scholarly journals Importance of the description of light interception in crop growth models

2021 ◽  
Author(s):  
Shouyang Liu ◽  
Frédéric Baret ◽  
Mariem Abichou ◽  
Loïc Manceau ◽  
Bruno Andrieu ◽  
...  

Abstract Canopy light interception determines the amount of energy captured by a crop, and is thus critical to modelling crop growth and yield, and may substantially contribute to the prediction uncertainty of crop growth models (CGMs). We thus analyzed the canopy light interception models of the 26 wheat (Triticum aestivum) CGMs used by the Agricultural Model Intercomparison and Improvement project (AgMIP). Twenty-one CGMs assume that the light extinction coefficient (K) is constant, varying from 0.37 to 0.80 depending on the model. The other models take into account the illumination conditions and assume either that all green surfaces in the canopy have the same inclination angle (θ) or that θ distribution follows a spherical distribution. These assumptions have not yet been evaluated due to a lack of experimental data. Therefore, we conducted a field experiment with five cultivars with contrasting leaf stature sown at normal and double row spacing, and analyzed θ distribution in the canopies from 3-dimensional canopy reconstructions. In all the canopies, θ distribution was well represented by an ellipsoidal distribution. We thus carried out an intercomparison between the light interception models of the AgMIP-Wheat CGMs ensemble and a physically based K model with ellipsoidal leaf angle distribution and canopy clumping (KCell). Results showed that the (KCell) model outperformed current approaches under most illumination conditions and that the uncertainty in simulated wheat growth and final grain yield due to light models could be as high as 45%. Therefore, our results call for an overhaul of light interception models in CGMs.

Agronomy ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 85
Author(s):  
Jorge Lopez-Jimenez ◽  
Nicanor Quijano ◽  
Alain Vande Wouwer

Climate change and the efficient use of freshwater for irrigation pose a challenge for sustainable agriculture. Traditionally, the prediction of agricultural production is carried out through crop-growth models and historical records of the climatic variables. However, one of the main flaws of these models is that they do not consider the variability of the soil throughout the cultivation area. In addition, with the availability of new information sources (i.e., aerial or satellite images) and low-cost meteorological stations, it is convenient that the models incorporate prediction capabilities to enhance the representation of production scenarios. In this work, an agent-based model (ABM) that considers the soil heterogeneity and water exchanges is proposed. Soil heterogeneity is associated to the combination of individual behaviours of uniform portions of land (agents), while water fluxes are related to the topography. Each agent is characterized by an individual dynamic model, which describes the local crop growth. Moreover, this model considers positive and negative effects of water level, i.e., drought and waterlogging, on the biomass production. The development of the global ABM is oriented to the future use of control strategies and optimal irrigation policies. The model is built bottom-up starting with the definition of agents, and the Python environment Mesa is chosen for the implementation. The validation is carried out using three topographic scenarios in Colombia. Results of potential production cases are discussed, and some practical recommendations on the implementation are presented.


Author(s):  
Rafael Battisti ◽  
Derblai Casaroli ◽  
Jéssica Sousa Paixão ◽  
José Alves Júnior ◽  
Adão Wagner Pêgo Evangelista ◽  
...  

1983 ◽  
Vol 31 (4) ◽  
pp. 313-323 ◽  
Author(s):  
C.T. de Wit ◽  
F.W.T.P. de Vries

For the simulation of organ formation and assimilate partitioning, information is required on the current level of activities like CO2 assimilation and the growth of various organs, as well as state variables such as leaf and root wt., N content and carbohydrate reserves and exogenous variables like radiation and temp. This information may be retained in auxiliary state variables by considering the dynamic equilibrium between growth of roots and shoots. Auxiliary state variables are not tangible quantities but mathematical artefacts of the simulation program; it is speculated that in real plants similar information may be retained and transferred by the hormonal system. A hormonal system is a communication system and such systems may be analysed either in terms of means (of the hardware used) or in terms of purpose (of the messages transferred). In dynamic models of crop growth, interest should be focused on the latter. Wheat, maize and ryegrass are used as examples. (Abstract retrieved from CAB Abstracts by CABI’s permission)


2019 ◽  
Vol 224 ◽  
pp. 105746 ◽  
Author(s):  
Si Mokrane Siad ◽  
Vito Iacobellis ◽  
Pandi Zdruli ◽  
Andrea Gioia ◽  
Ilan Stavi ◽  
...  

2003 ◽  
Author(s):  
Joel O. Paz ◽  
William D. Batchelor ◽  
David E. Clay ◽  
Sharon A. Clay ◽  
Cheryl Reese

PLoS ONE ◽  
2020 ◽  
Vol 15 (6) ◽  
pp. e0233951
Author(s):  
Yusuke Toda ◽  
Hitomi Wakatsuki ◽  
Toru Aoike ◽  
Hiromi Kajiya-Kanegae ◽  
Masanori Yamasaki ◽  
...  

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