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[科技图书] Chapter 7 - Agricultural Remote Sensing and Data Science in China 进入全文

Federal Data Science

Data science and big data have been booming in the past 5 years in China. Remote sensing is one of the most important fields using big data. Agriculture is one of the most important and popular fields of remote sensing applications and data science. In the past decade, there have been rapid developments in agricultural remote sensing and data science, in China and all over the world. In this chapter, the research and applications in remote sensing for agriculture and data science in China are reviewed. Substantial progress in agricultural quantitative remote sensing inversion of crop and environmental parameters has been made. Remote sensing applications in cropland classification and crop mapping, crop growth monitoring, and crop yield estimation are widely presented. The operational China's agriculture remote sensing monitoring system is presented as an example to showcase remote sensing applications in agriculture. The second part of the chapter briefly describes the development of data science in China and the status quo of big data applications. Some examples of data science resource sharing and service platforms are also presented.

[学术文献] Land degradation and poverty 进入全文

Nature Sustainability

Land is one of the few productive assets owned by the rural poor, and almost all such households engage in some form of agriculture. Over 2000–2010 the rural poor on degrading agricultural land increased in low-income countries and in sub-Saharan Africa and South Asia. Although degradation threatens the livelihoods of the poor, this interaction is complex and conditioned by key economic, social and environmental factors. These factors also limit the poverty-reducing impacts of economic growth and economy-wide reforms. A comprehensive development strategy requires investments that improve the livelihoods of affected populations and regions, and facilitates outmigration in severely impacted areas.

[学术文献] Agriculture, climate change and sustainability: The case of EU-28 进入全文

Ecological Indicators

Agriculture and climate change are characterized by a complex cause-effect relationship. The agricultural sector generates significant quantities of gas emissions that affect climate. The rise in the concentration of greenhouse gases in the atmosphere, the increase in temperatures as well as changes in the precipitation regime have repercussions on the volume, quality and stability of the agricultural and zoo technical production, but also on the natural environment in which agriculture is practiced. Based on the above, the purpose of the paper is twofold. Firstly, through the Wroclaw Taxonomic Method, we construct a composite indicator, called the Index of Sustainable Agriculture (ISA), and analyse 28 countries that have joined the European Union from 1 July 2013 to today (EU-28) over the period 2005–2014, according to 16 variables. Secondly, the Granger-causality test for panel data is implemented in order to verify the causal relationship among the ISA, climate change and agricultural production. In other words, we test which of the three analyzed variables turns out to be the cause variable and which, instead, turns out to be the effect variable. Furthermore, we test if there is a bidirectional causality among the variables. This analysis provides a wide overview on how European countries rank according to the ISA and its three crucial pillars, i.e. environmental, economic and social. Moreover, important causality relationships among the ISA, climate changes (approximated by mean annual temperature and mean annual precipitation and provided by the Climate Research Unit (CRU) Time-Series (TS) Version 3.22 of the University of East Anglia) and agricultural production (approximated by wheat and spelt yields and provided by EUROSTAT) are identified. In particular, the following hypotheses are verified: 1) there is a negative bidirectional relationship between climate change and agricultural yields; 2) there is a negative bidirectional causal relationship between climate change and sustainable agriculture; 3) conventional agriculture negatively affects sustainable agriculture.

[学术文献] Agricultural plastic waste mapping using GIS. A case study in Italy 进入全文

Resources, Conservation & Recycling

Plastic materials used in agriculture mostly derive from synthetic petro-chemical polymers. They require at the end of their lifetime a suitable waste management system for the collection and treatment. A research was carried out in order to define a GIS methodology for mapping the agricultural plastic waste on the land. The use in agriculture of plastics in Barletta-Andria-Trani Province – Apulia Region – was investigated by applying orthophotos analysis and remote sensing survey. Besides purposed Plastic Waste Indexes were created to release land use to waste generation. The data were organized in a specific geo-database. The analysis showed that the agricultural plastic waste yearly produced from covering films was 627 kg ha−1 , from the anti-hail nets was 159 kg ha−1 , from nets for crop protection was 192 kg ha−1 , from shading nets was 131 kg ha−1 , from irrigation pipes was 104 kg ha−1 . Through GIS, the areas with high density of plastic wastes were pointed out and the suitable location of collection centres was defined. The produced maps and the GIS database can be always updatable tools, useful for monitoring and optimizing the collection of agricultural plastic waste from the farms and their transport to the recycling companies.

[学术文献] Quantitative assessment of soil saline degradation using remote sensing indices in Siwa Oasis 进入全文

Remote Sensing Applications: Society and Environment

Unsuitable practices and improper land management lead to soil degradation and therefore deviates land from optimum productivity. Remote sensing indices and spatial variability of soil properties were implemented in Arc GIS model-Builder for quantitative assessment of land degradation in Siwa Oasis, western desert, Egypt. Semivariogram model through Kriging techniques was used to produce maps of soil properties in two dates 2002 and 2017. This was done in order to calculate soil degradation rates and its areas in the studied area. The results indicated that geostatistical approach and ArcGIS model-builder can directly reveal the spatial variability of soil properties and measure accurately the changes in soil properties. The results will help the farmers and decision makers for improving the soil-water management. The cross-validation results illustrated the smoothing effect of the spatial prediction. Physical and chemical properties of 90 soil profiles were analyzed and chemical parameters were analyzed of 30 groundwater sample, collected from irrigation-wells. Landsat images of five different periods were collected to monitor the changes of the surface features of soil salinity and water logging. Soil analyses show a wide variability. The very saline, non-sodic soils cover most of the suited soils. Agricultural areas, saline soils and water logged areas were increased. The increment of saline soil and water logged areas is associated with poor drainage and increment in crop irrigation. There is degradation in groundwater quality which indicated by its salinity. The studied soils are salt-affected and this prompts the need of a proper land reclamation program and prods the development of effective irrigation and drainage systems.

[学术文献] Carbon uptake by European agricultural land is variable, and in many regions could be increased: Evidence from remote sensing, yield statistics and models of potential productivity 进入全文

Science of the Total Environment

Agricultural plants, covering large parts of the global land surface and important for the livelihoods of people worldwide, fix carbon dioxide seasonally via photosynthesis. The carbon allocation of crops, however, remains relatively understudied compared to, for example, forests. For comprehensive consistent resource assessments or climate change impact studies large-scale reliable vegetation information is needed. Here, we demonstrate how robust data on carbon uptake in croplands can be obtained by combining multiple sources to enhance the reliability of estimates. Using yield statistics, a remote-sensing based productivity algorithm and climate-sensitive potential productivity, we mapped the potential to increase crop productivity and compared consistent carbon uptake information of agricultural land with forests. The productivity gap in Europe is higher in Eastern and Southern than in Central-Western countries. At continental scale, European agriculture shows a greater carbon uptake in harvestable compartments than forests (agriculture 1.96 vs. forests 1.76 t C ha−1 year−1 ). Mapping productivity gaps allows efforts to enhance crop production to be prioritized by, for example, improved crop cultivars, nutrient management or pest control. The concepts and methods for quantifying carbon uptake used in this study are applicable worldwide and allow forests and agriculture to be included in future carbon uptake assessments.

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