Agricultural and Biological Systems Simulation

Semester Taught - Spring

Catalog Description

Credits: 3

Basic concepts of systems analysis, modeling, and computer simulation of dynamic biological and agricultural systems. Methods for working with models, including sensitivity analysis, parameter estimation, and model evaluation. Applications of models in agricultural and biological systems.


MAC 2312, CGS 3460 or CIS 3020

Course Objectives

Objectives will include model evaluation, parameter estimation, sensitivity analysis, and application of biological models, including crops, disease and soils.

  1. To learn basic modeling and simulation methods for biological and agricultural systems
    • Systems Approach
    • Model development
    • Example models
    • Numerical Simulation
  2. To learn methods for working with dynamic models
    • Sensitivity analysis
    • Parameter estimation
    • Evaluation
    • Applications


Dr. Senthold Asseng
Phone: (352) 392-1864 ext 224
E-Mail: sasseng@ufl.edu

Material/Supply Fees


Class Materials Required


Wallach, D, Makowski, D, Jones, JW & Brun, F 2014. Working with Dynamic Crop Models, 2nd Edition, Methods, Tools and Examples for Agriculture and Environment. Academic Press, ISBN : 9780123970084, pp. 520

Handouts will include pages from

  • Keen, R.E. and J.D. Spain. 1992. Computer simulation in Biology: A Basic Introduction. Wiley-Liss Inc. New York. (Selected Chapters - Book out of print.)
  • Jones, J.W. and Luyten, J.C. 1998. simulation of Biological Processes. In: Peart, R.M. and Curry, R.B. (eds). Agricultural Systems Modeling and Simulation. Marcel Dekker Inc. ISBN 0-827-0041-4.
  • Thornley, John H.M. and Ian R. Johnson.2000. Pland and Crop Modeling: A Mathematical Approach to Plant and Crop Physiology. Oxford University Press. New York. Blackburn Press (Second Printing.)

Outside Readings

  • De Wit, C.T., 1992. Resource use efficiency in agriculture. Elsevier Applied Science, London.
  • Gifford, R., Angus, J., Barrett, D., Passioura, J., Rawson, H., Richards, R., Stapper, M., Wood, J., 1998. Climate change and Australian wheat yield. Nature 391, 448-449.
  • Godden, D., Batterham, R., Drynan, R., 1998. Climate change and Australian wheat yield. Nature 391, 447-448.
  • Landau, S., Mitchell, R.A.C., Barnett, V., Colls, J.J., Craigon, J., Moore, K.L., Payne, R.W., 1998. Testing winter wheat simulation models' predictions against observed UK grain yields. Agricultural and Forest Meteorology 89, 85-99.
  • Lobell, D.B., Cassman, K.G., Field, C.B., 2009. Crop Yield Gaps: Their Importance, Magnitudes, and Causes. Annual Review of Environment and Resources 34, 179-204.
  • Lobell, D.B., Field, C.B., 2007. Global scale climate - crop yield relationships and the impacts of recent warming. Environmental Research Letters 2.
  • Nicholls, N., 1997. Increased Australian wheat yield due to recent climate trends. Nature 387, 484-485.
  • Sinclair, T.R., Muchow, R.C., 1999. Radiation use efficiency. Advances in Agronomy 65, 215-265.
  • van Ittersum, M.K., Leffelaar, P.A., van Keulen, H., Kropff, M.J., Bastiaans, L., Goudriaan, J., 2003. On approaches and applications of the Wageningen crop models. European Journal of Agronomy 18, 201 - 234.

Course Outline


The course will contain two components. First, students will be exposed to basic concepts of systems analysis, modeling and computer simulation of agricultural and biological systems.  Emphasis will be placed on continuous simulation of dynamic models with examples that give students a broad exposure to dynamic models. Most of the reading material for this part of the course will be handed out by the instructor.

The second part of the course will introduce students to various methods for working with dynamic models, starting with sensitivity analysis, going into parameter estimation and model evaluation. An overview of applications of models in agricultural and biological systems will be given. The text for this part of the course is the book by Wallach et al. (2006) entitled “Working with Dynamic Crop Models: Evaluation, Analysis, Parameterization, and Applications”. During this part of the class, students will also be exposed to uncertainties in models associated with uncertainties in model parameters, inputs, and structure.



Reading Material


Course Overview

Introduction to Systems and Modeling Diagrams used in Systems Analysis

Jones & Luyten (1998) handout
Keen & Spain Chapter 1,2,3


Computer Simulation of Dynamic Models

Finite Difference, continuous states, discrete time Errors in Numerical Simulation, Choice of time step

Thornley & Johnson, 1990, Chapter 1, 2


Potential Growth

Model applications, input/output, seasonal variability, trait analysis, seasonal forecasts  


Lobell et al. 2009
De Wit, 1992
Van Ittersum et al. 2003
Landau et al. 1998


Crop Development

Effects of temperature on developmental processes in plants, Degree-day models: basis for and use of Temperature

Degree Days Handout
Curry Chapter on Development
Paper by Salazar et al.


Biological Growth

Crop growth, radiation use efficiency concept, Farquhar equation, respiration, harvest index/yield components, tuber growth, radiation data

Sinclair and Muchow, 1999
Farquhar et al. 1980


ET and Soil Water

ET approaches, soil water: cascading approach, Richards equation, root growth, nutrient uptake   


Zotarelli et al. 2010


Climate Change Impact

CO2 effect, Heat, rainfall, Statistical models, Global carbon

Nicholls, 1997
Gifford et al. 1998
Godden et al. 1998
Lobell and Field, 2007


Review for Exam
Mid Term Exam



Spring Break



Review of Basic Statistics, Random Variables

Why these are important in simulation
Working with Statistics in Simulation
Approximation of distributions from numerical outputs
Expected Values (Mean, Variance, Covariance – 2 methods)
Random Sampling, Monte Carlo Methods
Bayesian Statistics       


Statistical Notes
Models as functions
Supplemental Stat Notes
Wallach et al. 2014, Appendix
Wallach et al. 2014, Chapter 1
Other References


Two Forms of Crop Models

Evaluating [Crop] Models
Comparing a model with data
- Graphical, errors
- Measures of agreement (bias in mean, variance)
- Evaluation of predictive quality
Cross validation
Bootstrap estimation
Effects of errors in observations on MSEP

Wallach et al. (2006) Ch 1, 2
Also see chapters 12-13 for examples


Uncertainty and Sensitivity Analysis

Objectives for Uncertainty, Sensitivity Analyses
Notation with Example
Describing Uncertainty, Sensitivity Analysis Factors
Methods Overview (Sensitivity and Uncertainty)
Sensitivity Analysis
Local sensitivity analysis (absolute, relative)
Global Sensitivity Analysis
Analysis of output variance (ANOVA)
Monte Carlo sampling

Wallach et al. (2014) Chapter 3
Also see chapters 14, 15, 16 for examples
Morris Paper on SensAnal
Alam Paper on SensAnal


Parameter Estimation

Least Squares (non-linear)
Maximum Likelihood
Search Methods (Simplex, Gradient, Simulated Annealing)
Choice of parameters
Multiple types of observations; GLS and weighting errors
Bayesian methods for parameter estimation
Metropolis-Hastings (MCMC methods)Parameter uncertainty and correlation

Wallach et al. (2014) Chapter 4
Makowski et al. (2002)
Wang et al. (2005)
Beven and Binley (1992)


Optimization with Simulation Models
Data Assimilation with Dynamic Models

Wallach et al. (2014) Ch 5,6
Jones et al. paper - 2004

Refer to class syllabus

Last day of classes


Final Exam





Homework 30%
Mid-Term Exam 25%
Final Exam 25%
Project 20%

Academic Honesty

In 1995 the UF student body enacted an honor code and voluntarily committed itself to the highest standards of honesty and integrity. When students enroll at the university, they commit themselves to the standard drafted and enacted by students.

The Honor Pledge: We, the members of the University of Florida community, pledge to hold ourselves and our peers to the highest standards of honesty and integrity.

On all work submitted for credit by students at the university, the following pledge is either required or implied: "On my honor, I have neither given nor received unauthorized aid in doing this assignment."

Students should report any condition that facilitates dishonesty to the instructor, department chair, college dean, Student Honor Council, or Student Conduct and Conflict Resolution in the Dean of Students Office.

(Source: 2012-2013 Undergraduate Catalog)

It is assumed all work will be completed independently unless the assignment is defined as a group project, in writing by the instructor.

This policy will be vigorously upheld at all times in this course.

Accommodation for Students with Disabilities

The Disability Resource Center coordinates the needed accommodations of students with disabilities. This includes registering disabilities, recommending academic accommodations within the classroom, accessing special adaptive computer equipment, providing interpretation services and mediating faculty-student disability related issues.

0001 Reid Hall, 352-392-8565, www.dso.ufl.edu/drc/  

Software Use

All faculty, staff and students of the university are required and expected to obey the laws and legal agreements governing software use. Failure to do so can lead to monetary damages and/or criminal penalties for the individual violator. Because such violations are also against university policies and rules, disciplinary action will be taken as appropriate.

UF Counseling and Career Services

Resources are available on-campus for students having personal problems or lacking clear career and academic goals which interfere with their academic performance. These resources include:

  1. University Counseling and Wellness Center, 3190 Radio Road, Gainesville, FL 32611
  2. Career Connections Center, Reitz Union, 392-1601, career development assistance and counseling.