Current Climate Conditions Research Articles

Estimation of the impact of combined sewer overflows on surface water quality in a sparsely monitored area

Combined sewer overflows (CSOs) can have a severe negative, local impact on surface water systems. To assure good ecological surface water quality and drinking water production that meets the demands, the impact of sewer system overflows on the surrounding water bodies for current and future climate conditions needs to be assessed. Typically, integrated, detailed hydrological and hydrodynamic water quantity and quality models are used for this purpose, but often data and computational resource requirements limit their applicability. Therefore, an alternative computationally efficient, integrated water quantity and quality model of sewer systems and their receiving surface waters is proposed to assess the impact of CSOs on surface water quality in a sparsely observed area. A conceptual model approach to estimate CSO discharges is combined with an empirical model for estimating CSO pollutant concentrations based on waste water treatment plant influent observations. Both methods are compared with observations and independent results of established reference methods as to evaluate their performance. The methodology is demonstrated by modelling the current impact of CSOs on the water abstraction area of a major drinking water production centre in Flanders, Belgium. It is concluded that the proposed conceptual models achieve similar results for daily WWTP effluent and CSO frequency, whereby the accumulated CSO volume is similar to more detailed full hydrodynamic models. Further, the estimated pollutant concentrations correspond with another dataset based on high resolution sampled overflows. As a result, the proposed computational efficient method can give insights in the impact of CSOs on the water quality at a catchment level and can be used for planning monitoring campaigns or performing analyses of e.g. the current and future water availability for a data scarce areas.

Potential geographical distribution and its multi-factor analysis of Pinus massoniana in China based on the maxent model

Pinus massoniana, an important timber, producing, and silvicultural species in southern China, exhibits high adaptability and wide distribution. This study utilizes the Maximum Entropy Model (MaxEnt), a species distribution model based on the theory of maximum entropy, to forecast the potential suitable distribution areas of P. massoniana in China under four climate change scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) for both present and future (2080) conditions. The research integrates and analyzes the effects of various environmental factors, including topography, soil, and population, on the distribution of P. massoniana. Additionally, a geographical detector is employed to assess the interaction between different environmental factors and their contribution to the variation in suitability zones.The findings indicate that the MaxEnt model accurately predicts the potential distribution areas of P. massoniana, with AUC values exceeding 0.94. Precipitation in the driest month (BIO14), population density (POP), and annual precipitation (BIO12) emerge as the main factors influencing the current distribution of P. massoniana. Notably, BIO14 has the greatest impact on the species' distribution (43%), followed by POP (32.7%). Furthermore, lower BIO14 values correspond to higher probabilities of pine distribution, while higher POP values correlate with increased pine distribution probabilities. The potential distribution of P. massoniana is primarily concentrated in southern China under current climatic conditions, encompassing a total suitable survival zone of 25.24 × 105 km2, accounting for 26.29% of China's total area. Among the regions, Guangxi exhibits the largest suitable area for survival, reaching 28.9 × 104 km2, implying that the environmental characteristics of Guangxi are conducive to P. massoniana's survival. Under future climate scenarios, the overall distribution pattern of the potential range of P. massoniana remains similar to the present one, with an increasing trend in area. Notably, the SSP3-7.0 emissions scenario shows the most significant increase in area, totaling 4.71 × 104 km2, suggesting that this particular scenario is more favorable for the distribution of P. massoniana. This study provides valuable scientific insights for the management, conservation, and rational site selection of P. massoniana.

Pluvial flood adaptation using nature-based solutions: An integrated biophysical-economic assessment

Globally, flood events are considered the costliest natural hazard. Changes in precipitation patterns and large areas of impervious surfaces in urban environments are increasing the sensitivity of these systems to runoff production. At the same time, projected global sea-level rise may further increase the frequency of compound flooding due to simultaneous storm surge, sea-level rise and pluvial runoff that cause vast socio-economic and ecological impacts to coastal cities. In this context, over the last decade, the role of Nature-Based Solutions (NBS) has been recognised to support climate change adaptation by addressing ideas of multi-functionality, non-linearity and heterogeneity in urban design. Thus, increasing awareness about NBS benefits increases the willingness to accept these solutions. However, empirical evidence of NBS effectiveness at the urban catchment scale is still subject to debate. This study develops a spatial biophysical-economic framework that allows for the integrated assessment of NBS flood risk mitigation impacts, costs and benefits in the face of climate change, combining the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model, benefit transfer methods and Geographic Information System (GIS) tools. Specifically, the InVEST Urban Flood Risk Mitigation model was used to assess the biophysical impacts of NBS on urban pluvial flood risk, benefit-transfer methods were used to evaluate the economic implications of such solutions, and GIS was used to integrate and map biophysical impacts and economic implications. For the case of the coastal lagoon city of Aveiro (Portugal), NBS scenarios of green roofs and bioswales under current and future climate conditions were assessed. The main findings of this study show that green roofs scenarios would save 32 % of the flood damages to buildings and infrastructures every year, while bioswales help save only 0.1 %. Moreover, green roofs implementation provides larger benefits in the future climate scenario (representative concentration pathway – RCP – 4.5). The findings confirm the extent to which knowledge on NBS benefits and costs is partial and uncertain, thus requiring constant progress through biophysical-economic assessment to support an evolutive decision making process in climate adaptation planning.

Current and future potential geographical distribution of Bactericera cockerelli: an invasive pest of increasing global importance

AbstractDue to ongoing climate change and the spread of invasive pests, understanding and predicting climatic suitability for invasive insect species has shown growing demand from government and industry biosecurity managers. The invasive pest Bactericera cockerelli, (Šulc) (Hemiptera: Triozidae), commonly known as tomato potato psyllid (TPP), is native to North America and has recently invaded Australasia. TPP is also the vector of the bacterial plant pathogen Candidatus Liberibacter solanacearum (CLso), which has caused severe economic losses for potato growers worldwide. We used the niche modelling software CLIMEX to predict the potential geographical distribution of TPP in Australasia and worldwide under current and future climatic scenarios. Our model prediction of the current climate conditions closely agrees with all the known distributions of TPP. In its native range (North America), TPP is predicted to expand its current geographical range in semi‐arid, temperate, and continental climates. Within Australia, along with the known occurrence of TPP in Western Australia, potential expansion into South Australia, Victoria, New South Wales and Queensland is predicted. The predicted distribution closely matches all the known records with higher climatic suitability in New Zealand. Globally, the model projected that the pest‐free countries in Europe and East Asia are climatically more suitable for TPP. Predictions under the future climate change scenarios (A1B, CSIRO Mk 3.0 for 2090) showed a significant reduction of the known geographical range of TPP with a possible expansion towards higher latitudes. Areas in North America and Australia are projected to be less climatically suitable for the survival of TPP in future climates. However, our model suggested that Europe and New Zealand will remain unchanged or will become more favourable in the future. These CLIMEX projections for current and future climatic distribution provide valuable information for existing and future biosecurity preparedness and management programmes, which may prove helpful in risk assessments and identifying potential areas that are likely to be susceptible to a TPP invasion.

Controlled Drainage Effectiveness in Reducing Nutrient Outflow in Light of Climate Changes

This modeling study focused on the hydrological and water quality effects of controlled drainage (CD) when operated using a subsurface drainage system in an agricultural field in the Wielkopolska region. The DRAINMOD hydrologic model was well calibrated and validated in an experimental field. This model was used in the performance of CD and free drainage (FD) combinations (108 and 27, respectively) in a near-future climate change scenario. The objective was to understand the potential of CD on the groundwater table (GWT), drainage outflow, surface runoff, and nitrogen and phosphorus reduction under projected climate conditions in Poland during the 21st century with shared socioeconomic pathway SSP370. The results indicated that the earliest start of CD practice is the most effective in increasing GWT. Compared to current climatic conditions, when applying CD on 1 March in the near future, with an initial GWT of 60 and 80 cm b.s.l. in wet years, drainage outflows will increase by 33% and 80% for the GFDL model, by 30% and 40% for the MPI model, and by 17% and 23% for the UKESM model. Comparing the surface runoff values obtained to current climate conditions, the MPI, GFDL, and UKESM models predict a significant increase in surface runoff in the near future, which is due to the predicted increase in precipitation. The annual NO3–N reduction was by 22, 19, and 15 kg per hectare for wet, normal, and dry years, respectively, in the near future. Among the climate scenarios, the UKESM model predicted higher NO3–N and PO4 leaching values compared to the MPI and GFDL models.

Invasion risk by fruit trees mealybug Rastrococcus invadens (Williams) (Homoptera: Pseudococcidae) under climate warming

The mango mealybug Rastrococcus invadens (Williams) (Homoptera: Pseudococcidae) is a destructive and important insect pest of fruit trees in Africa and Asia, especially the mango. Females and nymphs feed on plant leaves and fruits and produce honeydew that causes sooty mold, leading to yield reduction. Although it is an important pest, the distribution of R. invadens under different climate change scenarios has not been established. In this study, we predicted the suitable habitat for R. invadens occurrence under current and future [two Shared Socioeconomic Pathways (SSPs) scenarios: (SSP2-4.5 and SSP5-8.5) for the years 2050s and 2070s], using environmental variables and four ecological niche models viz., maxent, random forest, boosted regression trees, and support vector machines. The performance and accuracy of these models were evaluated using the area under the curve (AUC), the true skill statistic (TSS), correlation (COR), and deviance. All models had high accuracy (AUC ≥ 0.96, TSS ≥ 0.88, COR ≥ 0.74 and deviance ≤ 0.3) in predicting the potential distribution of R. invadens. Among the four models, the random forest algorithm had the highest performance (AUC = 0.99, TSS = 0.95, COR = 0.91 and deviance = 0.14) in predicting the potential distribution of R. invadens, followed by maxent (AUC = 0.97, TSS = 0.90, COR = 0.81 and deviance = 0.22). However, the maxent model was the best among the four algorithms in predicting the ecological niche of R. invadens. The precipitation of the wettest month (bio13) was the most crucial environmental variable that contributed to the predictions of the four models. The results revealed that most areas in East, Central, and West Africa were projected with high suitability for R. invadens to occur under current climatic conditions. Similarly, Bangladesh, Laos, Myanmar, India, Thailand, Vietnam and Cambodia in Asia, as well as Brazil, and Venezuela in South America showed high suitability for R. invadens establishment. However, under future climatic conditions (the years 2050s and 2070s), the suitable areas for R. invadens will increase regardless of the SSPs scenario (SSP2-4.5 and SSP5-8.5) indicating an expansion of the geographical range for this pest. This expansion is projected to be higher for the years 2070s than the 2050s. Similarly, the invasion risk of R. invadens is predicted to be higher under SSP2-4.5 scenario compared to SSP5-8.5 scenario, regardless of the year of the projection. Our results serve as an early warning tool that could serve as a guide to prevent further spread and invasion of this pest to new areas as well as help in developing an effective management strategy against R. invadens.

RAINFALL PREDICTION USING SEASONAL AUTOREGRESSIVE INTEGRATED MOVING AVERAGE AND GEOGRAPHIC INFORMATION SYSTEM APPROACH

Rainfall is one indicator to determine the estimated adequacy of groundwater on agricultural land. The groundwater availability produced by rain can determine cropping patterns in an area. The availability of rainfall data depends on the accuracy of information on current climate conditions. This case causes the related parties to find difficulty determining the classification of cropping patterns in the future. Accurate rainfall prediction models are needed to overcome the problem of shifting rain patterns. Rainfall prediction models in determining cropping patterns are recommended by FAO, such as linear regression, which is still widely used today. This study aims to develop a new model of rainfall prediction by using the method SARIMA to determine cropping patterns to increase crop yields. Rainfall data was used from 2010 to 2020 from seven rainfall collection stations in Sleman Regency, and they are used as training data to predict future rainfall. The output of the data analysis is a prediction of rainfall in the range of January-April, which is predicted to be high, May-August, which is predicted to be low; and September-December, which is predicted to be moderate. In addition, based on the identified cropping patterns, recommendations can be given to farmers to set cropping schedules and strategies to increase the productivity of the farmland. The testing of accuracy forecasting used relative mean absolute error (RMAE) for 12 months. The results of the forecasting accuracy test for 12 months in Sleman Regency showed RMAE average of 1.46 was considered low, for it was still below 10%.

Aroma precursors of Grignolino grapes (Vitis vinifera L.) and their modulation by vintage in a climate change scenario.

Current climatic conditions may cause significant changes in grapevine phenology and maturity dynamics linked often with changes to ecoclimatic indicators. The influence exerted by different meteorological conditions during four consecutive years on the aromatic potential of Grignolino grapes was investigated for the first time. The samples were collected from three vineyards characterized by different microclimatic conditions mainly related to the vineyard exposure and by a different age of the plants. Important differences as far as temperature and rainfall patterns are concerned during ripening were observed among the 4 years. Grape responses to abiotic stress, with particular emphasis on aromatic precursors, were evaluated using gas chromatography coupled to mass spectrometry. The results highlighted significant differences among the vintages for each vineyard in terms of the berry weight and the aromatic precursor concentration. For the grapes of the younger-vine vineyard, the content of aroma compounds showed a different variability among the vintages if compared to the old-vine vineyards. Optimal conditions in terms of temperature and rainfall during the green phase followed by a warm and dry post-veraison period until harvest favored all classes of compounds especially terpenoids mainly in the grapes of the old vines. High-temperature (>30°C) and low-rainfall pattern before veraison led to high benzenoid contents and increased differences among vineyards such as berry weight, whereas cooler conditions favored the terpenoid levels in grapes from southeast-oriented vineyards. In a hilly environment, lack of rainfall and high temperature that lately characterize the second part of berry development seem to favor the grape quality of Grignolino, a cultivar of medium-late ripening, by limiting the differences on bunch ripening, allowing a greater accumulation of secondary metabolites but maintaining at the same time an optimum balance sugar/acidity.

Impact of uncertainty induced by fatality function on future tropical cyclone risk assessment

Tropical cyclones (TCs) are among the deadliest extreme events occurring under a warming climate. Future TC risk assessment depend on TC projection from climate models and impact function relating TC to its possible consequence. Few studies have explored the uncertainty of impact function in future TC risk assessment compared to uncertainty in future TC characteristics. In this study, we investigate the uncertainty in TC fatality risk assessment induced by geographic and TC category-dependence of fatality function. We focus on all provinces in the mainland of China with historically recorded TC-induced fatalities and examine their TC fatality risks by assessing the difference in the annual average fatalities between current and future climate conditions. Synthetic TCs derived from four climate models and fatality functions parameterized from three grouped historical TC disaster datasets are used to observe the uncertainty induced by climate model and fatality function. Results show that the changes in the TC frequency, wind, TC-induced rainfall intensity, and exposure due to climate change in each province are dependent on the climate models. And the changes in the annual average fatality of each province are dependent on both the climate models and fatality functions. Climate models play a dominant role in determining the spatial pattern of future risk, while the fatality functions can alter the direction and magnitude of the risk change for certain provinces. Our results highlight the role of fatality function in detecting future TC risk under climate change, and inspire further TC impact studies that consider the heterogeneity of both climate conditions and geographical locations.

A spatially-dependent synthetic global dataset of extreme sea level events

Current coastal flood risk assessments fail to capture flood spatial dependence at large scales. In this paper, we develop the first global synthetic dataset of spatially-dependent extreme sea level events, by applying an existing conditional multivariate statistical model to 40-year global reanalysis sea levels. The resulting dataset contains 10,000 years of extreme events with realistic spatial dependence under current climate conditions. The benchmarking against reanalysis data demonstrates a high agreement, with a coefficient of determination (R2) of 0.96 for the mean event footprint sizes and a mean bias of −0.04 m for 1 in 50-year water levels. By comparing well-known historic events, we show that our approach can produce events with similar spatial characteristics. Our dataset enables the future development of spatially-dependent flood hazard maps for deriving accurate large-scale risk profiles, which can help yield new insights into the spatial patterns of coastal flooding and support coastal communities in devising effective management plans.

Taking advantage of water scarcity? Concentration of agricultural land and the politics behind water governance in Chile

Chile is currently facing a major drought that has caused several problems, most of them concentrated in terms of the availability of water for both human consumption and irrigation for agriculture. Under such conditions, the main instrument the government has at hand to assign water for agricultural use is the Water Scarcity Decree (WSD), which, among other aspects, allows for the extraction of underground water. However, this practice requires an important investment from the agricultural producer, making it only affordable by relatively larger producers. Therefore, under the current climatic conditions and a generalized lack of water, larger agricultural producers are the ones who benefit the most from the establishment of a WSD and thus have the incentives to use their political power to pressure for its issuing. Whereas conventional wisdom suggest that this is indeed the case, there is no previous evidence trying to link the size of agricultural exploitations and the likelihood of the establishment of a WSD. In the paper we use the share of large exploitations at the municipality level, as a measure that can proxy for local political power, and the establishment, the number, and duration of WSD within any given year. Consistent with the hypothesis, our results show that areas dominated by larger producers/exploitations are more likely to be declared as water scarce, to have more decrees in a year, and to have them in place for longer periods of time, even after controlling for socioeconomic characteristics and climatic conditions, such as precipitations and water flow.

Balancing wildlife protection and wildfire threat mitigation using a network optimization approach

In boreal forests of North America, land managers often carry out preventive treatments of forest fuel for the protection of human infrastructure from wildfires. However, these treatments may negatively affect other ecosystem services, such as the capacity to sustain wildlife populations. Here, we examine the efficacy of a strategy aimed at preserving a critical movement corridor for boreal woodland caribou (Rangifer tarandus caribou) in northern Québec, Canada, by raising high-voltage power line conductors above the forest canopy. To assess the interplay between the caribou protection objectives and a reduction in power line's exposure to wildfires, we developed an optimization model that combines the objectives of protecting the power line from wildfires via fuel treatments and maintaining a suitable movement corridor for caribou. The model combines a critical node detection (CND) problem with a habitat connectivity problem that allocates a minimum-resistance fixed-width habitat corridor between isolated wildlife refuges. Our results identify the best locations to perform fire fuel treatments to lessen the threat of fire damage to human infrastructure while maintaining a connectivity corridor for caribou in present and future climate scenarios. The selected fuel treatment locations aimed to mitigate wildfire exposure to a power line. In small-budget solutions, the exposure of power line infrastructure to wildfires was reduced by 36–39% in current climate conditions and by 20–31% in future climate, compared with no-treatment scenarios. Despite the detrimental effects of wildfire on both the industrial asset and caribou habitat, the approach provides strategies that help achieve a compromise between these two values. Such knowledge is timely to help mitigate the negative impacts of climate change on human livelihoods and natural ecosystems.

Applying RUSLE for soil erosion estimation in Romania under current and future climate scenarios

Soil erosion is one of the main land degradation processes, often enhanced by unsuitable human activities. In the context of global climate change, soil erosion also exhibits quantitative and qualitative changes induced by the temporally dynamic erosion factors, namely rainfall erosivity and land use. Our study attempts to forecast the evolution of soil erosion rates under climate change scenarios over the Romanian territory. Firstly, we apply the Revised Universal Soil Loss Equation (RUSLE) methodology to achieve a spatial model of soil erosion for the current climate conditions. Secondly, we use projections of rainfall erosivity to derive soil erosion models for two future climate change scenarios (Representative Concentration Pathway - RCP 4.5 and 8.5) and two periods of time (2041–2060, 2061–2080). Thirdly, we compute the differences between future projected and current erosion rates to identify spatial patterns of soil erosion evolution. The results show that, under the influence of rainfall erosivity dynamics, soil erosion tends to increase during the next decades. Overall, an enhancement of soil erosion rates on 84–90% of the country is expected to take place. Most of the erosion rates will increase slightly, with <1 t ha−1 yr−1, from an overall average of 2.94 t ha−1 yr−1, at present, to 3.67 (2061–2080, RCP 8.5) – 3.74 (2041–2060, RCP 8.5) t ha−1 yr−1 at country level. Locally, the erosion rates may increase with >5–10 t ha−1 yr−1 on areas estimated to cumulate >8000 km2. The most affected areas will be the soils situated in hilly and plateau regions, leading to an enhancement of their fertility degradation.

Range Dynamics of Striped Field Mouse (Apodemus agrarius) in Northern Eurasia under Global Climate Change Based on Ensemble Species Distribution Models.

The striped field mouse (Apodemus agrarius Pallas, 1771) is a widespread species in Northern Eurasia. It damages crops and carries zoonotic pathogens. Its current and future range expansion under climate change may negatively affect public health and the economy, warranting further research to understand the ecological and invasive characteristics of the species. In our study, we used seven algorithms (GLM, GAM, GBS, FDA, RF, ANN, and MaxEnt) to develop robust ensemble species distribution models (eSDMs) under current (1970-2000) and future climate conditions derived from global circulation models (GCMs) for 2021-2040, 2041-2060, 2061-2080, and 2081-2100. Simulation of climate change included high-, medium-, and low-sensitivity GCMs under four scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). We analyzed the habitat suitability across GCMs and scenarios by constructing geographical ranges and calculating their centroids. The results showed that the range changes depended on both the sensitivity of GCMs and scenario. The main trends were range expansion to the northeast and partial loss of habitat in the steppe area. The striped field mouse may form a continuous range from Central Europe to East Asia, closing the range gap that has existed for 12 thousand years. We present 49 eSDMs for the current and future distribution of A. agrarius (for 2000-2100) with quantitative metrics (gain, loss, change) of the range dynamics under global climate change. The most important predictor variables determining eSDMs are mean annual temperature, mean diurnal range of temperatures, the highest temperature of the warmest month, annual precipitation, and precipitation in the coldest month. These findings could help limit the population of the striped field mouse and predict distribution of the species under global climate change.

Spatial and temporal patterns of near‐surface ground temperature in the Arctic mountain catchment

AbstractWe consider the case of a mountain catchment representative of southwest Spitsbergen to investigate the spatial and temporal variation in temperature of the near‐surface ground. We set up 20 thermistor strings distributed in different parts of the catchment, combine the measured data with land surface temperature (LST) retrieved from Landsat imagery, and verify the depth of ground thawing using electrical resistivity tomography. Under current climatic conditions, the thickness of the active layer (ALT) is at least 1.5 m, and in the lower parts of the catchment, between 3.5 and 4 m. The maximum ground surface temperature in the study area can be 27°C, and the temperature at a depth of 1.5 m, 5.4°C. We identified unfrozen ground (taliks) to a depth of 10–30 m in zones of water concentration at foot slopes and under river channels. The persistence of year‐round taliks at lake shores is possible. We verify the hypothesis that topographic parameters significantly determine the spatial variation of near‐surface ground temperature. For each level of temperature aggregation (from 6 h to 12 months data) and each examined the depth of the active layer, it is possible to identify environmental variables—related to energy conditions, heights, terrain relief, and ground surface moisture—significantly correlated with ground temperature, and consequently to specify multiple regression models. Topographic exposure and LST (used only in daily models), altitude, and various approximations of relative height are crucial in their construction. The ground temperature variance, unexplained in the regression analysis, prompts recognition of the critical role of factors not included in the modelling (groundwater, soil structure).

Water use efficiency and climate legacies dominate beech growth at its rear edge

Abstract Rear‐edge tree populations are experiencing a combination of higher temperatures and more intense droughts that might push individuals beyond their tolerance limits. This trend towards rising atmospheric [CO2] is concurrent with an increase in intrinsic water use efficiency (iWUE), which theoretically enhances photosynthesis and decrease evapotranspiration rates, consequently improving tree resistance to drought. However, it remains unclear whether iWUE is favouring tree growth under current climate conditions, particularly when climate and iWUE legacy effects are simultaneously considered. We evaluated this question with an extensive sampling along Iberian rear‐edge (dry) populations comprising four mountain ranges and two distinct altitudes. We simultaneously examined the effects of climate and iWUE on secondary growth using annually resolved basal area increments (BAIs) for the period 1901–2017. We used linear mixed models including second‐order autocorrelation and 1‐year legacy effects of iWUE and summer drought. BAI and iWUE increased across the studied period. iWUE increase was driven by changes in atmospheric CO2 concentration and water availability during the growing season. Climate and iWUE exerted direct and lagged effects on beech growth. Water availability during growing season was the main driver of tree growth, combining direct and indirect effects through its impact on iWUE. Legacy effects of water availability and iWUE were more important than growing season conditions. The net effect of iWUE shifted when lagged effects were considered, resulting in a net negative impact on tree growth. Synthesis: Our results reveal that climate and iWUE legacy effects must be considered to assess the net iWUE effect on secondary growth. Considering lagged effects, the current increase in iWUE is constraining tree growth. Modelling efforts of tree growth response to climate warming should include climate and iWUE legacy effects to adequately assess terrestrial ecosystem carbon balance.

The Drought Regime in Southern Africa: A Systematic Review

Drought is one natural disaster with the greatest impact worldwide. Southern Africa (SA) is susceptible and vulnerable to drought due to its type of climate. In the last four decades, droughts have occurred more frequently, with increasing intensity and impacts on ecosystems, agriculture, and health. The work consists of a systematic literature review on the drought regime’s characteristics in the SA under current and future climatic conditions, conducted on the Web of Science and Scopus platforms, using the PRISMA2020 methodology, with usual and appropriate inclusion and exclusion criteria to minimize/eliminate the risk of bias, which lead to 53 documents published after the year 1987. The number of publications on the drought regime in SA is still very small. The country with the most drought situations studied is South Africa, and the countries with fewer studies are Angola and Namibia. The analysis revealed that the main driver of drought in SA is the ocean–atmosphere interactions, including the El Niño Southern Oscillation. The documents used drought indices, evaluating drought descriptors for some regions, but it was not possible to identify one publication that reports the complete study of the drought regime, including the spatial and temporal distribution of all drought descriptors in SA.

The invasive twospot livebearer's biology, its current and potential global distribution.

Poeciliids are widely recognised as successful invaders, possessing traits associated with invasion success. Native to Central America and south-eastern Mexico, the twospot livebearer (Pseudoxiphophorus bimaculatus) is a species recently recognised as invasive in both Central and Northern Mexico. Despite its invasive status, limited research exists on its invasion process and the potential threats it poses to native species. In this study, we conducted a comprehensive review of the current knowledge on the twospot livebearer and mapped its current and potential distribution worldwide. The twospot livebearer shares similar traits with other successful invaders within the same family. Notably, it exhibits high fecundity throughout the year and demonstrates resilience to highly polluted and oxygen-deprived water conditions. This fish serves as a host for several parasites, including generalists, and has been extensively translocated for commercial purposes. Recently, it has also been used for biocontrol within its native range. Apart from existing outside its native range, the twospot livebearer, under current climate conditions and if transported there, could readily colonise biodiversity hotspots in tropical zones worldwide, including the Caribbean Islands, the Horn of Africa, North of the Madagascar Island, the south-eastern Brazil, and Asia-Pacific. Given that this fish is highly plastic and our Species Distribution Model, we consider that all areas with a habitat suitability >0.2 should prevent its arrival and establishment. Our findings underscore the urgent need to recognise this species as a threat to freshwater native topminnows and prevent its introduction and spread. This article is protected by copyright. All rights reserved.

ASSESSMENT OF THE STATE AND TRENDS IN THE IMPLEMENTATION OF INNOVATIVE TECHNOLOGICAL SOLUTIONS IN BREEDING DAIRY FARMS OF UKRAINE

The global dairy sector has been significantly impacted in recent years, with experts assessing the consequences as a serious threat to the food security of the world's population. The biggest destabilising factors for the industry were the effects of the pandemic, war, inflation and high energy prices. Despite the steady increase in demand for milk, there is currently no trend towards a recovery in production growth. Dairy producers are under constant pressure from the above factors, resulting in lower margins, difficult access to competitive capital, and constant adaptation to new regulations. In the current political, economic and climatic conditions, one of the possible ways to increase the profitability of the dairy industry as a major incentive to increase production is the widespread introduction of innovative technological approaches to milk production.&#x0D; The purpose of the article is to assess the state and trends in the implementation of innovative technological solutions in dairy farming in Ukraine on the example of Ukrainian breeding dairy farms.&#x0D; Research results. In order to ensure the survival of the national dairy industry during the active military phase, as well as its rapid development in the post-war period, created a Roadmap for the restoration of the dairy industry. The Roadmap is primarily aimed at ensuring sustainable long-term development of the dairy industry; creation and operation of highly efficient and financially sustainable dairy farms; stimulating the construction of modern dairy enterprises with widespread introduction of innovations aimed at increasing the international competitiveness of the industry.&#x0D; Our research has shown that in recent years, the dynamics of modernisation processes in Ukrainian dairy farming has accelerated. In particular, in the Holstein herds, the productivity of cows kept in dairy complexes increased by 1882 kg per cow, or 470 kg per year on average, in 2016–2020.&#x0D; At the same time, increasing the efficiency of milk production through the introduction of innovative technological solutions will result in a mega-trend of farm consolidation in the future. For example, in the US, 50.3% of milk is produced at industrial large-scale dairies with more than 1.000 cows, where the latest science and technology is used. The influence of the factor of production concentration on the efficiency of the use of breeding animals of dairy and dairy-meat cattle breeds was studied. The average number of cows in the controlled breeding herds of dairy breeds in Ukraine (n = 350) in 2019 was 400 cows. The average milk yield per cow was 7.540 kg. According to the results of the analysis, as the number of cows in the farms grows, economic indicators such as revenue, profitability, and profitability increase.&#x0D; Conclusions. In recent years, a quarter of breeding farms for the most numerous specialised dairy breeds have been involved in the innovation process in pedigree dairy cattle breeding, with more than 50% of the number of breeding cows of these breeds concentrated there. The small herds used traditional technologies for keeping and using cows.&#x0D; The introduction of the latest innovative technological solutions for industrial milk production ensures an increase in revenues from its sale due to the growth of cow productivity. As the concentration of cows increases, the industry's performance indicators, such as milk yield, feed intake and the intensity of land use, are also increasing. The industry's profitability is increasing from 14.7% to over 24.2%.&#x0D; When creating new livestock complexes in the post-war period, the best international and domestic experience of the industry should be taken into account. The development of the dairy farming sector should be aimed at breeding specialised dairy cattle breeds and introducing innovative technologies for their maintenance, feeding and operation. Domestic specialised dairy breeds are at the level of the best European analogues in terms of productivity in industrial dairy complexes, and are superior in terms of reproduction and health, are economically viable and should become the basis for further development of the dairy cattle industry in Ukraine.

Modeling interactions of planting date and phenology in Louisiana rice under current and future climate conditions

AbstractThe performance of novel genetic combinations under untested environmental scenarios and management practices can be virtually examined using process‐based crop models. Indeed, there has been a long‐standing interest in the crop modeling community to expand the utility of process‐based models to broader germplasm panels (e.g., breeding lines or diversity panels). Yet, there is often a misalignment between data needed to parameterize process‐based crop models and data routinely collected by breeding programs. To address this gap, we leverage a dataset from a long‐term trial on advanced experimental lines and released varieties from the Louisiana rice breeding program to calibrate and evaluate the decision support for agrotechnology transfer (DSSAT) CSM‐CERES‐Rice model. Next, we use data collected by the same program on a large collection of breeding lines to generate numerous in silico genotypes and evaluate their performance across different management practices (different planting dates) and three climatic conditions (current climate and two future scenarios based on CMIP6‐SSP5‐8.5 climate projections). Our simulations indicate that shifting the current planting date (i.e., March) back by 1–2 months (to January) under moderate warming conditions (+1.3°C warmer and 41% higher CO2 level), and 2–3 months (to December) under extreme warming conditions (+4.1°C warmer and 133% higher CO2 level) could potentially offset the negative impacts of the increased future temperature. Given earlier planting, shorter duration varieties (i.e., those with shorter growing degree day requirements during the vegetative and grain filling periods) are found to be more favorable for supporting high yields. Such varieties with a shorter thermal time to anthesis are found to remain just outside of the current pool of variation for this trait. Opportunities and challenges for leveraging breeding data in process‐based modeling to derive insights into adaptation strategies for future climates are further discussed.