Farmers' decision to use drought early warning system in developing countries

An Enhanced IDBO-CNN-BiLSTM Model for Sentiment Analysis of Natural Disaster Tweets

The Internet's development has prompted social media to become an essential channel for disseminating disaster-related information. Increasing the accuracy of emotional polarity recognition in tweets is conducive to the government or rescue organizations understanding the public's demands and responding appropriately. Existing sentiment analysis models have some limitations of applicability. Therefore, this research proposes an IDBO-CNN-BiLSTM model combining the swarm intelligence optimization algorithm and deep learning methods. First, the Dung Beetle Optimization (DBO) algorithm is improved by adopting the Latin hypercube sampling, integrating the Osprey Optimization Algorithm (OOA), and introducing an adaptive Gaussian-Cauchy mixture mutation disturbance. The improved DBO (IDBO) algorithm is then utilized to optimize the Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) model's hyperparameters. Finally, the IDBO-CNN-BiLSTM model is constructed to classify the emotional tendencies of tweets associated with the Hurricane Harvey event. The empirical analysis indicates that the proposed model achieves an accuracy of 0.8033, outperforming other single and hybrid models. In contrast with the GWO, WOA, and DBO algorithms, the accuracy is enhanced by 2.89%, 2.82%, and 2.72%, respectively. This study proves that the IDBO-CNN-BiLSTM model can be applied to assist emergency decision-making in natural disasters.

Drought stress detection technique for wheat crop using machine learning

The workflow of this research is based on numerous hypotheses involving the usage of pre-processing methods, wheat canopy segmentation methods, and whether the existing models from the past research can be adapted to classify wheat crop water stress. Hence, to construct an automation model for water stress detection, it was found that pre-processing operations known as total variation with L1 data fidelity term (TV-L1) denoising with a Primal-Dual algorithm and min-max contrast stretching are most useful. For wheat canopy segmentation curve fit based K-means algorithm (Cfit-kmeans) was also validated for the most accurate segmentation using intersection over union metric. For automated water stress detection, rapid prototyping of machine learning models revealed that there is a need only to explore nine models. After extensive grid search-based hyper-parameter tuning of machine learning algorithms and 10 K fold cross validation it was found that out of nine different machine algorithms tested, the random forest algorithm has the highest global diagnostic accuracy of 91.164% and is the most suitable for constructing water stress detection models.

Interlinkages between Climate Change and Food Systems: The Impact on Child Malnutrition-Narrative Review

The pandemics of obesity, undernutrition, and climate change represent severe threats to child health. They co-occur; interact with each other to produce sequelae at biological, psychological, or social levels; and share common underlying drivers. In this paper, we review the key issues concerning child diet and nutritional status, focusing on the interactions with climate and food systems. Inadequate infant and young child feeding practices, food insecurity, poverty, and limited access to health services are the leading causes of malnutrition across generations. Food system industrialization and globalization lead to a double burden of malnutrition, whereby undernutrition (i.e., stunting, wasting, and deficiencies in micronutrients) coexists with overweight and obesity, as well as to harmful effects on climate. Climate change and the COVID-19 pandemic are worsening child malnutrition, impacting the main underlying causes (i.e., household food security, dietary diversity, nutrient quality, and access to maternal and child health), as well as the social, economic, and political factors determining food security and nutrition (livelihoods, income, infrastructure resources, and political context). Existing interventions have the potential to be further scaled-up to concurrently address undernutrition, overnutrition, and climate change by cross-cutting education, agriculture, food systems, and social safety nets. Several stakeholders must work co-operatively to improve global sustainable nutrition.

Local reports of climate change impacts in Sierra Nevada, Spain: sociodemographic and geographical patterns

While we know that climate change is having different impacts on various ecosystems and regions of the world, we know less how the perception of such impacts varies within a population. In this study, we examine patterns of individual variation in climate change impacts reports using data from a sample (n = 238) drawn from 33 mountainous municipalities of Sierra Nevada, Spain. Sierra Nevada inhabitants report multiple climate change impacts, being the most frequently reported changes in snowfall and snow cover, abundance of terrestrial fauna, freshwater availability, and extreme temperatures. Reports of climate change impacts vary according to informants' sociodemographic characteristics and geographical location. People with life-long bonds with the environment and higher connection and dependence upon ecosystem services report more climate change impacts than other informants, as do people with lower level of schooling. We also found that reports of climate change impacts vary according to geographic areas, which reinforces the idea that climate change generates differentiated impacts even at small geographical scales. Understanding intracultural variation in reports of climate change impacts not only gives an enriched picture of the human dimensions of climate change but might also help design more targeted mitigation and adaptation responses.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10113-022-01981-5.

A conceptual model for adaptation to climate variability in rangelands

Exploiting medicinal plants on rangelands is a climate-sensitive strategy in Iran. In other words, there is an urgent need for the transition toward resilience under current climatic pressures and risks. In addition, a deep understanding about awareness, risk perceptions, and adaptation strategies of different rural groups can play a significant role in the mitigation of climate change impacts and the development of the adaptation capacity. Therefore, the development of a conceptual model for adaptation to climate variability in rangelands was determined as the main purpose of the present study. To this end, we used analysis of awareness, risk perceptions, and adaptation strategies of medicinal plant exploiters toward climate variability. This research was carried out in Sought Khorasan province of Iran, which is one of the climate-sensitive and leading areas for the exploitation of medicinal plants in the country. Required data for this study were gathered through 13 focus group discussions. The number of members of these focus groups was between 4 and 12 people. The total number of participants in the focus groups was 91 medicinal plant exploiters. The results revealed that exploiters have a relatively favorable awareness of the current climate situation. However, they perceived huge constraints in financial supports and resources which lead to increasing social conflicts, decreasing social relations, leaving the job, increasing migration, unemployment, and psychological impacts. As expected, various adaptation strategies were used aiming at conserving, developing, improving, and managing income resources, but many of them are short of resilience orientation. Finally, research findings were articulated in the form of a conceptual model and some practical recommendations were presented to enhance adaptation of rangelands' exploiters.

Drought Early Warning in Agri-Food Systems

Droughts will increase in frequency, intensity, duration, and spread under climate change. Drought affects numerous sectors in society and the natural environment, including short-term reduced crop production, social conflict over water allocation, severe outmigration, and eventual famine. Early action can prevent escalation of impacts, requiring drought early warning systems (DEWSs) that give current assessments and sufficient notice for active risk management. While most droughts are relatively slow in onset, often resulting in late responses, flash droughts are becoming more frequent, and their sudden onset poses challenging demands on DEWSs for timely communication. We examine several DEWSs at global, regional, and national scales, with a special emphasis on agri-food systems. Many of these have been successful, such as some of the responses to 2015–2017 droughts in Africa and Latin America. Successful examples show that early involvement of stakeholders, from DEWS development to implementation, is crucial. In addition, regional and global cooperation can cross-fertilize with new ideas, reduce reaction time, and raise efficiency. Broadening partnerships also includes recruiting citizen science and including seemingly subjective indigenous knowledge that can improve monitoring, data collection, and uptake of response measures. More precise and more useful DEWSs in agri-food systems will prove even more cost-effective in averting the need for emergency responses, improving global food security.