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1. Soil-smart cropping for climate-smart production

Author

Zhongkui Luo, Shuai Zhang, Zhigan Zhao, Budiman Minasny, Jinfeng Chang, Jingyi Huang, Baohai Li, Zhou Shi, Enli Wang, Mingming Wang, Yushan Wu, Liujun Xiao, and Su Ye

Subjects
Sustainable agriculture, Cropping systems, Root architecture, Root-soil interactions, Soil nutrients, Deep soil, Science
Abstract

Agriculture faces the dual challenge of sustainably increasing productivity to meet the food demand of a rapidly growing population and adapting to climate change. Despite significant efforts to develop more adaptive and productive crop cultivars and to improve water and nutrient management practices, the potential of crops to tackle this challenge by optimizing soil resource utilization remains underexplored. Here, we propose that optimizing root systems to promote the efficient acquisition of soil resources can increase yield, improve resilience to climate variability, and reduce environmental impacts. This optimization can be achieved through genetic manipulation at the crop species level and effective management of cropping systems at the field level (e.g., intercropping, rotation, and agroforestry). Advances in three-dimensional soil data collection, linking root traits to plant performance, and modelling of climate-soil–plant-management interactions are paving the way for soil-smart cropping. Effective communication and knowledge exchange with stakeholders beyond the scientific community are vital for accelerating the development and adoption of soil-smart practices for climate-smart and sustainable agricultural production.

Published
2024
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2. Optimization of inter-seasonal nitrogen allocation increases yield and resource-use efficiency in a water-limited wheat–maize cropping system in the North China Plain

Author

Xiaonan Zhou, Chenghang Du, Haoran Li, Zhencai Sun, Yifei Chen, Zhiqiang Gao, Zhigan Zhao, Yinghua Zhang, Zhimin Wang, and Ying Liu

Subjects
Cropping system, Water-saving irrigation, North China Plain, Nitrogen optimization, Sustainable intensification, Agriculture, Agriculture (General), S1-972
Abstract

Winter wheat–summer maize cropping system in the North China Plain often experiences drought-induced yield reduction in the wheat season and rainwater and nitrogen (N) fertilizer losses in the maize season. This study aimed to identify an optimal interseasonal water- and N-management strategy to alleviate these losses. Four ratios of allocation of 360 kg N ha−1 between the wheat and maize seasons under one-time presowing root-zone irrigation (W0) and additional jointing and anthesis irrigation (W2) in wheat and one irrigation after maize sowing were set as follows: N1 (120:240), N2 (180:180), N3 (240:120) and N4 (300:60). The results showed that under W0, the N3 treatment produced the highest annual yield, crop water productivity (WPC), and nitrogen partial factor productivity (PFPN). Increased N allocation in wheat under W0 improved wheat yield without affecting maize yield, as surplus nitrate after wheat harvest was retained in the topsoil layers and available for the subsequent maize. Under W2, annual yield was largest in the N2 treatment. The risk of nitrate leaching increased in W2 when N application rate in wheat exceeded that of the N2 treatment, especially in the wet year. Compared to W2N2, the W0N3 maintained 95.2% grain yield over two years. The WPC was higher in the W0 treatment than in the W2 treatment. Therefore, following limited total N rate, an appropriate fertilizer N transfer from maize to wheat season had the potential of a “triple win” for high annual yield, WPC and PFPN in a water-limited wheat–maize cropping system.

Published
2024
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3. Combining field data and modeling to better understand maize growth response to phosphorus (P) fertilizer application and soil P dynamics in calcareous soils

Author

Weina Zhang, Zhigan Zhao, Di He, Junhe Liu, Haigang Li, and Enli Wang

Subjects
maize, phosphorus availability, modeling, APSIM maize, APSIM SoilP, Agriculture (General), S1-972
Abstract

We used field experimental data to evaluate the ability of the agricultural production system model (APSIM) to simulate soil P availability, maize biomass and grain yield in response to P fertilizer applications on a fluvo-aquic soil in the North China Plain. Crop and soil data from a 2-year experiment with three P fertilizer application rates (0, 75 and 300 kg P2O5 ha−1) were used to calibrate the model. Sensitivity analysis was carried out to investigate the influence of APSIM SoilP parameters on the simulated P availability in soil and maize growth. Crop and soil P parameters were then derived by matching or relating the simulation results to observed crop biomass, yield, P uptake and Olsen-P in soil. The re-parameterized model was further validated against 2 years of independent data at the same sites. The re-parameterized model enabled good simulation of the maize leaf area index (LAI), biomass, grain yield, P uptake, and grain P content in response to different levels of P additions against both the calibration and validation datasets. Our results showed that APSIM needs to be re-parameterized for simulation of maize LAI dynamics through modification of leaf size curve and a reduction in the rate of leaf senescence for modern stay-green maize cultivars in China. The P concentration limits (maximum and minimum P concentrations in organs) at different stages also need to be adjusted. Our results further showed a curvilinear relationship between the measured Olsen-P concentration and simulated labile P content, which could facilitate the initialization of APSIM P pools in the NCP with Olsen-P measurements in future studies. It remains difficult to parameterize the APSIM SoilP module due to the conceptual nature of the pools and simplified conceptualization of key P transformation processes. A fundamental understanding still needs to be developed for modelling and predicting the fate of applied P fertilizers in soils with contrasting physical and chemical characteristics.

Published
2024
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4. Did Wheat Breeding Simultaneously Improve Grain Yield and Quality of Wheat Cultivars Releasing over the Past 20 Years in China?

Author

Naiyue Hu, Chenghang Du, Wanqing Zhang, Ying Liu, Yinghua Zhang, Zhigan Zhao, and Zhimin Wang

Subjects
wheat breeding, wheat yield and quality, genetic improvement, genetic yield potential, negative correlation, Agriculture
Abstract

Grain yield and quality of wheat are both important components for food security. Great effort has been made in the genetic improvement of wheat grain yield in China. However, wheat grain quality (i.e., protein concentration and protein quality) has received much less attention and is often overlooked in efforts to improve grain yield. A timely summary of the recent process of wheat breeding for increasing yield and quality (which can be used to guide future breeding strategies) is essential but still lacking. This study evaluated the breeding efforts on grain yield and grain quality of 1908 wheat varieties in China over the past two decades, from 2001 to 2020. We found wheat yields show a 0.64–1.03% annual growth in the three-dominant wheat-growing regions in China. At the same time, there was no significant decrease in wheat protein concentration. Genetic yield potential was increased, and the genetic yield gap was closed. High grain yields and better quality can likely be achieved simultaneously by genomic selection in future wheat breeding.

Published
2022
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5. Evidence for increasing global wheat yield potential

Author

Jose Rafael Guarin, Pierre Martre, Frank Ewert, Heidi Webber, Sibylle Dueri, Daniel Calderini, Matthew Reynolds, Gemma Molero, Daniel Miralles, Guillermo Garcia, Gustavo Slafer, Francesco Giunta, Diego N L Pequeno, Tommaso Stella, Mukhtar Ahmed, Phillip D Alderman, Bruno Basso, Andres G Berger, Marco Bindi, Gennady Bracho-Mujica, Davide Cammarano, Yi Chen, Benjamin Dumont, Ehsan Eyshi Rezaei, Elias Fereres, Roberto Ferrise, Thomas Gaiser, Yujing Gao, Margarita Garcia-Vila, Sebastian Gayler, Zvi Hochman, Gerrit Hoogenboom, Leslie A Hunt, Kurt C Kersebaum, Claas Nendel, Jørgen E Olesen, Taru Palosuo, Eckart Priesack, Johannes W M Pullens, Alfredo Rodríguez, Reimund P Rötter, Margarita Ruiz Ramos, Mikhail A Semenov, Nimai Senapati, Stefan Siebert, Amit Kumar Srivastava, Claudio Stöckle, Iwan Supit, Fulu Tao, Peter Thorburn, Enli Wang, Tobias Karl David Weber, Liujun Xiao, Zhao Zhang, Chuang Zhao, Jin Zhao, Zhigan Zhao, Yan Zhu, and Senthold Asseng

Subjects
yield increase, radiation use efficiency, wheat potential yield, crop model ensemble, global food security, Environmental technology. Sanitary engineering, TD1-1066, Environmental sciences, GE1-350, Science, Physics, QC1-999
Abstract

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.

Published
2022
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6. Grain Yield and Water Use Efficiency in Extremely-Late Sown Winter Wheat Cultivars under Two Irrigation Regimes in the North China Plain.

Author

Bin Wang, Yinghua Zhang, Baozhen Hao, Xuexin Xu, Zhigan Zhao, Zhimin Wang, and Qingwu Xue

Subjects
Medicine, Science
Abstract

Wheat production is threatened by water shortages and groundwater over-draft in the North China Plain (NCP). In recent years, winter wheat has been increasingly sown extremely late in early to mid-November after harvesting cotton or pepper. To improve water use efficiency (WUE) and guide the extremely late sowing practices, a 3-year field experiment was conducted under two irrigation regimes (W1, one-irrigation, 75 mm at jointing; W2, two-irrigation, 75 mm at jointing and 75 mm at anthesis) in 3 cultivars differing in spike size (HS4399, small spike; JM22, medium spike; WM8, large spike). Wheat was sown in early to mid-November at a high seeding rate of 800-850 seeds m(-2). Average yields of 7.42 t ha(-1) and WUE of 1.84 kg m(-3) were achieved with an average seasonal evapotranspiration (ET) of 404 mm. Compared with W2, wheat under W1 did not have yield penalty in 2 of 3 years, and had 7.9% lower seasonal ET and 7.5% higher WUE. The higher WUE and stable yield under W1 was associated with higher 1000-grain weight (TGW) and harvest index (HI). Among the 3 cultivars, JM22 had 5.9%-8.9% higher yield and 4.2%-9.3% higher WUE than WM8 and HS4399. The higher yield in JM22 was attributed mainly to higher HI and TGW due to increased post-anthesis biomass and deeper seasonal soil water extraction. In conclusion, one-irrigation with a medium-sized spike cultivar JM22 could be a useful strategy to maintain yield and high WUE in extremely late-sown winter wheat at a high seeding rate in the NCP.

Published
2016
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7. Global Nitrogen Needs to Improve Wheat Yield Under Climate Change

Author

Pierre Martre, Sibylle Dueri, Jose Rafael Guarin, Frank Ewert, Heidi Webber, Daniele Calderini, Gemma Molero, Matthew Reynolds, Daniel Miralles, Guillermo Garcia, Hamish Brown, Mike George, Rob Craigie, Jean-Pierre Cohan, Jean-Charles Deswarte, Gustavo Slafer, Francesco Giunta, Davide Cammarano, Roberto Ferrise, Thomas Gaiser, Yujing Gao, Zvi Hochman, Gerrit Hoogenboom, Leslie A. Hunt, Kurt C. Kersebaum, Claas Nendel, Gloria Padovan, Alex C. Ruane, Amit Kumar Srivastava, Tommaso Stella, Iwan Supit, Peter Thorburn, Enli Wang, Joost Wolf, Chuang Zhao, Zhigan Zhao, and Senthold Asseng

Subjects
Meteorology and Climatology
Abstract

Increasing global food demand will require more food production without further exceeding the planetary boundaries, while at the same time adapting to climate change. We used an ensemble of wheat simulation models, with improved yield traits from the highest-yielding wheat genotypes to quantify potential yield gains and associated N requirements. This was explored for current and climate change scenarios across representative sites of major world wheat producing regions. The improved yield traits increased yield by 16% with current N fertilizer applications under both current climate and mid-century climate change scenarios. To achieve the full yield potential, a 52% increase in global average yield under a mid-century high warming climate scenario (RCP8.5), fertilizer use would need to increase fourfold over current use, which would unavoidably lead to higher environmental impacts from wheat production. Our results show the need to improve soil N availability and N use efficiency, along with yield potential.

Published
2024

8. Vernalisation and photoperiod responses of diverse wheat genotypes

Author

Maxwell T. Bloomfield, Corinne Celestina, James R. Hunt, Neil Huth, Bangyou Zheng, Hamish Brown, Zhigan Zhao, Enli Wang, Katia Stefanova, Jessica Hyles, Tina Rathjen, and Ben Trevaskis

Subjects
Plant Science, Agronomy and Crop Science
Abstract

Context Wheat (Triticum aestivum L.) adaptation is highly dependent on crop lifecycle duration, particularly the time at which flowering occurs in a specific environment. Frost, low solar radiation, heat and drought can significantly reduce yield if a crop flowers too early or late. Wheat genotypes have different lifecycle durations determined by plant responses to temperature (thermal time accumulation and vernalisation) and photoperiod. These responses are largely controlled by five phenology genes (two PPD1 and three VRN1 genes). Advances in crop phenology modelling suggest that flowering time under field conditions could be accurately predicted with parameters derived from photoperiod and vernalisation responses obtained in controlled environments. Aims This study quantified photoperiod and vernalisation responses of 69 Australian wheat genotypes selected for diversity at the PPD1 and VRN1 loci. Methods Spring and winter genotypes were grown in four controlled environments at a constant temperature of 22°C with photoperiod (17 or 8 h) and vernalisation (0 or 8 weeks) treatments as factors. Key results Thermal time from coleoptile emergence to flowering in spring genotypes was typically decreased more by long photoperiod than by vernalisation; the opposite was true for winter genotypes. Spring genotypes that were sensitive to vernalisation contained a sensitive allele at the Vrn-A1 locus. Conclusions There is large diversity in phenological responses of wheat genotypes to photoperiod and vernalisation, including among those with matching multi-locus genotype. Implications Data from this study will be used to parameterise and test a wheat phenology model in a future study.

Published
2023
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10. The nitrogen price of improved wheat yield under climate change

Author

Pierre Martre, Sibylle Dueri, Jose Guarin, F Ewert, Heidi Webber, Daniel Calderini, Gemma Molero, Matthew Reynolds, Daniel Miralles, Guillermo Garcia, Hamish Brown, Mike George, Rob Craigie, Jean-Pierre Cohan, Jean-Charles Deswarte, Gustavo Slafer, F Giunta, Davide Cammarano, Roberto Ferrise, Thomas GAISER, Yujing Gao, Zvi Hochman, Gerrit Hoogenboom, Leslie A Hunt, Kurt Kersebaum, Claas Nendel, Gloria Padovan, Alex Ruane, Tommaso Stella, Iwan Supit, Amit Srivast, Peter Thorburn, Enli Wang, Joost Wolf, Chuang Zhao, Zhigan Zhao, and Senthold Asseng

Abstract

Increasing global food demand will require more food production without further exceeding the planetary boundaries, while at the same time adapting to climate change. We used an ensemble of wheat simulation models, with sink-source improved traits from the highest-yielding wheat genotypes to quantify potential yield gains and associated N requirements. This was explored for current and climate change scenarios across representative sites of major world wheat producing regions. The sink-source traits emerged as climate neutral with 16% yield increase with current N fertilizer applications under both current climate and mid-century climate change scenarios. To achieve the full yield potential, a 52% increase in global average yield under a mid-century RCP8.5 climate scenario, fertilizer use would need to increase fourfold over current use, which would unavoidably lead to higher environmental impacts from wheat production. Our results show the need to improve soil N availability and N use efficiency, along with yield potential.

Published
2023
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12. Scales of development for wheat and barley specific to either single culms or a population of culms

Author

Corinne Celestina, James Hunt, Hamish Brown, Neil Huth, Mariana Andreucci, Zvi Hochman, Maxwell Bloomfield, Kenton Porker, Melissa McCallum, Felicity Harris, Mary Matthews, Ben Biddulph, Ghazwan Al Yaseri, Dion Nicol, Jessica Hyles, Enli Wang, Bangyou Zheng, Zhigan Zhao, and Michele Kohout

Subjects
Soil Science, Plant Science, Agronomy and Crop Science
Published
2023
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14. Understanding soil water effects on nitrogen release from controlled‐release fertilizers

Author

Kirsten Verburg, Jacinta M. Poole, Zhigan Zhao, Tim H. Muster, Mike Wong, Gordon McLachlan, Kathy Wittwer-Schmid, James I. Mardel, and Keith L. Bristow

Subjects
Absorption of water, Soil Science, chemistry.chemical_element, Soil science, 04 agricultural and veterinary sciences, 010501 environmental sciences, complex mixtures, 01 natural sciences, Nitrogen, Controlled release, Permeability (earth sciences), Soil core, chemistry.chemical_compound, Water potential, chemistry, Soil water, 040103 agronomy & agriculture, Urea, 0401 agriculture, forestry, and fisheries, Environmental science, 0105 earth and related environmental sciences
Abstract

Controlled‐release fertilizers (CRFs) have the potential to deliver crop production and environmental benefits through better control of applied nitrogen (N) in cropping systems. Whereas N release from CRFs can be impacted by several factors, there has been a widely held view that soil water has little effect on N release from polymer‐coated CRFs. Past research has often studied the soil water effect as a function of soil water content. This limits the transferability of results. In this study we measured N release from a polymer‐coated urea (PCU) and a polymer‐sulfur–coated urea (PSCU) using undisturbed soil cores at set matric potentials. Soil matric potential had a significant effect on N release from PCU. Release at −1,000 kPa was delayed by up to 30 d compared with −10 kPa. Optical stereo microscopy clarified that this was linked to differences in the rate of water absorption. Theoretical considerations demonstrate that these relatively large differences could not be explained by the effect of soil matric potential on vapor flow. It is possible that soil matric potential interacted with the properties of the coating to change its permeability or the involvement of liquid flow. The effect of soil water on N release from PSCU was less clear. The magnitude of the soil water effect is, therefore, product‐specific and dependent on coating properties. The soil matric potential provided a consistent description of release patterns between soils with contrasting soil water retention characteristics. Soil water effects should hence be studied as a function of soil matric potential.

Published
2021
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15. The Uncertainty of Crop Yield Projections Is Reduced by Improved Temperature Response Functions

Author

Wang, Enli, Martre, Pierre, Zhigan, Zhao, Ewert, Frank, Maiorano, Andrea, Rotter, Reimund P, Kimball, Bruce A, Ottman, Michael J, Wall, Gerard W, White, Jeffrey, W, Reynolds, Matthew P, Alderman, Phillip D, Aggarwal, Pramod K, Anothai, Jakarat, Basso, Bruno, Biernath, Christian, Cammarano, Davide, Challinor, Andrew J, De Sanctis, Giacomo, Doltra, Jordi, Fereres, Elias, Garcia-Vila, Margarita, Gayler, Sebastian, Hoogenboom, Gerrit, Hunt, Leslie A, Izaurralde, Roberto C, Jabloun, Mohamed, Jones, Curtis D, Kersebaum, Kurt C, Koehler, Ann-Kristin, Liu, Leilei, Muller, Christoph, Kumar, Naresh Soora, Nendel, Claas, O'Leary, Garry, Oleson, Jorgen E, Palosuo, Taru, Priesack, Eckhart, Rezaei, Ehsan Eyshi, Ripoche, Dominique, Ruane, Alex C, Semenov, Mikhail A, Scherbak, Lurii, Stockle, Claudio, Stratonovitch, Pierre, Streck, Thilo, Supit, Iwan, Tao, Fulu, Thorburn, Peter, Waha, Katharina, Wallach, Daniel, Wang, Zhimin, Wolf, Joost, Zhu, Yan, and Asseng, Senthold

Subjects
Meteorology And Climatology
Published
2017

17. Management and spatial resolution effects on yield and water balance at regional scale in crop models

Author

Eric Casellas, Edwin Haas, Frank Ewert, Matthias Kuhnert, Thomas Gaiser, Jacques-Eric Bergez, Xenia Specka, Zhigan Zhao, Jagadeesh Yeluripati, Helene Raynal, Ganga Ram Maharjan, Luca Doro, Enli Wang, Ana Villa, Giacomo Trombi, Holger Hoffmann, Marco Bindi, Julie Constantin, Balász Grosz, Claas Nendel, Kurt Christian Kersebaum, Lutz Weihermüller, Henrik Eckersten, Steffen Klatt, Pier Paolo Roggero, Elisabet Lewan, Marco Moriondo, AGroécologie, Innovations, teRritoires (AGIR), Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), Institut National de la Recherche Agronomique (INRA), Crop Science Group, INRES, University of Bonn, Partenaires INRAE, Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, Desertification Research Centre and Department of Agricultural Sciences, University of Sassari, Texas A&M University [College Station], Department of Crop Production Ecology, Swedish University of Agricultural Sciences (SLU), Institute of Meteorology and Climate Research – Atmospheric Environmental Research, Karlsruhe Institute of Technology, Leibniz Centre for Agricultural Landscape Research (ZALF), Information and Computational Sciences Group, The James Hutton Insitite, Carigiebuckler, Departement of Soil and Environment, CSIRO Land and Water, ACT, and Institute of Bio- & Geosciences, Agrosphere (IBG-3)

Subjects
0106 biological sciences, Atmospheric Science, WINTER-WHEAT YIELD, 010504 meteorology & atmospheric sciences, ASSIMILATION, [SDV]Life Sciences [q-bio], Agricultural engineering, 01 natural sciences, Scaling, [SHS]Humanities and Social Sciences, SOWING DATES, CARBON, Crop, Aggregation, Water balance, DATA AGGREGATION, IRRIGATION, Evapotranspiration, ddc:550, [INFO]Computer Science [cs], [MATH]Mathematics [math], Drainage, Spatial analysis, AREA INDEX, 0105 earth and related environmental sciences, 2. Zero hunger, Global and Planetary Change, PRODUCTIVITY, Crop yield, Sowing, Forestry, 15. Life on land, Adaptive management, [SDE]Environmental Sciences, GROWTH, Environmental science, Decision rules, INPUT DATA, Agronomy and Crop Science, 010606 plant biology & botany
Abstract

International audience; Due to the more frequent use of crop models at regional and national scale, the effects of spatial data input resolution have gained increased attention. However, little is known about the influence of variability in crop management on model outputs. A constant and uniform crop management is often considered over the simulated area and period. This study determines the influence of crop management adapted to climatic conditions and input data resolution on regional-scale outputs of crop models. For this purpose, winter wheat and maize were simulated over 30 years with spatially and temporally uniform management or adaptive management for North Rhine-Westphalia ((similar to)34 083 km(2)), Germany. Adaptive management to local climatic conditions was used for 1) sowing date, 2) N fertilization dates, 3) N amounts, and 4) crop cycle length. Therefore, the models were applied with four different management sets for each crop. Input data for climate, soil and management were selected at five resolutions, from 1 x 1 km to 100 x 100 km grid size. Overall, 11 crop models were used to predict regional mean crop yield, actual evapotranspiration, and drainage. Adaptive management had little effect (< 10% difference) on the 30-year mean of the three output variables for most models and did not depend on soil, climate, and management resolution. Nevertheless, the effect was substantial for certain models, up to 31% on yield, 27% on evapotranspiration, and 12% on drainage compared to the uniform management reference. In general, effects were stronger on yield than on evapotranspiration and drainage, which had little sensitivity to changes in management. Scaling effects were generally lower than management effects on yield and evapotranspiration as opposed to drainage. Despite this trend, sensitivity to management and scaling varied greatly among the models. At the annual scale, effects were stronger in certain years, particularly the management effect on yield. These results imply that depending on the model, the representation of management should be carefully chosen, particularly when simulating yields and for predictions on annual scale.

Published
2019
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18. Modelling impact of early vigour on wheat yield in dryland regions

Author

Scott Chapman, Enli Wang, Zhigan Zhao, Bangyou Zheng, and Greg J. Rebetzke

Subjects
0106 biological sciences, 0301 basic medicine, Genotype, Physiology, Triticum aestivum, Growing season, Plant Science, Biology, 01 natural sciences, Available water capacity, 03 medical and health sciences, establishment, Yield (wine), Water-use efficiency, Triticum, Biomass (ecology), Crop yield, Australia, food and beverages, Research Papers, Plant Leaves, 030104 developmental biology, Agronomy, Soil water, Trait, environment, management, APSIM next generation, 010606 plant biology & botany
Abstract

Early vigour was simulated to increase wheat yield due to increased biomass and grain number at wetter sites, but there was no benefit at dryer sites., Early vigour, or faster early leaf area development, has been considered an important trait for rainfed wheat in dryland regions such as Australia. However, early vigour is a genetically complex trait, and results from field experiments have been highly variable. Whether early vigour can lead to improved water use efficiency and crop yields is strongly dependent on climate and management conditions across the entire growing season. Here, we present a modelling framework for simulating the impact of early vigour on wheat growth and yield at eight sites representing the major climate types in Australia. On a typical soil with plant available water capacity (PAWC) of 147 mm, simulated yield increase with early vigour associated with larger seed size was on average 4% higher compared with normal vigour wheat. Early vigour through selection of doubled early leaf sizes could increase yield by 16%. Increase in yield was mainly from increase in biomass and grain number, and was reduced at sites with seasonal rainfall plus initial soil water

Published
2019
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19. Modelling soybean and maize growth and grain yield in strip intercropping systems with different row configurations

Author

Xin Liu, Zhigan Zhao, Neil Huth, Feng Yang, Wanzhuo Gong, Taiwen Yong, Xiaochun Wang, Liang Yu, Wopke van der Werf, Tian Pu, Wenyu Yang, Jiang Liu, Junbo Du, Enli Wang, Weiguo Liu, Chunyan Liu, Di He, and Yushan Wu

Subjects
0106 biological sciences, Canopy, Soil Science, Biomass, Agricultural engineering, 01 natural sciences, Modelling, Yield (wine), APSIM, Mathematics, 2. Zero hunger, Light interception, biology, Sowing, Intercropping, 04 agricultural and veterinary sciences, 15. Life on land, biology.organism_classification, PE&RC, Efficiency, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Grain yield, Interception, Crop and Weed Ecology, Soybean, Agronomy and Crop Science, 010606 plant biology & botany
Abstract

Intercropping of two or more crop species increases the efficiency of resource use and often produces a greater yield per unit land area. The relative efficiency of intercropping depends on row configuration, but there is a shortage of modelling-based evaluation of alternative intercropping options due to the inadequacy of standard process-based crop models to simulate resource capture, growth and yield formation when the canopy is spatially structured in strips. We implemented a light interception model for strip crops into the APSIM Classic model and combined it with a quasi-Bayesian approach to derive the model parameters to simulate crop growth and grain yield in maize-soybean strip intercropping. We used 4 years of field data for 5 different row configurations to derive key model parameters for simulation of light interception, LAI dynamics, biomass growth and grain yield of maize and soybean intercrops. Key model parameters (e.g. RUE, k etc.) were found to change with row-spacing and configuration, posing challenges to simulate different configurations with a single parameter set. The potential ranges of these key parameters were derived by constraining the model to observed data. The model can be potentially used to evaluate impact of planting configurations on productivity of strip intercropping systems, but the variability of key model parameters among configuration treatments calls for further in-depth research to improve modelling physiology of strip intercrops.

Published
2021
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20. Climate change impact and adaptation for wheat protein

Author

Mohamed Jabloun, Pierre Martre, Garry O'Leary, Dominique Ripoche, Bruno Basso, Pramod K. Aggarwal, Daniel Wallach, Matthew P. Reynolds, Marijn van der Velde, John R. Porter, Heidi Webber, Enli Wang, Frank Ewert, Joost Wolf, Christian Klein, Belay T. Kassie, Christian Biernath, Margarita Garcia-Vila, M. Ali Babar, Pierre Stratonovitch, Yujing Gao, Glenn J. Fitzgerald, Davide Cammarano, Bing Liu, Peter J. Thorburn, Fulu Tao, Andrew J. Challinor, Reimund P. Rötter, Christine Girousse, Zhigan Zhao, Christoph Müller, Ann-Kristin Koehler, Jørgen E. Olesen, Elias Fereres, Iwan Supit, Andrea Maiorano, Marco Bindi, Sebastian Gayler, Kurt Christian Kersebaum, Giacomo De Sanctis, Alex C. Ruane, Rosella Motzo, Juraj Balkovic, Manuel Montesino San Martin, Roberto Ferrise, Mikhail A. Semenov, Claudio O. Stöckle, Soora Naresh Kumar, Gerrit Hoogenboom, Benjamin Dumont, Ehsan Eyshi Rezaei, Mukhtar Ahmed, Senthold Asseng, Thilo Streck, Yan Zhu, R. Cesar Izaurralde, Katharina Waha, Ahmed M. S. Kheir, Taru Palosuo, Liujun Xiao, Sara Minoli, Eckart Priesack, Heidi Horan, Curtis D. Jones, Francesco Giunta, Zhao Zhang, Claas Nendel, International Food Policy Research Institute (US), CGIAR (France), European Commission, Institut National de la Recherche Agronomique (France), National Natural Science Foundation of China, Federal Ministry of Food and Agriculture (Germany), Biotechnology and Biological Sciences Research Council (UK), Innovation Fund Denmark, China Scholarship Council, Ministero delle Politiche Agricole Alimentari e Forestali, Academy of Finland, Finnish Ministry of Agriculture and Forestry, Federal Ministry of Education and Research (Germany), Department of Agriculture and Water Resources (Australia), University of Melbourne, Grains Research and Development Corporation (Australia), National Institute of Food and Agriculture (US), German Research Foundation, Gorgan University, Department of Agricultural and Biological Engineering [Gainesville] (UF|ABE), Institute of Food and Agricultural Sciences [Gainesville] (UF|IFAS), University of Florida [Gainesville] (UF)-University of Florida [Gainesville] (UF), Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), European Food Safety Authority = Autorité européenne de sécurité des aliments, Tropical Plant Production and Agricultural Systems Modelling (TROPAGS), Georg-August-University = Georg-August-Universität Göttingen, Centre for Biodiversity and Sustainable Land-use [University of Göttingen] (CBL), Department of Economic Drt and Resources, Grains Innovation Park, Agriculture Victoria Research, Department of Economic Development, Jobs, Transport and Resources, Faculty of Veterinary and Agricultural Science [Melbourne], Génétique Diversité et Ecophysiologie des Céréales (GDEC), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020]), Department of Agricultural Sciences, University of Naples Federico II = Università degli studi di Napoli Federico II, World Food Crops Breeding, Department of Agronomy, IFAS, University of Florida [Gainesville] (UF), International Maize and Wheat Improvement Center (CIMMYT), Consultative Group on International Agricultural Research [CGIAR] (CGIAR), Soils, Water and Environment Research Institute, Agricultural Research Center (ARC), Agriculture and Food, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), International Maize and Wheat Improvement Centre [Inde] (CIMMYT), Consultative Group on International Agricultural Research [CGIAR] (CGIAR)-Consultative Group on International Agricultural Research [CGIAR] (CGIAR), Biological Systems Engineering, University of Wisconsin-Madison, Department of Agronomy, University of El-Tarf, Ecosystem Services and Management Program, International Institute for Applied Systems Analysis (IIASA), Department of Soil Science, Faculty of Natural Sciences, Comenius University in Bratislava, W. K. Kellogg Biological Station (KBS), Michigan State University [East Lansing], Michigan State University System-Michigan State University System, Department of Earth and Environmental Sciences [Ann Arbor], University of Michigan [Ann Arbor], University of Michigan System-University of Michigan System, Institute of Biochemical Plant Pathology, Research Center for Environmental Health, Helmholtz Zentrum München = German Research Center for Environmental Health, Department of Agri‐food Production and Environmental Sciences (DISPAA), Università degli Studi di Firenze = University of Florence (UniFI), The James Hutton Institute, Institute for Climate and Atmospheric Science [Leeds] (ICAS), School of Earth and Environment [Leeds] (SEE), University of Leeds-University of Leeds, Collaborative Research Program from CGIAR and Future Earth on Climate Change, Agriculture and Food Security (CCAFS), International Center for Tropical Agriculture, GMO Unit, European Food Safety Authority, Department Terra & AgroBioChem, Gembloux Agro‐Bio Tech, Université de Liège, Institute of Crop Science and Resource Conservation [Bonn] (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, Department of Crop Sciences, Instituto de Agricultura Sostenible - Institute for Sustainable Agriculture (IAS CSIC), Consejo Superior de Investigaciones Científicas [Madrid] (CSIC), Institute of Soil Science and Land Evaluation, University of Hohenheim, Food Systems Institute [Gainesville] (UF|IFAS), Department of Geographical Sciences, College Park, University of Maryland [College Park], University of Maryland System-University of Maryland System, Texas A and M AgriLife Research, Texas A&M University System, Department of Agroecology, Aarhus University [Aarhus], Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Institute of Biochemical Plant Pathology [Neuherberg], German Research Center for Environmental Health - Helmholtz Center München (GmbH), National Engineering and Technology Center for Information Agriculture, Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricutural University, Member of the Leibniz Association, Potsdam Institute for Climate Impact Research (PIK), Department of Plant and Environmental Sciences [Copenhagen], Faculty of Science [Copenhagen], University of Copenhagen = Københavns Universitet (UCPH)-University of Copenhagen = Københavns Universitet (UCPH), Centre for Environment Science and Climate Resilient Agriculture [New Delhi], Indian Agricultural Research Institute (IARI), Natural Resources Institute Finland (LUKE), Fonctionnement et conduite des systèmes de culture tropicaux et méditerranéens (UMR SYSTEM), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre International de Hautes Etudes Agronomiques Méditerranéennes - Institut Agronomique Méditerranéen de Montpellier (CIHEAM-IAMM), Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), University of Lincoln, Agroclim (AGROCLIM), Institut National de la Recherche Agronomique (INRA), Rothamsted Research, Biotechnology and Biological Sciences Research Council (BBSRC), Water & Food and Water Systems & Global Change Group, Wageningen University and Research [Wageningen] (WUR), Institute of geographical sciences and natural resources research [CAS] (IGSNRR), Chinese Academy of Sciences [Beijing] (CAS), Joint Research Centre (IPTS), Commission Européenne, AGroécologie, Innovations, teRritoires (AGIR), Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT), CSIRO Agriculture and Food (CSIRO), Plant Production Systems, State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University (BNU), Department of Agronomy and Biotechnology, China Agricultural University (CAU), National Research Foundation for the Doctoral Program of Higher Education of China, Grant/Award Number: 20120097110042, International Food Policy, European Project: 267196,EC:FP7:PEOPLE,FP7-PEOPLE-2010-COFUND,AGREENSKILLS(2012), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Georg-August-University [Göttingen], Centre of Biodiversity and Sustainable Land Use (CBL), University of Göttingen - Georg-August-Universität Göttingen, University of Naples Federico II, Helmholtz-Zentrum München (HZM), Universtiy of Florence, University of Copenhagen = Københavns Universitet (KU)-University of Copenhagen = Københavns Universitet (KU), Natural Resources Institute Finland, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Wageningen University, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences (CAS), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Agricultural & Biological Engineering Department, University of Florida [Gainesville], Georg-August-Universität Göttingen, University of Goettingen, Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP), Research Program on Climate Change, Agriculture and Food Security, BISA‐CIMMYT, Consultative Group on International Agricultural Research (CGIAR), Comenius University [Bratislava], Helmholtz Zentrum München, Institute of Crop Science and Resource Conservation INRES, University of Bonn, IAS‐CSIC, Universidad de Cordoba, Institute for Sustainable Food Systems, Texas A&M AgriLife Research and Extension Center, Aarhus University, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre International de Hautes Etudes Agronomiques Méditerranéennes - Institut Agronomique Méditerranéen de Montpellier (CIHEAM-IAMM), Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), UE Agroclim (UE AGROCLIM), UMR : AGroécologie, Innovations, TeRritoires, Ecole Nationale Supérieure Agronomique de Toulouse, Wageningen University and Research Center (WUR), Beijing Normal University, and China Agricultural University

Subjects
[SDV.SA]Life Sciences [q-bio]/Agricultural sciences, 0106 biological sciences, 010504 meteorology & atmospheric sciences, Water en Voedsel, 01 natural sciences, grain protein, adaptation au milieu, climate change adaptation, climate change impact, food security, wheat, Co2 concentration, adaptation to the environment, Triticum, General Environmental Science, 2. Zero hunger, changement climatique, Global and Planetary Change, Food security, Ecology, Temperature, food and beverages, Adaptation, Physiological, Droughts, Nitrogen, Climate Change, Climate change, 010603 evolutionary biology, blé, Crop production, Food Quality, Grain quality, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, Environmental Chemistry, Grain Proteins, global change, 0105 earth and related environmental sciences, WIMEK, Water and Food, Global change, Carbon Dioxide, Models, Theoretical, 15. Life on land, Agronomy, 13. Climate action, Grain yield, Environmental science, Water Systems and Global Change, Protein concentration
Abstract

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 32‐multi‐model 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 low‐rainfall 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., B.L received support from the International Food Policy Research Institute (IFPRI) through the Global Futures and Strategic Foresight project, the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) and the CGIAR Research Program on Wheat. A.M. received support from the EU Marie Curie FP7 COFUND People Programme, through an AgreenSkills fellowship under grant agreement no. PCOFUND‐GA‐2010‐267196. PM, A.M., D.R., and D.W. acknowledge support from the FACCE JPI MACSUR project (031A103B) through the metaprogram Adaptation of Agriculture and Forests to Climate Change (AAFCC) of the French National Institute for Agricultural Research (INRA). L.X. and Y.Z. were supported by the National High‐Tech Research and Development Program of China (2013AA100404), the National Natural Science Foundation of China (31271616), the National Research Foundation for the Doctoral Program of Higher Education of China (20120097110042), and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD). F.T. and Z.Z. were supported by the National Natural Science Foundation of China (41571088, 41571493 and 31561143003). R.R. received support from the German Ministry for Research and Education (BMBF) through project SPACES‐LLL. Rothamsted Research receives support from the Biotechnology and Biological Sciences Research Council (BBSRC) Designing Future Wheat programme [BB/P016855/1]. M.J. and J.E.O. were supported by Innovation Fund Denmark through the MACSUR project. L.X. and Y.G. acknowledge support from the China Scholarship Council. M.B and R.F. were funded by JPI FACCE MACSUR2 through the Italian Ministry for Agricultural, Food and Forestry Policies and thank A. Soltani from Gorgan Univ. of Agric. Sci. & Natur. Resour. for his support. R.P.R., T.P., and F.T. received financial support from the FACCE MACSUR project funded through the Finnish Ministry of Agriculture and Forestry (MMM) and from the Academy of Finland through the projects NORFASYS (decision nos. 268277 and 292944) and PLUMES (decision nos. 277403 and 292836). K.C.K. and C.N. received support from the German Ministry for Research and Education (BMBF) within the FACCE JPI MACSUR project. S.M. and C.M. acknowledge financial support from the MACMIT project (01LN1317A) funded through BMBF. G.J.O. and G.J.F. acknowledge support from the Victorian Department of Economic Development, Jobs, Transport and Resources, the Australian Department of Agriculture and Water Resources, The University of Melbourne and the Grains Research Development Corporation, Australia. P.K.A.'s work was implemented as part of the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), which is carried out with support from the CGIAR Trust Fund and through bilateral funding agreements. For details please visit https://ccafs.cgiar.org/donors. The views expressed in this document cannot be taken to reflect the official opinions of these organizations.. B.B. received financial support from USDA NIFA‐Water Cap Award 2015‐68007‐23133. F.E. acknowledges support from the FACCE JPI MACSUR project through the German Federal Ministry of Food and Agriculture (2815ERA01J) and from the German Science Foundation (project EW 119/5‐1).

Published
2019
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21. Performance of different cropping systems across precipitation gradient in North China Plain

Author

Xin Qin, Enli Wang, Zhimin Wang, and Zhigan Zhao

Subjects
0106 biological sciences, Atmospheric Science, Global and Planetary Change, Irrigation, business.industry, Crop yield, Forestry, 04 agricultural and veterinary sciences, Multiple cropping, Water pricing, 01 natural sciences, Agriculture, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, Precipitation, Water resource management, business, Agronomy and Crop Science, Cropping, Groundwater, 010606 plant biology & botany
Abstract

Overuse of groundwater for irrigation under the current high yielding wheat-maize double cropping system has led to quick depletion of groundwater resources in North China Plain (NCP). Experimental work conducted for a relatively short period (several years) to explore water-saving cropping systems at limited sites offers useful, but limited insights regarding future sustainable cropping systems in NCP to balance productivity and groundwater usage. An understanding of the performance of various alternative cropping systems under the long-term inter-annual climate variability across the precipitation gradient in NCP is needed, but yet lacking. Our study provides a systematic assessment on productivity and environmental impact of 5 alternative cropping systems along a precipitation gradient across NCP, through combination of cropping systems modelling and scenario analysis. Our results show that the groundwater neutral cropping systems change from single summer maize, single spring maize, to wheat-maize double rotation from dry to wet areas across the precipitation gradient. Water restriction will inevitably lead to crop yield reduction (by 2–9 t/ha) and water saving depending on the choice of alternative cropping systems and location precipitation. Reduced water availability for irrigation will also reduce the amount of mineral nitrogen required and the associated N loss to the atmosphere and to the ground water. The reduction on N input was estimated to be 20–190 kg N/ha/year and N loss was 5–35 kg N/ha/year for the most productive cropping systems under rainfed conditions. A value for groundwater use is also estimated based on the extra grain yield produced with irrigation. The results provide the scientific basis for the design of future sustainable cropping systems, and future policies for water pricing and agricultural inputs (water and nitrogen).

Published
2018
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22. Improving water use efficiency and grain yield of winter wheat by optimizing irrigations in the North China Plain

Author

Zhigan Zhao, Jinpeng Li, Xuexin Xu, Meng Zhang, Yinghua Zhang, Zuqiang Liu, Shun-Li Zhou, and Zhimin Wang

Subjects
0106 biological sciences, Irrigation, Soil Science, Sowing, 04 agricultural and veterinary sciences, 01 natural sciences, Anthesis, Agronomy, Evapotranspiration, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Leaf area index, Water-use efficiency, Agronomy and Crop Science, Water content, Water use, 010606 plant biology & botany, Mathematics
Abstract

Achieving the combination of high water use efficiency (WUE) and high yield is very important for the sustainable development of wheat production in the North China Plain (NCP). For this study, we investigated how to optimize timing of two irrigations to improve winter wheat grain yield and WUE under field conditions. No-irrigation after sowing (W0) as a control, and six irrigation treatments as follows: irrigation of 75 mm each at late tillering and booting (TB), at late tillering and anthesis (TA); at late tillering and medium milk (TM), at jointing and anthesis (JA), at jointing and medium milk (JM) and at booting and medium milk (BM). Experiments were conducted between the 2014–2016 growing seasons. In all the treatments, JA achieved the highest grain yield (9,267.6 kg ha−1) and WUE (20.2 kg ha−1 mm−1). Compared with TB, TA and TM, JA coordinated pre- and post-anthesis water use, reduced pre-anthesis and total evapotranspiration (ET), and increased post-anthesis water use amount and ratio; JA reduced biomass at anthesis, but optimized allocation of assimilation, increased spike partitioning index and maintained high fruiting efficiency, and thus obtained the highest grain number per m2 (GN, 23.7 103 m−2). Meanwhile, JA optimized crop characteristics with appropriate leaf area index (LAI), delayed leaf senescence, extended grain filling duration by 1–3 days, then increased biomass post-anthesis and harvest index (HI). Compared with JM and BM, JA increased GN, biomass post-anthesis and grain yield as well. These results demonstrated that irrigation at jointing and anthesis could improve grain yield and WUE by increasing biomass post-anthesis, HI and GN. Therefore, we propose that under adequate soil moisture conditions before sowing, two irrigations at jointing and anthesis with 150 mm irrigation amount is the optimal limited irrigation practice for wheat production in NCP.

Published
2018
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23. Making sense of cosmic-ray soil moisture measurements and eddy covariance data with regard to crop water use and field water balance

Author

Caleb Smith, Ben Macdonald, James R. Hunt, Peter Isaac, Hongtao Xing, Enli Wang, O. T. Denmead, Steve Zeglin, and Zhigan Zhao

Subjects
Nutrient cycle, 0208 environmental biotechnology, Eddy covariance, Soil Science, Soil science, 02 engineering and technology, 020801 environmental engineering, Water balance, Evapotranspiration, Greenhouse gas, Soil water, Environmental science, Leaching (agriculture), Agronomy and Crop Science, Water content, Earth-Surface Processes, Water Science and Technology
Abstract

Changes in soil moisture influence the water availability to crop plants and soil ecological processes like carbon and nutrient cycling, impacting on crop productivity and environmental performance (greenhouse gas emissions, leaching) of agricultural systems. While traditional soil moisture measurements are done using point-based methods, the recent development of the cosmic-ray soil moisture neutron sensor (CRNS) offers the opportunity to measure soil water at the field scale. However, due to its shallow (

Published
2018
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24. Does maize hybrid intercropping increase yield due to border effects?

Author

Yunqi Wang, Shun-Li Zhou, Zhimin Wang, Jinpeng Li, Yinghua Zhang, Zhigan Zhao, and Meng Zhang

Subjects
0106 biological sciences, biology, Crop yield, Soil Science, Biomass, Intercropping, 04 agricultural and veterinary sciences, Interspecific competition, biology.organism_classification, 01 natural sciences, Agronomy, Yield (wine), 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Grain yield, Cultivar, Agronomy and Crop Science, Cropping, 010606 plant biology & botany, Mathematics
Abstract

In China, maize hybrid intercropping has been practiced by farmers for its higher grain yield, but the underlying mechanism remains unclear. Here, we present field experimental data on yield and yield components of maize hybrid cultivars under intercropping system. These cultivars differ in plant heights (i.e. Zhengdan958 with plant height of 270 cm, Xianyu335 of 325 cm, Huayi5 of 330 cm, and Xundan20 of 273 cm) and were planted in 8 different intercropping configurations, along with 6 sole cropping treatments in two years. Yield advantages of 0.15–1.7 t ha−1 were detected under maize hybrid intercropping treatments as compared to sole cropping treatments in one year. Seasonal climate variation and hybrid configuration influenced the interspecific interactions between hybrid cultivars, resulting in different increase of grain yield. Cultivar Zhengdan958 had a lower grain yield per plant and thousand kernel weight under intercropping comparing to sole cropping, while cultivars Xianyu335 and Huayi5 under intercropping increased grain yield per plant and thousand kernel weight, resulting in a land equivalent ratio greater than 1. Additionally, the land equivalent ratio was positively and significantly (P

Published
2017
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25. Modelling sugarcane nitrogen uptake patterns to inform design of controlled release fertiliser for synchrony of N supply and demand

Author

Kirsten Verburg, Zhigan Zhao, and Neil Huth

Subjects
0106 biological sciences, Irrigation, Growing region, Lag, food and beverages, Soil Science, Sowing, chemistry.chemical_element, 04 agricultural and veterinary sciences, Subtropics, Biology, 01 natural sciences, Nitrogen, Ratooning, Crop, Agronomy, chemistry, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Agronomy and Crop Science, 010606 plant biology & botany
Abstract

The use of controlled release fertilisers (CRF) is being promoted in the Australian sugarcane industry to improve nitrogen (N) use efficiency and reduce N losses through better matching of N release with crop N demand. Little is known, however, about the required synchrony due to limited information on both N uptake patterns of sugarcane and N release patterns from CRF under different crop growing conditions. This paper uses APSIM scenario modelling to characterise N uptake patterns of sugarcane plant and ratoon crops in response to seasonal variability and different climatic and management conditions (e.g. level of water and N input, time of planting/ratooning) at five sites within the Australian sugarcane growing region. The results showed considerable variations in crop N uptake patterns across seasons, sites and under different management scenarios. However, for a given site, crop N uptake patterns during the early crop growth stage varied little between seasons (

Published
2017
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26. Value of groundwater used for producing extra grain in North China Plain

Author

Zhigan Zhao, Chao Chen, Xin Qin, Zhimin Wang, Hecang Zang, and Yinghua Zhang

Subjects
Irrigation, 010504 meteorology & atmospheric sciences, Agroforestry, Deficit irrigation, Irrigation statistics, Soil Science, 04 agricultural and veterinary sciences, Water pricing, 01 natural sciences, Water conservation, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, Water-use efficiency, Water resource management, Agronomy and Crop Science, Productivity, Groundwater, 0105 earth and related environmental sciences
Abstract

Irrigation has led to increased crop productivity in the North China Plain (NCP) in the past three decades, but also caused quick depletion of groundwater resources with potentially severe hydrological consequences. Future policy development needs to balance crop production and groundwater use for increased sustainability. This requires an understanding of how crop production can be impacted by reduced irrigation and the value of the groundwater used for producing extra grains across NCP. We used cropping systems modelling to assess how the productivity of the wheat-maize system are influenced by spatial climate variability and changes in irrigation water supply across a north-south rainfall gradient in NCP. Our results show that irrigation has minimised the north-south difference in crop productivity, leading to a production gain as large as 9 t/ha/year grain with a total value of up to 19,000 CNY/ha/year in the north. The high water use efficiency (WUE) of irrigation water in the north was similar to the WUE of natural rainfall in the south, allowing the farmers at dry sites to obtain very similar production value to those at the wet sites. The value of groundwater used for producing the extra grains was estimated to be 1.8–8.6 CNY m−3 water. The results provide the scientific basis for the design of future policies for water pricing and sustainable agricultural development in NCP.

Published
2017
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27. Global wheat production with 1.5 and 2.0°C above pre‐industrial warming

Author

Mónica Espadafor, Bing Liu, Frank Ewert, Mukhtar Ahmed, Liujun Xiao, Thilo Streck, Senthold Asseng, Soora Naresh Kumar, Gerrit Hoogenboom, John R. Porter, Sara Minoli, Margarita Garcia-Vila, Joost Wolf, Juraj Balkovic, Alex C. Ruane, Giacomo De Sanctis, Pierre Martre, Roberto C. Izaurralde, Marijn van der Velde, Dominique Ripoche, Roberto Ferrise, Davide Cammarano, Fulu Tao, Bruno Basso, Christoph Müller, Heidi Webber, Yujing Gao, Andrea Maiorano, Christian Klein, Ann-Kristin Koehler, Andrew J. Challinor, Reimund P. Rötter, Garry O'Leary, Manuel Montesino San Martin, Eckart Priesack, Peter J. Thorburn, Heidi Horan, Kurt Christian Kersebaum, Iwan Supit, Zhigan Zhao, Taru Palosuo, Belay T. Kassie, Christian Biernath, Pramod K. Aggarwal, Katharina Waha, Sebastian Gayler, Daniel Wallach, Yan Zhu, Marco Bindi, Zhao Zhang, Claas Nendel, Enli Wang, Curtis D. Jones, Ehsan Eyshi Rezaei, Mikhail A. Semenov, Claudio O. Stöckle, Benjamin Dumont, National Science Foundation (US), National Natural Science Foundation of China, International Food Policy Research Institute (US), CGIAR (France), Institut National de la Recherche Agronomique (France), Federal Ministry of Education and Research (Germany), Biotechnology and Biological Sciences Research Council (UK), China Scholarship Council, Department of Agriculture and Water Resources (Australia), Ministero delle Politiche Agricole Alimentari e Forestali, Gorgan University, Victoria State Government, National Institute of Food and Agriculture (US), Federal Ministry of Food and Agriculture (Germany), German Research Foundation, Academy of Finland, LabEx Agro, Natural Resources Institute Finland, National Engineering and Technology Center for Information Agriculture, Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricutural University, Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Institut für Nutzpflanzenwissenschaften und Ressourcenschutz (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Fonctionnement et conduite des systèmes de culture tropicaux et méditerranéens (UMR SYSTEM), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre International de Hautes Etudes Agronomiques Méditerranéennes - Institut Agronomique Méditerranéen de Montpellier (CIHEAM-IAMM), Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), University of Copenhagen = Københavns Universitet (KU), Lincoln University, University of Leeds, CGIAR-ESSP Program on Climate Change,Agriculture and Food Security, International Center for Tropical Agriculture, Potsdam Institute for Climate Impact Research (PIK), NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), Agriculture and Food, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), International Maize and Wheat Improvement Centre [Inde] (CIMMYT), International Maize and Wheat Improvement Center (CIMMYT), Consultative Group on International Agricultural Research [CGIAR] (CGIAR)-Consultative Group on International Agricultural Research [CGIAR] (CGIAR), Biological Systems Engineering, Washington State University (WSU), Department of Agronomy, University of El-Tarf, International Institute for Applied Systems Analysis, Ecosystem Services and Management Program, affiliation inconnue, Comenius University in Bratislava, Department of Earth and Environmental Sciences [East Lansing], Michigan State University [East Lansing], Michigan State University System-Michigan State University System, W. K. Kellogg Biological Station (KBS), Institute of Biochemical Plant Pathology (BIOP), German Research Center for Environmental Health - Helmholtz Center München (GmbH), Department of Agrifood Production and Environmental Sciences (DISPAA), Università degli Studi di Firenze = University of Florence [Firenze] (UNIFI), The James Hutton Institute, GMO Unit, European Food Safety Authority = Autorité européenne de sécurité des aliments, Université de Liège, University of Córdoba, Department of Crop Sciences, Georg-August-University [Göttingen], Institute of Soil Science and Land Evaluation, University of Hohenheim, Department of Agricultural and Biological Engineering [Gainesville] (UF|ABE), Institute of Food and Agricultural Sciences [Gainesville] (UF|IFAS), University of Florida [Gainesville] (UF)-University of Florida [Gainesville] (UF), Food Systems Institute [Gainesville] (UF|IFAS), Department of Geographical Sciences, College Park, University of Maryland [College Park], University of Maryland System-University of Maryland System, Texas A and M AgriLife Research, Texas A&M University System, European Food Safety Authority (EFSA), Centre for Environment Science and Climate Resilient Agriculture (CESCRA), Indian Agricultural Research Institute (IARI), Agriculture Victoria Research, Institute for Natural Resources, Agroclim (AGROCLIM), Institut National de la Recherche Agronomique (INRA), Tropical Plant Production and Agricultural Systems Modelling (TROPAGS), Centre of Biodiversity and Sustainable Land Use (CBL), University of Göttingen - Georg-August-Universität Göttingen, Rothamsted Research, Wageningen University and Research Centre (WUR), Institute of geographical sciences and natural resources research, Chinese Academy of Sciences [Changchun Branch] (CAS), European Commission - Joint Research Centre [Ispra] (JRC), AGroécologie, Innovations, teRritoires (AGIR), Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Plant Production Systems, Wageningen University and Research [Wageningen] (WUR), State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University (BNU), Department of Agronomy and Biotechnology, China Agricultural University (CAU), Agricultural Model Intercomparison and Improvement Project (AgMIP), and Biotechnology and Biological Sciences Research Council (BBSRC), Grant/Award Number: BB/, P016855/1

Subjects
[SDV.SA]Life Sciences [q-bio]/Agricultural sciences, 0106 biological sciences, 010504 meteorology & atmospheric sciences, Food prices, Water en Voedsel, Climate change, Atmospheric sciences, 010603 evolutionary biology, 01 natural sciences, Model ensemble, Extreme low yields, model ensemble, 1.5°C warming, Temperate climate, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, Environmental Chemistry, 0105 earth and related environmental sciences, General Environmental Science, 2. Zero hunger, Global and Planetary Change, WIMEK, Water and Food, Food security, Ecology, business.industry, wheat production, Crop yield, Global warming, food security, 15. Life on land, PE&RC, climate change, Plant Production Systems, 13. Climate action, Agriculture, Plantaardige Productiesystemen, extreme low yields, 8. Economic growth, Environmental science, Wheat production, Water Systems and Global Change, business, Cropping
Abstract

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, We thank the Agricultural Model Intercomparison and Improvement Project (AgMIP) for support. B.L., L.X., and Y.Z. were supported by the National Science Foundation for Distinguished Young Scholars (31725020), the National Natural Science Foundation of China (31801260, 51711520319, and 31611130182), the Natural Science Foundation of Jiangsu province (BK20180523), the 111 Project (B16026), and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD). S.A. and B.K. received support from the International Food Policy Research Institute (IFPRI) through the Global Futures and Strategic Foresight project, the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), and the CGIAR Research Program on Wheat. P.M, D.R., and D.W. acknowledge support from the FACCE JPI MACSUR project (031A103B) through the metaprogram Adaptation of Agriculture and Forests to Climate Change (AAFCC) of the French National Institute for Agricultural Research (INRA). F.T. and Z.Z. were supported by the National Natural Science Foundation of China (41571088, 41571493, 31761143006, and 31561143003). R.R. acknowledges support from the German Federal Ministry for Research and Education (BMBF) through project “Limpopo Living Landscapes” project (SPACES program; grant number 01LL1304A). Rothamsted Research receives grant‐aided support from the Biotechnology and Biological Sciences Research Council (BBSRC) Designing Future Wheat project [BB/P016855/1]. L.X. and Y.G. acknowledge support from the China Scholarship Council. M.B and R.F. were funded by JPI FACCE MACSUR2 through the Italian Ministry for Agricultural, Food and Forestry Policies and thank A. Soltani from Gorgan Univ. of Agric. Sci. & Natur. Resour for his support. K.C.K. and C.N. received support from the German Ministry for Research and Education (BMBF) within the FACCE JPI MACSUR project. S.M. and C.M. acknowledge financial support from the MACMIT project (01LN1317A) funded through BMBF. G.J.O. acknowledges support from the Victorian Department of Economic Development, Jobs, Transport and Resources, the Australian Department of Agriculture and Water Resources. P.K.A. was supported by the multiple donors contributing to the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS). B.B. received financial support from USDA NIFA‐Water Cap Award 2015‐68007‐23133. F.E. acknowledges support from the FACCE JPI MACSUR project through the German Federal Ministry of Food and Agriculture (2815ERA01J) and from the German Science Foundation (project EW 119/5‐1). J.R.P. acknowledges the support of the Labex Agro (Agropolis no. 1501‐003). La. T.P. and F.T. received financial support from the Academy of Finland through the project PLUMES (decision nos. 277403 and 292836) and from Natural Resources Institute Finland through the project ClimSmartAgri.

Published
2019
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28. Optimizing irrigation to reduce N leaching and maintain high crop productivity through the manipulation of soil water storage under summer monsoon climate

Author

Yang Lu, Zhigan Zhao, Ailing Tian, Xiuwei Liu, Xiying Zhang, and Enli Wang

Subjects
0106 biological sciences, Irrigation, Irrigation scheduling, Soil Science, Growing season, 04 agricultural and veterinary sciences, 01 natural sciences, Water balance, Agronomy, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, Leaching (agriculture), Drainage, Water-use efficiency, Irrigation management, Agronomy and Crop Science, 010606 plant biology & botany
Abstract

Reducing nitrate (N) leaching is important for reducing pollution sources to groundwater worldwide. For the annual double cropping system of winter wheat and summer maize in the North China Plain (NCP), irrigation management in the dry winter wheat growing season affects the water balance in the summer rainy maize growing season and therefore influences the water drainage and N leaching from the crop root zone. Measurement data from a field experiment with three irrigation schedules for winter wheat and maize from 2006 to 2009 were used to calibrate and validate the APSIM model (Agricultural Production Systems sIMulator). The evaluated model was used to simulate the long-term effects of different irrigation schedules for the winter wheat season on N leaching during the summer maize season from 1981 to 2018, and to investigate strategies with minimum irrigation to maintain crop yield while reducing N leaching. Simulation results showed that when irrigation application to winter wheat was less than twice (total irrigation amount being less than 140 mm during the growing season), soil water depletion by winter wheat was greater than 130 mm for the top 2 m soil profile, which led to a significant reduction in drainage and N leaching during the summer rainy season. The average drainage during the summer maize season varied from 1 mm up to 134 mm following rainfed to five irrigations during the winter wheat season. The average N leaching corresponding to the drainage changes varied from 0.6 kg/ha to 67.1 kg/ha. The results indicated that the reduction in irrigation water use during the dry season significantly increased soil water use, allowing more rainfall to be stored in the root zone soil profile without being leached during the rainy season. Although the reduction in irrigation water use reduced the yield of winter wheat, using moderate irrigation scheduling (one or two irrigations) significantly improved water use efficiency for grain yield with only a small yield penalty. Therefore, reducing the N leaching potential by increasing soil water use through irrigation management is an effective measure in the NCP.

Published
2021
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29. Late sowing with high seeding rate increases wheat water use efficiency under deficit irrigation

Author

B. Wang, Y. Zhang, Zhigan Zhao, Qingwu Xue, B. Hao, M. Han, and Z. Wang

Subjects
0106 biological sciences, Field experiment, Deficit irrigation, Soil Science, Biomass, Sowing, 04 agricultural and veterinary sciences, Biology, 01 natural sciences, Anthesis, Agronomy, Evapotranspiration, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Cultivar, Water-use efficiency, Agronomy and Crop Science, 010606 plant biology & botany, Nature and Landscape Conservation, Water Science and Technology
Abstract

In the North China Plain (NCP), drought and groundwater overdraft threaten winter wheat (Triticum aestivum L.) production. The objective of this study was to improve water use efficiency (WUE) under deficit irrigation. A three-year field experiment was conducted with four sowing dates (SD; SD1: October 10, SD2: October 20, SD3: October 30, and SD4: November 10) using two cultivars (Jimai22 [JM22] and Shimai15 [SM15]). As SD was delayed, seeding rate (SR) increased from 450 seeds m−2 in SD1 to 600, 700, and 800 seeds m−2 in SD2, SD3, and SD4, respectively. Relative to the normal SD (SD1), moderately delaying SD to SD2 with increased SR did not reduce yield (SD1: 8.79 Mg ha−1 and SD2: 8.72 Mg ha−1). Delaying SD to SD3 and SD4 reduced yield by 9% and 11.3% (SD3: 8 Mg ha−1 and SD4: 7.74 Mg ha−1). Delaying SD increased WUE from 1.83 kg m−3 in SD1 to 2.07 kg m−3 in SD4. The increase in WUE associated with delayed SD was attributed to shorter vegetative stage and decreased preanthesis evapotranspiration (ET). Under deficit irrigation, soil water availability and photosynthetic rate (Pn) during grain filling were higher as SD was delayed. As SD was delayed, the ratios of spike number to maximum stem number, postanthesis ET to seasonal ET, spike biomass to biomass at anthesis, and postanthesis biomass to total biomass all increased, resulting in higher harvest index and WUE. The yield reduction on SD4 was attributed to a reduced number of grains per spike due to decreased preanthesis biomass and thousand-grain weight (TGW). Comparing two cultivars, JM22 had higher Pn, postanthesis biomass, and TGW, but lower biomass remobilization (BMR) during grain filling. On SD4, JM22 had higher yield and WUE than SM15. In conclusion, under deficit irrigation in the NCP, moderately delaying sowing (SD2) with increased SR can obtain both high yield and high WUE. Extremely late sowing (SD4) with increased SR can maximize WUE and minimize yield loss, especially in cultivar JM22.

Published
2017
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30. The contribution of spike photosynthesis to wheat yield needs to be considered in process-based crop models

Author

Meng Zhang, Zhimin Wang, Tony Fischer, Zhigan Zhao, Yinghua Zhang, Yanmei Gao, Enli Wang, and Xiaonan Zhou

Subjects
0106 biological sciences, Irrigation, Yield (finance), Soil Science, Biomass, 04 agricultural and veterinary sciences, Photosynthesis, 01 natural sciences, Crop, Agronomy, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Spike (software development), Biomass partitioning, Interception, Agronomy and Crop Science, 010606 plant biology & botany, Mathematics
Abstract

Process-based crop models are increasingly used to simulate Genotype by Environment by Management interactions (GxExM), and to evaluate importance of physiological traits to assist in breeding selections. This requires the model to correctly simulate the key physiological processes that determine grain yield. Although spike photosynthesis contributes to grain yield of wheat, it has not been explicitly simulated in most crop models. Here we present experimental data from China and Mexico, derived from estimates of spike contributions to grain yield of wheat, and compared them with previous results. We then used the APSIM model to investigate the consequences of excluding and including spike contributions on simulation results, against the data from China consisting of 4 irrigation and plant density treatments. Our results show that the contribution of spikes photosynthesis to grain yield ranged from 9.8% to 39.0% with an average of 20.1%, consistent with results from previous studies. However, despite the omission of the contribution from spike photosynthesis to grain yield, APSIM captured the dynamics of LAI and biomass and grain yield with acceptable accuracy. Across the treatments, the APSIM (version 7.9) explained >78% of the variation in yield (RMSE = 737 kg ha-1) and biomass (RMSE = 1438 kg ha-1) compared with the experimental data. This highlight the fact that APSIM performed well in simulating yield partly for the wrong reasons. Adding spike contribution to APSIM improved the simulations of biomass and yield, particularly under high yield level. More detailed analysis revealed that APSIM overestimated leaf contribution to compensate neglection of spike contribution, explained by the overestimation of leave biomass and underestimation of stem biomass. While APSIM captured the trend of changes in pre-anthesis remobilization contribution to grain yield, it also underestimated this contribution across treatments. Future improvements should include the inclusion of spike photosynthesis contribution with correct spike light interception and RUE, and improved modelling of biomass partitioning to different organs and the remobilization of biomass to grain from pre-anthesis growth.

Published
2020
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31. Improving process-based crop models to better capture genotype×environment×management interactions

Author

Zhigan Zhao, Hamish E. Brown, Greg J. Rebetzke, Enli Wang, Scott Chapman, and Bangyou Zheng

Subjects
0106 biological sciences, 0301 basic medicine, Crops, Agricultural, Candidate gene, Genotype, Models, Genetic, Physiology, Computer science, Process (engineering), Functional genes, Plant Science, Computational biology, Linkage (mechanical), 01 natural sciences, law.invention, Crop, 03 medical and health sciences, 030104 developmental biology, Phenotype, law, Allele, Environment management, Triticum, 010606 plant biology & botany
Abstract

In spite of the increasing expectation for process-based crop modelling to capture genotype (G) by environment (E) by management (M) interactions to support breeding selections, it remains a challenge to use current crop models to accurately predict phenotypes from genotypes or from candidate genes. We use wheat as a target crop and the APSIM farming systems model (Holzworth et al., 2014) as an example to analyse the current status of process-based crop models with a major focus on need to improve simulation of specific eco-physiological processes and their linkage to underlying genetic controls. For challenging production environments in Australia, we examine the potential opportunities to capture physiological traits, and to integrate genetic and molecular approaches for future model development and applications. Model improvement will require both reducing the uncertainty in simulating key physiological processes and enhancing the capture of key observable traits and underlying genetic control of key physiological responses to environment. An approach consisting of three interactive stages is outlined to (i) improve modelling of crop physiology, (ii) develop linkage from model parameter to genotypes and further to loci or alleles, and (iii) further link to gene expression pathways. This helps to facilitate the integration of modelling, phenotyping, and functional gene detection and to effectively advance modelling of G×E×M interactions. While gene-based modelling is not always needed to simulate G×E×M, including well-understood gene effects can improve the estimation of genotype effects and prediction of phenotypes. Specific examples are given for enhanced modelling of wheat in the APSIM framework.

Published
2018

32. Modelling to increase the eco-efficiency of a wheat–maize double cropping system

Author

Xiying Zhang, Zhigan Zhao, Enli Wang, Peter Carberry, Zhimin Wang, Chunsheng Hu, Yinghua Zhang, Shun-Li Zhou, and Xin Qin

Subjects
Irrigation, Denitrification, Ecology, chemistry.chemical_element, Multiple cropping, Nitrogen, Agronomy, chemistry, Environmental science, Animal Science and Zoology, Cropping system, Leaching (agriculture), Drainage, Agronomy and Crop Science, Groundwater
Abstract

The winter wheat- summer maize rotation is the main cropping system in the North China Plain (NCP). While its productivity has been constantly increased, the sustainability of the double cropping system has been increasingly questioned due to the high resource use and negative environmental impact. This paper combined field data with scenario modelling to investigate the productivity, resource (water and nitrogen) use efficiency and environmental impact of the wheat–maize rotation in response to management changes at a representative site Wuqiao in NCP. In addition the potential options to increase the system’s eco-efficiency, i.e., producing more with less input and less negative impact on the environment were explored. The results show that 180 kg/ha/year of nitrogen could be considered as the optimal N rate for summer maize. For the wheat–maize double cropping rotation, 225 mm of irrigation water for wheat and 330 kg N/ha (150 for wheat and 180 for maize) are required to maintain the potential grain yield at 18 t/ha and to have minimum impact on environment in terms of N leaching to the groundwater and N loss due to denitrification. These derived rates are 175 mm/year less and 120 kg N/ha less than the highest irrigation and N rates currently practiced. The reduced water and N inputs could reduce the annual totals of drainage, N leaching and denitrification N loss to a minimum of 117 mm, 12 kg N/ha and 28 kg N/ha, respectively. Our simulation results also enabled derivation of yield response curves to water and nitrogen inputs, which can be used to optimise N applications under various levels of irrigation water inputs.

Published
2015
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33. Classifying multi-model wheat yield impact response surfaces showing sensitivity to temperature and precipitation change

Author

Miroslav Trnka, Paola A. Deligios, Alfredo Rodríguez, Pierre Stratonovitch, Françoise Ruget, Ingrid Jacquemin, Marco Moriondo, Louis François, Katharina Waha, Alessia Perego, Mikhail A. Semenov, Fulu Tao, Benjamin Dumont, Christoph Müller, Frank Ewert, Petr Hlavinka, Per Bodin, Margarita Ruiz-Ramos, Marie-France Destain, Lianhai Wu, Marco Acutis, Cezary Sławiński, Senthold Asseng, Nina Pirttioja, Piotr Baranowski, Marco Bindi, Manuel Montesino, Davide Cammarano, Taru Palosuo, M. Ines Minguez, Claas Nendel, Timothy R. Carter, Iwan Supit, Bruno Basso, Kurt Christian Kersebaum, Zhigan Zhao, Samuel Buis, Alex C. Ruane, Thomas Gaiser, Chris Kollas, Jaromir Krzyszczak, Roberto Ferrise, Ignacio J. Lorite, Julien Minet, Holger Hoffmann, Reimund P. Rötter, Enli Wang, Isik Öztürk, Stefan Fronzek, Mattia Sanna, Finnish Environment Institute (SYKE), Università degli Studi di Firenze = University of Florence (UniFI), Rheinische Friedrich-Wilhelms-Universität Bonn, Institut für Nutzpflanzenwissenschaften und Ressourcenschutz (INRES), Natural Resources Institute Finland (LUKE), Centro de Estudios e Investigación para la Gestión de Riesgos Agrarios y Medioambientales (CEIGRAM), Universidad Politécnica de Madrid (UPM), Institute of Agrosystems and Bioclimatology, Mendel University in Brno (MENDELU), Global Change Research Centre (CzechGlobe), Institut de Recherche pour le Développement (IRD [Nouvelle-Calédonie]), University of Florida [Gainesville] (UF), Institute of Agrophysics, Polska Akademia Nauk = Polish Academy of Sciences (PAN), Michigan State University [East Lansing], Michigan State University System, Lund University [Lund], Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), The James Hutton Institute, Università degli Studi di Sassari = University of Sassari [Sassari] (UNISS), Université de Liège, Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Potsdam Institute for Climate Impact Research (PIK), Instituto Andaluz de Investigación y Formación Agraria y Pesquera (IFAPA), University of Copenhagen = Københavns Universitet (UCPH), Istituto di Biometeorologia [Firenze] (IBIMET), National Research Council of Italy | Consiglio Nazionale delle Ricerche (CNR), NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), Rothamsted Research, Biotechnology and Biological Sciences Research Council (BBSRC), Wageningen University and Research [Wageningen] (WUR), Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), China Agricultural University (CAU), Tropical Plant Production and Agricultural Systems Modelling (TROPAGS), Georg-August-University = Georg-August-Universität Göttingen, Centre for Biodiversity and Sustainable Land Use (CBL), cademy of Finland 277276 277403 292836, Finnish Ministry of Agriculture and Forestry (FACCE-MACSUR), Polish National Centre for Research and Development BIOSTRATEG1/271322/3/NCBR/2015, BIOSTRATEG2 298782, German Federal Ministry of Food and Agriculture through the Federal Office for Agriculture and Food 2851ERA01J, FACCE MACSUR 2812ERA 147, German Federal Ministry of Education and Research via the 'Limpopo Living Landscapes' project within the SPACES programme 01LL1304A, MACMIT project 01LN1317A, Italian Ministry of Agricultural Food and Forest Policies (AGROSCENARI Project), Italian Ministry of Education, University and Research (FIRB) RBFR12B2K4_004, European Project: 603416,EC:FP7:ENV,FP7-ENV-2013-two-stage,IMPRESSIONS(2013), University of Florence (UNIFI), Natural Resources Institute Finland, CEIGRAM-AgSystems, Mendel University in Brno, University of Florida [Gainesville], Polish Academy of Sciences, Lund University, University of Sassari, Andalusian Agricultural Research Institute (IFAPA), University of Copenhagen = Københavns Universitet (KU), Consiglio Nazionale delle Ricerche (CNR), Aarhus University Hospital, Wageningen University and Research Center (WUR), China Agricultural University, Georg-August-Universität Göttingen, Università degli Studi di Firenze = University of Florence [Firenze] (UNIFI), Natural resources institute Finland, and Georg-August-University [Göttingen]

Subjects
Spatial correlation, ble tendre, 010504 meteorology & atmospheric sciences, modèle multivariable, influence de la température, Water en Voedsel, 01 natural sciences, modèle de culture, Statistics, Range (statistics), Climate change, Crop model, Mathematics, 2. Zero hunger, changement climatique, Mathematical model, Agricultura, 04 agricultural and veterinary sciences, ta4111, Classification, classification des sols, winter wheat, soft wheat, Wheat, rendement agricole, Sensitivity analysis, Meteorology, [SDE.MCG]Environmental Sciences/Global Changes, ta1171, Precipitation, global change, 0105 earth and related environmental sciences, WIMEK, Water and Food, triticum, Simulation modeling, simulation de croissance, 15. Life on land, Hierarchical clustering, Euclidean distance, 13. Climate action, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Animal Science and Zoology, Water Systems and Global Change, soil taxonomy, Agronomy and Crop Science, Ensemble, analyse de sensibilité
Abstract

Crop growth simulation models can differ greatly in their treatment of key processes and hence in their response to environmental conditions. Here, we used an ensemble of 26 process-based wheat models applied at sites across a European transect to compare their sensitivity to changes in temperature (−2 to +9°C) and precipitation (−50 to +50%). Model results were analysed by plotting them as impact response surfaces (IRSs), classifying the IRS patterns of individual model simulations, describing these classes and analysing factors that may explain the major differences in model responses. The model ensemble was used to simulate yields of winter and spring wheat at four sites in Finland, Germany and Spain. Results were plotted as IRSs that show changes in yields relative to the baseline with respect to temperature and precipitation. IRSs of 30-year means and selected extreme years were classified using two approaches describing their pattern. The expert diagnostic approach (EDA) combines two aspects of IRS patterns: location of the maximum yield (nine classes) and strength of the yield response with respect to climate (four classes), resulting in a total of 36 combined classes defined using criteria pre-specified by experts. The statistical diagnostic approach (SDA) groups IRSs by comparing their pattern and magnitude, without attempting to interpret these features. It applies a hierarchical clustering method, grouping response patterns using a distance metric that combines the spatial correlation and Euclidian distance between IRS pairs. The two approaches were used to investigate whether different patterns of yield response could be related to different properties of the crop models, specifically their genealogy, calibration and process description. Although no single model property across a large model ensemble was found to explain the integrated yield response to temperature and precipitation perturbations, the application of the EDA and SDA approaches revealed their capability to distinguish: (i) stronger yield responses to precipitation for winter wheat than spring wheat; (ii) differing strengths of response to climate changes for years with anomalous weather conditions compared to period-average conditions; (iii) the influence of site conditions on yield patterns; (iv) similarities in IRS patterns among models with related genealogy; (v) similarities in IRS patterns for models with simpler process descriptions of root growth and water uptake compared to those with more complex descriptions; and (vi) a closer correspondence of IRS patterns in models using partitioning schemes to represent yield formation than in those using a harvest index. Such results can inform future crop modelling studies that seek to exploit the diversity of multi-model ensembles, by distinguishing ensemble members that span a wide range of responses as well as those that display implausible behaviour or strong mutual similarities.

Published
2018
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34. The Hot Serial Cereal Experiment for modeling wheat response to temperature: Field experiments and AgMIP-Wheat multi-model simulations

Author

Roberto C. Izaurralde, Jakarat Anothai, Andrew J. Challinor, Reimund P. Rötter, Jørgen E. Olesen, Curtis D. Jones, Bing Liu, T. Palosuo, Peter J. Thorburn, Kurt Christian Kersebaum, Mohamed Jabloun, Iwan Supit, Frank Ewert, Mikhail A. Semenov, Margarita Garcia-Vila, Claudio O. Stöckle, Benjamin Dumont, Belay T. Kassie, Jordi Doltra, Christian Biernath, Dominique Ripoche, Giacomo De Sanctis, Enli Wang, Elias Fereres, Bruce A. Kimball, Thilo Streck, Gerard W. Wall, L. A. Hunt, Pierre Stratonovitch, Fulu Tao, Jeffrey W. White, Christoph Müller, Bruno Basso, Senthold Asseng, Katharina Waha, Ann-Kristin Koehler, Andrea Maiorano, Ehsan Eyshi Rezaei, Eckart Priesack, Soora Naresh Kumar, Claas Nendel, Gerrit Hoogenboom, Davide Cammarano, David B. Lobell, Joost Wolf, Pierre Martre, Pramod K. Aggarwal, Garry O'Leary, Zhigan Zhao, Michael J. Ottman, Sebastian Gayler, Yan Zhu, Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Agricultural Research Service / US Arid Land Agricultural Research Center, United States Department of Agriculture, The School of Plant Sciences, University of Arizona, Department of Agricultural and Biological Engineering [Gainesville] (UF|ABE), Institute of Food and Agricultural Sciences [Gainesville] (UF|IFAS), University of Florida [Gainesville] (UF)-University of Florida [Gainesville] (UF), Institute of Crop Science and Resource Conservation [Bonn] (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, Consultative Group on International Agricultural Research (CGIAR), AgWeatherNet Program, Washington State University (WSU), Department of Geological Sciences and W. K. Kellogg Biological Station, Michigan State University [East Lansing], Michigan State University System-Michigan State University System, German Research Center for Environmental Health - Helmholtz Center München (GmbH), University of Leeds, GMO Unit, European Food Safety Authority = Autorité européenne de sécurité des aliments, Catabrian Agricultural Research and Training Center (CIFA), Universidad de Córdoba [Cordoba], Instituto de Agricultura Sostenible - Institute for Sustainable Agriculture (IAS CSIC), Consejo Superior de Investigaciones Científicas [Madrid] (CSIC), Institute of Soil Science and Land Evaluation, University of Hohenheim, Department of Plant Agriculture, University of Guelph, Department of Geographical Sciences, University of Maryland [College Park], University of Maryland System-University of Maryland System, Texas A and M AgriLife Research, Texas A&M University System, Department of Agroecology, Aarhus University [Aarhus], Leibniz Association, Potsdam Institute for Climate Impact Research (PIK), Indian Agricultural Research Institute (IARI), National Engineering and Technology Center for Information Agriculture, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricutural University, Department of Environmental Earth System Science and Center on Food Security and the Environment, Stanford University, Landscape & Water Sciences, Department of Environment of Victoria, Natural Resources Institute Finland (LUKE), Agroclim (AGROCLIM), Institut National de la Recherche Agronomique (INRA), Computational and Systems Biology Department, Rothamsted Research, Biological Systems Engineering, PPS and WSG &CALM, Wageningen University and Research [Wageningen] (WUR), Institute of geographical sciences and natural resources research, Chinese Academy of Sciences [Changchun Branch] (CAS), Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), College of Agronomy and Biotechnology, and Southwest University

Subjects
Simulations, 0106 biological sciences, Irrigation, 010504 meteorology & atmospheric sciences, Water en Voedsel, Wheat, Field experimental data, Heat stress, Crop model simulations, AgMIP, Hot Serial Cereal, 01 natural sciences, donnée expérimentale, Crop, blé, température, Life Science, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, Relative humidity, Cultivar, 0105 earth and related environmental sciences, 2. Zero hunger, WIMEK, Water and Food, Vegetal Biology, Global warming, Sowing, essai en plein champ, food and beverages, série climatique, Modélisation et simulation, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Agronomy, Plant Production Systems, 13. Climate action, Plantaardige Productiesystemen, Modeling and Simulation, Frost, Weather data, Environmental science, Water Systems and Global Change, stress hydrique, Biologie végétale, modèle de production, 010606 plant biology & botany
Abstract

The data set reported here includes the part of a Hot Serial Cereal Experiment (HSC) experiment recently used in the AgMIP-Wheat project to analyze the uncertainty of 30 wheat models and quantify their response to temperature. The HSC experiment was conducted in an open-field in a semiarid environment in the southwest USA. The data reported herewith include one hard red spring wheat cultivar (Yecora Rojo) sown approximately every six weeks from December to August for a two-year period for a total of 11 planting dates out of the 15 of the entire HSC experiment. The treatments were chosen to avoid any effect of frost on grain yields. On late fall, winter and early spring plantings temperature free-air controlled enhancement (T-FACE) apparatus utilizing infrared heaters with supplemental irrigation were used to increase air temperature by 1.3°C/2.7°C (day/night) with conditions equivalent to raising air temperature at constant relative humidity (i.e. as expected with global warming) during the whole crop growth cycle. Experimental data include local daily weather data, soil characteristics and initial conditions, detailed crop measurements taken at three growth stages during the growth cycle, and cultivar information. Simulations include both daily in-season and end-of-season results from 30 wheat models. Data access via doi 10.7910/DVN/M9ZT0F

Published
2018
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35. Erratum: The uncertainty of crop yield projections is reduced by improved temperature response functions

Author

Enli Wang, Pierre Martre, Zhigan Zhao, Frank Ewert, Andrea Maiorano, Reimund P. Rötter, Bruce A. Kimball, Michael J. Ottman, Gerard W. Wall, Jeffrey W. White, Matthew P. Reynolds, Phillip D. Alderman, Pramod K. Aggarwal, Jakarat Anothai, Bruno Basso, Christian Biernath, Davide Cammarano, Andrew J. Challinor, Giacomo De Sanctis, Jordi Doltra, Elias Fereres, Margarita Garcia-Vila, Sebastian Gayler, Gerrit Hoogenboom, Leslie A. Hunt, Roberto C. Izaurralde, Mohamed Jabloun, Curtis D. Jones, Kurt C. Kersebaum, Ann-Kristin Koehler, Leilei Liu, Christoph Müller, Soora Naresh Kumar, Claas Nendel, Garry O’Leary, Jørgen E. Olesen, Taru Palosuo, Eckart Priesack, Ehsan Eyshi Rezaei, Dominique Ripoche, Alex C. Ruane, Mikhail A. Semenov, Iurii Shcherbak, Claudio Stöckle, Pierre Stratonovitch, Thilo Streck, Iwan Supit, Fulu Tao, Peter Thorburn, Katharina Waha, Daniel Wallach, Zhimin Wang, Joost Wolf, Yan Zhu, and Senthold Asseng

Subjects
Plant Science
Published
2017
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36. The uncertainty of crop yield projections is reduced by improved temperature response functions

Author

Alex C. Ruane, Peter J. Thorburn, Mikhail A. Semenov, Joost Wolf, Claudio O. Stöckle, Pramod K. Aggarwal, Gerard W. Wall, Margarita Garcia-Vila, Matthew P. Reynolds, Eckart Priesack, Jørgen E. Olesen, Enli Wang, Bruce A. Kimball, Jordi Doltra, Iurii Shcherbak, Ehsan Eyshi Rezaei, Jeffrey W. White, Leilei Liu, L. A. Hunt, Senthold Asseng, Frank Ewert, Yan Zhu, Fulu Tao, Christoph Müller, Daniel Wallach, Christian Biernath, Davide Cammarano, Mohamed Jabloun, Zhigan Zhao, Michael J. Ottman, Pierre Martre, Sebastian Gayler, Garry O'Leary, Zhimin Wang, Jakarat Anothai, Elias Fereres, Claas Nendel, Bruno Basso, Thilo Streck, Curtis D. Jones, Andrea Maiorano, Phillip D. Alderman, Andrew J. Challinor, Reimund P. Rötter, Taru Palosuo, Iwan Supit, Katharina Waha, Giacomo De Sanctis, Kurt Christian Kersebaum, Soora Naresh Kumar, Gerrit Hoogenboom, Dominique Ripoche, Pierre Stratonovitch, Ann-Kristin Koehler, Roberto C. Izaurralde, Commonwealth Scientific and Industrial Research Organisation (Australia), Chinese Academy of Sciences, China Scholarship Council, Ministry of Education of the People's Republic of China, Institut National de la Recherche Agronomique (France), European Commission, International Food Policy Research Institute (US), CGIAR (France), Department of Agriculture (US), Federal Ministry of Education and Research (Germany), Deutsche Gesellschaft für Internationale Zusammenarbeit, Danish Council for Strategic Research, Federal Ministry of Food and Agriculture (Germany), Finnish Ministry of Agriculture and Forestry, National Natural Science Foundation of China, Helmholtz Association, Grains Research and Development Corporation (Australia), Texas AgriLife Research, Texas A&M University, National Institute of Food and Agriculture (US), CSIRO, Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), College of Agronomy and Biotechnology, Southwest University, Institute of Crop Science and Resource Conservation, Division of Plant Nutrition-University of Bonn, Institute of Landscape Systems Analysis, Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Department of Crop Sciences, University of Goettingen, Centre of Biodiversity and Sustainable Land Use (CBL), United States Department of Agriculture - Agricultural Research Service, The School of Plant Sciences, University of Arizona, Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), Washington State University (WSU), Department of Earth and Environmental Sciences and W.K. Kellogg Biological Station, Michigan State University [East Lansing], Michigan State University System-Michigan State University System, Institute of Biochemical Plant Pathology (BIOP), German Research Center for Environmental Health - Helmholtz Center München (GmbH), Agricultural and Biological Engineering Department, Purdue University [West Lafayette], Institute for Climate and Atmospheric Science [Leeds] (ICAS), School of Earth and Environment [Leeds] (SEE), University of Leeds-University of Leeds, GMO Unit, European Food Safety Authority = Autorité européenne de sécurité des aliments, Cantabrian Agricultural Research and Training Centre, Dep. Agronomia, University of Córdoba [Córdoba], Spanish National Research Council (CSIC), Institute of Soil Science and Land Evaluation, University of Hohenheim, Department of Plant Agriculture, University of Guelph, Department of Geographical Sciences, University of Maryland [College Park], University of Maryland System-University of Maryland System, Texas A&M AgriLife Research and Extension Center, Texas A&M University System, Department of Agroecology, Aarhus University [Aarhus], National Engineering and Technology Center for Information Agriculture, Nanjing Agricutural University, Potsdam Institute for Climate Impact Research (PIK), Centre for Environment Science and Climate Resilient Agriculture (CESCRA), Indian Agricultural Research Institute (IARI), Department of Economic Development, Department of Economic Development, Jobs, Transport and Resources (DEDJTR), Natural Resources Institute Finland, Institute of Crop Science and Resource Conservation (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, UE Agroclim (UE AGROCLIM), Institut National de la Recherche Agronomique (INRA), NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), Computational and Systems Biology Department, Rothamsted Research, Biological Systems Engineering, University of Wisconsin-Madison, PPS and WSG &CALM, Wageningen University and Research Center (WUR), Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences [Beijing] (CAS), Agriculture and Food, Universidad de La Rioja (UR), UMR : AGroécologie, Innovations, TeRritoires, Ecole Nationale Supérieure Agronomique de Toulouse, China Agricultural University, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), China Agricultural University (CAU), Institute of Crop Science and Resource Conservation [Bonn], Georg-August-University [Göttingen], Arid-Land Agricultural Research Center, Consultative Group on International Agricultural Research [CGIAR] (CGIAR)-Consultative Group on International Agricultural Research [CGIAR] (CGIAR), CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), Department of Agricultural and Biological Engineering [Gainesville] (UF|ABE), Institute of Food and Agricultural Sciences [Gainesville] (UF|IFAS), University of Florida [Gainesville] (UF)-University of Florida [Gainesville] (UF), University of Leeds, European Food Safety Authority (EFSA), Catabrian Agricultural Research and Training Center (CIFA), Consejo Superior de Investigaciones Científicas [Madrid] (CSIC), AgWeatherNet Program, Texas A and M AgriLife Research, Jiangsu Collaborative Innovation Center for Modern Crop Production, Landscape and Water Sciences, Natural Resources Institute Finland (LUKE), Agroclim (AGROCLIM), Wageningen University and Research [Wageningen] (WUR), Chinese Academy of Agricultural Sciences (CAAS), AGroécologie, Innovations, teRritoires (AGIR), Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, European Project: 267196,EC:FP7:PEOPLE,FP7-PEOPLE-2010-COFUND,AGREENSKILLS(2012), Écophysiologie des Plantes sous Stress environnementaux ( LEPSE ), Institut National de la Recherche Agronomique ( INRA ) -Centre international d'études supérieures en sciences agronomiques ( Montpellier SupAgro ) -Institut national d’études supérieures agronomiques de Montpellier ( Montpellier SupAgro ), Leibniz Centre for Agricultural Landscape Research, International Maize and Wheat Improvement Center ( CIMMYT ), CGIAR Research Program on Climate Change, Agriculture and Food Security ( CCAFS ), Washington State University ( WSU ), Michigan State University, Institute of Biochemical Plant Pathology ( BIOP ), Institute for Climate and Atmospheric Science, School of Earth and Environment, European Food Safety Authority, Spanish National Research Council ( CSIC ), Texas A and M University ( TAMU ), Potsdam Institute for Climate Impact Research ( PIK ), Centre for Environment Science and Climate Resilient Agriculture ( CESCRA ), Indian Agricultural Research Institute ( IARI ), Department of Economic Development, Jobs, Transport and Resources ( DEDJTR ), University of Bonn (Rheinische Friedrich-Wilhelms), UE Agroclim ( UE AGROCLIM ), Institut National de la Recherche Agronomique ( INRA ), NASA Goddard Institute for Space Studies ( GISS ), NASA Goddard Space Flight Center ( GSFC ), University of Wisconsin-Madison [Madison], Wageningen University and Research Center ( WUR ), Chinese Academy of Sciences [Beijing] ( CAS ), Universidad de La Rioja ( UR ), University of Bonn-Division of Plant Nutrition, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), USDA-ARS : Agricultural Research Service, Consultative Group on International Agricultural Research [CGIAR]-Consultative Group on International Agricultural Research [CGIAR], Natural resources institute Finland, Georg-August-University = Georg-August-Universität Göttingen, Universidad de Córdoba = University of Córdoba [Córdoba], Biotechnology and Biological Sciences Research Council (BBSRC)-Biotechnology and Biological Sciences Research Council (BBSRC), and Université de Toulouse (UT)-Université de Toulouse (UT)

Subjects
Crops, Agricultural, 010504 meteorology & atmospheric sciences, [SDV]Life Sciences [q-bio], Yield (finance), Water en Voedsel, Growing season, Climate change, klim, Plant Science, Agricultural engineering, Models, Biological, 01 natural sciences, [SHS]Humanities and Social Sciences, Crop, Life Science, Computer Simulation, Productivity, 0105 earth and related environmental sciences, 2. Zero hunger, [ SDE.BE ] Environmental Sciences/Biodiversity and Ecology, WIMEK, Water and Food, Food security, business.industry, Crop yield, Temperature, Agriculture, 04 agricultural and veterinary sciences, 15. Life on land, Climate Resilience, Agronomy, Klimaatbestendigheid, 13. Climate action, [SDE]Environmental Sciences, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, Water Systems and Global Change, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, business
Abstract

Increasing the accuracy of crop productivity estimates is a key element in planning adaptation strategies to ensure global food security under climate change. Process-based crop models are effective means to project climate impact on crop yield, but have large uncertainty in yield simulations. Here, we show that variations in the mathematical functions currently used to simulate temperature responses of physiological processes in 29 wheat models account for >50% of uncertainty in simulated grain yields for mean growing season temperatures from 14 °C to 33 °C. We derived a set of new temperature response functions that when substituted in four wheat models reduced the error in grain yield simulations across seven global sites with different temperature regimes by 19% to 50% (42% average). We anticipate the improved temperature responses to be a key step to improve modelling of crops under rising temperature and climate change, leading to higher skill of crop yield projections., E.W. acknowledges support from the CSIRO project ‘Enhanced modelling of genotype by environment interactions’ and the project ‘Advancing crop yield while reducing the use of water and nitrogen’ jointly funded by CSIRO and the Chinese Academy of Sciences (CAS). Z.Z. received a scholarship from the China Scholarship Council through the CSIRO and the Chinese Ministry of Education PhD Research Program. P.M., A.M. and D.R. acknowledge support from the FACCE JPI MACSUR project (031A103B) through the metaprogram Adaptation of Agriculture and Forests to Climate Change (AAFCC) of the French National Institute for Agricultural Research (INRA). A.M. received the support of the EU in the framework of the Marie-Curie FP7 COFUND People Programme, through the award of an AgreenSkills fellowship under grant agreement No. PCOFUND-GA-2010-267196. S.A. and D.C. acknowledge support provided by the International Food Policy Research Institute (IFPRI), CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), the CGIAR Research Program on Wheat and the Wheat Initiative. C.S. was funded through USDA National Institute for Food and Agriculture award 32011-68002-30191. C.M. received financial support from the KULUNDA project (01LL0905 L) and the FACCE MACSUR project (031A103B) funded through the German Federal Ministry of Education and Research (BMBF). F.E. received support from the FACCE MACSUR project (031A103B) funded through the German Federal Ministry of Education and Research (2812ERA115) and E.E.R. was funded through the German Federal Ministry of Economic Cooperation and Development (Project: PARI). M.J. and J.E.O. were funded through the FACCE MACSUR project by the Danish Strategic Research Council. K.C.K. and C.N. were funded by the FACCE MACSUR project through the German Federal Ministry of Food and Agriculture (BMEL). F.T., T.P. and R.P.R. received financial support from the FACCE MACSUR project funded through the Finnish Ministry of Agriculture and Forestry (MMM); F.T. was also funded through the National Natural Science Foundation of China (No. 41071030). C.B. was funded through the Helmholtz project ‘REKLIM-Regional Climate Change: Causes and Effects’ Topic 9: ‘Climate Change and Air Quality’. M.P.R. and PD.A. received funding from the CGIAR Research Program on Climate Change, Agriculture, and Food Security (CCAFS). G.O'L. was funded through the Australian Grains Research and Development Corporation and the Department of Economic Development, Jobs, Transport and Resources Victoria, Australia. R.C.I. was funded by Texas AgriLife Research, Texas A&M University. B.B. was funded by USDA-NIFA Grant No: 2015-68007-23133.

Published
2017
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37. A reappraisal of the critical nitrogen concentration of wheat and its implications on crop modeling

Author

Zhigan Zhao, John Angus, Yunpeng Liu, Enli Wang, Zhimin Wang, and Hecang Zang

Subjects
urogenital system, Simulation modeling, North china, Nitrogen management, food and beverages, Soil Science, Biomass, chemistry.chemical_element, Nitrogen, Dilution, Crop, Above ground, chemistry, Agronomy, parasitic diseases, embryonic structures, Agronomy and Crop Science, Mathematics
Abstract

The concept of critical nitrogen (N) concentration (Ncc) has been used for both diagnostic purposes and modelling of wheat-N relations. Ncc has been derived with two contrasting approaches: one against above ground biomass (Ncc-biomass), and one against developmental stages (Ncc-stage). While the former has been claimed in diagnostic use, both approaches are adopted in wheat simulation models. This paper provides data from North China Plain (NCP) to re-exam the Ncc-stage relationships used in two widely used wheat models (APSIM and CERES) and to compare the Ncc-biomass vs. Ncc-stage relationships. The results revealed significant higher maximum and critical N concentrations in leaves of wheat in NCP than the values used in the APSIM-wheat model. Recalibration of the APSIM model with the new N concentrations led to improved simulations for wheat biomass and N uptake, particularly under low N input. Our results also show that the Ncc-stage relationship appeared to be more robust than the Ncc-biomass relationship, and it helped explain the variations in wheat Ncc-biomass curves from different regions. This likely reflects the fact that Ncc-stage curve captures the stage-driven formation of structural biomass and carbohydrate reserves of wheat, which is the main cause for N dilution. The implications of the findings on modelling of wheat–nitrogen relationships and on nitrogen management practices are also discussed.

Published
2014
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38. Accuracy of root modelling and its impact on simulated wheat yield and carbon cycling in soil

Author

Zhigan Zhao, Yongcheng Wu, Enli Wang, Hecang Zang, Jingting Zhang, Xin Qin, Zhimin Wang, and Lihua Xue

Subjects
Crop, Agronomy, Shoot, Soil Science, Biomass, Soil horizon, Environmental science, Soil carbon, Root system, Carbon sequestration, Agronomy and Crop Science, Carbon cycle
Abstract

Accurate modelling of root biomass and root distribution of crop plants has become increasingly important in order to address issues related to carbon sequestration in soil and resource use efficiency of crops under different environmental and management conditions. However, the performance of crop models for simulating crop root system has been rarely tested in many environments due to lack of detailed data on roots. In this paper, we present detailed measurement data on root biomass and distribution in 0–200 cm soil profile at key developmental stages of wheat crop at Wuqiao in the North China Plain, and compare them with the root dynamics simulated by the agricultural systems model APSIM. The objectives are to test the model performance for simulating root biomass and distribution, and to investigate the potential impact of errors in root modelling on simulated wheat yield and soil carbon change. Compared to the measurements at the study site, APSIM version 7.5 underestimated the rooting front advance and final rooting depth of winter wheat, but overestimated the root biomass and root–shoot ratio at maturity by 100–200%. Modifications to root growth parameters in the model led to improved simulations of root depth and biomass dynamics. The modifications had little impact on simulated shoot biomass and grain yield under conditions of sufficient nitrogen supply, but led to higher simulated grain yield when nitrogen was deficient. They also resulted in reduction in simulated soil organic carbon in the top (0–20 cm) soil layer by 0.02%. A meta-analysis combining wheat root and shoot data from literature indicated that the APSIM model simulated a much tighter root–shoot relationship than that observed in the data, and that both versions of the model simulated the root–shoot relationships within the measured range. The relationship simulated from the original model better matched the data points from dry climate and dryland conditions. While the modified model based on Wuqiao data simulated a relationship better matching the data points from wet climate and irrigated conditions.

Published
2014
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39. Using fertiliser to maintain soil inorganic nitrogen can increase dryland wheat yield with little environmental cost

Author

Hongtao Xing, Ben Macdonald, Caleb Smith, O. T. Denmead, Steve Zeglin, James R. Hunt, Zhigan Zhao, and Enli Wang

Subjects
0106 biological sciences, Crop residue, Denitrification, Ecology, Soil organic matter, Yield gap, 04 agricultural and veterinary sciences, 010603 evolutionary biology, 01 natural sciences, Crop, Agronomy, Soil water, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, Animal Science and Zoology, Leaching (agriculture), Agronomy and Crop Science, Cropping
Abstract

Nitrogen (N) deficiency is responsible for a large proportion of the exploitable yield gap that exists in Australian wheat production. In order to maximise N use efficiency (NUE), growers attempt to match N fertiliser to seasonal water limited yield potential, which in the southern grain growing region is largely determined by in-crop rainfall. Australia’s variable rainfall makes water limited yield potential very difficult to predict, and growers tend to under fertilise, fearing economic and environmental losses of N if crops are over-fertilised. However, environmental losses of excess N are low in the semi-arid, winter dominant rainfall environments with high water holding capacity soils that comprise much of the wheat producing regions of south eastern Australia. This is particularly the case when high carbon (C) to N ratio crop residues are retained which facilitate rapid immobilisation of any mineral N surplus to crop requirements. We therefore propose that the exploitable yield gap in wheat could be closed if a longer-term strategy of N fertiliser application was adopted. In this strategy, growers would simply use N fertiliser to maintain an environmentally appropriate base level of inorganic N (N bank) rather than attempting to match fertiliser inputs to seasonal conditions. We used field studies with 15N labelled fertiliser and cropping systems simulations to demonstrate that fertiliser N applied at rates calculated to maintain fixed thresholds of inorganic N (N bank criteria) and top-dressed immediately prior to the period of rapid crop uptake can substantially increase yields and profit in comparison to current practice, even when applications exceed crop demand in some years. A further benefit of this strategy is an increase in soil organic matter. Both field and simulation studies indicate that the environmental cost of such a strategy would be minimal, with only a small increase in leaching and denitrification despite higher overall rates of N application. This strategy needs to be compared with current practices in longer term field experiments to confirm production increases and lack of environmental impacts.

Published
2019
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40. Combine observational data and modelling to quantify cultivar differences of soybean

Author

Wenyu Yang, Zhigan Zhao, Yushan Wu, Enli Wang, Di He, Wanzhuo Gong, Xin Liu, Neil Huth, and Sotirios V. Archontoulis

Subjects
0106 biological sciences, biology, Phenology, Soil Science, Biomass, Intercropping, 04 agricultural and veterinary sciences, Plant Science, biology.organism_classification, 01 natural sciences, Grain size, Crop, Agronomy, Yield (wine), 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Cultivar, Leaf area index, Agronomy and Crop Science, 010606 plant biology & botany, Mathematics
Abstract

This paper combines the analysis of soybean growth data with modelling to quantify cultivar differences and to evaluate the ability of the APSIM-Soybean model for simulation of soybean biomass and grain yield. Crop data include main developmental stages, leaf area index (LAI), above ground biomass, grain yield and grain number of 72 soybean cultivars in China. We derived the parameters controlling soybean flowering and maturity time in the lack of data from contrasting environments, then tested APSIM to simulate LAI, biomass and grain yield. Our results show that once phenology is correctly simulated, APSIM was able to capture the LAI and biomass growth dynamics of multiple cultivars and across different years. Our data showed a strong correlation of grain yield with grain number, but not with grain size. Future model improvement could include the simulation of grain setting (grain number and grain filling rate) and their difference between cultivars so that yield difference can be simulated. Further scenario modelling demonstrated that APSIM also captured the genotype by environment interactions as they impact on yield. The derived parameters for 72 soybean cultivars provide the first data set in APSIM for improved modelling of soybean genotypes in China and beyond, adding to the current APSIM validation database of soybean. This also formed the basis for future modelling of intercropping systems with soybean, e.g. maize/soybean intercropping.

Published
2019
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41. The International Heat Stress Genotype Experiment for modeling wheat response to heat: field experiments and AgMIP-Wheat multi-model simulations

Author

L. A. Hunt, David B. Lobell, Phillip D. Alderman, Alex C. Ruane, Zhigan Zhao, T. Palosuo, Mohamed Jabloun, Margarita Garcia-Vila, Andrew J. Challinor, Reimund P. Rötter, Jordi Doltra, Dominique Ripoche, Jeffrey W. White, Bing Liu, Jakarat Anothai, Fulu Tao, Katharina Waha, Eckart Priesack, Sebastian Gayler, Pierre Stratonovitch, Andrea Maiorano, Davide Cammarano, Christoph Müller, Bruno Basso, Ehsan Eyshi Rezaei, Senthold Asseng, Claas Nendel, Joost Wolf, Curtis D. Jones, Ann-Kristin Koehler, Matthew P. Reynolds, Enli Wang, Belay T. Kassie, Christian Biernath, Soora Naresh Kumar, Pierre Martre, Frank Ewert, Iwan Supit, Jørgen E. Olesen, Gerrit Hoogenboom, Giacomo De Sanctis, Thilo Streck, Elias Fereres, Yan Zhu, Kurt Christian Kersebaum, Mikhail A. Semenov, Claudio O. Stöckle, Benjamin Dumont, Roberto C. Izaurralde, Peter J. Thorburn, Garry O'Leary, and Pramod K. Aggarwal

Subjects
Simulations, 0106 biological sciences, Water en Voedsel, klim, 01 natural sciences, Heat stress, Crop, heat stress, Anthesis, Yield (wine), wheat, Life Science, Cultivar, field experimental data, Biomass (ecology), WIMEK, Water and Food, Field experimental data, Sowing, 04 agricultural and veterinary sciences, PE&RC, Productivity (ecology), Agronomy, Plant Production Systems, Plantaardige Productiesystemen, Wheat, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, Water Systems and Global Change, simulations, Cropping, 010606 plant biology & botany
Abstract

All data are available via DOI http://doi.org/10.7910/DVN/ECSFZG, he data set contains a portion of the International Heat Stress Genotype Experiment (IHSGE) data used in the AgMIP-Wheat project to analyze the uncertainty of 30 wheat crop models and quantify the impact of heat on global wheat yield productivity. It includes two spring wheat cultivars grown during two consecutive winter cropping cycles at hot, irrigated, and low latitude sites in Mexico (Ciudad Obregon and Tlaltizapan), Egypt (Aswan), India (Dharwar), the Sudan (Wad Medani), and Bangladesh (Dinajpur). Experiments in Mexico included normal (November-December) and late (January-March) sowing dates. Data include local daily weather data, soil characteristics and initial soil conditions, crop measurements (anthesis and maturity dates, anthesis and final total above ground biomass, final grain yields and yields components), and cultivar information. Simulations include both daily in-season and end-of-season results from 30 wheat models.

Published
2017
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42. How accurately do maize crop models simulate the interactions of atmospheric CO2 concentration levels with limited water supply on water use and yield?

Author

Patrick Bertuzzi, Jon I. Lizaso, Jean-Louis Durand, Dennis Timlin, Julián Ramírez Villegas, Fulu Tao, Kurt Christian Kersebaum, Sabine I. Seidel, Lajpat R. Ahuja, Christoph Müller, Delphine Deryng, Amit Kumar Srivastava, Bruno Basso, James W. Jones, Heidi Webber, F. Ewert, Dominique Ripoche, Eckart Priesack, Christian Biernath, Cynthia Rosenzweig, Remy Manderscheid, Alex C. Ruane, Hans Johachim Weigel, Thomas Gaiser, Christian Baron, Claas Nendel, Tracy E. Twine, Enli Wang, Kenneth J. Boote, Saseendran S. Anapalli, Soo-Hyung Kim, Zhigan Zhao, Sebastian Gayler, Florian Heinlein, Albert Olioso, Reimund P. Rötter, Kenel Delusca, Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères (P3F), Institut National de la Recherche Agronomique (INRA), University of Florida [Gainesville] (UF), CEIGRAM, Technical University of Madrid, Johann Heinrich von Thünen Institut, NASA Goddard Space Flight Center (GSFC), CPSRU, USDA-ARS : Agricultural Research Service, Department of Geological Sciences, University of Oregon [Eugene], Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Agroclim (AGROCLIM), Institute of Biochemical Plant Pathology, German Research Center for Environmental Health - Helmholtz Center München (GmbH), Computation Institute, Loyola University of Chicago, Institute of Crop Science and Resource Conservation [Bonn] (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, Institute of Soil Science and Land Evaluation, Section Biogeophysics, University of Hohenheim, School of Environmental and Forest Sciences, University of Washington [Seattle], Potsdam Institute for Climate Impact Research (PIK), Leibniz-Zentrum für Agrarlandschaftsforschung (ZALF), Leibniz Association, Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), School of Earth and Environment (UWA), The University of Western Australia (UWA), International Center for Tropical Agriculture [Colombie] (CIAT), Consultative Group on International Agricultural Research [CGIAR] (CGIAR), Natural resources institute Finland, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences [Beijing] (CAS), Crop Systems and Global Change Laboratory, Department of Soil, Water, & Climate, University of Minnesota System, Land and Water, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), Institut für Nutzpflanzenwissenschaften und Ressourcenschutz (INRES), China Agricultural University (CAU), University of Florida [Gainesville], Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS), UE Agroclim (UE AGROCLIM), Institute of Crop Science and Resource Conservation (INRES), CGIAR Research Program on Climate Change Colombia International Center for Tropical Agriculture (CIAT), Agriculture and Food Security (CCAFS), Natural Resources Institute Finland, China Agricultural University, and Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre National de la Recherche Scientifique (CNRS)

Subjects
010504 meteorology & atmospheric sciences, Water supply, Plant Science, 01 natural sciences, modèle de culture, Atmospheric carbon dioxide concentration, Evapotranspiration, Zea Mays, Atmospheric Carbon Dioxide Concentration, Multi-model Ensemble, Stomata Conductance, Grain Number, Water Use, Photosynthèse, Transpiration, 2. Zero hunger, Multi-model ensemble, U10 - Informatique, mathématiques et statistiques, 04 agricultural and veterinary sciences, Rendement des cultures, Stomatal conductance, Irrigation, Grain number, Soil Science, approvisionnement eau, Zea mays, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, Leaf area index, weather data, 0105 earth and related environmental sciences, carbonic anhydride, business.industry, culture de mais, Modèle de simulation, 15. Life on land, Évapotranspiration, donnée météorologique, F61 - Physiologie végétale - Nutrition, Agronomy, 13. Climate action, Soil water, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, business, estimation de rendement, Agronomy and Crop Science, Water use, concentration atmosphérique, Dioxyde de carbone
Abstract

Conference: International Crop Modelling Symposium on Crop Modelling for Agriculture and Food Security under Global Change (iCropM) - Proceedings Paper Berlin, GERMANY 2016; 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 Thünen 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.

Published
2017
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43. The implication of input data aggregation on up-scaling soil organic carbon changes

Author

Gang Zhao, Elisabet Lewan, Sren Gebbert, Claas Nendel, Zhigan Zhao, Thomas Gaiser, Edmar Teixeira, Edwin Haas, Elsa Coucheney, Kurt Christian Kersebaum, Reimund Rtter, Giacomo Trombi, Frank Ewert, Enli Wang, Jagadeesh Yeluripati, Davide Cammarano, Marco Moriondo, Stefan Siebert, Matthias Kuhnert, Julie Constantin, Luca Doro, Daniel Wallach, Xenia Specka, Marco Bindi, Belay T. Kassie, Holger Hoffmann, Fulu Tao, Helene Raynal, Henrik Eckersten, Balzs Grosz, Rene Dechow, Senthold Asseng, Pier Paolo Roggero, Lutz Weihermller, Thünen Institute of Climate Smart Agriculture, Crop Science Group, Rheinische Friedrich-Wilhelms-Universität Bonn, UMR : AGroécologie, Innovations, TeRritoires, Ecole Nationale Supérieure Agronomique de Toulouse, Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), Institut National de la Recherche Agronomique (INRA), Department of Soil and Environment, Swedish University of Agricultural Sciences (SLU), Department of Crop Production Ecology, Institute of Landscape Systems Analysis, Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Biological and Environmental Sciences, School of Biological Sciences, University of Aberdeen, The James Hutton Institute, Institut für Meteorologie und Klimaforschung - Atmosphärische Umweltforschung (IMK-IFU), Karlsruher Institut für Technologie (KIT), Systems Modeling Team (Sustainable Production Group), New Zealand Institute for Plant and Food Research Limited, Department of Agri-food Production and Environmental Sciences, University of Florence (UNIFI), Istituto di Biometeorologia [Firenze] (IBIMET), Consiglio Nazionale delle Ricerche (CNR), Desertification Research Centre, University of Sassari, Desertification Research Centre - Department of Agricultural Sciences, CSIRO, Climate Impacts Group, Department of Crop Sciences, Georg-August-Universität Göttingen, Agricultural & Biological Engineering Department, University of Florida [Gainesville], Institute of Bio- and Geosciences Agrosphere (IBG-3), AGroécologie, Innovations, teRritoires (AGIR), Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Departement of Soil and Environment, Plant & Food Research, Università degli Studi di Firenze = University of Florence [Firenze] (UNIFI), Georg-August-University [Göttingen], Department of Agricultural and Biological Engineering [Gainesville] (UF|ABE), Institute of Food and Agricultural Sciences [Gainesville] (UF|IFAS), and University of Florida [Gainesville] (UF)-University of Florida [Gainesville] (UF)

Subjects
[SDV.SA]Life Sciences [q-bio]/Agricultural sciences, Biogeochemical cycle, Environmental Engineering, 010504 meteorology & atmospheric sciences, data aggregation, biogeochemical model, up-scaling error, Ecological Modeling, Up scaling, Soil science, 04 agricultural and veterinary sciences, Soil carbon, Common method, 15. Life on land, 01 natural sciences, Data availability, Data aggregator, soil organic carbon, North west, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, Limit (mathematics), Software, 0105 earth and related environmental sciences
Abstract

In up-scaling studies, model input data aggregation is a common method to cope with deficient data availability and limit the computational effort. We analyzed model errors due to soil data aggregation for modeled SOC trends. For a region in North West Germany, gridded soil data of spatial resolutions between 1km and 100km has been derived by majority selection. This data was used to simulate changes in SOC for a period of 30 years by 7 biogeochemical models. Soil data aggregation strongly affected modeled SOC trends. Prediction errors of simulated SOC changes decreased with increasing spatial resolution of model output. Output data aggregation only marginally reduced differences of model outputs between models indicating that errors caused by deficient model structure are likely to persist even if requirements on the spatial resolution of model outputs are low. Analysis of soil data aggregation on model errors of up-scaled SOC trends.Determination of factors controlling aggregation effects (AE) on modeled SOC trends.Comparison of variability between 7 biogeochemical models and AE.Development of ex ante methods to approximate AE for SOC simulation studies.

Published
2017
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44. Similar estimates of temperature impacts on global wheat yield by three independent methods

Author

Frank Ewert, Jakarat Anothai, P. V. Vara Prasad, Davide Cammarano, Curtis D. Jones, Elias Fereres, Margarita Garcia-Vila, Soora Naresh Kumar, Eckart Priesack, Phillip D. Alderman, Andrew J. Challinor, Reimund P. Rötter, Alex C. Ruane, Christian Folberth, Gerrit Hoogenboom, Pierre Martre, Roberto C. Izaurralde, Fulu Tao, Pramod K. Aggarwal, Mohamed Jabloun, Jordi Doltra, Joshua Elliott, Christoph Müller, Bing Liu, Iurii Shcherbak, Jeffrey W. White, Bruno Basso, Senthold Asseng, Pierre Stratonovitch, Peter J. Thorburn, Claas Nendel, Taru Palosuo, Joost Wolf, Ann-Kristin Koehler, Thilo Streck, Jørgen E. Olesen, David B. Lobell, Kurt Christian Kersebaum, Delphine Deryng, L. A. Hunt, Garry O'Leary, Katharina Waha, Giacomo De Sanctis, Daniel Wallach, Yan Zhu, James W. Jones, Elke Stehfest, Mikhail A. Semenov, Christian Biernath, Claudio O. Stöckle, Thomas A. M. Pugh, Matthew P. Reynolds, Enli Wang, Bruce A. Kimball, Erwin Schmid, Iwan Supit, Zhigan Zhao, Michael J. Ottman, Sebastian Gayler, Cynthia Rosenzweig, Ehsan Eyshi Rezaei, Gerard W. Wall, National Engineering and Technology Center for Information Agriculture, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricutural University, Department of Agricultural and Biological Engineering [Gainesville] (UF|ABE), Institute of Food and Agricultural Sciences [Gainesville] (UF|IFAS), University of Florida [Gainesville] (UF)-University of Florida [Gainesville] (UF), Potsdam Institute for Climate Impact Research (PIK), Institute of Crop Science and Resource Conservation [Bonn] (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, Leibniz-Zentrum für Agrarlandschaftsforschung (ZALF), Leibniz Association, Center for Climate Systems Research [New York] (CCSR), Columbia University [New York], Computation Institute, Loyola University of Chicago, Department of Environmental Earth System Science and Center on Food Security and the Environment, Stanford University, Génétique Diversité et Ecophysiologie des Céréales (GDEC), Université Blaise Pascal - Clermont-Ferrand 2 (UBP)-Institut National de la Recherche Agronomique (INRA), NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), AGroécologie, Innovations, teRritoires (AGIR), Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, CGIAR Research Program on Climate Change, Agriculture and Food Security, Borlaug Institute for South Asia, CIMMYT, Consultative Group on International Agricultural Research (CGIAR), Department of Plant and Soil Sciences, Mississippi State University [Mississippi], Department of Plant Science, Faculty of Natural Resources, Prince of Songkla University (PSU), Department of Geological Sciences, University of Oregon [Eugene], W. K. Kellogg Biological Station (KBS), Michigan State University [East Lansing], Michigan State University System-Michigan State University System, Institute of Soil Ecology [Neuherberg] (IBOE), Helmholtz-Zentrum München (HZM), The James Hutton Institute, Institute for Climate and Atmospheric Science [Leeds] (ICAS), School of Earth and Environment [Leeds] (SEE), University of Leeds-University of Leeds, CGIAR-ESSP Program on Climate Change, Agriculture and Food Security, International Center for Tropical Agriculture, European Commission - Joint Research Centre [Ispra] (JRC), Cantabrian Agricultural Research and Training Centre, Department of Agronomy, Purdue University [West Lafayette], Department of Geography, University of Liverpool, Ecosystem Services and Management Program, International Institute for Applied Systems Analysis (IIASA), Institute of Soil Science and Land Evaluation, University of Hohenheim, AgWeatherNet Program, Washington State University (WSU), Department of Plant Agriculture, University of Guelph, Department of Geographical Sciences, University of Maryland [College Park], University of Maryland System-University of Maryland System, Texas A and M AgriLife Research, Texas A&M University System, Department of Agroecology, Aarhus University [Aarhus], US Arid-Land Agricultural Research Center, United States Department of Agriculture, Centre for Environment Science and Climate Resilient Agriculture (CESCRA), Indian Agricultural Research Institute (IARI), Institute of Landscape Systems Analysis, Leibniz Centre for Agricultural Landscape Research, Landscape & Water Sciences, Department of Environment of Victoria, The School of Plant Sciences, University of Arizona, Natural resources institute Finland, Institute of Ecology, German Research Center for Environmental Health, Institut für Meteorologie und Klimaforschung - Atmosphärische Umweltforschung (IMK-IFU), Karlsruher Institut für Technologie (KIT), School of Geography, Earth and Environmental Sciences [Birmingham], University of Birmingham [Birmingham], Birmingham Institute of Forest Research (BIFoR), International Maize and Wheat Improvement Center (CIMMYT), Consultative Group on International Agricultural Research [CGIAR] (CGIAR), Center for Development Research (ZEF), Environmental Impacts Group, Georg-August-University [Göttingen], Universität für Bodenkultur Wien [Vienne, Autriche] (BOKU), Computational and Systems Biology Department, Rothamsted Research, Biotechnology and Biological Sciences Research Council, Netherlands Environmental Assessment Agency, Department of Biological Systems Engineering, University of Wisconsin-Madison, PPS, WSG and CALM, Wageningen University and Research [Wageningen] (WUR), Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences [Beijing] (CAS), Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), USDA-ARS, Arid-Land Agricultural Research Center, China Agricultural University (CAU), National High-Tech Research and Development Program of China (2013AA100404), the National Natural Science Foundation of China (31271616, 31611130182, 41571088 and 31561143003), the National Research Foundation for the Doctoral Program of Higher Education of China (20120097110042), the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), China Scholarship Council., IFPRI through the Global Futures and Strategic Foresight project, the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), the CGIAR Research Program on Wheat, the Agricultural Model Intercomparison and Improvement Project (AgMIP), Agricultural & Biological Engineering Department, University of Florida [Gainesville], Institute of Crop Science and Resource Conservation, University of Bonn-Division of Plant Nutrition, Stanford University [Stanford], Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), UMR : AGroécologie, Innovations, TeRritoires, Ecole Nationale Supérieure Agronomique de Toulouse, Prince of Songkla University, Texas A&M AgriLife Research and Extension Center, Natural Resources Institute Finland, Georg-August-Universität Göttingen, Wageningen University and Research Center (WUR), China Agricultural University, Division of Plant Nutrition-University of Bonn, Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), University of Florida, Potsdam Institute for Climate Impact Research ( PIK ), Leibniz Centre for Agricultural Landscape Research, Institute for Landscape Biogeochemistry, Center for Climate Systems Research [New York] ( CCSR ), Écophysiologie des Plantes sous Stress environnementaux ( LEPSE ), Institut National de la Recherche Agronomique ( INRA ) -Centre international d'études supérieures en sciences agronomiques ( Montpellier SupAgro ) -Institut national d’études supérieures agronomiques de Montpellier ( Montpellier SupAgro ), NASA Goddard Institute for Space Studies ( GISS ), NASA Goddard Space Flight Center ( GSFC ), Consultative Group on International Agricultural Research ( CGIAR ), W.K. Kellogg Biological Station, Institute of Soil Ecology [Neuherberg] ( IBOE ), Helmholtz-Zentrum München ( HZM ), James Hutton Institute, Institute for Climate and Atmospheric Science, School of Earth and Environment, University of Leeds, European Commission - Joint Research Centre [Ispra] ( JRC ), International Institute for Applied Systems Analysis ( IIASA ), Washington State University ( WSU ), Texas A and M University ( TAMU ), Leibniz Centre for Agricultural Landscape Research (ZALF), Centre for Environment Science and Climate Resilient Agriculture ( CESCRA ), Indian Agricultural Research Institute ( IARI ), Institut für Meteorologie und Klimaforschung - Atmosphärische Umweltforschung ( IMK-IFU ), Karlsruher Institut für Technologie ( KIT ), School of Geography, Earth & Environmental Science and Birmingham Institute of Forest Research, University of Birmingham, International Maize and Wheat Improvement Center ( CIMMYT ), Bonn Universität [Bonn], University of Natural Resources and Life Sciences, University of Wisconsin-Madison [Madison], Wageningen University and Research Center ( WUR ), Chinese Academy of Sciences [Beijing] ( CAS ), Commonwealth Scientific and Industrial Research Organisation, Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP), Université de Toulouse (UT)-Université de Toulouse (UT), Helmholtz Zentrum München = German Research Center for Environmental Health, Natural Resources Institute Finland (LUKE), Georg-August-University = Georg-August-Universität Göttingen, Universität für Bodenkultur Wien = University of Natural Resources and Life [Vienne, Autriche] (BOKU), Biotechnology and Biological Sciences Research Council (BBSRC), and Institute of geographical sciences and natural resources research [CAS] (IGSNRR)

Subjects
0106 biological sciences, [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, [ SDV.BV ] Life Sciences [q-bio]/Vegetal Biology, régression statistique, 010504 meteorology & atmospheric sciences, impact sur le rendement, klim, Atmospheric sciences, 01 natural sciences, incertitude, wheat, uncertainty, [ SDV.SA ] Life Sciences [q-bio]/Agricultural sciences, 2. Zero hunger, changement climatique, Regression analysis, statistical regression, simulation, PE&RC, [ SDE.MCG ] Environmental Sciences/Global Changes, sécurité alimentaire, Plant Production Systems, modèle de récolte, Yield (finance), comparaison de modèles, [SDE.MCG]Environmental Sciences/Global Changes, Climate change, Environmental Science (miscellaneous), Earth System Science, blé, température, Life Science, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, réchauffement climatique, global change, 0105 earth and related environmental sciences, Hydrology, WIMEK, Global temperature, business.industry, Crop yield, Global warming, Climate Resilience, 13. Climate action, Agriculture, Klimaatbestendigheid, Plantaardige Productiesystemen, Environmental science, Leerstoelgroep Aardsysteemkunde, Climate model, business, Social Sciences (miscellaneous), 010606 plant biology & botany
Abstract

The potential impact of global temperature change on global crop yield has recently been assessed with different methods. Here we show that grid-based and point-based simulations and statistical regressions (from historic records), without deliberate adaptation or CO2 fertilization effects, produce similar estimates of temperature impact on wheat yields at global and national scales. With a 1 °C global temperature increase, global wheat yield is projected to decline between 4.1% and 6.4%. Projected relative temperature impacts from different methods were similar for major wheat-producing countries China, India, USA and France, but less so for Russia. Point-based and grid-based simulations, and to some extent the statistical regressions, were consistent in projecting that warmer regions are likely to suffer more yield loss with increasing temperature than cooler regions. By forming a multi-method ensemble, it was possible to quantify ‘method uncertainty’ in addition to model uncertainty. This significantly improves confidence in estimates of climate impacts on global food security. The potential impact of global temperature change on global crop yield has recently been assessed with different methods. Here we show that grid-based and point-based simulations and statistical regressions (from historic records), without deliberate adaptation or CO2 fertilization effects, produce similar estimates of temperature impact on wheat yields at global and national scales. With a 1 °C global temperature increase, global wheat yield is projected to decline between 4.1% and 6.4%. Projected relative temperature impacts from different methods were similar for major wheat-producing countries China, India, USA and France, but less so for Russia. Point-based and grid-based simulations, and to some extent the statistical regressions, were consistent in projecting that warmer regions are likely to suffer more yield loss with increasing temperature than cooler regions. By forming a multi-method ensemble, it was possible to quantify ‘method uncertainty’ in addition to model uncertainty. This significantly improves confidence in estimates of climate impacts on global food security.

Published
2016
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45. Impacts of soil and weather data aggregation in spatial modelling of net primary production of croplands

Author

Matthias Kuhnert, Yeluripati, Jagadeesh B., Pete Smith, Holger Hoffmann, Julie Constantin, Elsa Coucheney, Rene Dechow, Henrik Eckersten, Thomas Gaiser, Balázs Grosz, Edwin Haas, Kurt-Christian Kersebaum, Ralf Kiese, Klatt, S., Elisabet Lewan, Claas Nendel, Helene Raynal, Sosa, C., Xenia Specka, Edmar Teixeira, Wang, E., Lutz Weihermueller, Gang Zhao, Zhigan Zhao, Frank Ewert, School of Biological Sciences [Aberdeen], University of Aberdeen, The James Hutton Institute, Crop Science Group, Rheinische Friedrich-Wilhelms-Universität Bonn, UMR : AGroécologie, Innovations, TeRritoires, Ecole Nationale Supérieure Agronomique de Toulouse, Department of Soil and Environment, Swedish University of Agricultural Sciences (SLU), Thünen Institute of Climate Smart Agriculture, Department of Crop Production Ecology, Karlsruhe Institute of Technology (KIT), Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), Institut National de la Recherche Agronomique (INRA), New Zealand Institute for Plant and Food Research Limited, CSIRO, Institute of Bio- and Geosciences Agrosphere (IBG-3), AGroécologie, Innovations, teRritoires (AGIR), Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Departement of Soil and Environment, and Plant & Food Research

Subjects
[SDV]Life Sciences [q-bio]
Abstract

Impacts of soil and weather data aggregation in spatial modelling of net primary production of croplands. iCROPM 2016 International Crop Modelling Symposium "Crop Modelling for Agriculture and Food Security under Global Change"

Published
2016

46. Impact analysis of climate data aggregation at different spatial scales on simulated net primary productivity for croplands

Author

Frank Ewert, Stephen M. Ogle, Lutz Weihermüller, Holger Hoffmann, Gang Zhao, Edmar Teixeira, Pete Smith, Xenia Specka, Balász Grosz, Thomas Gaiser, Zhigan Zhao, Ralf Kiese, Helene Raynal, Matthias Kuhnert, Kurt Christian Kersebaum, Rene Dechow, Jagadeesh Yeluripati, Elisabet Lewan, Claas Nendel, Enli Wang, Elsa Coucheney, Edwin Haas, Marcel van Oijen, Carmen Sosa, Julie Constantin, Henrik Eckersten, Steffen Klatt, Institute of Biological and Environmental Sciences, (SFIRC), Crop Science Group, INRES, Rheinische Friedrich-Wilhelms-Universität Bonn, Centre for Ecology and Hydrology, UMR : AGroécologie, Innovations, TeRritoires, Ecole Nationale Supérieure Agronomique de Toulouse, Department of Soil and Environment, Swedish University of Agricultural Sciences (SLU), Thünen Institute of Climate-Smart Agriculture, Department of Crop Production Ecology, Institut für Meteorologie und Klimaforschung - Atmosphärische Umweltforschung (IMK-IFU), Karlsruher Institut für Technologie (KIT), Institute of Landscape Systems Analysis, Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), Institut National de la Recherche Agronomique (INRA), Systems Modelling Team (Sustainable Production Group), New Zealand Institute for Plant and Food Research Limited, CSIRO, Institute of Bio- and Geosciences Agrosphere (IBG-3), Colorado State University [Fort Collins] (CSU), AGroécologie, Innovations, teRritoires (AGIR), Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Departement of Soil and Environment, and Plant & Food Research

Subjects
extreme events, 010504 meteorology & atmospheric sciences, data aggregation, [SDV]Life Sciences [q-bio], Soil Science, Growing season, Plant Science, Atmospheric sciences, 01 natural sciences, Ecology and Environment, Extreme weather, Range (statistics), Data and Information, Spatial analysis, climate, 0105 earth and related environmental sciences, 2. Zero hunger, npp, Ecology, Phenology, scaling, Primary production, net primary production, 04 agricultural and veterinary sciences, 15. Life on land, Agriculture and Soil Science, Agronomy, 13. Climate action, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, Monoculture, Scale (map), Agronomy and Crop Science, crop modelling
Abstract

For spatial crop and agro-systems modelling, there is often a discrepancy between the scale of measured driving data and the target resolution. Spatial data aggregation is often necessary, which can introduce additional uncertainty into the simulation results. Previous studies have shown that climate data aggregation has little effect on simulation of phenological stages, but effects on net primary production (NPP) might still be expected through changing the length of the growing season and the period of grain filling. This study investigates the impact of spatial climate data aggregation on NPP simulation results, applying eleven different models for the same study region (∼34,000 km2), situated in Western Germany. To isolate effects of climate, soil data and management were assumed to be constant over the entire study area and over the entire study period of 29 years. Two crops, winter wheat and silage maize, were tested as monocultures. Compared to the impact of climate data aggregation on yield, the effect on NPP is in a similar range, but is slightly lower, with only small impacts on averages over the entire simulation period and study region. Maximum differences between the five scales in the range of 1–100 km grid cells show changes of 0.4–7.8% and 0.0–4.8% for wheat and maize, respectively, whereas the simulated potential NPP averages of the models show a wide range (1.9–4.2 g C m−2 d−1 and 2.7–6.1 g C m−2 d−1 for wheat and maize, respectively). The impact of the spatial aggregation was also tested for shorter time periods, to see if impacts over shorter periods attenuate over longer periods. The results show larger impacts for single years (up to 9.4% for wheat and up to 13.6% for maize). An analysis of extreme weather conditions shows an aggregation effect in vulnerability up to 12.8% and 15.5% between the different resolutions for wheat and maize, respectively. Simulations of NPP averages over larger areas (e.g. regional scale) and longer time periods (several years) are relatively insensitive to climate data aggregation. However, the scale of climate data is more relevant for impacts on annual averages of NPP or if the period is strongly affected or dominated by drought stress. There should be an awareness of the greater uncertainty for the NPP values in these situations if data are not available at high resolution. On the other hand, the results suggest that there is no need to simulate at high resolution for long term regional NPP averages based on the simplified assumptions (soil and management constant in time and space) used in this study.

Published
2016
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47. Uncertainties in scaling-up crop models for large-area climate change impact assessments

Author

Frank Ewert, Lenny G. J. van Bussel, Gang Zhao, Holger Hoffmann, Thomas Gaiser, Xenia Specka, Claas Nendel, Kurt-Christian Kersebaum, Carmen Sosa, Elisabet Lewan, Jagadeesh Yeluripati, Matthias Kuhnert, Fulu Tao, Reimund Rötter, Julie Constantin, Helene Raynal, Daniel Wallach, Edmar Teixeira, Balasz Grosz, Michaela Bach, Luca Doro, Pier Paolo Roggero, Zhigan Zhao, Enli Wang, Ralf Kiese, Edwin Haas, Henrik Eckersten, Giacomo Trombi, Marco Bindi, Christian Klein, Christian Biernath, Florian Heinlein, Eckart Priesack, Davide Cammarano, Senthold Asseng, Joshua Elliott, Michael Glotter, Bruno Basso, Guillermo A. Baigorria, Consuelo C. Romero, Marco Moriondo, Institute of Crop Science and Resource Conservation, Division of Plant Nutrition-University of Bonn, Wageningen University and Research Center (WUR), Instutute of Landscape Biogeochemistry, Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Department of Soil and Environment, Swedish University of Agricultural Sciences (SLU), Biological and Environmental Sciences, University of Stirling, Agrifood Research Finland, UMR : AGroécologie, Innovations, TeRritoires, Ecole Nationale Supérieure Agronomique de Toulouse, Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), Institut National de la Recherche Agronomique (INRA), University of Canterbury, Thünen Institute of Climate Smart Agriculture, University of Sassari, CSIRO, Karlsruhe Institute of Technology (KIT), Dipartimento di Scienze delle Produzioni Agroalimentari e dell’Ambiente (DISPAA ), Helmholtz-Zentrum München (HZM), University of Florida [Gainesville], University of Chicago, Michigan State University [East Lansing], Michigan State University System, University of Nebraska [Lincoln], University of Nebraska System, Institute of Food Sciences of National Research Council (IFS - CNR), University of Bonn-Division of Plant Nutrition, Institute of Crop Science and Resource Conservation [Bonn] (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, Wageningen University and Research [Wageningen] (WUR), Departement of Soil and Environment, AGroécologie, Innovations, teRritoires (AGIR), Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, University of Canterbury [Christchurch], and University of Florida [Gainesville] (UF)

Subjects
010504 meteorology & atmospheric sciences, Operations research, [SDV]Life Sciences [q-bio], Climate change, 01 natural sciences, Life Science, 0105 earth and related environmental sciences, 2. Zero hunger, Sustainable development, Food security, business.industry, Impact assessment, Environmental resource management, 04 agricultural and veterinary sciences, 15. Life on land, Geography, Environmental Systems Analysis, 13. Climate action, Agriculture, Obstacle, Milieusysteemanalyse, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Economic model, Climate model, business
Abstract

Problems related to food security and sustainable development are complex (Ericksenet al., 2009) and require consideration of biophysical, economic, political, and social factors, as well as their interactions, at the level of farms, regions, nations, and globally. While the solution to such societal problems may be largely political, there is a growing recognition of the need for science to provide sound information to decision-makers (Meinke et al., 2009). Achieving this, particularly in light of largely uncertain future climate and socio-economic changes, will necessitate integrated assessment approaches and appropriate integrated assessment modeling (IAM) tools to perform them. Recent (Ewertet al., 2009; van Ittersumet al., 2008) and ongoing (Rosenzweiget al., 2013) studies have tried to advance the integrated use of biophysical and economic models to represent better the complex interactions in agricultural systems that largely determine food supply and sustainable resource use. Nonetheless, the challenges for model integration across disciplines are substantial and range from methodological and technical details to an often still-weak conceptual basis on which to ground model integration (Ewertet al., 2009; Janssenet al., 2011). New generations of integrated assessment models based on well-understood, general relationships that are applicable to different agricultural systems across the world are still to be developed. Initial efforts are underway towards this advancement (Nelsonet al., 2014; Rosenzweiget al., 2013). Together with economic and climate models, crop models constitute an essential model group in IAM for large-area cropping systems climate change impact assessments. However, in addition to challenges associated with model integration, inadequate representation of many crops and crop management systems, as well as a lack of data for model initialization and calibration, limit the integration of crop models with climate and economic models (Ewertet al., 2014). A particular obstacle is the mismatch between the temporal and spatial scale of input/output variables required and delivered by the various models in the IAM model chain. Crop models are typically developed, tested, and calibrated for field-scale application (Booteet al., 2013; see also Part 1, Chapter 4 in this volume) and short time-series limited to one or few seasons. Although crop models are increasingly used for larger areas and longer time-periods (Bondeauet al., 2007; Deryng et al., 2011; Elliottet al., 2014) rigorous evaluation of such applications is pending. Among the different sources of uncertainty related to climate and soil data, model parameters, and structure, the uncertainty from methods used to scale-up crop models has received little attention, though recent evaluations indicate that upscaling of crop models for climate change impact assessment and the resulting errors and uncertainties deserve attention in order to advance crop modeling for climate change assessment (Ewertet al., 2014; R¨ otteret al., 2011). This reality is now reflected in the scientific agendas of new international research projects and programs such as the Agricultural Model Intercomparison and Improvement Project (AgMIP; Rosenzweiget al., 2013) and MACSUR (MACSUR, 2014). In this chapter, progress in evaluation of scaling methods with their related uncertainties is reviewed. Specific emphasis is on examining the results of systematic studies recently established in AgMIP and MACSUR. Main features of the respective simulation studies are presented together with preliminary results. Insights from these studies are summarized and conclusions for further work are drawn.

Published
2015
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48. The AgMIP Coordinated Climate-Crop Modeling Project (C3MP): Methods and Protocols

Author

Sonali P. McDermid, Alexander C. Ruane, Cynthia Rosenzweig, Nicholas I. Hudson, Monica D. Morales, Prabodha Agalawatte, Shakeel Ahmad, L. R. Ahuja, Istiqlal Amien, Saseendran S. Anapalli, Jakarat Anothai, Senthold Asseng, Jody Biggs, Federico Bert, Patrick Bertuzzi, Virender S. Bhatia, Marco Bindi, Ian Broad, Davide Cammarano, Ramiro Carretero, Ashfaq Ahmad Chattha, Uran Chung, Stephanie Debats, Paola Deligios, Giacomo De Sanctis, Thanda Dhliwayo, Benjamin Dumont, Lyndon Estes, Frank Ewert, Roberto Ferrise, Thomas Gaiser, Guillermo Garcia, Sika Gbegbelegbe, Vellingiri Geethalakshmi, Edward Gerardeaux, Richard Goldberg, Brian Grant, Edgardo Guevara, Jonathan Hickman, Holger Hoffmann, Huanping Huang, Jamshad Hussain, Flavio Barbosa Justino, Asha S. Karunaratne, Ann-Kristin Koehler, Patrice K. Kouakou, Soora Naresh Kumar, Arunachalam Lakshmanan, Mark Lieffering, Xiaomao Lin, Qunying Luo, Graciela Magrin, Marco Mancini, Fabio Ricardo Marin, Anna Dalla Marta, Yuji Masutomi, Theodoros Mavromatis, Greg McLean, Santiago Meira, Monoranjan Mohanty, Marco Moriondo, Wajid Nasim, Lamyaa Negm, Francesca Orlando, Simone Orlandini, Isik Ozturk, Helena Maria Soares Pinto, Guillermo Podesta, Zhiming Qi, Johanna Ramarohetra, Muhammad Habib ur Rahman, Helene Raynal, Gabriel Rodriguez, Reimund Rötter, Vaishali Sharda, Lu Shuo, Ward Smith, Val Snow, Afshin Soltani, K. Srinivas, Benjamin Sultan, Dillip Kumar Swain, Fulu Tao, Kindie Tesfaye, Maria I. Travasso, Giacomo Trombi, Alex Topaj, Eline Vanuytrecht, Federico E. Viscarra, Syed Aftab Wajid, Enli Wang, Hong Wang, Jing Wang, Erandika Wijekoon, Lee Byun-Woo, Yang Xiaoguang, Ban Ho Young, Jin I. Yun, Zhigan Zhao, and Lareef Zubair

Subjects
P40 - Météorologie et climatologie, U10 - Informatique, mathématiques et statistiques, business.industry, Impact assessment, Crop yield, Environmental resource management, Climate change, Water resources, F01 - Culture des plantes, Agriculture, Soil retrogression and degradation, Environmental science, Climate model, business, Environmental planning, Downscaling
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)...

Published
2015

49. Temperature and precipitation effects on wheat yield across a European transect: a crop model ensemble analysis using impact response surfaces

Author

Alex C. Ruane, Kurt Christian Kersebaum, Roberto Ferrise, Frank Ewert, Lianhai Wu, Mikhail A. Semenov, Benjamin Dumont, Marco Acutis, Bruno Basso, Senthold Asseng, Timothy R. Carter, Enli Wang, Miroslav Trnka, Pierre Stratonovitch, Ingrid Jacquemin, Davide Cammarano, Alfredo Rodríguez, Stefan Fronzek, Per Bodin, Paola A. Deligios, Mattia Sanna, Marco Bindi, Nina Pirttioja, Manuel Montesino, Chris Kollas, Thomas Gaiser, Samuel Buis, Ignacio J. Lorite, Cezary Sławiński, Fulu Tao, Christoph Müller, Reimund P. Rötter, Marco Moriondo, Louis François, Katharina Waha, Piotr Baranowski, Taru Palosuo, Claas Nendel, Petr Hlavinka, Isik Öztürk, Françoise Ruget, Alessia Perego, Margarita Ruiz-Ramos, Zhigan Zhao, Marie-France Destain, Jaromir Krzyszczak, Julien Minet, Holger Hoffmann, M. I. Mínguez, Iwan Supit, Finnish Environment Institute (SYKE), University of Florence (UNIFI), INRES, Rheinische Friedrich-Wilhelms-Universität Bonn, Natural Resources Institute Finland, CEIGRAM-AgSystems, Universidad Politécnica de Madrid (UPM), Institute of Agrosystems and Bioclimatology, Mendel University in Brno, v.v.i, Global Change Research Centre AS CR, University of Milan, University of Florida [Gainesville], Institute of Agrophysics, Polska Akademia Nauk (PAN), University of Michigan [Ann Arbor], University of Michigan System, Lund University [Lund], Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), The James Hutton Institute, University of Sassari, Université de Liège, v.v.i., Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), IFAPA, University of Copenhagen = Københavns Universitet (KU), Istituto di Biometeorologia [Firenze] (IBIMET), Consiglio Nazionale delle Ricerche (CNR), Potsdam Institute for Climate Impact Research (PIK), Aarhus University [Aarhus], NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), Center for Climate Systems Research [New York] (CCSR), Columbia University [New York], Rothamsted Research, Wageningen University and Research Centre [Wageningen] (WUR), CSIRO Agriculture Flagship, China Agricultural University (CAU), financial support from the following sources: FACCE JPI, MACSUR, the Academy of Finland for the A-LA-CARTE (decisions: 140806 and 140870) and PLUMES (decisions: 277276 and 277403) projects, the European Commission Seventh Framework Programme IMPRESSIONS project (grant agreement no. 603416), the Italian Ministry of Agriculture (AGROSCENARI Project), the Italian Ministry of Research (FIRB 2012, RBFR12B2K4_004), the German Federal Office for Agriculture and Nutrition, the MTT strategic project MODAGS, the MULCLIVAR project from the Spanish Ministerio de Economia y Competitividad (MINECO) CGL2012-38923-C02-02, the Belgium funding agency DGO-3(SPW-Belgium), University of Sassari, Polish National Centre for Research and Development, the German Federal Ministries of Education and Research, and Food and Agriculture (grant no. 2812ERA115), the Czech National Agency for Agricultural Research (project no. QJ1310123) and KONTAKT project no. LD13030, Università degli Studi di Firenze = University of Florence [Firenze] (UNIFI), Mendel University in Brno (MENDELU), University of Florida [Gainesville] (UF), Polska Akademia Nauk = Polish Academy of Sciences (PAN), and Wageningen University and Research [Wageningen] (WUR)

Subjects
mallintaminen, [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, Atmospheric Science, Yield, Yield (finance), Climate, IRS, ta1172, ta1171, Triticum aestivum, Bivariate analysis, Ensemble analysis, Atmospheric sciences, Earth System Science, Saksa, Crop, sato, Suomi, ilmasto, Environmental Chemistry, herkkyys, Geología, Precipitation, Crop model, Transect, General Environmental Science, 2. Zero hunger, Hydrology, Crop yield, sensitiivisyys, Agricultura, Espanja, mallit, 15. Life on land, ta4111, ilmastonmuutokset, Climate Resilience, Geography, 13. Climate action, Klimaatbestendigheid, analyysi, Wheat, Leerstoelgroep Aardsysteemkunde, vehnä, Crop simulation model, Impact response surface, Sensitivity analysis
Abstract

This study explored the utility of the impact response surface (IRS) approach for investigating model ensemble crop yield responses under a large range of changes in climate. IRSs of spring and winter wheat Triticum aestivum yields were constructed from a 26-member ensemble of process-based crop simulation models for sites in Finland, Germany and Spain across a latitudinal transect. The sensitivity of modelled yield to systematic increments of changes in temperature (-2 to + 9 degrees C) and precipitation (-50 to + 50%) was tested by modifying values of baseline (1981 to 2010) daily weather, with CO2 concentration fixed at 360 ppm. The IRS approach offers an effective method of portraying model behaviour under changing climate as well as advantages for analysing, comparing and presenting results from multi-model ensemble simulations. Though individual model behaviour occasionally departed markedly from the average, ensemble median responses across sites and crop varieties indicated that yields decline with higher temperatures and decreased precipitation and increase with higher precipitation. Across the uncertainty ranges defined for the IRSs, yields were more sensitive to temperature than precipitation changes at the Finnish site while sensitivities were mixed at the German and Spanish sites. Precipitation effects diminished under higher temperature changes. While the bivariate and multi-model characteristics of the analysis impose some limits to interpretation, the IRS approach nonetheless provides additional insights into sensitivities to inter-model and inter-annual variability. Taken together, these sensitivities may help to pinpoint processes such as heat stress, vernalisation or drought effects requiring refinement in future model development.

Published
2015
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50. Effect of weather data aggregation on regional crop simulation for different crops, production conditions, and response variables

Author

Gunther Krauss, Marco Moriondo, Holger Hoffmann, Claas Nendel, Matthias Kuhnert, Julie Constantin, Frank Ewert, Lenny G.J. van Bussel, Elisabet Lewan, Zhigan Zhao, Thomas Gaiser, Henrik Eckersten, Balázs Grosz, Edmar Teixeira, Eline Vanuytrecht, Ralf Kiese, Xenia Specka, Reimund P. Rötter, Jagadeesh Yeluripati, Stefan Siebert, Pier Paolo Roggero, Davide Cammarano, Kurt Christian Kersebaum, Edwin Haas, Giacomo Trombi, Luca Doro, Gang Zhao, Enli Wang, Daniel Wallach, Carmen Sosa, Marco Bindi, Andreas Enders, Fulu Tao, Helene Raynal, Senthold Asseng, Michaela Bach, Institute of Crop Science and Resource Conservation [Bonn] (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, Plant Production Systems Group, Wageningen University and Research [Wageningen] (WUR), Institute of landscape systems analysis, Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Swedish University of Agricultural Sciences (SLU), INSTITUTE OF BIOLOGICAL AND ENVIRONMENTAL SCIENCES, University of Aberdeen, The James Hutton Institute, Agrifood Research Finland, AGroécologie, Innovations, teRritoires (AGIR), Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Systems Modelling Team (Sustainable Production Group), Plant & Food Research, Johann Heinrich von Thünen Institut, Università degli Studi di Sassari, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), Karlsruhe Institute of Technology (KIT), Department of Crop Production Ecology, Division Soil and Water Management, Catholic University of Leuven - Katholieke Universiteit Leuven (KU Leuven), Department of Agri-Food Production and Environmental Sciences, University delgi Studi di Firenze, Istituto di Biometeorologia [Firenze] (IBIMET), Consiglio Nazionale delle Ricerche (CNR), Department of Agricultural and Biological Engineering [Gainesville] (UF|ABE), Institute of Food and Agricultural Sciences [Gainesville] (UF|IFAS), University of Florida [Gainesville] (UF)-University of Florida [Gainesville] (UF), University of Florida [Gainesville] (UF), BMBF/BMELV project on 'Modeling European Agriculture with Climate Change for Food Security (MACSUR)' [2812ERA115], Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning [220-2007-1218], strategic funding 'Soil-Water-Landscape' from the faculty of Natural Resources and Agricultural Sciences (Swedish University of Agricultural Sciences), Royal Society of New Zealand, and Climate Changes, Impacts and Implications for New Zealand Project (CCII)

Subjects
Atmospheric Science, Meteorology, Mean squared error, [SDV]Life Sciences [q-bio], Growing season, Plant Ecology and Nature Conservation, Atmospheric sciences, Scaling, [SHS]Humanities and Social Sciences, Crop, Evapotranspiration, Environmental Chemistry, Water-use efficiency, Crop model, General Environmental Science, 2. Zero hunger, Spatial resolution, Model comparison, 15. Life on land, PE&RC, Spatial heterogeneity, Data aggregation, Geography, Plant Production Systems, 13. Climate action, Plantaardige Productiesystemen, [SDE]Environmental Sciences, Plantenecologie en Natuurbeheer, Spatial variability, Cropping
Abstract

International audience; We assessed the weather data aggregation effect (DAE) on the simulation of cropping systems for different crops, response variables, and production conditions. Using 13 process-based crop models and the ensemble mean, we simulated 30 yr continuous cropping systems for 2 crops (winter wheat and silage maize) under 3 production conditions for the state of North Rhine-Westphalia, Germany. The DAE was evaluated for 5 weather data resolutions (i.e. 1, 10, 25, 50, and 100 km) for 3 response variables including yield, growing season evapotranspiration, and water use efficiency. Five metrics, viz. the spatial bias (Delta), average absolute deviation (AAD), relative AAD, root mean squared error (RMSE), and relative RMSE, were used to evaluate the DAE on both the input weather data and simulated results. For weather data, we found that data aggregation narrowed the spatial variability but widened the., especially across mountainous areas. The DAE on loss of spatial heterogeneity and hotspots was stronger than on the average changes over the region. The DAE increased when coarsening the spatial resolution of the input weather data. The DAE varied considerably across different models, but changed only slightly for different production conditions and crops. We conclude that if spatially detailed information is essential for local management decision, higher resolution is desirable to adequately capture the spatial variability for heterogeneous regions. The required resolution depends on the choice of the model as well as the environmental condition of the study area.

Published
2015
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