Agricultural & Environmental Letters最新文献

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.

Transformative technologies such as artificial intelligence (AI) make difficult tasks more accessible and convenient. Since 2018, the use of AI in research has increased drastically, with annual publication rates of 3–5 times higher than pre-2017. Currently, >100,000 manuscripts using AI are published annually within science and engineering, and >20,000 of these belong to the agricultural and environmental fields. Given the magnitude of use, clear communication on how AI is used and how it helps advance scientific knowledge is essential. Clear communication is perhaps more necessary with AI than previous technologies due to its broad and flexible spectrum of uses, the “black-box” nature of deep-learning algorithms, and ongoing debates regarding AI's predictive power versus knowledge of first-principles mechanistic and process-based theories and models. In this commentary, we provide guidelines and discussion points to the scientific community to ensure transparent and effective communication of AI research in agricultural and environmental research publications.

Both agricultural lands and the role of crop advisors remain comparatively understudied in the Intermountain West (IMW) when it comes to the topic of soil health. Data from a survey of crop advisors in Utah is used to understand current and future soil health work in the region. Not all crop advisors engage in soil health work, but more are discussing it with clients than in the past. Respondents noted that information and costs are key barriers for farmers to managing soil health. Advisors also do not always feel they have the information and answers about soil health practices that farmers need. While crop advisors are one option for promoting producer understanding about soil health in the IMW, work is needed to better prepare them, and farmers will need other options and support to be successful in managing soil health.

The oasis effect, characterized by atmospheric cooling due to excessive evapotranspiration (ET) and the inflow of warm air from the surroundings, has been well documented in vegetated oases. Despite its significant ET rates, the atmospheric cooling phenomenon in rice paddies has not received extensive exploration. This study investigates the oasis effect during July and August, the peak months for ET in rice fields in temperate climate. Over 3 years (2020–2022), energy flux observations using the eddy covariance method were conducted to analyze atmospheric cooling in paddy fields. The findings revealed a pronounced atmospheric cooling effect associated with negative sensible heat in paddy fields. Moreover, this cooling phenomenon exhibited heightened activity during periods of increased wind speeds (>3.5 m/s) and subdued net radiation (<400 W/m²). These results highlight rice paddies' potential to cool the atmosphere, acting as a countermeasure against global warming and the urban heat island effect.