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A nivel internacional, estudios como el Informe Horizon (Educase, 2021), sostiene que los recursos educativos abiertos representarán —en el mediano plazo— una alternativa educativa que posibilitará a los estudiantes el acceso a alternativas académicas en las que, inclusive, podrán aprender sin necesidad de contar con la presencia de un profesor. Sin embargo, en este mismo documento se afirma que más que pensar en la desaparición de los docentes, estos deberán transformar su labor y enfocarla a la atención de las demandas y necesidades de los estudiantes, así como aspectos relacionados con su salud mental y su estado emocional. Asimismo, es preciso plantear que ante situaciones extraordinarias como la pandemia derivada del Covid-19, estudiantes y docentes han experimentado una serie de dificultades y barreras que afectaron la manera en la que se desarrolla el vínculo de comunicación entre estos, situación que ha representado un reto sin igual para mantener el flujo de información de forma efectiva. En ese mismo contexto, aunque los estudiantes valoran de forma positiva la manera en la que los docentes han hecho frente a esta contingencia, encuentran importantes limitantes para desarrollar el ambiente de aprendizaje adecuado para su desarrollo. En esta investigación se analiza la perspectiva de los estudiantes sobre la forma en la que los profesores aplican diversas herramientas para construir relaciones estrechas con ellos; asimismo, se evalúan las estrategias que los profesores aplican para crear el ambiente óptimo para el aprendizaje de sus alumnos y facilitar su aprovechamiento.

The plant net genetic merit is a linear combination of trait breeding values weighted by its respective economic weights whereas a linear selection index (LSI) is a linear combination of phenotypic or genomic estimated breeding values (GEBV) which is used to predict the net genetic merit of candidates for selection. Because economic values are difficult to assign, some authors developed economic weight-free LSI. The economic weights LSI are associated with linear regression theory, while the economic weight-free LSI is associated with canonical correlation theory. Both LSI can be unconstrained or constrained. Constrained LSI imposes restrictions on the expected genetic gain per trait to make some traits change their mean values based on a predetermined level, while the rest of the traits change their values without restriction. This work is geared towards plant breeders and researchers interested in LSI theory and practice in the context of wheat breeding. We provide the phenotypic and genomic unconstrained and constrained LSI, which together cover the theoretical and practical cornerstone of the single-stage LSI theory in plant breeding. Our main goal is to offer researchers a starting point for understanding the core tenets of LSI theory in plant selection.

Chromosome doubling is an important step in the production of maize doubled haploid (DH) lines to induce fertility in the male and female reproductive organs of haploid plants. Chromosomal doubling is routinely accomplished by treating haploid seedlings with mitosis-inhibiting chemicals. However, chromosomal doubling involves several labor-intensive steps and toxic chemicals. Spontaneous chromosomal doubling without any chemical treatments occurs at high frequency in haploids from a few maize genotypes. This study focused on elucidating the genetic components of two traits important for using spontaneous doubling in maize-breeding programs, namely, haploid male fertility (HMF) and haploid fertility (HF). In two different sets of diallel crosses, haploids were derived and assessed for HMF and HF in two environments in replicated trials. The results revealed significant genotypic variations for both traits. The general combining ability (GCA) and specific combining (SCA) were significant for both traits. Significant and positive GCA effects of up to 14% and 9% were found for HMF and HF, respectively. No significant reciprocal effects and genotype-by-environment (G&times, E) interactions were found for HF in both experiments, but HMF showed significant effects for both in one of the experiments. The GCA effects were more important than the SCA effects for HMF and HF across environments, implying that selection could facilitate their improvement. The high correlations between F1-hybrid performance and mid-parent values, as well as that between F1-hybrid performance and GCA effects, also supports the assumption that these traits are controlled by a few genes. SCA effects also played a role, especially when lines with low spontaneous doubling were used as parents. Overall, spontaneous doubling can be introgressed and improved in elite germplasm with selection, and it has the potential to be employed in DH pipelines.

Since 1984, the ‘Chilero’ spring wheat line developed by CIMMYT has proven to be highly resistant to leaf rust and stripe rust. Amid efforts to understand the basis of resistance of this line, a recombinant inbred line (RIL) population derived from a cross between Avocet and Chilero was studied. The parents and RILs were characterized in field trials for leaf rust and stripe rust in three locations in Mexico between 2012 and 2015 and genotyped with DArT-array, DArT-GBS, and SSR markers. A total of 6,168 polymorphic markers were used to construct genetic linkage maps. Inclusive composite interval mapping detected four colocated resistance loci to both rust diseases and two stripe rust resistant loci in the Avocet × Chilero population. Among these, the quantitative trait locus (QTL) on chromosome 1BL was identified as a pleotropic adult plant resistance gene Lr46/Yr29, whereas QLr.cim-5DS/QYr.cim-5DS was a newly discovered colocated resistance locus to both rust diseases in Chilero. Additionally, one new stripe rust resistance locus on chromosome 7BL was mapped in the current population. Avocet also contributed two minor colocated resistance QTLs situated on chromosomes 1DL and 4BS. The flanking SNP markers can be converted to breeder friendly Kompetitive Allele Specific PCR (KASP) markers for wheat breeding programs.

Permanent raised beds (PB) are a conservation agriculture option for irrigated conditions that can improve soil quality, increase soil moisture conservation and stabilize yields compared to conventional furrow irrigation. In irrigated wheat (Triticum sp.) production, wet sowing (i.e. applying irrigation before sowing) is most widely used. It allows pre-sowing weed control but reduces sowing time flexibility. Dry sowing, i.e. applying irrigation after sowing, reduces options for weed control but improves water use efficiency and sowing time flexibility. This study evaluated the performance of durum wheat (Triticum durum L.) under conventionally tilled (CTB) and PB with wet and dry sowing in northwestern Mexico. In those four tillage-sowing irrigation environments (ENV), five nitrogen (N) fertilization treatments were tested. Plant stand, grain yield, and grain quality were measured for ten years and fertilizer-based N use efficiency indices were assessed in three years. Plant stand, wheat yield and quality were significantly affected by ENV. The lowest plant stand and yield were found in PB-Dry sowing. On average, only 54 plants m-2 emerged in PB-Dry whereas 159 plants m-2 emerged in CTB-Wet. Plant stand showed high yearly fluctuations, with plant stand in dry sowing favored by lower reference evapotranspiration, with CTB-Dry favored more by high minimum temperature and PB-Dry by high maximum temperature. Yield ranged between 4.20 and 7.94 t ha-1. Yield in PB-Dry was on average 0.35 to 0.50 t ha-1 lower than in the remaining ENV, but positive interactions between year and dry sowing systems were associated with high minimum temperatures at germination and tillering. N fertilization management affected wheat quality, but not wheat yield, possibly due to high levels of soil mineral N available at sowing that were not measured in this study. Split application of N increased grain N content compared to basal N application. Research should address reduced plant stands with dry sowing in conservation agriculture to find management options that improve wheat emergence. Further efforts to optimize N fertilizer management in PB are required to improve grain quality components.

Genomic selection predicts the genomic estimated breeding values (GEBVs) of individuals not previously phenotyped. Several studies have investigated the accuracy of genomic predictions in maize but there is little empirical evidence on the practical performance of lines selected based on phenotype in comparison with those selected solely on GEBVs in advanced testcross yield trials. The main objectives of this study were to (1) empirically compare the performance of tropical maize hybrids selected through phenotypic selection (PS) and genomic selection (GS) under well-watered (WW) and managed drought stress (WS) conditions in Kenya, and (2) compare the cost-benefit analysis of GS and PS. For this study, we used two experimental maize data sets (stage I and stage II yield trials). The stage I data set consisted of 1492 doubled haploid (DH) lines genotyped with rAmpSeq SNPs. A subset of these lines (855) representing various DH populations within the stage I cohort was crossed with an individual single-cross tester chosen to complement each population. These testcross hybrids were evaluated in replicated trials under WW and WS conditions for grain yield and other agronomic traits, while the remaining 637 DH lines were predicted using the 855 lines as a training set. The second data set (stage II) consists of 348 DH lines from the first data set. Among these 348 best DH lines, 172 lines selected were solely based on GEBVs, and 176 lines were selected based on phenotypic performance. Each of the 348 DH lines were crossed with three common testers from complementary heterotic groups, and the resulting 1042 testcross hybrids and six commercial checks were evaluated in four to five WW locations and one WS condition in Kenya. For stage I trials, the cross-validated prediction accuracy for grain yield was 0.67 and 0.65 under WW and WS conditions, respectively. We found similar responses to selection using PS and GS for grain yield other agronomic traits under WW and WS conditions. The top 15% of hybrids advanced through GS and PS gave 21%-23% higher grain yield under WW and 51%-52% more grain yield under WS than the mean of the checks. The GS reduced the cost by 32% over the PS with similar selection gains. We concluded that the use of GS for yield under WW and WS conditions in maize can produce selection candidates with similar performance as those generated from conventional PS, but at a lower cost, and therefore, should be incorporated into maize breeding pipelines to increase breeding program efficiency.

Several studies have shown differences in the abilities of maize genotypes to facilitate or impede Azospirillum brasilense colonization and to receive benefits from this association. Hence, our aim was to study the genetic control, heterosis effect and the prediction accuracy of the shoot and root traits of maize in response to A. brasilense. For that, we evaluated 118 hybrids under two contrasting scenarios: i) N stress (control) and ii) N stress plus A. brasilense inoculation. The diallel analyses were performed using mixed model equations, and the genomic prediction models accounted for the general and specific combining ability (GCA and SCA, respectively) and the presence or not of G×E effects. In addition, the genomic models were fitted considering parametric (G-BLUP) and semi-parametric (RKHS) kernels. The genotypes showed significant inoculation effect for five root traits, and the GCA and SCA were significant for both. The GCA in the inoculated treatment presented a greater magnitude than the control, whereas the opposite was observed for SCA. Heterosis was weakly influenced by the inoculation, and the heterozygosity and N status in the plant can have a role in the benefits that can be obtained from this Plant Growth-Promoting Bacteria (PGPB). Prediction accuracies for N stress plus A. brasilense ranged from 0.42 to 0.78, depending on the scenario and trait, and were higher, in most cases, than the non-inoculated treatment. Finally, our findings provide an understanding of the quantitative variation of maize responsiveness to A. brasilense and important insights to be applied in maize breeding aiming the development of superior hybrids for this association.

A genomic selection index (GSI) is a linear combination of genomic estimated breeding values that uses genomic markers to predict the net genetic merit and select parents from a nonphenotyped testing population. Some authors have proposed a GSI; however, they have not used simulated or real data to validate the GSI theory and have not explained how to estimate the GSI selection response and the GSI expected genetic gain per selection cycle for the unobserved traits after the first selection cycle to obtain information about the genetic gains in each subsequent selection cycle. In this paper, we develop the theory of a GSI and apply it to two simulated and four real data sets with four traits. Also, we numerically compare its efficiency with that of the phenotypic selection index (PSI) by using the ratio of the GSI response over the PSI response, and the PSI and GSI expected genetic gain per selection cycle for observed and unobserved traits, respectively. In addition, we used the Technow inequality to compare GSI vs. PSI efficiency. Results from the simulated data were confirmed by the real data, indicating that GSI was more efficient than PSI per unit of time.

When reporting on well-conducted research, a characteristic of a complete and proper manuscript is one that includes analyses and interpretations of all interactions. Our purpose is to show how to analyze and interpret interactions in agronomy and breeding research by means of three data sets comprising random and fixed effects. Experiment 1 tested wheat (Triticum aestivum L.) at two N and four P fertilizer rates in two soil types. For this data set, we used a fixed-effect linear model with the highest order (three-way) interaction considered first and then worked down through the lower order interactions and main effects to illustrate the importance of interactions in data analysis. Experiment 2 evaluated maize (Zea mays L.) hybrids under four rates of N for 3 yr. For this data set, we used a linear mixed model and partitioned the four N rates into orthogonal polynomials. Experiment 3 evaluated genotypes in six environments where the objective was to show how to study genotype × environment interactions. Researchers must analyze all interactions, determine if they are due to changes in rank (crossover) or only to changes in scale, and then judge whether reporting on significant main effects or interactions would best explain the biological responses in their experiments. In an experiment with more than one factor, complete and correct analysis of interactions is essential for reporting and interpreting the research properly.

Understanding the factors that define a given interaction is important in agricultural, agronomic, and plant breeding research, where agronomic treatments or genotypes are evaluated under several environmental conditions and where interactions usually complicate a researcher’s decisions. We give examples of how interactions, in common agricultural experiments, can be examined and studied to make use of the rich information available on the interaction term of the model. Examples with different levels of interaction complexity are used to illustrate how to analyze and interpret interactions and how interaction components can be partitioned into comparisons with sensible biological interpretations. It will offer researchers a greater understanding of how to exploit interaction information beyond the standard statistical tests performed in the usual analysis of variance. Simple SAS codes for performing standard interaction contrasts and defining interaction covariables are provided.

Multienvironment trials (METs) enable the evaluation of the same genotypes under a variety of environments and management conditions. We present META (Multi Environment Trial Analysis), a suite of 33 SAS programs that analyze METs with complete or incomplete block designs, with or without adjustment by a covariate. Th e entire program is run through a graphical user interface. Th e program can produce boxplots or histograms for all traits, as well as univariate statistics. It also calculates best linear unbiased estimators (BLUEs) and best linear unbiased predictors (BLUPs) for the main response variable and BLUEs for all other traits. For all traits, it calculates variance components by restricted maximum likelihood, least signifi cant diff erence, coeffi cient of variation, and broad-sense heritability using PROC MIXED. Th e program can analyze each location separately, combine the analysis by management conditions, or combine all locations. Th e fl exibility and simplicity of use of this program makes it a valuable tool for analyzing METs in breeding and agronomy. Th e META program can be used by any researcher who knows only a few fundamental principles of SAS.

Genetic biofortification to improve zinc (Zn) and iron (Fe) concentrations in bread wheat ( Triticum aestivum L.) could reduce micronutrient malnutrition-related problems in the developing world. A breeding program on wheat was started to enhance Zn and Fe concentrations and other essential traits needed in a successful commercial variety. The first set of advanced lines derived from crosses of high yielding wheats with genetic resources possessing high Zn and Fe such as Triticum spelta , landraces and synthetic wheat based on Triticum dicoccon were tested at nine locations in South Asia and Mexico for Zn and Fe concentration, grain yield and other traits. Analyses of variance across locations revealed significant genotypic, environmental and genotype × environment (G × E) effects for grain Zn and Fe concentrations and grain yield. Variances associated with environmental effects were larger than the genotypic and G × E effects for all three traits, suggesting that environmental effects have relatively greater influence. Although G × E interaction was significant, high heritabilities were observed for Zn and Fe concentrations at individual sites and across environments, reflecting non-crossover type of interaction. This trend was confirmed by the high genetic correlations between locations that showed similar ranking of entries across locations, indicating that it is possible to select the best adapted entries with high Zn and Fe concentration. Pooled data across locations showed increments of 28% and 25% over the checks for Zn and Fe. A considerable number of entries exceeded intermediate to full breeding target Zn concentrations, indicating that it is possible to develop Zn-biofortified varieties with competitive yields and other farmer preferred agronomic traits. The positive and moderately high correlation between Zn and Fe concentration suggest good prospects of simultaneous improvement for both micronutrients.

For in vivo production of doubled haploid (DH) lines in maize, the rate of haploid induction is of crucial importance. Maternal haploid induction depends primarily on the inducer used as a pollinator. However, the source germplasm used as a maternal parent and the environmental conditions for induction may also influence haploid induction and these aspects have not been examined in tropical maize so far. The objectives of our study were to (i) monitor the variation for haploid induction rate (HIR) among diverse source germplasm in tropical maize, (ii) determine the relative importance of general (GCA) and specific (SCA) combining abilities for HIR, and (iii) investigate the influence of summer and winter seasons and genotype × season interactions on this trait. Ten inbreds were mated in a half diallel design. The resulting 45 F1 single crosses were pollinated with the haploid inducer hybrid RWS × UH400 during the summer 2008 and winter 2009 seasons in a lowland tropical environment in Mexico. HIR of the single crosses averaged over seasons ranged from 2.90 to 9.66% with an overall mean of 6.74%. Mean HIR was significantly (P

Great variation for many traits seems to be available in Capsicum genetic resources but yet to be fully used by plant breeders. Adequate characterization of genebank accessions is needed to facilitate the utilization of germplasm by end-users. The aim of this research was to use multivariate techniques with both qualitative and quantitative descriptors in the five domesticated species of Capsicum for grouping them after assessing inter- and intra-specific variation. Key qualitative descriptors (seed color, corolla color and spot, calyx constriction, numbers of flowers per node and filament color) served for assigning most accessions to their respective species, whereas intra-specific multivariate diversity was better assessed by quantitative descriptors such as fruit length/width ratio, numbers of days to flowering, leaf width, and anther, filament and pedicel length. The modified location model an adequate method for classifying Capsicum accessions using quantitative descriptors.

Wheat breeders rarely apply population improvement schemes or select parental sources according to combining ability and heterotic patterns. They rely on pedigree selection methods for breeding new cultivars. This experiment was undertaken to assess the advantages of using diallel crosses to define combining ability and understand heterosis in a broad-based wheat-breeding population across different environments affected by yellow rust. Sixty-four genotypes derived from a full diallel mating scheme were assessed for grain yield in two contrasting growing seasons at two locations for two consecutive years. Parental genotypes showed significant combining ability for grain yield that was affected by yellow rust and genotype-by-environment (GE) interactions, both of which affected heterosis for grain yield. Significant GE interactions suggested that decentralized selection for specific environments could maximize the use of this wheat germplasm. Cultivar effects and specific heterosis were the most important factors influencing grain yield. Some crosses capitalized on additive genetic variation for grain yield. This research shows the power of available quantitative breeding tools to help breeders choose parental sources in a population improvement programme.

Maize (Zea mays L.) landraces are an important source for the genetic improvement of the crop. Classification of genetic resources requires both appropriate descriptors as well as sound numerical and statistical methods. This research was undertaken to assess the use of six internal ear traits for classifying a set of four related Peruvian highland maize races comprising a total of 24 accessions. Several accessions of the four races were included in field trials planted in Peru’s inter-Andean valley. The trials were sown on two planting dates (normal and late) in two consecutive years. Variance components among races and among accessions with races were used to estimate broad-sense heritability and repeatability for each internal ear trait. The Ward-Modified Location model (MLM) and canonical analysis were undertaken for clustering the 24 accessions. For most traits, the variance components among races were more important than the accession within races, and the variance components for race × environment or accession within race × environment were, for the most part, negligible. Results suggest that internal ear traits such as cob and pith diameter, as well as cupule sizes and glume texture, are among the most appropriate for clustering these materials in their respective races. The numerical classification maintained the structure of the more differentiated races but identified two distinct accessions in one race and separated them into a homogeneous group. The Ward-MLM numerical method produced groups with distinct characteristics in terms of internal ear variables.

Spot blotch is an important disease of wheat (Triticum aestivum L.) in South Asia. Division of test sites for this disease into homogenous subregions is expected to contribute to more efficient evaluation and better differentiation of cultivars. Data from a collaborative regional program of South Asia conducted by CIMMYT were analyzed to group testing sites into relatively homogenous subregions for spot blotch area under the disease progress curve (AUDPC). Five-year data of eight locations from Eastern Gangetic Plains Nursery (EGPSN) and five locations of the Eastern Gangetic Plains Yield Trial (EGPYT) conducted in three countries (India, Nepal, and Bangladesh) of South Asia were used. A hierarchical cluster analysis was used to group locations on the basis of genotype x location interaction effects for spot blotch AUDPC. Cluster analysis divided South Asia into two broad regions and four subregions. This classification was not entirely consistent with the geographic distribution of locations, but clusters mostly followed general geographic-climatic locations. The locations Varanasi (India) and Bhairahawa (Nepal) were identified as the most suitable sites for evaluation of spot blotch, followed by Rampur (Nepal). The major determinant for the clustering was mean temperature. The results suggest that the major wheat region of South Asia can be divided into subregions, which may reduce the cost of resistance evaluation and aid in developing wheat with resistance to this disease.

Spring sown bread wheat is grown at high latitudes in Europe, Asia and North America. However, it is not clear what the associations are among environments, particularly in Asia and North America, and whether or not cultivars developed in one region may adapt in another. A yield trial comprised of cultivars developed in northern Kazakhstan, western Siberia, the Canadian Prairies, northern USA, northeastern China and broadly adapted genotypes bred by CIMMYT in Mexico was planted in all the above mentioned environments in 2002–2004. In general, cultivars performed best within the regions they were developed. However, cultivars developed in northern Kazakhstan/western Siberia were the most broadly adapted at high latitudes; they were not significantly different for grain yield from the locally developed cultivars in both China and Canada. Stronger photoperiod response, greater plant height and larger seed weight appeared to be key adaptive features of these materials. At lower latitudes, the Kazakh/Siberian cultivars were significantly lower yielding than all other materials. When low latitude Mexican sites were removed from the analysis, the Chinese locations tended to associate, whereas most Canadian and Kazak/Siberian locations were negatively associated with those from China. SSR analysis of the cultivars from each region split the materials into two general groups, one based on North American cultivars and one comprised of Kazakh/Siberian and Chinese cultivars. Lines developed in Mexico were spread across these two groupings. Evidence suggests that considerable scope exists to improve bread wheat adaptation at high latitudes globally through intercrossing materials originating from Asia and North America.

Selection indices are estimates of the net genetic merit of the individual candidates for selection and are calculated based on phenotyping and molecular marker information collected on plants under selection in a breeding program. They reflect the breeding value of the plants and help breeders to choose the best ones for next generation. Rindsel is an R package that calculates phenotypic and molecular selection indices.

Linear selection indexes of zooplankton prey taxa (Li) were calculated for 250 hatchery-reared Florida largemouth bass (Micropterus salmoides floridanus). Fish began feeding at a mean total length of 6.7 mm and exhibited positive selection for copepod nauplii. Sequentially, selection shifted to copepod adults for 7.5-10.5-mm fish, to adult copepods and cladocerans for 10.9-20.4-mm fish, and, lastly, to predominantly immature insects for 22.8-mm fish. Although selection values were negative for 7.5-10.5-mm fish feeding on cladocerans, 30% more cladocerans than copepod adults were consumed. No rotifers were found in gut contents of fish even though they were present in high numbers in ponds.

El desarrollo de un sistema integrado de libros de campo para mejoramiento (IBFieldbook, por sus siglas en ingles) para diferentes cultivos incluye la generacion, manejo y analisis de grandes volumenes de datos. El manejo de integracion de la informacion fenotipica, ambiente y pedigri para analisis de datos requiere de un programa computacional ("software") apropiado y exitoso, que facilite a los mejoradores, tecnicos e investigadores la administracion de la informacion recolectada en el campo de manera facil, eficiente e interactiva. Los usuarios del programa tambien necesitan metodos para intercambiar informacion con diferentes dispositivos usados para almacenar informacion en el campo. La informacion recolectada necesita ademas ser analizada adentro y afuera de la aplicacion, y generar reportes para el mejoramiento del germoplasma

Background Modern agriculture uses hyperspectral cameras to obtain hundreds of reflectance data measured at discrete narrow bands to cover the whole visible light spectrum and part of the infrared and ultraviolet light spectra, depending on the camera. This information is used to construct vegetation indices (VI) (e.g., green normalized difference vegetation index or GNDVI, simple ratio or SRa, etc.) which are used for the prediction of primary traits (e.g., biomass). However, these indices only use some bands and are cultivar-specific; therefore they lose considerable information and are not robust for all cultivars. Results This study proposes models that use all available bands as predictors to increase prediction accuracy; we compared these approaches with eight conventional vegetation indexes (VIs) constructed using only some bands. The data set we used comes from CIMMYT’s global wheat program and comprises 1170 genotypes evaluated for grain yield (ton/ha) in five environments (Drought, Irrigated, EarlyHeat, Melgas and Reduced Irrigated); the reflectance data were measured in 250 discrete narrow bands ranging between 392 and 851 nm. The proposed models for the simultaneous analysis of all the bands were ordinal least square (OLS), Bayes B, principal components with Bayes B, functional B-spline, functional Fourier and functional partial least square. The results of these models were compared with the OLS performed using as predictors each of the eight VIs individually and combined. Conclusions We found that using all bands simultaneously increased prediction accuracy more than using VI alone. The Splines and Fourier models had the best prediction accuracy for each of the nine time-points under study. Combining image data collected at different time-points led to a small increase in prediction accuracy relative to models that use data from a single time-point. Also, using bands with heritabilities larger than 0.5 only in Drought as predictor variables showed improvements in prediction accuracy.