Banana is one of the main fruit crops in the world as it is a rich source of nutrients and has recently become popular for its fibre, particularly as a raw material in many industries. Mathematical models are crucial for strategic and forecasting applications; however, models related to the banana crop are less common, and reviews on previous modelling efforts are scarce, emphasizing the need for evidence-based studies on this topic. Therefore, we reviewed 75 full-text articles published between 1985 and 2021 for information on mathematical models related to banana growth and, fruit and fibre yield. We analysed results in order to provide a descriptive synthesis of selected studies. According to the co-occurrence analysis, most studies were conducted on the mathematical modelling of banana fruit production. Modellers often used multiple linear regression models to estimate banana plant growth and fruit yield. Existing models incorporate a range of predictor variables, growth conditions, varieties, modelling approaches and evaluation methods, which limits comparative evaluation and selection of the best model. However, the banana process-based simulation model ‘SIMBA’ and artificial neural network have proven their robust applicability to estimate banana plant growth. This review shows that there is insufficient information on mathematical models related to banana fibre yield. This review could aid stakeholders in identifying the strengths and limitations of existing models, as well as providing insight on how to build novel and reliable banana crop-related mathematical models.

Further research into seismicity caused by natural gas production from the Groningen field is necessary to improve the assessment of seismic risk and develop means to control and reduce it. Research into subsurface aspects is primarily of relevance to assess the seismic hazard component in the cause-and-effect chain that governs the seismic risk. It requires a wide range of research activities that can be broadly classified as follows:

• • Increasing understanding of the physical mechanisms that govern production-induced seismicity, in particular source mechanisms, compaction behaviour, propagation of energy to the surface, and the effects of fluctuating production.

• • Reducing uncertainty by acquiring additional field data to improve statistical inference, and developing statistical methods and procedures that can cope with the non-stationary nature of the process.

• • Developing tools and techniques to improve risk management, and support operational control and policy measures under uncertainty.

An essential requirement for further research will be the possibility of developing competing theories for many aspects of the modelling chain. This requires an overall hazard and risk assessment methodology that can accommodate multiple models, and an organisational structure that facilitates the comparison of competing approaches while safeguarding their independent development. This will have to be supported by the availability of reliable data via shared databases. Finally, the scientific community should be prepared to make a major effort to translate their research results into popular scientific versions in order to keep stakeholders abreast of progressive insight into the origin, predictability and prevention of induced seismicity.

The ability to predict weed phenological development under field conditions is fundamental to the development of mechanistic weed–crop competition models. We studied how phenological development of common ragweed grown under field conditions could be explained using temperature and photoperiod responses derived from growth room experiments. We also determined the relationship between phenological development and common ragweed leaf area, dry matter production, and partitioning. Phenological development of common ragweed emerging at different times in the field was described by photothermal time based on temperature and photoperiod responses derived from growth room experiments. Estimated dates of phenological events of common ragweed were within 4 d of recorded values. Common ragweed seedling density did not influence phenological development. Common ragweed leaf area development, biomass partitioning, and total biomass were related to photothermal time accumulation. The results of this study are consistent with our hypothesis that phenological development is a major factor influencing the outcome of weed–crop competition. Results obtained from this study can be incorporated into a mechanistic model of weed–crop competition.

Implementation of an integrated weed management system requires prediction of the effect of weed competition on crop yield. Predicting outcomes of weed competition is complicated by genetic and environmental variation across years, locations, and management. Mechanistic models have the potential to account for this variability. Weed phenological development is an essential component of such models. Growth cabinet studies were conducted to characterize common ragweed's phenological response to temperature, photoperiod, and irradiance. Ragweed development occurred over a temperature range of 8.0 to 31.7 C, and this response to temperature was best characterized using a nonlinear function. A maximum leaf appearance rate of 1.02 leaves d−1 occurred at 31.7 C. Ragweed has a short juvenile phase, during which it was not sensitive to photoperiod. Following this juvenile phase, sensitivity to photoperiod was constant and continued until pistillate flowers were observed. Photoperiods of 14 h or less were optimal and resulted in maximum rates of development. Irradiance level affected ragweed phenological development only when combined with the additional stress of low temperatures. Data generated in this study can be used for the development of mechanistic weed competition models.

We comment on Zaida Lentini's summary of Session V (titled “Estimating Likelihood and Exposure”) of the 9th International Symposium on the Biosafety of Genetically Modified Organisms. We provide an explanation of the drawbacks of using empirical pollen dispersion models, based largely on the general representativeness of the data used to generate the empirical models. We exemplify the drawbacks by highlighting the limited data used to develop the empirical model of Gustafson (presented in the same Symposium session). We provide a discussion of the meaning of “worst-case” assessments for pollen dispersion, how “worst-case” assumptions are commonly used in environmental impact assessments and how regulators will view worst-case impact assessments differently from the regulated (biotech) community. Finally, we clarify the advantages and disadvantages of mechanistic models and explain why they are often used in preference to empirical models in environmental impact assessments.

Mechanistic animal growth models can incorporate a description of the genotype as represented by underlying biological traits that aim to specify the animal's genetic potential for performance, independent from the environmental factors captured by the models. It can be argued that these traits may therefore be more closely associated to genetic potential, or components of genetic merit that are more robust across environments, than the environmentally dependent phenotypic traits currently used for genetic evaluation. The prediction of merit for underlying biological traits can be valuable for breeding and development of selection strategies across environments.

Model inversion has been identified as a valid method for obtaining estimates of phenotypic and genetic components of the biological traits representing the genotype in the mechanistic model. The present study shows how these estimates were obtained for two existing pig breeds based on genetic and phenotypic components of existing performance trait records. Some of the resulting parameter estimates associated with each breed differ substantially, implying that the genetic differences between the breeds are represented in the underlying biological traits. The estimated heritabilities for the genetic potentials for growth, carcass composition and feed efficiency as represented by biological traits exceed the heritability estimates of related phenotypic traits that are currently used in evaluation processes for both breeds. The estimated heritabilities for maintenance energy requirements are however relatively small, suggesting that traits associated with basic survival processes have low heritability, provided that maintenance processes are appropriately represented by the model.

The results of this study suggest that mechanistic animal growth models can be useful to animal breeding through the introduction of new biological traits that are less influenced by environmental factors than phenotypic traits currently used. Potential value comes from the estimation of underlying biological trait components and the explicit description of their expression across a range of environments as predicted by the model equations.