Editorial: Smallholder targeted Agriculture 4.0 in temperature limited cropping systems

African and Asian food production relies to 70% (of calories provided) on smallholder production systems (Fanzo, 2017). More than 475 million small farms produce food often under marginal conditions, with little input or market access (Lowder, Skoet, Saito, Diack, Dieng, & Ndiaye, 2015). Changes to the original set-up due to fluctuating fertilizer prices, erratic weather patterns or disturbances in water supply can be accounted for on time, and the recommendations to farmers can be adapted considering the latest developments. Similar, server-based growth models can improve their performance by having access to the latest regional weather forecasts, allowing not just to suggest optimal cropping calendars to the farmers, but also to adjust crop management strategies such as fertilizer application and weed management. In order to support this kind of app-based extension approaches, the models behind have to be calibrated and validated using a wide range of input data. This is especially the case when even well-known varieties of staple crops such as rice are being introduced to new environments, for example irrigated paddy rice to the highlands of East Africa. How do these varieties respond to the thermal characteristics (cold or hot spells, Abera et al., 2020, Stuerz et al., 2020, this issue), how to adapt cropping calendars to the respective speed and timing of the varieties phenological development (Razafindrazaka et al., 2020, this issue), and do the plants respond to external influences such as fertilizer application in the same way as in their “native” environment (Boshuwenda et al., 2020, Senthilkumar et al., 2020, this issue)? Answering questions like these is crucial for the success of the modelling approaches behind these apps. Without these data, it will not be possible to apply the apps to the wide range of agricultural environments found in smallholder farms throughout the developing world, especially when facing the challenge of securing food security in a world of changing climate (Cotter et al., 2020, this issue). On larger scales digitalization does not come in the form of apps but requires GIS based, spatially and temporally explicit models. For this, conceptual models need to be established first that scrutinize the current situation and propose a way forward. An example of such approaches applies to Asian Mega Deltas that are under pressure from sea level rise and salt intrusion into rice production systems. These deltas are so vast that only well-calibrated models are able to yield recommendations for land-use adaptation to the climate change-induced changes (Schneider et al., 2020, this issue). The articles published in this issue are based on research that was presented during the conference “Smallholder targeted Agriculture 4.0 in temperature limited cropping systems (STATCROPS)” organized by the Hans-Ruthenberg-Institute for Tropical Agricultural Sciences at the University of Hohenheim and the Africa Rice Center, 20–21 September 2018. The STATCROPS conference addressed the possibilities that modern digitalization and modelling approaches offer to develop tools targeted to smallholder farmers, especially in temperature-limited environments. The conference aimed at linking research, modelling and application by discussing ideas, concepts, and experiences to build feedback- and feed-forward loops targeted at future options for such cropping systems. The authors would like to thank the Deutsche Forschungsgemeinschaft (DFG) for their support for this conference.