Structures and processes required for research, higher education and technology transfer in the agricultural sciences: a policy appraisal

Evidence is forwarded of a will for rationalisation of higher education, research and technology transfer processes; but the actions which have taken place in all three structures have, in contrast, produced irrationality and inefficiency. Tertiary education institutions are proposed as the spine for reconstruction, but pre-requisite is a logical hierarchy of missions appropriate to the various educational sectors. This done, research institutions may usefully coalesce with the universities, while development and advisory agencies may beneficially integrate into the polytechnic sector from which their information flow is sourced. There is strong mutually supportive efficiencies from education, research and extension emanating from a single resource base; but that base needs to be tiered according to the aptitude and requirement (science, technology, skills), and integrated with the industry. These proposals are not founded only as coping strategies in the face of funding withdrawals, but as optimisation movements bringing benefits of sharing of common human and physical resource for the three sectors; education, research, and technology transfer. An optimisation lost by their separation, and by competition amongst organisations within each sector (but especially education) striving for similar goals and for limited resources when the national requirement is for diversity. © 1998 Elsevier Science B.V. All rights reserved

Real-time Control of Pig Growth through an Integrated Management System.

This paper describes the development and testing of the first prototype closed-loop, model-based, real-time system for the integrated control of pig growth and pollutant emissions. In each of two trials, growing pigs were reared from 30–50 to 65–125 kg in groups of 12 in 12 separate pens under controlled environment conditions at ADAS Terrington (Norfolk, England). They were fed ad libitum diets in which the protein content was controlled for each pen. Weight, estimated by visual image analysis, and feed intake were recorded daily for each pig. The control system was based on a mechanistic growth model. Each week, two model parameters were optimised using the data to improve the prediction, then the diet for each pen was optimised by adjusting the crude protein content between 140 and 190 g/kg [dry matter] to minimise the model error from a target for weight or fat depth. Part of the trial set weight gain targets of 50 and 60 kg over 70 days using two pens for each target. In three of the four pens the final mean weight of the pigs was within 2 kg of the target; in the fourth, growth was on target until it was interrupted close to the end of the trial. This trial has demonstrated the potential of the system to control the growth rate of pigs and has given encouraging but not conclusive results for the control of back fat depth

A simulation model for predicting the voluntary feed intake of a growing pig

The main goal of this simulator is to predict voluntary feed intake based on the effects of temperature and stocking density. The model indicates the limiting factors relative to diet ( protein, energy or ash), housing environmental conditions and stocking density. The concepts of compensatory protein growth, correction of lipid growth, the desired feed intake to meet energy, protein and ash requirements, and influences of stocking density, genotype and sex are also introduced in this model. This study draws a flow chart and steps to predict feed intake of a growing pig to make it clear how the model works. The model simulates the outcomes of feed intake, energy and protein requirements for maintenance, the energy cost for cold thermogenesis, and protein and lipid retention on a daily basis until slaughter weight. This model was also validated by comparison with published experiments