06 ). Other heuristic methods have also been used, such as particle swarm optimization ( Qi et al. , 2010 ) and neural networks ( Fan et al. , 2015 ). We illustrate here the parameter estimations on maize and coffee. Data and sources codes are available as Supplementary Data S1 . These are also available from the following link http://greenlab.cirad.fr/StemGL/AoB_19945R_Codes.zip . These case studies are somewhat iconic. The study of maize is interesting as it has a simple non-branched deterministic structure but complex organ expansions, due to their duration and sink strength variation. The fruits are not numerous, but their biomass is significant due to their high sink strength. Thus, thi
Open resource ↗lines:306-320Unverified paper record
Two decades of research with the GreenLab model in agronomy.
Annals of botany · 1 Feb 2021 · 10.1093/aob/mcaa172
Abstract
Background With up to 200 published contributions, the GreenLab mathematical model of plant growth, developed since 2000 under Sino-French co-operation for agronomic applications, is descended from the structural models developed in the AMAP unit that characterize the development of plants and encompass them in a conceptual mathematical framework. The model also incorporates widely recognized crop model concepts (thermal time, light use efficiency and light interception), adapting them to the level of the individual plant. Scope Such long-term research work calls for an overview at some point. That is the objective of this review paper, which retraces the main history of the model's development and its current status, highlighting three aspects. (1) What are the key features of the GreenLab model? (2) How can the model be a guide for defining relevant measurement strategies and experimental protocols? (3) What kind of applications can such a model address? This last question is answered using case studies as illustrations, and through the Discussion. Conclusions The results obtained over several decades illustrate a key feature of the GreenLab model: owing to its concise mathematical formulation based on the factorization of plant structure, it comes along with dedicated methods and experimental protocols for its parameter estimation, in the deterministic or stochastic cases, at single-plant or population levels. Besides providing a reliable statistical framework, this intense and long-term research effort has provided new insights into the internal trophic regulations of many plant species and new guidelines for genetic improvement or optimization of crop systems.
Plant phenotyping relevance
GreenLabモデルの開発史と、植物構造・成長の測定戦略およびパラメータ推定用実験プロトコルを中心にレビューしており、植物形質の取得・モデル化手法が主要テーマである。
abstractWhat are the key features of the GreenLab model? (2) How can the model be a guide for defining relevant measurement strategies and experimental protocols?
abstractit comes along with dedicated methods and experimental protocols for its parameter estimation, in the deterministic or stochastic cases, at single-plant or population levels.
Code and data availability
The review explicitly states that the data and source codes for its maize and coffee GreenLab calibration case studies are publicly available as Supplementary Data S1 and via the authors' URL http://greenlab.cirad.fr/StemGL/AoB_19945R_Codes.zip. The GLUVED eLearning site is a general course resource, not a paper-phenot
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