Abstract in English:

Abstract: Background: Numerous studies have described junglerice (Echinochloa colona) competitiveness against crops, but its behavior concerning plant density as an outcome of intraspecific competition has not been well documented. Objective: This study aimed to characterize morphology based on population density and determine the degree of density dependence. Methods: Junglerice was grown in field conditions in a range of densities from 0.25 to 300 plants m-2. Plant height and width, tillering, aerial dry weight, seed weight, seed number, and hundred-seed weight were determined and related to growth habits by principal components analysis. The variables were fitted to the growth habits using a general linear model. Aerial dry weight, tiller number, seed number, and seed weight per plant (pl-1) and per area (m-2) were fitted to plant density using non-linear functions. Results: Plants exhibited prostrate habit at 0.25 plants m-2, semi-prostrate habit at 2-4 plants m-2, and erect habit at 35 plants m-2. Prostrate plants displayed a width of 196 cm, a height of 40 cm, 158 tillers per plant, and over 32,000 seeds per plant, whereas erect plants had an average width of 13.7 cm, a height of 114.5 cm, 10 tillers per plant, and approximately 1,700 seeds per plant. The aerial dry matter per area adhered to the law of constant final yield. In contrast, tiller number, seed number, and weight per area had a nonlinear dome-shaped relationship. Conclusions: Junglerice modifies its aerial structures according to population density, showcasing extensive morphological plasticity. This characteristic allows junglerice to adopt different growth habits.

Abstract in English:

Abstract: Background: The resistance of grass weeds to herbicides is expanding in wheat fields. An effective strategy for managing herbicide resistance is to prevent the likelihood of resistance development spreading from field margins to within fields. Objective: This study was conducted to evaluate the resistance development in winter wild oat (Avena sterilis subsp. ludoviciana) populations collected from within fields and field margins of 11 winter wheat fields to the commonly used ACCase and ALS-inhibiting herbicides. Methods: Seeds of 22 A. sterilis subsp. ludoviciana populations were collected, both from field margins and within winter wheat fields. The seeds were grown in greenhouse, and the seedlings at the three- to four-leaf stage were treated with the recommended field rates of the following four herbicides, clodinafop-propargyl (CP), pinoxaden (PN), mesosulfuron+iodosulfuron (MI), and mesosulfuron+iodosulfuron+diflufenican (MD). Results: All populations from within fields evolved resistance to CP, but none showed cross-resistance to PN. Importantly, there were notable variations in CP and MI resistance, with less than half of the samples from within fields demonstrating higher resistance, while for the remaining populations no differences were observed between field-margin and within-fields samples. Contradictory trend was observed in response to MD, where around 70% of populations followed the trend for CP and MI, surprisingly, around 30% of populations exhibited opposite results. Conclusions: Overall, the incidence of herbicide resistance to CP, MI, and MD was more common in populations from within fields, suggesting stronger selection pressure. However, some field margin populations showed MD resistance, underscoring the need for weed control in field margins as a proactive resistance management strategy.

Abstract in English:

Abstract: Background: In a prior study, Lasiodiplodia theobromae (Pat.) Griffiths and Maubl. and Bipolaris bicolor (Mitra) Shoemaker., were found to suppress the growth of Eleusine indica (L.) Gaertn, but limited information exists on their response to environmental factors and herbicides for integrated E. indica control. Objective: This study aimed to determine the tolerance levels of L. theobromae and B. bicolor to pH, temperature, photoperiod, relative humidity, and herbicides. Methods: The mycelia and conidia of L. theobromae and B. bicolor were exposed to a range of environmental conditions and herbicides in a controlled setting, including different levels of temperature (25, 30, 35 ⁰C), pH (4, 6, 8, 10), photoperiod (24 hours of darkness; alternating 12 hours of blue light followed by 12 hours of darkness; and alternating cycles of 6 hours of blue light and 6 hours of darkness), and relative humidity (75, 85, 95%). Herbicides such as topramezone, diuron, oxyfluorfen, and imazethapyr were applied at their recommended rates. Conidial germination was assessed by counting after an 18-hour incubation using a haemocytometer, while the diameter of mycelium growth was measured after 3 days of incubation, except for the herbicide effects evaluation, which were extended over 7 days. Results: Laboratory assays demonstrated that L. theobromae exhibited higher conidial germination of 85-95% and superior mycelial growth under varied pH, temperature, and photoperiod conditions compared to B. bicolor. Lasiodioplodia theobromae's mycelia remained unaffected by herbicides at full labelled rates, but they inhibited the conidial germination of the fungus. For B. bicolor, the conidia were not affected by imazethapyr and topramezone, but its mycelial growth was reduced by imazethapyr, oxyfluorfen, and diuron. Conclusions: These results indicate that the mycelia of L. theobromae presents a more favorable option for tank mixing with test herbicides, offering potential for the formulation of an integrated control strategy against E. indica.

Abstract in English:

Abstract: Background: Echinochloa colona (junglerice), a troublesome weed in rice, is resistant to 15 herbicide active ingredients. High temperatures are linked to reduction of herbicide efficacy. Objective: Evaluate growth and seed production of junglerice, after five generations of recurrent selection with sublethal dose of rice herbicides under heat stress. Methods: Junglerice plants previously subjected to recurrent selection with herbicides and heat stress for three cycles were exposed to further iterative cycles of selection with heat stress (45 °C) and sublethal dose of florpyrauxifen-benzyl (FPB, 0.125x), imazethapyr (0.125x), quinclorac (0.25x), and glufosinate (0.25x). Plant injury was evaluated 1 and 3 weeks after treatment. Panicle number; plant height; and dry biomass of shoot, panicles, and seeds were recorded. Seed number plant-1 and reproductive effort were estimated. Results: The joint effect of heat stress and sublethal dose of herbicides reduced the sensitivity of junglerice to all herbicides tested. Plants treated with FPB and quinclorac produced more shoot biomass and seed number after five generations. Heat-stressed plants treated with FPB produced more seeds than non-stressed plants. Imazethapyr reduced seed production. Heat-stressed junglerice treated with glufosinate produced fewer seeds than non-stressed plants. Conclusions: The joint effect of heat stress and sublethal dose of herbicides reduced junglerice sensitivity to the four rice herbicides tested after five generations. Heat stress and sublethal dose of the relatively recent auxinic herbicide florpyrauxifen-benzyl reduced junglerice sensitivity and increased seed production.

Abstract in English:

Abstract: In recent times, there has been an environmental pressure to reduce the amount of pesticides applied to crops and, consequently, the crop production costs. Therefore, investments have been made in technologies that could potentially reduce the usage of herbicides on weeds. Among such technologies, Machine Learning approaches are rising in number of applications and potential impact. Therefore, this article aims to identify the main machine learning algorithms used in integrated crop-livestock systems for weed management. Based on a systematic literature review, it was possible to determine where the selected studies were performed and which crop types were mostly used. The main research terms in this study were: "machine learning algorithms" + "weed management" + "integrated crop-livestock system". Although no results were found for the three terms altogether, the combinations involving "weed management" + "integrated crop-livestock system" and "machine learning algorithms" + "weed management" returned a significant number of studies which were subjected to a second layer of refinement by applying an eligibility criteria. The achieved results show that most of the studies were from the United States and from nations in Asia. Machine vision and deep learning were the most used machine learning models, representing 28% and 19% of all cases, respectively. These systems were applied to different practical solutions, the most prevalent being smart sprayers, which allow for a site-specific herbicide application.