AGROECOLOGY
- MATHEMATICAL MODELS
In
an article published last May in Agropalca, an agricultural newspaper from
Canary Islands, Ginés de Haro, agronomist and agricultural adviser specialized
in banana crop, wrote an interesting article about the development of a
mathematical model for making a reliable harvest prediction in banana crop in
highly variable conditions of Canary Islands.
Original
article in Spanish, page 21: http://www.palca.es/REVISTA%20AGROPALCA%2033.pdf
Picture: http://www.infonortedigital.com/portada/images/noticias/Canarias2015/canarias2016/plataneras.jpg
You
may not know that agriculture uses every day a large number of mathematical
models that allow it to constantly improve agronomic results, with a reduced
environmental impact and increasing controlled production costs.
Why does agriculture need
mathematical models?
To
address the continuous need to adapt to biological cycles, of plants and
animals, of crops and environment, of pests and their predators, which can be
highly variable. Climatic variations are one of the key factors of variability,
with diversity of crop conditions (species, varieties, method of cultivation,
soil types, types of irrigation, altitude, slope and orientation, geography,
etc.).
But
plants and animals react to a relatively homogeneous way, despite widely
varying conditions. This uniformity of reaction and behavior, is what
mathematical models seek to demonstrate, measure, and anticipate.
We
can predict behaviors in all situations, if we know how to put all the variable
conditions in comparison with each other. Mathematical models are one of the
most effective ways to predict agricultural and biological cycles.
They
allow in particular to anticipate developments of insects or many diseases, to
anticipate the needs of plants, their cycles, their blooming dates, their
climatic adaptations.
This
is for example through the development of a specific mathematical model that
health authorities can prevent flights of desert locusts in Africa, and avoid
or reduce the famines they can cause.
Picture: http://www.scidev.net/objects_store/thumbnail/4E6020899E91132C60690CE6CCFD3FFE.jpg
On what are based mathematical
models suitable for agriculture?
They
are mostly based on climate records, which constitute a reliable information,
easy to collect and process, which will be used as a reference, and on
biological observations such as cycle of plants, or such as evolution of pest
and predator populations.
The
comparison of these two categories of elements, and the accumulation of
references from previous years, allows one hand to establish correlations, and
secondly to confirm and refine them year after year. This work over the long
term helps to establish the reliability of the model and to determine the
safety margins.
Once
phytosanitary models developed, they allow farmers to anticipate pest or
disease damages, through specific actions or curative sprayings in some cases
preventive in others, placed at the perfect time, or to develop monitoring or
prophylaxis methods.
Let's take a
very common example, which is also one of the first reliable models established
in fruit production. It is the Mills table, published in 1944, then adjusted by
several scientific teams. The development of this model enabled at the time of
its disclosure, a drastic reduction in the number of sprayings against scab,
combined with a huge increase in the effectiveness of protection, which gave it
a great popularity among apples and pears growers.
Scab is a
fungus that grows after rain. But some rains cause contaminations, and others
not, and their severity varies. Furthermore, protection against scab is
primarily preventive, since the spots, once seen, are almost impossible to
stop, require a large amount of sprayings to prevent them from getting worse,
and cause severe fruit depreciation.
Picture: http://www.omafra.gov.on.ca/english/crops/facts/apscabf6.jpg
What can the
farmer do to prevent damage?
He has to
spray before seeing spots. But again nothing is simple.
Has been
contaminating this rain?
Can a single
fog cause damage?
How soon
after rain should I intervene to prevent damage?
How long
after rain will the damage appear?
The grower
could for example decide to spray once a week, whatever happens, not to make
his life harder. He should therefore spray very frequently, not necessarily
with a sufficient efficiency, and with an exorbitant environmental and economic
cost.
He therefore
has the solution to use mathematical models like the Mills table or one of more
modern models. He just needs to be equipped with a humectometer, which measures
the duration of leaf wetness, and a recording thermometer, all equipment easy
to find and affordable, or he can simply take a subscription to a specialized
agro-climatic stations of his region.
Whatever the
way he chooses, he thus has, in real time, a reliable information about risks.
This information will allow him to take the most appropriate decision on the
need to spray and the severity of the contamination, thus also on the types of
products to use.
The
farmer, whatever production method he chooses, organic, conventional or
integrated, uses the same references and the same methods. The only change is
the list of pesticides to use in each situation.
Picture: http://www.agrometeo.fr/img/Tavelure_automne.jpg
The system
also works in reverse, ie by linking the cycle of crop with the weather
conditions, to determine periods of sensitivity to certain health or
physiological problems, and act accordingly.
Ginés de
Haro explains:
"We took different altitudes
above the sea and orientations (some farms to the north, others to the south)
(...)
We can draw some conclusions from
the model. As expected, the cycles are directly related with the altitude above
sea level (as the temperature drops gradually as we rise), but also an
influence of direction. For example a farm at 45 meters in Fuencaliente has a
cycle of two weeks shorter than a farm in Galdar at 16 meters, regardless of
the period."
In
the case of the Canary Islands, it is particularly important to the extent that
they are islands, quite far from each other, varied and mountainous, over which
the variability factors are huge, and despite this, the model allows reliable
forecasts in all situations.
The use of
mathematics in agriculture, even is a challenge for the future, as precision
agriculture, recent concept that wants to maximize agricultural production by
the accuracy of techniques used, is an unavoidable way for the future of the
humanity. This precision agriculture passes, among other things, by modeling a
variety of concepts, in order to make predictable all the currently
unpredictable aspect of agriculture, that is to say the influence of the
variable climatic conditions on living beings.
See what
happened this year across Europe. Abnormal weather has seriously disrupted the
harvest of many crops. French cereal farmers were strongly affected by these
disturbances.
But they
recognize they have been surprised. At the end of June, the wheat had normal
bulk density, the plants were beautiful. No one imagined that ears were so
little filled.
This is the
kind of problem it is important to learn to avoid, or at least to manage.
The farmer,
little by culture, little by force of circumstances, often live from day to
day, and the anticipation of future events is sometimes difficult.
The
use models can be a great help in optimizing the work.
The
emergence of new technologies in agriculture has been the opportunity to
develop appropriate mathematical models. For example, satellite imagery,
combined with GPS coordinates, invention of new sensors and adapted equipment,
allowed a 5% reduction of fertilizer inputs, sometimes more. http://www.agronewscastillayleon.com/el-uso-de-nuevas-tecnologias-como-el-gps-permite-ahorrar-hasta-un-5-en-fertilizantes
How?
Satellite imagery provides a state of the homogeneity of the field, by a simple
image, or by colorimetry (photosynthesis intensity), or by thermal imaging
(temperature of the leaf, so capacity of the plant to regulate its own
temperature). Images are indicative of the health condition and homogeneity of
crop, and allow, through adapted mathematical models, to accurately adjust the
dose of fertilizer to each sector of the plot. These technologies can also be
used for targeted pesticide applications, for accurate seeding and are being
studied for irrigation.
Picture: http://geovantage.com/app/uploads/2013/04/Sample1_20100709_NDVI-1024x653.jpg
To what extend mathematical models
are going to play an important role in the development of agroecology?
One
of the foundations of agroecology is agriculture, therefore food and renewable
raw materials production for human needs. The goal is to minimize the negative
environmental impact of agriculture which must be ever more productive in order
to cover the needs of the population. These needs are increasing with the
increase in population, and with the improvement of living conditions of the
poorest populations. But it is essential not to increase the agricultural area,
so as not to accentuate the loss of biodiversity, and not to exacerbate the
negative environmental impact of the increase in population.
Mathematical
models have the ultimate goal of optimizing agricultural production, to reduce
the impact on the environment, but also to reduce food waste, at least for the
important part that occurs in the fields.
Most
existing models were oriented to agricultural crop optimization, or for
optimizing cultivation and phytosanitary actions.
The
environment is a very complex system, in which all human action has an impact,
more or less strong, more or less disruptive, and more or less negative. It is
clear that agriculture is one part of human activities whose environmental
impact is important. It's necessary to be able to measure it. The farmer works
in a production goal, the best he can, but often without the means of measuring
or estimating the environmental impact of his work.
It is now
important that scientific teams are concerned to develop models for farmers,
which relate agronomic actions with their environmental effects. This is still
the weak point of the agricultural system.
The water
footprint or carbon footprint calculations are the first step, but they are
extremely complex, slow and expensive, generally require to appeal to
specialists, and don't give a concrete solution to the farmer in his daily
practices. It's just an index to measure a specific situation at a specific
moment. This explains why very few farmers are actually involved in.
Farmers need
quick and simple systems, that they can establish themselves on their own farm,
and that helps them to work more efficiently.
Picture: http://asi.ucdavis.edu/programs/sarep/research-initiatives/are/files/AREbannerimage.png
The
application of mathematical models, combined with the implementation of
techniques and cultivation methods from precision agriculture and agroecology,
should enable agriculture to meet the tremendous challenge of feeding a
steadily increasing world population, without increasing its environmental
impact through better use of available resources, while adapting to climate
change.
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