular or flowchart forms) have been prepared in advance. They
are based on
the knowledge of local environmental/agricultural conditions (reference
the measurement of local “decision parameters” by local extension officer
or farmer;
economic considerations where applicable.
Crop weather models can improve response farming in two different ways. To
start with, the decision tables can be improved by using simulation models to
better understand the impact of local weather on local crops. Next, models
could be run with data of the current year to experiment with themanagement
options and decide on the most appropriate strategies.
In developing countries, the decision-tools must be prepared by National
Agrometeorological Services in collaboration with Agricultural Extension
Services and subsequently disseminated to farmers. The third operation will
be the most difficult in practice (WMO/CTA, 1992).
A similar concept to response farming is flexcropping; it is used in the context
of a crop rotation where summer fallow is a common practice, especially in
dry areas, like the Canadian prairies. Rotations are often described as 50:50
(1 year crop, 1 year fallow) or 2 in 3 (2 years crop, 1-year fallow). The term
flex crop has emerged to describe a less rigid system where a decision to re-
crop (or not) is made each year based on available soil moisture and the
prospect of getting good moisture during the upcoming growing season
(Zentner et al., 1993; Peter Dzikowski and Andy Bootsma, personal
Weisensel et al. (1991) have modelled the relative profitability and riskiness of
different crop decision models that might be used in an extensive setting. Of
particular interest is the value of information added by the availability of spring
A simple example of this could be, for instance, a threshold of air moisture or sunshine
duration to decide on pest risk, or a threshold of salt content of water to decide on irrigation-
salinity risk. Normally, other parameters (economic) also play an important part.
soil moisture data and by dynamic optimisation. The simulation has shown
that flexcropping based on available soil moisture at seeding time is the most
profitable cropping strategy. The authors stress the importance of accurate
soil moisture information.
The writer is not aware of flexcropping applications in developing countries,
but the concept would be easy to implement.
5.3 Farm management and planning in modern farming
Farmers have been using weather forecasts directly for a number of years to
plan their operations, from planting wheat to harvesting hay and spraying
fungicides! Models, however, have not really entered the farm in spite of their
potential. The main causes seem to be a mixture of lack of confidence and
lack of data (Rijks, 1997).
Basically three categories of applications can be identified:
what-if experiments to optimise the economic return from farms, including
real-time irrigation management. This is the only area where models are
well established, including in some developing countries (Smith, 1992);
optimisation of resources (pesticides, fertiliser) in the light of increasing
environmental concern (and pressure);
risk assessments, including the assessment of probabilities of pest and
disease outbreaks and the need to take corrective action..
Contrary to most other applications, on-farm real-time operations demand well
designed software that can be used by the non-expert, as well as a regular
supply of data.
In theory, some input could be taken automatically from recording weather
stations, but the reporter did not come across many specific examples. What
comes closest is probably a publication by Hess (1996). The author
underlines the sensitivity of an irrigation simulation package to errors in the
on-farm weather readings.
Systems have been described where some of the non-weather inputs come
from direct measurement (Thomson and Ross, 1996). Model (PNUTGRO)
parameters were adjusted as the system was used based on soil water
sensor responses to drying. An expert system determined which sensor
readings were valid before they could be used to adjust parameters.
Irrigation systems have a lot to gain from using weather forecasts rather than
climatological averages for future water demand. Fouss and Willis (1994)
show how daily weather forecasts, including real-time rainfall likelihood data
from the daily National Weather Service forecasts can assist in optimising the
operational control of soil water and scheduling agrochemical applications.
The authors indicate that the computer models will be incorporated into
decision support models (Expert Systems) which can be used by farmers and
farm managers to operate water-fertiliser-pest management systems.
Cabelguenne et al. (1997) use forecast weather to schedule irrigation in
combination with a variant of EPIC. The approach is apparently so efficient
that discrepancies between actual and weather forecasts led to a difference in
tactical irrigation management.
5.4 Implementation of “modern” response farming in developing countries
The tools described above are not only for the developed farmer. They can
contribute towards greater food security through less weather-dependent
practices. This can be achieved by more efficient agrometeorological advisory
services to farmers to stabilise their yields through better management of
agroclimatic resources as well as other inputs (fertiliser, pesticides). The
section below is based on a project document, which was recently prepared
for the Mekong delta, the main rice-growing areas of Vietnam
Agrometeorological advisory services to farmers can be improved only
through better co-ordination between the Agricultural Extension Services
(AExS) , the National Agrometeorological Services (NAmS) and the Media. In
particular, “response farming” has to be “evolved” (experimented) first under
local pilot conditions with improved data collected by NAmS and AExS before
it can be applied on a wider scale.
The general approach includes several steps: the preparation of the
methodology (development of the decision tools) by NAmS and AExS, the
experimentation of the management scheme by several pilot areas (farmers,
communes), the critical evaluation of the impact of the advise on farm output
in terms of quantities and regularity, and eventually the adoption of the
method in a larger area. Public information and the involvement of the media
constitute an essential component of the methodology at all stages.
The central issue, as indicated above, is the preparation of the decision
tables. It is proposed to adopt the three steps below as the main objectives
of a “modern” response farming project.
A. Identification of main focus for agrometeorological advisory services and
response farming
Work out the technical and operational details of the pilot work to be
conducted in several geographic areas and several thematic fields, for
instance pest and disease risk for some of the main crops, flood and typhoon
warnings, cold weather risk, irrigation water salinity...
Through contacts with the responsible government offices (mainly AExS)
assess the interest/willingness of farmers to be involved on a pilot basis, and
select several (5?) “pilot” farmers in some pilot areas (municipalities,
communes; 12 ?)
Provide each pilot area with the minimum required equipment
, and brief
farmers and train observers on their use as part of a more comprehensive
training package.
This section is largely based on Gommes, 1998c.
Tides move up the delta, and farmers often have to wait until the residual salt has been
washed out by rainfall or river water.
This will include maximum and minimum temperatures, air moisture, rainfall, wind speed
and direction (all stations) and water salinity (MD only).
Work out operational detail of the collection of the local parameters, as well as
their regular and timely transmission to the NAmS and AExS to help
develop/evaluate the experimental “decision tools”.
B. Tested decision tools
Find out the most suitable institutional arrangement for the development of the
Decision Tools. This includes the level of involvement of central (AExS,
NAmS) and Regional offices, agronomic research, etc. Briefing and training
sessions will be necessary at the regional offices (sub-offices). A more
sustainable system can be developed if the provincial/regional offices are fully
Carry-out the statistical, agronomic and economic analysis of the response of
the local crop production system as a function of the local Decision
Parameters. This takes into consideration “reference” data, i.e. knowledge
about statistical behaviour of variables like rainfall and water salinity, soil
types, likely yield with and without the management decision. Additional
parameters can be estimated by the sub-offices or the NAmS , for instance
estimates of local (pilot area) radiation based on the interpolation carried out
at the sub-office using ground and satellite data.
Transmit the experimental advice to farmers through the agreed channels,
and monitor implementation. Make provision to be able to compare output
“with” and “without” advice. It is also necessary that the farmers participating
in the exercise should be, if necessary, compensated for losses due to the
advice. The quantitative evaluation of the economic gain of the
agrometeorological advice will be given due consideration.
Phenology forecasts (which should be routinely produced by NAmS), yield
forecasts, regular weather forecasts and price information should be made
available and their use monitored, in order to assess their potential for the
farmers’ population at large at a later stage of the project.
Ensure regular contacts with the local and national media (written, radio and
television) and ensure that farmers are made aware of the rationale, methods
etc. behind the agrometeorological advisory services outside the pilot areas
as well.
C. Agrometeorological advisory services are expanded beyond the pilot area
AExS and NAmS jointly critically evaluate the outcome of the pilot phase, and
make final decision on the methodology and the feasibility and relevance of
expanding the approach to a wider area (several provinces).
Based on the analyses above, prepare details of plans to expand the method,
i.e. decide which geographic areas to cover, print the documentation about
the methodology, prepare the new decision tools (inclusive of programmable
calculator programmes), prepare training and briefing material and train
provincial officers in the relevant ministries, work out the most appropriate
channels to develop the new decision tools and channel the advice, etc.
D. A comprehensive plan to improve Agrometeorological advisory services
Based on the experience gained during the pilot phase, prepare an economic
evaluation of agrometeorological advice to farmers in the whole country,
including different agro-economic and agro-climatic zones. Evaluate the
potential to apply the experience acquired so far in other fields, for instance
grain drying, crop insurance, planting dates...
Critically assess the shortcomings and successes of the activity carried-out so
far, in particular as regards institutional working arrangements and bottle-
necks, the potential role of other partners beyond AExS and NAmS, but also
the role of agricultural research, University, etc. Identify areas where
additional information/studies will be required to improve the efficiency of
agrometeorological advice to farmers.
Institutionalise the working arrangements developed during the pilot and
subsequent phase, and prepare long-term plan to cover more areas and more
crops, in particular cash crops...
6. Conclusions
Modern agrometeorology can resort to a number of sources of data and
techniques of analysis, such crop models, Geographic Information Systems,
Random Weather Generators and spatial interpolation techniques.
In addition, the transmission of crop and weather data from the rural areas to
the national agrometeorological services is now easier than in the past.
National meteorological services should focus on two aspects to reduce the
wasteful use of climate resources:
develop decision tools that are calibrated against local (village) data, for
use by farmers in combination with the observation of the local weather;
based on the local information and the use of models, evaluate the best
tactical management decisions for farmers, and broadcast the information
in real time together with the weather forecasts.
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Risk information products are building stones of agrometeorological services as for example shown for various forms of land degradation in Africa by Stigter et al. (2005) . This picture was drawn even for Africa as early as more than 10 years ago (Olufayo et al. 1998). To design improvements in farm applications of risk information products in agrometeorology, a questionnaire/interview approach is necessary to know the present status of such applications and to make improved assessments of farmers’ needs (e.g. Abdalla et al. 2002; Onyewotu et al. 2003; Stigter 2007a). The theory was discussed in Stigter et al. (2005). In this same context Stigter et al. (2008) state that after having heard so many examples in China now, it would be very helpful if studies were made into the efficiency of the information channels and the opinion of farmers on the services and these channels (look at the example in Box III.2.23). And also on eventual alternatives or additions in services and information channels, in the ways suggested by the work of Stigter et al., as presented in the CAgM workshop in New Delhi in 2006 (Stigter et al. 2007).
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Inexpensive portable computers are now qualitatively equivalent to micro-computers in most respects. Their features preclude their use for applications which require high speeds, substantial random-access memory, or both. However, their low price makes them very attractive for many uses in training and operational agrometeorology and for the development and use of unsophisticated models.-from Author
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A real-time irrigation scheduling computer package for use on farms is described. The package comprises four models; a reference crop evapotranspiration model, an actual evapotranspiration model, a soil water balance model and an irrigation forecast model. The models used have been shown to produce reliable estimates of the soil water balance, however, the predictions are sensitive to the accuracy of the input data measured on the farm. This paper summarises the experience of applying such a program to supplementary irrigation in the UK.
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An irrigation scheduling tool was developed around a previously developed system that adjusts key parameters influencing the water balance components of the PNUTGRO crop model. These parameters were adjusted as the system was used based on soil water sensor responses to drying. An expert system determined which sensor readings were valid before they could be used to adjust parameters. A field test of the irrigation scheduling algorithms indicated that sensors could be relied on less as better predictions of soil water status were made. Comparisons of two very different sensor-based scheduling environments (one for Florida and one for Virginia) indicated potential improvements to the algorithms.
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Agrometeorological information, used for decision making, represents part of a continuum; at the other end is scientific knowledge and understanding. Other components of this continuum are the collection of data and transforming data into useful information. Information has value when it is disseminated in such a way that the end-users get the maximum benefit in applying its content. This paper explores the potential of the new information and communications technologies to improve the access to agrometeorological information. The Internet will play an important role in the collection and transfer of information. In developing countries, Multi-Purpose Community Telecentres (MCTs) will be the equivalent of an information supermarket. Radio can be used to transfer information from MCTs to rural areas. Using response farming as an example, a prototype information system that can have wide applicability is suggested. Procedures on evaluating the impact of agrometeorological information are provided. Future concerns about the information needs of diverse end-users, information on a fee basis, and the training needs of end-users and intermediaries are discussed. Although modern technology has improved agrometeorological information and increased the number of end-users, continued improvements are necessary to ensure that the content of the information is adequate to fulfill the requirements of the farming communities.
This workshop, in response to developments in information technology, was divided into three main areas; methods of obtaining weather data, methods of changing data into weather related information for agriculture and forestry, and methods of disseminating this information.-from US Govt Reports Announcements, 16, 1982
The economic returns and riskiness of spring wheat (Triticum aestivum L.) production using fixed sequence rotations were compared to flexible cropping systems wherein the annual crop/fallow decisions are based on the level of available water at or near the time of planting. The study used 25 yr of data from a long-term crop rotation experiment conducted on a medium-textured, Orthic Brown Chernozemic soil at Swift Current, Saskatchewan. Fixed cropping systems included fallow-wheat (F-W), fallow-wheat-wheat (F-W-W), and continuous wheat (CW), while flex-cropping systems included 2YR-IF, 3YR-IF, and CW-IF. The study concluded that widespread use of flex-cropping practices by producers in southwestern Saskatchewan could increase farm-level net returns and reduce risks of financial loss, while potentially reducing soil degradation -from Authors
The real time EPIC-PHASE model developed at Toulouse Auzeville INRA was tested on a maize crop. The main objective of the trial was to evaluate the potential of the model in real time tactical irrigation management based on model predictions every 5 days. The trial compared the results of management using simple decision-making rules (SDR) with those resulting from model management (MOD).Compared with the standard version of EPIC, which is capable of simulating the evolution of soil water content, the option of real time enables integration of intermediate states and realization of step-by-step simulations. The trial reported here has demonstrated that it is possible to simulate different irrigation tactics and, hence, different irrigation amounts with weather forecasts.Results from the study showed that discrepancies between actual and weather forecasts led to a difference in tactical irrigation management. Retrospective simulation carried out with observed data demonstrated the capabilities of the model under non-limited water supply, to be used as an irrigation management tool to save water and reach a similar level of yield. Use of the model also reduced the risk of overirrigation and associated nitrogen leaching.
Monte Carlo simulation is used to compare the expected net returns and relative riskiness of alternative cropping strategies in Saskatchewan. The strategies include traditional fixed rotations and flexible ones that have been recommended by researchers. Three important conclusions follow. (a) Flexcropping based on available soil moisture at seeding time is the most profitable cropping strategy. (b) However, the value of measuring spring soil moisture depends crucially on the level of confidence the decision maker has in these measurements. (c) Finally, simple flexcrop strategies based on a break-even formula are an effective extension tool, perhaps more so than those found using dynamic optimization.