Research Article
Grain Yield Stability Evaluation In Field Pea Using AMMI Model in the Highlands of Bale, Southern Ethiopia
Tadele Tadesse*,
Belay Asmare,
Amanuel Tekalign,
Mesud Aliy
Issue:
Volume 10, Issue 2, June 2025
Pages:
24-30
Received:
24 July 2025
Accepted:
7 August 2025
Published:
8 September 2025
Abstract: To identify high yielding and stable field pea genotypes in the highlands of bale, twelve field pea genotypes were evaluated at three locations, Sinana, Goba and Agarfa in the highlands of Bale southeastern Ethiopia, for three consecutive years 2022-2024 cropping season using randomized complete block design with three replications. The combined analysis over locations and years revealed significant variation of mean grain yield for genotypes, environments and genotype by environment interaction at P<0.01. The AMMI analysis also revealed significant variation for all AMMI components. The genotypes contributed about 59.32% of the total sum squares followed by environment which is responsible for 22.09% of the total sum square and 18.60% of the variation is accounted due to genotypes by environment interaction. The first AMMI1 is accounted for 43.07% of the interaction sum of square whereas the second AMMI components accounted for 23.56% variation of the interaction sum of square. The two components cumulatively accounted for 66.63% of the variation of the interaction sum of square. Using AMMI Stability Value (ASV) G4, G11, G8, and G10 are considered as stable genotypes. Since the most stable genotypes are not high yielder, the GSI revealed G10, and G11 are the stable once whereas G7 and G8 are moderately stable. Since G7 and G10 having high mean grain yield over the checks with yield advantage of 22.4 and 13.4, respectively and also showed stable performance over the testing environments, they have been identified as candidate genotypes to be verified for possible release for the highlands of bale and similar agro-ecologies.
Abstract: To identify high yielding and stable field pea genotypes in the highlands of bale, twelve field pea genotypes were evaluated at three locations, Sinana, Goba and Agarfa in the highlands of Bale southeastern Ethiopia, for three consecutive years 2022-2024 cropping season using randomized complete block design with three replications. The combined an...
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Research Article
Assessing the Efficiency of Generalized Additive Mixed Models (GAMMs) Using Milk Production Variables: Evidence from Tanzania’s National Panel Surveys
Issue:
Volume 10, Issue 2, June 2025
Pages:
31-42
Received:
11 June 2025
Accepted:
27 June 2025
Published:
26 September 2025
Abstract: The study aimed to evaluate the performance of Generalized Additive Mixed Models (GAMMs) in fitting and predicting cattle milk production variables in Tanzania. The study used the National Panel Survey data collected between 2012 and 2021. GAMM (gamma), GAMM (lognormal), and GAMM (inverse normal) were fitted, and their performance was evaluated using Akaike Information Criterion (AIC), proportional of deviance explained, Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). The findings show that the GAMM with the lognormal distribution performed best, having a balanced good fit and predictive performance (AIC=4714.36, deviance explained = 94%, RMSE = 4.72, and MAE=2.94). Although the GAMM with the gamma distribution had a slightly better prediction accuracy (RMSE = 4.27 and MAE =2.77), it had a higher AIC (5027.75) and a smaller proportion of deviance explained (59%), making it less suitable for identifying factors affecting cattle milk production. The GAMM with the inverse normal distribution showed the poorest performance, with the highest AIC (5366.34), lowest deviance explained (37.3%), and largest errors (RMSE = 13.64, MAE = 4.46). Despite the poor performance of the GAMM with the inverse normal distribution, the overall results demonstrate that GAMMs are an effective tool for fitting and predicting cattle milk production variables, particularly when data are collected repeatedly from the same units over time. The GAMM with the lognormal distribution not only provided accurate predictions but also offered valuable insights into nonlinear relationships between milk production and household inputs. These insights can guide dairy farmers and policymakers in optimizing milk production by focusing on key factors such as improved breeding practices, proper feeding, health management, and labour efficiency.
Abstract: The study aimed to evaluate the performance of Generalized Additive Mixed Models (GAMMs) in fitting and predicting cattle milk production variables in Tanzania. The study used the National Panel Survey data collected between 2012 and 2021. GAMM (gamma), GAMM (lognormal), and GAMM (inverse normal) were fitted, and their performance was evaluated usi...
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,
Rosalia Katapa
