ABSTRAKHasil penelitian menunjukkan bahwa distribusi normal 60 genotipe kedelai di kedua lokasi pada karakter waktu berbunga (32-48 hari), waktu berpolong (35-55 hari), waktu matang fisiologis (75-92 hari), dan waktu panen (78-99 hari). Diperoleh empat genotipe berumur genjah (umur masak tergenjah 75,3 hari dan warna pubescent coklat), tiga genotipe umur dalam (umur masak terdalam 89,7 hari dan warna pubescent abu-abu). Jumlah alel SSR total 237, rataan alel per lokus 12,6 (3-29), rataan nilai PIC 0,78 (0,55-0,89). Tingkat kesamaan genetik berkisar 74,8-95%. Pada kemiripan 77% membagi genotipe menjadi enam klaster (empat genotipe umur genjah ada pada klaster III dan IV dan tiga genotipe umur dalam ada pada klaster II). Berdasarkan analisis data umur, warna pubescent, dan analisis filogenetik terpilih tujuh tetua yang digunakan untuk pengembangan marka terkait umur genjah, yaitu empat tetua berumur genjah B1430, B2973, B3611, B4433 dan tiga tetua berumur dalam B1635, B1658, dan B3570. Dua belas populasi F 2 dibentuk menggunakan bantuan marka Satt300 dan Satt516. Dua di antara populasi tersebut dapat digunakan untuk pengembangan marka molekuler umur genjah. The Indonesian soybean productivity is still very low with the national average of 1.3 t/ha. One means to improve national soybean productivity is by manipulating harvest index by cultivating very early maturing soybean cultivars. Development of early maturing soybean cultivars can be expedited by using marker-aided selection. The objective of this study was to select parental lines having contrasted maturity traits and selected parents must be genetically distance. The parents then were used to develop F 2 populations for detecting early maturity QTL in soybean. Maturity tests of 60 soybean genotypes were conducted at two locations, Cikeumeuh (Bogor) and Pacet (Cianjur) using a randomized block design with three replications. Genomic DNA of the 60 genotypes were analyzed using 18 SSR markers and genetic relationship was constructed using the Unweighted Pair-Group Method Arithmatic through Numerical Taxonomy and Multivariate System program version 2.1-pc. Results showed that the 60 genotypes demonstrated normal distribution in both locations for days to R1 (32-48d), days to R3 (35-55d), days to R7 (75-92d), and days to R8 (78-99d). Four early maturing genotypes and three late genotypes were obtained. Total SSR alleles observed were 237 with average allele per locus of 12.6 (3-29), and average PIC value of 0.78 (0.55-0.89). Genetic similarity among genotypes ranges from 74.8-95%. At similarity level 77% divided the genotypes into six clusters (the four selected early maturing genotypes located in clusters III and IV, while the three late genotypes located in cluster II). Based on maturity data, pubescent color, and phygenetic analysis seven parents were selected (four early maturing genotypes B1430, B2973, B3611, B4433 and three late genotypes B1635, B1658, and B3570). Twelve F 2 populations were developed with the aid of SSR markers Satt300 dan Satt516. Two of ...

Land-use change has an impact on growing physical flood vulnerability. Geographic Information System (GIS) and Analytic Hierarchy Process (AHP) approaches are increasingly being used for flood vulnerability assessments. However, none has used timeseries land cover data for evaluation and rainfall over various return periods for prediction simultaneously, especially in Indonesia. Therefore, this study aims to evaluate and predict physical flood vulnerability using time-series land cover data and rainfall data over various return periods. Eight criteria were considered in the assessment: elevation, topographic wetness index, slope, distance to the river, distance downstream, soil type, rainfall, and land cover. The criteria weights were determined using the AHP method based on expert judgment. The multi-criteria model was built and validated using flood inundation data. Based on the validated model, the effect of land cover changes on flood vulnerability was evaluated. The flood vulnerability changes were also predicted based on rainfall over various return periods. The evaluation and prediction models have shown reliable findings. The criterion elevation and distance to the river significantly influenced the physical flood vulnerability by 41% and 20%. The evaluation model showed a strong correlation between the built-up area and the area with high flood vulnerability (r 2 = 0.96). Furthermore, the model predicted an inundation area expansion for rainfall over various return periods. Further research using spatial data with higher resolution and more advanced validation techniques is needed to improve the model accuracy.

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