Progressive Transient Photon Beams

In this work we introduce a novel algorithm for transient rendering in participating media. Our method is consistent, robust, and is able to generate animations of time-resolved light transport featuring complex caustic light paths in media. We base our method on the observation that the spatial continuity provides an increased coverage of the temporal domain, and generalize photon beams to transient-state. We extend the beam steady-state radiance estimates to include the temporal domain. Then, we develop a progressive version of spatio-temporal density estimations, that converges to the correct solution with finite memory requirements by iteratively averaging several realizations of independent renders with a progressively reduced kernel bandwidth. We derive the optimal convergence rates accounting for space and time kernels, and demonstrate our method against previous consistent transient rendering methods for participating media

Impact of long-term (1764-2017) air temperature on phenology of cereals and vines in two locations of northern Italy

Understanding how long-term temperature variability affects the phenology of the main agricultural crop is critical to develop targeted adaptation strategies to near and far future climate impacts. The objective of this study was to use crop phenology as a proxy to quantify the impact of a long-term temperature variability series (1764-2017) on a summer cereal crop (maize), spring wheat, winter wheat, and four different vines (perennials) in two locations representative of the main agricultural areas in northern Italy. To develop the phenological models for cereals and grapevines, the minimum (TDmin) and maximum (TDmax) daily temperatures for Milano and Bologna, northern Italy, from 1763 to 2017 were used. Results showed that wheat (spring and winter) has experienced a reduction in the growing period of 13 days for each °C of air temperature increase during the growing season. Vernalization requirements of winter wheat indicated that further increase in air temperature will determine a shift towards a supraoptimal range. The subsequent delay in vernalization fulfilment causes the grain filling phase to occur in warmer conditions and will be further shortened with consequences for final yield. Chilling accumulation in vines was fulfilled over the entire period under study with 90% effective chilling. Highlights - Long-term weather series show how the mean air temperature and its extremes have changed over the years. - Simulation of cereals and perennials phenology using long-term weather series showed a shortening of the growing season and a shift of developmental stages. - The number of days when the air temperature is above the crops' physiological threshold increased, with implications for development and senescence rates

Examining wheat yield sensitivity to temperature and precipitation changes for a large ensemble of crop models using impact response surfaces

Impact response surfaces (IRSs) depict the response of an impact variable to changes in two explanatory variables as a plotted surface. Here, IRSs of spring and winter wheat yields were constructed from a 25-member ensemble of process-based crop simulation models. Twenty-one models were calibrated by different groups using a common set of calibration data, with calibrations applied independently to the same models in three cases. The sensitivity of modelled yield to changes in temperature and precipitation was tested by systematically modifying values of 1981-2010 baseline weather data to span the range of 19 changes projected for the late 21st century at three locations in Europe

Evidence for increasing global wheat yield potential

Wheat is the most widely grown food crop, with 761 Mt produced globally in 2020. To meet the expected grain demand by mid-century, wheat breeding strategies must continue to improve upon yield-advancing physiological traits, regardless of climate change impacts. Here, the best performing doubled haploid (DH) crosses with an increased canopy photosynthesis from wheat field experiments in the literature were extrapolated to the global scale with a multi-model ensemble of process-based wheat crop models to estimate global wheat production. The DH field experiments were also used to determine a quantitative relationship between wheat production and solar radiation to estimate genetic yield potential. The multi-model ensemble projected a global annual wheat production of 1050 ± 145 Mt due to the improved canopy photosynthesis, a 37% increase, without expanding cropping area. Achieving this genetic yield potential would meet the lower estimate of the projected grain demand in 2050, albeit with considerable challenges.Fil: Guarin, Jose Rafael. National Aeronautics and Space Administration; Estados Unidos. Columbia University; Estados Unidos. Florida State University; Estados UnidosFil: Martre, Pierre. Institut Agro Montpellier SupAgro; FranciaFil: Ewert, Frank. Universitat Bonn; Alemania. Leibniz Centre for Agricultural Landscape Research; AlemaniaFil: Webber, Heidi. Universitat Bonn; Alemania. Leibniz Centre for Agricultural Landscape Research; AlemaniaFil: Dueri, Sibylle. Institut Agro Montpellier SupAgro; FranciaFil: Calderini, Daniel Fernando. Universidad Austral de Chile; ChileFil: Reynolds, Matthew. International Maize and Wheat Improvement Center ; MéxicoFil: Molero, Gemma. KWS; FranciaFil: Miralles, Daniel Julio. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; ArgentinaFil: Garcia, Guillermo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; ArgentinaFil: Slafer, Gustavo Ariel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; Argentina. Universitat de Lleida; España. Institució Catalana de Recerca i Estudis Avancats; EspañaFil: Giunta, Francesco. Consiglio Nazionale Delle Ricerche. Istituto Di Scienze Dell Atmosfera E del Clima.; ItaliaFil: Pequeno, Diego N.L.. International Maize and Wheat Improvement Center; MéxicoFil: Stella, Tommaso. Universitat Bonn; Alemania. Leibniz Centre for Agricultural Landscape Research; AlemaniaFil: Ahmed, Mukhtar. University Of Pakistan; PakistánFil: Alderman, Phillip D.. Oklahoma State University; Estados UnidosFil: Basso, Bruno. Michigan State University; Estados UnidosFil: Berger, Andres G.. Instituto Nacional de Investigacion Agropecuaria;Fil: Bindi, Marco. Università degli Studi di Firenze; ItaliaFil: Bracho-Mujica, Gennady. Universität Göttingen; AlemaniaFil: Cammarano, Davide. Purdue University; Estados UnidosFil: Chen, Yi. Chinese Academy of Sciences; República de ChinaFil: Dumont, Benjamin. Université de Liège; BélgicaFil: Rezaei, Ehsan Eyshi. Leibniz Institute Of Plant Genetics And Crop Plant Research.; AlemaniaFil: Fereres, Elias. Universidad de Córdoba; EspañaFil: Ferrise, Roberto. Michigan State University; Estados UnidosFil: Gaiser, Thomas. Universitat Bonn; AlemaniaFil: Gao, Yujing. Florida State University; Estados UnidosFil: Garcia Vila, Margarita. Universidad de Córdoba; EspañaFil: Gayler, Sebastian. Universidad de Hohenheim; Alemani

Comparison of site sensitivity of crop models using spatially variable field data from Precision Agriculture

Site conditions and soil properties have a strong influence on impacts of climate change on crop production. Vulnerability of crop production to changing climate conditions is highly determined by the ability of the site to buffer periods of adverse climatic situations like water scarcity or excessive rainfall.  Therefore, the capability of models to reflect crop responses and water and nutrient dynamics under different site conditions is essential to assess climate impact even on a regional scale. To test and improve sensitivity of models to various site properties such as soil variability and hydrological boundary conditions, spatial variable data sets from precision farming of two fields in Germany and Italy were provided to modellers. For the German 20 ha field soil and management data for 60 grid points for 3 years (2 years wheat, 1 year triticale) were provided. For the Italian field (12 ha) information for 100 grid points were available for three growing seasons of durum wheat. Modellers were asked to run their models using a) the model specific procedure to estimate soil hydraulic properties from texture using their standard procedure and use in step b) fixed values for field capacity and wilting point derived from soil taxonomy. Only the phenology and crop yield of one grid point provided for a basic calibration. In step c) information for all grid points of the first year (yield, soil water and mineral N content for Germany, yield, biomass and LAI for Italy) were provided. First results of five out of twelve participating models are compared against measured state variables analysing their site specific response and consistency across crop and soil variables.(Main text to be published in a peer-reviewed journal

Comparison of site sensitivity of crop models using spatially variable field data from Precision Agriculture

Site conditions and soil properties have a strong influence on impacts of climate change on crop production. Vulnerability of crop production to changing climate conditions is highly determined by the ability of the site to buffer periods of adverse climatic situations like water scarcity or excessive rainfall.  Therefore, the capability of models to reflect crop responses and water and nutrient dynamics under different site conditions is essential to assess climate impact even on a regional scale. To test and improve sensitivity of models to various site properties such as soil variability and hydrological boundary conditions, spatial variable data sets from precision farming of two fields in Germany and Italy were provided to modellers. For the German 20 ha field soil and management data for 60 grid points for 3 years (2 years wheat, 1 year triticale) were provided. For the Italian field (12 ha) information for 100 grid points were available for three growing seasons of durum wheat. Modellers were asked to run their models using a) the model specific procedure to estimate soil hydraulic properties from texture using their standard procedure and use in step b) fixed values for field capacity and wilting point derived from soil taxonomy. Only the phenology and crop yield of one grid point provided for a basic calibration. In step c) information for all grid points of the first year (yield, soil water and mineral N content for Germany, yield, biomass and LAI for Italy) were provided. First results of five out of twelve participating models are compared against measured state variables analysing their site specific response and consistency across crop and soil variables.(Main text to be published in a peer-reviewed journal

Accounting For Alignment Uncertainty in Phylogenomics

Uncertainty in multiple sequence alignments has a large impact on phylogenetic analyses. Little has been done to evaluate the quality of individual positions in protein sequence alignments, which directly impact the accuracy of phylogenetic trees. Here we describe ZORRO, a probabilistic masking program that accounts for alignment uncertainty by assigning confidence scores to each alignment position. Using the BALIBASE database and in simulation studies, we demonstrate that masking by ZORRO significantly reduces the alignment uncertainty and improves the tree accuracy

Evidence for increasing global wheat yield potential

Wheat is the most widely grown food crop, with 761 Mt produced globally in 2020. To meet the expected grain demand by mid-century, wheat breeding strategies must continue to improve upon yield-advancing physiological traits, regardless of climate change impacts. Here, the best performing doubled haploid (DH) crosses with an increased canopy photosynthesis from wheat field experiments in the literature were extrapolated to the global scale with a multi-model ensemble of process-based wheat crop models to estimate global wheat production. The DH field experiments were also used to determine a quantitative relationship between wheat production and solar radiation to estimate genetic yield potential. The multi-model ensemble projected a global annual wheat production of 1050 +/- 145 Mt due to the improved canopy photosynthesis, a 37% increase, without expanding cropping area. Achieving this genetic yield potential would meet the lower estimate of the projected grain demand in 2050, albeit with considerable challenges

Global wheat production with 1.5 and 2.0°C above pre‐industrial warming

Efforts to limit global warming to below 2°C in relation to the pre‐industrial 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 >2°C 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 2.0°C warming above the pre‐industrial period) on global wheat production and local yield variability. A multi‐crop and multi‐climate 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 1.5°C scenario and −2.4% to 10.5% under the 2.0°C scenario, compared to a baseline of 1980–2010, 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 inter‐annual variability will increase under both warming scenarios for some of the hot growing locations, including locations from the second largest global wheat producer—India, which supplies more than 14% of global wheat. The projected global impact of warming <2°C on wheat production is therefore not evenly distributed and will affect regional food security across the globe as well as food prices and trade