Climate change is projected to constitute a significant threat to food security, if no adaptation actions are taken 1,2 . Transformation of agricultural systems, e.g. switching crop types or moving out of agriculture, is projected to be necessary in some cases [3][4][5] .However, little attention has been paid to the timing of these transformations. Here, we develop a temporal uncertainty framework using the CMIP5 ensemble to assess when and where cultivation of key crops in Sub-Saharan African becomes unviable.We report potential transformational changes for all major crops during the 21 st century, as climates shift and areas become unsuitable. For most crops, however, transformation is limited to small pockets (<15 % of area), and only for beans, maize and banana is transformation more widespread (~30 % area for maize and banana, 60 % for beans). We envision three overlapping adaptation phases to enable projected transformational changes: an incremental adaptation phase focused on improvements to crops and management, a preparatory phase that establishes appropriate policies and enabling environments, and a transformational adaptation phase in which farmers substitute crops, explore alternative livelihoods strategies, or relocate. To best align policies with production triggers for no-regret actions, monitoring capacities to track farming systems as well as climate are needed.Agricultural activities are the main means to reduce poverty and improve food security among 850 million undernourished people 2 . Numerous studies have shown that climate change can be a significant threat to food availability and stability by reducing agricultural productivity and increasing inter-annual variations in yields 1,2,6 . Adaptation will be required if food production is to be increased in both quantity and stability in order to meet food security needs during the 21st century. A recent global meta-analysis 1 reported that decreases of ca. 5 % in crop productivity are expected for every degree of warming above historical levels, and that adapted crops yield roughly 7 % greater than non-adapted crops.Yield gains from adaptation through crop management and varietal substitution, however, are highest with moderate or low (< +3 ºC) levels of warming 1,6 , suggesting that more profound systemic and/or transformational changes may be required when and where higher levels of warming occur 5 .Transformational adaptation is defined by the IPCC 7 as a response to the effects of climate change that "changes the fundamental attributes of a system" (see Text S1 for definitions).Transformational change implies shifts in locations for production of specific crops and livestock, or shifting to farming systems new to a region or resource system 3,5 . Here, we consider one type of transformation: switching of staple crop type grown over a large geographic area of 0.3 million ha (the grid cell size of our analysis) or more. We analyze when and where major cropping systems transformations are likely to occur for important crops in Sub-Saharan A...

Understanding the feedback mechanisms between soil water content (SWC) and biomass production is important for sustainable resources management. Here we present a new method enabling simultaneous noninvasive measurements of SWC and biomass dynamics based on cosmic ray neutron sensing (CRNS). Recently, it was suggested that the neutron ratio (Nr) between thermal neutron (TN) and fast neutron (FN) intensity contains information on other hydrogen pools like vegetation, canopy interception, and snow. The aim of this study is to evaluate the accuracy of simultaneous measurements of SWC and biomass dynamics during agricultural drought conditions using CRNS probes. To this end, we instrumented an arable field cropped with sugar beet with CRNS probes and a wireless in situ SWC sensor network. Belowground and aboveground biomass were sampled in monthly intervals. We found a linear relationship between Nr and the aboveground biomass that allowed to continuously quantify the dry aboveground biomass development throughout the growing season with a root‐mean‐square error from 0.14 to 0.22 kg/m2. This information was used together with measured belowground biomass to correct for the effect of biomass on SWC determination with CRNS probes, which increased the accuracy of the SWC estimates considerably as indicated by the decrease of the root‐mean‐square error from 0.046 to 0.013 cm3/cm3. We anticipate that future research on the Nr can further improve the accuracy of SWC and biomass estimates and extend the application of CRNS to include canopy interception, ponding water, and snow water equivalent estimation for both stationary and roving CRNS systems.

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