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Direct human influence on atmospheric CO2 seasonality from increased cropland productivity
Abstract
Ground- and aircraft-based measurements show that the seasonal amplitude of Northern Hemisphere atmospheric carbon dioxide (CO2) concentrations has increased by as much as 50 per cent over the past 50 years. This increase has been linked to changes in temperate, boreal and arctic ecosystem properties and processes such as enhanced photosynthesis, increased heterotrophic respiration, and expansion of woody vegetation. However, the precise causal mechanisms behind the observed changes in atmospheric CO2 seasonality remain unclear. Here we use production statistics and a carbon accounting model to show that increases in agricultural productivity, which have been largely overlooked in previous investigations, explain as much as a quarter of the observed changes in atmospheric CO2 seasonality. Specifically, Northern Hemisphere extratropical maize, wheat, rice, and soybean production grew by 240 per cent between 1961 and 2008, thereby increasing the amount of net carbon uptake by croplands during the Northern Hemisphere growing season by 0.33 petagrams. Maize alone accounts for two-thirds of this change, owing mostly to agricultural intensification within concentrated production zones in the midwestern United States and northern China. Maize, wheat, rice, and soybeans account for about 68 per cent of extratropical dry biomass production, so it is likely that the total impact of increased agricultural production exceeds the amount quantified here.
... Climate change [1][2][3][4] and direct human interference [5][6][7][8] have changed terrestrial vegetation dynamics greatly. Significant efforts have been undertaken to monitor these changes and to understand the mechanisms driving them, so as to better understand and project the Earth system [9]. ...
... Multiple ecological engineering projects are carried out here, such as the Three-North Shelter Forest program [34] and the Grain for Green project [35]. How the vegetation evolves in this semi-arid region not only affects the local environment and socio-economic development, but also has implications for the carbon cycle [6,24], water cycle [27,36], and energy exchange [29], at the local, regional, and this barren land. Crops are cultivated along the river valleys, where water conditions are favorable ( Figure 1a). ...
... In this semi-arid region, the magnitudes of green-up for cropland and grasslands were very similar during the period from 2000 to 2019. Previous studies (e.g., [6][7][8]) suggested that agricultural practices, such as fertilization and irrigation, promote greening in agricultural land. This is not the case in the semi-arid region in Northwest China. ...
The dynamics of terrestrial vegetation have changed a lot due to climate change and direct human interference. Monitoring these changes and understanding the mechanisms driving them are important for better understanding and projecting the Earth system. Here, we assessed the dynamics of vegetation in a semi-arid region of Northwest China for the years from 2000 to 2019 through satellite remote sensing using Vegetation Index (VI) data from the Moderate Resolution Imaging Spectroradiometer (MODIS), and analyzed the interannual covariation between vegetation and three climatic factors—air temperature, precipitation, and vapor pressure deficit (VPD)—at nine meteorological stations. The main findings of this research are: (1) herbaceous land greened up much more than forests (2.85%/year vs. 1.26%/year) in this semi-arid region; (2) the magnitudes of green-up for croplands and grasslands were very similar, suggesting that agricultural practices, such as fertilization and irrigation, might have contributed little to vegetation green-up in this semi-arid region; and (3) the interannual dynamics of vegetation at high altitudes in this region correlate little with temperature, precipitation, or VPD, suggesting that factors other than temperature and moisture control the interannual vegetation dynamics there.
... We know from a network of ecosystem-scale CO 2 exchange measurements 20,21 and satellite observations 22 that densely vegetated croplands have shorter but more intense C uptake periods than natural ecosystems and are one of the most productive systems on planet earth. Based on top-down and bottomup models, Gray et al. 23 and Zeng et al. 24 argued that the intensification of agriculture at northern temperate latitudes was a major, yet largely overlooked, driver of changes in the CO 2 seasonal cycle of the northern hemisphere during the past five decades, accounting for 17-45% of the enhanced C exchange needed to explain the increasing CO 2 seasonal amplitude. Corn alone constitutes about two-thirds of this agricultural forcing, owing mostly to increasingly concentrated corn production in the Midwestern United States (i.e., the U.S. Corn Belt) and northern China 23,24 . ...
... Based on top-down and bottomup models, Gray et al. 23 and Zeng et al. 24 argued that the intensification of agriculture at northern temperate latitudes was a major, yet largely overlooked, driver of changes in the CO 2 seasonal cycle of the northern hemisphere during the past five decades, accounting for 17-45% of the enhanced C exchange needed to explain the increasing CO 2 seasonal amplitude. Corn alone constitutes about two-thirds of this agricultural forcing, owing mostly to increasingly concentrated corn production in the Midwestern United States (i.e., the U.S. Corn Belt) and northern China 23,24 . However, due to the scarcity and limited time period of direct observations, considerable uncertainties remain with respect to the overall strength of this agricultural forcing and the extent to which heterogeneous terrestrial ecosystems at northern mid-latitudes will respond to future climate warming 11 . ...
... Therefore, although the agriculture intensification at northern mid-latitudes is believed to be an important driver of the increasing CO 2 seasonal amplitude in the northern hemisphere across the decadal to multi-decadal scales 23,24 , our results, based on direct observations over an intensively agricultural region, suggest a decoupling between crop yields and CO 2 exchange intensity at the interannual scale. Because the CO 2 seasonal amplitude is an integrated measure of annual CO 2 exchange, this decoupling may be due to compensating responses of photosynthesis and ecosystem respiration to variations in climatic forcings at sub-annual scales 5 . ...
The response of highly productive croplands at northern mid-latitudes to climate change is a primary source of uncertainty in the global carbon cycle, and a concern for future food production. We present a decadal time series (2007 to 2019) of hourly CO2 concentration measured at a very tall tower in the United States Corn Belt. Analyses of this record, with other long-term data in the region, reveal that warming has had a positive impact on net CO2 uptake during the early crop growth stage, but has reduced net CO2 uptake in both croplands and natural ecosystems during the peak growing season. Future increase in summer temperature is projected to reduce annual CO2 sequestration in the Corn Belt by 10–20%. These findings highlight the dynamic control of warming on cropland CO2 exchange and crop yields and challenge the paradigm that warming will continue to favor CO2 sequestration in northern mid-latitude ecosystems.
... Aridity is another potential factor, as the droughts have been reported to either induce the changes in the trend of vegetation greenness (and therefore, a change in the IAV assuming a constant trend) (Berdugo et al., 2022), or directly enhance the variability of carbon cycle by increasing tropical extreme droughts (Luo & Keenan, 2022). Land cover and land use changes (e.g., expansion of croplands), temperature or CO 2 induced the changes of respirations (Forkel et al., 2016;Piao et al., 2018), have been linked to the increase in seasonal amplitude of atmospheric CO 2 (i.e., an indicator of intra-annual variability of the carbon cycle) (Gray et al., 2014), and may thus further imply the changes in the IAV of vegetation activities. ...
... Based on the findings from previous studies (Baldocchi et al., 2016;Berdugo et al., 2022;Forkel et al., 2016;Gray et al., 2014;Luo & Keenan, 2022;Piao et al., 2015;Zhu et al., 2016), we investigated several factors that may serve as potential drivers for the IAV changes. These factors are aridity index (AI), mean annual air temperature (MAT), mean annual precipitation (MAP), land use and land cover change (LUCC), mean monthly LAI, the trend of LAI (LAI trend ) and nitrogen deposition (N deposition). ...
Changes in the interannual variability (IAV) of vegetation greenness and carbon sequestration are key indicators of the stability and climate sensitivities of terrestrial ecosystems. Recent studies have examined the changes in the vegetation IAV using atmospheric CO2 observations and dynamic global vegetation models (DGVMs), however, reported different and even contradictory IAV trends. Here, we investigate the changes in the IAV of vegetation greenness, quantified as coefficient of variability (CV), over the past few decades based on multiple satellite remote sensing products and DGVMs. Our results suggested that, on half of the global vegetated surface (mostly in the tropics), the CV trends detected by different satellite remote sensing products are conflicting. We found that 22.20% and 28.20% of the global vegetated surface (mostly in the non‐tropical land surface) show significant positive and negative CV trends (p ≤ 0.1), respectively. Regions with higher air temperature and greater aridity tend to have increasing CV trends, whereas greater vegetation greening trend and higher nitrogen deposition lead to smaller CV trends. DGVMs generally cannot capture the CV trends obtained from satellite remote sensing products, while the inconsistency among satellite remote sensing products is likely caused by their process algorithms rather than the sensors utilized. Our study closely examines the changes in the IAV of global vegetation greenness, and highlights substantial uncertainty when using satellite remote sensing to study the response of terrestrial ecosystems to climate change.
... Although croplands are typically not a major long-term carbon sink due to harvesting that exports carbon offsite and the respiration of residues Wolf et al., 2015), they are known to be highly productive ecosystems in terms of growing-season CO 2 uptake (Schulze et al., 2010). In addition, the outsized influence of cropland intensification on the increasing CO 2 seasonal amplitude in the Northern hemisphere (Gray et al., 2014;N. Zeng et al., 2014) appears to be incompatible with the weak cropland uptake in most model simulations. ...
... This continental-scale feature of strong cropland uptake also resonates with existing studies that find far-reaching global impacts of cropland productivity on the seasonal amplitudes of CO 2 in the Northern hemisphere (Gray et al., 2014;N. Zeng et al., 2014). ...
Large uncertainties in North American terrestrial carbon fluxes hinder regional climate projections. Terrestrial biosphere models (TBMs), the essential tools for understanding continental‐scale carbon cycle, diverge on whether temperate forests or croplands dominate carbon uptake in North America. Evidence from novel photosynthetic proxies, such as those based on chlorophyll fluorescence, has cast doubt on the “weak cropland, strong forest” carbon uptake patterns simulated by most TBMs. However, no systematic evaluation of TBMs has yet been attempted to pin down space‐time patterns that are most consistent with regional CO2 observational constraints. Here, we leverage atmospheric CO2 observations and satellite‐observed photosynthetic proxies to understand emergent space‐time patterns in North American carbon fluxes from a large suite of TBMs and data‐driven models. To do so, we evaluate how well the atmospheric signals resulting from carbon flux estimates reproduce the space‐time variability in atmospheric CO2, as is observed by a network of continuous‐monitoring towers over North America. Models with gross or net carbon fluxes that are consistent with the observed CO2 variability share a salient feature of growing‐season carbon uptake in Midwest US croplands. Conversely, the remaining models place most growing‐season uptake in boreal or temperate forests. Differences in model explanatory power depend mainly on the simulated annual cycles of cropland uptake—especially, the timing of peak uptake—rather than the distribution of annual mean fluxes across biomes. Our results suggest that improved model representation of cropland phenology is crucial to robust, policy‐relevant estimation of North American carbon exchange.
... m soils. The soil C pool is approximately three and four times the size of the atmospheric and terrestrial biotic C pools, respectively (Lal, 2004), and has an important impact on global C cycling (Gray et al., 2014). However, most current earth system models rarely take deep soil C into account (Balesdent et al., 2018;Pries et al., 2017), and soil C cycling models also rarely take SIC into account because of its longer turnover time compared with that of SOC (Yang et , 2018). ...
Soil carbon (C) is important to support sustainable agriculture, affect global C cycling, and influence the climate system. Manure fertilization is an important and widely used practice to increase agricultural productivity and soil organic carbon (SOC) pools, whereas its effect on soil inorganic carbon (SIC) and total C in deep soils is not reported. This knowledge gap restricts our ability to accurately evaluate C budget in agricultural soils because SIC in deep soils accounts for more than half of the global soil C pools, while current earth system models rarely take them into account. Herein, we examined changes of soil C along 0‐ to 3.0‐m depth after 35 years of application of manure in a dryland agricultural ecosystem. We also measured C concentrations in soil samples (0–0.2 m) from 1985 to 2019 to evaluate C dynamics in topsoils. The objective was to understand how SIC and SOC in deep soils respond to manure fertilization in semiarid ecosystem, where SIC accounts for a large fraction of total C. We showed a divergent effect of 35 years of manure application on SOC and SIC in 0–3.0 m soil from a dryland agricultural ecosystem. Either within or across the two cropping systems examined, manure increased SOC in top 0.8 m layer but decreased SIC in 0.8–3.0 m layer, which offset SOC increase and resulted in 63.8 Mg ha⁻¹ decrease of total C in 0–3.0 m soil layer. Given the importance of soil C for sustainable agriculture and that drylands contain 80% of the global SIC and ∼50% of world cropland, immediate attention should be paid to such divergent effects in both mechanisms understanding and model prediction.
... Cropland ecosystems are heavily impacted by human activity, affecting both the food supply and carbon cycle (Gray et al., 2014;Li et al., 2014;Medkova et al., 2017;She et al., 2017;Liu et al., 2022a). Accurate estimation of cropland net primary productivity (NPP) is crucial for understanding its ability to absorb atmospheric CO 2 , which is a critical indicator of the carbon balance in the ecosystem. ...
The accurate estimation of cropland net primary productivity (NPP) remains a significant challenge. We hypothesized that incorporating prior information on NPP simulated by process-based models into normalized difference vegetation index (NDVI) data would improve the accuracy of cropland ecosystem NPP estimations. We used NDVI, MNPP (NPP of process-based model), and SNPP (statistic-based NPP) data estimated by nine process-based models and yield statistics to build a learning ensemble of the random forest model (LERFM). We used the new model to re-evaluate the cropland NPP in China from 1982 to 2010. Large spatial discrepancies among MNPPs, which indicate uncertainties in cropland NPP estimation using different methods, were observed when compared to SNPP. The LERFM model showed a slightly underestimation of only −0.37%, while the multi-model average process-based model (MMEM) strongly underestimated −15.46% of the SNPP. LERFM accurately estimated cropland NPP with a high simulation skill score. A consistent increasing trend in the LERFM and MMEM NPP during 1982–2010 and a significant positive correlation (r = 0.795, p < 0.001) between their total NPP indicate that the LERFM model can better describe spatiotemporal dynamic changes in cropland NPP. This study suggests that a learning ensemble method that combines the NDVI and process-based simulation results can effectively improve cropland NPP.
... The vegetation gross primary productivity of croplands contributes to 12%-16% of global vegetation gross primary productivity (Cai et al 2014), and also partly controls the seasonal fluctuations of the terrestrial carbon cycle (Zeng et al 2014). Previous studies have also emphasized the positive relationship between the growth of cropland production and the seasonal variations in atmospheric CO 2 concentration (Gray et al 2014). Furthermore, over the past few decades, there has been a notable expansion of cropland at the expense of forests, grasslands, and other ecosystems (Winkler et al 2021), leading to an increased contribution to the global carbon cycle (Erb et al 2017). ...
Crop harvested carbon (HC) is one of the most important components of the carbon cycle in cropland ecosystems, with a significant impact on the carbon budget of croplands. China is one of the most important crop producers, however, it is still unknown on the spatial and temporal variations of HC. This study collected statistical data on crop production at the province and county levels in China for all ten crop types from 1981 to 2020 and analyzed the magnitude and long-term trend of harvested crop carbon. Our results found a substantial increase of HC in cropland from 0.185 Gt C yr⁻¹ in 1981 to 0.423 Gt C yr⁻¹ in 2020 at a rate of 0.006 Gt C yr⁻¹. The results also highlighted that the average annual carbon sink removal from crop harvesting in China from 1981 to 2020 was 0.32 Gt C yr⁻¹, which was comparable to the net carbon sink of the entire terrestrial ecosystems in China. This study further generated a gridded dataset of HC from 2001 to 2019 in China by using jointly the statistical crop production and distribution maps of cropland. In addition, a model-data comparison was carried out using the dataset and results from seven state-of-the-art terrestrial ecosystem models, revealing substantial disparities in HC simulations in China compared to the dataset generated in the study. This study emphasized the increased importance of HC for estimating cropland carbon budget, and the produced dataset is expected to contribute to carbon budget estimation for cropland ecosystems and the entire China.
... Model and remote sensing-based studies suggest that the growing CO 2 amplitude results from enhancements in the annual cycle of net ecosystem production, defined as the difference between net primary production and heterotrophic respiration (D'Arrigo et al., 1987;Forkel et al., 2016;He et al., 2022;Lin et al., 2020;Piao et al., 2018;Randerson et al., 1997;Welp et al., 2016;Yin et al., 2018). Proposed mechanisms for the increasing seasonality of net ecosystem production have focused primarily on photosynthesis responses to rising levels of atmospheric CO 2 (Bastos et al., 2019;Bonan, 1992;Chen et al., 2022), agricultural expansion and intensification (Gray et al., 2014;Zeng et al., 2014), and climate change (Forkel et al., 2016;Liu et al., 2020;Myneni et al., 1997;Piao et al., 2008;Randerson et al., 1999). However, recent findings suggest that existing ecosystem models fail to fully capture the magnitude of observed amplitude trends, emphasizing the need to incorporate longer-term adjustments in species composition and other forms of ecological change within the models (Forkel et al., 2016;Graven et al., 2013;Keeling & Graven, 2021). ...
Observations of the annual cycle of atmospheric CO2 in high northern latitudes provide evidence for an increase in terrestrial metabolism in Arctic tundra and boreal forest ecosystems. However, the mechanisms driving these changes are not yet fully understood. One proposed hypothesis is that ecological change from disturbance, such as wildfire, could increase the magnitude and change the phase of net ecosystem exchange through shifts in plant community composition. Yet, little quantitative work has evaluated this potential mechanism at a regional scale. Here we investigate how fire disturbance influences landscape-level patterns of photosynthesis across western boreal North America. We use Alaska and Canadian large fire databases to identify the perimeters of wildfires, a Landsat-derived land cover time series to characterize plant functional types (PFTs), and solar-induced fluorescence (SIF) from the Orbiting Carbon Observatory-2 (OCO-2) as a proxy for photosynthesis. We analyze these datasets to characterize post-fire changes in plant succession and photosynthetic activity using a space-for-time approach. We find that increases in herbaceous and sparse vegetation, shrub, and deciduous broadleaf forest PFTs during mid-succession yield enhancements in SIF by 8–40% during June and July for 2- to 59-year stands relative to pre-fire controls. From the analysis of post-fire land cover changes within individual ecoregions and modeling, we identify two mechanisms by which fires contribute to long-term trends in SIF. First, increases in annual burning are shifting the stand age distribution, leading to increases in the abundance of shrubs and deciduous broadleaf forests that have considerably higher SIF during early- and mid-summer. Second, fire appears to facilitate a long-term shift from evergreen conifer to broadleaf deciduous forest in the Boreal Plain ecoregion. These findings suggest that increasing fire can contribute substantially to positive trends in seasonal CO2 exchange without a close coupling to long-term increases in carbon storage.
... The increase in the seasonality of the CO 2 annual cycle has been observed in recent decades and is estimated to be as much as 50% in the last 50 years in the Northern Hemisphere. It has been attributed to a rise in the ecosystems and croplands' productivity (Gray et al., 2014;Forkel et al., 2016), and is projected to continue to increase in the future, with a doubling of its amplitude by 2,100 in the SSP5-8.5 scenario (Meinshausen et al., 2020). In addition, the fact that the CO 2 forcing data is originally a surface mole fraction is also consistent with the higher seasonality with respect to the satellite-derived XCO2 concentration. ...
The Middle East has major sources of anthropogenic carbon dioxide (CO2) emissions, but a dearth of ground-based measurements precludes an investigation of its regional and temporal variability. This is achieved in this work with satellite-derived estimates from the Orbiting Carbon Observatory-2 (OCO-2) and OCO-3 missions from September 2014 to February 2023. The annual maximum and minimum column (XCO2) concentrations are generally reached in spring and autumn, respectively, with a typical seasonal cycle amplitude of 3–8 ± 0.5 ppmv in the Arabian Peninsula rising to 8–10 ± 1 ppmv in the mid-latitudes. A comparison of the seasonal-mean XCO2 values with the CO2 emissions estimated using the divergence method stresses the role played by the sources and transport of CO2 in the spatial distribution of XCO2, with anthropogenic emissions prevailing in arid and semi-arid regions that lack persistent vegetation. In the 8-year period 2015–2022, the XCO2 concentration in the United Arab Emirates (UAE) increased at a rate of about 2.50 ± 0.04 ppmv/year, with the trend empirical orthogonal function technique revealing a hotspot over northeastern UAE and southern Iran in the summer where anthropogenic emissions peak and accumulate aided by low-level wind convergence. A comparison of the satellite-derived CO2 concentration with that used to drive climate change models for different emission scenarios in the 8-year period revealed that the concentrations used in the latter is overestimated, with maximum differences exceeding 10 ppmv by 2022. This excess in the amount of CO2 can lead to an over-prediction of the projected increase in temperature in the region, an aspect that needs to be investigated further. This work stresses the need for a ground-based observational network of greenhouse gas concentrations in the Middle East to better understand its spatial and temporal variability and for the evaluation of remote sensing observations as well as climate models.
... Any changes in either soil conditions or management practices will alter the geochemical or environmental chemical processes that subsequently impact the cycling of carbon and nitrogen in agroecosystems, which finally leads to the production of greenhouse gases (i.e., CH 4 , N 2 O, and CO 2 ) (Li et al., 2004;Sass et al., 2002). With increases in crop-specific yields (240% increase in global dry biomass production) (Gray et al., 2014) facilitated by the development and adoption of improved cultivars and management accompanied by technological advances in the past 50 years, atmospheric CO 2 has increased by as much as 50% in the Northern Hemisphere (Graven et al., 2013;Keeling et al., 1996). Increasing evidence has pointed to the role of soil microorganisms, which are important engines of decomposition and participate in terrestrial carbon source-sink dynamics (Glassman et al., 2018;Jansson & Hofmockel, 2020;Nunan et al., 2020;Tang et al., 2018). ...
Studying the functional heterogeneity of soil microorganisms at different spatial scales and linking it to soil carbon mineralization is crucial for predicting the response of soil carbon stability to environmental changes and human disturbance. Here, a total of 429 soil samples were collected from typical paddy fields in China, and the bacterial and fungal communities as well as functional genes related to carbon mineraliza-tion in the soil were analysed using MiSeq sequencing and GeoChip gene microarray technology. We postulate that CO 2 emissions resulting from bacterial and fungal carbon mineralization are contingent upon their respective carbon consumption strategies , which rely on the regulation of interactions between biodiversity and functional genes. Our results showed that the spatial turnover of the fungal community was 2-4 times that of the bacterial community from hundreds of meters to thousands of kilometres. The effect of spatial scale exerted a greater impact on the composition rather than the functional characteristics of the microbial community. Furthermore, based on the establishment of functional networks at different spatial scales, we observed that both bacteria and fungi within the top 10 taxa associated with carbon minerali-zation exhibited a prevalence of generalist species at the regional scale. This study emphasizes the significance of spatial scaling patterns in soil bacterial and fungal carbon degradation functions, deepening our understanding of how the relationship between microbial decomposers and soil heterogeneity impacts carbon mineralization and subsequent greenhouse gas emissions. K E Y W O R D S carbon decomposition strategies, carbon mineralization, distance decay relationship, functional genes, greenhouse gas emission
... However, these mechanisms have proven insufficient in explaining the full range and magnitude of the observed increase in seasonal CO 2 amplitude. An alternative hypothesis is that the intensification of agriculture through human land management primarily contributes to the seasonal changes in CO 2 exchange between the biosphere and the atmosphere 37,38 . Extensive greening across global croplands further underscores the significance of our findings 6,13,14 , thereby providing additional evidence in support of the alternative hypothesis. ...
Satellite data show the Earth has been greening and identify croplands in India as one of the most prominent greening hotspots. Though India’s agriculture has been dependent on irrigation enhancement to reduce crop water stress and increase production, the spatiotemporal dynamics of how irrigation influenced the satellite observed greenness remains unclear. Here, we use satellite-derived leaf area data and survey-based agricultural statistics together with results from state-of-the-art Land Surface Models (LSM) to investigate the role of irrigation in the greening of India’s croplands. We find that satellite observations provide multiple lines of evidence showing strong contributions of irrigation to significant greening during dry season and in drier environments. The national statistics support irrigation-driven yield enhancement and increased dry season cropping intensity. These suggest a continuous shift in India’s agriculture toward an irrigation-driven dry season cropping system and confirm the importance of land management in the greening phenomenon. However, the LSMs identify CO2 fertilization as a primary driver of greening whereas land use and management have marginal impacts on the simulated leaf area changes. This finding urges a closer collaboration of the modeling, Earth observation, and land system science communities to improve representation of land management in the Earth system modeling.
... Besides climatic factors, the land carbon cycle might be influenced by the rising CO 2 concentration [5,6], increasing atmosphere nitrogen deposition [7], changing vegetation cover [4], vegetation recovery [69], and the agricultural Green Revolution [70]. However, it was noteworthy that the primary productivity might be weakened by deforestation [7] and the increasing risks of drought and heat [71]. ...
The terrestrial gross primary productivity (GPP) has increased over the past two decades. However, the climatic attribution and the physiological and phenological processes that control the trends in the GPP are still unclear. Here, we used remote-sensing-based vegetation GPP and phenology datasets, analyzed the spatial and temporal variation in the GPP, investigated the influence of the growing season length (GSL) and the maximum value of gross primary productivity (GPPmax) on the annual GPP, and quantified the effect of climate variables on the annual GPP. Our results identified a significant increase in the annual GPP (11.97 gC/m2/yr) during 2001–2020 in China’s deciduous forest. The GPPmax trend dominated the trends in the GPP, when compared with the GSL. Moreover, climate warming in summer contributes to the increase in the GPP and the GPPmax, while the extension of the GSL is primarily due to the temperature rise in spring. The annual GPP of the planted forest showed a higher increasing rate than the natural forest, due to the significant enhancement of the GPPmax and the high sensitivity of the GSL to climatic factors in the planted forest. Our findings provide a new perspective on the phenological and physiological causes of the trends in the GPP, and emphasize the importance of capturing the variability in the GPPmax when modeling the GPP.
... 1. These crops were chosen because they represent the four main world agricultural commodities and account for about 64% of global caloric consumption (Gray et al., 2014). ...
... Environmental values that make up good food include avoiding chemicals, building healthy soil, and working with natural ecosystems. In general, the literature shows that organic farmers see their practices as an opposition to the negative impacts of the Green Revolution due to reducing biodiversity and intensive pesticide, herbicide, and synthetic fertilizer use [74][75][76][77][78][79]. The Green Revolution introduced technologies essential to the survival of millions of people at the time but caused unanticipated environmental damage, for example, the persistence of the pesticide dichloro-diphenyl-trichloroethane (DDT) in soils. ...
This research aimed to determine salient factors affecting the decision to become a beginning organic farmer. New and beginning organic farmers have unique characteristics, showcasing their dedication to environmental justice and social justice at the expense of their own businesses. This research aimed to determine why people with no background in agriculture would start a farm when it is a high-risk and low-return business. With multigenerational farmers aging out of agriculture, we investigated the new generation and shifting demographics of people entering farming that will replace retiring farmers and feed our future. This research employed a multiple-case case study design. We conducted semi-structured interviews with 40 first-generation farmers who operate organic farms in Arkansas, Florida, or Georgia. We analyzed interview transcripts using the qualitative analysis approach of coding. Our results reveal two primary reasons why people with little practical knowledge start farms. First, they are inspired by those around them who succeed, and second, they are encouraged by influential characters in the field who assure them they can do something they love and be profitable. This research showed that first-generation farmers find inspiration and develop values rooted in food justice. Our findings have implications for developing and implementing current and future programmatic activities that aim to enhance beginning farmer training and workforce development. We identified sources of inspiration that will help researchers and service providers target newer and beginning farmers to support a vibrant food system, including burgeoning market opportunities, developing strong communities around food, and building grassroots solutions.
... However, it remains highly uncertain how climate change will affect long-term trends in cropland productivity. For example, while some crops may be negatively affected by increased drought or extreme weather events (Amer, 2021;Sanchez, 2000), others may benefit from increased CO 2 fertilization, warming, and moisture (Gray et al., 2014;Smith et al., 2013). Additionally, agricultural experts focus on factors such as soil, crops, seed, and farming practices that impact crop yields (e.g., Ahmed et al., 2016); ecologists examine connections between landscape patterns and cropland productivity (e.g., Nelson et al., 2022); while policymakers consider the impact of public policy and investment on cropland productivity (e.g., Elahi et al., 2020). ...
Sustained productivity growth of China's stable cropland is crucial for meeting the food and nutritional needs of the 1.4 billion population amid global food market volatility, limited uncultivated land, and urban and ecological land squeezing agricultural space. Despite this, research on trend tracking and spatial pattern identifying in productivity of China's stable croplands at a national scale is currently lacking. Here, we attempted to fill this gap based on satellite observation data and cloud computing platform, using the crop growth index, quadratic regression model, and indicator-based spatial overlay analysis. Results show the productivity of China's stable cropland rose by ~31.07% from 2001 to 2020 but will fall to ~78.89% to ~85.78% of the 2015 level (baseline year of SDGs) by 2030. The declining trends can be attributed to three main factors: (1) 69.15% of cropland that previously exhibited significant productivity growth has started to decline; (2) the productivity of cropland with the highest agricultural suitability (Level 4-5) has been long-term declining, showing no signs of reversing; (3) 44.12% of stable cropland's productivity was volatile over the research period, leading to high uncertainty in productivity growth. The large-scale spatial overlap between high agricultural suitability and human activity intensity determined the spatial pattern of stable cropland productivity. Therefore, comprehending and mitigating urbanization's indirect and off-site detrimental impacts on cropland productivity are critical to ensuring China's future food security.
... Environmental values that make up good food include avoiding chemicals, building healthy soil, and working with natural ecosystems. In general, the literature shows that organic farmers see their practices as an opposition to the negative impacts of the Green Revolution due to reducing biodiversity and intensive pesticide, herbicide, and synthetic fertilizer use [70][71][72][73][74]. The Green Revolution introduced technologies essential to the survival of millions of people at the time but caused unanticipated environmental damage, for example, the persistence of the pesticide dichlorodiphenyl-trichloroethane (DDT) in soils. ...
This research aimed to determine salient factors affecting the decision to become a beginning organic farmer. New and beginning organic farmers have unique characteristics, showcasing their dedication to environmental justice and social justice at the expense of their own businesses. This research employed a multiple-case case study design. We conducted semi-structured interviews with 40 first-generation farmers who operate organic farms in Arkansas, Florida, or Georgia. We analyzed interview transcripts using the qualitative analysis approach of coding. Our results revealed the reasons that people with little practical knowledge start farms. They are inspired by those around them who succeed and encouraged by influential characters in the field who assure them they can do something they love and be profitable. This research showed that first-generation farmers find inspiration and develop values rooted in food justice. Our findings have implications for developing and implementing current and future programmatic activities that aim to enhance beginning farmer training and workforce development. Identifying sources of inspiration will help researchers and service providers target newer and beginning farmers to support a vibrant food system, including a burgeoning market opportunity, developing strong communities around food, and building grassroots solutions.
... The area is also occupied by several wealthier settlers who tend to colonize larger tracts of land for commercial farming (Mwangi et al., 2017) and are more likely to acquire land in regions of lower elevation and slope gradients that support mechanized farming. Through practices such as land clearing, tilling, and irrigation, continued cropland expansion and agricultural intensification will increase CO 2 emissions (Gray et al., 2014). ...
Land use land cover (LULC) change can modify local, regional, and global socio-environmental systems. Globally, much LULC change study is aimed at deforestation and urbanization with less focus on other LULC categories like grasslands and savannas. Here, we focus our LULC change study on southwestern Kenya, a topographically varying region of forest, grassland, and cropland. This study leveraged existing remotely sensed Landsat landcover data to evaluate uncertainty of LULC classification, quantify the nature and magnitude of changes, and illustrate the role of topography (slope and elevation) in determining the spatiotemporal characteristics of the landscape between 1990 and 2018. We also analyzed landscape metrics of fragmentation and dominance at the class-level and employed Kruskal-Wallis tests to examine the characteristics and statistical significance of the changes. We obtained an overall classification accuracy of 86% and establish that over the 28-year period, cropland increased by 22.5% and became less fragmented, forest decreased by 6.6% and became less fragmented, while grassland decreased by 16% and became more fragmented. Results showed statistical difference (p < 0.05) in LULC change among different topographic classes. Our results indicate that deforestation in the region has slowed in the past decades, likely due to conservation efforts, but conversion of grassland to cropland has accelerated. Grasslands, including pastures, provide essential ecosystem services both to people and the environment and should not be overlooked in conservation initiatives.
... Pugh et al. (2015) found that when land activities such as harvesting, grazing and tillage were considered in climate model, the land-use induced cumulative carbon losses were 70% greater than in simulations that ignored such processes. Recent studies suggested that increase in crop production during the 20th century accounts for about 25% of the observed increase in the amplitude of CO 2 annual cycle (Gray et al., 2014;Zeng et al., 2014). Changes in forested land such as trees for wood products or fuel is also significant and has substantial carbon cycle consequences (Zubizarreta-Gerendiain et al., 2016). ...
Understanding the climate effect of land use and land cover change (LULCC) is critical for guiding human activities towards environmental sustainability. Previous studies have reported the climate effects of global deforestation, vegetation greening and crop cultivation changes. However, the contribution of each type of land state, land transition and land management to LULCC's climate effects remain underexamined. In this study, we estimated global biophysical temperature effects of LULCC using CMIP6 climate models, with special attention on the relative contribution (RC) of 12 land state changes, 113 land transitions and 10 land managements. The results show a large difference in the simulated LULCC's temperature effect between CESM2 and UKESM1–0-LL, and the two models even disagree in the sign of LULCC's effects in most of northern hemisphere except for autumn. Based on the weighted mean of two models, we found that historical LULCC has exerted a global warming effect at a rate of 0.0025 °C/century, with the largest warming effect in autumn. Spatially, a significant (p < 0.05) cooling effect is found from 60°N to 40°N, while the warming trend dominates the areas from 40°N to 30°S. Based on regression modelling, historical changes in forested/non-forested secondary land, urban land and cropland have contributed over 70% to LULCC's temperature effect, with land transitions from secondary land to cropland and from cropland to urban land dominating the climate effect at global scale. For land management, the climate effect of irrigation is larger than that of nitrogen fertilizer application. Furthermore, the application of nitrogen fertilizer for C3 plant has larger impacts compared to C4 plants, while similar effects of irrigation are observed for different types of croplands. Besides, the large difference in temperature effect between CESM2 and UKESM1–0-LL may be the difference in the forestland and cropland changes. Our study calls for explicit examination of the climate effect induced by different types of land state-change, land transition and land management for developing targeted land use policies in the future.
... Thus, the intensification of agriculture looking for more cereal yields for livestock contributed to climate change for decades, being responsible for 30%-35% of the global greenhouse gas (GHG) emissions as a consequence of deforestation, methane emissions, and nitrous oxide emissions from fertilized soils (Foley et al., 2011). Gray et al. (2014) were the first to demonstrate that agricultural productivity is affecting the amplitude of the annual CO 2 cycle. Specifically, Gray's study analyzed how much C was taken up by the four major crop typesdcorn, wheat, rice, and soybeansdin the northern extra-tropics, annually from 1961 to 2008. ...
In the near future, bovine livestock would need to keep pace with the projected demands from population growth and environmental concerns. It is clear that the advances in the bovine sector worldwide in a sustainable scenario will require the use of new technological tools (biological or not) with the engagement of a wide variety of disciplines ranging from veterinarians, agronomists, economists, biologists, geneticists, microbiologists, food-policy makers, engineers, bioinformaticians, and farmers. This article considers new biotechnological approaches that range from new sources in cattle nutrition (genetically modified crops or insects); genetic improvement (including the effect of epigenetic or rumen microbiota manipulation); meat and milk quality and safety (functional foods); improvements in waste disposal on farm, to the use of technology (nanotechnology and/or wearable and nonwearable devices).
... The increase of AMP was first noted in the 1990s from long-term surface atmospheric CO 2 measurements in the Northern Hemisphere (NH) (2) and later ascribed to flux changes in the northern high latitudes from surface records and early aircraft observations (5). This signal was attributed to changes in terrestrial fluxes, including enhanced photosynthesis (5)(6)(7)(8)(9)(10), accelerated decomposition of soil organic matter (7,11), and greater cropland production (12,13). ...
The enhanced seasonal amplitude of atmospheric CO 2 has been viewed so far primarily as a Northern Hemisphere phenomenon. Yet, analyses of atmospheric CO 2 records from 49 stations between 1980 and 2018 reveal substantial trends and variations in this amplitude globally. While no significant trends can be discerned before 2000 in most places, strong positive trends emerge after 2000 in the southern high latitudes. Using factorial simulations with an atmospheric transport model and analyses of surface ocean Pco 2 observations, we show that the increase is best explained by the onset of increasing seasonality of air-sea CO 2 exchange over the Southern Ocean around 2000. Underlying these changes is the long-term ocean acidification trend that tends to enhance the seasonality of the air-sea fluxes, but this trend is modified by the decadal variability of the Southern Ocean carbon sink. The seasonal variations of atmospheric CO 2 thus emerge as a sensitive recorder of the variations of the Southern Ocean carbon sink.
... Land cover and land use change are fundamental properties of terrestrial ecosystems that constitute critical components of global environmental change and sustainability research (Foley et al., 2005;Turner et al., 2007). Land cover and land use change (LCLUC) have extensively modified the Earth's ecosystems and have introduced substantial perturbations to many Earth system processes including the global carbon cycle (Gray et al., 2014;Tagesson et al., 2020), water cycle (Bosmans et al., 2017), and surface energy balance (Duveiller et al., 2018). LCLUC has also been linked to declines in biodiversity through habitat conversion and fragmentation, changes in plant species composition, and degradation of soil and water resources (Newbold et al., 2015). ...
South America has been an epicenter of land cover and land use change (LCLUC) for over five decades due to rapid agricultural expansion along forest frontiers, the establishment of plantations in savannas, and desertification in drylands. Most attention has focused on LCLUC in tropical forests, and so information regarding the magnitude, geography, and rate of LCLUC across sub-tropical and temperate ecosystems in Argentina, Paraguay, and Uruguay (APU) is incomplete. To address this, we used Landsat to map changes in the fractional cover of bare ground, woody cover, and herbaceous vegetation at annual time steps from 1999 to 2019 over APU. Using field observations and Landsat imagery, we created a spectral library representative of these three cover types. We trained a machine learning model to map annual fractional cover at 30 m spatial resolution, and used a Bayesian change point algorithm to characterize spatial and temporal trajectories of LCLUC. Our results reveal that 11.6% of the study domain experienced changes in land cover composition over APU between 1999 and 2019. The most substantial changes were herbaceous cover gain (87,507 ± 2730 km²), woody cover loss (101,528 ± 2843 km²), and bare ground gain (31,354 ± 934 km²). Herbaceous cover in Paraguay increased by 51%, mostly because of deforestation for cattle ranching in the Dry Chaco and commodity crop agriculture in the Atlantic Forest. Uruguay showed a 62% increase in woody cover arising from the emergence and growth of pine and eucalyptus plantations. Argentina, the largest and most heterogeneous of the three countries, experienced a 38% increase in bare ground in the Patagonian Steppe due to climate and anthropogenic drivers, including reduced precipitation. Quantification of these abrupt and gradual LCLUC processes can be used to improve models of the carbon and energy budgets in southern South America, especially in arid and semi-arid ecoregions because they are increasingly understood to be important drivers of the interannual variability of the global carbon cycle.
... Phenology has been a prominent diagnostic proxy as well as an input in prognostic models that being widely used in areas such as food security (Lobell et al., 2008;Alemu & Henebry, 2017;Gao & Zhang, 2021;Gray et al., 2014a), frost hazard (Hanninen, 2006;Ge et al., 2013;Dai et al., 2013), drought (de Beurs & Henebry, 2008), forest fire risk (Bison et al., 2022), landscape dynamics, climate change (Jin et al., 2019;Brown et al., 2017;Jeganathan et al., 2014;, biogeochemical cycling Gray et al., 2014b). Satellite remote sensing, with its synoptic view of the Earth, has become an invaluable approach to monitoring phenology at global scale and in a continuous and highly consistent manner (Caparros-Santiago et al., 2021;Zeng et al., 2020). ...
Vegetation phenology has been viewed as the nature’s calendar and an integrative indicator of plant-climate interactions. The correct representation of vegetation phenology is important for models to accurately simulate the exchange of carbon, water, and energy between the vegetated land surface and the atmosphere. Remote sensing has advanced the monitoring of vegetation phenology by providing spatially and temporally continuous data that in together with conventional ground observations offering a unique contribution to our knowledge about environmental impact on ecosystems as well as the ecological adaptations and feedback to global climate change. Land surface phenology is defined as the use of satellites to monitor seasonal dynamics in vegetated land surfaces and to estimate phenological transition dates. Land surface phenology, as an interdisciplinary subject among remote sensing, ecology and biometeorology, has undergone rapid development over the past few decades. Recent advances in sensor technologies, as well as data fusion techniques, have enabled novel phenology retrieval algorithms that refine phenology details at even higher spatiotemporal resolutions, providing new insights into ecosystem dynamics. As such, here we summarize the recent advances in land surface phenology and the associated opportunities for science applications. We focus on the remaining challenges, promising techniques, and emerging topics that together we believe will truly form the very frontier of global land surface phenology research field.
... The amplified CO 2 seasonality has been suggested to be driven by increasing productivity of high-latitude forests in the Northern Hemisphere, mainly from Arctic and boreal regions (Forkel et al., 2016;Graven et al., 2013;Lin et al., 2020;Liu et al., 2020;Piao et al., 2018;Yin et al., 2018). Meanwhile, Gray et al. (2014) and Zeng et al. (2014) reported that increasing cropland productivity linked with land use change and management has a non-negligible impact on the CO 2 seasonality trend. Despite a general SCA increase observed at most surface sites across the NH, the magnitude and range of trends and interannual variations differ from site to site (Forkel et al., 2016;Graven et al., 2013;Piao et al., 2018). ...
An increase in the seasonal cycle amplitude (SCA) of atmospheric CO2 since the 1960s has been observed in the Northern Hemisphere (NH). However, the underlying dominant drivers are still debated. The peak season CO2 uptake by vegetation is critical in shaping the CO2 seasonality. Using satellite‐upscaled gross primary production (GPP) from FLUXCOM and near‐infrared reflectance of vegetation (NIRV), we demonstrate that peak GPP has increased across the NH over the last two decades. We relate this productivity increase to changes in the CO2 SCA using an atmospheric transport model. The increased photosynthesis has strongly contributed to CO2 SCA trends, but with substantial latitudinal and longitudinal variations. Despite a general increase in the CO2 SCA, there are distinct regional differences. These differences are mainly controlled by regional biosphere carbon fluxes, with the remainder explained by non‐biome factors, including large‐scale atmospheric transport, changes in fossil fuel combustion, biomass burning and oceanic fluxes. Using the global flask and in situ CO2 measurement sites, we find that SCA trends at high latitude are mainly driven by increasingly productive natural ecosystems, whereas mid latitude sites around the Midwest United States are mainly impacted by intensified agriculture and atmospheric transport. Averaging across the 15 long‐term surface sites, forests contribute 26% (7%) to the SCA trends, while crops contribute 17% (24%) and the combined shrubland, grassland and wetland regions contribute 23% (37%) for simulations driven by FLUXCOM (NIRv) ecosystem fluxes. Our findings demonstrate that satellite inferred trends of ecosystem fluxes can capture the observed CO2 SCA trend.
... Cropland ecosystem net primary productivity (NPP) is not only a key factor of carbon cycling in cropland ecosystem but also an important indicator of food security and human well-being [1][2][3]. The main processes of cropland ecosystem NPP, such as photosynthesis, respiration, and evapotranspiration, are very sensitive to climate change [4,5]. Ongoing climate change has impacted global cropland ecosystem and caused NPP decline and grain yield reduction [6,7]. ...
Investigating elevational gradient of climate driving effects on cropland ecosystem net primary productivity (NPP) plays an important role in food security in alpine region. We simulated cropland NPP by coupling a remote sensing model with an ecosystem process model and explored elevational gradient of climate driving effects on it in an alpine region of the southwest China during 1981–2014. The results showed that cropland NPP increased significantly with a rate of 3.85 gC m−2 year−1 year−1 under significant increasing solar radiation and climate warming and drying, among which the increasing solar radiation was the main driving factor of the increasing NPP. The driving effect of climate warming on cropland NPP shifted from negative at low elevations to positive at high elevations, which was caused by the fragile ecosystem characteristics and frequent drought at low elevations and a higher temperature sensitivity of cropland ecosystem at high elevations. Different effects of climate warming on NPP change at different elevations caused different results when we analyzed the climate-driving effects on cropland NPP at different spatial scales. These results reminded us that we should take the elevational gradient of climate driving effects into account when we manage food security in the alpine region.
... The proportion is much higher in some major agricultural production countries such as China (Liu et al., 2014). Therefore, CO 2 flux and carbon budget in cropland are important for developing carbon cycle management and climate change mitigation strategies (Schmidt et al., 2012;Gray et al., 2014). Since croplands are intensively managed, daily, seasonal and inter-annual variations of CO 2 fluxes and carbon budget in cropland are subjected to both environmental factors and agricultural management practices such as irrigation, fertilizer and tillage practices which are often more complex than those found in a natural ecosystem (de la Motte et al., 2016;Hunt et al., 2016;Vick et al., 2016;Liu et al., 2021;Wagle et al., 2021). ...
Investigating CO2 fluxes and carbon budget in agro-ecosystems is essential to develop climate smart agriculture. Here, we conducted a thorough analysis on the daily, seasonal and inter-annual variations in CO2 fluxes and carbon budget in a winter-wheat and summer-maize rotation system in the North China Plain (NCP) to better understand the CO2 flux exchange and the underlying mechanisms of carbon budget dynamics. During 2003-2010, the inter-annual variability of monthly gross primary productivity was significantly correlated with leaf area index (LAI), temperature and net radiation. The inter-annual variability of monthly ecosystem respiration was significantly correlated with LAI, soil temperature and moisture. Daily and monthly variability in net ecosystem exchange (NEE) was significantly correlated with LAI. At a seasonal scale, soil moisture was one of the primary factors controlling carbon sequestration of wheat system. Nitrogen application rate and water conditions were the primary factors controlling carbon sequestration of maize system. The NEE for winter wheat system, maize system, and winter wheat-maize rotation system in the NCP ranged from -418 to -29, -448 to -119, and -857 to -274 gCm⁻², respectively. The net biome productivity (NBP) for winter wheat system, maize system, and winter wheat-maize rotation system in the NCP ranged from -223 to 151, -236 to 94, and -239 to 237 gCm⁻², respectively. Taking greenhouse gas (GHG) emissions from irrigation, fertilization, herbicides, fungicide, insecticide, and field operations, the associated net GHG emissions ranged from -39 to 325 gCm⁻² for wheat system and -22 to 287 gCm⁻² for maize system. Wheat and maize systems in the region were a medium source of GHG emissions in most of years, mainly due to the large application rates of fertilization and irrigation. Our findings gain new insights into the mechanisms underlying the inter-annual variations in CO2 fluxes and carbon budget, highlighting the optimization of genotype, environment and management interactions to realize climate smart agriculture.
... In contrast, recent studies focusing on trends after 1990 suggest that the CO 2 uptake has become increasingly limited by water stress tied to continued warming (Jiao et al., 2021;Lian et al., 2020;Peñuelas et al., 2017;Wang et al., 2018;Zhang et al., 2020). The SCA trend has likely also been strongly influenced by warming-driven changes in vegetation cover (Forkel et al., 2016;Liu et al., 2020), and changes in agriculture and other land-use changes (Gray et al., 2014;Zeng et al., 2014). Interannual to decadal climate change, including soil temperature and water supply availability, has been suggested as a major source of SCA variability (Buermann et al., 2007). ...
Long‐term measurements at the Mauna Loa Observatory (MLO) show that the CO2 seasonal cycle amplitude (SCA) increased from 1959 to 2019 at an overall rate of 0.22 ± $\pm $ 0.034 ppm decade⁻¹ while also varying on interannual to decadal time scales. These SCA changes are a signature of changes in land ecological CO2 fluxes as well as shifting winds. Simulations with the TM3 tracer transport model and CO2 fluxes from the Jena CarboScope CO2 Inversion suggest that shifting winds alone have contributed to a decrease in SCA of −0.10 ± $\pm $ 0.022 ppm decade⁻¹ from 1959 to 2019, partly offsetting the observed long‐term SCA increase associated with enhanced ecosystem net primary production. According to these simulations and MIROC‐ACTM simulations, the shorter‐term variability of MLO SCA is nearly equally driven by varying ecological CO2 fluxes (49%) and varying winds (51%). We also show that the MLO SCA is strongly correlated with the Pacific Decadal Oscillation (PDO) due to varying winds, as well as with a closely related wind index (U‐PDO). Since 1980, 44% of the wind‐driven SCA decrease has been tied to a secular trend in the U‐PDO, which is associated with a progressive weakening of westerly winds at 700 mbar over the central Pacific from 20°N to 40°N. Similar impacts of varying winds on the SCA are seen in simulations at other low‐latitude Pacific stations, illustrating the difficulty of constraining trend and variability of land CO2 fluxes using observations from low latitudes due to the complexity of circulation changes.
... With the seasonal influences of vegetation growth in terrestrial ecosystems, especially in the Northern Hemisphere, the contribution of fossil fuels to CO 2 seasonality is less than 20% (Randerson et al., 1997). Globally, including in the Northern Hemisphere, CO 2 concentrations were lower than other seasons during summer by 2100 ( Figure S1 in Supporting Information S1), which was mainly caused by feedback of the vegetation growth and ecosystem respiration (Gray et al., 2014;Yuan et al., 2017). The seasonality of CO 2 concentration in summer could affect GPP through physiological and radiative effects. ...
Atmospheric carbon dioxide (CO2) would be increasing much more if it were not for terrestrial carbon (C) uptake, fueling the drawdown of atmospheric CO2 in vegetation and soil on decadal to centennial time scales. Here, we used a global Earth system model (BNU‐ESM) with two different CO2 data sets (i.e., uniform CO2 vs. non‐uniform CO2 data sets) to simulate the responses of the C balance, particularly to the non‐uniform CO2 effect. Under future conditions of 2071–2100, accounting for spatial variations of CO2 concentrations resulted in 0.51 Pg C yr⁻¹ or 19% additional global net ecosystem production (NEP) inductions relative to the uniform conditions. The reduction in NEP in the future was mostly caused by the reduction in the Northern Hemisphere, within which summer was the season that accounted for the largest fraction of this reduction. Changes in NEP under future conditions differed largely to those under present conditions, resulting from changes in the circulation caused by the non‐uniform CO2—for example, reductions in evapotranspiration limit water vapor contributions to the lower atmosphere, and substantially diminish convective precipitation, which led to decreased precipitation. Our findings call for more attention to be paid to the influence of spatial variations in CO2 concentration—particularly in the Northern Hemisphere—to better constrain the projected C uptake under future conditions. Also, it highlights the fundamental importance of non‐uniform CO2 in determining the pattern, response, and magnitude of C uptake through to 2100.
... While it is important to address deforestation in the Amazon and Cerrado, it is also key to improve land management practices of existing cropland in order to better understand the conditions under which agricultural fields may become C sinks (Galford et al., 2011). Agricultural land plays an important role in regulating the global C balance (Anthoni et al., 2004;Gray et al., 2014). Agriculture also has great potential to mitigate C emissions through changes in land management (Ciais et al., 2011;Schmidt et al., 2012), and the current challenge in managing C on existing cropland in Brazil is to increase agricultural productivity without increasing greenhouse gas emissions (Galford et al., 2013). ...
The Cerrado is one of the most important agricultural production areas in Brazil where soybean and maize have expanded in recent decades through deforestation. The effects of cropland on the carbon (C) balance still needs to be understood at the ecosystem scale to better situate the role of land management in the tropical C cycle. In this study, we measured the C exchange of two fields (irrigated and rainfed) with rotations from different cultures. Results showed that both fields were C sinks over the course of the crops’ development cycles, but this C was mostly removed via harvest when considering the C stored in the grain (as soybean, maize, rice, and bean crops). Maize, intercropped with Brachiaria, had the most positive C balance (as a loss of C from the field), in part due to its longer stay in the field. The rainfed and irrigated fields acted as a net C source due to emissions to the atmosphere from periods of stubble and the Brachiaria intercrop, soil preparation and soybean planting. The irrigated field was a more important C sink than the rainfed field, suggesting that irrigation can reduce C losses resulting from possible drought, while at the same time allowing for a third harvest in the same calendar year. Our results confirm that practices such as no-till farming, crop rotation, intercropping, the reduction of fallow periods and the use of irrigation are key to mitigating C losses from agriculture in the Cerrado, while also helping reduce pressure on remaining natural forests in the region through agricultural intensification.
... Proposed causes of the trend in the amplitude of the seasonal cycle of CO 2 , and its amplification at higher latitudes, include increases in the summer productivity and/or increases in the magnitude of winter respiration of northern ecosystems (Barichivich et al., 2013;Graven et al., 2013;Forkel et al., 2016;Wenzel et al., 2016), increases in productivity throughout the Northern Hemisphere by CO 2 fertilization, and increases in the productivity of agricultural crops in northern mid-latitudes (Gray et al., 2014;Zeng et al., 2014). Recent studies have attempted to quantify the different contributions by comparing atmospheric CO 2 observations with ensembles of land surface model simulations. ...
The evidence for human influence on recent climate change strengthened from the IPCC Second Assessment Report to the IPCC Fifth Assessment Report, and is now even stronger in this assessment. The IPCC Second Assessment Report (1995) concluded ‘the balance of evidence suggests that there is a discernible human influence on global climate’. In subsequent assessments (TAR, 2001; AR4, 2007 and AR5, 2013), the evidence for human influence on the climate system was found to have progressively strengthened. AR5 concluded that human influence on the climate system is clear, evident from increasing greenhouse gas concentrations in the atmosphere, positive radiative forcing, observed warming, and physical understanding of the climate system. This chapter updates the assessment of human influence on the climate system for large-scale indicators of climate change, synthesizing information from paleo records, observations and climate models. It also provides the primary evaluation of large-scale indicators of climate change in this report, that is complemented by fitness-for-purpose evaluation in subsequent chapters.
... This point is confirmed by findings from Bastos et al. (2019) that attribute enhanced SCA in boreal Asia and Europe to increases in net biome productivity as a result of CO 2 fertilization. Although they do not address the increase in latitudinal gradients over time, Zeng et al. (2014) and Gray et al. (2014) argue that agricultural expansion in the Northern Hemisphere midlatitudes has resulted in increases in seasonal carbon exchange, which, in turn, result in larger SCA of CO 2 concentrations on a global scale. Barnes et al. (2016) suggest that it is actually the temperate forest between 30 and 50 • N that is the dominant driver of seasonal carbon exchange on global scales. ...
Satellite-based observations of atmospheric carbon dioxide (CO2) provide measurements in remote regions, such as the biologically sensitive but undersampled northern high latitudes, and are progressing toward true global data coverage. Recent improvements in satellite retrievals of total column-averaged dry air mole fractions of CO2 (XCO2) from the NASA Orbiting Carbon Observatory 2 (OCO-2) have allowed for unprecedented data coverage of northern high-latitude regions, while maintaining acceptable accuracy and consistency relative to ground-based observations, and finally providing sufficient data in spring and autumn for analysis of satellite-observed XCO2 seasonal cycles across a majority of terrestrial northern high-latitude regions. Here, we present an analysis of XCO2 seasonal cycles calculated from OCO-2 data for temperate, boreal, and tundra regions, subdivided into 5∘ latitude by 20∘ longitude zones. We quantify the seasonal cycle amplitudes (SCAs) and the annual half drawdown day (HDD). OCO-2 SCAs are in good agreement with ground-based observations at five high-latitude sites, and OCO-2 SCAs show very close agreement with SCAs calculated for model estimates of XCO2 from the Copernicus Atmosphere Monitoring Services (CAMS) global inversion-optimized greenhouse gas flux model v19r1 and the CarbonTracker2019 model (CT2019B). Model estimates of XCO2 from the GEOS-Chem CO2 simulation version 12.7.2 with underlying biospheric fluxes from CarbonTracker2019 (GC-CT2019) yield SCAs of larger magnitude and spread over a larger range than those from CAMS, CT2019B, or OCO-2; however, GC-CT2019 SCAs still exhibit a very similar spatial distribution across northern high-latitude regions to that from CAMS, CT2019B, and OCO-2. Zones in the Asian boreal forest were found to have exceptionally large SCA and early HDD, and both OCO-2 data and model estimates yield a distinct longitudinal gradient of increasing SCA from west to east across the Eurasian continent. In northern high-latitude regions, spanning latitudes from 47 to 72∘ N, longitudinal gradients in both SCA and HDD are at least as pronounced as latitudinal gradients, suggesting a role for global atmospheric transport patterns in defining spatial distributions of XCO2 seasonality across these regions. GEOS-Chem surface contact tracers show that the largest XCO2 SCAs occur in areas with the greatest contact with land surfaces, integrated over 15–30 d. The correlation of XCO2 SCA with these land surface contact tracers is stronger than the correlation of XCO2 SCA with the SCA of CO2 fluxes or the total annual CO2 flux within each 5∘ latitude by 20∘ longitude zone. This indicates that accumulation of terrestrial CO2 flux during atmospheric transport is a major driver of regional variations in XCO2 SCA.
... The predictor in this study is the sensitivity of the CO 2 seasonal cycle amplitude to rising atmospheric CO 2 concentrations, defined as the slope of the linear regression between the CO 2 seasonal cycle amplitude and the annual mean atmospheric CO 2 concentrations (see Figure B.1 for details). The emergent constraint is physically motivated by the hypothesis that increasing terrestrial GPP is the main driver for the observed changes in the CO 2 seasonal cycle amplitude (Gray et al. 2014;Keeling et al. 1996;Zhao and Zeng 2014). ...
Earth system models (ESMs) are common tools to project climate change. The main focus of this thesis is the analysis of climate projections from ESMs participating in the Coupled Model Intercomparison Project (CMIP) with the aim to reduce uncertainties in climate projections with observations. In a first step, climate sensitivity is evaluated in CMIP6 models. For the effective climate sensitivity (ECS), a multi-model range of 1.8-5.6 K is found. This range is higher than in any previous CMIP ensemble before. Possible reasons for this are changes in cloud parameterizations. To reduce uncertainties in the ECS of the CMIP6 models, 11 published emergent constraints on ECS are analyzed. Emergent constraints are approaches to reduce uncertainties in climate projections by combining observations and ESM output. The application of the emergent constraints to CMIP6 data shows a decrease in the skill of the emergent relationships. This is likely related to the increased multi-model spread of ECS in CMIP6, but may in some cases also be due to spurious statistical relationships. The results support previous studies concluding that emergent constraints should be based on independently verifiable physical mechanisms. To overcome these issues of emergent constraints, an alternative approach based on machine learning (ML) is introduced. As target variable, gross primary production (GPP) is studied. In a first step, an existing emergent constraint is used to constrain the global mean GPP at the end of the 21st century in Representative Concentration Pathway (RCP) 8.5 simulations with CMIP5 ESMs to (171 ± 12) GtC yr-1. In a second step, an ML model is used to constrain gridded future absolute GPP. For this, observational data is fed into the ML algorithm that has been trained on CMIP5 data to learn relationships between present-day physically relevant diagnostics and the target variable. In a perfect model setup, the ML model shows superior performance.
... Land surface phenology (LSP) plays an important role in understanding the response of terrestrial ecosystems to environmental changes [1,2]. Shifts in LSP have been frequently linked to the variability of climate patterns with significant influences on the cycling of land surface carbon, water and energy flows, and the interaction between different plant species [3][4][5][6]. ...
Accurate and timely land surface phenology (LSP) provides essential information for investigating the responses of terrestrial ecosystems to climate changes and quantifying carbon and surface energy cycles on the Earth. LSP has been widely investigated using daily Visible Infrared Imaging Radiometer Suite (VIIRS) or Moderate Resolution Imaging Spectroradiometer (MODIS) observations, but the resultant phenometrics are frequently influenced by surface heterogeneity and persistent cloud contamination in the time series observations. Recently, LSP has been derived from Landsat-8 and Sentinel-2 time series providing detailed spatial pattern, but the results are of high uncertainties because of poor temporal resolution. With the availability of data from Advanced Baseline Imager (ABI) onboard a new generation of geostationary satellites that observe the earth every 10–15 min, daily cloud-free time series could be obtained with high opportunities. Therefore, this study investigates the generation of synthetic high spatiotemporal resolution time series by fusing the harmonized Landsat-8 and Sentinel-2 (HLS) time series with the temporal shape of ABI data for monitoring field-scale (30 m) LSP. The algorithm is verified by detecting the timings of greenup and senescence onsets around north Wisconsin/Michigan states, United States, where cloud cover is frequent during spring rainy season. The LSP detections from HLS-ABI are compared with those from HLS or ABI alone and are further evaluated using PhenoCam observations. The result indicates that (1) ABI could provide ~3 times more high-quality observations than HLS around spring greenup onset; (2) the greenup and senescence onsets derived from ABI and HLS-ABI are spatially consistent and statistically comparable with a median difference less than 1 and 10-days, respectively; (3) greenup and senescence onsets derived from HLS data show sharp boundaries around the orbit-overlapped areas and shifts of ~13 days delay and ~15 days ahead, respectively, relative to HLS-ABI detections; and (4) HLS-ABI greenup and senescence onsets align closely to PhenoCam observations with an absolute average difference of less than 2 days and 5 days, respectively, which are much better than phenology detections from ABI or HLS alone. The result suggests that the proposed approach could be implemented the monitor of 30 m LSP over regions with persistent cloud cover.
... The influence of regionally important forcings such as land-use change, irrigation, aerosols, etc. also adds to the uncertainty (Bonfils, Duffy, et al., 2008). Recently, there has been considerable (and increasing) evidence for anthropogenic influence on other variables in climate (Santer et al.,2013(Santer et al., , 2018) and at regional scales (e.g., Bindoff et al., 2013;Gray et al., 2014;Henson et al., 2018;Mueller et al., 2018;Slangen et al., 2016;Undorf et al., 2018). At regional scale, the masking influence of anthropogenic aerosols can be even larger. ...
Human activities in terms of greenhouse gas emissions and aerosols resulting from the combustion of fossil fuels have been shown to have affected the temperature of the Earth on global and continental scales. The surface air temperature over India has also been observed to be increasing over the last 100 years. Understanding the underlying causes of regional climate change over India can help in developing appropriate mitigation and adaptation strategies. Differentiating signals of externally forced climate changes from the noise of natural internal variability generally becomes more difficult as spatial scale reduces. Therefore detecting and attributing the influence of external forcings such as greenhouse gases and aerosols is harder at local and regional scales. In this study, we applied a detection and attribution (D&A) method to study annual and seasonal mean surface air temperature over the Indian region. We found that the observed warming over India from 1906 to 2005 cannot be explained by natural climate variability alone. We found that the warming is largely driven by the increase in greenhouse gases, and partially offset by regional anthropogenic emissions of aerosols. These results were confirmed for the shorter 1956-2005 period, but results were sensitive to the choice of observational dataset. The changes cannot be explained by internal climate variability or natural external forcings alone, but are compatible with the responses to combined anthropogenic greenhouse gas and aerosol forcings.
... Land use also has significant effects on atmospheric CO 2 . Gray et al. (2014) and Zeng et al. (2014) suggested that agricultural improvements contributed to the increase in AMP at Mauna Loa by increasing the seasonal NBP uptake in cultivated lands. Goodale et al. (2002) suggested that over 80% of the estimated sink occurred in one-third of the forest area, in temperate regions affected by fire suppression, agricultural abandonment, and plantation forestry. ...
Over the recent decades, the increase of atmospheric carbon dioxide (CO2) concentration has caused large effects on the earth system. However, few studies have paid attention to the effects of heterogeneity of CO2 on the biosphere and the hydrosphere. Using a coupled diagnostic biophysical model (PML-V2) and comparing three heterogeneous CO2 datasets (GOSAT, CMIP6 and CarbonTracker) against a baseline homogeneous data (ESRL), this study investigated the effects of heterogeneous CO2 on gross primary production (GPP), actual evapotranspiration (ET) and water use efficiency (WUE) across the global. The results show that among the three heterogeneity CO2, CarbonTracker produced the highest CO2 concentration and showed the largest difference in ET (−6% to 2%), GPP (−2% to 5%) and WUE (4% to 11%) compared to those from the baseline. The most effects of the CO2 heterogeneity occurred in summer. Russia was identified as a vulnerable region with prominent decrease in GPP and an increase in ET due to CO2 heterogeneity. An obvious increase in GPP and a decrease in ET appeared in the Amazon rainforest, the Congo rainforest, and eastern Asia. On global scale, the effects of the CO2 heterogeneity on ET/GPP/WUE were not significant.
... After validating the simulated yield with observations from the research sites, root/shoot ratios of 0.10, 0.07 and 0.11 for winter pea, CT winter wheat, and NT winter wheat, respectively, were used from an adjacent experiment study (Williams et al., 2013). Root/shoot ratios of 0.37, 0.42, 0.96, and 1.35 were used for soybean, corn, alfalfa, and switchgrass, respectively (Bolinder et al., 2002;Gray et al., 2014). Harvest indices of 0.46 and 0.53 were used for soybean and corn, respectively (Johnson et al., 2006). ...
The potential of conservation practices to maintain soil organic C (SOC) and reduce climate change impacts on yields is unknown. This study aimed to validate the DayCent model with observed yield data and the CQESTR model with measured SOC in two agroecosystems, and predict the best practices to sustain SOC under projected climate change. Data were from a conventional tillage (CT) wheat (Triticum aestivum L.)–fallow rotation without N or with 135 kg N ha–1 fertilizer, and no‐till (NT) experiments, NTA (NTA0W‐F and NTA135W‐F) and NTB with a wheat–pea (Pisum sativum L.) cover crop (NTB0W‐P and NTB135W‐P) in Oregon; and crop rotations with CT and NT silage or grain corn (Zea mays L.)–soybean [Glycine max (L.) Merr.]–alfalfa (Medicago sativa L.) with or without manure or stover removal or a cover crop in Pennsylvania. Measured and CQESTR‐simulated SOC were significantly (p < .0001) correlated (r = .90). In Oregon, predicted SOC to 60 cm increased under NTA135W‐F and NTB135W‐P at 0.05 and 0.08 Mg ha–1 yr–1 under projected climate change, respectively. The NT management under dryland production provided a limited SOC increase. In Pennsylvania, predicted SOC to 1 m decreased at 0.07 Mg ha–1 yr–1 in corn–soybean under CT or NT with stover removal, but increased by 0.71 Mg ha–1 yr–1 in dairy forage NT and manure added under climate change. The responses of SOC to climate change were affected by management, biomass type, edaphic properties, local climate, and agroecosystem.
... 농경지는 긴 시간 규모에 대해 탄소중립(carbon-neutral) 으로 인정 받지만, 일별부터 계절까지 다소 짧은 시간 규모에 대해 대기 중 이산화탄소(CO 2 ) 농도의 변화에 강한 영향을 미치는 것으로 평가되었다. 선행 연구 [3,4] 농업생태계별로 비교해 보면, 생육기간 (6월~9월) 동안 평균 논의 광합성량(5.8 g C m -2 day -1 )이 콩(7.7 g C m -2 day -1 ) 과 사과(6.8 g C m -2 day -1 ) 보다 적지만 호흡량(1.3 ...
... 12 This intensification tripled cereal crop production 12 and contributed up to a quarter of the 50% increase in seasonal global atmospheric CO 2 fluctuations-highlighting the potential to manipulate CO 2 levels by altering agricultural practices. 13 More recently, direct attempts to increase photosynthetic efficiency (rather than crop yields) comprise an active area of research but have limited applications in the field thus far. Most research efforts have focused on improving the activity and selectivity of enzymes in the Calvin-Benson-Bassham (CBB) cycle-especially the carboxylation enzyme Rubisco, improving the mesophyll conductance and concentration of CO 2 in C3 plants, and modifying the photorespiratory pathway to reduce its energetic costs. ...
Photosynthesis is the dominant biotic carbon sink on earth and hence presents an opportunity for enhanced sequestration of CO2. If the average net carbon fixation efficiency of terrestrial plants could be increased by 3.3%, all anthropogenic CO2 accumulating in the atmosphere could instead be reduced and incorporated into terrestrial biomass. Plants make inefficient use of the overly abundant sunlight available to them, a result of having evolved to be competitive and survive highly dynamic environmental conditions rather than maximize photosynthetic productivity. We explore herein a phytophotonic approach to enhanced photosynthesis, whereby sunlight is redistributed by means of luminescent or persistent luminescent (PersL) materials. Phytophotonics has potential at varied scales, ranging from photobioreactors to greenhouses all the way to crops in the field, the latter having the potential to impact planetary CO2 levels. The approach is three-fold: a spectral redistribution to relieve high-light-stress at the top surface of leaves and increasingly drive photosynthesis deeper in leaves and canopies; a minute-scale temporal redistribution to bridge periods of intermittent shade and reduce shock associated with variable light conditions; and a multiple-hour temporal redistribution to shift a fraction of high-intensity midday lighting to evening hours. Based on simulations of photoluminescent materials and light quality experiments with a model algal system, it is shown that while lengthening daylight hours will require significant improvements in PersL materials, the other two approaches show more immediate promise. We demonstrate a means of concentrating PersL light from SrAl2O4:Eu,Dy, approaching levels needed to effectively bridge periods of natural shade, and outline the scientific questions and technical hurdles remaining to realize the benefits of the proposed spectral shift.
... increased . Although the underlying processes driving these trends are uncertain (Bastos et al., 2019;Forkel et al., 2016;Gray et al., 2014;Piao et al., 2018;Randerson et al., 1997;Zeng et al., 2014), it is generally agreed that both the increase in temperature and CO 2 are responsible (Bastos et al., 2019;Forkel et al., 2016;Piao et al., 2018;Randerson et al., 1997). Global mean CO 2 concentration has increased from 320 ppm in 1958 to~400 ppm in 2010s (https://www.esrl.noaa.gov/gmd/ccgg/trends/). ...
Since the 1960s, carbon cycling in the high‐latitude northern forest (HLNF) has experienced dramatic changes: Most of the forest is greening and net carbon uptake from the atmosphere has increased. During the same time period, the CO₂ seasonal cycle amplitude (SCA) has increased by ~50% or more. Disentangling complex processes that drive these changes has been challenging. In this study, we substitute spatial sensitivity to temperature for time to quantify the impact of temperature increase on gross primary production (GPP), total ecosystem respiration (TER), the fraction of Photosynthetic Active Radiation (fPAR), and the resulted contribution of these changes in amplifying the CO₂ SCA over the HLNF since 1960s. We use the spatial heterogeneity of GPP inferred from solar‐induced chlorophyll Fluorescence in combination with net ecosystem exchange (NEE) inferred from column CO₂ observations made between 2015 and 2017 from NASA's Orbiting Carbon Observatory‐2. We find that three quarters of the spatial variations in GPP can be explained by the spatial variation in the growing season mean temperature (GSMT). The long term hindcast captures both the magnitude and spatial variability of the trends in observed fPAR. We estimate that between 1960 and 2010, the increase in GSMT enhanced both GPP and the SCA of NEE by ~20%. The calculated enhancement of NEE due to increase in GSMT contributes 56–72% of the trend in the CO₂ SCA at high latitudes, much larger than simulations by most biogeochemical models.
Estimating gross primary productivity (GPP) over space and time is fundamental for understanding the response of the terrestrial biosphere to climate change. Eddy-covariance flux towers provide in situ estimates of GPP at the ecosystem scale, but their sparse geographical distribution limits larger scales inference. Machine learning (ML) techniques have been used to address this problem by extrapolating local GPP measurements over space using satellite remote sensing data. However, the accuracy of the regression model can be affected by uncertainties introduced by model selection, parametrization, and choice of predictor features. Recent advances in automated ML (AutoML) provide a novel automated way to select and synthesize different ML models. In this work, we explore the potential of AutoML by training three major AutoML frameworks on eddy-covariance measurements of GPP at 243 globally distributed sites. We compared their ability to predict GPP and its spatial and temporal variability based on different sets of remote sensing predictor variables. Predictor variables from only MODIS surface reflectance data and photosynthetically active radiation explained over 70 % of the monthly variability in GPP, while satellite-derived proxies for land surface temperature, evapotranspiration, soil moisture and plant functional types, and climate variables from reanalysis (ERA5-Land) further improved the frameworks' predictive ability. We found that the AutoML framework AutoSklearn consistently outperformed other AutoML frameworks as well as a classical Random Forest regressor in predicting GPP, reaching an overall r2 of 0.75. In addition, we deployed AutoSklearn to generate global wall-to-wall maps highlighting GPP patterns in good agreement with satellite-derived reference data. This research benchmarks the application of AutoML in GPP estimation and assesses its potential and limitations in quantifying global photosynthetic activity.
As the Arctic climate rapidly warms, there is a critical need for understanding variability and change in the Arctic carbon cycle, but sparse spatial coverage of observations has hindered progress. This work analyzes measurements of atmospheric CO2 in the Arctic from long‐term on‐ice measurements (the O‐Buoy Network), as well as coastal observatories from 2009 to 2016. The on‐ice measurements showed smaller seasonal amplitudes than coastal observatories, in contrast to the general observation of poleward increases of seasonal cycle amplitude. Average on‐ice measurements were also lower than their coastal counterparts during winter and spring, contradicting the expectation that CO2 increases poleward in boreal winter. We compared the observations to CO2 simulated in an updated version of GEOS‐Chem 3‐D chemical transport model, which includes new tracers of airmass history and CO2 sources and sinks. The model reproduced the observed features of the seasonal cycle and showed that terrestrial biosphere fluxes and synoptic transport explain most CO2 variability (both synoptic and interannual) over the Arctic Ocean surface. The polar airmass partially isolates the Arctic Ocean surface air from terrestrial CO2 exchange, which explains the reduced seasonal cycle amplitude and winter maxima. All Arctic coastal sites had similar CO2 interannual variability, particularly in summer, which was largely reproduced by the model. The interannual variability observed over sea ice, however, was distinct from the coastal sites and not reproduced by the model. Air‐sea CO2 exchange in and around sea ice, which was once thought to be negligible, may be an important driver of interannual variability over the Arctic Ocean.
The third edition of Gordon Bonan's comprehensive textbook introduces an interdisciplinary framework to understand the interaction between terrestrial ecosystems and climate change. Ideal for advanced undergraduate and graduate students studying ecology, environmental science, atmospheric science, and geography, it reviews basic meteorological, hydrological, and ecological concepts to examine the physical, chemical, and biological processes by which terrestrial ecosystems affect and are affected by climate. This new edition has been thoroughly updated with new science and references. The scope has been expanded beyond its initial focus on energy, water, and carbon to include reactive gases and aerosols in the atmosphere. The new edition emphasizes the Earth as a system, recognizing interconnections among the planet's physical, chemical, biological, and socioeconomic components, and emphasizing global environmental sustainability. Each chapter contains chapter summaries and review questions, and with over 400 illustrations, including many in color, this textbook will once again be an essential student guide.
Atmospheric CO2 measurements from a dense surface network can help to evaluate terrestrial biosphere model (TBM) simulations of Net Ecosystem Exchange (NEE) with two key benefits. First, gridded CO2 flux estimates can be evaluated over regional scales, not possible using flux tower observations at discrete locations for model evaluation. Second, TBM ability to explain atmospheric CO2 fluctuations due to the biosphere can be directly tested, an important objective for anthropogenic emissions monitoring using atmospheric observations. Here, we customize the Vegetation Photosynthesis and Respiration Model (VPRM) for an eastern North American domain with strong biological activity upwind of urban areas. Parameters are optimized using flux tower observations from a historical database with sites in (and near) the domain. In addition, the respiration model (originally a linear function of temperature) is modified to account for impacts of changing foliage, non‐linear temperature, and water stress. Flux estimates from VPRM, the Carnegie‐Ames‐Stanford Approach (CASA) model and the Simple Biosphere Model v4 (SiB4), are convolved with footprints from atmospheric transport models for evaluation with CO2 observations at 21 towers in the domain, with roughly half of the towers used here for the first time. Results show that the new respiration model in VPRM helps to correct a growing season sink bias in the atmosphere associated with underestimated summertime respiration using the original model with annual parameters. The new VPRM also better explains fine‐scale atmospheric CO2 variability compared to other TBMs, due to higher resolution diagnostic phenology, the new respiration model, domain‐specific parameters, and high‐quality input data sets.
Significance
Interactions between terrestrial carbon dioxide (CO 2 ) fluxes and climate or terrestrial ecosystem feedbacks exert a large uncertainty in climate projections. This uncertainty arises in part from poor quantification of gross CO 2 fluxes and their sensitivity to climate change over large spatial scales. Here, we demonstrate the usefulness of carbonyl sulfide (COS) for quantifying photosynthetic CO 2 uptake in the Arctic and Boreal ecosystems despite uncertainties in COS sources and sinks. The results highlight how the combination of atmospheric COS and CO 2 observations provides insights into past terrestrial ecosystem changes and can be utilized as a tool for direct quantification of these feedbacks impacted by climate change over the Arctic and Boreal ecosystems in the future.
Atmospheric CO2 is one of key parameters to estimate air-sea CO2 flux. The Orbiting Carbon Observatory-2 (OCO-2) satellite has observed the column-averaged dry-air mole fractions of global atmospheric carbon dioxide (XCO2) since 2014. In this study, the OCO-2 XCO2 products were compared between in-situ data from the Total Carbon Column Network (TCCON) and Global Monitoring Division (GMD), and modeling data from CarbonTracker2019 over global ocean and land. Results showed that the OCO-2 XCO2 data are consistent with the TCCON and GMD in situ XCO2 data, with mean absolute biases of 0.25×10−6 and 0.67×10−6, respectively. Moreover, the OCO-2 XCO2 data are also consistent with the CarbonTracker2019 modeling XCO2 data, with mean absolute biases of 0.78×10−6 over ocean and 1.02×10−6 over land. The results indicated the high accuracy of the OCO-2 XCO2 product over global ocean which could be applied to estimate the air-sea CO2 flux.
Paddy rice agriculture in Southern China, especially Hunan Province, has been suffered from soil contamination. Several policies including rice fallow and decreasing cropping intensity have been implemented for food safety here. It is thus important to monitor rice planting area and cropping intensity to understand the effectiveness of those land-use policies. However, it is challenging to map rice planting areas due to the complex cropping systems (mixed single- and double-cropping), persistent cloud covers, small crop fields, let alone cropping intensity. Here we used all the available Sentinel-2 and all-weather Sentinel-1 imagery to generate a time series data cube to extract paddy rice planting areas and the rice cropping intensity in the Changsha, Zhuzhou, and Xiangtan areas, which is a traditional rice-growing region with small farms in China. Specifically, we investigated the performances of different features (i.e., spectral, seasonal, polarization backscatter) by comparing five scenarios with different combinations of sensors and features, and identified the most suitable features for certain rice types (early, middle, and late rice). The random forest classifier was used for the classification in the Google Earth Engine (GEE) platform, and a reference map in 2017 based on visual interpretation on the GaoFen-2 images were used for collecting the training and validation samples. The results showed the combined data from Sentinel-1/2 generally outperformed classifications using only a single sensor (Sentinel-1/2), but the contribution of different sensors to certain rice types varied. The early, middle and late rice with the highest accuracies within the five scenarios had the overall accuracies of 85%, 95%, and 95%, respectively (F1 = 0.55, 0.85, and 0.85). The compositing of different types of rice allowed us to generate the rice cropping intensity map with an overall accuracy of 81%, which to our limited knowledge is the first effort to map cropping intensity at 10-m resolution in such a fragmented subtropical region. The result showed the single cropping dominated the rice cropping system in the study area 88%, which used to be a typical area with double cropping of rice. Our study demonstrates the potential of mapping rice cropping intensity in a cloudy and highly fragmented region in South China using all the available Sentinel-1/2 data, which would advance our understanding of regional rice production and mitigation of soil contamination.
The elevated CO2 (eCO2) has positive response on plant growth and negative response on insect pests. As a contemplation, the feeding pattern of the brown plant hopper, Nilaparvata lugens Stål on susceptible and resistant rice cultivars and their growth rates exposed to eCO2 conditions were analyzed. The eCO2 treatment showed significant differences in percentage of emergence and rice biomass that were consistent across the rice cultivars, when compared to the ambient conditions. Similarly, increase in carbon and decrese in nitrogen ratio of leaves and alterations in defensive peroxidase enzyme levels were observed, but was non-linear among the cultivars tested. Lower survivorship and nutritional indices of N. lugens were observed in conditions of eCO2 levels over ambient conditions. Results were nonlinear in manner. We conclude that the plant carbon accumulation increased due to eCO2, causing physiological changes that decreased nitrogen content. Similarly, eCO2 increased insect feeding, and did alter other variables such as their biology or reproduction.
Satellite-based observations of atmospheric carbon dioxide (CO2) provide measurements in remote regions, such as the biologically sensitive but under sampled northern high latitudes, and are progressing toward true global data coverage. Recent improvements in satellite retrievals of total column-averaged dry air mole fractions of CO2 (XCO2) from the NASA Orbiting Carbon Observatory 2 (OCO-2) have allowed for unprecedented data coverage of northern high latitude regions, while maintaining acceptable accuracy and consistency relative to ground-based observations, and finally providing sufficient data in spring and autumn for analysis of the satellite-observed XCO2 seasonal cycles across a majority of terrestrial northern high latitude regions. Here, we present an analysis of XCO2 seasonal cycles calculated from OCO-2 data for temperate, boreal, and tundra regions, subdivided into 5° latitude by 20° longitude zones. We quantify the seasonal cycle amplitudes (SCA) and the annual half drawdown day (HDD). OCO-2 SCA is in good agreement with ground-based observations at five high latitude sites and OCO-2 SCA show very close agreement with SCA calculated for model estimates of XCO2 from the Copernicus Atmospheric Monitoring Services (CAMS) global inversion-optimized greenhouse gas flux model v19r1. Model estimates of XCO2 from the GEOS-Chem CO2 simulation version 12.7.2 with underlying biospheric fluxes from CarbonTracker2019 yield SCA of larger magnitude and spread over a larger range than those from CAMS and OCO-2; however, GEOS-Chem SCA still exhibit a very similar spatial distribution across northern high latitude regions to that from CAMS and OCO-2. Zones in the Asian Boreal Forest were found to have exceptionally large SCA and early HDD, and both OCO-2 data and model estimates yield a distinct longitudinal gradient of increasing SCA from west to east across the Eurasian continent. Longitudinal gradients in both SCA and HDD are at least as pronounced as meridional gradients (with respect to latitude), suggesting an essential role for global atmospheric transport patterns in defining XCO2 seasonality. GEOS-Chem surface contact tracers show that the largest XCO2 SCA occurs in areas with the greatest contact with land surfaces, integrated over 15–30 days. The correlation of XCO2 SCA with these land contact tracers are stronger than the correlation of XCO2 SCA with the SCA of CO2 fluxes within each 5° latitude by 20° longitude zone. This indicates that accumulation of terrestrial CO2 flux during atmospheric transport is a major driver of regional variations in XCO2 SCA.
Gap-filling eddy covariance flux data using quantitative approaches has increased over the past decade. Numerous methods have been proposed previously, including look-up table approaches, parametric methods, process-based models, and machine learning. Particularly, the REddyProc package from the Max Planck Institute for Biogeochemistry and ONEFlux package from AmeriFlux have been widely used in many studies. However, there is no consensus regarding the optimal model and feature selection method that could be used for predicting different flux targets (Net Ecosystem Exchange, NEE; or Evapotranspiration –ET), due to the limited systematic comparative research based on the identical site-data. Here, we compared NEE and ET gap-filling/prediction performance of the least-square-based linear model, artificial neural network, random forest (RF), and support vector machine (SVM) using data obtained from four major row-crop and forage agroecosystems located in the subtropical or the climate-transition zones in the US. Additionally, we tested the impacts of different training-testing data partitioning settings, including a 10-fold time-series sequential (10FTS), a 10-fold cross validation (CV) routine with single data point (10FCV), daily (10FCVD), weekly (10FCVW) and monthly (10FCVM) gap length, and a 7/14-day flanking window (FW) approach; and implemented a novel Sliced Inverse Regression-based Recursive Feature Elimination algorithm (SIRRFE). We benchmarked the model performance against REddyProc and ONEFlux-produced results. Our results indicated that accurate NEE and ET prediction models could be systematically constructed using SVM/RF and only a few top informative features. The gap-filling performance of ONEFlux is generally satisfactory (R² = 0.39-0.71), but results from REddyProc could be very limited or even unreliable in many cases (R² = 0.01-0.67). Overall, SIRRFE-refined SVM models yielded excellent results for predicting NEE (R² = 0.46-0.92) and ET (R² = 0.74-0.91). Finally, the performance of various models was greatly affected by the types of ecosystem, predicting targets, and training algorithms; but was insensitive towards training-testing partitioning. Our research provided more insights into constructing novel gap-filling models and understanding the underlying drivers affecting boundary layer carbon/water fluxes on an ecosystem level.
Elevated CO2 has positive response on plant growth and negative response on insect pests. As a contemplation, the feeding pattern of the brown plant hopper, Nilaparvata lugens Stål on susceptible and resistant rice cultivars and their growth rates exposed to elevated CO2 conditions were analyzed. The elevated CO2 treatment showed significant differences in percentage of emergence and rice biomass that were consistent across the rice cultivars, when compared to the ambient conditions. Similarly, increase in carbon and nitrogen ratio of leaves and alterations in defensive peroxidase enzyme levels were observed, but was non-linear among the cultivars tested. Lower survivorship and nutritional indices of N. lugens were observed in conditions of elevated CO2 levels over ambient conditions. Results were nonlinear in manner. We conclude that the plant carbon accumulation increased due to elevated CO2, causing physiological changes that decreased nitrogen content. Similarly, elevated CO2 increased insect feeding, but did not alter other variables such as their biology or reproduction.
Multiple lines of evidence have demonstrated the persistence of global land carbon (C) sink during the past several decades. However, both annual net ecosystem productivity (NEP) and its inter-annual variation (IAVNEP) keep varying over space. Thus, identifying local indicators for the spatially varying NEP and IAVNEP is critical for locating the major and sustainable C sinks on land. Here, based on daily NEP observations from FLUXNET sites and large-scale estimates from an atmospheric-inversion product, we found a robust logarithmic correlation between annual NEP and seasonal carbon uptake–release ratio (i.e. U ∕ R). The cross-site variation in mean annual NEP could be logarithmically indicated by U ∕ R, while the spatial distribution of IAVNEP was associated with the slope (i.e. β) of the logarithmic correlation between annual NEP and U ∕ R. Among biomes, for example, forests and croplands had the largest U ∕ R ratio (1.06 ± 0.83) and β (473 ± 112 g C m−2 yr−1), indicating the highest NEP and IAVNEP in forests and croplands, respectively. We further showed that these two simple indicators could directly infer the spatial variations in NEP and IAVNEP in global gridded NEP products. Overall, this study provides two simple local indicators for the intricate spatial variations in the strength and stability of land C sinks. These indicators could be helpful for locating the persistent terrestrial C sinks and provide valuable constraints for improving the simulation of land–atmospheric C exchanges.
Carbon fixed by agricultural crops in the US creates regional CO<sub>2</sub> sinks where it is harvested and regional CO<sub>2</sub> sources where it is released back into the atmosphere. The quantity and location of these fluxes differ depending on the annual supply and demand of crop commodities. Data on the harvest of crop biomass, storage, import and export, and on the use of biomass for food, feed, fiber, and fuel were compiled to estimate an annual crop carbon budget for 2000 to 2008. With respect to US Farm Resource Regions, net sources of CO<sub>2</sub> associated with the consumption of crop commodities occurred in the Eastern Uplands, Southern Seaboard, and Fruitful Rim regions. Net sinks associated with the production of crop commodities occurred in the Heartland, Northern Crescent, Northern Great Plains, and Mississippi Portal regions. The national crop carbon budget was balanced to within 93 to 99% yr<sup>−1</sup> of total carbon uptake during the period of this analysis.
The effects of elevated atmospheric NH3 on growth and yield parameters of two winter wheat varieties, the high water and fertilizer-demanding variety Xiaoyan 6 (XY6) and the drought-resistant variety Changhan 58 (CH58), grown with two levels of N fertilization, were studied in Open-Top Chambers. The results showed that in combination with the high N treatment increasing the atmospheric NH3 concentration to 1000 nl/1 from the ambient level of 10 nl/1 NH3 significantly (P < 0.05) reduced the biomass and the root/shoot ratios of the plants, especially in XY6 plants, mainly because it negatively influenced root biomass production at anthesis and mature stages. In addition, the grain yield of XY6 was by 1.51% higher, while that of CH58 was 13.2% lower, following exposure to the elevated atmospheric NH3 concentration rather than the ambient concentration in combination with the high N treatment. In contrast, in combination with the low N treatment, elevated atmospheric NH3 had significantly and non-signifi- cantly positive effects on the grain yield of XY6 and CH58 plants, respectively. The Nitrogen Use Efficiency (NUE) and related parameters were all lower in plants of both varieties exposed to the high atmospheric NH3 concentration together with either the high or low N treatment.
Knowledge of the changes in physiological traits associated with genetic gains in yield potential is essential to improve understanding of yield-limiting factors and to inform future breeding strategies. Our objective was to identify physiological traits associated with genetic gains in grain yield of winter wheat (Triticum aestivum L.) in the UK. The growth and development of eight representative cultivars introduced from 1972 to 1995 (one tall rht-D1b cultivar and seven RHt-D1b, formerly RhT2, semidwarf cultivars) was examined in Field experiments at Sutton Boningfon in 1996-1997, 1997-1998, and 1998-1999. A linear genetic gain in grain yield of 0.12 Mg ha-1 yr-1 (1.2% yr-1) was positively correlated with both harvest index (HI) and aboveground biomass; a quadratic function fitted to the data showed that progress in HI was most apparent during the earlier phase of the 23-yr period, whereas biomass contributed most since about 1983. There was a linear increase across time of 217 grains m-2 yr-1, but no change in grain weight. Significant genetic changes across time and correlations with grain yield were also found for preanthesis radiation-use efficiency (RUE, 0.012 g MJ-1 yr-1) and water soluble carbohydrate (WSC) content of stems and leaf sheaths at anthesis (4.6 g m-2 yr-1). Our results suggest that recent genetic gains in grain yield have been based on a combination of improved growth rate in the preanthesis period, which has driven increases in number of grains per square meter, and a larger source for grain filling through increases in stem soluble carbohydrate reserves.
Significance
Global food and biofuel production and their vulnerability in a changing climate are of paramount societal importance. However, current global model predictions of crop photosynthesis are highly uncertain. Here we demonstrate that new space-based observations of chlorophyll fluorescence, an emission intrinsically linked to plant biochemistry, enable an accurate, global, and time-resolved measurement of crop photosynthesis, which is not possible from any other remote vegetation measurement. Our results show that chlorophyll fluorescence data can be used as a unique benchmark to improve our global models, thus providing more reliable projections of agricultural productivity and climate impact on crop yields. The enormous increase of the observational capabilities for fluorescence in the very near future strengthens the relevance of this study.
Roots, cereal crowns, and stems growing beneath the soil surface provide important resistance to soil erosion. Understanding the amount and distribution of this material in the soil profile could provide insight into resistance to soil erosion by water and improve the performance of soil erosion models, such as the revised universal soil loss equation (RUSLE) and the water erosion prediction project (WEPP). Erosion models use built-in or external crop growth models to populate crop yield and live aboveground and associated belowground biomass databases. We examined two data sets from the dryland small grain production region in the Pacific Northwest of the United States to determine root:shoot ratios, the vertical distribution of root and attached belowground biomass, and incorporated residue from previously grown crops. Data were collected in 1993, 1994, 1995, and 2000 from short-term no-till and conventional tillage experiments conducted near Pendleton, Oregon, and Pullman,Washington, and in 1999 and 2000 from long-term experiments representative of farming practices near Pendleton, Oregon. The crops sampled in the short-term data set included soft white winter and spring wheat (Triticum aestivum L.;WW and SW, respectively), spring peas (Pisum sativum L.; SP), and winter canola (Brassica napus L.;WC). Crops sampled in the long-term study included WW SW, and SP. Data were collected at harvest in both data sets and during three phenologic stages in each of the crops in the short-term data set. Soil samples were collected to a depth of 60 cm (23.6 in) in the short-term and 30 cm (11.9 in) in the long-term experiments. In both sets of measurements, we found greater than 70% of root mass is in the top 10 cm (3.9 in) of the soil profile with the exception of SP, which had 70% of root mass in the top 15 cm (5.9 in) of the soil profile.WC produced significantly more biomass near the soil surface than WW SW, or SP Root-to-shoot biomass ratios, in mature wheat ranged from 0.13 to 0.17 in the top 30 cm (11.9 in) of the soil profile, substantially lower than values suggested for use in WEPP (0.25). In the long-term experiments, soil of the conventionally tilled continuous winter wheat (CWW) plots contained significantly greater biomass than soil of conventionally tilled winter wheat/fallow (CR) and no-till winter wheat/fallow (NT) treatments. There was no significant difference between CWW and conventionally tilled winter wheat/spring pea (WP); however, CWW returned more residue to the soil than WP because SP produced less residue and these residues were incorporated with a field cultivator rather than a moldboard plow. More accurate representation of root development, particularly in winter crops, could improve RUSLE and WEPP performance in the Pacific Northwest where winter conditions have proven difficult to model.
Changes in atmospheric CO2 variability during the twenty-first century may provide insight about ecosystem responses to climate change and have implications for the design of carbon monitoring programs. This paper describes changes in the three-dimensional structure of atmospheric CO2 for several representative concentration pathways (RCPs 4.5 and 8.5) using the Community Earth System Model-Biogeochemistry (CESM1-BGC). CO2 simulated for the historical period was first compared to surface, aircraft, and column observations. In a second step, the evolution of spatial and temporal gradients during the twenty-first century was examined. The mean annual cycle in atmospheric CO2 was underestimated for the historical period throughout the Northern Hemisphere, suggesting that the growing season net flux in the Community Land Model (the land component of CESM) was too weak. Consistent with weak summer drawdown in Northern Hemisphere high latitudes, simulated CO2 showed correspondingly weak north-south and vertical gradients during the summer. In the simulations of the twenty-first century, CESM predicted increases in the mean annual cycle of atmospheric CO2 and larger horizontal gradients. Not only did the mean north-south gradient increase due to fossil fuel emissions, but east-west contrasts in CO2 also strengthened because of changing patterns in fossil fuel emissions and terrestrial carbon exchange. In the RCP8.5 simulation, where CO2 increased to 1150 ppm by 2100, the CESM predicted increases in interannual variability in the Northern Hemisphere midlatitudes of up to 60% relative to present variability for time series filtered with a 2-10-yr bandpass. Such an increase in variability may impact detection of changing surface fluxes from atmospheric observations.
Accurate assessments of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere is important to better understand the global carbon cycle, support the climate policy process, and project future climate change. Present-day analysis requires the combination of a range of data, algorithms, statistics and model estimates and their interpretation by a broad scientific community. Here we describe datasets and a methodology developed by the global carbon cycle science community to quantify all major components of the global carbon budget, including their uncertainties. We discuss changes compared to previous estimates, consistency within and among components, and methodology and data limitations. CO2 emissions from fossil fuel combustion and cement production (EFF) are based on energy statistics, while emissions from Land-Use Change (ELUC), including deforestation, are based on combined evidence from land cover change data, fire activity in regions undergoing deforestation, and models. The global atmospheric CO2 concentration is measured directly and its rate of growth (GATM) is computed from the concentration. The mean ocean CO2 sink (SOCEAN) is based on observations from the 1990s, while the annual anomalies and trends are estimated with ocean models. Finally, the global residual terrestrial CO2 sink (SLAND) is estimated by the difference of the other terms. For the last decade available (2002-2011), EFF was 8.3 ± 0.4 PgC yr-1, ELUC 1.0 ± 0.5 PgC yr-1, GATM 4.3 ± 0.1PgC yr-1, SOCEAN 2.5 ± 0.5 PgC yr-1, and SLAND 2.6 ± 0.8 PgC yr-1. For year 2011 alone, EFF was 9.5 ± 0.5 PgC yr -1, 3.0 percent above 2010, reflecting a continued trend in these emissions; ELUC was 0.9 ± 0.5 PgC yr-1, approximately constant throughout the decade; GATM was 3.6 ± 0.2 PgC yr-1, SOCEAN was 2.7 ± 0.5 PgC yr-1, and SLAND was 4.1 ± 0.9 PgC yr-1. GATM was low in 2011 compared to the 2002-2011 average because of a high uptake by the land probably in response to natural climate variability associated to La Niña conditions in the Pacific Ocean. The global atmospheric CO2 concentration reached 391.31 ± 0.13 ppm at the end of year 2011. We estimate that EFF will have increased by 2.6% (1.9-3.5%) in 2012 based on projections of gross world product and recent changes in the carbon intensity of the economy. All uncertainties are reported as ±1 sigma (68% confidence assuming Gaussian error distributions that the real value lies within the given interval), reflecting the current capacity to characterise the annual estimates of each component of the global carbon budget. This paper is intended to provide a baseline to keep track of annual carbon budgets in the future.
Seasonal variations of atmospheric carbon dioxide (CO2) in the Northern Hemisphere have increased since the 1950s, but sparse observations have prevented a clear assessment of
the patterns of long-term change and the underlying mechanisms. We compare recent aircraft-based observations of CO2 above the North Pacific and Arctic Oceans to earlier data from 1958 to 1961 and find that the seasonal amplitude at altitudes
of 3 to 6 km increased by 50% for 45° to 90°N but by less than 25% for 10° to 45°N. An increase of 30 to 60% in the seasonal
exchange of CO2 by northern extratropical land ecosystems, focused on boreal forests, is implicated, substantially more than simulated by
current land ecosystem models. The observations appear to signal large ecological changes in northern forests and a major
shift in the global carbon cycle.
We characterized decadal changes in the amplitude and shape of the seasonal cycle of atmospheric CO2 with three kinds of analysis. First, we calculated the trends in the seasonal cycle of measured atmospheric CO2 at observation stations in the National Oceanic and Atmospheric Administration Climate Monitoring and Diagnostic Laboratory network. Second, we assessed the impact of terrestrial ecosystems in various localities on the mean seasonal cycle of CO2 at observation stations using the Carnegie-Ames-Stanford Approach terrestrial biosphere model and the Goddard Institute for Space Studies (GISS) atmospheric tracer transport model. Third, we used the GISS tracer model to quantify the contribution of terrestrial sources and sinks to trends in the seasonal cycle of atmospheric CO2 for the period 1961–1990, specifically examining the effects of biomass burning, emissions from fossil fuel combustion, and regional increases in net primary production (NPP). Our analysis supports results from previous studies that indicate a significant positive increase in the amplitude of the seasonal cycle of CO2 at Arctic and subarctic observation stations. For stations north of 55°N the amplitude increased at a mean rate of 0.66% yr−1 from 1981 to 1995. From the analysis of ecosystem impacts on the mean seasonal cycle we find that tundra, boreal forest, and other northern ecosystems are responsible for most of the seasonal variation in CO2 at stations north of 55°N. The effects of tropical biomass burning on trends in the seasonal cycle are minimal at these stations, probably because of strong vertical convection in equatorial regions. From 1981 to 1990, fossil fuel emissions contributed a trend of 0.20% yr−1 to the seasonal cycle amplitude at Mauna Loa and less than 0.10% yr−1 at stations north of 55°N. To match the observed amplitude increases at Arctic and subarctic stations with NPP increases, we find that north of 30°N a 1.7 Pg C yr−1 terrestrial sink would be required. In contrast, over regions south of 30°N, even large NPP increases and accompanying terrestrial sinks would be insufficient to account for the increase in high-latitude amplitudes.
Large-scale assessments of the potential for food production and its impact on biogeochemical cycling require the best possible information on the distribution of cropland. This information can come from ground-based agricultural census data sets and/or spaceborne remote sensing products, both with strengths and weaknesses. Official cropland statistics for China contain much information on the distribution of crop types, but are known to significantly underestimate total cropland areas and are generally at coarse spatial resolution. Remote sensing products can provide moderate to fine spatial resolution estimates of cropland location and extent, but supply little information on crop type or management. We combined county-scale agricultural census statistics on total cropland area and sown area of 17 major crops in 1990 with a fine-resolution land-cover map derived from 1995–1996 optical remote sensing (Landsat) data to generate 0.5° resolution maps of the distribution of rice agriculture in mainland China. Agricultural census data were used to determine the fraction of crop area in each 0.5° grid cell that was in single rice and each of 10 different multicrop paddy rice rotations (e.g., winter wheat/rice), while the remote sensing land-cover product was used to determine the spatial distribution and extent of total cropland in China. We estimate that there were 0.30 million km2 of paddy rice cropland; 75% of this paddy land was multicropped, and 56% had two rice plantings per year. Total sown area for paddy rice was 0.47 million km2. Paddy rice agriculture occurred on 23% of all cultivated land in China.
The objective of this study was to clarify the effects of soil temperature in the stage from late tillering to panicle initiation (SI) and during the grain-fi lling stage (SII) on grain setting, dry matter production, photosynthesis, non-structural carbohydrate (NSC), xylem exudation and abscisic acid (ABA) levels in rice (Oryza sativa L. cv. Koshihikari). Rice plants were exposed to four different soil temperatures during SI or SII: 17.5, 25, 31.5 and 36.5°C (ST18, ST25, ST32 and ST37, respectively). The yield, yield components, grain filling and quality in SI were negatively influenced by high soil temperature of 37°C. On the other hand, there was no significant difference in those characters among temperature treatments in SII. The root/shoot ratio was smallest in the ST37 plants in both SI and SII, mainly due to their lighter root weight. At 7 days after initiation of treatment (DAT) in both SI and SII, the photosynthetic and xylem exudation rate tended to increase slightly as soil temperature increased up to 32°C. At 21 DAT, however, the photosynthetic rate was lowest in ST37, with concurrent decrease of diffusion conductance and SPAD value. In addition, decrease of NSC concentration in stem and xylem exudation rate, and increase of ABA level in leaves and xylem exudate were observed in ST37 plants at 21 DAT. These results suggested that high soil temperature before heading especially influenced yield, grain quality and plant growth. Possible mechanisms of the effect of soil temperature are discussed.
Accurate assessment of anthropogenic carbon dioxide (CO<sub>2</sub>) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere is important to better understand the global carbon cycle, support the climate policy process, and project future climate change. Present-day analysis requires the combination of a range of data, algorithms, statistics and model estimates and their interpretation by a broad scientific community. Here we describe datasets and a methodology developed by the global carbon cycle science community to quantify all major components of the global carbon budget, including their uncertainties. We discuss changes compared to previous estimates, consistency within and among components, and methodology and data limitations. Based on energy statistics, we estimate that the global emissions of CO<sub>2</sub> from fossil fuel combustion and cement production were 9.5 ± 0.5 PgC yr<sup>−1</sup> in 2011, 3.0 percent above 2010 levels. We project these emissions will increase by 2.6% (1.9–3.5%) in 2012 based on projections of Gross World Product and recent changes in the carbon intensity of the economy. Global net CO<sub>2</sub> emissions from Land-Use Change, including deforestation, are more difficult to update annually because of data availability, but combined evidence from land cover change data, fire activity in regions undergoing deforestation and models suggests those net emissions were 0.9 ± 0.5 PgC yr<sup>−1</sup> in 2011. The global atmospheric CO<sub>2</sub> concentration is measured directly and reached 391.38 ± 0.13 ppm at the end of year 2011, increasing 1.70 ± 0.09 ppm yr<sup>−1</sup> or 3.6 ± 0.2 PgC yr<sup>−1</sup> in 2011. Estimates from four ocean models suggest that the ocean CO<sub>2</sub> sink was 2.6 ± 0.5 PgC yr<sup>−1</sup> in 2011, implying a global residual terrestrial CO<sub>2</sub> sink of 4.1 ± 0.9 PgC yr<sup>−1</sup>. All uncertainties are reported as ±1 sigma (68% confidence assuming Gaussian error distributions that the real value lies within the given interval), reflecting the current capacity to characterise the annual estimates of each component of the global carbon budget. This paper is intended to provide a baseline to keep track of annual carbon budgets in the future.
All carbon data presented here can be downloaded from the Carbon Dioxide Information Analysis Center ( doi:10.3334/CDIAC/GCP_V2012 ).
Global temperature is increasing, especially over northern lands (>50° N), owing to positive feedbacks. As this increase is most pronounced in winter, temperature seasonality (ST)—conventionally defined as the difference between summer and winter temperatures—is diminishing over time, a phenomenon that is analogous to its equatorward decline at an annual scale. The initiation, termination and performance of vegetation photosynthetic activity are tied to threshold temperatures. Trends in the timing of these thresholds and cumulative temperatures above them may alter vegetation productivity, or modify vegetation seasonality (SV), over time. The relationship between ST and SV is critically examined here with newly improved ground and satellite data sets. The observed diminishment of ST and SV is equivalent to 4° and 7° (5° and 6°) latitudinal shift equatorward during the past 30 years in the Arctic (boreal) region. Analysis of simulations from 17 state-of-the-art climate models indicates an additional ST diminishment equivalent to a 20° equatorward shift could occur this century. How SV will change in response to such large projected ST declines and the impact this will have on ecosystem services are not well understood. Hence the need for continued monitoring of northern lands as their seasonal temperature profiles evolve to resemble those further south.
Food systems contribute 19%–29% of global anthropogenic greenhouse gas (GHG) emissions, releasing 9,800–16,900 megatonnes of carbon dioxide equivalent (MtCO2e) in 2008. Agricultural production, including indirect emissions associated with land-cover change, contributes 80%–86% of total food system emissions, with significant regional variation. The impacts of global climate change on food systems are expected to be widespread, complex, geographically and temporally variable, and profoundly influenced by socioeconomic conditions. Historical statistical studies and integrated assessment models provide evidence that climate change will affect agricultural yields and earnings, food prices, reliability of delivery, food quality, and, notably, food safety. Low-income producers and consumers of food will be more vulnerable to climate change owing to their comparatively limited ability to invest in adaptive institutions and technologies under increasing climatic risks. Some synergies among food security, adaptati...
In the coming decades, continued population growth, rising meat and dairy consumption and expanding biofuel use will dramatically increase the pressure on global agriculture. Even as we face these future burdens, there have been scattered reports of yield stagnation in the world's major cereal crops, including maize, rice and wheat. Here we study data from ∼2.5 million census observations across the globe extending over the period 1961-2008. We examined the trends in crop yields for four key global crops: maize, rice, wheat and soybeans. Although yields continue to increase in many areas, we find that across 24-39% of maize-, rice-, wheat- and soybean-growing areas, yields either never improve, stagnate or collapse. This result underscores the challenge of meeting increasing global agricultural demands. New investments in underperforming regions, as well as strategies to continue increasing yields in the high-performing areas, are required.
Assessments of agriculture at the national scale require the best possible information on the distribution of cropland and the management of that cropland. Official cropland statistics for China contain much information on the distribution of crop types, but are known to underestimate total cropland area and are generally at coarse spatial resolution. Remote sensing products can provide moderate to fine spatial resolution estimates of cropland location and extent, but usually supply little information on crop type or management. We combined 1990 county‐scale agricultural census statistics on total cropland area and sown area of 17 major crops with a fine‐resolution land‐cover map derived from 1995‐96 optical remote sensing (Landsat Thematic Mapper) data to generate 0.5° resolution maps of the distribution of 47 different single‐ and multi‐crop rotations in mainland China. We estimated that, of 1.3‐million km of cropland, approximately 60% was single‐cropped, 30% was double‐cropped, and 10% was triple‐cropped. Total sown or planted area was 2.0‐million km.
Competitive outcome between crops and weeds is affected by partitioning of new biomass to above- and belowground plant organs in response to nutrient supply. This study determined the fraction of biomass partitioned to roots vs. shoots in corn and velvetleaf in response to nitrogen (N) supply. Pots measuring 28 cm in diam and 60 cm deep were embedded in the ground and each contained one plant of either corn or velvetleaf. Each plant received one of three N treatments: 0, 1, or 3 g N applied as ammonium nitrate in 2001, and 0, 2, or 6 g N in 2002. Measurements of total above- and belowground biomass were made at 10 sampling dates during each growing season. The root:shoot ratio decreased over time for both corn and velvetleaf as a result of normal plant growth and as N supply increased. Root:shoot ratio was greater for corn than for velvetleaf at comparable stages of development and at all levels of N supply. Both corn and velvetleaf display true plasticity in biomass partitioning patterns in response to N supply. Velvetleaf root:shoot ratio increased by 46 to 82% when N was limiting in 2001 and 2002, respectively, whereas corn root:shoot ratio increased by only 29 to 45%. The greater increase in biomass partitioned to roots by velvetleaf might negatively impact its ability to compete with corn for light when N supply is limited. Nomenclature: Velvetleaf, Abutilon theophrasti Medic., ABUTH; corn, Zea mays L.
In the coming decades, a crucial challenge for humanity will be meeting future food demands without undermining further the integrity of the Earth's environmental systems. Agricultural systems are already major forces of global environmental degradation, but population growth and increasing consumption of calorie- and meat-intensive diets are expected to roughly double human food demand by 2050 (ref. 3). Responding to these pressures, there is increasing focus on 'sustainable intensification' as a means to increase yields on underperforming landscapes while simultaneously decreasing the environmental impacts of agricultural systems. However, it is unclear what such efforts might entail for the future of global agricultural landscapes. Here we present a global-scale assessment of intensification prospects from closing 'yield gaps' (differences between observed yields and those attainable in a given region), the spatial patterns of agricultural management practices and yield limitation, and the management changes that may be necessary to achieve increased yields. We find that global yield variability is heavily controlled by fertilizer use, irrigation and climate. Large production increases (45% to 70% for most crops) are possible from closing yield gaps to 100% of attainable yields, and the changes to management practices that are needed to close yield gaps vary considerably by region and current intensity. Furthermore, we find that there are large opportunities to reduce the environmental impact of agriculture by eliminating nutrient overuse, while still allowing an approximately 30% increase in production of major cereals (maize, wheat and rice). Meeting the food security and sustainability challenges of the coming decades is possible, but will require considerable changes in nutrient and water management.
Temperature is increasing at unprecedented rates across most of the tundra biome. Remote-sensing data indicate that contemporary climate warming has already resulted in increased productivity over much of the Arctic, but plot-based evidence for vegetation transformation is not widespread. We analysed change in tundra vegetation surveyed between 1980 and 2010 in 158 plant communities spread across 46 locations. We found biome-wide trends of increased height of the plant canopy and maximum observed plant height for most vascular growth forms; increased abundance of litter; increased abundance of evergreen, low-growing and tall shrubs; and decreased abundance of bare ground. Intersite comparisons indicated an association between the degree of summer warming and change in vascular plant abundance, with shrubs, forbs and rushes increasing with warming. However, the association was dependent on the climate zone, the moisture regime and the presence of permafrost. Our data provide plot-scale evidence linking changes in vascular plant abundance to local summer warming in widely dispersed tundra locations across the globe.
Knowledge of the changes in physiological traits associated with genetic gains in yield potential is essential to improve
understanding of yield-limiting factors and to inform future breeding strategies. The growth and development of eight representative
UK wheat (Triticum aestivum L.) cultivars introduced from 1972 to 1995 was examined in field experiments in 1997–1999. Significant genetic changes over
time and positive correlations with grain yield were found for pre-anthesis radiation-use efficiency ( 0.012 g MJ-1 yr-1 ; RUE) and water soluble carbohydrate (WSC) content of stems and leaf sheaths at anthesis (4.6 g m-2 yr-1) . Results suggested that yield of modern UK cultivars although still sink limited might be closer to source-limitation than
their predecessors. Therefore, breeders may eventually need to take further steps to increase source size post-anthesis alongside
improvements in grain sink size. In this respect, greater accumulation of stem carbohydrate reserve may be beneficial, providing
this is not competitive with ear growth. Results further suggested that the 1BL.1RS wheat-rye translocation may be associated
with greater harvest biomass in modern cultivars. The relationship between the amount of stem WSC measured shortly after flowering
and grain yield was further tested in two doubled haploid (DH) populations, Rialto x Spark and Beaver x Soissons in 2001 and
2002. There was a positive linear relationship between stem WSC and grain yield in both populations. The effects of 1BR.1RS
were further examined in two DH populations, Beaver (1BL.1RS) x Soissons (1B) and Drake-sib (1BL.1RS) x Welton (1B), in 1998–2002.
1BL.1RS increased harvest biomass in both populations, although grain yield was only increased in the Beaver x Soissons population.
In the Drake-sib x Welton population, the physiological basis of the increased harvest biomass was investigated, and found
to be associated with greater pre-anthesis biomass. There was no change in green area index or light extinction coefficient
in the pre-anthesis period indicating 1BL.1RS may have conferred an increase in RUE
A major challenge in predicting Earth's future climate state is to understand feedbacks that alter greenhouse-gas forcing.
Here we synthesize field data from arctic Alaska, showing that terrestrial changes in summer albedo contribute substantially
to recent high-latitude warming trends. Pronounced terrestrial summer warming in arctic Alaska correlates with a lengthening
of the snow-free season that has increased atmospheric heating locally by about 3 watts per square meter per decade (similar
in magnitude to the regional heating expected over multiple decades from a doubling of atmospheric CO2). The continuation of current trends in shrub and tree expansion could further amplify this atmospheric heating by two to
seven times.
Early planting of maize ( Zea mays L.) allows for longer‐season hybrids to be used in cool temperate regions. Given that a multidecadal trend toward earlier planting has been occurring across the Corn Belt, it was hypothesized that this shift has supported a portion of recent yield increases. The objectives were to quantify relationships among state level monthly climate variables, maize yields, and planting dates, and to investigate whether multidecadal trends of earlier planting contributed to rising yields during 1979 to 2005 in 12 central U.S. states. Year‐to‐year changes (i.e., first differences) of predictor variables (monthly mean temperature and precipitation and planting date) and yields were calculated, and multiple linear regression was used to estimate the effect of planting date trends on maize yield increases. In six of the 12 states, a significant relationship ( P < 0.05) existed between first differences of planting dates and yields. Multiple linear regression suggested that the management change has potentially contributed between 19 and 53% of the state level yield increases in Nebraska, South Dakota, Minnesota, Iowa, Wisconsin, and Michigan. Yield increases between 0.06 and 0.14 Mg ha ⁻¹ were attributed to each additional day of earlier planting, which likely reflects a gradual adoption of longer‐season hybrids. Thus, if these earlier planting trends were to suddenly abate, a falloff in annual yield increases may follow in several Corn Belt states. Maize production in northern U.S. states appears to have benefited more significantly from earlier planting due to a shorter growing season in contrast to more southern locations.
Differences in the seasonal pattern of assimilatory and respiratory
processes are responsible for divergences in seasonal net carbon
exchange among ecosystems. Using FLUXNET data
(http://www-eosdis.ornl.gov/FLUXNET) we have analyzed seasonal patterns
of gross primary productivity (GPP), and ecosystem respiration (RE) of
boreal and temperate, deciduous and coniferous forests, mediterranean
evergreen systems, rainforest, temperate grasslands, and C3 and C4
crops. Based on generalized seasonal patterns classifications of
ecosystems into vegetation functional types can be evaluated for use in
global productivity and climate change models. The results of this study
contribute to our understanding of respiratory costs of assimilated
carbon in various ecosystems. Seasonal variability of GPP and RE
increased in the order tropical, Mediterranean, temperate coniferous,
temperate deciduous, boreal forests. Together with boreal forests,
managed grasslands and crops show the largest seasonal variability. In
temperate coniferous forests, seasonal patterns of GPP and RE are in
phase, in temperate deciduous and boreal coniferous forests RE was
delayed compared to GPP, resulting in the greatest imbalance between
respiratory and assimilatory fluxes early in the growing season. Gross
primary productivity adjusted for the length of the growing season
decreased across functional types in the order C4 crops, and temperate
and boreal deciduous forests (7.5-8.3 g C m-2 d-1), temperate conifers,
C3 grassland and crops (5.7-6.9 g C m-2 d-1), rainforest and boreal
conifers (4.6-4.9 g C m-2 d-1). Annual GPP and NEP decreased across
climate zones in the order tropical, temperate, boreal. However, the
decrease in NEP was greater than the decrease in GPP, indicating a
larger contribution of respiratory (especially heterotrophic) processes
in boreal systems.
This study reports on two consecutive years of experimentation to evaluate ecological intensification options for smallholder irrigated rice production on an alluvial-marine soil in the eastern periphery of Amazonia. The 2007 system-comparison trial contrasts management packages of the System of Rice Intensification (SRI) with ‘conventional’ irrigated rice (CIR). In a bi-factorial layout, we compare SRI and CIR under low (10 Mg cow manure ha−1) and high (10 Mg manure + additional 100 kg N ha−1 as urea) fertilizer regimes. The 2008 factor-comparison trial investigated the relative effects of three key rice intensification management components: (i) aerobic-moist (as opposed to anaerobic-flooded) water regime, (ii) low-density transplanting (as opposed to high-density direct seeding), and (iii) organic (as opposed to synthetic) fertilization. Weed management was not investigated and therefore standardized to manual weeding. Since a full factorial scheme (3 × 3 treatments) was not feasible, we compare SRI and conventional management components under the conditions we deemed most relevant for irrigated rice in our region and beyond.
A primary objective of the Earth Observing System (EOS) is to develop and validate algorithms to estimate leaf area index (L), fraction of absorbed photosynthetically active radiation (fAPAR), and net primary production (NPP) from remotely sensed products. These three products are important because they relate to or are components of the metabolism of the biosphere and can be determined for terrestrial ecosystems from satellite-borne sensors. The importance of these products in the EOS program necessitates the need to use standard methods to obtain accurate ground truth estimates of L, fAPAR, and NPP that are correlated to satellite-derived estimates. The objective of this article is to review direct and indirect methods used to estimate L, fAPAR, and NPP in terrestrial ecosystems. Direct estimates of L, biomass, and NPP can be obtained by harvesting individual plants, developing allometric equations, and applying these equations to all individuals in the stand. Using non-site-specific allometric equations to estimate L and foliage production can cause large errors because carbon allocation to foliage is influenced by numerous environmental and ecological factors. All of the optical instruments that indirectly estimate L actually estimate “effective” leaf area index (LE) and underestimate L when foliage in the canopy is nonrandomly distributed (i.e., clumped). We discuss several methods, ranging from simple to complex in terms of data needs, that can be used to correct estimates of L when foliage is clumped. Direct estimates of above-ground and below-ground net primary production (NPPA and NPPB, respectively) are laborious, expensive and can only be carried out for small plots, yet there is a great need to obtain global estimates of NPP. Process models, driven by remotely sensed input parameters, are useful tools to examine the influence of global change on the metabolism of terrestrial ecosystems, but an incomplete understanding of carbon allocation continues to hamper development of more accurate NPP models. We summarize carbon allocation patterns for major terrestrial biomes and discuss emerging allocation patterns that can be incorporated into global NPP models. One common process model, light use efficiency or epsilon model, uses remotely sensed fAPAR, light use efficiency (LUE) and carbon allocation coefficients, and other meteorological data to estimates NPP. Such models require reliable estimates of LUE. We summarize the literature and provide LUE coefficients for the major biomes, being careful to correct for inconsistencies in radiation, dry matter and carbon allocation units.
The use of maize (Zea mays L.) stover as a feed-stock for cellulosic biofuels production will create demand for maize hybrids with greater stover yield. It is expected that grain yield will remain the most critical trait and continue to drive hybrid sales, requiring that any increases in stover yield be made without sacrificing grain yield potential. The objective of this review was to determine the relationship between grain yield, harvest index, and stover yield to illuminate the potential for increasing both grain and stover yield through breeding. In contrast to what has been observed in other crops, gains in maize grain yield over time in the U.S. Corn Belt have been accompanied by increases in stover yield. Where recurrent selection on grain yield has been most successful, stover yield also increased while harvest index has been relatively stable. The opposite situation has been observed in tropical germplasm, where gains in grain yield have been associated with increasing harvest index and relatively constant biomass yield. We expect that stover yield of Corn Belt hybrids will continue to increase along with grain yield, resulting in future hybrids capable of producing both more food and biofeedstock for energy production. If maize breeders pursue selection for increased stover yield, we found no evidence to suggest that breeding for stover yield will necessarily reduce rate of gain in grain yield.
Early planting of maize (Zea mays L.) allows for longer-season hybrids to be used in cool temperate regions. Given that a multidecadal trend toward earlier planting has been occurring across the Corn Belt, it was hypothesized that this shift has supported a portion of recent yield increases. The objectives were to quantify relationships among state level monthly climate variables, maize yields, and planting dates, and to investigate whether multidecadal trends of earlier planting contributed to rising yields during 1979 to 2005 in 12 central U.S. states. Year-to-year changes (i.e., first differences) of predictor variables (monthly mean temperature and precipitation and planting date) and yields were calculated, and multiple linear regression was used to estimate the effect of planting date trends on maize yield increases. In six of the 12 states, a significant relationship (P < 0.05) existed between first differences of planting dates and yields. Multiple linear regression suggested that the management change has potentially contributed between 19 and 53% of the state level yield increases in Nebraska, South Dakota, Minnesota, Iowa, Wisconsin, and Michigan. Yield increases between 0.06 and 0.14 Mg ha(-1) were attributed to each additional day of earlier planting, which likely reflects a gradual adoption of longer-season hybrids. Thus, if these earlier planting trends were to suddenly abate, a falloff in annual yield increases may follow in several Corn Belt states. Maize production in northern U.S. states appears to have benefited more significantly from earlier planting due to a shorter growing season in contrast to more southern locations.
The concentration of atmospheric carbon dioxide at Mauna Loa Observatory exhibits a seasonal pattern that repeats with striking regularity from year to year. The amplitude of this seasonal signal, expressed either by peak-to-peak changes in concentration or as a series of harmonic terms, increased at an average rate of about 0.7% per year from 1958 to 1982. The estimated standard error in the linear coefficient of increase is 0.09%. Thus the increase appears to be highly significant statistically. A detailed examination of methods of calibration and of data analysis during this long record do not reveal any inconsistencies large enough to be responsible for the increase. Because the seasonal cycle of COâ in the northern hemisphere is thought to be due principally to the metabolic activity of terrestrial vegetation, it is likely that at least part of the increase is a result of increasing plant activity.
Records of the annual variation of the atmospheric carbon dioxide concentration at Mauna Loa, Point Barrow, and Weathership P are examined for secular changes. The amplitude of the annual variation appears to have increased in recent years with a best estimate of the rate of change, based on the Mauna Loa data, of 0.45+/-0.42% yr-1. This change is discussed in terms of changes in biospheric respiration and photosynthesis and the use of fossil fuels. The analysis does not allow for the separation of several possible causes of amplitude change. However, if the change is interpreted as reflecting enhanced biospheric growth, the effect is equivalent to a 8% change in the net summer uptake of carbon over the years 1959-1978 and to a growth of the northern hemisphere seasonal biosphere of 0.5×1012 kg of carbon per year. Such a conclusion is consistent with recent inventory studies, which indicate that temperature zone forests have acted as a net sink of about 1012 kg of carbon per year in recent decades.
Plant harvest index, the ratio of grain weight to total plant weight, is an important trait associated with the dramatic increases in crop yields that have occurred in the twentieth century. Harvest index reflects the partitioning of photosynthate between the grain and the vegetative plant and improvements in harvest index emphasize the importance of carbon allocation in grain production. The objective of this review is to examine from an historical perspective some of the changes that have occurred in crop harvest index and to consider the importance of crop nitrogen accumulation associated with changing this trait. In modern times, harvest index has generally increased. Prior to the twentieth century, there is evidence that plant selections also resulted in changes in harvest index. One factor that may have influenced these changes was the relative value of grain, compared with straw. Historically, straw production was a high priority, making low harvest index a desirable trait. Another factor was the level of nitrogen available for the production of high grain yields. Accumulation of high levels of nitrogen is essential for high grain yields, and thus, high levels of nitrogen are commonly associated with crops having high harvest indices. Under conditions where nitrogen is limiting, a low harvest index crop is advantageous. Limited nitrogen can be partitioned into the low nitrogen concentration vegetative tissue, which results in high total production of plant mass. However, increasing grain yield and crop harvest index with high nitrogen grain requires a concomitant increase in crop nitrogen accumulation.
We studied crop harvested yield, as recorded in national agricultural statistics, to estimate net primary production (NPP) in agricultural regions where most of the land area is sown with a few, well-studied crops. We estimated the magnitudes and interannual variations in NPP in croplands in the U.S. Midwest using crop area and yield data obtained from the U.S. National Agricultural Statistics Service (NASS). Total NPP, including estimates of the above- and belowground components, was calculated from harvested-yield data by (1) conversion from reporting units of yield of the crop product (Usually in volume) to mass, (2) conversion from fresh mass to dry mass, (3) estimation of aboveground yield using crop harvest indices, defined as the ratio of economic product (e.g., grain) dry mass to plant aboveground dry mass, and (4) estimation of belowground yield as a function of aboveground biomass. This approach is applied to corn, soybean, sorghum, sunflower, oats, barley, wheat, and hay in Illinois, Indiana, Iowa, Wisconsin, Michigan, Minnesota, North Dakota, and Ohio for 1992, and in Iowa for 1982 through 1996. Many counties in the eight states had > 70% coverage of these crops. In Iowa. corn and soybean accounted for > 50% of the land area in most counties. County-level NPP in 1992 ranged from 4 Mg.ha(-1).yr(-1) biomass (x0.5 in terms of carbon) in North Dakota, Wisconsin, and Minnesota to > 17 Mg.ha(-1).yr(-1) in central Iowa, Illinois, and Ohio. Areas of highest NPP were dominated by corn and soybean cultivation. NPP for counties in Iowa varied among years by a factor of 2, with the lowest NPP in 1983 (which had an unusually wet spring), in 1988 (which was a drought year), and in 1993 (which experienced floods). A sensitivity analysis, conducted by varying harvest index and root:shoot ratio by 10-50%, indicated that the limit of accuracy of the method is similar to1 Mg.ha(-1).yr(-1).
A pot and a field experiment were conducted to assess the effects of root/shoot ratio (R/S) on the water use efficiency (WUE) and grain yield of winter wheat. The R/S was regulated by pruning the roots during the stem elongation stage, resulting in reduced root systems of the plants. At the heading stage, the root dry weight of root-pruned plants was less than that of intact-root plants, but their R/S was similar to that of intact-root plants under both experimental conditions. After tiller pruning, the R/S of root-pruned plants was significantly lower than that of intact-root plants (p < 0.05). Root pruning reduced the rate of leaf transpiration and lowered the number of tillers per plant (p < 0.05) during the vegetative stage. As a result, root-pruned wheat showed reduced water use when compared to intact-root plants before heading (p < 0.05). At anthesis, there was no significant difference in transpiration between plants with intact roots and those with pruned roots in the pots. However, under field conditions, transpiration of root-pruned plants was significantly higher than that of intact-root plants at anthesis. Additionally, at anthesis root-pruned plants had a higher rate of leaf photosynthesis and lower rate of root respiration, which resulted in a significantly higher grain yield at maturity when compared to plants with intact roots. Under both experimental conditions, there were no significant differences in shoot dry weight per plant between root-pruned and intact-root plants grown in monoculture. When root-pruned plants were grown with intact-root plants, the root-pruned wheat was less productive and had a lower relative shoot dry weight (0.78 and 0.86, respectively) than the intact-root plants (1.24 and 1.16, respectively). These results suggest that plants with pruned roots had a lower ability to compete and to acquire and use the same resources in the mixture when compared with intact-root plants. Root pruning improved the WUE of winter wheat under both experimental conditions. This suggests that appropriate management for the root system/tillers in wheat crops can be used to increase grain yield and water use efficiency. Specifically, lowering the R/S improved the grain yield and WUE of winter wheat significantly by lowering its competitive ability and improving root efficiency. Therefore, drought-resistance breeding to improve the grain yield and WUE, at least for wheat, should be made by targeted selection of less competitive progeny with a small R/S for Cultivation in and and semiarid areas.
Genetic improvement of short-season soybean [Glycine max (L.) Merr.] cultivars has resulted in a 0.5% annual gain in yield. Although yield is the product of dry matter (DM) accumulation and partitioning, the relative contributions of these two components of yield to genetic improvement has not been documented. Furthermore, the mechanism by which higher DM accumulation or harvest index (HI) is accomplished in the newer cultivars is unclear. The objective of the current study was to characterize DM accumulation and partitioning in cultivars which differ in yield potential, and determine the role of these traits in yield improvement. Two older (low yield potential) and two newer (higher yield potential) soybean cultivars of similar maturity were grown in side-by-side trials in 1996 and 1997. Plant samples were taken during each growing season and separated into leaves, stems + petioles, roots, and seeds. Dry matter accumulation and leaf area indices were measured. Seed yield of the new cultivars was 30% greater than their older counterparts. Increased DM accumulation contributed 78% and increased HI contributed 22% towards the genetic gain in yield. Total plant dry weight increased to a maximum around R4/R5 and subsequently declined during the seed-filling period (SFP) as pod development increased and leaf senescence began. This decline in dry weight during the SFP was greater for the old than for the new cultivars. The newer cultivars maintained leaf area further into the SFP than the old cultivars enabling continued dry matter accumulation. The results of this experiment indicate that genetic yield improvement in the short-season soybean cultivars examined was mainly associated with longer leaf area duration and the subsequently greater DM accumulation.
We combine satellite and ground observations during 1950-2011 to study the long-term links between multiple climate (air temperature and cryospheric dynamics) and vegetation (greenness and atmospheric CO2 concentrations) indicators of the growing season of northern ecosystems (>45 degrees N) and their connection with the carbon cycle. During the last three decades, the thermal potential growing season has lengthened by about 10.5days (P<0.01, 1982-2011), which is unprecedented in the context of the past 60years. The overall lengthening has been stronger and more significant in Eurasia (12.6days, P<0.01) than North America (6.2days, P>0.05). The photosynthetic growing season has closely tracked the pace of warming and extension of the potential growing season in spring, but not in autumn when factors such as light and moisture limitation may constrain photosynthesis. The autumnal extension of the photosynthetic growing season since 1982 appears to be about half that of the thermal potential growing season, yielding a smaller lengthening of the photosynthetic growing season (6.7days at the circumpolar scale, P<0.01). Nevertheless, when integrated over the growing season, photosynthetic activity has closely followed the interannual variations and warming trend in cumulative growing season temperatures. This lengthening and intensification of the photosynthetic growing season, manifested principally over Eurasia rather than North America, is associated with a long-term increase (22.2% since 1972, P<0.01) in the amplitude of the CO2 annual cycle at northern latitudes. The springtime extension of the photosynthetic and potential growing seasons has apparently stimulated earlier and stronger net CO2 uptake by northern ecosystems, while the autumnal extension is associated with an earlier net release of CO2 to the atmosphere. These contrasting responses may be critical in determining the impact of continued warming on northern terrestrial ecosystems and the carbon cycle.
THROUGHOUT the Northern Hemisphere the concentration of atmospheric carbon dioxide rises in winter and declines in summer, mainly in response to the seasonal growth in land vegetation1–4. In the far north the amplitude of the seasonal cycle, peak to trough, is between 15 and 20 parts per million by volume5. The annual amplitude diminishes southwards to about 3 p.p.m. near the Equator, owing to the diminishing seasonally of plant activity towards the tropics. In spite of atmospheric mixing processes, enough spatial variability is retained in the seasonal cycle of CO2 to reveal considerable regional detail in seasonal plant activity6. Here we report that the annual amplitude of the seasonal CO2 cycle has increased by 20%, as measured in Hawaii, and by 40% in the Arctic, since the early 1960s. These increases are accompanied by phase advances of about 7 days during the declining phase of the cycle, suggesting a lengthening of the growing season. In addition, the annual amplitudes show maxima which appear to reflect a sensitivity to global warming episodes that peaked in 1981 and 1990. We propose that the amplitude increases reflect increasing assimilation of CO2 by land plants in response to climate changes accompanying recent rapid increases in temperature.
Two types of source contribute photosynthate for grain filling in wheat (Triticum aestivum L.) and barley (Hordeum vulgare L.), current photosynthate transferred directly to the grain and photosynthate redistributed from reserve pools in vegetative tissues. Reserve pools provide the substrate needed to maintain transport and the supply of assimilate to grains during the dark period of the diurnal cycle and during the latter part of grain filling, when the photosynthetic apparatus is senescing and the rate of dry matter accumulation of grains exceeds the rate of dry matter accumulation of the total crop. In addition reserve pools provide a means by which the current rates of photosynthate production and of photosynthate use in grain filling are allowed to proceed (at least in part) independently from each other. There is evidence that all photosynthetic organs (leaves, glumes and exposed portion of the peduncle) contain one or more diurnal carbohydrate storage pools. Diurnal storage of sucrose seems to be much more important than the transient storage of starch. There is little evidence for fructan pools serving a role as a net source of carbon during the dark period of the diurnal cycle. However, fructan is the most important longer-term reserve carbohydrate of vegetative tissues. Fructan accumulation occurs mainly in the extended internodes and leaf sheaths and usually terminates within three weeks following anthesis, after which fructan is gradually lost until grain maturity. The accumulation and loss of fructan are greatly influenced by environmental conditions and treatments that alter the longer-term balance between photosynthate production by the plant and photosynthate use in grain filling. Observations are consistent with the view that fructan accumulation in expanded vegetative tissue is not competitive with grain filling, but the fructan pools accept surplus photosynthate during periods of low demand by grains and provide photosynthate during longer-term deficits in current photosynthate production. Even under optimal conditions for photosynthesis it is likely that half or more of the photosynthate in mature grains is temporarily deposited in one or more reserve pools before being transferred to the grain. Neither the efficiency of reserve utilization in grain filling nor its potential variability in different genotypes and environments are known. Also, very little is known about the mechanisms that control the partitioning of photosynthate between the grain and reserve pools and the allocation to different types of reserve pools. Future progress in the understanding of photosynthesis-yield relationships will likely depend to a great extent on improved knowledge of the controls that govern photosynthate deposition and redistribution in the different pools of reserve carbohydrate.
This review charts the use of the concept of harvest index in crop improvement and physiology, concentrating on the literature from the last 20 years. Evidence from abstract journals indicates that the term has been applied most to small grain cereal crops and pulses, in India, Western Europe and the USA, and that it has been less useful for maize and tuber crops. Standard methods of measuring harvest index, the associated problems of measurement and interpretation, and representative values for a range of world species are reviewed. The values for modern varieties of most intensively-cultivated grain crops fall within the range 0.4 to 0.6. Variation between varieties of the same species is illustrated by trends in the harvest indices of old, outclassed and recent varieties of temperate and mediterranean wheat and barley (compared under uniform conditions); this shows a progressive increase throughout the present century, although improvement has been much slower in Australia and Canada than in the UK. In most cases, the improvement in harvest index has been a consequence of increased grain population density coupled with stable individual grain weight. The high heritability of harvest index is explored by examining its (rather weak) response to variation in environmental factors (fertilisation, population density, application of growth regulators) in the absence of severe stress. A fuller perspective is gained by reviewing aspects of the harvest index of rice, maize and tropical pulses. With rice, attention must be paid to the fact that the adhering lemma and palea (not primarily part of economic yield) can make up 20% of grain weight; and there are important interactions among biomass, grain yield and season length. Maize differs from most small grain crops in that harvest index (in N. American varieties) was already high at the start of this century, and increases in yield potential have been largely the consequence of increased biomass production. The harvest index of many pulse species and varieties tends to be low because selection has been for some yield in all seasons. Extension of the harvest index concept to express the partitioning of mineral nutrients as well as dry matter (e.g. the nitrogen harvest index) has provided a range of responses whose implications for production and breeding remain to be explored. It is concluded that even though the principal cereal crops appear to be approaching the upper limit of harvest index, and future yield gains will have to be sought by increased biomass production, there will still be a need for the concept of harvest index as a tool in interpreting crop response to different environments and climatic change.
This paper provides new estimates of area planted to the rice (Oryza sativa L.)–wheat (Triticum aestivum L.) rotation in China by combining the results obtained from two methodologies. One methodology uses official statistics at the province-level for sown area of rice and wheat, which allows construction of annual estimates from 1979 to 2001. The other methodology uses remote sensing data and county level Agricultural Census data on sown area of 17 major crops, which allows for construction of one estimate appropriate for the middle of the 1990s. The first methodology suggests that the area planted to the rice–wheat rotation has declined sharply in recent years. A combination of the two methodologies results in an estimate of rice–wheat area in China in 2001 of 3.4 Mha. This is substantially below other figures in the literature that reach as high as 13 Mha. This estimate, and the reasons for its declining trend over time, is important for setting priorities in crop research and for understanding how farmers might react to possible new productivity-enhancing technologies.
There is a potential to sequester carbon in soil by changing agricultural management practices. These changes in agricultural management can also result in changes in fossil-fuel use, agricultural inputs, and the carbon emissions associated with fossil fuels and other inputs. Management practices that alter crop yields and land productivity can affect the amount of land used for crop production with further significant implications for both emissions and sequestration potential. Data from a 20-year agricultural experiment were used to analyze carbon sequestration, carbon emissions, crop yield, and land-use change and to estimate the impact that carbon sequestration strategies might have on the net flux of carbon to the atmosphere. Results indicate that if changes in management result in decreased crop yields, the net carbon flux can be greater under the new system, assuming that crop demand remains the same and additional lands are brought into production. Conversely, if increasing crop yields lead to land abandonment, the overall carbon savings from changes in management will be greater than when soil carbon sequestration alone is considered.
Aim To assemble a data set of global crop planting and harvesting dates for 19 major crops, explore spatial relationships between planting date and climate for two of them, and compare our analysis with a review of the literature on factors that drive decisions on planting dates.
Location Global.
Methods We digitized and georeferenced existing data on crop planting and harvesting dates from six sources. We then examined relationships between planting dates and temperature, precipitation and potential evapotranspiration using 30‐year average climatologies from the Climatic Research Unit, University of East Anglia (CRU CL 2.0).
Results We present global planting date patterns for maize, spring wheat and winter wheat (our full, publicly available data set contains planting and harvesting dates for 19 major crops). Maize planting in the northern mid‐latitudes generally occurs in April and May. Daily average air temperatures are usually c . 12–17 °C at the time of maize planting in these regions, although soil moisture often determines planting date more directly than does temperature. Maize planting dates vary more widely in tropical regions. Spring wheat is usually planted at cooler temperatures than maize, between c . 8 and 14 °C in temperate regions. Winter wheat is generally planted in September and October in the northern mid‐latitudes.
Main conclusions In temperate regions, spatial patterns of maize and spring wheat planting dates can be predicted reasonably well by assuming a fixed temperature at planting. However, planting dates in lower latitudes and planting dates of winter wheat are more difficult to predict from climate alone. In part this is because planting dates may be chosen to ensure a favourable climate during a critical growth stage, such as flowering, rather than to ensure an optimal climate early in the crop's growth. The lack of predictability is also due to the pervasive influence of technological and socio‐economic factors on planting dates.
Abstract Remote sensing of net primary production (NPP) is a critical tool for assessing spatial and temporal patterns of carbon exchange between the atmosphere and biosphere. However, satellite estimates suffer from a lack of large-scale field data needed for validation, as well as the need to parameterize plant light-use efficiencies (LUEs). In this study, we estimated cropland NPP with the Carnegie-Ames-Stanford-Approach (CASA), a biogeochemical model driven by satellite observations, and then compared these results with field estimates based on harvest data from United States Department of Agriculture National Agriculture Statistics Service (NASS) county statistics. Observed interannual variations in NPP over a 17-year period were well modelled by CASA, with exceptions mainly due to occasional difficulties in estimating NPP from harvest yields. The role of environmental stressors in agriculture was investigated by running CASA with and without temperature and moisture down-regulators, which are used in the model to simulate climate impacts on plant LUE. In most cases, correlations with NASS data were highest with modelled stresses, while the opposite was true for irrigated and temperature resistant crops. Analysis of the spatial variability in computed LUE revealed significantly higher values for corn than for other crops, suggesting a simple parameterization of LUE for future studies based on the fraction of area with corn. Absolute values of LUE were much lower than those reported in field trials, due to uncommonly high yields in most field trials, as well as overestimates of absorbed radiation in CASA attributed to bias from temporal compositing of satellite data. Total NPP for US croplands, excluding Alaska and Hawaii, was estimated as 0.62 Pg C year−1, representing ∼20% of total US NPP, and exhibited a positive trend of 3.7 Tg C year−2. These results have several implications for large-scale carbon cycle research that are discussed, and are especially relevant for studies of the role of agriculture in the global carbon balance.
This study uses a global terrestrial carbon cycle model (the Carnegie-Ames-Stanford Approach (CASA) model), a satellite-derived map of existing vegetation, and global maps of natural vegetation to estimate the effects of human-induced land cover change on carbon emissions to the atmosphere and net primary production. We derived two maps approximating global land cover that would exist for current climate in the absence of human disturbance of the landscape, using a procedure that minimizes disagreements between maps of existing and natural vegetation that represent artifacts in the data. Similarly, we simulated monthly fields of the Normalized Difference Vegetation Index, required as input to CASA, for the undisturbed land cover case. Model results estimate total carbon losses from human-induced land cover changes of 182, and 199 Pg for the two simulations, compared with an estimate of 124 Pg for total flux between 1850 and 1990 [Houghton, 1999], suggesting that land cover change prior to 1850 accounted for approximately one-third of total carbon emissions from land use change. Estimates of global carbon loss from the two independent methods, the modeling approach used in this paper and the accounting approach of Houghton [1999], are comparable taking into account carbon losses from agricultural expansion prior to 1850 estimated at 48-57 Pg. However, estimates of regional carbon losses vary considerably, notably in temperate midlatitudes where our estimates indicate higher cumulative carbon loss. Overall, land cover changes reduced global annual net primary productivity (NPP) by approximately 5 percent, with large regional variations. High-input agriculture in North America and Europe display higher annual NPP than the natural vegetation that would exist in the absence of cropland. However, NPP has been depleted in localized areas in South Asia and Africa by up to 90 percent. These results provide initial crude estimates, limited by the spatial resolution of the data sets used as input to the model and by the lack of information about transient changes in land cover. The results suggest that a modeling approach can be used to estimate spatially-explicit effects of land cover change on biosphere-atmosphere interactions.
In examining the global food supply and demand, the balance of research has favored analysis of the prospects for increased crop production, at the expense of examination of the potential for reducing the production requirements by increases in efficiency and productivity, or by shifts in diets. This has implied that there is a lack of coherent evaluations of efficiency and diet as options for keeping down the long-term production requirements for crops and other food phytomass. This paper presents estimates of current efficiency and phytomass appropriation of major food commodities, performed by modeling all principal flows of biomass in the food system. Estimated overall efficiencies varied from 0.35% for beef cattle meat, to 31% for starchy root tubers (global averages, gross energy basis). The results indicate that there is a most substantial potential for efficiency improvements within the animal food sector, particularly for ruminant systems in non-industrialized regions. It is also concluded that a considerable reduction of the phytomass production might be achieved by shifts in diet, even if assuming no changes with respect to the total share of meat in the diet.
Differences in the seasonal pattern of assimilatory and respiratory processes are responsible for divergences in seasonal net carbon exchange among ecosystems. Using FLUXNET data (http://www.eosdis.ornl.gov/FLUXNET) we have analyzed seasonal patterns of gross primary productivity (FGPP), and ecosystem respiration (FRE) of boreal and temperate, deciduous and coniferous forests, Mediterranean evergreen systems, a rainforest, temperate grasslands, and C3 and C4 crops. Based on generalized seasonal patterns classifications of ecosystems into vegetation functional types can be evaluated for use in global productivity and climate change models. The results of this study contribute to our understanding of respiratory costs of assimilated carbon in various ecosystems.Seasonal variability of FGPP and FRE of the investigated sites increased in the order . Together with the boreal forest sites, the managed grasslands and crops show the largest seasonal variability. In the temperate coniferous forests, seasonal patterns of FGPP and FRE are in phase, in the temperate deciduous and boreal coniferous forests FRE was delayed compared to FGPP, resulting in the greatest imbalance between respiratory and assimilatory fluxes early in the growing season.FGPP adjusted for the length of the carbon uptake period decreased at the sampling sites across functional types in the order C4 crops, temperate and boreal deciduous forests conifers, C3 grassland and crops conifers (4.6 g C m−2 per day). Annual FGPP and net ecosystem productivity (FNEP) decreased across climate zones in the order tropical>temperate>boreal. However, the decrease in FNEP with latitude was greater than the decrease in FGPP, indicating a larger contribution of respiratory (especially heterotrophic) processes in boreal systems.
The atmospheric CO2 concentration is increasing, due primarily to fossil-fuel combustion and deforestation. Sequestering atmospheric C in agricultural soils is being advocated as a possibility to partially offset fossil-fuel emissions. Sequestering C in agriculture requires a change in management practices, i.e. efficient use of pesticides, irrigation, and farm machinery. The C emissions associated with a change in practices have not traditionally been incorporated comprehensively into C sequestration analyses. A full C cycle analysis has been completed for agricultural inputs, resulting in estimates of net C flux for three crop types across three tillage intensities. The full C cycle analysis includes estimates of energy use and C emissions for primary fuels, electricity, fertilizers, lime, pesticides, irrigation, seed production, and farm machinery. Total C emissions values were used in conjunction with C sequestration estimates to model net C flux to the atmosphere over time. Based on US average crop inputs, no-till emitted less CO2 from agricultural operations than did conventional tillage, with 137 and 168 kg C ha−1 per year, respectively. Changing from conventional tillage to no-till is therefore estimated to both enhance C sequestration and decrease CO2 emissions. While the enhanced C sequestration will continue for a finite time, the reduction in net CO2 flux to the atmosphere, caused by the reduced fossil-fuel use, can continue indefinitely, as long as the alternative practice is continued. Estimates of net C flux, which are based on US average inputs, will vary across crop type and different climate regimes. The C coefficients calculated for agricultural inputs can be used to estimate C emissions and net C flux on a site-specific basis.
A crop managed in a traditional way was monitored over a complete sugar beet/winter wheat/potato/winter wheat rotation cycle from 2004 to 2008. Eddy covariance, automatic and manual soil chamber, leaf diffusion and biomass measurements were performed continuously in order to obtain the daily and seasonal Net Ecosystem Exchange (NEE), Gross Primary Productivity (GPP), Total Ecosystem Respiration (TER), Net Primary Productivity (NPP), autotrophic respiration, heterotrophic respiration and Net Biome Production (NBP). The results showed that GPP and TER were subjected to important inter-annual variability due to differences between crops and to climate variability. A significant impact of intercrop assimilation and of some farmer interventions was also detected and quantified. Notably, the impact of ploughing was found to be limited in intensity (1–2 μmol m−2 s−1) and duration (not more than 1 day). Seasonal budgets showed that, during cropping periods, the TER/GPP ratio varied between 40 and 60% and that TER was dominated mainly by the autotrophic component (65% of TER and more). Autotrophic respiration was closely related to GPP during the growth period. The whole cycle budget showed that NEE was negative and the rotation behaved as a sink of 1.59 kgC m−2 over the 4-year rotation. However, if exports are deducted from the budget, the crop became a small source of 0.22 (±0.14) kgC m−2. The main causes of uncertainty with these results were due to biomass samplings and eddy covariance measurements (mainly, uncertainties about the u* threshold determination). The positive NBP also suggested that the crop soil carbon content decreased. This could be explained by the crop management, as neither farmyard manure nor slurry had been applied to the crop for more than 10 years and because cereal straw had been systematically exported for livestock. The results were also strongly influenced by the particular climatic conditions in 2007 (mild winter, and dry spring) that increased the fraction of biomass returned to the soil at the expense of harvested biomass, and therefore mitigated the source intensity. If 2007 had been a ‘normal’ year, this intensity would have been twice as great. This suggests that, in general, the rotation behaved as a small carbon source, which accords with similar studies based on multi-year eddy covariance measurements and export assessment and with modelling or inventory studies analysing the evolution of crop soil organic carbon (SOC) on a decennial scale.
It is widely believed that soil disturbance by tillage was a primary cause of the historical loss of soil organic carbon (SOC) in North America, and that substantial SOC sequestration can be accomplished by changing from conventional plowing to less intensive methods known as conservation tillage. This is based on experiments where changes in carbon storage have been estimated through soil sampling of tillage trials. However, sampling protocol may have biased the results. In essentially all cases where conservation tillage was found to sequester C, soils were only sampled to a depth of 30 cm or less, even though crop roots often extend much deeper. In the few studies where sampling extended deeper than 30 cm, conservation tillage has shown no consistent accrual of SOC, instead showing a difference in the distribution of SOC, with higher concentrations near the surface in conservation tillage and higher concentrations in deeper layers under conventional tillage. These contrasting results may be due to tillage-induced differences in thermal and physical conditions that affect root growth and distribution. Long-term, continuous gas exchange measurements have also been unable to detect C gain due to reduced tillage. Though there are other good reasons to use conservation tillage, evidence that it promotes C sequestration is not compelling.