Cayenne development is not limited to improving production but also offers opportunities to reach new consumer markets through its aesthetic value. Consequently, cayenne cultivation can be directed towards both functional and ornamental purposes to bolster household food resilience. Therefore, this study aimed to identify the characteristics that make ornamental cayenne desirable to consumers by evaluating F5 cayenne breeding lines through complementary agronomic and consumer preference assessments of the ornamental cayenne. The study utilized Alpha Lattice design, involving 25 genotypes selected from a F4 generation alongside three check varieties, resulting in 28 combinations per replicate. The plants were grown in two replicates, each containing three incomplete blocks. Agronomic data were initially analyzed using Best Linear Unbiased Prediction (BLUP) under an alpha-lattice design, followed by correlation analysis. The correlated traits were transformed into family indices and fitness values, which were subsequently integrated through principal component analysis (PCA) and the Weighted Average of Absolute Scores (WAAS) to construct the agronomic index (AI) of each selected lines. Preference assessment involving 45 panelists who evaluated the visual appeal of the selected lines based on AI. The optimal agronomic traits identified for selection included canopy width, stem diameter, fruit number, and yield, all of which exhibited high heritability, which is crucial in fitness-WAAS selection. The fitness-WAAS analysis indicated that lines G25 (0.78) and G13 (0.46) had higher indices than the best check variety, Bara (0.44). Based on the standard deviation to relative check variety, six lines were recommended in the preference analysis, with line G25 (0.75) being the most promising, followed by the other five. These lines are recommended for yield potential evaluation and multi-location trials using a participatory breeding concept. Although further refinement through metabolomic, transcriptomic, and molecular analyses is required, this approach provides a systematic framework for selecting ornamental cayenne lines by integrating agronomic performance and consumer preferences while offering a preliminary indication of the visual characteristics preferred by panelists.

Developing new tomato varieties requires testing in several locations to determine how they perform in different environments, especially at different altitudes above sea level. This testing involves a careful study of the genetic potential of genotypes and the variation in their performance. Best Linear Unbiased Prediction (BLUP) analysis, multivariate analysis, and adaptability tests should be performed on tomato types at different altitudes. Therefore, this study aimed to develop a comprehensive selection framework for broad adaptation of tomato in various environments at different altitudes and to select potential lines with wide adaptation. The study was designed as a nested group randomized design, in which replications were nested in three locations. The main factor in this study was the 45 lines, and four check varieties were repeated thrice, resulting in a total of 441 experimental units. This study assessed tomato genotypes across environments using BLUP for heritability, multivariate analyses to identify key yield traits, and adaptability tests (Finlay–Wilkinson regression and RMSE). This study showed that using BLUP, factor, and path analyses helped in choosing tomato lines that adapt well to multiple environments. Focusing on the number of fruits per plant as a secondary trait, the developed selection index formulation was as follows: selection index = 0.04 yield + 0.16 number of fruits per plant. This index suggested 29 promising tomato lines. Based on adaptability using Finlay-Wilkinson regression and RMSE, 24 had a good adaptability index (AI > 0), and seven of them were consistently adapted to three different altitude environments. Therefore, the seven genotypes are recommended for evaluation in multilocation testing.

The development of new corn varieties is necessary to meet the corn demand. Using full diallel crosses is a method for developing high-yielding hybrid corn. This development requires systematic selection methods that incorporate various approaches in developing selection indices. This study aimed to develop a selection index concept for two full diallel cross populations and select potential hybrid crosses for preliminary yield evaluation. The study involved two populations of 100 corn seed genotypes from full diallel crosses (90 F1 genotypes and 10 selfing elders) and five check varieties per population, planted using a Type II Augmented RCBD in eight blocks. Agronomic characteristics were analyzed using analysis of variance, heritability, factor analysis, and path analysis, with selection criteria aligned with heterotic potential, specific combining analysis, and heterobeltiosis. Analysis revealed significant genetic variation and moderate-to-high heritability for most traits. Correlation, factor, and path analyses identified cob diameter, number of rows per cob, and seeds per row as optimal selection criteria. Selection indices were developed by integrating standard heterosis, specific combining ability, and heterobeltiosis, with weights based on heritability and direct effects. Forty-four hybrid crosses showed potential for preliminary yield tests, with seven having the best final index compared to the reference variety. The p17 × p23 cross had the best potential for the final index. This study demonstrates the effectiveness of integrating multivariate analysis and selection indices in developing superior hybrid corn crosses. Further optimization is recommended through preliminary yield tests and molecular approaches.

Tomato breeding success depends on identifying superior genotypes with high yield stability and desirable agronomic traits. This study evaluated genetic variability, heritability, trait associations, and heterosis expression in a tomato population consisting of 122 genotypes, including 102 F1 hybrid lines, 15 parental lines, and 5 commercial checks. The experiment was conducted using an augmented design with four blocks. Seventeen quantitative traits were measured, including vegetative growth, reproductive traits, and yield components. Significant genetic variation was observed among genotypes for all traits. Heritability estimates were generally high (H2 > 50 %), with the highest values recorded for fruit thickness (99.60 %), productive bunches per plant (96.95 %), and number of flowers per bunch (94.47 %). Yield showed high heritability (71.61 %) and moderate genetic variability, indicating suitability for direct selection. Correlation analysis revealed strong positive associations between yield and reproductive efficiency traits, particularly fruit weight, productive bunches per plant, and number of fruits per bunch. Path analysis confirmed the number of fruits per bunch (0.88), fruit weight (0.74), and productive bunches per plant (0.45) as the strongest direct contributors to yield performance. Heterosis assessment identified several crosses with superior performance. Genotype G13/G5 showed the highest heterosis and heterobeltiosis for yield (78.47 % and 57.23 %, respectively), followed by G5/G13 and G5/G3. These results highlight the potential for exploiting hybrid vigor in selected parental combinations. Overall, the study demonstrates that reproductive traits are key determinants of yield expression in tomato and provides strong evidence supporting direct selection and hybrid improvement strategies. The identified superior genotypes represent promising breeding material for the development of high-yielding and commercially competitive tomato cultivars.

Climate change results in rainfall distribution anomalies that trigger drought, resulting in reduced crop yields. Additionally, the scarcity of nitrogen fertilizer is an obstacle in increasing production yields. Therefore, an intensification approach is needed in corn cultivation. In corn cultivation, quick and accurate decision-making is essential, which necessitates the use of technological inputs. One technology that can be utilized is the UAV (Unmanned Aerial Vehicle). UAV can be used to obtain vegetation indices such as NDVI (Normalized Difference Vegetation Index) and canopy cover density (CCD). NDVI development has been extensively studied, but NDVI applications in Indonesia remain limited due to differences in agroecosystems and lack of localized calibration. As a result, NDVI has not yet been utilized in Indonesia. Thus, the development of NDVI is necessary. The objective of this study was to develop a predictive model for corn productivity using NDVI and agronomic traits under varying irrigation intervals and nitrogen doses. This research was conducted from July to October 2024 at the Bajeng Balitsereal Experimental Farm, Pabentengan Village, Bajeng Subdistrict, Gowa District, South Sulawesi. This study was designed using a split-plot design, where irrigation intervals were the main plots with three irrigation intervals (5 days, 10 days, and 15 days), and nitrogen doses were the subplots with five dose levels (0, 100, 150, 200, and 250 N kg/ha). Each treatment combination was replicated three times, resulting in 45 experimental units. Each experimental plot was 16 m2 in size. Drone imagery was taken at 09:00. Agronomic data will be analyzed using analysis of variance, correlation analysis, and path analysis. After identifying potential agronomic characteristics, these characteristics were analyzed using linear regression and multiple regression on NDVI and canopy cover density based on pixels. Regression results were validated using the coefficient of determination (R 2) and Root Mean Square Error (RMSE). Based on multivariate analysis results, plant height, male flowering age, female flowering age, peel cob weight, and number of seeds per row were identified as the agronomic traits with the greatest influence on productivity. These agronomic traits were then analyzed using linear and multiple regression against NDVI and CCD traits. The productivity (ton/ha) model formulation based on the linear regression approach combined with NDVI (45.55 (NDVI) – 5.15) was considered more effective than other model formulations because it has a high and stable R 2 (train: 0.8555, and test: 0.8543).

Improving tomato fruit characteristics is a crucial step to address the decline in production, which is primarily caused by biotic stress and the limited adaptability of existing varieties in lowland areas. This improvement can be achieved through plant breeding programs involving crossbreeding techniques. The primary objective of developing superior varieties is to produce tomatoes with high fruit quality and excellent productivity. The selection criteria were further refined using both principal component analysis (PCA) and path analysis. PCA was employed to identify the primary traits contributing to variability, while path analysis helped establish the strength and direction of relationships between key traits and supporting characteristics. The most significant direct impact will be incorporated into the index value to determine the genotype with the best overall performance. The findings identified 12 F1 tomato breeding lines deemed suitable for progression to the next generation, offering significant potential for enhancing tomato production. This study underscores the effectiveness of targeted breeding strategies. It contributes to developing more sustainable and efficient approaches to tomato cultivation, paving the way for improved productivity and quality in future varieties.

Amphibious rice varieties are a promising solution to improve rice production resilience under climate change, especially with increasing uncertainty in rainfall patterns. This study uses two complementary methods: bibliometric analysis to explore global research trends on amphibious rice, and field research to optimize the Biobestari variety. The bibliometric analysis identifies key topics, collaborations, and publication patterns. The primary study tests Biobestari using two planting spacing methods, double row with alternating row width (Jajar Legowo is an Indonesian term), square planting and five levels of fertilizer application. The agronomic and economic performance of each combination was evaluated. Results show that amphibious rice, combined with efficient planting and eco-friendly fertilizers, improves productivity and achieves a profit ratio of 1.91. This suggests that amphibious rice is well suited for areas with irregular rainfall. Its adoption should be supported by government programs and farmer training. The study highlights the importance of integrating genetic improvement, good farming practices, economic feasibility, and policy support to build climate-resilient rice systems.

The stress tolerance index is widely used to detect genotypes' tolerance levels under stress conditions, such as tomato plants. Therefore, determining secondary characters requires an appropriate statistical approach, one of which is the concept of multivariate analysis. This study aims to determine the main secondary characters and select tomato lines that are adaptive to drought stress. The research was conducted in parallel at 2 locations from August to December 2023 at the Experimental Garden of the Faculty of Agriculture, Hasanuddin University, located in Tamalanrea District, Makassar City, at an altitude of 12 m above sea level and the Maros Youth Learning Center Garden, Purnakarya Village, Tanralili District, Maros Regency at an altitude of 31 m above sea level, South Sulawesi. This study used Augmented design with Nested Design as the environment design. Location 1 irrigated environment and location 2 drought stress comprised 126 genotypes of 121 F6 lines and five parental varieties (Karuna, Mawar, Chung, Gustavi, and Gammara). This study consisted of 5 blocks; each genotype consisted of 8 plants in each location. The stress tolerance index analysis results showed that the 12 best tomato genotypes have adapted to drought stress, namely genotypes MC10. 4.5.5, KM23.3.3.10, MC74.12.8.1, MC10.7.2.3, MC12.3.2.1, MC29.4.6.4, KM5.3.4.12, MC8.3.3.2, MC10.7.2.1, MC79.2.7.9, MC8.11.5.1 and MC27.12.1.6. These genotypes were selected based on a comprehensive performance review across all measured parameters. Based on the overall review, the results of this evaluation are recommended as a consideration for the selection of F7 lines in supporting the direction of releasing tomato varieties from environmentally adaptive results.

The development of transgressive segregant (TS) selection on convergent breeding populations of S4 maize is a concept that is rarely applied. However, the development of TS is necessary to accelerate maize breeding pipelines. Therefore, the objectives of this study were (1) to develop the concept of TS selection and (2) to select S4 TS maize to be developed as hybrid cross parents. The study was carried out using two experiments. The first experiment was designed with an augmented design of 6 replications for control genotypes. This design is just one factor focused on maize genotypes. However, it was divided into two sets: non-replicated of 32 TS lines and replicated of four check hybrid varieties. The second experiment focused on validation using a three-way cross. The experiment used a randomized complete block design with three replications. Based on the resulting study, the combination of ratio analysis, path analysis, best linear unbiased prediction, relative fitness, and selection indices is an objective approach to assessing the genetic potential of the S4 TS. The selection index formed was 0.53 ear weight + 0.24-grain yield percentage + yield. The index selection resulted in 11 S4 TS lines being further evaluated for their hybrid potential, with the TS line CB2.23.1 being the best. However, these TSs are expected to focus on identifying and combining ability through diallel crosses in the future. Furthermore, the three-way cross hybrid lines assessment also revealed SG 3.35.12 × JH37 F1 and CB 2.23.1 × JH37 F1 to be promising hybrid lines.

Developing F3 transgenic segregants has significant potential to improve cayenne pepper varieties. However, current evaluation methods are often inconsistent and inaccurate, hindering the identification of effective traits. Traditional approaches only focus on a few aspects, thus not covering the full potential performance of the genotype. Utilizing morphometric image processing and categorical parameter assessment can fill the gap of traditional approaches to improving accuracy and objectivity in evaluation. In addition, environmental factors affecting the evaluation process are not adequately considered, making the results unreliable. Therefore, a systematic evaluation framework integrating morphometric analysis, categorical assessment, and environmental correction is essential for optimizing F3 cayenne transgressive segregants. The study aims to develop a synchronized assessment and selection approach based on agronomic, fruit morphometric, and categorical traits in evaluating F3 cayenne transgressive segregants. This research was designed with a randomized completed block design with 16 transgressive segregant genotypes and three check varieties. Each genotype was repeated three times, resulting in 57 experimental units. Based on the results of this study, quantitative and categorical indices could be used to selectively and systematically evaluate potential transgressive segregants in F3 cayenne peppers. The quantitative index is formed from outcome selection criteria, number of productive branches, area, and major axes weighted through an unbiased linear estimation approach, heritability, and best path analysis. Seven genotypes demonstrated superior transgressive performance based on quantitative indices, with G10.9.2, G10.7.1, and G6.8.5 excelling in both agronomic traits and categorical evaluations. These lines can be recommended for yield evaluation and hybrid cross-parents.

Nitrogen is essential nutrient that supports the growth and yield of corn. The correct dose of nitrogen fertilization is one of the keys to increasing corn productivity by its yield potential. Using unmanned aerial vehicle (UAV) drones, the normalized difference vegetation index (NDVI) can be obtained, which can provide accurate information about the health condition of plant vegetation directly. Therefore, this study aimed to determine the effect of nitrogen fertilizer dose and type of maize variety on crop production and vegetation index obtained through UAV technology. This study was designed with a separate plot design and a group randomized design as the environmental design. The research was conducted by applying various doses of nitrogen (0, 50, 100, 150, 200, and 250) and maize varieties (Sinhas, Nasa 29, HJ 36, Bisi 18, and Pioneer). The combination of all treatments resulted in 35 combinations and was repeated three times, resulting in 105 experimental units. Vegetation condition measurements were conducted using drones at time intervals (40, 55, and 70 DAP). Selection criteria were determined systematically through Pearson correlation, path, and principal component analysis (PCA). The results showed that higher nitrogen doses increased NDVI values, which reflected better vegetation health and contributed to increased crop yields. The PCA results showed that four principal components had eigenvalues greater than 1 with a cumulative proportion of 0.21. This research indicates that using optimal nitrogen doses and vegetation health monitoring using UAVs can significantly increase maize yields. These findings provide valuable insights to increase maize production through the best maize cultivation technologies that farmers can use.

An assessment of the stability and adaptability of released varieties is needed to ensure their potential. Analysis of both approaches can be performed through PBSTAT-GE. However, the application of PBSTAT-GE in combination with index selection for elucidating stability and adaptability in hybrid maize has not been reported in depth. Therefore, this study aimed to identify suitable high-yielding maize hybrids based on stability and adaptability analyses using PBSTAT-GE software followed by index selection. The study was conducted in eight locations having different agro-climates in 2023, including eight test hybrids and two check varieties. The experiment used a randomized complete block design with three replications in each environment, so there are 300 experimental units in this study. This study focused on the grain yield, which was analyzed for potential stability and adaptability in the PBSTAT-GE. Based on the results of this study, PBSTAT-GE has the potential to be applied for comprehensive stability and adaptability analysis. The max–min standardization-based accumulation index can combine parametric stability-based assessment, non-parametric stability, and productivity potential of a genotype. Based on this approach, MAI-UH 08 and MAI-UH 03 are recommended for hybrid maize variety release with good stability and adaptability potential in both. In addition, lines MAI-UH 01, MAI-UH 02, and MAI-UH 05 can be recommended in Tomohon and Boyolali based on good adaptability potential. In conclusion, PBSTAT-GE is highly suitable and recommended for stability and adaptability analysis in identifying high-yielding maize hybrids, especially using a max–min standardization-based accumulation index.

Early maturing rice varieties are crucial for climate-resilient agriculture, yet nitrogen optimization in these varieties remains under-explored. Most existing studies focus on conventional varieties and lack an integrated approach combining agronomic traits, remote sensing, and statistical modeling. The objective of this study was to determine evaluation criteria and develop a model to predict the productivity of short-season rice varieties. Experiments were conducted in different seasons at two locations in Sidenreng Rappang and Maros, South Sulawesi, using a nested split-plot design with three replicates. The main plots consisted of five nitrogen levels, while the subplots included five early maturing rice varieties and two moderate age as control. Key findings of this study is that the stepwise regression model combining NDVI and yield per clump showed strong performance, with R2 = 0.65/0.73, RMSE = 0.65/0.61, and MAPE = 9.72%/10.81% for training/testing, respectively. This regression model effectively evaluates how rice growth responds to varying nitrogen fertilizer doses, particularly in early-maturing varieties. Therefore, it can be reliably used to predict the future yield of these varieties.