WHOSE GAP COUNTS? THE ROLE OF YIELD GAP ANALYSIS WITHIN A DEVELOPMENT-ORIENTED AGRONOMY
Bibliographic record
Abstract
SUMMARY Yield gaps have become a useful tool for guiding development-related agronomy, especially in the global South. While critics have challenged some aspects of the yield gap methodology, and the relevance of food security advocacy based on yield gaps, very few studies question the actual relevance, application and scalability of yield gaps for smallholder farmers (and researchers) in the tropics. We assess these limitations using two contrasting case studies: maize-based farming systems in Western Kenya and rice-based farming systems in Central Luzon, the Philippines. From these two cases, we propose improvements in the use of yield gaps that would acknowledge both the riskiness of crop improvement options and the role that yield increases might play within local livelihoods. Participatory research conducted in Western Kenya calls into question the actual use and up-scaling of yield measurements from on-station agronomic trials to derive estimates of actual and water-limited yields in the region. Looking at maize yield gaps as cumulative probabilities demonstrates the challenges of assessing the real magnitude of yield gaps in farmers’ fields and of deciding whose yield gaps count for agricultural development in Kenya. In the case of rice-based farming systems, we use a historical dataset (1966–2012) to assess changes in rice yields, labour productivity, gross margin and rice self-sufficiency in Central Luzon, the Philippines. While large rice yield gaps persist here, there appear to be few incentives to close that gap once we consider the position of crop production within local livelihoods. In this context, economic returns to labour for farm work were marginal: labour productivity increased over time in both wet and dry seasons, but gross margins decreased in the wet season while no trend was observed for the dry season. Since most households were rice self-sufficient and further increases in crop production would offer minimal returns while relying increasingly on hired labour, we question who should close which yield gap. Our case studies show the importance of contextualising yield gaps within the broader livelihood context in which farmers operate. We propose that this should be done at farm and/or farming systems level while considering the risks associated with narrowing yield gaps and looking into multiple performance indicators.
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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 | 0.059 | 0.184 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".