Soil ecosystem perturbation due to agronomic practices can negatively impact soil productivity by altering the diversity and function of soil health determinants. Currently, the influence of rice cultivation and off-season periods on the dynamics of soil health determinants is unclear. Therefore, soil enzyme activities (EAs) and bacterial community compositions in rice-cultivated fields at postharvest (PH) and after a 5-month off-season period (5mR), and fallow-fields (5-years-fallow, 5YF; 10-years-fallow, 10YF and/or one-year-fallow, 1YF) were assessed in two agroecological regions of Mozambique. EAs were mostly higher in fallow fields than in PH, with significant (p < 0.05) differences detected for β-glucosidase and acid phosphatase activities. Only β-glucosidase activity was significantly (p < 0.05) different between PH and 5mR, suggesting that β-glucosidase is responsive in the short-term. Bacterial diversity was highest in rice-cultivated soil and correlated with NO3−, NH4+ and electrical conductivity. Differentially abundant genera, such as Agromyces, Bacillus, Desulfuromonas, Gaiella, Lysobacter, Micromonospora, Norcadiodes, Rubrobacter, Solirubrobacter and Sphingomonas were mostly associated with fallow and 5mR fields, suggesting either negative effects of rice cultivation or the fallow period aided their recovery. Overall, rice cultivation and chemical parameters influenced certain EAs and shaped bacterial communities. Furthermore, the 5-month off-season period facilitates nutrient recovery and proliferation of plant-growth-promoting bacteria.

The objective of this study was to evaluate soil physical properties under different cropping systems for onion. The study was conducted on an Inceptisol in Ituporanga, Santa Catarina state, Brazil. We evaluated eight cropping systems for onion: T1: maize-onion succession; T2: common vetch-maize/rye+fodder radish-onion-maize/rye+fodder radish-common bean; T3: rye-onionmaize/black oat-maize; T4: onion-velvet bean succession; T5: rye-onion-millet/black oat-onionmillet; T6: rye-onion-velvet bean succession; T7: onion-velvet bean+millet+sunflower succession.Treatments from T1 to T7 were conducted under no-tillage system (NTS) and T8: maize-onion succession under a conventional tillage system. We used the randomized complete block design, with five replications. Undisturbed soil samples were collected at the 0-5, 5-10 and 10-15 cm soil layers and we evaluated the following soil physical properties: geometric mean diameter of aggregates, total porosity, macroporosity, microporosity, bulk density and the ratio macroporosity/ total porosity. The data analysis was performed using univariate statistics, principal components analysis (PCA) and cluster analysis (CA). Based on the cropping systems characteristics, the CA divided them into three groups: group 1: T7; group 2: T1, T2, T3, T4, T5, T6; and group 3: T8. T8 showed evidence of soil physical quality deterioration at the soil surface, T7 presented the best soil physical properties.

The present study aimed to compare selection indices, namely: Smith and Hazel multiplicative, Mulamba and Mock's, and the support vector machines algorithm for sugarcane families selection. We used two datasets, from Moreira et al. (2021) and from Ferreira et al. (2022), both related to the sugarcane breeding program conducted at the Center for Sugar cane Research and Breeding at the Federal University of Viçosa, Oratórios, Minas Gerais. Both experiments were conducted in a randomized complete block design. We constructed the selection indices via mixed models approach. We adopted a selection percentage of 18% of the top families for the selection process. In both studies, we considered as explanatory traits: the number of stalks, stalks diameter and stalk height, and as the response trait the tons of stalks per hectare per family. In the dataset from Ferreira et al. (2022), the support vector machine was a better approach to select sugarcane families by learning from the data after multivariate simulation. Whereas in the dataset from Moreira et al. (2021), using similar methodology, lower performance for support vector machines was obtained. Keywords: Synthetic data. Indirect selection. Yield prediction. Machine learning. BLUP

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The aim of this study was to evaluate the biofilm-forming ability and the resistance of planktonic cells and biofilm to methicillin (MIC and MBC, and log CFU/cm2 reduction, respectively). Thirty-four isolates were used, including coagulase-positive Staphylococcus (CPS) and coagulase-negative Staphylococcus (CNS), recovered from ready-to-eat (RTE) foods. Biofilm-forming ability was based on enumeration of viable cells from biofilms formed on three types of surfaces, namely stainless steel, polystyrene, and glass. Thirteen of the thirty-four isolates (38.2%) were methicillin-resistant (MIC higher than 4 µg/mL). Staphylococcus aureus (CPS) and Staphylococcus saprophyticus (CNS) were the species that showed the highest percentage of resistance (50% and 71.4%, respectively). Staphylococcus warneri was the only species susceptible to methicillin. In 20 of the 34 isolates, the MBC value was twice the MIC value. The CPS (S. aureus) and the CNS (the other species) were not significantly different (p > 0.05) on biofilm-forming ability on the three surfaces used. However, when comparing the averages obtained for the biofilm-forming ability of the isolates, the values obtained (log CFU/cm2) in polystyrene were significantly higher (p < 0.05) than those obtained with the isolates on stainless steel. On the other hand, data from biofilm-forming ability of the isolates on glass and stainless steel are positively and significantly correlated with each other (r = 0.54; p = 0.02). It was not possible to determine the concentration of methicillin that promotes biofilm removal since log reductions were less than 3 log CFU/cm2. The results of this study indicate that foodborne CPS and CNS can form biofilms on different types of material. As these biofilms are resistant to high concentrations of methicillin, their occurrence in food environments and their spread to medical settings can result in staphylococcal food poisoning or, in the worst-case scenario, septicemia, respectively. Good hygiene and good manufacturing practices (GHP and GMP) are therefore mandatory to prevent contamination with Staphylococcus spp.