Estimates of crop responses to climate change with quantified ranges of uncertainty

In estimating responses of crops to future climate realisations, it is necessary to understand and differentiate between the sources of uncertainty in climate models and how these lead to errors in estimating the past climate and biases in future projections, and how these affect crop model estimates. This paper investigates the complexities in using climate model projections representing different spatial scales within climate change impacts and adaptation studies. This is illustrated by simulating spring barley with three crop models run using site-specific observed, original (50•50 km) and bias corrected downscaled (site-specific) hindcast (1960-1990) weather data from the HadRM3 Regional Climate Model (RCM). Original and bias corrected downscaled weather data were evaluated against the observed data. The comparisons made between the crop models were in the light of lessons learned from this data evaluation. Though the bias correction downscaling method improved the match between observed and hindcast data, this did not always translate into better matching of crop models estimates. At four sites the original HadRM3 data produced near identical mean simulated yield values as from the observed weather data, despite differences in the weather data, giving a situation of ‘right results for the wrong reasons’. This was likely due to compensating errors in the input weather data and non-linearity in crop models processes, making interpretation of results problematic. Overall, bias correction downscaling improved the quality of simulated outputs. Understanding how biases in climate data manifest themselves in crop models gives greater confidence in the utility of the estimates produced using downscaled future climate projections. The results indicate implications on how future projections of climate change impacts are interpreted. Fundamentally, considerable care is required in determining the impact weather data sources have in climate change impact and adaptation studies, whether from individual models or ensembles

Editorial

The Italian Society of Agronomy (SIA) has changed the Editor in Chief and the Editorial board of the Italian Journal of Agronomy (IJA). The new Editorial board is being integrated with new expertise and includes three Associate editors: Michael D. Casler from USDA-ARS, USA, Davide Cammarano from Purdue University, USA and Michele Rinaldi from Council for Agricultural Research and Economics, Italy, the former co-editor. The Editorial board is redeveloping the Journal with a more pro-active publishing policy, that is consistent to the changing editorial demand of agronomy scientists worldwide. The international scientific publishing industry is facing a sharp transition, pulled by the increasing demand of rapid publication in the publish-or-perish or highly-cited paradigm and pushed towards full open access publishing by research funders and end-users. Minimizing the time between manuscript submission and paper publication is threatening the quality of the peer-review process, which is constrained by time pressure on highly qualified scientists, who end up being overloaded with reviews and editorial duties. The open access scientific journal industry is struggling between increasing the impact factor/cite score of the journals and maximizing the number of published articles, which is directly proportional to the publisher's business. This is generating an increasing number of open access scientific publications worldwide: +75% between 2008-10 and 2015-17 in the 'Agronomy and crop science' subject category (Source: Scopus) while the non-open access publications in the same domain and time span increased by only +27%. This situation and the evolution of long term open-theme research funding schemes into short-term projectified finalized research funding programs are deeply influencing the topics of research in Agronomy. Long term agronomic facilities and field scale research are becoming rare and are often being replaced by short-term easily-published studies. However, international scientific exchanges are facilitating the development of permanent regional and global networks of researchers (e.g. AgMip, Global Research Alliance) that are developing unprecedented long-term research efforts on global issues around agronomy, involving hundreds of post-docs and young researchers worldwide. In this developing context, the Italian Journal of Agronomy, own by the Italian Society of Agronomy, a non-profit scientific organization, is developing a new editorial policy to contribute to the progress of agronomic science through an open-access, low-cost and authoritative scientific literature space, with particular attention to young scientists. There are number of reasons why an agronomy scientist should publish an article in the Italian Journal of Agronomy, including: i) to get a rapid and careful peer review assessment of the submissions by an authoritative editorial board with specific expertise in Agronomy and receive careful support on how to address major revisions when required; ii) to ensure maximum visibility for published articles through the open access system; iii) to contribute to the agronomic scientific literature through an open access Scopus/WOS scientific Journal owned by a non-profit scientific society at a fair price; iv) to compete for the SIA grants and prizes for best articles or best reviewers of the year. The new editorial policy of IJA includes a more pro-active publishing strategy aiming at widening the arena of international scientists contributing to the journal's scope, including invited papers and special conditions for the publication of special issues on cutting-edge agronomy topics, promotion of the journal during scientific conferences and events, rewarding of the best articles and peer-reviewers contributing to the journal's development. IJA is solely focused on the free diffusion of agroecosystem science, not on any other business: we trust that authors and readers will appreciate that IJA's editorial board members work toward this mission without compensation and that the article fee is necessary only to cover the publisher's net costs. We are very grateful to the past and new Editorial board and all peer reviewers for their invaluable contribution to the development of our Journal. Michele Perniola, President of the Italian Society of Agronomy Pier Paolo Roggero, Editor in ChiefMichael D. Casler, Associate EditorDavide Cammarano, Associate EditorMichele Rinaldi, Associate Edito

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

Exome sequences and multi-environment field trials elucidate the genetic basis of adaptation in barley

Broadening the genetic base of crops is crucial for developing varieties to respond to global agricultural challenges such as climate change. Here, we analysed a diverse panel of 371 domesticated lines of the model crop of barley to explore the genetics of crop adaptation. We first collected exome sequence data and phenotypes of key life history traits from contrasting multi-environment common garden trials. Then we applied refined statistical methods, including based on exomic haplotype states, for genotype-by-environment (G 7E) modelling. Sub-populations defined from exomic profiles were coincident with barley's biology, geography and history, and explained a high proportion of trial phenotypic variance. Clear G 7E interactions indicated adaptation profiles that varied for landraces and cultivars. Exploration of circadian clock-related genes, associated with the environmentally-adaptive days to heading trait (crucial for the crop's spread from the Fertile Crescent), illustrated complexities in G 7E effect directions, and the importance of latitudinally-based genic context in the expression of large effect alleles. Our analysis supports a gene-level scientific understanding of crop adaption and leads to practical opportunities for crop improvement, allowing the prioritisation of genomic regions and particular sets of lines for breeding efforts seeking to cope with climate change and other stresses

Why do crop models diverge substantially in climate impact projections? A comprehensive analysis based on eight barley crop models

Robust projections of climate impact on crop growth and productivity by crop models are key to designing effective adaptations to cope with future climate risk. However, current crop models diverge strongly in their climate impact projections. Previous studies tried to compare or improve crop models regarding the impact of one single climate variable. However, this approach is insufficient, considering that crop growth and yield are affected by the interactive impacts of multiple climate change factors and multiple interrelated biophysical processes. Here, a new comprehensive analysis was conducted to look holistically at the reasons why crop models diverge substantially in climate impact projections and to investigate which biophysical processes and knowledge gaps are key factors affecting this uncertainty and should be given the highest priorities for improvement. First, eight barley models and eight climate projections for the 2050s were applied to investigate the uncertainty from crop model structure in climate impact projections for barley growth and yield at two sites: Jokioinen, Finland (Boreal) and Lleida, Spain (Mediterranean). Sensitivity analyses were then conducted on the responses of major crop processes to major climatic variables including temperature, precipitation, irradiation, and CO2, as well as their interactions, for each of the eight crop models. The results showed that the temperature and CO2 relationships in the models were the major sources of the large discrepancies among the models in climate impact projections. In particular, the impacts of increases in temperature and CO2 on leaf area development were identified as the major causes for the large uncertainty in simulating changes in evapotranspiration, above-ground biomass, and grain yield. Our findings highlight that advancements in understanding the basic processes and thresholds by which climate warming and CO2 increases will affect leaf area development, crop evapotranspiration, photosynthesis, and grain formation in contrasting environments are needed for modeling their impacts.Peer reviewe

Multimodel Ensembles of Wheat Growth: More Models are Better than One

Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop models can give valuable information about model accuracy and uncertainty, but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 24-38% for the different end-of-season variables including grain yield (GY) and grain protein concentration (GPC). There was little relation between error of a model for GY or GPC and error for in-season variables. Thus, most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamics. Ensemble simulations, taking either the mean (e-mean) or median (e-median) of simulated values, gave better estimates than any individual model when all variables were considered. Compared to individual models, e-median ranked first in simulating measured GY and third in GPC. The error of e-mean and e-median declined with an increasing number of ensemble members, with little decrease beyond 10 models. We conclude that multimodel ensembles can be used to create new estimators with improved accuracy and consistency in simulating growth dynamics. We argue that these results are applicable to other crop species, and hypothesize that they apply more generally to ecological system models

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

Statistical Analysis of Large Simulated Yield Datasets for Studying Climate Effects

Many studies have been carried out during the last decade to study the effect of climate change on crop yields and other key crop characteristics. In these studies, one or several crop models were used to simulate crop growth and development for different climate scenarios that correspond to different projections of atmospheric CO2 concentration, temperature, and rainfall changes (Semenov et al., 1996; Tubiello and Ewert, 2002; White et al., 2011). The Agricultural Model Intercomparison and Improvement Project (AgMIP; Rosenzweig et al., 2013) builds on these studies with the goal of using an ensemble of multiple crop models in order to assess effects of climate change scenarios for several crops in contrasting environments. These studies generate large datasets, including thousands of simulated crop yield data. They include series of yield values obtained by combining several crop models with different climate scenarios that are defined by several climatic variables (temperature, CO2, rainfall, etc.). Such datasets potentially provide useful information on the possible effects of different climate change scenarios on crop yields. However, it is sometimes difficult to analyze these datasets and to summarize them in a useful way due to their structural complexity; simulated yield data can differ among contrasting climate scenarios, sites, and crop models. Another issue is that it is not straightforward to extrapolate the results obtained for the scenarios to alternative climate change scenarios not initially included in the simulation protocols. Additional dynamic crop model simulations for new climate change scenarios are an option but this approach is costly, especially when a large number of crop models are used to generate the simulated data, as in AgMIP. Statistical models have been used to analyze responses of measured yield data to climate variables in past studies (Lobell et al., 2011), but the use of a statistical model to analyze yields simulated by complex process-based crop models is a rather new idea. We demonstrate herewith that statistical methods can play an important role in analyzing simulated yield data sets obtained from the ensembles of process-based crop models. Formal statistical analysis is helpful to estimate the effects of different climatic variables on yield, and to describe the between-model variability of these effects

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

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