Characterization of Soybean Yield Variability Using Crop Growth Models and 13C Discrimination

2003 ◽  
Author(s):  
Joel O. Paz ◽  
William D. Batchelor ◽  
David E. Clay ◽  
Sharon A. Clay ◽  
Cheryl Reese
Keyword(s):  
Growth Models ◽  
Crop Growth ◽  
Yield Variability ◽  
Soybean Yield ◽  
13C Discrimination ◽  
Crop Growth Models

scholarly journals An Agent-Based Crop Model Framework for Heterogeneous Soils

Agronomy ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 85
Author(s):  
Jorge Lopez-Jimenez ◽  
Nicanor Quijano ◽  
Alain Vande Wouwer
Keyword(s):  
Growth Models ◽  
Control Strategies ◽  
Low Cost ◽  
Soil Heterogeneity ◽  
Crop Growth ◽  
Negative Effects ◽  
Model Framework ◽  
Agent Based ◽  
Crop Growth Models ◽  
Definition Of

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.

Assessment of Soybeans Crop Management Strategies Using Crop Growth Models for Central Brazil

2020 ◽  
pp. 525-543
Author(s):  
Rafael Battisti ◽  
Derblai Casaroli ◽  
Jéssica Sousa Paixão ◽  
José Alves Júnior ◽  
Adão Wagner Pêgo Evangelista ◽  
...  
Keyword(s):  
Growth Models ◽  
Management Strategies ◽  
Crop Management ◽  
Crop Growth ◽  
Central Brazil ◽  
Crop Growth Models

scholarly journals Crop growth models without hormones.

1983 ◽  
Vol 31 (4) ◽  
pp. 313-323 ◽  
Author(s):  
C.T. de Wit ◽  
F.W.T.P. de Vries
Keyword(s):  
Dynamic Models ◽  
Dynamic Equilibrium ◽  
Growth Models ◽  
Co2 Assimilation ◽  
Crop Growth ◽  
State Variables ◽  
System A ◽  
Similar Information ◽  
Hormonal System ◽  
Crop Growth Models

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)

scholarly journals A review of coupled hydrologic and crop growth models

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

scholarly journals Predicting biomass of rice with intermediate traits: Modeling method combining crop growth models and genomic prediction models

PLoS ONE ◽  
2020 ◽  
Vol 15 (6) ◽  
pp. e0233951
Author(s):  
Yusuke Toda ◽  
Hitomi Wakatsuki ◽  
Toru Aoike ◽  
Hiromi Kajiya-Kanegae ◽  
Masanori Yamasaki ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Prediction Models ◽  
Growth Models ◽  
Crop Growth ◽  
Modeling Method ◽  
Crop Growth Models ◽  
Intermediate Traits

scholarly journals Role of Crop Modeling in scenario Development Analysis and Developing Strategic Recommendation: A Review

2019 ◽  
Vol 7 (01) ◽  
Author(s):  
Tesfaye Wossen Dejenie
Keyword(s):  
Decision Support ◽  
Agricultural Research ◽  
Growth Models ◽  
Crop Growth ◽  
Crop Modeling ◽  
Scenario Development ◽  
Management Options ◽  
Field Level ◽  
Crop Growth Models

The agricultural scientists and planners are facing formidable challenges to ensure continued increases in agricultural productivity to meet the food grain requirements of ever increasing population across the globe. Thus, the works on development and use of crop growth models to answer strategic and tactical questions concerning agricultural planning as well as on-farm soil and crop management are essential. Scenarios is a tool for evaluating decisions and testing policy options by indicating possible future situations which indicate the possible effects of decisions. Crop growth models are powerful tools in agricultural decision support at operational, strategic and exploratory levels. Models through the scenario analysis system plays an important role in the interface between farmers, researchers and advisors in participatory research approaches where as agricultural research, model development and testing, and application of mode-based decision support system can be mutually enhancing for better understanding and reaction future situations. This review paper is devoted to crop modeling and scenario development for planning and field level management options in crop production. This helps researchers to understand the role of crop modeling for scenario development to adjust and develop field level recommendation by considering future conditions and developing alternative strategic decisions to reduce the expected negative impact and maximize the benefit.

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