ALBERT

Description: Climate change is estimated to exacerbate existing challenges faced by smallholder farmers in Sub-Sahara Africa. However, limited studies quantify the extent of variation in climate change impact under these systems at the local scale. The Decision Support System for Agro-technological Transfer (DSSAT) was used to quantify variation in climate change impacts on maize yield under current agricultural practices in semi-arid regions of Senegal (Nioro du Rip) and Ghana (Navrongo and Tamale). Multi-benchmark climate models (Mid-Century, 20402069 for two Representative Concentration Pathways, RCP4.5 and RCP8.5), and multiple soil and management information from agronomic surveys were used as input for DSSAT. The average impact of climate scenarios on grain yield among farms ranged between 9% and 39% across sites. Substantial variation in climate response exists across farms in the same farming zone with relative standard deviations from 8% to 117% at Nioro du Rip, 13% to 64% in Navrongo and 9% to 37% in Tamale across climate models. Variations in fertilizer application, planting dates and soil types explained the variation in the impact among farms. This study provides insight into the complexities of the impact of climate scenarios on maize yield and the need for better representation of heterogeneous farming systems for optimized outcomes in adaptation and resilience planning in smallholder systems.

Description: This chapter describes methods developed by the Agricultural Model Intercomparison and Improvement Project (AgMIP) to implement a transdisciplinary, systems-based approach for regional-scale (local to national) integrated assessment of agricultural systems under future climate, biophysical, and socio-economic conditions. These methods were used by the AgMIP regional research teams in Sub-Saharan Africa and South Asia to implement the analyses reported in their respective chapters of this book. Additional technical details are provided in Appendix 1.The principal goal that motivates AgMIP's regional integrated assessment (RIA) methodology is to provide scientifically rigorous information needed to support improved decision-making by various stakeholders, ranging from local to national and international non-governmental and governmental organizations.

Description: The X-ray integrated field unit (X-IFU) in European Space Agency's (ESA's) Athena mission will be the first high-resolution X-ray spectrometer in space using a large-format transition-edge sensor microcalorimeter array. Motivated by optimization of detector performance for X-IFU, we have conducted an extensive campaign of parametric characterization on transition-edge sensor (TES) detectors with nominal geometries and physical properties in order to establish sensitivity trends relative to magnetic field, dc bias on detectors, operating temperature, and to improve our understanding of detector behavior relative to its fundamental properties such as thermal conductivity, heat capacity, and transition temperature. These results were used for validation of a simple linear detector model in which a small perturbation can be introduced to one or multiple parameters to estimate the error budget for X-IFU. We will show here results of our parametric characterization of TES detectors and briefly discuss the comparison with the TES model.

Description: We describe the development, launch into space, and initial results from a prototype wide eld-of-view (FOV) soft X-ray imager that employs Lobster-eye optics and targets heliophysics, planetary, and astrophysics science. The Sheath Transport Observer for the Redistribution of Mass (STORM) is the rst instrument using this type of optics launched into space and provides proof-of-concept for future ight instruments capable of imaging structures such as the terrestrial cusp, the entire dayside magnetosheath from outside the magnetosphere, comets, the moon, and the solar wind interaction with planetary bodies like Venus and Mars.

Description: We develop superconducting transition-edge sensor (TES) microcalorimeter focal planes for versatility in meeting the specifications of X-ray imaging spectrometers, including high count rate, high energy resolution, and large field of view. In particular, a focal plane composed of two subarrays: one of fine pitch, high count-rate devices and the other of slower, larger pixels with similar energy resolution, offers promise for the next generation of astrophysics instruments, such as the X-ray Integral Field Unit Instrument on the European Space Agencys ATHENA mission. We have based the subarrays of our current design on successful pixel designs that have been demonstrated separately. Pixels with an all-gold X-ray absorber on 50 and 75 micron pitch, where the Mo/Au TES sits atop a thick metal heatsinking layer, have shown high resolution and can accommodate high count rates. The demonstrated larger pixels use a silicon nitride membrane for thermal isolation, thinner Au, and an added bismuth layer in a 250-sq micron absorber. To tune the parameters of each subarray requires merging the fabrication processes of the two detector types. We present the fabrication process for dual production of different X-ray absorbers on the same substrate, thick Au on the small pixels and thinner Au with a Bi capping layer on the larger pixels to tune their heat capacities. The process requires multiple electroplating and etching steps, but the absorbers are defined in a single-ion milling step. We demonstrate methods for integrating the heatsinking of the two types of pixel into the same focal plane consistent with the requirements for each subarray, including the limiting of thermal crosstalk. We also discuss fabrication process modifications for tuning the intrinsic transition temperature (T(sub c)) of the bilayers for the different device types through variation of the bilayer thicknesses. The latest results on these 'hybrid' arrays will be presented.

Description: Efforts to limit global warming to below 2C in relation to the preindustrial level are under way, in accordance with the 2015 Paris Agreement. However, most impact research on agriculture to date has focused on impacts of warming 〉2C on mean crop yields, and many previous studies did not focus sufficiently on extreme events and yield interannual variability. Here, with the latest climate scenarios from the Half a degree Additional warming, Prognosis and Projected Impacts (HAPPI) project, we evaluated the impacts of the 2015 Paris Agreement range of global warming (1.5 and 2.0C warming above the preindustrial period) on global wheat production and local yield variability. A multicrop and multiclimate model ensemble over a global network of sites developed by the Agricultural Model Intercomparison and Improvement Project (AgMIP) for Wheat was used to represent major rainfed and irrigated wheat cropping systems. Results show that projected global wheat production will change by 2.3% to 7.0% under the 1.5C scenario and 2.4% to 10.5% under the 2.0C scenario, compared to a baseline of 19802010, when considering changes in local temperature, rainfall, and global atmospheric CO2 concentration, but no changes in management or wheat cultivars. The projected impact on wheat production varies spatially; a larger increase is projected for temperate high rainfall regions than for moderate hot low rainfall and irrigated regions. Grain yields in warmer regions are more likely to be reduced than in cooler regions. Despite mostly positive impacts on global average grain yields, the frequency of extremely low yields (bottom 5 percentile of baseline distribution) and yield interannual variability will increase under both warming scenarios for some of the hot growing locations, including locations from the second largest global wheat producerIndia, which supplies more than 14% of global wheat. The projected global impact of warming 〈2C on wheat production is therefore not evenly distributed and will affect regional food security across the globe as well as food prices and trade.

Description: Current rates of agricultural water use are unsustainable in many regions, creating an urgent need to identify improved irrigation strategies for water limited areas. Crop models can be used to quantify plant water requirements, predict the impact of water shortages on yield, and calculate water productivity (WP) to link water availability and crop yields for economic analyses. Many simulations of crop growth and development, especially in regional and global assessments, rely on automatic irrigation algorithms to estimate irrigation dates and amounts. However, these algorithms are not well suited for water limited regions because they have simplistic irrigation rules, such as a single soil-moisture based threshold, and assume unlimited water. To address this constraint, a new modeling framework to simulate agricultural production in water limited areas was developed. The framework consists of a new automatic irrigation algorithm for the simulation of growth stage based deficit irrigation under limited seasonal water availability; and optimization of growth stage specific parameters. The new automatic irrigation algorithm was used to simulate maize and soybean in Gainesville, Florida, and first used to evaluate the sensitivity of maize and soybean simulations to irrigation at different growth stages and then to test the hypothesis that water productivity calculated using simplistic irrigation rules underestimates WP. In the first experiment, the effect of irrigating at specific growth stages on yield and irrigation water use efficiency (IWUE) in maize and soybean was evaluated. In the reproductive stages, IWUE tended to be higher than in the vegetative stages (e.g. IWUE was 18% higher than the well watered treatment when irrigating only during R3 in soybean), and when rainfall events were less frequent. In the second experiment, water productivity (WP) was significantly greater with optimized irrigation schedules compared to non-optimized irrigation schedules in water restricted scenarios. For example, the mean WP across 38 years of maize production was 1.1 kg/cu m for non-optimized irrigation schedules with 50 mm of seasonal available water and 2.1 kg/cu m optimized ion schedules, a 91% improvement in WP with optimized irrigation schedules. The framework described in this work could be used to estimate WP for regional to global assessments, as well as derive location specific irrigation guidance.

Description: We are developing kilo-pixel arrays of transition-edge sensors (TESs) for the X-ray Integral Field Unit on ESA's (European Space Agency's) Athena observatory. Previous measurements of AC-biased Mo/Au TESs have highlighted a frequency-dependent loss mechanism that results in broader transitions and worse spectral performance compared to the same devices measured under DC (Direct Current) bias. In order to better understand the nature of this loss, we are now studying TES pixels in different geometric configurations. We present measurements on devices of different sizes and with different metal features used for noise mitigation and X-ray absorption. Our results show how the loss mechanism is strongly dependent upon the amount of metal in close proximity to the sensor and can be attributed to induced eddy current coupling to these features. We present a finite element model that successfully reproduces the magnitude and geometry dependence of the losses. Using this model, we present mitigation strategies that should reduce the losses to an acceptable level.

Description: Wheat grain protein concentration is an important determinant of wheat quality for human nutrition that is often overlooked in efforts to improve crop production. We tested and applied a 32multimodel ensemble to simulate global wheat yield and quality in a changing climate. Potential benefits of elevated atmospheric CO2 concentration by 2050 on global wheat grain and protein yield are likely to be negated by impacts from rising temperature and changes in rainfall, but with considerable disparities between regions. Grain and protein yields are expected to be lower and more variable in most lowrainfall regions, with nitrogen availability limiting growth stimulus from elevated CO2. Introducing genotypes adapted to warmer temperatures (and also considering changes in CO2 and rainfall) could boost global wheat yield by 7% and protein yield by 2%, but grain protein concentration would be reduced by 1.1 percentage points, representing a relative change of 8.6%. Climate change adaptations that benefit grain yield are not always positive for grain quality, putting additional pressure on global wheat production.

Description: This study presents results of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Coordinated Global and Regional Assessments (CGRA) of +1.5 and +2.0 C global warming above pre-industrial conditions. This first CGRA application provides multi-discipline, multi-scale, and multi-model perspectives to elucidate major challenges for the agricultural sector caused by direct biophysical impacts of climate changes as well as ramifications of associated mitigation strategies. Agriculture in both target climate stabilizations is characterized by differential impacts across regions and farming systems, with tropical maize (Zea mays) experiencing the largest losses while soy (Glycine max) mostly benefits. The result is upward pressure on prices and area expansion for maize and wheat (Triticum), while soy prices and area decline (results for rice, Oryza sativa, are mixed). An example global mitigation strategy encouraging bioenergy expansion is more disruptive to land use and crop prices than the climate change impacts alone, even in the +2.0 C World which has a larger climate signal and lower mitigation requirement than the +1.5 C World. Coordinated assessments reveal that direct biophysical and economic impacts can be substantially larger for regional farming systems than global production changes. Regional farmers can buffer negative effects or take advantage of new opportunities via mitigation incentives and farm management technologies. Primary uncertainties in the CGRA framework include the extent of CO2 benefits for diverse agricultural systems in crop models, as simulations without CO2 benefits show widespread production losses that raise prices and expand agricultural area