A comparison of three models used to determine water fluxes over the Albany Thicket, Eastern Cape, South Africa:

The Albany Thicket (AT) biome contains outstanding global biodiversity as well as the potential to achieve carbon credits associated with water-efficient Crasslucean acid metabolism (CAM). Understanding the water fluxes in the AT is crucial to determining carbon (C) sequestration rates and water-use efficiency. Despite large variation in water fluxes across the AT, only a few studies have been conducted in this region with their results validated against short periods of observed data. This study aims to evaluate three models of water fluxes over AT against data from an eddy covariance (EC) system active from October 2015 to May 2018. ET was modelled using the BioGeoChemistry Management (BGC-MAN) model, a biophysical model (Penman-Monteith-Leuning (PML)) and a remotely-sensed product (MOD16), and their results compared with that from the EC system

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

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