地下滴灌埋管深度对冬小麦根冠生长及水分利用效率的影响

在试验室内 ,进行了地下滴灌埋管深度对不同生育时期冬小麦根系生长和地上部分生长影响的试验研究。结果表明 :2 0 cm和 4 0 cm埋管深度的水分分布特征 ,在生育早期抑制地上部分营养生长 ,促进冬小麦蹲苗 ;而中后期则促进生殖生长 ,产量和水分利用效率较高。不考虑犁底层影响时 ,在重壤土上 4 0 cm是冬小麦进行地下滴灌的较好埋深

Precipitation-use efficiency along a 4500-km grassland transect

National Natural Science Foundation of China [30590381, 40971027]; State Key Technologies RD Program [2006BAC08]; Chinese Academy of Sciences ; National Key Research and Development Program [2010CB833501]

Spatial and temporal flows of China's forest resources: Development of a framework for evaluating resource efficiency

Science & Technology Basic Work Program of China: Scientific Survey of the Middle-lower Reaches of Lantsang River and the Great Shangri-La Region [2008FY110300]; National Basic Research Program of China (973 Program): Ecosystem Services and Ecological Safety of the Major Terrestrial Ecosystems of China [2009CB421106]; National Natural Science Foundation of China [30670374]; EU ; European Commission, DG Research [003874]

Evaluation for use efficiency of agricultural resources in grain production: A case study of Changshu, Taihe and Ansai in China

National Natural Science Foundation of China [70673097]

Morphological Adaptations to Drought and Reproductive Strategy of the Moss Syntrichia caninervis in the Gurbantunggut Desert, China

The biological soil crusts (BSCs) in the Gurbantunggut Desert, the largest fixed and semi-fixed desert in China, feature moss-dominated BSCs, which play an indispensable role in sand fixation. Syntrichia caninervis Mitt. (S. caninervis) serves as one of the most common species in BSCs in the desert. In this study we examined the morphological structure of S. caninervis from leafy gametophyte to protonema using light and scanning electron microscopy (SEM). We also examined the relationships between the morphological structure of S. caninervis and environmental factors. We found that: (1) this moss species is commonly tufted on the sand surface, and its leaves are folded upwards and twisted around the stem under dry conditions; (2) the cells on both upper and lower leaf surfaces have C-shaped dark papillae, which may reflect sunlight to reduce the damage from high temperature; (3) the leaf costa is excurrent, forming an awn with forked teeth; and (4) the protonema cells are small and thickset with thick cell walls and the cytoplasm is highly concentrated with a small vacuole. In addition, we also found that the protonema cells always form pouches on the tip of the mother cells during the process of cell polarization. Our results suggest that S. caninervis has, through its life cycle, several morphological and structural characteristics to adapt to dry environmental conditions. These morphological features of S. caninervis may also be found in other deserts in the world due to the world-wide distribution of the species.

Radiation-use efficiency and the harvest index of winter wheat at different nitrogen levels and their relationships to canopy spectral reflectance

Radiation-use efficiency (RUE, g/MJ) and the harvest index (HI, unitless) are two helpful characteristics in interpreting crop response to environmental and climatic changes. They are also increasingly important for accurate crop yield simulation, but they are affected by various environmental factors. In this study, the RUE and HI of winter wheat and their relationships to canopy spectral reflectance were investigated based on the massive field measurements of five nitrogen (N) treatments. Crop production can be separated into light interception and RUE. The results indicated that during a long period of slow growth from emergence to regreening, the effect of N on crop production mainly showed up in an increased light interception by the canopy. During the period of rapid growth from regreening to maturity, it was present in both light interception and RUE. The temporal variations of RUEAPAR (aboveground biomass produced per unit of photosynthetically active radiation absorbed by the canopy) during the period from regreening to maturity had different patterns corresponding to the N deficiency, N adequacy and N-excess conditions. Moreover, significant relationships were found between the RUEAPAR and the accumulative normalised difference vegetation index (NDVI) in the integrated season (R-2 = 0.68), between the HI and the accumulative NDVI after anthesis (R-2 = 0.89), and between the RUEgrain (ratio of grain yield to the total amount of photosynthetically active radiation absorbed by the canopy) and the accumulative NDVI of the whole season (R-2 = 0.89) and that after anthesis (R-2 = 0.94). It suggested that canopy spectral reflectance has the potential to reveal the spatial information of the RUEAPAR, HI and RUEgrain. It is hoped that this information will be useful in improving the accuracy of crop yield simulation in large areas.

Study of remote sensing based parameter uncertainty in Production Efficiency Models

The remote sensing based Production Efficiency Models (PEMs), springs from the concept of "Light Use Efficiency" and has been applied more and more in estimating terrestrial Net Primary Productivity (NPP) regionally and globally. However, global NPP estimates vary greatly among different models in different data sources and handling methods. Because direct observation or measurement of NPP is unavailable at global scale, the precision and reliability of the models cannot be guaranteed. Though, there are ways to improve the accuracy of the models from input parameters. In this study, five remote sensing based PEMs have been compared: CASA, GLO-PEM, TURC, SDBM and VPM. We divided input parameters into three categories, and analyzed the uncertainty of (1) vegetation distribution, (2) fraction of photosynthetically active radiation absorbed by the canopy (fPAR) and (3) light use efficiency (e). Ground measurements of Hulunbeier typical grassland and meteorology measurements were introduced for accuracy evaluation. Results show that a real-time, more accurate vegetation distribution could significantly affect the accuracy of the models, since it's applied directly or indirectly in all models and affects other parameters simultaneously. Higher spatial and spectral resolution remote sensing data may reduce uncertainty of fPAR up to 51.3%, which is essential to improve model accuracy.