Agricultural & Environmental Letters最新文献

Healthy soil supports the global carbon cycle, the water cycle, and many nutrient cycles to stabilize ecosystems. We take these processes for granted, and yet, disruptions to these cycles would be devastating if soils became defunct and plants could not photosynthesize. As with the health of the human body to which we rely on to carry out our daily lives, so too does the health of soil give essential life to our world. Strong corollaries exist between the functioning of the human body and the soil body. This essay explores these two bodies through simile. Just as we wish others good health, so too should each of us (and society) wish a world with excellent soil health. A foundational pathway laid by strong science, but pitched to engage more of the public in this effort to foster better soil health might be through non-traditional impressionistic storylines.

In situ soil respiration is driven by annual patterns of temperature and soil moisture, but what about extracellular enzyme activities responsible for depolymerizing soil organic matter? We conducted biweekly measurements of potential soil β-glucosidase activities during a 4-month period from March soil thawing through July in annually cropped field plots in eastern South Dakota. Our objective was to determine the best sampling time to resolve the effects of crop rotational diversity on soil microbial activities. Potential β-glucosidase activities were elevated immediately following soil thaw, peaked in May, and declined to their lowest value in mid-summer. Temperature and precipitation had no value in predicting enzyme activities; however, enzyme activities were affected by crop rotational diversity and responded to current crop and previous crop. These findings are pertinent to the use of soil extracellular enzymes in soil health assessments and as indicators of microbial substrate preference with implications for soil carbon stabilization.

Soil organic matter (SOM) plays key roles in sloping land erosion control. This study explores SOM content across Hubei Province, China, focusing on four soil types under various conservation strategies. Field samples (n = 243) were collected under 27 monitoring sites employing diverse conservation strategies in runoff plots. Results indicated substantial variability in SOM content among soil types, with calcareous soils exhibiting the highest levels (12.63 g kg⁻¹). Conversely, red soils displayed the lowest SOM content (6.32 g kg⁻¹). However, short-term conservation strategies and their interaction with soil type did not significantly influence SOM. The findings underscore the intricate relationship between soil types and SOM dynamics. This study contributes to the understanding of SOM dynamics in diverse landscapes, offering valuable guidance for policymakers and land managers to apply practices in mitigating erosion and enhancing soil health.

Agrometeorological data are essential for understanding production using digital agriculture techniques. However, integrating agrometerological observations from multiple sources remains a challenge. Often, digital agriculture scientists download and clean the same datasets many times. We present a prototype system that simplifies the process of collecting, cleaning, integrating, and aggregating data from meteorological data sources by providing a simplified user interface, database, and application programming interface. The prototype provides a standard interface for querying multiple geospatial formats (raster and vector) and integrates observation networks including the National Oceanic and Atmospheric Administration Global Historical Climatology Network (NOAA GHCN), NOAA NClim-Grid (NOAA's Gridded Climate Normals), and Ameriflux BASE. The system automatically checks and updates data, saving storage space and processing time, and allows users to summarize data spatially and temporally. Provided as open source code and browser-based user interface, the application and integration system can be run across Windows, Linux, and Mac environments to support broader use of multi-source agrometeorology data.

Nitrous oxide (N₂O) is a significant greenhouse gas and the most important currently emitted ozone depleting substance, primarily via agricultural fertilization. Current N₂O emission estimation methods at the national scale are predominantly via emission factors. Models estimating national-scale emissions are focused on growing season emissions. However, a substantial fraction of N₂O can be emitted during non-growing season periods. Using newly published off-season N₂O emission ratio maps and high-resolution nitrogen application data, this study explores the potential magnitude of underestimated N₂O emissions if using only the default growing-season focused methodology. Although there is large variation at county scales (12%–35%), non-growing season national emissions are estimated at 31% of the total, a potential 12,000 Gg CO₂e year⁻¹. Further work should better refine emission estimates spatially as well as fully integrate estimates across growing and non-growing seasons.