FOOD PRODUCTION
Agriculture and food production is the world's largest industry. More people are involved in agricultural
activities world-wide than all other occupations combined. Due to the tremendous changes in farming
techniques that have occurred in the last decades, yields have increased dramatically and politicians and
scientist spoke about the so-called ‘green revolution’.
Despite this green revolution, there are still regions of the world that do not have sufficient food.
Organisations like the Food and Agriculture Organisation (FAO) of the UN are running various projects to
help countries develop sustainable agricultural systems. The overall aim is to increase crop production and
productivity, and to improve food security, as well as the general promotion of economic development.
Earth observation techniques provide these projects with information on crop types and locations and
identify areas where control of the health and development of the crops is needed. Combined with other
spatial data (weather forecasts, soil parameters… ) this information is used to establish yield prediction
models that prevent or minimise the effects of food crises.
In developed countries, individual farmers use this technology to optimise their farm management and to
increase the cost/benefit of crop production. Through the availability of powerful and low-cost hardware,
together with simple image processing and task specific software, it is now possible for the farmer to
integrate aerial photographs and GIS.
Three application fields of remote sensing techniques shall be described in the following sections:
•
Crop inventory
•
Yield prediction
•
Precision farming
GAF / GeoVille
EC / JRC / CEO
EO Education and Training for Decision Makers
Contract No.: 14040-1998-06 F1PC ISP DE
Food Production
CROP INVENTORY
The survey and control of crops is one of the main applications of Earth observation for agriculture. There
is a world-wide need for crop statistics. Driven by this demand, along with improvements in the satellite
systems and in hardware/software, remote sensing has become increasingly important during the last
decade. It has emerged from fundamental research to operational use in this field. In developing countries,
these data can be used to reform the traditional agriculture methods and to set farms up more efficiently.
In the developed countries (i.e. in the EU), crop inventory will be used to control food production and to
regulate over-production.
Issues and information requirements
The target is to generate crop statistics on a global, national and regional level. It is therefore not
necessary that the entire area has to be observed by satellites, but various sampling methods can
considerably reduce the amount of information gathered. Specifically, the location, the area under
cultivation and the type of crop need to be assessed. In semi-arid or arid countries, the mapping of
irrigated crops is of major importance as well and is an important prerequisite to derive valid crop
inventory information. Crop inventories are rarely made on a one-off basis, and normally they are carried
out for other monitoring programmes.
An important aspect of crop inventories is the timely availability of information. To be useful for the
market, most of the required information must be available within a short time interval, depending on the
length of the growth season and cycle. Earth observation has proven to be flexible enough for this
purpose, unlike the more traditional information sources like aerial photographs and ground surveys.
How EO can help
Satellites are regarded as an objective instrument for the generation of agricultural statistics. The
extraction of this information is to some extent either relatively simple (as for crop location and area),
extractable with experience and knowledge (as for crop type and development) or is available after further
research and integration with other data (as for crop yield). In the EU for instance, satellites have been
proposed as an optional tool to support the administrative verification of the declared cultivated crop areas
and set-aside fields for agricultural subsidies.
Sample projects
• Irrigation Map Production for River Tajo, Spain
http://ewse.ceo.org/anonymous/construct/build.pl/637300
• Monitoring the UK Potato Crop using Satellite Earth Observation Data
http://ewse.ceo.org/anonymous/construct/build.pl/619362
• Remote control of subsidised crops http://www.gaf.de/agricul/seta.htm
• ALIS, an Agricultural Resource Monitoring Aid for Egypt
http://ewse.ceo.org/anonymous/construct/build.pl/643197
• Crop Information System in Romania http://ewse.ceo.org/anonymous/construct/build.pl/636137
• Synergistic use of SAR and SPOT data in the framework of crop control by remote sensing
http://ewse.ceo.org/anonymous/construct/build.pl/669317
• Synthetic aperture radar for agricultural monitoring
http://ewse.ceo.org/anonymous/construct/build.pl/619146
• Development and implementation of an expert geographic information system for the management of
sugar beet cultivation http://ewse.ceo.org/anonymous/construct/build.pl/639868
• The Rapid Estimate of Crop Area changes at the Level of the European Union
http://ewse.ceo.org/anonymous/construct/build.pl/607180
• U.S. Department of Agriculture Buys Indian IRS Products
http://www.spaceimage.com/home/apps/irs/usda.html
GAF / GeoVille
EC / JRC / CEO
EO Education and Training for Decision Makers
Contract No.: 14040-1998-06 F1PC ISP DE
FOOD PRODUCTION
CROP INVENTORY
Ü
Quantitative estimation of areas occupied by various crops in a
given region or country for the generation of crop statistics is a typical
task for supervision and planning for decision makers at the
governmental level or for agribusiness companies. EO data have
proven to be a useful tool for this application. It can provide accurate
acreage of cultivated land and - using multitemporal datasets - it can be
used to distinguish a range of different crop types and monitor crop
development as an indicative for potential yield.
Ü
For details refer to: http://www. gaf.de/agricul/seta.htm
The images below show four presentations of the same area by
different EO sensors and different acquisition dates. Vegetation
appears in different hues of red and bare soil and settlements in bluish
tones. The spectral appearance and its changes over time are used to
distinguish crop types and indicate potential yield.
Courtesy of: GAF
EO Education and Training for Decision Makers
Contract No.: 14040-1998-06 F1PC ISP DE
Food Production
YIELD PREDICTION
Good or bad harvests still have a significant impact on the economy of developed and developing
countries. In the US or Canada, the exports and hence the trade balance are affected, while in lesserdeveloped countries a bad harvest can cause severe food shortages and even famine. Therefore, the
quantitative estimation of crop yields is an issue of interest to policy decision-makers, the agribusiness
market and even the military.
According to FAO statistics, over 40% of the world’s labour force work in the agriculture sector. While
this percentage drops to less than 10% for developed countries, it is 61% in Africa, and 22% and 56% in
South America and Asia respectively.
The FAO established the Global Information and Early Warning System for Food and Agriculture
(GIEWS) in 1975 in the wake of a world food crisis caused by climatic events that took place between
1972 and 1973.
Issues and information requirements
Today various yield prediction models are in use or under development. These models also depend largely
on good sampling strategies for the derivation of the basic input data and the production of results with a
sound statistical basis.
As for crop inventories, the basic information needed for yield prediction include location, area under
cultivation and crop types. This information has to be collected at several moments during the growing
season in order to reflect the seasonal development. Daily meteorological information and derived
phenological data are thus required to model expected yields. In some cases a lot of such detailed and upto-date field data can be gathered easily and EO data only plays a marginal role. However, in areas and
countries with limited field surveys, satellite systems are available for the collection of this urgently
needed information.
How EO can help
Operational yield predictions are mostly based on readily available agro-meteorological and satellite
derived data. They do not depend on expensive and labour intensive ground surveys and are easily revised
as new data become available. Forecasts can be issued early and at regular intervals from the time of
planting until harvest. Within these prediction models, EO data serve mainly to generate information on
crop areas and crop development over the growing cycle.
Sample projects
• Sugar Beet Yield Prediction and Management
http://ewse.ceo.org/anonymous/construct/build.pl/618838
• The Cereal YES system http://ewse.ceo.org/anonymous/construct/build.pl/687531
• Improvement of the Yield Estimation of Maize with Remote Sensing
http://ewse.ceo.org/anonymous/construct/build.pl/672128
• Energy and water balance products for crop yield forecasting in Europe and Africa
http://ewse.ceo.org/anonymous/construct/build.pl/607893
• Agrometeorological Crop Forecasting http://www.fao.org/sd/eidirect/agromet/forecast.htm
• Agriculture and Commodities Monitoring and Forecasting
http://www.digitalglobe.com/applications/01.html
• The MARS Project - Monitoring Agriculture with Remote Sensing http://ceo-www.jrc.it/agridocs/mars.html
• Crop yield estimation via remote sensing project of the state institute of statistics (SIS) of Turkey
http://www.die.gov.tr/PROJECTS/CROP/crop.html
• Crop forecasting and statistics in Russia http://ewse.ceo.org/anonymous/construct/build.pl/662670
GAF / GeoVille
EC / JRC / CEO
EO Education and Training for Decision Makers
Contract No.: 14040-1998-06 F1PC ISP DE
FOOD PRODUCTION
YIELD PREDICTION
Ü A crop assessment and monitoring system was developed
to provide Russia with regular, accurate and up-to-date
information on crop production.
Ü EO data will be used to manage crop variability, estimate
importation requirements and ensure self-sufficiency in food
production.
Ü Three test sites (40km x 40km) in different Russian regions
(fig.1) were chosen to test the crop classification (mainly
cereals) for different climatic and soil conditions. Figure 2
shows one of the segments of the Krasnodar site.
Ü The results of field surveys in combination with computer-
For details refer to: http://ewse.ceo.org/anonymous /construct/build.pl/662670
aided image processing to identify crop type and maturity
(fig. 3) were input into a database to estimate the annual crop
yields.
Courtesy of: SCOT CONSEIL
EO Education and Training for Decision Makers
Contract No.: 14040-1998-06 F1PC ISP DE
Food Production
PRECISION FARMING
Farmers world-wide make substantial annual investments in farm chemicals and fertilisers, but still lose
crops due to pest infestations, plant diseases, and poor farming practices. The recent introduction of new
technologies such as EO, Geographic Information Systems (GIS) and Global Positioning Systems (GPS)
offer new possibilities for farm management. Precision Farming is a developing technology that has
introduced a new set of tools for the farm manager or even individual farmer. The particular goal of
precision farming is not necessarily to achieve maximum yield, but to optimise the methods and resources
used in farm management and to increase the long-term cost/benefit and sustainability of the soil.
Practically speaking, considerable savings can be made by the localised application of pesticides and other
production chemicals, fertilisers, and certain irrigation methods.
Issues and information requirements
The overall aim of precision farming is to assist farmers by offering them better spatial information about
the status of their soil and crops for the management of their fields. The economic factors justifying the
application of precision farming tools are increased income for farmers through yield improvements and
lower expenditures on agro-chemicals. Besides the economic considerations, precision farming is better
for the environment as well.
The required information is based on precise locations, therefore GPS is a prerequisite for precision
farming. The most important EO derived information for precision farming is the Normalised Difference
Vegetation Index (NDVI), which gives average information on the extent of leaf cover or biomass in a
field.
How EO can help
The reason for using Earth observation to support precision farming is the availability of up-to-date spatial
information related to the crop type, crop health, and their supply of water and nutrients. It is usually
assumed that crop development within a given plot or parcel is uniform. In practise of course this rarely
applies. By knowing the spatial variability due to, for example, crop diseases or basic soil properties with
a reliable geometric accuracy (2-5m is regarded sufficient in practise), fields and crops can be managed
more “precisely”, and treatments can be applied only to those parts which need it. This can be achieved by
specialised consultants, or by investing in small, dedicated hard-and software systems (such as combined
harvester yield mappers).
Sample projects
• Agimage (Satellite imagery and additional information for farmers)
http://ewse.ceo.org/anonymous/construct/details.pl/667637
• Assessment of Farming Practices http://www.digitalglobe.com/applications/02.html
• Management guidelines for precision farming
http://www.silsoe.cranfield.ac.uk/cpf/projects/HGCABMP/HGCABMP.htm
• Precision Farming; An Introduction http://www.silsoe.cranfield.ac.uk/cpf/papers/cabi/pfifinal.htm
• High Resolution Imagery will enhance the effectiveness of Precision Farming
http://www.spaceimaging.com/home/pubs/papers/ag_innov.html
• Remote Sensing and Precision Agriculture http://www.uswcl.ars.ag.gov/epd/remsen/rspreag.htm
• Introduction to Remote Sensing for Agriculture http://www.uswcl.ars.ag.gov/epd/remsen/rsagintr.htm
• Precision Farming, GIS and GPS: A Wave of the Future http://www.agro.com/spf.html
• Satellite Monitoring for Farm Decision Support Systems
http://ewse.ceo.org/anonymous/construct/build.pl/661955
GAF / GeoVille
EC / JRC / CEO
EO Education and Training for Decision Makers
Contract No.: 14040-1998-06 F1PC ISP DE
FOOD PRODUCTION
PRECISION FARMING
Ü Crop classification and evaluation of the condition of
crops by EO provides farmers with information about their
fields and hence assists in farm management decisions.
effected by clubroot disease on a cauliflower crop. The
images below show areas with broccoli crop affected by
mildew, a common fungal disease.
Ü Using such images to monitor their crops on a continual
basis, farmers can detect disease, water stress and other
factors that influence their crops at an early stage. It also
allows farmers to localise treatments (e.g. pesticides) to only
the parts of the field that require it. This helps to preserve the
environment, saves money and protects the consumer.
For details refer to: http://www .spaceimaging .com/home/pubs/papers /ag_innov.html
Ü The yellow arrows in the images above indicate areas
Courtesy of: Space Imaging
EO Education and Training for Decision Makers
Contract No.: 14040-1998-06 F1PC ISP DE