Mitigating heat and chilling stress by adjusting the sowing date of maize in the North China Plain

2018 ◽  
Vol 205 (1) ◽  
pp. 77-87 ◽  
Author(s):  
Beijing Tian ◽  
Jincheng Zhu ◽  
Yanshun Nie ◽  
Cailong Xu ◽  
Qingfeng Meng ◽  
...  
Keyword(s):  
North China Plain ◽  
North China ◽  
Chilling Stress ◽  
Sowing Date ◽  
The North ◽  
The North China Plain

scholarly journals Optimum Sowing Dates for High-Yield Maize when Grown as Sole Crop in the North China Plain

Agronomy ◽  
2019 ◽  
Vol 9 (4) ◽  
pp. 198 ◽  
Author(s):  
Xuepeng Zhang ◽  
Jiali Cheng ◽  
Biao Wang ◽  
Peng Yan ◽  
Hongcui Dai ◽  
...  
Keyword(s):  
North China Plain ◽  
North China ◽  
Grain Filling ◽  
Maize Yield ◽  
Sowing Date ◽  
Thermal Time ◽  
High Yield ◽  
The North ◽  
The North China Plain ◽  
Sowing Dates

The maize sole cropping system solves problems related to ground water resource shortages and guarantees food security in the North China Plain. Using optimal sowing dates is an effective management practice for increasing maize yield. The goal of this study was to explore an optimum sowing date for high-yield maize. Six sowing dates (SDs) from early April to late June with intervals of 10 to 20 days between SD—SD1 (early April), SD2 (mid to late April), SD3 (early May), SD4 (mid to late May), SD5 (early June), SD6 (late June)—were applied from 2012 to 2017. The results showed that yield was correlated with the sowing date based on the thermal time before sowing (r = 0.62**), which was defined as the pre-thermal time (PTt), and that the yield was steadily maintained at a high level (>10,500 kg ha−1) when PTt was greater than 479 °C. To satisfy the growing degree-days required for maturity, maize needs to be sown before a PTt of 750 °C. Data analysis of the results from 2014, 2015, and 2017 revealed the following: i) Most of the grain-filling parameters of late-sown dates (SD4, SD5 and SD6) were better than those in early-sown dates (SD1, SD2, and SD3) in all years, because of the high daily maximum temperature (Tmax) and wide diurnal temperature (Td) from silking to blister (R1–R2) of early-sown dates. The weight of maximum grain-filling rate (Wmax) of SD3 decreased compare with SD4 by the narrow Td from blister to physiological maturity (R2–R6) in all years (−5, −12, and −33 mg kernel−1 in 2014, 2015, and 2017, respectively). ii) In 2017, the pollination failure rates of early-sown dates were 8.4~14.5%, which was caused by the high Tmax and Td of R1–R2. The apical kernel abortion rates were 28.6 (SD2) and 38.7% (SD3), which were affected by Tmax and Td during R2–R6. iii) Compared with late-sown dates, the wide Td of early-sown dates in R1–R2 was caused by higher Tmax, but the narrow Td in R2-R6 was caused by higher Tmin. Our results indicate that high-yielding maize can be obtained by postponing the sowing date with a PTt of 480~750 °C, which can prevent the negative effects of the high Tmax of R1–R2 and high Tmin of R2–R6 on kernel number and weight formation. Moreover, these above-mentioned traits should be considered for heat tolerance breeding to further increase the maize yield.

Adjusting the sowing date of spring maize did not mitigate against heat stress in the North China Plain

2021 ◽  
Vol 298-299 ◽  
pp. 108274
Author(s):  
Zhen Gao ◽  
Han-Yu Feng ◽  
Xiao-Gui Liang ◽  
Shan Lin ◽  
Xue Zhao ◽  
...  
Keyword(s):  
Heat Stress ◽  
North China Plain ◽  
North China ◽  
Sowing Date ◽  
The North ◽  
The North China Plain ◽  
Spring Maize

scholarly journals Divergent climate feedbacks on winter wheat growing and dormancy periods as affected by sowing date in the North China Plain

2021 ◽  
Vol 18 (7) ◽  
pp. 2275-2287
Author(s):  
Fengshan Liu ◽  
Ying Chen ◽  
Nini Bai ◽  
Dengpan Xiao ◽  
Huizi Bai ◽  
...  
Keyword(s):  
Winter Wheat ◽  
North China Plain ◽  
North China ◽  
Sowing Date ◽  
Climate Feedbacks ◽  
Crop Phenology ◽  
Radiative Process ◽  
The North ◽  
The North China Plain ◽  
Biophysical Processes

Abstract. Crop phenology exerts measurable impacts on soil surface properties, biophysical processes and climate feedbacks, particularly at local or regional scales. Nevertheless, the response of surface biophysical processes to climate feedbacks as affected by sowing date in winter wheat croplands has been overlooked, especially during winter dormancy. The dynamics of leaf area index (LAI), surface energy balance and canopy temperature (Tc) were simulated by a modified SiBcrop (Simple Biosphere) model under two sowing date scenarios (early sowing, EP; late sowing, LP) at 10 stations in the North China Plain. The results showed that the SiBcrop model with a modified crop phenology scheme well simulated the seasonal dynamic of LAI, Tc, phenology and surface heat fluxes. An earlier sowing date had a higher LAI with earlier development than a later sowing date. But the response of Tc to the sowing date exhibited opposite patterns during the dormancy and active-growth periods: EP led to higher Tc (0.05 K) than LP in the dormancy period and lower Tc (−0.2 K) in the growth period. The highest difference (0.6 K) between EP and LP happened at the time when wheat was sown in EP but was not in LP. The higher LAI captured more net radiation with a warming effect but partitioned more energy into latent heat flux with cooling. The climate feedback of the sowing date, which was more obvious in winter in the northern areas and in the growing period in the southern areas, was determined by the relative contributions of the albedo radiative process and partitioning non-radiative process. The study highlights the surface biophysical process of land management in modulating climate.

scholarly journals Divergent climate feedbacks in the growing period and the dormancy period to sowing date shift of winter wheat in the North China Plain

2020 ◽  
Author(s):  
Fengshan Liu ◽  
Ying Chen ◽  
Nini Bai ◽  
Dengpan Xiao ◽  
Huizi Bai ◽  
...  
Keyword(s):  
Winter Wheat ◽  
North China Plain ◽  
North China ◽  
Sowing Date ◽  
Climate Feedback ◽  
Radiative Process ◽  
The North ◽  
The North China Plain ◽  
Dormancy Period ◽  
Biophysical Process

Abstract. The land cover and management changes have strong feedbacks to climate through surface biophysical and biochemical processes. Agricultural phenology dynamic exerted measurable impacts on land surface properties, biophysical process and climate feedback in particular times at local/regional scale. But the responses of climate feedback through surface biophysical process to sowing date shift in the winter wheat ecosystem have been overlooked, especially at winter dormancy period. Considering the large cultivation area, unique surface property and phenology shift of winter wheat in the North China Plain, we first validated the SiBcrop model. Then, we used it to investigate the dynamics of leaf area index (LAI) and canopy temperature (Tc) under two planting date scenarios (Early Sowing: EP; Late Sowing: LP) of winter wheat at 10 selected stations. Finally, the surface energy budget was analyzed and interpreted. The results showed that the SiBcrop with a modified crop phenology scheme better simulated the seasonal dynamic of LAI, Tc, phenology, and surface heat fluxes. Earlier sowing date had higher LAI with earlier development than later sowing date. But the response of Tc to sowing date exhibited opposite patterns during the dormancy and active growth periods: EP led to higher Tc (0.05 K) than LP in the dormancy period and lower Tc (−0.2 K) in the growth period. The highest difference (0.6 K) between EP and LP happened at the time when wheat was sown in EP but wasn't in LP. The higher LAI captured more net radiation with lower surface albedo for warming, whist surface energy partitioning exerted cooling effect. The relative contributions of albedo-radiative process and partitioning-non-radiative process determined the climate effect of sowing date shift. The spatial pattern of the climate response to sowing date was influence by precipitation and air temperature. The study highlight that the climate effects of the sowing date shift in winter dormancy period are worthy of attention.

ROx Budgets and O3 Formation during Summertime at Xianghe Suburban Site in the North China Plain

2021 ◽  
Author(s):  
Min Xue ◽  
Jianzhong Ma ◽  
Guiqian Tang ◽  
Shengrui Tong ◽  
Bo Hu ◽  
...  
Keyword(s):  
North China Plain ◽  
North China ◽  
The North ◽  
The North China Plain ◽  
Suburban Site

scholarly journals Improving the Resolution and Accuracy of Groundwater Level Anomalies Using the Machine Learning-Based Fusion Model in the North China Plain

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 46
Author(s):  
Gangqiang Zhang ◽  
Wei Zheng ◽  
Wenjie Yin ◽  
Weiwei Lei
Keyword(s):  
Machine Learning ◽  
Data Fusion ◽  
Groundwater Level ◽  
North China Plain ◽  
North China ◽  
Gradient Boosting ◽  
Fusion Model ◽  
The North ◽  
The North China Plain ◽  
Prediction Module

The launch of GRACE satellites has provided a new avenue for studying the terrestrial water storage anomalies (TWSA) with unprecedented accuracy. However, the coarse spatial resolution greatly limits its application in hydrology researches on local scales. To overcome this limitation, this study develops a machine learning-based fusion model to obtain high-resolution (0.25°) groundwater level anomalies (GWLA) by integrating GRACE observations in the North China Plain. Specifically, the fusion model consists of three modules, namely the downscaling module, the data fusion module, and the prediction module, respectively. In terms of the downscaling module, the GRACE-Noah model outperforms traditional data-driven models (multiple linear regression and gradient boosting decision tree (GBDT)) with the correlation coefficient (CC) values from 0.24 to 0.78. With respect to the data fusion module, the groundwater level from 12 monitoring wells is incorporated with climate variables (precipitation, runoff, and evapotranspiration) using the GBDT algorithm, achieving satisfactory performance (mean values: CC: 0.97, RMSE: 1.10 m, and MAE: 0.87 m). By merging the downscaled TWSA and fused groundwater level based on the GBDT algorithm, the prediction module can predict the water level in specified pixels. The predicted groundwater level is validated against 6 in-situ groundwater level data sets in the study area. Compare to the downscaling module, there is a significant improvement in terms of CC metrics, on average, from 0.43 to 0.71. This study provides a feasible and accurate fusion model for downscaling GRACE observations and predicting groundwater level with improved accuracy.

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