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Better agronomic management increases climate resilience of maize to drought in Tanzania

Xiong, W. and Tarnavsky, E. ORCID: (2020) Better agronomic management increases climate resilience of maize to drought in Tanzania. Atmoshpere, 11 (9). 982. ISSN 2073-4433

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To link to this item DOI: 10.3390/atmos11090982


Improved access to better seeds and other inputs, as well as to market and financing, provides greater harvest security for smallholder farmers in Africa, boosting their incomes and increasing food security. Since 2015, a variety of agronomic measures have been introduced and adopted by smallholder farmers under a program led by the United Nations’ World Food Program (WFP) called the Patient Procurement Platform (PPP). Here, we integrate a variety of agronomic measures proposed by the PPP to more than 20,000 smallholder farmers in Tanzania into 18 management strategies. We apply these across the country through grid-based crop model (DSSAT) simulations in order to quantify their benefits and risk to regional food security and smallholder farmers’ livelihoods. The simulation demonstrates current maize yields are far below potential yields in the country. Simulated yields across the nation were slightly higher than the mean of reported values from 1984 to 2014. Periodic droughts delayed farmers’ sowing and reduced maize yield, leading to high risk and low sustainability of maize production in most of the maize areas of the country. Better agronomic management strategies, particularly the combination of long-maturity, drought tolerance cultivars, with high fertilizer input, can potentially increase national maize production by up to five times, promoting Tanzania as a regional breadbasket. Our study provides detailed spatial and temporal information of the yield responses and their spatial variations, facilitating the adoption of various management options for stakeholders.

Item Type:Article
Divisions:Interdisciplinary Research Centres (IDRCs) > Walker Institute
Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
ID Code:93418


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