Highlights- Efficacy of UAVs and emergence predictive models for weed control has been confirmed. - Combination of time-specific and site-specific weed control provides optimal results.- Use of timely prescription maps can substantially reduce herbicide use. Remote sensing using unmanned aerial vehicles (UAVs) for weed detection is a valuable asset in agriculture and is vastly used for site-specific weed control. Alongside site-specific methods, time-specific weed control is another critical aspect of precision weed control where, by using different models, it is possible to determine the time of weed species emergence. In this study, site-specific and time-specific weed control methods were combined to explore their collective benefits for precision weed control. Using the AlertInf model, which is a weed emergence prediction model, the cumulative emergence of Sorghum halepense was calculated, following the selection of the best date for UAV survey when the emergence was predicted to be at 96%. The survey was executed using a UAV with visible range sensors, resulting in an orthophoto with a resolution of 3 cm, allowing for good weed detection. The orthophoto was post-processed using two separate methods: an artificial neural network (ANN) and the visible atmospherically resistant index (VARI) to discriminate between the weeds, the crop and the soil. Finally, a model was applied for the creation of prescription maps with different cell sizes (0.25 m2, 2 m2, and 3 m2) and with three different decision-making thresholds based on pixels identified as weeds (>1%, >5%, and >10%). Additionally, the potential savings in herbicide use were assessed using two herbicides (Equip and Titus Mais Extra) as examples. The results show that both classification methods have a high overall accuracy of 98.6% for ANN and 98.1% for VARI, with the ANN having much better results concerning user/producer accuracy and Cohen's Kappa value (k=83.7 ANN and k=72 VARI). The reduction percentage of the area to be sprayed ranged from 65.29% to 93.35% using VARI and from 42.43% to 87.82% using ANN. The potential reduction in herbicide use was found to be dependent on the area. For the Equip herbicide, this reduction ranged from 1.32 L/ha to 0.28 L/ha for the ANN; with VARI the reduction in the amounts used ranged from 0.80 L/ha to 0.15 L/ha. Meanwhile, for Titus Mais Extra herbicide, the reduction ranged from 46.06 g/ha to 8.19 g/ha in amounts used with the ANN; with VARI the reduction in amounts used ranged from 27.77 g/ha to 5.32 g/ha. These preliminary results indicate that combining site-specific and time-specific weed control, has the potential to obtain a significant reduction in herbicide use with direct benefits for the environment and on-farm variable costs. Further field studies are needed for the validation of these results.

Agriculture faces big challenges, such as feeding a growing population and providing an increasing amount of biomass for energy production. Land is, however, a limited resource and intensification of agricultural practices is deprecated because of the negative impacts on natural resources. Effective answers should therefore be fostered by the development of smarter spatial configurations of agricultural activities. The improvement of farming systems therefore requires agronomy to interact with geography and other disciplines that deal with spatially-explicit aspects of agricultural land management. Different research approaches have supported agronomy in the development of a landscape approach and in this paper we focus on the interactions with geography fostering the enhancement of a common language about the way farming practices are observed and understood by the two disciplines. For this purpose, we compare land management units, identified in recent agronomic literature, with the aim to facilitate future synergies of landscape-oriented research about farming system design. We conclude by arguing for the enhancement of the interface between agronomy and geography and discussing some perspectives on the use of the various land management units in the design of future farming systems with a landscape approach.

Miscanthus is an emerging crop with high potential for bioenergy production. Its effective sustainability depends greatly on the spatial location of this crop, although few modelling approaches have been based on real maps. To fill this gap, we propose a spatially explicit method based on real location data. We mapped all of the miscanthus fields in the supply area of a transformation plant located in east-central France. Then, we used a boosted regression tree, machine learning method, to model miscanthus presence/absence at the level of the farmer's block as mapped in the French land parcel identification system. Each of these modelling spatial units was characterised on agronomical, morphological and contextual variables selected from in-depth spatially explicit farm surveys. The model fostered a two-fold aim: to assess the farmers' decision criteria and predict miscanthus location probability. In addition, we evaluated the consequence of possible legislative constraints, which could prevent the miscanthus to be planted in protected areas or in place of grasslands. The small and complex-shaped farmer's blocks that were predicted by our model to be planted with miscanthus were also characterised by their great distance from the farm and the roads. This kind of result could provide a different perspective on the definition of “marginal land” by integrating also the farm management criteria. In conclusion, our approach elicited real farmers' criteria regarding miscanthus location to capture local specificities and explore different miscanthus location probabilities at the farm and landscape levels