 The study came about with the need to preserve farmers' corn varieties also referred as native corn varieties through continued cultivation. Our strategy is to identify unique nutritional properties to promote their utilization, and hence, varietal preservation and cultivation. Historically, corn production and utilization started when the early Americans domesticated this tall leafy stalk plant and harvested the seeds from it. Its use as food was first recognized by Christopher Columbus in 1492, and this led to the spread of corn throughout Europe. This figure shows the projected population growth in 2045 as presented in green bars. Significant population increase will be observed from the two regions, Asia and Africa, of which high population rates were already observed in 2013. With a growing population rate, demand for food is a concern. To address the food security issue, grain yield is expected to increase by almost 100% by 2050. For past decades, trait selection for corn as well as for wheat and other crops has been concentrated in yield. However, food security is not only addressed by increasing production. Nutritional value is also necessary. With increase in yield, grain quality decreases. Starch is a function of grain yield, and several studies reported the negative correlation between starch and protein and oil contents. Hence, there's a need to improve grain quality. In the Philippines, corn was hypothesized to be introduced during the Acapulco galleon trade which is more than 500 years ago. Being a tropical country, it is common to have two cropping seasons per year. Therefore, native varieties may represent more than 1,000 seasons of natures and farmer selection. The variability in climate across the country contributes to varietal adaptation as well as to crop diversity. Therefore, we have a rich germplasm pool. Among the native corn varieties are the farmer's selections, where in selection at the farmer's level is mostly based on eating quality. This is because most of these varieties are grown in major corn eating areas of the country. In addition, these varieties consist of a heterogeneous population that may provide a diverse pool to explore unique grain quality traits for better nutrition. Examples of corn types used for food is blue corn with high ethosionine, upe corn that has been improved for protein content, and deep orange kernels that have high beta carotene. The goals of this study are to characterize grain quality traits of selected farmers varieties and to explore their potential for varietal improvement to strengthen germplasm conservation through its utilization. What we did with this study is to first collect farmer's varieties from major corn-growing areas in the country. This figure illustrates the distribution of corn production areas in the Philippines. The major corn-growing areas are represented in red. We initially did our collection in the northern part of Luzon, Visayas, and Mindanao. This project is tied up with a national program, the Corn Germplasm Utilization through Advanced Research and Development, also known as Seaguard. We coordinated with the different regions under the Department of Agriculture to assist in germplasm collection. Seed samples from the initial collection of 46 varieties were used to determine the proximate composition and total antioxidants. Cluster analysis on this data was performed using Ward's minimum variants to classify the varieties into groups based on similarities of their grain quality traits. Data for carotenoid contents were also obtained as well as agronomic traits and downy mildew resistance. Seventy varieties were crossed with broad-based testers, population 62, IPB var 6, IPB var 8, IPB var 9, IPB var 11, and IPB var 13. These are six elite locally bred corn cultivars that were used to determine combining ability and possible heterotic grouping based on yield. Trials were conducted in Isabela and Pangasinan. Separate analysis of variants was performed for a single location before performing combined analysis. Based on phenotypic characterization and performance evaluation, population improvement for high starch, protein antioxidant, and carotenoid contents will be conducted in selected population. For the results of this study, this figure illustrates the dendogram of genetic relationships among selected farmers varieties formed by two groups, the high protein in red box and high starch group in blue box. Among the varieties, UP, LBC, and N11 and UP, LBC, and N15 have high total antioxidants with scavengic activity of 13.52% and 12.98% and UP, LBC, and N15 also had good resistance to downy mildew resistance with less than 20% incidence. Using BACROS method, increase in resistance to downy mildew resistance was observed between IPB var 6 and BACROS 1F1 and BACROS 1F2 populations. Summarize here are the nutritional benefits observed in different native varieties. UP, LBC, and N15, UP, LBC, and N46, and UP, LBC, and N32 have high antioxidant activity. High protein was observed for UP, LBC, and N2 and high starch for improved eating quality was observed for UP, LBC, and N39. Among the farmers varieties with high carotenoid contents are the three varieties coded as UP, LBC, and N20, UP, LBC, and N32, and UP, LBC, and N36. These varieties have comparable carotenoid contents with IPB var 13 and elite yellow open pollinated cultivar. Since carotenoids are associated with yellow kernel color, white corn varieties IPB var 6 and IPB var 8 have low values. Test cross trials were conducted in Isabela and Pangasinan and separate ANOVA were performed. The combined analysis across Pangasinan and Isabela revealed a high significantly difference among test cross, native population, and tester effects. The GCA estimates for yield were calculated for both testers and native populations. SAA estimates were calculated for test crosses. The GCA estimates for test cross range from minus 0.26 to 0.22 with IPB var 13 having the highest estimate. And IPB var 6 obtained the least and negative GCA estimate for yield among the testers. The negative GCA estimate contributes decrease in yield when used in cross. On the other hand, the GCA estimates for native population range from minus 0.79 to 1.44 with UP, LBC, and N108 having the highest estimate. Followed by UP, LBC, and N22 with 1.22 and UP, LBC, and N53 obtained the least and negative GCA estimate for yield among the native populations. The UP, LBC, and N108 had high test cross performance for yield on all six testers. While UP, LBC, and N22 obtained the highest average yield among the selected test crosses. These native populations are candidates for population improvement. The SAA estimates highlighted in blue and red illustrate the relative magnitude for each native population across all the testers. The relative magnitude of the SAA estimates for each native population provides information on the strength of combining ability of the native population with the different testers. The blue cells show favorable response to cross contributing to increase in yield while red cells show unfavorable response to cross as it contributes to decrease in yield. The SAA estimates for test cross range from minus 1.02 to 1.17. With UP, LBC, and N88 by IPV var 8 having the highest estimate and UP, LBC, and N22 by IPV var 9 having the least and negative SAA estimate for yield. The UP, LBC, and N88 and IPV var 8 can be further evaluated to assess its performance on population hybrid since it is also observed to be the highest yielding test cross among all the test crosses. Stability analysis for test cross was performed using the SAA estimates for yield of the test crosses. The analysis was done to determine the combining ability of the testers with the native populations. IPV var 9 showed the most stable performance or the least variation in terms of combining ability with native populations. This reflects the poor ability of the tester to differentiate its combining ability with the native populations. On the other hand, IPV var 13 was the least stable in terms of combining ability with native populations, followed by IPV var 11. And these two testers also rank first and second based on GCA estimates. In addition, IPV var 11 showed the highest mean yield among the testers. The elite open pollinated cultivars, IPV var 13 and IPV var 11 can be recommended as testers to evaluate other native corn varieties and similar germ plasams of the IPV corn breeding program. The biplot analysis was performed to identify which among the testers have similar combining ability with the native populations and to determine grouping of the testers based on their performance with the native populations. The six testers were divided into four groups. Group 1 consists of IPV var 6, IPV var 9, and population 62. While group 2 is represented by IPV var 11, group 3 by IPV var 8, and group 4 represented by IPV var 13. The testers belonging to group 1 showed similar combining ability. Based on the GCA estimates and stability analysis, population 62 can be used to represent group 1. On the other hand, the grouping can be validated by test cross performance. The native populations contained in each group where the tester belongs are heterotic with the tester. UPLBCNN88 is contained in group 3 that is represented by IPV var 8 and the high SCA estimate observed in their cross confirms their heterotic relationship. This figure illustrates the heterotic grouping among the native population, which was determined using Ward's minimum variance based on SCA estimates for yield. Three heterotic groupings were formed and based on bi-plat analysis and GCA and SCA estimates, testers for each heterotic group are identified. IPV var 13 can serve as tester for heterotic group 1, IPV var 11, and population 62 can serve as tester for heterotics group 2 and 3 respectively. Composites can be formed from selected native populations from each heterotic group. Listed here are the selected native populations for each heterotic group to be used to form composites. Now how can we proceed with varital improvement? For each varital selection in corn and in any other crops, we should consider the overall performance of the variety, considering all the traits. A high population mean to expect high level of improvement or success, its performance in process that is reflected in the combining ability, and the target trait in this case is nutritional composition. Based on combining ability and heterotic grouping, the appropriate breeding strategy for selected varieties would be intra-population improvement for selected varieties with particular desirable grain quality traits. UPLBCNN20, UPLBCNN32, and UPLBCNN36, for example, can be improved for carotenoid contents. And since these three varieties belong to heterotic group 3, composites can also be developed among them. Right now we have several sets of population improvement programs implemented to initiate the first cycle of selection to improve starch, protein, antioxidant, and carotenoid contents. A BACRAS program is also already been implemented to incorporate downy-milch resistance to IPBVAR6 using UPLBCNN15, a source for resistance. Population hybrids can also be developed by identifying native varieties with high SEA, with a tester, for example, UPLBCNN88 by IPBVAR8. In summary, the pool of farmers varieties evaluated has variable response in their nutritional composition. These can be explored to improve current corn cultivars for better nutrition and to develop the next generation of healthier corn products. The knowledge on heterotic group among native corn varieties and current corn germplasm facilitates directed and predicted response to increased productivity and other important traits. Hence, strengthening Philippine corn germplasm.