PROTOCOL: The Effects of Training, Innovation and New Technology on African Smallholder Farmers' Wealth and Food Security: A Systematic Review
Bibliographic record
Abstract
A large proportion of the world's poor live in rural areas, dependent on subsistence farming for their survival (FAO, 2011). Smallholder farmers have been credited with providing up to 80 per cent of food in developing countries (IFAD, 2012) and have the potential to feed themselves and also supply urban markets. Vietnam's smallholder farmers are often credited, for example, with transforming the country from a net-importer of food, to a major exporter (ibid). Whilst definitions of smallholder farming vary, the concept usually incorporates a number of key elements (Morton, 2007): farms on which labour is predominantly family (‘family farms’) (IFAD, 2009); farmers and farms that are resource poor (Nagayets 2005; Dixon et al., 2003); farms of a particular size, most commonly two hectares (Nagayets, 2005; Hazell et al. 2010; Wiggins et al., 2010; World Bank 2003; IFAD, 2011a); and farms which are predominantly subsistence, but might also include a mix of commercial and subsistence activities (Narayan and Gulati, 2002). Smallholder farming is of particular significance to Africa for a number of reasons. Africa's economy is dominated by agriculture (Massett et al., 2011) and the vast majority of farmers in Africa are smallholders (World Bank, 2007). Smallholder farmers contribute significantly to food security on the continent, for example, in sub-Saharan African smallholder farmers contribute up to 80 per cent of the food supply (IFAD, 2011b). Smallholder farmers also include many of the continent's poorest and most marginalised people (World Bank, 2007). In southern Africa in particular, large numbers of women and girls rely on smallholder farming, and it provides a survival strategy for many of the continent's young people, many of whom are orphans and head of their households. Furthermore, supporting Africa's development is a priority within the G8, and working towards increasing food security in the region is high on the agendas of the majority of international donors, including the Canadian Foreign Affairs, Trade and Development agency (DFATD), who have commissioned this review. Given both the importance of smallholder farming in Africa and its potential to contribute to the food security of so many, it is not surprising that considerable efforts are being invested in its success. Both national and international agencies are investing in improving the productivity of smallholder farming, including the International Fund for Agricultural Development (IFAD) and the Canadian Foreign Affairs, Trade and Development agency (DFATD). Additionally, in 2009, the G8's L'Aquila initiative pledged $22 billion USD for agriculture in developing countries (G8, 2009). In 2012, IFAD launched the Adaptation for Small Holder Agriculture Programme (www.ifad.org/climate/asap/). On a national level, heads of state in Africa are increasingly stressing the need for support for smallholder farmers. For instance, South African President Jacob Zuma emphasized the need for support of smallholder famers in his 2013 State of the Nation Address (RSA, 2013). Parallel efforts are being invested in agricultural research, such as impact evaluations and systematic reviews, to assess the effectiveness of these agricultural programmes. Agricultural productivity of particularly starchy cereals is important since this category of crop accounts for two thirds of the region's energy intake as well as 70 per cent of the income of the extremely poor population living in Africa (AGRA, 2013). The 2013 Africa agriculture report issued by AGRA (ibid) identifies the general production trend for Africa as somewhat erratic, but with most countries reporting a steady increase in production. According to the report, Kenya, Ghana, Mali, Tanzania, Uganda, Zambia, and possibly Mozambique have all reported steady increases in agricultural production (AGRA, 2013, 21). Areas that have experienced civil unrest, political instability, or mismanagement of the macroeconomics of the country in the last decade have seen a decrease in agricultural productivity; included among these are Sierra Leone and Liberia (AGRA, 2013, 21). Technologies typically used to increase agricultural productivity in the region include the “increased use of agricultural inputs, modern farming techniques, and reduced market inefficiencies” (AGRA, 2013, 20). However, a much larger array of factors come to bear on agricultural productivity. Political, technological, physical environmental, and micro- and macroeconomic factors related to each country play a pivotal role in shaping the region's agricultural productivity. World prices of inputs and outputs, and international trade policies also influence the agricultural productivity within countries (AGRA, 2013). As such, any technology addressing any one of these aspects may be expected to have an influence on the food security or income of smallholder farmers in Africa. Examples of specific technologies include treadle pump irrigation technology (Adeoti et al., 2009); biofortification and health information (de Brauw et al., 2013); and adopting an export crop and marketing techniques (Ashraf et al., 2008). In the context of this considerable and growing emphasis on smallholder farming, there is a need to understand the relative effectiveness of the different interventions targeting smallholder agriculture in achieving various outcomes. Today, there are a multitude of agricultural interventions in place across Africa (Sapa, 2009). The focus of these interventions has shifted as understanding of the relationship between agriculture and poverty has developed (Massett et al., 2011). Early interventions focused on increasing productivity to meet a perceived lack of food. With the realisation that undernourishment persists alongside high levels of production (Reutlinger & Pellekaan, 1986), structural issues came to the fore and the concept of food security was introduced (Sen, 1981). Interventions shifted towards income generation, access to markets and the production of more nutritious and calorific foods. Two groups of interventions have specifically sought to increase food security and reduce poverty by farmers and or to agricultural and Interventions that are as the of a farming or of this of is the of to increase the intake of A technology emphasis on the of a agricultural be a different of or interventions place emphasis on providing of to farmers. The of such may not be to but However, that interventions on the of technology For instance, an of this of is Brauw et of biofortification and a health information on the food security of smallholder farmers A systematic of the of interventions targeting smallholder farmers et al., that there in the African a lack of systematic addressing various on the of smallholder and a lack of of the impact of interventions on food The of the has been by these as well as by with and interventions for farmers interventions focus on and interventions are also often as a for which to and people in agricultural production to and to and as a to have more in et al., in which may be one of agricultural use a more to and et al., 2009). are and in and on and by farmers by et al., in developed to an on have been across 80 countries important of these interventions to the there is a in the between that are to agricultural and increase example, on resource and which focus on aspects of example, the most of the of interventions is to of the or the the of the and the of the of an of this of is et al. who a in improving the income and food security of smallholder farmers a number of including to the farmers and as well as providing on marketing et al. on the agricultural interventions that with groups and farmers with information and to export and and with Agricultural interventions to of technologies more nutritious and et al., 2013); and these with et al., 2008). and a of these in of and The a number of of evaluations of these and For example, et al. the impact of a the of in South Africa. on the a the effectiveness of of on factors such as smallholder farming income and resource in The of these are from or to income generation, food and poverty (World Bank, 2007). In particular, there is increasing emphasis international on the of food security and poverty Smallholder farming has been credited with the potential to food (Sen, & Pellekaan, The is that it is both an subsistence strategy and a potential poor farmers to food (IFAD, Furthermore, it is to of the population that are most to the of and (World Bank, 2007). Whilst there is for of the effectiveness of and and technology on the and food security of smallholder the by which these interventions are and are dependent on factors such as the political and as well as more elements such as to their and increase their productivity. As there are key on the to food security and of of and increase in and productivity. include that to the of food the of crop as well as crop et al., 2013). may include of the impact of interventions the or of of the of or and food et al., 2013). is by and in the systematic of African as of various such as of resource reduced on labour and of et al., 2013). The importance of smallholder farming in and the multitude of interventions to increase the and food security of smallholder farmers has been with agencies and supporting these farmers to their for to their programmes. on the of interventions and their the need for on the effectiveness of and or and their on both poverty and food to the to which these more on one but not the that and which to in and and for more on this A of systematic of to smallholder farming in Africa two & et al., and a is et al. 2013). 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A be to the within two of are not the or are to for an the has the to the or the to The also has the to or the it not meet the of the the for the in of and and and the for the to as with the The support of the and the in is to the and in the in is However, a systematic be or the its in not in are for in the to include a the in any of the review. understand the to a and to in the on of the 2013
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
| gpt | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".