Experiments measuring the interaction between genotypes and environments measure the spatial (e.g., locations) and temporal (e.g., years) separation and/or combination of these factors. The genotype-by-environment interaction (GEI) is very important in plant breeding programs. Over the past six decades, the propensity to model the GEI led to the development of several models and mathematical methods for deciphering GEI in multi-environmental trials (METs) called “stability analyses”. However, its size is hidden by the contribution of improved management in the yield increase, and for this reason comparisons of new with old varieties in a single experiment could reveal its real size. Due to the existence of inherent differences among proposed methods and analytical models, it is necessary for researchers that calculate stability indices, and ultimately select the superior genotypes, to dissect their usefulness. Thus, we have collected statistics, as well as models and their equations, to explore these methods further. This review introduces a complete set of parametric and non-parametric methods and models with a selection pattern based on each of them. Furthermore, we have aligned each method or statistic with a matched software, macro codes, and/or scripts.

Drastic changes in the climate and ecosystem due to natural or anthropogenic activities have severely affected crop production globally. This concern has raised the need to develop environmentally friendly and cost-effective strategies, particularly for keeping pace with the demands of the growing population. The use of nanobiofertilizers in agriculture opens a new chapter in the sustainable production of crops. The application of nanoparticles improves the growth and stress tolerance in plants. Inoculation of biofertilizers is another strategy explored in agriculture. The combination of nanoparticles and biofertilizers produces nanobiofertilizers, which are cost-effective and more potent and eco-friendly than nanoparticles or biofertilizers alone. Nanobiofertilizers consist of biofertilizers encapsulated in nanoparticles. Biofertilizers are the preparations of plant-based carriers having beneficial microbial cells, while nanoparticles are microscopic (1–100 nm) particles that possess numerous advantages. Silicon, zinc, copper, iron, and silver are the commonly used nanoparticles for the formulation of nanobiofertilizer. The green synthesis of these nanoparticles enhances their performance and characteristics. The use of nanobiofertilizers is more effective than other traditional strategies. They also perform their role better than the common salts previously used in agriculture to enhance the production of crops. Nanobiofertilizer gives better and more long-lasting results as compared to traditional chemical fertilizers. It improves the structure and function of soil and the morphological, physiological, biochemical, and yield attributes of plants. The formation and application of nanobiofertilizer is a practical step toward smart fertilizer that enhances growth and augments the yield of crops. The literature on the formulation and application of nanobiofertilizer at the field level is scarce. This product requires attention, as it can reduce the use of chemical fertilizer and make the soil and crops healthy. This review highlights the formulation and application of nanobiofertilizer on various plant species and explains how nanobiofertilizer improves the growth and development of plants. It covers the role and status of nanobiofertilizer in agriculture. The limitations of and future strategies for formulating effective nanobiofertilizer are mentioned.

Durum wheat (Triticum turgidum var. durum) is one of the most important cereal crops widely cultivated all over the world with high economic value. In the present study, genetic variation in a mini-core collection of durum wheat germplasm, including 25 breeding lines and 18 landraces, was evaluated using 15 inter-simple sequence repeat (ISSR) and six start codon targeted (SCoT) markers. High levels of polymorphism were observed; 98.70% (ISSR) and 100% (SCoT), which indicated that these markers are useful tools for detection of genetic variation in the collection. Analysis of molecular variance revealed that the major part of genetic variations (90% and 93% for ISSR and SCoT, respectively) occurred within genotypes set. Comparing the genetic variation of breeding lines and landraces based on genetic parameters showed that effective number of alleles (Ne), Nei's gene diversity (He) and Shannon's Information index (I) in landraces were higher than in breeding lines. Although cluster analysis, based on both markers, separated the genotypes in five groups, the dendrogram obtained from SCoT provided the best clustering pattern. Inter-population differentiation (Gst) estimated on the basis of two marker systems representing that a vast portion of the total genetic diversity refers to variation within two sets of genotypes. In conclusion, the results verified a high level of genetic variation among the durum wheat mini-core collection, particularly among landraces, which can be interesting for future breeding programmes.

Premise of the Study: Access to improved crop cultivars is the foundation for successful agriculture. New cultivars must have improved yields that are determined by quantitative and qualitative traits. Genotype‐by‐environment interactions ( GEI ) occur for quantitative traits such as reproductive fitness, longevity, height, weight, yield, and disease resistance. The stability of genotypes across a range of environments can be analyzed using GEI analysis. GEI analysis includes univariate and multivariate analyses with both parametric and non‐parametric models. Methods and Results: The program STABILITYSOFT is online software based on JavaScript and R to calculate several univariate parametric and non‐parametric statistics for various crop traits. These statistics include Plaisted and Peterson's mean variance component ( θ _i ), Plaisted's GE variance component ( θ _(i) ), Wricke's ecovalence stability index ( W _i ² ), regression coefficient ( b _i ), deviation from regression ( S _di ² ), Shukla's stability variance ( σ _i ² ), environmental coefficient of variance ( CV _i ), Nassar and Huhn's statistics ( S ⁽¹⁾ , S ⁽²⁾ ), Huhn's equation ( S ⁽³⁾ and S ⁽⁶⁾ ), Thennarasu's non‐parametric statistics ( NP ⁽ⁱ⁾ ), and Kang's rank‐sum. These statistics are important in the identification of stable genotypes; hence, this program can compare and select genotypes across multiple environment trials for a given data set. This program supports both the repeated data across environments and matrix data types. The accuracy of the results obtained from this software was tested on several crop plants. Conclusions: This new software provides a user‐friendly interface to estimate stability statistics accurately for plant scientists, agronomists, and breeders who deal with large volumes of quantitative data. This software can also show ranking patterns of genotypes and describe associations among different statistics with yield performance through a heat map plot. The software is available at https://mohsenyousefian.com/stabilitysoft/ .