Agricultural Modeling Overview

Principles

Definition

Agricultural modeling is a methodological approach or research field that utilizes mathematical and computational models to represent, simulate, and analyze the structure and dynamics of agricultural systems. It investigates the complex interactions between biological components (e.g., crops, livestock), environmental factors (e.g., climate, soil), management practices, and socio-economic aspects to enhance understanding, predict system responses, optimize resource use, and inform decision-making for sustainable agriculture.

Ontological type

Core Methods

Model Applications

System Components

Key developments

Key Figures

Process-based Modeling era

Peter M. Vitousek [1], associated with Stanford University [3] and the University of North Carolina at Chapel Hill [4], helped shape mechanistic process-based perspectives in the Process-based Modeling era. His key contribution, embodied in the 1993 paper Terrestrial ecosystem production: A process model based on global satellite and surface data [6], introduced a process-based framework that links satellite observations with ecosystem production, enabling cross-scale evaluation and calibration essential to the era. James T. Randerson [2], with affiliations at Stanford University [3] and the University of California, Berkeley [5], contributed to the era's modeling program. The 1993 Terrestrial ecosystem production paper [6] encapsulates Randerson's contributions by applying the process-based approach to space-borne and surface data, underscoring its importance for scenario analysis, calibration with weather observations, and cross-model comparability in agroecosystem research.

Global Scale Standardization era

Arjen Y. Hoekstra [1] is a leading scholar in this era, affiliated with Columbia University [3] and the National University of Singapore [4]. His key contribution in this era is the green, blue and grey water footprint of crops and derived crop products [7], which established standardized water-footprint accounting across green, blue, and grey categories, enabling cross-regional comparability of crop water use at continental to global scales. Navin Ramankutty [2] is a central figure in this era, associated with the University of Wisconsin–Madison [5] and the University of Maryland, College Park [6]. His work farming the planet: 1. Geographic distribution of global agricultural lands in the year 2000 [8] advanced spatially explicit global land-cover mapping and crop-area inventories, laying the groundwork for standardized, interoperable global agricultural models and comparative scenario analyses.

Data-driven Precision Agronomy era

Alessandro Dal’Cól Lúcio[1] is a notable figure in data-driven precision agronomy during the 2018-2024 era, with affiliations at Universidade Federal de Santa Maria[2] and Serviço Nacional de Aprendizagem Comercial[3]. His key contribution in this era is the metan: An R package for multi-environment trial analysis[4], which enables standardized, scalable analysis of multi-environment trial data and supports robust yield forecasting and management decision support. This work addressed the need for reproducible analytics across diverse environments, aligning with the era's emphasis on learning-based, cloud-enabled models for field-to-continental decision making[4]. By linking academic development at Universidade Federal de Santa Maria[2] and the practical training orientation of Serviço Nacional de Aprendizagem Comercial[3], Dal’Cól Lúcio helped accelerate adoption of open-source tools in precision agronomy during this era[1].