Photo-thermal models of rice growth duration for various varietal types in China

Gao, Liangzhi; Jin, Zhiqing; Li, L.

doi:10.1016/0168-1923(87)90038-4

Cited by 31 publications

(8 citation statements)

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“…During rice growing season, air temperature was recorded daily at 30-min intervals with the ZDR-11 (made at Zhejiang University of China; measuring range: -20°C~60°C; measuring accuracy: ±0.5°C; 3.6V lithium battery, the power under saving mode can be used continuously for more than 1 year; communication Interface: RS-232; sampling interval (optional): 2 seconds~24 hours). Data were downloaded every month and used for calculating thermal time (TT,°C·d) with a base temperature of 10°C for Japonica and 12°C for Indica cultivars [ 27 ]. In addition, N contents and dry weights of different organs from the sampled plants were measured by micro-Kjeldahl method [ 28 ] and an electronic scale, respectively.…”

Section: Methodsmentioning

confidence: 99%

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Modeling the leaf angle dynamics in rice plant

Zhang

Tang²,

Liu³

et al. 2017

PLoS ONE

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The leaf angle between stem and sheath (SSA) is an important rice morphological trait. The objective of this study was to develop and validate a dynamic SSA model under different nitrogen (N) rates for selected rice cultivars. The time-course data of SSA were collected in three years, and a dynamic SSA model was developed for different main stem leaf ranks under different N rates for two selected rice cultivars. SSA increased with tiller age. The SSA of the same leaf rank increased with increase in N rate. The maximum SSA increased with leaf rank from the first to the third leaf, then decreased from the third to the final leaf. The relationship between the maximum SSA and leaf rank on main stem could be described with a linear piecewise function. The change of SSA with thermal time (TT) was described by a logistic equation. A variety parameter (the maximum SSA of the 3rd leaf on main stem) and a nitrogen factor were introduced to quantify the effect of cultivar and N rate on SSA. The model was validated against data collected from both pot and field experiments. The relative root mean square error (RRMSE) was 11.56% and 14.05%, respectively. The resulting models could be used for virtual rice plant modeling and plant-type design.

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Section: Methodsmentioning

confidence: 99%

“… where a and b are equation coefficients, 5.18 and 0.52, respectively. AGB is above-ground biomass of rice plant [ 27 ].…”

Section: Methodsmentioning

confidence: 99%

Modeling the leaf angle dynamics in rice plant

Zhang

Tang²,

Liu³

et al. 2017

PLoS ONE

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“…To verify the cultivation period, rice phenology was examined. The available rice phenology models were growing DDs (Tollenaar et al 1979), photothermal (Gao et al 1987), bilinear (Olsen et al 1993), beta function (Yin and Kropff 1996), ORYZA2000 (Bouman et al 2001), and leaf appearance and final leaf number models (Lee et al 2001a, b). We used the leaf appearance and final leaf number models to estimate the rice cultivation period because estimated parameters for Korean rice cultivars are available.…”

Section: Rice Transplanting Timementioning

confidence: 99%

Predicting temporal shifts in the spring occurrence of overwintered Scotinophara lurida (Hemiptera: Pentatomidae) and rice phenology in Korea with climate change

Lee

Kang²,

Ahn³

et al. 2015

Int J Biometeorol

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Climate change could shift the phenology of insects and plants and alter their linkage in space and time. We examined the synchrony of rice and its insect pest, Scotinophara lurida (Burmeister), under the representative concentration pathways (RCP) 8.5 climate change scenario by comparing the mean spring immigration time of overwintered S. lurida with the mean rice transplanting times in Korea. The immigration time of S. lurida was estimated using an overwintered adult flight model. The rice transplanting time of three cultivars (early, medium, and medium-late maturing) was estimated by forecasting the optimal cultivation period using leaf appearance and final leaf number models. A temperature increase significantly advanced the 99% immigration time of S. lurida from Julian day 192.1 in the 2000s to 178.4 in the 2050s and 163.1 in the 2090s. In contrast, rice transplanting time was significantly delayed in the early-maturing cultivar from day 141.2 in the 2000s to 166.7 in the 2050s and 190.6 in the 2090s, in the medium-maturing cultivar from day 130.6 in the 2000s to 156.6 in the 2050s and 184.7 in the 2090s, and in the medium-late maturing cultivar from day 128.5 in 2000s to 152.9 in the 2050s and 182.3 in the 2090s. These simulation results predict a significant future phenological asynchrony between S. lurida and rice in Korea.

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“…For temperature-based phenology models (i.e., DD10), developmental rate is generally modeled in relation to thermal time accumulation (Gao et al, 1987;Yin et al, 1996). It is assumed that a certain amount of thermal time (degrees per day, °C d −1 ) is needed to complete a given developmental stage (Gao et al, 1992;Summerfield et al, 1992).…”

Section: Using Stage-dependent Temperature Parameters To Improve Phenmentioning

confidence: 99%

Using Stage‐Dependent Temperature Parameters to Improve Phenological Model Prediction Accuracy in Rice Models

Sharifi

Hijmans²,

Hill

2017

Crop Science

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Crop phenology models that use constant temperature parameters across developmental stages may be less accurate and have temperature‐dependent systematic prediction error (bias). Using the DD10 model, we evaluated default and optimized (DD_Opt) temperature parameters using data from seven California rice (Oryza sativa L.) cultivars grown in six locations over 3 yr (2012–2014). Furthermore, we evaluated the effect of using stage‐dependent temperature parameters on model performance using two‐ and three‐stage optimization approaches. Optimized temperature parameters, or DD_Opt (RMSE: 2.3–5.4 d), performed better than DD10 (RMSE: 2.9–7.3 d). A temperature sensitivity analysis indicated that the time from planting to panicle initiation was most sensitive to temperature (every 1°C increase decreased days to panicle initiation by 1.8 d) while time from heading to R7 (marked by the appearance of one yellow hull on the main stem panicle) was not affected by temperature. Optimized temperature parameters varied between stages, with base temperature decreasing and optimum temperature increasing with plant development. Compared to the DD_Opt, two‐stage optimization (planting–heading and heading–R7) reduced the RMSE by 0.8 d and the systematic error by 0.6 d °C−1. Three‐stage optimization (planting–panicle initiation, panicle initiation–heading, and heading–R7) further reduced RMSE by 1.1 d and systematic error by 1.4 d °C−1 for preheading. These results demonstrate the importance of using stage‐dependent parameters to improve accuracy of phenological models, which may be important when models are used to study the crop response to climate change, field management options, ecosystem productivity, breeding, and yield gap analysis.

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Photo-thermal models of rice growth duration for various varietal types in China

Cited by 31 publications

References 0 publications

Modeling the leaf angle dynamics in rice plant

Modeling the leaf angle dynamics in rice plant

Predicting temporal shifts in the spring occurrence of overwintered Scotinophara lurida (Hemiptera: Pentatomidae) and rice phenology in Korea with climate change

Using Stage‐Dependent Temperature Parameters to Improve Phenological Model Prediction Accuracy in Rice Models

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