Chinese sisal estates and smallholder land uses and livelihoods, Kilosa, Central Tanzania

• Published in: World development Vol. 177; p. 106558
• Main Authors: Li, Puyang, Zhang, Yujia, Turner, B.L., Li, Wei
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.05.2024
• Subjects: Estate agriculture; Google earth engine; Land rush; Land system science; Land-use/cover; Smallholder livelihood; Land rush; Land-use/cover; Google earth engine; Land system science; Smallholder livelihood; Estate agriculture
• ISSN: 0305-750X; 1873-5991
• DOI: 10.1016/j.worlddev.2024.106558

•This study assesses the complex impacts of land acquisitions in central Tanzania.•The revival of estates leads to significant smallholder land-livelihood dynamics.•The mix of estate and household farming improves nearby smallholder livelihoods.•Fiber estates bring positive effects to enrich smallholder livelihood portfolios.•The land system dynamics offer insights into various African agribusiness claims. The impacts of large-scale agribusiness, commonly associated with land grabbing, on smallholder livelihoods in Africa have been the subject of an expansive array of research that tends to focus on socioeconomic or environmental issues. This study examines the nexus of smallholder land-livelihood dynamics from 2000 to 2018 owing to the resurrection of fiber estates in the “sisal belt” of Kilosa, Tanzania. Here, Chinese firms have restored former colonial sisal estates, offering wage opportunities to neighboring communities, giving rise to commercial rice production among smallholders and incentivizing in-migration with its increasing demands for subsistence agricultural land. The combination of estate, subsistence, and rice cultivation has enhanced smallholder livelihoods on average, suggesting positive impacts of the fiber estates. These emerging conditions, however, have generated significant landscape changes, which foretell impacts on environmental services. These dynamics of land systems are revealed through the lens of remote sensing data, field surveys and observations, and SURE models.