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Modelling neglected and underutilised crops: A systematic review of progress, challenges, and opportunities

Creator: Chimonyo, V.G.P.
Creator: Chibarabada, T.P.
Creator: Choruma, D.J.
Creator: Kunz, R.
Creator: Walker, S.
Creator: Massawe, F.
Creator: Modi, A.T.
Creator: Mabhaudhi, T.
Year: 2022
URI: https://hdl.handle.net/10883/22314
Language: English
Publisher: MDPI
Copyright: CIMMYT manages Intellectual Assets as International Public Goods. The user is free to download, print, store and share this work. In case you want to translate or create any other derivative work and share or distribute such translation/derivative work, please contact CIMMYT-Knowledge-Center@cgiar.org indicating the work you want to use and the kind of use you intend; CIMMYT will contact you with the suitable license for that purpose
Type: Article
Place of Publication: Basel (Switzerland)
Issue: 21
Volume: 14
DOI: 10.3390/su142113931
Keywords: Crop Simulation Modelling
Description: Developing and promoting neglected and underutilised crops (NUS) is essential to building resilience and strengthening food systems. However, a lack of robust, reliable, and scalable evidence impedes the mainstreaming of NUS into policies and strategies to improve food and nutrition security. Well-calibrated and validated crop models can be useful in closing the gap by generating evidence at several spatiotemporal scales needed to inform policy and practice. We, therefore, assessed progress, opportunities, and challenges for modelling NUS using a systematic review. While several models have been calibrated for a range of NUS, few models have been applied to evaluate the growth, yield, and resource use efficiencies of NUS. The low progress in modelling NUS is due, in part, to the vast diversity found within NUS that available models cannot adequately capture. A general lack of research compounds this focus on modelling NUS, which is made even more difficult by a deficiency of robust and accurate ecophysiological data needed to parameterise crop models. Furthermore, opportunities exist for advancing crop model databases and knowledge by tapping into big data and machine learning.
Agrovoc: CLIMATE RESILIENCE
Agrovoc: ECOPHYSIOLOGY
Agrovoc: SUSTAINABILITY
Agrovoc: CROPS
Related Datasets: https://www.mdpi.com/2071-1050/14/21/13931#app1-sustainability-14-13931
ISSN: 2071-1050
Journal: Sustainability (Switzerland)
Article number: 13931


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  • Sustainable Intensification
    Sustainable intensification agriculture including topics on cropping systems, agronomy, soil, mechanization, precision agriculture, etc.

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