Plant species diversity in urban areas of Northwest Siberia

The flora of two towns and two settlements in the northern taiga and forest-tundra of Northwest Siberia (Russia) are considered. Urban species lists are limited (61–119 vascular plant species) and mainly consist of native species with a predominance of perennial herbs. Various urban functional zones (industrial, residential, recreational, vegetable patches) differ by species composition, and this difference increases in the course of city development. In the industrial zone, vegetation composition is closer to the native flora, because of the low number of adventive species. Maximal diversity is due to decorative plants, cultivars and southern weed distribution, and is typical to residential neighborhoods. Species diversity increases mainly because of woody plants introduction. Alien species are represented generally by a small number of individuals. The severe climate and poor soils limit their abilities to spread in the Far North. Urbanization forces the reduction of aboriginal biodiversity, but in northern areas where native species have the adaptive advantage, this effect is minimal. Changes in flora since 1995 were analyzed in the town of Novy Urengoy (Yamalo-Nenets Autonomous Okrug, Russia). Plant diversity increased by about 20 % in all functional zones, although some alien and natural species were not recorded in 2018.

A survey evaluating the spatial and temporal distribution of horseweed (Conyza canadensis) late season in Ohio soybeanfields from 2013 to 2017

On-site surveys of weed populations provide information on the relative occurrence and density of weeds that can be useful to growers in that region. Data generated by weed surveys can aid in the management of weed issues by monitoring the movement of problem weeds and forecasting areas susceptible to infestations. Currently, on-site surveys are often performed on a small scale, within single fields or counties. Questionnaire surveys are helpful for assessing relative abundance, but don’t always provide detailed information on weed distribution in time or space. A survey was conducted annually in Ohio from 2013 through 2017 in 49 counties with soybean [Glycine max (L.) Merr.] production to assess the late-season occurrence of horseweed [Conyza canadensis (L.) Cronquist]. The objectives of this research were to: 1) determine the frequency, level of infestation, and distribution of C. canadensis in soybean fields in the primary soybean-producing Ohio counties over five years; 2) identify significant spatial clusters or movement trends over time. Conyza canadensis was encountered in each county from 2013 through 2017. Spatial cores of interest, or counties identified as having significant levels of C. canadensis infestations or a lack thereof, relative to surrounding counties, were identified in all years except 2017. The lowest frequency of C. canadensis encountered at all rating levels occurred in 2017, which coincided with second highest frequency of infestations (highest density level) among years. There was no distinct distribution or pattern of C. canadensis movement within the state from year to year, but there was an increase in counties with infestations over time compared to the early years of the survey where many counties had few to no infestations. These results suggest that C. canadensis persists as a common and troublesome threat to Ohio soybean producers, and that growers should continue making C. canadensis management a priority when developing weed control programs.

Can Commercial Low-Cost Drones and Open-Source GIS Technologies Be Suitable for Semi-Automatic Weed Mapping for Smart Farming? A Case Study in NE Italy

Weed management is a crucial issue in agriculture, resulting in environmental in-field and off-field impacts. Within Agriculture 4.0, adoption of UASs combined with spatially explicit approaches may drastically reduce doses of herbicides, increasing sustainability in weed management. However, Agriculture 4.0 technologies are barely adopted in small-medium size farms. Recently, small and low-cost UASs, together with open-source software packages, may represent a low-cost spatially explicit system to map weed distribution in crop fields. The general aim is to map weed distribution by a low-cost UASs and a replicable workflow, completely based on open GIS software and algorithms: OpenDroneMap, QGIS, SAGA and OpenCV classification algorithms. Specific objectives are: (i) testing a low-cost UAS for weed mapping; (ii) assessing open-source packages for semi-automatic weed classification; (iii) performing a sustainable management scenario by prescription maps. Results showed high performances along the whole process: in orthomosaic generation at very high spatial resolution (0.01 m/pixel), in testing weed detection (Matthews Correlation Coefficient: 0.67–0.74), and in the production of prescription maps, reducing herbicide treatment to only 3.47% of the entire field. This study reveals the feasibility of low-cost UASs combined with open-source software, enabling a spatially explicit approach for weed management in small-medium size farmlands.

Weed Density Detection Method Based on Absolute Feature Corner Points in Field

Field weeds identification is challenging for precision spraying, i.e., the automation identification of the weeds from the crops. For rapidly obtaining weed distribution in field, this study developed a weed density detection method based on absolute feature corner point (AFCP) algorithm for the first time. For optimizing the AFCP algorithm, image preprocessing was firstly performed through a sub-module processing capable of segmenting and optimizing the field images. The AFCP algorithm improved Harris corner to extract corners of single crop and weed and then sub-absolute corner classifier as well as absolute corner classifier were proposed for absolute corners detection of crop rows. Then, the AFCP algorithm merged absolute corners to identify crop and weed position information. Meanwhile, the weed distribution was obtained based on two weed density parameters (weed pressure and cluster rate). At last, the AFCP algorithm was validated based on the images that were obtained using one typical digital camera mounted on the tractor in field. The results showed that the proposed weed detection method manifested well given its ability to process an image of 2748 × 576 pixels using 782 ms as well as its accuracy in identifying weeds reaching 90.3%. Such results indicated that the weed detection method based on AFCP algorithm met the requirements of practical weed management in field, including the real-time images computation processing and accuracy, which provided the theoretical base for the precision spraying operations.

Understanding the nexus of rising CO2, climate change, and evolution in weed biology

Rapid increases in herbicide resistance have highlighted the ability of weeds to undergo genetic change within a short period of time. That change, in turn, has resulted in an increasing emphasis in weed science on the evolutionary ecology and potential adaptation of weeds to herbicide selection. Here we argue that a similar emphasis would also be invaluable for understanding another challenge that will profoundly alter weed biology: the rapid rise in atmospheric carbon dioxide (CO2) and the associated changes in climate. Our review of the literature suggests that elevated CO2 and climate change will impose strong selection pressures on weeds and that weeds will often have the capacity to respond with rapid adaptive evolution. Based on current data, climate change and rising CO2 levels are likely to alter the evolution of agronomic and invasive weeds, with consequences for distribution, community composition, and herbicide efficacy. In addition, we identify four key areas that represent clear knowledge gaps in weed evolution: (1) differential herbicide resistance in response to a rapidly changing CO2/climate confluence; (2) shifts in the efficacy of biological constraints (e.g., pathogens) and resultant selection shifts in affected weed species; (3) climate-induced phenological shifts in weed distribution, demography, and fitness relative to crop systems; and (4) understanding and characterization of epigenetics and the differential expression of phenotypic plasticity versus evolutionary adaptation. These consequences, in turn, should be of fundamental interest to the weed science community.

Effect of tillage system on the structure of weed infestation of winter wheat

The study aimed to evaluate the structure of weed infestation of winter wheat grown in different weeding systems: conventional tillage (CT), reduced tillage (RT), and herbicide treatment (HT). In CT system, shallow ploughing and pre-sow ploughing were conducted after the harvest of the previous crop. In RT system, shallow ploughing was replaced by cultivator tillage, whereas pre-sow ploughing by a tillage set. In HT system, shallow ploughing was replaced by spraying with glyphosate and pre-sow ploughing by cultivator tillage. At the tillering stage (22-23 in BBCH scale), species composition and number of weeds/m2 were determined with the botanical-gravimetric method, whereas at the stage of waxy maturity of wheat (82-83 BBCH) analyses were conducted for species composition as well as density, air-dry weight, and weed distribution in crop levels. The Shannon-Wiener’s diversity index (H’) and degrees of phytosociological constancy (S) of weeds were determined as well. The study showed that more weeds occurred in RT and HT systems than in the CT system and they produced higher biomass in RT than in CT and HT systems. The tillage system affected weed distribution in crop levels. In CT system, the highest weed density was identified in the ground and lower levels, whereas in RT and HT systems in the ground and middle levels. Values of the species diversity index (H’) indicate a similar diversity of weed species composition between weeding systems and more diverse between study years.

Incorporating Surface Elevation Information in UAV Multispectral Images for Mapping Weed Patches

Accurate mapping of weed distribution within a field is a first step towards effective weed management. The aim of this work was to improve the mapping of milk thistle (Silybum marianum) weed patches through unmanned aerial vehicle (UAV) images using auxiliary layers of information, such as spatial texture and estimated vegetation height from the UAV digital surface model. UAV multispectral images acquired in the visible and near-infrared parts of the spectrum were used as the main source of data, together with texture that was estimated for the image bands using a local variance filter. The digital surface model was created from structure from motion algorithms using the UAV image stereopairs. From this layer, the terrain elevation was estimated using a focal minimum filter followed by a low-pass filter. The plant height was computed by subtracting the terrain elevation from the digital surface model. Three classification algorithms (maximum likelihood, minimum distance and an object-based image classifier) were used to identify S. marianum from other vegetation using various combinations of inputs: image bands, texture and plant height. The resulting weed distribution maps were evaluated for their accuracy using field-surveyed data. Both texture and plant height have helped improve the accuracy of classification of S. marianum weed, increasing the overall accuracy of classification from 70% to 87% in 2015, and from 82% to 95% in 2016. Thus, as texture is easier to compute than plant height from a digital surface model, it may be preferable to be used in future weed mapping applications.

Distribution, Density, and Abundance of Parthenium Weed (Parthenium hysterophorus L.) at Kuala Muda, Malaysia

Knowledge of distribution, density, and abundance of weed in a place is a prerequisite for its proper management. Parthenium hazard is a national agenda in Malaysia, and Kedah is the worst infested state in the country. Despite it, the distribution and abundance of the weed is not systematically documented. Periodical weed surveys were conducted at Kuala Muda, Kedah, during March and September 2015 to identify infested locations, to determine density, abundance, and severity of infestation, and to do mapping of weed distribution of the area. Geographic locations were recorded using a GPS. Weed density was measured following the list count quadrat method. The mapping of weed infestation was done by the ArcGIS software using data of GPS and weed density. Different letters were used to indicate the severity of infestation. Results indicated that in Kuala Muda, sixteen sites are infested having average weed density of 10.6 weeds/m2. The highest density was noted at Kg. Kongsi 6 (24.3 plants/m2). The relative density was highest at Semeling (27.25%) followed by Kg. Kongsi 6 (23.14%). The average severity of infestation was viewed as the medium. Parthenium abundance and relative density increased by 18.0% and 27%, respectively, in the second survey conducted. The intervention of concerned authority to tackle the weed problem using integrated weed management approach is emphasized.

Weed Flora of Citrus Orchards and Factors Affecting its Distribution in Western Mediterranean Region of Turkey

ABSTRACT: Citrus is an important export commodity, mostly grown on Mediterranean and Aegean coasts of Turkey. Weeds are hidden foes impairing citrus productivity. Limited knowledge of weed distribution and factors affecting the distribution are among major hurdles in successful weed management. In this study, weed flora of citrus orchards and factors affecting its distributions in Mugla province of Turkey were determined. Sixty orchards were surveyed in spring and autumn seasons of 2010 and 2011. Data relating to frequency, coverage and density of weed species were recorded. Soil samples (0-30 cm depth) were collected and analyzed for physicochemical properties. Climatic variables, altitude and soil properties were correlated with weed flora. Sixty-eight weed species belonging to 30 families were documented. Higher number of weed species (54) was recorded in spring season compared with autumn (29 weed species). Annuals and therophytes were the most dominant growth and life forms, respectively. Canonical Correspondence Analysis (CCA) to correlate soil properties and weed vegetation data yielded three distinct groups dominated by phosphorus, sand and silt contents, which affected weed distribution. CCA to correlate vegetation data and weather attributes produced two distinct groups affected by altitude and precipitation. Generally, cosmopolitan weeds adapted to different ecosystems were observed during the survey. Keeping in view the spatial variability of soil and nature of weeds, site-specific/orchard-specific weed management practices are recommended to be opted for successful weed management.