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[学术文献 ] Adoption of digital technologies in agriculture—an inventory in a european small-scale farming region 进入全文
Precision Agriculture
As digitalization in the agricultural sector has intensified, the number of studies addressing adoption and use of digital technologies in crop production and livestock farming has also increased. However, digitalization trends in the context of small-scale farming have mainly been excluded from such studies. The focus of this paper is on investigating the sequential adoption of precision agriculture (PA) and other digital technologies, and the use of multiple technologies in a small-scale agricultural region in southern Germany. An online survey of farmers yielded a total of 2,390 observations, of which 1,820 operate in field farming, and 1,376 were livestock farmers. A heuristic approach was deployed to identify adoption patterns. Probable multiple uses of 30 digital farming technologies and decision-support applications, as well as potential trends of sequential technology adoption were analyzed for four sequential points of adoption (entry technology, currently used technologies, and planned short-term and mid-term investments). Results show that Bavarian farmers cannot be described as exceedingly digitalized but show potential adoption rates of 15–20% within the next five years for technologies such as barn robotics, section control, variable-rate applications, and maps from satellite data. Established use of entry technologies (e.g., automatic milking systems, digital field records, automatic steering systems) increased the probability of adoption of additional technologies. Among the most used technologies, the current focus is on user-friendly automation solutions that reduce farmers’ workload. Identifying current equipment and technology trends in small-scale agriculture is essential to strengthen policy efforts to promote digitalization.
[学术文献 ] Handling of Big Data in Agricultural Remote Sensing 进入全文
Encyclopedia of Smart Agriculture Technologies
The acquisition, processing, storage, analysis, visualization, and application of agricultural remote sensing big data are critical to guide the success of precision agriculture. This entry overviews available data resources for agricultural remote sensing, recent development of technologies for remote sensing big data management, and remote sensing data processing and management for precision agriculture. Remote sensing technology has been developed for earth observation (EO) with massive remotely sensed data for various research needs and practical applications. As of September 1, 2021, there are 4550 active man-made satellites in space orbiting earth and 22% of the satellites serve for EO remote sensing (DEWEsoft 2022). It can be predicted that this number will increase rapidly, especially for those satellites that will be launched into low orbits around the earth for near earth observation. For remote sensing, satellites are equipped with one or more sensors or instruments to collect various observation data of earth surface, including land, water, and atmosphere. These satellite sensors acquire images of earth surface unceasingly at different spatial, spectral, and temporal resolutions so that huge volume of remotely sensed images is available in many countries and international agencies with the data volume growing every day, every hour, and even every second. Sustainable development of agriculture is the goal for global food security. Modern agricultural development is the result from the development of precision agriculture. Precision agriculture began to renovate agricultural operations in the 1980s when the theory and practice were established based on agricultural mechanization through the integration of global positioning system (GPS), geographic information system (GIS), and remote sensing technologies (Zhang et al. 2002). Over the past four years, precision agriculture has evolved from strategic monitoring using satellite imagery for regional decision-making to tactical monitoring and control prescribed by the information from low-altitude remotely sensed data by unmanned aerial vehicles (UAVs) for field-scale site-specific management to deal with within-field variability of soil properties, weed distributions, crop stress of insects, diseases, nutrients and water, biomass, and yield. Now data science and big data technology are gradually merged into precision agricultural schemes so that the data can be analyzed rapidly in time for decision-making although research remains for how to manipulate big data and convert the big data to “small” data and actionable information for specific issues or fields for accurate precision agricultural operation. Agricultural remote sensing is a key technology that, with global positioning data, produces spatially varied data and information for agricultural planning and prescription for precision agricultural operations with GIS (Yao and Huang 2013). Agricultural remote sensing data appear in different forms and are acquired from different sensors and at different intervals and scales. Agricultural remote sensing data all have characteristics of big data in complexity and volume. The acquisition, processing, storage, analysis, and visualization of agricultural remote sensing big data are critical to the success of precision agriculture. With the most recent and coming advances of information and electronics technologies and remote sensing big data support, precision agriculture will be developed into the stage of smart, intelligent agriculture.
[学术文献 ] 基于区块链的农业科学数据管理场景模型构建研究 进入全文
情报科学
【目的/意义】区块链技术与农业科学数据管理在应用场景方面有很多共同的特点，非常适合在三个应用比较广泛的场景中进行融合，可以实现农业科学数据价值的增值。【方法/过程】结合区块链技术的特点，对现有研究成果进行文献梳理，构建了农业科学数据共享模型、农业科学数据溯源模型和农业科学数据隐私计算模型三个场景模型。【结果/结论】该模型能够提高农业科学数据管理的可信性、安全性、高效性，为科研组织和科研工作者提供农业科学数据服务，为农业科学数据管理提供了一种新的解决思路和方法。【创新/局限】将区块链技术和农业科学数据管理相结合，推动了农业科学数据管理的场景优化。
[学术文献 ] Remote Sensing for Agricultural Water Management in Jordan 进入全文
Remote Sensing
This study shows how remote sensing methods are used to support and provide means for improving agricultural water management (AWM) in Jordan through detailed mapping of irrigated areas and irrigation water consumption (IWC). Digital processing and classification methods were applied on multi-temporal data of Landsat 8 and Sentinel-2 to derive maps of irrigated areas for the period 2017–2019. Different relationships were developed between the normalized difference vegetation index (NDVI) and the crop coefficient (Kc) to map evapotranspiration (ET). Using ground data, ET maps were transferred to IWC for the whole country. Spatial analysis was then used to delineate hotspots where shifts between ET and groundwater abstraction were observed. Results showed that the applied remote sensing methods provided accurate maps of irrigated areas. The NDVI-Kc relationships were significant, with coefficients of determination (R2) ranging from 0.89 to 0.93. Subsequently, the ET estimates from the NDVI-Kc relationships were in agreement with remotely sensed ET modeled by SEBAL (NSE = 0.89). In the context of Jordan, results showed that irrigated areas in the country reached 98 thousand ha in 2019, with 64% of this area located in the highlands. The main irrigated crops were vegetables (55%) and fruit trees and olives (40%). The total IWC reached 702 MCM in 2019, constituting 56% of the total water consumption in Jordan, with 375 MCM of this amount being pumped from groundwater, while reported abstraction was only 235 MCM. The study identified the hotspots where illegal abstraction or incorrect metering of groundwater existed. Furthermore, it emphasized the roles of remote sensing in AWM, as it provided updated figures on groundwater abstraction and forecasts for future IWC, which would reach 986 MCM in 2050. Therefore, the approach of ET and IWC mapping would be highly recommended to map ET and to provide estimates of present and future IWC.
[学术文献 ] Soil Erosion Satellite-Based Estimation in Cropland for Soil Conservation 进入全文
Remote Sensing
Intensive cropland expansion for an increasing population has driven soil degradation worldwide. Modeling how agroecosystems respond to variations in soil attributes, relief and crop management dynamics can guide soil conservation. This research presents a new approach to evaluate soil loss by water erosion in cropland using the RUSLE model and Synthetic Soil Image (spectroscopy technique), which uses time series remotely sensed environmental, agricultural and anthropic variables, in the southeast region of São Paulo State, Brazil. The availability of the open-access satellite images of Tropical Rainfall Measuring Mission (TRMM) and Landsat satellite images provided ten years of rainfall data and 35 years of exposed soil surface. The bare soil surface and agricultural land use were extracted, and the multi-temporal rainfall erosivity was assessed. We predict soil maps’ attributes (texture and organic matter) through innovative soil spectroscopy techniques to assess the soil erodibility and soil loss tolerance. The erosivity, erodibility, and topography obtained by the Earth observations were adopted to estimate soil erosion in four scenarios of sugarcane (Saccharum spp.) residue coverage (0%, 50%, 75%, and 100%) in five years of the sugarcane cycle: the first year of sugarcane harvest and four subsequent harvesting years from 2013 to 2017. Soil loss tolerance means 4.3 Mg ha−1 exceeds the minimum rate in 40% of the region, resulting in a total soil loss of ~6 million Mg yr−1 under total coverage management (7 Mg ha−1). Our findings suggest that sugarcane straw production has not been sufficient to protect the soil loss against water erosion. Thus, straw removal is unfeasible unless alternative conservation practices are adopted, such as minimum soil tillage, contour lines, terracing and other techniques that favor increases in organic matter content and soil flocculating cations. This research also identifies a spatiotemporal erosion-prone area that requests an immediately sustainable land development guide to restore and rehabilitate the vulnerable ecosystem service. The high-resolution spatially distribution method provided can identify soil degradation-prone areas and the cropland expansion frequency. This information may guide farms and the policymakers for a better request of conservation practices according to site-specific management variation.
[学术文献 ] Progress and challenges in sustainable land management initiatives: A global review 进入全文
Science of The Total Environment
Sustainable land management (SLM) is widely recognized as the key to reducing rates of land degradation, and preventing desertification. Many efforts have been made worldwide by various stakeholders to adopt and/or develop various SLM practices. Nevertheless, a comprehensive review on the spatial distribution, prospects, and challenges of SLM practices and research is lacking. To address this gap, we gathered information from a global SLM database provided by the World Overview of Conservation Approaches and Technologies (WOCAT) and two bibliographic databases of academic research. Over 1900 SLM practices and 1181 academic research papers from 129 and 90 countries were compiled and analyzed. Relatively better SLM dissemination was observed in dry subhumid countries and countries with medium scores on the Human Development Index (HDI), whereas dissemination and research were both lower in humid countries with low HDI values. Cropland was the main land use type targeted in both dissemination and research; degradation caused by water erosion and mitigation aimed at water erosion were also the main focus areas. Other dominant land use types (e.g., grazing) and SLM purposes (e.g., economic benefits) have received relatively less research attention compared to their dissemination. Overall, over 75 % of the 60 countries experiencing high soil erosion rates (>10 t ha−1 yr−1) also have low HDI scores, as well as poor SLM dissemination and research implying the limited evidence-based SLM dissemination in these countries. The limitation of research evidence can be addressed in the short term through integrating existing scientific research and SLM databases by adopting the proposed Research Evidence for SLM framework. There is, however, a great need for additional detailed studies of country-specific SLM challenges and prospects to create appropriate evidence-based SLM dissemination strategies to achieve multiple SLM benefits.
