Genotype×year interaction of pod and seed mass and stability of Pongamia pinnata families in a semi-arid region

Sixteen pongamia families were evaluated in a field experiment for eight consecutive years in dryland conditions to identify stable,high-yielding families.The trial was conducted in a randomized complete block design with three replications.Each family,consisting of nine trees per replication,was planted at a spacing of3 m x 3 m.Yield stability was analyzed using(1)Eberhart and Russel’s regression coefficient(β_i)and deviation from regression(S_d^2),(2)Wrike’s ecovalence(W_i);(3)Shukla stability variance(σ_i^2);and(4)Piepho and Lotito’s stability index(L_i).Families were also analyzed for adaptability and stability using AMMI and GGE biplots graphical methods.The study revealed significant variances due to family and family x year interaction for pod and seed yield.Families performed differently and ranked differently across years.The performance of families was influenced by both genetic factor and environmental conditions in different years.Among families tested,TNMP20,Acc14,TNMP14 and Acc30 were high yielders for pods,and Acc14,Acc30,TNMP6,RAK19 and TNMP14 were high for seed yield.According to the Eberhart and Russell model,Acc30,TNMP14 and TNMP3 were stable across years.In the graphical view of family x year interaction based on AMMI methods,TNMP3,TNMP4 and TNMP14 had greater stability with moderate seed yield,and Acc14 and Acc30 had moderate stability with high seed yield.On the other hand,GGE biplots revealed Acc14,Acc30 and TNMP14 as high yielders with moderate stability.AMMI and GGE biplots were able to capture nonlinear parts of the family x year interaction that were not be captured by the Eberhart and Russel model while also identifying stable families.Based on different methodologies,Acc14,Acc30 and TNMP14 were identified as high yielding and stable families for promoting pongamia cultivation as a biofuel crop for semi-arid regions.

Multi-Environment Evaluation and Genotype ×Environment Interaction Analysis of Sorghum [<i>Sorghum bicolor</i>(L.) Moench] Genotypes in Highland Areas of Ethiopia

Sorghum [Sorghum bicolor (L.) Moench] is a high-yielding, nutrient-use efficient, and drought tolerant crop that can be cultivated on over 80 per cent of the world’s agricultural land. However, a number of biotic and abiotic factors are limiting grain yield increase. Diseases (leaf and grain) are considered as one of the major biotic factors hindering sorghum productivity in the highland and intermediate altitude sorghum growing areas of Ethiopia. In addition, the yield performance of crop varieties is highly influenced by genotype × environment (G × E) interaction which is the major focus of researchers while generating improved varieties. In Ethiopia, high yielding and stable varieties that withstand biotic stress in the highland areas are limited. In line with this, the yield performance of 21 sorghum genotypes and one standard check were evaluated across 14 environments with the objectives of estimating magnitude G × E interaction for grain yield and to identify high yielder and stable genotypes across environments. The experiment was laid out using Randomized Complete Block Design with three replications in all environments. The combined analysis of variance across environments revealed highly significant differences among environments, genotypes and G × E interactions of grain yield suggesting further analysis of the G × E interaction. The results of the combined AMMI analysis of variance indicated that the total variation in grain yield was attributed to environments effects 71.21%, genotypes effects 4.52% and G × E interactions effects 24.27% indicating the major sources of variation. Genotypes 2006AN7010 and 2006AN7011 were high yielder and they were stable across environments and one variety has been released for commercial production and can be used as parental lines for genetic improvement in the sorghum improvement program. In general, this research study revealed the importance of evaluating sorghum genotypes for their yield and stability across diverse highland areas of Ethiopia before releasing for commercial production.

One compound approach combining factor-analytic model with AMMI and GGE biplot to improve multi-environment trials analysis

To improve multi-environmental trial(MET)analysis,a compound method—which combines factor analytic(FA)model with additive main effect and multiplicative interaction(AMMI)and genotype main effect plus genotype-by-environment interaction(GGE)biplot—was conducted in this study.The diameter at breast height of 36 open-pollinated(OP)families of Pinus taeda at six sites in South China was used as a raw dataset.The best linear unbiased prediction(BLUP)data of all individual trees in each site was obtained by fitting the spatial effects with the FA method from raw data.The raw data and BLUP data were analyzed and compared by using the AMMI and GGE biplot.BLUP results showed that the six sites were heterogeneous and spatial variation could be effectively fitted by spatial analysis with the FA method.AMMI analysis identified that two datasets had highly significant effects on the site,family,and their interactions,while BLUP data had a smaller residual error,but higher variation explaining ability and more credible stability than raw data.GGE biplot results revealed that raw data and BLUP data had different results in mega-environment delineation,test-environment evaluation,and genotype evaluation.In addition,BLUP data results were more reasonable due to the stronger analytical ability of the first two principal components.Our study suggests that the compound method combing the FA method with the AMMI and GGE biplot could improve the analysis result of MET data in Pinus teada as it was more reliable than direct AMMI and GGE biplot analysis on raw data.

Detection and Analysis of High Temperature Sensitivity of TGMS Lines in Rice Using AMMI Model

With the AMMI (additive main effects and multiplicative interaction) analysis model, thedetermination of the sensitivity to temperature among different TGMS (thermo-sensitivegenic male sterile) lines was performed. To assess the genetic differences due to hightemperature stress at the fertility-sensitive stage (10-20d before heading), sevengenotypes (six TGMS lines and the control Pei-Ai64S) were grown from May 4 at sevendifferent stages with 10d intervals. The temperatures at the fertility-sensitive stagesinvolved twelve levels from<20 to>℃ under the regime natural conditions in Hangzhou,China. There was considerable variation in pollen fertility among genotypes in responseto high temperature. Five genotypes identified as TGMS lines as their percentages offertile pollens were lower than or close to that of the control except for the unstableline RTS19 (V6). When the temperatures at the fertility-sensitive stage were at Ⅰ-Ⅳ,Ⅴ-Ⅵ and Ⅶ-Ⅻ, the percentages of fertile pollens varied in the ranges of 46.46-48.49%,19.62-22.79% and 3.49-5.87%, respectively. The critical temperatures of sterility andfertility in the five TGMS lines were 25.1 and 23.0℃, respectively. Considering theamounts and directions of main effect and their IPCA (interaction principal componentsanalysis), we can classify the lines and temperature levels into different groups, anddescribe the characteristics of genotypetemperature interaction, offering the informationand tools for the development and utility of thermo-sensitive male sterile lines.Several TGMS rice lines with their reproductive sensitivity to high temperature that canbe screened using the AMMI model may add valuable germplasm to the breeding program ofhybrid rice.

The Influence of Solonetz Soil Limited Growth Conditions on Bread Wheat Yield

The wheat yield variation on solonetz and chernozem soil in six environments was in study in order to obtain information for use of genetic variability and for building strategy in plant breeding for less productive and marginal environments. The sample of eight bread wheat varieties： Rcnesansa, Pobeda, Rapsodija, Dragana, Cipovka, Evropa 90, NSR-5 and Nevesinjka, which are characterized by tolerance to stressful growing conditions and broader adaptability, was selected for the study. The trial was established by Randomized Complete Block Design in three replications at two locations in the Pannonian Plain, Northern Serbia in two vegetation periods 2004/2005 and 2008/2009. Locations differed in a soil type, primarily. The tested locality was on solonetz, while control locality was on chernozem soil type. Additive Main and Multiplicative Interaction model （AMMI） grouped varieties that exhibited strong reaction to environmental improvement （Nevesinjka and Evropa 90）, varieties showing fairly small GE interaction （Renesansa, Cipovka and Pobeda） and varieties having the ability for maximum use of less productive soil in better meteorological conditions （Dragana, Rapsodija and NSR-5）. Meteorological conditions significantly influenced the effect of soil quality variation on grain yield in trial. Varieties have interacted differently with the environment, depending on their genetic background.

Prediction of spatial heterogeneity in nutrient-limited sub-tropical maize yield:Implications for precision management in the eastern Indo-Gangetic Plains

Knowledge of the factors influencing nutrient-limited subtropical maize yield and subsequent prediction is crucial for effective nutrientmanagement,maximizing profitability,ensuring food security,and promoting environmental sustainability.Weanalyzed data fromnutrient omission plot trials(NOPTs)conducted in 324 farmers'fields across ten agroecological zones(AEZs)in the Eastern Indo-Gangetic Plains(EIGP)of Bangladesh to explain maize yield variability and identify variables controlling nutrient-limited yields.An additive main effect and multiplicative interaction(AMMI)model was used to explain maize yield variability with nutrient addition.Interpretable machine learning(ML)algorithms in automatic machine learning(AutoML)frameworks were subsequently used to predict attainable yield relative nutrient-limited yield(RY)and to rank variables that control RY.The stack-ensemble model was identified as the best-performing model for predicting RYs of N,P,and Zn.In contrast,deep learning outperformed all base learners for predicting RYK.The best model's square errors(RMSEs)were 0.122,0.105,0.123,and 0.104 for RY_(N),RY_(P),RY_(K),and RY_(Zn),respectively.The permutation-based feature importance technique identified soil pH as the most critical variable controlling RY_(N)and RY_(P).The RY_(K)showed lower in the eastern longitudinal direction.Soil N and Zn were associated with RYZn.The predicted median RY of N,P,K,and Zn,representing average soil fertility,was 0.51,0.84,0.87,and 0.97,accounting for 44,54,54,and 48%upland dry season crop area of Bangladesh,respectively.Efforts are needed to update databases cataloging variability in land type inundation classes,soil characteristics,and INS and combine them with farmers'crop management information to develop more precise nutrient guidelines for maize in the EIGP.