收费全文 | 524篇 |
免费 | 53篇 |
国内免费 | 1篇 |
林业 | 35篇 |
农学 | 27篇 |
基础科学 | 2篇 |
64篇 | |
综合类 | 36篇 |
农作物 | 21篇 |
水产渔业 | 92篇 |
畜牧兽医 | 230篇 |
园艺 | 5篇 |
植物保护 | 66篇 |
2024年 | 2篇 |
2023年 | 11篇 |
2022年 | 12篇 |
2021年 | 22篇 |
2020年 | 27篇 |
2019年 | 28篇 |
2018年 | 22篇 |
2017年 | 27篇 |
2016年 | 21篇 |
2015年 | 18篇 |
2014年 | 27篇 |
2013年 | 31篇 |
2012年 | 44篇 |
2011年 | 35篇 |
2010年 | 19篇 |
2009年 | 23篇 |
2008年 | 19篇 |
2007年 | 24篇 |
2006年 | 19篇 |
2005年 | 18篇 |
2004年 | 10篇 |
2003年 | 23篇 |
2002年 | 12篇 |
2001年 | 11篇 |
2000年 | 14篇 |
1999年 | 9篇 |
1998年 | 1篇 |
1997年 | 9篇 |
1996年 | 1篇 |
1995年 | 1篇 |
1994年 | 1篇 |
1993年 | 2篇 |
1992年 | 4篇 |
1991年 | 6篇 |
1990年 | 2篇 |
1989年 | 4篇 |
1988年 | 1篇 |
1987年 | 6篇 |
1986年 | 4篇 |
1985年 | 3篇 |
1984年 | 1篇 |
1983年 | 2篇 |
1972年 | 1篇 |
1970年 | 1篇 |
Purpose
In this study, we quantified soil organic carbon (SOC) stocks and analyzed their relationship with biophysical factors and soil properties.Materials and methods
The study region was Veracruz State, located in the eastern part of Mexico, covering an area of 72,410 km2. A soil database that contains physicochemical analyses of soil horizons such as carbon concentration data was the source of information used in this study. The database consisted of 163 soil profiles representing 464 genetic horizons. Statistical analysis was used to investigate the effect of each factor (climate, altitude, slope) on SOC stock to 0.50 m depth and to assess differences in the distribution of SOC stock in terms of soil depth (0.0–0.20, 0.20–0.40, 0.40–0.60, 0.60–0.80, 0.80–1.0 m) and land use. In order to compute the spatial distribution of SOC stock to 0.50 m depth based on the soil sampling location, the kriging method was used.Results and discussion
Results indicated that SOC stock (0.50 m depth) ranged between 0.44 and 41.2 kg C m?2. Regression analysis showed that SOC stocks (0.50 m depth) are negatively correlated with temperature (r?=??0.38; P?<?0.001) and positively correlated with altitude (r?=?0.40; P?<?0.001) and slope (r?=?0.40; P?<?0.001). In addition, by multiple regression, temperature combined with precipitation explained more SOC stock variations (r?=?0.43; P?<?0.001) than the regression model with precipitation (r?=?0.13; P?=?0.16) alone. Also, slope combined with temperature and precipitation explained more SOC stock variations (r?=?0.46; P?<?0.001) than the regression model with slope alone. Forest lands, grasslands, and croplands have higher SOC stocks in the 0.0–0.20-m soil layer than in deeper layers. On average, forest lands, grasslands, croplands, and other lands (wetland and dunes) had a SOC stock of 13.6, 14.6, 15.1, and 8.5 kg C m?2 at 1 m depth, respectively. Soil color correlated (?0.25 ≤ r ≤ ?0.89) with SOC content.Conclusions
Overall, these results indicate the influence of major interactions between biophysical factors and SOC stocks. This research indicated that SOC stock decreased with soil depth, but with slight variations depending on land use. Thus, there remains a need for more SOC data that include an improved distribution of soil sampling points in order to entirely understand the contributions of biophysical factors to SOC stocks in Veracruz State. 相似文献Soil is one of the most important factors for agricultural production. In tropical regions, soil variability is considerable, with the most diverse combinations of physical and chemical characteristics, an influence factor in crop growth and productivity. In this research, the main objective was to identify how soil characteristics and parent material can influence sugarcane development over time using remote sensing. An area located in Sao Paulo, Brazil, of 182 ha (one point per ha with soil analysis), with high variability in the parent material and soil types, was selected. Images from the Sentinel2-MSI satellite were used to describe the spectral behavior of sugarcane over a period of one year. The NDRE (normalized difference red-edge index) was calculated for each image and then the leaf area index (LAI) was obtained from it. Maps of soil classes, soil properties at two depths (0–0.20 and 0.80–1.0 m), and parent material classes were related to sugarcane LAI variability over time. Production environment zones, which is a classification based on soil characteristics to support sugarcane development, were also obtained and related to LAI variability. Spectral signatures of the crop presented different behaviors through the season, soil types and soil attributes provided useful responses for this variability. At the beginning of the season, the surface and subsurface soil properties (texture and fertility) impacted differently on crop development. On the other hand, soil classes and parent material influenced LAI in all production environments studied. The results indicated that the soil types and their properties at different depths have a significant impact on sugarcane development. Furthermore, RS was able to monitor the plant evolution and be related to soil types which may assist in plant management. The results can bring light on how better sugarcane management can be conducted using remote sensing data and soils variability.
相似文献