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Resende RT, Hickey L, Amaral CH, Peixoto LL, Marcatti GE, Xu Y. Satellite-enabled enviromics to enhance crop improvement. MOLECULAR PLANT 2024; 17:848-866. [PMID: 38637991 DOI: 10.1016/j.molp.2024.04.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 04/04/2024] [Accepted: 04/11/2024] [Indexed: 04/20/2024]
Abstract
Enviromics refers to the characterization of micro- and macroenvironments based on large-scale environmental datasets. By providing genotypic recommendations with predictive extrapolation at a site-specific level, enviromics could inform plant breeding decisions across varying conditions and anticipate productivity in a changing climate. Enviromics-based integration of statistics, envirotyping (i.e., determining environmental factors), and remote sensing could help unravel the complex interplay of genetics, environment, and management. To support this goal, exhaustive envirotyping to generate precise environmental profiles would significantly improve predictions of genotype performance and genetic gain in crops. Already, informatics management platforms aggregate diverse environmental datasets obtained using optical, thermal, radar, and light detection and ranging (LiDAR)sensors that capture detailed information about vegetation, surface structure, and terrain. This wealth of information, coupled with freely available climate data, fuels innovative enviromics research. While enviromics holds immense potential for breeding, a few obstacles remain, such as the need for (1) integrative methodologies to systematically collect field data to scale and expand observations across the landscape with satellite data; (2) state-of-the-art AI models for data integration, simulation, and prediction; (3) cyberinfrastructure for processing big data across scales and providing seamless interfaces to deliver forecasts to stakeholders; and (4) collaboration and data sharing among farmers, breeders, physiologists, geoinformatics experts, and programmers across research institutions. Overcoming these challenges is essential for leveraging the full potential of big data captured by satellites to transform 21st century agriculture and crop improvement through enviromics.
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Affiliation(s)
- Rafael T Resende
- Universidade Federal de Goiás (UFG), Agronomy Department, Plant Breeding Sector, Goiânia (GO) 74690-900, Brazil; TheCROP, a Precision-Breeding Startup: Enviromics, Phenomics, and Genomics, No Zip-code, Operating Virtually, Goiânia (GO) and Sete Lagoas (MG), Brazil.
| | - Lee Hickey
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD, Australia
| | - Cibele H Amaral
- Earth Lab, Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO 80303, USA; Environmental Data Science Innovation & Inclusion Lab, Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO 80303, USA
| | - Lucas L Peixoto
- Universidade Federal de Goiás (UFG), Agronomy Department, Plant Breeding Sector, Goiânia (GO) 74690-900, Brazil
| | - Gustavo E Marcatti
- TheCROP, a Precision-Breeding Startup: Enviromics, Phenomics, and Genomics, No Zip-code, Operating Virtually, Goiânia (GO) and Sete Lagoas (MG), Brazil; Universidade Federal de São João del-Rei, Forest Engineering Department, Campus Sete Lagoas, Sete Lagoas (MG) 35701-970, Brazil
| | - Yunbi Xu
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China; Peking University Institute of Advanced Agricultural Sciences, Weifang, Shandong 261325, China; BGI Bioverse, Shenzhen 518083, China.
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Diancoumba M, Kholová J, Adam M, Famanta M, Clerget B, Traore PCS, Weltzien E, Vacksmann M, McLean G, Hammer GL, van Oosterom EJ, Vadez V. APSIM-based modeling approach to understand sorghum production environments in Mali. AGRONOMY FOR SUSTAINABLE DEVELOPMENT 2024; 44:25. [PMID: 38660316 PMCID: PMC11035133 DOI: 10.1007/s13593-023-00909-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 05/31/2023] [Indexed: 04/26/2024]
Abstract
Sorghum production system in the semi-arid region of Africa is characterized by low yields which are generally attributed to high rainfall variability, poor soil fertility, and biotic factors. Production constraints must be well understood and quantified to design effective sorghum-system improvements. This study uses the state-of-the-art in silico methods and focuses on characterizing the sorghum production regions in Mali for drought occurrence and its effects on sorghum productivity. For this purpose, we adapted the APSIM-sorghum module to reproduce two cultivated photoperiod-sensitive sorghum types across a latitude of major sorghum production regions in Western Africa. We used the simulation outputs to characterize drought stress scenarios. We identified three main drought scenarios: (i) no-stress; (ii) early pre-flowering drought stress; and (iii) drought stress onset around flowering. The frequency of drought stress scenarios experienced by the two sorghum types across rainfall zones and soil types differed. As expected, the early pre-flowering and flowering drought stress occurred more frequently in isohyets < 600 mm, for the photoperiod-sensitive, late-flowering sorghum type. In isohyets above 600 mm, the frequency of drought stress was very low for both cultivars. We quantified the consequences of these drought scenarios on grain and biomass productivity. The yields of the highly-photoperiod-sensitive sorghum type were quite stable across the higher rainfall zones > 600 mm, but was affected by the drought stress in the lower rainfall zones < 600 mm. Comparatively, the less photoperiod-sensitive cultivar had notable yield gain in the driest regions < 600 mm. The results suggest that, at least for the tested crop types, drought stress might not be the major constraint to sorghum production in isohyets > 600 mm. The findings from this study provide the entry point for further quantitative testing of the Genotype × Environment × Management options required to optimize sorghum production in Mali. Supplementary Information The online version contains supplementary material available at 10.1007/s13593-023-00909-5.
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Affiliation(s)
- Madina Diancoumba
- International Crops Research Institute for the Semiarid Tropics (ICRISAT), BP 320, Bamako, Mali
- Leibniz Centre for Agricultural Landscape Research (ZALF), Eberswalder Straße 84, 15374 Müncheberg, Germany
- International Crops Research Institute for the Semiarid Tropics (ICRISAT), Patancheru, Andhra Pradesh 502324 India
| | - Jana Kholová
- International Crops Research Institute for the Semiarid Tropics (ICRISAT), Patancheru, Andhra Pradesh 502324 India
- Department of Information Technologies, Faculty of Economics and Management, Czech University of Life Sciences Prague, Kamýcká 129, Prague, 165 00 Czech Republic
| | - Myriam Adam
- Centre de coopération internationale en recherche agronomique pour le développement (CIRAD), Avenue Agropolis, Cedex 5, 34398 Montpellier, France
| | - Mahamoudou Famanta
- Institut Polytechnique Rural de Formation et de Recherche Appliquée (IPR/IFRA), BP 06-Katibougou, Koulikoro, Mali
| | - Benoît Clerget
- Centre de coopération internationale en recherche agronomique pour le développement (CIRAD), Avenue Agropolis, Cedex 5, 34398 Montpellier, France
| | - Pierre C. S. Traore
- agCelerant-Senegal, Manobi Africa Group company, Almadies Rue 12 Lot 14, BP 25026, Dakar, Sénégal
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Ouakam Rue OKM29 Villa BP 24265, Dakar, Senegal
| | - Eva Weltzien
- International Crops Research Institute for the Semiarid Tropics (ICRISAT), BP 320, Bamako, Mali
- Agronomy Department, University of Wisconsin - Madison, 371 Moore Hall, Wisconsin, USA
| | - Michel Vacksmann
- Centre de coopération internationale en recherche agronomique pour le développement (CIRAD), Avenue Agropolis, Cedex 5, 34398 Montpellier, France
- Institut d’Economie Rurale (IER), BP 1813, Bamako, Mali
| | - Greg McLean
- Queensland Alliance for Agriculture and Food Innovation, University of Queensland, Brisbane, QLD 4072 Australia
| | - Graeme L. Hammer
- Queensland Alliance for Agriculture and Food Innovation, University of Queensland, Brisbane, QLD 4072 Australia
| | - Erik J. van Oosterom
- Queensland Alliance for Agriculture and Food Innovation, University of Queensland, Brisbane, QLD 4072 Australia
| | - Vincent Vadez
- International Crops Research Institute for the Semiarid Tropics (ICRISAT), Patancheru, Andhra Pradesh 502324 India
- French National Research Institute for Sustainable Development (IRD), UMR DIADE, University of Montpellier, 911 Av Agropolis BP65401, 34394 Montpellier, France
- LMI LAPSE, CERAAS-ISRA, Thiès, Senegal
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3
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Toda Y, Sasaki G, Ohmori Y, Yamasaki Y, Takahashi H, Takanashi H, Tsuda M, Kajiya-Kanegae H, Tsujimoto H, Kaga A, Hirai M, Nakazono M, Fujiwara T, Iwata H. Reaction norm for genomic prediction of plant growth: modeling drought stress response in soybean. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2024; 137:77. [PMID: 38460027 PMCID: PMC10924738 DOI: 10.1007/s00122-024-04565-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 01/30/2024] [Indexed: 03/11/2024]
Abstract
KEY MESSAGE We proposed models to predict the effects of genomic and environmental factors on daily soybean growth and applied them to soybean growth data obtained with unmanned aerial vehicles. Advances in high-throughput phenotyping technology have made it possible to obtain time-series plant growth data in field trials, enabling genotype-by-environment interaction (G × E) modeling of plant growth. Although the reaction norm is an effective method for quantitatively evaluating G × E and has been implemented in genomic prediction models, no reaction norm models have been applied to plant growth data. Here, we propose a novel reaction norm model for plant growth using spline and random forest models, in which daily growth is explained by environmental factors one day prior. The proposed model was applied to soybean canopy area and height to evaluate the influence of drought stress levels. Changes in the canopy area and height of 198 cultivars were measured by remote sensing using unmanned aerial vehicles. Multiple drought stress levels were set as treatments, and their time-series soil moisture was measured. The models were evaluated using three cross-validation schemes. Although accuracy of the proposed models did not surpass that of single-trait genomic prediction, the results suggest that our model can capture G × E, especially the latter growth period for the random forest model. Also, significant variations in the G × E of the canopy height during the early growth period were visualized using the spline model. This result indicates the effectiveness of the proposed models on plant growth data and the possibility of revealing G × E in various growth stages in plant breeding by applying statistical or machine learning models to time-series phenotype data.
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Affiliation(s)
- Yusuke Toda
- Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan
| | - Goshi Sasaki
- Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan
| | - Yoshihiro Ohmori
- Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan
| | - Yuji Yamasaki
- Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan
- Arid Land Research Center, Tottori University, Tottori, Japan
| | - Hirokazu Takahashi
- Graduate School of Bioagricultural Sciences, Nagoya University, Nagoya, Japan
| | - Hideki Takanashi
- Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan
| | - Mai Tsuda
- Tsukuba-Plant Innovation Research Center (T-PIRC), University of Tsukuba, Tsukuba, Japan
| | | | | | - Akito Kaga
- Institute of Crop Science, NARO, Tsukuba, Japan
| | - Masami Hirai
- RIKEN Center for Sustainable Resource Science, Tsukuba, Japan
| | - Mikio Nakazono
- Graduate School of Bioagricultural Sciences, Nagoya University, Nagoya, Japan
| | - Toru Fujiwara
- Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan
| | - Hiroyoshi Iwata
- Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan.
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Guo T, Wei J, Li X, Yu J. Environmental context of phenotypic plasticity in flowering time in sorghum and rice. JOURNAL OF EXPERIMENTAL BOTANY 2024; 75:1004-1015. [PMID: 37819624 PMCID: PMC10837014 DOI: 10.1093/jxb/erad398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 10/17/2023] [Indexed: 10/13/2023]
Abstract
Phenotypic plasticity is an important topic in biology and evolution. However, how to generate broadly applicable insights from individual studies remains a challenge. Here, with flowering time observed from a large geographical region for sorghum and rice genetic populations, we examine the consistency of parameter estimation for reaction norms of genotypes across different subsets of environments and searched for potential strategies to inform the study design. Both sample size and environmental mean range of the subset affected the consistency. The subset with either a large range of environmental mean or a large sample size resulted in genetic parameters consistent with the overall pattern. Furthermore, high accuracy through genomic prediction was obtained for reaction norm parameters of untested genotypes using models built from tested genotypes under the subsets of environments with either a large range or a large sample size. With 1428 and 1674 simulated settings, our analyses suggested that the distribution of environmental index values of a site should be considered in designing experiments. Overall, we showed that environmental context was critical, and considerations should be given to better cover the intended range of the environmental variable. Our findings have implications for the genetic architecture of complex traits, plant-environment interaction, and climate adaptation.
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Affiliation(s)
- Tingting Guo
- Hubei Hongshan Laboratory, Wuhan, Hubei, China
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, Hubei, China
| | - Jialu Wei
- Department of Agronomy, Iowa State University, Ames, IA, USA
| | - Xianran Li
- USDA, Agricultural Research Service, Wheat Health, Genetics, and Quality Research Unit, Pullman, WA, USA
| | - Jianming Yu
- Department of Agronomy, Iowa State University, Ames, IA, USA
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5
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Khanna A, Anumalla M, Ramos J, Cruz MTS, Catolos M, Sajise AG, Gregorio G, Dixit S, Ali J, Islam MR, Singh VK, Rahman MA, Khatun H, Pisano DJ, Bhosale S, Hussain W. Genetic gains in IRRI's rice salinity breeding and elite panel development as a future breeding resource. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2024; 137:37. [PMID: 38294550 PMCID: PMC10830834 DOI: 10.1007/s00122-024-04545-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 01/05/2024] [Indexed: 02/01/2024]
Abstract
KEY MESSAGE Estimating genetic gains and formulating a future salinity elite breeding panel for rice pave the way for developing better high-yielding salinity tolerant lines with enhanced genetic gains. Genetic gain is a crucial parameter to check the breeding program's success and help optimize future breeding strategies for enhanced genetic gains. To estimate the genetic gains in IRRI's salinity breeding program and identify the best genotypes based on high breeding values for grain yield (kg/ha), we analyzed the historical data from the trials conducted in the IRRI, Philippines and Bangladesh. A two-stage mixed-model approach accounting for experimental design factors and a relationship matrix was fitted to obtain the breeding values for grain yield and estimate genetic trends. A positive genetic trend of 0.1% per annum with a yield advantage of 1.52 kg/ha was observed in IRRI, Philippines. In Bangladesh, we observed a genetic gain of 0.31% per annum with a yield advantage of 14.02 kg/ha. In the released varieties, we observed a genetic gain of 0.12% per annum with a 2.2 kg/ha/year yield advantage in the IRRI, Philippines. For the Bangladesh dataset, a genetic gain of 0.14% per annum with a yield advantage of 5.9 kg/ha/year was observed in the released varieties. Based on breeding values for grain yield, a core set of the top 145 genotypes with higher breeding values of > 2400 kg/ha in the IRRI, Philippines, and > 3500 kg/ha in Bangladesh with a reliability of > 0.4 were selected to develop the elite breeding panel. Conclusively, a recurrent selection breeding strategy integrated with novel technologies like genomic selection and speed breeding is highly required to achieve higher genetic gains in IRRI's salinity breeding programs.
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Affiliation(s)
- Apurva Khanna
- Rice Breeding Innovation Platform, International Rice Research Institute (IRRI), 4031, Los Baños, Laguna, Philippines
| | - Mahender Anumalla
- Rice Breeding Innovation Platform, International Rice Research Institute (IRRI), 4031, Los Baños, Laguna, Philippines
| | - Joie Ramos
- Rice Breeding Innovation Platform, International Rice Research Institute (IRRI), 4031, Los Baños, Laguna, Philippines
| | - Ma Teresa Sta Cruz
- Rice Breeding Innovation Platform, International Rice Research Institute (IRRI), 4031, Los Baños, Laguna, Philippines
| | - Margaret Catolos
- Rice Breeding Innovation Platform, International Rice Research Institute (IRRI), 4031, Los Baños, Laguna, Philippines
| | - Andres Godwin Sajise
- Rice Breeding Innovation Platform, International Rice Research Institute (IRRI), 4031, Los Baños, Laguna, Philippines
| | - Glenn Gregorio
- Southeast Asian Regional Center for Graduate Study and Research in Agriculture (SEARCA) and University of Philippines, 4031, Los Baños, Laguna, Philippines
| | - Shalabh Dixit
- Rice Breeding Innovation Platform, International Rice Research Institute (IRRI), 4031, Los Baños, Laguna, Philippines
| | - Jauhar Ali
- Rice Breeding Innovation Platform, International Rice Research Institute (IRRI), 4031, Los Baños, Laguna, Philippines
| | - Md Rafiqul Islam
- IRRI South Asia Regional Center (IRRI-SA Hub), Hyderabad, Telangana, 502324, India
| | - Vikas Kumar Singh
- IRRI South Asia Regional Center (IRRI-SA Hub), Hyderabad, Telangana, 502324, India
| | - Md Akhlasur Rahman
- Plant Breeding Division, Bangladesh Rice Research Institute (BRRI), Gazipur, 1701, Bangladesh
| | - Hasina Khatun
- Plant Breeding Division, Bangladesh Rice Research Institute (BRRI), Gazipur, 1701, Bangladesh
| | - Daniel Joseph Pisano
- Rice Breeding Innovation Platform, International Rice Research Institute (IRRI), 4031, Los Baños, Laguna, Philippines
| | - Sankalp Bhosale
- Rice Breeding Innovation Platform, International Rice Research Institute (IRRI), 4031, Los Baños, Laguna, Philippines
| | - Waseem Hussain
- Rice Breeding Innovation Platform, International Rice Research Institute (IRRI), 4031, Los Baños, Laguna, Philippines.
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Knopf O, Castro A, Bendig J, Pude R, Kleist E, Poorter H, Rascher U, Muller O. Field phenotyping of ten wheat cultivars under elevated CO 2 shows seasonal differences in chlorophyll fluorescence, plant height and vegetation indices. FRONTIERS IN PLANT SCIENCE 2024; 14:1304751. [PMID: 38259917 PMCID: PMC10800489 DOI: 10.3389/fpls.2023.1304751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 12/05/2023] [Indexed: 01/24/2024]
Abstract
In the context of climate change and global sustainable development goals, future wheat cultivation has to master various challenges at a time, including the rising atmospheric carbon dioxide concentration ([CO2]). To investigate growth and photosynthesis dynamics under the effects of ambient (~434 ppm) and elevated [CO2] (~622 ppm), a Free-Air CO2 Enrichment (FACE) facility was combined with an automated phenotyping platform and an array of sensors. Ten modern winter wheat cultivars (Triticum aestivum L.) were monitored over a vegetation period using a Light-induced Fluorescence Transient (LIFT) sensor, ground-based RGB cameras and a UAV equipped with an RGB and multispectral camera. The LIFT sensor enabled a fast quantification of the photosynthetic performance by measuring the operating efficiency of Photosystem II (Fq'/Fm') and the kinetics of electron transport, i.e. the reoxidation rates Fr1' and Fr2'. Our results suggest that elevated [CO2] significantly increased Fq'/Fm' and plant height during the vegetative growth phase. As the plants transitioned to the senescence phase, a pronounced decline in Fq'/Fm' was observed under elevated [CO2]. This was also reflected in the reoxidation rates Fr1' and Fr2'. A large majority of the cultivars showed a decrease in the harvest index, suggesting a different resource allocation and indicating a potential plateau in yield progression under e[CO2]. Our results indicate that the rise in atmospheric [CO2] has significant effects on the cultivation of winter wheat with strong manifestation during early and late growth.
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Affiliation(s)
- Oliver Knopf
- Institute of Bio- and Geosciences: Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Antony Castro
- Institute of Bio- and Geosciences: Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Juliane Bendig
- Institute of Bio- and Geosciences: Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Ralf Pude
- INRES-Renewable Resources, University of Bonn, Rheinbach, Germany
| | - Einhard Kleist
- Institute of Bio- and Geosciences: Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Hendrik Poorter
- Institute of Bio- and Geosciences: Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, Jülich, Germany
- Department of Natural Sciences, Macquarie University, North Ryde, NSW, Australia
| | - Uwe Rascher
- Institute of Bio- and Geosciences: Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Onno Muller
- Institute of Bio- and Geosciences: Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, Jülich, Germany
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Gepts P. Biocultural diversity and crop improvement. Emerg Top Life Sci 2023; 7:ETLS20230067. [PMID: 38084755 PMCID: PMC10754339 DOI: 10.1042/etls20230067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 11/26/2023] [Accepted: 11/27/2023] [Indexed: 12/30/2023]
Abstract
Biocultural diversity is the ever-evolving and irreplaceable sum total of all living organisms inhabiting the Earth. It plays a significant role in sustainable productivity and ecosystem services that benefit humanity and is closely allied with human cultural diversity. Despite its essentiality, biodiversity is seriously threatened by the insatiable and inequitable human exploitation of the Earth's resources. One of the benefits of biodiversity is its utilization in crop improvement, including cropping improvement (agronomic cultivation practices) and genetic improvement (plant breeding). Crop improvement has tended to decrease agricultural biodiversity since the origins of agriculture, but awareness of this situation can reverse this negative trend. Cropping improvement can strive to use more diverse cultivars and a broader complement of crops on farms and in landscapes. It can also focus on underutilized crops, including legumes. Genetic improvement can access a broader range of biodiversity sources and, with the assistance of modern breeding tools like genomics, can facilitate the introduction of additional characteristics that improve yield, mitigate environmental stresses, and restore, at least partially, lost crop biodiversity. The current legal framework covering biodiversity includes national intellectual property and international treaty instruments, which have tended to limit access and innovation to biodiversity. A global system of access and benefit sharing, encompassing digital sequence information, would benefit humanity but remains an elusive goal. The Kunming-Montréal Global Biodiversity Framework sets forth an ambitious set of targets and goals to be accomplished by 2030 and 2050, respectively, to protect and restore biocultural diversity, including agrobiodiversity.
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Affiliation(s)
- Paul Gepts
- Department of Plant Sciences, Section of Crop and Ecosystem Sciences, University of California, Davis, CA 95616-8780, U.S.A
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Antichi D, Pampana S, Tramacere LG, Biarnes V, Stute I, Kadžiulienė Ž, Howard B, Duarte I, Balodis O, Bertin I, Makowski D, Guilpart N. An experimental dataset on yields of pulses across Europe. Sci Data 2023; 10:708. [PMID: 37848459 PMCID: PMC10582191 DOI: 10.1038/s41597-023-02606-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 09/28/2023] [Indexed: 10/19/2023] Open
Abstract
Future European agriculture should achieve high productivity while limiting its impact on the environment. Legume-supported crop rotations could contribute to these goals, as they request less nitrogen (N) fertilizer inputs, show high resource use efficiency and support biodiversity. However, legumes grown for their grain (pulses) are not widely cultivated in Europe. To further expand their cultivation, it remains crucial to better understand how different cropping and environmental features affect pulses production in Europe. To address this gap, we collected the grain yields of the most cultivated legumes across European countries, from both published scientific papers and unpublished experiments of the European projects LegValue and Legato. Data were integrated into an open-source, easily updatable dataset, including 5229 yield observations for five major pulses: chickpea (Cicer arietinum L.), faba bean (Vicia faba L.), field pea (Pisum sativum L.), lentil (Lens culinaris Medik.), and soybean (Glycine max (L.) Merr.). These data were collected in 177 field experiments across 21 countries, from 37° N (southern Italy) to 63° N (Finland) of latitude, and from ca. 8° W (western Spain) to 47° E (Turkey), between 1980 and 2020. Our dataset can be used to quantify the effects of the soil, climate, and agronomic factors affecting pulses yields in Europe and could contribute to identifying the most suitable cropping areas in Europe to grow pulses.
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Affiliation(s)
- Daniele Antichi
- Department of Agriculture, Food and Environment, University of Pisa, Via del Borghetto 80, Pisa, 56124, Italy.
- Centre for Agri-environmental Research "Enrico Avanzi", University of Pisa, Via Vecchia di Marina 2, San Piero a Grado, 56122, Italy.
| | - Silvia Pampana
- Department of Agriculture, Food and Environment, University of Pisa, Via del Borghetto 80, Pisa, 56124, Italy
- Centre for Agri-environmental Research "Enrico Avanzi", University of Pisa, Via Vecchia di Marina 2, San Piero a Grado, 56122, Italy
| | - Lorenzo Gabriele Tramacere
- Department of Agriculture, Food and Environment, University of Pisa, Via del Borghetto 80, Pisa, 56124, Italy
- Centre for Agri-environmental Research "Enrico Avanzi", University of Pisa, Via Vecchia di Marina 2, San Piero a Grado, 56122, Italy
| | - Véronique Biarnes
- Terres Inovia, Avenue Lucien Bretignières, Campus de Grignon, Thiverval-Grignon, 78850, France
| | - Ina Stute
- Fachhochschule Südwestfalen, Lübecker Ring 2, Soest, 59494, Germany
| | - Žydrė Kadžiulienė
- Lithuanian Research Centre for Agriculture and Forestry, Instituto al. 1, Akademija, Kėdainiai, LT-58344, Lithuania
| | - Becky Howard
- PGRO Research Limited, The Research Station, Great North Road, Thornhaugh, Peterborough, PE8 6HJ, UK
| | - Isabel Duarte
- Instituto Nacional de Investigaçao Agraria e Veterinaria, Estrada de Gil Vaz, Apartado 6, 7351-901, Elvas, Portugal
| | - Oskars Balodis
- Faculty of Agriculture, Latvia University of Agriculture, Lielâ iela 2, Jelgava, LV-3001, Latvia
| | - Iris Bertin
- Université Paris-Saclay, AgroParisTech, INRAE, UMR Agronomie, 91120, Palaiseau, France
| | - David Makowski
- University Paris-Saclay, AgroParisTech, INRAE, UMR MIA Paris-Saclay, 91120, Palaiseau, France
| | - Nicolas Guilpart
- Université Paris-Saclay, AgroParisTech, INRAE, UMR Agronomie, 91120, Palaiseau, France
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9
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Echarte L, Alfonso CS, González H, Hernández MD, Lewczuk NA, Nagore L, Echarte MM. Influence of management practices on water-related grain yield determinants. JOURNAL OF EXPERIMENTAL BOTANY 2023; 74:4825-4846. [PMID: 37490359 DOI: 10.1093/jxb/erad269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 07/24/2023] [Indexed: 07/27/2023]
Abstract
Adequate management of N supply, plant density, row spacing, and soil cover has proved useful for increasing grain yields and/or grain yield stability of rainfed crops over the years. We review the impact of these management practices on grain yield water-related determinants: seasonal crop evapotranspiration (ET) and water use efficiency for grain production per unit of evapotranspired water during the growing season (WUEG,ET,s). We highlight a large number of conflicting results for the impact of management on ET and expose the complexity of the ET response to environmental factors. We analyse the influence of management practices on WUEG,ET,s in terms of the three main processes controlling it: (i) the proportion of transpiration in ET (T/ET), (ii) transpiration efficiency for shoot biomass production (TEB), and (iii) the harvest index. We directly relate the impact of management practices on T/ET to their effect on crop light interception and provide evidence that management practices significantly influence TEB. To optimize WUEG,ET,s, management practices should favor soil water availability during critical periods for seed set, thereby improving the harvest index. The need to improve the performance of existing crop growth models for the prediction of water-related grain yield determinants under different management practices is also discussed.
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Affiliation(s)
- Laura Echarte
- IPADS (INTA-CONICET), Ruta 226 Km 73.5, Balcarce, Argentina
- Facultad de Ciencias Agrarias, Universidad Nacional de Mar del Plata, Ruta 226 Km 73.5, Balcarce, Argentina
| | - Carla S Alfonso
- Facultad de Ciencias Agrarias, Universidad Nacional de Mar del Plata, Ruta 226 Km 73.5, Balcarce, Argentina
| | - Hugo González
- IPADS (INTA-CONICET), Ruta 226 Km 73.5, Balcarce, Argentina
| | - Mariano D Hernández
- Facultad de Ciencias Agrarias, Universidad Nacional de Mar del Plata, Ruta 226 Km 73.5, Balcarce, Argentina
| | | | - Luján Nagore
- Facultad de Ciencias Agrarias, Universidad Nacional de Mar del Plata, Ruta 226 Km 73.5, Balcarce, Argentina
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10
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Messina CD, Gho C, Hammer GL, Tang T, Cooper M. Two decades of harnessing standing genetic variation for physiological traits to improve drought tolerance in maize. JOURNAL OF EXPERIMENTAL BOTANY 2023; 74:4847-4861. [PMID: 37354091 PMCID: PMC10474595 DOI: 10.1093/jxb/erad231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 06/15/2023] [Indexed: 06/26/2023]
Abstract
We review approaches to maize breeding for improved drought tolerance during flowering and grain filling in the central and western US corn belt and place our findings in the context of results from public breeding. Here we show that after two decades of dedicated breeding efforts, the rate of crop improvement under drought increased from 6.2 g m-2 year-1 to 7.5 g m-2 year-1, closing the genetic gain gap with respect to the 8.6 g m-2 year-1 observed under water-sufficient conditions. The improvement relative to the long-term genetic gain was possible by harnessing favourable alleles for physiological traits available in the reference population of genotypes. Experimentation in managed stress environments that maximized the genetic correlation with target environments was key for breeders to identify and select for these alleles. We also show that the embedding of physiological understanding within genomic selection methods via crop growth models can hasten genetic gain under drought. We estimate a prediction accuracy differential (Δr) above current prediction approaches of ~30% (Δr=0.11, r=0.38), which increases with increasing complexity of the trait environment system as estimated by Shannon information theory. We propose this framework to inform breeding strategies for drought stress across geographies and crops.
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Affiliation(s)
- Carlos D Messina
- Horticultural Sciences Department, University of Florida, Gainesville, FL, USA
- ARC Centre of Excellence for Plant Success in Nature and Agriculture, The University of Queensland, Brisbane, Qld 4072, Australia
| | - Carla Gho
- School of Agriculture & Food Sciences, The University of Queensland, Brisbane, Qld 4072, Australia
| | - Graeme L Hammer
- ARC Centre of Excellence for Plant Success in Nature and Agriculture, The University of Queensland, Brisbane, Qld 4072, Australia
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, Qld 4072, Australia
| | - Tom Tang
- Corteva Agrisciences, Johnston, IA, USA
| | - Mark Cooper
- ARC Centre of Excellence for Plant Success in Nature and Agriculture, The University of Queensland, Brisbane, Qld 4072, Australia
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, Qld 4072, Australia
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11
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Lin Y, Li S, Duan S, Ye Y, Li B, Li G, Lyv D, Jin L, Bian C, Liu J. Methodological evolution of potato yield prediction: a comprehensive review. FRONTIERS IN PLANT SCIENCE 2023; 14:1214006. [PMID: 37564384 PMCID: PMC10410453 DOI: 10.3389/fpls.2023.1214006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 07/06/2023] [Indexed: 08/12/2023]
Abstract
Timely and accurate prediction of crop yield is essential for increasing crop production, estimating planting insurance, and improving trade benefits. Potato (Solanum tuberosum L.) is a staple food in many parts of the world and improving its yield is necessary to ensure food security and promote related industries. We conducted a comprehensive literature survey to demonstrate methodological evolution of predicting potato yield. Publications on predicting potato yield based on methods of remote sensing (RS), crop growth model (CGM), and yield limiting factor (LF) were reviewed. RS, especially satellite-based RS, is crucial in potato yield prediction and decision support over large farm areas. In contrast, CGM are often utilized to optimize management measures and address climate change. Currently, combined with the advantages of low cost and easy operation, unmanned aerial vehicle (UAV) RS combined with artificial intelligence (AI) show superior potential for predicting potato yield in precision management of large-scale farms. However, studies on potato yield prediction are still limited in the number of varieties and field sample size. In the future, it is critical to employ time-series data from multiple sources for a wider range of varieties and large field sample sizes. This study aims to provide a comprehensive review of the progress in potato yield prediction studies and to provide a theoretical reference for related research on potato.
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Affiliation(s)
- Yongxin Lin
- State Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing, China
- College of Agronomy and Biotechnology, Southwest University, Chongqing, China
| | - Shuang Li
- State Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Shaoguang Duan
- State Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Yanran Ye
- State Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Bo Li
- Seeds Development, Syngenta Jealott’s Hill International Research Centre, Bracknell, United Kingdom
| | - Guangcun Li
- State Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Dianqiu Lyv
- College of Agronomy and Biotechnology, Southwest University, Chongqing, China
| | - Liping Jin
- State Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Chunsong Bian
- State Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Jiangang Liu
- State Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing, China
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12
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Großkinsky DK, Faure JD, Gibon Y, Haslam RP, Usadel B, Zanetti F, Jonak C. The potential of integrative phenomics to harness underutilized crops for improving stress resilience. FRONTIERS IN PLANT SCIENCE 2023; 14:1216337. [PMID: 37409292 PMCID: PMC10318926 DOI: 10.3389/fpls.2023.1216337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 06/08/2023] [Indexed: 07/07/2023]
Affiliation(s)
- Dominik K. Großkinsky
- AIT Austrian Institute of Technology, Center for Health and Bioresources, Bioresources Unit, Tulln a. d. Donau, Austria
| | - Jean-Denis Faure
- Université Paris-Saclay, INRAE, AgroParisTech, Institut Jean-Pierre Bourgin, Versailles, France
| | - Yves Gibon
- INRAE, Univ. Bordeaux, UMR BFP, Villenave d’Ornon, France
- Bordeaux Metabolome, INRAE, Univ. Bordeaux, Villenave d’Ornon, France
| | | | - Björn Usadel
- IBG-4 Bioinformatics, CEPLAS, Forschungszentrum, Jülich, Germany
- Biological Data Science, Heinrich Heine University, Universitätsstrasse 1, Düsseldorf, Germany
| | - Federica Zanetti
- Department of Agricultural and Food Sciences (DISTAL), Alma Mater Studiorum - Università di Bologna, Bologna, Italy
| | - Claudia Jonak
- AIT Austrian Institute of Technology, Center for Health and Bioresources, Bioresources Unit, Tulln a. d. Donau, Austria
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13
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Inclusive collaboration across plant physiology and genomics: Now is the time! PLANT DIRECT 2023; 7:e493. [PMID: 37214275 PMCID: PMC10192722 DOI: 10.1002/pld3.493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 03/16/2023] [Accepted: 03/27/2023] [Indexed: 05/24/2023]
Abstract
Within the broad field of plant sciences, what are the most pressing challenges and opportunities to advance? Answers to this question usually include food and nutritional security, climate change mitigation, adaptation of plants to changing climates, preservation of biodiversity and ecosystem services, production of plant-based proteins and products, and growth of the bioeconomy. Genes and the processes their products carry out create differences in how plants grow, develop, and behave, and thus, the key solutions to these challenges lie squarely in the space where plant genomics and physiology intersect. Advancements in genomics, phenomics, and analysis tools have generated massive datasets, but these data are complex and have not always generated scientific insights at the anticipated pace. Further, new tools may need to be created or adapted, and field-relevant applications tested, to advance scientific discovery derived from such datasets. Meaningful, relevant conclusions and connections from genomics and plant physiological and biochemical data require both subject matter expertise and the collaborative skills needed to work together outside of specific disciplines. Bringing the best expertise to bear on complex problems in plant sciences requires enhanced, inclusive, and sustained collaboration across disciplines. However, despite significant efforts to enable and sustain collaborative research, a variety of challenges persist. Here, we present the outcomes and conclusions of two workshops convened to address the need for collaboration between scientists engaged in plant physiology, genetics, and genomics and to discuss the approaches that will create the necessary environments to support successful collaboration. We conclude with approaches to share and reward collaboration and the need to train inclusive scientists that will have the skills to thrive in interdisciplinary contexts.
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14
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Waters DL, van der Werf JHJ, Robinson H, Hickey LT, Clark SA. Partitioning the forms of genotype-by-environment interaction in the reaction norm analysis of stability. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2023; 136:99. [PMID: 37027025 PMCID: PMC10082108 DOI: 10.1007/s00122-023-04319-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 02/07/2023] [Indexed: 05/13/2023]
Abstract
KEY MESSAGE The reaction norm analysis of stability can be enhanced by partitioning the contribution of different types of G × E to the variation in slope. The slope of regression in a reaction norm model, where the performance of a genotype is regressed over an environmental covariable, is often used as a measure of stability of genotype performance. This method could be developed further by partitioning variation in the slope of regression into the two sources of genotype-by-environment interaction (G × E) which cause it: scale-type G × E (heterogeneity of variance) and rank-type G × E (heterogeneity of correlation). Because the two types of G × E have very different properties, separating their effect would enable a clearer understanding of stability. The aim of this paper was to demonstrate two methods which seek to achieve this in reaction norm models. Reaction norm models were fit to yield data from a multi-environment trial in Barley (Hordeum vulgare), with the adjusted mean yield from each environment used as the environmental covariable. Stability estimated from factor-analytic models, which can disentangle the two types of G × E and estimate stability based on rank-type G × E, was used for comparison. Adjusting the reaction norm slope to account for scale-type G × E using a genetic regression more than tripled the correlation with factor-analytic estimates of stability (0.24-0.26 to 0.80-0.85), indicating that it removed variation in the reaction norm slope that originated from scale-type G × E. A standardisation procedure had a more modest increase (055-0.59) but could be useful when curvilinear reaction norms are required. Analyses which use reaction norms to explore the stability of genotypes could gain additional insight into the mechanisms of stability by applying the methods outlined in this study.
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Affiliation(s)
- Dominic L Waters
- School of Environmental and Rural Science, University of New England, Armidale, NSW, 2351, Australia.
| | - Julius H J van der Werf
- School of Environmental and Rural Science, University of New England, Armidale, NSW, 2351, Australia
| | - Hannah Robinson
- InterGrain Pty Ltd, Perth, WA, Australia
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD, Australia
| | - Lee T Hickey
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD, Australia
| | - Sam A Clark
- School of Environmental and Rural Science, University of New England, Armidale, NSW, 2351, Australia
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15
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Fichtl L, Hofmann M, Kahlen K, Voss-Fels KP, Cast CS, Ollat N, Vivin P, Loose S, Nsibi M, Schmid J, Strack T, Schultz HR, Smith J, Friedel M. Towards grapevine root architectural models to adapt viticulture to drought. FRONTIERS IN PLANT SCIENCE 2023; 14:1162506. [PMID: 36998680 PMCID: PMC10043487 DOI: 10.3389/fpls.2023.1162506] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 02/27/2023] [Indexed: 05/31/2023]
Abstract
To sustainably adapt viticultural production to drought, the planting of rootstock genotypes adapted to a changing climate is a promising means. Rootstocks contribute to the regulation of scion vigor and water consumption, modulate scion phenological development and determine resource availability by root system architecture development. There is, however, a lack of knowledge on spatio-temporal root system development of rootstock genotypes and its interactions with environment and management that prevents efficient knowledge transfer into practice. Hence, winegrowers take only limited advantage of the large variability of existing rootstock genotypes. Models of vineyard water balance combined with root architectural models, using both static and dynamic representations of the root system, seem promising tools to match rootstock genotypes to frequently occurring future drought stress scenarios and address scientific knowledge gaps. In this perspective, we discuss how current developments in vineyard water balance modeling may provide the background for a better understanding of the interplay of rootstock genotypes, environment and management. We argue that root architecture traits are key drivers of this interplay, but our knowledge on rootstock architectures in the field remains limited both qualitatively and quantitatively. We propose phenotyping methods to help close current knowledge gaps and discuss approaches to integrate phenotyping data into different models to advance our understanding of rootstock x environment x management interactions and predict rootstock genotype performance in a changing climate. This could also provide a valuable basis for optimizing breeding efforts to develop new grapevine rootstock cultivars with optimal trait configurations for future growing conditions.
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Affiliation(s)
- Lukas Fichtl
- Department of General and Organic Viticulture, Hochschule Geisenheim University, Geisenheim, Germany
| | - Marco Hofmann
- Department of General and Organic Viticulture, Hochschule Geisenheim University, Geisenheim, Germany
| | - Katrin Kahlen
- Department of Modeling and Systems Analysis, Hochschule Geisenheim University, Geisenheim, Germany
| | - Kai P. Voss-Fels
- Department of Grapevine Breeding, Hochschule Geisenheim University, Geisenheim, Germany
| | - Clément Saint Cast
- EGFV, University of Bordeaux, Bordeaux Sciences Agro, INRAE, ISVV, Villenave d’Ornon, France
| | - Nathalie Ollat
- EGFV, University of Bordeaux, Bordeaux Sciences Agro, INRAE, ISVV, Villenave d’Ornon, France
| | - Philippe Vivin
- EGFV, University of Bordeaux, Bordeaux Sciences Agro, INRAE, ISVV, Villenave d’Ornon, France
| | - Simone Loose
- Department of Wine and Beverage Business, Hochschule Geisenheim University, Geisenheim, Germany
| | - Mariem Nsibi
- Department of Grapevine Breeding, Hochschule Geisenheim University, Geisenheim, Germany
| | - Joachim Schmid
- Department of Grapevine Breeding, Hochschule Geisenheim University, Geisenheim, Germany
| | - Timo Strack
- Department of Grapevine Breeding, Hochschule Geisenheim University, Geisenheim, Germany
| | - Hans Reiner Schultz
- Department of General and Organic Viticulture, Hochschule Geisenheim University, Geisenheim, Germany
| | - Jason Smith
- Gulbali Institute for Agriculture, Water and Environment, Charles Sturt University, Orange, NSW, Australia
| | - Matthias Friedel
- Department of General and Organic Viticulture, Hochschule Geisenheim University, Geisenheim, Germany
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16
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Cooper M, Powell O, Gho C, Tang T, Messina C. Extending the breeder's equation to take aim at the target population of environments. FRONTIERS IN PLANT SCIENCE 2023; 14:1129591. [PMID: 36895882 PMCID: PMC9990092 DOI: 10.3389/fpls.2023.1129591] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Accepted: 02/10/2023] [Indexed: 06/18/2023]
Abstract
A major focus for genomic prediction has been on improving trait prediction accuracy using combinations of algorithms and the training data sets available from plant breeding multi-environment trials (METs). Any improvements in prediction accuracy are viewed as pathways to improve traits in the reference population of genotypes and product performance in the target population of environments (TPE). To realize these breeding outcomes there must be a positive MET-TPE relationship that provides consistency between the trait variation expressed within the MET data sets that are used to train the genome-to-phenome (G2P) model for applications of genomic prediction and the realized trait and performance differences in the TPE for the genotypes that are the prediction targets. The strength of this MET-TPE relationship is usually assumed to be high, however it is rarely quantified. To date investigations of genomic prediction methods have focused on improving prediction accuracy within MET training data sets, with less attention to quantifying the structure of the TPE and the MET-TPE relationship and their potential impact on training the G2P model for applications of genomic prediction to accelerate breeding outcomes for the on-farm TPE. We extend the breeder's equation and use an example to demonstrate the importance of the MET-TPE relationship as a key component for the design of genomic prediction methods to realize improved rates of genetic gain for the target yield, quality, stress tolerance and yield stability traits in the on-farm TPE.
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Affiliation(s)
- Mark Cooper
- Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, Brisbane, QLD, Australia
- Australian Research Council Centre of Excellence for Plant Success in Nature and Agriculture, The University of Queensland, Brisbane, QLD, Australia
| | - Owen Powell
- Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, Brisbane, QLD, Australia
- Australian Research Council Centre of Excellence for Plant Success in Nature and Agriculture, The University of Queensland, Brisbane, QLD, Australia
| | - Carla Gho
- School of Agriculture & Food Sciences, The University of Queensland, Brisbane, QLD, Australia
| | - Tom Tang
- Corteva Agriscience, Johnston, IA, United States
| | - Carlos Messina
- Horticultural Sciences Department, University of Florida, Gainesville, FL, United States
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17
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Cooper M, Messina CD. Breeding crops for drought-affected environments and improved climate resilience. THE PLANT CELL 2023; 35:162-186. [PMID: 36370076 PMCID: PMC9806606 DOI: 10.1093/plcell/koac321] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 11/01/2022] [Indexed: 05/12/2023]
Abstract
Breeding climate-resilient crops with improved levels of abiotic and biotic stress resistance as a response to climate change presents both opportunities and challenges. Applying the framework of the "breeder's equation," which is used to predict the response to selection for a breeding program cycle, we review methodologies and strategies that have been used to successfully breed crops with improved levels of drought resistance, where the target population of environments (TPEs) is a spatially and temporally heterogeneous mixture of drought-affected and favorable (water-sufficient) environments. Long-term improvement of temperate maize for the US corn belt is used as a case study and compared with progress for other crops and geographies. Integration of trait information across scales, from genomes to ecosystems, is needed to accurately predict yield outcomes for genotypes within the current and future TPEs. This will require transdisciplinary teams to explore, identify, and exploit novel opportunities to accelerate breeding program outcomes; both improved germplasm resources and improved products (cultivars, hybrids, clones, and populations) that outperform and replace the products in use by farmers, in combination with modified agronomic management strategies suited to their local environments.
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Affiliation(s)
| | - Carlos D Messina
- Horticultural Sciences Department, University of Florida, Gainesville, Florida 32611, USA
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18
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Bowerman AF, Byrt CS, Roy SJ, Whitney SM, Mortimer JC, Ankeny RA, Gilliham M, Zhang D, Millar AA, Rebetzke GJ, Pogson BJ. Potential abiotic stress targets for modern genetic manipulation. THE PLANT CELL 2023; 35:139-161. [PMID: 36377770 PMCID: PMC9806601 DOI: 10.1093/plcell/koac327] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 11/03/2022] [Indexed: 05/06/2023]
Abstract
Research into crop yield and resilience has underpinned global food security, evident in yields tripling in the past 5 decades. The challenges that global agriculture now faces are not just to feed 10+ billion people within a generation, but to do so under a harsher, more variable, and less predictable climate, and in many cases with less water, more expensive inputs, and declining soil quality. The challenges of climate change are not simply to breed for a "hotter drier climate," but to enable resilience to floods and droughts and frosts and heat waves, possibly even within a single growing season. How well we prepare for the coming decades of climate variability will depend on our ability to modify current practices, innovate with novel breeding methods, and communicate and work with farming communities to ensure viability and profitability. Here we define how future climates will impact farming systems and growing seasons, thereby identifying the traits and practices needed and including exemplars being implemented and developed. Critically, this review will also consider societal perspectives and public engagement about emerging technologies for climate resilience, with participatory approaches presented as the best approach.
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Affiliation(s)
- Andrew F Bowerman
- ARC Training Centre for Accelerated Future Crops Development, The Australian National University, Canberra, Australian Capital Territory, Australia
| | - Caitlin S Byrt
- ARC Training Centre for Accelerated Future Crops Development, The Australian National University, Canberra, Australian Capital Territory, Australia
| | - Stuart John Roy
- ARC Training Centre for Accelerated Future Crops Development, University of Adelaide, South Australia, Australia
- School of Agriculture, Food and Wine & Waite Research Institute, University of Adelaide, Glen Osmond, South Australia, Australia
| | - Spencer M Whitney
- ARC Training Centre for Accelerated Future Crops Development, The Australian National University, Canberra, Australian Capital Territory, Australia
| | - Jenny C Mortimer
- ARC Training Centre for Accelerated Future Crops Development, University of Adelaide, South Australia, Australia
- School of Agriculture, Food and Wine & Waite Research Institute, University of Adelaide, Glen Osmond, South Australia, Australia
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA
| | - Rachel A Ankeny
- ARC Training Centre for Accelerated Future Crops Development, University of Adelaide, South Australia, Australia
- School of Humanities, University of Adelaide, North Terrace, South Australia, Australia
| | - Matthew Gilliham
- ARC Training Centre for Accelerated Future Crops Development, University of Adelaide, South Australia, Australia
- School of Agriculture, Food and Wine & Waite Research Institute, University of Adelaide, Glen Osmond, South Australia, Australia
| | - Dabing Zhang
- ARC Training Centre for Accelerated Future Crops Development, University of Adelaide, South Australia, Australia
- School of Agriculture, Food and Wine & Waite Research Institute, University of Adelaide, Glen Osmond, South Australia, Australia
| | - Anthony A Millar
- ARC Training Centre for Accelerated Future Crops Development, The Australian National University, Canberra, Australian Capital Territory, Australia
| | - Greg J Rebetzke
- CSIRO Agriculture & Food, Canberra, Australian Capital Territory, Australia
| | - Barry J Pogson
- ARC Training Centre for Accelerated Future Crops Development, The Australian National University, Canberra, Australian Capital Territory, Australia
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19
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Barmukh R, Roorkiwal M, Garg V, Khan AW, German L, Jaganathan D, Chitikineni A, Kholova J, Kudapa H, Sivasakthi K, Samineni S, Kale SM, Gaur PM, Sagurthi SR, Benitez‐Alfonso Y, Varshney RK. Genetic variation in CaTIFY4b contributes to drought adaptation in chickpea. PLANT BIOTECHNOLOGY JOURNAL 2022; 20:1701-1715. [PMID: 35534989 PMCID: PMC9398337 DOI: 10.1111/pbi.13840] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 04/28/2022] [Indexed: 05/26/2023]
Abstract
Chickpea production is vulnerable to drought stress. Identifying the genetic components underlying drought adaptation is crucial for enhancing chickpea productivity. Here, we present the fine mapping and characterization of 'QTL-hotspot', a genomic region controlling chickpea growth with positive consequences on crop production under drought. We report that a non-synonymous substitution in the transcription factor CaTIFY4b regulates seed weight and organ size in chickpea. Ectopic expression of CaTIFY4b in Medicago truncatula enhances root growth under water deficit. Our results suggest that allelic variation in 'QTL-hotspot' improves pre-anthesis water use, transpiration efficiency, root architecture and canopy development, enabling high-yield performance under terminal drought conditions. Gene expression analysis indicated that CaTIFY4b may regulate organ size under water deficit by modulating the expression of GRF-INTERACTING FACTOR1 (GIF1), a transcriptional co-activator of Growth-Regulating Factors. Taken together, our study offers new insights into the role of CaTIFY4b and on diverse physiological and molecular mechanisms underpinning chickpea growth and production under specific drought scenarios.
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Affiliation(s)
- Rutwik Barmukh
- Centre of Excellence in Genomics and Systems BiologyInternational Crops Research Institute for the Semi‐Arid Tropics (ICRISAT)HyderabadIndia
- Department of GeneticsOsmania UniversityHyderabadIndia
| | - Manish Roorkiwal
- Centre of Excellence in Genomics and Systems BiologyInternational Crops Research Institute for the Semi‐Arid Tropics (ICRISAT)HyderabadIndia
- Khalifa Center for Genetic Engineering and BiotechnologyUnited Arab Emirates UniversityAl‐AinUnited Arab Emirates
- The UWA Institute of AgricultureThe University of Western AustraliaPerthWestern AustraliaAustralia
| | - Vanika Garg
- Centre of Excellence in Genomics and Systems BiologyInternational Crops Research Institute for the Semi‐Arid Tropics (ICRISAT)HyderabadIndia
| | - Aamir W. Khan
- Centre of Excellence in Genomics and Systems BiologyInternational Crops Research Institute for the Semi‐Arid Tropics (ICRISAT)HyderabadIndia
| | - Liam German
- Centre for Plant ScienceSchool of BiologyUniversity of LeedsLeedsUK
| | - Deepa Jaganathan
- Centre of Excellence in Genomics and Systems BiologyInternational Crops Research Institute for the Semi‐Arid Tropics (ICRISAT)HyderabadIndia
| | - Annapurna Chitikineni
- Centre of Excellence in Genomics and Systems BiologyInternational Crops Research Institute for the Semi‐Arid Tropics (ICRISAT)HyderabadIndia
| | - Jana Kholova
- Crop Physiology and ModellingInternational Crops Research Institute for the Semi‐Arid Tropics (ICRISAT)HyderabadIndia
| | - Himabindu Kudapa
- Centre of Excellence in Genomics and Systems BiologyInternational Crops Research Institute for the Semi‐Arid Tropics (ICRISAT)HyderabadIndia
| | - Kaliamoorthy Sivasakthi
- Crop Physiology and ModellingInternational Crops Research Institute for the Semi‐Arid Tropics (ICRISAT)HyderabadIndia
| | - Srinivasan Samineni
- Crop BreedingInternational Crops Research Institute for the Semi‐Arid Tropics (ICRISAT)HyderabadIndia
| | - Sandip M. Kale
- Centre of Excellence in Genomics and Systems BiologyInternational Crops Research Institute for the Semi‐Arid Tropics (ICRISAT)HyderabadIndia
| | - Pooran M. Gaur
- The UWA Institute of AgricultureThe University of Western AustraliaPerthWestern AustraliaAustralia
- Crop BreedingInternational Crops Research Institute for the Semi‐Arid Tropics (ICRISAT)HyderabadIndia
| | | | | | - Rajeev K. Varshney
- Centre of Excellence in Genomics and Systems BiologyInternational Crops Research Institute for the Semi‐Arid Tropics (ICRISAT)HyderabadIndia
- The UWA Institute of AgricultureThe University of Western AustraliaPerthWestern AustraliaAustralia
- Murdoch’s Centre for Crop & Food InnovationState Agricultural Biotechnology CentreFood Futures InstituteMurdoch UniversityMurdochWestern AustraliaAustralia
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20
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Senapati N, Semenov MA, Halford NG, Hawkesford MJ, Asseng S, Cooper M, Ewert F, van Ittersum MK, Martre P, Olesen JE, Reynolds M, Rötter RP, Webber H. Global wheat production could benefit from closing the genetic yield gap. NATURE FOOD 2022; 3:532-541. [PMID: 37117937 DOI: 10.1038/s43016-022-00540-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 05/20/2022] [Indexed: 04/30/2023]
Abstract
Global food security requires food production to be increased in the coming decades. The closure of any existing genetic yield gap (Yig) by genetic improvement could increase crop yield potential and global production. Here we estimated present global wheat Yig, covering all wheat-growing environments and major producers, by optimizing local wheat cultivars using the wheat model Sirius. The estimated mean global Yig was 51%, implying that global wheat production could benefit greatly from exploiting the untapped global Yig through the use of optimal cultivar designs, utilization of the vast variation available in wheat genetic resources, application of modern advanced breeding tools, and continuous improvements of crop and soil management.
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Affiliation(s)
- Nimai Senapati
- Plant Sciences Department, Rothamsted Research, Harpenden, UK.
| | | | - Nigel G Halford
- Plant Sciences Department, Rothamsted Research, Harpenden, UK
| | | | - Senthold Asseng
- TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Mark Cooper
- The University of Queensland, Queensland Alliance for Agriculture and Food Innovation, Centre for Crop Science, Brisbane, Queensland, Australia
| | - Frank Ewert
- Institute of Crop Science and Resource Conservation INRES, University of Bonn, Bonn, Germany
- Leibniz Centre for Agricultural Landscape Research, Müncheberg, Germany
| | | | - Pierre Martre
- LEPSE, Université Montpellier, INRAE, Institut Agro Montpellier, Montpellier, France
| | - Jørgen E Olesen
- Department of Agroecology, Aarhus University, Tjele, Denmark
| | - Matthew Reynolds
- International Maize and Wheat Improvement Center, Texcoco, Mexico
| | - Reimund P Rötter
- Tropical Plant Production & Agricultural Systems Modelling (TROPAGS), University of Göttingen, Göttingen, Germany
- Centre of Biodiversity and Sustainable Land Use (CBL), University of Göttingen, Göttingen, Germany
| | - Heidi Webber
- Institute of Crop Science and Resource Conservation INRES, University of Bonn, Bonn, Germany
- Leibniz Centre for Agricultural Landscape Research, Müncheberg, Germany
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21
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Hu P, Chapman SC, Sukumaran S, Reynolds M, Zheng B. Phenological optimization of late reproductive phase for raising wheat yield potential in irrigated mega-environments. JOURNAL OF EXPERIMENTAL BOTANY 2022; 73:4236-4249. [PMID: 35383843 PMCID: PMC9232205 DOI: 10.1093/jxb/erac144] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Accepted: 04/04/2022] [Indexed: 06/14/2023]
Abstract
Increasing grain number through fine-tuning duration of the late reproductive phase (LRP; terminal spikelet to anthesis) without altering anthesis time has been proposed as a genetic strategy to increase yield potential (YP) of wheat. Here we conducted a modelling analysis to evaluate the potential of fine-tuning LRP in raising YP in irrigated mega-environments. Using the known optimal anthesis and sowing date of current elite benchmark genotypes, we applied a gene-based phenology model for long-term simulations of phenological stages and yield-related variables of all potential germplasm with the same duration to anthesis as the benchmark genotypes. These diverse genotypes had the same duration to anthesis but varying LRP duration. Lengthening LRP increased YP and harvest index by increasing grain number to some extent and an excessively long LRP reduced YP due to reduced time for canopy construction for high biomass production of pre-anthesis phase. The current elite genotypes could have their LRP extended for higher YP in most sites. Genotypes with a ratio of the duration of LRP to pre-anthesis phase of about 0.42 ensured high yields (≥95% of YP) with their optimal sowing and anthesis dates. Optimization of intermediate growth stages could be further evaluated in breeding programmes to improve YP.
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Affiliation(s)
- Pengcheng Hu
- CSIRO Agriculture and Food, Queensland Biosciences Precinct, 306 Carmody Rd, St Lucia, Queensland 4067, Australia
- The University of Queensland, School of Agriculture and Food Sciences, St Lucia, Queensland 4072, Australia
| | - Scott C Chapman
- The University of Queensland, School of Agriculture and Food Sciences, St Lucia, Queensland 4072, Australia
| | - Sivakumar Sukumaran
- International Maize and Wheat Improvement Centre (CIMMYT), Carretera México-Veracruz Km 45, El Batán, Texcoco, México, CP 56237, Mexico
| | - Matthew Reynolds
- International Maize and Wheat Improvement Centre (CIMMYT), Carretera México-Veracruz Km 45, El Batán, Texcoco, México, CP 56237, Mexico
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22
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Physiological adaptive traits are a potential allele reservoir for maize genetic progress under challenging conditions. Nat Commun 2022; 13:3225. [PMID: 35680899 PMCID: PMC9184527 DOI: 10.1038/s41467-022-30872-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Accepted: 05/23/2022] [Indexed: 11/08/2022] Open
Abstract
Combined phenomic and genomic approaches are required to evaluate the margin of progress of breeding strategies. Here, we analyze 65 years of genetic progress in maize yield, which was similar (101 kg ha-1 year-1) across most frequent environmental scenarios in the European growing area. Yield gains were linked to physiologically simple traits (plant phenology and architecture) which indirectly affected reproductive development and light interception in all studied environments, marked by significant genomic signatures of selection. Conversely, studied physiological processes involved in stress adaptation remained phenotypically unchanged (e.g. stomatal conductance and growth sensitivity to drought) and showed no signatures of selection. By selecting for yield, breeders indirectly selected traits with stable effects on yield, but not physiological traits whose effects on yield can be positive or negative depending on environmental conditions. Because yield stability under climate change is desirable, novel breeding strategies may be needed for exploiting alleles governing physiological adaptive traits.
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23
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Fernández-Calleja M, Ciudad FJ, Casas AM, Igartua E. Hybrids Provide More Options for Fine-Tuning Flowering Time Responses of Winter Barley. FRONTIERS IN PLANT SCIENCE 2022; 13:827701. [PMID: 35432439 PMCID: PMC9011329 DOI: 10.3389/fpls.2022.827701] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Accepted: 02/07/2022] [Indexed: 06/14/2023]
Abstract
Crop adaptation requires matching resource availability to plant development. Tight coordination of the plant cycle with prevailing environmental conditions is crucial to maximizing yield. It is expected that winters in temperate areas will become warmer, so the vernalization requirements of current cultivars can be desynchronized with the environment's vernalizing potential. Therefore, current phenological ideotypes may not be optimum for future climatic conditions. Major genes conferring vernalization sensitivity and phenological responses in barley (Hordeum vulgare L.) are known, but some allelic combinations remain insufficiently evaluated. Furthermore, there is a lack of knowledge about flowering time in a hybrid context. To honor the promise of increased yield potentials, hybrid barley phenology must be studied, and the knowledge deployed in new cultivars. A set of three male and two female barley lines, as well as their six F1 hybrids, were studied in growth chambers, subjected to three vernalization treatments: complete (8 weeks), moderate (4 weeks), and low (2 weeks). Development was recorded up to flowering, and expression of major genes was assayed at key stages. We observed a gradation in responses to vernalization, mostly additive, concentrated in the phase until the initiation of stem elongation, and proportional to the allele constitution and dosage present in VRN-H1. These responses were further modulated by the presence of PPD-H2. The duration of the late reproductive phase presented more dominance toward earliness and was affected by the rich variety of alleles at VRN-H3. Our results provide further opportunities for fine-tuning total and phasal growth duration in hybrid barley, beyond what is currently feasible in inbred cultivars.
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Affiliation(s)
- Miriam Fernández-Calleja
- Department of Genetics and Plant Production, Aula Dei Experimental Station - Spanish National Research Council (EEAD-CSIC), Zaragoza, Spain
| | - Francisco J. Ciudad
- Agricultural Technology Institute of Castilla and León (ITACYL), Valladolid, Spain
| | - Ana M. Casas
- Department of Genetics and Plant Production, Aula Dei Experimental Station - Spanish National Research Council (EEAD-CSIC), Zaragoza, Spain
| | - Ernesto Igartua
- Department of Genetics and Plant Production, Aula Dei Experimental Station - Spanish National Research Council (EEAD-CSIC), Zaragoza, Spain
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24
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Carcedo AJP, Mayor L, Demarco P, Morris GP, Lingenfelser J, Messina CD, Ciampitti IA. Environment Characterization in Sorghum ( Sorghum bicolor L.) by Modeling Water-Deficit and Heat Patterns in the Great Plains Region, United States. FRONTIERS IN PLANT SCIENCE 2022; 13:768610. [PMID: 35310654 PMCID: PMC8929132 DOI: 10.3389/fpls.2022.768610] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 01/31/2022] [Indexed: 05/26/2023]
Abstract
Environmental characterization for defining the target population of environments (TPE) is critical to improve the efficiency of breeding programs in crops, such as sorghum (Sorghum bicolor L.). The aim of this study was to characterize the spatial and temporal variation for a TPE for sorghum within the United States. APSIM-sorghum, included in the Agricultural Production Systems sIMulator software platform, was used to quantify water-deficit and heat patterns for 15 sites in the sorghum belt. Historical weather data (∼35 years) was used to identify water (WSP) and heat (HSP) stress patterns to develop water-heat clusters. Four WSPs were identified with large differences in the timing of onset, intensity, and duration of the stress. In the western region of Kansas, Oklahoma, and Texas, the most frequent WSP (∼35%) was stress during grain filling with late recovery. For northeast Kansas, WSP frequencies were more evenly distributed, suggesting large temporal variation. Three HSPs were defined, with the low HSP being most frequent (∼68%). Field data from Kansas State University sorghum hybrid yield performance trials (2006-2013 period, 6 hybrids, 10 sites, 46 site × year combinations) were classified into the previously defined WSP and HSP clusters. As the intensity of the environmental stress increased, there was a clear reduction on grain yield. Both simulated and observed yield data showed similar yield trends when the level of heat or water stressed increased. Field yield data clearly separated contrasting clusters for both water and heat patterns (with vs. without stress). Thus, the patterns were regrouped into four categories, which account for the observed genotype by environment interaction (GxE) and can be applied in a breeding program. A better definition of TPE to improve predictability of GxE could accelerate genetic gains and help bridge the gap between breeders, agronomists, and farmers.
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Affiliation(s)
- Ana J. P. Carcedo
- Department of Agronomy, Kansas State University, Manhattan, KS, United States
| | - Laura Mayor
- Corteva Agriscience, Johnston, IA, United States
| | - Paula Demarco
- Department of Agronomy, Kansas State University, Manhattan, KS, United States
| | - Geoffrey P. Morris
- Department of Soil and Crop Science, Colorado State University, Fort Collins, CO, United States
| | - Jane Lingenfelser
- Department of Agronomy, Kansas State University, Manhattan, KS, United States
| | - Carlos D. Messina
- Horticultural Sciences Department, University of Florida, Gainesville, FL, United States
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25
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Diepenbrock CH, Tang T, Jines M, Technow F, Lira S, Podlich D, Cooper M, Messina C. Can we harness digital technologies and physiology to hasten genetic gain in US maize breeding? PLANT PHYSIOLOGY 2022; 188:1141-1157. [PMID: 34791474 PMCID: PMC8825268 DOI: 10.1093/plphys/kiab527] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Accepted: 10/01/2021] [Indexed: 05/26/2023]
Abstract
Plant physiology can offer invaluable insights to accelerate genetic gain. However, translating physiological understanding into breeding decisions has been an ongoing and complex endeavor. Here we demonstrate an approach to leverage physiology and genomics to hasten crop improvement. A half-diallel maize (Zea mays) experiment resulting from crossing 9 elite inbreds was conducted at 17 locations in the USA corn belt and 6 locations at managed stress environments between 2017 and 2019 covering a range of water environments from 377 to 760 mm of evapotranspiration and family mean yields from 542 to 1,874 g m-2. Results from analyses of 35 families and 2,367 hybrids using crop growth models linked to whole-genome prediction (CGM-WGP) demonstrated that CGM-WGP offered a predictive accuracy advantage compared to BayesA for untested genotypes evaluated in untested environments (r = 0.43 versus r = 0.27). In contrast to WGP, CGMs can deal effectively with time-dependent interactions between a physiological process and the environment. To facilitate the selection/identification of traits for modeling yield, an algorithmic approach was introduced. The method was able to identify 4 out of 12 candidate traits known to explain yield variation in maize. The estimation of allelic and physiological values for each genotype using the CGM created in silico phenotypes (e.g. root elongation) and physiological hypotheses that could be tested within the breeding program in an iterative manner. Overall, the approach and results suggest a promising future to fully harness digital technologies, gap analysis, and physiological knowledge to hasten genetic gain by improving predictive skill and definition of breeding goals.
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Affiliation(s)
| | - Tom Tang
- Research & Development, Corteva Agriscience, Johnston, Iowa 50131, USA
| | - Michael Jines
- Research & Development, Corteva Agriscience, Windfall, Indiana 46076, USA
| | - Frank Technow
- Research & Development, Corteva Agriscience, Tavistock, ON N4S 7W1, Canada
| | - Sara Lira
- Research & Development, Corteva Agriscience, Johnston, Iowa 50131, USA
| | - Dean Podlich
- Research & Development, Corteva Agriscience, Johnston, Iowa 50131, USA
| | - Mark Cooper
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, Queensland 4072, Australia
| | - Carlos Messina
- Research & Development, Corteva Agriscience, Johnston, Iowa 50131, USA
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26
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Langstroff A, Heuermann MC, Stahl A, Junker A. Opportunities and limits of controlled-environment plant phenotyping for climate response traits. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:1-16. [PMID: 34302493 PMCID: PMC8741719 DOI: 10.1007/s00122-021-03892-1] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/19/2020] [Accepted: 06/17/2021] [Indexed: 05/19/2023]
Abstract
Rising temperatures and changing precipitation patterns will affect agricultural production substantially, exposing crops to extended and more intense periods of stress. Therefore, breeding of varieties adapted to the constantly changing conditions is pivotal to enable a quantitatively and qualitatively adequate crop production despite the negative effects of climate change. As it is not yet possible to select for adaptation to future climate scenarios in the field, simulations of future conditions in controlled-environment (CE) phenotyping facilities contribute to the understanding of the plant response to special stress conditions and help breeders to select ideal genotypes which cope with future conditions. CE phenotyping facilities enable the collection of traits that are not easy to measure under field conditions and the assessment of a plant's phenotype under repeatable, clearly defined environmental conditions using automated, non-invasive, high-throughput methods. However, extrapolation and translation of results obtained under controlled environments to field environments is ambiguous. This review outlines the opportunities and challenges of phenotyping approaches under controlled environments complementary to conventional field trials. It gives an overview on general principles and introduces existing phenotyping facilities that take up the challenge of obtaining reliable and robust phenotypic data on climate response traits to support breeding of climate-adapted crops.
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Affiliation(s)
- Anna Langstroff
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University Giessen, Heinrich Buff-Ring 26, 35392, Giessen, Germany
| | - Marc C Heuermann
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Corrensstr. 3, OT Gatersleben, 06466, Seeland, Germany
| | - Andreas Stahl
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University Giessen, Heinrich Buff-Ring 26, 35392, Giessen, Germany
- Institute for Resistance Research and Stress Tolerance, Federal Research Centre for Cultivated Plants, Julius Kühn-Institut (JKI), Erwin-Baur-Strasse 27, 06484, Quedlinburg, Germany
| | - Astrid Junker
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Corrensstr. 3, OT Gatersleben, 06466, Seeland, Germany.
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27
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Washburn JD, Cimen E, Ramstein G, Reeves T, O'Briant P, McLean G, Cooper M, Hammer G, Buckler ES. Predicting phenotypes from genetic, environment, management, and historical data using CNNs. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:3997-4011. [PMID: 34448888 DOI: 10.1007/s00122-021-03943-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Accepted: 08/18/2021] [Indexed: 06/13/2023]
Abstract
Convolutional Neural Networks (CNNs) can perform similarly or better than standard genomic prediction methods when sufficient genetic, environmental, and management data are provided. Predicting phenotypes from genetic (G), environmental (E), and management (M) conditions is a long-standing challenge with implications to agriculture, medicine, and conservation. Most methods reduce the factors in a dataset (feature engineering) in a subjective and potentially oversimplified manner. Deep neural networks such as Multilayer Perceptrons (MPL) and Convolutional Neural Networks (CNN) can overcome this by allowing the data itself to determine which factors are most important. CNN models were developed for predicting agronomic yield from a combination of replicated trials and historical yield survey data. The results were more accurate than standard methods when tested on held-out G, E, and M data (r = 0.50 vs. r = 0.43), and performed slightly worse than standard methods when only G was held out (r = 0.74 vs. r = 0.80). Pre-training on historical data increased accuracy compared to trial data alone. Saliency map analysis indicated the CNN has "learned" to prioritize many factors of known agricultural importance.
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Affiliation(s)
- Jacob D Washburn
- United States Department of Agriculture, Agricultural Research Service, Columbia, MO, 65211, USA.
| | - Emre Cimen
- Institute for Genomic Diversity, Cornell University, Ithaca, NY, 14853, USA
- Computational Intelligence and Optimization Laboratory, Industrial Engineering Department, Eskisehir Technical University, Eskisehir, Turkey
| | - Guillaume Ramstein
- Institute for Genomic Diversity, Cornell University, Ithaca, NY, 14853, USA
- Center for Quantitative Genetics and Genomics, Aarhus University, 8000, Aarhus, Denmark
| | - Timothy Reeves
- Institute for Genomic Diversity, Cornell University, Ithaca, NY, 14853, USA
| | - Patrick O'Briant
- Institute for Genomic Diversity, Cornell University, Ithaca, NY, 14853, USA
| | - Greg McLean
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St. Lucia, Brisbane, QLD, 4072, Australia
| | - Mark Cooper
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St. Lucia, Brisbane, QLD, 4072, Australia
| | - Graeme Hammer
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St. Lucia, Brisbane, QLD, 4072, Australia
| | - Edward S Buckler
- Institute for Genomic Diversity, Cornell University, Ithaca, NY, 14853, USA
- Department of Agriculture, Agricultural Research Service, Ithaca, NY, 14850, USA
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28
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Kholová J, Urban MO, Cock J, Arcos J, Arnaud E, Aytekin D, Azevedo V, Barnes AP, Ceccarelli S, Chavarriaga P, Cobb JN, Connor D, Cooper M, Craufurd P, Debouck D, Fungo R, Grando S, Hammer GL, Jara CE, Messina C, Mosquera G, Nchanji E, Ng EH, Prager S, Sankaran S, Selvaraj M, Tardieu F, Thornton P, Valdes-Gutierrez SP, van Etten J, Wenzl P, Xu Y. In pursuit of a better world: crop improvement and the CGIAR. JOURNAL OF EXPERIMENTAL BOTANY 2021; 72:5158-5179. [PMID: 34021317 PMCID: PMC8272562 DOI: 10.1093/jxb/erab226] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Accepted: 05/20/2021] [Indexed: 05/10/2023]
Abstract
The CGIAR crop improvement (CI) programs, unlike commercial CI programs, which are mainly geared to profit though meeting farmers' needs, are charged with meeting multiple objectives with target populations that include both farmers and the community at large. We compiled the opinions from >30 experts in the private and public sector on key strategies, methodologies, and activities that could the help CGIAR meet the challenges of providing farmers with improved varieties while simultaneously meeting the goals of: (i) nutrition, health, and food security; (ii) poverty reduction, livelihoods, and jobs; (iii) gender equality, youth, and inclusion; (iv) climate adaptation and mitigation; and (v) environmental health and biodiversity. We review the crop improvement processes starting with crop choice, moving through to breeding objectives, production of potential new varieties, selection, and finally adoption by farmers. The importance of multidisciplinary teams working towards common objectives is stressed as a key factor to success. The role of the distinct disciplines, actors, and their interactions throughout the process from crop choice through to adoption by farmers is discussed and illustrated.
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Affiliation(s)
- Jana Kholová
- International Crops Research Institute for the Semi-Arid Tropics, Hyderabad-502324, India
| | - Milan Oldřich Urban
- International Center for Tropical Agriculture, Km 17 Recta Cali-Palmira, CP 763537, A.A. 12 6713, Cali, Colombia
| | - James Cock
- International Center for Tropical Agriculture, Km 17 Recta Cali-Palmira, CP 763537, A.A. 12 6713, Cali, Colombia
| | - Jairo Arcos
- HarvestPlus, Km 17 Recta Cali-Palmira, CP 763537, A.A. 12 6713, Cali, Colombia
| | - Elizabeth Arnaud
- Bioversity International, Parc scientifique Agropolis II, 1990 Boulevard de la Lironde, 34397 Montpellier, France
| | | | - Vania Azevedo
- International Crops Research Institute for the Semi-Arid Tropics, Hyderabad-502324, India
| | | | | | - Paul Chavarriaga
- International Center for Tropical Agriculture, Km 17 Recta Cali-Palmira, CP 763537, A.A. 12 6713, Cali, Colombia
| | | | - David Connor
- Department of Agriculture and Food, The University of Melbourne, Australia
| | - Mark Cooper
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, Qld 4072, Australia
| | - Peter Craufurd
- CIMMYT, 1st floor, National Plant Breeding and Genetics Centre, NARC Research Station, Khumaltor, Lalitpur, PO Box 5186, Kathmandu, Nepal
| | - Daniel Debouck
- International Center for Tropical Agriculture, Km 17 Recta Cali-Palmira, CP 763537, A.A. 12 6713, Cali, Colombia
| | - Robert Fungo
- International Center for Tropical Agriculture, PO Box 6247, Kampala, Uganda
- School of Food Technology, Nutrition & Bio-Engineering, Makerere University, PO Box, 7062, Kampala, Uganda
| | - Stefania Grando
- Independent Consultant, Corso Mazzini 256, 63100 Ascoli Piceno, Italy
| | - Graeme L Hammer
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, Qld 4072, Australia
| | - Carlos E Jara
- Independent Consultant, Hacienda Real, Torre 2, CP 760033, Cali, Colombia
| | - Charlie Messina
- Corteva Agriscience, 7200 62nd Avenue, Johnston, IA 50131, USA
| | - Gloria Mosquera
- International Center for Tropical Agriculture, Km 17 Recta Cali-Palmira, CP 763537, A.A. 12 6713, Cali, Colombia
| | - Eileen Nchanji
- International Center for Tropical Agriculture, African hub, Box 823-00621, Nairobi, Kenya
| | - Eng Hwa Ng
- International Maize and Wheat Improvement Center (CIMMYT); México-Veracruz, El Batán Km. 45, 56237, Mexico
| | - Steven Prager
- International Center for Tropical Agriculture, Km 17 Recta Cali-Palmira, CP 763537, A.A. 12 6713, Cali, Colombia
| | - Sindhujan Sankaran
- Department of Biological Systems Engineering, Washington State University, 1935 E. Grimes Way, PO Box 646120, Pullman, WA 99164, USA
| | - Michael Selvaraj
- International Center for Tropical Agriculture, Km 17 Recta Cali-Palmira, CP 763537, A.A. 12 6713, Cali, Colombia
| | - François Tardieu
- INRA Centre de Montpellier, Montpellier, Languedoc-Roussillon, France
| | - Philip Thornton
- CGIAR Research Program on Climate Change, Agriculture 37 and Food Security (CCAFS), International Livestock Research Institute (ILRI), Nairobi, Kenya
| | - Sandra P Valdes-Gutierrez
- International Center for Tropical Agriculture, Km 17 Recta Cali-Palmira, CP 763537, A.A. 12 6713, Cali, Colombia
| | - Jacob van Etten
- Bioversity International, Parc scientifique Agropolis II, 1990 Boulevard de la Lironde, 34397 Montpellier, France
| | - Peter Wenzl
- International Center for Tropical Agriculture, Km 17 Recta Cali-Palmira, CP 763537, A.A. 12 6713, Cali, Colombia
| | - Yunbi Xu
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China
- International Maize and Wheat Improvement Center (CIMMYT), El Batan Texcoco 56130, Mexico
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29
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Kholová J, Urban MO, Cock J, Arcos J, Arnaud E, Aytekin D, Azevedo V, Barnes AP, Ceccarelli S, Chavarriaga P, Cobb JN, Connor D, Cooper M, Craufurd P, Debouck D, Fungo R, Grando S, Hammer GL, Jara CE, Messina C, Mosquera G, Nchanji E, Ng EH, Prager S, Sankaran S, Selvaraj M, Tardieu F, Thornton P, Valdes-Gutierrez SP, van Etten J, Wenzl P, Xu Y. In pursuit of a better world: crop improvement and the CGIAR. JOURNAL OF EXPERIMENTAL BOTANY 2021. [PMID: 34021317 DOI: 10.5281/zenodo.4638248] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
The CGIAR crop improvement (CI) programs, unlike commercial CI programs, which are mainly geared to profit though meeting farmers' needs, are charged with meeting multiple objectives with target populations that include both farmers and the community at large. We compiled the opinions from >30 experts in the private and public sector on key strategies, methodologies, and activities that could the help CGIAR meet the challenges of providing farmers with improved varieties while simultaneously meeting the goals of: (i) nutrition, health, and food security; (ii) poverty reduction, livelihoods, and jobs; (iii) gender equality, youth, and inclusion; (iv) climate adaptation and mitigation; and (v) environmental health and biodiversity. We review the crop improvement processes starting with crop choice, moving through to breeding objectives, production of potential new varieties, selection, and finally adoption by farmers. The importance of multidisciplinary teams working towards common objectives is stressed as a key factor to success. The role of the distinct disciplines, actors, and their interactions throughout the process from crop choice through to adoption by farmers is discussed and illustrated.
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Affiliation(s)
- Jana Kholová
- International Crops Research Institute for the Semi-Arid Tropics, Hyderabad-502324, India
| | - Milan Oldřich Urban
- International Center for Tropical Agriculture, Km 17 Recta Cali-Palmira, CP 763537, A.A. 12 6713, Cali, Colombia
| | - James Cock
- International Center for Tropical Agriculture, Km 17 Recta Cali-Palmira, CP 763537, A.A. 12 6713, Cali, Colombia
| | - Jairo Arcos
- HarvestPlus, Km 17 Recta Cali-Palmira, CP 763537, A.A. 12 6713, Cali, Colombia
| | - Elizabeth Arnaud
- Bioversity International, Parc scientifique Agropolis II, 1990 Boulevard de la Lironde, 34397 Montpellier, France
| | | | - Vania Azevedo
- International Crops Research Institute for the Semi-Arid Tropics, Hyderabad-502324, India
| | | | | | - Paul Chavarriaga
- International Center for Tropical Agriculture, Km 17 Recta Cali-Palmira, CP 763537, A.A. 12 6713, Cali, Colombia
| | | | - David Connor
- Department of Agriculture and Food, The University of Melbourne, Australia
| | - Mark Cooper
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, Qld 4072, Australia
| | - Peter Craufurd
- CIMMYT, 1st floor, National Plant Breeding and Genetics Centre, NARC Research Station, Khumaltor, Lalitpur, PO Box 5186, Kathmandu, Nepal
| | - Daniel Debouck
- International Center for Tropical Agriculture, Km 17 Recta Cali-Palmira, CP 763537, A.A. 12 6713, Cali, Colombia
| | - Robert Fungo
- International Center for Tropical Agriculture, PO Box 6247, Kampala, Uganda
- School of Food Technology, Nutrition & Bio-Engineering, Makerere University, PO Box, 7062, Kampala, Uganda
| | - Stefania Grando
- Independent Consultant, Corso Mazzini 256, 63100 Ascoli Piceno, Italy
| | - Graeme L Hammer
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, Qld 4072, Australia
| | - Carlos E Jara
- Independent Consultant, Hacienda Real, Torre 2, CP 760033, Cali, Colombia
| | - Charlie Messina
- Corteva Agriscience, 7200 62nd Avenue, Johnston, IA 50131, USA
| | - Gloria Mosquera
- International Center for Tropical Agriculture, Km 17 Recta Cali-Palmira, CP 763537, A.A. 12 6713, Cali, Colombia
| | - Eileen Nchanji
- International Center for Tropical Agriculture, African hub, Box 823-00621, Nairobi, Kenya
| | - Eng Hwa Ng
- International Maize and Wheat Improvement Center (CIMMYT); México-Veracruz, El Batán Km. 45, 56237, Mexico
| | - Steven Prager
- International Center for Tropical Agriculture, Km 17 Recta Cali-Palmira, CP 763537, A.A. 12 6713, Cali, Colombia
| | - Sindhujan Sankaran
- Department of Biological Systems Engineering, Washington State University, 1935 E. Grimes Way, PO Box 646120, Pullman, WA 99164, USA
| | - Michael Selvaraj
- International Center for Tropical Agriculture, Km 17 Recta Cali-Palmira, CP 763537, A.A. 12 6713, Cali, Colombia
| | - François Tardieu
- INRA Centre de Montpellier, Montpellier, Languedoc-Roussillon, France
| | - Philip Thornton
- CGIAR Research Program on Climate Change, Agriculture 37 and Food Security (CCAFS), International Livestock Research Institute (ILRI), Nairobi, Kenya
| | - Sandra P Valdes-Gutierrez
- International Center for Tropical Agriculture, Km 17 Recta Cali-Palmira, CP 763537, A.A. 12 6713, Cali, Colombia
| | - Jacob van Etten
- Bioversity International, Parc scientifique Agropolis II, 1990 Boulevard de la Lironde, 34397 Montpellier, France
| | - Peter Wenzl
- International Center for Tropical Agriculture, Km 17 Recta Cali-Palmira, CP 763537, A.A. 12 6713, Cali, Colombia
| | - Yunbi Xu
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China
- International Maize and Wheat Improvement Center (CIMMYT), El Batan Texcoco 56130, Mexico
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Langridge P, Braun H, Hulke B, Ober E, Prasanna BM. Breeding crops for climate resilience. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:1607-1611. [PMID: 34046700 DOI: 10.1007/s00122-021-03854-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 05/04/2021] [Indexed: 05/12/2023]
Abstract
In enhancing the resilience of our crops to the impacts of climate change, selection objectives need to address increased variability in the production environment. This encompasses the effects of more variable rainfall and temperatures than currently experienced, including extreme weather events, and changes in pest and pathogens distribution with the increased likelihood of major pest and disease outbreaks as well as occurrence of novel pathogens. Farmers manage the inevitable risks associated with cropping by planting varieties that deliver high yields and good quality under optimal conditions but minimise losses when the seasons are bad. Breeders and agronomists work to support farmers in specific target environments, but increased climate variability has meant that they need to broaden the adaptability of varieties grown and increase the yield stability to help minimise climate-induced risks and build resilience.
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Affiliation(s)
- Peter Langridge
- School of Agriculture Food and Wine, University of Adelaide, Glen Osmond, SA, 5064, Australia.
- Wheat Initiative, Julius-Kühn-Institute, 14195, Berlin, Germany.
| | | | - Brent Hulke
- Sunflower and Plant Biology Research Unit, USDA-ARS Edward T Schafer Agricultural Research Center, Fargo, ND, USA
| | - Eric Ober
- NIAB, 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK
| | - B M Prasanna
- CIMMYT, ICRAF Campus, UN Avenue, Gigiri, Nairobi, Kenya
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Smith DT, Potgieter AB, Chapman SC. Scaling up high-throughput phenotyping for abiotic stress selection in the field. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:1845-1866. [PMID: 34076731 DOI: 10.1007/s00122-021-03864-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 05/13/2021] [Indexed: 05/18/2023]
Abstract
High-throughput phenotyping (HTP) is in its infancy for deployment in large-scale breeding programmes. With the ability to measure correlated traits associated with physiological ideotypes, in-field phenotyping methods are available for screening of abiotic stress responses. As cropping environments become more hostile and unpredictable due to the effects of climate change, the need to characterise variability across spatial and temporal scales will become increasingly important. The sensor technologies that have enabled HTP from macroscopic through to satellite sensors may also be utilised here to complement spatial characterisation using envirotyping, which can improve estimations of genotypic performance across environments by better accounting for variation at the plot, trial and inter-trial levels. Climate change is leading to increased variation at all physical and temporal scales in the cropping environment. Maintaining yield stability under circumstances with greater levels of abiotic stress while capitalising upon yield potential in good years, requires approaches to plant breeding that target the physiological limitations to crop performance in specific environments. This requires dynamic modelling of conditions within target populations of environments, GxExM predictions, clustering of environments so breeding trajectories can be defined, and the development of screens that enable selection for genetic gain to occur. High-throughput phenotyping (HTP), combined with related technologies used for envirotyping, can help to address these challenges. Non-destructive analysis of the morphological, biochemical and physiological qualities of plant canopies using HTP has great potential to complement whole-genome selection, which is becoming increasingly common in breeding programmes. A range of novel analytic techniques, such as machine learning and deep learning, combined with a widening range of sensors, allow rapid assessment of large breeding populations that are repeatable and objective. Secondary traits underlying radiation use efficiency and water use efficiency can be screened with HTP for selection at the early stages of a breeding programme. HTP and envirotyping technologies can also characterise spatial variability at trial and within-plot levels, which can be used to correct for spatial variations that confound measurements of genotypic values. This review explores HTP for abiotic stress selection through a physiological trait lens and additionally investigates the use of envirotyping and EC to characterise spatial variability at all physical scales in METs.
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Affiliation(s)
- Daniel T Smith
- The University of Queensland, St Lucia, Brisbane, QLD, 4072, Australia
| | - Andries B Potgieter
- Centre for Crop Science, Queensland Alliance for Agriculture and Food Innovation, University of Queensland, Brisbane, QLD, 4072, Australia
| | - Scott C Chapman
- The University of Queensland, St Lucia, Brisbane, QLD, 4072, Australia.
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Cooper M, Messina CD. Can We Harness "Enviromics" to Accelerate Crop Improvement by Integrating Breeding and Agronomy? FRONTIERS IN PLANT SCIENCE 2021; 12:735143. [PMID: 34567047 PMCID: PMC8461239 DOI: 10.3389/fpls.2021.735143] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Accepted: 08/16/2021] [Indexed: 05/02/2023]
Abstract
The diverse consequences of genotype-by-environment (GxE) interactions determine trait phenotypes across levels of biological organization for crops, challenging our ambition to predict trait phenotypes from genomic information alone. GxE interactions have many implications for optimizing both genetic gain through plant breeding and crop productivity through on-farm agronomic management. Advances in genomics technologies have provided many suitable predictors for the genotype dimension of GxE interactions. Emerging advances in high-throughput proximal and remote sensor technologies have stimulated the development of "enviromics" as a community of practice, which has the potential to provide suitable predictors for the environment dimension of GxE interactions. Recently, several bespoke examples have emerged demonstrating the nascent potential for enhancing the prediction of yield and other complex trait phenotypes of crop plants through including effects of GxE interactions within prediction models. These encouraging results motivate the development of new prediction methods to accelerate crop improvement. If we can automate methods to identify and harness suitable sets of coordinated genotypic and environmental predictors, this will open new opportunities to upscale and operationalize prediction of the consequences of GxE interactions. This would provide a foundation for accelerating crop improvement through integrating the contributions of both breeding and agronomy. Here we draw on our experience from improvement of maize productivity for the range of water-driven environments across the US corn-belt. We provide perspectives from the maize case study to prioritize promising opportunities to further develop and automate "enviromics" methodologies to accelerate crop improvement through integrated breeding and agronomic approaches for a wider range of crops and environmental targets.
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Affiliation(s)
- Mark Cooper
- Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, Brisbane, QLD, Australia
- *Correspondence: Mark Cooper,
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Yan W. A Systematic Narration of Some Key Concepts and Procedures in Plant Breeding. FRONTIERS IN PLANT SCIENCE 2021; 12:724517. [PMID: 34603352 PMCID: PMC8481876 DOI: 10.3389/fpls.2021.724517] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/13/2021] [Accepted: 08/09/2021] [Indexed: 05/22/2023]
Abstract
The goal of a plant breeding program is to develop new cultivars of a crop kind with improved yield and quality for a target region and end-use. Improved yield across locations and years means better adaptation to the climatic, soil, and management conditions in the target region. Improved or maintained quality renders and adds value to the improved yield. Both yield and quality must be considered simultaneously, which constitutes the greatest challenge to successful cultivar development. Cultivar development consists of two stages: the development of a promising breeding population and the selection of the best genotypes out of it. A complete breeder's equation was presented to cover both stages, which consists of three key parameters for a trait of interest: the population mean (μ), the population variability (σ G ), and the achieved heritability (h 2 or H), under the multi-location, multi-year framework. Population development is to maximize μσ G and progeny selection is to improve H. Approaches to improve H include identifying and utilizing repeatable genotype by environment interaction (GE) through mega-environment analysis, accommodating unrepeatable GE through adequate testing, and reducing experimental error via replication and spatial analysis. Related concepts and procedures were critically reviewed, including GGE (genotypic main effect plus genotype by environment interaction) biplot analysis, GGE + GGL (genotypic main effect plus genotype by location interaction) biplot analysis, LG (location-grouping) biplot analysis, stability analysis, spatial analysis, adequate testing, and optimum replication. Selection on multiple traits includes independent culling and index selection, for the latter GYT (genotype by yield*trait) biplot analysis was recommended. Genomic selection may provide an alternative and potentially more effective approach in all these aspects. Efforts were made to organize and comment on these concepts and procedures in a systematic manner.
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