Processes of water retention and movement and the hydraulic conductivity are altered in the rhizosphere. The aim of this study was to investigate the physical-hydric properties of soil aggregates in the rhizosphere of annual ryegrass (Lolium multiflorum) cropped in a Kandiudalfic Eutrudox, taking into account aspects related to soil aggregate stability. Soil aggregates from rhizosphere soil (RZS) and soil between plant rows (SBP) were used to determine soil water retention curves (SWRCs) and saturated hydraulic conductivity (K_sat). In addition, properties related to soil aggregate stability, such as water-dispersible clay, soil organic carbon (SOC), and microbial activity, were also assessed. The higher microbial activity observed in the RZS was facilitated by increased SOC and microbial activity, resulting in improved soil aggregation (less water-dispersible clay). For nearly all measured matric potentials, RZS had a higher water content than SBP. This was attributed to the stability of aggregates, increase in SOC content, and the root exudates, which improved soil water retention. The increase in total porosity in RZS was associated with improved soil aggregation, which prevents deterioration of the soil pore space and results in higher K_sat and hydraulic conductivity as a function of the effective relative saturation in RZS compared to SBP.

The interaction of soil with plant roots in the rhizosphere plays an important role in various ecosystem services and food production, and it has been the focus of numerous studies. In turn, statistical modeling can aid in a more comprehensive understanding of this interaction, such as the application of regression models to rhizosphere data. Thus, the main objective of this work was to develop the first bibliometric analysis on regression models applied to rhizosphere data. Bibliometric data were obtained from Web of Science and Scopus databases. We use the topic retrival as ((“Rhizosphere”) AND (“Regression models” OR “Regression model” OR “Generalized Linear Models” OR “Generalized Linear Model”)) to search for scientific articles that contain these keywords in their title, abstract, or keywords. The search encompassed articles published from 1900 to 2022, resulting in 34 articles, with the earliest record dating back to 1985. While studies of the rhizosphere are increasing, few studies apply regression models to their data. The use of more advanced techniques, such as Generalized Linear Models (MLG), Artificial Neural Network (ANN), Random Forest Model (RFM), Support Vector Machines (SVM), and Generalized Boosted Regression Modeling (GBM), became evident from 2016 onwards, which was associated with the computational advances and the development of artificial intelligence. Some articles demonstrated that the use of more robust models can provide more meaningful results to the researcher. Only one article was published in a journal dedicated to statistics, highlighting the diffusion of regression models into various fields. Collaborations involving co-authorship between researchers from different countries have led to higher citation rates, increasing the importance of the research to the scientific community. Perhaps one of the most notable limitations to increasing research using regression models is the absence of a statistician or researcher within the research groups who is well versed in statistical models and procedures.

Soil aggregation is a key ecosystem process that results in soil structure formation and stabilization and controls several soil functions. The root system and its mycorrhiza symbiosis contribution to aggregate stabilization have been already assessed. However, few studies considered several cropping systems under different seasons within tropical environments. Our goal was to better understand the response of aggregate stability in the rhizosphere (RS) and bulk soil (BS) to six cropping systems in an Oxisol, in the summer and winter seasons. Readily-dispersible clay and mechanically-dispersible clay were evaluated as aggregate stability indicators. RS and BS were also characterized for soil physical (i.e., water content), chemical (i.e., soil organic carbon, K, P, Ca, Mg, Al, potential acidity), and physicochemical (i.e., pH, ΔpH, and PZC) properties. Dispersible clay was lower (p<0.05) within the RS than in the BS, which suggests that the crop root systems increased aggregate stability, but their influence on soil aggregation differed among cropping systems. Aggregate stability was higher in the winter compared to the summer season. Overall, these results reflect ( i ) the positive root system influence of the winter crops on soil structure stability, ( ii ) the negative response of soil structure stability to the addition of high doses of chemical fertilizers to soybean and maize (cash crops) in the summer season, ( iii ) the minimal influence of soybean and maize root system on formation and stabilization of the aggregates in the summer season, and ( iv ) the different weather condition in the summer and winter seasons. Thus, better erosion control practices and crops that promote soil aggregation should be adopted in the summer period. Finally, attention should also be given to the root exudates and their relationship with soil, as they can either increase or decrease soil aggregation.