Sorbent Materials Characterization Based on Mechanical or Thermal Pretreated Montmorillonite Modified by Surfactant Loading for Improved Chromium Retention

Water, Air, & Soil Pollution - The treatment efficiency of Chlorella sorokiniana and Scenedesmus species, immobilized in sodium alginate, was evaluated for removing nitrate from groundwater....     

Short-term spatiotemporal variation of soil CO2 emission,temperature, moisture and aeration in sugarcane field reform areas under the influence of precipitation events

Soil CO₂ emission (FCO₂) in agricultural areas results from the interaction of different factors such as climate and soil conditions. Our objective was to investigate the spatiotemporal variation of FCO₂, temperature (T_soil), moisture (M_soil) and air-filled pore space (AFPS), as well as their interactions, during the sugarcane field reform. The study was conducted on a 90 × 90 m sampling grid with 100 points at 10 m spacings. Ten assessments of FCO₂, T_soil and M_soil were carried out at each point over a 28-day period. The greatest mean values of FCO₂ (0.74 g m⁻² hr⁻¹) and M_soil (31.7%) were obtained on Julian day 276, 2013, being associated with precipitation events at the study site. Also, the smallest values of AFPS (19.17%) and T_soil (20.90°C) were observed on the same day. The spatial variability of FCO₂, T_soil, M_soil and AFPS was best described by an adjusted spherical model, although an exponential model better fitted some results. The spatial pattern of all soil attributes showed little temporal persistency, indicating a high complexity for FCO₂ during precipitation. Correlation maps assisted in identifying regions where M_soil and AFPS better controlled the emission process and where T_soil was important. A major challenge for world agriculture is to increase the efficiency of conventional soil management practices. We highlight the importance of the spatial pattern of soil properties that directly influence the CO₂ emission dynamics. Future mitigation actions should involve less intense tillage and ensure homogeneous applications of soil inputs, thereby reducing production costs and the contribution of these activities to CO₂ emissions during the sugarcane field reform.     

Shading effects on canopy and tillering characteristics of continuously stocked palisadegrass in a silvopastoral system in the Amazon biome

In silvopastoral (SP) systems, forage responses depend on the microenvironment in which the plants develop. Our objective was to evaluate canopy and tillering characteristics of shaded 'Marandu' palisadegrass [Brachiaria brizantha (Hochst A Rich) Stapf, syn. Urochloa brizantha] under continuous stocking in a SP system. Treatments were one full sun (FS) and three shaded systems (silvopasture, SP) corresponding to distances from tree groves: 7.5 m north (SP1), and 15 m (SP2) and 7.5 m south (SP3) studied during two rainy seasons (Year 1 and Year 2). The tree in the SP system was Eucalyptus urograndis (hybrid of Eucalyptus grandis W. Hill ex Maiden × Eucalyptus urophylla S. T. Blake). The photosynthetic active radiation was greater in FS (923 μmol m^-2 s^-1), followed by SP2 (811 μmol m^-2 s^-1), SP1 (727 μmol m^-2 s^-1) and SP3 (673 μmol m^-2 s^-1). Forage accumulation in FS was 15% greater than the mean of SP1, SP2 and SP3 (10,663 kg DM/ha). There was no difference in net accumulation of leaf, stem and dead material, averaging 3,302, 3,420 and 4,063 kg DM/ha respectively. Leaf accumulation and accumulation rate were greater in Year 2, and leaf accumulation rate was similar among treatments (19 kg DM ha⁻¹ day⁻¹). Leaf proportion increased 14% from Year 1 to Year 2. Specific leaf area was greater for treatments SP1 and SP3 (193 cm²/g). Tiller population density was similar across treatments in Year 1. Shaded palisadegrass maintains leaf productivity similar to FS under continuous stocking in an SP system.     

Genomic prediction for meat and carcass traits in Nellore cattle using a Markov blanket algorithm

This study was carried out to evaluate the advantage of preselecting SNP markers using Markov blanket algorithm regarding the accuracy of genomic prediction for carcass and meat quality traits in Nellore cattle. This study considered 3675, 3680, 3660 and 524 records of rib eye area (REA), back fat thickness (BF), rump fat (RF), and Warner–Bratzler shear force (WBSF), respectively, from the Nellore Brazil Breeding Program. The animals have been genotyped using low-density SNP panel (30 k), and subsequently imputed for arrays with 777 k SNPs. Four Bayesian specifications of genomic regression models, namely Bayes A, Bayes B, Bayes Cπ and Bayesian Ridge Regression methods were compared in terms of prediction accuracy using a five folds cross-validation. Prediction accuracy for REA, BF and RF was all similar using the Bayesian Alphabet models, ranging from 0.75 to 0.95. For WBSF, the predictive ability was higher using Bayes B (0.47) than other methods (0.39 to 0.42). Although the prediction accuracies using Markov blanket of SNP markers were lower than those using all SNPs, for WBSF the relative gain was lower than 13%. With a subset of informative SNPs markers, identified using Markov blanket, probably, is possible to capture a large proportion of the genetic variance for WBSF. The development of low-density and customized arrays using Markov blanket might be cost-effective to perform a genomic selection for this trait, increasing the number of evaluated animals, improving the management decisions based on genomic information and applying genomic selection on a large scale.