Grain yield stability analysis using parametric and nonparametric statistics in oat (<i>Avena sativa</i> L.) genotypes in Ethiopia

Kebede, Gezahagn; Worku, Walelign; Jifar, Habte; Feyissa, Fekede

doi:10.1002/glr2.12056

Cited by 3 publications

(3 citation statements)

References 52 publications

(89 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several other empirical results exist for crops not covered in our analysis, e.g., for oats in Ethiopia (Kebede et al 2023), for chickpeas in India (Kumar et al 2020), energy crops in the EU (Popp et al 2021), maize in several locations (Kang 1993), and cereals and vegetables in Europe and North America Hollósy 2019a, 2019b), using various methods for evaluating yield stability, but all agree that both climate impacts and the impacts of management decisions regarding farming technology interact in creating actual yield fluctuations. Shojaei et al (2021) used the AMMI methodology for maize in Iran, comparing 12 maize hybrids under different environmental conditions for a two-year study using similar stability measures as Bose et al (2014).…”

Section: Discussionmentioning

confidence: 58%

Technological Advancements and the Changing Face of Crop Yield Stability in Asia

Hollósy,

Ma’ruf,

Bacsi

2023

Economies

View full text Add to dashboard Cite

Recent technological advancements have revolutionized agriculture in Asia, leading to significant changes in crop yield stability. This study examines the changing face of crop yield stability in Asia resulting from the increasing adoption of innovative technologies in agriculture. Through a review of current research and case studies, the impact of technology-driven changes on yield levels, variability, and predictability is explored. The study applies a yield stability index (YSI) to evaluate the yield stability of six crops in seven Asian countries during two periods (1961–1994 and 1995–2020), comparing the countries, crops, and stability changes between the two segments. The novelty of the research is the application of YSI, which, contrary to usual stability metrics, can distinguish between rare large extreme yields and frequent minor fluctuations, and based on this feature, evaluates the suitability of the prevailing technologies to local environmental conditions. The YSI is used to evaluate the stability of technologies, indicating whether the technologies can respond well to the annual variations of environmental conditions. Positive YSI values indicate stable technologies that can respond well to the annual variations of environmental conditions, and the concept of a well-technologized crop is used for crops in countries with stable positive YSI values, indicating the suitability of the actual crop to the actual geographical environment. These results can guide production technology developments and the introduction or abandonment of certain crops in certain geographical zones, especially regarding the implications of climate change and global warming. This study highlights the transformative power of technology in improving crop yield stability and food security in Asia, while discussing the potential challenges associated with these changes and the need for continued research to address them.

show abstract

Section: Discussionmentioning

confidence: 58%

Technological Advancements and the Changing Face of Crop Yield Stability in Asia

Hollósy,

Ma’ruf,

Bacsi

2023

Economies

View full text Add to dashboard Cite

show abstract

“…This was based on stability factors such as lower values of Wi 2 , σ 2i , P i , and s 2 di, a high BLUP factor, and bi equal to 1. The use of different parametric stability assessments has also been reported in wheat (Bornhofen et al, 2017), rice (Lee et al, 2023), safflower (Afzal et al, 2021), and oat (Kebede et al, 2023).…”

Section: Discussionmentioning

confidence: 99%

Deciphering genotype‐by‐environment interaction in new soybean lines based on multiple traits using different adaptability and stability methods

Majidian,

Masoudi,

Hezarjaribi

et al. 2024

Food Science & Nutrition

View full text Add to dashboard Cite

The multi‐environmental trials aid breeders in selecting the best genotypes for specific or general adaptability to different environments before commercial release. This study aimed to assess the stability of 13 new soybean pure lines, along with two controls, in terms of seed yield and important agronomic traits. The assessment was based on a completely randomized block design with three replications across four areas during 2020–2022. Various adaptability methods, including parametric, AMMI, GGE biplot, PCA, and SIIG were employed. The mixed analysis showed that the effects of environment, genotype, and genotype–environment (GE) interaction were significant for most studied traits. The AMMI showed the highest portion of environment (65.89%) in soybean seed yield. Based on all stability parameters, lines 2 and 5 were selected for their average seed yields of 3349 and 3142 kg ha−1, respectively. Additionally, lines 6 and 5 showed the most stability, yielding higher than the average, which were 2992 and 3142 kg ha−1, respectively, according to GGE biplot results. Furthermore, lines 2, 5, and 8 were identified as the ideal genotypes concerning seed yield and other agronomic traits, with high SIIG parameters and yields exceeding the average. Finally, the soybean line 5 was deemed the most suitable due to its higher yield, stability, and early maturity (128‐day growth period). Therefore, line 5 is considered appropriate for its high stability and earliness in various regions of Iran.

show abstract

“…Cohen's d effect size was employed to measure the magnitude of the mean differences between the two groups. Mathematically, Cohen's d is expressed by the following equations 68 :…”

Section: Cohen Testmentioning

confidence: 99%

Nature‐ınspired algorithms for optimizing fractional order PID controllers in time‐delayed systems

Güven,

Mengi

2024

Optim Control Appl Methods

View full text Add to dashboard Cite

Time‐delayed systems frequently appear, especially in sectors such as fluid flow processes, chemical procedures, and the food industry. This paper addresses the optimization of parameters for a fractional order PID (FOPID) controller, which is used to control a time‐delayed system, using five distinct algorithms inspired by nature. These algorithms are NewBAT, Cuckoo search (CS), Firefly (FF), Gray Wolf Optimizer (GWO), and Whale optimization algorithm (WOA). The FOPID controller parameters, namely KP, KI, KD, λ and μ, have been optimized using these algorithms. During the optimization process, the integral of the time absolute error (ITAE) was considered as the primary measurement criterion. In addition to this value, the maximum overshoot, settling time, time to reach the maximum value, and error values were examined. Simulations conducted with the obtained parameters tested the system's resilience to disturbances introduced at the output, and the controller responses were also evaluated during these tests. The reactions of the determined parameters to different reference inputs were analyzed, and the results are presented in graphs and tables. The efficiency and reliability of the optimization algorithms were substantiated by comprehensive statistical analyses. These analyses play a critical role in algorithm selection and objective evaluation of the results. Simulation studies were conducted in the Matlab and Simulink environments. The FOMCON Toolbox was used for fractional‐order processes.

show abstract

Grain yield stability analysis using parametric and nonparametric statistics in oat (Avena sativa L.) genotypes in Ethiopia

Cited by 3 publications

References 52 publications

Technological Advancements and the Changing Face of Crop Yield Stability in Asia

Technological Advancements and the Changing Face of Crop Yield Stability in Asia

Deciphering genotype‐by‐environment interaction in new soybean lines based on multiple traits using different adaptability and stability methods

Nature‐ınspired algorithms for optimizing fractional order PID controllers in time‐delayed systems

Contact Info

Product

Resources

About