Genetic Variation in Drought-Tolerance Traits and Their Relationships to Growth in Pinus radiata D. Don Under Water Stress

The selection of drought-tolerant genotypes is globally recognized as an effective strategy to maintain the growth and survival of commercial tree species exposed to future drought periods. New genomic selection tools that reduce the time of progeny trials are required to substitute traditional tree breeding programs. We investigated the genetic variation of water stress tolerance in New Zealand-grown Pinus radiata D. Don using 622 commercially-used genotypes from 63 families. We used quantitative pedigree-based (Genomic Best Linear Unbiased Prediction or ABLUP) and genomic-based (Genomic Best Linear Unbiased Prediction or GBLUP) approaches to examine the heritability estimates associated with water stress tolerance in P. radiata. Tree seedling growth traits, foliar carbon isotope composition (δ13C), and dark-adapted chlorophyll fluorescence (Y) were monitored before, during and after 10 months of water stress. Height growth showed a constant and moderate heritability level, while the heritability estimate for diameter growth and δ13C decreased with water stress. In contrast, chlorophyll fluorescence exhibited low heritability after 5 and 10 months of water stress. The GBLUP approach provided less breeding value accuracy than ABLUP, however, the relative selection efficiency of GBLUP was greater compared with ABLUP selection techniques. Although there was no significant relationship directly between δ13C and Y, the genetic correlations were significant and stronger for GBLUP. The positive genetic correlations between δ13C and tree biomass traits under water stress indicated that intraspecific variation in δ13C was likely driven by differences in the genotype’s photosynthetic capacity. The results show that foliar δ13C can predict P. radiata genotype tolerance to water stress using ABLUP and GBLUP approaches and that such approaches can provide a faster screening and selection of drought-tolerant genotypes for forestry breeding programs.

Genetic Parameters and Selection in Full-Sib Families of Tall Fescue Using Best Linear Unbiased Prediction (BLUP) Analysis of Animal Breeding and Genetic

Background: Better understanding of genetic structure of economic traits is crucial for identification and selection of superior genotypes in specific breeding programs. Best linear unbiased prediction (BLUP) is the most efficient method in this regards, which is poorly used in forage plant breeding. The present study aimed to assess genetic variation, estimate genetic parameters, and predict breeding values of five essential traits in full sib families (recognized by EST-SSR markers) of tall fescue using REML/BLUP procedure. Method: Forty-two full-sib families of tall fescue (included of 120 individual genotypes), recognized by EST-SSR markers’ along with twenty-one their corresponding parental genotypes were assessed for biomass production and agro-morphological traits at three harvests (spring, summer, and autumn) in the field during 4 years (2017-2020). Results: Considerable genotypic variability was observed for all traits. Low narrow-sense heritability (h2n) for dry forage yield (DFY) at three harvest indicates that non-additive gene actions may play an important role in the inheritance of this trait. Higher h2n of yield related traits and flowering time and also significant genetic correlation of these traits with forage yield, suggests that selection based on these traits via developing an index may lead to indirect genetic improvement of DFY. Conclusion: Our results showed the adequacy of REML/BLUP procedure for identification and selection of preferable parental genotypes and progenies with higher breeding values for future breeding programs such as variety development in tall fescue. Parental genotypes 21M, 1M, and 20L were identified as superior and stable genotypes and could also produce the best hybrid combinations when they were mostly used as maternal parent.

​Best Linear Unbiased Prediction with Additive Main and Multiplicative Interaction for Stability Analysis of Barley Genotypes Evaluated in Coordinated Program

Background: Additive main and multiplicative interaction (AMMI) analysis had been exploited for multi environment trials for most of the crops. Usage of the best linear unbiased prediction (BLUP), along with AMMI tools, of the genotypes would improve the estimation of interaction effects. Methods: AMMI based measures of adaptability have been enriched with the incorporation of BLUP of genotypes by new Superiority index that allowed variable weights for stability and yield of genotypes. Result: Stability measure weighted average of absolute scores (WAASB) based on all significant interaction principal components ranked suitability of KB1754, RD3000, NDB1445 genotypes. Superiority index while weighting 0.65 and 0.35 for mean yield and stability arranged DWRB201, NDB1445, RD2552 as of stable high yield performance of barley genotypes. Corrected measure Modified AMMI Stability Value (MASV1) found RD2552, DWRB201, KB1762 and Modified AMMI Stability Value (MASV) ranked DWRB201, RD2552, KB1762. ASTAB measure achieved the desirable lower values for DWRB201 DWRB207, HUB268 genotypes. Biplot graphical analysis based on 60.7% of variation of the stability measures observed MASV1, ASTAB (AMMI based stability parameter), EV(Averages of the squared eigenvector values), SIPC (Sums of the absolute value of the IPC scores), Za (Absolute value of the relative contribution of IPCs to the interaction), W3, WAASB and MASV had been clubbed together. For the second year lower value of WAASB measure had observed for RD3016, KB1815 HUB273. Ranking of genotypes as per Superiority index found RD3017, RD2907, HUB274 as of stable high yield performance. Genotypes RD3017, RD2907 and NDB1173 pointed out by MASV1 while RD3017, RD2907, NDB1173 identified by MASV as the genotypes of choice. RD3017 NDB1173, RD2907 genotypes were selected as per values of ASTAB measure. Total of 71.8% of variation of the considered measures in biplot analysis expressed larger cluster comprised of AMMI based measures and a separate cluster of Superiority indexes as per mean, Geometric Adaptability Index (GAI) and HMGV also observed.

Genomic studies with pre-selected markers reveal dominance effects influencing growth traits in Eucalyptus nitens

Genomic selection (GS) is being increasingly adopted by the tree breeding community. Most of the GS studies in trees are focused on estimating additive genetic effects. Exploiting the dominance effects offers additional opportunities to improve genetic gain. To detect dominance effects, trait relevant markers may be important compared to non-selected markers. Here we used pre-selected markers to study the dominance effects in a Eucalyptus nitens (E. nitens) breeding population consisting of open-pollinated (OP) and controlled-pollinated (CP) families. We used 8221 trees from six progeny trials in this study. Of these, 868 progeny and 255 parents were genotyped with the E. nitens marker panel. Three traits; diameter at breast height (DBH), wood basic density (DEN) and kraft pulp yield (KPY) were analysed. Two types of genomic relationship matrices based on identity-by-state (IBS) and identity-by-descent (IBD) were tested. Performance of the genomic best linear unbiased prediction (GBLUP) models with IBS and IBD matrices were compared with pedigree-based additive best linear unbiased prediction (ABLUP) models with and without the pedigree reconstruction. Similarly, the performance of the single-step GBLUP (ssGBLUP) with IBS and IBD matrices were compared with ABLUP models using all 8221 trees. Significant dominance effects were observed with the GBLUP-AD model for DBH. The predictive ability of DBH is higher with the GBLUP-AD model compared to other models. Similarly, the prediction accuracy of genotypic values is higher with GBLUP-AD compared to the GBLUP-A model. Among the two GBLUP models (IBS and IBD), no differences were observed in predictive abilities and prediction accuracies. While the estimates of predictive ability with additive effects were similar among all four models, prediction accuracies of ABLUP were lower than the GBLUP models. The prediction accuracy of ssGBLUP-IBD is higher than the other three models while the theoretical accuracy of ssGBLUP-IBS is consistently higher than the other three models across all three groups tested (parents, genotyped, non-genotyped). Significant inbreeding depression was observed for DBH and KPY. While there is a linear relationship between inbreeding and DBH, the relationship between inbreeding and KPY is non-linear and quadratic. These results indicate that the inbreeding depression of DBH is mainly due to directional dominance while in KPY it may be due to epistasis. Inbreeding depression may be the main source of the observed dominance effects in DBH. The significant dominance effect observed for DBH may be used to select complementary parents to improve the genetic merit of the progeny in E. nitens.

M-quantile Chambers-Dunstan Untuk Pendugaan Area Kecil

Metode Small Area Estimations (SAE) digunakan sebagai pendekatan yang reliabel dalam mengatasi kendala ketidakcukupan sampel pada survei sampel. BPS memproduksi statistik area kecil menggunakan metode SAE popular seperti Empirical Best Linear Unbiased Prediction dalam model Fay-Herriot (EBLUP-FH). Metode EBLUP-FH sebagai pendekatan parametrik memerlukan asumsi normalitas dan terbebas dari outliers pada kedua komponen random effect-nya. Namun, hal tersebut sulit dipenuhi karena seringkali data di lapangan berperilaku ekstrim. Metode SAE M-quantile Chambers-Dunstan (CD) merelaksasi asumsi parametrik dan robust dalam inferensi terhadap outliers. Penelitian ini mengkaji metode M-quantile CD dalam meningkatkan robustness pendugaan area kecil melalui penerapannya pada data riil untuk estimasi rata-rata pengeluaran rumah tangga per kapita tingkat kecamatan di DI Yogyakarta tahun 2018. Penelitian ini menggunakan data Susenas 2018 dan Podes 2018. Hasil implementasi pada data riil menunjukkan model M-quantile CD berhasil memperbaiki presisi EBLUP-FH. Dengan mengimplementasikan M-quantile CD diharapkan estimasi data berperilaku ekstrim lebih akurat untuk pengambilan kebijakan di daerah.

Penerapan Empirical Best Linear Unbiased Prediction (EBLUP) pada Pendugaan Tingkat Pengangguran Terbuka Level Kecamatan di Provinsi Banten

Pengangguran merupakan masalah yang kompleks baik disebabkan maupun memberikan dampak terhadap banyak faktor. Di Indonesia, indikator pengangguran diukur melalui tingkat pengangguran terbuka (TPT). Indikator ini dikumpulkan melalui Survei Angkatan Kerja Nasional (Sakernas). Banten merupakan provinsi dengan TPT tertinggi serta selalu masuk dalam lima besar TPT tertinggi di Indonesia sejak tahun 2016 hingga 2018. Sebagai upaya percepatan penurunan angka pengangguran, perlu adanya informasi sampai ke level terkecil. Namun, sampel yang digunakan oleh Sakernas tidak mencukupi untuk pendugaan TPT secara langsung. Tujuan dari penelitian ini adalah menghasilkan penduga tingkat pengangguran terbuka (TPT) level kecamatan dengan presisi yang lebih baik melalui Small Area Estimation (SAE). Metode SAE yang digunakan adalah Empirical Best Linear Unbiased Prediction (EBLUP). Data yang digunakan adalah indikator penyusun TPT dari Sakernas 2018 dan variabel penyerta yang berasal dari Podes 2018 Provinsi Banten. Hasil dari penelitian ini menunjukkan bahwa terdapat sepuluh variabel penyerta yang berkorelasi dengan TPT. Kemudian, nilai RRMSE menunjukkan bahwa EBLUP meningkatkan presisi pendugaan jika dibandingkan dengan penduga langsung. Terdapat dua kecamatan yang memiliki hasil TPT sangat tinggi yaitu kecamatan Curugbitung dan Koroncong. Hasil estimasi EBLUP pada TPT level kecamatan ini dapat digunakan oleh pemerintah daerah untuk menurunkan TPT secara lebih terfokus dan lebih tepat sasaran, utamanya pada kecamatan-kecamatan dengan TPT yang tinggi.

Implementasi Empirical Best Linear Unbiased Prediction Fay-Herriot dalam Menduga Rata-Rata Pengeluaran per Kapita Level Kecamatan di Provinsi Jawa Timur dengan Tambahan Informasi Cluster

Salah satu indikator ekonomi yang dapat mengukur tingkat kesejahteraan adalah kemiskinan. Penduduk tergolong miskin apabila rata-rata pengeluaran per kapita setiap bulannya dibawah garis kemiskinan. Provinsi Jawa Timur terpilih sebagai lokus penelitian dikarenakan memiliki jumlah penduduk miskin tertinggi di Indonesia selama satu dekade terakhir. Data yang digunakan berasal dari Susenas Maret 2019 dan Podes 2018 dengan 666 observasi level kecamatan. Upaya pengentasan kemiskinan memerlukan data yang akurat dan menjangkau hingga wilayah terkecil. Akan tetapi tidak semua wilayah memiliki sampel yang cukup atau bahkan tidak memiliki sampel sama sekali. Hal ini tidak memungkinkan untuk melakukan estimasi langsung. Oleh karena itu dibutuhkan metode statistik untuk dapat mengestimasi area kecil dengan baik. Metode yang dapat digunakan untuk menduga area kecil adalah Small Area Estimation (SAE). Penelitian ini menggunakan metode SAE dengan Model Empirical Best Linear Unbiased Prediction Fay-Herriot. Hasil yang diperoleh bahwa metode SAE dapat memberikan pendugaan yang lebih baik dibanding estimasi langsung yang ditunjukan dengan nilai Relative Root Mean Square Error (RRMSE) lebih kecil dibanding estimasi langsung. Estimasi pada non-sample area dilakukan dengan memanfaatkan informasi cluster.

Small Area Estimation untuk Estimasi Angka Kematian Bayi di Indonesia menggunakan Metode Empirical Best Linear Unbiased Prediction

Angka Kematian Bayi (AKB) adalah jumlah kematian bayi usia di bawah satu tahun untuk setiap 1000 kelahiran bayi lahir hidup dalam kurun waktu satu tahun. IMR merupakan indikator penting dari status kesehatan dari masyarakat dalam suatu daerah. Hal ini sejalan dengan Sustainable Development Goals (SDG’s) yang ke tiga yaitu memastikan kehidupan yang sehat dan mendukung kesejahteraan bagi semua untuk semua usia. AKB dihasilkan melalui estimasi langsung dari Survei Demografi dan Kesehatan Indonesia (SDKI). Akan tetapi, dalam SDKI 2017, AKB hanya bisa menghasilkan indikator pada level nasional. Hal ini disebabkan estimasi langsung dari AKB di beberapa provinsi memiliki nilai Relatif Standard Error (RSE) yang besar dan ukuran sampel yang tidak mencukupi. Dalam jurnal ini, kami mempelajari Small Area Estimation (SAE) menggunakan metode Empirical Best Linear Unbiased Prediction (EBLUP) level area untuk mengatasi keterbatasan estimasi AKB di level provinsi. SAE dilakukan dengan meminjam kekuatan beberapa variabel dari data Potensi Desa (PODES) yang berkorelasi kuat dengan AKB tingkat provinsi di Indonesia. Hasil penelitian menunjukkan bahwa SAE menggunakan metode EBLUP memiliki nilai RSE yang lebih kecil dibandingkan estimasi langsung dari SDKI. Sehingga, dapat dikatakan bahwa SAE menggunakan metode EBLUP baik untuk memperkirakan AKB level provinsi di Indonesia pada tahun 2017.