Integrating soil conservation and fodder production as climate adaptation strategy in Ethiopia

Like many smallholder farmers in Africa, the farming families of southern Ethiopia are facing three major challenges: the need to intensify and diversify their farm production, on very small areas, in a context of high population growth. In these rural areas, given the fertility rate, the population will double by 2050; agricultural practices must preserve the environment to maintain its productive capacity, for current and future generations; the necessity to rapidly adapt farming practices to cope with climate changes. Regarding these three challenges, Inter Aide has developed an innovative approach that consists in combining fodder production and soil and water conservation. The basic idea is simple: to plant fodder on anti-erosive structures and in unproductive places on the farm

The effect of climate-smart agriculture on soil fertility, crop yield, and soil carbon in Southern Ethiopia

It is critical to develop technologies that simultaneously improve agricultural production,offset impacts of climate change, and ensure food security in a changing climate. Within this context,considerable attention has been given to climate-smart agricultural practices (CSA). This study wasconducted to investigate the effects of integrating different CSA practices on crop production, soilfertility, and carbon sequestration after being practiced continuously for up to 10 years. The CSApractices include use of soil and water conservation (SWC) structures combined with biologicalmeasures, hedgerow planting, crop residue management, grazing management, crop rotation, andperennial crop-based agroforestry systems. The landscapes with CSA interventions were comparedto farmers’ business-as-usual practices (i.e., control). Wheat (Triticumsp.) yield was quantified from245 households.The results demonstrated that yield was 30–45% higher under CSA practices than thecontrol (p< 0.05). The total carbon stored at a soil depth of 1 m was three- to seven-fold higher underCSA landscapes than the control. CSA interventions slightly increased the soil pH and exhibited2.2–2.6 and 1.7–2.7 times more total nitrogen and plant-available phosphorus content, respectively,than the control. The time series Normalized Difference Water Index (NDWI) revealed higher soilmoisture content under CSA. The findings illustrated the substantial opportunity of integrating CSApractices to build climate change resilience of resource-poor farmers through improving crop yield,reducing nutrient depletion, and mitigating GHG emissions through soil carbon sequestratiom

Identification of Climate-Smart Bread Wheat (Triticum aestivum L.) Germplasm for Optimum Moisture Areas of Ethiopia

Ethiopia's Bread Wheat Breeding Program conducts annual multi-environment yield trials to develop advanced wheat genotypes for Ethiopian wheat cultivation, ensuring a steady supply of new and improved varieties to meet production and marketing challenges. The objective of this research was to assess potential yield and the interactions between genotype and environment in wheat across multiple environments, as every cultivar has a distinct response to soil and climate. The BLUP analysis reveals that the 22AA and 22KU trials in 2022, along with 21KU trials in 2021 yielded high grain production, indicating optimal testing locations for distinguishing bread wheat genotypes and agroecologies. The study found that seven out of sixteen trials exhibited a higher genetic variance for yield, indicating high genotype discriminating power, with estimates ranging from 0.043 to 0.989 for genetic variance, 0.084 to 1.147 for error variance, and 72.7 to 96.4 for heritability. EBW202471 and EBW202473 are stable genotypes with good yield performance across correlated locations, with EBW202471 showing the highest yield (4.98 t/ha) and Deka showing a lower yield (4.07 t/ha). Three wheat genotypes, EBW202471, EBW202472, and EBW202473, were found to be moderately resistant to moderately susceptible to stem and yellow rust among 20 wheat genotypes. Finally, the two genotypes, EBW202471 and EBW202473, were advanced to National Performance Trials to evaluate their performance alongside top genotypes from regional federal research centers and to be released as new varieties

Graphical Analysis of Multi-environmental Trials for Bread Wheat (Triticum aestivum L.) Grain Yield Based on GGE Bi-Plot Analysis

Bread wheat (Triticum aestivum L.) is a crucial crop in Ethiopia, and breeders test newly developed elite lines for superiority to existing cultivars to boost national productivity. The study was undertaken during the 2021–22 to 2022–23 cropping seasons at seven environments in optimum moisture areas of Ethiopian using 36 diverse and advanced bread wheat genotypes to evaluate the GEI by the graphical method of GGE biplot and to identify the genotypes with high mean yield performance and stability. Field experiments were conducted at the Adet, Asasa, Kulumsa, and Sinana research centers in Ethiopia. The experiments were planted in an alpha lattice design replicated three times in six rows of 2.5m long. Row-to-row distance and distance between blocks were 0.2m and 1.5m, respectively.&nbsp; The analysis of variance revealed that genotype, environment, and their interaction showed a highly significant effect on the yield as reflected in the GGE model and the GGE model indicated the suitability of the genotypes EBW202136 (33), Boru (1), and EBW202172 (12), with high mean yield and stability, whereas the genotypes EBW202185 (16) and Deka (36) produced high mean yield, but unstable. Likewise, the genotypes EBW202164 (27) and EBW202192 (29) produced low mean yield and unstable. The AMMI analysis of variance for grain yield across the environments showed that 17.26% of the total variation was attributed to genotypic effects, 64.03% to environmental effects, and 18.71% to GEI effects. Two mega environments were identified based on GGE biplot analysis and the which-won–model indicated the adaptation of genotypes Boru (1), EBW202159 (4), EBW202172 (12), EBW202171 (19), and EBW202136 (33) to first mega-environment and genotypes EBW202157 (3), EBW202166 (5), EBW202160 (6), EBW202162 (9), EBW202185 (16), Dursa (17) and Deka (36) in the second. These approaches allowed the identification of stable and high-yielding genotypes (EBW202136 (33) and EBW202172 (12)) which can be included in the national verification program, with a plan to release a new variety, and other genotypes with high yield could be utilized in breeding programs to further improve grain yield in bread wheat

Registration of Bread Wheat (Triticum aestivum L.) Variety Kulumsa for the Midlands of Ethiopia

Bread wheat (Triticum aestivum L.) is a crucial crop in Ethiopia, and breeders test newly developed elite lines for superiority to existing cultivars to boost national productivity. Recently, commercial wheat varieties with higher genetic gain for economic traits have been released, which outperform older varieties. One such variety is Kulumsa, which has the pedigree “PFAU/MILAN/5/CHEN/ AEGILOPSSQUARROSA(TAUS)/BCN/3/VEE#7/BOW/4/PASTOR/6/2*BAVIS#1/7/BORL14” and selection history “CMSS13B00513S-099M-099NJ-099NJ-15Y-0WGY”. It was developed and released by Kulumsa Agricultural Research Center for mid to high altitudes of wheat-growing agroecology of Ethiopia. Kulumsa has higher grain yield performance than the check and has good agronomic characteristics and medium maturing type compared to the current varieties. It consistently out-yielded other tested bread wheat genotypes over two years. Compared to Wane, Danda'a, and Lemu checks, Kulumsa demonstrated significant improvement in agronomic characteristics and enhanced yield by 60%, 62%, and 68%, respectively. Wane (30.2g), Lemu (29.6g), and Danda'a (32.7g) have lower thousand kernel weights than Kulumsa (39.6g). Kulumsa had a 31%, 21%, and 34% thousand kernel weight advantage over Wane, Danda'a, and Lemu, respectively. The new variety has a better hectoliter weight than Wane, Lemu, and Danda'a by 18%, 13%, and 11%, respectively. The newly released bread wheat varieties are moderately resistant to stem rust, and yellow rust, and comparable for leaf rust disease and Septoria with the checks Wane, Danda'a, and Lemu. Kulumsa proved to be more resistant to stem yellow and leaf rust than all currently produced varieties in the mid to high-land part of wheat-growing agroecology. It offers new hope for farmers of Ethiopia and has a white grain color with good general acceptance for bread with high quality

Evaluation of Bread Wheat (Tritium aestivum L.) Genotype in Multi-environment Trials Using Enhanced Statistical Models

In varietal selection field trials, spatial variation and genotype by environment (GxE) interaction are frequent and present a major challenge to plant breeders comparing the genetic potential of several cultivars. To consistently select superior cultivars that increase agricultural production, bread wheat breeding studies must be evaluated using efficient statistical techniques. By modeling the interactions of geographical field trends and genotypes by environment interaction, this work aimed to forecast the genetic potential of bread wheat varieties across settings and improve selection tactics. The dataset utilized in this investigation consisted of sixteen multi-environment trials (MET) that were carried out using a randomized complete block design (RCBD), with two replications arranged in plot arrays of rows and columns. The findings showed that the factor analytical and spatial models were effective ways to analyze the data for this study under the linear mixed model. By ranking average Best Linear Unbiased Predictions (BLUPs) within clusters, the 16 bread wheat environments were grouped into three mega environments (C1, C2, and C3) based on yield. This served as a selection indicator. Ranking average BLUPs helped in the selection of superior and stable genotypes. The first cluster (C1)'s mean BLUP values were used to score the genotypes' performance; C2 and C3 were excluded because of their limited genetic variety and low genetic connection with the other trials. The genotypes with the highest potential based on this cluster were EBW192346 and EBW192347, chosen for a subsequent verification study to release a variety. The estimates for variance component parameters ranged from 0.013 to 3.024 for genetic variance and from 0.072 to 0.37 for error variance.&nbsp; Hence, scaling up the use of this efficient analysis method will improve the selection of superior bread wheat varieties