Demographic and conservation genomic assessment of the threatened marbled teal (Marmaronetta angustirostris).

Genetic assessment of species that have experienced dramatic population declines provides critical information that is instrumental for the design of conservation recovery programs. Here, we use different sources of molecular data (mtDNA and ddRAD-seq) to evaluate the genetic status of wild and captive populations of marbled teal (Marmaronetta angustirostris), a duck species classified as critically endangered in Spain and near threatened at a global scale. First, we determined the evolutionary and demographic trajectories of the wild population from Spain and the currently much larger population from Iraq, which is also the documented source of European zoo stocks. Second, we evaluated the suitability of the different captive populations for ongoing restocking programs in Spain and assessed their potential impact on the genetic composition of wild populations. Populations from Spain and Iraq were assigned to distinct genetic clusters, albeit with an overall low level of genetic differentiation, in line with their recent divergence (<8000 years ago) and lack of phylogeographic structure in the species. Demogenomic inferences revealed that the two populations have experienced parallel demographic trajectories, with a marked bottleneck during the last glacial period followed by a sudden demographic expansion and stability since the onset of the Holocene. The wild population from Spain presented high levels of inbreeding, but we found no evidence of recent genetic bottlenecks compatible with the human-driven decline of the species during the past century. The captive populations from the two Spanish centers involved in restocking programs showed genetic introgression from European zoos; however, we found limited evidence of introgression from the zoo genetic stock into the wild population from Spain, suggesting captive-bred birds have limited breeding success in the wild. Our study illustrates how ex situ conservation programs should consider the genetic distinctiveness of populations when establishing breeding stocks and highlights the importance of genetically assessing captive populations prior to reinforcement actions.

Modeling of soil moisture movement and wetting behavior under point-source trickle irrigation.

The design and selection of ideal emitter discharge rates can be aided by accurate information regarding the wetted soil pattern under surface drip irrigation. The current field investigation was conducted in an apple orchard in SKUAST- Kashmir, Jammu and Kashmir, a Union Territory of India, during 2017-2019. The objective of the experiment was to examine the movement of moisture over time and assess the extent of wetting in both horizontal and vertical directions under point source drip irrigation with discharge rates of 2, 4, and 8 L h-1. At 30, 60, and 120 min since the beginning of irrigation, a soil pit was dug across the length of the wetted area on the surface in order to measure the wetting pattern. For measuring the soil moisture movement and wetted soil width and depth, three replicas of soil samples were collected according to the treatment and the average value were considered. As a result, 54 different experiments were conducted, resulting in the digging of pits [3 emitter discharge rates × 3 application times × 3 replications × 2 (after application and 24 after application)]. This study utilized the Drip-Irriwater model to evaluate and validate the accuracy of predictions of wetting fronts and soil moisture dynamics in both orientations. Results showed that the modeled values were very close to the actual field values, with a mean absolute error of 0.018, a mean bias error of 0.0005, a mean absolute percentage error of 7.3, a root mean square error of 0.023, a Pearson coefficient of 0.951, a coefficient of correlation of 0.918, and a Nash-Sutcliffe model efficiency coefficient of 0.887. The wetted width just after irrigation was measured at 14.65, 16.65, and 20.62 cm; 16.20, 20.25, and 23.90 cm; and 20.00, 24.50, and 28.81 cm in 2, 4, and 8 L h-1, at 30, 60, and 120 min, respectively, while the wetted depth was observed 13.10, 16.20, and 20.44 cm; 15.10, 21.50, and 26.00 cm; 19.40, 25.00, and 31.00 cm, respectively. As the flow rate from the emitter increased, the amount of moisture dissemination grew (both immediately and 24 h after irrigation). The soil moisture contents were observed 0.4300, 0.3808, 0.2298, 0.1604, and 0.1600 cm3 cm-3 just after irrigation in 2 L h-1 while 0.4300, 0.3841, 0.2385, 0.1607, and 0.1600 cm3 cm-3 were in 4 L h-1 and 0.4300, 0.3852, 0.2417, 0.1608, and 0.1600 cm3 cm-3 were in 8 L h-1 at 5, 10, 15, 20, and 25 cm soil depth in 30 min of application time. Similar distinct increments were found in 60, and 120 min of irrigation. The findings suggest that this simple model, which only requires soil, irrigation, and simulation parameters, is a valuable and practical tool for irrigation design. It provides information on soil wetting patterns and soil moisture distribution under a single emitter, which is important for effectively planning and designing a drip irrigation system. Investigating soil wetting patterns and moisture redistribution in the soil profile under point source drip irrigation helps promote efficient planning and design of a drip irrigation system.

Soil erosion control from trash residues at varying land slopes under simulated rainfall conditions.

Trash mulches are remarkably effective in preventing soil erosion, reducing runoff-sediment transport-erosion, and increasing infiltration. The study was carried out to observe the sediment outflow from sugar cane leaf (trash) mulch treatments at selected land slopes under simulated rainfall conditions using a rainfall simulator of size 10 m × 1.2 m × 0.5 m with the locally available soil material collected from Pantnagar. In the present study, trash mulches with different quantities were selected to observe the effect of mulching on soil loss reduction. The number of mulches was taken as 6, 8 and 10 t/ha, three rainfall intensities viz. 11, 13 and 14.65 cm/h at 0, 2 and 4% land slopes were selected. The rainfall duration was fixed (10 minutes) for every mulch treatment. The total runoff volume varied with mulch rates for constant rainfall input and land slope. The average sediment concentration (SC) and sediment outflow rate (SOR) increased with the increasing land slope. However, SC and outflow decreased with the increasing mulch rate for a fixed land slope and rainfall intensity. The SOR for no mulch-treated land was higher than trash mulch-treated lands. Mathematical relationships were developed for relating SOR, SC, land slope, and rainfall intensity for a particular mulch treatment. It was observed that SOR and average SC values correlated with rainfall intensity and land slope for each mulch treatment. The developed models' correlation coefficients were more than 90%.

Forecasting of stage-discharge in a non-perennial river using machine learning with gamma test.

Knowledge of the stage-discharge rating curve is useful in designing and planning flood warnings; thus, developing a reliable stage-discharge rating curve is a fundamental and crucial component of water resource system engineering. Since the continuous measurement is often impossible, the stage-discharge relationship is generally used in natural streams to estimate discharge. This paper aims to optimize the rating curve using a generalized reduced gradient (GRG) solver and the test the accuracy and applicability of the hybridized linear regression (LR) with other machine learning techniques, namely, linear regression-random subspace (LR-RSS), linear regression-reduced error pruning tree (LR-REPTree), linear regression-support vector machine (LR-SVM) and linear regression-M5 pruned (LR-M5P) models. An application of these hybrid models was performed and test to modeling the Gaula Barrage stage-discharge problem. For this, 12-year historical stage-discharge data were collected and analyzed. The 12-year historical daily flow data (m3/s) and stage (m) from during the monsoon season, i.e., June to October only from 03/06/2007 to 31/10/2018, were used for discharge simulation. The best suitable combination of input variables for LR, LR-RSS, LR-REPTree, LR-SVM, and LR-M5P models was identified and decided using the gamma test. GRG-based rating curve equations were found to be as effective and more accurate as conventional rating curve equations. The outcomes from GRG, LR, LR-RSS, LR-REPTree, LR-SVM, and LR-M5P models were compared to observed values of daily discharge based on Nash Sutcliffe model efficiency coefficient (NSE), Willmott Index of Agreement (d), Kling-Gupta efficiency (KGE), mean absolute error (MAE), mean bias error (MBE), relative bias in percent (RE), root mean square error (RMSE) Pearson correlation coefficient (PCC) and coefficient of determination (R2). The LR-REPTree model (combination 1: NSE = 0.993, d = 0.998, KGE = 0.987, PCC(r) = 0.997, and R2 = 0.994 and minimum value of RMSE = 0.109, MAE = 0.041, MBE = -0.010 and RE = -0.1%; combination 2; NSE = 0.941, d = 0.984, KGE = 0. 923, PCC(r) = 0. 973, and R2 = 0. 947 and minimum value of RMSE = 0. 331, MAE = 0.143, MBE = -0.089 and RE = -0.9%) performed superior to the GRG, LR, LR-RSS, LR-SVM, and LR-M5P models in all input combinations during the testing period. It was also noticed that the performance of the alone LR and its hybrid models (i.e., LR-RSS, LR-REPTree, LR-SVM, and LR-M5P) was better than the conventional stage-discharge rating curve, including the GRG method.

Statistical and spatial analysis for soil heavy metals over the Murray-Darling river basin in Australia.

Heavy metals (HMs) are a vital elements for investigating the pollutant level of sediments and water bodies. The Murray-Darling river basin area located in Australia is experiencing severe damage to increased crop productivity, loss of soil fertility, and pollution levels within the vicinity of the river system. This basin is the most effective primary production area in Australia where agricultural productivity is increased the gross domastic product in the entire mainland. In this study, HMs contaminations are examined for eight study sites selected for the Murray-Darling river basin where the inverse Distance Weighting interpolation method is used to identify the distribution of HMs. To pursue this, four different pollution indices namely the Geo-accumulation index (Igeo), Contamination factor (CF), Pollution load index (PLI), single-factor pollution index (SPLI), and the heavy metal pollution index (HPI) are computed. Following this, the Pearson correlation matrix is used to identify the relationships among the two HM parameters. The results indicate that the conductivity and N (%) are relatively high in respect to using Igeo and PLI indexes for study sites 4, 6, and 7 with 2.93, 3.20, and 1.38, respectively. The average HPI is 216.9071 that also indicates higher level pollution in the Murray-Darling river basin and the highest HPI value is noted in sample site 1 (353.5817). The study also shows that the levels of Co, P, Conductivity, Al, and Mn are mostly affected by HMs and that these indices indicate the maximum HM pollution level in the Murray-Darling river basin. Finally, the results show that the high HM contamination level appears to influence human health and local environmental conditions.

New perception about the use of nanofungicides in sustainable agriculture practices.

Protecting plants from pathogens using synthetic nanofungicides is not very effective, because it is harmful to the environment. However, it is synthetic fungicides that farmers are familiar with and commonly use. In this modern era, nanotechnology offers a smart solution to environmental issues at the nanoscale level. It is an emergent field and nanoparticles can be synthesized through various methods. Nanofungicides are efficient due to their solubility and permeability, low dose-dependent toxicity, low dose, enhanced bioavailability, targeted delivery, enhanced bioavailability, and controlled release. There are many metallic compounds, such as Cu, Zn, Ag, and TiO2 available which are used as nanofungicides. There is a contrary relationship between the size of the nanoparticles and their efficacy and antifungal potential. This review article offers a wide knowledge about formulation of nanomaterials as nanofungicides and their role in disease management in plants.

Aptamer Based Nanoprobes for Detection of Foodborne Virus in Food and Environment Samples: Recent Progress and Challenges.

Accepting the fact that there is a huge number of virus particles in food that lead to several infectious diseases, eliminating of the foodborne virus from food is tangible. In 2020, the appearance of new SARS-CoV-2 variants had remarked the importance of food safety in our lives. Detection virus is a dynamic domain. Recently, many papers have tried to detect several foodborne viruses by using conventional sensing platforms including ELISA (enzyme-linked immunosorbent assay), PCR (polymerase chain reaction-based methods) and NASBA (nucleic acid sequence-based amplification). However, small sizes, low infective doses and discrete distribution of the foodborne virus have converted these microorganisms into the most challengeable pathogen in the food samples matrix. Foodborne virus detection exploiting aptamer-based biosensors has attracted considerable attention toward the numerous benefits of sourcing from aptamers in which a variety of viruses could be detected by conjugation of aptamer-virus. The development of multiple sensing methodologies and platforms in terms of aptasensor application in real food and environment samples has demonstrated promising results. In this review, we present the latest developments in myriad types of aptasensors (including electrochemical, optical and piezoelectric aptasensor) for the quantification of foodborne viruses. Working strategies, benefits and disadvantages of these platforms are argued.