The purpose of this study was to evaluate the quality of Marandu grass (Brachiaria brizantha) haylage according to different dry matter (DM) contents in storage. The design adopted was completely randomized with four treatments and five replications. The treatments were DM contents of the plant at the moment of storage (in natura, 30–40, 40–50 and 50–60% DM). The analyses to assess the quality of the haylage were performed after 90 days of storage. The chemical composition, microbiological population, gas quantification, pH, N-NH3, volatile fatty acids, soluble carbohydrates (CHO) and the aerobic stability were evaluated. The means were compared through the Tukey's test and linear regression. The treatment with 50–60% DM presented the highest DM and CHO contents which were 563.8 and 42.0 g/kg, respectively. There was a higher presence of oxygen in the haylage of in natura material, which was 4.8%. There was no difference between treatments for the population of lactic acid bacteria; however, the treatment with 50–60% DM had the highest concentration of enterobacteria. The haylage with 30–40% DM and 50–60% DM presented high concentrations of acetic acid. There was no break in aerobic stability for any treatment within 120 h after opening the bales. There was a smaller amount of N-NH3 in treatments with 40–50% DM and 50–60% DM. The Marandu grass with a DM content of 50–60% for haylage making demonstrated better quality characterization of conserved forage.

Integrated pest management (IPM) seeks to minimize the environmental impact of pesticide application, and reduce risks to human and animal health. IPM is based on two important aspects – prevention and monitoring of diseases and insect pests – which today are being assisted by sensing and artificial-intelligence (AI) techniques. In this paper, we surveyed the detection and diagnosis, with AI, of diseases and insect pests, in cotton, which have been published between 2014 and 2021. This research is a systematic literature review. The results show that AI techniques were employed – mainly – in the context of (i) classification, (ii) image segmentation and (iii) feature extraction. The most used algorithms, in classification, were support vector machines, fuzzy inference, back-propagation neural-networks and recently, convolutional neural networks; in image segmentation, k-means was the most used; and, in feature extraction, histogram of oriented gradients, partial least-square regression, discrete wavelet transform and enhanced particle-swarm optimization were equally used. The most used sensing techniques were cameras, and field sensors such as temperature and humidity sensors. The most investigated insect pest was the whitefly, and the disease was root rot. Finally, this paper presents future works related to the use of AI and sensing techniques, to manage diseases and insect pests, in cotton; for instance, implement diagnostic, predictive and prescriptive models to know when and where the diseases and insect pests will attack and make strategies to control them.

Yarkoni argues that one solution is to abandon quantitative methods for qualitative ones. While we agree that qualitative methods are undervalued, we argue that both are necessary for thoroughgoing psychological research, complementing one another through the use of causal analysis. We illustrate how directed acyclic graphs can bridge qualitative and quantitative methods, thereby fostering understanding between different psychological methodologies.