R. Michael Lehman, Shannon L. Osborne, Patrick M. Ewing
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.
{"title":"When are you measuring soil β-glucosidase activities in cropping systems?","authors":"R. Michael Lehman, Shannon L. Osborne, Patrick M. Ewing","doi":"10.1002/ael2.70002","DOIUrl":"https://doi.org/10.1002/ael2.70002","url":null,"abstract":"<p>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.</p>","PeriodicalId":48502,"journal":{"name":"Agricultural & Environmental Letters","volume":"9 2","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ael2.70002","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142525099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
土壤有机质(SOM)在坡耕地水土流失控制中起着关键作用。本研究以不同水土保持策略下的四种土壤类型为重点,探讨了中国湖北省的土壤有机质含量。在 27 个采用不同水土保持策略的监测点的径流地块中采集了田间样本(n = 243)。结果表明,不同土壤类型的 SOM 含量差异很大,其中石灰性土壤的 SOM 含量最高(12.63 g kg-1)。相反,红壤的 SOM 含量最低(6.32 g kg-1)。然而,短期保护策略及其与土壤类型的相互作用对 SOM 并无显著影响。研究结果凸显了土壤类型与 SOM 动态之间错综复杂的关系。这项研究有助于人们了解不同地貌中的 SOM 动态,为政策制定者和土地管理者提供有价值的指导,以减少侵蚀和提高土壤健康水平。
{"title":"Soil organic matter characteristics of four soil types under different conservation strategies across Hubei Province","authors":"Wei Yang, Yangbo He, Xingsheng Song, Lirong Lin, Zhengchao Tian, Ying Zhou","doi":"10.1002/ael2.70000","DOIUrl":"https://doi.org/10.1002/ael2.70000","url":null,"abstract":"<p>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 (<i>n</i> = 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<sup>−1</sup>). Conversely, red soils displayed the lowest SOM content (6.32 g kg<sup>−1</sup>). 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.</p>","PeriodicalId":48502,"journal":{"name":"Agricultural & Environmental Letters","volume":"9 2","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ael2.70000","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142448994","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Logan Gall, Tom Glancy, Michael Kantar, Bryan C. Runck
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.
农业气象数据对于利用数字农业技术了解生产情况至关重要。然而,整合多个来源的农业气象观测数据仍是一项挑战。数字农业科学家通常要多次下载和清理相同的数据集。我们提出了一个原型系统,通过提供简化的用户界面、数据库和应用程序接口,简化了从气象数据源收集、清理、整合和汇总数据的过程。原型系统提供了查询多种地理空间格式(栅格和矢量)的标准接口,并整合了观测网络,包括美国国家海洋和大气管理局全球历史气候学网络(NOAA GHCN)、NOAA NClim-Grid(NOAA 的网格气候标准)和 Ameriflux BASE。该系统可自动检查和更新数据,节省存储空间和处理时间,并允许用户对数据进行空间和时间汇总。该应用和集成系统以开放源代码和基于浏览器的用户界面提供,可在 Windows、Linux 和 Mac 环境中运行,支持更广泛地使用多源农业气象数据。
{"title":"A tool for integrating agrometeorological observation data for digital agriculture: A Minnesota case study","authors":"Logan Gall, Tom Glancy, Michael Kantar, Bryan C. Runck","doi":"10.1002/ael2.20147","DOIUrl":"https://doi.org/10.1002/ael2.20147","url":null,"abstract":"<p>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.</p>","PeriodicalId":48502,"journal":{"name":"Agricultural & Environmental Letters","volume":"9 2","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ael2.20147","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142439117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"Including non-growing season emissions of N2O in US maize could raise net CO2e emissions by 31% annually","authors":"Brian Buma","doi":"10.1002/ael2.20146","DOIUrl":"https://doi.org/10.1002/ael2.20146","url":null,"abstract":"<p>Nitrous oxide (N<sub>2</sub>O) is a significant greenhouse gas and the most important currently emitted ozone depleting substance, primarily via agricultural fertilization. Current N<sub>2</sub>O 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 N<sub>2</sub>O can be emitted during non-growing season periods. Using newly published off-season N<sub>2</sub>O emission ratio maps and high-resolution nitrogen application data, this study explores the potential magnitude of underestimated N<sub>2</sub>O 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 CO<sub>2</sub>e year<sup>−1</sup>. Further work should better refine emission estimates spatially as well as fully integrate estimates across growing and non-growing seasons.</p>","PeriodicalId":48502,"journal":{"name":"Agricultural & Environmental Letters","volume":"9 2","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ael2.20146","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}