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Foyer, C. H., Siddique, K. H. M., Tai, A. P. K., Anders, S., Fodor, N., Wong, F. - L., et al. (2019). Modelling predicts that soybean is poised to dominate crop production across Africa. Plant Cell Environ., 42(1), 373–385.
Abstract: The superior agronomic and human nutritional properties of grain legumes (pulses) make them an ideal foundation for future sustainable agriculture. Legume-based farming is particularly important in Africa, where small-scale agricultural systems dominate the food production landscape. Legumes provide an inexpensive source of protein and nutrients to African households as well as natural fertilization for the soil. Although the consumption of traditionally grown legumes has started to decline, the production of soybeans (Glycine max Merr.) is spreading fast, especially across southern Africa. Predictions of future land-use allocation and production show that the soybean is poised to dominate future production across Africa. Land use models project an expansion of harvest area, whereas crop models project possible yield increases. Moreover, a seed change in farming strategy is underway. This is being driven largely by the combined cash crop value of products such as oils and the high nutritional benefits of soybean as an animal feed. Intensification of soybean production has the potential to reduce the dependence of Africa on soybean imports. However, a successful “soybean bonanza” across Africa necessitates an intensive research, development, extension, and policy agenda to ensure that soybean genetic improvements and production technology meet future demands for sustainable production.
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Faye, B., Webber, H., Naab, J. B., MacCarthy, D. S., Adam, M., Ewert, F., et al. (2018). Impacts of 1.5 versus 2.0 degrees C on cereal yields in the West African Sudan Savanna. Environ. Res. Lett., 13(3), 034014.
Abstract: To reduce the risks of climate change, governments agreed in the Paris Agreement to limit global temperature rise to less than 2.0 degrees C above pre-industrial levels, with the ambition to keep warming to 1.5 degrees C. Charting appropriate mitigation responses requires information on the costs of mitigating versus associated damages for the two levels of warming. In this assessment, a critical consideration is the impact on crop yields and yield variability in regions currently challenged by food insecurity. The current study assessed impacts of 1.5 degrees C versus 2.0 degrees C on yields of maize, pearl millet and sorghum in the West African Sudan Savanna using two crop models that were calibrated with common varieties from experiments in the region with management reflecting a range of typical sowing windows. As sustainable intensification is promoted in the region for improving food security, simulations were conducted for both current fertilizer use and for an intensification case (fertility not limiting). With current fertilizer use, results indicated 2% units higher losses for maize and sorghum with 2.0 degrees C compared to 1.5 degrees C warming, with no change in millet yields for either scenario. In the intensification case, yield losses due to climate change were larger than with current fertilizer levels. However, despite the larger losses, yields were always two to three times higher with intensification, irrespective of the warming scenario. Though yield variability increased with intensification, there was no interaction with warming scenario. Risk and market analysis are needed to extend these results to understand implications for food security.
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Schauberger, B., Rolinski, S., & Müller, C. (2016). A network-based approach for semi-quantitative knowledge mining and its application to yield variability. Environ. Res. Lett., 11(12), 123001.
Abstract: Variability of crop yields is detrimental for food security. Under climate change its amplitude is likely to increase, thus it is essential to understand the underlying causes and mechanisms. Crop models are the primary tool to project future changes in crop yields under climate change. Asystematic overview of drivers and mechanisms of crop yield variability (YV) can thus inform crop model development and facilitate improved understanding of climate change impacts on crop yields. Yet there is a vast body of literature on crop physiology and YV, which makes a prioritization of mechanisms for implementation in models challenging. Therefore this paper takes on a novel approach to systematically mine and organize existing knowledge from the literature. The aim is to identify important mechanisms lacking in models, which can help to set priorities in model improvement. We structure knowledge from the literature in a semi-quantitative network. This network consists of complex interactions between growing conditions, plant physiology and crop yield. We utilize the resulting network structure to assign relative importance to causes of YV and related plant physiological processes. As expected, our findings confirm existing knowledge, in particular on the dominant role of temperature and precipitation, but also highlight other important drivers of YV. More importantly, our method allows for identifying the relevant physiological processes that transmit variability in growing conditions to variability in yield. We can identify explicit targets for the improvement of crop models. The network can additionally guide model development by outlining complex interactions between processes and by easily retrieving quantitative information for each of the 350 interactions. We show the validity of our network method as a structured, consistent and scalable dictionary of literature. The method can easily be applied to many other research fields.
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Fan, F., Henriksen, C. B., & Porter, J. (2018). Long-term effects of conversion to organic farming on ecosystem services – a model simulation case study and on-farm case study in Denmark. Agroecology and Sustainable Food Systems, 42(5), 504–529.
Abstract: Organic agriculture aims to produce food while establishing an ecological balance to augment ecosystem services (ES) and has been rapidly expanding in the world since the 1980s. Recently, however, in several European countries, including Denmark, organic farmers have converted back to conventional farming. Hence, understanding how agricultural ES are affected by the number of years since conversion to organic farming is imperative for policy makers to guide future agricultural policy. In order to investigate the long-term effects of conversion to organic farming on ES we performed i) a model simulation case study by applying the Daisy model to simulate 14 different conversion scenarios for a Danish farm during a 65 year period with increasing number of years under organic farming, and ii) an on-farm case study in Denmark with one conventional farm, one organic farm under conversion, and three organic farms converted 10, 15 and 58 years ago, respectively. Both the model simulation case study and the on-farm case study showed that non-marketable ES values increased with increasing number of years under organic farming. Trade-offs between marketable and non-marketable ES were not evident, since also marketable ES values generally showed an increasing trend, except when the price difference between organic and conventional products in the model simulation study was the smallest, and when an alfalfa pre-crop in the on-farm case study resulted in a significantly higher level of plant available nitrogen, which boosted the yield and the associated marketable ES of the subsequent winter rye crop. These results indicate a possible benefit of preserving long-term organic farms and could be used to argue for agricultural policy interventions to offset further reduction in the number of organic farms or the land area under organic farming.
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Webber, H., Ewert, F., Olesen, J. E., Müller, C., Fronzek, S., Ruane, A. C., et al. (2018). Diverging importance of drought stress for maize and winter wheat in Europe. Nat. Comm., 9, 4249.
Abstract: Understanding the drivers of yield levels under climate change is required to support adaptation planning and respond to changing production risks. This study uses an ensemble of crop models applied on a spatial grid to quantify the contributions of various climatic drivers to past yield variability in grain maize and winter wheat of European cropping systems (1984-2009) and drivers of climate change impacts to 2050. Results reveal that for the current genotypes and mix of irrigated and rainfed production, climate change would lead to yield losses for grain maize and gains for winter wheat. Across Europe, on average heat stress does not increase for either crop in rainfed systems, while drought stress intensifies for maize only. In low-yielding years, drought stress persists as the main driver of losses for both crops, with elevated CO2 offering no yield benefit in these years.
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