Crop rotation in winter cereals in semiarid Mediterranean climates is highly desirable to prevent weed infestations, but the climatic conditions make it difficult to implement alternative crops to winter cereals. Camelina (Camelina sativa (L.) Crantz) is an interesting option, as it is able to produce profitable yields thanks to its tolerance to cold and drought. In this work, three autumn–winter sowing dates (SD1, October; SD2, December; SD3, January) and two sowing rates (R1, 8 kg ha⁻¹; R2, 11 kg ha⁻¹) were chosen to evaluate the effectiveness of these cultural methods to control weeds over three seasons, and to determine effects on camelina seed yield. Results showed that a significant reduction of weed coverage was obtained by delaying the sowing date. Sowing rates did not show differences in weed coverage. In contrast, no apparent yield penalty was observed among sowing dates and rates. These results show that the introduction of camelina as a rotational crop in semiarid Mediterranean climates is a feasible option for helping to suppress winter weeds, as well as to provide productive seed yield in these climatic conditions.

The establishment and management of cover crops are common practices widely used in irrigated viticulture around the world, as they bring great benefits not only to protect and improve the soil, but also to control vine vigor and improve the yield quality, among others. However, these benefits are often reduced when cover crops are infested by Cynodon dactylon (bermudagrass), which impacts crop production due to its competition for water and nutrients and causes important economic losses for the winegrowers. Therefore, the discrimination of Cynodon dactylon in cover crops would enable site-specific control to be applied and thus drastically mitigate damage to the vineyard. In this context, this research proposes a novel, automatic and robust image analysis algorithm for the quick and accurate mapping of Cynodon dactylon growing in vineyard cover crops. The algorithm was developed using aerial images taken with an Unmanned Aerial Vehicle (UAV) and combined decision tree (DT) and object-based image analysis (OBIA) approaches. The relevance of this work consisted in dealing with the constraint caused by the spectral similarity of these complex scenarios formed by vines, cover crops, Cynodon dactylon, and bare soil. The incorporation of height information from the Digital Surface Model and several features selected by machine learning tools in the DT-OBIA algorithm solved this spectral similarity limitation and allowed the precise design of Cynodon dactylon maps. Another contribution of this work is the short time needed to apply the full process from UAV flights to image analysis, which can enable useful maps to be created on demand (within two days of the farmer´s request) and is thus timely for controlling Cynodon dactylon in the herbicide application window. Therefore, this combination of UAV imagery and a DT-OBIA algorithm would allow winegrowers to apply site-specific control of Cynodon dactylon and maintain cover crop-based management systems and their consequent benefits in the vineyards, and also comply with the European legal framework for the sustainable use of agricultural inputs and implementation of integrated crop management.

The implementation of cover crops in vineyards is an environment‐friendly soil management technique that has several advantages, including weed suppression. The effectiveness of the management depends on the characteristics of both the weed community and the cover crop species. The objectives of this study were to evaluate the dynamics of Cynodon dactylon patches and the rest of the weed community composition under five different cover crops (four grasses and one dicotyledonous species) in a commercial vineyard in Spain: (1) the perennial Festuca arundinacea and the annual species (2) Hordeum vulgare, (3) Vulpia ciliata, (4) a mixture of Bromus species and (5) Medicago rugosa. The annual species were sown in November every year after tillage (from 2015 to 2018) and shredded in June, while F. arundinacea was sown in 2015 and shredded in June and in November every year. Results showed that cover crop species affected C. dactylon soil coverage and frequency across the field. After three seasons, F. arundinacea and H. vulgare were the best cover crops which controlled C. dactylon, resulting in the lowest weed coverage of 4.6% and 3.0% respectively. Although the expansion of the weed was restrained by cover crops, the soil tillage prior to sowing the annual species spreads the rhizomes and stolons to weed free areas of the field. Canonical Correspondence Analysis (CCA) showed that the weed community composition varied depending on the cover crop species, revealing the importance of its management in the weed assemblage.

The authors wish to make the following correction to this paper