A comparison of popular approaches to optimize landscape resistance surfaces

Landscape Ecology - Landscape resistance surfaces are often used to address questions related to movement, dispersal, or population connectivity. However, modeling landscape resistance is...     

Prediction of Genetic Gain in Sweet Corn using Selection Indexes

Journal of Crop Science and Biotechnology - The cultivation of sweet corn is expanding in Brazil, but there are serious constraints about the availability of commercial cultivars. The selection of...     

Soil quality assessments in integrated crop–livestock–forest systems: A review

Integrated crop–livestock–forest is a promising strategy to improve soil quality. It comprises four different integrated farming systems: crop–livestock, crop–forest, forest–livestock and crop–livestock–forest. This work systematically reviewed studies about integrated crop–livestock–forest systems and soil quality. A total of 92 papers were retrieved from the Web of Science—Clarivate Analytics platform, and the following information was analysed: publication year, institution, region of the studied site, type of integrated system, soil type, tillage system, maximum soil depth and the soil quality indicators assessed. Most studies were published in the second half of the 2010s. Brazil is a prominent focus of research about soil quality and integrated crop–livestock–forest systems, with significant contribution from its central and southern regions. The Embrapa was the main publishing institution, present in over one‐third of the studies. Crop–livestock was the most common integrated system, Ferralsols was the most common soil group, and most of the studied soils were clayey. No tillage was the main tillage system. Most studies focused on the topsoil, assessing physical and/or chemical soil quality indicators. More emphasis on biological indicators of soil quality is required, as well as assessments integrating biological, physical and chemical indicators of soil quality. Future works should compare different integrated systems, including assessments deeper in the soil profile, especially in systems with the forest component, and also in sandy and silty soils. Soil quality indicators that have been rarely used should be further tested. Novel indicators should be added to better understand the promotion of soil quality by integrated crop–livestock–forest systems.     

Proteomic profiles during adventitious rooting of Eucalyptus species relevant to the cellulose industry

New Forests - Adventitious rooting (AR) is an obligatory step for vegetative propagation of commercial woody species. Paper industries have interest in Eucalyptus globulus Labill and its hybrids...     

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.     

Evaluation of the Effects of Different Stunning Methods on the Stress Responses and Meat Quality of the Amazon hybrid surubim,Pseudoplatystoma fasciatum female × Leiarius marmoratus male

Different stunning methods (carbon dioxide [CO₂] and hypothermia) used in industrial fish processing were compared with asphyxia in air, which is used in traditional fishing, to evaluate the effects of these methods on the stress responses and the meat quality of the Amazon hybrid surubim, Pseudoplatystoma fasciatum female × Leiarius marmoratus male. After the application, blood and behavioral indicators were evaluated. Following death by gill cutting, the fish were gutted, and analyses of muscle pH, rigor mortis, the exudation of the meat, and coloration were performed. Significantly higher levels of cortisol and glucose were exhibited by the fish subjected to asphyxia than by the fish stunned by either CO₂ or hypothermia. The fish subjected to asphyxia displayed decreasing muscle pH during the first 2 h after death and showed higher rates of rigor mortis after 3 h than the fish that were stunned by the other two methods. The asphyxia is a practice that exposes fish to suffering by causing an increase in stress responses, which affects meat quality. The immersion of fish in water and ice (hypothermia) for 5 min was more effective stunning method than the use of CO₂, resulting in higher loss of sensibility and greater welfare.     

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.