## 6 Discussion
### Limits, Margin of Progress, and Recommendations for Future New Zealand Development  
With more than six decades of multidisciplinary contribution to concepts and tools for agricultural systems modelling, the scientific community considers models critical to make informed agricultural decisions [39]. However, despite all data and models available, models often fail to inform policy. For example, the European Commission defined a strategy to halt biodiversity loss affected by the agricultural sector by 2020. Those efforts were supported by the Common Agricultural Policy (CAP) subsidies (40% of the EU budget, EUR 362.8 billion) for the 2014-2020 period. However, the agricultural reforms failed on biodiversity, mainly due to underestimation of intensive farming, increasing of chemicals, and machinery use (according to European Court of Auditors). In addition to limited modelling, other reasons for failure include the dilution of ambitions, targeting large farms only, multiple possible exemptions, and abandonment of previous working measures (e.g., reduction of permanent grassland) [27; 141]. A large number of other models have been developed since 2014 to understand the failed processes [142; 143] and define recommendations for the 2030 strategy. They highlighted the need for more flexibility to regional adaptations and a focus on land-use processes rather than quantity (e.g., connectivity of landscape element to support overall diversity) [141; 142]. In the context of a future development of the NZ agricultural system model, the example of the European model limits are to be considered. The chosen or developed model must take into account the complexity of the whole system, but it must also maintain an expert analysis phase downstream from the modelling outputs.  
The next generation of models should be led by the use of AI, and should include advances in technology, such as precision agriculture, biotech, and others. During the analysis process, we did not find any national or regional agricultural systems of DST model based on AI, as proper training of AI systems requires large volumes of data, and in a sense, AI is still in its infancy in environmental applications. However, as agricultural systems modelling has always capitalised from technology advances, operating with big data and generalising cloud computing is a major avenue of future development. The current state of agricultural system models' complexity is sufficient for powerful applications [83].  
To select the ideal model or combination of models for future agricultural system modelling initiatives in NZ, modellers and users should consider the following:  
1. Clearly identify the question(s) to be answered or the objectives of the modelling initiative.
2. Assess the data available for modelling (qualitative, quantitative, statistical databases, field observations, GIS maps, etc.). The data available will help refine the modelling approach to use and model thematic application, and if needed, help identify what additional data needs to be collected.
3. Evaluate existing models and their adaptation potential to address the modelling requirements. Consideration for the type of approach needed, model thematic application, and model availability will depend on items 1 and 2 above. Furthermore,consideration should be given to a combination of modelling approaches to address all requirements, if necessary.