Project description:Being subject to climate change and human intervention, the land-use pattern in the agro-pastoral ecotone of Northern China has undergone complex changes over the past few decades, which may jeopardize the provision of ecosystem services. Thus, for sustainable land management, ecosystem services should be evaluated and monitored. In this study, based on Landsat TM/ETM data, we quantitatively evaluated the losses of ecosystem service values (ESV) in three sections of the agro-pastoral ecotone from 1980-2015. The results were as follows: (1) the main characteristic of the land conversions was that a large area of grassland was converted into cultivated land in the agro-pastoral ecotone; (2) on the spatial scale, the ESV losses of the agro-pastoral ecotone can be called an "inclined surface" in the direction of the northeast to southwest, and the northeastern section of the agro-pastoral ecotone lost more ESV than the middle and northwest sections (p < 0.05), on the temporal scale, the order of losses was 1990-2000 > 1980-1990 > 2000-2015; (3) the agro-pastoral ecotone lost more ESV, which was mainly due to four kinds of land conversion, which were grassland that was transformed into cultivated land, grassland transformed into unused land, grassland transformed into built-up areas, and cultivated land transformed into built-up areas; (4) although these land conversions were curbed after the implementation of protection policies at the end of the 1990s, due to reduced precipitation and increasing temperatures, the agro-pastoral ecotone will face a more severe situation in the future; and, (5) during the period of 1990-2015, the overall dynamic processes of increasing population gradually expanded to the sparsely populated pastoral area. Therefore, we believe that human interventions are the main cause of ecological deterioration in the agro-pastoral ecotone. This study provides references for fully understanding the regional differences in the ecological and environmental effects of land use change and it helps to objectively evaluate ecological civilization construction in the agro-pastoral ecotone of Northern China.
Project description:The agro-pastoral ecotone of northern China is one of the areas most sensitive to global temperature change. To analyze the temporal and spatial trends of extreme temperature events in this area, we calculated the values of 16 extreme-temperature indices from 1960 to 2016 based on data from 45 national meteorological stations. We found that the coldest-temperature indices decreased significantly and the warmest-temperature indices increased significantly. The warming of night temperatures contributed more than warming of day temperatures to the overall warming trend. In addition, the warm-temperature indices appeared to be increasing since the late 1980s and early 1990s. Overall, though the four extremal indices showed an increasing trend, the rate of change in the minimum temperature was greater than that of the maximum temperature; thus, the minimum temperature contributed most strongly to the overall temperature increases. The growing season is being prolonged in higher-elevation areas, but vegetation maturation in lower-elevation areas has been accelerated by the high temperatures, potentially leading to a shorter growing season at low altitudes. However, the impacts of land-use changes caused by human activities on the temperature increases will require additional study.
Project description:Land use land cover (LULC) changes frequently in ecotones due to the large climate and soil gradients, and complex landscape composition and configuration. Accurate mapping of LULC changes in ecotones is of great importance for assessment of ecosystem functions/services and policy-decision support. Decadal or sub-decadal mapping of LULC provides scenarios for modeling biogeochemical processes and their feedbacks to climate, and evaluating effectiveness of land-use policies, e.g. forest conversion. However, it remains a great challenge to produce reliable LULC maps in moderate resolution and to evaluate their uncertainties over large areas with complex landscapes. In this study we developed a robust LULC classification system using multiple classifiers based on MODIS (Moderate Resolution Imaging Spectroradiometer) data and posterior data fusion. Not only does the system create LULC maps with high statistical accuracy, but also it provides pixel-level uncertainties that are essential for subsequent analyses and applications. We applied the classification system to the Agro-pasture transition band in northern China (APTBNC) to detect the decadal changes in LULC during 2003-2013 and evaluated the effectiveness of the implementation of major Key Forestry Programs (KFPs). In our study, the random forest (RF), support vector machine (SVM), and weighted k-nearest neighbors (WKNN) classifiers outperformed the artificial neural networks (ANN) and naive Bayes (NB) in terms of high classification accuracy and low sensitivity to training sample size. The Bayesian-average data fusion based on the results of RF, SVM, and WKNN achieved the 87.5% Kappa statistics, higher than any individual classifiers and the majority-vote integration. The pixel-level uncertainty map agreed with the traditional accuracy assessment. However, it conveys spatial variation of uncertainty. Specifically, it pinpoints the southwestern area of APTBNC has higher uncertainty than other part of the region, and the open shrubland is likely to be misclassified to the bare ground in some locations. Forests, closed shrublands, and grasslands in APTBNC expanded by 23%, 50%, and 9%, respectively, during 2003-2013. The expansion of these land cover types is compensated with the shrinkages in croplands (20%), bare ground (15%), and open shrublands (30%). The significant decline in agricultural lands is primarily attributed to the KFPs implemented in the end of last century and the nationwide urbanization in recent decade. The increased coverage of grass and woody plants would largely reduce soil erosion, improve mitigation of climate change, and enhance carbon sequestration in this region.
Project description:In the Tibetan agro-pastoral ecotone, which has an altitude of 4000 m above sea level, small-scale cropland tillage has been exploited on the grassland surrounding the houses of farmers and herdsmen. However, knowledge of the effects of land change from grassland to cropland on soil nutrients and microbial communities is poor. Here, we investigated the structure and assembly mechanism of bacterial communities in cropland (tillage) and grassland (non-tillage) from an agro-pastoral ecotone of Tibet. Results indicated that soil nutrients and composition of bacterial communities changed dramatically in the process of land-use change from grassland to cropland. The pH value and the content of total nitrogen, organic material, total potassium, and total phosphorus in cropland soil were well above those in grassland soil, whereas the soil bulk density and ammonia nitrogen content in grassland soil were higher than those in cropland soil. Proteobacteria (30.5%) and Acidobacteria (21.7%) were the key components in cropland soil, whereas Proteobacteria (31.5%) and Actinobacteria (27.7%) were the main components in grassland soils. Tillage promotes uniformity of bacterial communities in cropland soils. In particular, the higher migration rate may increase the coexistence patterns of the bacterial community in cropland soils. These results also suggest that the tillage promotes the migration and coexistence of bacterial communities in the grassland soil of an agro-pastoral ecotone. In addition, the stochastic process was the dominant assembly pattern of the bacterial community in cropland, whereas, in grassland soil, the community assembly was more deterministic. These findings provide new insights into the changes in soil nutrients and microbial communities during the conversion of grassland to cropland in the agro-pastoral ecotone.
Project description:Improving plant water use efficiency is a key strategy for the utilization of regional limited water resources as well as the sustainable development of agriculture industry. To investigate the effects of different land use types on plant water use efficiency and their mechanisms, a randomized block experiment was designed in the agro-pastoral ecotone of northern China during 2020-2021. The differences in dry matter accumulation, evapotranspiration, soil physical and chemical properties, soil water storage and water use efficiency and their relationships among cropland, natural grassland and artificial grassland were studied. The results show that: In 2020, the dry matter accumulation and water use efficiency of cropland were significantly higher than those of artificial and natural grassland. In 2021, dry matter accumulation and water use efficiency of artificial grassland increased significantly from 364.79 g·m-2 and 24.92 kg·ha-1·mm-1 to 1037.14 g·m-2 and 50.82 kg·ha-1·mm-1, respectively, which were significantly higher than cropland and natural grassland. The evapotranspiration of three land use types showed an increasing trend in two years. The main reason affecting the difference of water use efficiency was that land use type affected soil moisture and soil nutrients, and then changed the dry matter accumulation and evapotranspiration of plants. During the study period, the water use efficiency of artificial grassland was higher in years with less precipitation. Therefore, expanding the planted area of artificial grassland may be one of the effective ways to promote the full utilization of regional water resources.
Project description:Using national crop and livestock production records from 1961-2003 and satellite-derived data on pasture greenness from 1982-2003 we show that the productivity of crops, livestock, and pastures in Africa is predictably associated with the El Niño Southern Oscillation and the North Atlantic Oscillation. The causal relations of these results are partly understandable through the associations between the atmospheric fluctuations and African rainfall. The range of the explained among-year variation in crop production in Africa as a whole corresponds to the nutritional requirements for approximately 20 million people. Results suggest reduced African food production if the global climate changes toward more El Niño-like conditions, as most climate models predict. Maize production in southern Africa is most strongly affected by El Niño events. Management measures include annual changes in crop selection and storage strategies in response to El Niño Southern Oscillation-based and North Atlantic Oscillation-based predictions for the next growing season.
Project description:Four fertilization treatments were set up in the long-term fertilization experiment, including chemical fertilization alone, organic manure alone, organic manure with chemical fertilization and non-fertilization control. Raw sequence reads
Project description:Chengde city is located in the agro-pastoral transitional zone in northern China near the capital city of Beijing, which has experienced large-scale ecological construction in the past three decades. This study quantitatively assessed the environmental changes in Chengde through observation records of water resources, water environment, atmospheric environment, and vegetation activity and investigated the possible causes. From the late 1950s to 2002, the streamflow presented a downward trend induced by climate variability and human activities, with contribution ratios of 33.2% and 66.8%, respectively. During 2001-2012, the days of levels I and II air quality presented clear upward trends. Moreover, the air pollutant concentration was relatively low compared with that in the adjacent areas, which means the air quality has improved more than that in the neighboring areas. The water quality, which deteriorated during 1993-2000, began to improve in 2002. The air and water quality changes were closely related to pollutant emissions induced by anthropogenic activities. During 1982-2012, the vegetation in the southeastern and central regions presented restoration trends, whereas that in the northwestern area showed degradation trends. The pixels with obvious degradation trends correlated significantly with annual mean temperature and annual precipitation. Ecological engineering also played a positive role in vegetation restoration. This analysis can be beneficial to environment managers in the active response and adaptation to the possible effects of future climate change, population growth, and industrial development and can be used to ensure sustainable development and environmental safety.
Project description:Evapotranspiration (ET) plays a crucial role in hydrological and energy cycles, as well as in the assessments of water resources and irrigation demands. On a regional scale, particularly in the agro-pastoral ecotone, clarification of the distribution of surface ET and its influencing factors is critical for the rational use of water resources, restoration of the ecological environment, and protection of ecological water sources. The SEBAL model was used to invert the regional ET based on Landsat8 images in the agro-pastoral ecotone of northwest China. The results were indirectly verified by monitoring data from meteorological stations. The correlation between ET and surface parameters was analyzed. Thus, the main factors that affect the surface ET were identified. The results show that the SEBAL model determines an accurate inversion, with a correlation coefficient of 0.81 and an average root mean square error of 0.9 mm/d, which is highly suitable for research on water resources. The correlation coefficients of normalized vegetation index, surface temperature, land surface albedo, net radiation flux with daily ET were 0.5830, 0.8425, 0.3428 and 0.9111, respectively. The normalized vegetation index and the net radiation flux positively correlated with the daily ET, while the surface temperature and land surface albedo negatively correlated with the daily ET. The correlation from strong to weak is the net radiation flux > surface temperature > normalized vegetation index > surface albedo. In terms of spatial distribution, the daily ET of water was the highest, followed by woodland, wetland, cropland, built-up land, shrub land, grassland and bare land. However, the SEBAL model overestimates the inversion of daily ET of built-up land.