Fadhli N, Farid M, Rafiuddin, Efendi R, Azrai M, Anshori MF. 2020. Multivariate analysis to determine secondary characters in selecting adaptive hybrid corn lines under drought stress. Biodiversitas 21: 3617-3624. The development of adaptive hybrid corn varieties under drought stress needs an effective selection. Multivariate analysis has been reported can increase the effectiveness of selection in plants by attaching the secondary characters in the selection. Therefore, this concept also can be applied to develop adaptive corn varieties under drought stress. The objectives of this study are to determine the main secondary characters and select the best hybrid lines adaptive to drought stress. The experiment was arranged by a nested design, where replications nested under two environmental conditions, namely normal and drought stress. The main factor was genotypes consisted of 30 genotypes and was repeated three times. Moreover, the observations of this research consisted of 20 variables. The result of this research showed that the weight of harvested cob was an effective secondary character as a selection criterion along with productivity in selecting adaptive maize genotypes under drought stress. The number of green leaves was the character outside of the yield component could be as an alternative secondary character besides the weight of harvested cob. The selection results based on the weight of the harvested cob and productivity resulted in 12 hybrid corn lines considered adaptive to drought stress.

Improving the tolerance of crop species to abiotic stresses that limit plant growth and productivity is essential for mitigating the emerging problems of global warming. In this context, imaged data analysis represents an effective method in the 4.0 technology era, where this method has the non-destructive and recursive characterization of plant phenotypic traits as selection criteria. So, the plant breeders are helped in the development of adapted and climate-resilient crop varieties. Although image-based phenotyping has recently resulted in remarkable improvements for identifying the crop status under a range of growing conditions, the topic of its application for assessing the plant behavioral responses to abiotic stressors has not yet been extensively reviewed. For such a purpose, bibliometric analysis is an ideal analytical concept to analyze the evolution and interplay of image-based phenotyping to abiotic stresses by objectively reviewing the literature in light of existing database. Bibliometricy, a bibliometric analysis was applied using a systematic methodology which involved data mining, mining data improvement and analysis, and manuscript construction. The obtained results indicate that there are 554 documents related to image-based phenotyping to abiotic stress until 5 January 2023. All document showed the future development trends of image-based phenotyping will be mainly centered in the United States, European continent and China. The keywords analysis major focus to the application of 4.0 technology and machine learning in plant breeding, especially to create the tolerant variety under abiotic stresses. Drought and saline become an abiotic stress often using image-based phenotyping. Besides that, the rice, wheat and maize as the main commodities in this topic. In conclusion, the present work provides information on resolutive interactions in developing image-based phenotyping to abiotic stress, especially optimizing high-throughput sensors in image-based phenotyping for the future development.

The genotype evaluation process requires analysis of GxE interactions to ascertain the responsiveness of a genotype to various environments, including the development of early maturing rice. However, the concept of interaction is relatively specific to grain yield. In contrast, grain yield is highly polygenic, so assessment should be carried out with multivariate approaches. Therefore, multivariate assessment in evaluating GxE interactions should be developed, especially for early maturing rice genotypes. The study aimed to develop a comprehensive multivariate approach to improve the comprehensiveness and responsiveness of GxE interaction analysis. The study was conducted in Bone and Soppeng districts, South Sulawesi, Indonesia, in two seasons. The study used a randomized complete block design, where replications were nested across two seasons and locations. Two check varieties and five early maturing varieties were replicated three times in each environment. Based on this study, a new approach to GxE interaction analysis based on multiple regression index analysis, BLUP analysis, factor analysis, and path analysis was considered adequate, especially for evaluating early maturing rice. This approach combined days to harvest, biological yield, and grain yield in multiple linear regression with weighting based on the combination of all analyses. The effectiveness of the GxE interaction assessment was reflected by high coefficient of determination (R²) and gradient (b) values above 0.8 and 0.9, respectively. Inpari 13 (R^2 = 0.9; b=1.05), Cakrabuana (R^2 = 0.98; b=0.99), and Padjajaran (R^2 = 0.95; b=1.07) also have good grain yield with days to harvesting consideration, namely 7.83 ton ha^-1, 98.12 days; 7.37 ton ha^-1, 95.52 days; and 7.29 ton ha^-1, 97.23 days, respectively. Therefore, this index approach can be recommended in GxE interaction analysis to evaluate early maturing rice genotypes. Furthermore, Inpari 13, Cakrabuana, and Padjajaran are recommended as adaptive early maturing varieties.

Interaction of Genetic and Cultivation Technology in Maize Prolific and Productivity Increase

The development of salinity tolerant varieties can be done by using doubled haploid technology. In the determination of the salinity tolerance trait, hydroponic screening at the seedling stage is the most common screening method. However, it needs improvement on the assignment of the tolerance traits. Cluster heatmap analysis has been reported to select lines by combining various data. The objective of the study was to select tolerant doubled-haploid rice lines to salinity stress at the seedling phase and evaluate the use of cluster heatmap analysis by combining tolerance score and growth characters. The study was conducted in a greenhouse using a nested factorial design (repetition nested in NaCl concentrations), with 3 replications. The plant materials used consisted of 62 genotypes and NaCl concentration levels consisted of 0 (1 dS/m) and 120 mM (13.3 dS/m). Hydroponic cultures used a Yoshida solution. Each container contained 14 experimental units and 2 units of check i.e. sensitive and tolerant controls. Observations was carried out on tolerance scoring and growth parameters. The results showed that tolerant and moderate genotypes have a lower pattern of decline than sensitive genotypes, especially in shoot-related characters. Based on the cluster heatmap analysis, there were 38 doubled haploid rice lines considered good tolerance traits under saline condition. The cluster heatmap assignment was considered more selective than only focused on tolerance score, especially in negative selection to sensitive lines.

Rice-fish cultivation, also known as Manipadi in Indonesia, is one of the many integrated farming systems where fish are grown concurrently with rice. However, this integrated system needs to be optimized by organic fertilizer. The application of organic fertilizers, such as compost and local biofertilizers, when implemented into this integrated system, can optimize the yield per land for both rice and fish. However, this combination must be evaluated systematically by multiple evaluation criteria and statistical analyses. Therefore, this study aimed to identify the combination of compost doses and/or local liquid organic fertilizer concentrations, based on multiple evaluation criteria, that supports the productivity of rice-fish integration agriculture. The experiment was carried out in the Wanuae farmer group of the Barebbo District, Bone Regency, Indonesia, between September 2018 and January 2019. The experiment employed a split plot design with a randomized complete block design (RCBD). The main plot consisted of compost (four levels), and the subplot was the local biofertilizer source (four levels). The results demonstrated that the number of fill grains was significantly affected by the compost treatment and the type of local biofiller. The effect of the compost dosing was more dominant in comparison with the local liquid organic fertilizer character and its interactions. A compost dosage of four tons ha⁻¹ and a local liquid organic fertilizer, sourced from soaked coconut fiber with cow urine, has been recommended as the combination treatment to optimize the rice and fish yield potential. The Minapadi (rice fish) with additional high composting effectively increased the farmer’s outcome with a 2-point R/C ratio, more so than those without composting. Moreover, the compost dosage in this Minapadi study requires further exploration for a better understanding.