Journal Paper Digests

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Journal Paper Digests 2022 #17

  • An evaluation of carbon indicators of soil health in long-term agricultural experiments
  • A new criterion based on estimator variance for model sampling in precision agriculture
  • Forest and Freshwater Ecosystem Responses to Climate Change and Variability at US LTER Sites
  • Cross-Site Comparisons of Dryland Ecosystem Response to Climate Change in the US Long-Term Ecological Research Network
  • Responses of Coastal Ecosystems to Climate Change: Insights from Long-Term Ecological Research
  • Long-Term Ecological Research on Ecosystem Responses to Climate Change
  • Spatiotemporal assessments of nutrients and water quality in coastal areas using remote sensing and a spatiotemporal deep learning model
  • Tillage strategies optimize SOC distribution to reduce carbon footprint

Tillage strategies optimize SOC distribution to reduce carbon footprint

Tillage methods and nitrogen (N) application are critical for soil organic carbon (SOC) sequestration and crop production. However, both tillage and N are the main contributors to the carbon footprint (CF) in agricultural production. A 6-year-long field experiment was conducted under a winter wheat-summer maize cropping system in Northern China to test how tillage methods (RT, annual rotary tillage; DT, annual deep tillage; and TT, RT applied annually with a DT interval of two years) and N rates (300 kg ha(-1), N300; 225 kg ha(-1), N225; 165 kg ha(-1), N165) affect SOC sequestration, greenhouse gas (GHG) emissions, and annual grain yield. And, the CF was used to evaluate ecological sustainability. RT preferentially sequestrated SOC in the 0-10 cm soil layer. In the 10-30 cm soil layers, 2.87-3.82 and 1.85-2.53 Mg ha(-1) greater SOC were respectively observed under DT and TT than RT, which was conducive to maximizing annual grain yield with less N application (N225) relative to traditional farming practice (RT-N300). Both increasing N rate and deep tillage resulted in obvious increases in the total GHG emissions. N fertilizer production and transportation were the greatest contributors, accounting for 40.4-47.0% of the total GHG emissions, followed by direct N2O and CH4 emissions (23.5-30.5%). TT-N225 significantly reduced CF (CF including SOC sequestration was 0.49 Mg CO2 eq ha(-1) year(-1) lower than DT -N225, and was 1.87 Mg CO2 eq ha(-1) year(-1) lower than RT-N300) and maintained high crop productivity while creating appropriate soil conditions and thus may be a much cleaner agricultural strategy in Northern China. And, the strategy of reducing N application in agricultural production by improving soil properties of the plow layer in this study can also be referred to in other ecological regions.

Spatiotemporal assessments of nutrients and water quality in coastal areas using remote sensing and a spatiotemporal deep learning model

Revealing the spatiotemporal variations of nutrients in coastal waters is crucial to the understanding and evaluation of coastal environment, thereby providing efficient guidance for the aquatic environmental treatment. This study proposed a spatiotemporal-incorporated deep learning model, which is easily applicable to establish the quantitative relationships between measured environmental factors and large-scale satellite maps, and can reduce estimation errors by more than 40% compared with non-spatiotemporal-incorporated deep learning model. The spatiotemporal distributions of dissolved inorganic nitrogen (DIN) and dissolved inorganic phosphate (DIP) over 44400 km(2) of the East China Sea on 8-day scale from 2010 to 2018 were obtained. Based on the spatiotemporal variations, the water quality patterns were depicted, and the fluctuation variations of the two essential nutrients were found in the harbors with complex anthropogenic influences, in the typical estuaries with multiple river inputs, and in the open seas with important fisheries. Although the concentration of DIN and DIP decreased by 24% and 19% in 9 years, respectively, the water quality level in the inshore sea has not been significantly improved, especially in autumn and winter. Further, we quantitatively analyzed the main factors of deteriorated water and provided scientific suggestions for targeted monitoring and regional cooperative governances.

Long-Term Ecological Research on Ecosystem Responses to Climate Change

In this article marking the 40th anniversary of the US National Science Foundation’s Long Term Ecological Research (LTER) Network, we describe how a long-term ecological research perspective facilitates insights into an ecosystem’s response to climate change. At all 28 LTER sites, from the Arctic to Antarctica, air temperature and moisture variability have increased since 1930, with increased disturbance frequency and severity and unprecedented disturbance types. LTER research documents the responses to these changes, including altered primary production, enhanced cycling of organic and inorganic matter, and changes in populations and communities. Although some responses are shared among diverse ecosystems, most are unique, involving region-specific drivers of change, interactions among multiple climate change drivers, and interactions with other human activities. Ecosystem responses to climate change are just beginning to emerge, and as climate change accelerates, long-term ecological research is crucial to understand, mitigate, and adapt to ecosystem responses to climate change.

Responses of Coastal Ecosystems to Climate Change: Insights from Long-Term Ecological Research

Coastal ecosystems play a disproportionately large role in society, and climate change is altering their ecological structure and function, as well as their highly valued goods and services. In the present article, we review the results from decade-scale research on coastal ecosystems shaped by foundation species (e.g., coral reefs, kelp forests, coastal marshes, seagrass meadows, mangrove forests, barrier islands) to show how climate change is altering their ecological attributes and services. We demonstrate the value of site-based, long-term studies for quantifying the resilience of coastal systems to climate forcing, identifying thresholds that cause shifts in ecological state, and investigating the capacity of coastal ecosystems to adapt to climate change and the biological mechanisms that underlie it. We draw extensively from research conducted at coastal ecosystems studied by the US Long Term Ecological Research Network, where long-term, spatially extensive observational data are coupled with shorter-term mechanistic studies to understand the ecological consequences of climate change.

Cross-Site Comparisons of Dryland Ecosystem Response to Climate Change in the US Long-Term Ecological Research Network

Long-term observations and experiments in diverse drylands reveal how ecosystems and services are responding to climate change. To develop generalities about climate change impacts at dryland sites, we compared broadscale patterns in climate and synthesized primary production responses among the eight terrestrial, nonforested sites of the United States Long-Term Ecological Research (US LTER) Network located in temperate (Southwest and Midwest) and polar (Arctic and Antarctic) regions. All sites experienced warming in recent decades, whereas drought varied regionally with multidecadal phases. Multiple years of wet or dry conditions had larger effects than single years on primary production. Droughts, floods, and wildfires altered resource availability and restructured plant communities, with greater impacts on primary production than warming alone. During severe regional droughts, air pollution from wildfire and dust events peaked. Studies at US LTER drylands over more than 40 years demonstrate reciprocal links and feedbacks among dryland ecosystems, climate-driven disturbance events, and climate change.

Forest and Freshwater Ecosystem Responses to Climate Change and Variability at US LTER Sites

Forest and freshwater ecosystems are tightly linked and together provide important ecosystem services, but climate change is affecting their species composition, structure, and function. Research at nine US Long Term Ecological Research sites reveals complex interactions and cascading effects of climate change, some of which feed back into the climate system. Air temperature has increased at all sites, and those in the Northeast have become wetter, whereas sites in the Northwest and Alaska have become slightly drier. These changes have altered streamflow and affected ecosystem processes, including primary production, carbon storage, water and nutrient cycling, and community dynamics. At some sites, the direct effects of climate change are the dominant driver altering ecosystems, whereas at other sites indirect effects or disturbances and stressors unrelated to climate change are more important. Long-term studies are critical for understanding the impacts of climate change on forest and freshwater ecosystems.

An evaluation of carbon indicators of soil health in long-term agricultural experiments

Soil organic carbon (SOC) is closely tied to soil health. However, additional biological indicators may also provide insight about C dynamics and microbial activity. We used SOC and the other C indicators (potential C mineralization, permanganate oxidizable C, water extractable organic C, and beta-glucosidase enzyme activity) from the North American Project to Evaluate Soil Health Measurements to examine the continental-scale drivers of these indicators, the relationships among indicators, and the effects of soil health practices on indicator values. All indicators had greater values at cooler temperatures, and most were greater with increased precipitation and clay content. The indicators were strongly correlated with each other at the site-level, with the strongest relationship between SOC and permanganate oxidizable C. The indicator values responded positively to decreased tillage, inclusion of cover crops, application of organic nutrients, and retention of crop residue, but not the number of harvested crops in a rotation. The effect of decreased tillage on the C indicators was generally greater at sites with higher precipitation. The magnitude and direction of the response to soil health practices was consistent across indicators within a site but measuring at least two indicators would provide additional confi-dence of the effects of management, especially for tillage. All C indicators responded to management, an essential criterion for evaluating soil health. Balancing the cost, sensitivity, interpretability, and availability at commercial labs, a 24-hr potential C mineralization assay could deliver the most benefit to measure in conjunction with SOC.

A new criterion based on estimator variance for model sampling in precision agriculture

Model sampling has proven to be an interesting approach to optimize the sampling of an agronomic variable of interest at the field level. The use of a model improves the quality of the estimates by making it possible to integrate the information provided by one or more auxiliary data. It has been shown that such an approach gives better estimations compared to more traditional approaches.Through a statistical work describing the properties of model sampling variance, this paper details how the different factors either related to sample characteristics or to the correlation between the auxiliary data and the variable of interest, affect estimation error. The resulting equations show that the use of samples with a mean close to the field mean and with a substantial dispersion reduces the estimation variance. On the basis of these statistical considerations, a variance criterion is defined to compare sample properties. The lower the value of the criterion of a sample, the lower the variance of the estimate and the expected errors. These theoretical insights were applied to real commercial vine fields in order to validate the demonstration.Nine vine fields were considered with the objective to provide the best yield estimation. High resolution vegetative index derived from airborne multispectral image was used to drive the sampling and the estimation. The theoretical considerations were verified on the nine fields; as the observed estimation errors correspond quite well to the values predicted by the equations. The selection of a large number of random samples from these fields confirms that samples associated with higher values of the chosen criterion result, on average, in larger yield estimation errors. Samples with the highest criterion values are associated with mean estimation errors up to two times larger than those of average samples. Random sampling is also compared to two target sampling approaches (Clustering based on quantiles or on k-means algorithm) commonly considered in the literature, whose characteristics improve the value of the proposed criterion. It is shown that these sampling strategies produce samples associated with criterion values up to 100 times smaller than random sampling. The use of these easy-to-implement methods thus guarantees to reduce the variance of the estimation and the esti-mation errors.

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