Types of Research
Our initial agriculture capacity building search revealed best practices including institutional partnership building, cross-border opportunities such as ‘twinning,’ and views that these practices are most effective when accompanied by appropriate policies and regulatory frameworks to incentivize return on education to home countries. In addition, the literature explained the historical and political context in which some countries successfully built higher educational capacity, suggesting a set of socio-political conditions necessary for a ‘surge’ in capacity building to occur. Our results raised questions about challenges shaping these best practices (e.g. “brain drain” leading to the need for cross-border opportunities) as well as possible approaches to address these underlying issues. To further examine identified challenges from our initial findings, we re-oriented our search to investigate retention strategies, regional or intra-national network capacity building approaches, and whether there is in fact a need for higher education capacity in all countries through comparative advantage or otherwise. This report presents a review of the literature on the best and worst practices for national agricultural capacity building when investing in a country's higher education system or when investing directly in national or relevant global research capacity. We find that several countries have successfully employed a variety of retention, return, and diaspora strategies to build capacity by capitalizing on the feedback loops of international mobility. In addition, several countries in Africa have employed strategies to address the rural-to-urban “brain drain” by prioritizing education of students with post-secondary rural agricultural work experience and strong ties to rural communities in order to return the benefit of this education to local communities. The report discusses these and other strategies as well as analysis related to the ‘whole system effect’ of higher education and subsequent ‘need’ for Higher Agricultural Education (HAE) capacity in all countries.
This literature review examines the returns to tertiary agricultural sciences education, particularly in Sub-Saharan Africa (SSA). We include information from organizations’ program documents and gray literature, including the World Bank, UNESCO, ILO, IFPRI, ASTI, various Ministries of Education, country-specific NARS, and ADBG. We find no calculated rate of return (RoR) to tertiary agricultural science, including in SSA. We do find estimates for the return on tertiary education in general, ranging from 12-30% in SSA, along with qualitative support for the value of agricultural science education. The private value of this education can be somewhat inferred from the unmet demand of African students for agricultural science training in North America, Europe, and Australia, and the private and social value from the demand for educated researchers in NARS and SSAQ labor markets. Educated agricultural scientists are hypothesized to affect agricultural productivity via research and development and their influence on policy. Despite the dearth of quantitative ROR evidence, we do find several articles describing the need for increased higher agricultural education and proposing recommendations toward this aim. In this report, we summarize these qualitative results as evidence of the value of tertiary education.
This brief explores how two datasets – The Tanzania National Panel Survey (TZNPS) and the TNS-Research International Farmer Focus (FF) – predict the determinants of inorganic fertilizer use among smallholder farmers in Tanzania by using regression analysis. The (TZNPS) was implemented by the Tanzania National Bureau of Statistics, with support from the World Bank Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA) team and includes extensive information on crop productivity and input use. The FF survey was funded by the Bill and Melinda Gates Foundation and implemented by TNS Research International and focuses on the on the behaviors and attitudes of smallholder farmers in Tanzania. The two datasets produce relatively comparable results for the primary predictors of inorganic fertilizer use: agricultural extension and whether or not a household grows cash crops. However, other factors influencing input use produce results that vary in magnitude and direction of the effect across the two datasets. Distinct survey instrument designs make it difficult to test the robustness of the models on input use other than inorganic fertilizer. This brief uses data inorganic fertilizer use, rather than adoption per se. The TZNPS did not ask households how recently they began using a certain product and although the FF survey asked respondents how many new inputs were tried in the past four planting seasons, they did not ask specifically about inorganic fertilizer.