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[学术文献 ] Toward to agricultural green development by multi-objective zoning and nitrogen nutrient management: a case study in the Baiyangdian Basin, China 进入全文
Frontiers of Agricultural Science and Engineering
Although China has achieved great advancements toward national food security, the country is still confronted with a range of challenges, including natural resource stress, imbalanced diets and environmental pollution. Optimized management of crop–livestock systems is the key measure to realize agricultural green transformation. However, optimized management of crop–livestock systems that use multi-objective zoning is lacking. This study employed a multi-objective zoning management approach to comprehensively analyze four indicators: ammonia volatilization, nitrogen surplus, soil carrying capacity and ecological red line area. With its significant ecological integrity and a strong emphasis on sustainability, the Baiyangdian Basin serves as a unique and suitable test case for conducting analyses on multi-objective nutrient optimization management, with the aim to facilitate the agricultural green transformation. This study finds that less than 8% of the area in the Baiyangdian Basin meet the acceptable environmental indicator standard, whereas around 50% of the area that had both nitrogen surplus and ammonia volatilization exceeded the threshold. Implementation of unified management, that is, the same management technique across the study areas, could result in an increase of areas meeting environmental indicator thresholds to 21.1%. This project developed a novel multi-indicator partition optimization method, in which distinct measures are tailored for different areas to satisfy multiple environmental indicators. Implementation of this method, could potentially bring more than 50% area below the threshold, and areas with ammonia emissions and nitrogen surplus could be reduced to 15.8%. The multi-indicators partition optimization method represents a more advanced and efficiency-oriented management approach when compared to unified management. This approach could be regarded as the best available option to help China achieve agricultural transformation to improve efficient production and reduce environmental pollution. It is recommended that current policies aimed at nutrient management toward sustainable agricultural development should shift toward the application of multi-indicators partition optimization.
[学术文献 ] Spatio-temporal detection of agricultural disaster vulnerability in the world and implications for developing climate-resilient agriculture 进入全文
Science of The Total Environment
Drought and floods seriously affect agriculture across the world. It is of great importance to lower down the agricultural vulnerability to disasters to build climate-resilient agriculture. The paper aims to investigate the spatio-temporal changes of agricultural vulnerability to drought and floods in the world in the period 2003–2019. Research results show that (1) the agricultural vulnerability to drought and floods is at a low level across the globe owning to the dual effects of decreasing exposure and increasing adaptability; (2) the northern parts of United States, northeastern parts of China, and the border between Russia and Kazakhstan are identified as most vulnerable areas to drought and floods; and (3) spatio-temporal mismatch of precipitation is the main factor to cause floods and drought while better adaption is beneficial to minimize the adverse effects of disasters. Based on analysis on the drivers and spatial patterns of drought and floods risk in all dimensions, tailored measures and policies are put forwards to make adaptive strategies of agriculture to climate change.
[前沿资讯 ] "Drop industrial agriculture": Major study reports that people and environment both benefit from diversified farming, while bottom lines also thrive 进入全文
EurekAlert
Mixing livestock and crops, integrating flower strips and trees, water and soil conservation and much more: Massive new global study led by the University of Copenhagen and University of Hohenheim, has examined the effects of diversified agriculture. The conclusion is abundantly clear – positive effects increase with every measure, while negative effects are hard to find. Laura Vang Rasmussen of the University of Copenhagen can finally wipe the sweat from her brow. For the last four years, she has served as the link between 58 researchers on five continents and as lead author of a major agricultural study which gathered data from 24 research projects, along with colleague Ingo Grass of the University of Hohenheim in Germany.
[学术文献 ] Enhanced prediction of vegetation responses to extreme drought using deep learning and Earth observation data 进入全文
Ecological Informatics
The advent of abundant Earth observation data enables the development of novel predictive methods for forecasting climate impacts on the state and health of terrestrial ecosystems. Here, we predict the spatial and temporal variations of land surface reflectance and vegetation greenness, measuring the density of green vegetation and active foliage area, conditioned on current and past weather and the local topography. We train two alternative recurrent deep learning models that combine Long Short-Term Memory cells with convolutional layers (ConvLSTM) for forecasting the spatially resolved deviation of surface reflectance across a heterogeneous landscape from a specified initial state. Using data from diverse ecosystems and land cover types across Europe and following a standardized model evaluation framework (EarthNet2021 Challenge), our results indicate increased performance in predicting surface greenness during extreme drought events of the models presented here, compared to currently published benchmarks. This demonstrates how deep learning methods for optical Earth observation time series enable an early-warning of vegetation responses to the impacts of climatic extreme events, such as the drought-related loss of green foliage.
[学术文献 ] Rebound of surface and terrestrial water resources in Mongolian plateau following sustained depletion 进入全文
Ecological Indicators
Water resources have always played an important role in ensuring industrial and agricultural production, as well as maintaining ecosystem security in the Mongolian Plateau (MP), a typical arid to semi-arid region. Previous studies have reported the considerable shrinkages of surface water bodies affected by intense human disturbance in the MP before 2010. However, it is still unclear about the effects of those key ecological restoration efforts (e.g., the construction of ecological civilization since 2012) on water resources in the recent decade. Here, using all the available Landsat-5/7/8 surface reflectance observations, a robust water mapping algorithm based on spectral indices and thresholds, and the Google Earth Engine (GEE) cloud computing platform, we examined the changes in surface water area (SWA) in the MP during 1991–2021. In addition, based on the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) Mascon data products, we investigated the inter-annual variability and trends of terrestrial water storage (TWS) from 2002 to 2021. We found that SWA experienced remarkable increases (85.5 km2/yr) since 2009 after continuous shrinkage of surface water bodies (-205.9 km2/yr) for over 20 years, in which Inner Mongolia played a dominant role in the recovery of SWA (72.2 km2/yr). Also, TWS had undergone continuous decline before 2012 and fluctuating rebound after that. The most significant recovery of TWS mainly happened in the northern part of the MP. Quantitative attribution analyses showed that the key ecological restoration projects in China, especially the construction of ecological civilization since 2012, were the major drivers for the recovery of surface and terrestrial water resources. While previous studies reported the considerable decline of surface water resources induced by human activities in the MP since the 1990s, our research provided gratifying satellite evidence for the significant recoveries of surface and terrestrial water resources in the plateau during the past decade under the influence of ecological restoration efforts.
[学术文献 ] The grain Food-Energy-Water nexus in China: Benchmarking sustainability with generalized data envelopment analysis 进入全文
Science of The Total Environment
Food insecurity can be considered as a significant cause to instability in some regions around the world. Grain production utilizes a multiple of inputs, such as: water resources, fertilizers, pesticides, energy, machinery, and labor. In China, grain production has led to huge irrigation water use, non-point source pollution, and greenhouse gas emissions. It is necessary to emphasize the synergy between food production and ecological environment. In this study, a grain Food-Energy-Water nexus is delivered and an eco-efficiency sustainability evaluation metric is introduced, Sustainability of Grain Inputs (SGI), for investigating the sustainability of water and energy use in grain production across China. SGI is constructed by using generalized data envelopment analysis to comprehensively incorporate differences of water and energy inputs (including indirect energy use contained in agricultural chemicals such as fertilizers, pesticides, agricultural film, and direct energy use such as the electricity and diesel used for irrigation and agricultural machinery) in different regions across China. Both water and energy are considered by the new metric at the same time, which is built on the single resources metrices that are often used in the sustainability literature. This study evaluates the water and energy use of wheat and corn production in China. Wheat production uses water and energy sustainably in Sichuan, Shandong, and Henan; Corn production has the highest combined sustainability index in Shandong, Jilin, Liaoning, and Henan. In these areas, the grain sown area could be increased. However, wheat production in Inner Mongolia and corn production in Xinjiang rely on unsustainable water and energy inputs, and their grain sown areas could be reduced. The SGI is a tool that researchers and policy makers can use to better quantify the sustainability of water and energy inputs to grain production. It facilitates formulating policies about water saving and carbon emission reduce of grain production.
