Significant differences in the estimation of amino acid requirements exist between the available factorial methods. This study aimed to compare current factorial models used to estimate the individual and population standardised ileal digestible (SID) lysine (Lys) requirements of growing pigs during a 26-day feeding phase. Individual daily feed intake and BW data from 40 high-performance pigs (25-kg initial BW) were smoothed by linear regression. Body weight gain was constant (regression slope not different from 0) for all the pigs. The CV of the SID Lys requirements ranged from 22% at the beginning of the trial to 8% at the end. The population Brazilian tables (BT-2017) and National Research Council (NRC-2012) SID Lys requirements for the average pig were 16% higher than the average requirement estimated by the individual precision-feeding model (IPF), but similar to the estimated for the population assuming that population requirements are those of the 80th-percentile pig of the population (IPF-80). Meaning that, the IPF-80, BT-2017, and NRC-2012 models would yield similar recommendations when pigs are group-fed in conventional multi-phase systems. Additionally, the IPF-80 estimates are independent of the phase length, whereas the BT-2017 and NRC-2012 models use average population values in the middle of the feeding phase for the calculations and therefore, conventional requirement estimations decrease as the length of the feeding phase increases. In conclusion, the BT-2017 and NRC-2012 methods were calibrated for maximum population responses, which explains why these methods yield higher values than those estimated for the average pig by the IPF model. This study shows the limitations of conventional factorial methods to estimate amino acid requirements for precision-feeding systems.

The salt content of processed foods is important because of the high intake of Na by most New Zealanders. A database of Na concentrations in fifty-eight processed foods was compiled from existing and new data and combined with 24 h diet recall data from two national nutrition surveys (5771 respondents) to derive salt intakes for seven population groups. Mean salt intakes from processed foods ranged from 6·9 g/d for young males aged 19–24 years to 3·5 g/d for children aged 5–6 years. A total of  ≥ 50 % of children aged 5–6 years, boys aged 11–14 years and young males aged 19–24 years had salt intakes that exceeded the upper limit for Na, calculated as salt (3·2–5·3 g/d), from processed foods only. Bread accounted for the greatest contribution to salt intake for each population group (35–43 % of total salt intake). Other foods that contributed 2 % or more and common across most age groups were sausage, meat pies, pizza, instant noodles and cheese. The Na concentrations of key foods have changed little over the 16-year period from 1987 to 2003 except for corned beef and whole milk that have decreased by 34 and 50 % respectively. Bread is an obvious target for salt reduction but the implication on iodine intake needs consideration as salt is used as a vehicle for iodine fortification of bread.

The purpose of the present paper is to provide an introduction to modelling, particularly mathematical modelling, for nutritional researchers with little or no experience of the modelling process. It aims to outline the function of modelling, and to give some guidance on factors to consider when designing protocols to generate data as part of the modelling process. It is not intended in any way to be a comprehensive guide to mathematical modelling. The paper discusses the uses of modelling, and presents a ‘hydrodynamic analogy’ to compartmental modelling, to explain the process to the non-mathematically-minded and to examine some of the pitfalls to be avoided when using stable-isotope tracers. Examples of the use of modelling in nutrition are presented, including methods for determining absorption, as well as a discussion of possible future avenues for nutritional modelling.