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Crop models are useful tools to evaluate the effects of agricultural management on ecosystem services. However, before they can be applied with confidence, it is important to calibrate and validate crop models in the region of interest. In this study, the Environmental Policy Integrated Climate (EPIC) model was evaluated for its potential to simulate maize yield using limited data from field trials on two maize cultivars. Two independent fields at the Cradock Research Farm were used, one for calibration and one for validation. Before calibration, mean simulated yield was 8 t ha −1 while mean observed yield was 11.26 t ha −1 . Model calibration improved mean simulated yield to 11.23 t ha −1 with a coefficient of determination, (r 2 ) = 0.76 and a model efficiency (NSE) = 0.56. Validation with grain yield was satisfactory with r 2 = 0.85 and NSE = 0.61. Calibration of potential heat units (PHUs) and soil-carbon related parameters improved model simulations. Although the study only used grain yield to calibrate and evaluate the model, results show that the calibrated model can provide reasonably accurate simulations. It can be concluded that limited data sets from field trials on maize can be used to calibrate the EPIC model when comprehensive experimental data are not available.Agronomy 2019, 9, 494 2 of 16 used to test the effectiveness of alternative agricultural land management practices under varying climate change scenarios. However, to yield meaningful results, it is prudent to calibrate crop models in the region of intended use before their application [11].Model calibration is the procedure where model parameters are fine-tuned to increase the agreement between model simulations and real-world observations [12]. Calibration is important to increase model accurateness and decrease model prediction uncertainty [13]. Calibration is done by judiciously choosing model parameter values, adjusting them within recommended ranges, for example, from literature or expert opinions, and comparing the simulated outputs with observed data for a given set of conditions [14]. A successful calibration would be when the model reproduces observed data within a satisfactory degree of accuracy and precision for the intended model use [15,16]. Once calibrated and validated, the model can be reasonably applied in the area of interest. Calibrated crop growth models can, therefore, be useful tools to complement field experiments and support decision making for sustainable agricultural land management.The Environmental Policy Integrated Climate (EPIC) model [17], originally developed in the United States of America (USA), is a process-based, field-scale model with a daily time scale. It simulates the chemical processes occurring in the soil-water-plant interaction under different agricultural management regimes [18]. The main components of EPIC are weather simulation, crop growth, carbon and nutrient cycling, tillage, soil erosion, and hydrology [19]. Globally, the model has been applied to study crop yield responses to nutrients and...

Globally, farmers remain the key ecosystem managers responsible for increasing food production while simultaneously reducing the associated negative environmental impacts. However, research investigating how farmers' agricultural management practices are influenced by the values they assign to ecosystem services is scarce in South Africa. To address this gap, a survey of farmers' agricultural management practices and the values they assigned towards ecosystem services was conducted in the Eastern Cape, South Africa. Results from the survey show that farmers assign a high value on food provisioning ecosystem services compared to other ecosystem services. Irrigation and fertiliser decisions were mostly based on achieving maximum crop yields or good crop quality. The majority of farmers (86%) indicated a willingness to receive payments for ecosystem services (PES) to manage their farms in a more ecosystems-oriented manner. To encourage farmers to shift from managing ecosystems for single ecosystem services such as food provision to managing ecosystems for multiple ecosystem services, market-oriented plans such as PES may be employed. Effective measures for sustainable intensification of food production will depend on the inclusion of farmers in the development of land management strategies and practices as well as increasing farmers' awareness and knowledge of the ecosystem services concept.

Climate change has been projected to impact negatively on African agricultural systems. However, there is still an insufficient understanding of the possible effects of climate change on crop yields in Africa. In this study, a previously calibrated Environmental Policy Integrated Climate (EPIC) model was used to assess the effects of future climate change on maize (Zea mays L.) yield in the Eastern Cape Province of South Africa. The study aimed to compare maize yields obtained from EPIC simulations using baseline (1980–2010) weather data with maize yields obtained from EPIC using statistically downscaled future climate data sets for two future periods (mid-century (2040–2069) and late century (2070–2099)). We used three general circulation models (GCMs): BCC-CSM1.1, GFDL-ESM2M and MIROC-ES under two Representative Concentration Pathways (RCPs), RCP 4.5 and RCP 8.5, to drive the future maize yield simulations. Simulation results showed that for all three GCMs and for both future periods, a decrease in maize production was projected. Maize yield was projected to decrease by as much as 23.8% for MIROC, RCP 8.5, (2070–2099). The temperature was projected to rise by over 50% in winter under RCP 8.5 for both future periods. For both future scenarios, rainfall was projected to decrease in the summer months while increasing in the winter months. Overall, this study provides preliminary evidence that local farmers and the Eastern Cape government can utilise to develop local climate change adaptation strategies.

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.