Calibration of GOSSYM: Theory and practice

• Published in: Computers and electronics in agriculture Vol. 9; no. 3; pp. 193 - 203
• Main Authors: Boone, M.Y.L., Porter, D.O., McKinion, J.M.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.11.1993; Elsevier
• Subjects: Agronomy. Soil science and plant productions; Biological and medical sciences; Biometrics, statistics, experimental designs, modeling, agricultural computer applications; CRECIMIENTO; CROISSANCE; Fundamental and applied biological sciences. Psychology; Generalities. Biometrics, experimentation. Remote sensing; GOSSYPIUM; GROWTH; MODELE DE SIMULATION; MODELOS DE SIMULACION; SIMULATION MODELS; VARIEDADES; VARIETE; VARIETIES; Malvaceae; Growth; Climatic data; Theory; Gossypium; Expert system; Environmental factor; Calibration; Fiber crop; Accuracy; Model calibration; Dicotyledones; Angiospermae; Computerized processing; Development; Spermatophyta; Simulation model; Mathematical model; Cultivar
• ISSN: 0168-1699; 1872-7107
• DOI: 10.1016/0168-1699(93)90038-3

GOSSYM-COMAX, the cotton simulation model and expert system, has been in use across the cotton belt for the last 6 years. Behind the capability of GOSSYM to simulate a wide range of cultivars is the VARIETY file. This variety file contains 50 parameters that modify the growth and development of twelve varieties from the Delta, Acala, Stripper, and PIMA cultivars. Whenever enhancements to GOSSYM are made and before a new version is released, the model is calibrated with standard data sets to verify the accuracy of the model for each variety class. These standard data sets have developmental and carbohydrate partitioning data such as: height; number of nodes, squares, and bolls; dry weights of stems, leaves and fruits; and lai. These data sets, representative of various cultivars, are used to modify variety-dependent model parameters to obtain an optimal simulation of field data. This paper presents the procedure undertaken in the calibration process and some of the recognized knowledge gaps.