Deciphering global patterns of forest canopy rainfall interception (FCRI): A synthesis of geographical, forest species, and methodological influences.

Forest canopy rainfall interception (FRCI) is an essential hydrological process that governs water and biogeochemical cycles in forest ecosystems. Identifying patterns and relationships of FCRI using a systematic review is key to improving our knowledge supporting new experiment research, modeling, and application. In this meta-analysis, we aimed to delineate the canopy interception (CI), throughfall (TF), and stemflow (SF) concerning geographical and forest variables and experimental methodologies. We leveraged peer-reviewed 170 articles across 234 sites globally, extracting TF, CI, SF, geographical, forest, and experimental aspects. We applied multivariate statistical procedures to discern the principal influences on TF, CI, and SF and examined their multicollinearity. In addition, we developed Generalized Linear Models (GLM) for CI and TF. Global TF experiments indicate that the predominant rainfall devices, number of sample trees, number of events, and monitoring length are 10-20 devices (81% fixed), 3-6 trees, 30-50 events, and 10-30 months. Predominant global values of TF, CI, and SF are 70-80% (median = 73%), 20%-30% (median = 23.9%), and <1.0% (median = 1.87%), respectively. Global models of CI and TF were responsive to T, LAI, and D (respectively, R2adj of 0.196** and 0.206**). Temperate forests mirrored the global model (R2adj of 0.274** and 0.31**, respectively). The Subtropical CI model was fitted based on P and DBH (R2adj = 0.245*), and the TF model was based on E, D, and LAI (R2adj = 0.532**); the Mediterranean CI model was based on T, Basal, and LAI (R2adj = 0.45*), while TF was based on P, Basal, and LAI (R2adj = 0.671**). The Tropical CI model was based on T and H (R2adj = 0.396*), and the TF model, LAI, and P (R2adj = 0.35*). This meta-analysis underscores the importance of comprehending the hydrological processes in forested areas as they are pivotal in mitigating climate change impacts.

Performance assessment of spatio-temporal regression kriging with GAMLSS models as trends.

The main objective of this study is to propose different probabilistic models for adjusting the trend component, since it significantly influences the quality of the spatio-temporal interpolation of rainfalls. We used the monthly total precipitation data of the São Francisco River Basin (SFRB) for the period of 31 years, 1989-2019. The SFRB occupies 8% of the whole Brazilian territory, mostly located in the Northeast Brazilian region. For the trend component, we propose the fitted GAMLSS models by comparing different probability distribution families, which in most cases include the characteristics of these data. The results indicate the existence of a spatio-temporal pattern of the residues obtained from the adjustment of the trend with zero adjusted Gamma distribution for the accumulated monthly precipitation. The adjustment revealed a spatial dependence of up to 873 km between the pluviometric stations and temporal autocorrelation of approximately 1.6 months. The methodology used in this study enabled us to create rainfall maps, interpolating unobserved locations in differences years. The projection of these maps to the SFRB is considered extremely important for planning and implementing activities related to water resources across the river basin.

Yeast Beta-Glucans Ingestion Does Not Influence Body Weight: A Systematic Review and Meta-Analysis of Pre-Clinical Studies.

Dietary fiber supplementation has been studied as a promising strategy in the treatment of obesity and its comorbidities. A systematic review and meta-analysis were performed to verify whether the consumption of yeast beta-glucan (BG) favors weight loss in obese and non-obese rodents. The PICO strategy was employed, investigating rodents (Population), subjected to the oral administration of yeast BG (Intervention) compared to animals receiving placebo (Comparison), evaluating body weight changes (Outcome), and based on preclinical studies (Study design). Two reviewers searched six databases and the grey literature. We followed the PRISMA 2020 guidelines, and the protocol was registered on PROSPERO (CRD42021267788). The search returned 2467 articles. Thirty articles were selected for full-text evaluation, and seven studies remained based on the eligibility criteria. The effects of BG intake on body weight were analyzed based on obese (n = 4 studies) and non-obese animals (n = 4 studies). Even though most studies on obese rodents (75%) indicated a reduction in body weight (qualitative analysis), the meta-analysis showed this was not significant (mean difference -1.35 g-95% CI -5.14:2.45). No effects were also observed for non-obese animals. We concluded that the ingestion of yeast BG barely affects the body weight of obese and non-obese animals.

Application of Bayesian networks to the prediction of the AMEn: a new methodology in broiler nutrition.

Designing balanced rations for broilers depends on precise knowledge of nitrogen-corrected apparent metabolizable energy (AMEn) and the chemical composition of the feedstuffs. The equations that include the measurements of the chemical composition of the feedstuff can be used in the prediction of AMEn. In the literature, there are studies that obtained prediction equations through multiple regression, meta-analysis, and neural networks. However, other statistical methodologies with promising potential can be used to obtain better predictions of energy values. The objective of the present study was to propose and evaluate the use of Bayesian networks (BN) to the prediction of the AMEn values of energy and protein feedstuffs of vegetable origin used in the formulation of broiler rations. In addition, verify that the predictions of energy values using this methodology are the most accurate and, consequently, are recommended to Animal Science professionals area for the preparation of balanced feeds. BN are models that consist of graphical and probabilistic representations of conditional and joint distributions of the random variables. BN uses machine learning algorithms, being a methodology of artificial intelligence. The bnlearn package in R software was used to predict AMEn from the following covariates: crude protein, crude fiber, ethereal extract, mineral matter, as well as food category, i.e., energy (corn, corn by-products, and others) or protein (soybean, soy by-products, and others) and the type of animal (chick or cockerel). The data come from 568 feeding experiments carried out in Brazil. Additional data from metabolic experiments were obtained from the Federal University of Lavras (UFLA) - Lavras, Minas Gerais, Brazil. The model with the highest accuracy (mean squared error = 66529.8 and multiple coefficients of determination = 0.87) was fitted with the max-min hill climbing algorithm (MMHC) using 80% and 20% of the data for training and test sets, respectively. The accuracy of the models was evaluated based on their values of mean squared error, mean absolute deviation, and mean absolute percentage error. The equations proposed by a new methodology in avian nutrition can be used by the broiler industry in the determination of rations.

Volume equations for Khaya ivorensis A. Chev. plantations in Brazil.

African mahogany (Khaya spp.) plantations are in expansion in Brazil and in the world. This fact justifies the need for studies related to its growth and yield. This paper aimed to evaluate the performance of single-entry and double-entry models for estimating merchantable and total volume for Khaya ivorensis plantations before the first thinning (7 years) and expected final cut (15 years). Volume data was from 100 and 46 trees in Minas Gerais and Pará states, respectively, by using an electronic dendrometer (Criterion RD 1000). Observed volumes were calculated by Smalian's formula. To validate the optical dendrometer, 10 trees were felled and had their volume measured, and compared with the volumes measured indirectly. The results showed that observed and estimated volumes were statistically equal, and that double-entry models were more precise than single-entry models. Schumacher and Hall model was the best equation to estimate merchantable volume for first thinning and for final cut in Minas Gerais stands. Spurr logarithmized model was the best equation to estimate total volume for first thinning and Spurr model for final cut in Pará stands. All chosen equations can be used to quantify merchantable and total volumes of Khaya ivorensis grown under similar conditions.

Biogenic amine profile in unripe Arabica coffee beans processed according to dry and wet methods.

Immature coffee fruit processing contributes to a high amount of defective beans, which determines a significant amount of low-quality coffee sold in the Brazilian internal market. Unripe bean processing was tested, taking the levels of bioactive amines as criteria for evaluating the extent of fermentation and establishing the differences between processing methods. The beans were processed by the dry method after being mechanically depulped immediately after harvest or after a 12 h resting period in a dry pile or immersed in water. Seven bioactive amines were quantified: putrescine, spermine, spermidine, serotonin, cadaverine, histamine, and tyramine, with global amounts ranging from 71.8 to 80.3 mg/kg. The levels of spermine and spermidine were lower in the unripe depulped coffee than in the natural coffee. The specific conditions of dry and wet processing also influenced cadaverine levels, and histamine was reduced in unripe depulped coffee. A resting period of 12 h does not induce significant alteration on the beans and can be improved if performed in water. These results confirm that peeling immature coffee can decrease fermentation processes while providing more uniform drying, thus reducing the number of defects and potentially increasing beverage quality.