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Nitrous oxide (N2O) emissions from dairy-grazing pastures can be dominated by large emissions from small areas (‘hotspots’) frequently used by grazing dairy cattle (i.e., water troughs and gateways). N2O emissions from these hotspots are quantified by investigating whether N2O emissions and emission factors (% of applied N emitted as N2O, EF3) from potential hotspots are different from non-hotspots. To better characterise N2O emissions from hotspots and non-hotspots of farms to understand their contributions to national agricultural greenhouse gas inventory calculations, a series of measurements were conducted during winter and spring on two NZ typical dairy farms with contrasting soil drainage (poorly versus well drained). Before measurements were taken, the soils either received a cow urine application or remained untreated. The results showed that changes in water-filled pore space (WFPS) and mineral N around water troughs and gateways, due to additional stock movements and disproportionate excreta-N deposition during previous grazing events, affected both background and total N2O emissions. But there was little impact on EF3 values (calculated using IPCC guidelines) from deposited urine between hotspot and pasture areas. These results suggest the same EF3 values can be used for both to calculate emissions from urine deposited on grazed pastures. However, these results raise concerns about higher background emission in hotspots subtracted from measured emissions from urine-N deposition in calculating EF3 values and discounting the effects of disproportionate N inputs in intensive agriculture on increased background emissions (legacy effect). This IPCC inventory method does not account for the legacy effect of N loading prior to the measurements which may underestimate the emissions. Thus, an allowance for higher hotspot background emissions could be included in the Inventory to accurately estimate total emissions from agriculture.

Soil temperature is a key parameter affecting changes in the balance among energy flow, water flow, nutrient cycling, and ecological stability. In previous research on soil temperature estimation, field measurement has been valuable. However its value is uncertain for regional scale research. A regression equation algorithm approach introduces the environmental factors into consideration. The correlations between soil temperature and environmental covariates may not be linear, and most of them conform to a parabolic relationship. Therefore, based on 53 years' data (1958-2010) from 140 meteorological stations in Australia, this study applied a 3-parameter cosine wave model to fit the monthly air and soil temperature. Further, this study identified the correlations between the parameters and landscape factors using stepwise regression. The root mean square error (RMSE) and Nash-Sutcliffe efficiency coefficient (NS) were used to evaluate the accuracy of the model. The results showed that: (1) The cosine-wave function can effectively identify the monthly air/soil temperature fluctuation and the prediction accuracy increases gradually from north to south in Australia. (2) The precipitation, horizontal irradiation and elevation are predominant factors that affecting soil temperature fluctuation given a defined air temperature fluctuation. (3) Landscape factors regulate the hysteresis of air/soil temperature fluctuation. The hysteresis of soil temperature response to air temperature in Australia is in order of duration: central > south > north. Therefore, the application of the cosine function is a useful and novel technique for estimation of soil temperature, which could provide better understanding of how soil temperature fluctuation responds to the global warming.

The effects of combined biochar and double inhibitor application on gaseous nitrogen (N; nitrous oxide [N2O] and ammonia [NH3]) emissions and N leaching in paddy soils remain unclear. We investigated the effects of biochar application at different rates and double inhibitor application (hydroquinone [HQ] and dicyandiamide [DCD]) on NH3 and N2O emissions, N leaching, as well as rice yield in a paddy field, with eight treatments, including conventional urea N application at 280 kg N ha−1 (CN); reduced N application at 240 kg N ha−1 (RN); RN + 7.5 t ha−1 biochar (RNB1); RN + 15 t ha−1 biochar (RNB2); RN + HQ + DCD (RNI); RNB1 + HQ + DCD (RNIB1); RNB2 + HQ + DCD (RNIB2); and a control without N fertilizer. When compared with N leaching under RN, biochar application reduced total N leaching by 26.9–34.8% but stimulated NH3 emissions by 13.2–27.1%, mainly because of enhanced floodwater and soil NH4+-N concentrations and pH, and increased N2O emission by 7.7–21.2%, potentially due to increased soil NO3−-N concentrations. Urease and nitrification inhibitor addition decreased NH3 and N2O emissions, and total N leaching by 20.1%, 21.5%, and 22.1%, respectively. Compared with RN, combined biochar (7.5 t ha−1) and double inhibitor application decreased NH3 and N2O emissions, with reductions of 24.3% and 14.6%, respectively, and reduced total N leaching by up to 45.4%. Biochar application alone or combined with double inhibitors enhanced N use efficiency from 26.2% (RN) to 44.7% (RNIB2). Conversely, double inhibitor application alone or combined with biochar enhanced rice yield and reduced yield-scaled N2O emissions. Our results suggest that double inhibitor application alone or combined with 7.5 t ha−1 biochar is an effective practice to mitigate NH3 and N2O emission and N leaching in paddy fields.