Abstract
Climate change impacts the sustainability of agriculture. Physiological processes are very essential for improving crop modeling. The objective of this paper was to assess the integrated effects of crop modeling and crop physiology on the response of rice crop to climate change. Some models have weakness and this weakness rectified by better understanding of physiological features which is correlated to the growth and development of the plant. Process-based dynamic crop models are able to estimate a range of crop response to the environment and to assess the biophysical effects of future climate scenarios on growth and yield. Crop modelling has the potential to enable society to assess the efficacy of G × E technologies to mitigate and adapt crop production systems to climate change. An advanced integration of crop modeling and crop physiology will enable a gene-to-farm design of resilient and sustainable crop production systems under a changing climate at regional-to-global scales. High night temperature increases carbon loss from night respiration and reduces the activity of source–sink enzymes resulting in lower biomass, poor grain filling, and reduced grain weight of rice. ORYZA and CERES-Rice models are the most familiar physiological based rice models currently used for rice crop modeling studies. Interaction between plant physiology and modeling is essential to improving the existing models, for creating new models, and for improving predictions on crop responses to climate changes and variability. Keywords: Crop modeling, Physiology, climate change, crop response, Rice DOI: 10.7176/JNSR/13-4-01 Publication date: February 28 th 2022
Highlights
Due to the complexity of agricultural systems and the complex nature of climate change, crop models are often used to understand the impact of climate change on agriculture
The highest response to elevated CO2 is found under water limiting conditions because higher CO2 concentrations increase leaf and plant water use efficiency (Weigel, 2007)
Minimizing the emissions of atmospheric greenhouse gas concentrations are stabilized at levels that restrict the adverse consequences of climate change
Summary
The global climate is changing, and agriculture will need to adapt to the changes to ensure sustainability and survival. The significant challenge of increasing crop production to provide food security for the world population is understanding the projected environment and ecosystem functioning. Regarding to this such approaches has been accepted as a predictor of future impact of climate changes because their algorithms are supposed to depend on the state of the art of the physiological and physical principles for a given species (Rosenzweig et al 2013). Higher concentrations of CO2 are expected to act as a fertilizer by improving net photosynthesis rates and increasing water use efficiency (Deryng et al, 2016) This positive effect is higher in C3 plants such as wheat, rice and soybean, due to the limited photosynthetic output of photorespiratory carbon losses. The use of crop models as decision support tools for a climate impact assessment will be beneficial, but suitability of models for representative growing conditions need to be verified and the ability of CERES-Rice and ORYZA crop models predict rice yield
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