Water uptake in crop growth models for land use systems analysis

2002 ◽  
Vol 92 (1) ◽  
pp. 21-36 ◽  
Author(s):  
M. van den Berg ◽  
P.M. Driessen
Author(s):  
Peter B. Tinker ◽  
Peter Nye

In this chapter we deal with vegetation growing in the field. This introduces new and challenging questions of scale and heterogeneity, in time and space, of the environment in which plants grow. It builds on the concepts and methods explained in earlier chapters, especially the movement of water and solutes (chapters 2, 3 and 4) and the distribution of roots (chapter 9) in field soils. In some cases, it requires changes and simplifications in the methods that we have used earlier. The problems of dealing with water and nutrient movement and uptake at the field scale are discussed first. The modelling approach that we developed in the earlier chapters of this book, up to the end of chapter 10, logically resumes at section 11.3. This covers both uptake models and the more complex combined crop growth and uptake models that simulate the main interactions with the environment. This chapter considers increasingly complex systems: first, uniform monocultures, including models of a ‘green leaf crop’, a root crop, a cereal, and a tree crop. At this level, the presence of weeds or groundcover is deliberately ignored. Interspecies competition is included later, with vegetation composed of more or less regularly spaced plants of more than one species. This occurs in many agricultural systems, such as mixtures of forage species and agroforestry systems. The competition processes become even more complicated where there is no spatial symmetry, and models of crop/weed mixtures, grass/legume mixtures, and planted woodlands are used as examples. Progress with crops has been more rapid because of their more regular structure, so we deal mainly with these, but we believe that similar ideas will be applied to natural vegetation also, and this is discussed in section 11.5. Most of these models have a water submodel, or, if not, one could be added. As the physical basis is normally rather similar for all water models, one model for water uptake is explained in some detail (section 11.1.2), but elsewhere water uptake is dealt with very briefly. For each model, the preferred order of discussion is water; growth, including economic yield; nitrogen; potassium; phosphorus; and other nutrients, unless the logic of the subject demands a different order.


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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