Simulation of crop and water productivity for rice (Oryza sativa L.) using APSIM under diverse agro-climatic conditions and water management techniques in Sri Lanka

Amarasingha, R.P.R.K.; Suriyagoda, L. D. B.; Marambe, B.; Gaydon, Donald S.; Galagedara, Lakshman; Punyawardena, Ranjith; Silva, G.L.L.P.; Nidumolu, Uday; Howden, Mark

doi:10.1016/j.agwat.2015.07.001

Cited by 56 publications

(26 citation statements)

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“…The simulation of soil and crop variables started on 1 April of the year 2000 and continued until 31 December 2010, as this period represents dry, wet, and normal years. We considered the rice direct-seeding for this simulation, as it is the most common practice of field establishment for rice crops in Sri Lanka [52]. The cultivation decisions of Sri Lankan rainfed rice farmers are dependent on the onset of rainfall [52].…”

Section: Apsim Simulationmentioning

confidence: 99%

“…We considered the rice direct-seeding for this simulation, as it is the most common practice of field establishment for rice crops in Sri Lanka [52]. The cultivation decisions of Sri Lankan rainfed rice farmers are dependent on the onset of rainfall [52]. In simulations, we sowed rice when 20 mm of rainfall was accumulated over three days between November 15 to 30 or on December 1 in each year from 2000 to 2009.…”

Section: Apsim Simulationmentioning

confidence: 99%

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A Functional Analysis of Pedotransfer Functions Developed for Sri Lankan soils: Applicability for Process-Based Crop Models

et al. 2020

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As measurements are expensive and laborious, the estimation of soil hydraulic properties using pedotransfer functions (PTFs) has become popular worldwide. However, the estimation of soil hydraulic properties is not the final aim but an essential input value for other calculations and simulations, mostly in environmental and crop models. This modeling approach is a popular way to assess agricultural and environmental processes. However, it is rarely used in Sri Lanka because soil hydraulic data are rare. We evaluated the functionality of PTFs (developed to estimate field capacity (FC) and the permanent wilting point (PWP) of Sri Lankan soils) for process-based crop models. We used the Agricultural Production Systems sIMulator (APSIM) as the test model. Initially, we confirmed the importance of PWP (LL15) and FC (DUL) by assessing the sensitivity of the soil input parameters on the growth and yield of rice under rainfed conditions. We simulated the growth and yield of rice and the four selected outputs related to the APSIM soil module using the measured and estimated values of FC and PWP. These simulations were conducted for ten years in 16 locations of Sri Lanka, representing wet, intermediate, and dry zones. The simulated total aboveground dry matter and weight of the rough rice, using both input conditions (the measured and PTF-estimated soil hydraulic properties), showed good agreement, with no significant differences between each other. Outputs related to the soil module also showed good agreement, as no significant differences were found between the two input conditions (measured and PTF-estimated soil hydraulic properties). Although the DUL and LL15 are the most influential parameters for the selected outputs of APSIM-Oryza, the estimated FC and PWP values did not change the predictive ability of APSIM. In this way, the functionality of PTFs for APSIM crop modeling is confirmed.Agronomy 2020, 10, 285 2 of 18 world, especially in tropical regions [1]. Pedotransfer functions (PTFs) are predictive functions used to estimate difficult-to-measure soil parameters with easily measurable soil parameters [2]. Point-based PTFs are used to determine soil parameters at specific, conventional values of matric potential [3]. The moisture content at −10 and −33 kPa (representing the field capacity (FC)) and moisture content at -1500 kPa (representing the permanent wilting point (PWP)) are the most common reference values for the soil moisture characteristic curve (SMCC) used for point PTFs [4].Point PTFs were developed [5] for Sri Lankan soil and confirmed the statistical accuracy of these PTFs for Sri Lankan conditions. However, these PTFs cannot perfectly calculate soil hydraulic parameters. In other words, there are always some errors that could be over or underestimated according to soil type. These errors may be different according to the application of the PTFs. Hence, the final estimation of the model quality should be defined by the model's applicability [6] because the estimation of hydraulic properties is not the...

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Section: Apsim Simulationmentioning

confidence: 99%

Section: Apsim Simulationmentioning

confidence: 99%

A Functional Analysis of Pedotransfer Functions Developed for Sri Lankan soils: Applicability for Process-Based Crop Models

et al. 2020

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“…This model also allows assessment of a continuous cropping system. APSIM-Oryza has been calibrated and used in Korea [29] and Sri Lanka [30] to simulate the different nitrogen treatments in rice cropping systems. The model has satisfactorily simulated the dynamics of the rice variables measured, such as phenological stages, yield, and biomass.…”

Section: Crop Modelingmentioning

confidence: 99%

Quantification of Cultivar Change in Double Rice Regions under a Warming Climate during 1981–2009 in China

et al. 2019

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Maintaining high double rice productivity in China is very important for ensuring the food security of China. However, the double rice production system is sensitive to changes in both climate and management practices. Previous studies showed that rice production has been negatively impacted by global warming without considering the changes of cultivars and management practices. However, cultivar improvements and the impact of cultivar change must not be ignored in any assessment. In the current study, we combined data analysis with crop modeling to investigate the impacts of changes in climate and cultivars on rice productivity at three different double rice sites (Nanchang, Hengyang, and Gaoyao) in China. The results showed a warming trend at the study sites during 1981–2009, and the temperature increase rates (maximum, average, and minimum temperatures) in the late rice growing season were larger than in the early rice growing season. Global warming has led to a reduction in the length of the rice growth period. Adopting new rice cultivars may partially mitigate the declining trend of the growing duration and grain yield, but it would not completely compensate for the negative impact observed in double rice regions. In general, the changes in cultivars prolonged the growing duration by increasing the basic vegetative phase and the photoperiod formation phase. The main reasons for yield improvement were the increase in the percentage of filled grains for early rice and the increase in grain number per spike for late rice. In the face of future warming, breeding efforts are necessary for producing new cultivars that are resilient to the negative impacts of future climate change on agriculture.

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“…APSIM-Oryza is a model for rice growth simulation, and it has been increasingly used in related studies because of the widely-accepted APSIM (Agricultural Production Systems sIMulator) platform (Amarasingha et al, 2015;Gaydon et al, 2012;Gaydon et al, 2017;Holzworth et al, 2014;Radanielson et al, 2018;Zhang et al, 2007). The crop growth process of APSIM-Oryza was borrowed from the Oryza2000 model (https://sites.google.com/a/irri.org/oryza2000/, Bouman et al, 2001;Bouman and Van Laar, 2006;Li et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Global sensitivity analysis of the APSIM-Oryza rice growth model under different environmental conditions

Liu

Zhu

et al. 2019

Science of The Total Environment

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This study conducted the global sensitivity analysis of the APSIM-Oryza rice growth model under eight climate conditions and two CO2 levels using the extended Fourier Amplitude Sensitivity Test method. Two output variables (i.e. total aboveground dry matter WAGT and dry weight of storage organs WSO) and twenty parameters were analyzed. T 30% and s of base values were used as the ranges of parameter variation, and local fertilization and irrigation managements were considered. Results showed that the influential parameters were the same under different environmental conditions, but their orders were often different. Climate conditions had obvious influence on the sensitivity index of several parameters (e.g.RGRLMX, WGRMX and SPGF). In particular, the sensitivity index of RGRLMX was larger under cold climate than under warm climate. Differences also exist for parameter sensitivity of early and late rice in the same site. The CO2 concentration did not have much influence on the results of sensitivity analysis. The range of parameter variation affected the stability of sensitivity analysis results, but the main s study. Compared with existing studies, our study performed the sensitivity analysis of APSIM-Oryza under more environmental conditions, thereby providing more comprehensive insights into the model and its parameters.

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Simulation of crop and water productivity for rice (Oryza sativa L.) using APSIM under diverse agro-climatic conditions and water management techniques in Sri Lanka

Cited by 56 publications

References 40 publications

A Functional Analysis of Pedotransfer Functions Developed for Sri Lankan soils: Applicability for Process-Based Crop Models

A Functional Analysis of Pedotransfer Functions Developed for Sri Lankan soils: Applicability for Process-Based Crop Models

Quantification of Cultivar Change in Double Rice Regions under a Warming Climate during 1981–2009 in China

Global sensitivity analysis of the APSIM-Oryza rice growth model under different environmental conditions

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