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.     

Is single-step genomic REML with the algorithm for proven and young more computationally efficient when less generations of data are present?

Efficient computing techniques allow the estimation of variance components for virtually any traditional dataset. When genomic information is available, variance components can be estimated using genomic REML (GREML). If only a portion of the animals have genotypes, single-step GREML (ssGREML) is the method of choice. The genomic relationship matrix (G) used in both cases is dense, limiting computations depending on the number of genotyped animals. The algorithm for proven and young (APY) can be used to create a sparse inverse of G (GAPY~-1) with close to linear memory and computing requirements. In ssGREML, the inverse of the realized relationship matrix (H⁻¹) also includes the inverse of the pedigree relationship matrix, which can be dense with a long pedigree, but sparser with short. The main purpose of this study was to investigate whether costs of ssGREML can be reduced using APY with truncated pedigree and phenotypes. We also investigated the impact of truncation on variance components estimation when different numbers of core animals are used in APY. Simulations included 150K animals from 10 generations, with selection. Phenotypes (h² = 0.3) were available for all animals in generations 1–9. A total of 30K animals in generations 8 and 9, and 15K validation animals in generation 10 were genotyped for 52,890 SNP. Average information REML and ssGREML with G⁻¹ and GAPY~-1 using 1K, 5K, 9K, and 14K core animals were compared. Variance components are impacted when the core group in APY represents the number of eigenvalues explaining a small fraction of the total variation in G. The most time-consuming operation was the inversion of G, with more than 50% of the total time. Next, numerical factorization consumed nearly 30% of the total computing time. On average, a 7% decrease in the computing time for ordering was observed by removing each generation of data. APY can be successfully applied to create the inverse of the genomic relationship matrix used in ssGREML for estimating variance components. To ensure reliable variance component estimation, it is important to use a core size that corresponds to the number of largest eigenvalues explaining around 98% of total variation in G. When APY is used, pedigrees can be truncated to increase the sparsity of H and slightly reduce computing time for ordering and symbolic factorization, with no impact on the estimates.     

Updated Trends on the Biodiscovery of New Marine Natural Products from Invertebrates

From 1990–2019, a total of 15,442 New Marine Natural Products from Invertebrates (NMNPIs) were reported. The 2010s saw the most prolific decade of biodiscovery, with 5630 NMNPIs recorded. The phyla that contributed most biomolecules were the Porifera (sponges) (47.2%, 2659 NMNPIs) and the Cnidaria (35.3%, 1989 NMNPIs). The prevalence of these two phyla as the main sources of NMNPIs became more pronounced in the 2010s. The tropical areas of the Pacific Ocean yielded more NMNPIs, most likely due to the remarkable biodiversity of coral reefs. The Indo-Burma biodiversity hotspot (BH) was the most relevant area for the biodiscovery of NMNPIs in the 2010s, accounting for nearly one-third (1819 NMNPIs) of the total and surpassing the top BH from the 1990s and the 2000s (the Sea of Japan and the Caribbean Islands, respectively). The Chinese exclusive economic zone (EEZ) alone contributed nearly one-quarter (24.7%) of all NMNPIs recorded during the 2010s, displacing Japan’s leading role from the 1990s and the 2000s. With the biodiscovery of these biomolecules steadily decreasing since 2012, it is uncertain whether this decline has been caused by lower bioprospecting efforts or the potential exhaustion of chemodiversity from traditional marine invertebrate sources.     

The antihistaminic chlorpheniramine inhibitsin vitro growth of several fungi isolated from harvested fruits

Chlorpheniramine (CPA) is an antihistaminic that changes the conformation of DNA and inhibits polyamine biosynthesis in mammalian cells. In the present work, we tested the effect of CPA on four genera of fungi species (Altemaria alternata, Botrytis cinerea, Cladosporium cladosporioides and threePenicillium spp.) grownin vitro. Similar growth inhibitions of these genera were produced by 0.5 raM iprodione, CPA and histidinol, but CPA was the most effective. The CPA sensitivities of the twoB. cinerea strains were different. Putrescine did not restore the fungal growth inhibited by CPA.     

Effect of Stocking Density on Growth,Survival, and Condition of the Mexican Cichlid Cichlasoma beani

The Mexican cichlid Cichlasoma beani is currently exploited regionally as food and can be commercialized in the aquarium trade. Natural populations of C. beani may already be negatively affected by anthropogenic alteration of the areas in which it is distributed. The aim of the present study was to examine the effect on growth, survival, and condition of C. beani cultured in three stocking densities: three (D3), six (D6), and nine (D9) fish per each 40 L tank. At the end of a 6‐wk trial the fish cultured in D3 were longer, heavier, and grew faster than the rest of the treatments but their survival was the lowest compared to D6 and D9. The mortalities were caused by a strong aggressive behavior in D3.     

Emerging issues in genomic selection

Genomic selection (GS) is now practiced successfully across many species. However, many questions remain, such as long-term effects, estimations of genomic parameters, robustness of genome-wide association study (GWAS) with small and large datasets, and stability of genomic predictions. This study summarizes presentations from the authors at the 2020 American Society of Animal Science (ASAS) symposium. The focus of many studies until now is on linkage disequilibrium between two loci. Ignoring higher-level equilibrium may lead to phantom dominance and epistasis. The Bulmer effect leads to a reduction of the additive variance; however, the selection for increased recombination rate can release anew genetic variance. With genomic information, estimates of genetic parameters may be biased by genomic preselection, but costs of estimation can increase drastically due to the dense form of the genomic information. To make the computation of estimates feasible, genotypes could be retained only for the most important animals, and methods of estimation should use algorithms that can recognize dense blocks in sparse matrices. GWASs using small genomic datasets frequently find many marker-trait associations, whereas studies using much bigger datasets find only a few. Most of the current tools use very simple models for GWAS, possibly causing artifacts. These models are adequate for large datasets where pseudo-phenotypes such as deregressed proofs indirectly account for important effects for traits of interest. Artifacts arising in GWAS with small datasets can be minimized by using data from all animals (whether genotyped or not), realistic models, and methods that account for population structure. Recent developments permit the computation of P-values from genomic best linear unbiased prediction (GBLUP), where models can be arbitrarily complex but restricted to genotyped animals only, and single-step GBLUP that also uses phenotypes from ungenotyped animals. Stability was an important part of nongenomic evaluations, where genetic predictions were stable in the absence of new data even with low prediction accuracies. Unfortunately, genomic evaluations for such animals change because all animals with genotypes are connected. A top-ranked animal can easily drop in the next evaluation, causing a crisis of confidence in genomic evaluations. While correlations between consecutive genomic evaluations are high, outliers can have differences as high as 1 SD. A solution to fluctuating genomic evaluations is to base selection decisions on groups of animals. Although many issues in GS have been solved, many new issues that require additional research continue to surface.