Exploring the nexus of climate variability, population dynamics, and maize production in Togo: Implications for global warming and food security
Bibliographic record
Abstract
Maize, which serves as the primary staple crop in Togo, plays a crucial role in supporting rural communities and ensuring food security. Comprehending the complex relationship between Togo's maize production, population dynamics, and climate variability is crucial for mitigating the impacts of global warming and guaranteeing long-term food security. The present study examines the climate patterns and their impact on maize cultivation in Togo for the period spanning from 1990 to 2020. During this temporal interval, significant variations were seen in important climate indices. The levels of rainfall and the frequency of wet days exhibited notable fluctuations, whereas the temperature and concentrations of air pollutants, specifically carbon dioxide (CO2) and nitrous oxide (N2O), had a persistent rising trajectory. The recorded temperature exhibited an increase from 27 °C in 1990 to 29 °C in 2020. The levels of CO2 emissions had a substantial increase from 1000 kt in 1990 to an estimated 2500 kt in 2020. Similarly, the emissions of N2O witnessed a notable rise of more than 800 kt over the same time frame. Using time series analysis, we examined the temporal characteristics and employed ARIMA predictive models to project maize production over the upcoming decade, considering the gradual decrease in rural population density. The studies highlight the significant implications of global warming on a worldwide scale, exerting a profound impact on the Earth. The agriculture sector, specifically maize production, had notable variations in yield and overall output. Notwithstanding the inherent fluctuations in output, maize continues to maintain its position as the most widely consumed and preferred crop in Togo, even in the face of the country's rapidly expanding population. As a result, Togo has increased its imports of maize to satisfy the growing demand. The present study sheds light on the intricate relationship between production parameters, including yield and quantity, and a range of environmental variables such as the frequency of precipitation, temperature, and levels of N2O, in addition to rural and urban population density. The statement underscores the pressing necessity to confront the issue of global warming and its subsequent impacts on agricultural methodologies. Furthermore, the results underscore the significance of considering population dynamics and climate variables in the prediction of forthcoming agricultural outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".