Agricultural & Environmental Letters最新文献

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.

The decision whether to manage agriculture according to organic farming principles or conventional farming is a question bigger than scientific inquiry; it constitutes a political question. Similarly, deciding the regulations governing organic and conventional production does not fall within the pursuit of science. Rather, science should show how different management practices influence the environment. The regulatory framework of organic farming is derived from normative values rather than scientific principles, which now categorizes the production.

McGuire (2017) contend that ideology and science do not blend well. However, researchers inherently possess normative values, which shape their research interests and perspectives. It could be argued that this is only problematic when the goal of the scientific pursuit and ideology crosses, thus becoming activistic. This can harm the scientific process by drawing wrongful conclusions upon poorly constructed experiments, and thus the scientific process in general. All scientific decisions—for example., formulating a research question, designing the study, and analyzing the data—are conducted by humans, with values and experiences influencing their choices, therefore including some normative values (Reed, 2011; Risjord, 2016). While this is generally recognized by social sciences, natural sciences often neglect it.

Analysis of studies comparing the environmental impacts of organic and conventional farming show variation in environmental impact, as for dairy production (Cederberg & Mattsson, 2000; De Boer, 2003; Kristensen et al., 2011; Thomassen et al., 2008). When assessing the two production regimes the production level between the systems is seemingly important. This is because emission or environmental impact are often divided upon the emission per produced product, which as an effect of production levels obtained is favoring higher production. Organic farming utilizes less resources per produced product, but often has a lower productivity. Organic farming, however, often claims other ideologic values besides production, such as health, ecology, fairness, and care (IFOAM, 2005).

Comparison of organic and conventional management also raises the question of whether the production systems are similar enough to be comparable. Both organic and conventional production can be described with the goal to produce goods to sell, while somehow having different aims. Organic farming emphasizes different values, complicating direct statistical comparisons with conventional systems, since these values are not described with a reductionistic approach. The external values in organic production seem to have a cost, often resulting in lower productivity than conventional production.

The reasoning of McGuire (2017), who advocates that organic agriculture should change its

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.