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Traditional Ecological Knowledge (TEK) is one of the components of the Globally Important Agricultural Heritage Systems (GIAHS), which are good examples of evolutionary adapted socio-ecosystems in human history. The Hani Rice Terraces System, located in China's southwestern Yunnan Province, is a living example of GIAHS. The Hani Rice Terraces system has existed for more than one thousand years, following TEK related to cultivation and natural resources management, which was collected and practiced continually. Over this long time period, TEK has enabled the Hani people to manage their terraces and other natural resources in a sustainable way. This paper concentrates on the TEK transferring in the current Hani community, taking a small village, Mitian, as an example. Grouping the interviewees into three different age groups (young group, OPEN ACCESSSustainability 2014, 6 4498 0-30 years old; middle-age group, 31-50 years old; old group > 50 years old), we investigated their understanding and participation in 13 items of TEK in relation to rice cultivation and water utilization. The items of TEK were divided into four categories, namely "Festivals", "Beliefs", "Folk Songs", and "Water Management". From the data collected, it was learned that all the items of TEK are well known, but not necessarily practiced. Age and gender have significant influences on farmers' understanding and participation in TEK. Our analysis suggested that both the knowledge and the practice showed declining trends from the older to the younger age group. Men and women behave differently in practices. In general, it is shown that TEK is declining in the Hani villages which will affect the rice terrace system in ways that are yet unknown. It is likely that a blended TEK, with old and new knowledge and practices, will emerge to sustain the upland rice terrace systems of Yunnan.

Abstract: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 30ˆ30 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.

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Crop production and water use in rainfed cropland are vulnerable to climate change. This study was to quantify diverse responses of winter wheat (Triticum aestivum L.) yield and water use to climate change on the Loess Plateau (LP) under different combinations of climatic variables. The crop model APSIM was validated against field experimental data and applied to calculate yield and water use at 18 sites on the LP during 1961 to 2010. The coefficient of variation of yield ranged from 12 to 66%, in which the vulnerability of yield increased from the southeast (12%) to the northwest (66%). This change was attributed to the gradual increase in precipitation variation from the southeast to the northwest. An obvious warming trend during 1961 to 2010 resulted in a significant decrease in the growth duration by 1 to 5 d decade -1 . The yield at 12 sites was significantly reduced by 120 to 720 kg ha -1 decade -1 . Evapotranspiration was significantly decreased by 1 to 26 mm decade -1 ; however, water use efficiency at most sites showed no significant trend. Eighteen sites were classified into three climatic zones by cluster analysis: high temperature-high precipitation-low radiation (HHL), medium temperature-medium precipitation-medium radiation (MMM), and low temperature-low precipitation-high radiation (LLH). The trend of decreasing yield was smallest in the HHL cluster because of a minimal reduction in precipitation, while decreasing trends in yield and evapotranspiration were larger in the LLH and MMM because of larger reductions in precipitation. The results imply that among strategies such as breeding for long duration or drought tolerance, modification of the planting date will be necessary to avoid high temperatures associated with climate change. Abbreviations: ET, evapotranspiration; HHL, high temperature-high precipitation-low radiation cluster; LAI, leaf area index; LLH, low temperature-low precipitation-high radiation cluster; LP, Loess Plateau; MMM, medium temperature-medium precipitation-medium radiation cluster.Climatic factors such as solar radiation, precipitation, and temperature are major determinants of crop production and water use. Climate change is characterized by increased surface temperature and modified precipitation patterns (Intergovernmental Panel on Climate Change, 2007). Thus, crop production and water use are influenced by climate change. Consequently, a better understanding of the interactions between climatic factors and their impacts on crop production is essential for optimizing crop management, improving water use efficiency, and adopting reasonable strategies to mitigate climate change (Yu et al., 2014).The Loess Plateau (LP, Fig. 1) is located in northern China, where dryland agriculture is the primary economic activity. The LP has a typical continental monsoon climate, which means that it is cold in the winter and warm and humid in the summer. The average temperature ranges from 3.6°C in the north to 14.3°C in the south (Sun et al., 2010). Agriculture on the LP is vulner...

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The study of wildlife activity patterns is an effective approach to understanding fundamental ecological and evolutionary processes. However, traditional statistical approaches used to conduct quantitative analysis have thus far had limited success in revealing underlying mechanisms driving activity patterns. Here, we combine wavelet analysis, a type of frequency‐based time‐series analysis, with high‐resolution activity data from accelerometers embedded in GPS collars to explore the effects of internal states (e.g., pregnancy) and external factors (e.g., seasonal dynamics of resources and weather) on activity patterns of the endangered giant panda (Ailuropoda melanoleuca). Giant pandas exhibited higher frequency cycles during the winter when resources (e.g., water and forage) were relatively poor, as well as during spring, which includes the giant panda's mating season. During the summer and autumn when resources were abundant, pandas exhibited a regular activity pattern with activity peaks every 24 hr. A pregnant individual showed distinct differences in her activity pattern from other giant pandas for several months following parturition. These results indicate that animals adjust activity cycles to adapt to seasonal variation of the resources and unique physiological periods. Wavelet coherency analysis also verified the synchronization of giant panda activity level with air temperature and solar radiation at the 24‐hr band. Our study also shows that wavelet analysis is an effective tool for analyzing high‐resolution activity pattern data and its relationship to internal and external states, an approach that has the potential to inform wildlife conservation and management across species.