Gene-editing technologies for developing climate resilient rice crops in sub-Saharan Africa: Political priorities and space for responsible innovation
Bibliographic record
Abstract
Over the last 2 decades, rice has become one of the most important staple crops for sub-Saharan Africa. Estimates show that average consumption of rice has tripled over the last 3 decades, from 9.2 million metric tons (Mt) in the early 1990s to 31.5 million Mt in 2018, with West and Central Africa accounting for nearly two-thirds of this share. The demand for rice, however, has placed an enormous economic burden on African countries, whereby they spent over USD 5.5 billion per year on rice imports over the past few years. To address this challenge, over 32 countries have established National Rice Development Strategies to increase local production and to achieve rice self-sufficiency. Several of these countries have shown policy interest to use modern biotechnological advancements, including gene editing, to ensure increases in rice productivity and reduce food imports, in the context of extreme climate vulnerability and acceleration of the effects of biotic and abiotic stresses. This review article examines the role of biotechnology in African countries’ efforts to achieve rice self-sufficiency, particularly the potential for genome-editing technologies toward the genetic improvement of rice and to Africa’s nascent research programs. This article notes that while gene editing offers important advances in crop breeding, like genetic engineering, it faces some persistent sociopolitical challenges and low societal acceptability. As such, international partnerships advancing genome editing in Africa’s rice-subsectors development could benefit from adopting key principles from “responsible research and innovation” to help these projects achieve their potential, while bringing about more inclusive and reflexive processes that strive to anticipate the benefits and limits associated with new biotechnologies as they relate to local contexts. Such an approach could create the necessary political space to test and assess the benefits (and risks) related to adopting gene-editing technologies in Africa’s rice sectors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".