Dynamic agricultural supply response under Agricultural Transformation Agenda in Nigeria
Why this work is in the frame
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Bibliographic record
Abstract
Nigeria has been experiencing declining agricultural production over the past two decades despite the implementation of different policies to boost agricultural production. One such policy was the Agricultural Transformation Agenda (ATA), implemented in 2011. However, it remains unclear whether ATA succeeded in boosting agricultural supply; this study examines that issue by assessing the effect of ATA on the supply responses of six major crops in Nigeria. We used secondary data spanning l980-20l9 from reliable institutions in Nigeria. The growth model, Vector Error Correction Model (VECM), and Generalized Method of Moments (GMM) estimator were used to analyze the dynamic nature of crop supply response to address endogeneity and simultaneity bias. The findings of this study show that the trends of selected crop acreage, production and yield from l980 to 20l9 fluctuated more during the Agricultural Transformation Agenda era (ATA). Aside from the cassava crop, yield growth rates were abysmal for the remaining five crops, with a negative rice rate indicative of poor productivity during the ATA period. In addition, production was steady with high growth rates, similar to the land devoted to production (acreage) during the ATA period. Additionally, an increase in the price of cassava causes a decrease in maize production in the short and long run. There is an inverse relationship between own price and rice production in the short and long run. The non-price variables affecting maize production were acreage, exchange rate, fertilizer consumption, and school enrollment. Likewise, the level of rainfall, crop production index, exchange rate, and school enrolment influence rice production. In conclusion, production was steady with high growth rates, similar to land devoted for production (acreage) under the ATA period. We suggest that the Nigerian government and farmers should invest in technologies that shift their reliance to rain-fed agriculture.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it