Extreme Weather Events in Agriculture: A Systematic Review

Cogato, Alessia; Meggio, Franco; Migliorati, Massimiliano De Antoni; Marinello, Francesco

doi:10.3390/su11092547

Cited by 115 publications

(77 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…It can be concluded that extreme weather record may cause an exceptional prediction result that year and is valuable for future prediction. An increase of extreme and uncertain events is characteristic of the most recent climate scenarios, which can help DL networks learn various cases and become more universal [61].…”

Section: Resultsmentioning

confidence: 99%

County-Level Soybean Yield Prediction Using Deep CNN-LSTM Model

Sun

et al. 2019

Sensors

246

108

View full text Add to dashboard Cite

Yield prediction is of great significance for yield mapping, crop market planning, crop insurance, and harvest management. Remote sensing is becoming increasingly important in crop yield prediction. Based on remote sensing data, great progress has been made in this field by using machine learning, especially the Deep Learning (DL) method, including Convolutional Neural Network (CNN) or Long Short-Term Memory (LSTM). Recent experiments in this area suggested that CNN can explore more spatial features and LSTM has the ability to reveal phenological characteristics, which both play an important role in crop yield prediction. However, very few experiments combining these two models for crop yield prediction have been reported. In this paper, we propose a deep CNN-LSTM model for both end-of-season and in-season soybean yield prediction in CONUS at the county-level. The model was trained by crop growth variables and environment variables, which include weather data, MODIS Land Surface Temperature (LST) data, and MODIS Surface Reflectance (SR) data; historical soybean yield data were employed as labels. Based on the Google Earth Engine (GEE), all these training data were combined and transformed into histogram-based tensors for deep learning. The results of the experiment indicate that the prediction performance of the proposed CNN-LSTM model can outperform the pure CNN or LSTM model in both end-of-season and in-season. The proposed method shows great potential in improving the accuracy of yield prediction for other crops like corn, wheat, and potatoes at fine scales in the future.

show abstract

Section: Resultsmentioning

confidence: 99%

County-Level Soybean Yield Prediction Using Deep CNN-LSTM Model

Sun

et al. 2019

Sensors

246

108

View full text Add to dashboard Cite

show abstract

“…An individual's preference and behavior are likely to change with disasters and accidents (Teng et al 2015;Rajesh 2018;Cogato et al 2019), as revealed in the literature on the endogenous formation of individual preferences that these preferences are not constant across time and change under some circumstances (Fehr and Hoff 2011;Chuang and Schechter 2015;Yusuke and Yasuyuki 2019). In this situation, stockpiling the scarce food will become very common.…”

Section: Literature Reviewmentioning

confidence: 99%

Consumer food stockpiling behavior and willingness to pay for food reserves in COVID-19

et al. 2020

View full text Add to dashboard Cite

Consumer behavior changes differently in emergencies. Understanding consumer food stockpiling behavior during COVID-19 pandemic can provide critical information for governments and policymakers to adjust inventory and response strategies. This paper analyzed consumer food stockpiling behavior, including the change of food reserve scale and willingness to pay for fresh food reserves in COVID-19. Our paper shows that the scale of food reserve extends from 3.37 to 7.37 days after the outbreak of COVID-19; if available, consumers on average are willing to pay 18.14 yuan (60.47%) premium for fresh products reserves. The result shows that food stockpiling behavior is fueled by a set of multiple motivations and subjective risk perception. Female, high education level and high-income consumers were more likely to reserve larger scale food reserves, but consumers' willingness to pay for fresh food reserves is determined by income. This study was conducted when new infection cases continued to rise in the world. The story of food stockpiling during the COVID-19 pandemic in China is similar with the rest of world. Consumer stockpiling behavior in China can also be expanded to other countries to predict the change of food demand and understand more about consumer preferences in emergencies.

show abstract

“…The Elsevier Scopus database has been selected to obtain the sample of studies to analyse, in the same way as other articles that have analysed the dynamics followed by a certain line of research [68][69][70][71]. The search parameters selected include all the terms related to the principal topics of the research in greenhouse technology (GT), such as soil, energy, structure, climate, and irrigation.…”

Section: Data Processingmentioning

confidence: 99%

An Analysis of Global Research Trends on Greenhouse Technology: Towards a Sustainable Agriculture

Aznar-Sánchez

Velasco-Muñoz

López-Felices

et al. 2020

IJERPH

View full text Add to dashboard Cite

Greenhouse farming is an agricultural management system that has demonstrated its efficiency in intensifying food production. These systems constitute a feasible alternative for ensuring food supply, which is one of the greatest challenges faced by humankind in the twenty-first century. Technology has been able to meet the challenges related to greenhouse farming in both contributing to overcoming its limitations, correcting adverse impacts and ensuring system sustainability. The objective of this article is to analyse the global research trends in greenhouse technology over the last two decades, in order to identify the main driving agents, the most outstanding research lines and possible gaps in the literature. Different methodologies have been used for the analysis; both quantitative and qualitative. The principal results show that there are different relevant lines of research related to different aspects of greenhouse farming: the use of water for irrigation, the design of the optimum structure of the greenhouse, conserving the soil in the best growing conditions, energy consumption of the system as a whole, climate control within the facility and pest control. The research is characterized by the being composed largely of ad hoc studies, which hinders the international collaboration between researchers and institutions. The research approach has shifted from being focused on increasing production and cost savings to aspects related to resource conservation and sustainability.

show abstract

Extreme Weather Events in Agriculture: A Systematic Review

Cited by 115 publications

References 46 publications

County-Level Soybean Yield Prediction Using Deep CNN-LSTM Model

County-Level Soybean Yield Prediction Using Deep CNN-LSTM Model

Consumer food stockpiling behavior and willingness to pay for food reserves in COVID-19

An Analysis of Global Research Trends on Greenhouse Technology: Towards a Sustainable Agriculture

Contact Info

Product

Resources

About