Combining Ability Analysis of Maize Inbred Lines in Ethiopia

The study was initiated to estimate combining ability of maize inbred lines and crosses using line by tester analysis. Fifty entries consists 48 F1 single crosses developed from 24 inbred lines and 2 testers using line x tester design and two commercial check hybrids used in the study. The experiment was conducted using alpha lattice design with two replications. Analysis of variance revealed existence of significant genetic variation among genotypes for all studied traits except for plant aspect (PA). Location x entry interaction for most of the traits was not significant which suggests hybrid performance was consistent across tested locations. Line x tester analysis of variance showed that mean squares due to GCA of lines were significant (p< 0.01 or p< 0.05) for all studied traits. Mean squares of tester GCA and SCA were significant for most of studied traits. This indicates that both additive and non-additive gene effects had contributed for the variation of the crosses. However, higher proportional contribution of additive gene action for all studied traits was obtained. Several lines and crosses were identified as good general and specific combiners for yield and yield related traits. Lines L23, L11, L15 and crosses L2xT1, L3xT1, L8xT1, L11xT1, L23xT1 and L13xT2 were found to be good general and specific combiners, respectively. In conclusion, the stated inbred lines with desirable gca effects and cross combinations with desirable sca effects for grain yield and yield related traits could be used as useful genetic material

Combing ability analysis of among early generation maize inbred lines

Combining ability estimates are important genetic attributes ina maize breeding program aiming to develop stable andhigh yielding hybrids and synthetic varieties. The objectives of this study were to estimate combining ability effects of locally developed and introduced early generation maize inbred lines for grain yield, yield related traits, and reaction to gray leaf spot (GLS) and northern corn leaf blight (NCLB) diseases; and (2) identify promising hybridsthat could be used in the breeding programs or for commercial production. Twenty-nine early generation maize inbred lines were crossed to two testers(SC22 and Guto-LMS5) using line xtester mating design. The resulting F1 progenies along with two check hybrids were tested across three locations (Hawassa, Arekaand Bako) in Ethiopia. Analysis of variance revealed significant difference among the hybrids for all studied traits. General combining ability (GCA) and specific combining ability (SCA) effects were also significant, indicating the contributions of both additive and non-additive gene actionsin controlling the traits studied. However, the relative magnitudes of GCA and SCA sum of squares indicated the preponderance of additive gene effects for all characters studied. Parental lines 2, 8, 9, 15 and 20 showed significantly positive GCA effects for grain yield. For GLS parents 1,7,23, and 26, and for TLB parents 5, 6and 7 revealed significantly negative GCA effects. These inbredlines couldbe good sources ofgenes for the improvement of the traitsunder considerationin the breeding programs.Five crosses, namely,L5 x GuttoLMS5,L7x Gutto LMS5, L8 x Guto LMS5, L15 x SC22 and L20 x TSC22) gave significantly higher grain yield advantage over the two standard checks. Further evaluation of these crosses can give reliable information about their performances to recommend the crossesfor commercial production

Combining ability and heterotic relationships between CIMMYT and Ethiopian maize inbred lines

Knowledge of combining ability and heterotic relation of exotic inbred lines with the locally available ones would be helpful for efficient breeding program. The objectives of the current study were to estimate: (i) combining ability effects between Ethiopian and CIMMYT (International Centre for Maize and Wheat Improvement) maize inbred lines and (ii) possible heterotic relationships between the two sources of inbred lines. Forty-two crosses were produced using North Carolina Design II mating scheme by crossing six Ethiopian with seven CIMMYT inbred lines. Combined analyses over three locations showed significant differences among the hybrids for all the studied traits. Both general (GCA) and specific (SCA) effects were significant for most traits, indicating the importance of both additive and non-additive effects for these traits. Female parents E2 and E4 showed significant and positive GCA effects for grain yield. Other female lines that showed desirable GCA effects were E1 for ear height, E2 for days to anthesis, ear and plant heights and E5 for days to anthesis and silkng. Among male parents, C1 was the best general combiner for all agronomic traits, but not for grain yield. Inbred lines C2 and C6 were good general combiners for plant height and days to silking, respectively. Hybrids E4 x C2 and E5 x C3 showed superior SCA effects for grain yield while few other combinations showed desirable SCA effects for days to anthesis, ear and plant heights. The results of this study indicated potential heterotic relationships between CIMMYT and Ethiopian inbred lines for use in hybrid and synthetic development and introgression of germplasm

Genotype x environment interaction and stability analysis of grain yield in QPM hybrid varieties

Maize (Zea mays L.) is a major staple cereal widely cultivated in different agro-climatic environments of Ethiopia.Maize productivity in the tropical highland region of the country is known by low average yield mainly due to thelack of high yielding and widely adapted improved cultivars. The objectives of this study were to determine G×Einteraction and yield stability of quality protein maize (QPM) experimental hybrids,to identify ideal genotype withhigh average yield depending on the differential genotypic responses to environment, and to form homogeneousgrouping of environments. The study was conducted at seven environments representing the tropical-highlandsub-humid maize growing agro-ecology of Ethiopia in 2015/2016. Thirty-three QPM hybrids and three-commercial hybridchecks were evaluated using a 4 ×9 alpha lattice design. Yield data was analyzed using AMMI and GGEbi-plot methods. Using AMMI analysis, four promising QPM hybrids designated asG31, G7, G19, G29, and G22were identified based on combined stability and average yield.GGEbi-plot displayed that variety Jibatwas closestto the ideal genotype, can be considered as best hybrid whereas G29, G22 were considered as desirably stable genotypes.GGE bi-plot also displayed Holeta as ideal environment and thus considered useful in discriminating thehybrids and representativeness as suitable environment. The GGE analysis delineated the test environments intothree mega-environments useful for targeted evaluation of genotypes. The result of this study indicated specificallyand widely adapted high yielding stable genotypes and also revealed homogeneous test environments

Mega-Environment Targeting of Maize Varieties using Ammi and GGE Bi-Plot Analysis in Ethiopia

አህፅሮት በቆሎ በኢትዮጵያ  ከሚመረቱ የምግብ ሰብሎች መካከል በምርትና ምርታማነቱ ግንባር ቀደም ስፍራን የያዘ ሰብል ነው፡፡  የሰብሉን ምርታማነት ከሚደግፉ የተለያዩ መንስዔዎች  ውስጥ በዋናነት ከፍተኛውን  ቦታ  የሚይዙት ከጥናትና ምርምር  የተገኙ የተሻሻሉ ዝርያዎች ቢሆኑም ሁሉም ዝርያዎች   በበቆሎ አብቃይ ስነ-ምህዳሮች  ላይ ተዘርተው  በምርታማነታቸው ወጥነት የማያሳዩ መሆናቸው ይታወቃል፡፡ እንደየአካባቢው የአይር ፀባይ፤ የአፈር ዓይነትና የዝናብ መጠን እንዲሁም የመሬት ከባህር ወለል ከፍታ ልዩነት የተነሳ በምርታማነታቸው ለየአካባቢው ተመራጭና ተመራጭ ያልሆኑ ዝርያዎችን መለየት ይቻላል፡፡ በዚህ ምክንያት ለተለያዩ ዝርያዎች ምርታማነት ተስማሚና ወካይ የሆኑ ስፍራዎችን  ለይቶ በማወቅ የትኛው ዝርያ በየትኛው ስፍራ ላይ ቢዘራ  ሁለንተናዊ የአካባቢ ባህሪያትን  ተላብሶ ከፍተኛ ምርት ሊሰጥ ይችላል?  እንዲሁም የትኛቹ ስፍራዎች በአየር ንብረት ተቀራራቢነት በጥቅል ተደምረው አንድ ዝርያ በወጥነት  በሁሉም ስፍራ ተዘርቶ ምርታማ የሚያደርጋቸውን አካባቢዎች ለይቶ ለማወቅ ጥናቱ ተደረገ፡፡ ጥናቱ ለምርት በምርምር የተለቀቁ  19 ዲቃላ የበቆሎ ዝርያዎችን በማካተት  ወይናደጋማና ደጋማ ስፍራዎች ላይ ተዘርተው የተለያዩ መረጃዎችን  በማሰባሰብ እንዲጠናቀር ከተደረገ በኋላ ለጥናቱ ስኬት   ከፍተኛ ትኩረት ተሰጥቶት  ለውሳኔ  እንዲያመች ከየአካባቢው የተሰበሰቡ የዝርያዎቹ ምርት አግባብ ባላቸው ሳይንሳዊ ዘዴዎች እንዲሰሉ ተደረገ፡፡ በስሌቱ መሰረት ከዝርያዎቹ በአማካይ በሔክታር 4.47 ( BH545)  እስከ 7.49  ( BH546) ቶን  ምርት ተመዘገበ፡፡ እንዲሁም በተደረገው ስሌት G14  እና  G1  ተብለው የተለዩ ዝርያዎች ለአብዛኞቹ የጥናቱ ስፍራዎች ተስማሚ  እንደሆኑ  ቢታወቅም  BH546  በሚባል ስያሜ የሚለየው ዝርያ በከፍተኛ ደረጃ ተመራጭ እንደሆነ ለማረጋገጥ ተችለሏል፡፡ በሌላ በኩል E9  በተባለ ምህፃረ-ቃል የሚለይ ስፍራ በአብዛኛው ዝርያዎች  ተመራጭ እንደሆነ ስሌቱ ሲያሳይ ፤ E1  የተባለው ግን ተመራጭ እንዳልሆነ ታውቋል፡፡ ሆኖም ግን 11 የጥናት ስፍራዎች በሶስት ዋና ዋና ፤ እያንዳንዳቸው በዝርዎቹ ምርታማነት የጎላ ልዩነት በሚታይባቸው ወጥ ክፍሎች እንደተከፈሉ የስሌቱ ውጤት ለይቶ አሳይቷል፡፡ በዚህ መሰረት E9 በሚል ስያሜ የሚለየው ስፍራ በብቸኝንት እንደ አንድ ዋና ክፍል የተከፈለ ሲሆን በሁለተኛ  ክፍል ውስጥ  በጥቅል  ዘጠኝ  አካባቦዎች  E1, E2, E3, E5, E6, E7, E8  እና E11  በአንድነት ተደመሩ፤ እንዲሁም  E4 እና  E10 በሶስተኛው ክፍል ውስጥ ተመደቡ፡፡ E3, E5 and, E7 በተባሉ ምህፃረ-ቃል የተለዩ ስፍራዎች ለዝርዎቹ ምርታማነት ወካይና ተመራጭ መሆናቸውን ጥናቱ አሳየ፡፡  ነገር ግን E4, E9 and E10  የተባሉ አካባቢዎች በውስን ስፍራዎች ውስጥ  ምርታማ የሚሆኑ  ዝርያዎችን መለየት የሚችሉ መሆናቸውን ጥናቱ ያረጋግጣል፡፡  በሌላ በኩል E8 and E11 የተባሉ ስፍራዎች የዝርያዎችን ምርታማነትና ተመራጭነት  በጉልህ ለማሳየት ምንም አስተዋፅዖ ያላበረከቱ መሆናቸውን ጥናቱ አሳይቷል፡፡ በመጨረሻም የዚህ ጥናት ውጤት ወጥነት ያላቸው ሶስት ዋና ዋና ስነ-ምህዳራትን ለይቷል፤  ዝርይዎች በምርታማነታቸው   ተመራጭነት  የሚኖራቸውንና  የማይኖራቸውነ  ለይተው የሚያሳዩ ስፍራዎችን  ጠቁሟል እንዲሁም በምርታማነቱና ለአብዛኛው አካባቢዎች  በወጥነት ተስማሚነቱን የሜያሳይ ዝርያ ለይቶ አሳይቷል፡፡ Abstract In multi-location experimental trials, test locations must be selected to properly discriminate between varieties and to be representative of the target regions. The objective of this study were to evaluate test locations in terms of discrimination ability, representativeness, and desirability, and to investigate the presence of mega-environments using AMMI and GGE models and to suggest representative environments for breeding and variety testing purposes.  Among 19 maize varieties tested across 11 environments, mean grain yield ranged between 4.47 t/ha (BH545) to 7.49 t/ha (BH546). Both AMMI and GGE  models identified   G14 and G1 as  desirable hybrids for cultivation   because they combined stability and higher average yield. Nonetheless, as confirmed by GGE analysis BH546 was most closest to the ideal genotype hence, considered as best hybrid.  Environment wise, E9 and E4 were the most stable and unstable test environments, respectively. The 11 test environments fell into three apparent mega-environments.  E9 formed one group by its own, E1, E2, E3, E5, E6, E7, E8 and E11 formed the second group and E4 and E10 formed the third group.  E3, E5 and, E7 were both discriminating and representative therefore are favorable environments for selecting generally adapted genotypes. E4, E9 and E10 were discriminating but non-representative test environments thus are useful for selecting specifically adapted genotypes. E8 and E11 were non-discriminating test environments hence little information about the genotypes. The results of this study helped to identify mega-environments, also representativeness and discriminating power of test environments better visualized with the GGE bi-plot model

Reference soil groups map of Ethiopia based on legacy data and machine learning-technique: EthioSoilGrids 1.0

Up-to-date digital soil resource information and its comprehensive understanding are crucial to supporting crop production and sustainable agricultural development. Generating such information through conventional approaches consumes time and resources, and is difficult for developing countries. In Ethiopia, the soil resource map that was in use is qualitative, dated (since 1984), and small scaled (1 : 2 M), which limit its practical applicability. Yet, a large legacy soil profile dataset accumulated over time and the emerging machine-learning modeling approaches can help in generating a high-quality quantitative digital soil map that can provide better soil information. Thus, a group of researchers formed a Coalition of the Willing for soil and agronomy data-sharing and collated about 20 000 soil profile data and stored them in a central database. The data were cleaned and harmonized using the latest soil profile data template and 14 681 profile data were prepared for modeling. Random forest was used to develop a continuous quantitative digital map of 18 World Reference Base (WRB) soil groups at 250 m resolution by integrating environmental covariates representing major soil-forming factors. The map was validated by experts through a rigorous process involving senior soil specialists or pedologists checking the map based on purposely selected district-level geographic windows across Ethiopia. The map is expected to be of tremendous value for soil management and other land-based development planning, given its improved spatial resolution and quantitative digital representation.</p

Measuring progress from 1990 to 2017 and projecting attainment to 2030 of the health-related Sustainable Development Goals for 195 countries and territories: a systematic analysis for the Global Burden of Disease Study 2017

Background: Efforts to establish the 2015 baseline and monitor early implementation of the UN Sustainable Development Goals (SDGs) highlight both great potential for and threats to improving health by 2030. To fully deliver on the SDG aim of “leaving no one behind”, it is increasingly important to examine the health-related SDGs beyond national-level estimates. As part of the Global Burden of Diseases, Injuries, and Risk Factors Study 2017 (GBD 2017), we measured progress on 41 of 52 health-related SDG indicators and estimated the health-related SDG index for 195 countries and territories for the period 1990–2017, projected indicators to 2030, and analysed global attainment. Methods: We measured progress on 41 health-related SDG indicators from 1990 to 2017, an increase of four indicators since GBD 2016 (new indicators were health worker density, sexual violence by non-intimate partners, population census status, and prevalence of physical and sexual violence [reported separately]). We also improved the measurement of several previously reported indicators. We constructed national-level estimates and, for a subset of health-related SDGs, examined indicator-level differences by sex and Socio-demographic Index (SDI) quintile. We also did subnational assessments of performance for selected countries. To construct the health-related SDG index, we transformed the value for each indicator on a scale of 0–100, with 0 as the 2\ub75th percentile and 100 as the 97\ub75th percentile of 1000 draws calculated from 1990 to 2030, and took the geometric mean of the scaled indicators by target. To generate projections through 2030, we used a forecasting framework that drew estimates from the broader GBD study and used weighted averages of indicator-specific and country-specific annualised rates of change from 1990 to 2017 to inform future estimates. We assessed attainment of indicators with defined targets in two ways: first, using mean values projected for 2030, and then using the probability of attainment in 2030 calculated from 1000 draws. We also did a global attainment analysis of the feasibility of attaining SDG targets on the basis of past trends. Using 2015 global averages of indicators with defined SDG targets, we calculated the global annualised rates of change required from 2015 to 2030 to meet these targets, and then identified in what percentiles the required global annualised rates of change fell in the distribution of country-level rates of change from 1990 to 2015. We took the mean of these global percentile values across indicators and applied the past rate of change at this mean global percentile to all health-related SDG indicators, irrespective of target definition, to estimate the equivalent 2030 global average value and percentage change from 2015 to 2030 for each indicator. Findings: The global median health-related SDG index in 2017 was 59\ub74 (IQR 35\ub74–67\ub73), ranging from a low of 11\ub76 (95% uncertainty interval 9\ub76–14\ub70) to a high of 84\ub79 (83\ub71–86\ub77). SDG index values in countries assessed at the subnational level varied substantially, particularly in China and India, although scores in Japan and the UK were more homogeneous. Indicators also varied by SDI quintile and sex, with males having worse outcomes than females for non-communicable disease (NCD) mortality, alcohol use, and smoking, among others. Most countries were projected to have a higher health-related SDG index in 2030 than in 2017, while country-level probabilities of attainment by 2030 varied widely by indicator. Under-5 mortality, neonatal mortality, maternal mortality ratio, and malaria indicators had the most countries with at least 95% probability of target attainment. Other indicators, including NCD mortality and suicide mortality, had no countries projected to meet corresponding SDG targets on the basis of projected mean values for 2030 but showed some probability of attainment by 2030. For some indicators, including child malnutrition, several infectious diseases, and most violence measures, the annualised rates of change required to meet SDG targets far exceeded the pace of progress achieved by any country in the recent past. We found that applying the mean global annualised rate of change to indicators without defined targets would equate to about 19% and 22% reductions in global smoking and alcohol consumption, respectively; a 47% decline in adolescent birth rates; and a more than 85% increase in health worker density per 1000 population by 2030. Interpretation: The GBD study offers a unique, robust platform for monitoring the health-related SDGs across demographic and geographic dimensions. Our findings underscore the importance of increased collection and analysis of disaggregated data and highlight where more deliberate design or targeting of interventions could accelerate progress in attaining the SDGs. Current projections show that many health-related SDG indicators, NCDs, NCD-related risks, and violence-related indicators will require a concerted shift away from what might have driven past gains—curative interventions in the case of NCDs—towards multisectoral, prevention-oriented policy action and investments to achieve SDG aims. Notably, several targets, if they are to be met by 2030, demand a pace of progress that no country has achieved in the recent past. The future is fundamentally uncertain, and no model can fully predict what breakthroughs or events might alter the course of the SDGs. What is clear is that our actions—or inaction—today will ultimately dictate how close the world, collectively, can get to leaving no one behind by 2030

Measuring progress from 1990 to 2017 and projecting attainment to 2030 of the health-related Sustainable Development Goals for 195 countries and territories: a systematic analysis for the Global Burden of Disease Study 2017.

BACKGROUND: Efforts to establish the 2015 baseline and monitor early implementation of the UN Sustainable Development Goals (SDGs) highlight both great potential for and threats to improving health by 2030. To fully deliver on the SDG aim of 'leaving no one behind', it is increasingly important to examine the health-related SDGs beyond national-level estimates. As part of the Global Burden of Diseases, Injuries, and Risk Factors Study 2017 (GBD 2017), we measured progress on 41 of 52 health-related SDG indicators and estimated the health-related SDG index for 195 countries and territories for the period 1990-2017, projected indicators to 2030, and analysed global attainment. METHODS: We measured progress on 41 health-related SDG indicators from 1990 to 2017, an increase of four indicators since GBD 2016 (new indicators were health worker density, sexual violence by non-intimate partners, population census status, and prevalence of physical and sexual violence [reported separately]). We also improved the measurement of several previously reported indicators. We constructed national-level estimates and, for a subset of health-related SDGs, examined indicator-level differences by sex and Socio-demographic Index (SDI) quintile. We also did subnational assessments of performance for selected countries. To construct the health-related SDG index, we transformed the value for each indicator on a scale of 0-100, with 0 as the 2·5th percentile and 100 as the 97·5th percentile of 1000 draws calculated from 1990 to 2030, and took the geometric mean of the scaled indicators by target. To generate projections through 2030, we used a forecasting framework that drew estimates from the broader GBD study and used weighted averages of indicator-specific and country-specific annualised rates of change from 1990 to 2017 to inform future estimates. We assessed attainment of indicators with defined targets in two ways: first, using mean values projected for 2030, and then using the probability of attainment in 2030 calculated from 1000 draws. We also did a global attainment analysis of the feasibility of attaining SDG targets on the basis of past trends. Using 2015 global averages of indicators with defined SDG targets, we calculated the global annualised rates of change required from 2015 to 2030 to meet these targets, and then identified in what percentiles the required global annualised rates of change fell in the distribution of country-level rates of change from 1990 to 2015. We took the mean of these global percentile values across indicators and applied the past rate of change at this mean global percentile to all health-related SDG indicators, irrespective of target definition, to estimate the equivalent 2030 global average value and percentage change from 2015 to 2030 for each indicator

The global burden of cancer attributable to risk factors, 2010-19 : a systematic analysis for the Global Burden of Disease Study 2019

Background Understanding the magnitude of cancer burden attributable to potentially modifiable risk factors is crucial for development of effective prevention and mitigation strategies. We analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to inform cancer control planning efforts globally. Methods The GBD 2019 comparative risk assessment framework was used to estimate cancer burden attributable to behavioural, environmental and occupational, and metabolic risk factors. A total of 82 risk-outcome pairs were included on the basis of the World Cancer Research Fund criteria. Estimated cancer deaths and disability-adjusted life-years (DALYs) in 2019 and change in these measures between 2010 and 2019 are presented. Findings Globally, in 2019, the risk factors included in this analysis accounted for 4.45 million (95% uncertainty interval 4.01-4.94) deaths and 105 million (95.0-116) DALYs for both sexes combined, representing 44.4% (41.3-48.4) of all cancer deaths and 42.0% (39.1-45.6) of all DALYs. There were 2.88 million (2.60-3.18) risk-attributable cancer deaths in males (50.6% [47.8-54.1] of all male cancer deaths) and 1.58 million (1.36-1.84) risk-attributable cancer deaths in females (36.3% [32.5-41.3] of all female cancer deaths). The leading risk factors at the most detailed level globally for risk-attributable cancer deaths and DALYs in 2019 for both sexes combined were smoking, followed by alcohol use and high BMI. Risk-attributable cancer burden varied by world region and Socio-demographic Index (SDI), with smoking, unsafe sex, and alcohol use being the three leading risk factors for risk-attributable cancer DALYs in low SDI locations in 2019, whereas DALYs in high SDI locations mirrored the top three global risk factor rankings. From 2010 to 2019, global risk-attributable cancer deaths increased by 20.4% (12.6-28.4) and DALYs by 16.8% (8.8-25.0), with the greatest percentage increase in metabolic risks (34.7% [27.9-42.8] and 33.3% [25.8-42.0]). Interpretation The leading risk factors contributing to global cancer burden in 2019 were behavioural, whereas metabolic risk factors saw the largest increases between 2010 and 2019. Reducing exposure to these modifiable risk factors would decrease cancer mortality and DALY rates worldwide, and policies should be tailored appropriately to local cancer risk factor burden. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.Peer reviewe