Colegio de Postgraduados

Fungi play major roles in ecosystem processes, but the determinants of fungal diversity and biogeographic patterns remain poorly understood. Using DNA metabarcoding data from hundreds of globally distributed soil samples, we demonstrate that fungal richness is decoupled from plant diversity. The plant-to-fungus richness ratio declines exponentially toward the poles. Climatic factors, followed by edaphic and spatial variables, constitute the best predictors of fungal richness and community composition at the global scale. Fungi show similar latitudinal diversity gradients to other organisms, with several notable exceptions. These findings advance our understanding of global fungal diversity patterns and permit integration of fungi into a general macroecological framework.

Ruminant livestock are important sources of human food and global greenhouse gas emissions. Feed degradation and methane formation by ruminants rely on metabolic interactions between rumen microbes and affect ruminant productivity. Rumen and camelid foregut microbial community composition was determined in 742 samples from 32 animal species and 35 countries, to estimate if this was influenced by diet, host species, or geography. Similar bacteria and archaea dominated in nearly all samples, while protozoal communities were more variable. The dominant bacteria are poorly characterised, but the methanogenic archaea are better known and highly conserved across the world. This universality and limited diversity could make it possible to mitigate methane emissions by developing strategies that target the few dominant methanogens. Differences in microbial community compositions were predominantly attributable to diet, with the host being less influential. There were few strong co-occurrence patterns between microbes, suggesting that major metabolic interactions are non-selective rather than specific.

Many modern genomic data analyses require implementing regressions where the number of parameters (p, e.g., the number of marker effects) exceeds sample size (n). Implementing these large-p-with-small-n regressions poses several statistical and computational challenges, some of which can be confronted using Bayesian methods. This approach allows integrating various parametric and nonparametric shrinkage and variable selection procedures in a unified and consistent manner. The BGLR R-package implements a large collection of Bayesian regression models, including parametric variable selection and shrinkage methods and semiparametric procedures (Bayesian reproducing kernel Hilbert spaces regressions, RKHS). The software was originally developed for genomic applications; however, the methods implemented are useful for many nongenomic applications as well. The response can be continuous (censored or not) or categorical (either binary or ordinal). The algorithm is based on a Gibbs sampler with scalar updates and the implementation takes advantage of efficient compiled C and Fortran routines. In this article we describe the methods implemented in BGLR, present examples of the use of the package, and discuss practical issues emerging in real-data analysis.

Progress towards understanding the extent to which mycorrhizal fungi are involved in the mobilization of nitrogen (N) and phosphorus (P) from natural substrates is reviewed here. While mycorrhiza research has emphasized the role of the symbiosis in facilitation of capture of these nutrients in ionic form, attention has shifted since the mid-1980s to analysing the mycorrhizal fungal abilities to release N and P from the detrital materials of microbial faunal and plant origins, which are the primary sources of these elements in terrestrial ecosystems. Ericoid, and some ectomycorrhizal fungi have the potential to be directly involved in attack both on structural polymers, which may render nutrients inaccessible, and in mobilization of N and P from the organic polymers in which they are sequestered. The advantages to the plant of achieving intervention in the microbial mobilization-immobilization cycles are stressed. While the new approaches may initially lack the precision achieved in studies of readily characterized ionic forms of N and P, they do provide insights of greater ecological relevance. The results support the hypothesis that selection has favoured ericoid and ectomycorrhizal systems with well developed saprotrophic capabilities in those ecosystems characterized by retention of N and P as organic complexes in the soil. The need for further investigation of the abilities of arbuscular mycorrhizal fungi to intervene in nutrient mobilization processes is stressed.

Concerns about secondary use of data and limited opportunities for benefit-sharing have focused attention on the tension that Indigenous communities feel between (1) protecting Indigenous rights and interests in Indigenous data (including traditional knowledges) and (2) supporting open data, machine learning, broad data sharing, and big data initiatives. The International Indigenous Data Sovereignty Interest Group (within the Research Data Alliance) is a network of nation-state based Indigenous data sovereignty networks and individuals that developed the ‘CARE Principles for Indigenous Data Governance’ (Collective Benefit, Authority to Control, Responsibility, and Ethics) in consultation with Indigenous Peoples, scholars, non-profit organizations, and governments. The CARE Principles are people– and purpose-oriented, reflecting the crucial role of data in advancing innovation, governance, and self-determination among Indigenous Peoples. The Principles complement the existing data-centric approach represented in the ‘FAIR Guiding Principles for scientific data management and stewardship’ (Findable, Accessible, Interoperable, Reusable). The CARE Principles build upon earlier work by the Te Mana Raraunga Maori Data Sovereignty Network, US Indigenous Data Sovereignty Network, Maiam nayri Wingara Aboriginal and Torres Strait Islander Data Sovereignty Collective, and numerous Indigenous Peoples, nations, and communities. The goal is that stewards and other users of Indigenous data will ‘Be FAIR and CARE.’ In this first formal publication of the CARE Principles, we articulate their rationale, describe their relation to the FAIR Principles, and present examples of their application.

Grain yields of eight representative semidwarf spring wheat ( Triticum aestivum L.) cultivars released in northwest Mexico between 1962 and 1988 have increased linearly across years as measured in this region during 6 yr under favorable management and irrigation. To understand the physiological basis of this progress and possibly assist future selection for grain yield, leaf traits were determined during 3 yr in the same study. Stomatal conductance ( g s ), maximum photosynthetic rate (A max , and canopy temperature depression (CTD), averaged over the 3 yr, were closely and positively correlated with progress in the 6‐yr mean yield. The correlation was greatest with g s ( r = 0.94, P < 0.01). Compared with the overall yield increase of 27%, g s increased 63%, A max increased 23%, and canopies were 0.6°C cooler. Carbon‐13 isotope discrimination was also positively associated with yield progress ( r = 0.71, P < 0.05), but other leaf traits such as flag leaf area, specific leaf weight, percentage N and greeness were not, nor was crop growth rate around anthesis. The causal basis of the leaf activity interrelationships is reasonably clear, with both increased intercellular CO 2 concentration and increased mesophyll activity contributing to the increase in A max . However, causal links to the yield progress, and the accompanying increase in kernels per square meter, are not clear. It is concluded that g s and CTD should be further investigated as potential indirect selection criteria for yield.

The availability of dense molecular markers has made possible the use of genomic selection (GS) for plant breeding. However, the evaluation of models for GS in real plant populations is very limited. This article evaluates the performance of parametric and semiparametric models for GS using wheat (Triticum aestivum L.) and maize (Zea mays) data in which different traits were measured in several environmental conditions. The findings, based on extensive cross-validations, indicate that models including marker information had higher predictive ability than pedigree-based models. In the wheat data set, and relative to a pedigree model, gains in predictive ability due to inclusion of markers ranged from 7.7 to 35.7%. Correlation between observed and predictive values in the maize data set achieved values up to 0.79. Estimates of marker effects were different across environmental conditions, indicating that genotype × environment interaction is an important component of genetic variability. These results indicate that GS in plant breeding can be an effective strategy for selecting among lines whose phenotypes have yet to be observed.

The Plant Transcription Factor Database (PlnTFDB; http://plntfdb.bio.uni-potsdam.de/v3.0/) is an integrative database that provides putatively complete sets of transcription factors (TFs) and other transcriptional regulators (TRs) in plant species (sensu lato) whose genomes have been completely sequenced and annotated. The complete sets of 84 families of TFs and TRs from 19 species ranging from unicellular red and green algae to angiosperms are included in PlnTFDB, representing >1.6 billion years of evolution of gene regulatory networks. For each gene family, a basic description is provided that is complemented by literature references, and multiple sequence alignments of protein domains. TF or TR gene entries include information of expressed sequence tags, 3D protein structures of homologous proteins, domain architecture and cross-links to other computational resources online. Moreover, the different species in PlnTFDB are linked to each other by means of orthologous genes facilitating cross-species comparisons.

New methods that incorporate the main and interaction effects of high-dimensional markers and of high-dimensional environmental covariates gave increased prediction accuracy of grain yield in wheat across and within environments. In most agricultural crops the effects of genes on traits are modulated by environmental conditions, leading to genetic by environmental interaction (G × E). Modern genotyping technologies allow characterizing genomes in great detail and modern information systems can generate large volumes of environmental data. In principle, G × E can be accounted for using interactions between markers and environmental covariates (ECs). However, when genotypic and environmental information is high dimensional, modeling all possible interactions explicitly becomes infeasible. In this article we show how to model interactions between high-dimensional sets of markers and ECs using covariance functions. The model presented here consists of (random) reaction norm where the genetic and environmental gradients are described as linear functions of markers and of ECs, respectively. We assessed the proposed method using data from Arvalis, consisting of 139 wheat lines genotyped with 2,395 SNPs and evaluated for grain yield over 8 years and various locations within northern France. A total of 68 ECs, defined based on five phases of the phenology of the crop, were used in the analysis. Interaction terms accounted for a sizable proportion (16 %) of the within-environment yield variance, and the prediction accuracy of models including interaction terms was substantially higher (17-34 %) than that of models based on main effects only. Breeding for target environmental conditions has become a central priority of most breeding programs. Methods, like the one presented here, that can capitalize upon the wealth of genomic and environmental information available, will become increasingly important.

This open access book presents the state of the art genome base prediction models and statistical learning tools

Genomic selection (GS) has been implemented in animal and plant species, and is regarded as a useful tool for accelerating genetic gains. Varying levels of genomic prediction accuracy have been obtained in plants, depending on the prediction problem assessed and on several other factors, such as trait heritability, the relationship between the individuals to be predicted and those used to train the models for prediction, number of markers, sample size and genotype × environment interaction (GE). The main objective of this article is to describe the results of genomic prediction in International Maize and Wheat Improvement Center's (CIMMYT's) maize and wheat breeding programs, from the initial assessment of the predictive ability of different models using pedigree and marker information to the present, when methods for implementing GS in practical global maize and wheat breeding programs are being studied and investigated. Results show that pedigree (population structure) accounts for a sizeable proportion of the prediction accuracy when a global population is the prediction problem to be assessed. However, when the prediction uses unrelated populations to train the prediction equations, prediction accuracy becomes negligible. When genomic prediction includes modeling GE, an increase in prediction accuracy can be achieved by borrowing information from correlated environments. Several questions on how to incorporate GS into CIMMYT's maize and wheat programs remain unanswered and subject to further investigation, for example, prediction within and between related bi-parental crosses. Further research on the quantification of breeding value components for GS in plant breeding populations is required.

Abstract. The Intergovernmental Technical Panel on Soils has completed the first State of the World's Soil Resources Report. Globally soil erosion was identified as the gravest threat, leading to deteriorating water quality in developed regions and to lowering of crop yields in many developing regions. We need to increase nitrogen and phosphorus fertilizer use in infertile tropical and semi-tropical soils – the regions where the most food insecurity among us are found – while reducing global use of these products overall. Stores of soil organic carbon are critical in the global carbon balance, and national governments must set specific targets to stabilize or ideally increase soil organic carbon stores. Finally the quality of soil information available for policy formulation must be improved – the regional assessments in the State of the World's Soil Resources Report frequently base their evaluations on studies from the 1990s based on observations made in the 1980s or earlier.

The fall armyworm, Spodoptera frugiperda (J. E. Smith), is one of the main pests of corn in many areas of the American continent. The reliance on pesticides to control fall armyworm has led to the development of insecticide resistance in many regions. We determined the resistance levels of fall armyworm to insecticides of different modes of action in fall armyworm populations from Puerto Rico and several Mexican states with different insecticide use patterns. Mexican populations that expressed higher resistance ratios (RR50) were: Sonora (20-fold to chlorpyriphos), Oaxaca (19-fold to permethrin), and Sinaloa (10-fold to flubendamide). The Puerto Rico population exhibited a remarkable field-evolved resistance to many pesticides. The RR50 to the insecticides tested were: flubendiamide (500-fold), chlorantraniliprole (160-fold), methomyl (223-fold), thiodicarb (124-fold), permethrin (48-fold), chlorpyriphos (47-fold), zeta-cypermethrin (35-fold), deltamethrin (25-fold), triflumuron (20-fold), spinetoram (14-fold). Spinosad (eightfold), emamectin benzoate and abamectin (sevenfold) displayed lower resistance ratio. However, these compounds are still effective to manage fall armyworm resistance in Puerto Rico. Fall armyworm populations from Mexico show different levels of susceptibility, which may reflect the heterogeneity of the pest control patterns in this country. The status of insecticide resistance in the fall armyworm from Puerto Rico indicates a challenging situation for the control of this pest with these insecticides in the close future. Lessons learned from this research might be applied in regions with recent invasions of fall armyworm in Africa.

and allied fusarioid genera (www.fusarium.org).

Drought is the second major constraint to common bean ( Phaseolus vulgaris L) production after disease. This study examined yield under drought, yield potential, drought susceptibility index, harvest index, and geometric mean as potential indicators of drought resistant genotypes. The performance of two common bean populations, consisting of 78 and 95 recombinant inbred lines, was examined under moisture stress and nonstress regimes. Experiments were conducted at seven locations (1990–1994) in Michigan and Mexico to identify effective selection criteria for drought resistance. Two genotypes from each population yielded in the top 10% under both stress and nonstress conditions. Heritability estimates for yield in the Sierra/AC1028 population, based on 5 yr of data, ranged from 0.55 to 0.59 for stress and nonstress, respectively, and from 0.20 to 0.19 for stress and nonstress, respectively, in the Sierra/Lef‐2RB population. Heritability for plant biomass was 0.52 for stress and 0.55 for nonstress in the Sierra/AC1028 population and 0.15 under stress and 0.05 under nonstress in the Sierra/Lef‐2RB population. One‐hundred seed weight was the most highly heritable trait in both populations with heritability estimates of 0.80 for the Sierra/AC1028 population and 0.65 for the Sierra/Lef‐2RB population. The geometric mean of the two moisture regimes was the single strongest indicator of performance under stress and nonstress, and a breeding strategy that involves selection based first on the geometric mean, followed by selection based on yield under stress, was suggested as the most effective strategy to improve drought resistance in common bean.

META-R (multi-environment trial analysis in R) is a suite of R scripts linked by a graphical user interface (GUI) designed in Java language. The objective of META-R is to accurately analyze multi-environment plant breeding trials (METs) by fitting mixed and fixed linear models from experimental designs such as the randomized complete block design (RCBD) and the alpha-lattice/lattice designs. META-R simultaneously estimates the best linear and unbiased estimators (BLUEs) and the best linear and unbiased predictors (BLUPs). Additionally, it computes the variance-covariance parameters, as well as some statistical and genetic parameters such as the least significant difference (LSD) at 5% significance, the coefficient of variation in percentage (CV), the genetic variance, and the broad-sense heritability. These parameters are very important in the selection of top performing genotypes in plant breeding. META-R also computes the phenotypic and genetic correlations among environments and between traits, as well as their statistical significance. The genetic correlations between environments or traits can be visualized in a biplot graph or a tree diagram (dendrogram). Genetic correlations are very important for identifying environments with similar behavior or making indirect selection and identifying the most highly associated traits. META-R performs multi-environment analyses by using the residual maximum likelihood (REML) method; these analyses can be done by environment, across environments by grouping factors (stress conditions, nitrogen content, etc.) and across environments; the analyses across environments can be done with a pre-defined degree of heritability.

Varietal data from 27 crop species from five continents were drawn together to determine overall trends in crop varietal diversity on farm. Measurements of richness, evenness, and divergence showed that considerable crop genetic diversity continues to be maintained on farm, in the form of traditional crop varieties. Major staples had higher richness and evenness than nonstaples. Variety richness for clonal species was much higher than that of other breeding systems. A close linear relationship between traditional variety richness and evenness (both transformed), empirically derived from data spanning a wide range of crops and countries, was found both at household and community levels. Fitting a neutral "function" to traditional variety diversity relationships, comparable to a species abundance distribution of "neutral ecology," provided a benchmark to assess the standing diversity on farm. In some cases, high dominance occurred, with much of the variety richness held at low frequencies. This suggested that diversity may be maintained as an insurance to meet future environmental changes or social and economic needs. In other cases, a more even frequency distribution of varieties was found, possibly implying that farmers are selecting varieties to service a diversity of current needs and purposes. Divergence estimates, measured as the proportion of community evenness displayed among farmers, underscore the importance of a large number of small farms adopting distinctly diverse varietal strategies as a major force that maintains crop genetic diversity on farm.

BACKGROUND: Several conventional genomic Bayesian (or no Bayesian) prediction methods have been proposed including the standard additive genetic effect model for which the variance components are estimated with mixed model equations. In recent years, deep learning (DL) methods have been considered in the context of genomic prediction. The DL methods are nonparametric models providing flexibility to adapt to complicated associations between data and output with the ability to adapt to very complex patterns. MAIN BODY: We review the applications of deep learning (DL) methods in genomic selection (GS) to obtain a meta-picture of GS performance and highlight how these tools can help solve challenging plant breeding problems. We also provide general guidance for the effective use of DL methods including the fundamentals of DL and the requirements for its appropriate use. We discuss the pros and cons of this technique compared to traditional genomic prediction approaches as well as the current trends in DL applications. CONCLUSIONS: The main requirement for using DL is the quality and sufficiently large training data. Although, based on current literature GS in plant and animal breeding we did not find clear superiority of DL in terms of prediction power compared to conventional genome based prediction models. Nevertheless, there are clear evidences that DL algorithms capture nonlinear patterns more efficiently than conventional genome based. Deep learning algorithms are able to integrate data from different sources as is usually needed in GS assisted breeding and it shows the ability for improving prediction accuracy for large plant breeding data. It is important to apply DL to large training-testing data sets.

This study tested whether providing cows a 4-wk period on pasture would improve gait and change lying behavior. Eighteen groups, each of 4 lactating Holstein cows initially housed in a freestall barn, were assigned to either continued housing in the same freestall barn, or moved to pasture to provide changes in both physical environment and diet. To assess lameness, gait scores (1 to 5) were recorded weekly for 4 wk. Gait improved by an average of 0.22 units/wk for those cows kept on pasture. We also recorded 4 specific gait attributes (head bob, back arch, tracking up, and reluctance to bear weight evenly on all 4 hooves), and found that the latter 2 attributes also improved during the pasture period. Improved gait for cows on pasture was not because of increased lying times. Cows on pasture actually spent less time lying down than cows kept indoors (10.9 vs. 12.3 h/d), although this lying time was spread over a larger number of bouts (15.3 vs. 12.2 bouts). Cows housed on pasture also lost more weight and produced less milk relative to cows in freestalls, likely because of reduced nutrient intake. These results indicate that a period on pasture can be used to help lame cattle recover probably because pasture provides a more comfortable surface upon which cows stand, helping them to recover from hoof and leg injuries.

Genotyping-by-sequencing (GBS) technologies have proven capacity for delivering large numbers of marker genotypes with potentially less ascertainment bias than standard single nucleotide polymorphism (SNP) arrays. Therefore, GBS has become an attractive alternative technology for genomic selection. However, the use of GBS data poses important challenges, and the accuracy of genomic prediction using GBS is currently undergoing investigation in several crops, including maize, wheat, and cassava. The main objective of this study was to evaluate various methods for incorporating GBS information and compare them with pedigree models for predicting genetic values of lines from two maize populations evaluated for different traits measured in different environments (experiments 1 and 2). Given that GBS data come with a large percentage of uncalled genotypes, we evaluated methods using nonimputed, imputed, and GBS-inferred haplotypes of different lengths (short or long). GBS and pedigree data were incorporated into statistical models using either the genomic best linear unbiased predictors (GBLUP) or the reproducing kernel Hilbert spaces (RKHS) regressions, and prediction accuracy was quantified using cross-validation methods. The following results were found: relative to pedigree or marker-only models, there were consistent gains in prediction accuracy by combining pedigree and GBS data; there was increased predictive ability when using imputed or nonimputed GBS data over inferred haplotype in experiment 1, or nonimputed GBS and information-based imputed short and long haplotypes, as compared to the other methods in experiment 2; the level of prediction accuracy achieved using GBS data in experiment 2 is comparable to those reported by previous authors who analyzed this data set using SNP arrays; and GBLUP and RKHS models with pedigree with nonimputed and imputed GBS data provided the best prediction correlations for the three traits in experiment 1, whereas for experiment 2 RKHS provided slightly better prediction than GBLUP for drought-stressed environments, and both models provided similar predictions in well-watered environments.