Increasing phosphorus (P) use efficiency in agricultural systems is urgent and essential to significantly reduce the global demand for this nutrient. Applying phosphate-solubilizing and plant growth-promoting bacteria in the rhizosphere represents a strategy worthy of attention. In this context, the present work aimed to select and validate bacterial strains capable of solubilizing phosphorous and promoting maize growth, aiming to develop a microbial inoculant to be used in Brazilian agriculture. Bacterial strains from the maize rhizosphere were evaluated based on their ability to solubilize phosphate and produce indole acetic acid. Based on these characteristics, 24 strains were selected to be further evaluated under laboratory, greenhouse, and field conditions. Among the selected strains, four (I04, I12, I13, and I17) showed a high potential to increase maize root growth and shoot P content. Strains I13 (Ag87) and I17 (Ag94) were identified by genomic sequencing as Bacillus megaterium and Lysinibacillus sp., respectively. These strains presented superior yield increments relative to the control treatment with 30% P. In addition, combining Ag87 and Ag94 resulted in even higher yield gains, indicating a synergistic effect that could be harnessed in a commercial inoculant for Brazilian agriculture.

Deoxynivalenol (DON) is a predisposing factor for necrotic enteritis. This study aimed to investigate the effects of a DON and Clostridium perfringens (CP) challenge on the intestinal morphology, morphometry, oxidative stress, and immune response of broilers. Additionally, we evaluated the potential of a Lactobacillus spp. mixture as an approach to mitigate the damage induced by the challenge. One-day-old broiler chickens (n = 252) were divided into seven treatment groups: Control, DON, CP, CP + DON, VL (DON + CP + viable Lactobacillus spp. mixture), HIL (DON + CP + heat-inactivated Lactobacillus spp. mixture), and LCS (DON + CP + Lactobacillus spp. mixture culture supernatant). Macroscopic evaluation of the intestines revealed that the CP + DON group exhibited the highest lesion score, while the VL and HIL groups showed the lowest scores. Microscopically, all Lactobacillus spp. treatments mitigated the morphological changes induced by the challenge. DON increased levels of reactive oxygen species (ROS) in the jejunum, and CP increased ROS levels in the jejunum and ileum. Notably, the Lactobacillus spp. treatments did not improve the antioxidant defense against CP-induced oxidative stress. In summary, a Lactobacillus spp. mixture, whether used as a probiotic, paraprobiotic, or postbiotic, exerted a partially protective effect in mitigating most of the intestinal damage induced by DON and CP challenges.

Regression analysis is highly relevant to agricultural sciences since many of the factors studied are quantitative. Researchers have generally used polynomial models to explain their experimental results, mainly because much of the existing software perform this analysis and a lack of knowledge of other models. On the other hand, many of the natural phenomena do not present such behavior; nevertheless, the use of non-linear models is costly and requires advanced knowledge of language programming such as R. Thus, this work presents several regression models found in scientific studies, implementing them in the form of an R package called AgroReg. The package comprises 44 analysis functions with 66 regression models such as polynomial, non-parametric (loess), segmented, logistic, exponential, and logarithmic, among others. The functions provide the coefficient of determination (R²), model coefficients and the respective p-values from the t-test, root mean square error (RMSE), Akaike’s information criterion (AIC), Bayesian information criterion (BIC), maximum and minimum predicted values, and the regression plot. Furthermore, other measures of model quality and graphical analysis of residuals are also included. The package can be downloaded from the CRAN repository using the command: install.packages(“AgroReg”). AgroReg is a promising analysis tool in agricultural research on account of its user-friendly and straightforward functions that allow for fast and efficient data processing with greater reliability and relevant information.

The main strawberry cultivars grown in tropical regions originated from breeding programs developed in temperate countries, which limit the expression of their maximum productive potential when grown in warm climates. Additionally, strawberry cultivation in these regions depends almost entirely on imported strawberry young plants, increasing production costs. Obtaining adapted cultivars with a lower requirement for the accumulation of chilling hours is the main objective of breeding programs in the southern hemisphere. Thus, the objective of this study was to evaluate short-day strawberry genotypes, pre-selected based on yield and fruit quality components through multivariate analysis, for cultivation in tropical conditions. Two experiments were performed. In the first one, 36 pre-selected genotypes and their parents were assessed based on productive traits (total fruit mass, commercial fruit mass, and mean mass of commercial fruits), soluble solids content, and fruit firmness. For selection, multivariate analysis was applied using the genotype (ideotype) selection index and principal component analysis. In the second experiment, the fruit of the best-ranked genotypes, the parents, and the Camarosa and Dover cultivars were assessed for soluble solids (SS), titratable acidity (TA), SS/TA ratio, ascorbic acid, phenolic compounds, and total anthocyanins. To identify the most promising genotypes, the data were analyzed using principal component analysis. The RVFS06CR-105, RVDA11CR-61, RVDA11CR-125, and RVCS44CR-130 were the most promising genotypes for cultivation in tropical conditions, based on yield and fruit quality components. The means found for these genotypes were higher than the commercial cultivars Dover, Camino Real, and Camarosa. The use of multivariate analysis was efficient in selecting the genotypes, with repeatability of information between the methods guaranteeing consistency in the information. The selected genotypes had higher yields than those used as the control, which is a promising result considering the independence in relation to genetic material, mainly in the expansion of the cultivation area in the tropical regions of the southern hemisphere. In addition, these genotypes are being evaluated in different tropical climate regions with the aim of validating them for submission to the registration and release process for farmers. Additionally, they are being used as a source of genes for crossings and new selections.