test

STICS soil-crop model.

Conceptual framework, equations and uses

Beaudoin N., Lecharpentier P., Ripoche-Wachter D., Strullu L., Mary B., Léonard J., Launay M., Justes É.


This book was funded with support from the French National Fund for Open Science (project AAPFNSO2019OPEN-STICS-13999), from CIRAD and from the Agroecosystems division of INRAE.

                       

This book is licenced under CC-by-NC-ND 4.0.  


For all requests for permission to reproduce or translate, please contact stics@inrae.fr

© Éditions Quæ, 2023


To cite this book:

Beaudoin N., Lecharpentier P., Ripoche-Wachter D., Strullu L., Mary B., Léonard J., Launay M., Justes É., eds. 2022. STICS soil-crop model. Conceptual framework, equations and uses, Versailles, Éditions Quæ.

Éditions Quæ
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ISBN papier: 978-2-7592-3678-7
ISBN pdf: 978-2-7592-3679-4
ISBN ePub: 978-2-7592-3680-0

Foreword

Peter Thorburn

Cropping systems are complicated non-linear biophysical systems, made complex by drivers that can’t currently be predicted, namely climate and management actions executed in response to numerous socio-economic-political drivers. How do we, agricultural scientists, make sense of these systems and help land managers meet their goals and the goals of the societies in which they live? Models are tools used by agricultural scientists to make sense of these systems for over 100 years. This 2nd edition of the “STICS red book” represents an important milestone in the evolution of cropping systems models (soil-crop including management practices) over that period.

Models have evolved from simple equations of plant growth in the early- and mid-1900’s to today’s sophisticated cropping systems models (Keating and Thorburn, 2018). An important part of this evolution was the “leap” from crop models, which coupled models of growth of a single crop to models of soil processes, to “cropping system” models in the 1990’s. Cropping systems models allowed realistic representation of crop rotations and so reflected more closely the way farmers viewed and managed their fields. As these models developed descriptions of them were published: A landmark was papers by the major modelling groups around the world in 2003 Special Issue of the European Journal of Agronomy (Volume 18, Issues 3–4) followed by updates in a Thematic Issue of Environmental Modelling and Software in 2014 (Volume 62). Overviews of STICS were included in both (Bergez et al., 2014; Brisson et al., 2003). However, journal papers come with length restrictions and the “overviews” of complicated tools like cropping systems models in those papers are inadequate resources for new and experienced users alike. To me, the 1st edition of the “STICS red book” represented the commitment of the STICS team to support those users and expose the detail of the concepts, structures and approaches in the model to their modelling peers.

The 2nd edition of the “STICS red book” shows how comprehensive the STICS cropping systems model has become. Long gone are the days (for STICS and other models) when simulating a “crop-fallow-crop” rotation was challenging and novel. This is now one of the first tasks given to students learning cropping systems modelling. Developments since the 1st edition of the “STICS red book” include the capability to address contemporary issues such as climate change impacts and adaptation, greenhouse gas (GHG) emissions and abatement, organic agriculture, spatial application, and coupling with other models (e.g. of hydrology, pest and diseases, etc).

Some of these applications have indirect or direct links with government policy. Modelling in this context raises new challenges for model development and application coming from the increased scrutiny to which the results will be subjected (Moore et al., 2014). In agriculture, models started as tools of scientific enquiry. For example, CT de Wit’s interest in modelling was sparked by the desire to know the potential yield of a crop (Keating and Thorburn, 2018). The scrutiny of such modelling was likely limited to scientific peers who likely understood and accepted the strengths and weaknesses of modelling [although the was not always the case; e.g. Passioura (1996)]. As models developed and modellers started using them to inform farmers how to improve their management, scrutiny expanded to include farmer stakeholders as well as scientific peers. However, in many farmer interactions the model (or simulation output) acted as a “boundary object” facilitating discussions between the modeller and farmer (Jakku and Thorburn, 2010). The modeller explaining to the farmer the simulation results and their meaning built trust in the farmer of the modeller (provided the explanations made sense to the farmer!). This was/is essentially a social process and the technicalities of the model application itself were not necessarily scrutinised – if the farmer trusted the modeller, she trusted the model. Further, the farmer was free to change farm management, or not, as a result of these interactions, and solely bore the consequences of any changes (whether positive or negative).

In public policy applications, the link between the modeller and (government) stakeholder is likely to be much less personal than between modellers and farmers or scientific peers. Further, the application of the policy will often create “winners” and “losers”. It is natural for the “losers” to want to scrutinise the technical basis behind the policy impacting them. A recent example of this is the examination of modelling behind water quality policy for agricultural lands in New Zealand (Johnson et al., 2021). The “losers”, and other stakeholders, will likely ask questions about the quality of the science in the model, whether that is accurately implemented in the code (and for the specific version of the model used in the analysis) and whether the model was competently run. Publication in the peer reviewed literature is often the means of assuring the quality of the science. As a community, however, cropping systems modellers have less established methods of quality assurance for implementation and running models than some other communities. For example, just for calibration of phenology, an important but limited part of running a crop model, there is a huge diversity of approaches used by different modellers (Seidel et al., 2018) and there will be benefits from having some consistency in the approach (Wallach et al., 2021). Conversely, ensuring accurate implementation of science in model code, i.e. having good software development practices, has received less attention across cropping systems models (Holzworth et al., 2015). It is therefore significant that this issue has been discussed in the 2nd edition of the “STICS red book”.

What does the future hold for cropping systems models? That question has been addressed in a number of recent papers (Jones et al., 2017; Keating and Thorburn, 2018; Silva and Giller, 2020) and their conclusions do not need repeating here. However, those authors agree that application of cropping systems models will be an important methodology in meeting the coming challenges faced by food and agricultural systems and the models will need to be further improved and developed. That development will necessitate increasing efforts in collecting data to underpin those developments. Data availability has always been both a limitation and driver of model development: in many respects cropping systems models have been created to overcome the scarcity of data. As we enter the age of “big data”, data will increasingly be available from remote and proximal sensors. That raises the questions of how those data will aid model development and/or application, and how will they affect the relevance of cropping systems modelling? An example of the first question is the potential use of multi-year high resolution data on crop growth and development to inversely parameterise models, e.g. soil water (He and Wang, 2019) or phenology (Araya et al., 2016) parameters, aiding subsequent application. The implications of the second question are less clear. With rich data, possibly less biophysical detail is needed in a model if it is designed for use in conjunction with those data (e.g. Donohue et al., 2018). Even further, there may be no role for a biophysical model at all. However, this “struggle” between models and data for prediction and understanding is not new. An example is the prediction of the optimum rates of nitrogen fertiliser in the mid-west corn-belt of USA. Large datasets have been gathered and developed into a tool for forecasting the Maximum Return to Nitrogen (Sawyer et al. (2006); http://cnrc.agron.iastate.edu). However, recently developed approaches based on cropping systems modelling are showing promise in increasing accuracy of those forecasts (Puntel et al., 2018). And, unlike purely data-driven approaches, the cause of a result from a cropping systems model can be tracked down and understood, enlightening the modeller and their stakeholders. Thus, it is unlikely there will be a single “winner” in the “struggle” between models and data for prediction. What is clear however, is that modelling systems (structure and software) will need to evolve to be easily applied with these new sources of data. The STICS model is well advanced down that development road and thus will remain relevant for a long time. I forecast there will be a 3rd edition of the “STICS Red Book” in the future!

Dedication

This book is dedicated to Nadine Brisson … Nadine Brisson was an ever-enthusiastic captain of the STICS ship and kept her sights set far ahead. With her intelligence, energy, insightfulness, determination and presence, she was always able to bring people on board with her who would remain ever faithful. Her vessel covered great distances, towards the shores of all the continents, sometimes facing storms along the way but continuing cheerfully and steadfastly onwards, committed to fulfilling her pledge: to give the scientific community a tool to help tackle food security, climate change, agroecological transition and other major challenges. She would have been so pleased with this new edition, which is available at zero cost (free numeric version), in keeping with the values of Open Science that she promoted before the movement had even emerged. Her life ended much too soon, but it was full of professional and personal adventures. She became fast friends with nearly everyone she met, and this is how we will forever remember her.

To Nadine

Au rendez-vous des bons copains	When to a rendezvous they’d go
Y avait pas souvent de lapins	Not often was there a no-show
Quand l’un d’entre eux manquait a bord	If one of them was not on board,
C’est qu’il était mort	it means he was no more
Oui, mais jamais, au grand jamais	But never could their friendship dim
Son trou dans l’eau n’se refermait	as the deep seas closed ever him
Cent ans après, coquin de sort	A hundred years after the peal,
Il manquait encore	they mourn him still

Source: LyricFind

Parolier: Georges Brassens (1964)

Extrait des paroles de Les Copains d’abord © Universal Music Publishing Group

Preface

Nicolas Beaudoin, Patrice Lecharpentier and Dominique Ripoche-Wachter

The first book

In 2009, the initial project team produced the book Conceptual Basis, Formalisations and Parameterization of the STICS Crop Model (Brisson et al., 2009), often referred as the ‘STICS Red Book’, published by Editions Quae.

Figure 0.1. Cover of the first edition of the book.

The first edition of this book was written primarily by Nadine Brisson and was quite original in that synthesised scientific knowledge about cropping systems. The book covered the STICS model formalisms in an exhaustive way . But, more than ten years on, it was in need of a comprehensive update following the profound changes to the capabilities of the STICS model.

The following authors contributed to the original formalisations according to their affiliations:

• INRA (now INRAE): R. Antonioletti, N. Beaudoin, P. Bertuzzi, T. Boulard, N. Brisson, S. Buis, P. Burger, F. Bussière, Y.M. Cabidoche, P. Cellier, P. Debaeke, F. Devienne-Barret, C. Durr, M Duru, B. Gabrielle, I. García de Cortázar Atauri, C. Gary, F. Gastal, J.P. Gaudillère, S. Génermont, M. Guérif, G. Helloux, C. Hénault, B. Itier, M.H. Jeuffroy, E. Justes, M. Launay , S. Lebonvallet , G. Lemaire, B. Mary, T. Morvan, B. Nicolardot, B. Nicoullaud, H. Ozier-Lafontaine, L. Pagès, S. Recous, G. Richard, R. Roche, J. Roger-Estrade, F. Ruget, C. Salon, B. Seguin, J. Sierra, H. Sinoquet, R. Tournebize, C. Valancogne, A.S. Voisin

• ESA-Angers: Y. Crozat

• ARVALIS Institut du végétal: P. Gate

• CEMAGREF (now INRAE): B. Rebière, J. Tournebize, D. Zimmer

• CIRAD: F. Maraux

A new book based on an innovative approach

In 2019 ten years after the first book was published, the STICS project team decided to update it by integrating all the STICS skill extensions which have since been developed, evaluated and published. Several key changes deal with:

• the roles of carbon and nitrogen reserves in perennial crops,

• the biological destruction of mulch from crop residues,

• soil \(\mathrm{N_{2}O}\) emissions,

• forage harvest management.

All of the STICS project team members worked together in a dynamic collaborative way to produce this new book.

Their work was supported by an innovative editorial approach, thanks to the involvement of Patrice Lecharpentier, who oversaw the feasibility study for the project, the design and finally the implementation of the writing workflow.

Another base part of the work is the bibliographic database management (under Zotero ¹) the workflow is depending on. The STICS database organisation and maintenance was possible thanks to the support of Christine Le Bas.

This dynamic workflow aims to maintain a close link between changes in model formalisms (including associated data) and the book content with regular updates (Figure 0.2).

The collaborative dimension is crucial and based on the experience of the project team. The use of reproducible science tools is of the utmost importance.

Figure 0.2. The workflow for the new book showing the dynamic interaction between the three activities of the STICS project team

The project, named ‘Open-STICS’, was selected for the 2019 French National Fund for Open Science (FNSO) call for projects, which is aimed at supporting such kind of open science editorial projects.

The main objectives were to offer the STICS user community a written, open access resource in English, with content that can be updated regularly according to the standard versions of the model.

Specific tools were chosen to produce the book in order to easily incorporate updates, including making corrections, adding new formalisms, modifying settings, extending application domains and adding new plants species.