Submitted to: Agricultural Systems
Publication Type: Literature review Publication Acceptance Date: 1/10/2004 Publication Date: 2/24/2004 Citation: Ma, L., Ahuja, L.R. 2004. Book review: mathematical modeling for system analysis in agricultural research by karel d. vohnout. Agricultural Systems. Vol 81, pp. 273274. Interpretive Summary: The author assumes that mathematical modeling in agricultural systems is essentially an empirical process, with only few feasible theoretical considerations. Free choice of mathematical models of agricultural systems is assumed. Thus the book deals with empirical modeling of agricultural system components based on statistical regression relationships. Less consideration is given to the entire systems. The author neglects the fact that, over the past 30 years, models of agricultural systems have increasingly incorporated theoretical cause and effect relationships in soilwaterplantatmosphere processes, even though there are still some empirical relations used when knowledge gaps are encountered. For example, the movement of water through the soilplantatmosphere continuum is theoretically based, so are the transformation and transport of carbon and nitrogen. On the other hand, the empirical modeling approaches described in the book can be useful in developing and testing theoretical hypothesis of different components of a system, as well as of component interactions. The book may be useful for graduate level teaching of the empirical statistical approaches to modeling. However, the level of the knowledge of mathematics required by the students will have to be much higher than the current level of graduate students in agricultural sciences in general. The book is more suitable for graduate students in agricultural statistics and engineering. Technical Abstract: The author assumes that mathematical modeling in agricultural systems is essentially an empirical process, with only few feasible theoretical considerations. Free choice of mathematical models of agricultural systems is assumed. Thus the book deals with empirical modeling of agricultural system components based on statistical regression relationships. Less consideration is given to the entire systems. The author neglects the fact that, over the past 30 years, models of agricultural systems have increasingly incorporated theoretical cause and effect relationships in soilwaterplantatmosphere processes, even though there are still some empirical relations used when knowledge gaps are encountered. For example, the movement of water through the soilplantatmosphere continuum is theoretically based, so are the transformation and transport of carbon and nitrogen. On the other hand, the empirical modeling approaches described in the book can be useful in developing and testing theoretical hypothesis of different components of a system, as well as of component interactions. The book may be useful for graduate level teaching of the empirical statistical approaches to modeling. However, the level of the knowledge of mathematics required by the students will have to be much higher than the current level of graduate students in agricultural sciences in general. The book is more suitable for graduate students in agricultural statistics and engineering.
