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Creator Dr Amelia Hood describes what their systematic map of cassava farming practices revealed about our data gaps round this essential staple crop.
Cassava is a potato-like tuber used to supply a wide selection of savoury and candy dishes, both for direct consumption (e.g. tasty chips!) or following processing into flour (e.g. tapioca, farinha, garri).
Cassava – also called mandioca and yuca – is a major meals supply for many individuals throughout the tropics and subtropics, and is commonly consumed by 10% of the worldwide inhabitants. It’s largely grown by small-scale farmers and eaten domestically, with secondary makes use of as animal feed and biofuel.
Cassava underneath local weather change
Local weather change and inhabitants progress will considerably enhance stress on international meals safety this century because of the mixed impacts of water shortage, excessive climate occasions, biodiversity loss, and pest and illness unfold. Sub-Saharan Africa, which produces 50% of cassava globally, is probably the most susceptible area to those growing pressures.
Cassava is a vastly promising crop underneath local weather change as it may well maintain producing meals underneath drought situations when different main crops can not. It will also be grown in soils with low fertility and harvested at any time, which is advantageous when unpredictable local weather occasions alter anticipated harvesting dates.
A number of worldwide authorities on cassava farming, such because the Worldwide Heart for Tropical Agriculture (CIAT), the Worldwide Institute of Tropical Agriculture (IITA) and the UN’s Meals and Agriculture Organisation (FAO) have been prioritising and main cassava analysis, however giant data gaps (understudied matters) stay, and figuring out and filling these will probably be key to optimising sustainable cassava manufacturing and creating climate-resilient farming methods.
In our article, we reviewed the analysis on cassava agriculture by producing a scientific map.
A scientific map of cassava agriculture
A scientific map is a analysis technique used to collate and describe present literature on a specific matter. In contrast to a scientific assessment, which solutions a particular analysis query (e.g. what’s the impression of intercropping cassava on crop yield?), a scientific map identifies and describes present analysis on a specific matter (e.g. which research have investigated the impression of intercropping cassava?). Systematic maps can be utilized by researchers, funders, practitioners and coverage makers to determine related analysis or data gaps requiring additional consideration.
For our map, we adopted a broadcast protocol to determine which research had measured the impacts of cassava farming interventions (e.g. intercropping/tilling) on agricultural, financial and environmental outcomes (e.g. yield/soil). We assessed which interventions and outcomes had been studied the place and when, and the standard of the analysis (e.g. examine designs). Interventions and outcomes had been developed into new hierarchical ontologies (Agri-ontologies 1.0), which aren’t described right here.
We discovered 36,580 related data and included 1,599 research within the closing systematic map. For anybody unfamiliar with this analysis technique, a scientific map this dimension takes a very long time to construct – in our case a number of years with a workforce of individuals!
Cassava analysis wants higher prioritisation
Our systematic map confirmed that:
1. Analysis is clustered, with minimal analysis in some areas of main manufacturing
We discovered robust clustering of analysis with 46% of research carried out in Nigeria, Colombia, and Brazil alone, and 70% of research carried out over simply ten nations.
This partly stems from manufacturing, as most main cassava-producing nations are in included on this high ten, however a number of areas are understudied. For instance, the Democratic Republic of Congo is the second highest producer, with solely 11 research, and Ghana, Vietnam, and Indonesia are main producers, every with fewer than 50 research.
We suggest that researchers and funders urgently goal these areas, notably as common yields in these areas are 60% decrease than potential yields (18 T/ha vs 44 T/ha).
2. Majority of analysis targeted on manufacturing, with little analysis into wider impacts
There have been giant data clusters when it comes to the studied interventions and outcomes.
For interventions, most studied interventions associated to crop administration (70%), and testing completely different cultivars particularly. Soil, water and crop land administration (19%), and non-crop habitat administration (e.g. margins) (34%) had been studied much less.
Probably the most studied outcomes associated to crops (58%), and crop yield particularly, with soil (17%), water (2%), pollution (1.5%) or wildlife (4%) receiving much less consideration.
While a major operate of agricultural land is to supply meals, figuring out and implementing best-practice administration for the soil, water, and biodiversity will assist with this intention by stabilising crop yield fluctuations and selling resilience to ecological disturbances, akin to excessive climate occasions, along with wider societal advantages (e.g. carbon sequestration and water high quality). Subsequently, we strongly suggest that stakeholders use this systematic map to determine and fill these key data gaps.
3. Poor reporting requirements, short-term initiatives and publication delays are hampering analysis
Reporting requirements confer with the descriptive knowledge which can be printed to supply the context for a examine (e.g. location). We discovered that reporting requirements had been poor (e.g. 24% of research didn’t report experimental begin dates) and suggest that reporting checklists/pointers are promoted to enhance this (e.g. AgroEcoList 1.0 for agricultural ecology).
Lengthy-term initiatives also needs to be prioritised, as common examine period was two years which is temporary for figuring out ecological impacts that may take years to accrue. We additionally discovered that common publication delay (time from experiment finish to publication) was 4 years, which implies decision-makers should not getting probably the most related analysis in a well timed method. Systemic adjustments are wanted throughout academia to handle this (see Christie et al. 2021).
Given the significance of cassava as a staple crop, we urge researchers, funders, policymakers and different stakeholders to prioritise cassava analysis, and use our systematic map to determine data clusters for syntheses and data gaps for brand spanking new experiments.
Particularly, we suggest focusing on the under-researched areas recognized right here, and interventions and outcomes associated to the soil, water, and biodiversity. This can assist to shut present yield gaps, scale back the impacts of cassava manufacturing on the broader setting, and promote meals safety underneath local weather change.
Learn the complete Stage 2 Registered Report : “A scientific map of cassava farming practices and their agricultural and environmental impacts utilizing new ontologies: Agri-ontologies 1.0” in Ecological Options and Proof.
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