Nitrous oxide (N₂O) is the third important long‐lived greenhouse gas next to carbon dioxide and methane and croplands are considered biogeochemical hotspots of soil N₂O emissions. To reduce soil N₂O and other greenhouse emissions, climate‐smart agricultural practices including biochar application have been applied. Many studies have been conducted with biochar application but results from these studies are not conclusive. To address this issue, meta‐analysis, a quantitative review that synthesizes results from multiple independent studies, has been widely used. The results from different meta‐analyses also differ but are seldomly evaluated. In this study, we evaluated meta‐analyses on the effects of biochar application on soil N₂O emissions. A grand mean response ratio (RR) was further proposed to estimate an overall effect and the impacts of experiment setting, properties of biochar and soil, and agricultural practices. We found 18 meta‐analysis papers were published between 2014 and 2022. Sample size (publications or experiments) varied from less than 30 to more than 1000, with a mean sample size of 275. RR was calculated in all studies except one. While four meta‐analyses did not find a significant effect of biochar application on soil N₂O emissions, all others reported reductions of soil N₂O emissions, but the magnitude ranged from −10.5% to −54.8%. Synthesizing all results from these meta‐analyses, we found that biochar application overall significantly reduced the soil N₂O emissions by 38.8%. The impacts increased with experimental duration till one and half years and reduced after that. Biochar application rate and C:N ratio had large influence on the effects of biochar application on soil N₂O emissions. This study demonstrated that while meta‐analysis provides a more comprehensive and better estimation, the inconsistence among these studies may need to be further evaluated. A grand mean RR based on meta‐analyses could be more accurate and representative than single meta‐analysis.

The large areal extent of hypoxia in the northern Gulf of Mexico has been partially attributed to substantial nitrogen (N) loading from the Mississippi River basin, which is driven by multiple natural and human factors. The available water quality monitoring data and most of the current models are insufficient to fully quantify N load magnitude and the underlying controls. Here we use a process‐based Dynamic Land Ecosystem Model to examine how multiple factors (synthetic N fertilizer, atmospheric N deposition, land use changes, climate variability, and increasing atmospheric CO₂) have affected the loading and delivery of total nitrogen (TN) consisting of ammonium and nitrate (dissolved inorganic N) and total organic nitrogen from the Mississippi River basin during 1901–2014. The model results indicate that TN export during 2000–2014 was twofold larger than that in the first decade of twentieth century: Dissolved inorganic N export increased by 140% dominated by nitrate; total organic nitrogen export increased by 53%. The substantial enrichment of TN export since the 1960s was strongly associated with increased anthropogenic N inputs (synthetic N fertilizer and atmospheric N deposition). The greatest export of TN was in the spring. Although the implementation of N reduction has been carried out over the past three decades, total N loads to the northern Gulf of Mexico have not decreased significantly. Due to the legacy effect from historical N accumulation in soils and riverbeds, a larger reduction in synthetic N fertilizer inputs as well as improved N management practices are needed to alleviate ocean hypoxia in the northern Gulf of Mexico.

Irrigation is crucial to agriculture in arid and semi-arid areas and significantly contributes to crop development, food diversity and the sustainability of agro-ecosystems. For a specific crop, the separation of its irrigated and rainfed areas is difficult, because their phenology is similar and therefore less distinguishable, especially when there are phenology shifts due to various factors, such as elevation and latitude. In this study, we present a simple, but robust method to map irrigated and rainfed wheat areas in a semi-arid region of China. We used the Normalized Difference Vegetation Index (NDVI) at a 30 × 30 m spatial resolution derived from the Chinese HJ-1A/B (HuanJing(HJ) means environment in Chinese) satellite to create a time series spanning the whole growth period of wheat from September 2010 to July 2011. The maximum NDVI and time-integrated NDVI (TIN) that usually exhibit significant differences between irrigated and rainfed wheat were selected to establish a classification model using a support vector machine (SVM) algorithm. The overall accuracy of the Google-Earth testing samples was 96.0%, indicating that the classification results are accurate. The estimated irrigated-to-rainfed ratio was 4.4:5.6, close to the estimates provided by the agricultural sector in Shanxi Province. Our results illustrate that the SVM classification model can effectively avoid empirical thresholds in supervised classification and realistically capture the magnitude and spatial patterns of rainfed and irrigated wheat areas. The approach in this study can be applied to map irrigated/rainfed areas in other regions when field observational data are available.