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Stirbet A, Guo Y, Lazár D, Govindjee G. From leaf to multiscale models of photosynthesis: applications and challenges for crop improvement. PHOTOSYNTHESIS RESEARCH 2024; 161:21-49. [PMID: 38619700 DOI: 10.1007/s11120-024-01083-9] [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: 01/26/2024] [Accepted: 01/29/2024] [Indexed: 04/16/2024]
Abstract
To keep up with the growth of human population and to circumvent deleterious effects of global climate change, it is essential to enhance crop yield to achieve higher production. Here we review mathematical models of oxygenic photosynthesis that are extensively used, and discuss in depth a subset that accounts for diverse approaches providing solutions to our objective. These include models (1) to study different ways to enhance photosynthesis, such as fine-tuning antenna size, photoprotection and electron transport; (2) to bioengineer carbon metabolism; and (3) to evaluate the interactions between the process of photosynthesis and the seasonal crop dynamics, or those that have included statistical whole-genome prediction methods to quantify the impact of photosynthesis traits on the improvement of crop yield. We conclude by emphasizing that the results obtained in these studies clearly demonstrate that mathematical modelling is a key tool to examine different approaches to improve photosynthesis for better productivity, while effective multiscale crop models, especially those that also include remote sensing data, are indispensable to verify different strategies to obtain maximized crop yields.
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Affiliation(s)
| | - Ya Guo
- Key Laboratory of Advanced Process Control for Light Industry, Ministry of Education Jiangnan University, Wuxi, 214122, China
| | - Dušan Lazár
- Department of Biophysics, Faculty of Science, Palacký Univesity, Šlechtitelů 27, 78371, Olomouc, Czech Republic
| | - Govindjee Govindjee
- Department of Biochemistry, Department of Plant Biology, and the Center of Biophysics & Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA.
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Kumar R, Mishra SK, Singh K, Al-Ashkar I, Iqbal MA, Muzamil MN, Habib ur Rahman M, El Sabagh A. Impact analysis of moisture stress on growth and yield of cotton using DSSAT-CROPGRO-cotton model under semi-arid climate. PeerJ 2023; 11:e16329. [PMID: 38025731 PMCID: PMC10640844 DOI: 10.7717/peerj.16329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 09/30/2023] [Indexed: 12/01/2023] Open
Abstract
Adequate soil moisture around the root zone of the crops is essential for optimal plant growth and productivity throughout the crop season, whereas excessive as well as deficient moisture is usually detrimental. A field experiment was conducted on cotton (Gossipium hirsuttum) with three water regimes (viz. well-watered (control); rainfed after one post-sowing irrigation (1-POSI) and rainfed after two post-sowing irrigations (2-POSI)) in main plots and application of eight osmoprotectants in sub plots of Split plot design to quantify the loss of seed cotton yield (SCY) under high and mild moisture stress. The DSSAT-CROPGRO-cotton model was calibrated to validate the response of cotton crop to water stress. Results elucidated that in comparison of well watered (control) crop, 1-POSI and 2-POSI reduced plant height by 13.5-28.4% and lower leaf area index (LAI) by 21.6-37.6%. Pooled analysis revealed that SCY under control was higher by 1,127 kg ha-1 over 1-POSI and 597 kg ha-1 than 2-POSI. The DSSAT-CROPGRO-cotton model fairly simulated the cotton yield as evidenced by good accuracy (d-stat ≥ 0.92) along with lower root mean square error (RMSE) of ≤183.2 kg ha-1; mean absolute percent error (MAPE) ≤6.5% under different irrigation levels. Similarly, simulated and observed biomass also exhibited good agreement with ≥0.98 d-stat; ≤533.7 kg ha-1 RMSE; and ≤4.6% MAPE. The model accurately simulated the periodical LAI, biomass and soil water dynamics as affected by varying water regimes in conformity with periodical observations. Both the experimental and the simulated results confirmed the decline of SCY with any degree of water stress. Thus, a well calibrated DSSAT-CROPGRO-cotton model may be successfully used for estimating the crop performance under varying hydro-climatic conditions.
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Affiliation(s)
- Rotash Kumar
- Punjab Agricultural University, Regional Research Station, Faridkot, Punjab, India
| | - Sudhir Kumar Mishra
- Punjab Agricultural University, Regional Research Station, Faridkot, Punjab, India
| | - Kulvir Singh
- Punjab Agricultural University, Regional Research Station, Faridkot, Punjab, India
| | - Ibrahim Al-Ashkar
- Plant Production Department, College of Food and Agriculture Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Muhammad Aamir Iqbal
- Department of Agronomy, Faculty of Agriculture, University of Poonch, Rawalakot, Pakistan
| | | | - Muhammad Habib ur Rahman
- Institute of Crop Science and Resource Conservation (INRES), Crop Science, University of Bonn, Bonn, Germany
- Department of Seed Science and Technology, Institute of Plant Breeding and Biotechnology (IPBB), MNS-University of Agriculture, Multan, Punjab, Pakistan
| | - Ayman El Sabagh
- Department of Agronomy, Faculty of Agriculture, Kafrelsheikh University, Kafr El-Shaikh, Egypt
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Sun Q, Zhao Y, Zhang Y, Chen S, Ying Q, Lv Z, Che X, Wang D. Heat stress may cause a significant reduction of rice yield in China under future climate scenarios. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 818:151746. [PMID: 34801492 DOI: 10.1016/j.scitotenv.2021.151746] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 11/13/2021] [Accepted: 11/13/2021] [Indexed: 05/12/2023]
Abstract
Extreme heat events have become more frequent and severe under climate change and seriously threaten rice growth. Most existing crop models tend to underestimate the impacts of heat stress on rice yields. Heat stress modules in crop models have not been extensively explored, particularly on a large scale. This study modeled rice growth under heat stress at the flowering and filling stages through two heat stress models which coupled into the CERES-Rice model. We evaluated the advanced model with provincial statistics and Gridded Observed Rice Yield. Our improved CERES-Rice model produced more accurate estimates on rice yield than the original model evidenced by an increased correlation coefficient (R) of 12.72% and d-index of 0.02%. The RMSE and MAE decreased by 5.94% and 6.01%, respectively. Most pseudo positive correlations between rice yield and the number of heat days were corrected to the negative ones by the improved model. The future projections from the improved model signifies multi-model ensemble yield projection without CO2 effect (MME-I-NOCO2) has an apparent fall from 2020 to 2099 under RCP4.5, RCP6.0 and RCP8.5 with the decreasing percentages of 6%, 14%, and 37%, respectively, whereas the decreasing trend (12%) only occurs under RCP8.5 with CO2 effect (MME-I-CO2). The apparently decreasing trends of yield projection from MME-I-NOCO2 will occur in most rice-planted regions of China with the decreasing rate < 50 kg/ha/a especially in the central-south and southern cropping regions, and this decreasing trend will be slowed down for MME-I-CO2. Relative to rice yield of historical period, rice yield variations of MME-I-NOCO2 for different growing seasons show a downward trend with the decrease of approximately 54%, 60%, and 43%, respectively. Our study highlights the importance of modeling crop yields under heat stress to food security, agricultural adaptation and mitigation to climate change.
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Affiliation(s)
- Qing Sun
- State Key Laboratory of Severe Weather (LASW), Chinese Academy of Meteorological Sciences, Beijing 100081, China
| | - Yanxia Zhao
- State Key Laboratory of Severe Weather (LASW), Chinese Academy of Meteorological Sciences, Beijing 100081, China.
| | - Yi Zhang
- State Key Laboratory of Severe Weather (LASW), Chinese Academy of Meteorological Sciences, Beijing 100081, China
| | - Sining Chen
- Tianjin Climate Center, Tianjin 300074, China
| | - Qing Ying
- Department of Geosciences, Texas Tech University, Lubbock, TX 79430, USA
| | - Zunfu Lv
- College of Agriculture & Food Science and the Key Laboratory for Quality Improvement of Agricultural Products of Zhejiang Province, Zhejiang A & F University, Lin'an 311300, Zhejiang, China
| | - Xianghong Che
- Chinese Academy of Surveying & Mapping, Beijing 100830, China
| | - Delong Wang
- Beijing Institute of Applied Meteorology, Beijing 100029, China
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Adnan A, Diels J, Jibrin J, Kamara A, Shaibu A, Craufurd P, Menkir A. CERES-Maize model for simulating genotype-by-environment interaction of maize and its stability in the dry and wet savannas of Nigeria. FIELD CROPS RESEARCH 2020; 253:107826. [PMID: 32817743 PMCID: PMC7255407 DOI: 10.1016/j.fcr.2020.107826] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/07/2019] [Revised: 04/01/2020] [Accepted: 04/26/2020] [Indexed: 06/11/2023]
Abstract
When properly calibrated and evaluated, dynamic crop simulation models can provide insights into the different components of genotype by environment interactions (GEIs). Modelled outputs could be used to complement data from multi-environment trials. Field experiments were conducted in the rainy and dry seasons of 2015 and 2016 across four locations in maize growing regions of Northern Nigeria using 16 maize varieties planted under near-optimal conditions of moisture and soil nitrogen. The CERES-Maize model was calibrated using data from three locations and two seasons (rainy and dry) and evaluated using data from one location and two seasons all in 2015. Observed data from the four locations and two seasons in 2016 was used to create eight different environments. Two profile pits were dug in each location and were used separately in the simulations for each environment to provide replicated data for stability analysis in a combined ANOVA. The effects of the environment, genotype and GEI were highly significant (p = 0.001) for both observed and simulated grain yields. The environment explained 67 % and 64 % of the variations in observed and simulated grain yields respectively. The variance component of GEI (13 % for observed and 15 % for simulated) were lower but still considerable when compared to that of genotypes (19 % for observed and 21 % for simulated). From the stability analysis of the observed and simulated grain yields using six different stability models, three models (ASV, Ecovalence, and Sigma) ranked Ife Hybrid as the most stable variety. The slope of the regression (bi) model ranked Sammaz 11 as the most stable variety, while the Shukla model ranked Sammaz 28 as the most stable variety. Long-term seasonal analysis with the CERES-Maize model revealed that early and intermediate maturing varieties produce high yields in both wet and dry savannas, early and extra-early varieties produce high yields only in the dry savannas, while late maturing varieties produce high yields only in the wet savannas. When properly calibrated and evaluated, the CERES-Maize model can be used to generate data for GEI and stability studies of maize genotype in the absence of observed field data.
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Affiliation(s)
- A.A Adnan
- Department of Agronomy, Bayero University Kano, 70001, Kano, Nigeria
- Department of Earth and Environmental Sciences, Division of Soil and Water Management, KU Leuven, Celestijnenlaan 200E, 3001 Leuven, Belgium
- Centre for Dryland Agriculture (CDA), Bayero University Kano, 70001, Kano, Nigeria
| | - J. Diels
- Department of Earth and Environmental Sciences, Division of Soil and Water Management, KU Leuven, Celestijnenlaan 200E, 3001 Leuven, Belgium
| | - J.M. Jibrin
- Centre for Dryland Agriculture (CDA), Bayero University Kano, 70001, Kano, Nigeria
| | - A.Y Kamara
- International Institute of Tropical Agriculture, Ibadan, Nigeria. c/o IITA Ltd, Carolyn House, 26 Dingwall Road, Croydon CR9 3 EE, United Kingdom
| | - A.S Shaibu
- Department of Agronomy, Bayero University Kano, 70001, Kano, Nigeria
- Centre for Dryland Agriculture (CDA), Bayero University Kano, 70001, Kano, Nigeria
| | - P Craufurd
- International Maize and Wheat Improvement Center (CIMMYT) World Agroforestry Centre (ICRAF) House United Nations Avenue, Gigiri P.O. Box 1041–00621, Nairobi, Kenya
| | - Abebe Menkir
- International Institute of Tropical Agriculture, Ibadan, Nigeria. c/o IITA Ltd, Carolyn House, 26 Dingwall Road, Croydon CR9 3 EE, United Kingdom
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