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Monitoring screen of the computer piloting the Phenoarch platform. © INRA, SLAGMULDER Christian

Modelling and agrosystems

STICS, an agronomy dynamo

STICS, an interdisciplinary simulator for standard crops, models crop development at plot level across all agronomic criteria: climate, soil, and agricultural practices. It is also able to model intercropping systems and crop rotation cycles.

By Pascale Mollier, translated by Daniel McKinnon
Updated on 09/03/2014
Published on 05/30/2013

The STICS model was created by INRA in 1996. Since then, a number of other integrated models have been developed with STICS as the foundation. STICS is available free of charge on INRA Avignon’s AGROCLIM research unit website and has hundreds of users, using it mostly for research and education purposes. People with STICS training can also use the model to shape agricultural advice in areas such as variety selection and irrigation practices.

A large number of researchers and development stakeholders contributed to the design of STICS in order to integrate knowledge from across many fields of study, including bioclimatology, plant ecophysiology, agronomics, hydrology, soil science, and epidemiology. The model has more than 50 coauthors, making STICS an invaluable tool to capitalise on expertise and experiences.

STICS can be used to estimate crop yields

Input variables include:

  • climate: daily maximum and minimum temperatures, radiation, precipitation, etc. Climate data is input for every day of the crop cycle from sowing to harvest;
  • soil: available water capacity, organic matter content (which determines the nitrogen mineralisation rate);
  • agricultural practices: sowing dates and densities, varieties, fertilisation rates, irrigation, crop rotations, harvesting method.

STICS is able to calculate, based on input variables, the characteristics of the agricultural output, such as crop yields, harvest quality, and plant nitrogen and water consumption rates.

“Of course, actual yields can only be determined once climate conditions for the entire growing period are known, that is to say, when the crop is harvested” explains Françoise Ruget. “Nevertheless, forecasting is very useful during the growing period in order to adjust fertilisation and irrigation rates in response to climate conditions and to plant development. After the harvest, we can compare estimated yields with actual yields and determine the cause of any discrepancies, be they due to poorly suited agricultural practices, pests, and so on.”

“As a forecasting tool, STICS is not used in isolation” says Marie Launay. “It is used in conjunction with other tools such as photographs taken as crops grow, statistical data from previous years, and climate indicators.”

STICS is the only available method for calculating grassland yields

Actual yield data is not available for grasslands because forage is, in part, eaten by Growth from 20/07/2011 to 20/08/2011. Darker colours represent areas of less growth. White areas indicate no growth.. © INRA, ISOP©
Growth from 20/07/2011 to 20/08/2011. Darker colours represent areas of less growth. White areas indicate no growth. © INRA, ISOP©
livestock. Yield estimates calculated by ISOP, a model built on the basis of STICS, therefore represent the only information available on grassland yields. As with STICS, ISOP is not used to make predictions, but rather to forecast the impact of situations such as drought.

STICS also able to assess the environmental impact of crops

In addition to agricultural output characteristics, STICS is also able to assess environmental criteria such as nitrate leaching, and greenhouse gas emissions such as CO2 and N2O. STICS can be used not only at plot level but also on a regional scale by aggregating data from a number of plots.

“In the wider research world, STICS can be paired with an epidemiology model to gauge the impact of a certain illness” says Launay. “In this way, STICS represents a knowledge based model, used extensively by researchers. It can even be used to supply variables missing from global vegetation models, which are themselves used in meteorological models."

STICS models a wide range of crops

STICS’ generic design means it can be used for 24 major, temperate, perennial and annual crops.  Its hierarchical tree structure starts from a common basis and branches off with customisation options for each crop. At present, INRA is looking to expand the model so that it can be used for more “exotic” crops, such as rice, sugarcane, turmeric, and marigold, or with crops not widely grown in France, such as soybean and sorghum. To do so, new data must be added to model. In the case of rice, for example, information on rice cultivation in the Camargue could be applied.

Examples of STICS applications

STICS is able to scale the results of experiments. Data fed into the model can be used to extrapolate for an analogous situation without the need for new testing each time. For example, maize can be studied over a number of years, or in a range of climates.

STICS has been used in two INRA expert reports:

- to study the effects of climate change on various regions of France (Report on drought and agriculture, 2006);
- to study the impact of cover crops for trapping (Study on using cover crops to reduce nitrate leaching: Impact to water, nitrogen, and other ecosystem services, 2012).

Contact(s)
Scientific contact(s):

Associated Division(s):
Environment and Agronomy
Associated Centre(s):
Provence-Alpes-Côte d'Azur

References

. © INRA
© INRA
"Conceptual basis, formalisations and parameterisation of the STICS crop model", coauthored by more than 50 experts, published by Éditions Quæ in January 2009.

Tribute to Nadine Brisson

Portrait of Nadine Brisson. © INRA
Portrait of Nadine Brisson © INRA
On 16 October 2012, the French Association of Agronomy organised a conference to honour the memory of Nadine Brisson, who passed away on 18 October 2011.

“Nadine Brisson was the untiring architect of the STICS model. … Her vision was largely based on two concepts: the model should be inclusive, and should be built around common objectives. … With time, STICS became a clearinghouse for consolidating and linking together knowledge. It is, in essence, a way to share findings. Now, other scientific communities use STICS as an agronomy go-to point.”