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

doi:10.1007/s11442-017-1423-3

Abstract

Abstract:

Response and feedback of land surface process to climate change is one of the research priorities in the field of geoscience. The current study paid more attention to the impacts of global change on land surface process, but the feedback of land surface process to climate change has been poorly understood. It is becoming more and more meaningful under the framework of Earth system science to understand systematically the relationships between agricultural phenology dynamic and biophysical process, as well as the feedback on climate. In this paper, we summarized the research progress in this field, including the fact of agricultural phenology change, parameterization of phenology dynamic in land surface progress model, the influence of agricultural phenology dynamic on biophysical process, as well as its feedback on climate. The results showed that the agriculture phenophase, represented by the key phenological phases such as sowing, flowering and maturity, had shifted significantly due to the impacts of climate change and agronomic management. The digital expressions of land surface dynamic process, as well as the biophysical process and atmospheric process, were improved by coupling phenology dynamic in land surface model. The agricultural phenology dynamic had influenced net radiation, latent heat, sensible heat, albedo, temperature, precipitation, circulation, playing an important role in the surface energy partitioning and climate feedback. Considering the importance of agricultural phenology dynamic in land surface biophysical process and climate feedback, the following research priorities should be stressed: (1) the interactions between climate change and land surface phenology dynamic; (2) the relations between agricultural phenology dynamic and land surface reflectivity at different spectrums; (3) the contributions of crop physiology characteristic changes to land surface biophysical process; (4) the regional differences of climate feedbacks from phenology dynamic in different climate zones. This review is helpful to accelerate understanding of the role of agricultural phenology dynamic in land surface process and climate feedback.

Key words: agricultural phenology, land surface biophysical process, land surface process model, climate feedback

Fengshan LIU, Ying CHEN, Wenjiao SHI, Shuai ZHANG, Fulu Tao, Quansheng GE. Influences of agricultural phenology dynamic on land surface biophysical process and climate feedback[J].Journal of Geographical Sciences, 2017, 27(9): 1085-1099.

Figures/Tables 3

Table 1

Characteristics of phenological change and controlling factors for staple crops around the world"

Country	Crop	Phenology variation	Drivers
China (Tao et al., 2012; Xiao et al., 2013; 2015)	Winter wheat	Sowing, seedling, and dormant stage delayed by 1.5, 1.7, and 1.5 days/decade, respectively. Spring green up, flowering, and mature stage advanced by 1.1, 2.7, and 1.4 days/decade, respectively.	Increased temperature reduces the growth period, variety renewal with higher GDD extends the reproductive stage, and reduced day length extends the vegetative period.
China (Tao et al., 2014; Wang et al., 2016; Xiao et al., 2016)	Summer maize	36.6% of sites with extended mature stage, and 41.1% of sites with extended growth period. Reproductive growth period extended by 2.4-3.7 days/decade.	Increased average temperature shortens the growth period. Variety renewal delays the flowering and mature stages. Advancement of planting date to adapt to increased temperature.
China (Wang et al., 2016)	Maize	Advanced planting, jointing, and flowering stages. Delayed mature stage. Shortened vegetative period. Prolonged reproductive period.	Global warming speeds up the development of maize and shortens the growth period. Precipitation reduces growth period to a certain extent. Variety renewal extends growth period.
America (Sacks and Kucharik, 2011)	Maize	Sowing stage advanced by 4.2 days/decade. Duration from sowing to harvest increased by 5 days/decade. Duration from mature to harvest shorted by 3 days/decade.	GDD increased by 14% in the reproductive stage. Maize variety with longer growth period. Interaction with more irrigation, added fertilizer, and variety renewal.
America (Sacks and Kucharik 2011)	Soybeans	Sowing stage advanced by 4.9 days/decade. Harvest stage advanced by 4.9 days/decade.	Higher temperature in favor of sowing advancement and long duration of maturity. Variety renewal with longer reproductive period.
Northern and Central Europe (Olesen et al., 2012)	Gramineae (wheat, oats, and maize)	Sowing stage advanced by 1-3 weeks. Flowering and mature stages advanced by 1-3 weeks.	Model parameter setting based on 1500 site records: development from sowing to flowering stage depends on temperature and day length for oat and wheat and on temperature for maize and development from flowering to mature stage depends on temperature.
Spain (Oteros et al., 2015)	Cereal crops (oats, wheat, rye, barley, and maize)	Phenology in spring advanced for winter wheat. Flag leaf sheath swelling advanced by 30 days/decade. Flowering stage advanced by 10 days/decade.	Temperature prior to certain phenology was the main factor. Human intervention mitigates the impact of phenology change on yield.
Germany (Chmielewski et al., 2004)	Maize	Sowing, seedling, and initial harvest stages advanced by 1.7, 3.3, and 1.3 days/decade, respectively. Duration from sowing to seedling reduced by 1.6 days/decade. Duration from seedling to harvest increased by 2.1 days/decade.	Increased spring temperatures make sowing in advance possible. Strong warming in May accelerates plant growth and imposes serious effects on the seedling stage.
Kazakhstan (de Beurs and Henebry, 2004)	Wheat	NDVI peak 4-7 days in advance.	Increased GDD. Collapse of the Soviet Union.

Table 1

Table 2

Contribution of land surface and crop model coupling to understanding surface energy and water balance"

Model^a	Crop type		Result			Reason
Agro-IBIS Dynamic crop growth model(Sacks and Kucharik, 2011)	Maize and soybean in America		Agricultural phenology dynamic changed the surface heat and water balance; Earlier planting increased latent heat in June, reduced maturity-harvest duration, and increased net radiation in October.			Using GDD to express phenology change
BATS CERES3.0 (Chen and Xie, 2011)	Farmland in China		Canopy interception, transpiration, evaporation, latent and sensible heat fluxes had significantly impacted; Decrease the systematic error of LAI and surface moisture; Increase the simulation accuracy of surface flux.			Addition of the process of crop development and growth
BATS CERES-Maize (Tsvetsinskaya et al., 2001)	Maize in America		Latent heat changed by 30%-45%, sensible heat changed by 20%-35% with LAI changed from 5 to 1; The contribution of evaporation and transpiration to latent was influenced by LAI.			Phenophase and organic matter accumulation and allocation process based on physiology
CLASS Carbon and nitrogen model (Chang et al., 2014)	Farmland in Canada		Increase the determination coefficient between simulated and observed data of NEP; More rational distribution process of organic matter.			Addition of the agricultural phenological scheme and management measure
CLM CornSoy (Chen et al., 2015)	Maize and soybean in America		Closely connection between carbon flux and phenology in simulation; Better correlation between simulated and observed data for LAI, energy and CO₂ flux.			The expression of emergence-filling stage and filling-harvest stage using GDD; Remove the restriction to maximum LAI.
CLM Agricultural phenology model (Levis et al., 2012)	Maize, soybean and cereals in North America		More real LAI, spring planting, autumn harvest in simulation; Better representation of latent heat in lower LAI period			The seasonal dynamics of agricultural phenology and carbon allocation driven by temperature.
ISAM Dynamic crop growth model (Song et al., 2013)	Maize-soybean rotation system America		Dynamic growth model improves simulation of seasonal change of LAI, canopy height, root depth, soil moisture absorption and evaporation, fluxes of energy, water and carbon.			The containment of the stress of light, water and nutrient on crop dynamic; Increased simulation accuracy of LAI seasonal dynamic; Better simulation of soil moisture absorption and transpiration.
JULES InfoCrop (Tsarouchi et al., 2014)	Farmland in India		Simulation error of evapotranspiration decreased from 7.5-24.4 to 5.4-11.6 mm month^-1in wet season and from 10-17 to 2.2-3.4 mm month^-1 in dry season			Addition of the crop growth model
JULES SUCROS (Van den Hoof et al., 2011)	Farmland in Europe		Significantly increased correlation between simulated and measured data in farmland; Better expression in the spatial-temporal characteristics of crop growth; The importance of crop structure and phenology to land-air interaction.			Dynamic crop growth; The expression of phenology from planting to harvest.
LPJ DGVMs (Bondeau et al., 2007)	Global farmland		Better expression in planting date, canopy seasonal dynamic of crop in temperate zone; Farmland expansion decreased transpiration by 5%, and increased evaporation by 40%.			Parameterization of phenology and its connection with LAI.
ORCHIDEE STICS (Gervois et al., 2004)		Winter wheat and maize in France and America		Better simulation of evapotranspiration, biomass accumulation process in different climatic zones.	Added simulation of LAI, nutrition stress, and plant height; Improved simulation of organic matter distribution, water stress, and carboxylation.
SiB2 Agricultural phenology model (Lokupitiya et al., 2009)		Wheat, soybean, maize in America		Better simulation of the beginning and ending of growth season, harvest, seasonal dynamic of rotation system; Increased LAI and carbon flux.	Phenological scheme and corresponding physiological parameters for specific crop.
SiB2 Agricultural phenology model (Lei et al., 2010)		Winter wheat-summer maize rotation system in North China Plain		Precisely simulation of LAI, carbon flux, latent flux, soil water content and yield	Phenological scheme and corresponding physiological parameters for specific crop.

Table 2

Figure 1

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