• Energy Research
• 15. Life on land close
• CN close
• University of North Texas close

Quantification of the trade-offs among greenhouse gas (GHG) emissions, yield, and farmers’ incomes is essential for proposing economic and environmental nitrogen (N) management strategies for optimizing agricultural production. A four-year (2017–2020) field experiment (including four treatments: basic N fertilizer treatment (BF), suitable utilization of fertilization (SU), emission reduction treatment (ER), and high fertilization (HF)) was conducted on maize (Zea mays L.) in the North China Plain. The Life Cycle Assessment (LCA) method was used in this study to quantify the GHG emissions and farmers’ incomes during the whole maize production process. The total GHG emissions of BF, SU, ER, and HF treatments in the process of maize production are 10,755.2, 12,908.7, 11,950.1, and 14,274.5 kg CO2-eq ha−1, respectively, of which the direct emissions account for 84.8%, 76.8%, 74.9%, and 71.0%, respectively. Adding inhibitors significantly reduced direct GHG emissions, and the N2O and CO2 emissions from the maize fields in the ER treatment decreased by 30.0% and 7.9% compared to those in the SU treatment. Insignificant differences in yield were found between the SU and ER treatments, indicating that adding fertilizer inhibitors did not affect farmers’ incomes while reducing GHG emissions. The yield for SU, ER, and HF treatments all significantly increased by 12.9–24.0%, 10.0–20.7%, and 2.1–17.4% compared to BF, respectively. In comparison with BF, both SU and ER significantly promoted agricultural net profit (ANP) by 16.6% and 12.2%, with mean ANP values of 3101.0 USD ha−1 and 2980.0 USD ha−1, respectively. Due to the high agricultural inputs, the ANP values in the HF treatment were 11.2%, 16.6%, and 12.4% lower than those in the SU treatment in 2018–2020. In conclusion, the combination of N fertilizer and inhibitors proved to be an environmentally friendly, high-profit, and low-emissions production technology while sustaining or even increasing maize yields in the North China Plain, which was conducive to achieving agricultural sustainability.

The recent widespread mountain pine beetle (MPB) outbreak in the Southern Rocky Mountains presents an opportunity to investigate the relative influence of anthropogenic, biologic, and physical drivers that have shaped the spatiotemporal patterns of the outbreak. The aim of this study was to quantify the landscape-level drivers that explained the dynamic patterns of MPB mortality, and simulate areas with future potential MPB mortality under projected climate-change scenarios in Grand County, Colorado, USA. The outbreak patterns of MPB were characterized by analysis of a decade-long Landsat time-series stack, aided by automatic attribution of change detected by the Landsat-based Detection of Trends in Disturbance and Recovery algorithm (LandTrendr). The annual area of new MPB mortality was then related to a suite of anthropogenic, biologic, and physical predictor variables under a general linear model (GLM) framework. Data from years 2001–2005 were used to train the model and data from years 2006–2011 were retained for validation. After stepwise removal of non-significant predictors, the remaining predictors in the GLM indicated that neighborhood mortality, winter mean temperature anomaly, and residential housing density were positively associated with MPB mortality, whereas summer precipitation was negatively related. The final model had an average area under the curve (AUC) of a receiver operating characteristic plot value of 0.72 in predicting the annual area of new mortality for the independent validation years, and the mean deviation from the base maps in the MPB mortality areal estimates was around 5%. The extent of MPB mortality will likely expand under two climate-change scenarios (RCP 4.5 and 8.5) in Grand County, which implies that the impacts of MPB outbreaks on vegetation composition and structure, and ecosystem functioning are likely to increase in the future.

Large numbers of plant cell-wall (CW)-related genes have been identified or predicted in several plant genomes such as Arabidopsis thaliana, Oryza sativa (rice), and Zea mays (maize), as results of intensive studies of these organisms in the past 2 decades. However, no such gene list has been identified in switchgrass (Panicum virgatum), a key bioenergy crop. Here, we present a computational study for prediction of CW genes in switchgrass using a two-step procedure: (i) homology mapping of all annotated CW genes in the fore-mentioned species to switchgrass, giving rise to a total of 991 genes, and (ii) candidate prediction of CW genes based on switchgrass genes co-expressed with the 991 genes under a large number of experimental conditions. Specifically, our co-expression analyses using the 991 genes as seeds led to the identification of 104 large clusters of co-expressed genes, each referred to as a co-expression module (CEM), covering 830 of the 991 genes plus 823 additional genes that are strongly co-expressed with some of the 104 CEMs. These 1653 genes represent our prediction of CW genes in switchgrass, 112 of which are homologous to predicted CW genes in Arabidopsis. Functional inference of these genes is conducted to derive the possible functional relations among these predicted CW genes. Overall, these data may offer a highly useful information source for cell-wall biologists of switchgrass as well as plants in general.

Remnants of native tallgrass prairie experience elevated atmospheric nitrogen (N) deposition in urban areas, with potential effects on species traits that are important for N cycling and species composition. We quantified bulk (primarily wet) inorganic N (NH4+-N + NO3--N) deposition at six sites along an urban development gradient (6–64% urban) in the Dallas-Fort Worth metropolitan area from April 2014 to October 2015. In addition, we conducted a phytometer experiment with two common native prairie bunchgrass species––one well studied (Schizachyrium scoparium) and one little studied (Nasella leucotricha)––to investigate ambient N deposition effects on plant biomass and tissue quality. Bulk inorganic N deposition ranged from 6.1–9.9 kg ha-1 yr-1, peaked in spring, and did not vary consistently with proportion of urban land within 10 km of the sites. Total (wet + dry) inorganic N deposition estimated using bulk deposition measured in this study and modeled dry deposition was 12.9–18.2 kg ha-1 yr-1. Although the two plant species studied differ in photosynthetic pathway, biomass, and tissue N, they exhibited a maximum 2-3-fold and 2-4-fold increase in total biomass and total plant N, respectively, with 1.6-fold higher bulk N deposition. In addition, our findings indicate that while native prairie grasses may exhibit a positive biomass response to increased N deposition up to ~18 kg ha-1 yr-1, total inorganic N deposition is well above the estimated critical load for herbaceous plant species richness in the tallgrass prairie of the Great Plains ecoregion and thus may negatively affect these plant communities.

Dense high voltage power transmission lines limit human living space and affect the natural landscape and environment. Concerns about the environment in the corridor of high voltage power transmission lines and the occupied land area need to be addressed. Trees are a dissipative medium that can affect polarization in electric fields, leading to excitation of the induced electric field and shielding the impact of electric fields. A mathematical model for the electric field calculation of trees was established by selecting, mapping, and calculating the larch tree species in an experimental forest field at Northeast Forestry University, Heilongjiang Province, China. This study used the finite element method of equivalent excitation sources and the equivalent tree model to estimate the electric field shielding effectiveness of trees. The shielding effect of trees on the electric field of high voltage transmission lines was obvious. Planting trees reduced the transmission lines corridor width by 50.7%, and the maximum electric field in the ground decreased by 95.9%. Thus, planting trees to reduce the electric field of power transmission lines was more effective than the currently widely used method of erecting wires.

Global warming and dimming/brightening have significant implications for crop systems and exhibit regional variations. It is important to clarify the changes in regional thermal and solar radiation resources and estimate the impacts on potential crop production spatially and temporally. Based on daily observation data during 1961–2015 in the North China Plain (NCP), the impacts of climate change associated with climate warming and global dimming/brightening on potential light–temperature productivity (PTP) of summer maize were assessed in this study. Results show that the NCP experienced a continuous warming and dimming trend in maize growing season during the past 55 years, and both ATT10 and solar radiation had an abrupt change in the mid-1990s. The period of 2000–2015 was warmer and dimmer than any other previous decade. Assuming the maize growing season remains unchanged, climate warming would increase PTP of summer maize by 4.6% over the period of 1961–2015, which mainly occurred in the start grain filling–maturity stage. On the other hand, as negative contribution value of solar radiation to the PTP was found in each stage, dimming would offset the increase of PTP due to warming climate, and lead to a 15.6% reduction in PTP in the past 55 years. This study reveals that the changes in thermal and solar radiation have reduced the PTP of summer maize in the NCP. However, the actual maize yield could benefit more from climate warming because solar radiation is not a limiting factor for the current low production level.

AbstractIndividual tree structure mapping in cities is important for urban environmental studies. Despite mapping products for tree canopy cover and biomass are reported at multiple spatial scales using various approaches, spatially explicit mapping of individual trees and their three-dimensional structure is sparse. Here we produced an individual tree dataset including tree locations, height, crown area, crown volume, and biomass over the entire New York City, USA for 6,005,690 trees. Individual trees were detected and mapped from remotely sensed datasets along with their height and crown size information. Tree biomass in 296 field plots was measured and modelled using i-Tree Eco. Wall-to-wall tree biomass was mapped using relationships between field measurements and remotely sensed datasets and downscaled to individual trees. Validation using field-plot measurements indicated that our mapping products overestimated tree number, mean tree height and maximum tree height by 11.1%, 8.6%, and 5.3%, respectively. These overestimations were mainly due to the spatial and temporal mis-match between field measurements and remote sensing observations and uncertainties in tree segmentation algorithms. This dataset enables the evaluation of urban forest ecosystem services including regulating urban heat and promoting urban health, which can provide valuable insights for urban forest management and policy making.

The water and energy in the land surface and lower atmosphere have a strong coupling relationship. Apart from the land surface temperature (Ts) and air temperature (Ta), the land surface-air temperature difference (Ts-Ta) is also an essential parameter reflecting the coupling process. However, the global spatiotemporal variations and influencing factors of Ts-Ta remain not well explored. Here, ERA5-land reanalysis data, GIMMS NDVI data, and elevation data were used to analyze the global spatiotemporal heterogeneity and influencing factors of Ts-Ta. It was found that annual mean Ts-Ta exhibited a decreasing trend from the equator to polar areas. And the annual Ts-Ta increased at 0.009 °C/10a from 1981 to 2020. The variations of global net radiation mainly determined the spatiotemporal heterogeneity of global Ts-Ta. The different properties of the land surface and near-surface atmosphere were the main factors affecting the Ts-Ta, including soil moisture, vegetation, snow cover, and the water vapor content in the atmosphere. In addition, Ts and Ta also affected each other. These findings are conducive to a better understanding of the land-atmosphere coupling, and it is of great significance to take better measures to adapt the global climate change.