Nitrous oxide (N₂O) emissions are highly episodic in response to nitrogen additions and changes in soil moisture. Automated gas sampling provides the necessary high temporal frequency to capture these emission events in real time, ensuring the development of accurate N₂O inventories and effective mitigation strategies to reduce global warming. This paper outlines the design and operational considerations of automated chamber systems including chamber design and deployment, frequency of gas sampling, and options in terms of the analysis of gas samples. The basic hardware and software requirements for automated chambers are described, including the major challenges and obstacles in their implementation and operation in a wide range of environments. Detailed descriptions are provided of automated systems that have been deployed to assess the impacts of agronomy on the emissions of N₂O and other significant greenhouse gases. This information will assist researchers across the world in the successful deployment and operation of automated N₂O chamber systems.

Nitrous oxide (N₂O) is a potent greenhouse gas that is primarily emitted from agriculture. Sampling limitations have generally resulted in discontinuous N₂O observations over the course of any given year. The status quo for interpolating between sampling points has been to use a simple linear interpolation. This can be problematic with N₂O emissions, since they are highly variable and sampling bias around these peak emission periods can have dramatic impacts on cumulative emissions. Here, we outline five gap‐filling practices: linear interpolation, generalized additive models (GAMs), autoregressive integrated moving average (ARIMA), random forest (RF), and neural networks (NNs) that have been used for gap‐filling soil N₂O emissions. To facilitate the use of improved gap‐filling methods, we describe the five methods and then provide strengths and challenges or weaknesses of each method so that model selection can be improved. We then outline a protocol that details data organization and selection, splitting of data into training and testing datasets, building and testing models, and reporting results. Use of advanced gap‐filling methods within a standardized protocol is likely to increase transparency, improve emission estimates, reduce uncertainty, and increase capacity to quantify the impact of mitigation practices.

Heatwaves are common in many viticultural regions of Australia. We evaluated the potential of satellite-based remote sensing to detect the effects of high temperatures on grapevines in a South Australian vineyard over the 2016–2017 and 2017–2018 seasons. The study involved: (i) comparing the normalized difference vegetation index (NDVI) from medium- and high-resolution satellite images; (ii) determining correlations between environmental conditions and vegetation indices (Vis); and (iii) identifying VIs that best indicate heatwave effects. Pearson’s correlation and Bland–Altman testing showed a significant agreement between the NDVI of high- and medium-resolution imagery (R = 0.74, estimated difference −0.093). The band and the VI most sensitive to changes in environmental conditions were 705 nm and enhanced vegetation index (EVI), both of which correlated with relative humidity (R = 0.65 and R = 0.62, respectively). Conversely, SWIR (short wave infrared, 1610 nm) exhibited a negative correlation with growing degree days (R = −0.64). The analysis of heat stress showed that green and red edge bands—the chlorophyll absorption ratio index (CARI) and transformed chlorophyll absorption ratio index (TCARI)—were negatively correlated with thermal environmental parameters such as air and soil temperature and growing degree days (GDDs). The red and red edge bands—the soil-adjusted vegetation index (SAVI) and CARI2—were correlated with relative humidity. To the best of our knowledge, this is the first study demonstrating the effectiveness of using medium-resolution imagery for the detection of heat stress on grapevines in irrigated vineyards.

Manure application to land and deposition of urine and dung by grazing animals are major sources of ammonia (NH₃) and nitrous oxide (N₂O) emissions. Using data on NH₃ and N₂O emissions following land‐applied manures and excreta deposited during grazing, emission factors (EFs) disaggregated by climate zone were developed, and the effects of mitigation strategies were evaluated. The NH₃ data represent emissions from cattle and swine manures in temperate wet climates, and the N₂O data include cattle, sheep, and swine manure emissions in temperate wet/dry and tropical wet/dry climates. The NH₃ EFs for broadcast cattle solid manure and slurry were 0.03 and 0.24 kg NH₃–N kg^–1 total N (TN), respectively, whereas the NH₃ EF of broadcast swine slurry was 0.29. Emissions from both cattle and swine slurry were reduced between 46 and 62% with low‐emissions application methods. Land application of cattle and swine manure in wet climates had EFs of 0.005 and 0.011 kg N₂O–N kg^–1 TN, respectively, whereas in dry climates the EF for cattle manure was 0.0031. The N₂O EFs for cattle urine and dung in wet climates were 0.0095 and 0.002 kg N₂O–N kg^–1 TN, respectively, which were three times greater than for dry climates. The N₂O EFs for sheep urine and dung in wet climates were 0.0043 and 0.0005, respectively. The use of nitrification inhibitors reduced emissions in swine manure, cattle urine/dung, and sheep urine by 45–63%. These enhanced EFs can improve national inventories; however, more data from poorly represented regions (e.g., Asia, Africa, South America) are needed.

Nitrous oxide (N₂O), ammonia (NH₃), and methane (CH₄) emissions from the manure management chain of livestock production systems are important contributors to greenhouse gases (GHGs) and NH₃ emitted by human activities. Several studies have evaluated manure‐related emissions and associated key variables at regional, national, or continental scales. However, there have been few studies focusing on the drivers of these emissions using a global dataset. An international project was created (DATAMAN) to develop a global database on GHG and NH₃ emissions from the manure management chain (housing, storage, and field) to identify key variables influencing emissions and ultimately to refine emission factors (EFs) for future national GHG inventories and NH₃ emission reporting. This paper describes the “field” database that focuses on N₂O and NH₃ EFs from land‐applied manure and excreta deposited by grazing livestock. We collated relevant information (EFs, manure characteristics, soil properties, and climatic conditions) from published peer‐reviewed research, conference papers, and existing databases. The database, containing 5,632 observations compiled from 184 studies, was relatively evenly split between N₂O and NH₃ (56 and 44% of the EF values, respectively). The N₂O data were derived from studies conducted in 21 countries on five continents, with New Zealand, the United Kingdom, Kenya, and Brazil representing 86% of the data. The NH₃ data originated from studies conducted in 17 countries on four continents, with the United Kingdom, Denmark, Canada, and The Netherlands representing 79% of the data. Wet temperate climates represented 90% of the total database. The DATAMAN field database is available at http://www.dataman.co.nz.