Designing profitable and climate-smart farms using virtual reality

is harvested. This time gap means the potential revenue from forestry does not support the farm cash flow at the time of the transition from pasture. For this reason, the potential long- term revenue from forestry operations was also not accounted for. Future development of the application should consider including the potential revenue from carbon credits and forestry. Landscape aesthetic and tacit knowledge Aesthetic. The VR application combines high-resolution DEM (i.e., one meter), high-res- olution aerial imagery (i.e., 300 mm) and realistic 3D tree models to provide highly realistic predictions of the landscape aesthetic (Fig 7). To improve the realism of the landscape texture, the aerial imagery has been augmented with a ‘texture effect’ that has been built within the UE and combines soil and grass effects (Fig 7F). The high level of image fidelity that was sought was expected to enhance the emotional emersion of the user and therefore a more holistic con- sideration of alternative future scenarios [4, 42, 43]. To preserve the performance of the appli- cation, the augmented texture is activated for the player’s direct surroundings only. To preserve the performance of the application, the augmented texture is activated for the player’s direct surroundings only. We believe VR applications similar to the one we have developed can simulate accurately the aesthetic value of different planting strategies and therefore con- tribute to landscape decision making [44]. Predicting the aesthetic dimension of potential future plantings is important when design-

ing carbon neutral farms, because tree species have a significant effect on the aesthetic feel of a landscape and therefore, an individual’s connection to it [45]. For instance, people tend to value forested landscape when seen from proximity, but in terms of landscape structure, pref- erence for mosaic landscapes that have smaller areas of forest mixed with agricultural land has been reported [46, 47]. There is likely to be a large variation in an individual’s landscape aes- thetic preference which will reflect their own context, such as the place of residence and Fig 7. Screen captures form the virtual reality application. (A) and (B) helicopter views of two different areas of the farm. The coloured polygons on overlapping the pastures represent the different productivity classes. Dark green, light green, yellow and red polygons represent the very high, high, low and very low productive pasture respectively. (C) Represents trees being planted. (D) represents the predicted farm carbon emissions (in kg/year) and farm profit values (in $/year) and can be accessed by the user at any time. The aesthetic of Pinus radiata forest can also be assessed from the ground level. (E) Represents stands of Pinus radiata from a helicopter view. (F) represents the build texture that is used on top of the digital elevation model and the high-resolution aerial imagery to improve the realism of the rocky areas. https://doi.org/10.1371/journal.pone.0286723.g007 PLOS ONE Designing profitable and climate-smart farms using virtual reality

PLOS ONE | https://doi.org/10.1371/journal.pone.0286723 June 2, 2023 10 / 16