Rechercher

In recent years, plastic greenhouse vegetable cultivation (PGVC) has expanded worldwide, particularly in China, where it accounts for more than 90% of all global PGVC operations. As compared with conventional agricultural methods, PGVC has doubled crop yields by extending growing seasons and intensifying agriculture. PGVC also offers more ecosystem services relative to conventional approaches, including greater soil carbon sequestration, lower water consumption, and improved soil protection at regional scales. The economic benefits of this easily implemented agricultural method are attractive to small‐holder farmers. However, greater environmental impacts (eg greenhouse‐gas emissions, generation of large amounts of plastic waste) are associated with PGVC than with conventional approaches. Here, we review what is currently known about PGVC and identify future research priorities that will comprehensively assess the ecosystem services offered by this method of cultivation, as well as its environmental impacts and socioeconomic benefits.

Abstract Sources of methane ( CH 4 ) become highly variable for countries undergoing a heightened period of development due to both human activity and climate change. An urgent need therefore exists to budget key sources of CH 4 , such as wetlands (rice paddies and natural wetlands) and lakes (including reservoirs and ponds), which are sensitive to these changes. For this study, references in relation to CH 4 emissions from rice paddies, natural wetlands, and lakes in C hina were first reviewed and then reestimated based on the review itself. Total emissions from the three CH 4 sources were 11.25 Tg CH 4  yr −1 (ranging from 7.98 to 15.16 Tg CH 4  yr −1 ). Among the emissions, 8.11 Tg CH 4  yr −1 (ranging from 5.20 to 11.36 Tg CH 4  yr −1 ) derived from rice paddies, 2.69 Tg CH 4  yr −1 (ranging from 2.46 to 3.20 Tg CH 4  yr −1 ) from natural wetlands, and 0.46 Tg CH 4  yr −1 (ranging from 0.33 to 0.59 Tg CH 4  yr −1 ) from lakes (including reservoirs and ponds). Plentiful water and warm conditions, as well as its large rice paddy area make rice paddies in southeastern C hina the greatest overall source of CH 4 , accounting for approximately 55% of total paddy emissions. Natural wetland estimates were slightly higher than the other estimates owing to the higher CH 4 emissions recorded within Q inghai‐ T ibetan P lateau peatlands. Total CH 4 emissions from lakes were estimated for the first time by this study, with three quarters from the littoral zone and one quarter from lake surfaces. Rice paddies, natural wetlands, and lakes are not constant sources of CH 4 , but decreasing ones influenced by anthropogenic activity and climate change. A new progress‐based model used in conjunction with more observations through model‐data fusion approach could help obtain better estimates and insights with regard to CH 4 emissions deriving from wetlands and lakes in C hina.

Abstract With a pace of about twice the observed rate of global warming, the temperature on the Qinghai‐Tibetan Plateau (Earth's ‘third pole’) has increased by 0.2 °C per decade over the past 50 years, which results in significant permafrost thawing and glacier retreat. Our review suggested that warming enhanced net primary production and soil respiration, decreased methane ( CH 4 ) emissions from wetlands and increased CH 4 consumption of meadows, but might increase CH 4 emissions from lakes. Warming‐induced permafrost thawing and glaciers melting would also result in substantial emission of old carbon dioxide ( CO 2 ) and CH 4 . Nitrous oxide ( N 2 O ) emission was not stimulated by warming itself, but might be slightly enhanced by wetting. However, there are many uncertainties in such biogeochemical cycles under climate change. Human activities (e.g. grazing, land cover changes) further modified the biogeochemical cycles and amplified such uncertainties on the plateau. If the projected warming and wetting continues, the future biogeochemical cycles will be more complicated. So facing research in this field is an ongoing challenge of integrating field observations with process‐based ecosystem models to predict the impacts of future climate change and human activities at various temporal and spatial scales. To reduce the uncertainties and to improve the precision of the predictions of the impacts of climate change and human activities on biogeochemical cycles, efforts should focus on conducting more field observation studies, integrating data within improved models, and developing new knowledge about coupling among carbon, nitrogen, and phosphorus biogeochemical cycles as well as about the role of microbes in these cycles.

Abstract Spatial analog techniques consist in identifying locations whose historical climate is similar to the anticipated future climate at a reference location. In the process of identifying analogs, one key step is the quantification of the dissimilarity between two climates separated in time and space, which involves the choice of a metric. In this study, six a priori suitable metrics are described (the standardized Euclidean distance, the Kolmogorov–Smirnov statistic, the nearest-neighbor distance, the Zech–Aslan energy statistic, the Friedman–Rafsky runs statistic, and the Kullback–Leibler divergence) and criteria are proposed and investigated in an attempt to identify the best metric for selecting spatial analogs. The case study involves the use of numerical simulations performed with the Canadian Regional Climate Model (CRCM, version 4.2.3), from which three annual indicators (total precipitation, heating degree-days, and cooling degree-days) are calculated over 30-yr periods (1971–2000 and 2041–70). It is found that the six metrics identify comparable analog regions at a relatively large scale but that best analogs may differ substantially. For best analogs, it is shown that the uncertainty stemming from the metric choice does not generally exceed that stemming from the simulation or model choice. On the basis of the set of criteria considered in this study, the Zech–Aslan energy statistic stands out as the most recommended metric for analog studies, whereas the Friedman–Rafsky runs statistic is the least recommended.

A bstract Developing models to predict tree mortality using data from long‐term repeated measurement data sets can be difficult and challenging due to the nature of mortality as well as the effects of dependence on observations. Marginal (population‐averaged) generalized estimating equations (GEE) and random effects (subject‐specific) models offer two possible ways to overcome these effects. For this study, standard logistic, marginal logistic based on the GEE approach, and random logistic regression models were fitted and compared. In addition, four model evaluation statistics were calculated by means of K ‐fold cross‐valuation. They include the mean prediction error, the mean absolute prediction error, the variance of prediction error, and the mean square error. Results from this study suggest that the random effects model produced the smallest evaluation statistics among the three models. Although marginal logistic regression accommodated for correlations between observations, it did not provide noticeable improvements of model performance compared to the standard logistic regression model that assumed impendence. This study indicates that the random effects model was able to increase the overall accuracy of mortality modeling. Moreover, it was able to ascertain correlation derived from the hierarchal data structure as well as serial correlation generated through repeated measurements.