Seasonal Time Research Articles

Pollinator competition and the contingency of nectar depletion during an early spring resource pulse.

Concerns about competition between pollinators are predicated on the assumption of floral resource limitation. Floral resource limitation, however, is a complex phenomenon involving the interplay of resource production by plants, resource demand by pollinators, and exogenous factors-like weather conditions-that constrain both plants and pollinators. In this study, we examined nectar limitation during the mass flowering of rosaceous fruit trees in early spring. Our study was set in the same region as a previous study that found severe nectar limitation in summer grasslands. We used this seasonal contrast to evaluate two alternative hypotheses concerning the seasonal dynamics of floral resource limitation: either (H1) rates of resource production and consumption are matched through seasonal time to maintain a consistent degree of resource limitation, or (H2) a mismatch of high floral resource production and low pollinator activity in early spring creates a period of relaxed resource limitation that intensifies later in the year. We found generally much lower depletion in our spring study compared to the near 100% depletion found in the summer study, but depletion rates varied markedly through diel time and across sampling days, with afternoon depletion rates sometimes exceeding 80%. In some cases, there were also pronounced differences in depletion rates across simultaneously sampled floral species, indicating different degrees of nectar exploitation. These findings generally support the seasonal mismatch hypothesis (H2) but underscore the complex contingency of nectar depletion. The challenge of future work is to discern how the fluctuation of resource limitation across diel, inter-diel, and seasonal time scales translates into population-level outcomes for pollinators.

Long-term pollen season trends of Fraxinus (ash), Quercus (oak) and Ambrosia artemisiifolia (ragweed) as indicators of anthropogenic climate change impact

The ongoing climatic change, together with atmospheric pollution, influences the timing, duration and intensity of pollen seasons of some allergenic plant taxa. To study these influences, we correlated the trends in the pollen season characteristics of both woody (Fraxinus, Quercus) and herbaceous (Ambrosia) taxa from two pollen monitoring stations in Slovakia with the trends in meteorological factors and air pollutants during the last two decades. In woody species, the increased temperature during the formation of flower buds in summer and autumn led to an earlier onset and intensification of next year’s pollen season, especially in Quercus. The increase of relative air humidity and precipitation during this time also had a positive influence on the intensity of the pollen season of trees. The pollen season of the invasive herbaceous species Ambrosia artemisiifolia was prolonged by increased temperature and humidity during the summer and autumn of the same year, which extended the blooming period and delayed the end of the pollen season. From the studied air pollutants, only three were found to correlate with the intensity of the pollen season of the studied taxa, CO − positively and SO2 and NO2 − negatively. It is important to study these long-term trends since they not only give us valuable insight into the response of plants to changing conditions but also enable the prognosis of the exacerbations of pollen-related allergenic diseases.

Timing patterns of initial respiratory syncytial virus infection and factors influencing disease severity in hospitalized infants with different health status.

This study aimed to determine the timing patterns of the initial respiratory syncytial virus (RSV) infection and to identify the factors influencing disease severity in infants of varying health status. A retrospective study was conducted at the Affiliated Children's Hospital of Chongqing Medical University from 2012 to 2022. The timing of the first RSV infection was estimated in infants with differing health status using correlation analysis, considering their birth time. Logistic regression was utilized to identify factors influencing severe RSV infection in these infants.RSV detection primarily occurred in the winter and spring. Epidemic season and peak timing of RSV were not significantly affected by health status or the COVID-19 pandemic. A strong positive correlation was observed between the age at RSV infection and the interval from birth to the RSV peak season. Infants born during the RSV epidemic season exhibited a higher likelihood of infection within the first 2 months postbirth. In contrast, those born outside the RSV epidemic season were more susceptible to infection during the subsequent peak. Notably, infants with pre-existing health conditions contracted RSV at an earlier age compared to their healthy counterparts. Among healthy infants, severe RSV infection was associated with sex, age, and timing of infection. For infants with underlying conditions, severe RSV infection was primarily related to age and timing of infection. The initial timing of RSV infection in infants varied depending on their health status. Young age and infection timing during the RSV epidemic season were significant risk factors for severe RSV infection. These findings provide a theoretical basis for optimizing immunization strategies for infants with diverse health conditions.

Influence of Seasonal Level Shift Outlier on the Residuals of SARMA(1, 1) Model

Time series often contain discrepant observations which are called outliers. Seasonal Level Shift (SLS) outlier is one such which occurs in seasonal time series. And the importance of residuals in time series model building is well known. Thus in this paper, through analytical expressions, we establish the effect of the presence of an SLS outlier on the residuals of the Seasonal Autoregressive Moving Average model of order (1, 1) (SARMA(1, 1)), followed by a simulation study. One of the principle conclusions of this paper is that the SLS outlier not only affects the residuals at its time of incidence but also at the subsequent seasons. And, the extent of the effect depends on the magnitude of the outlier and parameters of the underlying model.. KEYWORDS :Time series, Seasonal level shift, Residuals, Seasonal autoregressive moving average.

Extreme drought with seasonal timing consistently promotes CH4 uptake through inconsistent pathways in a temperate grassland, China

Extreme drought with seasonal timing consistently promotes CH4 uptake through inconsistent pathways in a temperate grassland, China

Seasonal and Time-Series Analysis of Road Traffic Accidents

Road Traffic Accidents (RTAs) contribute a significant and escalating rate in harm, hospitalization, and mortality. This study assesses the seasonal trend and time series analysis of RTAs cases brought to casualty department of Liaquat University Hospital, Hyderabad, Pakistan. Objective: To conduct seasonal and time series analysis of RTA cases at Casualty Department of Liaquat University Hospital Hyderabad. Methods: Nine hundred and sixty-six road traffic accidents-related cases were examined through prospective study at casualty department, Liaquat University Hospital, Hyderabad, Pakistan from January 2020 to December 2022. The seasonal trend was demonstrated by drawing seasonal index plot and time series plot. Results: The largest occurrences of RTA-related cases were reported in the month of December, having 103 total cases with a mean of 34.33 ± 18.92. July had the second highest frequency of RTAs-related cases, with a total of 94 cases having a mean of 31 ± 5.43 cases. The second-highest number of cases was observed in July and November with approximately equal mean number of RTAs. The time series analysis demonstrated an increasing trend of RTAs-related cases in the last three months of the year. Conclusions: The number of cases connected to RTAs increased during seasons of extreme weather, such as the summer and winter, and it also showed an increasing tendency in the last quarter of 2022.

Multiscale estimation of the cooling effect of urban greenspace in subtropical and tropical cities

Urban greenspace has been widely recognized for its beneficial role in mitigating the urban heat island (UHI) effect and enhancing human thermal comfort. However, understanding on the thermal effects of greenspace still remains limited, particularly in the areas of regional differences, scale issues, and modeling approaches. This study employs a multiscale approach to evaluate the cooling effect of greenspace in Hong Kong and Singapore by combining remote sensing techniques at the territory level, a hybrid modeling approach at the urban district level, and field measurements at the micro level. Results demonstrate that the approach generally performed well in evaluating the relationship between cooling effect and various explanatory variables. At the city scale, the average reduction in LST by greenspace (△LSTG−UF) was found to be 3.08 ℃ in Hong Kong and 2.84 ℃ in Singapore, respectively. However, the cooling effect of greenspace is much reduced at the urban district level. Of the evaluated urban districts, greenspace was found to have a LST reduction effect ranging from 0.27 to 1.56 ℃ in Hong Kong and from −0.02–2.69 ℃ in Singapore, respectively. At the micro level, the study reveals that building height and greenspace volume have the most significant influence on ambient temperature variations during the hottest time of the summer season. These findings not only highlight the importance of considering scale and time variations when evaluating the thermal effects of greenspace but also help identify effective greenspace deployment strategies to combat excessive urban heat.

Evaluation of Gridded Rainfall Products in Three West African Basins

In recent years, accessing rainfall data from ground observation networks maintained by national meteorological services in West Africa has become increasingly challenging. This is primarily due to high acquisition costs and the often sparse distribution of rainfall gauges across the region, which limits their use in hydrological studies and related research. At the same time, the rising availability of precipitation products derived from satellite/earth observations, reanalysis datasets, and in situ measurements presents exciting prospects for hydrological applications. Nonetheless, these datasets constitute indirect measurements, necessitating rigorous validation against ground-based rainfall data. This study comprehensively assesses twenty-three gridded rainfall products, including sixteen from satellites, six from reanalysis data, and one from in situ measurements, across the Senegal, Gambia, and Casamance River basins. Performance evaluation is conducted across distinct climatic zones, both pre- and post-resampling against observed rainfall data gathered from forty-nine rainfall stations over a six-year period (2003–2008). Evaluation criteria include the Kling–Gupta Efficiency (KGE) and Percentage of Bias (PBIAS) metrics, assessed at daily, monthly, and seasonal time steps. The results reveal distinct performance levels among the evaluated rainfall products. RFE, ARC2, and CPC notably yield the highest KGE scores at the daily time step, while GPCP, CHIRP, CHIRPS, RFE, MSWEP, ARC2, CPC, TAMSAT, and CMORPHCRT demonstrate superior performance at the monthly time step. During the rainy season, these products generally exhibit robustness. However, rainfall estimates derived from reanalysis datasets (ERA5, EWEMBI, MERRA2, PGF, WFDEICRU, and WFDEIGPCC) perform poorly in the studied basins. Based on the PBIAS metric, most products tend to underestimate precipitation values, while only PERSIANN and PERSIANNCCS lead to significant overestimations. Spatially, optimal performance of the products is observed in the Casamance basin and the Sudanian and Sahelian climatic zones within the Gambia and Senegal basins. Conversely, in the Guinean zone of the Gambia and Senegal Rivers, the rainfall products displayed the poorest performance.

Impact of Hurricane Irma on coral reef sediment redistribution at Looe Key Reef, Florida, USA

Abstract. Understanding event-driven sediment transport in coral reef environments is essential to assessing impacts on reef species, habitats, restoration, and mitigation, yet a global knowledge gap remains due to limited quantitative studies. Hurricane Irma made landfall in the Lower Florida Keys with sustained 209 km h−1 winds and waves greater than 8 m on 10 September 2017, directly impacting the Florida Reef Tract (FRT) and providing an opportunity to perform a unique comprehensive, quantitative assessment of its impact on coral reef structure and sediment redistribution. We used lidar and multibeam derived digital elevation models (DEMs) collected before and after the passing of Hurricane Irma over a 15.98 km2 area along the lower FRT including Looe Key Reef to quantify changes in seafloor elevation, volume, and structure due to storm impacts. Elevation change was calculated at over 4 million point locations across 10 habitat types within this study area for two time periods using data collected (1) approximately 1 year before the passing of Irma and 3 to 6 months following the storm's impact as well as (2) 3 to 6 months after and up to 16.5 months after the storm. Elevation change data were then used to generate triangulated irregular network (TIN) models in ArcMap to calculate changes in seafloor volume during each time period. Our results indicate that Hurricane Irma was primarily a depositional event that increased mean seafloor elevation and volume at this study site by 0.34 m and up to 5.4 Mm3, respectively. Sediment was transported primarily west-southwest (WSW) and downslope, modifying geomorphic seafloor features including the migration of sand waves and rubble fields, formation of scour marks in shallow seagrass habitats, and burial of seagrass and coral-dominated habitats. Approximately 16.5 months after Hurricane Irma (during a 13-month period between 2017 and 2019), net erosion was observed across all habitats with mean elevation change of −0.15 m and net volume change up to −2.46 Mm3. Rates of elevation change during this post-storm period were 1 to 2 orders of magnitude greater than decadal and multi-decadal rates of change in the same location, and changes showed erosion of approximately 50 % of sediment deposited during the storm event as seafloor sediment distribution began to re-equilibrate to non-storm sea-state conditions. Our results suggest that higher-resolution elevation change data collected over seasonal and annual time periods could enhance characterization and understanding of short-term and long-term rates and processes of seafloor change.

Spatio-temporal characterization of drought variability in data-scarce regions using global precipitation data: a case study in Cauto river basin, Cuba.

Drought is considered the most severe water-related disaster in the Cauto river basin, which is the longest river and the main agricultural producer in Cuba. Better understanding of drought characteristics is crucial to drought management. Given the sparsity of ground-based precipitation observations in the Cauto, this study aims at using gridded global precipitation to analyze the spatio-temporal variations of drought in this river basin. Firstly, the monthly Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) was calibrated with the gauged precipitation using the Thiessen polygon-based method and linear least squares regression equations. Then, the gridded standardized precipitation index (SPI) with time scales of 3, 6, 9months and drought characteristics, namely, drought frequency, duration and intensity were calculated using the calibrated CHIRPS. Finally, the spatio-temporal analysis was performed to investigate the variations of drought in the Cauto river basin in time and space. The obtained results show that the calibrated CHIRPS is highly consistent with the gauged observations and is capable of determining the magnitude, time, and spatial extent of drought events in the Cauto river basin. The trend analysis by the Mann-Kendall test reveals that although the trend is not statistically significant, the SPI tends to decrease with time in the dry season, which indicates the more severe drought. The spatial analysis indicates that the lower altitude area of the Cauto river basin is suffered from longer drought duration and higher drought intensity than the upper one. This study expresses the importance of open global precipitation data sources in monitoring and quantifying drought characteristics in data-scarce regions.

Wildfire smoke reduces lake ecosystem metabolic rates unequally across a trophic gradient

Wildfire smoke covers entire continents, depositing aerosols and reducing solar radiation fluxes to millions of freshwater ecosystems, yet little is known about impacts on lakes. Here, we quantified trends in the spatial extent of smoke cover in California, USA, and assessed responses of gross primary production and ecosystem respiration to smoke in 10 lakes spanning a gradient in water clarity and nutrient concentrations. From 2006 − 2022, the maximum extent of medium or high-density smoke occurring between June-October increased by 300,000 km2. In the three smokiest years (2018, 2020, 2021), lakes experienced 23 − 45 medium or high-density smoke days, characterized by 20% lower shortwave radiation fluxes and five-fold higher atmospheric fine particulate matter concentrations. Ecosystem respiration generally declined during smoke cover, especially in low-nutrient, cold lakes, whereas responses of primary production were more variable. Lake attributes and seasonal timing of wildfires will mediate the effects of smoke on lakes.

Skill assessment of seasonal forecasts of ocean variables

There is growing demand for seasonal forecast products for marine applications. The availability of consistent and sufficiently long observational records of ocean variables permits the assessment of the spatial distribution of the skill of ocean variables from seasonal forecasts. Here we use state-of-the-art temporal records of sea surface temperature (SST), sea surface height (SSH) and upper 300m ocean heat content (OHC) to quantify the distribution of skill, up to 2 seasons ahead, of two operational seasonal forecasting systems contributing to the seasonal multi-model of the Copernicus Climate Change Services (C3S). This study presents the spatial distribution of the skill of the seasonal forecast ensemble mean in terms of anomaly correlation and root mean square error and compares it to the persistence and climatological benchmarks. The comparative assessment of the skill among variables sheds light on sources/limits of predictability at seasonal time scales, as well as the nature of model errors. Beyond these standard verification metrics, we also evaluate the ability of the models to represent the observed long-term trends. Results show that long-term trends contribute to the skill of seasonal forecasts. Although the forecasts capture the long-term trends in general, some regional aspects remain challenging. Part of these errors can be attributed to specific aspects of the ocean initialization, but others, such as the overestimation of the warming in the Eastern Pacific are also influenced by model error. Skill gains can be obtained by improving the trend representation in future forecasting systems. In the meantime, a forecast calibration procedure that corrects the linear trends can produce substantial skill gains. The results show that calibrated seasonal forecasts beat both the climatological and persistence benchmark almost at every location for all initial dates and lead times. Results demonstrate the value of the seasonal forecasts for marine applications and highlight the importance of representing the decadal variability and trends in ocean heat content and sea level.

Growth, Productivity, Yield Components and Seasonality of Different Genotypes of Forage Clover Lotus corniculatus L. under Varied Soil Moisture Contents.

This study aimed to evaluate the response to water deficit of different ecotypes and a variety of Lotus corniculatus on growth, productivity, and yield components, through seasonal times. A randomized block experimental design in a 2 × 5 factorial arrangement with three replicates was used. The first variation factor was soil moisture contents: field capacity (FC) was 26.5% ± 1.5, and water deficit (WD) was 85% of FC (22.5% ± 1.5); the second variation factor was the ecotypes identified with the codes 255301, 255305, 202700 and 226792 and of the variety Estanzuela Ganador. The best responses in plant cover and weight of accumulated fresh biomass were obtained in the ecotype 202700 under WD, with values of 1649.0 cm2 and 583.7 g plant-1, and 1661.2 cm2 and 740.1 g plant-1 in ecotype 255305 under FC. The leaf clover was the main component of yield during the summer and autumn seasons. Ecotype 226792 was tolerant to low temperatures during the winter season with better leaf development. Ecotype 202700 is the best option for forage clover production when water is limited, and ecotype 255305 when water is not resource-limited, but these preliminary conclusions need to be confirmed in field studies.

Assessment of future trends and spatial orientation of groundwater resources as an essential climate variable in the Ganga basin

The dynamic climatic conditions, and anthropogenic activities have a significant impact on the Ganga basin, which is crucial for sustaining the eco-hydrological processes within the agricultural landscape of northern India. Additionally, the Ganga basin represents one of the largest and most densely inhabited aquifer systems globally. Inadequate management practices of groundwater in the current context are likely to contribute to a shortage of food insecurity in the near future. The multi-point statistics using non-parametric test based on the well data point for the period of 1996–2016, were analysed and geoprocessed for the seasonal (pre-monsoon, monsoon, post-monsoon) trend of groundwater depth, fluctuation, and drought in the Ganga basin. The zonal statistical mean of groundwater depletion/loss were maximum observed in post-monsoon season (10 ± 21 cm/year), followed by pre-monsoon(9 ± 22 cm/year), and monsoon (9 ± 21 cm/year). The groundwater depth fluctuation trend magnitude in the Ganga basin ranges from −0.57 m/year to 0.85 m/year, having mean of −0.01 ± 0.07 m/year. The areal percentage of the Ganga basin showing the groundwater drought propagation regions for the pre-monsoon, monsoon, and post-monsoon are found as 74.86%, 74.17%, and 78.52%, respectively. The severity of groundwater drought increased 22 times during pre-monsoon, 9 times in monsoon, and 7 times in the post-monsoon season from 1996 to 2016. The negative groundwater depth fluctuation leads to the groundwater mining at an alarming rate, and formation of dark zone. The decadal trend migration of the mean center shows high spatial variability based on the overexploitation of groundwater. The spatial mapping of declining depth, and increasing drought magnitude of groundwater leads to the groundwater scarcity in most of the regions, which could affect to achieve UN-SDG, Sendai Framework for Disaster Risk Reduction (SFDRR) (2015–2030).

Probabilistic Causal Network Modeling of Southern Hemisphere Jet Subseasonal to Seasonal Predictability

Abstract Skillful prediction of the Southern Hemisphere (SH) eddy-driven jet is crucial for representation of mid-to-high-latitude SH climate variability. In the austral spring-to-summer months, the jet and the stratospheric polar vortex variabilities are strongly coupled. Since the vortex is more predictable and influenced by long-lead drivers 1 month or more ahead, the stratosphere is considered a promising pathway for improving forecasts in the region on subseasonal to seasonal (S2S) time scales. However, a quantification of this predictability has been lacking, as most modeling studies address only one of the several interacting drivers at a time, while statistical analyses quantify association but not skill. This methodological gap is addressed through a knowledge-driven probabilistic causal network approach, quantified with seasonal ensemble hindcast data. The approach enables to quantify the jet’s long-range predictability arising from known late-winter drivers, namely, El Niño–Southern Oscillation (ENSO), Indian Ocean dipole (IOD), upward wave activity flux, and polar night jet oscillation, mediated by the vortex variability in spring. Network-based predictions confirm the vortex as determinant for skillful jet predictions, both for the jet’s poleward shift in late spring and its equatorward shift in early summer. ENSO, IOD, late-winter wave activity flux, and polar night jet oscillation only provide moderate prediction skill to the vortex. This points to early spring submonthly variability as important for determining the vortex state leading up to its breakdown, creating a predictability bottleneck for the jet. The method developed here offers a new avenue to quantify the predictability provided by multiple, interacting drivers on S2S time scales. Significance Statement Predictions of the Southern Hemisphere midlatitude jet stream are crucial for skillful forecasts of the austral mid-to-high latitudes. Several oceanic and atmospheric phenomena could, if better represented in models, improve spring-to-summer jet predictions on subseasonal to seasonal time scales. However, the combined potential skill arising from the inclusion of such phenomena has not been quantified. This study does so by using a probabilistic causal network model, representing the connections between those drivers and the jet with conditional probabilities, trained on large sets of model data. The stratospheric polar vortex is confirmed as crucial predictor of jet variability but is itself hard to predict a month in advance due to submonthly variability, creating a predictability bottleneck for the jet.

Factors affecting the number of influenza patients before and during COVID-19 pandemic, Thailand.

This study was aimed to explore the association between potential factors including public health and social measures and the number of influenza patients in Thailand between 2014-2021. Secondary data from relevant agencies were collected. Generalized Estimating Equation (GEE) and regression coefficient (β) were performed at a significance level of 0.05. We found factors associated with number of influenza patients during the time prior to COVID-19 pandemic were monthly income per household (Adjusted β = -0.02; 95% CI: -0.03, -0.01), population density (Adjusted β = 1.00; 95% CI: 0.82, 1.18), rainy season (Adjusted β = 137.15; 95% CI: 86.17, 188.13) and winter time (Adjusted β = 56.46; 95% CI: 3.21, 109.71). During the time of COVID-19 pandemic, population density (Adjusted β = 0.20; 95% CI: 0.15, 0.26), rainy season (Adjusted β = -164.23; 95% CI: -229.93, -98.52), winter time (Adjusted β = 61.06; 95% CI: 0.71, 121.41), public health control measures (prohibition of entering to into an area with high number of COVID-19 infections (Adjusted β = -169.34; 95% CI: -233.52, -105.16), and restriction of travelling also reduced the number of influenza patients (Adjusted β = -66.88; 95% CI: -125.15, -8.62) were associated with number of influenza patients. This study commends strategies in monitoring influenza patients to focus on the areas with low income, high population density, and in specific seasons. Public health and social measures which can be implemented are prohibition of entering to risk-areas (lock down), and restriction of travelling across provinces which their effectiveness in reducing influenza infections.

Dynamic regulation of water potential in Juniperus osteosperma mediates ecosystem carbon fluxes.

Some plants exhibit dynamic hydraulic regulation, in which the strictness of hydraulic regulation (i.e. iso/anisohydry) changes in response to environmental conditions. However, the environmental controls over iso/anisohydry and the implications of flexible hydraulic regulation for plant productivity remain unknown. In Juniperus osteosperma, a drought-resistant dryland conifer, we collected a 5-month growing season time series of in situ, high temporal-resolution plant water potential ( ) and stand gross primary productivity (GPP). We quantified the stringency of hydraulic regulation associated with environmental covariates and evaluated how predawn water potential contributes to empirically predicting carbon uptake. Juniperus osteosperma showed less stringent hydraulic regulation (more anisohydric) after monsoon precipitation pulses, when soil moisture and atmospheric demand were high, and corresponded with GPP pulses. Predawn water potential matched the timing of GPP fluxes and improved estimates of GPP more strongly than soil and/or atmospheric moisture, notably resolving GPP underestimation before vegetation green-up. Flexible hydraulic regulation appears to allow J. osteosperma to prolong soil water extraction and, therefore, the period of high carbon uptake following monsoon precipitation pulses. Water potential and its dynamic regulation may account for why process-based and empirical models commonly underestimate the magnitude and temporal variability of dryland GPP.

Harmonized NDVI time-series from Landsat and Sentinel-2 reveal phenological patterns of diverse, small-scale cropping systems in East Africa

Capturing the phenological responses of crops in diverse, small-scale agricultural systems is one of the most notable remote sensing research gaps in Eastern Africa. Because of the heterogeneous and complex agricultural environments, remote sensing methodologies successfully implemented elsewhere yield poor results in Eastern Africa. The reliance on local rainfall and dry spells can lead to alterations in planting patterns, impacting growing season timing and yield, underscoring the importance of high temporal resolution for capturing crop phenology. We generated a high-resolution land use/land cover (LULC) classification map of 10 m using Sentinel-1 and Sentinel-2 imagery together with a large set of reference data acquired in situ in 2022. The LULC classification yielded an overall accuracy of 87.9% and Cohen's kappa value of 0.83. Based on the LULC map, we investigated the phenological metrics of the most important crops in the Kamuli district of Eastern Uganda by combining a multi-sensor harmonization approach, thresholding method, and cloud computing. Previously unavailable Landsat 9 band adjustment coefficients were derived. By harmonizing Landsat (LS) 7 Enhanced Thematic Mapper Plus (ETM+), Landsat 8 and 9 Operational Land Imager (OLI), with Sentinel-2 Multispectral Instrument (MSI), the phenological patterns during the March to May growing season were captured using a dense temporal Normalized Difference Vegetation Index (NDVI) time series. The seasonal onset for most crop types was in the end of March to middle of April while the end of the growing season occurred from the end of June to middle of July. Investigation of the 13 crop types using a high-resolution LULC map and dense, harmonized NDVI time series signifies the first field-level phenological metrics estimation of smallholder farming systems in Eastern Africa. Future research should focus on expanding the methodology in space and time to provide insights into past and current growing season development.

Coexisting in the Surf Zone: Age and Feeding Habits of the Spotted Seabass (Dicentrarchus punctatus) and European Seabass (Dicentrarchus labrax) on the Gulf of Cádiz Beaches (Southwest Iberian Peninsula)

Various commercial fish species utilize different coastal habitats as nurseries in their juvenile stages, with surf zones being particularly crucial due to the protection and abundant food resources they offer. Among the species that rely on these areas are the spotted seabass (Dicentrarchus punctatus) and the European seabass (Dicentrarchus labrax). This study aimed to investigate the age and feeding habits of surf zone specimens of these species and explore their environmental adaptations. The average size for D. punctatus was 16.94 ± 4.05 cm, and for D. labrax, it was 23.23 ± 6.30 cm. The length–weight relationship for D. punctatus was TW = 0.013*TL2.885 (parameter a: 95% CI: 0.012–0.014; parameter b: 95% CI: 2.854–2.917), and for D. labrax, it was TW = 0.008*TL3.095 (parameter a: 95% CI: 0.006–0.009; parameter b: 95% CI: 3.040–3.151). In both D. punctatus (79.20%) and D. labrax (75.92%), the predominant age classes were 1+ and 2+. Significant variations in age class abundance were observed in D. punctatus based on the time of day, lunar phase, and season, while D. labrax showed variations only by season. A diet analysis revealed that both species primarily consume mysids and fish, with D. punctatus showing dietary variations related to the time of day, lunar phase, and season, and D. labrax showing variations with the time of day and season.

Utilizing Machine Learning Framework to Evaluate the Effect of Climate Change on Maize and Soybean Yield

In recent years, climate change patterns and extreme weather events have adversely affected agricultural production and have raised concerns about its effect on crop yields. These changes can affect the crop yield in many ways including the changes in the length of the growing season, planting and harvest time windows, precipitation amount and frequency, and the growing degree days, etc. So, it is important to analyze the effect of climate change on yield variability for a better understanding of its effect on the crops. This study aims to analyze the historical monthly county-level data for the state of Ohio to quantify maize (Zea mays) and soybean (Glycine max) yield temporal variability and to study the impact of proposed climate change scenarios on the yield. Machine learning algorithms were used to model the effect of monthly temperature and precipitation levels on yield variability, and to study the effect of climate change scenarios on yield. In addition, yield prediction models were integrated with proposed climate change scenarios for higher and lower emissions scenarios to predict the maize and soybean yield for the year 2100. To model the effect of weather parameters on temporal yield variability, the Random Forest model outperformed with RMSE of 0.61 Mt/ha and R2 of 0.73 for maize and with RMSE of 0.21 Mt/ha, R2 of 0.64 for soybean crop. Maximum temperature for July which has a negative correlation with yield was found to be the most dominant parameter for maize yield followed by the precipitation in August, July, and June which have a positive correlation with maize yield. Precipitation in August was found to be the most dominant weather parameter for soybean yield followed by maximum temperature in July. Based on the projected average increase in Ohio’s temperature and precipitation levels alone, maize yield was predicted to drop by 13.2% or 18.5% respectively due to lower emissions and higher emissions climate change scenario of Ohio. Similarly, a 6.64% and a 9.63% drop is expected in the soybean yield by the year 2100 for both lower emissions and higher emissions climate change scenarios. In economic terms based on current commodity values, this is an astounding loss of 254.7 to 369.7 million USD for maize and 535.9 – 751.1 million USD for soybean crop to the state of Ohio.