Multi-objective optimization as a tool to identify possibilities for future agricultural landscapes

Agricultural landscapes provide many ecosystem services simultaneously including food production, regulation of water and regulation of greenhouse gases. Thus, it is challenging to make land management decisions, particularly transformative changes, that improve on one service without unintended consequences on other services. To make informed decisions the trade-offs between different landscape functions must be considered. Here, we use a multi-objective optimization algorithm with a model of crop production that also simulates environmental effects such as nitrous oxide emissions to identify trade-off frontiers for agricultural management using wheat production across three soil types as an example. The optimisation algorithm identifies trade-offs between different objectives and allows them to be visualised. For example, we observed a highly non-linear trade-off between wheat yield and nitrous oxide emissions, illustrating where small changes might have a large impact. There were more possible strategies for achieving desirable environmental outcomes and remaining profitable when the management of different soil types was considered together. Interestingly, it was on the soil capable of the highest potential profit that lower profit strategies were identified as useful for combined management. Meanwhile to maintain average profitability across the soils it was necessary to maximise the profit from the soil with the lowest potential profit. These results are somewhat counterintuitive and so the range of strategies supplied by the model could be used to stimulate discussion amongst stakeholders. We also used a cluster analysis to identify distinct management strategies that have similar effects on objectives. As the key objectives can be met in different ways, this would allow stakeholders to discuss the impact of these management strategies on other objectives not quantified by the model. We also highlight the need for future optimization algorithms to retain some strategies that are slightly sub-optimal, as these may better suit stakeholders’ additional priorities (i.e. those not quantified by the model).