Ruminating on soil carbon: Applying current understanding to inform grazing management.

Among options for atmospheric CO2 removal, sequestering soil organic carbon (SOC) via improved grazing management is a rare opportunity because it is scalable across millions of globally grazed acres, low cost, and has high technical potential. Decades of scientific research on grazing and SOC has failed to form a cohesive understanding of how grazing management affects SOC stocks and their distribution between particulate (POM) and mineral-associated organic matter (MAOM)-characterized by different formation and stabilization pathways-across different climatic contexts. As we increasingly look to grazing management for SOC sequestration on grazinglands to bolster our climate change mitigation efforts, we need a clear and collective understanding of grazing management's impact on pathways of SOC change to inform on-the-ground management decisions. We set out to review the effects of grazing management on SOC through a unified plant ecophysiology and soil biogeochemistry conceptual framework, where elements such as productivity, input quality, soil mineral capacity, and climate variables such as aridity co-govern SOC accumulation and distribution into POM and MAOM. To maximize applicability to grazingland managers, we discuss how common management levers that drive overall grazing pattern, including timing, intensity, duration, and frequency can be used to optimize mechanistic pathways of SOC sequestration. We discuss important research needs and measurement challenges, and highlight how our conceptual framework can inform more robust research with greater applicability for maximizing the use of grazing management to sequester SOC.

Machine learning based estimation of field-scale daily, high resolution, multi-depth soil moisture for the Western and Midwestern United States.

Background: High-resolution soil moisture estimates are critical for planning water management and assessing environmental quality. In-situ measurements alone are too costly to support the spatial and temporal resolutions needed for water management. Recent efforts have combined calibration data with machine learning algorithms to fill the gap where high resolution moisture estimates are lacking at the field scale. This study aimed to provide calibrated soil moisture models and methodology for generating gridded estimates of soil moisture at multiple depths, according to user-defined temporal periods, spatial resolution and extent. Methods: We applied nearly one million national library soil moisture records from over 100 sites, spanning the U.S. Midwest and West, to build Quantile Random Forest (QRF) calibration models. The QRF models were built on covariates including soil moisture estimates from North American Land Data Assimilation System (NLDAS), soil properties, climate variables, digital elevation models, and remote sensing-derived indices. We also explored an alternative approach that adopted a regionalized calibration dataset for the Western U.S. The broad-scale QRF models were independently validated according to sampling depths, land cover type, and observation period. We then explored the model performance improved with local samples used for spiking. Finally, the QRF models were applied to estimate soil moisture at the field scale where evaluation was carried out to check estimated temporal and spatial patterns. Results: The broad-scale QRF model showed moderate performance (R2 = 0.53, RMSE = 0.078 m3/m3) when data points from all depth layers (up to 100 cm) were considered for an independent validation. Elevation, NLDAS-derived moisture, soil properties, and sampling depth were ranked as the most important covariates. The best model performance was observed for forest and pasture sites (R2 > 0.5; RMSE < 0.09 m3/m3), followed by grassland and cropland (R2 > 0.4; RMSE < 0.11 m3/m3). Model performance decreased with sampling depths and was slightly lower during the winter months. Spiking the national QRF model with local samples improved model performance by reducing the RMSE to less than 0.05 m3/m3 for grassland sites. At the field scale, model estimates illustrated more accurate temporal trends for surface than subsurface soil layers. Model estimated spatial patterns need to be further improved and validated with management data. Conclusions: The model accuracy for top 0-20 cm soil depth (R2 > 0.5, RMSE < 0.08 m3/m3) showed promise for adopting the methodology for soil moisture monitoring. The success of spiking the national model with local samples showed the need to collect multi-year high frequency (e.g., hourly) sensor-based field measurements to improve estimates of soil moisture for a longer time period. Future work should improve model performance for deeper depths with additional hydraulic properties and use of locally-selected calibration datasets.

Impact of Irrigation Strategies on Tomato Root Distribution and Rhizosphere Processes in an Organic System.

Root exploitation of soil heterogeneity and microbially mediated rhizosphere nutrient transformations play critical roles in plant resource uptake. However, how these processes change under water-saving irrigation technologies remains unclear, especially for organic systems where crops rely on soil ecological processes for plant nutrition and productivity. We conducted a field experiment and examined how water-saving subsurface drip irrigation (SDI) and concentrated organic fertilizer application altered root traits and rhizosphere processes compared to traditional furrow irrigation (FI) in an organic tomato system. We measured root distribution and morphology, the activities of C-, N-, and P-cycling enzymes in the rhizosphere, the abundance of rhizosphere microbial N-cycling genes, and root mycorrhizal colonization rate under two irrigation strategies. Tomato plants produced shorter and finer root systems with higher densities of roots around the drip line, lower activities of soil C-degrading enzymes, and shifts in the abundance of microbial N-cycling genes and mycorrhizal colonization rates in the rhizosphere of SDI plants compared to FI. SDI led to 66.4% higher irrigation water productivity than FI, but it also led to excessive vegetative growth and 28.3% lower tomato yield than FI. Our results suggest that roots and root-microbe interactions have a high potential for coordinated adaptation to water and nutrient spatial patterns to facilitate resource uptake under SDI. However, mismatches between plant needs and resource availability remain, highlighting the importance of assessing temporal dynamics of root-soil-microbe interactions to maximize their resource-mining potential for innovative irrigation systems.

Temperature enhances the affinity of soil alkaline phosphatase to Cd.

Both elevated temperature and heavy metal contamination can have profound effects on microbial function and soil biogeochemical cycling. However, the interactive effects of heavy metal toxicity and temperature on microbial activity have been poorly understood. The aim of this study was to quantify the effect of temperature and cadmium (Cd) toxicity on alkaline phosphatase (ALP) produced by microbes to acquire phosphorus. To determine whether these effects were dependent on soil properties, we utilized 11 soil types from cropland throughout China. We measured ALP activities and kinetics across a temperature (17, 27, 37, and 47 °C) and Cd concentration gradient (0, 0.6, 5, 25, 50, 100, 200, 300, and 500 mg kg-1). We found that the half saturation constant (Km) and the velocity constant (k) of ALP increased nonlinearly with temperature across all soil types. However, the maximum reaction velocity (Vmax) increased linearly with temperature. Regardless of soil type and temperature, Cd had a non-competitive inhibitory mechanism. Soil pH, TOC, and clay content were the major factors controlling the affinity of ALP for Cd (Ki). The ecology dose (ED50) for Vmax and k, and Ki were negatively related to temperature, indicating that the toxicity of Cd on ALP is temperature-dependent. Additionally, higher temperatures led to more inhibition of Cd on ALP activity in alkaline soils than that in acidic and neutral soils. Our results suggest that global warming might accelerate the deficiency of available phosphorus in Cd contaminated soils due to higher inhibition of Cd on ALP activity, particularly in alkaline soils.

Temperature response of soil respiration largely unaltered with experimental warming.

The respiratory release of carbon dioxide (CO2) from soil is a major yet poorly understood flux in the global carbon cycle. Climatic warming is hypothesized to increase rates of soil respiration, potentially fueling further increases in global temperatures. However, despite considerable scientific attention in recent decades, the overall response of soil respiration to anticipated climatic warming remains unclear. We synthesize the largest global dataset to date of soil respiration, moisture, and temperature measurements, totaling >3,800 observations representing 27 temperature manipulation studies, spanning nine biomes and over 2 decades of warming. Our analysis reveals no significant differences in the temperature sensitivity of soil respiration between control and warmed plots in all biomes, with the exception of deserts and boreal forests. Thus, our data provide limited evidence of acclimation of soil respiration to experimental warming in several major biome types, contrary to the results from multiple single-site studies. Moreover, across all nondesert biomes, respiration rates with and without experimental warming follow a Gaussian response, increasing with soil temperature up to a threshold of ∼25 °C, above which respiration rates decrease with further increases in temperature. This consistent decrease in temperature sensitivity at higher temperatures demonstrates that rising global temperatures may result in regionally variable responses in soil respiration, with colder climates being considerably more responsive to increased ambient temperatures compared with warmer regions. Our analysis adds a unique cross-biome perspective on the temperature response of soil respiration, information critical to improving our mechanistic understanding of how soil carbon dynamics change with climatic warming.

Emerging land use practices rapidly increase soil organic matter.

The loss of organic matter from agricultural lands constrains our ability to sustainably feed a growing population and mitigate the impacts of climate change. Addressing these challenges requires land use activities that accumulate soil carbon (C) while contributing to food production. In a region of extensive soil degradation in the southeastern United States, we evaluated soil C accumulation for 3 years across a 7-year chronosequence of three farms converted to management-intensive grazing. Here we show that these farms accumulated C at 8.0 Mg ha(-1) yr(-1), increasing cation exchange and water holding capacity by 95% and 34%, respectively. Thus, within a decade of management-intensive grazing practices soil C levels returned to those of native forest soils, and likely decreased fertilizer and irrigation demands. Emerging land uses, such as management-intensive grazing, may offer a rare win-win strategy combining profitable food production with rapid improvement of soil quality and short-term climate mitigation through soil C-accumulation.