How can data mining help us to predict the influence of climate change on Mediterranean agriculture

Fathi, Maroi Tsouli; Ezziyyani, Mostafa

doi:10.1504/ijsami.2019.10023185

Cited by 2 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Spatiotemporal analysis of long-term climatic data can supply considerable information for insights into the extreme variability of weather events within growing seasons (Zhang, et al, 2014;Gummadi et al, 2018;Haghtalab, et al, 2019;. Several authors have conducted trend analysis on historical rainfall data to grant a full perception of the characteristics of agriculture-influencing weather events over distinct geographical zones in the world (Zaytseva et al, 2016;Usman et al, 2018;Batool et al, 2019;Fathi and Ezziyyani, 2019;Ekwueme and Agunwamba, 2021). Factors associated with the variabilities in weather extremes (flooding and/or drought) become more explicit when subjected to time series data analysis.…”

Section: Introductionmentioning

confidence: 99%

Assessing rainfall and extreme weather variability for climate resilient agricultural production systems in Nigeria

Olayide,

Alabi,

Oyedepo

et al. 2024

Preprint

View full text Add to dashboard Cite

Nigeria's food production heavily relies on smallholder rainfed agriculture. Unfortunately, this sector is highly vulnerable to the negative impacts of climate change, such as floods and droughts. It is crucial to examine the frequency, intensity, and magnitude of these events and develop effective mitigation and adaptation strategies to ensure the resilience of Nigeria's agricultural system. This study analyses rainfall variability in Nigeria's agroecological zones (AEZs) over the past sixty years. The research evaluates annual, quarterly, and seasonal rainfall patterns and uses the Standardised Precipitation-Evapotranspiration Index (SPEI) to examine drought and flood frequency and intensity. The study's findings reveal that the period from 1981 to 1990 experienced the weakest rainfall patterns across all AEZs, while the best occurred from 1961 to 1970. The humid forest zone consistently received the highest rainfall, although with a declining trend over the decades. Conversely, the Arid/Sahel and Semi-arid zones exhibited the lowest with a slight upward trend in precipitation, while others experienced a reduction. Spatial analysis of drought patterns also reveals that most of the AEZs face high drought risks, with over 65% of the country experiencing a considerable increase in drought in the last three decades. The rainfall pattern was less erratic in zones of lower latitudes than in the higher altitudes. Heavy rainfall events exceeding 10 mm and 20 mm revealed a decreasing trend from the Arid to the Humid Forest zone. Extreme weather events have significant economic consequences. Policymakers and stakeholders must take action to ensure climate resilient and sustainable agricultural production systems.

show abstract

Section: Introductionmentioning

confidence: 99%

Assessing rainfall and extreme weather variability for climate resilient agricultural production systems in Nigeria

Olayide,

Alabi,

Oyedepo

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…CCSs are tools for validating various climate change models (De Castro et al 2007;Belda et al 2014;Skalák et al 2018). CCS is considered an essential tool for characterizing locations suitable or inappropriate for agricultural activity, considering future climate change scenarios (Fathi and Ezziyyani 2019).…”

Section: Introductionmentioning

confidence: 99%

Climate change in Brazil: future scenarios classified by Thornthwaite (1948)

Lima

Aparecido

Lorençone

et al. 2021

Theor Appl Climatol

View full text Add to dashboard Cite

Climate Classification System (CCS) is an important tool for validating climate change models, subsidizing the characterization of new areas suitable or unfit for agricultural activity according to future climate change scenarios. This study aims to classify the climate of the Brazilian territory in the various climate change scenarios of the IPCC through the Thornthwaite system (1948). We used a 30-year historical series of climatic data of average air temperature (°C) and rainfall (mm), obtained from the National Aeronautics and Space Administration/Prediction of Worldwide Energy Resources platform (NASA/POWER). Potential evapotranspiration (ETP) was estimated by the method of Camargo (1971); the climatological water balance (CWB) was calculated by the method of Thornthwaite and Mather (1955), using 100 mm of soil water storage capacity. CWB extracts were combined for classification by Thornthwaite (1948). The scenarios used were based on the IPCC (2014) projections and the study of Pirttioja et al. (2015). The Brazilian territory had an average air temperature of 22.20 °C (± 3.20) °C and annual precipitation of 1987 mm (± 725) mm. The climatic classification of Thornthwaite presented 108 climatic classes for the current scenario with a more significant predominance of the classes ArAʹaʹ, B4rAʹaʹ, and B3rAʹaʹ representing 20.54%, 15.62%, and 9.46% of the Brazilian territory, respectively. The climate class ArAʹaʹ had 39.20% in the North and 14.97% in the Midwest. The South region has a predominance of 24.31% for the class ArBʹ3aʹ. In the Southeast and Northeast, the climate classes B2rBʹ3aʹ and DdBʹ2aʹ represented 14.80% and 15.26% of the regions, respectively. The S5 scenario was considered more favorable to establishing crops, with 48.04% of Brazil represented by the climate class ArAʹaʹ. Furthermore, the most catastrophic scenarios for crops were S3 and S4, promoting Brazil a predominance of classes B3rAʹaʹ in 18.02% and B1rAʹaʹ in 21.04%, respectively, favoring the occurrence of arid and dry climates in large part of the Brazilian territory.

show abstract

How can data mining help us to predict the influence of climate change on Mediterranean agriculture

Cited by 2 publications

References 0 publications

Assessing rainfall and extreme weather variability for climate resilient agricultural production systems in Nigeria

Assessing rainfall and extreme weather variability for climate resilient agricultural production systems in Nigeria

Climate change in Brazil: future scenarios classified by Thornthwaite (1948)

Contact Info

Product

Resources

About