Ethiopia's transforming wheat landscape: tracking variety use through DNA fingerprinting

Multivariate analysis of phenotypic diversity elite bread wheat (Triticum aestivum L.) genotypes from ICARDA in Ethiopia

Wheat is an important crop for food security, providing a source of protein and energy for the growing population in Ethiopia. However, both biotic and abiotic factors limit national wheat productivity. The availability of genetically diverse wheat genotypes is crucial for developing new wheat varieties that are both high-yielding and resilient to stress. Therefore, this field trial aimed to assess phenotypic variation and relationship among ICARDA-derived bread wheat genotypes using multivariate analysis techniques. The trial was conducted at three locations: Enewari, Wogere, and Kulumsa using an alpha lattice design with two replications during the main cropping seasons of 2022 and 2023. Phenotypic data on eight agronomic traits and the severity of yellow rust were collected and R programming was used for data analysis. Individual and combined location data analysis of variance showed significant differences (p ≤ 0.05) among genotypes for most of the studied traits. The highest heritability and genetic advance as a percentage of the mean were observed in days to heading (90.8, 21.29), plant height (72.4, 28.6), seeds per spike (61.7, 28), thousand kernel weight (61.9, 12), and area under the disease progress curve (67, 39.8), suggesting a predominance of additive gene action. Grain yield showed a strong positive correlation with days to maturity, plant height, spike length, spikelet per spike, and thousand kernel weight for each location. Dendrogram and phylogenetic tree methods were used to group genotypes into four genetically distinct clusters. Cluster II and III had the greatest inter-cluster distance, indicating higher diversity among their genotypes. This study identified new candidate genotypes with superior agronomic performance, high grain yield traits, and robust resistance to yellow rust, making them valuable for both current and future wheat breeding programs. Additionally, the comprehensive dataset produced in this study could facilitate the identification of genetic variations influencing desirable traits through genome-wide association analysis.

Genotype-by-environment interaction and stability analysis of grain yield of bread wheat (Triticum aestivum L.) genotypes using AMMI and GGE biplot analyses

Bread wheat is a vital staple crop worldwide; including in Ethiopia, but its production is prone to various environmental constraints and yield reduction associated with adaptation. To identify adaptable genotypes, a total of 12 bread wheat genotypes (G1 to G12) were evaluated for their genotype-environment interaction (GEI) and stability across three different environments for two years using Additive Main Effect and Multiplicative Interaction (AMMI) and genotype main effect plus genotype-by-environment interaction (GGE) biplots analysis. GEI is a common phenomenon in crop improvement and is of significant importance in genotype assessment and recommendation. According to combined analysis of variance, grain yield was considerably impacted by environments, genotypes, and GEI. AMMI and GGE biplots analysis also provided insights into the performance and stability of the genotypes across diverse environmental conditions. Among the 12 genotypes, G6 was selected by AMMI biplot analysis as adaptive and high-yielding genotype; G5 and G7 demonstrated high stability and minimal interaction with the environment, as evidenced by their IPCA1 values. G7 was identified as the most stable and high-yielding genotype. The GGE biplot's polygon view revealed that the highest grain yield was obtained from G6 in environment three (E3). E3 was selected as the ideal environment by the GGE biplot. The top three stable genotypes identified by AMMI stability value (ASV) were G5, G7, and G10, while the most stable genotype determined by Genotype Selection Index (GSI) was G7. Even though G6 was a high yielder, it was found to be unstable according to ASV and ranked third in stability according to GSI. Based on the study's findings, the GGE biplot genotype view for grain yield identified Tay genotype (G6) to be the most ideal genotype due to its high grain yield and stability in diverse environments. G7 showed similar characteristics and was also stable. These findings provide valuable insights to breeders and researchers for selecting high-yielding and stable, as well as high-yielding specifically adapted genotypes.

Associations between women's bargaining power and the adoption of rust-resistant wheat varieties in Ethiopia

The dynamics in intra-household decision-making are often neglected in literature on the adoption of agricultural innovations. However, households' farm management decisions are often made following negotiations between female and male farmers. These may differ in terms of individual bargaining power and personal preferences. A better understanding of the links between gender roles in household decision-making and the adoption of technologies is postulated to enhance the uptake of innovations in smallholder farming systems. In this study, we use survey data from 1,088 wheat-producing households in Ethiopia to analyze the links between women's role in household decisions concerning crop production and the adoption and turnover rates of rust-resistant wheat varieties. We interviewed female and male respondents from the same households, but separately, which facilitated capturing individual perceptions and the intra-household dynamics in decision-making. To account for observed heterogeneity that may simultaneously determine the level of women's agency and varietal adoption by households, we employed Inverse Probability-Weighted Regression Adjustment (IPWRA). A positive association was found between women's role in decision-making concerning choice of wheat seed and household adoption of rust-resistant wheat varieties and wheat varietal turnover. Spouses may be in agreement or have different opinions regarding their decision-making roles. The disagreement scenario in which the wife claims to have a role in decision-making is associated with lower adoption rates of rust-resistant wheat varieties and less frequent testing of new varties in recent growing seasons, compared to a scenario where both spouses agree that wives do not have a role. We conclude that gender-disaggregated data and the examination of intra-household decision-making can offer novel and valuable insights for designing and implementing strategies to enhance the uptake of agricultural technologies among smallholders. The results emphasize the need to include complementary perspectives on the intra-household decision-making process.

Gender, rainfall endowment, and farmers' heterogeneity in wheat trait preferences in Ethiopia

Wheat is a vital cereal crop for smallholders in Ethiopia. Despite over fifty years of research on wheat varietal development, consideration of gendered trait preferences in developing target product profiles for wheat breeding is limited. To address this gap, our study used sex-disaggregated survey data and historical rainfall trends from the major wheat-growing regions in Ethiopia. The findings indicated heterogeneity in trait preferences based on gender and rainfall endowment. Men respondents tended to prefer wheat traits with high straw yield and disease-resistance potential, while women showed a greater appreciation for wheat traits related to good taste and cooking quality. Farmers in high rainfall areas seemed to prioritize high straw yield and disease resistance traits, while those in low rainfall areas valued good adaptation traits more highly. Most of the correlation coefficients among the preferred traits were positive, indicating that farmers seek wheat varieties with traits that serve multiple purposes. Understanding men's and women's preferences and incorporating them in breeding and seed systems could contribute to the development of more targeted and effective wheat varieties that meet the diverse needs of men and women farmers in Ethiopia.

The potential of UAV and very high-resolution satellite imagery for yellow and stem rust detection and phenotyping in Ethiopia

Very high (spatial and temporal) resolution satellite (VHRS) and high-resolution unmanned aerial vehicle (UAV) imagery provides the opportunity to develop new crop disease detection methods at early growth stages with utility for early warning systems. The capability of multispectral UAV, SkySat and Pleiades imagery as a high throughput phenotyping (HTP) and rapid disease detection tool for wheat rusts is assessed. In a randomized trial with and without fungicide control, six bread wheat varieties with differing rust resistance were monitored using UAV and VHRS. In total, 18 spectral features served as predictors for stem and yellow rust disease progression and associated yield loss. Several spectral features demonstrated strong predictive power for the detection of combined wheat rust diseases and the estimation of varieties' response to disease stress and grain yield. Visible spectral (VIS) bands (Green, Red) were more useful at booting, shifting to VIS-NIR (near-infrared) vegetation indices (e.g., NDVI, RVI) at heading. The top-performing spectral features for disease progression and grain yield were the Red band and UAV-derived RVI and NDVI. Our findings provide valuable insight into the upscaling capability of multispectral sensors for disease detection, demonstrating the possibility of upscaling disease detection from plot to regional scales at early growth stages.

Ethiopia's wheat production pathways to self-sufficiency through land area expansion, irrigation advance, and yield gap closure

Ethiopia is the second largest wheat producer in Africa. Though wheat production has been increasing steadily in the past decades, the demand for the crop outstripped domestic supply and forced the country to cover about 30 % of the deficit through commercial imports and food aid. The utilization of wheat is rising at 9 % annually while the production is increasing at 7.8 %, showing a continued widening between consumption and production. With a growing demand due to bourgeoning population, increase in income, and preference toward wheat-based products, the country has a long journey to achieve wheat self-sufficiency and save the scarce foreign currency reserve spent on import. The government of Ethiopia is committed to self-sufficiency through initiatives such as wheat area expansion, irrigation development and yield gap closure. In this review, we explored wheat production trends and the roles of the recent government initiatives toward wheat self-sufficiency. The review indicated that wheat production and productivity have increased in Ethiopia, but the wheat self-sufficiency of the country has declined from 99 % in the 1960s to 70 % at present. The future land area expansion in traditionally wheat-producing areas is limited, and wheat land suitability and yield potential is likely to reduce under climate change. Thus, the options to transform the wheat sector while reducing greenhouse gas emissions is through yield gap closure through intensification on existing cropland and judicious temporal and spatial expansion of irrigated wheat areas. The yield gap closure requires higher and more efficient input supply and utilization, investments in modern technologies, as well as supportive agricultural policies. To improve the productivity of small landholder farmers and increase intensity of production through irrigation investment on irrigation structures and facilities is required. Finally, we recommend further studies on yield gap analysis, the role of government initiatives, and wheat land suitability under current and future climate change scenarios at the local level.

Genomics-driven breeding for local adaptation of durum wheat is enhanced by farmers' traditional knowledge

In the smallholder, low-input farming systems widespread in sub-Saharan Africa, farmers select and propagate crop varieties based on their traditional knowledge and experience. A data-driven integration of their knowledge into breeding pipelines may support the sustainable intensification of local farming. Here, we combine genomics with participatory research to tap into traditional knowledge in smallholder farming systems, using durum wheat (Triticum durum Desf.) in Ethiopia as a case study. We developed and genotyped a large multiparental population, called the Ethiopian NAM (EtNAM), that recombines an elite international breeding line with Ethiopian traditional varieties maintained by local farmers. A total of 1,200 EtNAM lines were evaluated for agronomic performance and farmers' appreciation in three locations in Ethiopia, finding that women and men farmers could skillfully identify the worth of wheat genotypes and their potential for local adaptation. We then trained a genomic selection (GS) model using farmer appreciation scores and found that its prediction accuracy over grain yield (GY) was higher than that of a benchmark GS model trained on GY. Finally, we used forward genetics approaches to identify marker-trait associations for agronomic traits and farmer appreciation scores. We produced genetic maps for individual EtNAM families and used them to support the characterization of genomic loci of breeding relevance with pleiotropic effects on phenology, yield, and farmer preference. Our data show that farmers' traditional knowledge can be integrated in genomics-driven breeding to support the selection of best allelic combinations for local adaptation.

Soil type and fertilizer rate affect wheat (Triticum aestivum L.) yield, quality and nutrient use efficiency in Ayiba, northern Ethiopia

The blanket NP fertilizer recommendation over the past five decades in Ethiopia did not result in a significant increment of crop productivity. The main lack of success was highly linked to the extrapolating approach of one site success to others without considering the climate, soil, and ecological setting and variations. As a result, a new fertilization approach was desperately needed, and with this premise, new blended fertilizers are now being introduced to replace the conventional approach. Thus, the objective of this study was to examine the effect of NPSZnB blended fertilizer on bread wheat yield attributes, quality traits and use efficiency in two different soil types under rain-fed conditions in Ayiba, northern Ethiopia. Relevant agronomic data were evaluated and recorded from plots of each soil types for analysis. The analysis of variance revealed a significant (p < 0.001) variation on all the agronomic and grain quality traits due to the main and interaction effects of soil type and fertilizer treatment factors. Most agronomic and quality characteristics recorded the highest result in the highest treatment applications (175 and 150 kg NPSZnB ha^-1) in both soils. Yield and grain quality traits of bread wheat was also found better under fertilized plots than unfertilized plots. In both soil types increasing application of the new blended fertilizer rate from 50-175 kg NPSZnB ha^-1 showed an increasing trend in grain yield from 1.6 to 4.3 and 2.5 to 5.4 t ha^-1 in Vertisol and Cambisol soils, respectively. The varied yield as a response of fertilizer treatments across soils signifies soil-specific fertilization approach is critically important for production increment. On the other hand, based on the partial budget analysis the highest net benefit with the highest marginal rate of return in both Vertisol and Cambisol soils were obtained when treated with 100 and 125 kg NPSZnB ha^-1, respectively. Therefore, to produce optimum bread wheat yield under rainfed conditions in Ayiba (northern Ethiopia) fertilizing Vertisols with 100 kg NPSZnB ha^-1 and fertilizing Cambisols with 125 kg NPSZnB ha^-1 is recommended.

A Large-Scale Dataset of Barley, Maize and Sorghum Variety Identification Using DNA Fingerprinting in Ethiopia

The data described in this paper were part of a large-scale nationally representative household survey, the Ethiopian Socioeconomic Survey (ESS 2018/19). Grain samples of barley, maize and sorghum were collected in six regions in Ethiopia. Variety identification was assessed by matching samples to a reference library composed of released improved materials, using approximately 50,000 markers from DArTseq platforms. This data were part of a study documenting the reach of CGIAR-related germplasms in Ethiopia. These objective measures of crop varietal adoption, unique in the public domain, can be analyzed along with a large set of variables related to agro-ecologies, household characteristics and plot management practices, available in the Ethiopian Socioeconomic Survey 2018/19.

Wheat rust epidemics damage Ethiopian wheat production: A decade of field disease surveillance reveals national-scale trends in past outbreaks

Wheat rusts are the key biological constraint to wheat production in Ethiopia-one of Africa's largest wheat producing countries. The fungal diseases cause economic losses and threaten livelihoods of smallholder farmers. While it is known that wheat rust epidemics have occurred in Ethiopia, to date no systematic long-term analysis of past outbreaks has been available. We present results from one of the most comprehensive surveillance campaigns of wheat rusts in Africa. More than 13,000 fields have been surveyed during the last 13 years. Using a combination of spatial data-analysis and visualization, statistical tools, and empirical modelling, we identify trends in the distribution of wheat stem rust (Sr), stripe rust (Yr) and leaf rust (Lr). Results show very high infection levels (mean incidence for Yr: 44%; Sr: 34%; Lr: 18%). These recurrent rust outbreaks lead to substantial economic losses, which we estimate to be of the order of 10s of millions of US-D annually. On the widely adopted wheat variety, Digalu, there is a marked increase in disease prevalence following the incursion of new rust races into Ethiopia, which indicates a pronounced boom-and-bust cycle of major gene resistance. Using spatial analyses, we identify hotspots of disease risk for all three rusts, show a linear correlation between altitude and disease prevalence, and find a pronounced north-south trend in stem rust prevalence. Temporal analyses show a sigmoidal increase in disease levels during the wheat season and strong inter-annual variations. While a simple logistic curve performs satisfactorily in predicting stem rust in some years, it cannot account for the complex outbreak patterns in other years and fails to predict the occurrence of stripe and leaf rust. The empirical insights into wheat rust epidemiology in Ethiopia presented here provide a basis for improving future surveillance and to inform the development of mechanistic models to predict disease spread.