Replication Data for: What influences transfer of training in an African agricultural research network?

Purpose: A study was conducted to determine the extent to which transfer of training back to work among trainees from national partners of an international bean research network in Africa was perceived to have taken place; and to determine the factors that predicted transfer of training back to the job. Methodology/approach: Online data collection using the Learning Transfer Systems Inventory (LTSI) from 139 respondents was made and analyzed using bivariate correlations and hierarchical multiple regression. Findings: An average of 75% of the training skills were perceived as transferred. Personal capacity significantly predicted transfer, while motivation to transfer, transfer design, supervisor and peer support positively correlated with training transfer. Theoretical implications: The Learning Transfer System Inventory factors remain relevant explanations for training transfer with in African agricultural research and development organizations. Certain work environments are likely to have new factors such as ‘Peer and supervisor’ support which operated as one explanatory factor for training transfer, showing the closeness of peers and supervisors in agricultural research and development settings. Practical implications: The international agricultural research network needs to pay attention to the trainees’ ability to transfer new training, particularly on workload related hindrances. Originality/value: The study has tested out the applicability of the LTSI for international agencies that conduct training for agricultural research and development in Africa. Understanding personal capacity to transfer is critical in this context, suggesting that institutions need policies that enhance trainee capacity to transfer enacted, facilitated and enforced

Crop improvement, adoption and impact of improved bean varieties in Sub Saharan Africa

The crop improvement research effort of the Consultative Group on International Agricultural Research (CGIAR) centers and their national agricultural research systems (NARS) partners has had a large impact on world food production. Although bean impact has been documented in a number of past studies, the last comprehensive study of the international crop improvement effort, organized by the Standing Panel for Impact Assessment (SPIA, formerly the Impact Assessment and Evaluation Group), was based on data collected a decade ago (Evenson and Gollin, 2003 based on 1997-98 data). Important changes have occurred in the funding and conduct of the international crop improvement effort and in the general climate for agriculture in the developing world since the completion of the Evenson and Gollin study. The level and focus of funding for research in the NARS and in the CGIAR centers have fluctuated greatly, and the role of the private sector has evolved. Yet, the importance of the CGIAR/NARS crop improvement effort in feeding the world is arguably as important today as it has been at any time in history. The steady uptake and turnover of crop varieties is fundamental to realizing a Green Revolution in Africa, and it is still important for helping achieve income growth for numerous poor rural households. But our present understanding of improved variety adoption—by crop, by location, by adopter and by source—is limited in Africa. The data seeks to redress this anomaly, by providing a versatile database on bean variety adoption by crop, by location, by adopter and by source in sub-saharan countries. The following countries are covered: Burundi, DRCongo, Ethiopia, Malawi, Mozambique, Rwanda, Tanzania, Uganda and Zambia

Bean production areas in sub-Saharan Africa

176 bean production areas were identified and reviewed using a form of the Delphi method of consensus building among experts from almost all bean producing countries of sub-Saharan Africa. Data were collected for: bean production, cropping systems and producers; seed systems; bean use and marketing; bean grain types and varieties; and abiotic and biotic constraints to bean production and storage. Data on harvested area and production were compiled from a variety of sources and allocated among bean production areas using local expert knowledge and/or sub-national statistics. Bean environments were computed and allocated to each bean production area. The dataset includes raw data from each workshop; for some variables the data were later standardised to ensure that the cumulative values were 100%. We recommend using the standardised data. (2019-11-27

Training beneficiaries in the PABRA Project

Pan African Bean Research Alliance (PABRA) specifically invests finance, human resource and time in ensuring that the continents bean researchers and staff are up to date and relevant with skills they require. The data sets presented here have been assembled from multiple sources to provide and indicative position of skill and knowledge building initiatives by PABRA and its various partners. The data sets show the number of people trained between the year 2003 t0 2016. Though the data sets provide the numbers, discussion on capacity building as a whole is available in the capacity building section of the PABRA website

Training beneficiaries in the PABRA Project

Pan African Bean Research Alliance (PABRA) specifically invests finance, human resource and time in ensuring that the continents bean researchers and staff are up to date and relevant with skills they require. The data sets presented here have been assembled from multiple sources to provide and indicative position of skill and knowledge building initiatives by PABRA and its various partners. The data sets show the number of people trained between the year 2003 t0 2016. Though the data sets provide the numbers, discussion on capacity building as a whole is available in the capacity building section of the PABRA website

Common Bean variety releases in Africa

The Pan Africa Bean Research Alliance is a network of national agricultural research centers (NARS), and private and public sector institutions that work to deliver better beans with consumer and market preferred traits to farmers. The datasets presented here draw from 17 Sub Saharan countries that are members of PABRA. The dataset on released bean varieties is a collection of 513 bean varieties released by NARS and there characteristics. The dataset on bean varieties and the relationship to constraints provides the 513 bean varieties on the basis of resistance to constraints such as fungal, bacterial, viral, diseases and tolerance to abiotic stresses. There is also a dataset of bean varieties that have been released in more than one country, useful for moving seed from one country to another and facilitating regional trade. The dataset on Niche market traits provides the market defined classifications for bean trade in Sub Saharan Africa as well as varieties that fall into these classifications. The datasets are an update to the 2011 discussion on PABRAs achievement in breeding and delivery of bean varieties in Buruchara et. 2011 in pages 236 and 237 here: http://www.ajol.info/index.php/acsj/article/view/74168 . It is also an update to a follow up to this discussion in Muthoni, R. A., Andrade, R. 2015 on the performance of bean improvement programmes in sub-Saharan Africa from the perspectives of varietal output and adoption in chapter 8. here: http://dx.doi.org/10.1079/9781780644011.0148. The data is extracted from the PABRA M&E database available here (http://database.pabra-africa.org/?location=breeding)

Common Bean variety releases in Africa

The Pan Africa Bean Research Alliance is a network of national agricultural research centers (NARS), and private and public sector institutions that work to deliver better beans with consumer and market preferred traits to farmers. The datasets presented here draw from 17 Sub Saharan countries that are members of PABRA. The dataset on released bean varieties is a collection of 357 bean varieties released by NARS and there characteristics. The dataset on bean varieties and the relationship to constraints provides the 357 bean varieties on the basis of resistance to constraints such as fungal, bacterial, viral, diseases and tolerance to abiotic stresses. There is also a dataset of bean varieties that have been released in more than one country, useful for moving seed from one country to another and facilitating regional trade. The dataset on Niche market traits provides the market defined classifications for bean trade in Sub Saharan Africa as well as varieties that fall into these classifications. The datasets are an update to the 2011 discussion on PABRAs achievement in breeding and delivery of bean varieties in Buruchara et. 2011 in pages 236 and 237 here: http://www.ajol.info/index.php/acsj/article/view/74168 . It is also an update to a follow up to this discussion in Muthoni, R. A., Andrade, R. 2015 on the performance of bean improvement programmes in sub-Saharan Africa from the perspectives of varietal output and adoption in chapter 8. here: http://dx.doi.org/10.1079/9781780644011.0148. The data is extracted from the PABRA M&E database available here ( http://database.pabra-africa.org/?location=breeding)