Uncertainty in Calculations of Net Primary Production for Grasslands

Lauenroth, William K.; Wade, Alisa A.; Williamson, Matthew A.; Ross, Beth E.; Kumar, Suresh; Cariveau, Daniel P.

doi:10.1007/s10021-005-0072-z

Cited by 52 publications

(32 citation statements)

References 14 publications

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“…In addition, DATsrc contributed considerable uncertainty with 27% contribution, and MODfrm contributed 13% uncertainty in grassland biomass estimation. The amount of variability of the three NDVI dataset used in the calculation was more important than the specific mathematical operations, which was also illustrated in Lauenroth et al (2006). If the model is very sensitive to an input with very small uncertainty, then the uncertainty contribution of this input might be small, and vice versa (He et al, 2014).…”

Section: Uncertainty Partitioning In Grassland Biomass Estimationsmentioning

confidence: 99%

Estimation and uncertainty analyses of grassland biomass in Northern China: Comparison of multiple remote sensing data sources and modeling approaches

Jia

Liu

Yang

et al. 2016

Ecological Indicators

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a b s t r a c tAccurate estimation of grassland biomass and its dynamics are crucial not only for the biogeochemical dynamics of terrestrial ecosystems, but also for the sustainable use of grassland resources. However, estimations of grassland biomass on large spatial scale usually suffer from large variability and mostly lack quantitative uncertainty analyses. In this study, the spatial grassland biomass estimation and its uncertainty were assessed based on 265 field measurements and remote sensing data across Northern China during [2001][2002][2003][2004][2005]. Potential sources of uncertainty, including remote sensing data sources (DATsrc), model forms (MODfrm) and model parameters (biomass allocation, BMallo, e.g. root:shoot ratio), were determined and their relative contribution was quantified. The results showed that the annual grassland biomass in Northern China was 1268.37 ± 180.84 Tg (i.e., 532.02 ± 99.71 g/m 2 ) during 2001-2005, increasing from western to eastern area, with a mean relative uncertainty of 19.8%. There were distinguishable differences among the uncertainty contributions of three sources (BMallo > DATsrc > MODfrm), which contributed 52%, 27% and 13%, respectively. This study highlighted the need to concern the uncertainty in grassland biomass estimation, especially for the uncertainty related to BMallo.

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Section: Uncertainty Partitioning In Grassland Biomass Estimationsmentioning

confidence: 99%

Estimation and uncertainty analyses of grassland biomass in Northern China: Comparison of multiple remote sensing data sources and modeling approaches

Jia

Liu

Yang

et al. 2016

Ecological Indicators

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“…The half-hourly data provide important insights into many short-term physiological processes, but most ecological and management-relevant questions are framed over even longer timeframes -from days to years. A matter of particular interest to both ecologists and ecosystem managers is the inter-annual variability of primary production and carbon storage (Lauenroth et al, 2006). Semi-arid savannas are characterised by high inter-annual variability, in response to highly variable rainfall.…”

Section: Introductionmentioning

confidence: 99%

Drivers of inter-annual variability in Net Ecosystem Exchange in a semi-arid savanna ecosystem, South Africa

et al. 2009

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Abstract. Inter-annual variability in primary production and ecosystem respiration was explored using eddy-covariance data at a semi-arid savanna site in the Kruger Park, South Africa. New methods of extrapolating night-time respiration to the entire day and filling gaps in eddy-covariance data in semi-arid systems were developed. Net ecosystem exchange (NEE) in these systems occurs as pulses associated with rainfall events, a pattern not well-represented in current standard gap-filling procedures developed primarily for temperate flux sites. They furthermore do not take into account the decrease in respiration at high soil temperatures. An artificial neural network (ANN) model incorporating these features predicted measured fluxes accurately (MAE 0.42 gC/m 2 /day), and was able to represent the seasonal patterns of photosynthesis and respiration at the site. The amount of green leaf area (indexed using satellite-derived estimates of fractional interception of photosynthetically active radiation f APAR ), and the timing and magnitude of rainfall events, were the two most important predictors used in the ANN model. These drivers were also identified by multiple linear regressions (MLR), with strong interactive effects. The annual integral of the filled NEE data was found to range from −138 to +155 g C/m 2 /y over the 5 year eddy covariance measurement period. When applied to a 25 year time series of meteorological data, the ANN model predicts an annual mean NEE of 75(±105) g C/m 2 /y. The main correlates of this inter-annual variability were found to be variation in the amount of absorbed photosynthetically active radiation (APAR), length of the growing season, and number of days in the year when moisture was available in the soil.

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“…In addition, frequencies of cutting also decrease the weight of roots. Calculation of BNPP is mainly based on changes in total below-ground dry mass which is accompanied by many methodologist problems (Lauenroth et al 2006). Another useful approach which enables to estimate root production is in-growth core technique (Milchunas 2009).…”

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confidence: 99%

Effect of fertilization on root growth in the wet submontane meadow

Holub¹,

Tůma²,

Fiala³

2013

PLANT SOIL ENVIRON

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Root net primary productivity (RNPP) was assessed in the wet submontane meadow in the highland region of the Czech Republic. RNPP was studied from 1992 to 1995 with help of the in-growth core technique. The effect of different intensities of fertilization on root growth was covered. In comparison with unfertilized stands, the application of fertilizers (90 kg N/ha) resulted, mostly significantly, in greater root productivity (297 g/m 2 /year and 2.08 g/m 2 /day, on average). This represented an increase by 72% and 71%, respectively. Variability in the fraction of RNPP to total net primary productivity was examined. Results indicate that this fraction varied from 0.18 to 0.25 across the compared treatments and decreased with increasing fertilization. Data show how different roles can grasslands play in accumulation of plant matter due to different levels of fertilization.

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Uncertainty in Calculations of Net Primary Production for Grasslands

Cited by 52 publications

References 14 publications

Estimation and uncertainty analyses of grassland biomass in Northern China: Comparison of multiple remote sensing data sources and modeling approaches

Estimation and uncertainty analyses of grassland biomass in Northern China: Comparison of multiple remote sensing data sources and modeling approaches

Drivers of inter-annual variability in Net Ecosystem Exchange in a semi-arid savanna ecosystem, South Africa

Effect of fertilization on root growth in the wet submontane meadow

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