Annual Temperature Research Articles

Increased global warming potential during freeze-thaw cycle is primarily due to the contribution of N2O rather than CO2

While freeze-thaw cycle (FTC) can influence greenhouse gas emissions, the specific greenhouse gas that responds most strongly to FTC, as well as the underlying mechanisms, remain unclear. Here, we conducted a meta-analysis to explore the responses of global warming potential (GWP) and the fluxes of CO2 and N2O to FTC. Our results showed that FTC treatment significantly increased GWP, N2O flux, cumulative GWP, and cumulative N2O emissions by 23.1 %, 53.2 %, 14.5 %, and 164.6 %, respectively, but did not affect CO2 flux, indicating that the enhanced GWP during the FTC period may be primarily due to the contribution of N2O flux rather than CO2 flux. The responses of GWP (+68.6 %), CO2 (+21.0 %), and N2O fluxes (+136.3 %) in croplands was higher than those in other ecosystems, exhibiting a strong dependence on ecosystem types. The effect size of FTC treatment on greenhouse gas emissions escalated with decreasing freezing temperature and diminished with increasing FTC frequency. Moreover, mean annual temperature (MAT) and FTC patterns were key factors influencing GWP during the FTC period. These findings provide critical insights into the variations in greenhouse gas emissions due to FTC and its influencing factors, allowing for more accurate predictions of the future impact of global climate change on GWP.

Biogeographical Variation in Termite Distributions Alters Global Deadwood Decay

ABSTRACTAimTermites are a crucial group of macroinvertebrates regulating rates of deadwood decomposition across tropical and subtropical regions. When examining global patterns of deadwood decay, termites are treated as a homogenous group. There exist key biogeographical differences in termite distribution. One such clear distinction is the distribution of fungus‐growing termites (FGT, subfamily Macrotermitinae). Considering that climate will have shaped termite distribution and ecosystem processes, we evaluate the roles of termite distribution (presence of FGT) and climate (aridity) on global patterns in deadwood decay.LocationBetween 46° N‐43° S and 175° E‐85° W.Time PeriodPresent (between 2016 and 2021).Major Taxa StudiedTermites (Blattodea: Termitoidae).MethodsWe add salient data to an existing global dataset on deadwood decomposition, including new data from five existing sites and seven additional African sites. We analyse a dataset spanning six continents, 16 countries and 102 experimental sites. Firstly, we evaluate climatic differences (mean annual temperature, mean annual precipitation and mean annual aridity) between sites with and without FGT. Secondly, using aridity as a single comparative climate metric between sites that accounts for temperature and precipitation differences, we examine the interaction between FGT and aridity on global patterns of termite deadwood discovery and decay through multivariate logistic and linear regressions.ResultsTermite‐driven decay and wood discovery increased with aridity; however, responses differed between FGT and NFGT sites. Wood discovery increased with aridity in FGT sites only, suggesting a greater role of FGT to deadwood decay in arid environments. On average, both termite discovery and decay of deadwood were approximately four times greater in regions with FGT compared with regions without FGT.Main ConclusionsTermite discovery and decay of deadwood is climate dependent, and higher decay may be through greater discovery of deadwood in FGT sites. Inclusion of biogeographical differences in termite distribution could potentially alter current and future global estimates of deadwood turnover.

Assessing the Impacts of Climate Change on Geographical Distribution of Tea (Camellia sinensis L.) in Kenya with Maximum Entropy Model

Climate change has been disturbing the present species distribution ranges, resulting in the shifting of cultivation areas and decreases in production and quality. Tea (Camellia sinensis L.), which seeks optimum climatic resources, is a key cash crop economically in Kenya. In this study, the shifting of tea suitability was projected with the MaxEnt model under the SSP (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) climate scenarios for the 2050s and 2090s relative to the 1970–2000 distribution. Analysis under the current climatic condition showed that the proportions of optimal and medium- and marginal-suitable areas were 2%, 3%, and 24% of the total area, respectively, and located in south-western (SW), central, and north-eastern (NE) Kenya and, to some extent, in the Rift Valley. It was projected that the potential suitable tea-growing areas would migrate from the western areas to the central, eastern, and north-eastern highlands in Kenya. It was detected that the precipitation of the driest period (July), precipitation of the wettest quarter (April, May, and June), and annual temperature range could be the main climatic factors determining the shift in tea distribution. Compared to the current distribution (29%), the climatically suitable areas for tea production could reach 32.58% of Kenya’s land area under the SSP1-2.6 scenarios in the 2050s and 35.08% in the 2090s under the SSP5-8.5 scenario. On the contrary, it was found that the optimal climate-suitable habitats were projected to shrink by 2% and 1% in the 2050s and 2090s under all scenarios on the west side of the Great Rift Valley compared to the current distribution. In comparison, the sizes of medium- and marginal-suitable habitats would increase by 1% and 3%, respectively. The findings indicated that unless adaptive climate actions are taken, climate change could reduce the tea planting areas in western Kenya. Meanwhile, climate suitability was projected to expand upward on the east side of the Rift Valley, enhancing the potential distribution of tea. The developed climate information could be used to design and implement adaptation interventions in the lower elevation areas. Finally, we highlight that the available scientific literature on the climate suitability of tea in Kenya should be broadened by adding non-climatic factors.

An intelligent method for temperature load of arch dams

Temperature load significantly affects the deformation and stress of arch dams. Traditional methods for determining temperature loads may not adapt to complex environments, such as high-altitude regions with large temperature variations. This study proposes an intelligent method for temperature load of arch dams. Based on the measured data from existing dams, a physics-informed convolutional neural network is developed, enabling direct application to newly designed dams. The intelligent method delivers annual temperature boundaries on the dam surface, aiding the finite element model in predicting deformation and stress during operational phases. Validation demonstrates accurate results for dam body temperature and deformation, aligning closely with measured data, while also pinpointing critical stress areas within the dam. This method advances the AI-assisted design by predicting temperature loads during dam operation, providing strong support for determining the most unfavorable loading without measured data and ensuring the safety of the structure.

Modeling habitat suitability to predict the potential distribution of the Sri Lankan Green Pit Viper

Snake distribution and habitat modeling serve multiple valuable purposes, one of which involves establishing a systematic approach for collecting and assessing data related to the presence and influence of diverse factors on snake distribution patterns. The main objective of this study is to explore the habitat association of the Sri Lankan Green Pit Viper (Peltopelor trigonocephalus) and develop models to assess the suitability of different habitats within the island Sri Lanka. We modeled the suitable habitat for this species using the maximum entropy algorithm, combining presence data collected over two years (from April 2021 to April 2023) with a set of seven environmental variables (annual precipitation, annual mean temperature, precipitation of coldest quarter, elevation, land use, isothermality, Euclidean distance to water). We assessed the importance of different environmental variables through jackknife tests. We evaluated the predictive ability of the models using the area under the receiver operating characteristic curve. We discovered that the primary explanatory variables influencing the distribution of the species were annual mean temperature, annual precipitation, and elevation, contributing significantly at 35%, 31.6%, and 20%, respectively. The Sri Lankan Green pit viper inhabited riparian, forest, and open habitats, with the highest number of individuals recorded in riparian areas. We presented the first habitat suitability models for the Sri Lankan Green Pit Viper, offering valuable insights for conservation biologists and land managers involved in preserving this species.

Green Manure Mediated Improvement in Saline Soils in China: A Meta-Analysis

The application of green manure is a traditional and valuable practice to improve the fertility of saline soil. However, the impact of environmental factors, green manure types and returning methods on the changes in soil fertility and soil salinity remain poorly quantified at a large scale. In the present study, we conducted a meta-analysis to generate a comprehensive evaluation of the effects of green manure on soil organic carbon (SOC), soil salt content, and soil nutrients compared to bare soil in China. The results showed that compared with bare soil, green manure planting could significantly increase the SOC content of saline soil, reduce salt content, and improve the soil total nitrogen (N), soil available phosphorus (P) and soil available potassium (K) contents. On average, green manure significantly enhanced SOC by 34.82% (percentage change), soil total N by 32.23%, soil available P by 34.34% and soil available K by 17.43%, while reducing soil salt content by 47.75%, compared to bare soil. In areas with a mean annual temperature (MAT) of <10 °C or a mean annual precipitation (MAP) of 200–400 mm, green manure had the largest increase in SOC, soil total N, soil available P, and soil available K. The smallest increases were observed in areas with an MAT above 15 °C and MAP greater than 800 mm. Green manure types influenced the improvement effect of green manure on saline soil. Green manure mixtures were more conducive to increases in SOC, while the increases in soil total N resulting from mixed green manure were lower in comparison to those from both legumes and non-legumes. In addition, the initial salt content, experimental years, and returning method influenced the improvement effect of green manure on saline soil. Therefore, this meta-analysis identified green manure as a promising practice for significantly improved saline soil in China.

Alterations in Suitable Cultivation Area for Scutellaria baicalensis under Future Climatic Scenarios in China: Geodetector-Based Prediction

The dried roots of Scutellaria baicalensis (S. baicalensis) have been widely used as a traditional medicine. Recently, climate change and human activities have caused the degeneration of its wildlife habitat. However, there is rare knowledge on the effect and interactive effect of different variables on the spatial heterogeneity of S. baicalensis and how the pattern of suitable cultivation area in China would shift in response to climate change. Based on the Geodetector and Habitat Suitability Index (HSI) method, we proposed an assessment model to identify the critical environmental variable(s) affecting the distribution of suitable cultivation area for S. baicalensis in China and to project its shift under climate change. The results showed that soil and mean annual temperature are two determining variables in its spatial heterogeneity in China. Compared to 1981–2010, future climate change may result in a decrease in its suitable area, and yet may result in an increase in the extremely suitable area (about 1.00–1.35 million km2). S. baicalensis in the southern and northwestern part of its current distribution and the southwestern part and small area of northern China may experience expansion during the 21st century, while S. baicalensis in southern China, the Huang-Huai-Hai plain, and the midwest of northwestern China may experience contractions. Meanwhile, climate warming is expected to shift its distribution northwest through an expansion at the northern (at least 84 km) and western (at least 62 km) boundary and contraction at the southern (at least 529 km) boundary, respectively. These results could provide valuable information to policy-makers for the conservation and scientific introduction of S. baicalensis.

Identifying temperature refuges in Utah using temperature, biota, and habitat data

AbstractUnderstanding where on landscapes to make investments, such as designating protected areas, is a critical component of biodiversity management. Locations for management actions should achieve current management objectives while also having the best chance of continued success in the future. Climate change has the potential to undermine biodiversity management, as it may lead to substantial changes in environmental conditions that are outside local managers' control. Following changes in environmental conditions, areas on the landscape may become unsuitable for the species or habitats that the initial actions were intended to benefit. The potential for local actions to be undermined by global‐scale threats makes it essential to account for and minimize exposure to temperature change. We present a series of analyses identifying priority areas for wildlife and habitat management. We conducted our analyses using a systematic landscape planning approach that identifies areas within species' ranges or current distributions of key habitats that are predicted to be less affected by future temperature change. We used the ranges of 142 animal and 149 plant species identified as species of greatest conservation need (SGCN) together with the distributions of 14 terrestrial and 19 aquatic key habitats in Utah, USA. We measured temperature change in 2 ways: as changes in mean annual temperature between 2020 and the year 2100 (temperature difference) and by quantifying how far a species range or habitat would have to shift to maintain its current temperature envelope (climate velocity). We identified the sub‐watersheds with hydrologic unit code 12 (HUC 12) that collectively encompassed the ranges of our SGCNs and key habitats while minimizing overall exposure to temperature change. These high priority HUC 12s represented areas that were not only hotspots for SGCNs and key habitats but also acted as temperature refugia, where management actions are likely to be robust to temperature change. We hope that our identification of high‐priority HUC 12s will help inform and guide future management actions to improve their long‐term outcomes.

Enhanced enrichment of rare earth elements during pedogenesis under subtropical climate

Rare earth elements (REE) are crucial strategic resources, and weathering-crust rare earth deposits are one of the primary sources. To systematically understand the geochemical behavior (e.g., enrichment and leaching) of REE in soils (or weathering crusts) formed from diverse parent rocks under varying climatic conditions, 171 soil profiles (weathering crusts) developed from three main types of parent rocks (granite, basalt, and carbonate rock) worldwide were studied. Granite shows the highest concentration of rare earth elements at 264 (interquartile range (IQR): 278) ppm, 171 (IQR: 151) ppm and 11.9 (IQR: 36.4) ppm in basalt and carbonate rock, respectively (median test: p < 0.05). The median REE values within the soil profiles were significantly different (median test: p < 0.05), with the concentration of 318 (IQR: 441) ppm, 267 (IQR: 217) ppm and 207 (IQR: 417) ppm in soils derived from granite, carbonate rock, and basalt, respectively. Principal component analysis (PCA) and Linear mixed-effects models (LME) revealed that soils developed from granite and basalt inherit the mineral characteristics of their parent rocks, with REE concentrations primarily influenced by the REE content of the parent rock and climate. In contrast, the REE concentrations in soils developed from carbonate rocks are predominantly controlled by climate. LME and correlation analysis indicates that the enrichment of REE (Q REE ) shows a trend of initially increasing and then decreasing with rising temperature and precipitation, due to variations host clay minerals. The greatest enrichment occurs in the subtropical region (mean annual temperature (MAT) = 15 ̶ 23 °C; mean annual precipitation (MAP) = 1000 ̶ 2000 mm); weathering-crust type REE deposits are primarily found in the subtropics.

Assessing Larix principis-rupprechtii productivity and its determinants based on national forest inventory data in Hebei Province, China

Tree productivity is not only determined by stand structure, but also influenced by soil chemical properties, climate and topography. However, the relative importance of each indicator on larch (Larix principis-rupprechtii) productivity were uncertain. In this study, 76 pure larch forest plots were selected based on national forest inventory (NFI) data in Hebei Province, China. Structural equation model (SEM) was used to analyze the direct and indirect effects of stand structure, soil chemical properties, climate and topography on larch productivity, and to quantify the relative importance of each indicator in determining productivity. The results showed that stem volume growth (SVG) of larch was influenced by a combination of stand density, diameter at breast height (DBH), mean winter snow (PAS), annual temperature range (TD), slope, and alkali-hydrolysis nitrogen (AN). SVG tended to increase with decreasing stand density and AN content and increasing DBH. Stand density, DBH and AN were more important than PAS, TD, and slope in explaining SVG variation. The results can provide a scientific basis for adaptive management of larch forests.

Effects of Nitrogen Addition on Soil Organic Carbon and Its Fractions in Karst Farmland and Forest Ecosystems of China Based on Meta-analysis

In recent decades, with the intensification of human activities, atmospheric nitrogen （N） deposition has been increasing. N deposition affects carbon （C） cycling in terrestrial ecosystems, especially in fragile karst ecosystems. Karst ecosystems are considered to be an important C pool. To evaluate the impact of N deposition on soil organic C （SOC） and its fractions in karst ecosystems of China, we collected and collated 14 English literature published through the end of March 2023, yielding a total of 460 sets of experimental data. The meta-analysis examined the effect of N addition levels [low N: ≤50 kg·（hm2·a）-1, medium N: 50-100 kg·（hm2·a）-1, and high N: >100 kg·（hm2·a）-1, in terms of N] on SOC and its fractions [particular organic C （POC）, readily oxidized organic C （ROC）, microbial biomass C （MBC）, and dissolved organic C （DOC）]. The results showed that N addition levels significantly affected the responses of farmland and forest soil SOC and their active fractions to N addition. Specifically, low and high N additions significantly increased SOC concentration in farmland ecosystems, whereas medium N addition significantly increased SOC concentration in forest ecosystems. In addition, soil active C fraction concentrations increased under high N addition in farmland ecosystems and under low and medium N addition in forest ecosystems. Without considering the level of N addition, N addition significantly enhanced soil organic matter （SOM） mineralization in both farmland and forest ecosystems and increased the SOC concentration in farmland ecosystems but not forest ecosystems. The responses of different active C fractions to N addition were diverse. In farmland ecosystems, the POC and ROC concentrations increased, but DOC did not change with N addition. In forest ecosystems, the DOC and POC concentrations increased, but there was no significant effect on MBC. Moreover, the response ratios （RR） of SOC and its fractions in different ecosystems to N addition were influenced by different environmental factors. In farmland ecosystems, the response ratio of SOC was related to the annual average temperature and soil pH. The response ratio of DOC was affected by the annual average temperature, mean annual precipitation, and N addition rate. The POC response ratio was related to the N addition rate. In forest ecosystems, the effects of N addition on the SOC response ratio were significantly altered by the annual average temperature, mean annual precipitation, and soil pH. However, the response ratios of DOC, POC, and MBC were not affected by the annual average temperature, mean annual precipitation, soil pH, and N addition rate. Consequently, these findings indicate that N addition could enhance soil SOC concentration and promote soil C sequestration in farmland and forest ecosystems in karst regions, but this effect relies on the level of N addition. This provides a scientific basis for predicting the soil C sink function in karst ecosystems under climate change scenarios.

Spatiotemporal changes and driving factors of alpine land cover in Tianshan world natural heritage sites

Alpine natural heritage sites hold significant value due to their unique global resources. Studying land cover changes in these areas is crucial for maintaining and preserving multiple their values. This study takes Kalajun-Kuerdening, one of the components of Xinjiang Tianshan World Natural Heritage Site, as an example to analyze land cover changes and their driving factors in alpine heritage sites. Highlights include: (1) Between 1994 and 2023, Forest and Grassland increased by 55.96 km2 and 18.16 km2, with notable forest growth from 2007 to 2017. Trends in Forest changes align with forest protection policies, and a substantial amount of Bareland converted to Grassland indicates an increase in vegetation cover. (2) Elevation, precipitation, temperature, and evapotranspiration are key drivers of land cover changes, as validated by Random Forest algorithm and Geodetector model. (3) Favorable conditions for Grassland to Forest transition include annual precipitation between 275 and 375 mm, annual temperature between −2 and 3 °C, annual evapotranspiration between 580 and 750 mm, elevation between 1800 and 2600 m, and aspect between 0 to 110° and 220 to 259.9°. Continuous monitoring of land cover changes and their driving factors in mountain heritage sites contributes to the protection of the ecological environment and provides data and information support for addressing climate change, resource management, and policy making.

Analysis of the Influence of Driving Factors on Vegetation Changes Based on the Optimal-Parameter-Based Geographical Detector Model in the Yima Mining Area

The growth of vegetation directly maintains the ecological security of coal mining areas. It is of great significance to monitor the dynamic changes in vegetation in mining areas and study the driving factors of vegetation spatial division. This study focuses on the Yima mining area in Henan Province. Utilizing MODIS and multi-dimensional explanatory variable data, the Theil–Sen Median + Mann–Kendall trend analysis, variation index, Hurst index, and optimal-parameter-based geographical detector model (OPGD) are employed to analyze the spatiotemporal changes and future trends in the EVI (enhanced vegetation index) from 2000 to 2020. This study further investigates the underlying factors that contribute to the spatial variation in vegetation. The results indicate the following: (1) During the period studied, the Yima mining area was primarily characterized by a moderate-to-low vegetation cover. The area exhibited significant spatial variation, with a notable pattern of “western improvement and eastern degradation”. This pattern indicated that the areas that experienced improvement greatly outnumbered the areas that underwent degradation. Moreover, there was an inclination towards a deterioration in vegetation in the future. (2) Based on the optimal parameter geographic detector, it was found that 2 km was the optimal spatial scale for the analysis of the driving factors of vegetation change in this area. The optimal parameter combination was determined by employing five spatial data discretization methods and selecting an interval classification range of 5–10. This approach effectively addresses the subjective bias in spatial scales and data discretization, leading to enhanced accuracy in vegetation change analysis and the identification of its driving factors. (3) The spatial heterogeneity of vegetation is influenced by various factors, such as topography, socio-economic conditions, climate, etc. Among these factors, population density and mean annual temperature were the primary driving forces in the study area, with Q > 0.29 and elevation being the strongest explanatory factor (Q = 0.326). The interaction between temperature and night light was the most powerful explanation (Q = 0.541), and the average Q value of the interaction between the average annual temperature and other driving factors was 0.478, which was the strongest cofactor among the interactions. The interactions between any two factors enhanced their impact on the vegetation’s spatial changes, and each driving factor had its suitable range for affecting vegetative growth within this region. This research provides scientific support for conserving vegetation and restoring the ecological system.

Climate Warming Has Contributed to the Rise of Timberlines on the Eastern Tibetan Plateau but Slowed in Recent Years

The alpine timberline is a component of terrestrial ecosystems and is highly susceptible to climate change. Since 2000, the Tibetan Plateau’s high-altitude zone has been experiencing a persistent warming, clarifying that the response of the alpine timberline to climate warming is important for mitigating the negative impacts of global warming. However, it is difficult for traditional field surveys to clarify changes in the alpine timberline over a wide range of historical periods. Therefore, alpine timberline sites were extracted from 2000–2021, based on remote sensing data sources (LANDSAT, MODIS), to quantify the timberline vegetation growth in the Gexigou National Nature Reserve and to explore the impacts of climate change on timberline vegetation growth. The results show that the mean temperature increased significantly from 2000 to 2021 (R2 = 0.35, p = 0.0036) at a rate of +0.03 °C/year. The alpine timberline continued to shift upwards, but at a slower rate, by +22.87 m, +23.23 m, and +2.73 m in 2000–2007, 2007–2014, and 2014–2021, respectively. The sample plots of the timberline showing an upward shift experienced a decreasing trend. The timberline NDVI increased significantly from 2000 to 2021 (R2 = 0.2678, p = 0.0136) with an improvement in its vegetation. The timberline NDVI is positively correlated with the annual mean temperature (p < 0.05), February mean temperature (p < 0.05), June minimum temperature (p < 0.05), February maximum temperature (p < 0.01), June maximum temperature (p < 0.01), and June mean temperature (p < 0.01). It was also found to be negatively correlated with annual precipitation (p < 0.01). The study showcases the practicality of using remote sensing techniques to investigate the alpine timberline shifts and timberline vegetation. The findings are valuable in developing approaches to the sustainable management of timberline ecosystems.

Hindcasted Body Temperatures Reveal Underestimated Thermal Stress Faced by Intertidal Species

ABSTRACTAimAs global climate changes, there is a clear mismatch between the temporal and spatial characteristics of body temperature and environmental temperature, confounding the assessment of thermal stress for organisms in many ecological studies. Here, we hindcast the hourly body temperatures of intertidal molluscs to explore the differences between them and environmental temperatures (air and water temperatures) in multiple metrics of thermal stress.LocationIntertidal shores in East Asia (0°–45°N, 100°E–140°E).Time Period40 years, 1980 to 2019.Major Taxa StudiedMollusca.MethodsWe collected habitat zonation data and measured the morphological characteristics of 25 intertidal molluscs living in East Asia. For three different types of intertidal molluscs (i.e., bivalves, limpets and snails), we built corresponding heat budget models (HBMs) to hindcast the hourly body temperatures from 1980 to 2019. We analysed the thermal stress of intertidal species faced in three metrics, annual extreme high temperatures (T99), seasonal daily maximum temperatures (DMT) and heatwaves, and compared them with environmental temperatures.ResultsWe found that T99 of body temperatures and their interannual warming rates are significantly higher than those of environmental temperatures. Moreover, there were non‐negligible mismatches between the seasonal thermal pattern and heatwaves of body temperatures and environmental temperatures, suggesting that the deleterious impacts of global warming on intertidal species are underestimated and cannot be directly revealed by environmental temperatures.Main ConclusionsThermal stress patterns of body temperature were significantly different from those of environmental temperature, and the thermal stress faced by intertidal species had been persistently underestimated. These results emphasise that body temperature should be used as the appropriate metric for evaluating and predicting the impacts of global warming and weather extremes in the intertidal biological system.

From crisis to opportunity: climate change benefits livestock production in Somalia

Abstract While livelihoods of Somalian livestock smallholders rely heavily on seasonal climate conditions, little is known of long-term implications of the changing climate for this nation. Here, we quantify implications of the changing climate on the productivity and profitability of livestock smallholders across a rainfall gradient in northwestern Somalia. Using the Sustainable Grazing Systems (SGS) model, we explore 80 future climate realisations, with global climate model projections including low- and high-impact socio-economic pathways (SSP245 and SSP585), two climate horizons (2040 and 2080) and four case study farm regions. In general, future seasonal and annual rainfall and temperature relative to the baseline period (1981–2020) increased for most regions. Mean annual temperatures increased by 9%–14%, while cumulative annual precipitation increased by 37%–57% from mid to late century, respectively. Grassland production increased with later climate horizons, as higher average annual rainfall together with elevated atmospheric carbon dioxide drove up growth rates in spring and autumn. Under the low emissions scenario (SSP245), changes in farm profit were modest or positive, ranging from negative 4% in Berbera–20% plus in Sheikh. Under the higher emissions scenario (SSP585), farm profits were higher, ranging from 23% to 42% above baseline profits, largely due to greater pasture production and lower requirements for supplementary feed. We conclude that future climates will benefit the productivity and profitability of smallholder farmers in Somalia, although more agile farm management will be required to cope with increased seasonal climate variability.

Surface Temperature Changes and Effects on Cocoa Yield in the Top Four Cocoa Producing Countries of Africa: A Comparative Analysis

Cocoa is a leading export crop to the world market and Cote D’Ivoire, Ghana, Nigeria and Cameroon in Africa, accounts for about 70% of the global supply. However, increasing global warming with attendant high ambient temperature had affected yields in the tropical regions of Africa. The data of 61 years (1961 – 2021), area harvested (ha), yield (kg/ha) and temperature (OC) were sourced from the database of Food and Agricultural Organisation Corporate Statistical (FAOSTAT). Least squares regression and correlation analysis models were used for data analysis. Results showed that mean ambient temperature in Cote D’Ivoire, Ghana, Nigeria and Cameroon have increased by 0.7661, 0.7507, 0.7333 and 0.6400 OC for a total of 51, 51, 50 and 49 years respectively, while mean decreases were 0.1541, 0.1693, 0.1710, 0.1392oC for 10, 10, 11 and 12 years respectively. Annual average temperature increase was highest (0.0243oC) in Cote D’Ivoire with regression coefficient (R2) of 0.7232 and least (0.0200oC) in Nigeria at R2 of 0.6380. However, in both countries, annual temperature decreased by 0.0012o and 0.0007o with R2 of 0.0052 and 0.0014 respectively. The mean annual temperature increase in Ghana and Cameroon were 0.023o and 0.0204o with R2 of 0.7364 and 0.6965 respectively, while the decreases were 0.0019o and -0.0011o with R2 of 0.0152 and 0.0431 respectively. The cocoa cumulative yield (kg/ha) reduced by 105.8, 90.8 and 75.0kg/ha respectively in Cote D’Ivoire, Ghana and Cameroon when temperature decreased, but increased by 12.5kg/ha in Nigeria during same temperature decreasing trend. In Ghana and Cameroon, cocoa annual yield was higher in the years of temperature increase, while decreased ambient temperature enhanced cocoa yield in Nigeria and Cote D’Ivoire. Hence, to enhance cocoa yield in the top four cocoa producing countries of Africa, the ambient temperatures should be controlled through the adoption of Good Agricultural Practices (GAP).

Assessing the impact of climate change on agricultural production in central Afghanistan

Afghanistan has faced extreme climatic crises such as drought, rising temperature, and scarce precipitation, and these crises will likely worsen in the future. Reduction in crop yield can affect food security in Afghanistan, where the majority of population and economy are completely dependent on agriculture. This study assessed the interaction between climate change and crop yield in Kabul of Afghanistan during the reference (1990–2020) and future (2025–2100) periods. Climate data (1990–2020) were collected from four meteorological stations and three local organizations, and wheat yield data (1990–2020) were acquired from the United States Agriculture Department. Data during the reference period (1990–2020) were used for the validation and calibration of the statistical downscaling models such as the Statistical Downscaling Model (SDSM) and Long Ashton Research Station Weather Generator (LARS-WG). Furthermore, the auto-regression model was used for trend analysis. The results showed that an increase in the average annual temperature of 2.15°C, 2.89°C, and 4.13°C will lead to a reduction in the wheat yield of 9.14%, 10.20%, and 12.00% under Representative Concentration Pathway (RCP)2.6, RCP4.5, and RCP8.5 during the future period (2025–2100), respectively. Moreover, an increase in the annual maximum temperature of 1.79°C, 2.48°C, and 3.74°C also causes a significant reduction in the wheat yield of 2.60%, 3.60%, and 10.50% under RCP2.6, RCP4.5, and RCP8.5, respectively. Furthermore, an increase in the annual minimum temperature of 2.98°C, 2.23°C, and 4.30°C can result in an increase in the wheat yield of 6.50%, 4.80%, and 9.30% under RCP2.6, RCP4.5, and RCP8.5, respectively. According to the SDSM, the decrease of the average monthly precipitation of 4.34%, 4.10%, and 5.13% results in a decrease in the wheat yield of 2.60%, 2.36%, and 3.18% under RCP2.6, RCP4.5, and RCP8.5, respectively. This study suggests that adaptation strategies can be applied to minimize the consequences of climate change on agricultural production.

Assessing machine learning models to generate permafrost distribution map in Solukhumbu, Nepal

Permafrost is one of the key components of the cryosphere. Previous studies show that the extent of permafrost has shifted to higher elevations in Nepal. These researches, however, has been hampered by inconsistency in their study period. Proxies like rock glaciers and climatic variables, such as multi-decadal annual air temperature, are used to link towards the likely occurrence of permafrost. Here, the rock glacier inventory of Solukhumbu was prepared, and classified based on their activity (Intact/Relict) from Google Earth. Talus-based rock glaciers were observed more than glacier-derived ones. These rock glaciers were highly correlated with Mean Annual Air Temperature, followed by potential incoming solar radiation and slope. Three machine learning models (Logistic Regression, Random Forest and Support Vector Machines) were trained to generate permafrost probability distribution maps based on their prediction of the probability of rock glaciers being intact as opposed to relict. Logistic Regression and Support Vector Machines were able to produce a similar spatial distribution of permafrost. However, the Random Forest has low precision of spatial variation. The permafrost distribution map suggests the likely occurrence of permafrost to be above 5000 m, indicating a potential for rock and landslides should it thaw in the future. While higher-resolution input data can improve the results, this approach remains promising for application in permafrost regions where information about the ice content of rock glaciers is very limited.

"Two zones and three centers" distribution and suitable areas shift of an evergreen oak in subtropical China under climate scenarios.

Understanding the impact of climate change on the geographical distribution of species is a fundamental requirement for biodiversity conservation and resource management. Quercus oxyphylla, an evergreen oak endemic to China, plays a crucial role in maintaining the ecological stability in subtropical regions and high economic value attributed to its dark and high-density heartwood, but the existing resources are close to endangered. Currently, limited knowledge exists regarding its distribution and potential influences of climate change on suitable areas. This study utilized 63 occurrence records and Biomod2 platform, to predict changes in suitable areas for Q. oxyphylla under future climate change. The results revealed that (1) Q. oxyphylla showed a pattern of three disjunctive geographical centers in the eastern subregion of subtropical evergreen broad-leaved forest region (IVA): Qinling-Daba Mountains, Nanling Mountains and Wuyi Mountains center. Currently, the highly suitable areas concentrated in two zones divided by the Yangtze River, that is, the northern subtropical evergreen and deciduous broad-leaved forest zone (IVAii) and the mid-subtropical evergreen broad-leaved forest zone (IVAi). (2) The temperature-related variables, such as annual temperature range (Bio7), the mean diurnal range (Bio2), and annual mean temperature (Bio1), were identified as the key determinants of the distribution pattern. Because of its considerable climatic variations in temperature and water conditions, Q. oxyphylla's habitat displayed a wider climate niche and strong physiological tolerance to climate change. (3) Under future climate scenarios, the suitable area of the species was expected to overall expand with significant regional differences. The suitable area in IVAi was expected to expand significantly northward while that in IVAii was expected to gradually shrink. To address the impact of climate change, it is necessary to develop conservation plans focused around the three distribution centers, implement localized and regional conservation policies, and conduct educational outreach among local people.