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Sulfur, as an essential nutrient for plant growth, has increasingly been used in fertiliser applications for many crops. This increase is coincident with declines in atmospheric sulfur deposition in response to air quality improvements in the United States and Europe. Here, we evaluate trends in sulfur fertiliser sales by mass, as a proxy for fertiliser applications, and estimate total atmospheric sulfur deposition across the Midwestern United States. Crop acreage, yield and sulfur fertiliser application substantially increased between 1985 and 2015, coincident with declines in atmospheric sulfur deposition. The increase in sulfur fertiliser has outpaced the relative rate of change in other major nutrient fertilisers including nitrogen, phosphorus and potassium, by approximately 7-fold prior to 2009, and 29-fold after 2009. We suggest that there is a critical need to develop sulfur management tools that optimize fertiliser applications to maintain crop yields while minimizing the consequences of excess sulfur in the environment.

 

Globally, sulfur (S) applications to croplands result in S inputs that often exceed historical atmospheric deposition. Sulfur is applied to crops as a fertilizer, fungicide, soil conditioner, pH regulator, and carrier for other elements. However, excess S in soils and aquatic ecosystems can have detrimental ecological and biogeochemical consequences, including soil base cation depletion, surface water acidification, hydrogen sulfide toxicity, and increased production of methyl mercury. The dichotomy between S benefits to crops and environmental consequences parallels that of nitrogen and phosphorus; however, there has not yet been a focus on developing sustainable S management plans in agriculture. We review the current literature on S cycling in agricultural systems and propose solutions that reduce S inputs, losses, and ecological consequences, including field applications of organic matter, adaptation of precision agriculture, and implementation of total maximum daily loads. We suggest opportunities for technological innovation, including analysis of remote sensing imagery to identify location and timing of S deficiencies and stresses, crop genetic modification to reduce S requirements, inoculation of plants with arbuscular mycorrhizal fungi to enhance plant S acquisition, and remediation of wetlands and other anoxic environments with high S loads. We conclude with areas for continued research on S biogeochemistry.

 

Soil physical properties, such as soil texture, color, bulk density, and porosity are important determinants of water flow (e.g., infiltration and drainage), biogeochemical cycling, and plant community composition. In addition, they reflect the environment in which the soil developed, giving insight into climate, mineralogy, and land cover. While many soil assessments require sophisticated laboratory equipment, some can be made simply by a trained individual, requiring only practice and reference materials. For students in environmental fields, it is particularly important and empowering to learn how to make informed soil observations that provide insights from the soil pedon to the landscape and that can be done within the field setting. Drawing on updated pedagogical approaches, including active learning, small group collaboration, and metacognitive exercises, this paper presents a course module for teaching soil texture and color analysis in the field that can be modified for students from secondary through graduate school. The combination of asynchronous, pre-course readings and assessment; synchronous, in-class instruction, hands-on practice, and application activities; and post-class reflection give students the opportunity to build a strong foundation for making soil observations. This course module is suitable for both in-person and remote learning modalities and can be adapted to a number of course topics across environmental disciplines. Ultimately, the goal is to provide students with exciting, hands-on training that inspires them to learn more about soils regardless of the learning platform.

 

The environmental fates and consequences of intensive sulfur (S) applications to croplands are largely unknown. In this study, we used S stable isotopes to identify and trace agricultural S from field-to-watershed scales, an initial and timely step toward constraining the modern S cycle. We conducted our research within the Napa River Watershed, California, US, where vineyards receive frequent fungicidal S sprays. We measured soil and surface water sulfate concentrations ([SO₄²⁻]) and stable isotopes (δ³⁴S–SO₄²⁻), which we refer to in combination as the ‘S fingerprint’. We compared samples collected from vineyards and surrounding forests/grasslands, which receive background atmospheric and geologic S sources. Vineyardδ³⁴S–SO₄²⁻values were 9.9 ± 5.9‰ (median ± interquartile range), enriched by ∼10‰ relative to forests/grasslands (−0.28 ± 5.7‰). Vineyards also had roughly three-fold higher [SO₄²⁻] than forests/grasslands (13.6 and 5.0 mg SO₄²⁻–S l⁻¹, respectively). Napa Riverδ³⁴S–SO₄²⁻values, reflecting the watershed scale, were similar to those from vineyards (10.5 ± 7.0‰), despite vineyard agriculture constituting only ∼11% of the watershed area. Combined, our results provide important evidence that agricultural S is traceable at field-to-watershed scales, a critical step toward determining the consequences of agricultural alterations to the modern S cycle.

 

Climate warming in alpine regions is changing patterns of water storage, a primary control on alpine plant ecology, biogeochemistry, and water supplies to lower elevations. There is an outstanding need to determine how the interacting drivers of precipitation and the critical zone (CZ) dictate the spatial pattern and time evolution of soil water storage. In this study, we developed an analytical framework that combines intensive hydrologic measurements and extensive remotely-sensed observations with statistical modeling to identify areas with similar temporal trends in soil water storage within, and predict their relationships across, a 0.26 km 2 alpine catchment in the Colorado Rocky Mountains, U.S.A. Repeat measurements of soil moisture were used to drive an unsupervised clustering algorithm, which identified six unique groups of locations ranging from predominantly dry to persistently very wet within the catchment. We then explored relationships between these hydrologic groups and multiple CZ-related indices, including snow depth, plant productivity, macro- (10 2 ->10 3 m) and microtopography (<10 0 -10 2 m), and hydrological flow paths. Finally, we used a supervised machine learning random forest algorithm to map each of the six hydrologic groups across the catchment based on distributed CZ properties and evaluated their aggregate relationships at the catchment scale. Our analysis indicated that ~40–50% of the catchment is hydrologically connected to the stream channel, lending insight into the portions of the catchment that likely dominate stream water and solute fluxes. This research expands our understanding of patch-to-catchment-scale physical controls on hydrologic and biogeochemical processes, as well as their relationships across space and time, which will inform predictive models aimed at determining future changes to alpine ecosystems.