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The presence of straw on the soil is crucial for enhancing its physical, chemical, and biological conditions in no-tillage cropping systems, while contributing to weed control. This study aimed to determine the optimal spray volume for desiccating ground cover plants using glyphosate applied by an unmanned aerial vehicle (UAV). Treatments included an untreated control and glyphosate spraying at 5, 10, 15, 20, and 25 L ha−1 spray solution. The study was conducted in three fields in the State of Paraná, Brazil. Spraying variables were assessed using water-sensitive paper and the e-Sprinkle software, and included, covered area (%), recovered spray volume (L ha−1), droplet impact density, coefficient of variation for droplet diameter (CV%), and relative amplitude (SPAN). Herbicidal efficacy and dry biomass of cover plants were also evaluated. Increasing spray volume improved covered area, recovered volume, droplet impact density, and droplet diameter-spectrum variation but had no effect on droplet spectrum homogeneity, control of black oat (Avena strigosa), turnip (Raphanus sativus), or buckwheat (Fagopyrum esculentum). Spray volumes ranging from 5.0 to 25 L ha−1, applied with a UAV, provided satisfactory desiccation percentages of ground cover plants irrespective of species or location. Thus, for desiccating ground cover plants with glyphosate applied by UAV, a volume as low as 5.0 L ha−1 is effective. • There is influence of spray volume for drying plants for soil cover with glyphosate sprayed by drone? • The glyphosate herbicide spraying for drying with spray volumes of 5, 10, 15, 20 and 25 L ha−1. • The experiment was replicated in three farmers in the Paraná state, Brazil. • The variables evaluated were: spray quality, control of plants and their dry mass. • Spray volumes from 5.0 to 25 L ha−1 applied with drone provided satisfactory levels of control of ground cover plants. [ABSTRACT FROM AUTHOR]

Research efforts on ambulance response times for Emergency Medical Services (EMS) calls have been made for decades, especially in developed countries, using different techniques and with varying objectives. In Brazil, a developing country, the scarce scientific production on this vital indicator prioritizes scenarios for EMS in cities with more than one million inhabitants. This shows the importance of extending research to the reality of small and medium-sized cities. This paper presents SAMU, the Brazilian EMS that follows the Franco-German emergency medicine model, compiling numbers related to service at the national level. The use of quantile regression allows the identification of the RT for the EMS and helps to explain the effects of factors at the system level, at the patient level, and specific factors on response time intervals of Southwest Paraná SAMU. This specific EMS, characterized as an inter-municipal consortium of prehospital services, is responsible for prehospital emergency care for an approximate population of 635,000 inhabitants in 42 small towns in the State of Paraná in southern Brazil. From the analysis of the records of 12,050 ambulance dispatches, it was possible to identify the average ambulance response time of 14 min and 25 s. The regression model was able to explain the influence of the independent variables at the system level (presumed severity of the emergency, ambulance dispatch time, and ambulance travel time), at the patient level (age, gender, and characteristic of the emergency) and specific variables of the emergency (day of the week and time of day) on the dependent variable response time over the quantiles, showing that the dispatch time, travel time, time of day, service to male patients and critical cases influence the ambulance response time. This work contributes to deepening the understanding of the management of EMS operations in a developing country, allows the comparison of the RT identified in relation to other countries, and identifies factors that impact the RT for other actors directly or indirectly involved. The practical implications are also presented, as well as how the study impacts the decision-making and management process of the EMS in the short, medium and long term. • In this study, the use of ambulance service was surveyed, and the RT was identified. • Using descriptive statistics, it was possible to determine the average RT of 14min 25s. • It also describe the EMS in terms of its structure, its internal and external processes and its technological limitations. • This work characterizes the Brazilian EMS, as well as the influences exerted by the Franco-German and Anglo-American models. [ABSTRACT FROM AUTHOR]

The United Nations included reducing harvest and postharvest losses as a Sustainable Millennium Development Goal in 2015, leading to increased research and policymaker interest in reducing losses to insure food security. This article analyzes the factors associated with self-reported harvest loss among soybean farmers in Paraná, Brazil, using a survey of 243 farmers. The principal–agent problem is the most important contributor to harvest and postharvest losses on the farm. Loss is lowered when the combines are operated at slower speeds and are adjusted properly, but combine operators have incentives to complete harvesting jobs and deliver soybean to storage facilities quickly. Farmers report training of the combine operators as one of the most important causes of postharvest loss. Empirical results show that farmers report a 1.5 percentage points higher harvest loss when they ask a third party to harvest. Similarly, the farmers' education level is negatively associated with harvest loss; farmers with college completion have 1.6 percentage points less loss compared to farmers with less than fourth grade. However, larger planted areas are associated with higher harvest loss. The paper suggests that who harvests the crop is critical in reducing harvest loss. Policies and contract designs that align incentives between farmers and combine operators might reduce harvest and postharvest loss. • Reducing harvest and postharvest losses contributes to greater food security. • The principal–agent problem contributes to harvest losses. • Farmers report combine operators' training as the primary cause of harvest loss. • The farmers' education level is negatively associated with harvest loss. • Aligning incentives between farmers and combine operators may reduce harvest loss. [ABSTRACT FROM AUTHOR]