Digitalisation of agriculture in Zimbabwe: Challenges and opportunities

Cosmas Parwada () and Hillary Marufu ()

International Journal of Sustainable Agricultural Research, 2023, vol. 10, issue 1, 32-41

Abstract: To provide insights on the potential of digitalised agriculture in improving output exploring its challenges among the smallholder farmers in Zimbabwe. Literature on the application of digital agriculture was gathered. The research from countries with same or similar Zimbabwean agricultural conditions were then selected in this review. Notably, there are disparities in complexity and level of digitalisation between the developing and developing countries. Unlike in the developing countries, digitalised agriculture is more advanced and highly applied in developed countries. In Zimbabwe specifically, application of digitalized agriculture is skewed towards the commercial farmers than the smallholder communal farmers. The application of digital agriculture e-agriculture) has gained momentum world over in recent years but still low in Zimbabwe where it is more common to the highly literate and resource endowed farming communities than poorly resourced farmers. The digital agriculture is a useful modern technology applied in agricultural production systems in enhancing precision application of resources e.g water, fertilizers, pesticides etc increasing the technical efficiency that translates into high farm outputs (both quantity and quality). Machine Learning (ML) which is a subset of Al, developed to handle various challenges faced during the formation of knowledge-based farming systems. Therefore, digitalisation of agriculture ranges from the use of simple offline programmed production systems installed into information and communications technology (ICTs) gadgets to complex algorithms run by computers. In advanced digitalisation, algorithms are applied in different agronomic practices of crops as well as in animal husbandry.

Keywords: Agricultural management; Agricultural production; Artificial intelligent; Big data; Increased efficiency. (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
https://archive.conscientiabeam.com/index.php/70/article/view/3280/7323 (application/pdf)
https://archive.conscientiabeam.com/index.php/70/article/view/3280/7529 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pkp:ijosar:v:10:y:2023:i:1:p:32-41:id:3280

Access Statistics for this article

More articles in International Journal of Sustainable Agricultural Research from Conscientia Beam
Bibliographic data for series maintained by Dim Michael ().