Sunflower produces achenes and oil of good quality, besides serving for production of silage, forage and biodiesel. Growth modeling allows knowing the growth pattern of the crop and optimizing the management. The research characterized the growth of the Rhino sunflower cultivar using the Logistic and Gompertz models and to make considerations regarding management based on critical points. The data used come from three uniformity trials with the Rhino confectionery sunflower cultivar carried out in the experimental area of the Federal University of Santa Maria - Campus Frederico Westphalen in the 2019/2020 agricultural harvest. In the first, second and third trials 14, 12 and 10 weekly height evaluations were performed on 10 plants, respectively. The data were adjusted for the thermal time accumulated. The parameters were estimated by ordinary least square’s method using the Gauss-Newton algorithm. The fitting quality of the models to the data was measured by the adjusted coefficient of determination, Akaike information criterion, Bayesian information criterion, and through intrinsic and parametric nonlinearity. The inflection points (IP), maximum acceleration (MAP), maximum deceleration (MDP) and asymptotic deceleration (ADP) were determined. Statistical analyses were performed with Microsoft Office Excel® and R software. The models satisfactorily described the height growth curve of sunflower, providing parameters with practical interpretations. The Logistics model has the best fitting quality, being the most suitable for characterizing the growth curve. The estimated critical points provide important information for crop management. Weeds must be controlled until the MAP. Covered fertilizer applications must be carried out between the MAP and IP range. ADP is an indicator of maturity, after reaching this point, the plants can be harvested for the production of silage without loss of volume and quality.
Brazilian wheat farming is characterized by a high occurrence of seed-transmitted diseases that can cause significant damage to grain production. The objective of this study was to evaluate the influence of seed treatments on the physiological and sanitary quality of wheat cultivar seeds. Seeds from three commercial wheat cultivars (TBIO Sossego, TBIO Sinuelo, and TBIO Toruk) were used, along with four seed treatments (Control, Chemical, Biological, and Chemical + Biological). The physiological quality of the seeds was determined based on first germination count, germination percentage, quantification of abnormal seedlings, shoot and radicle length, whole seedling dry weight, and emergence velocity index (EVI). The sanitary quality was assessed using the Blotter test. Overall, the chemical treatment combined with the seeds of the TBIO Toruk cultivar provided better indices of physiological and sanitary quality.
The aim of this study was to assess yield components and grain yield of soybean cultivars in response to sowing densities. For this, two soybean cultivars and five sowing densities were tested, in a two-factor scheme. The following yield components were measured by the end of the cycle: plant height; insertion height of the first pod; number of nodes per plant; number of pods with one, two, three and four grains; number of pods per plant; number of grains per plant; weight of a thousand grains; humidity and grain yield. Sowing densities did not cause significant variations of grain yield (bags ha -1 ) for any cultivar, however, higher populational densities promoted a reduction in the number of pods with two and three grains, as well as a reduction in the total number of pods and grains per plant for both cultivars. Cultivar NS 5700 IPRO was the most productive, with a higher number of pods with two and three grains and number of pods and grains per plant.
White mold is a disease with a wide distribution worldwide. Temperatures between 18-23 °C and high humidity conditions favor the occurrence of the pathogen. For the control of the disease it is fundamental to understand the morphology and pathogenicity of the fungus. The objective of this study was to characterize the morphological and pathogenic characteristics of Sclerotinia sclerotiorum isolates from the state of Rio Grande do Sul. Sclerodes were disinfested, placed in the center of plates containing culture medium and incubated under controlled conditions. The evaluations were performed daily, during a period of 30 days, from the incubation of sclerotia. The experimental design was completely randomized, with four plaques per isolate, each plate one replicate. The characteristics evaluated for the mycelium characterization were: time required for the fungus to occupy the plate; density of the formed mycelium; coloration of the colonies and mycelial growth rate. Scleroderma assessments were based on training or not; time for formation of the first sclerodium; total amount formed per plate; Format; distribution in the colony and weight. The isolates were pathogenically characterized by the methodology of inoculation of the detached leaf. All data were submitted to analysis of variance and the means were compared by the Skott-Knott test, at 1% probability. The evaluated populations presented wide variability for the studied characteristics. It was not possible to verify the existence of common groups that could be related to the origin of the isolates, due to the high genetic diversity. The isolates showed different levels of aggressiveness, the two being more aggressive LF02 and LF06.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2025 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.