Quantifying Crop Yield, Bioenergy Production And Greenhouse Gas Emissions From Cropland And Marginal Land Using A Model-Data Fusion Approach

Bioenergy is becoming increasingly attractive to many countries, but has sparked an intensive debate regarding energy, economy, society and environment. Biofuels provide alternative energy to conventional fossil fuels. However, the environmental impact of producing and using biofuel is a major concern to our society. This study is dedicated to quantifying and evaluating biofuel production and potential climate change mitigation due to potential large-scale bioenergy expansion in the conterminous United States, using model-data fusion approaches. Biofuel made from conventional (e.g., maize (Zea mays L.)) and cellulosic crops (e.g., switchgrass (Panicum virgatum L.) and Miscanthus (Miscanthus × giganteus)) provides alternative energy to fossil fuels and has been considered to mitigate greenhouse gas emissions. To estimate the large-scale carbon and nitrogen dynamics of these biofuel ecosystems, process-based models are needed. Here, we developed an agroecosystem model (AgTEM) based on the Terrestrial Ecosystem Model for these ecosystems. The model incorporated biogeochemical and ecophysiological processes including crop phenology, biomass allocation, nitrification and denitrification as well as agronomic management of irrigation and fertilization. It was used to estimate crop yield, biomass, net carbon exchange, and nitrous oxide (N2O) emissions at an ecosystem level. We found that AgTEM reproduces the observed annual net primary production and N2O emissions of most sites, with over 85% of total variations explained by the model. Local sensitivity analysis indicated that the model sensitivity varies among different ecosystems. Net primary production of maize is sensitive to temperature, precipitation, cloudiness, fertilizer and irrigation and less sensitive to atmospheric carbon dioxide (CO2) concentrations. In contrast, the net primary production of switchgrass and Miscanthus is most sensitive to temperature among all factors. The N2O emissions are sensitive to management in maize ecosystems, and sensitive to climate factors in cellulosic ecosystems. The developed model should help advance our understanding of carbon and nitrogen dynamics of these biofuel ecosystems at both field and regional scales. Next, we estimated the potential emissions of greenhouse gases from bioenergy ecosystems with AgTEM, assuming maize, switchgrass and Miscanthus will be grown on the current maize-producing areas in the conterminous United States. The modeling experiments suggested that, the maize ecosystem acts as a mild net carbon source while cellulosic ecosystems (i.e., switchgrass and Miscanthus) act as mild sinks. Nitrogen fertilizer use is an important factor affecting biomass production and N2O emissions, especially in the maize ecosystem. To maintain high biomass productivity, the maize ecosystem emits much more greenhouse gases, including CO2 and N2O, than switchgrass and Miscanthus cosystems, when high-rate nitrogen fertilizers are applied. For maize, the global warming potential amounts to 1-2 Mg CO2eq ha-1 yr-1, with a dominant contribution of over 90% from N2O emissions. Cellulosic crops contribute to the global warming potential of less than 0.3 Mg CO2eq ha-1 yr-1. Among all three bioenergy crops, Miscanthus is the most biofuel productive and the least GHG intensive at a given cropland. Regional model simulations suggested that, substituting Miscanthus for maize to produce biofuel could potentially save land and reduce GHG emissions. Since growing biomass from marginal lands is becoming an increasingly attractive choice for producing biofuel, we looked further into bioenergy potential and possible GHG emissions from bioenergy crops grown on marginal lands in the United States. Two broadly tested cellulosic crops, switchgrass and Miscanthus, were assumed to be grown on the abandoned land and mixed crop-vegetation land with marginal productivity. Production of biomass and biofuel as well as net carbon exchange and N2O emissions were estimated in a spatially explicit manner, using AgTEM. Modeling experiments showed that, cellulosic crops, especially Miscanthus, could produce a considerable amount of biomass and thus ethanol. For every hectare of marginal land, switchgrass and Miscanthus could produce 1.4-2.3 kL and 4.1-6.9 kL ethanol, respectively. The actual amount of ethanol production depends on nitrogen fertilization rate and biofuel conversion efficiency. Switchgrass has high global warming intensity (100-190 g CO2eq L-1 ethanol), in terms of GHG emissions per unit ethanol produced. Miscanthus, however, emits only 21-36 g CO2eq to produce every liter of ethanol. To reach the mandated cellulosic ethanol target of 21 billion gallons by 2022 in the United States, growing Miscanthus on the marginal lands could save a large amount of land and reduce GHG emissions in comparison to growing switchgrass. It should be noted that, ecosystem modeling may be useful for evaluating ecosystem services and environmental impacts, and the results could be informative for policy making concerning energy, food security and sustainability. However, the modeling results are limited in terms of advising agricultural management practices, land use change and energy system analysis, due to modeling uncertainties, data unavailability, and simulation scale and boundary limitations. High-accuracy data assimilation, model improvement and life cycle assessment still await future study

Animal waste use and implications to agricultural greenhouse gas emissions in the United States

Acknowledgements: Z. Q. and S. D. have been partially supported by the National Basic Research Program of China (2016YFA0602701), the National Natural Science Foundation of China (41975113), and the Guangdong Provincial Department of Science and Technology (2019ZT08G090). The input of P. S. contributed to the following projects: DEVIL (NE/M021327/1) and Soils-R-GRREAT (NE/P019455/1). Data availability: The data that support the findings of this study are openly available at the following URL/DOI: https://greet.es.anl.gov/. Publisher Copyright: © 2021 The Author(s). Published by IOP Publishing Ltd. Creative Commons Attribution 4.0 license, Original content from this work may be used under the terms of the . Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.Peer reviewedPublisher PD

Delayed impact of natural climate solutions

Acknowledgement: This work was supported by the National Basic Research Program of China (2016YFA0602701), the National Natural Science Foundation of China (41975113; 91937302), and the Guangdong Provincial Department of Science and Technology (2019ZT08G090). We appreciate the support from the China Association for Science and Technology Working Group for UN Environment Consultation. The authors declare no conflict of interests.Peer reviewedPostprin

Robust paths to net greenhouse gas mitigation and negative emissions via advanced biofuels

ACKNOWLEDGEMENTS We thank Dennis Ojima and Daniel L. Sanchez for their encouragement on this topic. The authors gratefully acknowledge partial support as follows: J.L.F., L.R.L., T.L.R., E.A.H.S., and J.J.S., the Sao Paulo Research Foundation (FAPESP grant# 2014/26767-9); J.L.F., L.R.L., K.P., and T.L.R., The Center for Bioenergy Innovation, a U.S. Department of Energy Research Center supported by the Office of Biological and Environmental Research in the DOE Office of Science (grant# DE-AC05-00OR22725); L.R.L., the Sao Paulo Research Foundation, and the Link Foundation; J.L.F. and K.P., USDA/NIFA (grant# 2013-68005-21298 and 2017-67019-26327); T.L.R., USDA/NIFA (grant# 2012-68005-19703); D.S.L. and S.P.L., the Energy Biosciences Institute. Data availability The DayCent model (https://www2.nrel.colostate.edu/projects/daycent/) is freely available upon request. Specification of DayCent model runs and automated model initialization, calibration, scenario simulation, results analysis, and figure generation were implemented in Python 2.7, using the numpy module for data processing and the matplotlib module for figure generation. Analysis code is available in a version-controlled repository (https://github.com/johnlfield/Ecosystem_dynamics). A working copy of the code, all associated DayCent model inputs, and analysis outputs are also available in an online data repository (https://figshare.com/s/4c14ec168bd550db4bad; note this URL is for accessing a private version of the repository, and will eventually be replaced with an updated URL for the public version of the repository, which will only be accessible after the journal-specified embargo date).Peer reviewedPostprintPublisher PD

A global, empirical, harmonised dataset of soil organic carbon changes under perennial crops

A global, unified dataset on Soil Organic Carbon (SOC) changes under perennial crops has not existed till now. We present a global, harmonised database on SOC change resulting from perennial crop cultivation. It contains information about 1605 paired-comparison empirical values (some of which are aggregated data) from 180 different peer-reviewed studies, 709 sites, on 58 different perennial crop types, from 32 countries in temperate, tropical and boreal areas; including species used for food, bioenergy and bio-products. The database also contains information on climate, soil characteristics, management and topography. This is the first such global compilation and will act as a baseline for SOC changes in perennial crops. It will be key to supporting global modelling of land use and carbon cycle feedbacks, and supporting agricultural policy development

Soil organic carbon (SOC) dynamic of China's croplands over the past 40 years

Soil organic carbon (SOC) dynamic of China's croplands over the past 40 years, estimated by this publication:Lin Z, Xu Y, Lu X, Sun W, Yu Y, Zhang W, Mishra U, Kuzyakov Y, Wang G, Qin Z. Straw return to croplands has been a major determining factor in the increase of soil organic carbon in China’s croplands over the past 40 years. </p

Soil organic carbon sequestration potential of cropland in China

Soil organic carbon (SOC) in cropland is of great importance to the global carbon (C) balance and to agricultural productivity, but it is highly sensitive to human activities such as irrigation and crop rotation. It has been observed that under certain improved management practices, cropland soils can sequestrate additional C beyond their existing SOC level before reaching the C saturation state. Here we use data from worldwide, long-term agricultural experiments to develop two statistical models to determine the saturated SOC level (SOCS) in upland and paddy agroecosystems, respectively. We then use the models to estimate SOC sequestration potential (SOCP) in Chinese croplands. SOCP is the difference between SOCS and existing SOC level (SOCE). We find that the models for both the upland and paddy agroecosystems can reproduce the observed SOCS data from long-term experiments. The SOCE and SOCS stock in Chinese upland and paddy croplands (0-30cm soil) are estimated to be 5.2 and 7.9 Pg C with national average densities of 37.4 and 56.8Mg C ha(-1), respectively. As a result, the total SOC sequestration potential is estimated to be 2.7 Pg C or 19.4Mg C ha(-1) in Chinese cropland. Paddy has a relatively higher SOCE (45.4Mg C ha(-1)) than upland (34.7Mg C ha(-1)) and also a greater SOCP at 26.1Mg C ha(-1) compared with 17.2Mg C ha(-1) in the upland. The SOC varies dramatically among different regions. Northeast China has the highest SOCE and SOCS density, while the Loess Plateau has the greatest SOCP density. The time required to reach SOC saturation in Chinese cropland is highly dependent on management practices applied. Chinese cropland has relatively low SOC density in comparison to the global average but could have great potentials for C sequestration under improved agricultural management strategies

Soil indigenous nutrients increase the resilience of maize yield to climatic warming in China

Climate warming leads to crop yield loss. Although investigations have shown the region-specific effect of climate warming on maize yield in China, the determinants of this region-specific effect are poorly known. Using county-level data from 1980 to 2010 for China, we investigated the dependence of yield change under climate warming on soil indigenous nutrients. Analysis of the data indicated an average decrease of 2.6% in maize yield for 1 degrees C warming. Warming-related yield loss occurred mostly in western China, the North China Plain, and the southwest region of Northeast China. By contrast, climate warming did not decline maize yield in the northern region of Northeast China, south, and southwest China. Summer maize is more sensitive to warming than spring maize. A 1 degrees C warming resulted in an average loss of 3.3% for summer maize and 1.8% for spring maize. The region-specific change in yield can be well quantified by a combination of soil indigenous total nitrogen (STN), available phosphorus (SAP), and available potassium (SAK). Under climate warming, maize yields in regions with high STN generally increased, while the risk of yield reduction appeared in regions with high SAK. Areas that were vulnerable (defined as a yield loss higher than 1% for a 1 degrees C increase) to climate warming accounted for 62%, while areas that showed resilience (defined as a yield increase higher than 1% for a 1 degrees C increase) to climate warming accounted for 27% of the planting area. An increase in nitrogen fertilizer application is expected to reduce the risk of yield reduction in regions with low STN. Our findings highlight soil resilience to climate warming and underline the practice of fertilizer management to mitigate yield loss due to climate warming