Search

Copyright of Revista Brasileira de Engenharia Agricola e Ambiental - Agriambi is the property of Revista Brasileira de Engenharia Agricola e Ambiental and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Several factors contribute to the increase in irrigation demand: population growth, demand for higher value-added products, and the impacts of climate change, among others. High-quality water is essential for irrigation, so knowledge of water quality is critical. Additionally, water use in agriculture has been increasing in the last decades. Lack of water quality can cause drip clog, a lack of application uniformity, cross-contamination, and direct and indirect impacts on plants and soil. Currently, there is a need for more automated methods for evaluating and monitoring water quality for irrigation purposes, considering different aspects, from impacts on soil to impacts on irrigation systems. This work proposes a data-driven method to address this gap and implemented it in a case study in the PCJ river basin in Brazil. The methodology contains nine components and considers the main steps of the data lifecycle and the traditional machine learning workflow, allowing for automated knowledge extraction and providing important information for improving decision making. The case study illustrates the use of the methodology, highlighting its main advantages and challenges. Clustering different scenarios in three hydrological years (high, average, and lower streamflows) and considering different inputs (soil-related metrics, irrigation system-related metrics, and all metrics) helped generate new insights into the area that would not be easily obtained using traditional methods. [ABSTRACT FROM AUTHOR]

Background: Bariatric surgery leads to weight loss and to cardiometabolic risk improvement. Although prediabetes remission after bariatric surgery is biologically plausible, data on this topic is scarce. We aimed to assess prediabetes remission rate and clinical predictors of remission in a 4 year follow up period. Methods: Observational longitudinal study including patients with obesity and prediabetes who had undergone bariatric surgery in our centre. Prediabetes was defined as having a baseline glycated haemoglobin (A1c) between 5.7% and 6.4% and absence of anti-diabetic drug treatment. We used logistic regression models to evaluate the association between the predictors and prediabetes remission rate. Results: A total of 669 patients were included, 84% being female. The population had a mean age of 45.4 ± 10.1 years-old, body mass index of 43.8 ± 5.7 kg/m2, and median A1c of 5.9 [5.8, 6.1]%. After bariatric surgery, prediabetes remission rate was 82%, 73%, 66%, and 58%, respectively in the 1st, 2nd, 3rd, and 4th years of follow-up. Gastric sleeve (GS) surgery was associated with higher prediabetes remission rate than Roux-en-Y gastric bypass surgery in the 3rd year of follow-up. Men had a higher remission rate than women, in the 1st and 3nd years of follow-up in the unadjusted analysis. Younger patients presented a higher remission rate comparing to older patients in the 3rd year of follow-up. Conclusion: We showed a high prediabetes remission rate after bariatric surgery. The remission rate decreases over the follow-up period, although most of the patients maintain the normoglycemia. Prediabetes remission seems to be more significant in patients who had undergone GS, in male and in younger patients. [ABSTRACT FROM AUTHOR]

Introduction: The COVID-19 pandemic has led to a worldwide lockdown, which affected physical exercise habits, as well as having a detrimental effect on psychological health and follow-up visits of patients submitted to bariatric surgery. The aim of this study was to evaluate the impact of COVID-19 lockdown on the 2-year weight loss of patients submitted to bariatric surgery in our center. Methods: This was an observational study comparing the weight loss of patients who underwent bariatric surgery from January to March 2020 with a control group submitted to surgery between January and March 2017. Percentage of total weight loss (% TWL) and excess weight loss (% EWL) were assessed 6, 12, and 24 months after surgery. Results: A total number of 203 patients were included in this study, 102 had bariatric surgery during the selected period in 2020 and 101 underwent surgery during the same period in 2017. There was no statistically significant difference in weight loss between the 2017 and 2020 groups which was reported as % TWL (mean 27.08 ± 7.530 vs. 28.03 ± 7.074, 33.87 ± 8.507 vs. 34.07 ± 8.979 and 34.13 ± 9.340 vs. 33.98 ± 9.993; p = 0.371) and % EWL (mean 66.83 ± 23.004 vs. 69.71 ± 17.021, 83.37 ± 24.059 vs. 84.51 ± 21.640 and 83.47 ± 24.130 vs. 84.27 ± 23.651; p = 0.506) at 6, 12, and 24 months post-surgery. Conclusion: Despite social limitations imposed by the COVID-19 lockdown, we found no significant difference between weight loss at 2 years postoperatively in the 2020 group when compared with a control group who underwent bariatric surgery in 2017. These results show that the outcomes of bariatric surgery during the COVID-19 lockdown were comparable with those recorded before the pandemic, supporting the efficacy of bariatric procedures' metabolic effects during the first 2 years after surgery, regardless of lifestyle habits. [ABSTRACT FROM AUTHOR]

Chlorophyll (Chl) concentration is one of the factors that affects crop productivity. This study investigated the prediction of chlorophyll concentrations in Mombaça grass' leaves using hyperspectral data and machine learning techniques. Chlorophyll variations were induced by different levels of nitrogen fertilization (104, 208, 312, and 416 kg ha−1). Spectral signatures (400–2500 nm) and chlorophyll contents of the leaves were obtained in October, November, and December 2017, and January 2018. Models were generated using Partial Least Square Regression (PLSR), Random Forest (RF), and Support Vector Regression (SVR). Two validation techniques were employed: holdout, dividing the data into training (75%) and testing (25%) sets; and leave‐one‐date‐out cross‐validation (LOOCV), in which one date was omitted during model training and used to predict the omitted date's value. Chlorophyll concentrations varied according to N doses, with the highest concentrations observed in October and December. In these months, there were greater variations in spectral reflectance in the green and red bands (530–680 nm). December was identified as the ideal period for chlorophyll quantification, for both holdout and LOOCV validation techniques. The SVR technique performed best (R2 = 0.71, RMSE = 0.23 mg g−1, dr = 0.72) compared to RF (R2 = 0.63, RMSE = 0.27 mg g−1, dr = 0.66) and PLSR (R2 = 0.60, RMSE = 0.27 mg g−1, dr = 0.67). Therefore, the prediction of chlorophyll in Mombaça grass using spectroradiometry is promising and applicable across different cultivation periods. [ABSTRACT FROM AUTHOR]