Evaluation of methods and marker Systems in Genomic Selection of oil palm (Elaeis guineensis Jacq.)

Barrera

Rev. Fac. Nac. Agron. Medellín

et al. 2018

Fue empleado el método REML/BLUP para estimar los parámetros genéticos, y seleccionar los mejores individuos provenientes de una población de hermanos germanos de Dura x Dura, a partir de un análisis de correlación entre caracteres, realizando un agrupamiento de familias por disimilaridad multivariada y determinación del número de medidas repetidas necesarias para la selección de las características (número y peso medio de racimos). Fueron evaluadas 24 familias procedentes de tres ensayos del banco de germoplasma de la estación experimental Santo Domingo del INIAP en Ecuador. La evaluación fue realizada en un periodo de cinco años, empleando un diseño en bloques al azar, con doce plantas por parcela y cuatro repeticiones. La variabilidad de la población en relación a las características evaluadas y heredabilidad de individuos dentro del bloque, similar a la encontrada dentro de familias en las parcelas. La ganancia genética de las 10 plantas seleccionadas representa un 43% superior a la media general. La correlación fue baja y negativa para número de racimos y peso medio de racimos. Con base en el agrupamiento de Tocher se obtuvieron seis grupos, donde el grupo IV agrupa las familias seleccionadas por el Rank-medio multivariado (3A, 5C y 7B). Se puede concluir que las estimativas obtenidas por el BLUP, estimulan la continuidad del programa de mejoramiento genético de racimos, con posibilidad de maximizar las ganancias genéticas en generaciones futuras.

Section: Resultsmentioning

confidence: 99%

“…However, according to Ramalho and Lambert (2004), Kwong et al (2017) the success of intrapopulation improvement depends on the crossing of plants of the best but most divergent families. Thus, the 27 full-sib families were grouped by dissimilarity (Table 6), based on the standardized mean Euclidean distance.…”

Section: Resultsmentioning

confidence: 99%

Estimates of parameters, prediction and selection of an oil palm population in Ecuador

Barrera

Rev. Fac. Nac. Agron. Medellín

et al. 2018

“…One of the studies included in this group explains the results of various marker systems and modelling procedures for the implementation of genome selection (GS) in the introgressive hybridization of the dura family. The discussed dura family "Deli x Nigerian," the resultant of a "Deli dura x Nigerian dura," was considered in the analysis [41]. The observed family is a valuable source of new palm trees with higher oil yields and improved bunch characteristics.…”

Section: Multipurpose Classificationmentioning

confidence: 99%

Oil Palm and Machine Learning: Reviewing One Decade of Ideas, Innovations, Applications, and Gaps

et al. 2021

Machine learning (ML) offers new technologies in the precision agriculture domain with its intelligent algorithms and strong computation. Oil palm is one of the rich crops that is also emerging with modern technologies to meet global sustainability standards. This article presents a comprehensive review of research dedicated to the application of ML in the oil palm agricultural industry over the last decade (2011–2020). A systematic review was structured to answer seven predefined research questions by analysing 61 papers after applying exclusion criteria. The works analysed were categorized into two main groups: (1) regression analysis used to predict fruit yield, harvest time, oil yield, and seasonal impacts and (2) classification techniques to classify trees, fruit, disease levels, canopy, and land. Based on defined research questions, investigation of the reviewed literature included yearly distribution and geographical distribution of articles, highly adopted algorithms, input data, used features, and model performance evaluation criteria. Detailed quantitative–qualitative investigations have revealed that ML is still underutilised for predictive analysis of oil palm. However, smart systems integrated with machine vision and artificial intelligence are evolving to reform oil palm agri-business. This article offers an opportunity to understand the significance of ML in the oil palm agricultural industry and provides a roadmap for future research in this domain.

“…The main attraction of GS for perennial crops may be that it can accelerate breeding cycles, thereby increasing the gain per unit time and reducing field trial costs [3,23,24]. Sweet cherry [25], peach [26], oil palm [27][28][29], citrus [30], apple [31,32], and pear [33,34] researchers have evaluated the use of GS to increase genetic gain in their breeding programs. A recent study in Japanese chestnut [35] achieved high prediction accuracies for harvest date (r = 0.84) and insect infestation (0.60), though yield was not studied.…”

Section: Introductionmentioning

confidence: 99%

Genomic selection and genetic gain for nut yield in an Australian macadamia breeding population

et al. 2021

Background Improving yield prediction and selection efficiency is critical for tree breeding. This is vital for macadamia trees with the time from crossing to production of new cultivars being almost a quarter of a century. Genomic selection (GS) is a useful tool in plant breeding, particularly with perennial trees, contributing to an increased rate of genetic gain and reducing the length of the breeding cycle. We investigated the potential of using GS methods to increase genetic gain and accelerate selection efficiency in the Australian macadamia breeding program with comparison to traditional breeding methods. This study evaluated the prediction accuracy of GS in a macadamia breeding population of 295 full-sib progeny from 32 families (29 parents, reciprocals combined), along with a subset of parents. Historical yield data for tree ages 5 to 8 years were used in the study, along with a set of 4113 SNP markers. The traits of focus were average nut yield from tree ages 5 to 8 years and yield stability, measured as the standard deviation of yield over these 4 years. GBLUP GS models were used to obtain genomic estimated breeding values for each genotype, with a five-fold cross-validation method and two techniques: prediction across related populations and prediction across unrelated populations. Results Narrow-sense heritability of yield and yield stability was low (h2 = 0.30 and 0.04, respectively). Prediction accuracy for yield was 0.57 for predictions across related populations and 0.14 when predicted across unrelated populations. Accuracy of prediction of yield stability was high (r = 0.79) for predictions across related populations. Predicted genetic gain of yield using GS in related populations was 474 g/year, more than double that of traditional breeding methods (226 g/year), due to the halving of generation length from 8 to 4 years. Conclusions The results of this study indicate that the incorporation of GS for yield into the Australian macadamia breeding program may accelerate genetic gain due to reduction in generation length, though the cost of genotyping appears to be a constraint at present.