According to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5) for Representative Concentration Pathway (RCP) under RCP 4.5, the near-surface mean temperature is indicated at 1–1. 65 ☌ over the past five decades and extreme rainfall events have increased in frequency with the projected likelihood of continuance. The average annual temperatures over South Africa has increased by at least 1.5 times above the observed global average of 0. Due to global emissions of greenhouse gases (IPCC, 2013), climate change affects areas worldwide, with places, like South Africa, experiencing temperature increases and decreasing rainfall patterns. Similarly, climate change has been cited to be a key concern within South Africa due to the significant threat it poses to South Africa’s water resources, food security, ecosystem services, and biodiversity. Based on this scenario, the question which arises is how is South Africa going to solve the issues of meeting the increase in the demand for agricultural products given that the country is already facing challenges due to the increased pressures on land use, water scarcity, and other natural resources? To complicate matters further, they have to solve this by depending on the same natural resources in the country. The import for this increase is that food production in South Africa will have to be increased by such measures to be able to meet and sustain the demands of the rapidly growing population. The South African population has increased between 20 and the estimated overall growth rate increased from around 1.17% between 20 to 1.61% for the period 2016 to 2017 and 1.28% between 20. 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.Īccording 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. 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. 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. We advocate a hybrid approach for obtaining input data for model calibration and validation. 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. 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. 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.
Even when a suitable choice of a crop simulation model is selected, data limitations hamper some of the models’ effective role for projections. In some cases, available input data may not be in the quantity and quality needed to drive most crop models.
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.
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.
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