Simulation of Winter Wheat Phenology in Beijing Area with DSSAT-CERES Model

Feng, Haikuan; Li, Zhenhai; He, Ping; Jin, Xiuliang; Yang, Guijun; Yu, Haiyang; Fuqin, Yang

doi:10.1007/978-3-319-48354-2_27

Cited by 3 publications

(4 citation statements)

References 21 publications

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“…Model calibration was performed using data from three-year experiment of different winter wheat sowing time results. Main genetic coefficient values of model were close to the recent author studies of winter wheat modeling shown in Table 3, except grain filling phase duration parameter (P5) where it reached 894.6 °C and compared to other author findings it was slightly greater (Feng et al 2015, Attia et al 2016, Tang et al 2018). On the other hand, our observed experimental data shows that grain filling thermal time requirement takes from 850 to 966 °C.…”

Section: Discussionsupporting

confidence: 84%

“…One of the most popular crop models is DSSAT (Decision Support System for Agrotechnology Transfer) created by Jones et al (2003) and Hoogenboom et al (2019) and used by many researchers around the world (Feng et al 2015, Yao et al 2015, Attia et al 2016, Adhikari et al 2017, Tang et al 2018). The DSSAT model simulates crop growth, phenological crop development and crop yield on a uniform area of land under completed or simulated management.…”

Section: Introductionmentioning

confidence: 99%

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Effects of different sowing time to phenology and yield of winter wheat

Klepeckas

Januškaitienė

Vagusevičienė

et al. 2020

AFSci

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This study aims to determine optimal sowing date for winter wheat (Triticum aestivum L.) and invoke DSSAT Nwheat model to analyze impact of modified sowing date to phenological development and grain yield. Wheat was grown for three seasons in two locations across Lithuania and sowing was carried out one time per week from 1 September to 29 September. Average three-year temperature of vegetation period gets lower every week by 0.16 °C, and available GDD by 94.5 °C when sowing time is delayed. Modeling results showed that tillering rate is affected by sowing time winter wheat grew 232 (tillers m-2) less with each week of delayed sowing. Grain filling stage was shortened by 1.25 days with each delayed sowing week. Depending on sowing time yield varied from 8.58 t ha-1 to 6 t ha-1 while simulated harvest was 8% lower. We conclude that best winter wheat sowing time for current climatic conditions in Lithuania is mid-September and DSSAT model proves as a useful tool in anticipating sowing time while facing problems caused by a climate change.

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Section: Discussionsupporting

confidence: 84%

Section: Introductionmentioning

confidence: 99%

Effects of different sowing time to phenology and yield of winter wheat

Klepeckas

Januškaitienė

Vagusevičienė

et al. 2020

AFSci

View full text Add to dashboard Cite

show abstract

“…Timsina and Humphreys [81] observed the nRMSE of 4 to 5% anthesis and maturity time in the CERES-Wheat model. Feng et al [82] noticed RMSE of 1.83 and 3.56 days for winter wheat anthesis and maturity time. The average RMSE values were less than 300 kg ha −1 , 800 kg ha −1 , and 0.03, and average r values were 0.95, 0.77, and 0.68 for grain yield, total dry matter, and harvest index, respectively, in both growing seasons.…”

Section: Discussionmentioning

confidence: 99%

Warming Climate and Elevated CO2 Will Enhance Future Winter Wheat Yields in North China Region

et al. 2022

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The projected climate change substantially impacts agricultural productivity and global food security. The cropping system models (CSM) can help estimate the effects of the changing climate on current and future crop production. The current study evaluated the impact of a projected climate change under shared socioeconomic pathways (SSPs) scenarios (SSP2-4.5 and SSP5-8.5) on the grain yield of winter wheat in the North China Plain by adopting the CSM-DSSAT CERES-Wheat model. The model was calibrated and evaluated using observed data of winter wheat experiments from 2015 to 2017 in which nitrogen fertigation was applied to various growth stages of winter wheat. Under the near-term (2021–2040), mid-term (2041–2060), and long-term (2081–2100) SSP2-4.5 and SSP5-8.5 scenarios, the future climate projections were based on five global climate models (GCMs) of the sixth phase of the Coupled Model Intercomparison Project (CMIP6). The GCMs projected an increase in grain yield with increasing temperature and precipitation in the near-term, mid-term, and long-term projections. In the mid-term, 13% more winter wheat grain yield is predicted under 1.3 °C, and a 33 mm increase in temperature and precipitation, respectively, compared with the baseline period (1995–2014). The increasing CO2 concentration trends projected an increase in average grain yield from 4 to 6%, 4 to 14%, and 2 to 34% in the near-term, mid-term, and long-term projections, respectively, compared to the baseline. The adaptive strategies were also analyzed, including three irrigation levels (200, 260, and 320 mm), three nitrogen fertilizer rates (275, 330, and 385 kg ha−1), and four sowing times (September 13, September 23, October 3, and October 13). An adaptive strategy experiments indicated that sowing winter wheat on October 3 (traditional planting time) and applying 275 kg ha−1 nitrogen fertilizer and 260 mm irrigation water could positively affect the grain yield in the North China Plain. These findings are beneficial in decision making to adopt and implement the best management practices to mitigate future climate change impacts on wheat grain yields.

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“…This process helps field managers schedule and carry out various activities such as irrigation and fertilization at the right time. Moreover, it provides essential references for estimating the growth stage of crop yield [1]. Rice is one of the three significant staple crops globally, with Asia hosting 90% of its planting area.…”

Section: Introductionmentioning

confidence: 99%

Deep-Learning-Based Rice Phenological Stage Recognition

Qin

Yuan

et al. 2023

Remote Sensing

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Crop phenology is an important attribute of crops, not only reflecting the growth and development of crops, but also affecting crop yield. By observing the phenological stages, agricultural production losses can be reduced and corresponding systems and plans can be formulated according to their changes, having guiding significance for agricultural production activities. Traditionally, crop phenological stages are determined mainly by manual analysis of remote sensing data collected by UAVs, which is time-consuming, labor-intensive, and may lead to data loss. To cope with this problem, this paper proposes a deep-learning-based method for rice phenological stage recognition. Firstly, we use a weather station equipped with RGB cameras to collect image data of the whole life cycle of rice and build a dataset. Secondly, we use object detection technology to clean the dataset and divide it into six subsets. Finally, we use ResNet-50 as the backbone network to extract spatial feature information from image data and achieve accurate recognition of six rice phenological stages, including seedling, tillering, booting jointing, heading flowering, grain filling, and maturity. Compared with the existing solutions, our method guarantees long-term, continuous, and accurate phenology monitoring. The experimental results show that our method can achieve an accuracy of around 87.33%, providing a new research direction for crop phenological stage recognition.

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Simulation of Winter Wheat Phenology in Beijing Area with DSSAT-CERES Model

Cited by 3 publications

References 21 publications

Effects of different sowing time to phenology and yield of winter wheat

Effects of different sowing time to phenology and yield of winter wheat

Warming Climate and Elevated CO2 Will Enhance Future Winter Wheat Yields in North China Region

Deep-Learning-Based Rice Phenological Stage Recognition

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