Impact of artificial intelligence on the total productivity of agricultural factors in Africa.
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| Title: | Impact of artificial intelligence on the total productivity of agricultural factors in Africa. |
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| 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 |
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| FullText | Links: – Type: pdflink Text: Availability: 1 |
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| Header | DbId: enr DbLabel: Energy & Power Source An: 194093184 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Impact of artificial intelligence on the total productivity of agricultural factors in Africa. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Donfouet%2C+Olivier%22">Donfouet, Olivier</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> donfouetolivier1@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Ngouhouo%2C+Ibrahim%22">Ngouhouo, Ibrahim</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> ngouhouo@yahoo.fr</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Environment%2C+Development+%26+Sustainability%22">Environment, Development & Sustainability</searchLink>. Jun2026, Vol. 28 Issue 6, p13789-13817. 29p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br />*<searchLink fieldCode="DE" term="%22Agricultural+productivity%22">Agricultural productivity</searchLink><br />*<searchLink fieldCode="DE" term="%22Marginal+productivity%22">Marginal productivity</searchLink><br />*<searchLink fieldCode="DE" term="%22Policy+sciences%22">Policy sciences</searchLink><br />*<searchLink fieldCode="DE" term="%22Sub-Saharan+Africans%22">Sub-Saharan Africans</searchLink><br />*<searchLink fieldCode="DE" term="%22Digital+technology%22">Digital technology</searchLink><br />*<searchLink fieldCode="DE" term="%22Econometrics%22">Econometrics</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Africa%22">Africa</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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] |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=194093184 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10668-024-05528-y Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 29 StartPage: 13789 Subjects: – SubjectFull: Artificial intelligence Type: general – SubjectFull: Agricultural productivity Type: general – SubjectFull: Marginal productivity Type: general – SubjectFull: Policy sciences Type: general – SubjectFull: Sub-Saharan Africans Type: general – SubjectFull: Digital technology Type: general – SubjectFull: Econometrics Type: general – SubjectFull: Africa Type: general Titles: – TitleFull: Impact of artificial intelligence on the total productivity of agricultural factors in Africa. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Donfouet, Olivier – PersonEntity: Name: NameFull: Ngouhouo, Ibrahim IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 1387585X Numbering: – Type: volume Value: 28 – Type: issue Value: 6 Titles: – TitleFull: Environment, Development & Sustainability Type: main |
| ResultId | 1 |
