Modelling Neglected and Underutilised Crops: A Systematic Review of Progress, Challenges, and Opportunities
Abstract
:1. Introduction
2. Materials and Methods
2.1. Phase 1 Progress: Literature Search
2.2. Phase 2: Identifying Research Challenges and Opportunities
2.3. Limitations of the Review
3. Results
3.1. Progress and Challenges in Modelling NUS
3.2. Opportunities for Modelling NUS
3.3. Genomics Opportunities
3.4. Mapping the Underlying Eco-Physiology in NUS
3.5. NUS Phenology
3.6. The Role of Information and Computer Technologies and Data Management
3.7. Considerations for Informing the Modelling of NUS
Consideration | Description | Key Feature/Requirements | Comment |
---|---|---|---|
Modelling and model goals | The first criterion when choosing a crop model should be the main purpose of applying it. | Simulate plant growth and development, biophysical processes, resource use and management, inter-plant competition and climate change impacts | For NUS, one of the primary research objectives is to develop production guidelines for mainstreaming them into the existing cropping system. Hence, the selected crop model should be able to simulate various management levers and, more importantly, climate variability and change scenarios to address some of the existing knowledge gaps on NUS |
A model’s goal should be to inform what function the model is to perform and what degree of accuracy is required in the model’s outputs | Simulate plant growth and development, biophysical processes, resource use and management, inter-plant competition and climate change impacts | For NUS, models should consider the various influences of climate change drivers such as rainfall and temperature while accounting for key crop physiological processes and biophysical aspects of the crop–soil–atmosphere systems to solve crop production and natural resource management issues. | |
Model availability | The essential aspect to consider is whether the selected model is freely available for use. Not all crop models are freely available. Based on 70 models reviewed by [104], only 29 are freely available | Most established models can be downloaded free of charge | The use of generic crop models can allow several NUS to be adapted for individual crop varieties under specific conditions. |
Model type | The complexity, degree of detail, level of comprehensiveness, and the scale of application (specific cultivar, field, catchment, and region) of crop models differs | Models can be described as either simple empirical types or complex mechanistic models | Mechanistic crop models exhibit increased robustness |
Most models are “source-driven”, thus assuming thatgrowth is limited by factors that drive the production and partitioning of assimilates | Growth engine can be either carbon-driven (World Food Studies simulation WOFOST model), solar radiation-driven (APSIM) or water-driven (AquaCrop) | Across sub-Saharan Africa, current climate change projections indicate increased atmospheric CO2 concentration and weather excesses like heatwaves, droughts, and floods. It is in this context of the expected impacts of climate change and climate change adaptation that carbon-, solar radiation and water-driven models should be considered | |
System boundaries | Predictive capabilities are generally most robust within the boundaries of the data used to develop a model | To rely on a model’s results to make decisions, it is vital to use a model that is not “built on oversimplified and unrealistic assumptions about natural processes.” | |
Model input | Model complexity increases with the number of input parameters required by the model | A model’s descriptive or predictive ability depends on the data quality used to populate it. | As shown by [59], the number of cultivar-specific parameters ranged from 2 (e.g., AquaCrop) to 22 (General Large Area Model GLAM) |
Spatialisation | The impacts of climate variability and change on the agricultural system are interrelated, with cascading and interchanging biophysical layers of air, water, soil, and crops at spatiotemporal scales | To run the models non-stop and at a regional and national level | NUS have been produced and studied in a few agroecologies, where the temporal drivers for their growth and production operate at much smaller resolutions than the courser scale at which most crop models are presented |
Model output | Outputs generated by the model allow the user to fully answer and analyse the questions or objectives of the modelling exercise | ||
Intersectionality | Intersectionality refers to how different processes within the model interact to create a distinct outcome based on different management levers | The model needs to include the representation of several physiological processes to describe or predict outcomes |
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Search Scope | NUS Database | |
---|---|---|
Web of Science | Scopus | |
1 NUS—Title, abstract, keywords “Sorghum” OR “Finger Millet” OR “Tef” OR “Barnyard grass” OR “Bambara groundnut” OR “Lablab” OR “Pigeon pea” OR “Sword bean” OR “Cowpea” OR “Velvet bean” OR “Marama bean” OR “Taro” OR “Sweet potato” OR “Cassava” OR “African yam bean” OR “Cocoyam” OR “Bottle gourd” OR “Blackjack” OR “African Eggplant” OR “Jews Mallow” OR “Roselle” OR “Spider plant” OR “Amaranth” OR “Nightshade” OR “Chinese Cabbage” OR “Sunberry” OR “Wild mustard” OR “Wild Water Melon” | 68,971 | 80,538 |
2 Crop simulation modelling—Title, abstract, keywords “Crop simulation model *” OR “crop model *” OR “crop growth model” | 5547 | 6630 |
Combined search NUS Modelling (1 AND 2) | 322 | 275 |
Retained after removing duplicates in the combined search for NUS Modelling | 362 | |
* Further screening of search by reading through titles, abstracts, and keywords for NUS Modelling | 167 | |
Retained and available for final review NUS Modelling | 167 |
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Chimonyo, V.G.P.; Chibarabada, T.P.; Choruma, D.J.; Kunz, R.; Walker, S.; Massawe, F.; Modi, A.T.; Mabhaudhi, T. Modelling Neglected and Underutilised Crops: A Systematic Review of Progress, Challenges, and Opportunities. Sustainability 2022, 14, 13931. https://doi.org/10.3390/su142113931
Chimonyo VGP, Chibarabada TP, Choruma DJ, Kunz R, Walker S, Massawe F, Modi AT, Mabhaudhi T. Modelling Neglected and Underutilised Crops: A Systematic Review of Progress, Challenges, and Opportunities. Sustainability. 2022; 14(21):13931. https://doi.org/10.3390/su142113931
Chicago/Turabian StyleChimonyo, Vimbayi Grace Petrova, Tendai Polite Chibarabada, Dennis Junior Choruma, Richard Kunz, Sue Walker, Festo Massawe, Albert Thembinkosi Modi, and Tafadzwanashe Mabhaudhi. 2022. "Modelling Neglected and Underutilised Crops: A Systematic Review of Progress, Challenges, and Opportunities" Sustainability 14, no. 21: 13931. https://doi.org/10.3390/su142113931