Your search found 7 records
1 Yamaguchi-Shinozaki, K.; Kasuga, M.; Liu, Q.; Nakashima, K.; Sakuma, Y.; Abe, H.; Shinwari, Z. K.; Seki, M.; Shinozaki, K. 2002. Development of drought-resistant and water-stress tolerant crops through biotechnology. In Yajima, M.; Okada, K.; Matsumoto, N. (Eds.), Water for sustainable agriculture in developing regions รป More crop for every scarce drop: Proceedings of the 8th JIRCAS International Symposium, Tsukuba, 27-28 November 2001. Ibaraki, Japan: JIRCAS. pp.23-34.
(Location: IWMI-HQ Call no: 631.7.2 G000 YAJ Record No: H031510)
2 Yuan, F.; Xie, Z. H.; Liu, Q.; Xia, J. 2005. Simulating hydrologic changes with climate change scenarios in the Haihe River Basin. Pedosphere, 15(5):595-600.
River basins ; Climate change ; Hydrology ; Simulation models ; Calibration ; Runoff / China / Haihe River Basin
(Location: IWMI-HQ Call no: PER Record No: H037922)
3 Hao, W.; Mei, X.; Cai, Xueliang; Du, J.; Liu, Q.. 2011. Crop planting extraction based on multi-temporal remote sensing data in Northeast China. In Chinese. Transactions of the Chinese Society of Agricultural Engineering, 27(1):201-207. [doi: https://doi.org/ 10.3969/j.issn.1002-6819.2011.01.033]
(Location: IWMI HQ Call no: e-copy only Record No: H043831)
(1.88 MB)
Crop area and its spatial distribution are generally considered to be essential data inputs for crop yield estimation, assessment of water productivity and adjustment of cropping structure to support science and policy applications focused on understanding the role and response of the agricultural sector to environmental change issues. The objective of this research was to evaluate the applicability of time-series MODIS 250m normalized difference vegetation index (NDVI) data for large-area crop mapping over Northeast China. Spatial pattern of crop planting was obtained based on 16-day time-series MODIS 250m NDVI data from 2007 to 2008, Landsat enhanced thematic mapper plus (ETM+) images, and ground truth data using Optimal Iteration Unsupervised Classification, spectral matching technique (SMT) and Google Earth. Sub-pixel area fraction estimate was applied to estimate cropland area, rice area, spring maize area and soybean area. We found that the position precision was 85.7%, their correlation coefficient compared with statistic was 0.916, 0.685, 0.746 and 0.681 respectively, and that there was significant difference between these groups by using paired samples test. Results indicated that the method can accurately reflect various crop distributions in Northeast China and be applied for large-area crops classification and crop planting extraction.
4 Liu, Q.; Yan, C.; Yang, J.; Mei, X.; Hao, W.; Ju, H. 2015. Impacts of climate change on crop water requirements in Huang-Huai-Hai Plain, China. In Hoanh, Chu Thai; Johnston, Robyn; Smakhtin, Vladimir. Climate change and agricultural water management in developing countries. Wallingford, UK: CABI. pp.48-62. (CABI Climate Change Series 8)
Climate change ; Water requirements ; Weather ; Meteorological stations ; Crop production ; Evapotranspiration ; Winter wheat ; Precipitation ; Solar radiation ; Wind speed ; Relative humidity ; Temperature / China / Huang-Huai-Hai Plain
(Location: IWMI HQ Call no: IWMI Record No: H047371)
(740 KB)
5 Liu, Q.. 2017. WEF nexus cases from California with climate change implication. In Salam, P. A.; Shrestha, S.; Pandey, V. P.; Anal, A. K. (Eds.). Water-energy-food nexus: principles and practices. Indianapolis, IN, USA: Wiley. pp.151-162.
Climate change ; Water resources ; Water availability ; Water supply ; Water use ; Food security ; Energy resources ; Energy demand ; Nexus ; Groundwater ; Food production ; Electricity ; Greenhouse gases ; Emission ; Drought / USA / California
(Location: IWMI HQ Call no: IWMI Record No: H048745)
6 Wu, J.; Wang, X.; Zhong, B.; Yang, A.; Jue, K.; Wu, J.; Zhang, L.; Xu, W.; Wu, S.; Zhang, N.; Liu, Q.. 2020. Ecological environment assessment for greater Mekong Subregion based on pressure-state-response framework by remote sensing. Ecological Indicators, 117:106521. (Online first) [doi: https://doi.org/10.1016/j.ecolind.2020.106521]
Environmental Impact Assessment ; Ecological indicators ; Remote sensing ; Landsat ; Biodiversity ; Vegetation ; Land use ; Land cover ; Spatial distribution ; Farmland ; Ecosystems ; Anthropogenic factors ; Evapotranspiration ; Sustainable development / China / Myanmar / Lao People's Democratic Republic / Greater Mekong Subregion / Yunnan / Sipsongpanna
(Location: IWMI HQ Call no: e-copy only Record No: H049753)
(6.71 MB)
The environment project in the greater Mekong sub-region was the largest multi-field environmental cooperation launched by six countries (China, Vietnam, Laos, Myanmar, Thailand and Cambodia) in 2006, since the cooperation mechanism was established by Asian Development Bank (ADB) in 1992. How to establish the indicators to assess the achievements of the biological corridor construction and the status of ecological environment quantitatively is one of the prerequisites for the future project ongoing phase. The popular Pressure-State-Response (PSR) framework was employed in this study to assess the natural and human pressure, the healthy state of regional natural environment, and the subsequent response of ecosystem dynamic change in the Greater Mekong Subregion. Instead of using surveying based data as driving parameters, large amount of driving factors were retrieved from multi-source remote sensing data from 2000 to 2017, which provides access to larger updated and real-time databases, more tangible data allowing more objective goal management, and better spatially covered. The driving factors for pressure analysis included digital elevation, land surface temperature, evapotranspiration, light index, road network map, land cover dynamic change and land use degree, which were derived directly and indirectly from remote sensing. The indicators for state evaluation were composed of vegetation index, leaf area index, and fractional vegetation cover from remote sensing directly. The comprehensive response index was mainly determined by the pressure and state indicators. Through the analysis based on an overlay technique, it showed that the ecological environment deteriorated firstly from 2000 to 2010 and then started to improve from 2010 to 2017. The proofs indicated that the natural forest and wetland ecosystems were improved and the farmland area was decreased between 2000 and 2017. This study explored effective indicators from remote sensing for the ecological and environmental assessment, which can provide a strong decision-making basis for promoting the sustainable development of the ecological environment in the greater Mekong subregion, as well as the technological support for the construction of the biodiversity corridor.
7 Liu, Q.; Sun, X.; Wu, W.; Liu, Z.; Fang, G.; Yang, P. 2022. Agroecosystem services: a review of concepts, indicators, assessment methods and future research perspectives. Ecological Indicators, 142:109218. [doi: https://doi.org/10.1016/j.ecolind.2022.109218]
Agroecosystems ; Ecosystem services ; Indicators ; Assessment ; Agricultural development ; Sustainable Development Goals ; Agricultural production ; Crop yield ; Farmland ; Soil conservation ; Biodiversity
(Location: IWMI HQ Call no: e-copy only Record No: H051329)
https://www.sciencedirect.com/science/article/pii/S1470160X22006902/pdfft?md5=9828c9361aaaa1d72866eac891be5d91&pid=1-s2.0-S1470160X22006902-main.pdf
https://vlibrary.iwmi.org/pdf/H051329.pdf
https://vlibrary.iwmi.org/pdf/H051329.pdf
(7.29 MB) (7.29 MB)
Agroecosystems benefit from many ecosystem services and are frequently managed to increase productivity. In recent years, agricultural industrialization has caused the loss of some important ecosystem services in agroecosystems, hindering some sustainable development goals (SDGs). In order to promote sustainable agricultural development, it is necessary to restore the damaged agroecosystems and improve agroecosystem services (AES). However, there are relatively few studies on AES, and fewer studies concerning the definition or connotation of AES. Therefore, this paper reviews current AES research, indicators, and assessment methods, as well as directions for future research. AES are determined by agroecosystem functions and human agricultural practices, with both positive and negative effects, scale effects, and trade-offs and synergies between AES. AES indicators can be classified as provisioning services, regulating services, and cultural services, with a few studies including supporting services. Currently, the main AES assessment methods include public participation, empirical model, mechanism model, and value estimation. Multi-source data fusion for integrated models to assess multiple AES will be the future research trend. In addition, AES research should develop additional promising topics, including considering both AES and agroecosystem disservices (AEDS); assessing AES supply, demand, and flow; and analyzing AES trade-offs and synergies comprehensively. This will extend the research field to the links between AES and SDGs and their applications in agricultural landscape planning and governance. This review highlights the importance of AES research to more effectively manage agroecosystems and promote sustainable agricultural development.
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