Influences of agricultural phenology dynamic on land surface biophysical process and climate feedback

Liu, Fengshan; Chen, Ying; Shi, Wenjiao; Zhang, Shuai; Tao, Fulu; Ge, Quansheng

doi:10.1007/s11442-017-1423-3

Cited by 15 publications

(9 citation statements)

References 70 publications

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“…Interannual variation in phenology could also be impacted by other factors, such as snow cover in mountain and cold environments and precipitation in semiarid regions [21][22][23] . The large interannual variations in phenological events have significant impacts on the feedbacks of the seasonality of albedo, surface roughness length, canopy conductance, and fluxes of water, energy, and CO 2 [24][25][26][27] . Extreme phenological events could occur in response to weather and climate extremes, which could have the greatest direct impacts on human health, agricultural development, and terrestrial ecosystems 28,29 .…”

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confidence: 99%

Effects of temperature variability and extremes on spring phenology across the contiguous United States from 1982 to 2016

Liu

Zhang

2020

Sci Rep

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Warming climate and its impact on vegetation phenological trends have been widely investigated. However, interannual variability in temperature is considerably large in recent decades, which is expected to trigger an increasing trend of variation in vegetation phenology. To explore the interannual phenological variation across the contiguous United States (CONUS), we first detected the onset of vegetation greenup using the time series of the daily two-band Enhanced Vegetation Index (EVI2) observed from the AVHRR Long-Term Data Record (1982–1999) and the MODIS Climate Modeling Grid (2000–2016). We then calculated the interannual variation in greenup onset during four decadal periods: 1982–1989, 1990–1999, 2000–2009 and 2010–2016. Further, the trend of interannual variation in greenup onset from 1982 to 2016 was analyzed at pixel and state levels. Extreme phenological events were also determined using a greenup onset anomaly for each pixel. Similar approaches were applied to spring temperatures to detect extreme years and to the temporal trend of interannual variation to explain the phenological variation. The results revealed that 62% of pixels show an increasing interannual variation in greenup onset, and in 44% of pixels, this variation could be explained by the temperature. Although extreme phenology occurred locally in different years, three nationwide extreme phenological years were distinguished. The extreme warm spring that occurred in 2012 resulted in the occurrence of greenup onset as much as 20 days earlier than normal in large parts of the CONUS. In contrast, greenup onset was much later (up to 30 days) in 1983 and 1996 due to cool spring temperatures. These findings suggest that interannual variation in spring phenology could be much stronger in the future in response to climate variation, which could have more significant impacts on terrestrial ecosystems than the regular long-term phenological trend.

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confidence: 99%

Effects of temperature variability and extremes on spring phenology across the contiguous United States from 1982 to 2016

Liu

Zhang

2020

Sci Rep

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“…The main reasons for crop phenology include climate warming and variety renewal (Mirschel et al, 2005;Eyshi Rezaei et al, 2017;F. Liu et al, 2017).…”

Section: Contributions Of Surface Energy Balance Components To the Scenario Difference In T Cmentioning

confidence: 99%

“…During the dormancy period in winter, the aboveground canopy of winter wheat remained constant for more than 2 months (Xiao et al, 2013). In view of the close relationships between surface biophysical processes and the aboveground canopy (Boisier et al, 2012;Chen et al, 2015;F. Liu et al, 2017), the length from the sowing date to start of dormancy would be the determinant factor at surface biophysical processes in winter where winter wheat is widely distributed, such as the NCP, Pacific Northwest (Wuest, 2010) and southern Great Plains of the USA (Bagley et al, 2017), Australia, and numerous countries surrounding the Mediterranean Sea (Mahdi et al, 1998;Schillinger, 2011).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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.

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“…Many studies have shown that the climate impacts on agricultural ecosystems are reflected by variations in crop phenology, such as the advanced or delayed planting and harvesting dates [10][11][12]. For example, He et al [13] reported that soybean planting dates were delayed by an average of 1.78 days/decade, and the growing season length was shortened by an average of 1.16 days/decade during 1981-2010 across the major soybeanproducing areas in China.…”

Section: Introductionmentioning

confidence: 99%

Detecting Recent Crop Phenology Dynamics in Corn and Soybean Cropping Systems of Kentucky

et al. 2021

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Accurate phenological information is essential for monitoring crop development, predicting crop yield, and enhancing resilience to cope with climate change. This study employed a curve-change-based dynamic threshold approach on NDVI (Normalized Differential Vegetation Index) time series to detect the planting and harvesting dates for corn and soybean in Kentucky, a typical climatic transition zone, from 2000 to 2018. We compared satellite-based estimates with ground observations and performed trend analyses of crop phenological stages over the study period to analyze their relationships with climate change and crop yields. Our results showed that corn and soybean planting dates were delayed by 0.01 and 0.07 days/year, respectively. Corn harvesting dates were also delayed at a rate of 0.67 days/year, while advanced soybean harvesting occurred at a rate of 0.05 days/year. The growing season length has increased considerably at a rate of 0.66 days/year for corn and was shortened by 0.12 days/year for soybean. Sensitivity analysis showed that planting dates were more sensitive to the early season temperature, while harvesting dates were significantly correlated with temperature over the entire growing season. In terms of the changing climatic factors, only the increased summer precipitation was statistically related to the delayed corn harvesting dates in Kentucky. Further analysis showed that the increased corn yield was significantly correlated with the delayed harvesting dates (1.37 Bu/acre per day) and extended growing season length (1.67 Bu/acre per day). Our results suggested that seasonal climate change (e.g., summer precipitation) was the main factor influencing crop phenological trends, particularly corn harvesting in Kentucky over the study period. We also highlighted the critical role of changing crop phenology in constraining crop production, which needs further efforts for optimizing crop management practices.

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Influences of agricultural phenology dynamic on land surface biophysical process and climate feedback

Cited by 15 publications

References 70 publications

Effects of temperature variability and extremes on spring phenology across the contiguous United States from 1982 to 2016

Effects of temperature variability and extremes on spring phenology across the contiguous United States from 1982 to 2016

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

Detecting Recent Crop Phenology Dynamics in Corn and Soybean Cropping Systems of Kentucky

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