Journal Paper Digests

Reading time ~10 minutes

Journal Paper Digests 2021 #41

  • The Four Ways to Consider Measurement Noise in Bayesian Model Selection-And Which One to Choose
  • Spatial-temporal characteristics of ecosystem health in Central Asia
  • Responses of soil pH to no-till and the factors affecting it: A global meta-analysis
  • Distribution of soil organic matter fractions are altered with soil priming
  • Soil rock fragments: Unquantified players in terrestrial carbon and nitrogen cycles
  • Protect, manage and then restore lands for climate mitigation
  • Soil quality and grain yield: A win-win combination in clayey tropical oxisols

Soil quality and grain yield: A win-win combination in clayey tropical oxisols

Relationships between a soil quality index (SQI(FERTBIO)) and soil functions with both relative cumulative grain yields (RCY) and soil organic carbon (SOC), were evaluated in a tropical clayey Cerrado Oxisol. Soil quality (SQ) was quantified integrating chemical (FERT) and biological (BIO) indicators in a weighted-additive soil quality index combining three soil functions and respective indicators: (F1) nutrient cycling (based on the activities of soil enzymes beta-glucosidase and arylsulfatase), (F2) nutrient storage (SOC and cation exchange capacity, CEC) and (F3) nutrient supply (Ca+2, Mg+2, K+, P, pH, H + Al; Al+3, sum of bases (SB) and base saturation (%BS)). The contribution of each indicator for the related soil function was scored using standardized scoring functions (SSF) that transform the indicator value on a scale between 0 and 1. Soil samples (0 to 10 cm depth) were collected in 2013 and 2015 from 21 treatments from three long-term field experiments, with different P fertilizer management strategies. P-fertilization resulted in higher cumulative crop yields that in turn were associated with better SQI(FERTBIO) and soil functions scores. Through linear regression analyses, critical limits (CLs) to interpret the individual scores of the SQI and of the soil functions, based on RCY and SOC were defined. The proposed SQI was positively associated with RCY (p < 0,001; R-2 = 0,95) and SOC (p < 0,001; R-2 = 0,88). Similar significant relationships were found between the three soil functions and RCY or SOC. Overall, the lower and upper limits of the interpretative classes for the SQI(FERTBIO) and soil functions scores (where the index scores are equivalent, respectively, to 40% and 80% of the optimal RCY or SOC) were 0.42 and 0.64. The combination of chemical and biological indicators in different soil functions and in the SQI(FERTBIO) proved to be a simple and useful strategy to assess SQ in tropical Oxisols, linking agronomic management practices to both SQ, yield benefits and SOC. These results will help extensionists, agronomists and soil scientists, when assisting farmers, in soil health assessments, by making easier to explain the importance of SQ for the economic performance of farms.

Protect, manage and then restore lands for climate mitigation

Natural climate solutions, along with reduction in fossil fuel emissions, are critical to mitigating climate change and meeting climate goals. This Perspective outlines a hierarchy for decision-making regarding protecting, managing and then restoring natural systems for climate mitigation.

Limited time and resources remain to constrain the climate crisis. Natural climate solutions represent promising options to protect, manage and restore natural lands for additional climate mitigation, but they differ in (1) the magnitude and (2) immediacy of mitigation potential, as well as (3) cost-effectiveness and (4) the co-benefits they offer. Counter to an emerging preference for restoration, we use these four criteria to propose a general rule of thumb to protect, manage and then restore lands, but also show how these criteria explain alternative prioritization and portfolio schemes. This hierarchy offers a decision-making framework for public and private sector actors to optimize the effectiveness of natural climate solutions in an environment in which resources are constrained, and time is short.

Soil rock fragments: Unquantified players in terrestrial carbon and nitrogen cycles

Soils with a significant proportion of rock fragments (RFs, with diameter >2 mm) are widely distributed in terrestrial ecosystems. However, a lack of quantitative information about RF characteristics restricts our ability to explain and predict the related soil carbon (C) and nitrogen (N) processes, and induces biases of C and N investigations in areas where soils contain significant RFs. In this paper, we reviewed the direct and indirect effects of RFs on soil C and N cycles, including via affecting soil C and N stocks, hydrology, temperature, and via chemical weathering that releases C, N, cations and anions, consumes CO2 and regulates pH. Based on this, we discussed current challenges and potential solutions in investigating C and N cycles of high RF content soils. First, we proposed a series of approaches to quantifying the effects of RFs on C and N stocks and soil hydrology across scales. Second, we emphasized the evolutions of soil RFs’ properties over time, and recommended including RF physicochemical properties in the routine soil inventory. Third, we suggested that both the effects of RFs and the evolutions of soil RFs over time should be considered in model simulation. Finally, we provided a framework by coupling monitoring, mapping and modelling to investigate the roles of RFs on soil C and N cycles from earth’s critical zone to global scale. This review will improve our understandings of RFs and terrestrial C and N cycles, and their responses to global change.

Distribution of soil organic matter fractions are altered with soil priming

Soil organic matter (SOM) plays a central role in mediating soil productivity through its impacts on nutrient cycling and retention, aggregate stability and water retention. Thus, management techniques or technologies including novel soil amendments could benefit farmers through the accumulation of carbon (C) and other nutrients in SOM. However, these same inputs can also lead to accelerated mineralization of native SOM through the process known as priming. This unresolved paradox may be due to the limited understanding of how different SOM fractions respond to priming and in which direction. In this study, we examine the response of functionally distinct SOM fractions to priming when soils are amended with lactobionate, a low molecular weight sugar acid byproduct of cheese manufacturing. Liquid-based 13C lactobionate was added to an agricultural silty loam soil to study its persistence, priming effects, and response of different SOM fractions to lactobionate over 84 days. Cumulative soil carbon dioxide (CO2) was greater in lactobionate-amended soils versus control and by the end of the experiment, 53% of added lactobionate was mineralized. In total, positive priming of 40% of extant SOM was observed from 14 to 84 days. Lactobionate-induced changes to SOM fractions were determined at days 14, 28, 56 and 84 of the incubation to examine if and how priming altered the distribution of C between fast and slowcycling SOC fractions. In response to lactobionate, the total C content of the water extractable organic matter (WEOM) fraction initially increased by 100% from the dissolved lactobionate we added, but then declined and at a faster rate than other SOM fractions. In addition, the total C of the light-fraction particulate organic matter (LFPOM) fraction also declined. At the same time, we observed total C increases in the slower-cycling sand-sized POM (H-POM) and mineral-associated organic (MAOM) C fractions, in response to lactobionate additions. We also saw a marginal increase in total soil C in the lactobionate-amended soils. Our findings therefore suggest that the application of lactobionate to soils may induce positive priming of the faster cycling LF-POM and WEOM fractions, but also concurrent gains in the H-POM and MAOM C fractions associated with long-term persistence and relative resiliency to disturbance with no net loss of total soil carbon. Thus, the application of low-molecular weight C-based materials such as lactobionate presents an avenue to building more persistent SOM through its impacts on the internal cycling and transformation of SOM fractions.

Responses of soil pH to no-till and the factors affecting it: A global meta-analysis

No-till (NT) is a sustainable option because of its benefits in controlling erosion, saving labor, and mitigating climate change. However, a comprehensive assessment of soil pH response to NT is still lacking. Thus, a global meta-analysis was conducted to determine the effects of NT on soil pH and to identify the influential factors and possible consequences based on the analysis of 114 publications. When comparing tillage practices, the results indicated an overall significant decrease by 1.33 +/- 0.28% in soil pH under NT than that under conventional tillage (p < .05). Soil texture, NT duration, mean annual temperature (MAT), and initial soil pH are the critical factors affecting soil pH under NT. Specifically, with significant variations among subgroups, when compared to conventional tillage, the soil under NT had lower relative changes in soil pH observed on clay loam soil (-2.44%), long-term implementation (-2.11% for more than 15 years), medium MAT (-1.87% in the range of 8-16celcius), neutral soil pH (-2.28% for 6.5 < initial soil pH < 7.5), mean annual precipitation (-1.95% in the range of 600-1200 mm), in topsoil layers (-2.03% for 0-20 cm), with crop rotation (-1.98%), N fertilizer input (the same for NT and conventional tillage) of 100-200 kg N ha(-1) (-1.83%), or crop residue retention (-1.52%). Changes in organic matter decomposition under undisturbed soil and with crop residue retention might lead to a higher concentration of H+ and lower of basic cations (i.e., calcium, magnesium, and potassium), which decrease the soil pH, and consequently, impact nutrient dynamics (i.e., soil phosphorus) in the surface layer under NT. Furthermore, soil acidification may be aggravated by NT within site-specific conditions and improper fertilizer and crop residue management and consequently leading to adverse effects on soil nutrient availability. Thus, there is a need to identify strategies to ameliorate soil acidification under NT to minimize the adverse consequences.

Spatial-temporal characteristics of ecosystem health in Central Asia

Land-use/land-cover change in Central Asia has led to ecological disasters such as draining of the Aral Sea, salinization, desertification that ultimately affect the ecosystem’s health. However, only a few studies have systematically assessed the spatial-temporal characteristics of ecosystem health in Central Asia using an integrated ecosystem assessment method. Therefore, this study aims to assess the ecosystem health of Central Asia using multi-source high-resolution data as the indicator and the integrated ecosystem vigor-ecosystem organization-ecosystem elasticity (V-O-E) model. The result showed that most of the areas in Central Asia are in a weak state of ecosystem vigor, ecosystem organization, ecosystem elasticity, and ecosystem health. The fragile state of the ecosystem health was highly due to the decreasing density of forest land, farmland, waterbody, and the increasing density of bare land and artificial land. The area of farmland gradually decreased, from 878639.08 km(2) in 2000 to 719128.08 km(2) in 2010 and 584251.36 km(2) in 2017. The forest of Central Asia has been declining continuously from 161998.69 km(2) in 2000 to 115343.96 km(2) in 2010 to 81185.10 km(2) in 2017 and has become spatially heterogeneous. Interestingly, the area of forest land in Xinjiang increased from 18216.14 km(2) in 2000 to 20970.92 km(2) in 2010 and 23098.12 km(2) in 2017, although there was a decrease in the overall forest land in other countries of Central Asia; this shows that China’s afforestation and reforestation policies during the last two decades had notable contributions to the ecosystem vigor and ecosystem elasticity in Xinjiang, which led to the increase in ecosystem health in Xinjiang. Hence, it is urgent to initiate integrated water management measures, limit the population growth and take adequate measures to enhance the techniques of farmland development to enhance the ecosystem health of Central Asia.

The Four Ways to Consider Measurement Noise in Bayesian Model Selection-And Which One to Choose

Bayesian model selection (BMS) is a statistically rigorous approach to assess the plausibility of competing models. It naturally accounts for uncertainties in models and data. In this study, we discuss the role of measurement noise in BMS deeper than in past literature. We distinguish between four cases, accounting for noise in models and/or data: (1) no-no, (2) no-yes, (3) yes-no, and (4) yes-yes. These cases differ mathematically and philosophically. Only two out of these four cases are logically consistent, and they represent two potentially conflicting research questions: “Which model is best in modeling the pure physics?” (Case 1) and “which model is best in predicting the data-generating process (i.e., physics plus noise)?” (Case 4). If we are interested in the “pure physics question,” we face two practical challenges: First, we would need noise-free data, which is impossible to obtain; and second, the numerical approximation of Bayesian model evidence can be hard when neglecting noise. We discuss how to address both challenges and reveal that a fallback to the easier “data-generation question” as a proxy for the “physics question” is not appropriate. We demonstrate on synthetic scenarios and a real-world hydrogeological case study that the choice of the case has a significant impact on the outcome of posterior model weights, and hence on results of the model ranking, model selection, model averaging, model confusion analysis, and uncertainty quantification. Reality might force us to use a different case than philosophy would suggest, and we provide guidance on how to interpret model probabilities under such conditions.

Journal Paper Digests

## Journal Paper Digests 2022 #17* An evaluation of carbon indicators of soil health in long-term agricultural experiments* A new criteri...… Continue reading

Journal Paper Digests

Published on August 15, 2022

Journal Paper Digests

Published on August 03, 2022