Dataset Information

Empirical and model-based estimates of spatial and temporal variations in net primary productivity in semi-arid grasslands of Northern China.

ABSTRACT: Spatiotemporal variations in net primary productivity (NPP) reflect the dynamics of water and carbon in the biosphere, and are often closely related to temperature and precipitation. We used the ecosystem model known as the Carnegie-Ames-Stanford Approach (CASA) to estimate NPP of semiarid grassland in northern China counties between 2001 and 2013. Model estimates were strongly linearly correlated with observed values from different counties (slope = 0.76 (p < 0.001), intercept = 34.7 (p < 0.01), R2 = 0.67, RMSE = 35 g C·m-2·year-1, bias = -0.11 g C·m-2·year-1). We also quantified inter-annual changes in NPP over the 13-year study period. NPP varied between 141 and 313 g C·m-2·year-1, with a mean of 240 g C·m-2·year-1. NPP increased from west to east each year, and mean precipitation in each county was significantly positively correlated with NPP-annually, and in summer and autumn. Mean precipitation was positively related to NPP in spring, but not significantly so. Annual and summer temperatures were mostly negatively correlated with NPP, but temperature was positively correlated with spring and autumn NPP. Spatial correlation and partial correlation analyses at the pixel scale confirmed precipitation is a major driver of NPP. Temperature was negatively correlated with NPP in 99% of the regions at the annual scale, but after removing the effect of precipitation, temperature was positively correlated with the NPP in 77% of the regions. Our data show that temperature effects on production depend heavily on recent precipitation. Results reported here have significant and far-reaching implications for natural resource management, given the enormous size of these grasslands and the numbers of people dependent on them.

SUBMITTER: Zhang S

PROVIDER: S-EPMC5675409 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Publications

Empirical and model-based estimates of spatial and temporal variations in net primary productivity in semi-arid grasslands of Northern China.

Zhang Shengwei S Zhang Rui R Liu Tingxi T Song Xin X A Adams Mark M

PloS one 20171107 11

Spatiotemporal variations in net primary productivity (NPP) reflect the dynamics of water and carbon in the biosphere, and are often closely related to temperature and precipitation. We used the ecosystem model known as the Carnegie-Ames-Stanford Approach (CASA) to estimate NPP of semiarid grassland in northern China counties between 2001 and 2013. Model estimates were strongly linearly correlated with observed values from different counties (slope = 0.76 (p < 0.001), intercept = 34.7 (p < 0.01) ...[more]

PMID: 29112982

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Project description:Soil microbial processes are crucial for understanding the ecological functions of arid and semi-arid lands which occupy approximately 40% of the global terrestrial ecosystems. However, how soil microbial metabolic activities may change across a wide aridity gradient in drylands remains unclear. Here, we investigated three soil microbial metabolic indices (soil organic carbon (SOC)-based microbial respiration, metabolic quotient, and microbial biomass as a proportion of total SOC) and the degree of carbon limitation for microbial respiration along a 3,200 km transect with a wide aridity gradient. The aridity gradient was customarily expressed using the aridity index (AI) which was calculated as the ratio of mean annual precipitation to mean annual evaporation, therefore, a lower AI value indicated a higher degree of aridity. Our results showed non-linear relationships between AI values and the metabolic indices with a clear aridity threshold for each of the three metabolic indices along the aridity gradient, respectively (AI = 0.13 for basal respiration, AI = 0.17 for metabolic quotient, and AI = 0.17 for MBC:SOC ratio). These metabolic indices linearly declined when AI was above the thresholds, but did not show any clear patterns when AI was below the thresholds. We also found that soil microbial respiration was highly limited by available carbon substrates at locations with higher primary production and relatively lower level of water limitation when AI was above the threshold, a counter-intuitive pattern that microbes were more starved in ecosystems with more substrate input. However, the increasing level of carbon limitation did correspond to the declining trend of the three metabolic indices along the AI gradient, which indicates that the carbon limitation influences microbial metabolism. We also found that the ratio of microbial biomass carbon to SOC in arid regions (AI < 0.2) with extremely low precipitation and primary production were not quantitatively related to SOC content. Overall, our results imply that microbial metabolism is distinctively different in arid lands than in non-arid lands.

Project description:BackgroundIn the arid and semi-arid areas of northern China, overexploitation of fertilizers and extensive irrigation with brackish groundwater have led to soil degradation and large areas of farmland have been abandoned. In order to improve the soil quality of abandoned farmland and make reasonable use of brackish groundwater, we conducted field trials in 2013 and 2014.MethodsIn our study, we used three fertilization modes (CF, chemical fertilizer; OM, organic manure and chemical fertilizer; NF, no fertilizer) and three deficit irrigation levels (I0: 0 mm; I75: 75 mm; I150: 150 mm).ResultsThe results showed that the activities of soil urease, alkaline phosphatase, invertase, catalase, and dehydrogenase in the OM treatment were significantly improved compared with those in the CF and NF treatments under the three deficit irrigation levels. Compared with NF, the OM treatment significantly increased soil organic carbon (SOC), water-soluble carbon (WSC), total nitrogen, microbial biomass carbon and nitrogen (MBC and MBN), and soil respiration rate, and significantly decreased soil C:N and MBC:MBN ratios and the metabolic quotient, thus improving the soil quality of abandoned farmland. Furthermore, the OM treatment increased alfalfa plant height, leaf area index, leaf chlorophyll content, and biomass yield. Under the CF and OM fertilization modes, the activities of urease and catalase in I150 were significantly higher than those in I0, whereas irrigating without fertilizer did not significantly increase the activity of these two enzymes. Regardless of fertilization, alkaline phosphatase activity increased with an increase in irrigation amount, whereas invertase activity decreased.DiscussionThe results showed that deficit irrigation with brackish groundwater under the OM treatment can improve soil quality. Over the two years of the study, maximum SOC, total nitrogen, WSC, MBC, and MBN were observed under the OM-I150 treatment, and the alfalfa biomass yield of this treatment was also significantly higher than that of the OM-I0 treatment. Therefore, the OM-I150 treatment could be used as a suitable measure not only to improve the quality of abandoned farmland soil but also to increase the alfalfa biomass yield in arid and semi-arid areas of northern China.

Dataset Information

Empirical and model-based estimates of spatial and temporal variations in net primary productivity in semi-arid grasslands of Northern China.

Publications

Empirical and model-based estimates of spatial and temporal variations in net primary productivity in semi-arid grasslands of Northern China.

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