Net Primary Production of Chinese Croplands From 1950 to 1999

Huang, Yao; Zhang, Wen; Sun, Weixia; Zheng, Xunhua

doi:10.1890/05-1792

Cited by 150 publications

(98 citation statements)

References 29 publications

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“…9). Cropland NPP in China was estimated to increase from 159 TgC yr −1 in the 1960s to 513 TgC yr −1 in the 1990s based on the National Agriculture Database (Statistics Bureau of China 2000) (Huang et al, 2007), and this study estimated the range as 286 TgC yr −1 in the 1960s to 559 TgC yr −1 in the 1990s. In tropical Asia, the new croplands were mainly derived from forests, which caused large amounts of carbon loss from both vegetation and soil Tao et al, 2013;West et al, 2010).…”

Section: Discussionmentioning

confidence: 95%

“…The HI varies for different crops, with a lower value for wheat (0.37-0.43) (Huang et al, 2007;Prince et al, 2001;Soltani et al, 2004) and higher values for rice (0.42-0.47) (Prasad et al, 2006;Witt et al, 1999) and maize (0.44-0.53) (Huang et al, 2007;Prince et al, 2001). We used a value of 0.45 for the year 2000, a typical value of the three major crops: maize, rice, and wheat (Haberl et al, 2007;Sinclair, 1998).…”

Section: Varietymentioning

confidence: 99%

“…However, these data report agricultural production, not NPP, and thus need crop-specific factors (dry matter fraction, harvest index (HI), root-to-shoot ratio, etc.) to calculate the NPP (Gray et al, 2014;Huang et al, 2007;Monfreda et al, 2008;Prince et al, 2001), which neglected the temporal evolution for crop-specific factors such as HI and root-to-shoot ratio (Lorenz et al, 2010;Sinclair, 1998). Remote sensing using satellites is a powerful tool for estimating global terrestrial NPP (Cleveland et al, 2015;Field et al, 1995;Nemani et al, 2003;Parazoo et al, 2014;Zhao and Running, 2010), yet croplands are coincident with natural vegetation, making it difficult to differentiate the two using remote sensing (Defries et al, 2000;Monfreda et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Estimating global cropland production from 1961 to 2010

et al. 2017

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Abstract. Global cropland net primary production (NPP) has tripled over the last 50 years, contributing 17-45 % to the increase in global atmospheric CO 2 seasonal amplitude. Although many regional-scale comparisons have been made between statistical data and modeling results, long-term national comparisons across global croplands are scarce due to the lack of detailed spatiotemporal management data. Here, we conducted a simulation study of global cropland NPP from 1961 to 2010 using a process-based model called Vegetation-Global Atmosphere-Soil (VEGAS) and compared the results with Food and Agriculture Organization of the United Nations (FAO) statistical data on both continental and country scales. According to the FAO data, the global cropland NPP was 1.3, 1.8, 2.2, 2.6, 3.0, and 3.6 PgC yr −1 in the 1960s, 1970s, 1980s, 1990s, 2000s, and 2010s, respectively. The VEGAS model captured these major trends on global and continental scales. The NPP increased most notably in the US Midwest, western Europe, and the North China Plain and increased modestly in Africa and Oceania. However, significant biases remained in some regions such as Africa and Oceania, especially in temporal evolution. This finding is not surprising as VEGAS is the first global carbon cycle model with full parameterization representing the Green Revolution. To improve model performance for different major regions, we modified the default values of management intensity associated with the agricultural Green Revolution differences across various regions to better match the FAO statistical data at the continental level and for selected countries. Across all the selected countries, the updated results reduced the RMSE from 19.0 to 10.5 TgC yr −1 (∼ 45 % decrease). The results suggest that these regional differences in model parameterization are due to differences in socioeconomic development. To better explain the past changes and predict the future trends, it is important to calibrate key parameters on regional scales and develop data sets for land management history.

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Section: Discussionmentioning

confidence: 95%

Section: Varietymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Estimating global cropland production from 1961 to 2010

et al. 2017

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“…To incorporate information on the harvest residues, the reported production amounts were divided by the crop-specific ratios of the residue to economic yield, which were obtained from the literature [14][15][16][17]. The harvest data were converted from fresh weight to dry weight using a literature-derived fraction of dry matter [18,19].…”

Section: Estimation Of Hanpp Harvmentioning

confidence: 99%

Quantitative Assessment of the Human Appropriation of Net Primary Production (HANPP) in the Coastal Areas of Jiangsu, China

2015

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Abstract:Global increases in population and consumption have raised concerns regarding the sustainability of the current and future use of natural resources. The human appropriation of net primary production (HANPP) provides a useful measure for determining human-derived alterations in the amount of biomass that is available in ecosystems each year. HANPP was calculated based on vegetation modelling, agricultural statistics, and remote sensing data on land use and land cover to assess the human impacts on ecosystems in the coastal areas of Jiangsu, China. The results showed that HANPP increased from 332 g¨C/m 2 /year in 2000 to 442 g¨C/m 2 /year in 2010, with an average annual increase of 2.9%. The proportion of appropriated net primary production increased from 50.3% to 71.0% of NPP pot , mainly driven by HANPP harv (harvested NPP) with an increase from 45.2% to 61.3% of NPP pot . Additionally, the spatial variation in average HANPP was striking among counties in the observed period with the lowest and highest values of 21.8% and 63.8% of NPP pot , respectively. Further analysis showed that observed levels of HANPP are high due to a high level of biomass harvest from cropland and the increases in fertilizer use, farmland irrigation rate and population and economic growth explain the trends in HANPP in the coastal areas of Jiangsu.

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“…and crop straw as well as dead roots and stubble from previous crops. Roots remaining in the soil can be accounted for using the root / shoot ratio (Huang et al, 2007). Stubble was assumed to represent one-tenth of the aboveground straw biomass.…”

Section: A4 Organic Matter Amendment In Rice Paddiesmentioning

confidence: 99%

Uncertainties in estimating regional methane emissions from rice paddies due to data scarcity in the modeling approach

Zhang

Huang

et al. 2014

Geosci. Model Dev.

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Abstract. Rice paddies are a major anthropogenic source of the atmospheric methane. However, because of the high spatial heterogeneity, making accurate estimations of the methane emission from rice paddies is still a big challenge, even with complicated models. Data scarcity is one of the substantial causes of the uncertainties in estimating the methane emissions on regional scales. In the present study, we discussed how data scarcity affected the uncertainties in model estimations of rice paddy methane emissions, from county/provincial scale up to national scale. The uncertainties in methane emissions from the rice paddies of China was calculated with a local-scale model and the Monte Carlo simulation. The data scarcities in five of the most sensitive model variables, field irrigation, organic matter application, soil properties, rice variety and production were included in the analysis. The result showed that in each individual county, the within-cell standard deviation of methane flux, as calculated via Monte Carlo methods, was 13.5-89.3 % of the statistical mean. After spatial aggregation, the national total methane emissions were estimated at 6.44-7.32 Tg, depending on the base scale of the modeling and the reliability of the input data. And with the given data availability, the overall aggregated standard deviation was 16.3 % of the total emissions, ranging from 18.3-28.0 % for early, late and middle rice ecosystems. The 95 % confidence interval of the estimation was 4.5-8.7 Tg by assuming a gamma distribution. Improving the data availability of the model input variables is expected to reduce the uncertainties significantly, especially of those factors with high model sensitivities.

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Net Primary Production of Chinese Croplands From 1950 to 1999

Cited by 150 publications

References 29 publications

Estimating global cropland production from 1961 to 2010

Estimating global cropland production from 1961 to 2010

Quantitative Assessment of the Human Appropriation of Net Primary Production (HANPP) in the Coastal Areas of Jiangsu, China

Uncertainties in estimating regional methane emissions from rice paddies due to data scarcity in the modeling approach

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