The impact of precipitation, temperature, and soil moisture on wheat yield gap quantification: evidence from Morocco
Bibliographic record
Abstract
Climate change has devastating impacts on agriculture, increasing the yield gap for most crops, especially in developing nations. This is likely to worsen food insecurity in some countries, calling for efforts to close the yield gap as much as possible. Estimating the yield gap and its drivers is essential for devising strategies to increase yields. This study quantifies the wheat yield gap in Morocco's five major wheat production regions. It analyzes the historical sensitivity of wheat yield to temperature, precipitation, and soil moisture, which are important factors affecting agricultural productivity. Furthermore, it evaluates how these yield gaps impact the revenue of producers in these regions. This analysis was conducted using datasets, including the Global Dataset of Historical Yield (GDHY) for yield gap assessment, soil moisture data, ERA5 reanalysis data, and CHIRPS datasets for climate assessment from 1982 to 2016. Pearson correlation and multiple linear regression analyses were employed to reflect the variation characteristics of wheat yield and to identify the impacts of precipitation, temperature, and soil moisture on wheat yield. High regional differences in wheat yield gaps were observed, with values ranging from 1.64 t/ha in Casablanca Settat to 4.12 t/ha in Marrakech Safi, and temporal variability ranging from 9 to 18%. Wheat yields were found to be strongly correlated with rainfall, particularly from December to March. Temperature fluctuations had a significant negative impact on wheat yield across the regions. Soil moisture was positively correlated with wheat yields throughout all growing periods, showing the strongest impacts during the early vegetative development phase. Additionally, losses due to wheat yield gaps were considerable, ranging between $ 194 and 891 per hectare. The revenue loss due to Yield Gap I ranged from 49 to 71%, while the loss due to Yield Gap II ranged from 240 to 444%, depending on the method used to calculate the wheat yield gap. Results reveal gaps in wheat yield, forming a basis for process-based modeling to understand crop yield gap drivers. Understanding yield gap drivers will play a pivotal role in evidence-based intervention strategies to enhance yields. By applying such strategies, it is possible to not only manage and reduce the variability in wheat production, but also ensure sustainable agricultural practices and achieve food security in Morocco and beyond.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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, 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".