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[学术文献 ] Feasibility and Effectiveness Assessment of Multi-Sectoral Climate Change Adaptation for Food Security and Nutrition 进入全文
Current Climate Change Reports
Purpose of Review This review aims to identify the evidence for the assessment of the effectiveness and feasibility of multi-sectoral climate adaptation for food security and malnutrition. This review and the assessments of the evidence inform the contents and confidence statements in section “multi-sectoral adaptation for malnutrition” and in the Executive Summary of the IPCC AR6 WGII Chapter 7: Health Wellbeing and Changing Community Structure. Recent Findings A review of adaptation for food security and nutrition FSN in West Africa concluded that food security and nutrition and climate adaptation are not independent goals, but often go under different sectors. Summary Most of the adaptation categories identified here are highly effective in reducing climate risks to food security and malnutrition, and the implementation is moderately or highly feasible. Categories include improved access to (1) sustainable, affordable, and healthy diets from climate-resilient, nutrition-sensitive agroecological food systems; (ii) health care (including child, maternal, and reproductive), nutrition services, water and sanitation; (iii) anticipatory actions, adoption of the IPC classification, EW-EA systems; and (iv) nutrition-sensitive adaptive social protection. Risk reduction, such as weather-related insurance, and risk management are moderately effective and feasible due to economic and institutional barriers. Women and girls’ empowerment, enhanced education, rights-based approaches, and peace building are highly relevant enablers for implementation of the adaptation options.
[学术文献 ] Embedding a precision agriculture service into a farm management information system - ifarma/PreFer 进入全文
Smart Agricultural Technology
Today, bridging precision agriculture (PA) with farm management is a high priority. To respond to this challenge, a precision fertilization service for extended crops, namely ‘PreFer’ (developed by ’Ecodevelopment’enterprise), was embedded as a new module in a cloud-based farm management information system (FMIS), namely ‘ifarma’ (developed by ’Agrostis’ enterprise). The new PreFer module preserves the full potential of the original service methodology, while taking advantage of all fundamental functionalities of the ifarma platform. PreFer as a service uses a GIS to store and process the farmers’ geodatabases, which are fed from multiple sources, such as soil surveys, satellite data, yield monitors, etc. This GIS is also used to feed the machine learning algorithms of ‘PreFer’ with the required data to produce the prescription maps for both broadcasting and topdressing fertilizations. Then, all tables and maps are transferred from the GIS to the platform upon their production, thus becoming immediately available to the farmers. The ‘ifarma/PreFer’ module was tested during the 2022 cultivation season, showing that fully meets farmers’ requirements. This work also indicates that synergies like this are more than necessary to create added-value in commercial precision agriculture.
[学术文献 ] Understanding the potential applications of Artificial Intelligence in Agriculture Sector 进入全文
Advanced Agrochem
Artificial Intelligence (AI) has been extensively applied in farming recently. To cultivate healthier crops, manage pests, monitor soil and growing conditions, analyse data for farmers, and enhance other management activities of the food supply chain, the agriculture sector is turning to AI technology. It makes it challenging for farmers to choose the ideal time to plant seeds. AI helps farmers choose the optimum seed for a particular weather scenario. It also offers data on weather forecasts. AI-powered solutions will help farmers produce more with fewer resources, increase crop quality, and hasten product time to reach the market. AI aids in understanding soil qualities. AI helps farmers by suggesting the nutrients they should apply to increase the quality of the soil. AI can help farmers choose the optimal time to plant their seeds. Intelligent equipment can calculate the spacing between seeds and the maximum planting depth. An AI-powered system known as a health monitoring system provides farmers with information on the health of their crops and the nutrients that need to be given to enhance yield quality and quantity. This study identifies and analyses relevant articles on AI for Agriculture. Using AI, farmers can now access advanced data and analytics tools that will foster better farming, improve efficiencies, and reduce waste in biofuel and food production while minimising the negative environmental impacts. AI and Machine Learning (ML) have transformed various industries, and the AI wave has now reached the agriculture sector. Companies are developing several technologies to make monitoring farmers' crop and soil health easier. Hyperspectral imaging and 3D laser scanning are the leading AI-based technologies that can help ensure crop health. These AI-powered technologies collect precise data on the health of the crops in greater volume for analysis. This paper studied AI and its need in Agriculture. The process of AI in Agriculture and some Agriculture parameters monitored by AI are briefed. Finally, we identified and discussed the significant applications of AI in agriculture.
[学术文献 ] Framing the response to IoT in agriculture: A discourse analysis 进入全文
Agricultural Systems
CONTEXT A technology in smart farming, the Internet of Things (IoT), is predicted to continue altering farm life by introducing opportunities and obstacles. However, there are limited studies on how farmers' views of IoT influence their decision-making regarding technology adoption. OBJECTIVE To understand what characterises farmers' experiences with IoT, we conducted a discourse analysis of 32 interviews with farmers in Ontario. METHODS Discourse analysis was used to understand the range of meanings associated with IoT by farmers. RESULTS AND CONCLUSIONS We find that two main discourses are present (1) the extent to which IoT was viewed as useful/helpful vs not useful/unhelpful and (2) the extent to which IoT was viewed as being their choice. The results indicate that farmers respond to IoT in four categories: embrace, accept, ignore, and caution. SIGNIFICANCE This paper contributes to the literature by categorising the farmers' responses to IoT implementation and highlighting why farmers adopt these categories. Current literature recognizes that diagnosing the current readiness and use of innovations is a proxy for their readiness to scale. Understanding how farmers view opportunities enabled by IoT and how they experience the diffusion of IoT is a foundation for suggesting recommendations for technology improvement and development in agriculture.
[学术文献 ] A trusted IoT data sharing and secure oracle based access for agricultural production risk management 进入全文
Computers and Electronics in Agriculture
Agricultural risks associated with weather events, soil conditions, diseases, and pests have risen in recent years due to climate change. These risks have burdened farmers with the shock of financial stress and endangered the region’s food security. Despite increasing risks, notable risk management tools like agricultural insurance have not effectively reached marginal farmers in developing economies. This can be attributed to the fact that existing centralized agricultural information systems lack trust factors in sharing and accessing agricultural risk data, which is accompanied by processing delays in insurance payouts. To this end, the emergence of promising technologies like blockchain helps establish trust and automates insurance-based payments with smart contracts. This work proposes a framework called AgriSSIOracle with two key contributions. The first is to have a trusted agricultural Internet of Things (IoT) data sharing that employs blockchain-based Self-Sovereign Identity (SSI) and provable credentials. Second is the decentralized oracle-based access control mechanism for smart contracts in agricultural insurance. A method for privacy-preserved authentication and data provenance for agricultural IoT devices is developed with SSI-based Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs). Next, the IoT data sharing scheme is illustrated using publish–subscribe and request–response communication patterns based on the device data credentials. Finally, the decentralized oracle mechanism for smart contracts is built based on multiparty computation for risk-related data access. The implementation of the framework is realized using a permissioned identity blockchain called Hyperledger Indy and Ethereum, which is a public blockchain for smart contracts. The security and privacy analysis confirms that the AgriSSIOracle framework ensures trust factors, particularly authenticity, privacy, data provenance, data integrity, and access control. The framework is evaluated with respect to the transaction throughput, latency, credential issue-verify-revoke time, and resource utilization metrics. The evaluation results prove that the AgriSSIOracle solution is scalable and efficient and meets the requirements of real-time deployment.
[学术文献 ] An integrated approach of remote sensing and geospatial analysis for modeling and predicting the impacts of climate change on food security 进入全文
Scientific Reports
The agriculture sector provides the majority of food supplies, ensures food security, and promotes sustainable development. Due to recent climate changes as well as trends in human population growth and environmental degradation, the need for timely agricultural information continues to rise. This study analyzes and predicts the impacts of climate change on food security (FS). For 2002–2021, Landsat, MODIS satellite images and predisposing variables (land surface temperature (LST), evapotranspiration, precipitation, sunny days, cloud ratio, soil salinity, soil moisture, groundwater quality, soil types, digital elevation model, slope, and aspect) were used. First, we used a deep learning convolutional neural network (DL-CNN) based on the Google Earth Engine (GEE) to detect agricultural land (AL). A remote sensing-based approach combined with the analytical network process (ANP) model was used to identify frost-affected areas. We then analyzed the relationship between climatic, geospatial, and topographical variables and AL and frost-affected areas. We found negative correlations of − 0.80, − 0.58, − 0.43, and − 0.45 between AL and LST, evapotranspiration, cloud ratio, and soil salinity, respectively. There is a positive correlation between AL and precipitation, sunny days, soil moisture, and groundwater quality of 0.39, 0.25, 0.21, and 0.77, respectively. The correlation between frost-affected areas and LST, evapotranspiration, cloud ratio, elevation, slope, and aspect are 0.55, 0.40, 0.52, 0.35, 0.45, and 0.39. Frost-affected areas have negative correlations with precipitation, sunny day, and soil moisture of − 0.68, − 0.23, and − 0.38, respectively. Our findings show that the increase in LST, evapotranspiration, cloud ratio, and soil salinity is associated with the decrease in AL. Additionally, AL decreases with a decreasing in precipitation, sunny days, soil moisture, and groundwater quality. It was also found that as LST, evapotranspiration, cloud ratio, elevation, slope, and aspect increase, frost-affected areas increase as well. Furthermore, frost-affected areas increase when precipitation, sunny days, and soil moisture decrease. Finally, we predicted the FS threat for 2030, 2040, 2050, and 2060 using the CA–Markov method. According to the results, the AL will decrease by 0.36% from 2030 to 2060. Between 2030 and 2060, however, the area with very high frost-affected will increase by about 10.64%. In sum, this study accentuates the critical impacts of climate change on the FS in the region. Our findings and proposed methods could be helpful for researchers to model and quantify the climate change impacts on the FS in different regions and periods.
