Impact of artificial intelligence on the total productivity of agricultural factors in Africa.
Saved in:
| Title: | Impact of artificial intelligence on the total productivity of agricultural factors in Africa. |
|---|---|
| Authors: | Donfouet, Olivier1 (AUTHOR) donfouetolivier1@gmail.com, Ngouhouo, Ibrahim1 (AUTHOR) ngouhouo@yahoo.fr |
| Source: | Environment, Development & Sustainability. Jun2026, Vol. 28 Issue 6, p13789-13817. 29p. |
| Subject Terms: | *Artificial intelligence, *Agricultural productivity, *Marginal productivity, *Policy sciences, *Sub-Saharan Africans, *Digital technology, *Econometrics |
| Geographic Terms: | Africa |
| Abstract: | This study analyzes the impact of artificial intelligence (AI) on agricultural total factor productivity (TFP) in 53 African countries from 2012 to 2020. The results of the propensity score matching analysis show that Artificial Intelligence improves Total Factor Productivity. The average treatment effect (ATT) is 12.40 with a T-test of 4.74, indicating a positive and significant effect at 1%. The graphs show strong data overlap, with no unsupported units. Robustness tests, including the difference-in-difference (DID) method, confirm these results. Other econometric techniques, such as ordinary and generalized least squares, fixed and random effects, also corroborate these findings. To take full advantage of Artificial Intelligence in African agriculture, it is crucial to strengthen policies in the areas of digital infrastructure, training, financing, innovation, data regulation and awareness-raising. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
| Abstract: | This study analyzes the impact of artificial intelligence (AI) on agricultural total factor productivity (TFP) in 53 African countries from 2012 to 2020. The results of the propensity score matching analysis show that Artificial Intelligence improves Total Factor Productivity. The average treatment effect (ATT) is 12.40 with a T-test of 4.74, indicating a positive and significant effect at 1%. The graphs show strong data overlap, with no unsupported units. Robustness tests, including the difference-in-difference (DID) method, confirm these results. Other econometric techniques, such as ordinary and generalized least squares, fixed and random effects, also corroborate these findings. To take full advantage of Artificial Intelligence in African agriculture, it is crucial to strengthen policies in the areas of digital infrastructure, training, financing, innovation, data regulation and awareness-raising. [ABSTRACT FROM AUTHOR] |
|---|---|
| ISSN: | 1387585X |
| DOI: | 10.1007/s10668-024-05528-y |
