Morphological and Genetic Diversity Study of Upland Rice Varieties under Rain-fed Environment

A field experiment was conducted at Fogera Northwest Amhara region to study the morphological traits to variability in 20 upland rice varieties, consisting of nine NERICA and eleven parents. The data were collected from ten randomly selected plants of each plot (plant height, panicle length, culm length, flag-leaf length, number of spikelet per panicle, number of grains per panicle, number of filled grains per panicle, numbers of fertile tillers per plant, yield per plant) and from plot bases (days to heading, days to maturity, grain-filling period, thousand-seed weight, biomass yield, grain yield, and harvest index). The results of the principal component analysis showed that four components account for 76.7% of the total variation, giving a clear idea of the structure underlying the variables analysed. Cluster analysis using un-weighted Pair Group Method using Arithmetic Average linkage (UWPGMA) classified the twenty varieties into five distinct groups. The maximum inter-cluster distances were; recorded 8.05 between cluster I & V, 6.67 between cluster I and IV; and 5.5 between Cluster I and III, indicating that the possibility of high heterosis if individuals from these clusters are cross bred. The results of the principal component analysis were closely in line with those of the cluster analysis. This study has provided useful information, on evaluation of genetic diversity of rice varieties and will indicate the way, how plant breeders screen out large populations and to develop new breeding protocols for rice improvement

Yield Gaps of Major Cereal and Grain Legume Crops in Ethiopia: A Review

In Ethiopia, smallholder farmers are responsible for most food production. Though yield levels in grain crops have improved greatly over the years, they are still much lower than their potential. The source of yield improvements and the causes of those yield gaps are not well understood. To explain the drivers of yield gaps and current sources of yield improvements in four major cereals (teff, maize, wheat, and sorghum) and three grain legumes (faba bean, common bean, and soybean), we accessed the databases of the Global Yield Gap Atlas, the Food and Agriculture Organization of the United Nations, and the Central Statistical Agency of Ethiopia. Refereed journal articles and grey literature were sought in online databases using keywords. The results showed large increases in production of grain crops with little or no increase in areas of production. The yield increases were primarily attributed to genetic gain rather than agronomic improvements. Farmers’ yields remain far lower than those from on-farm trials and on-station trials and the calculated water-limited yield potential. Currently, yields of wheat, maize, sorghum, and common bean in Ethiopia are about 26.8, 19.7, 29.3, and 35.5% of their water-limited yield potentials. Significant portions of the yield gaps stem from low adoption and use of improved varieties, low application of inputs, continual usage of un-optimized crop management practices, and uncontrolled biotic and abiotic stresses. Proper application of fertilizers and use of improved varieties increase yield by 2 to 3 fold and 24–160%, respectively. Cereal-legume intercropping and crop rotation practices increase yield while reducing severity of pests and the need for application of synthetic fertilizers. In contrast, abiotic stresses cause yield reductions of 20–100%. Hence, dissection of the water-limited yield gap in terms of technology, resource, and efficiency yield gaps will allow the prioritization of the most effective intervention areas

Genotype × Environment Interaction and Stability of Field Pea (Pisum sativum L.) Genotypes for Seed Yield in Northwestern Ethiopia

Field pea (Pisum sativum L.) is a self-pollinated diploid (2n=14) annual cool-season pulse crop. It is a major food legume with a valuable and cheap source of plant protein having essential amino acids that have high nutritional value for resource poorhouseholds. Biotic stress such as weed and insect pests and abiotic stresses like water logging, soil acidity, and low soil fertility are the major constraints to field pea production and productivity. Fourteen field pea genotypes, obtained from Holeta Agricultural Research Center, were evaluated in eight environments in Northwestern Ethiopia in the main production season (2018-2019) to identify stable and high-yielding field pea genotypes. The trial was laid out using a randomized complete block design and replicated three times. Combined analysis of variance for seed yield revealed that genotype, environments, and genotype-by-environment interaction effects were significant (P < 0.05). The lowest hundred seed weight value (12.83 g) was manifested by the local check, while the highest value (20.73 g) was revealed by EH 07007-3 genotype from the overall mean of location. The highest mean grain yield of 2400 kg.ha-1 was obtained from the EH08003-2 genotype, while the lowest yield 1660 kg.ha-1 was obtained from EH 08041-3. The maximum grain yield of 4140 kg.ha-1 was recorded from Debark by EH 09015-3 genotype, while the minimum grain yield of 560 kg.ha-1 was revealed by EH 08041-3. The environments, GxE, and genotypes accounted for 74.8%, 16.3%, and 7.0% of the total sum squares, respectively, indicating that field pea seed yield was significantly affected by the changes in the environment, followed by GxE interaction and genotypic effect. The candidate genotype, EH08003-2, was the most stable genotype followed by EH 09068-2 and EH 08042-2 having an IPCA score closer to zero with a yield advantage of 26.3% and 36.4% over the standard and local checks, respectively. Considering the eight environments’ data and field performance evaluation during the variety verification trial, the National Variety Releasing Committee has approved the official release of EH08003-2 for kik seed utilization class with a vernacular name of Hasset for high potential areas of Northwestern Ethiopia and similar agro-ecologies

Quality and sustainability of Ethiopia's national surgical indicators.

In 2015, the Ethiopian Federal Ministry of Health (FMOH) developed the Saving Lives through Safe Surgery (SaLTS) initiative to improve national surgical care. Previous work led to development and implementation of 15 surgical key performance indicators (KPIs) to standardize surgical data practices. The objective of this project is to investigate current practices of KPI data collection and assess quality to improve data management and strengthen surgical systems. The first portion of the study documented the surgical data collection process including methods, instruments, and effectiveness at 10 hospitals across 2 regions in Ethiopia. Secondly, data for KPIs of focus [1. Surgical Volume, 2. Perioperative Mortality Rate (POMR), 3. Adverse Anesthetic Outcome (AAO), 4. Surgical Site Infection (SSI), and 5. Safe Surgery Checklist (SSC) Utilization] were compared between registries, KPI reporting forms, and the DHIS2 (district health information system) electronic database for a 6-month period (January-June 2022). Quality was assessed based on data completeness and consistency. The data collection process involved hospital staff recording data elements in registries, quality officers calculating KPIs, completing monthly KPI reporting forms, and submitting data into DHIS2 for the national and regional health bureaus. Data quality verifications revealed discrepancies in consistency at all hospitals, ranging from 1-3 indicators. For all hospitals, average monthly surgical volume was 57 cases, POMR was 0.38% (13/3399), inpatient SSI rate was 0.79% (27/3399), AAO rate was 0.15% (5/3399), and mean SSC utilization monthly was 93% (100% median). Half of the hospitals had incomplete data within the registries, ranging from 2-5 indicators. AAO, SSC, and SSI were commonly missing data in registries. Non-standardized KPI reporting forms contributed significantly to the findings. Facilitators to quality data collection included continued use of registries from previous interventions and use of a separate logbook to document specific KPIs. Delayed rollout of these indicators in each region contributed to issues in data quality. Barriers involved variable indicator recording from different personnel, data collection tools that generate false positives (i.e. completeness of SSC defined as paper form filled out prior to patient discharge) or missing data because of reporting time period (i.e. monthly SSI may miss infections outside of one month), inadequate data elements in registries, and lack of standardized monthly KPI reporting forms. As the FMOH introduces new indicators and changes, we recommend continuous and consistent quality checks and data capacity building, including the use of routinely generated health information for quality improvement projects at the department level