Search

Growing prosperity in the South is accompanied by human diets that will claim more natural resources per capita. This reality, combined with growing populations, may raise the global demand for food crops two– to four–fold within two generations. Considering the large volume of natural resources and potential crop yields, it seems that this demand can be met smoothly. However, this is a fallacy for the following reasons. (i) Geographic regions differ widely in their potential food security: policy choices for agricultural use of natural resources are limited in Asia. For example, to ensure national self–sufficiency and food security, most of the suitable land (China) and nearly all of the surface water (India) are needed. Degradation restricts options further. (ii) The attainable level of agricultural production depends also on socio–economic conditions. Extensive poverty keeps the attainable food production too low to achieve food security, even when the yield gap is wide, as in Africa. (iii) Bio–energy, non–food crops and nature ‘compete’ with food crops for natural resources. Global and regional food security are attainable, but only with major efforts. Strategies to achieve alternative aims will be discussed.

The question of how many grams of an organism can grow heterotrophically from only 1·0 g of glucose and adequate minerals has been put forward many times. Only a few attempts have been made to answer this question theoretically and these attempts were rather rough. In this paper, it is demonstrated that the yield of a growth process may be accurately computed by considering the relevant biochemistry of conversion reactions and the cytological implications of biosynthesis and growth. Oxygen consumption and carbon dioxide production by these processes are also computed. The weight of the biomass synthesized from 1·0 g of substrate and the quantities of gases exchanged are independent of temperature. These results are obtained by adding the individual equations describing the formation of each compound synthesized by the organism from the substrate supplied. The sum represents an equation which accounts for all substrate molecules required for biosynthesis of the carbon skeletons of an end-product, whose chemical composition is given. It is then calculated how much energy is required for the non-synthetic processes which form a part of biosynthesis, such as intra- and intercellular transport of molecules and maintenance of RNA and enzymes. The additional amount of substrate required to provide this energy by combustion is easily calculated. Adding this substrate to the amount used for skeleton synthesis gives an overall equation which quantifies the substrate and oxygen demand as well as carbon dioxide evolution during biosynthesis of 1·0 g biomass. For example, it requires 1·34 g of glucose with adequate ammonia and minerals to synthesize 1·0 g maize plant biomass in darkness; during this process 0·14 g oxygen are consumed and 0·24 g carbon dioxide are produced. It has been described elsewhere that similar results were obtained experimentally with growing plants. Such results depend considerably upon the chemical composition of the biomass being synthesized and upon the state (oxidized or reduced) of the nitrogen source. Other parameters, such as the number of ATP molecules required for protein synthesis, the possibility for utilization of alternative pathways for synthesis or energy production, the presence or absence of compartmentation of synthetic processes and variations in the P/O ratio between two and three, under many conditions affect results of the computation less than 10%. Since maintenance of cellular structures is not considered, the approach concerns the gross yield of biosynthesis. It predicts therefore the dry matter yield of heterotrophic cells from a given quantity of substrate at high relative growth rates.

For the simulation of organ formation and assimilate partitioning, information is required on the current level of activities like CO2 assimilation and the growth of various organs, as well as state variables such as leaf and root wt., N content and carbohydrate reserves and exogenous variables like radiation and temp. This information may be retained in auxiliary state variables by considering the dynamic equilibrium between growth of roots and shoots. Auxiliary state variables are not tangible quantities but mathematical artefacts of the simulation program; it is speculated that in real plants similar information may be retained and transferred by the hormonal system. A hormonal system is a communication system and such systems may be analysed either in terms of means (of the hardware used) or in terms of purpose (of the messages transferred). In dynamic models of crop growth, interest should be focused on the latter. Wheat, maize and ryegrass are used as examples. (Abstract retrieved from CAB Abstracts by CABI’s permission)

Systems analysis and simulation have developed into advanced tools in agriculture. They are used in research, extension and education. The recent availability of powerful inexpensive microcomputers now allows the use of these techniques in developing countries. A training programme was developed and conducted to enable the implementation of systems analysis and simulation in several national research institutes in South and Southeast Asia. Eight teams of four researchers from different disciplines participated in the training programme, which included a case study, involving both experimentation and computer simulation at their home institutes. The evaluation indicated that this approach to training was effective. It is expected to be applicable on a wider scale. The interactions between team members from different disciplines were strengthened by the course experiences. Simulation provided a means and a reason for communication. However, reaching the ultimate goal of systems analysis and modelling at these national research institutes, i.e. to use these tools to contribute toward increased national agricultural production, is expected to require several years.

Prediction may be envisaged as organized thinking about the possible. For this purpose, dynamic models of the state-variable approach are important tools because they combine basic knowledge on the physical, chemical and physiological processes that underlie crop growth and agricultural production. At one extreme are comprehensive models that claim to integrate all aspects of growth and to focus attention on the main gaps in present operational knowledge. As such, they are research tools. At the other extreme are summarizing models that are especially geared to answer ‘w hat-if’ questions and are used for evaluating regional production potentials and constraints, for irrigation management and integrated control of pests, diseases and weeds. Examples of these types of models are given and their usefulness for predictive purposes is discussed.

A workshop on evaluation of simulation models was held in Wageningen. The object of the meeting was to discuss problems and points of view concerning evaluation of complex models. Most of the workshop was devoted to discussions. At the end, there was a considerable consensus on many points. This paper summarises the major conclusions.

The general objective of this thesis is to formulate a general model for fish production in integrated ponds and ricefields as a means of obtaining a better understanding of these production systems. Integrated culture systems produce fish without large industrial energy inputs and have positive effects on the whole farm system. A main characteristic is their environmental variability, notably dissolved oxygen concentration and temperature. A systems approach using mathematical models is advocated because it can lead to insights that have universal applicability while avoiding the pitfalls of site- and species-specific, expensive experimental work. Two modelling approaches are distinguished: descriptive models, generally the result of statistical analysis of datasets; and explanatory models, based on knowledge of the biological processes underlying fish production.Multiple regression analysis (a descriptive modelling technique) was used for the analysis of data from 15 integrated rice-fish production experiments with the Nile tilapia (Oreochromis niloticus L.) in the Philippines. Results showed that this technique led to insights that had not been obtained through separate analysis of the experiments. Main drawback of this method was that the models were not applicable to other production environments.An explanatory model (called Fish Growth Simulator, or FGS) for growth of O.niloticus was developed on the basis of an existing simulation model for the African catfish Clarias gariepinus Burchell (1822). After parameterization and calibration, the model gave good predictions of fish growth in independent datasets. Parameterization and calibration of the same model for the rainbow trout Onchorhynchus mykiss (Walbaum) demonstrated the generality of the model and it was concluded that, provided that enough data are available, the model may be used to predict growth in a wide range of fish species. Food amount and composition, and temperature were the environmental variables upon which the model based its predictions.FGS was expanded with a dissolved oxygen module to accomodate oxygen as an environmental variable. The module was based on the hypothesis that oxygen is needed in sufficient amounts for aerobic metabolism, and that gill surface area limits the supply of oxygen to fish. The resulting model allowed the simulation of fish growth under low dissolved oxygen concentration and also provided an explanation for differences in the final weight of fishes, both within and between species.FGS was used for simulation of food and oxygen limitations in waste-fed fish ponds in Honduras, Thailand and Rwanda. The model simulated fish growth for various combinations of environmental conditions: temperature, food availability and dissolved oxygen concentration. Validation, using data from Indonesia and Panama, was not successful because estimates of the food consumption rate in these countries were not reliable.In the last chapter methodology, the role of oxygen in fish metabolism and growth, model implications for the management of integrated agriculture-aquaculture systems and implications for further work are discussed.