## 7 Conclusions  
There is currently an urgent need for a national scale agricultural systems modelling in NZ to address key questions of the sector, due to critical global environmental, socio-economic, and technological disruptions. Although there are a number of models available for agricultural systems modelling, none are intended to be used for modelling major national or regional disruptions to agriculture, or their usability outside inbuilt geographic boundaries is low. Furthermore, most models are only intended for targeted applications for understanding the effects of land-use change, climate, or other specific changes. The FAO suite of models are freely globally accessible, provide generalised country-specific input data, and are quickly reusable for specific regional to national modelling initiatives; however, the weakness of these models is in their singular modelling approach, which limits their applicability to address a range of complex disruptions at once. Six broad modelling approaches were identified, and each of these approaches provides for specific strengths, weaknesses, and application complexities. The participatory approach allows a great stakeholder engagement; deterministic approaches allow for a direct link with field knowledge and physical processes; probabilistic approaches provide statistical modelling to explain uncertainties; system dynamics approaches allow for modelling complex systems and include feedback loops and delay response; agent-based approaches permit behaviour reproduction and testing, and artificial intelligence allows for deep learning of any source of data to provide understanding of previous changes, which can be used as a predictive tool. To better understand the current complex disruptions affecting the NZ agricultural sector and assess relevant policies to address future disruptions, a suite of physical crop-water-climate models (i.e., FAO models) should be linked to economic, trade, and production models (i.e., CAPRI model), ecosystem health models (i.e., InVest), and target socio-environmental global objectives (i.e., ISDG indices). An SD approach would be an ideal framework to allow for an integration of these models for both temporal and spatial analysis. Furthermore, ABM could be used to understand and test behaviour of stakeholders under various scenarios of disruptions. Free national datasets (i.e., NZ Stats, sectoral statistics Dairy NZ, Beef&Lamb, Irrigation NZ), international datasets (i.e., FAOSTAT) and maps (i.e., Global Land Cover, OurEnvironment NZ), together with data analysis of national or global datasets through Artificial Intelligence could provide for the necessary inputs to drive such models and also be used to calibrate and validate results through comparison to examples of historical disruptions to agriculture. The complexity of the NZ agricultural system and its economic, social, and environmental implications, requires integrative approaches, particularly at the national scale for policy and decision-making. A single modelling approach has limited usefulness for modelling of complex agricultural disruptions and thus future investment is needed by the NZ government and industry in integrative modelling development. Finally, this analysis suggests the use of a spatial DST, based on an SD approach, as the most suitable modelling system at a regional to national scale for modelling the NZ agricultural system to support policy development and anticipate main impacts and future disruptions. Conceptualization, C.V., T.A.C., L.B. and P.Z.R. ; methodology, C.V. and T.A.C. ; formal analysis, C.V.; data curation, C.V.; writing--original draft preparation, C.V. and T.A.C. ; writing--review and editing, T.A.C., L.B. and P.Z.R. ; supervision, T.A.C., L.B. and P.Z.R. ; project administration, T.A.C. ; funding acquisition, T.A.C., L.B. and P.Z.R. All authors have read and agreed to the published version of the manuscript. This research received no external funding.