Increasing zinc concentration in maize grown under contrasting soil types in Malawi through agronomic biofortification: Trial protocol for a field experiment to detect small effect sizes
Bibliographic record
Abstract
The prevalence of micronutrient deficiencies including zinc (Zn) is widespread in Malawi, especially among poor and marginalized rural populations. This is due to low concentrations of Zn in most staple cereal crops and limited consumption of animal source foods. The Zn concentration of cereal grain can be increased through application of Zn-enriched fertilizers; a process termed agronomic biofortification or agro-fortification. This trial protocol describes a field experiment which aims to assess the potential of agronomic biofortification to improve the grain Zn concentration of maize, the predominant staple crop of Malawi. The hypotheses of the study are that application of Zn-enriched fertilizers will create a relatively small increase in the concentration of Zn in maize grains that will be sufficient to benefit dietary supplies of Zn, and that the effectiveness of agronomic biofortification will differ between soil types. The study will be conducted at three sites, Chitedze, Chitala, and Ngabu Agricultural Research Stations, in Lilongwe, Salima, and Chikwawa Districts respectively. These three sites represent locations in the Central and Southern Regions of Malawi. At each site, two different sub-sites will be used, each corresponding to one of two agriculturally important soil types of Malawi, Lixisols, and Vertisols. Within each sub-site, three Zn fertilizer rates (1, 30, and 90 kg/ha) will be applied to experimental plots using standard soil application methods, in a randomized complete block design. The number of replicates at plot level has been informed by a power analysis from pilot study data, assuming that a minimum 10% increase in Zn concentration of grain at 90 kg/ha relative to the concentration at 1 kg/ha is of interest. Grain mass (yield), stover mass, and both stover and grain Zn concentrations will be measured at harvest. A second year of cropping will be used to establish whether there are any residual benefits to grain Zn concentration. The potential for Zn agronomic biofortification will be communicated to relevant academic and government stakeholders through a peer review journal article and a briefing paper.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".