Integrating genomics for chickpea improvement: achievements and opportunities

The implementation of novel breeding technologies is expected to contribute substantial improvements in crop productivity. While conventional breeding methods have led to development of more than 200 improved chickpea varieties in the past, still there is ample scope to increase productivity. It is predicted that integration of modern genomic resources with conventional breeding efforts will help in the delivery of climate-resilient chickpea varieties in comparatively less time. Recent advances in genomics tools and technologies have facilitated the generation of large-scale sequencing and genotyping data sets in chickpea. Combined analysis of high-resolution phenotypic and genetic data is paving the way for identifying genes and biological pathways associated with breeding-related traits. Genomics technologies have been used to develop diagnostic markers for use in marker-assisted backcrossing programmes, which have yielded several molecular breeding products in chickpea. We anticipate that a sequence-based holistic breeding approach, including the integration of functional omics, parental selection, forward breeding and genome-wide selection, will bring a paradigm shift in development of superior chickpea varieties. There is a need to integrate the knowledge generated by modern genomics technologies with molecular breeding efforts to bridge the genome-to-phenome gap. Here, we review recent advances that have led to new possibilities for developing and screening breeding populations, and provide strategies for enhancing the selection efficiency and accelerating the rate of genetic gain in chickpea

Efficiency, food security and differentiation in small-scale irrigation agriculture: Evidence from North West Nigeria

Ambiguity over the effectiveness of agricultural intervention is more pronounced in rural areas where the majority of North West Nigeria’s poor population, and those involved in agriculture, reside. Further characterising these areas is the paucity of research on the issue of differentiation within the smallholder community. Specifically, definite classification of households based on efficiency, food security and income status remains inadequate. The study explores smallholder households’ differentials on the basis of these three phenomena, and other factors that affect smallholder typologies. Data was collected from 306 randomly selected smallholders involved in the Middle Rima Valley Irrigation Project, Sokoto State, Nigeria. Smallholders’ technical efficiency and households’ Food Consumption Score (FCS) were assessed. Also, Pearson correlation analysis, a segmentation approach using cluster analysis and multinomial regression model were used for the study. The study showed that the mean efficiency level of smallholder farms was 85.9% and that the majority of the households were food insecure

Dataset on Agro-Pastoral Youth Participation in Development Interventions in East and West Hararghe Zones, Oromia Regional State, Ethiopia

The dataset contained in this data article comprises several sections. In all the sections, data were first presented for the full/pooled sample. Moreover, data were disaggregated by gender, Zone and Woreda. Data on socio-demographic characteristics were presented in Table 1 (for the full sample, male and female youth) and Figure 1 (for the two Zones and four Woredas). Data on youth employment and participation in the labour market for the full sample, male and female youth categories are given in Table 2. The Zonal and Woreda level data were presented in Figure 2. The engagement of youth in on-farm and non-/off-farm income-generating activities (IGAs) was given in Table 3 (for the pooled sample, male and female youth) and Figure 3 (for Zonal and Woreda comparisons). Concerning data on agricultural production, income and food security status, Table 4 presents a summary statistic on various aspects of livelihood activities and outcomes for the pooled sample and male and female youth categories. Whereas Figure 4 depicts the Zonal and Woreda level data on experience in farming, land holding size and livestock possessions, Figure 5 presents the status of land registration in the study Zones and Woredas. Likewise, Figure 6 depicts Zonal and Woreda level comparative data on expenditures for productive assets and farm income. Regarding food security, data on household dietary diversity score (HDDS) and food consumption score (FCS) were presented in Figure 7.Youth perception on whether agriculture can be a basic means of livelihood (Table 5 and Figure 8); whether agriculture can be a viable profession with a reasonable financial return (Table 6 and Figure 9); and level of satisfaction with current (agricultural) job (Table 7 and Figure 10) were all included herein. Data on youth access to basic services, infrastructure and facilities were given in Figure 11. Youth ownership of asset, control over use of income, and decision about credit is presented in Table 8 and Figure 12.Data on youth participation in public extension and advisory services, including farmer field schools (FFSs), farmer training centers (FTCs), and pastoral training centers (PTCs) is presented in Table 9 and Figure 13. Similarly, data on youth participation in microfinance institutions (MFI) and small and medium enterprise (SME) promotion activities is depicted in Table 10 and Figure 14. Concerning the participation of youth in community-based organizations (CBOs), networks and groups, data were presented in Table 11, Figure 15 and Figure 16. Furthermore, data on youth participation in the productive safety net program (PSNP) and cooperatives is given in Table 12 and Figure 17. The last part of this data article presents data on status of youth access to and participation in the activities of various NGOs operating in their vicinity (Table 13, Figure 18 and Figure 19)

Dataset on Agro-Pastoral Youth Participation in Development Interventions in East and West Hararghe Zones, Oromia Regional State, Ethiopia

The dataset contained in this data article comprises several sections. In all the sections, data were first presented for the full/pooled sample. Moreover, data were disaggregated by gender, Zone and Woreda. Data on socio-demographic characteristics were presented in Table 1 (for the full sample, male and female youth) and Figure 1 (for the two Zones and four Woredas). Data on youth employment and participation in the labour market for the full sample, male and female youth categories are given in Table 2. The Zonal and Woreda level data were presented in Figure 2. The engagement of youth in on-farm and non-/off-farm income-generating activities (IGAs) was given in Table 3 (for the pooled sample, male and female youth) and Figure 3 (for Zonal and Woreda comparisons). Concerning data on agricultural production, income and food security status, Table 4 presents a summary statistic on various aspects of livelihood activities and outcomes for the pooled sample and male and female youth categories. Whereas Figure 4 depicts the Zonal and Woreda level data on experience in farming, land holding size and livestock possessions, Figure 5 presents the status of land registration in the study Zones and Woredas. Likewise, Figure 6 depicts Zonal and Woreda level comparative data on expenditures for productive assets and farm income. Regarding food security, data on household dietary diversity score (HDDS) and food consumption score (FCS) were presented in Figure 7.Youth perception on whether agriculture can be a basic means of livelihood (Table 5 and Figure 8); whether agriculture can be a viable profession with a reasonable financial return (Table 6 and Figure 9); and level of satisfaction with current (agricultural) job (Table 7 and Figure 10) were all included herein. Data on youth access to basic services, infrastructure and facilities were given in Figure 11. Youth ownership of asset, control over use of income, and decision about credit is presented in Table 8 and Figure 12.Data on youth participation in public extension and advisory services, including farmer field schools (FFSs), farmer training centers (FTCs), and pastoral training centers (PTCs) is presented in Table 9 and Figure 13. Similarly, data on youth participation in microfinance institutions (MFI) and small and medium enterprise (SME) promotion activities is depicted in Table 10 and Figure 14. Concerning the participation of youth in community-based organizations (CBOs), networks and groups, data were presented in Table 11, Figure 15 and Figure 16. Furthermore, data on youth participation in the productive safety net program (PSNP) and cooperatives is given in Table 12 and Figure 17. The last part of this data article presents data on status of youth access to and participation in the activities of various NGOs operating in their vicinity (Table 13, Figure 18 and Figure 19).THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Dataset on Agro-Pastoral Youth Participation in Development Interventions in East and West Hararghe Zones, Oromia Regional State, Ethiopia

Data were collected from male and female youth (15-29 years old) located in 4 Woredas/districts of East and West Hararghe Zones, Oromia Regional State, Ethiopia. The research covered a total of 398 randomly selected youth. Roughly 50% of the sample were female youth. Selection criteria included: gender, education, economic and social status, and resource endowment.The STATA dataset (WORDOFA_Youth_Participation_Dataset_02 March 2023.dta) contains the following elements.1. Study area and participant identifiers (ZONE, WOREDA, Household/respondent ID);2. Socio-demographic characteristics (age, education, family size, gender). 3. Data on youth employment and participation in the labour market (using ‘availability of appropriate job opportunities’ and ‘youth engagement in on-farm and non-/off-farm income-generating activities’).4. Youth engagement in on-farm and non-/off-farm income-generating activities (IGAs): in addition to access to employment and status of employment, this section presents data on childhood job aspirations and interest of the youth to start their own businesses and IGAs.5. Data on agricultural production, income and food security status (experience in farming, land holding size, land registration certificate, livestock possessions, expenditure for productive inputs, on-farm income, food consumption score, household dietary diversity score)6. Data on youth perception on whether agriculture can be a basic means of livelihood; whether agriculture can be a viable profession with a reasonable financial return; and level of satisfaction with current (agricultural) job 7. Data on youth access to basic services, infrastructure and facilities, youth ownership of asset, control over use of income, and decision about credit.8. Data on youth participation in public extension and advisory services, including farmer field schools (FFSs), farmer training centers (FTCs), and pastoral training centers (PTCs) 9. Data on youth participation in microfinance institutions (MFI) and small and medium enterprise (SME) promotion activities.10. Youth engagement in community-based organizations (CBOs), networks and groups; youth participation in the productive safety net program (PSNP) and cooperatives.11. Data on status of youth access to and participation in the activities of various NGOs operating in their vicinity.The variable names, labels, values and descriptions of all the 64 variables contained in the STATA file are given in the file: Codebook - description of variables in the dataset.pdfTHIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Dataset on Agro-Pastoral Youth Participation in Development Interventions in East and West Hararghe Zones, Oromia Regional State, Ethiopia

Data were collected from male and female youth (15-29 years old) located in 4 Woredas/districts of East and West Hararghe Zones, Oromia Regional State, Ethiopia. The research covered a total of 398 randomly selected youth. Roughly 50% of the sample were female youth. Selection criteria included: gender, education, economic and social status, and resource endowment.The STATA dataset (WORDOFA_Youth_Participation_Dataset_02 March 2023.dta) contains the following elements.1. Study area and participant identifiers (ZONE, WOREDA, Household/respondent ID);2. Socio-demographic characteristics (age, education, family size, gender). 3. Data on youth employment and participation in the labour market (using ‘availability of appropriate job opportunities’ and ‘youth engagement in on-farm and non-/off-farm income-generating activities’).4. Youth engagement in on-farm and non-/off-farm income-generating activities (IGAs): in addition to access to employment and status of employment, this section presents data on childhood job aspirations and interest of the youth to start their own businesses and IGAs.5. Data on agricultural production, income and food security status (experience in farming, land holding size, land registration certificate, livestock possessions, expenditure for productive inputs, on-farm income, food consumption score, household dietary diversity score)6. Data on youth perception on whether agriculture can be a basic means of livelihood; whether agriculture can be a viable profession with a reasonable financial return; and level of satisfaction with current (agricultural) job 7. Data on youth access to basic services, infrastructure and facilities, youth ownership of asset, control over use of income, and decision about credit.8. Data on youth participation in public extension and advisory services, including farmer field schools (FFSs), farmer training centers (FTCs), and pastoral training centers (PTCs) 9. Data on youth participation in microfinance institutions (MFI) and small and medium enterprise (SME) promotion activities.10. Youth engagement in community-based organizations (CBOs), networks and groups; youth participation in the productive safety net program (PSNP) and cooperatives.11. Data on status of youth access to and participation in the activities of various NGOs operating in their vicinity.The variable names, labels, values and descriptions of all the 64 variables contained in the STATA file are given in the file: Codebook - description of variables in the dataset.pdfTHIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Examining Smallholder Farmers’ Intensity of Participation in Onfarm Agricultural Advisory Services: a Case Study in Haramaya District, Eastern Ethiopia

The immense role of agricultural advisory services (AASs) in transforming smallholder farming systems has been recognized in many developing countries. However, the benefit that farmers obtain out of participating in AASs and the resulting impact depends, to a great extent, by the (intensity of) farmers’ direct and indirect access to these services. The issue of intensity of farmers’ involvement in AASs is also especially important considering the various stages involved in farming – land preparation to post-harvest management. In this regard, we identify that the extent of farmers’ participation in such services and the determinants of their intensity of participation is not very well documented. In this study, we conducted a field-based household survey from May to October 2013 on a sample of 340 farm households in Haramaya district, Ethiopia, in order to analyze the predictors of farmers’ intensity of participation in on-farm training and demonstration. By employing both Poisson regression and negative binomial regression models on the primary data gathered for this purpose, we find that a host of factors – relating to human capital, financial capital, physical capital, social capital, and access to infrastructure and services – influence the farmers’ differential involvement in these services. On the basis of these findings, some conclusions are drawn and recommendations are suggested for improving smallholder farmers’ participation in on-farm AASs

The Effect of School-Linked Module-Based Friendly-Health Education on Adolescents’ Sexual and Reproductive Health Knowledge, Guji Zone, Ethiopia - Cluster Randomized Controlled Trial

Gobena Godana Boku,1,2 Sileshi Garoma Abeya,2,3 Nicola Ayers,4 Muluembet Abera Wordofa5 1Population and Family Health Department, Faculty of Public Health, Jimma University, Jimma, Oromia, Ethiopia; 2Medical Services Lead Executive Office, Federal Ministry of Health, Addis Ababa, Ethiopia; 3Public Health Department, Adama Hospital Medical College, Adama, Oromia, Ethiopia; 4School of Nursing, BPP University, London, UK; 5Population & Family Health Department Faculty of Public Health, Jimma University, Jimma, EthiopiaCorrespondence: Gobena Godana Boku, Medical Services Lead Executive Office, MoHE, P.O. Box 1234, Addis Ababa, Ethiopia, Tel +251911545441, Email [email protected]: Although access to sexual and reproductive health information is the right and a critical component of health policy, it is not well addressed in pastoral communities. This study assessed the effect of School-Lined Module-based friendly health education on adolescents’ sexual and reproductive health knowledge in the pastoral community of Guji Zone, Ethiopia.Methods: A two-arm cluster Randomized control trial study with pre-post evaluation was conducted among interventions (n=375) compared with control (n=384) in Gorodola and Wadara high schools. Comparing an intervention to a control group, pre-posttests, and post-posttests were used to evaluate the effectiveness of the intervention. The data was collected using 25 Self-administered questionnaires and analyzed using paired-sample independent t-tests and linear regressions to study the relationship between the outcome and independent variables.Results: We collected the data from 759 adolescents among 15 intervention and 15 control clusters. The results have shown that as compared to control arms, the mean sexual and reproductive health Knowledge score was significant higher in the intervention clusters (375) 73.3%, vs (384) 66.5%%, p< 0.001, 95% CI, (0.05395– 0.08347). Information (β: 0.038, 95% CI: 0.028– 0.052), confidence (β: 0.045, 95% CI: 0.033– 0.057), knowledge (β: 0.05, 95% CI: 0.035– 0.066), and compassionate care (β: 0.107, 95% CI: 0.092– 0.122) were significantly associated with SRH knowledge prediction. The proportion of SRH knowledge increased from 168(44%) baseline to 244(65%) end line in the intervention versus 235(60% to 238(62%) in control arms.Conclusion: The execution of school-linked module-based friendly health education has proved to have a significant effect on mean SRH knowledge. Individual-level and behavioral-level factors significantly explain variability in enhancing SRH knowledge in the pastoral community. We recommend scaling up the School-Linked Module-based friendly health education intervention.Trial Registration: We registered clinical trial PACTR202107905622610 on 16 July 2021.Keywords: adolescent, SRH knowledge, education, intervention, pastoral communit