The data set records the statistical data of grassland type area and livestock carrying capacity in Haidong area of Qinghai Province in 1988 and 2012. The data are classified and counted according to the grassland group code, such as: I represents Alpine dry grassland, II represents mountain dry grassland, III represents Alpine desert, B represents medium grass group, J represents shrub group, etc, For specific grassland group type codes and their corresponding meanings, see "description of grassland group type codes in Qinghai Province. PDF" in the data set. The data are compiled from the grassland station of Qinghai Province and the grassland resources statistics of Qinghai Province issued in 1988 and 2012. The data set contains three data tables, which are: statistical data of grassland area and livestock carrying capacity of various types in Haidong area (1988), statistical data of grassland area and livestock carrying capacity in Haidong area (2012) and description of grassland group code in Qinghai Province. The data table structure is similar. For example, there are 8 fields in the statistical data (2012) of grassland type, area and livestock carrying capacity in Haidong area: Field 1: type code Field 2: grassland type name Field 3: grassland area Field 4: available area of grassland Field 5: average unit yield of fresh grass Field 6: average unit yield of edible fresh grass Field 7: stocking capacity Field 8: grassland type, etc
AGRICULTURAL AND RURAL Department of Qinghai Province
The Tibetan Plateau in China covers six provinces including Tibet, Qinghai, Xinjiang, Yunnan, Gansu and Sichuan, including Tibet and Qinghai, as well as parts of Xinjiang, Yunnan, Gansu and Sichuan. The research on water and soil resources matching aims to reveal the equilibrium and abundance of water resources and land resources in a certain regional scale. The higher the level of consistency between regional water resources and the allocation of cultivated land resources, the higher the matching degree, and the superior the basic conditions of agricultural production. The general agricultural water resource measurement method based on the unit area of cultivated land is used to reflect the quantitative relationship between the water supply of agricultural production in the study area and the spatial suitability of cultivated land resources. The Excel file of the data set contains the generalized agricultural soil and water resource matching coefficient data of the Tibetan Plateau municipal administrative region in China from 2008 to 2015, the vector data is the boundary data of the Tibetan Plateau municipal administrative region in China in 2004, and the raster data pixel value is the generalized agricultural soil and water resource matching coefficient of the year in the region.
DONG Qianjin, DONG Lingxiao
By applying Supply-demand Balance Analysis, the water resource supply and demand of the whole river basin and each county or district were calculated and used to evaluate the vulnerability of the water resources system of the basin. The IPAT equation was used to set a future water resource demand scenario to establish the scenario by setting variables such as future population growth rate, economic growth rate, and unit GDP water consumption. By taking 2005 as the base year and using assorted forecasting data of population size and economic scale, the future water demand scenarios of various counties and cities from 2010 to 2050 were predicted. By applying the basic structure of the HBV conceptual hydrological model of the Swedish Hydrometeorological Institute, a model of the variation tendency of the basin under climate change was designed. The glacial melting scenario was used as the model input to construct the runoff scenario under climate change. According to the national regulations of the water resources allocation of the basin, a water distribution plan was set up to calculate the water supply comprehensively. Considering of the supply and demand situation, the water resource system vulnerability was evaluated by the water shortage rate. By calculating the (grain production) land pressure index of the major counties and cities in the basin, the balance of supply and demand of land resources under the climate change, glacial melt and population growth scenarios was analyzed, and the vulnerability of the agricultural system was evaluated. The Miami formula and HANPP model were used to calculate the human appropriation of net primary biomass and primary biomass in the major counties and cities for the future, and the vulnerability of ecosystems from the perspective of supply and demand balance was assessed.
YANG Linsheng, ZHONG Fanglei
The data set include crop leaf stomatal conductance observed at four sample regions, that is the soil moisture control experimental field at Daman county, and the super station, and Shiqiao sample plots at Wuxing village in Zhangye city. 1) Objective Crop leaf stomatal conductance, a key biophysical parameter, was observed as model parameter or a priori knowledge for crop growth model, or evapotranspiration estimation. 2) Measuring instruments Leaf porometer. 3) Measuring site a. the soil moisture control experimental field at Daman county, Twelve soil water treatments are set. The crop leaf stomatal conductance for each treatment is measured on 17, 23 and 29 May, and 3, 9, 14 and 24 June, and 5 and 12 July. b. the Super Station The crop leaf stomatal conductance at the super station is measured on 22 and 28 May, 5, 11, 18, and 25 June, and 1, 8, 15, 22 and 31 July, 9, 15 and 22 August, and 3 and 11 September. c. the Shiqiao sample site The crop leaf stomatal conductance at the Shiqiao village is measured on 17, 22 and 28 May, 4, 11, 17 and 25 June, 1, 8, 15, 22, and 30 July, 8, 16 and 27 August, and 9 September. 4) Data processing The observational data was recorded in the sheets and reorganized in the EXCEL sheets. The time used in this dataset is in UTC+8 Time.
Xu Fengying, Wang Jing, Huang Yongsheng, LI Xin, MA Mingguo
1. Overview of data This data is based on the latest googleearth remote sensing image data to establish the spatial distribution database of crops in Ganzhou District of Zhangye City. 2. Data content Based on the spatial distribution of maize seed production focused by the project, the land use types in the study area are divided into 14 types (maize seed production land, spring wheat land, vegetable land, greenhouse land, intercropping land, rice land, water area, wetland, forest land, urban and rural industrial and mining residential land, roads, railways and unused land). 3. Space-time range The data range includes 19 villages and towns including Pingshanhu, Shajing, Wujiang River, Jingan, Mingyong, Sanzha, Ganjun, Xindun, Shangqin, Jiantan, Chengguan Town, Liangjiadun, Chang 'an, Dangzhai, Xiaoman, Longqu, Daman, Huazhai and Anyang. The data type is vector polygon and stored in Shape format. The data range covers Ganzhou District.
According to the characteristics of the selected field and its surrounding area, a trime tube is arranged in the corn field, and 5 trime tubes are arranged in a direction perpendicular to the field path. When monitoring soil moisture content in the TDR vertical direction, the unit is every 10cm. Monitor down. Location: N 38 ° 52′27.6 ″ E 100 ° 21′14.0 ″ The submitted data includes the water content of the farmland and its surrounding soil (TDR monitoring) after three irrigations in a selected farmland in Yingke Irrigation District, encrypted monitoring after irrigation, one group every 3 hours within 24 hours, and 3 groups per day for the next 5 days. -10 days are two groups per day, and 10-15 days are one group per day.
HUANG Guanhua, JIANG Yao
The field experiments of water consumption and irrigation water productivity of corn and cotton were arranged in 2012 and 2013, and the field experiments of irrigation water productivity of corn and sunflower under different mulching and cultivation methods were arranged in 2014. The characteristics of water consumption and irrigation water demand of three crops under different soil conditions, as well as the relationship between key soil properties and crop yield and irrigation water productivity were obtained.
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