Pitaya production has been expanding in several regions of Brazil despite the lack of technical information on plant management. To obtain good crop productivity, weed control is essential; however, the use of herbicides can cause phytotoxicity and reduce plant growth and production. Thus, the present work aimed to evaluate the tolerance of red (Selenicereus polyrhizus) and white (S. undatus) pitaya seedlings to different herbicides applied after emergence. Two experiments were installed, one with red pitaya and the another with white pitaya, on August 15, 2020 in the Fruit Production Sector of the Federal University of Lavras. Experiments were arranged in a randomized block design, with four and three replicates, respectively, eight treatments and three plants per plot in both experiments. Treatments consisted of control, fomesafen, ammonium glufosinate, glyphosate, clethodim, carfentrazone-ethyl, imazethapyr and chlorimuron. Visual phytotoxicity symptoms were evaluated at 7, 14, 21 and 28 days after herbicide application. At 30 and 60 days after application, percentage of necrosis, cladode length and number of new shoots were evaluated. Herbicide fomesafen caused greater damage to red pitaya seedlings and fomesafen and glyphosate caused greater damage to white pitaya seedlings. For both pitaya species, herbicide clethodim can be an alternative for controlling narrow-leaf weeds without causing visual damage to the growth and development of pitaya plants.

Citriculture has worldwide importance, and monitoring the nutritional status of plants through leaf analysis is essential. Recently, proximal sensing has supported this process, although there is a lack of studies conducted specifically for citrus. The objective of this study was to evaluate the application of portable X-ray fluorescence spectrometry (pXRF) combined with machine learning algorithms to predict the nutrient content (B, Ca, Cu, Fe, K, Mg, Mn, P, S, and Zn) of citrus leaves, using inductively coupled plasma optical emission spectrometry (ICP-OES) results as a reference. Additionally, the study aimed to differentiate 15 citrus scion/rootstock combinations via pXRF results and investigate the effect of the sample condition (fresh or dried leaves) on the accuracy of pXRF predictions. The samples were analyzed with pXRF both fresh and after drying and grinding. Subsequently, the samples underwent acid digestion and analysis via ICP-OES. Predictions using dried leaves yielded better results (R² from 0.71 to 0.96) than those using fresh leaves (R² from 0.35 to 0.87) for all analyzed elements. Predictions of scion/rootstock combinations were also more accurate with dry leaves (Overall accuracy = 0.64, kappa index = 0.62). The pXRF accurately predicted nutrient contents in citrus leaves and differentiated leaves from 15 scion/rootstock combinations. This can significantly reduce costs and time in the nutritional assessment of citrus crops.

The use of herbicides to control weeds in passion fruit orchards is a more accessible and lower-cost alternative. However, the lack of registered agrochemicals for the crop, combined with the scarcity of studies on their phytotoxic effects, makes this aspect one of the main obstacles to production. Thus, this study aimed to evaluate the tolerance of advanced sour passion fruit (Passiflora edulis Sims) seedlings to the application of different herbicides. The experimental design was randomized blocks, with seven treatments, four replications, and three plants per plot. The treatments consisted of a control (without herbicide application), sulfentrazone, diuron, S-metolachlor, ammonium glufosinate, imazethapyr, and terbuthylazine. It was observed that S-metolachlor caused the lowest levels of visual phytotoxicity, followed by imazethapyr and terbuthylazine, throughout the evaluation period. In contrast, ammonium glufosinate, sulfentrazone, and diuron caused greater phytotoxic effects, negatively affecting plant development. Overall, herbicide tolerance in sour passion fruit seedlings varies significantly depending on the active ingredient.