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Pipitone, U. (2007)
Este ensayo constituye un intento preliminar de interpretación del largo estancamiento estructural de Oaxaca enfocado en dos aspectos: una agricultura comunitaria de baja productividad e instituciones que no terminan de institucionalizarse. Oaxaca ha visto una sucesión de elites políticas más interesadas en mantener el control sobre los recursos públicos que en estimular y gobernar el cambio institucional. En el siglo pasado hemos sido testigos del éxito sorpresivo (en términos de estabilidad política) de una revolución en el poder que cambió todo manteniendo, en la sustancia, todo igual. En una realidad de pobreza difundida, erosión de los suelos, emigración, remesas, ausencia virtual de movilidad social y escasa acción colectiva permanente, la alternativa política más reciente proviene de un multiculturalismo que ofrece como solución una visión idealizada del pasado indígena.
This essay is a preliminary attempt to understand the long structural stagnation of Oaxaca, focused on the traces of indigenous low productivity agriculture and the historically poor institutionalization of regional institutions. The southern state of Mexico has experienced a long sequence of political elites more interested in conserving their almost discretional control over public resources, than to govern the change. In the past century we have witnessed the surprising success (in terms of political stability) of a revolution in power that changed everything in order to maintain everything as it used to be. In a reality of diffused poverty, soil erosion, migration, remittances, no social mobility and no continuous collective action, the newest cultural feature seems to be a multiculturalism that offers to the indigenous peoples a solution of their problems in an idealized past.
When information on multiple genotypes evaluated in multiple environments is recorded, a multi-environment single trait model for assessing genotype · environment interaction (G · E) is usually employed. Comprehensive models that simultaneously take into account the correlated traits and trait · genotype · environment interaction (T · G · E) are lacking. In this research, we propose a Bayesian model for analyzing multiple traits and multiple environments for whole-genome prediction (WGP) model. For this model, we used Half-t priors on each standard deviation term and uniform priors on each correlation of the covariance matrix. These priors were not informative and led to posterior inferences that were insensitive to the choice of hyper-parameters. We also developed a computationally efficient Markov Chain Monte Carlo (MCMC) under the above priors, which allowed us to obtain all required full conditional distributions of the parameters leading to an exact Gibbs sampling for the posterior distribution. We used two real data sets to implement and evaluate the proposed Bayesian method and found that when the correlation between traits was high (.0.5), the proposed model (with unstructured variance–covariance) improved prediction accuracy compared to the model with diagonal and standard variance–covariance structures. The R-software package Bayesian Multi-Trait and Multi-Environment (BMTME) offers optimized C++ routines to efficiently perform the analyses.
Modern agriculture uses hyperspectral cameras with hundreds of reflectance data at discrete narrow bands measured in several environments. Recently, Montesinos-López et al. (Plant Methods 13(4):1–23, 2017a. https ://doi.org/10.1186/s1300 7-016-0154-2; Plant Methods 13(62):1–29, 2017b. https ://doi.org/10.1186/s1300 7-017-0212- 4) proposed using functional regression analysis (as functional data analyses) to help reduce the dimensionality of the bands and thus decrease the computational cost. The purpose of this paper is to discuss the advantages and disadvantages that functional regression analysis offers when analyzing hyperspectral image data. We provide a brief review of functional regression analysis and examples that illustrate the methodology. We highlight critical elements of model specification: (i) type and number of basis functions, (ii) the degree of the polynomial, and (iii) the methods used to estimate regression coefficients. We also show how functional data analyses can be integrated into Bayesian models. Finally, we include an in-depth discussion of the challenges and opportunities presented by functional regression analysis. Results: We used seven model-methods, one with the conventional model (M1), three methods using the B-splines model (M2, M4, and M6) and three methods using the Fourier basis model (M3, M5, and M7). The data set we used comprises 976 wheat lines under irrigated environments with 250 wavelengths. Under a Bayesian Ridge Regression (BRR), we compared the prediction accuracy of the model-methods proposed under different numbers of basis functions, and compared the implementation time (in seconds) of the seven proposed model-methods for different numbers of basis. Our results as well as previously analyzed data (Montesinos-López et al. 2017a, 2017b) support that around 23 basis functions are enough. Concerning the degree of the polynomial in the context of B-splines, degree 3 approximates most of the curves very well. Two satisfactory types of basis are the Fourier basis for period curves and the B-splines model for non-periodic curves. Under nine different basis, the seven method-models showed similar prediction accuracy. Regarding implementation time, results show that the lower the number of basis, the lower the implementation time required. Methods M2, M3, M6 and M7 were around 3.4 times faster than methods M1, M4 and M5. Conclusions: In this study, we promote the use of functional regression modeling for analyzing high-throughput phenotypic data and indicate the advantages and disadvantages of its implementation. In addition, many key elements that are needed to understand and implement this statistical technique appropriately are provided using a real data set. We provide details for implementing Bayesian functional regression using the developed genomic functional regression (GFR) package. In summary, we believe this paper is a good guide for breeders and scientists interested in using functional regression models for implementing prediction models when their data are curves. Keywords: Hyperspectral data, Functional regression analysis, Bayesian functional regression, Functional data, Bayesian Ridge Regression.
Phenotypes Economic activities Statistical methods Regression analysis Hyperspectral Data Functional Regression Analyses Bayesian Functional Regression Functional Data Bayesian Ridge Regression DATA ANALYSIS REGRESSION ANALYSIS STATISTICAL METHODS BAYESIAN THEORY CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA
In genomic-enabled prediction, the task of improving the accuracy of the prediction of lines in environments is difficult because the available information is generally sparse and usually has low correlations between traits. In current genomic selection, although researchers have a large amount of information and appropriate statistical models to process it, there is still limited computing efficiency to do so. Although some statistical models are usually mathematically elegant, many of them are also computationally inefficient, and they are impractical for many traits, lines, environments, and years because they need to sample from huge normal multivariate distributions. For these reasons, this study explores two recommender systems: itembased collaborative filtering (IBCF) and the matrix factorization algorithm (MF) in the context of multiple traits and multiple environments. The IBCF and MF methods were compared with two conventional methods on simulated and real data. Results of the simulated and real data sets show that the IBCF technique was slightly better in terms of prediction accuracy than the two conventional methods and the MF method when the correlation was moderately high. The IBCF technique is very attractive because it produces good predictions when there is high correlation between items (environment–trait combinations) and its implementation is computationally feasible, which can be useful for plant breeders who deal with very large data sets.
Genomics Genotype environment interaction Statistical methods Precision agriculture Genomic Information Matrix Factorization Prediction Accuracy Collaborative Foltering GenPred Shared Data Resources GENOMICS GENOTYPE ENVIRONMENT INTERACTION STATISTICAL METHODS CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA
Drought is a devastating environmental stress in agriculture and hence a common target of plant breeding. A review of breeding progress on drought tolerance shows that, to a certain extent, selection for high yield in stress-free conditions indirectly improves yield in water-limiting conditions. The objectives of this study were to (i) assess the genotype × environment (GE) interaction for grain yield (GY) and other agronomic traits for maize (Zea mays L.) across East African agro-ecologies; and (ii) evaluate agronomic performance and stability in Uganda and Tanzania under optimum and random drought conditions. Data were recorded for major agronomic traits. Genotype main effect plus GE (GGE) biplot analysis was used to assess the stability of varieties within various environments and across environments. Combined analysis of variance across optimum moisture and random drought environments indicated that locations, mean-squares for genotypes and GE were significant for most measured traits. The best hybrids, CKDHH1097 and CKDHH1090, gave GY advantages of 23% and 43%, respectively, over the commercial hybrid varieties under both optimum-moisture and random-drought conditions. Across environments, genotypic variance was less than the GE variance for GY. The hybrids derived from doubled-haploid inbred lines produced higher GY and possessed acceptable agronomic traits compared with the commercial hybrids. Hybrid CKDHH1098 ranked second-best under optimum-moisture and drought-stress environments and was the most stable with broad adaptation to both environments. Use of the best doubled-haploids lines in testcross hybrids make-up, well targeted to the production environments, could boost maize production among farmers in East Africa.
Hybrids Maize Drought stress Correlation G-E Interaction GENOTYPE ENVIRONMENT INTERACTION DROUGHT STATISTICAL METHODS AGRICULTURAL SCIENCES AND BIOTECHNOLOGY GENOTYPE ENVIRONMENT INTERACTION HERITABILITY MANAGEMENT CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA
Genomic selection (GS) has become a tool for selecting candidates in plant and animal breeding programs. In the case of quantitative traits, it is common to assume that the distribution of the response variable can be approximated by a normal distribution. However, it is known that the selection process leads to skewed distributions. There is vast statistical literature on skewed distributions, but the skew normal distribution is of particular interest in this research. This distribution includes a third parameter that drives the skewness, so that it generalizes the normal distribution. We propose an extension of the Bayesian whole-genome regression to skew normal distribution data in the context of GS applications, where usually the number of predictors vastly exceeds the sample size. However, it can also be applied when the number of predictors is smaller than the sample size. We used a stochastic representation of a skew normal random variable, which allows the implementation of standard Markov Chain Monte Carlo (MCMC) techniques to efficiently fit the proposed model. The predictive ability and goodness of fit of the proposed model were evaluated using simulated and real data, and the results were compared to those obtained by the Bayesian Ridge Regression model. Results indicate that the proposed model has a better fit and is as good as the conventional Bayesian Ridge Regression model for prediction, based on the DIC criterion and cross-validation, respectively. A computing program coded in the R statistical package and C programming language to fit the proposed model is available as supplementary material.
Genomics Bayesian theory Genomic Selection Data Augmentation Assymetric Distributions GBLUP Ridge Regression GenPred Shared Data Resources BAYESIAN THEORY REGRESSION ANALYSIS STATISTICAL METHODS CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA
High-throughput phenotyping (HTP) technologies can produce data on thousands of phenotypes per unit being monitored. These data can be used to breed for economically and environmentally relevant traits (e.g., drought tolerance); however, incorporating high-dimensional phenotypes in genetic analyses and in breeding schemes poses important statistical and computational challenges. To address this problem, we developed regularized selection indices; the methodology integrates techniques commonly used in high-dimensional phenotypic regressions (including penalization and rank-reduction approaches) into the selection index (SI) framework. Using extensive data from CIMMYT?s (International Maize and Wheat Improvement Center) wheat breeding program we show that regularized SIs derived from hyper-spectral data offer consistently higher accuracy for grain yield than those achieved by standard SIs, and by vegetation indices commonly used to predict agronomic traits. Regularized SIs offer an effective approach to leverage HTP data that is routinely generated in agriculture; the methodology can also be used to conduct genetic studies using high-dimensional phenotypes that are often collected in humans and model organisms including body images and whole-genome gene expression profiles.
Modern agriculture uses hyperspectral cameras with hundreds of reflectance data at discrete narrow bands measured in several environments. Recently, Montesinos-López et al. (Plant Methods 13(4):1–23, 2017a. https://doi.org/10.1186/s13007-016-0154-2; Plant Methods 13(62):1–29, 2017b. https://doi.org/10.1186/s13007-017-0212-4) proposed using functional regression analysis (as functional data analyses) to help reduce the dimensionality of the bands and thus decrease the computational cost. The purpose of this paper is to discuss the advantages and disadvantages that functional regression analysis offers when analyzing hyperspectral image data. We provide a brief review of functional regression analysis and examples that illustrate the methodology. We highlight critical elements of model specification: (i) type and number of basis functions, (ii) the degree of the polynomial, and (iii) the methods used to estimate regression coefficients. We also show how functional data analyses can be integrated into Bayesian models. Finally, we include an in-depth discussion of the challenges and opportunities presented by functional regression analysis.
Agriculture Bayesian theory Phenotyping Hyperspectral Data Functional Regression Analyses Bayesian Functional Regression Functional Data Bayesian Ridge Regression DATA ANALYSIS REGRESSION ANALYSIS STATISTICAL METHODS BAYESIAN THEORY CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA
Gregorio Alvarado (2019)
Ali Akhter (2018)