Seasonal Net Primary Productivity Research Articles

Spatio-temporal dynamics of net primary productivity and the economic value of Spartina alterniflora in the coastal regions of China

This study employs an improved Carnegie–Ames–Stanford Approach (CASA) model to calculate the Net Primary Productivity (NPP) of Spartina alterniflora (SA) and various other land use/land cover types (LULC) across coastal China over multiple years. The research aims to provide significant theoretical and practical insights into carbon sink research in coastal zones, sustainable development, and resource management. Key findings include identifying the first εmax value of 2.219 g C/MJ for SA, addressing a critical data gap in CASA modeling research on invasive plants. SA's NPP exhibited higher values in Shanghai and Zhejiang due to factors such as genetic diversity, invasion duration, and tidal dynamics. In contrast, other LULC exhibited higher NPP values in southern and inland regions, characterized by greater vegetation cover and favorable growing conditions. In 2020, SA and other LULC sequestered 16.352 kt C and 0.821*106 kt C, respectively. From 2000 to 2020, the average annual NPP and total carbon storage of SA and other LULC increased significantly, primarily driven by Shanghai and deciduous needleleaf forests, respectively. Seasonal NPP trends followed summer> spring> autumn> winter, influenced by climate conditions and plant life activities. Economic assessments in 2020 estimated SA's carbon storage value at RMB0.409 billion (Market Value method) or RMB5.562 billion (Carbon Tax method), with RMB2.054 billion attributed to oxygen release values, underscoring its economic and ecological potential. Among other LULC, evergreen broadleaf forests showed the highest carbon storage value (RMB183.463 billion). The study emphasizes the critical role of all LULC in carbon storage and oxygen release, advocating for targeted conservation and land management strategies. It suggests that managing SA should balance stringent control in high-risk areas, lenient measures in low-risk areas, eradication of scattered populations, and maximizing ecological benefits in retention areas, with continuous monitoring and adaptive management strategies to balance conservation and development efforts.

Loss of Net primary production of seasonal winter wheat due to multiple drought types in the main Grain-Producing area of China

Droughts have a severe impact on ecosystems and food security. Net primary production (NPP), a critical component of the carbon cycle of an ecosystem, is strongly associated with crop yield and serves as an indicator of drought impact. In the present study, the loss of NPP in seasonal winter wheat was evaluated through an analysis of the changes in frequency and probability of exceeding NPP loss. This study also analyzed the drought thresholds for various types of drought, namely meteorological drought (MD), natural agricultural drought (NAD), and actual agricultural drought (AAD), in the North China Plain (NCP). The standardized precipitation evapotranspiration index, Palmer Drought Severity Index, and Vegetation Health Index were used to define these drought categories. The study findings reveal that although the NCP has experienced increased dryness, the severity of AAD has decreased. Within the study period, the frequency of winter wheat NPP loss due to drought in the area was within the range of 30% to 40%, with higher sensitivity to drought occurring during the middle growth period of winter wheat and in the NCP’s dry subhumid region. The highest probability of NPP loss was observed in March in the humid and dry subhumid regions of the NCP and in April in the semiarid region. Farmers in the NCP can effectively cope with extreme loss caused by drought. However, they have less experience with handling mild and moderate losses, which were the primary forms of NPP loss during the study period, particularly in the semiarid region. To ensure NPP can be sustained at a normal level or increased, MD and AAD must be prevented during the growing period of winter wheat, and NAD in the semiarid region must be managed to ensure its effects remain mild. Furthermore, farmers’ management of AAD may lead winter wheat to have a higher tolerance to MD and NAD. Our results deepen the understanding of seasonal NPP loss in winter wheat due to different types of drought, providing practical assistance to farmers in the region.

Low sensitivity of net primary productivity to climatic factors in three karst provinces in southwest China from 1981 to 2019

Karst areas are sensitive to environmental changes due to their fragile ecological environments. It is imperative to explore the temporal and spatial variations in the characteristics of vegetation net primary productivity (NPP) and evaluate the influence of different driving factors on NPP. Taking three karst provinces in southwest China as the study area, where the historical vegetation changes and the mechanisms influencing these changes remain unclear; therefore, the present study used vegetation NPP data from 1981 to 2019 to analyze the temporal and spatial variations in the characteristics of vegetation NPP using slope trend analyses and the sequential Mann-Kendall test. The influence of each driving factor on vegetation NPP and the interaction between each driving factor were investigated using the optimal parameters-based geographic detector (OPGD) model. The results were as follows: (1) The annual variation of the total NPP in the study area from 1981 to 2019 showed a fluctuating growth trend, with a slow growth rate before 2005 followed by a rapid increase. (2) The annual fluctuations in the seasonal NPP in the study area were generally upward, and the total NPP of vegetation in summer was the highest, but the growth rate of NPP in autumn was the fastest, and the area with the largest increase in NPP in autumn was the largest. (3) NPP was related to natural factors, with slope being the primary influencing factor at the annual scale. However, at the seasonal scale, there were differences in the explanatory power of the influencing factors on vegetation NPP. The highest explanatory power was observed for surface soil moisture in spring, air temperature in summer, and precipitation in autumn and winter. (4) The spatiotemporal evolution of NPP characteristics was affected by the interaction among various natural factors. At the annual and seasonal scales, the interaction between each climatic factor and slope greatly enhanced the explanatory power of individual climatic factors. Taken together, the interaction between slope and precipitation contributes the most to NPP.

Insights into Spatiotemporal Variations in the NPP of Terrestrial Vegetation in Africa from 1981 to 2018

The net primary productivity (NPP) of vegetation is an important indicator used to evaluate the quality of terrestrial ecosystems and characterize the carbon balance of ecosystems. In this study, the spatiotemporal distribution and dynamic change in NPP in Africa from 1981 to 2018 were analyzed using the long time series data of NPP. The results of the trend and fluctuation analysis showed that the NPP in the Sahara arid region in northern Africa and the arid region in South Africa exhibited a significant reduction and a high degree of fluctuation; most of the NPP in the tropical rainforests in central Africa and the deciduous broadleaved forests and deciduous needle-leaved forests on the north and south sides of the tropical rainforests increased and showed a low degree of fluctuation; the Congo basin, Gabon, Cameroon, Ghana, Nigeria, Tanzania, and other regions were affected by human activities, while the NPP in these regions exhibited a significant reduction and a high degree of fluctuation. Anomaly analysis showed that the NPP in Africa generally exhibited a slow upward trend during the period from 1981 and 2018. The trend was basically consistent in different seasons, and can be segmented into three phases: (1) a phase of descent from 1981 to 1992, with the NPP below the average value in most years; (2) a phase of steady growth from 1993 to 2000, reaching a peak in 2000; (3) a phase of fluctuations from 2001 to 2018, where the NPP value was above the average value in all years except 2015 and 2016, when the NPP value was low due to abnormally high temperatures and drought. The Mann–Kendall test further showed that the annual and seasonal NPP in Africa exhibited a significant upward trend, and the mutation time points occurred around 1995. The wavelet time series analysis revealed obvious periodic changes in the time series of NPP in Africa. The annual and seasonal NPP showed clear oscillations on time scales of 7, 20, 29, and 55 years. The 55-year period had the strongest signal, and was the first main period. The study can provide a scientific gist for the sustainable development of environmental ecology, agricultural production, and the social economy in Africa.

Response of nutrients and primary production to high wind and upwelling-favorable wind in the Arctic Ocean: A modeling perspective

Both remote sensing and numerical models revealed increasing net primary production (NPP) in the Arctic Ocean due to declining sea ice cover and increasing ice-free days. The NPP increases in some parts of the Arctic Ocean are also hypothesized to link to high wind (>10 m/s) and upwelling-favorable wind, however, the mechanism remains unclear. Using Regional Arctic System Model (RASM) to investigate the relationship between NPP and wind, we found that the seasonal NPP are statistically correlated to high wind frequency (HWF) in the Barents (Br) and Southern Chukchi Seas (SC) due to their high subsurface nutrients in the 20-50 m layer. Five high and five low HWF years along a zonally averaged section were chosen to understand the spatial variation of the correlation between HWF, NO3, and NPP in the SC. During high HWF years, the decrease in subsurface NO3 exceeds its increase in surface, implying the utilization by biological productivity. A more positive response of NPP to HWF in north SC than south was also found because more subsurface nutrients were entrained into the surface by higher HWF. The NPP are statistically correlated to easterly wind frequency (EWF) in the Beaufort and Canada Basin (BC), where the stronger EWF-induced upwelling could bring up higher nutrients from >100 m depth. While the nutrients and NPP in the south BC are normally higher than in the north, an increase of EWF can further enhance the nutrients and NPP in the south much more than those in the north. Differences between five high and five low EWF years reveal that the increase of EWF is most important around the shelf break region, where NO3 and NPP are also most enhanced. The enhancement of NPP by higher HWF in the Br and SC is less than that by higher ice-free days ratio (IFR), while the enhancement of NPP by higher EWF in BC is of similar magnitude to that by IFR. As the trend of declining sea ice cover continues, it’s necessary to advance our understanding on the nutrients and NPP response to changing wind regimes in different Arctic regions.

Quantifying the Effects of Snow on the Beginning of Vegetation Growth in the Mongolian Plateau

Snow is one of the important water sources for vegetation growth in the Mongolian Plateau, and temporal and spatial changes to it have a profound impact on terrestrial vegetation phenology. In recent years, due to global climate change, the snow associated with the different vegetation types of the Mongolian Plateau has changed substantially, and the mechanism of the resulting change in the vegetation growth date needs to be studied. To address this issue, we used the modified Carnegie Ames Stanford Approach (CASA) model was to estimate the start of growing season net primary productivity (SOSNPP) for different types of vegetation over the Mongolian Plateau from 2001 to 2019. An extensive study of the spatial changes in the SOSNPP and the responses reflected by the winter snow cover fraction (SCFWinter), spring snow melting date (SMDSpring), and SOSNPP to influencing factors is of great significance for ecosystem maintenance. We observed: (1) Different vegetation types exhibited similar changes; SCFWinter underwent a significant decrease of −0.2%, and SMDSpring followed a slow downward trend of −0.59 day of the year (DOY)/year for the whole study area. (2) In the Mongolia Plateau, SOSNPP showed a trend of significant decrease of −0.53 DOY/year. (3) The local hydrothermal condition coupling relationship effect on different vegetation types. Spring temperature (TEMSpring) has a direct effect on vegetation SOSNPP, with a path coefficient of −0.09 in the Mongolian Plateau. SCFWinter and SMDSpring were shown through a path analysis to employ different effects on vegetation SOSNPP. SMDSpring has a direct effect on vegetation SOSNPP, with a path coefficient of 0.53. (4) The SMDSpring and PRESpring factors have a significant impact on vegetation SOSNPP, and they account for 21.11% and 21.26% of the whole study area SOSNPP, respectively. This study is expected to promote the examination of the snow phonological parameters of different related vegetation types and theoretical research on SOSNPP.

Spatiotemporal patterns and drivers of net primary production in the terrestrial ecosystem of the Dajiuhu Basin, China, between 1990 and 2018

Net primary production (NPP) plays a vital role in both the evolution of ecosystems and the terrestrial carbon cycle and is influenced by geographical conditions and climate change. Understanding the terrestrial carbon balance requires an in-depth knowledge of the relationships between NPP and geographical and climatic conditions. This study aimed to simulate and map the daily spatiotemporal features of terrestrial NPP in the Dajiuhu Basin (DB), China, using the BEPS-TerrainLab V2.0 model. This area is highly sensitive to climate change and is a water source in the central path of the South-to-North Water Transfer Project. Changes in the distribution of daily and seasonal NPP between 1990 and 2018 were examined using the Mann-Kendall (MK) test, the moving t-test (MTT), and multiple regression analyses. It was found that: 1) The model explained 79% of the variation in eddy covariance (EC)-tower-measured NPP, and could thus be applied to the DB; 2) The mean annual NPP in the DB between 1990 and 2018 was 705 g C/m2/yr, with the terrestrial NPP decreasing before 1999 (−31.8 g C/m2/yr) and increasing after 1999 (0.87 g C/m2/yr); 3) The NPP first increased and then decreased with increasing altitude, with higher NPP values mainly found in the mountains on the periphery of the basin and lower NPP values in the central basin;4) Changes in NPP during autumn and summer contributed the most to the annual NPP trend. Temperature and NPP were positively correlated in summer and autumn, whereas they were negatively correlated in spring and winter. Precipitation and NPP were positively correlated in spring, autumn, and winter; 5) The sensitivities of NPP to temperature and precipitation differed across the different seasons. The sensitivities of the annual NPP to temperature and precipitation decreased and increased, respectively, compared with those before 1999. Although the contribution of precipitation to the NPP trend became more significant after 1999, that of temperature decreased. This study proposes an approach for a detailed study of daily changes in NPP and for examining the link between environmental factors, climatic conditions, and NPP distribution.

Operational framework to predict field level crop biomass using remote sensing and data driven models

Remote Sensing (RS) based monitoring provides opportunities to acquire timely and reliable information on crop growth at diverse scales. Crop yield forecasts can help decision makers to formulate policies on maintaining national food reserves, sustaining food supply chains and attaining national food security. Inter field crop yield heterogeneity arising from varied field management and agricultural practices necessitates this forecasting to be done at field-level. Such field level information can aid farmers to identify gaps in water and farm management practices and take corrective actions, if needed. So far, no scalable tools are available to predict crop yields at field level that leverage the availability of open-access high-resolution RS data.This research implements an operational framework to predict field level crop biomass by evaluating different regression algorithms to develop data driven models, leveraging historical and near real time open-access high resolution optical satellite data from Sentinel-2, radar data from Sentinel-1 and evapotranspiration (ETa) and Net Primary Production (NPP) data from Food and Agriculture Organization’s (FAO) Water Productivity through Open-access Remotely sensed data (WaPOR) platform. NPP is used as a proxy for biomass production/yield. Five of the most commonly used regression algorithms were tested to build a data-driven model for sugarcane NPP prediction in Wonji-Shoa sugarcane estate, located in the Awash Basin, Ethiopia. The models tested were the Multivariate Linear Regression (MLR), Stepwise Multivariate Linear Regression (SMLR), Boosted Regression Trees (BRT), Support Vector Regression (SVR), and Random Forest Regression (RFR).The results revealed that for seasonal sugarcane NPP predictions, the linear regression models (MLR and SMLR) yielded more accurate predictions than the non-linear machine learning models (BRT, SVR and RFR) tested. The highest accuracy was achieved for MLR models for which estimates with 89% accuracy could be made 4 months prior to the harvest and with accuracies of 79% up to 200 days (approx. 6.5 months) before the harvest. The non-linear machine learning models, however, could not provide reliable estimates of sugarcane NPP (accuracies < 61%). Cumulative vegetation indices (VIs) were found to have higher predictive power than standard VIs for predicting future sugarcane NPP. Cumulative Enhanced Vegetation Index (EVI) was found to be the variable with the highest predictive power, followed by VH polarized Sentinel-1 Synthetic Aperture Radar data and WaPOR ETa. The study shows the usefulness of high-resolution RS information to predict seasonal NPP at field level. The methods presented here can be translated into an automated framework towards an operational system.

Spatiotemporal variation of net primary productivity and its response to drought in Inner Mongolian desert steppe

The desert steppe in Inner Mongolia is an important part of the temperate grasslands in Central Asia and plays a key role in sustaining the regional pastoral livelihood and securing the ecological environment in northern China. Grassland net primary productivity (NPP) is an important indicator to ecosystem health, and is largely affected by the variation of precipitation, especially the frequent droughts in the desert steppe region. Monitoring the spatiotemporal changes of the NPP in relation with climate variation, especially with the drought events, is of great significance for assessing the total amount of grassland resources and predicting its future change trend, which is critically important for the development of grassland management regimes with the consideration of current and future climate changes. In the present study, we estimated the spatiotemporal variation of the NPP of the desert steppe in Inner Mongolia for the period from 2000 to 2019. We calculated the standardized precipitation evapotranspiration index (SPEI) of the study area, and analyzed its effects on vegetation NPP, especially for the drought effects. We found that the temporal trend line and Mann-Kendall statistical curve of vegetation NPP were basically consistent. Over the 20-year period, the NPP showed an overall downward trend during the plant growing season, or in summer and autumn period, but a slight upward trend in spring. Spatially, the plant growing season NPP slightly decreased on the 74.89% of the studied desert steppe area, the NPP in summer slightly decreased on the 72.25% of the area, while the NPP in spring slightly increased on the 61.8% of the area, and NPP in autumn significantly decreased on the 25.9% of the area. From the perspective of the persistence on different time scales, the proportion of moderate persistence-slight increase area covered 11.72% of the study area, and weak persistence-slight increase in spring covered 50.10% of the study area. The proportion of weak persistence-slight decrease in summer was 59.68%. The proportion of moderate persistence-slight decrease covered 18.83% in autumn. In the growing season, a positive correlation between NPP and SPEI accounted for 91.18% of the desert steppe area. Over 8% of the area showed a highly significant positive correlation, and 25.21% showed a significant positive correlation. Overall, 92.56% of the area showed a positive correlation in spring, and 87.26% showed a positive correlation in summer. A positive correlation was observed over 60.71% of the area in autumn. The research results show that the NPP results of CASA model simulating desert vegetation in Inner Mongolia are reliable. The SPEI drought index has good applicability in the study area. On the whole, the dynamic changes of NPP are closely related to drought. This research has important reference significance for regional ecosystem management and construction.

Spatiotemporal variability of ecosystem water use efficiency in northern Ethiopia during 1982–2014

Water use efficiency (WUE) measures the trade-off between carbon gain and water loss and is an important link between the carbon and water cycles. A better understanding of spatiotemporal variation in WUE and its controlling factors will help to improve ecosystem management for adapting to and mitigating the impacts of climatic change. We examined the spatiotemporal variations of WUE and its controlling factors for five bioclimatic zones and four land covers for 1982–2014 in the Tekeze River basin in northern Ethiopia. We quantified mean annual and seasonal net primary productivity (NPP), actual evapotranspiration (ETa), and WUE (NPP/ETa) during this period and found that each increased significantly. NPP, ETa, and therefore WUE varied spatially amongst the bioclimatic zones and land covers, but with different patterns. WUE was higher in semi-arid than humid zones. Spatiotemporal changes in temperature and precipitation could be important controls on NPP and ETa variations and therefore WUE. Mild and severe droughts in the southern regions of the basin decreased annual WUE, and moderate and extreme droughts slightly increased it, indicating that the measure of environmental rehabilitation implemented since the 1980s have increased NPP in these regions, despite the water stress caused by frequent droughts. The variability of WUE was primarily controlled by climatic change and human activity, the latter dominant since the 2000s. Ecological restoration and measures for conserving soil and water could substantially increase the vegetation productivity of this region, and be a viable management strategy to both adapt to and mitigate the impacts of climate change.

Assessment of Drought Impact on Net Primary Productivity in the Terrestrial Ecosystems of Mongolia from 2003 to 2018

Drought has devastating impacts on agriculture and other ecosystems, and its occurrence is expected to increase in the future. However, its spatiotemporal impacts on net primary productivity (NPP) in Mongolia have remained uncertain. Hence, this paper focuses on the impact of drought on NPP in Mongolia. The drought events in Mongolia during 2003–2018 were identified using the Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI). The Boreal Ecosystem Productivity Simulator (BEPS)-derived NPP was computed to assess changes in NPP during the 16 years, and the impacts of drought on the NPP of Mongolian terrestrial ecosystems was quantitatively analyzed. The results showed a slightly increasing trend of the growing season NPP during 2003–2018. However, a decreasing trend of NPP was observed during the six major drought events. A total of 60.55–87.75% of land in the entire country experienced drought, leading to a 75% drop in NPP. More specifically, NPP decline was prominent in severe drought areas than in mild and moderate drought areas. Moreover, this study revealed that drought had mostly affected the sparse vegetation NPP. In contrast, forest and shrubland were the least affected vegetation types.

Modeling Phytoplankton Blooms and Inorganic Carbon Responses to Sea‐Ice Variability in the West Antarctic Peninsula

AbstractThe ocean coastal‐shelf‐slope ecosystem west of the Antarctic Peninsula (WAP) is a biologically productive region that could potentially act as a large sink of atmospheric carbon dioxide. The duration of the sea‐ice season in the WAP shows large interannual variability. However, quantifying the mechanisms by which sea ice impacts biological productivity and surface dissolved inorganic carbon (DIC) remains a challenge due to the lack of data early in the phytoplankton growth season. In this study, we implemented a circulation, sea‐ice, and biogeochemistry model (MITgcm‐REcoM2) to study the effect of sea ice on phytoplankton blooms and surface DIC. Results were compared with satellite sea‐ice and ocean color, and research ship surveys from the Palmer Long‐Term Ecological Research (LTER) program. The simulations suggest that the annual sea‐ice cycle has an important role in the seasonal DIC drawdown. In years of early sea‐ice retreat, there is a longer growth season leading to larger seasonally integrated net primary production (NPP). Part of the biological uptake of DIC by phytoplankton, however, is counteracted by increased oceanic uptake of atmospheric CO2. Despite lower seasonal NPP, years of late sea‐ice retreat show larger DIC drawdown, attributed to lower air‐sea CO2 fluxes and increased dilution by sea‐ice melt. The role of dissolved iron and iron limitation on WAP phytoplankton also remains a challenge due to the lack of data. The model results suggest sediments and glacial meltwater are the main sources in the coastal and shelf regions, with sediments being more influential in the northern coast.

Summer High‐Wind Events and Phytoplankton Productivity in the Arctic Ocean

AbstractAt the base of the marine food web, phytoplankton are an essential component of the Arctic Ocean ecosystem and carbon cycle. Especially after sea ice retreats and light becomes more available to the Arctic Ocean each summer, phytoplankton productivity is limited by nutrient availability, which can be replenished by vertical mixing of the water column. One potential mixing mechanism is gale‐force wind associated with summer storm activity. Past studies show that sustained high winds (&gt;10 m s−1) impart sufficient stress on the ocean surface to induce vertical mixing, and it has been speculated that greater storm activity may increase net primary productivity (NPP) on a year‐to‐year timescale. We test this idea using a combination of satellite products and reanalysis data from 1998 to 2018. After controlling for the amount of open water, sea‐surface temperature, and wind direction, we find evidence that greater frequency of high‐wind events in summer is associated with greater seasonal NPP in the Barents, Laptev, East Siberian, and southern Chukchi Seas. This relationship is only robust for the Barents and southern Chukchi Seas, which are more strongly impacted by inflow of relatively nutrient‐rich water from the Atlantic and Pacific Oceans, respectively. In other words, stormier summers may have higher productivity in several regions of the Arctic Ocean, but especially the two inflow seas. Additionally, a recent rise in high‐wind frequency in the Barents Sea may have contributed to the simultaneous increase in NPP.

Direct and Lagged Effects of Spring Phenology on Net Primary Productivity in the Alpine Grasslands on the Tibetan Plateau

As a key biotic factor, phenology exerts fundamental influences on ecosystem carbon sequestration. However, whether spring phenology affects the subsequent seasonal ecosystem productivity and the underlying resource limitation mechanism remains unclear for the alpine grasslands of the Tibetan Plateau (TP). In this study, we investigated the direct and lagged seasonal responses of net primary productivity (NPP) to the beginning of growing season (BGS) along a precipitation gradient by integrating field observations, remote sensing monitoring and ecosystem model simulations. The results revealed distinct response patterns of seasonal NPP to BGS. Specifically, the BGS showed a significant and negative correlation with spring NPP (R = −0.73, p &lt; 0.01), as evidenced by the direct boosting effects of earlier BGS on spring NPP. Moreover, spring NPP was more responsive to BGS in areas with more annual precipitation. The boosting effects of earlier BGS on NPP tended to weaken in summer compared with that in spring. Sequentially, BGS exhibited stronger positive correlation with autumn NPP in areas with less annual precipitation, which suggested the enhanced lagged suppressing effects of earlier spring phenology on ecosystem carbon assimilation during the later growing season under aggravated water stress. Overall, the strengthened NPP in spring was offset by its decrement in autumn, resulting in no obvious relationship between BGS and annual NPP (R = −0.34, p &gt; 0.05) for the entire grasslands on the TP. The findings of this study imply that the lagged effects of phenology on the ecosystem productivity during the subsequent seasons should not be neglected in the future studies.

Modifying the maximal light-use efficiency for enhancing predictions of vegetation net primary productivity on the Mongolian Plateau

ABSTRACTMaximal light-use efficiency (LUE), ɛmax, is a measure of the conversion efficiency of the photosynthetically active radiation absorbed by plants to net primary productivity (NPP), based on the principle that an optimal plant productive capability exists when the LUE is at its maximum. ɛmax is an important parameter for modelling regional NPP and is conventionally applied at the biome level using a constant value. In this study, we estimated type-specific ɛmax values for three dominant land cover types on the Mongolian Plateau: 0.621 g C MJ–1 (MJ is the mega Joules and 1 MJ = 106 J) for meadow steppe, 0.534 g C MJ–1 for typical steppe, and 0.520 g C MJ–1 for desert steppe. With these steppe-specific modified ɛmax values, we were able to examine changes in NPP for 2001–2015 on the plateau, as well as their likely responses to regional climate change. The use of different ɛmax values for each steppe type improved the accuracy of the Carnegie Ames Stanford Approach (CASA) Biosphere model predictions of grassland NPP by 18.8% (R2 = 0.48 to R2 = 0.57; R2 is the coefficient of determination) over the observation period. Previous studies based on a constant ɛmax (0.541 g C MJ–1) appear to underestimate ɛmax and NPP in meadow steppe, highlighting the importance of setting type-specific ɛmax values for different land cover types in the remote-sensing modelling of grassland NPP. However, more detailed maps of biome sub-classes, with species composition, would be valuable for future attempts to determine appropriate ɛmax values. The growing season (April–October) NPP on the plateau increased significantly from 2001 to 2015, with an annual increment of 4.44 g C m–2 y–1. This trend was strongly governed by the change in summer NPP across the plateau. In comparison, NPP in the spring and autumn did not influence the change in total NPP, which was likely due to their relatively small values. Summer precipitation and the related drought stress were the chief factors responsible for plateau-scale NPP changes, due to the high proportion of summer precipitation and NPP in the annual totals. This may induce important environmental features, such as dzud (a Mongolian term for a severe winter) and desertification on the plateau.

Seasonally and spatially varied controls of climatic factors on net primary productivity in alpine grasslands on the Tibetan Plateau

Vegetation net primary productivity (NPP) is a core parameter regulating carbon cycles of terrestrial ecosystem, which also has close relations with climates. The alpine ecosystems on the Tibetan Plateau (TP) are highly sensitive to climate changes. However, systematic analyses on the seasonal and annual responses of NPP to climatic factors in different grassland types on the TP are still lacking. In this study, the spatial and temporal patterns of NPP and their responses to temperature, precipitation and solar radiation during 2001–2015 at seasonal and annual temporal scales were investigated based on outputs of an improved Carnegie–Ames–Stanford Approach (CASA) model. The improved CASA model showed solid performances in simulating NPP in reference to field observations (R2 = 0.79, P < 0.001), resulting in mean error (ME) of −16.68, root mean square error (RMSE) of 87.59 g C·m−2·yr−1, and mean relative error (MRE) of −4.29%, respectively. The annual NPP displayed different altitude dependences between the regions below and above 3500 m, which could be attributed to the altitude associated precipitation variations. The temporal trends of the seasonal and annual NPP exhibited high spatial heterogeneity. For the entire alpine grasslands, solar radiation exerted stronger influences on annual NPP than temperature and precipitation did. The responses of NPP to climatic factors also varied among grassland types and seasons. For alpine meadow, solar radiation and temperature were the dominant climatic factors in controlling the NPP variability in spring and summer, respectively, while the effect of precipitation was weak in all seasons. On the contrary, precipitation played a more crucial role in influencing NPP than temperature and solar radiation in both summer and autumn for alpine steppe. Our results shed further lights on the mechanism underlying the responses of alpine ecosystem to climate changes. The improved understanding can provide guidelines for alpine grassland management.

Net primary productivity dynamics and associated hydrological driving factors in the floodplain wetland of China's largest freshwater lake

Wetlands are thought to be the most unique ecosystem in the world which plays an important role in water and material circulation. However, investigation of ecosystem dynamics in those lake floodplain wetlands that suffering rapid and significant short-term water level fluctuation is quite a challenge. In this study, the short- and long-term characteristics of vegetation NPP (net primary productivity) and their driving mechanism were investigated in the Poyang Lake floodplain wetland, an important international wetland that listed in the Global Eco-region by the World Wildlife Fund (WWF). Attempts were achieved through validating the Carnegie-Ames-Stanford Approach (CASA) model based on observed biomasses of different vegetation types and reconstructed continuous high spatiotemporal resolution (30 m and 16 days) of NDVI data during 2000–2015 according to the fused Landsat and MODIS data. Major result indicates that the intra-annual variation of NPP of most vegetation types shows two peaks in a year due to combined effects of vegetation growth rhythm and seasonal exposure of the lake floodplain. Annual NPP of the lake floodplain ranges in 360.09–735.94 gC/m2 and shows an increasing trend during the study period. The change of NPP in space indicates that the distribution elevation of the major vegetation types decreased and evoluted toward the center lake floodplain. Different from the terrestrial ecosystem, inundation duration is the dominant factor that controls NPP dynamics in the lake floodplain, while the influences of other meteorological factors are much weakened. Recent decline of lake water level was the major reason for the spatio-temporal evolution of annual and seasonal vegetation NPP in the lake floodplain.

Improved estimates of net primary production, growth, and standing crop of Macrocystis pyrifera in Southern California.

The giant kelp Macrocystis pyrifera forms subtidal forests on shallow reefs in temperate regions of the world. It is one of the fastest-growing multicellular autotrophs on Earth and its high productivity supports diverse marine food webs. In 2008, we published a method for estimating biomass and net primary production (NPP) of giant kelp along with five years of data, to provide a more integrated measure of NPP than those yielded by previous methods. Our method combines monthly field measurements of standing crop and loss rates with a model of kelp biomass dynamics to estimate instantaneous mass-specific growth rates and NPP for each season of each year. We have since improved our approach to account for several previously unresolved sources of biomass loss. These improvements have led to a near doubling of our prior estimates of growth and NPP. At our site with the most persistent stand of giant kelp, NPP averages ~5.2kgdrymass·m-2 ·yr-1 and results from the rapid growth (~3.5% per d) of a relatively small standing biomass (~0.4kgdry mass/m2 on average) that turns over ~12times annually. Here we provide revised estimates of seasonal biomass, growth, and NPP for the five years covered by our previous publication (2002-2006), along with more than a decade of additional data (2007-2017). We also present updated relationships for predicting giant kelp biomass and NPP from much more easily obtained measurements of frond density. These data can be used to understand the mechanisms that drive variation in giant kelp NPP at a wide range of temporal scales. No copyright or proprietary restrictions are associated with the use of this data set other than citation of this Data Paper.

Spatial and temporal variability in the net primary production of grassland in China and its relation to climate factors

As an important component of the terrestrial carbon (C) cycle, variability in net primary productivity (NPP) plays a crucial role in the C input and accumulation in grasslands system. In this study, the spatial and temporal variability of grassland NPP in China during 2001–2010 and its relation to climate factors were analyzed by using a modified model of Carnegie–Ames–Stanford Approach based on the Comprehensive and Sequential Classification System. The results show that monthly grassland NPP increases from January to July. While the seasonal variability of NPP indicates peak productivity in summer. Annual mean grassland NPP follows a significant increasing trend with fluctuation from 2001 to 2010. The spatial pattern of grassland NPP shows increasing gradients from the west to the east and from the north to the south of China. Annual NPP differs significantly among different grassland types, with the highest NPP in the grassland distributed in sub-tropical perhumid evergreen broad leaved forest and tropical-perhumid rain forest. Time-lag correlation analysis at the monthly scale shows that grassland NPP responded more rapidly to changes in temperature than to precipitation. Among the climate factors, grassland NPP shows the strongest correlation at 1-month lag with moisture index K. There is a significant positive correlation between seasonal NPP and K. The seasonal NPP is significantly correlated with >0 °C annual cumulative temperature. The highest and the lowest NPP sensitivity to precipitation, K, and temperature were observed in the grassland distributed in tropical forest and semi-desert. The results indicate a complex mechanism of climate factors that control grassland C sequestration in terrestrial ecosystems.

Net primary productivity and allocation to fine-root production in field-grown sweet cherry trees under different soil nitrogen regimes

Fine-roots have a high replacement rate or turnover during growing season, constituting an important sink for C allocation and a substantial metabolic cost for trees. However, studies in fruit-bearing trees are scarce. Here we report the annual net primary production (NPP) and relative allocation to seasonal fine-root production in field-grown sweet cherry trees (Prunus avium L. cv ‘Bing’ on Gisela®6) fertilized with two N rates (0 and 60kgha−1). Seasonal fine-root production and total tree NPP were estimated combining destructive and non-destructive methods. Vegetative growth variables, fruit yield, and air/soil variables were also monitored. N fertilization hastened the turnover rates of fine-roots, resulting in an increased absolute amount of fine-root biomass production, on average 70% higher than in control trees. Total NPP was unchanged, but the relative fraction of annual NPP accounted by fine-roots was higher in N-fertilized trees (24% versus 17%), which did not affect the fruit yield and quality. However, the relative C allocation to leaves and shoots was diminished, suggesting that N-fertilized trees preferentially allocated the fixed C into fruits and fine-roots at the expense of vegetative canopy growth. Inputs from fine-roots to soil organic cycle were 22kgNha−1 and 690kgCha−1 in control trees; while, 42kgNha−1 and 1170kgCha−1 in N-fertilized trees. Our results demonstrate that fine-root growth represents a major C cost for young sweet cherry trees, as well as an important contribution of C and nutrients to soil cycling.