Ideal Test Environment Research Articles

Identification of foxtail millet (Seteria italica (L.) P. Beauv.) genotypes for multi-season adaptability using GGE biplot analysis in Foothills of Nagaland

Foxtail millet is a main millets crop of Northeastern region of India. The genotype-by-environment analyses assist in understanding the potential performance of the genotypes over environments. The present study aimed to investigate the 30 foxtail genotypes over the four different environments to identify the genotypes with stable yield in this region. The investigation was carried out during July 2022 to May 2023 for four different dates of sowing with fifty-five days interval. Two environments maintained under rainfed condition and the remaining two environments are maintained under irrigated condition with seven days interval. The experiment was conducted in randomized complete block design with three replications in all environments. Genotype-environment interactions significantly influenced grain yield across four environments, while replicates were non-significant. Pooled analysis revealed significant genotypic effects and seasonal impacts. The “discriminating power vs. representativeness” GGE biplot showed that E4 (showing dates in January 2ndfort night) is the most ideal test environment for foxtail millet elite line selection based on discriminative ability and representativeness. The accessions that performed best in each environment based on “which-won-where” polygon biplot, genotypes G19 (FOX 4392) and G27 (FOX 4420) showed superior and stable performance in E1, however G25 (FOX 4341) and G1 (ELS 20) excelled in E2, E3, and E4.Mean vs stability biplots exposed G1 (ELS 20) is stable and high yielding performance. Ranking genotypes GGE biplot identify the G25 (FOX 4341) is an ideal genotype due to its higher yield and stability compared to the other genotypes. Keywords: GGE Biplots, Foxtail millet, Tukey's Honest Significant Difference (HSD) Test

Influence of Genotype × Environment Interaction on Yield Stability of Maize Hybrids with AMMI Model and GGE Biplot

Maize yields perform differently in different environments, so the selection of suitable genotypes in diverse environments is essential for variety selection to enable better site-specific planting. Hence, the objective of the study was to estimate the productivity of 11 maize hybrids (G) in 10 different environments (E) and select high-yield and stable varieties for adaptive cultivation in 2022 and 2023. The combined analysis of variance showed that G (4%), E (50%), and their interaction (31%) had a significant effect (p < 0.01) on maize yield, with E factors contributing the most. In addition, the average yield ranged from 9398 kg/ha to 10,574 kg/ha, and ZF-2208 and DY-519 performed relatively well in both years. The AMMI model showed that the varieties DY-213, DY-605, and DY-519 had high and stable production in 2022, whereas it was ZF-2209 and LX-24 in 2023. The “W-W-W” biplot showed that DY-519 and JG-18 were the optimal varieties in 2022, and ZF-2208 and ZF-2210 were optimal in 2023. The “mean vs. stability” biplot indicated that JG-18, DY-605, and DY-213 (in 2022) and ZF-2208, LX-24, and ZF-2209 (in 2023) were the optimal varieties. Additionally, both the discrimination and representative biplot and the ranking biplot reflected that BinChuan and ShiDian (in 2022) and GengMa and YongSheng (in 2023) were the ideal test environments. In conclusion, DY-519, DY-605, ZF-2208, and LX-24 hybrids could be used for variety promotion. Moreover, BinChuan, ShiDian, GengMa, and YongSheng were the ideal test environments for selecting varieties. Therefore, the AMMI model and GGE biplot can be used to complement each other for a comprehensive evaluation of maize yield. In this way, excellent maize hybrids with high yield and stability can be selected, which could promote the selection and popularization of varieties and shorten the breeding process.

Enhancing Genetic Gains in Grain Yield and Efficiency of Testing Sites of Early-Maturing Maize Hybrids under Contrasting Environments.

The major challenges of maize production and productivity in Sub-Saharan Africa (SSA) include Striga hermonthica infestation, recurrent drought, and low soil nitrogen (low N). This study assessed the following: (i) accelerated genetic advancements in grain yield and other measured traits of early-maturing maize hybrids, (ii) ideal test environments for selecting early-maturing multiple-stress tolerant hybrids, and (iii) high-yielding and stable hybrids across multiple-stress and non-stress environments. Fifty-four hybrids developed during three periods of genetic enhancement (2008-2010, 2011-2013, and 2014-2016) were evaluated in Nigeria, The Republic of Benin, and Ghana under multiple stressors (Striga infestation, managed drought, and Low N) and non-stress environments from 2017 to 2019. Under multiple-stress and non-stress environments, annual genetic gains from selection in grain yield of 84.72 kg ha-1 (4.05%) and 61 kg ha-1 (1.56%), respectively, were recorded. Three mega-environments were identified across 14 stress environments. Abuja was identified as an ideal test environment for selecting superior hybrids. The hybrid TZdEI 352 × TZEI 355 developed during period 3 was the most outstanding under multiple-stress and non-stress environments. On-farm testing of this hybrid is required to verify its superior performance for commercialization in SSA. Considerable progress has been made in the genetic improvement of early-maturing maize hybrids for tolerance of multiple stressors and high yield. The identified core testing sites of this study could be used to enhance the testing and selection of promising hybrids.

An evaluation based on the analytic hierarchy process and GGEbiplot on French fry potato genotypes in Yunnan, China.

A total of 33 potato (Solanum tuberosum L.) cultivars and breeding clones imported from the United States and two local cultivars (Yunshu 401 and Cooperation 88, CK) were planted and evaluated. To determine their suitability for processing into French fries at five locations (e1-e5) in Yunnan Province, China, we developed a comprehensive evaluation system using the analytical hierarchy process (AHP). Eleven evaluation indicators for French fry quality, yield, and agronomic characteristics with a relative importance (weight coefficients) of 0.483, 0.301 and 0.216, respectively, were used to analyze the 35 potato genotypes (designated g1-g35).The genotypes were ranked and the results revealed that (1) on the average, the 33 potato genotypes imported from the United States showed a lower performance compared to the local cultivars. Compared with the CK, they were classified as not vigorous (Mean 5.11 vs CK 7.75), matured earlier (Mean 5.79 vs CK 1.70), and had a low resistance to late blight (Mean 3735.59 vs CK 1418.55), requiring the use of fungicides to control the disease at the five trial locations. (2) The US cultivar 'Defender' (g3) ranked in the top six at all five test locations because it had higher yield (29.56t h m-2), better fry quality (4.64), higher dry matter content (20.41%), better tuber length/width ratio (1.99), longer tubers (13.57cm), stronger plant vigor (7.17) and higher resistance to late blight (AUDPC: 3134.2). (3) By using GGEbiplot analysis, superior genotypes with high and stable yields were g3 and 'Echo Russet' (g33). 'Yunshu 401' (g34) and 'Yukon Gem' (g4) had high but not stable yields. The ideal test environments and hence experimental locations were Luquan (LQ, e2) and Lijiang (LJ, e4) which resulted in the best discrimination between genotypes among the five experimental locations in Yunnan. Overall, the developed evaluation system based on AHP and GGEbiplot analysis including 11 evaluation indicators for French fry quality, yield and agricultural traits can be a model for evaluation and promotion of new French fry cultivars, and evaluating and selecting the test location.

Investigation of helium exhaust dynamics at the ASDEX Upgrade tokamak with full-tungsten wall

An efficient removal of He ash by active pumping in future fusion devices is necessary to avoid fuel dilution and not degrade the core confinement properties. Therefore, a deep understanding of the underlying physics mechanisms is mandatory. Helium exhaust has been experimentally investigated at the ASDEX Upgrade tokamak. This is an ideal test environment, thanks to the ITER-like divertor geometry, an extensive diagnostics coverage and the presence of plasma-facing components made of tungsten. The exhaust efficiency, characterized by the He compression in the divertor, was found to improve with increasing divertor neutral pressure but to degrade with detachment. A multi-reservoir particle balance model was developed to interpret the observed exhaust dynamics, accounting for plasma transport and wall retention. The limited performance of the pumping system and the efficient helium retention capability of the tungsten wall were identified to have the strongest impact on the exhaust dynamics.

GGE biplot analysis of genotype by environment interaction and grain yield stability of oat (Avena sativa L.) in Ethiopia

AbstractA total of 24 oat genotypes were evaluated across 9 environments using a randomized complete block design with 3 replications. The combined analysis of variance indicated that grain yield was significantly affected by genotype, environment, and genotype by environment interaction (GEI). The environment's main effect explained 44.62% of the total variation, whereas the genotype and GEI captured 28.84% and 26.54%, respectively. The genotype plus genotype by environment (GGE) biplot of the first two principal components also explained (PC1 = 68.46%) and (PC2 = 11.84%) of the GEI sum of squares. G8 (SRCPX80Ab2267), G17 (SRCPX80Ab2310), G5 (Sorataf), G14 (Was), G19 (Bonsa), G22 (SRCPX80Ab2691), G12 (SRCPX80Ab2291), G11 (SRCPX80Ab2806), G4 (CI‐8251), and G16 (Jassari), which were located at the left side of the PC1, showed better grain yield performance. The smaller PC1 values were observed for G19, G14, G8, G11, G12, G17, and G22 showing better grain yield stability. The ideal genotype located at the center of the concentric circles was G19, whereas the desirable genotypes were G14, G8, G11, G12, G17, and G22. The polygon view of the GGE biplot showed that the first mega‐environment contains environment E3 (Ginchi) with G16, whereas the second mega‐environment contains environments E7 (Bekoji) and E8 (Kuyu) with G12 being the winning genotypes. The vertices of the fourth mega‐environments (E6—Kulumsa) were G8 and G17, whereas the remaining environments grouped in the third mega‐environment without vertex genotype. Furthermore, E2 (Ada‐Berga) was an ideal test environment, whereas E5 (D/Zeit), E1 (Holetta), and E4 (Jeldu) were identified as favorable environments. Regarding the mean grain yield and stability, G19, G14, G8, G11, G12, G17, and G22 were the best genotypes that can be considered adapted genotypes to wider environments.

Performance and Yield Stability of Quality Protein Maize (Zea mays L.) Hybrids under Rainfed Condition

Abstract Quality protein maize (QPM) commercialization can combat the food and nutritional insecurity that is common in some developing nations of the world. Evaluation of QPM hybrids under varying rainfed environments provide valuable evidence for the selection of the most productive genotypes for the target environment. The present study was conducted at three locations to assess the agronomic performance and the grain yield stability of 11 QPM and 2 commercial hybrids (checks) over two years. There were significant (p ≥ 0.01) differences among the environments in the expression of all measured traits while genotype as well as genotype × environment interaction also differed significantly for all the traits except for husk cover and Anthesis-silking interval. The tested QPM hybrids outperformed the local and reference checks by 17.28% and 29.47% respectively. The biplot explained 85.3% of the total variation in yield, with 65.4% attributed to principal component 1 and 19.9% to principal component 2. Three vertex hybrids, EWQH-25, EWQH-21 and Local Check, were identified as the most responsive in the environments within which they fall. Hybrid EWQH-21 produced the highest yield across all environments but was unstable. Therefore, hybrids EWQH-22, EWQH-9, and EWQH-13, with similar yield values and more stable performances were recognized as ideal across environments. Lapai 2017 (E2) proves to be both representative and discriminative, making it the ideal test environment for selecting hybrids with broad adaptability. The study concludes that EWQH-9, EWQH-13, and EWQH-22 be recommended for cultivation across the environments while EWQH-21 be recommended for the specific area of adaptation.

Mitigating aflatoxin contamination in maize: Breeding for stable resistant cultivars

Maize is a major cereal crop for humans and animals. The production and consumption of maize are adversely affected by aflatoxin contamination resulting in reduced grain quality with high health risk to consumers. Unsafe aflatoxin levels above 20 ppb have been reported in maize from farmers’ fields in Ghana. A study was undertaken to evaluate the yield and aflatoxin contamination resistance stabilities of selected hybrids being developed in Ghana. Eighteen maize genotypes (14 hybrids with the following code names: GH01, GH02, GH03, GH04, GH05, GH06, GH07, GH08, GH09, GH10, GH11, GH12, GH13, and GH14 and four local checks: Etubi, Obotantim, Tintim, and Mamaba) were evaluated across six environments in two seasons. Artificial inoculation was done randomly using the side needle injection method 14 days after midsilk at a concentration of 9 × 107 conidia/ml. After the harvest levels of aflatoxin in the harvested grains were determined using high-performance liquid chromatography (HPLC). Data analysis showed that genotypic effects and environmental effects on some traits were consistently significant across environments, which mean that there was useful variation among the genotypes that could be utilized for the development of hybrids. Non-significant genotype by environment interaction effects for grain yield suggests that a promising genotype selected in one of these locations will also be suitable for production in the other locations in the same agroecological zone. GGE biplot analyses provided clear bases for determining stability and performance of the hybrids. Two hybrids have the potential for production in Ejura, Fumesua, Ohawu, Akumadan, and Kpeve and similar agroecological zones. GH04, GH10, and GH01 were lowest yielding, but GH10 was most stable. GH05 was identified as the most promising for production in Ejura, Ohawu, and Akumadan. Again, GH08 was the Vertex genotype for Wenchi environment. Ejura, located in the transition zone, was identified as the ideal testing environment for the hybrids. Most of the hybrids showed low levels of aflatoxin accumulation below 20 ppb despite artificial inoculation. The study revealed GH01, GH05, and GH08 as the most stable among the top yielding hybrids with aflatoxin levels of 11.9, 14.3, and 3.8 ppb, respectively, after artificial inoculation, which can be considered for cultivation as varieties to help mitigate aflatoxin contamination in maize in Ghana and beyond.

Genotype by environment interactions of grain yield performance and lodging incidence in advance breeding lines of rice across environments in Guyana

Fifteen advanced rice (Oryza sativa L.) breeding lines along with three check varieties were assessed for lodging incidence and grain yield stability under six environments in Guyana. The trials were conducted in the spring and autumn seasons of 2018. A RCBD with three replication was employed. Descriptive statistics was used to look at the lodging incidence and AMMI & GGE analysis was utilized to study the genotype and environment (G x E) interactions. All genotypes (except G15-11, FG15-01, and check GRDB 10) recorded mean lodging incidence of less than 5 percent. The average grain yield of the 18 genotypes ranged from 5504.44 to 7499.21 Kg/ha. The biplot results showed genotype FG12-259 demonstrated consistently high stable grain yield performance followed by FG12-49, FG12-23, FG14-43, G13-126, G15-11, G14-10 and G16-104. Likewise, AMMI analysis found the environment at Rice Research Station, Burma, Region # 5, Spring crop, 2018 (E2) and Anna Regina, Region # 2, Autumn crop, 2018 (E4) to be the most representative environment with smaller average - environment axis (AEA) angle and closeness to the ideal test environment. Also, E4 followed by Lesbeholden, Black Bush Polder, Region # 6, Autumn crop, 2018 (E6) recorded low IPCA1 scores and showed small interactions. The what-won-where biplot pattern suggested the target environment consisted of three mega-environments since four environment falls in one sector and one each in another two sectors. The three check cultivars GRDB 10 GRDB 12 and GRDB14 also expressed a similar higher level of stable grain yields over all the test environments. Based on the consistently high and stable grain yield performance, along with other superior grain qualities and plant characteristics the genotypes viz. FG12-259 and FG12-49 were selected, developed, and released as commercial rice varieties called ‘GRDB 16’ and ‘GRDB 15’, respectively in Guyana for wider cultivation by the farmers.

Yield Stability Analysis in Maize Hybrids of Southwest China under Genotype by Environment Interaction Using GGE Biplot

Selecting superior genotypes across different environments is vital for varietal release, crop planting, and commercial use. Therefore, the objectives of this research were to appraise the performance of hybrids approved in recent years in diverse environments, and recommend high-yielding and stable genotypes for wider adaptation. Fourteen single cross maize hybrid genotypes (G), including a check, were implemented across ten environments (E) in two crop seasons (2020 and 2021). The combined analysis of variance revealed that G, E, and their interactive (GEI) significantly (p < 0.01) affected the grain yield. Moreover, the mean grain yield ranged from 9333 kg ha−1 for HH-2 (2021) to 13,195 kg ha−1 for LD-18 (2020). The “which won where” GGE biplot revealed the existence of mega environments with their own best hybrids (LD-18 and LD-29 in 2020; LD-18, LD-19, and YY-1506 in 2021). The “mean vs. stable” GGE biplot suggested that LD-18 and ZY-811, with highest/middle productive and high stability across 10 environments, were closest to the ideal genotype. Furthermore, the “discriminating power vs. representativeness” GGE biplot showed that Xuanwei, Yanshan, Gengma, and Shiling were the most the ideal test environments for hybrid selecting, based on their discriminative ability and representativeness. Therefore, the GGE biplot analysis allowed for an efficient selection of high-yielding and stable maize hybrids to guide ecological planting and commercial use.

Genotypes by Environment Interaction of Bread Wheat (Triticum aestivum L.) Genotypes on Yield and Quality Parameters under Rainfed Conditions

The significant genotype (G) by environment (E) interaction and genetic diversity in the breeding programs are an essential issue for the breeder to develop new cultivars. The experiment was conducted in the Trakia region, Turkey at five environments during the 2015-2016 growing cycles. In the study, 25 advanced genotypes were used in randomized complete block design with four replications. Data on grain yield, days to heading, plant height, 1000-kernel weight, test weight, protein ratio, wet gluten content, gluten index, hardness, and sedimentation value were investigated. The results of variance analyses showed that there were significant differences (P<0.01) among genotypes based on all parameters investigated. Genotypes when tested across different environmental conditions often showed significant variation in grain yield. Mean grain yield across five locations ranged from the highest 6673 kg ha-1 to the smallest 5008 kg ha-1. Burgaz location was found near the ideal test environment of the average environment coordination. Therefore, location Burgaz should be regarded as the most suitable to select widely adapted genotypes. With the longest vectors from the origin, environments Edirne1 and Edirne2 were the most discriminating location. Considering simultaneously mean yield and stability, G7 and G12 were the best genotypes. G3 is more ideal genotype because it is located in the ideal center. So these genotypes can be used as for the evaluation of bread wheat genotypes in the region. With the longest vectors from the origin, traits plant height, gluten index and protein ratio was the most discriminating parameters. In the evaluation of genotypes, G2 and Pehlivan are quite stable because they are located close to the center of the horizontal axis. Genotype G3 is very favourable because it is located near the center of the horizontal axis and on all traits. G7, G24 and Aldane are located above the axis vertical genotypes are desirable based on parameters profiles.

Genotypes x Environment Interaction and Stability of Bread Wheat (Triticum Aestivum L.) Cultivar Under Rainfed Conditions

The significant genotype (G) and environment (E) interaction and genetic diversity in the breeding programs are essential issues for the breeder to develop new cultivars. The experiment was conducted in the experimental area of Trakia Agriculture Research Institute Edirne, Turkey at eight environments from 2006-2007 to 2013-2014 growing cycles. In the study, nine released cultivars were used in randomized complete block design with four replications. Grain yield data were subjected to analysis of variance (ANOVA), the additive main effect, and multiplicative interaction (AMMI) and genotype and genotype-by-environment (GGE) biplot analyses. Stable genotypes were identified with GGE biplot and AMMI models. ANOVA and AMMI analysis revealed highly significant (p < 0.01) differences among test environments (E), genotypes (G), and their interaction (G×E). The graphical result from PCI showed that the first principal component PC1 explained 49.43% of the interaction while the second principal component, PC2 explained 29.08% of some of the square interaction. The result of PCA revealed that the 2 principal components (PC1, PC2) contributed 78.51% of the total variability. The environmental effect was responsible for the greatest part of the variation, followed by genotype and genotype by location interaction effects. Genotypes, when tested across eight environmental conditions, showed significant variation in grain yield. The highest grain yield was performed by cultivar Bereket (G8) and followed by Selimiye (G7) and Gelibolu (G4). Environment E4 and E1was found near the ideal test environment of the average environment coordination. It was determined that cultivars G7 (Selimiye) and G8 (Bereket) were well adaptable to all environmental conditions. Cultivar G4 (Gelibolu) was well adaptable to well fertile environmental conditions.

Genotype by Environment Interaction of Cotton Genotypes for Seed Cotton Yield in Zambia

Cotton (Gossypium hirsutum L.) is an important cash crop in Zambia. The national seed cotton yield (SCY) per hectare ranges from 200-500 kg/ ha as compared to the potential of up to 2500 kg/ ha. Understanding the specific performance of several genotypes across different environments is an option, which may maximize specific genotypic performance. In addition, the performance of specific environments if known concerning specific and mean genotypic performance may guide the breeding approaches to these environments. This study therefore investigated the presence of SCY mega-production environments in Zambia and delineated the environments and identified the ideal test environment capable of discriminating yield differences among genotypes. Thirty (30) genotypes, were planted following a 6 x 5 lattice design with three replications in seven environments of Zambia. Additive main effects and multiplicative interaction (AMMI) model and genotype plus genotype by environment (GGE) biplot were used to explore the genotype by genotypic environmental interaction (GEI). Three mega environments (M1, M2 and M3) were identified. Genotype G27, G26 and G28 were the best performing genotypes in M1, M2 and M3 with overall mean SCY of 1416, 1320 and 960 kg/ ha respectively. Among the locations, Masumba was identified as an ideal test environment with mean SCY of 1249 kg/ ha. Therefore, testing seed cotton genotypic yield and selecting desirable genotypes in Masumba may be sufficient for evaluation.

Grain yield and stability of early-maturing single-cross hybrids of maize across contrasting environments

ABSTRACTTesting and identification of high-yielding and stable hybrids adapted to contrasting environments is crucial for sustainable maize (Zea mays L.) production in sub-Saharan Africa. The objectives of this study were to (i) assess the yield response of 150 single-cross hybrids under low soil nitrogen (low-N), Striga-infested and optimal growing environments, and (ii) identify the most suitable environments for the selection of superior and stable hybrids. The 150 hybrids were derived from 30 inbred parents via North Carolina design II mating scheme. They were tested, together with six checks, in a 13 × 12 alpha lattice design with two replications across 10 test environments in 2016 and 2017. Genotype by environment interaction was significant for yield, with high repeatability (0.70) across the test environments. Test environments KWDS-LN-16 and KWDS-OPT-16 were found to be the ideal test environments for selecting superior hybrids. About 62% of the hybrids were stable across environments. Hybrids TZEI 3A × TZdEI 192, TZdEI 216 × TZEI 3A and TZEI 379 × TZdEI 272 were identified as high yielding and most stable across environments. These outstanding hybrids with tolerance to both Striga and low-N should be further tested extensively on-farm for potential release in Ghana and other West African countries.

Differential grain yield performance of single cross hybrid maize in varied nitrogen conditions

Presence of genotypic differences in maize ( Zea mays L.) grain yield in multi-environments would help plant breeders to make logical decisions in improvement programmes of the crop. Hence, the grain yield performance, heterosis and stability in hybrid maize in varied soil nitrogen (N) conditions in Nigeria were assessed using multiple biometrical techniques. Grain yield performance, heterosis and stability of 150 single cross hybrid maize were assessed with checks in severe N stress, N stress and optimal conditions in a replicated trial laid out in 19 × 8 lattice design for 2 years. The grain yield was subjected to analysis of variance for each N condition and combined across years. Heterosis was estimated for the trait, additive mean effects and multiplicative interaction (AMMI); and genotype + genotype × environment (GGE) models were used to analyse the hybrids’ yield stability. The AMMI captured 93.7%; while GGE bi-plot accounted for 88.7% of total variation among the hybrids. Eight hybrids had high yields across the N conditions. Genotypes and environments affected heterosis for grain yield. Hybrids TZEI7×BD74-399, BD74-179×BD74-55 and BD74-175×BD74-147 were the most ideal genotypes for the N conditions; while severe N stress condition was ideal test environment. Hybrid BD74-170×BD74-31 adapted most to N stress; while TZEI1×BD74-399 adapted most to severe N stress and to optimal conditions. Based on yield, heterosis and stability, hybrids involving inbred line BD74-171 were recommended for N stress; while those with inbred lines TZEI1, TZEI4 BD74-170, BD74-128, BD74-179 and BD74-175 were adapted to N stress and optimal conditions. Key words: AMMI, GGE biplot, Zea mays

Inorganic carbonate composites as potential high temperature CO2 sorbents with enhanced cycle stability.

A calcium magnesium carbonate composite (CMC) material containing highly porous amorphous calcium carbonate (HPACC) and mesoporous magnesium carbonate (MMC) was synthesized. CMCs with varying HPACC : MMC mol ratios and high BET surface area (over 490 m2 g−1) were produced. The CMCs retained the morphology shared by HPACC and MMC. All these materials were built up of aggregated nanometer-sized particles. We tested the CO2 uptake properties of the synthesized materials. The CMCs were calcined at 850 °C to obtain the corresponding calcium magnesium oxide composites (CMOs) that contained CaO : MgO at different mol ratios. CMO with CaO : MgO = 3 : 1 (CMO-3) showed comparable CO2 uptake at 650 °C (0.586 g g−1) to CaO sorbents obtained from pure HPACC (0.658 g g−1) and the commercial CaCO3 (0.562 g g−1). Over 23 adsorption–desorption cycles CMOs also showed a lower CO2 uptake capacity loss (35.7%) than CaO from HPACC (51.3%) and commercial CaCO3 (79.7%). Al was introduced to CMO by the addition of Al(NO3)3 in the synthesis of CMC-3 to give ACMO after calcination. The presence of ∼19 mol% of Al(NO3)3 in ACMO-4 significantly enhanced its stability over 23 cycles (capacity loss of 5.2%) when compared with CMO-3 (calcined CMC-3) without adversely affecting the CO2 uptake. After 100 cycles, ACMO-4 still had a CO2 uptake of 0.219 g g−1. Scanning electron microscope images clearly showed that the presence of Mg and Al in CMO hindered the sintering of CaCO3 at high temperatures and therefore, enhanced the cycle stability of the CMO sorbents. We tested the CO2 uptake properties of CMO and ACMO only under ideal laboratory testing environment, but our results indicated that these materials can be further optimized as good CO2 sorbents for various applications.

Screening for blast resistance in rice using AMMI models to understand G x E interaction in Guyana

In rice, totally one hundred and three genotypes were evaluated for resistance against blast disease in three hot spot locations of Guyana. The experiments were conducted in spring and autumn seasons of 2015, 2016 and 2017. The modified upland blast nursery (UBN) method was adopted for screening blast resistance. The Additive Main effects and Multiplicative Interaction (AMMI) analysis was applied to understand the interaction between genotype (G) and environment (E). The results of all seasons at three locations showed the consistent expression of high resistance by genotype FL 127 followed by FG12–08 and FG12–273. The AMMI analysis of variance (ANOVA) revealed that, resistance was dominated by the genotype main effect. The magnitude of genotypes by environment (G x E) sum of square (SS) was 3.64 times smaller than genotype SS, and 6.74 times larger than environment SS. The analysis has indicated that the differences of the genotypes across the environment were not substantial. All of the seven environment vectors appeared to be positively correlated. The screening at Onverwagt Back, autumn 2016 (E7) and Black Bush Polder, spring 2015 (E5) were observed to be the most representative environment. This was clearly revealed by smaller average - environment axis (AEA) angle. Likewise, E4 (Canje, autumn 2015) was observed closer to the “ideal test environment”, thus suggested as an ideal environment for blast resistant screening. Also, genotypes clustered near the origin have showed stable blast disease resistant status and contributed little to both G and G x E. These genotypes proposed as useful ones for breeding disease resistant varieties and hybrids in rice.

Targeting test environments and rust-resistant genotypes in lentils (Lens culinaris) by using heritability-adjusted biplot analysis

Lentil rust incited by the fungus Uromyces viciae-fabae is a major impedance to lentil (Lens culinaris Medik.) production globally. Host-plant resistance is the most reliable, efficient and viable strategy among the various approaches to control this disease. In this study, 26 lentil genotypes comprising advanced breeding lines and released varieties along with a susceptible check were evaluated consecutively for rust resistance under natural incidence for two years and at five test locations in India. A heritability-adjusted genotype main effect plus genotype × environment interaction (HA-GGE) biplot program was used to analyse disease-severity data. The results revealed that, among the interactive factors, the GE interaction had the greatest impact (27.81%), whereas environment and genotype showed lower effects of 17.2% and 20.98%, respectively. The high GE variation made possible the evaluation of the genotypes at different test locations. The HA-GGE biplot method identified two sites (Gurdaspur and Pantnagar) as the ideal test environments in this study, with high efficiency for selection of durable and rust-resistant genotypes, whereas two other sites (Kanpur and Faizabad) were the least desirable test environments. In addition, the HA-GGE biplot analysis identified three distinct mega-environments for rust severity in India. Furthermore, the analysis identified three genotypes, DPL 62, PL 165 and PL 157, as best performing and durable for rust resistance in this study. The HA-GGE biplot analysis recognised the best test environments, restructured the ecological zones for lentil-rust testing, and identified stable sources of resistance for lentil rust disease, under multi-location and multi-year trials.

Assessing Genotype-By-Environment Interactions in Aspergillus Ear Rot and Pre-Harvest Aflatoxin Accumulation in Maize Inbred Lines

Aspergillus flavus, causal agent of the Aspergillus ear rot (AER) of maize, also produces aflatoxins that cause aflatoxicosis in humans and livestock. Ten maize inbred lines were evaluated in replicated trials in two aflatoxicosis outbreak hot spots in Kenya and in three maize-growing areas in South Africa for resistance to AER, A. flavus colonization, and pre-harvest aflatoxin accumulation during the 2012/13 growing season. AER severity was measured by visual assessment, while A. flavus colonization and aflatoxin content were quantified by real-time polymerase chain reaction (PCR) and liquid chromatography tandem mass spectrometry, respectively. Genotype by environment interaction (GEI) was determined using analysis of variance (ANOVA), additive main effects and multiplicative models (AMMI), and genotype plus by environment (GGE) biplot analyses. Stability of genotypes was evaluated using AMMI analysis. AER severity and fungal colonization significantly (p &lt; 0.001) varied between genotypes. GEI influenced the severity of AER symptoms and aflatoxin accumulation significantly (p &lt; 0.001), while fungal colonization was not affected. The inbred lines response was consistent for this trait in the test environments and was thus considered a desirable measure to indicate maize lines with a high risk of aflatoxin accumulation. CML495, CKL05019, LaPosta, and MIRTC5 were the least diseased lines, with the lowest aflatoxin contamination and a stable phenotypic response across the environments. Kiboko was determined as the ideal representative test environment, with discriminative ability of the genotypes for selection of the desired stable responses of the three traits.

Agile Testing Automation Framework

Software testing is one of the most important phases of Software Development Life Cycle and main technique to find bugs and ensure the quality of the software. Software Testing can be conducted manually as well as automated. In manual testing, testing is done without any tool. In automation testing, testing is done with the help of automated testing tools. In recent times, agile development and testing are growing in popularity. Agile testing tools vary from project management tools to automated testing tools. In this paper, we have explained the detailed working of Agile Testing Framework (ATF). The ATF is about implementing the ideal automated testing environment in an agile organization, to deliver quality software, fast. The ATF Architecture can be used to easily integrate with continuous integration tools and best in class third-party testing tools to allow for accessibility, security, web consistency and full performance (front-end, middleware and load) testing.