Light Use Efficiency over Two Temperate Steppes in Inner Mongolia, China

Wang, Yu; Zhou, Guangsheng

doi:10.1371/journal.pone.0043614

Cited by 11 publications

(8 citation statements)

References 39 publications

(75 reference statements)

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“…For the alpine meadow HaM site, the annual maximum LAI was 3.1 in August, 2001 and 3.8 in July, 2002 (Kato et al, 2004). More detailed ecological and climate information can be found in Chen et al (2009a,b) for sites of Xi1, Xi2, Du2, and Ku2, in Wang et al (2008) and Wang and Zhou (2012) for site Xfs, and in Kato et al (2004Kato et al ( , 2006 for site HaM.…”

Section: Study Area and Flux Tower Datamentioning

confidence: 97%

Net ecosystem productivity of temperate grasslands in northern China: An upscaling study

Zhang¹,

Guo²,

Jia³

et al. 2014

Agricultural and Forest Meteorology

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a b s t r a c tGrassland is one of the widespread biome types globally, and plays an important role in the terrestrial carbon cycle. We examined net ecosystem production (NEP) for the temperate grasslands in northern China from 2000 to 2010. We combined flux observations, satellite data, and climate data to develop a piecewise regression model for NEP, and then used the model to map NEP for grasslands in northern China. Over the growing season, the northern China's grassland had a net carbon uptake of 158 ± 25 g C m −2 during 2000-2010 with the mean regional NEP estimate of 126 Tg C. Our results showed generally higher grassland NEP at high latitudes (northeast) than at low latitudes (central and west) because of different grassland types and environmental conditions. In the northeast, which is dominated by meadow steppes, the growing season NEP generally reached 200-300 g C m −2 . In the southwest corner of the region, which is partially occupied by alpine meadow systems, the growing season NEP also reached 200-300 g C m −2 . In the central part, which is dominated by typical steppe systems, the growing season NEP generally varied in the range of 100-200 g C m −2 . The NEP of the northern China's grasslands was highly variable through years, ranging from 129 (2001) to 217 g C m −2 growing season −1 (2010). The large interannual variations of NEP could be attributed to the sensitivity of temperate grasslands to climate changes and extreme climatic events. The droughts in 2000, 2001, and 2006 reduced the carbon uptake over the growing season by 11%, 29%, and 16% relative to the long-term (2000-2010) mean. Over the study period (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010), precipitation was significantly correlated with NEP for the growing season (R 2 = 0.35, p-value < 0.1), indicating that water availability is an important stressor for the productivity of the temperate grasslands in semi-arid and arid regions in northern China. We conclude that northern temperate grasslands have the potential to sequester carbon, but the capacity of carbon sequestration depends on grassland types and environmental conditions. Extreme climate events like drought can significantly reduce the net carbon uptake of grasslands.

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Section: Study Area and Flux Tower Datamentioning

confidence: 97%

Net ecosystem productivity of temperate grasslands in northern China: An upscaling study

Zhang¹,

Guo²,

Jia³

et al. 2014

Agricultural and Forest Meteorology

Self Cite

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“…Using RS time series and meteorological data, apple fruit yields were also predicted by the CASA SR model. A previous study demonstrated that the parameters of the CASA model were often affected by several factors, such as the vegetation type, geographical location, and environmental conditions [13,35,36,56,57]. Therefore, this study optimized the parameters of the CASA model to improve its apple fruit yield prediction accuracy.…”

Section: The Casa Model For Apple Yield Predictionmentioning

confidence: 98%

“…The maximum light use efficiency of the original CASA model was assigned a value of 0.389 g C/MJ [35]. However, many studies have demonstrated that the maximum light use efficiency displays large differences among vegetation types and environmental conditions [56,57,60]. Zhu et al [58] proposed that the maximum light use efficiency values ranged from 0.159 to 2.553 g C/MJ for woody vegetation in China.…”

Section: The Casa Model For Apple Yield Predictionmentioning

confidence: 99%

Comparison of Machine-Learning and CASA Models for Predicting Apple Fruit Yields from Time-Series Planet Imageries

Bai

et al. 2021

Remote Sensing

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Apple (Malus domestica Borkh. cv. “Fuji”), an important cash crop, is widely consumed around the world. Accurately predicting preharvest apple fruit yields is critical for planting policy making and agricultural management. This study attempted to explore an effective approach for predicting apple fruit yields based on time-series remote sensing data. In this study, time-series vegetation indices (VIs) were derived from Planet images and analyzed to further construct an accumulated VI (∑VIs)-based random forest (RF∑VI) model and a Carnegie–Ames–Stanford approach (CASA) model for predicting apple fruit yields. The results showed that (1) ∑NDVI was the optimal predictor to construct an RF model for apple fruit yield, and the R2, RMSE, and RPD values of the RF∑NDVI model reached 0.71, 16.40 kg/tree, and 1.83, respectively. (2) The maximum light use efficiency was determined to be 0.499 g C/MJ, and the CASASR model (R2 = 0.57, RMSE = 19.61 kg/tree, and RPD = 1.53) performed better than the CASANDVI model and the CASAAverage model (R2, RMSE, and RPD = 0.56, 24.47 kg/tree, 1.22 and 0.57, 20.82 kg/tree, 1.44, respectively). (3) This study compared the yield prediction accuracies obtained by the models using the same dataset, and the RF∑NDVI model (RPD = 1.83) showed a better performance in predicting apple fruit yields than the CASASR model (RPD = 1.53). The results obtained from this study indicated the potential of the RF∑NDVI model based on time-series Planet images to accurately predict apple fruit yields. The models could provide spatial and quantitative information of apple fruit yield, which would be valuable for agronomists to predict regional apple production to inform and develop national planting policies, agricultural management, and export strategies.

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“…LUE demonstrated significant year-to-year variation in a peatland pasture in Canada during 2014-2015 [16]. LUE increased and decreased in the opposite direction on two different grassland types in Inner Mongolia, China [17]. Temperature, precipitation, radiation, soil moisture, and nutrients are key factors limiting the photosynthetic processes of grassland ecosystems in different regions [14,15,[17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Altered Trends in Light Use Efficiency of Grassland Ecosystem in Northern China

Yuan,

Zhang,

Yao

et al. 2023

Remote Sensing

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Light use efficiency (LUE) is a crucial indicator used to reflect the ability of terrestrial ecosystems to transform light energy. Understanding the long-term trends in LUE and its influencing factors are essential for determining the future carbon sink and carbon sequestration potential of terrestrial ecosystems. However, the long-term interannual variability of LUE in grasslands in northern China at the ecosystem scale is poorly understood due to the limitations of the year length and the coverage of the site data. In this study, we assessed the long-term LUE trends in the grasslands of northern China from 1982 to 2018 and then revealed the relationships between interannual variability in LUE and climate factors. Our study showed a substantial rising trend for LUE from 1982 to 2018 in the grasslands of northern China (3.42 × 10−3 g C/MJ/yr). Regarding the different grassland types, alpine meadow had the highest growth rate (4.85 × 10−3 g C/MJ/yr), while temperate steppe had the lowest growth rate (1.58 × 10−3 g C/MJ/yr). The climate factors driving LUE dynamics were spatially heterogeneous in grasslands. Increasing precipitation accelerated the interannual growth rate of LUE in temperate steppe, and increasing temperature accelerated the interannual growth rate of LUE in other types. In addition, the temporal dynamic of LUE showed different trends in relation to time scales, and the growth trend slowed down after 1998. Our results should be considered in developing future grassland management measures and predicting carbon cycle–climate interactions.

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Light Use Efficiency over Two Temperate Steppes in Inner Mongolia, China

Cited by 11 publications

References 39 publications

Net ecosystem productivity of temperate grasslands in northern China: An upscaling study

Net ecosystem productivity of temperate grasslands in northern China: An upscaling study

Comparison of Machine-Learning and CASA Models for Predicting Apple Fruit Yields from Time-Series Planet Imageries

Altered Trends in Light Use Efficiency of Grassland Ecosystem in Northern China

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