Orphan crops can contribute to building resilience of marginal cropping systems as a climate chnage adaptation strategy. Orphan crops play an important rol

Business as usual or transformative change? While the global agro-industrial food system is credited with increasing food production, availability and accessibility, it is also credited with giving birth to ‘new’ challenges such as malnutrition, biodiversity loss, and environmental degradation. We reviewed the potential of underutilized indigenous and traditional crops to bring about a transformative change to South Africa’s food system. South Africa has a dichotomous food system, characterized by a distinct, dominant agro-industrial, and, alternative, informal food system. This dichotomous food system has inadvertently undermined the development of smallholder producers. While the dominant agro-industrial food system has led to improvements in food supply, it has also resulted in significant trade-offs with agro-biodiversity, dietary diversity, environmental sustainability, and socio-economic stability, especially amongst the rural poor. This challenges South Africa’s ability to deliver on sustainable and healthy food systems under environmental change. The review proposes a transdisciplinary approach to mainstreaming underutilized indigenous and traditional crops into the food system, which offers real opportunities for developing a sustainable and healthy food system, while, at the same time, achieving societal goals such as employment creation, wellbeing, and environmental sustainability. This process can be initiated by researchers translating existing evidence for informing policy-makers. Similarly, policy-makers need to acknowledge the divergence in the existing policies, and bring about policy convergence in pursuit of a food system which includes smallholder famers, and where underutilized indigenous and traditional crops are mainstreamed into the South African food system.

Underutilised crops represent an important component of agro-biodiversity with potential to contribute to climate change adaptation, food security and sovereignty in poor rural areas. However, despite emerging research interest, they continue to occupy the peripheries of mainstream agriculture. There is a need to consolidate the gains made and propose a coherent strategy for translating underutilised crops into mainstream agriculture. The status of underutilised crops in South Africa (past, present and on-going research) was reviewed with a view to identifying existing gaps, opportunities and challenges for developing future research capacity. The review confirmed that several underutilised crops are drought tolerant, adapted to low levels of water use and thus suitable for cultivation in most marginal production areas typical of semi-arid and arid cropping systems. In addition, several are nutrient dense and could be used to improve dietary diversity among poor rural people. These characteristics make them ideal for inclusion in climate change adaptation and promotion of food sovereignty. There is need for a paradigm shift away from practices that have promoted a few major crops to an agro-ecology based land use classification system that recognises diversity and strengthens food networks. There is a need to identify those underutilised crops that show the greatest potential for success and can be fitted into semi-arid and arid cropping systems and prioritise them for future research, development and innovation.

In agriculture, land use and land classification address questions such as “where”, “why” and “when” a particular crop is grown within a particular agroecology. To date, there are several land suitability analysis (LSA) methods, but there is no consensus on the best method for crop suitability analysis. We conducted a scoping review to evaluate methodological strategies for LSA. Secondary to this, we assessed which of these would be suitable for neglected and underutilised crop species (NUS). The review classified LSA methods reported in articles as traditional (26.6%) and modern (63.4%). Modern approaches, including multi-criteria decision-making (MCDM) methods such as analytical hierarchy process (AHP) (14.9%) and fuzzy methods (12.9%); crop simulation models (9.9%) and machine learning related methods (25.7%) are gaining popularity over traditional methods. The MCDM methods, namely AHP and fuzzy, are commonly applied to LSA while crop models and machine learning related methods are gaining popularity. A total of 67 parameters from climatic, hydrology, soil, socio-economic and landscape properties are essential in LSA. Unavailability and the inclusion of categorical datasets from social sources is a challenge. Using big data and Internet of Things (IoT) improves the accuracy and reliability of LSA methods. The review expects to provide researchers and decision-makers with the most robust methods and standard parameters required in developing LSA for NUS. Qualitative and quantitative approaches must be integrated into unique hybrid land evaluation systems to improve LSA.

Determining maize water content variability is necessary for crop monitoring and in developing early warning systems to optimise agricultural production in smallholder farms. However, spatially explicit information on maize water content, particularly in Southern Africa, remains elementary due to the shortage of efficient and affordable primary sources of suitable spatial data at a local scale. Unmanned Aerial Vehicles (UAVs), equipped with light-weight multispectral sensors, provide spatially explicit, near-real-time information for determining the maize crop water status at farm scale. Therefore, this study evaluated the utility of UAV-derived multispectral imagery and machine learning techniques in estimating maize leaf water indicators: equivalent water thickness (EWT), fuel moisture content (FMC), and specific leaf area (SLA). The results illustrated that both NIR and red-edge derived spectral variables were critical in characterising the maize water indicators on smallholder farms. Furthermore, the best models for estimating EWT, FMC, and SLA were derived from the random forest regression (RFR) algorithm with an rRMSE of 3.13%, 1%, and 3.48%, respectively. Additionally, EWT and FMC yielded the highest predictive performance and were the most optimal indicators of maize leaf water content. The findings are critical towards developing a robust and spatially explicit monitoring framework of maize water status and serve as a proxy of crop health and the overall productivity of smallholder maize farms.