The sustainability of coffee production is a concern for producers around the world. To be sustainable, it is necessary to achieve satisfactory levels of coffee productivity and quality. Pests and diseases cause reduced productivity and can affect the quality of coffee beans. To ensure sustainability, producers need to monitor pests that can lead to substantial crop losses, such as the coffee leaf miner, Leucoptera coffeella (Lepidoptera: Lyonetiidae), which belongs to the Lepidoptera order and the Lyonetiidae family. This research aimed to use machine learning techniques and vegetation indices to remotely identify infestations of the coffee leaf miner in coffee-growing regions. Field assessments of coffee leaf miner infestation were conducted in September 2023. Aerial images were taken using remotely piloted aircraft to determine 13 vegetative indices with RGB (red, green, blue) images. The vegetation indices were calculated using ArcGis 10.8 software. A comprehensive database encompassing details of coffee leaf miner infestation, vegetation indices, and crop data. The dataset was divided into training and testing subsets. A set of four machine learning algorithms was utilized: Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), and Stochastic Gradient Descent (SGD). Following hyperparameter tuning, the test subset was employed for model validation. Remarkably, both the SVM and SGD models demonstrated superior performance in estimating coffee leaf miner infestations, with kappa indices of 0.6 and 0.67, respectively. The combined use of vegetation indices and crop data increased the accuracy of coffee leaf miner detection. The RF model performed poorly, while the SVM and SGD models performed better. This situation highlights the challenges of tracking coffee leaf miner infestations in fields with varying ages of coffee plants, different cultivars, and other environmental variables.

Polyethylene mulch can cause serious problems in the environment. A promising alternative to polyethylene mulch is biodegradable mulch. Therefore, a two-year experiment was carried out with zucchini (Cucurbita pepo L.) during the cropping seasons of 2017 and 2018 in Southeast Brazil to investigate the effects of four soil mulching treatments (white polyethylene mulch: WP, black polyethylene mulch: BP, biodegradable mulch of brown recycled paper: RP and nonmulched: NM) and two wetted soil percentages (50 and 70% of the soil surface) using a randomized block design with five repetitions. The average water consumption of the zucchini was 157 mm in the WP treatment, 155 mm in the BP treatment, 155 mm in the WP treatment and 215 mm in the NM treatment. The yield was higher in the WP (78.1 ton ha-1) and BP (69.1 ton ha-1) treatments than in the RP (66.6 ton ha-1) or NM (52.4 ton ha-1) treatments. The daily thermal amplitude was lowest in the RP treatment (3.6-6.0 °C) and highest in the BP treatment (7.3-9.0 °C). The lower wetted soil percentage (50%) reduced water consumption (by 5-7%) and improved water-use efficiency (by 11%). Weeds, including Cyperus roduntus, were more efficiently controlled in the RP treatment than in the WP and BP treatments. In general, compared to the nonmulched treatment, mulches were effective in reducing weed infestation (95%), increasing yield (36%) and improving water-use efficiency (94%). RP, as an alternative to polyethylene mulch, can save water and control weeds in zucchini crops. These results represent useful information that extends the mulching options for technicians and farmers.

Vegetable cultivation requires high water use and weed control. Soil cover using recycled paper, can be an alternative to polyethylene film to reduce weed incidence, soil temperature and increase water use efficiency beyond reduces costs and environmental pollutions. The objective of this study was to evaluate the use of biodegradable mulch in weed management and water use efficiency (WUE) in lettuce crop. The treatments were composed of brown recycled paper (RP), black polyethylene film (PF) and soil without cover with weed removal (WR) and without weed removal (WW). RP and PF were efficient to control weeds. The soil temperature with RP was 8.2 and 2.1ºC lower than with PF and WR, respectively. The lettuce yield with RP was 14.5 and 28.3% higher than WR, and with PF, respectively. The water volume applied with RP was 26.5% lower, and WUE was 55.6% higher compared to WR. Soil cover with recycled paper controlled weeds, reduced soil temperature and water consumption and increased yield and water use efficiency in lettuce crop.

Crescimento, produtividade e nitrato em frutos de pepino submetidos a fontes de adubos nitrogenados - DOI:10.5039/agraria.v12i2a5441

Bromeliad Quesnelia quesneliana (Brongniart) L.B. Smith has been reported in the Atlantic Forest, Rainforest, Mesophilic Semideciduous Seasonal Forest, Mangroves and Restingas in the Brazilian southeastern states of Rio de Janeiro and Espírito Santo, but information about their fruit and seed morphology, and germination is limited. The aim of this study was to characterize the external morphology of fruit and seeds, germination rate and post-seminal stages of Q. quesneliana. Fruits were collected from Restinga area in the Armação dos Búzios city, Rio de Janeiro, Brazil. The width and length of fruit and seeds (external morphology) were measured, the post-seminal development of the seeds was analyzed and botanical illustrations were made. The indexes t50, uniformity of germination, mean germination time and germination speed coefficient were also calculated. Germination was assessed for 20 days by counting individuals to obtain the post-seminal stages. Ripe Q. quesneliana fruits are pyriform, reddish-brown in color, with light spots, 26 mm long and 10 mm wide, with an average of 148 seeds per fruit and wrapped in a transparent mucilage. The seeds are 2 mm long and 1 mm wide, with epigeal germination, and its seedlings are cryptocotyledonary. The seeds of this species germinate quickly and have no dormancy.