Impact Of Climate Variability Research Articles

Late-glacial and Holocene shifts in the mountain landscapes of the Cantabrian range (northern Spain) in response to changing climate, fire occurrence and land use

A new well-dated (18 14C dates on short-lived terrestrial plant macrofossils, 210Pb and 137Cs dating) fine-resolution palaeoecological sequence (pollen, coprophilous fungi, conifer stomata, macrofossils, charcoal) from Lago de Isoba (1400 m a.s.l.) spanning the past c. 17,000 years provides novel insights into the long-term dynamics of the high-elevation Cantabrian landscape in response to climate variability and human activities. Vegetation changes were mainly climate-driven during the Late-glacial and the Early Holocene. Thus, steppe-like environments prevailed during the Greenland Stadial 2.1a (GS-2.1a ≈ ‘Oldest Dryas’). Warmer and moister conditions during the Greenland Interstadial 1 (GI-1 ≈ ’Bølling-Allerød’) enabled successive expansions of Juniperus, Betula and Pinus. Steppe-like vegetation re-expanded during the cooler and drier Greenland Stadial 1 (GS-1 ≈ ‘Younger Dryas’). Afterwards, the Early Holocene warming triggered the rapid spread of mixed pine-broad-leaved deciduous mountain forests. Pollen, stomata and macrofossils indicate continuous, long-lasting (c. 10,850–1250 cal yr BP) local occurrence of Pinus sylvestris stands. The records of Cerealia and Plantago lanceolata pollen types (first Holocene occurrences dated at c. 5100 cal yr BP) evidence increased land use during the Late Holocene, which triggered substantial changes in forest ecosystems. Following a phase of intensified arable and pastoral farming, which involved the use of fire to create and maintain forest clearings, pinewoods expanded and reached their maximum development between c. 3600 and 3300 cal yr BP. Pinus probably outcompeted temporarily other woody taxa thanks to its resilience to low-intensity disturbances. Later, pinewoods underwent a stepwise, human-induced decline ending in their final massive collapse after c. 1150 cal yr BP. The highest intensity of agricultural and pastoral farming took place during the Middle Ages, following maximum fire occurrence. We conclude that the present-day open landscape has been shaped by millennia of land use that partly blurred the impact of climate variability.

Spatial–Temporal Analysis of Impacts of Climate Variability on Maize Yield in Kenya

This study examined the spatial temporal impacts of climate variability on maize yield in Kenya. The maize yield data were obtained from the Kenya Maize Yield Database while climatic variable data were obtained from the Climatic Research Unit gridded Time Series (CRU TS) with a spatial resolution of 0.5° × 0.5°. The non-parametric Mann–Kendall and Sen’s slope tests showed no trend in the data for maximum temperature, minimum temperature and precipitation. The spatial maps patterns highlight the rampancy of wetter areas in the Lake Victoria basin and Highlands East of Rift Valley compared to other regions. Additionally, there is a decreasing trend in the spatial distribution of precipitation in wetter areas and an increasing trend in maximum temperature in dry areas, albeit not statistically significant. Spearman’s rank correlation test showed a strong positive correlation between maize yield and the climatic parameters for the Lake Victoria basin, Highlands East of Rift Valley, Coastal Strip and North Western Regions. The findings suggest that climate variability has a significant impact on maize yield for four out of six climatological zones. We recommend adoption of policies and frameworks that will augment adaptive capacity and build resilience to climatic changes.

Identifying the impact of climate variables on biological and chemical contaminants of preharvest foods and their associated food safety risks: a scoping review protocol

BackgroundFoodborne and waterborne illnesses affect over four million Canadians annually and pose a preventable burden on the nation’s healthcare system. Climate change can increase the risk of such illnesses by...

Assessment of climatic influences on net primary productivity along elevation gradients in temperate ecoregions

Elevation gradients significantly influence net primary productivity (NPP), but the relationship between elevation, climate variables, and vegetation productivity remains underexplored, particularly in diverse ecological zones. This study quantifies the impact of elevation and climatic variables on NPP in northern Pakistan, hypothesizing that elevation modulates NPP through its influence on temperature and precipitation patterns. Using remote sensing data (MODIS ERA5) and advanced ecological models like the Eddy Covariance-Light Use Efficiency (EC-LUE) model and the Thornthwaite Memorial Model (TMM), we analyzed Gross Primary Productivity (GPP) dynamics across various vegetation types and elevations from 2001 to 2023. Our findings show a mean annual NPP of 323.46 g C m-2 a-1, with an annual increase of 5.73 g C m-2 a-1. Significant elevation-dependent variations were observed, especially in mid-elevation zones (401 to 1600 meters), where NPP increased at a rate of 0.174 g C m-2 a-1 per meter (R² = 0.808, p < 0.01). In contrast, higher elevations (2800-5200 meters) exhibited a decline in NPP, decreasing by -0.171 g C m-2 a-1 per meter (R² = 0.905, p < 0.001). Temperature and precipitation were key drivers, with precipitation positively correlating with NPP across all vegetation types, particularly in Evergreen Needleleaf and Broadleaf Trees. The EC-LUE model's GPP estimates closely matched MODIS data (R² = 0.82), demonstrating the model's reliability. These findings highlight the critical role of elevation and climatic factors in vegetation productivity and underscore the need for targeted ecological management and conservation strategies. The insights from this research are vital for global climate adaptation policies and sustainable development goals, contributing to ecological resilience and carbon sequestration efforts worldwide.

Improving carbon flux estimation in tea plantation ecosystems: A machine learning ensemble approach

Tea plant (Camellia sinensis) is a major global crop consumed as a drink after water. Quantifying carbon flux, specifically the net ecosystem exchange (NEE), in tea plantations is essential for determining carbon sequestration and ecosystem carbon balance. The Eddy covariance (EC) system is widely used for continuous monitoring of carbon flux but high costs associated with installation and maintenance limit its widespread adoption. In addition, EC flux data is often discarded due to malfunction of instruments caused by adverse weather conditions. Therefore, additional approaches for estimating NEE are necessary to overcome these challenges and ensure accurate NEE measurement. For this purpose, three standalone tree-based machine learning (ML) models were used for NEE estimation using EC flux data collected from tea ecosystem located in subtropical region (Danyang county of Zhenjiang city) of China. To address the accuracy limitations inherent in standalone ML models, the ensemble mechanism based on voting regressor method was proposed. In addition, k-fold cross-validation based on early stopping process was also used to enhance the performance of standalone ML models. Based on visual plots (scatter diagram, heatMap, Taylor diagram) and performing indices (root-mean-square error (RMSE), determination coefficient (R2), mean absolute error (MAE), mean absolute percentage error (MAPE), Nash–Sutcliffe efficiency (NSE), correlation coefficient (r), Kling Gupta Efficiency (KGE) and index of agreement (d)), the findings indicated that non-linear ensemble-generalized regression neural network (NLE-GRNN) significantly improved standalone ML model's results. In current study, the highest NSE, r and d in case of standalone ML model (DT) achieved 0.49, 0.73 and 0.75 respectively while our proposed NLE-GRNN model improved 48 % in NSE value (NSE = 0.97), 25 % in r value (r = 0.98) and 24 % in d value (d = 0.99). Likewise, NLE-GRNN significantly reduce errors (MAE, MAPE and RMSE) and provides NEE estimate closet to the observed value. The impact of climatic variables on NEE using shapley additive explanations (SHAP) analysis revealed that Rg (solar radiation) and Tair (air temperature) were the prime factors controlling NEE variation in the tea ecosystem. Considering the high accuracy and stability of the studied ML models, it is recommended to apply developed ensemble ML model (NLE-GRNN) for significant improvement of NEE estimate in the tea biomes or other ecosystems.

Assessing spatiotemporal dynamics of water use efficiency in ecologically vulnerable karst landscapes

Study regionSouthwest China Study focusWater use efficiency (WUE) is a key variable for understanding the carbon and water cycling processes in ecosystems. There exists the world's largest continuous karst topography in Southwest China, rendering it a quintessential ecologically vulnerable region. Therefore, determining the trends in WUE changes between karst and non-karst ecosystems is of great significance for understanding the carbon and water cycling processes in ecologically vulnerable areas. In this study, various analysis methods were utilized to explore the spatiotemporal variations, persistence characteristics, and the impact of complex climatic variability of WUE in Southwest China. New hydrological insights for the region(i) The rate of increase and multi-year average of WUE in karst ecosystem is higher than that in non-karst ecosystem. (ii) In the future, the proportion of area with decreasing in WUE will be higher in karst ecosystem than in non-karst ecosystem. (iii) There are significant differences in the correlation between WUE and climatic factors in different ecosystems. (iv) The dominant climatic factor for WUE in karst ecosystem is vapor pressure, while in non-karst ecosystem, it is precipitation. The research results can provide a theoretical basis for studying the carbon and water cycling processes in karst ecosystem.

Investigating the impact of climate variables on the organic honey yield in Turkey using XGBoost machine learning.

The Turkish organic honey industry, a major player in the global market, faces challenges due to climate fluctuations. Understanding the influence of climate factors on honey production is vital for sustainable farming and economic stability. This study uses a machine learning approach with the XGBoost algorithm to analyze temperature, wind, humidity, precipitation and surface pressure over a 20-year period from 2004 to 2023. The results show that these factors significantly impact organic honey production, with temperature, wind, humidity, precipitation and surface pressure having effects of 41.20%, 26.50%, 12.47%, 11.42% and 8.41%, respectively. Sensitivity analysis reveals the model's sensitivity to even minor fluctuations in these variables. The results of this research underscore the necessity of integrating climate change mitigation and adaptation measures into agricultural policies and beekeeping practices. This study showcases the practical application of machine learning in deciphering the intricate relationship between climate change and the production of crops, emphasizing the importance of data-driven decision-making to guarantee long-term sustainability and financial stability in the sector. © 2024 The Author(s). Journal of the Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Adapting Seasonal Rice Cultivation Strategies for Food Security in Response to Climate Change Impacts

An in-depth examination of the effects of climate change on rice yield in China, encompassing various rice types, is crucial for ensuring the nation’s food security. This study develops an “economy-climate” theoretical model and employs Panel Corrected Standard Error Estimation (PCSE) on panel data spanning from 1978 to 2018, sourced from China’s primary grain-producing regions. The analysis delves into the impact of climate variables, including precipitation, temperature, and sunshine duration, on overall rice production and different rice types. Overall, the findings reveal a nonlinear relationship between precipitation, temperature, sunshine duration, and rice yield, characterized by an “inverted U-shaped” pattern. However, significant variations exist in the effects on different rice types across China’s main grain-producing areas. Increasing precipitation generally enhances early rice production across provinces and also augments mid-season and one-season-late rice production in the Inner Mongolia Autonomous Region, Hebei, Jilin, Heilongjiang, and Shandong Province. Conversely, it reduces mid-season and one-season-late rice output in Liaoning, Jiangsu, Anhui, Jiangxi, Henan, Hubei, and Hunan. Sichuan Province sees a rise in temperature favoring early and double-season-late rice production, unlike other provinces. For mid-season and one-season-late rice, temperature increases benefit output in Heilongjiang Province but not in other regions. Additionally, prolonged sunshine duration boosts early and double-season-late rice production across all provinces but does not have the same effect on mid-season and one-season-late rice in China’s primary grain-producing areas.

Effect of seasonal variability on the increased malaria positivity rate in drought-prone malaria endemic areas of Ethiopia.

Malaria is a climate-sensitive and season-dependent disease that mainly affects the populations of tropical and subtropical regions of the world. Climatic variables such as rainfall, temperature, and humidity are identified as the driving factors affecting the epidemiology of malaria. In Ethiopia, recently unexpectedly high malaria burden accompanied by substantial morbidity and mortality is observed. The incidence rate of vector-borne diseases such as malaria is largely influenced by fluctuation of seasonal variables due to the proceeding global warming. Therefore, the study aimed to assess the impact of seasonal and climate variability on the burden of malaria in one of drought-prone areas of north central Ethiopia. The study was conducted in the Oromia zone of the Amhara regional state in Ethiopia. Data from patients tested and diagnosed with malaria between 2014 and 2019 were extracted from the medical records of the patients registered in all health facilities found in the zone. Additionally, meteorological data such as rainfall and temperature (maximum and minimum) were collected from the regional meteorological agency. Data were analyzed using R software. The malaria positivity rate recorded in this study was 12.7% (64,522/508,057). Months from June to December were significantly (p < 0.05) associated with high malaria positivity. Rainfall had a significant negative effect on malaria positivity (p = 0.042). Also, three to four months of lagged rainfall is associated with high malaria positivity (p < 0.05). However, monthly minimum temperature positivity influenced the rate of malaria positivity (exp = 0.037, p = 0.0098). While monthly mean maximum temperature showed a negative association with malaria positivity, although it was insignificant. In addition, malaria positivity significantly decreased from 2014 to 2019 by 20.5% (exp = 0.205, p < 0.0001). The seasonal and climatic variables evaluated in this study substantially affected the malaria positivity rate. Monthly mean temperature was the leading and predictor variable for malaria infection. Despite vigorous efforts by health authorities in the country at this level, following the neglected implementation of interventional activities in the nation for a couple of years, malaria-associated burdens have increased. As seasonal and climate variables could play a key role in the rising burden, concerned national or local health authorities, and policymakers should prioritize urgent interventional activities to control malaria in one of the drought-prone areas of Ethiopia.

Comprehensive climatic variability analysis and its significance on flood occurrences in the Middle Ganga Plain, India

Climate variability significantly affects rainfall, air temperature, relative humidity, and solar radiation. In subtropical regions like India, intense monsoon rainfall primarily causes floods. This study aims to analyze the climatic factors contributing to flood occurrence in the Middle Ganga Plain (MGP) by examining meteorological parameters through various analyses, including monthly and yearly trends, spatial variability, exceedance probability, historical highest intensity rainfall, and wind trajectory analysis. By establishing a strong link between climatic variables and flood events, this research provides valuable insights into the impact of climate variability on flood patterns in the MGP. Results indicate that monsoon rainfall peaks from July to October, correlating with major flood events. During these months, high relative humidity increases the likelihood of rainfall. Spatial analysis shows higher rainfall in the northern Middle Ganga Plain (MGP), which aligns with observed flood patterns. Trend detection tests reveal increasing annual rainfall and rising average temperatures. Geostatistical analysis identifies the western and eastern MGP as areas prone to extreme rainfall. Exceedance probability analysis confirms the occurrence of substantial monsoon rainfall events. Wind trajectory analysis uncovers seasonal shifts that lead to low-pressure systems and heavy monsoon rainfall. The study establishes a strong link between climatic variables and flood events in the MGP region.

Impacts of climate change and variability on drought characteristics and challenges on sorghum productivity in Babile District, Eastern Ethiopia

ABSTRACT Examining the characteristics of drought indices in the context of climate variability and change, particularly in semi-arid water-stressed regions, requires adaptation. Observed climate data of the Babile station from 1980 to 2009 were used as a baseline for climate projection. Future climate projection was established under two Representative Concentration Pathway (RCP4.5 and RCP8.5) climate scenarios for the 21st century. Two drought indices, namely standard precipitation index and standard evapotranspiration index (SPI and SPEI) were employed based on temperature and rainfall to characterize droughts. Our study revealed that drought severity and intensity are more likely to increase under RCP4.5 climate forcing in the middle of the 21st century. The average drought severity (S) was 1.1, 1.53, 1.55, and 1.8 in SPEI 3-month time scale; 1.51, 2.1, 2.38, and 2.29 in SPEI 4-month time scale; and 2.15, 2.77, 3.44, and 2.91 in SPEI 6-month time scale, whereas, the drought severity (S) was 1.33, 1.37, and 1.79 in SPEI 3-month time scale; 1.79, 2.05, and 2.19 in SPEI 4-month time scale; and 2.47, 3.19, and 2.69 in SPEI 6-month time scale in observed, near, mid and end of the 21st century under RCP4.5 and 8.5 scenarios, respectively. Increase in drought frequency and severity occurrences under RCP4.5 scenario would have a negative impact on sorghum crop productivity. We recommended further study on practical soil water conservation to drought management in the study area.

Crack Length of Elastomeric Sealants and Their Service Life in Contrasting Canadian Climates: Effects of Climate Change.

The longevity of polymer-based sealant and jointing products, including elastomers, significantly depends on the level of exposure to sunlight and joint movement. These factors are particularly crucial in the application of polymers in construction due to their susceptibility to degradation under environmental conditions. For instance, diurnal cycles of contraction and dilation, arising from daily temperature fluctuations, impose significant stress on sealants and joints, impacting their durability over time. The elastic nature of polymeric sealants enables them to endure these cyclic mechanical loads. Athough there is considerable information on sealant durability obtained from laboratory accelerated aging, there is limited knowledge about the effect of climatic factors using historical and projected weather data on the durability and expected service life of these products. This study employed the Shephard crack growth model to predict the performance of sealants in a Canadian context; the crack growth and time-to-failure of hypothetical silicone sealants were investigated across 564 locations, for which historical climate data were obtained from 1998 to 2017, including gridded reanalysis data for the period of 1836-2015. The historical climate data were classified into four climate categories, and crack growth was estimated based on historical climatic data within the valid range for the Shephard model, revealing that locations in colder climates with lower levels of precipitation typically exhibit higher cumulative crack growth. The impact of climatic variation and environmental stressors on the longevity of sealants in the context of climate change was also investigated using future projected data.

Climate-adaptive fire smoke ventilation strategies for atrium-type metro stations: A NSGA-II multi-objective optimisation study

This study explores the impact of climate variability on the operation of sustainable ventilation systems in urban infrastructure, with a focus on smoke movement in metro stations under varied external climatic conditions. Smoke characteristics and ventilation strategies in an atrium-type metro station are investigated using numerical simulations. The simulations reveal the influence of external climate on smoke movement through the atrium and roof windows, with particular attention to outdoor temperature and wind velocity. The study designs different ventilation strategies for smoke control that incorporate meteorological data, evaluating smoke visibility, CO distribution, and smoke extraction efficiency. The findings indicate that under severe external climates of low temperatures and high wind velocities, smoke extraction from roof window outlets is challenging. The adverse effects of external climate decrease the natural smoke extraction efficiency, increase the CO concentration, and reduce the safe visibility area. However, improved ventilation measures, such as the platform make-up air system and roof mechanical extraction system, can significantly enhance smoke control performance and facilitate safe evacuation. The study also employs the NSGA-II multi-objective optimisation method to obtain optimal ventilation strategies for different climates, balancing three optimisation objectives of operating energy consumption, safe visibility area, and smoke extraction efficiency. The outcomes show that the recommended values of Vp are 20%–30 % (0 °C ≤ To≤40 °C) and 40 % (−40 °C ≤ To<0 °C), vr are 0.3 (10 °C ≤ To≤40 °C) and 0.4 (−40 °C ≤ To < −10 °C), and Vc are all lower than 4 %. As wind velocity increases, the recommended values need to be enhanced. This research contributes to sustainable urban development by offering a framework for managing public safety and energy efficiency in the context of climate variability.

SIMULATION OF SPATIO-TEMPORAL DYNAMICS OF STREAM FLOW TO CLIMATE VARIABILITY IN NJORO RIVER CATCHMENT, KENYA

Climate variability continues to alter hydrological regimes and the response of many catchments globally thus threatening water security. Njoro River Catchment has not been an exception. There is a steady recognition of climate variability and adverse influences like deterioration of ecosystems, surface, and groundwater sources. Therefore, the study focused on modelling the impact of climate variability on the spatial and temporal distribution of stream flow in the Njoro River Catchment, Kenya. Modelling of stream flow response to climate variability using SWAT was carried out based on USGS/NASA downloaded Digital Elevation Model, FAO soil data, Landsat (MSS 1-5) LULC of 1978, and meteorological data for the period (1978 - 2017). Simulation of spatial and temporal impacts of climate variability was then carried out. Based on the analysis, SUFI-2 algorithm results revealed that the most sensitive parameter in Njoro River Catchment is SCS curve number for antecedent moisture condition (II) (CN2), ranked according to the highest sensitivity tested at p &lt; 0.05. The least sensitive parameter is maximum canopy storage (CANMX). Overall uncertainty analysis results showed a good performance value of P-factor (0.72) and R-factor (0.38), which indicated that SWAT reproduced the dynamics of the catchment hydrologic response relatively well. The values of R2, NSE, and PBIAS for calibration and validation of monthly stream flow were 0.88 and 0.77, 0.86 and 0.74, and 5.51 % and -15.42 % respectively. From the spatio-temporal impacts of climate variability on hydrologic variables, actual evapotranspiration increased by about 5.6 %, 3 %, and 6.62 % in the 2nd, 3rd, and 4th decades. Surface runoff decreased in the 2nd decade by 34.56 % and increased by 4.55 % and 34.02 % in the 3rd and 4th decades. Annual water yield decreased in the 2nd decade by 29.69 % and increased by 4.25 % and 60.18 % in the 3rd and 4th decades respectively. On average, stream flow reduced by 30.91 % in the 2nd decade and increased by 5.47 % and 63.63 % in the 3rd and 4th decades respectively. These findings provide pertinent insights, which may perhaps enlighten decision-making in designing adaptable mitigation measures, catchment rehabilitation, and strategic initiatives for the integrated management of water resources.

A hybrid machine learning-based model for predicting failure of water mains under climatic variations: A Hong Kong case study

Effective functioning of water systems is critical to ensure the quality of human life. Therefore, failure prediction of water mains under climatic variations is necessary to avoid socio-economic and environmental losses. This paper aims to propose a hybrid model named STL-GC-LSTM for an accurate failure prediction of water mains under the impact of climatic variations. Firstly, the seasonal-trend decomposition based on Loess (STL) method is employed to decompose the failure time series. Next, significant climate variables are selected from the Granger causality (GC) test. Lastly, the final predicted failure of water mains is acquired by adding up the predictive results of the three components which are learned by Long Short-Term Memory (LSTM) models. Several evaluation metrics are used to assess the prediction performance. The results from a case study in Hong Kong imply that STL decomposition is promising for fully mining intrinsic properties of failure series. The developed hybrid models are effective in specifically identifying which component climatic variations exert influence on, and the final failure predictions show satisfactory agreement compared with peer models. This paper could provide an accurate estimation for failures of water mains ahead of time and be used as an essential complement to other numerical prediction models.

The importance of local observation wells, reanalysis, and satellite data on gravity anomaly, climate, and land use to improve groundwater management in the Urucuia aquifer system

The Urucuia Aquifer System (UAS) is an important water source for multiple users and the perennity of the San Francisco River. However, it has been subject to intensive land use transition (LUT), which provoked concerns due to continuous groundwater depletion. Therefore, understanding this depletion and potential drivers is essential for improved management towards water security. In this study, satellite data of the GRACE-project and in-situ data of the RIMAS-project were used to quantify the groundwater depletion between 2003 and 2021. Two important drivers were assessed for their impact on the UAS: (i) climate variability and (ii) LUT. The impact of climate variability was evaluated by using the vertical water balance (VWB; rainfall - evapotranspiration) on basis of validated rainfall and air temperature data from reanalysis (1979–2020). LUT was retrieved from the MAPBIOMAS database (1985–2022). The UAS was subdivided into quadrants of 0.75 × 0.75° (6700 km2) to assess the spatial variation of groundwater depletion and its drivers. Groundwater depletion was characterized by trend analysis and contrasted with the temporal behavior of VWB and LUT across the UAS. The results show a continuous decrease of the groundwater table in the study period (−22 mm/y) with a steeper decline between 2012 and 2018 (−52 mm/y). A direct cause-effect-relation can be observed with the negative trend of the VWB, pushed by decreasing rainfall and increasing air temperature over time. On the other hand, groundwater depletion correlates with LUT and is more accentuated where native vegetation is replaced by irrigated crops. Moreover, the GRACE dataset could be used for regional studies, but in-situ monitoring is recommended for local management as validation (R2 = 56%) indicates an underestimation of groundwater depletion by satellite data. In conclusion, extending the in-situ monitoring network, limiting LUT, and irrigation are crucial to long-term sustainability of the UAS.

Geospatial assessment of the impact of climate variability on millet and yam yield in FCT, Abuja

Geospatial assessment of the impact of climate variability on millet and yam yield in FCT, Abuja

Decoupled Hydroclimate of Central and Southwestern Iran Controlled by the Strength of Southerly‐Westerly Jets During Marine Isotope Stage 3

AbstractThe regional impact of abrupt glacial climate variability remains poorly constrained for arid southwestern Asia, particularly winter dynamics during Marine Isotope Stage 3, due to limited paleoarchives in the Middle East. Here, we present continuous speleothem records of δ18O and δ13C with robust chronologies for southwestern and central Iran, spanning ∼50–30 ka. Stable‐isotope signals in the two stalagmites are generally uncorrelated and do not exhibit a consistent response to Greenland stadials or interstadials; however, both show a positive δ18O excursion that coincides with Heinrich event 4. We explore the potential mechanisms for intermittent coupling of speleothem δ18O across Iran through isotope‐enabled atmospheric modeling outputs, from which we utilize the spatial δ18O gradient as a proxy for wintertime westerly versus southerly jet strength. Our results suggest that during Heinrich event 4 and several Greenland stadials, stronger westerly winds enhanced Mediterranean moisture contributions to both sites and reduced aridity in southern Iran.

Assessing the impact of climate variability on maize yields in the different regions of Ghana-A machine learning perspective.

Climate variability has become one of the most pressing issues of our time, affecting various aspects of the environment, including the agriculture sector. This study examines the impact of climate variability on Ghana's maize yield for all agro-ecological zones and administrative regions in Ghana using annual data from 1992 to 2019. The study also employs the stacking ensemble learning model (SELM) in predicting the maize yield in the different regions taking random forest (RF), support vector machine (SVM), gradient boosting (GB), decision tree (DT), and linear regression (LR) as base models. The findings of the study reveal that maize production in the regions of Ghana is inconsistent, with some regions having high variability. All the climate variables considered have positive impact on maize yield, with a lesser variability of temperature in the Guinea savanna zones and a higher temperature variability in the Volta Region. Carbon dioxide (CO2) also plays a significant role in predicting maize yield across all regions of Ghana. Among the machine learning models utilized, the stacking ensemble model consistently performed better in many regions such as in the Western, Upper East, Upper West, and Greater Accra regions. These findings are important in understanding the impact of climate variability on the yield of maize in Ghana, highlighting regional disparities in maize yield in the country, and highlighting the need for advanced techniques for forecasting, which are important for further investigation and interventions for agricultural planning and decision-making on food security in Ghana.

Bayesian analysis of cultivar and climate effects on seed and oil production in linseed

Linseed oil is utilised in a broad range of industrial and food applications, which make desirable a thorough understanding of the factors that influence its yield and quality. In this study, we used a Bayesian hierarchical model to investigate the impact of genotype and climatic variables on linseed seed and oil yield, as well as fatty acid composition, by utilising a germplasm collection database. Data were collected for 25 years at two nearby locations in Northern Italy, covering between 192 and 252 cultivars. After model optimisation, the cultivar-specific intercept coefficient exhibited the highest degree of differentiation across cultivars for all the variables under investigation. The response to temperature showed on the other hand no difference among cultivars, but it was generally negative for seed yield and seed oil content while it did not impact on fatty acids except for reducing the ratio of polyunsaturated to saturated fats in a few cultivars. A higher variability was detected for the cultivar response to rainfall, which affected seed yield and oil content either negatively or positively. Rainfall also influenced fatty acid composition, in particular the fraction of monounsaturated acids which were negatively impacted in all cultivars. Overall, the seed yield was the variable most affected by weather factors, which accounted for a value ranging between −27.9 % and 14.9 % on the average characteristic value of the cultivar, whereas for all other variables, the deviation was always less than 8 % in absolute value. The study also analysed the effect of autumn sowing compared to spring sowing, more commonly practiced in the study sites. Autumn sowing increased seed yield by up to 79.4 %, whereas oil content rose by no more than 1.6 %. While the saturated and monounsaturated fractions of fatty acids decreased, the polyunsaturated fraction rose to a maximum of 13.1 %. The ratio of polyunsaturated to saturated fats was increased up to 29.4 %.