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J. W. Jones - One of the best experts on this subject based on the ideXlab platform.

  • Short survey Scaling-up Crop Models for climate variability applications $
    2020
    Co-Authors: James W Hansen, J. W. Jones
    Abstract:

    Although most dynamic Crop Models have been developed and tested for the scale of a homogeneous plot, applications related to climate variability are often at broader spatial scales that can incorporate considerable heterogeneity. This study reviews issues and approaches related to applying Crop Models at scales larger than the plot. Perfect aggregate prediction at larger scales requires perfect integration of a perfect model across the range of variability of perfect input data. Aggregation error results from imperfect integration of heterogeneous inputs, and includes distortions of either spatial mean values of predictions or year-to-year variability of the spatial means. Approaches for reducing aggregation error include sampling input variability in geographic or probability space, and calibration of model inputs or outputs. Implications of scale and spatial interactions for model structure and complexity are a matter of ongoing debate. Large-scale Crop model applications must address limitations of soil, weather and management data. Distortion of weather sequences from spatial averaging is a particular danger. A case study of soybean in the state of Georgia, USA, illustrates several Crop model scaling approaches. # 2000 Elsevier Science Ltd. All rights reserved.

  • how accurately do maize Crop Models simulate the interactions of atmospheric co2 concentration levels with limited water supply on water use and yield
    European Journal of Agronomy, 2017
    Co-Authors: J L Durand, Alex C. Ruane, J. W. Jones, Cynthia Rosenzweig, K J Boote, Kenel Delusca, J I Lizaso, Remy Manderscheid, Hans Johachim Weigel, L.r. Ahuja
    Abstract:

    This study assesses the ability of 21 Crop Models to capture the impact of elevated CO2 concentration ([CO2]) on maize yield and water use as measured in a 2-year Free Air Carbon dioxide Enrichment experiment conducted at the Thunen Institute in Braunschweig, Germany (Manderscheid et al., 2014). Data for ambient [CO2] and irrigated treatments were provided to the 21 Models for calibrating plant traits, including weather, soil and management data as well as yield, grain number, above ground biomass, leaf area index, nitrogen concentration in biomass and grain, water use and soil water content. Models differed in their representation of carbon assimilation and evapotranspiration processes. The Models reproduced the absence of yield response to elevated [CO2] under well-watered conditions, as well as the impact of water deficit at ambient [CO2], with 50% of Models within a range of +/−1 Mg ha−1 around the mean. The bias of the median of the 21 Models was less than 1 Mg ha−1. However under water deficit in one of the two years, the Models captured only 30% of the exceptionally high [CO2] enhancement on yield observed. Furthermore the ensemble of Models was unable to simulate the very low soil water content at anthesis and the increase of soil water and grain number brought about by the elevated [CO2] under dry conditions. Overall, we found Models with explicit stomatal control on transpiration tended to perform better. Our results highlight the need for model improvement with respect to simulating transpirational water use and its impact on water status during the kernel-set phase.

  • how do various maize Crop Models vary in their responses to climate change factors
    Global Change Biology, 2014
    Co-Authors: Simona Bassu, Alex C. Ruane, J. W. Jones, Cynthia Rosenzweig, Myriam Adam, K J Boote, J L Durand, J I Lizaso, Nadine Brisson, Christian Baron
    Abstract:

    Potential consequences of climate change on Crop production can be studied using mechanistic Crop simulation Models. While a broad variety of maize simulation Models exist, it is not known whether different Models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [CO2], we present the largest maize Crop model intercomparison to date, including 23 different Models. These Models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames (USA), Rio Verde (Brazil) and Morogoro (Tanzania). While individual Models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of Models was able to simulate absolute yields accurately at the four sites even with low data for calibration, thus suggesting that using an ensemble of Models has merit. Temperature increase had strong negative influence on modeled yield response of roughly -0.5 Mg ha(-1) per degrees C. Doubling [CO2] from 360 to 720 mu mol mol(-1) increased grain yield by 7.5% on average across Models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [CO2] among Models. Model responses to temperature and [CO2] did not differ whether Models were simulated with low calibration information or, simulated with high level of calibration information.

  • working with dynamic Crop Models methods tools and examples for agriculture and environment
    2014
    Co-Authors: Daniel Wallach, J. W. Jones, David Makowski, Francois Brun
    Abstract:

    This second edition of Working with Dynamic Crop Models is meant for self-learning by researchers or for use in graduate level courses devoted to methods for working with dynamic Models in Crop, agricultural, and related sciences. Each chapter focuses on a particular topic and includes an introduction, a detailed explanation of the available methods, applications of the methods to one or two simple Models that are followed throughout the book, real-life examples of the methods from literature, and finally a section detailing implementation of the methods using the R programming language. The consistent use of R makes this book immediately and directly applicable to scientists seeking to develop Models quickly and effectively, and the selected examples ensure broad appeal to scientists in various disciplines. New to this edition: *50% new content - 100% reviewed and updated* Clearly explains practical application of the methods presented, including R language examples * Presents real-life examples of core Crop modeling methods, and ones that are translatable to dynamic system Models in other fields

  • putting mechanisms into Crop production Models
    Plant Cell and Environment, 2013
    Co-Authors: K J Boote, Senthold Asseng, J. W. Jones, Jeffrey W White, J I Lizaso
    Abstract:

    Crop growth Models dynamically simulate processes of C, N and water balance on daily or hourly time-steps to predict Crop growth and development and at season-end, final yield. Their ability to integrate effects of genetics, environment and Crop management have led to applications ranging from understanding gene function to predicting potential impacts of climate change. The history of Crop Models is reviewed briefly, and their level of mechanistic detail for assimilation and respiration, ranging from hourly leaf-to-canopy assimilation to daily radiation-use efficiency is discussed. Crop Models have improved steadily over the past 30–40 years, but much work remains. Improvements are needed for the prediction of transpiration response to elevated CO2 and high temperature effects on phenology and reproductive fertility, and simulation of root growth and nutrient uptake under stressful edaphic conditions. Mechanistic improvements are needed to better connect Crop growth to genetics and to soil fertility, soil waterlogging and pest damage. Because Crop Models integrate multiple processes and consider impacts of environment and management, they have excellent potential for linking research from genomics and allied disciplines to Crop responses at the field scale, thus providing a valuable tool for deciphering genotype by environment by management effects.

Frank Ewert - One of the best experts on this subject based on the ideXlab platform.

  • contribution of Crop Models to adaptation in wheat
    Trends in Plant Science, 2017
    Co-Authors: Karine Chenu, Frank Ewert, John R Porter, Pierre Martre, Bruno Basso, S C Chapman, M Bindi, Senthold Asseng
    Abstract:

    With world population growing quickly, agriculture needs to produce more with fewer inputs while being environmentally friendly. In a context of changing environments, Crop Models are useful tools to simulate Crop yields. Wheat (Triticum spp.) Crop Models have been evolving since the 1960s to translate processes related to Crop growth and development into mathematical equations. These have been used over decades for agronomic purposes, and have more recently incorporated advances in the modeling of environmental footprints, biotic constraints, trait and gene effects, climate change impact, and the upscaling of global change impacts. This review outlines the potential and limitations of modern wheat Crop Models in assisting agronomists, breeders, and policymakers to address the current and future challenges facing agriculture.

  • Crop modelling for integrated assessment of risk to food production from climate change
    Environmental Modelling and Software, 2015
    Co-Authors: Frank Ewert, Heidi Webber, R. P. Rötter, Kurt Christian Kersebaum, M K Van Ittersum, Marco Bindi, Miroslav Trnka, Jørgen E. Olesen, Sander Janssen, Mike Rivington
    Abstract:

    The complexity of risks posed by climate change and possible adaptations for Crop production has called for integrated assessment and modelling (IAM) approaches linking biophysical and economic Models. This paper attempts to provide an overview of the present state of Crop modelling to assess climate change risks to food production and to which extent Crop Models comply with IAM demands. Considerable progress has been made in modelling effects of climate variables, where Crop Models best satisfy IAM demands. Demands are partly satisfied for simulating commonly required assessment variables. However, progress on the number of simulated Crops, uncertainty propagation related to model parameters and structure, adaptations and scaling are less advanced and lagging behind IAM demands. The limitations are considered substantial and apply to a different extent to all Crop Models. Overcoming these limitations will require joint efforts, and consideration of novel modelling approaches. Extreme events and future climate uncertainty represent risk for food production.Crop Models are largely able to simulate Crop response to climate factors.Adaptations are best evaluated in integrated assessment Models (IAM).Key limitations for Crop Models in IAM are low data availability and integration.Cross-scale nature of IAM suggests novel modelling approaches are needed.

  • what role can Crop Models play in supporting climate change adaptation decisions to enhance food security in sub saharan africa
    Agricultural Systems, 2014
    Co-Authors: Heidi Webber, Thomas Gaiser, Frank Ewert
    Abstract:

    In Sub-Saharan Africa (SSA) efforts to achieve food security are challenged by poverty, low soil fertility, unequal global trade relationships, population growth, weak institutions and infrastructure, and future climate changes and variability. Crop Models are the primary tools available to assess the impacts of climate change and other drivers on Crop productivity, a key aspect of food security. This review examines their role and suitability for informing climate change adaptation decisions in the SSA context. Perception of climate change is rarely the only factor leading to changed farming practices, with labor availability, recent extreme climatic events (floods or droughts) and access to formal credit, constituting the main factors farmers respond to. Further, farmers’ socio-economic status constrains the adaptations they make in response to these drivers. Many Crop modeling studies reviewed investigating climate change adaptation currently do not capture many of these drivers, adaptations nor constraints. However, a number of areas were identified where Crop Models could aid in adaptations decision-making. For instance, Crop Models can: test which changes farmers are making are most robust to future climate scenarios; be used as tools for experimentation in farmer organizations to build farmer capacity, minimize risk and empower farmers; be linked to economic, farm systems or livestock Models to widen the scope of potential impacts, adaptations and farmer constraints considered, and to probe the interactions of Cropping systems with other systems; and evaluate various indicators of resilience. Finally it is suggested that one of the greatest benefits of linking Crop Models across disciplines and in integrated assessment frameworks may be providing a platform to bring specialists and stakeholders from diverse backgrounds together to assess climate change adaptation options to enhance food security in SSA.

  • What role can Crop Models play in supporting climate change adaptation decisions to enhance food security in Sub-Saharan Africa?
    Agricultural Systems, 2014
    Co-Authors: Heidi Webber, Thomas Gaiser, Frank Ewert
    Abstract:

    In Sub-Saharan Africa (SSA) efforts to achieve food security are challenged by poverty, low soil fertility, unequal global trade relationships, population growth, weak institutions and infrastructure, and future climate changes and variability. Crop Models are the primary tools available to assess the impacts of climate change and other drivers on Crop productivity, a key aspect of food security. This review examines their role and suitability for informing climate change adaptation decisions in the SSA context. Perception of climate change is rarely the only factor leading to changed farming practices, with labor availability, recent extreme climatic events (floods or droughts) and access to formal credit, constituting the main factors farmers respond to. Further, farmers' socio-economic status constrains the adaptations they make in response to these drivers. Many Crop modeling studies reviewed investigating climate change adaptation currently do not capture many of these drivers, adaptations nor constraints. However, a number of areas were identified where Crop Models could aid in adaptations decision-making. For instance, Crop Models can: test which changes farmers are making are most robust to future climate scenarios; be used as tools for experimentation in farmer organizations to build farmer capacity, minimize risk and empower farmers; be linked to economic, farm systems or livestock Models to widen the scope of potential impacts, adaptations and farmer constraints considered, and to probe the interactions of Cropping systems with other systems; and evaluate various indicators of resilience. Finally it is suggested that one of the greatest benefits of linking Crop Models across disciplines and in integrated assessment frameworks may be providing a platform to bring specialists and stakeholders from diverse backgrounds together to assess climate change adaptation options to enhance food security in SSA. © 2014 Elsevier Ltd.

  • A systematic approach for re-assembly of Crop Models: An example to simulate pea growth from wheat growth
    Ecological Modelling, 2013
    Co-Authors: Myriam Adam, Jacques Wery, Frank Ewert, Peter A. Leffelaar, Marc Corbeels, H. Van Keulen
    Abstract:

    The process of Crop modelling to develop operational software requires different skills, from conceptualization of the biophysical system to computer programming, involving three main scientific disciplines: agronomy, mathematics, and software engineering. Model building implies transforming a conceptual model into sets of mathematical equations and then translating these equations into a computer program. Although recent Crop modelling frameworks can technically support model building, the modelling process is not always well documented and difficult to repeat. The focus of this paper is therefore on developing and documenting an approach to reassemble Crop Models, i.e. develop a new model from an existing one, using a Crop modelling framework and Crop physiological knowledge. Modifications to an initial Crop model were classified according to three categories: (i) changes in parameter values, (ii) changes in equations, and (iii) changes in overall model structure. We illustrate the approach with a case study transforming a wheat Crop model into a pea Crop model. We discuss the role of each actor in the process to document diverse uncertainties related to the model (i.e. contextual situation, data, structure), and the general applicability of the approach for different Crop modelling frameworks. We conclude that the use of our approach to reassemble a Crop model within a modelling framework facilitates integration of different disciplines around a modelling objective, and facilitates creating transparent and reproducible Models.

K J Boote - One of the best experts on this subject based on the ideXlab platform.

  • how accurately do maize Crop Models simulate the interactions of atmospheric co2 concentration levels with limited water supply on water use and yield
    European Journal of Agronomy, 2017
    Co-Authors: J L Durand, Alex C. Ruane, J. W. Jones, Cynthia Rosenzweig, K J Boote, Kenel Delusca, J I Lizaso, Remy Manderscheid, Hans Johachim Weigel, L.r. Ahuja
    Abstract:

    This study assesses the ability of 21 Crop Models to capture the impact of elevated CO2 concentration ([CO2]) on maize yield and water use as measured in a 2-year Free Air Carbon dioxide Enrichment experiment conducted at the Thunen Institute in Braunschweig, Germany (Manderscheid et al., 2014). Data for ambient [CO2] and irrigated treatments were provided to the 21 Models for calibrating plant traits, including weather, soil and management data as well as yield, grain number, above ground biomass, leaf area index, nitrogen concentration in biomass and grain, water use and soil water content. Models differed in their representation of carbon assimilation and evapotranspiration processes. The Models reproduced the absence of yield response to elevated [CO2] under well-watered conditions, as well as the impact of water deficit at ambient [CO2], with 50% of Models within a range of +/−1 Mg ha−1 around the mean. The bias of the median of the 21 Models was less than 1 Mg ha−1. However under water deficit in one of the two years, the Models captured only 30% of the exceptionally high [CO2] enhancement on yield observed. Furthermore the ensemble of Models was unable to simulate the very low soil water content at anthesis and the increase of soil water and grain number brought about by the elevated [CO2] under dry conditions. Overall, we found Models with explicit stomatal control on transpiration tended to perform better. Our results highlight the need for model improvement with respect to simulating transpirational water use and its impact on water status during the kernel-set phase.

  • inter comparison of performance of soybean Crop simulation Models and their ensemble in southern brazil
    Field Crops Research, 2017
    Co-Authors: Rafael Battisti, Paulo Cesar Sentelhas, K J Boote
    Abstract:

    Abstract Crop simulation Models can help scientists, government agencies and growers to evaluate the best strategies to manage their Crops in the field, according to the climate conditions. Currently, there are many Crop Models available to simulate soybean growth, development, and yield, with different levels of complexity and performance. Based on that, the aim of this study was to assess five soybean Crop Models and their ensemble in Southern Brazil. The following Crop Models were assessed: FAO – Agroecological Zone; AQUACrop; DSSAT CSM–CropGRO–Soybean; APSIM Soybean; and MONICA. These Crop Models were calibrated using experimental data obtained during 2013/2014 growing season in different sites, sowing dates and Crop conditions (rainfed and irrigated) for cultivar BRS 284, totaling 17 treatments. The Crop variables assessed were: grain yield; Crop phases; harvest index; total above-ground biomass; and leaf area index. The calibration was made in three phases: using original coefficients from modelś default (no calibration); calibrating the coefficients related only with Crop life cycle phases; and calibrating all set of coefficients (below and above the soil). The results from the Models were analyzed individually and in an ensemble of them. The Crop Models showed an improvement of performance from no calibration to complete calibration. Crop phases were estimated efficiently, although different approaches were used by the Models. The estimated yield had RMSE of 650, 536, 548, 550 and 535 kg ha −1 , respectively, for FAO, AQUACrop, DSSAT, APSIM and MONICA, with d indices always higher than 0.90 for all of them. The best performance was obtained when an ensemble of all Models was considered, reducing yield RMSE to 262 kg ha −1 . The same tendency for ensemble being best was observed for leaf area index. The harvest index was the Crop variable with the poorest performance. In general, the results showed that an ensemble of completely calibrated Models were more efficient to simulate soybean yield than any single one, which was also observed when testing this procedure with independent data.

  • uncertainties in predicting rice yield by current Crop Models under a wide range of climatic conditions
    Global Change Biology, 2015
    Co-Authors: Tao Li, S. Bregaglio, Myriam Adam, Toshihiro Hasegawa, K J Boote, Samuel Buis, Roberto Confalonieri, Tamon Fumoto, D S Gaydon, Manuel Marcaida
    Abstract:

    Predicting rice (Oryza sativa) productivity under future climates is important for global food security. Ecophysiological Crop Models in combination with climate model outputs are commonly used in yield prediction, but uncertainties associated with Crop Models remain largely unquantified. We evaluated 13 rice Models against multi-year experimental yield data at four sites with diverse climatic conditions in Asia and examined whether different modeling approaches on major physiological processes attribute to the uncertainties of prediction to field measured yields and to the uncertainties of sensitivity to changes in temperature and CO2 concentration [CO2]. We also examined whether a use of an ensemble of Crop Models can reduce the uncertainties. Individual Models did not consistently reproduce both experimental and regional yields well, and uncertainty was larger at the warmest and coolest sites. The variation in yield projections was larger among Crop Models than variation resulting from 16 global climate model-based scenarios. However, the mean of predictions of all Crop Models reproduced experimental data, with an uncertainty of less than 10% of measured yields. Using an ensemble of eight Models calibrated only for phenology or five Models calibrated in detail resulted in the uncertainty equivalent to that of the measured yield in well-controlled agronomic field experiments. Sensitivity analysis indicates the necessity to improve the accuracy in predicting both biomass and harvest index in response to increasing [CO2] and temperature.

  • how do various maize Crop Models vary in their responses to climate change factors
    Global Change Biology, 2014
    Co-Authors: Simona Bassu, Alex C. Ruane, J. W. Jones, Cynthia Rosenzweig, Myriam Adam, K J Boote, J L Durand, J I Lizaso, Nadine Brisson, Christian Baron
    Abstract:

    Potential consequences of climate change on Crop production can be studied using mechanistic Crop simulation Models. While a broad variety of maize simulation Models exist, it is not known whether different Models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [CO2], we present the largest maize Crop model intercomparison to date, including 23 different Models. These Models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames (USA), Rio Verde (Brazil) and Morogoro (Tanzania). While individual Models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of Models was able to simulate absolute yields accurately at the four sites even with low data for calibration, thus suggesting that using an ensemble of Models has merit. Temperature increase had strong negative influence on modeled yield response of roughly -0.5 Mg ha(-1) per degrees C. Doubling [CO2] from 360 to 720 mu mol mol(-1) increased grain yield by 7.5% on average across Models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [CO2] among Models. Model responses to temperature and [CO2] did not differ whether Models were simulated with low calibration information or, simulated with high level of calibration information.

  • putting mechanisms into Crop production Models
    Plant Cell and Environment, 2013
    Co-Authors: K J Boote, Senthold Asseng, J. W. Jones, Jeffrey W White, J I Lizaso
    Abstract:

    Crop growth Models dynamically simulate processes of C, N and water balance on daily or hourly time-steps to predict Crop growth and development and at season-end, final yield. Their ability to integrate effects of genetics, environment and Crop management have led to applications ranging from understanding gene function to predicting potential impacts of climate change. The history of Crop Models is reviewed briefly, and their level of mechanistic detail for assimilation and respiration, ranging from hourly leaf-to-canopy assimilation to daily radiation-use efficiency is discussed. Crop Models have improved steadily over the past 30–40 years, but much work remains. Improvements are needed for the prediction of transpiration response to elevated CO2 and high temperature effects on phenology and reproductive fertility, and simulation of root growth and nutrient uptake under stressful edaphic conditions. Mechanistic improvements are needed to better connect Crop growth to genetics and to soil fertility, soil waterlogging and pest damage. Because Crop Models integrate multiple processes and consider impacts of environment and management, they have excellent potential for linking research from genomics and allied disciplines to Crop responses at the field scale, thus providing a valuable tool for deciphering genotype by environment by management effects.

Senthold Asseng - One of the best experts on this subject based on the ideXlab platform.

  • improving the use of Crop Models for risk assessment and climate change adaptation
    Agricultural Systems, 2018
    Co-Authors: Senthold Asseng, Daniel Wallach, Chetan Deva, Andrew J. Challinor, Christoph Muller, K J Nicklin, Eline Vanuytrecht, Stephen Whitfield, Julian Ramirezvillegas
    Abstract:

    Abstract Crop Models are used for an increasingly broad range of applications, with a commensurate proliferation of methods. Careful framing of research questions and development of targeted and appropriate methods are therefore increasingly important. In conjunction with the other authors in this special issue, we have developed a set of criteria for use of Crop Models in assessments of impacts, adaptation and risk. Our analysis drew on the other papers in this special issue, and on our experience in the UK Climate Change Risk Assessment 2017 and the MACSUR, AgMIP and ISIMIP projects. The criteria were used to assess how improvements could be made to the framing of climate change risks, and to outline the good practice and new developments that are needed to improve risk assessment. Key areas of good practice include: i. the development, running and documentation of Crop Models, with attention given to issues of spatial scale and complexity; ii. the methods used to form Crop-climate ensembles, which can be based on model skill and/or spread; iii. the methods used to assess adaptation, which need broadening to account for technological development and to reflect the full range options available. The analysis highlights the limitations of focussing only on projections of future impacts and adaptation options using pre-determined time slices. Whilst this long-standing approach may remain an essential component of risk assessments, we identify three further key components: 1. Working with stakeholders to identify the timing of risks. What are the key vulnerabilities of food systems and what does Crop-climate modelling tell us about when those systems are at risk? 2. Use of multiple methods that critically assess the use of climate model output and avoid any presumption that analyses should begin and end with gridded output. 3. Increasing transparency and inter-comparability in risk assessments. Whilst studies frequently produce ranges that quantify uncertainty, the assumptions underlying these ranges are not always clear. We suggest that the contingency of results upon assumptions is made explicit via a common uncertainty reporting format; and/or that studies are assessed against a set of criteria, such as those presented in this paper.

  • contribution of Crop Models to adaptation in wheat
    Trends in Plant Science, 2017
    Co-Authors: Karine Chenu, Frank Ewert, John R Porter, Pierre Martre, Bruno Basso, S C Chapman, M Bindi, Senthold Asseng
    Abstract:

    With world population growing quickly, agriculture needs to produce more with fewer inputs while being environmentally friendly. In a context of changing environments, Crop Models are useful tools to simulate Crop yields. Wheat (Triticum spp.) Crop Models have been evolving since the 1960s to translate processes related to Crop growth and development into mathematical equations. These have been used over decades for agronomic purposes, and have more recently incorporated advances in the modeling of environmental footprints, biotic constraints, trait and gene effects, climate change impact, and the upscaling of global change impacts. This review outlines the potential and limitations of modern wheat Crop Models in assisting agronomists, breeders, and policymakers to address the current and future challenges facing agriculture.

  • testing the responses of four wheat Crop Models to heat stress at anthesis and grain filling
    Global Change Biology, 2016
    Co-Authors: Senthold Asseng, Liang Tang
    Abstract:

    Higher temperatures caused by future climate change will bring more frequent heat stress events and pose an increasing risk to global wheat production. Crop Models have been widely used to simulate future Crop productivity but are rarely tested with observed heat stress experimental datasets. Four wheat Models (DSSAT-CERES-Wheat, DSSAT-Nwheat, APSIM-Wheat, and WheatGrow) were evaluated with 4 years of environment-controlled phytotron experimental datasets with two wheat cultivars under heat stress at anthesis and grain filling stages. Heat stress at anthesis reduced observed grain numbers per unit area and individual grain size, while heat stress during grain filling mainly decreased the size of the individual grains. The observed impact of heat stress on grain filling duration, total aboveground biomass, grain yield, and grain protein concentration (GPC) varied depending on cultivar and accumulated heat stress. For every unit increase of heat degree days (HDD, degree days over 30 °C), grain filling duration was reduced by 0.30-0.60%, total aboveground biomass was reduced by 0.37-0.43%, and grain yield was reduced by 1.0-1.6%, but GPC was increased by 0.50% for cv Yangmai16 and 0.80% for cv Xumai30. The tested Crop simulation Models could reproduce some of the observed reductions in grain filling duration, final total aboveground biomass, and grain yield, as well as the observed increase in GPC due to heat stress. Most of the Crop Models tended to reproduce heat stress impacts better during grain filling than at anthesis. Some of the tested Models require improvements in the response to heat stress during grain filling, but all Models need improvements in simulating heat stress effects on grain set during anthesis. The observed significant genetic variability in the response of wheat to heat stress needs to be considered through cultivar parameters in future simulation studies.

  • Soil nitrogen mineralisation simulated by Crop Models across different environments and the consequences for model improvment
    2016
    Co-Authors: Claas Nendel, Peter J Thorburn, Senthold Asseng, Myriam Adam, D. Melzer, C.e.p. Cerri, L. Claessens, Pramod K. Aggarwal, Carlos Angulo, Christian Baron
    Abstract:

    Soil nitrogen mineralisation simulated by Crop Models across different environments and the consequences for model improvment. iCropM2016 International Crop Modelling Symposium

  • The AgMIP Coordinated Climate-Crop Modeling Project (C3MP): Methods and Protocols
    Handbook of Climate Change and Agroecosystems, 2015
    Co-Authors: Sonali Mcdermid, Alex C. Ruane, Senthold Asseng, Cynthia Rosenzweig, L.r. Ahuja, Nicholas I. Hudson, Saseendran S. Anapalli, Jakarat Anothai, Benjamin Dumont, Federico Bert
    Abstract:

    Climate change is expected to alter a multitude of factors important to agricultural systems, including pests, diseases, weeds, extreme climate events, water resources, soil degradation, and socio-economic pressures. Changes to carbon dioxide concentration ([CO2]), temperature, andwater (CTW) will be the primary drivers of change in Crop growth and agricultural systems. Therefore, establishing the CTW-change sensitivity of Crop yields is an urgent research need and warrants diverse methods of investigation. Crop Models provide a biophysical, process-based tool to investigate Crop responses across varying environmental conditions and farm management techniques, and have been applied in climate impact assessment by using a variety of methods (White et al., 2011, and references therein). However, there is a significant amount of divergence between various Crop Models’ responses to CTW changes (R¨otter et al., 2011). While the application of a site-based Crop model is relatively simple, the coordination of such agricultural impact assessments on larger scales requires consistent and timely contributions from a large number of Crop modelers, each time a new global climate model (GCM) scenario or downscaling technique is created. A coordinated, global effort to rapidly examine CTW sensitivity across multiple Crops, Crop Models, and sites is needed to aid model development and enhance the assessment of climate impacts (Deser et al., 2012)...

Gerrit Hoogenboom - One of the best experts on this subject based on the ideXlab platform.

  • Toward large-scale Crop production forecasts for global food security
    IBM Journal of Research and Development, 2016
    Co-Authors: G. Badr, C. M. Albrecht, F. J. Marianno, X. Shao, L. J. Klein, Markus Freitag, Nigel Hinds, Gerrit Hoogenboom, S Lu, Hendrik F. Hamann
    Abstract:

    Predicting Crop production plays a critical role in food price forecasting and mitigating potential food shortages. Crop Models may require parameters from, for example, weather, Crop genotype, farm management, and soil. Sources for these data are often found in very different places. Researchers spend a significant amount of time to collect and curate them. In addition, in order to scale yield forecasts from the single-farm level up to the continental scale, Crop Models have to be coupled with a geospatial big data platform to provide the required data inputs. In a proof-of-concept case study, we investigate the coupling of a scalable geospatial big data platform, Physical Analytics Integrated Repository and Services (PAIRS), to the Decision Support System for Agrotechnology Transfer (DSSAT) Crop model. We envision running this system on a global scale. For geospatial analytics, PAIRS provides curation of heterogeneous data sources to simulate Crop Models using hundreds of terabytes of data.

  • assessment of maize growth and yield using Crop Models under present and future climate in southwestern ethiopia
    Agricultural and Forest Meteorology, 2015
    Co-Authors: A Araya, Gerrit Hoogenboom, Eike Luedeling, Kiros Meles Hadgu, Isaya Kisekka, L G Martorano
    Abstract:

    Abstract Maize yield productivity in Ethiopia has been below the genetic potential—constrained, among other factors, by frequent moisture stress due to local weather variability. Changes in climate may exacerbate these limitations to productivity, but current research on projecting responses of maize yields to climate change in Ethiopia is inadequate. The research objectives of this project were to (1) calibrate and evaluate the performance of the APSIM-maize and DSSAT CSM-CERES-Maize Models, and (2) assess the impact of climate change on future maize yield. The climate periods considered were near future (2010–2039), middle (2040–2069) and end of the 21st century (2070–2099). Climate simulations were conducted using 20 General Circulation Models (GCMs) and two Representative Concentration Pathways (RCPs; RCP4.5 and RCP8.5). Both Crop Models reasonably reproduced observations for time to anthesis, time to physiological maturity and Crop yields, with values for the index of agreement of 0.86, 0.80 and 0.77 for DSSAT, and 0.50, 0.89 and 0.60 for APSIM. Similarly root mean square errors were moderate for days to anthesis (1.3 and 3.7 days, for DSSAT and APSIM, respectively), maturity (4.5 and 3.1 days), and yield (1.1 and 1.2 tons). Deviations of simulated from observed values were low for days to anthesis (DSSAT: −2.4–2.3%; APSIM: 0–6%) and days to maturity (DSSAT: −0.6–4.4%; APSIM: −1.9–3.3%) but relatively high for yield (DSSAT: −18.5–21.2%; APSIM: −19.1–37.1%). Overall the goodness-of-fit measures indicated that Models were useful for assessing maize yield at the study site. Simulations for future climate scenarios projected slight increases in the median yield for the near future (1.7%–2.9% across Models and RCPs), with uncertainty increasing toward mid-century (0.6–4.2%). By the end of the 21st century, projections ranged between yield decreases by 6.3% and increases by 4%. Differences between the RCPs were small, probably due to factor interactions, such as higher temperatures reducing the CO 2 -induced yield gains for the higher RCP. Uncertainties in studies on the impact of climate change on maize might arise mostly from the choice of Crop model and GCM. Therefore, the use of multiple Crop Models along with multiple GCMs would be advisable in order to adequately consider uncertainties about future climate and Crop responses and to provide comprehensive information to policy makers and planners. Overall, results of this study (based on two different Crop simulation Models across 20 GCMs, and two RCPs under similar Crop management) consistently indicated a slight increase in yield.

  • Application of DSSAT Crop Models to Generate Alternative Production Activities Under Combined Use of Organic-Inorganic Nutrients in Rwanda
    Journal of Crop Improvement, 2012
    Co-Authors: J. C. Bidogeza, P. B.m. Berensten, Jan De Graaff, Gerrit Hoogenboom, Alfons G.j.m. Oude Lansink
    Abstract:

    The low agricultural productivity of Rwanda reflects the poor soil fertility status caused by a low organic matter and high soil acidity that characterizes a large part of the country. Experimental trials have shown that a combined use of organic and inorganic fertilizers can increase Crop yield. However, there are no guidelines for combined nutrients of different sources and qualities. Crop growth Models can assist in the evaluation of the integration of organic and inorganic fertilizers. The Decision Support System for Agrotechnology Transfer (DSSAT) presents a collection of such Crop Models. The objective of this study was to determine alternative production activities through yield prediction of several Crops under combined use of organic and inorganic fertilizers on Oxisols and Inceptisols in eastern Rwanda and to determine the best fertility management options. The DSSAT Crop Models were used to quantify the alternative production activities. The simulation of Crop yield showed that predicted Crop yield was distinctly higher than the actual yield for the current small-scale farming practices common in the region. The predicted yields for beans (Phaseolus vulgaris), groundnut (Arachis hypogaea), and cassava (Manihot esculenta) were approximately the same for all treatments, whereas the combined application of Tithonia diversifolia and Diammonium phosphate appeared to predict higher yields for maize (Zea mays) and sorghum (Sorghum bicolor). Yield prediction for all Crops was higher on the Inceptisols than on the Oxisols because of the better chemical and physical conditions of Inceptisols. This is in line with reality. © 2012 Copyright Taylor and Francis Group, LLC.

  • The DSSAT Cropping system model
    European Journal of Agronomy, 2003
    Co-Authors: J. W. Jones, L. A. Hunt, P. W. Wilkens, U. Singh, C. H. Porter, A. J. Gijsman, William D. Batchelor, Kenneth J. Boote, Gerrit Hoogenboom, J T Ritchie
    Abstract:

    The decision support system for agrotechnology transfer (DSSAT) has been in use for the last 15 years by researchers worldwide. This package incorporates Models of 16 different Crops with software that facilitates the evaluation and application of the Crop Models for different purposes. Over the last few years, it has become increasingly difficult to maintain the DSSAT Crop Models, partly due to fact that there were different sets of computer code for different Crops with little attention to software design at the level of Crop Models themselves. Thus, the DSSAT Crop Models have been re-designed and programmed to facilitate more efficient incorporation of new scientific advances, applications, documentation and maintenance. The basis for the new DSSAT Cropping system model (CSM) design is a modular structure in which components separate along scientific discipline lines and are structured to allow easy replacement or addition of modules. It has one Soil module, a Crop Template module which can simulate different Crops by defining species input files, an interface to add individual Crop Models if they have the same design and interface, a Weather module, and a module for dealing with competition for light and water among the soil, plants, and atmosphere. It is also designed for incorporation into various application packages, ranging from those that help researchers adapt and test the CSM to those that operate the DSSAT-CSM to simulate production over time and space for different purposes. In this paper, we describe this new DSSAT-CSM design as well as approaches used to model the primary scientific components (soil, Crop, weather, and management). In addition, the paper describes data requirements and methods used for model evaluation. We provide an overview of the hundreds of published studies in which the DSSAT Crop Models have been used for various applications. The benefits of the new, re-designed DSSAT-CSM will provide considerable opportunities to its developers and others in the scientific community for greater cooperation in interdisciplinary research and in the application of knowledge to solve problems at field, farm, and higher levels. © 2002 Elsevier Science B.V. All rights reserved.

  • the dssat Cropping system model
    European Journal of Agronomy, 2003
    Co-Authors: J. W. Jones, L. A. Hunt, P. W. Wilkens, U. Singh, C. H. Porter, Gerrit Hoogenboom, K J Boote, W D Batchelor, Arjan J Gijsman, J T Ritchie
    Abstract:

    The decision support system for agrotechnology transfer (DSSAT) has been in use for the last 15 years by researchers worldwide. This package incorporates Models of 16 different Crops with software that facilitates the evaluation and application of the Crop Models for different purposes. Over the last few years, it has become increasingly difficult to maintain the DSSAT Crop Models, partly due to fact that there were different sets of computer code for different Crops with little attention to software design at the level of Crop Models themselves. Thus, the DSSAT Crop Models have been re-designed and programmed to facilitate more efficient incorporation of new scientific advances, applications, documentation and maintenance. The basis for the new DSSAT Cropping system model (CSM) design is a modular structure in which components separate along scientific discipline lines and are structured to allow easy replacement or addition of modules. It has one Soil module, a Crop Template module which can simulate different Crops by defining species input files, an interface to add individual Crop Models if they have the same design and interface, a Weather module, and a module for dealing with competition for light and water among the soil, plants, and atmosphere. It is also designed for incorporation into various application packages, ranging from those that help researchers adapt and test the CSM to those that operate the DSSAT-CSM to simulate production over time and space for different purposes. In this paper, we describe this new DSSAT-CSM design as well as approaches used to model the primary scientific components (soil, Crop, weather, and management). In addition, the paper describes data requirements and methods used for model evaluation. We provide an overview of the hundreds of published studies in which the DSSAT Crop Models have been used for various applications. The benefits of the new, re-designed DSSAT-CSM will provide considerable opportunities to its developers and others in the scientific community for greater cooperation in interdisciplinary research and in the application of knowledge to solve problems at field, farm, and higher levels.