Register      Login
Crop and Pasture Science Crop and Pasture Science Society
Plant sciences, sustainable farming systems and food quality

Articles citing this paper

Genotype by environment interactions affecting grain sorghum. III. Temporal sequences and spatial patterns in the target population of environments

S. C. Chapman, G. L. Hammer, D. G. Butler and M. Cooper
51(2) pp.223 - 234


81 articles found in Crossref database.

Extending the breeder’s equation to take aim at the target population of environments
Cooper Mark, Powell Owen, Gho Carla, Tang Tom, Messina Carlos
Frontiers in Plant Science. 2023 14
Working with Dynamic Crop Models (2014)
Wallach Daniel, Makowski David, Jones James W., Brun François
Seasonal Effects and Genotypic Responses for Grain Yield in Semi-dwarf Wheat
. Mahboob Ali Sial, . M. Afzal Arain, . Mazhar H. Naqvi, . A.M. Soomro, . Sawan Laghari, . Nisar A. Nizamani, . Akbar Ali
Asian Journal of Plant Sciences. 2003 2(15). p.1097
Environmental characterization and yield gap analysis to tackle genotype-by-environment-by-management interactions and map region-specific agronomic and breeding targets in groundnut
Hajjarpoor Amir, Kholová Jana, Pasupuleti Janila, Soltani Afshin, Burridge James, Degala Subhash Babu, Gattu S., Murali T.V., Garin Vincent, Radhakrishnan Thankappan, Vadez Vincent
Field Crops Research. 2021 267 p.108160
Modelling the effect of plant water use traits on yield and stay-green expression in sorghum
Kholová Jana, Murugesan Tharanya, Kaliamoorthy Sivasakthi, Malayee Srikanth, Baddam Rekha, Hammer Graeme L., McLean Greg, Deshpande Santosh, Hash C. Thomas, Craufurd Peter Q., Vadez Vincent
Functional Plant Biology. 2014 41(11). p.1019
Breeding crops for drought-affected environments and improved climate resilience
Cooper Mark, Messina Carlos D
The Plant Cell. 2023 35(1). p.162
Can We Harness “Enviromics” to Accelerate Crop Improvement by Integrating Breeding and Agronomy?
Cooper Mark, Messina Carlos D.
Frontiers in Plant Science. 2021 12
Examining the yield potential of barley near-isogenic lines using a genotype by environment by management analysis
Ibrahim Ahmed, Harrison Matthew Tom, Meinke Holger, Zhou Meixue
European Journal of Agronomy. 2019 105 p.41
Genotype-specific P-spline response surfaces assist interpretation of regional wheat adaptation to climate change
Bustos-Korts Daniela, Boer Martin P, Chenu Karine, Zheng Bangyou, Chapman Scott, van Eeuwijk Fred A, Messina Carlos, Long Stephen P
in silico Plants. 2021 3(2).
Crop Physiology (2015)
Chenu Karine
Assessing environment types for maize, soybean, and wheat in the United States as determined by spatio-temporal variation in drought and heat stress
Couëdel Antoine, Edreira Juan Ignacio Rattalino, Pisa Lollato Romulo, Archontoulis Sotirios, Sadras Victor, Grassini Patricio
Agricultural and Forest Meteorology. 2021 307 p.108513
Identification of environment similarities using a crop model to assist the cultivation and breeding of a new crop in a new region
Chauhan Yashvir S., Sands Doug, Krosch Steve, Agius Peter, Frederiks Troy, Chenu Karine, Williams Rex, Palta Jairo
Crop & Pasture Science. 2023 75(1).
Towards a multiscale crop modelling framework for climate change adaptation assessment
Peng Bin, Guan Kaiyu, Tang Jinyun, Ainsworth Elizabeth A., Asseng Senthold, Bernacchi Carl J., Cooper Mark, Delucia Evan H., Elliott Joshua W., Ewert Frank, Grant Robert F., Gustafson David I, Hammer Graeme L., Jin Zhenong, Jones James W., Kimm Hyungsuk, Lawrence David M., Li Yan, Lombardozzi Danica L., Marshall-Colon Amy, Messina Carlos D., Ort Donald R., Schnable James C., Vallejos C. Eduardo, Wu Alex, Yin Xinyou, Zhou Wang
Nature Plants. 2020 6(4). p.338
Improving winter barley adaptation to freezing and heat stresses in the U.S. Midwest: bottlenecks and opportunities
Sadok Walid, Wiersma Jochum J., Steffenson Brian J., Snapp Sigelinde S., Smith Kevin P.
Field Crops Research. 2022 286 p.108635
Innovations in Dryland Agriculture (2016)
Sohail Quahir, Naheed Hafsa, Mohammadi Reza
How process-based modeling can help plant breeding deal with G x E x M interactions
Hajjarpoor Amir, Nelson William C.D., Vadez Vincent
Field Crops Research. 2022 283 p.108554
The E(NK) model: Extending the NK model to incorporate gene‐by‐environment interactions and epistasis for diploid genomes
Cooper Mark, Podlich Dean W.
Complexity. 2002 7(6). p.31
Genetic and genomic resources of sorghum to connect genotype with phenotype in contrasting environments
Boyles Richard E., Brenton Zachary W., Kresovich Stephen
The Plant Journal. 2019 97(1). p.19
Tackling G × E × M interactions to close on-farm yield-gaps: creating novel pathways for crop improvement by predicting contributions of genetics and management to crop productivity
Cooper Mark, Voss-Fels Kai P., Messina Carlos D., Tang Tom, Hammer Graeme L.
Theoretical and Applied Genetics. 2021 134(6). p.1625
Characterization of the main chickpea cropping systems in India using a yield gap analysis approach
Hajjarpoor Amir, Vadez Vincent, Soltani Afshin, Gaur Pooran, Whitbread Anthony, Suresh Babu Dharani, Gumma Murali Krishna, Diancoumba Madina, Kholová Jana
Field Crops Research. 2018 223 p.93
Scale and Complexity in Plant Systems Research (2007)
Hammer G.L., Jordan D.R.
Variation and impact of drought-stress patterns across upland rice target population of environments in Brazil
Heinemann Alexandre Bryan, Barrios-Perez Camilo, Ramirez-Villegas Julian, Arango-Londoño David, Bonilla-Findji Osana, Medeiros João Carlos, Jarvis Andy
Journal of Experimental Botany. 2015 66(12). p.3625
Crop Systems Biology (2016)
Bustos-Korts Daniela, Malosetti Marcos, Chapman Scott, van Eeuwijk Fred
The Impacts of Flowering Time and Tillering on Grain Yield of Sorghum Hybrids across Diverse Environments
Wang Xuemin, Hunt Colleen, Cruickshank Alan, Mace Emma, Hammer Graeme, Jordan David
Agronomy. 2020 10(1). p.135
Advances in Environmental Biotechnology (2017)
Talukdar Daizee, Sharma Rohit, Kumar Raman
Genomics-Assisted Crop Improvement (2007)
Cooper Mark, Podlich Dean W., Luo Lang
Yield stability and phenotypic plasticity of Populus spp. clones growing in environmental gradients: I-yield stability under field conditions
Alvarez Javier A., Cortizo Silvia C., Gyenge Javier E.
Forest Ecology and Management. 2020 463 p.117995
APSIM – Evolution towards a new generation of agricultural systems simulation
Holzworth Dean P., Huth Neil I., deVoil Peter G., Zurcher Eric J., Herrmann Neville I., McLean Greg, Chenu Karine, van Oosterom Erik J., Snow Val, Murphy Chris, Moore Andrew D., Brown Hamish, Whish Jeremy P.M., Verrall Shaun, Fainges Justin, Bell Lindsay W., Peake Allan S., Poulton Perry L., Hochman Zvi, Thorburn Peter J., Gaydon Donald S., Dalgliesh Neal P., Rodriguez Daniel, Cox Howard, Chapman Scott, Doherty Alastair, Teixeira Edmar, Sharp Joanna, Cichota Rogerio, Vogeler Iris, Li Frank Y., Wang Enli, Hammer Graeme L., Robertson Michael J., Dimes John P., Whitbread Anthony M., Hunt James, van Rees Harm, McClelland Tim, Carberry Peter S., Hargreaves John N.G., MacLeod Neil, McDonald Cam, Harsdorf Justin, Wedgwood Sara, Keating Brian A.
Environmental Modelling & Software. 2014 62 p.327
Characterization of spatial and temporal combinations of climatic factors affecting yields: An empirical model applied to the French barley belt
Beillouin Damien, Jeuffroy Marie-Hélène, Gauffreteau Arnaud
Agricultural and Forest Meteorology. 2018 262 p.402
Quantifying high temperature risks and their potential effects on sorghum production in Australia
Singh Vijaya, Nguyen Chuc T., McLean Greg, Chapman Scott C., Zheng Bangyou, van Oosterom Erik J., Hammer Graeme L.
Field Crops Research. 2017 211 p.77
Genetically Modified Food and Global Welfare (2011)
Herdt Robert W., Nelson Rebecca
What Should Students in Plant Breeding Know About the Statistical Aspects of Genotype × Environment Interactions?
van Eeuwijk Fred A., Bustos‐Korts Daniela V., Malosetti Marcos
Crop Science. 2016 56(5). p.2119
Characterization of drought stress environments for upland rice and maize in central Brazil
Heinemann Alexandre Bryan, Dingkuhn Michael, Luquet Delphine, Combres Jean Claude, Chapman Scott
Euphytica. 2008 162(3). p.395
Characterization of north-eastern Australian environments using APSIM for increasing rainfed maize production
Chauhan Y.S., Solomon K.F., Rodriguez D.
Field Crops Research. 2013 144 p.245
On to the next chapter for crop breeding: Convergence with data science
Ersoz Elhan S., Martin Nicolas F., Stapleton Ann E.
Crop Science. 2020 60(2). p.639
Gene-to-phenotype models and complex trait genetics
Cooper Mark, Podlich Dean W., Smith Oscar S.
Australian Journal of Agricultural Research. 2005 56(9). p.895
Integrating genetic gain and gap analysis to predict improvements in crop productivity
Cooper Mark, Tang Tom, Gho Carla, Hart Tim, Hammer Graeme, Messina Carlos
Crop Science. 2020 60(2). p.582
Sorghum (2019)
Hammer Graeme, McLean Greg, Doherty Al, van Oosterom Erik, Chapman Scott
Data-Driven Machine Learning for Pattern Recognition Supports Environmental Quality Prediction for Irrigated Rice in Brazil
Costa-Neto Germano, Matta David Henriques da, Fernandes Igor Kuivjogi, Stone Luís Fernando, Heinemann Alexandre Bryan
SSRN Electronic Journal . 2022
Genotypic variation for grain and stover yield of dryland (rabi) sorghum in India: 1. Magnitude of genotype×environment interactions
DeLacy I.H., Kaul S., Rana B.S., Cooper M.
Field Crops Research. 2010 118(3). p.228
Enviromics in breeding: applications and perspectives on envirotypic-assisted selection
Resende Rafael T., Piepho Hans-Peter, Rosa Guilherme J. M., Silva-Junior Orzenil B., e Silva Fabyano F., de Resende Marcos Deon V., Grattapaglia Dario
Theoretical and Applied Genetics. 2021 134(1). p.95
Adaptation to diverse semi-arid environments of sorghum genotypes having different plant type and sensitivity to photoperiod
Kouressy Mamoutou, Dingkuhn Michael, Vaksmann Michel, Heinemann Alexandre Bryan
Agricultural and Forest Meteorology. 2008 148(3). p.357
Accelerated Plant Breeding, Volume 3 (2020)
Kumar Shiv, Gupta Priyanka, Choukri Hasnae, Siddique Kadambot H. M.
Plant Breeding: The Arnel R. Hallauer International Symposium (2006)
Edmeades Gregory, Bänziger Marianne, Campos Hugo, Schussler Jeffrey
From QTLs to Adaptation Landscapes: Using Genotype-To-Phenotype Models to Characterize G×E Over Time
Bustos-Korts Daniela, Malosetti Marcos, Chenu Karine, Chapman Scott, Boer Martin P., Zheng Bangyou, van Eeuwijk Fred A.
Frontiers in Plant Science. 2019 10
Environmental clusters defining breeding zones for tropical irrigated rice in Brazil
Costa‐Neto Germano, Matta David Henriques da, Fernandes Igor Kuivjogi, Stone Luís Fernando, Heinemann Alexandre Bryan
Agronomy Journal. 2023
Nitrogen nutrition index predicted by a crop model improves the genomic prediction of grain number for a bread wheat core collection
Ly Delphine, Chenu Karine, Gauffreteau Arnaud, Rincent Renaud, Huet Sylvie, Gouache David, Martre Pierre, Bordes Jacques, Charmet Gilles
Field Crops Research. 2017 214 p.331
Genotype by environment interaction of newly developed sorghum lines in Namibia
Wanga Maliata Athon, Shimelis Hussein, Mashilo Jacob
Euphytica. 2022 218(10).
The shifting influence of drought and heat stress for crops in northeast Australia
Lobell David B., Hammer Graeme L., Chenu Karine, Zheng Bangyou, McLean Greg, Chapman Scott C.
Global Change Biology. 2015 21(11). p.4115
Sorghum drought and heat stress patterns across the Argentinean temperate central region
Carcedo Ana J.P., Gambin Brenda L.
Field Crops Research. 2019 241 p.107552
Genotypic Differences in Effects of Short Episodes of High‐Temperature Stress during Reproductive Development in Sorghum
Singh Vijaya, Nguyen Chuc T., Yang Zongjian, Chapman Scott C., van Oosterom Erik J., Hammer Graeme L.
Crop Science. 2016 56(4). p.1561
Concepts and strategies for plant adaptation research in rainfed lowland rice
Cooper Mark
Field Crops Research. 1999 64(1-2). p.13
Padrões de deficiência hídrica para a cultura de milho (safra normal e safrinha) no estado de Goiás e suas conseqüencias para o melhoramento genético
Heinemann Alexandre Bryan, Andrade Camilo de Lelis Teixeira de, Gomide Reinaldo Lúcio, Amorim André de O., Paz Rosidalva Lopes da
Ciência e Agrotecnologia. 2009 33(4). p.1026
Evaluating Plant Breeding Strategies by Simulating Gene Action and Dryland Environment Effects
Chapman Scott, Cooper Mark, Podlich Dean, Hammer Graeme
Agronomy Journal. 2003 95(1). p.99
Working with Dynamic Crop Models (2019)
Wallach Daniel, Makowski David, Jones James W., Brun François
Environment characterization as an aid to wheat improvement: interpreting genotype–environment interactions by modelling water-deficit patterns in North-Eastern Australia
Chenu K., Cooper M., Hammer G. L., Mathews K. L., Dreccer M. F., Chapman S. C.
Journal of Experimental Botany. 2011 62(6). p.1743
Water and thermal regimes for field pea in Australia and their implications for breeding
Sadras V. O., Lake L., Chenu K., McMurray L. S., Leonforte A.
Crop and Pasture Science. 2012 63(1). p.33
Yield stability evaluation of peanut lines: A comparison of an experimental versus a simulation approach
Banterng P., Patanothai A., Pannangpetch K., Jogloy S., Hoogenboom G.
Field Crops Research. 2006 96(1). p.168
Molecular Breeding of Forage Crops (2001)
Cooper M., Podlich D. W., Micallef K. P.
Predicting the future of plant breeding: complementing empirical evaluation with genetic prediction
Cooper Mark, Messina Carlos D., Podlich Dean, Totir L. Radu, Baumgarten Andrew, Hausmann Neil J., Wright Deanne, Graham Geoffrey
Crop and Pasture Science. 2014 65(4). p.311
Caracterização dos padrões de estresse hídrico para a cultura do arroz (ciclo curto e médio) no estado de Goiás e suas conseqüências para o melhoramento genético
Heinemann Alexandre Bryan
Ciência e Agrotecnologia. 2010 34(1). p.29
Plant Breeding Reviews (2005)
Barker T., Campos H., Cooper M., Dolan D., Edmeades G., Habben J., Schussler J., Wright D., Zinselmeier C.
Visualising the pattern of long‐term genotype performance by leveraging a genomic prediction model
Arief Vivi N., DeLacy Ian H., Payne Thomas, Basford Kaye E.
Australian & New Zealand Journal of Statistics. 2022 64(2). p.297
Modelling selection response in plant-breeding programs using crop models as mechanistic gene-to-phenotype (CGM-G2P) multi-trait link functions
Cooper M, Powell O, Voss-Fels K P, Messina C D, Gho C, Podlich D W, Technow F, Chapman S C, Beveridge C A, Ortiz-Barrientos D, Hammer G L, Vadez Vincent
in silico Plants. 2021 3(1).
Statistical models for genotype by environment data: from conventional ANOVA models to eco-physiological QTL models
van Eeuwijk Fred A., Malosetti Marcos, Yin Xinyou, Struik Paul C., Stam Piet
Australian Journal of Agricultural Research. 2005 56(9). p.883
Accelerating crop genetic gains with genomic selection
Voss-Fels Kai Peter, Cooper Mark, Hayes Ben John
Theoretical and Applied Genetics. 2019 132(3). p.669
Biotechnology in Agriculture
Herdt Robert W.
Annual Review of Environment and Resources. 2006 31(1). p.265
Use of crop models to understand genotype by environment interactions for drought in real-world and simulated plant breeding trials
Chapman Scott C.
Euphytica. 2008 161(1-2). p.195
Bioenergy Sorghum Crop Model Predicts VPD-Limited Transpiration Traits Enhance Biomass Yield in Water-Limited Environments
Truong Sandra K., McCormick Ryan F., Mullet John E.
Frontiers in Plant Science. 2017 8
Future contributions of crop modelling—from heuristics and supporting decision making to understanding genetic regulation and aiding crop improvement
Hammer G.L., Kropff M.J., Sinclair T.R., Porter J.R.
European Journal of Agronomy. 2002 18(1-2). p.15
Determination of grain number in sorghum
van Oosterom E.J., Hammer G.L.
Field Crops Research. 2008 108(3). p.259
Application of the Cropping System Model (CSM)‐CROPGRO‐Soybean for Determining Optimum Management Strategies for Soybean in Tropical Environments
Banterng P., Hoogenboom G., Patanothai A., Singh P., Wani S. P., Pathak P., Tongpoonpol S., Atichart S., Srihaban P., Buranaviriyakul S., Jintrawet A., Nguyen T. C.
Journal of Agronomy and Crop Science. 2010 196(3). p.231
An APSIM-powered framework for post-rainy sorghum-system design in India
Ronanki Swarna, Pavlík Jan, Masner Jan, Jarolímek Jan, Stočes Michal, Subhash Degala, Talwar Harvinder S., Tonapi Vilas A., Srikanth Mallayee, Baddam Rekha, Kholová Jana
Field Crops Research. 2022 277 p.108422
Agronomic model uses to predict cultivar performance in various environments and cropping systems. A review
Jeuffroy Marie-Hélène, Casadebaig Pierre, Debaeke Philippe, Loyce Chantal, Meynard Jean-Marc
Agronomy for Sustainable Development. 2014 34(1). p.121
Using Genomic Prediction to Characterize Environments and Optimize Prediction Accuracy in Applied Breeding Data
Heslot Nicolas, Jannink Jean‐Luc, Sorrells Mark E.
Crop Science. 2013 53(3). p.921
Physiological mechanisms of drought tolerance in sorghum, genetic basis and breeding methods: A review
Beyene Amelework, Hussien Shimelis, Pangirayi Tongoona, Mark Laing
African Journal of Agricultural Research. 2015 10(31). p.3029
Systems Modeling (2020)
Ahmed Mukhtar, Raza Muhammad Ali, Hussain Taimoor
From genome to crop: integration through simulation modeling
Hoogenboom Gerrit, White Jeffrey W., Messina Carlos D.
Field Crops Research. 2004 90(1). p.145
Large‐scale characterization of drought pattern: a continent‐wide modelling approach applied to the Australian wheatbelt – spatial and temporal trends
Chenu Karine, Deihimfard Reza, Chapman Scott C.
New Phytologist. 2013 198(3). p.801
Regression‐Based Evaluation of Ecophysiological Models
White Jeffrey W., Boote Kenneth J., Hoogenboom Gerrit, Jones Peter G.
Agronomy Journal. 2007 99(2). p.419
Plant Breeding Reviews (2022)
Cooper Mark, Messina Carlos D., Tang Tom, Gho Carla, Powell Owen M., Podlich Dean W., Technow Frank, Hammer Graeme L.

Committee on Publication Ethics


Abstract Export Citation Get Permission