A novel approach in analyzing agriculture and food systems: Review of modeling and its applications

Abstract

For the past decades, advances in computational devices have propelled mathematical modeling to become an effective tool for solving the black box of complex biological systems because of its prominent analytical power and comprehensive insight. Nevertheless, modeling is still limitedly used in the fields of agriculture and food which generally concentrate on producing experimental data rather than processing them. This study, hence, intends to introduce modeling in terms of its procedure types of structure, formulation, analyses, and software, with reviews of current notable studies from micro to macro scales so as to propose the modeling technique as a novel approach in discerning conundrums in agriculture and food systems. We expect this review to provide an eligible source for researchers who are willing to apply modeling techniques into the unexplored fields related to bio-systems that comprehensively include biology, nutrition, agriculture, food, animal science, and ecology.

Climate‐based ensemble modelling to evaluate the global distribution of Anoplophora glabripennis (Motschulsky)

“…In addition, Kappel et al (2017) estimated the time to maturity as a function of temperature by using an empirical model, and spatially mapped US territories at risk by A. glabripennis damage. Despite the recent application of species distribution modelling, such models still include predictive uncertainties like other biological models (Kim et al, 2016). Therefore, ensemble modelling, which simultaneously uses more than one model, has been used as a conservative approach for predicting species distributions (Shabani et al, 2016), showing its suitability for applications in Cydia pomonella (Lepidoptera: Tortricidae) (Kumar et al, 2015); Rhagoletis pomonella (Diptera: Tephritidae) (Kumar et al, 2016); Ricania shantungensis Chou & Lu (Hemiptera: Ricaniidae), which is a severe forest pest in South Korea (Baek et al, 2019); and Austropuccinia psidii, an invasive rust fungus (Narouei-Khandan et al, 2020).…”

Prediction of Phenolic Contents Based on Ultraviolet-B Radiation in Three-Dimensional Structure of Kale Leaves

“…Statistical modeling is an effective tool for quantifying the relationship between dependent and independent variables by the statistical significance of their correlations (Kim et al, 2016). Kim et al (2018) developed a statistical model for glucosinolate content in Chinese cabbage using a secondorder multi-polynomial equation with growth duration and temperature as independent variables.…”

“…This model approach could be extended to various plants and structures. Further steps in the modeling procedure for agriculture and food systems were model simulation and model-based analysis (Kim et al, 2016). These steps allow us to investigate numerous scenarios and predict the system responses with cost and input in an effective way (Kreutz and Timmer, 2009).…”

“…Statistical modeling has widely been used because it is simple but powerful for predicting and quantifying the relationship between variables (Kim et al, 2016). The plant developmental stage affects plant structures as well as their sensitivity to UV-B radiation (Yoon et al, 2021b).…”

Statistical modeling for estimating glucosinolate content in Chinese cabbage by growth conditions

“…Statistical modeling is an effective tool for analyzing empirical data and explains a dependent variable as a function of explanatory variables determined by the statistical significance of their correlation. The model identifies relationships between variables and provides quantitative predictions of the target with a simple model structure compared to other types of models, i.e., mechanistic model . Statistical modeling has been widely applied in various fields ranging from humanities and social science to agricultural/food science and engineering.…”