Abstract
A broad scope of crop models with varying demands on data inputs is being used for several purposes, such as possible adaptation strategies to control climate change impacts on future crop production, management decisions, and adaptation policies. A constant challenge to crop model simulation, especially for future crop performance projections and impact studies under varied conditions, is the unavailability of reliable historical data for model calibrations. In some cases, available input data may not be in the quantity and quality needed to drive most crop models. Even when a suitable choice of a crop simulation model is selected, data limitations hamper some of the models’ effective role for projections. To date, no review has looked at factors inhibiting the effective use of crop simulation models and complementary sources for input data in South Africa. This review looked at the barriers to crop simulation, relevant sources from which input data for crop models can be sourced, and proposed a framework for collecting input data. Results showed that barriers to effective simulations exist because, in most instances, the input data, like climate, soil, farm management practices, and cultivar characteristics, were generally incomplete, poor in quality, and not easily accessible or usable. We advocate a hybrid approach for obtaining input data for model calibration and validation. Recommended methods depending on the intended outputs and end use of model results include remote sensing, field, and greenhouse experiments, secondary data, engaging with farmers to model actual on-farm conditions. Thus, employing more than one method of data collection for input data for models can reduce the challenges faced by crop modellers due to the unavailability of data. The future of modelling depends on the goodness and availability of the input data, the readiness of modellers to cooperate on modularity and standardization, and potential user groups’ ability to communicate.
Highlights
According to the United Nations (U.N) (2019) projections, the population of South Africa is expected to grow to about 68 million by the year 2035 and 75 million by 2050
As a critical first step towards providing an integrated approach to obtaining input data for crop models, we propose various types of crop simulation models and the risk of their use (“Components of crop simulation models” section); minimum data requirements for running crop models (“Model calibration - minimum data requirements versus a full set of data” section); and the challenges involved in running of crop models in South Africa (“Challenges in using crop simulation models in South Africa” section)
In order to establish resilient and sustainable agricultural systems in the face of climate change, there is a need for effective adaptation measures to be established
Summary
According to the United Nations (U.N) (2019) projections, the population of South Africa is expected to grow to about 68 million by the year 2035 and 75 million by 2050. The long-term adaptation scenario flagship research programme (LTAS) (Department of Environmental Affairs (DEA)(2013) describes South Africa’s future climate up to 2050 and beyond using four fundamental climate scenarios with different degrees of change and likelihood that capture the impacts of global mitigation over time These scenarios include a warmer (3 °C above 1961–2000) and wetter with substantially greater frequency of extreme rainfall events; a warmer (< 3 °C above 1961–2000) and drier, with an increase in the frequency of drought events and somewhat greater frequency of extreme rainfall events; hotter (> 3 °C above 1961–2000) and wetter, with substantially greater frequency of extreme rainfall events; and hotter (> 3 °C above 1961–2000) and drier, with a substantial increase in the frequency of drought events and greater frequency of extreme rainfall events. This, presents a scenario where the information needed for agricultural decision-making at all levels from farm management to adaptation strategies and relief schemes are increasing and a method of supplying such information in relatively shorter time frames is needed
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