Bioclimatic and soil determinants of buckwheat cultivation prospects under global warming: A case study of the Ukrainian Polissya and Forest-Steppe

The Qinghai–Tibet Plateau is one of the regions most strongly affected by climate change. The climate feedback of the distribution of plateau pika, a key species, is closely related to the trophic structure of the plateau ecosystem and the development of agriculture and animal husbandry on the plateau. In order to understand the impact of future climate change on the suitable distribution area of plateau pika, potential suitable distribution areas of Plateau pika were predicted using the MaxEnt model under three climate scenarios (SSP 1-2.6, SSP 2-4.5, and SSP 5-8.5) in the near term (2021–2040) and medium term (2041–2060). The predictions were found to be highly accurate with AUC values of 0.997 and 0.996 for the training and test sets. The main results are as follows: (1) The precipitation of the wettest month (BIO 16), mean diurnal range (BIO 2), slope, elevation, temperature seasonality (BIO 4), and annual mean temperature (BIO 1) were the main influencing factors. (2) In the historical period, the total suitable distribution area of Plateau pika in the Qinghai–Tibet Plateau accounted for 29.90% of the total area at approximately 74.74 × 104 km2, concentrated in the eastern and central areas of the Qinghai–Tibet Plateau. (3) The total suitable distribution area of pika exhibited an expansion trend under SSP 1-2.6 and SSP 2-4.5 in the near term (2021–2040), and the expansion area was concentrated in the eastern and central parts of the Qinghai–Tibet Plateau. The expansion area was the largest in Qinghai Province, followed by Sichuan Province and Tibet. In contrast, the suitable distribution area shrank in the Altun Mountains, Xinjiang. Under SSP 5-8.5 in the near term and all scenarios in the medium term (2041–2060), the suitable distribution area of Plateau pika decreased to different degrees. The shrinkage area was concentrated at the margin of the Qaidam Basin, central Tibet, and the Qilian Mountains in the east of Qinghai Province. (4) Plateau pika migrated toward the east or southeast on the Qinghai–Tibet Plateau under the three climate scenarios. Under most of the scenarios, the migration distance was longer in the medium term than in the near term.

Previous studies have demonstrated an association between short-term exposure to ambient temperature and mortality. However, the long-term effects of elevated temperature and temperature variability on mortality have remained somewhat elusive in epidemiological studies. We conducted a comprehensive epidemiological study utilizing Chinese population census data from 2000 and 2010. Census-derived demographic and socioeconomic factors were paired with temperature data from the European Re-Analysis Land Dataset across 2823 counties. We employed a difference-in-difference approach to quantitatively examine the relationship between all-cause mortality and annual exposure to mean temperature and diurnal temperature range (DTR). Additionally, we evaluated the potential effects of socioeconomic and environmental covariate modifications on this relationship and calculated the attributable mortality. Lastly, we projected excess deaths attributable to annual temperature exposure under various shared socioeconomic pathways (SSPs, e.g. SSP126, SSP370, and SSP585). For each 1 °C rise in annual mean temperature and DTR, the mortality risk could increase by 6.12% (95% CI: 0.84%, 11.69%) and 7.72% (95% CI: 3.75%, 11.84%), respectively. Counties with high labor-force ratios and high NO2 and O3 concentrations appeared to be sensitive to the annual mean temperature and DTR. Climate warming from 2000 to 2010 may have resulted in 5.85 and 14.46 additional deaths per 10 000 people attributable to changes in annual mean temperature and DTR, respectively. The excess mortality related to changes in annual mean temperature and DTR is expected to increase in the future, with special attention warranted for long-term temperature changes in Southwest China. Our findings indicate that long-term mean temperature and DTR could significantly impact mortality rates. Given the spatial heterogeneity of increased mortality risk, the formulation of region-specific strategies to tackle climate change is crucial.

Reactive nitrogen losses from China's food system for the shared socioeconomic pathways (SSPs)

Linear ICs in instrumentation

Manufacturing X-Ray lithography inches closer

Both climate change and human activities play critical roles in shaping the spatial distribution of cotton cultivation, particularly in arid and semi-arid environments. However, existing studies have not sufficiently quantified their synergistic effects, and regional-scale applications remain limited. This study selected key variables from 31 environmental factors—including climate, soil, topography, and human footprint—and employed an optimized MaxEnt model to project cotton distribution across three Shared Socioeconomic Pathways (SSP126, SSP245, and SSP585). We developed models based on (i) current climate conditions, (ii) an integrated model incorporating both current climate conditions and human footprint, and (iii) future climate projections for the 2030s, 2050s, and 2070s. The results indicate that human footprint, mean diurnal temperature range (bio2), mean temperature of the coldest quarter (bio11), precipitation of the coldest quarter (bio19), and solar radiation intensity are the primary factors influencing cotton distribution. Under prevailing climate conditions, suitable cotton habitats are mainly located in Aksu, Kashgar, Tacheng, Bayingolin Mongol Autonomous Prefecture, and Changji, where human activities have significantly expanded the cultivation range. Future climate projections indicate a decrease in the extent of suitable cotton habitats, with its distribution center shifting toward lower-altitude areas. This study offers key empirical evidence and conceptual understanding to address climate-induced risks to cotton farming, forming a basis for informed strategies in sustainable cultivation and habitat conservation.

Phytophthora cinnamomi is a globally distributed plant-pathogenic oomycete that threatens economically important crops, including Lauraceae, Bromeliaceae, Fabaceae, and Solanaceae. Utilizing species occurrence records and 35 environmental variables (|R| < 0.8), we employed the MaxEnt model and ArcGIS spatial analysis to systematically predict the potential geographical distribution of P. cinnamomi under current (1970–2000) and future (2030S, 2050S, 2070S, 2090S) climate scenarios across three Shared Socioeconomic Pathways (SSPs). The results indicate that currently suitable habitats cover the majority of China’s provinces (>50% of their areas), with only sporadic low-suitability zones in Qinghai, Tibet, and Xinjiang. The most influential environmental variables were the mean diurnal temperature range, mean temperature of the warmest quarter, annual precipitation, precipitation of the driest month, and elevation. Under future climate scenarios, new suitable habitats emerged in high-latitude regions, while the highly suitable area expanded significantly, with the distribution centroid shifting northeastward. This study employs predictive modeling to elucidate the future distribution patterns of P. cinnamomi in China, providing a theoretical foundation for establishing a regional-scale disease early warning system and formulating ecological management strategies.

Moringa oleifera Lam and Moringa stenopetala (Baker f.) Cufod are being widely promoted as multipurpose trees across the tropics for their nutritional, medicinal and soil health benefits. Different parts of these species are edible, have therapeutic values and their seeds are used for water purification. Although the two species are similar in many ways, they have contrasting distributions. However, their current promotion is not guided by adequate knowledge of the suitability of the target areas. Information is also scanty on the suitability of habitats for these species under the current and future climate change scenarios. Therefore, the objective of this study was to predict the habitat suitability of M. oleifera and M. stenopetala under current and future climate change scenarios using an ensemble of models assuming four shared socio-economic pathways, namely, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 for 2050 and 2070. The results suggest that areas that are highly suitable for M. oleifera will increase by 0.1% and 3.2% under SSP1-2.6 to SSP5-8.5 by 2050, respectively. By 2070, the area suitable for M. oleifera would likely decrease by 5.4 and 10.6% under SSP1-2.6 and SSP5-8.5 scenarios, respectively. The habitat that is highly suitable for M. stenopetala was predicted to increase by 85–98% under SSP3-7.0 and SSP5-8.5 scenarios by 2050 and by 2070, while suitable areas could increase by up to 143.6% under SSP5-8.5. The most influential bioclimatic variables for both species were mean diurnal temperature range, mean temperature of driest quarter, precipitation of wettest month, and isothermality. Additionally, soil pH, elevation and water holding capacity were influential variables in the distribution of M. oleifera, while soil pH, soil salinity and slope were influential in M. stenopetala distribution. This study has provided baseline information on the current distribution and possible future habitat suitability, which will be helpful to guide formulation of good policies and practices for promoting Moringa species outside their current range.

Brown spot needle blight (BSNB), caused by Lecanosticta acicola (Thüm.) Syd., is an emerging forest disease of Pinus species originating from North America and introduced to Europe and Asia. Severity and spread of the disease has increased in the last two decades in North America and Europe as a response to climate change. No modeling work on spread, severity, climatic suitability, or potential distribution has been done for this important emerging pathogen. This study utilizes a global dataset of 2,970 independent observations of L. acicola presence and absence from the geodatabase, together with Pinus spp. distribution data and 44 independent climatic and environmental variables. The objectives were to (1) identify which bioclimatic and environmental variables are most influential in the distribution of L. acicola; (2) compare four modeling approaches to determine which modeling method best fits the data; (3) examine the realized distribution of the pathogen under climatic conditions in the reference period (1971–2000); and (4) predict the potential future global distribution of the pathogen under various climate change scenarios. These objectives were achieved using a species distribution modeling. Four modeling approaches were tested: regression-based model, individual classification trees, bagging with three different base learners, and random forest. Altogether, eight models were developed. An ensemble of the three best models was used to make predictions for the potential distribution of L. acicola: bagging with random tree, bagging with logistic model trees, and random forest. Performance of the model ensemble was very good, with high precision (0.87) and very high AUC (0.94). The potential distribution of L. acicola was computed for five global climate models (GCM) and three combined pathways of Shared Socioeconomic Pathway (SSP) and Representative Concentration Pathway (SSP-RCP): SSP1-RCP2.6, SSP2-RCP4.5, and SSP5-RCP8.5. The results of the five GCMs were averaged on combined SSP-RCP (median) per 30-year period. Eight of 44 studied factors determined as most important in explaining L. acicola distribution were included in the models: mean diurnal temperature range, mean temperature of wettest quarter, precipitation of warmest quarter, precipitation seasonality, moisture in upper portion of soil column of wettest quarter, surface downwelling longwave radiation of driest quarter, surface downwelling shortwave radiation of warmest quarter and elevation. The actual distribution of L. acicola in the reference period 1971–2000 covered 5.9% of Pinus spp. area globally. However, the model ensemble predicted potential distribution of L. acicola to cover an average of 58.2% of Pinus species global cover in the reference period. Different climate change scenarios (five GCMs, three SSP-RCPs) showed a positive trend in possible range expansion of L. acicola for the period 1971–2100. The average model predictions toward the end of the century showed the potential distribution of L. acicola rising to 62.2, 61.9, 60.3% of Pinus spp. area for SSP1-RCP2.6, SSP2-RCP4.5, SSP5-RCP8.5, respectively. However, the 95% confidence interval encompassed 35.7–82.3% of global Pinus spp. area in the period 1971–2000 and 33.6–85.8% in the period 2071–2100. It was found that SSP-RCPs had a little effect on variability of BSNB potential distribution (60.3–62.2% in the period 2071–2100 for medium prediction). In contrast, GCMs had vast impact on the potential distribution of L. acicola (33.6–85.8% of global pines area). The maps of potential distribution of BSNB will assist forest managers in considering the risk of BSNB. The results will allow practitioners and policymakers to focus surveillance methods and implement appropriate management plans.

Адаптивна технологія вирощування гречки з умістом в зерні CR+3 на фоні застосування органічних добрив, виготовлених за новітніми технологіями

A significant problem in the cultivation of buckwheat is its low yield, therefore, it is relevant to improve the elements of the technology for growing this crop. The purpose of the study was to establish optimal norms and terms for applying mineral fertilisers and foliar dressing with chelated microfertilisers in the forest-steppe conditions of Western Ukraine, in a zone of sufficient moisture to obtain stable and high yields of buckwheat grain. For this purpose, a two-factor experiment was conducted on the experimental fields of the Lviv National Agrarian University on dark grey podzolized light loamy soil, which included fertiliser rates: N20P20K20, P20K20+N20 (foliar dressing), N40P40K40, P40K40+N40 (foliar dressing), N60P60K60, P60K60+N60 (foliar dressing) and foliar dressing: control (without foliar dressing), Vuksal Boron 2.0 l/ha, Intermag Legumes 2.0 l/ha. Research methods: field studies – to determine the interaction of the object of research with weather factors and elements of the fertiliser system; calculation and weight – setting parameters of crop structure indicators and determining buckwheat yield; methods of mathematical statistics – dispersion, correlation, regressive and graphical display of findings. An increase in fertiliser rates from N20P20K20 to N60P60K60 provided an increase in all indicators of the buckwheat crop structure. Without foliar dressing, they reached the following values: the number of first-order branches – 1.56 pcs./plant, the number of inflorescences and flowers – 10.68 and 1,011 pcs, respectively, the number of full-size and undeveloped grains (pcs./plant) – 41.23 and 11.37, the weight of full-size grains – 1.15 g and the weight of 1,000 grains – 28.00 g. The introduction of nitrogen fertilisers in foliar dressing (at the beginning of flowering) has significant advantages over the introduction of nitrogen for pre-sowing cultivation. One of the proofs of this assumption is the increase in the number of grains, their weight, and the weight of 1,000 grains, respectively, from 35.43 pcs., 0.97 g, and 27.37 g (variant with N40P40K40) up to 37.27 pcs., 1.03 g, 27.80 g (variant with P40K40+N40). This trend is typical for all experiment designs. A positive effect of foliar dressing with microfertilisers on the elements of the crop structure was observed. The use of Vuksal Boron 2 l/ha was more effective. The maximum weight of full-size grains was in the variant P60K60+N60 (foliar dressing) + Vuksal Boron 2 l/ha – 1.21 g, while the use of Intermag Legumes 2 l/ha on a similar background of mineral fertilisers provided this indicator at the level of 1.17 g. Studies have established the positive effect of nitrogen application by foliar dressing during the beginning of flowering on the yield level, and buckwheat yield also increased with an increase in the rate of mineral fertiliser application from N20P20K20 to N60P60K60 and under the influence of foliar dressing with microfertilisers. The maximum yield indicator on average for three years of research is obtained in variant P60K60+N60 (foliar dressing) + Vuksal Boron 2.0 l/ha – 2.64 t/ha

To understand the potential suitable habitats of Limosa limosa and clarify the dominant environmental factors affecting its distribution, we collected a total of 239 distribution sites and 29 environmental variables, and simulated with the MaxEnt model. The results showed that the dominant factors influencing the suitable habitats for L. limosa during the breeding period included isothermality, slope, elevation, distance to major water, distance to paddy field, distance to village, and mean temperature of the coldest quarter. During the breeding period, L. limosa preferred inland wetlands and their surrounding grassland and farmland with high annual temperature variation, low winter temperature, medium elevation, low slope, near water sources and with little human activity. The dominant influencing factors during the overwintering period were precipitation of the coldest quarter, slope, distance to major water, normalized vegetation index, elevation, and mean diurnal temperature range. During the overwintering period, L. limosa preferred areas with a certain amount of rainfall during the coldest season, low elevation and slope, closer to water sources, low temperature variation and suitable degree of concealment. The highly suitable area for the L. limosa during the breeding period covered an area of 17.8×104 km2, and was mainly distributed in northern Xinjiang, central and northeastern Inner Mongolia, with a few breeding sites in Heilongjiang and Jilin provinces. The highly suitable area for the overwintering period covered an area of 6.1×104 km2, and was mainly distributed in the middle and lower reaches of the Yangtze River (such as Hunan, Hubei, Jiangxi provinces) and its southern coastal provinces (such as Fujian and Guangdong provinces). Due to the influence of human activities and global warming, the wintering area of the L. limosa has gradually shifted from the coastal areas to the middle and lower reaches of the Yangtze River in recent years. Our findings hold significant implications for the conservation of L. limosa population and habitat management.

Lygodium japonicum is a valuable medicinal plant with increasing demand in China, yet large-scale cultivation remains limited, relying heavily on wild populations. As climate change accelerates, its potential distribution is expected to shift, affecting suitable growth areas. Despite its medicinal importance, research on its adaptability and future habitat changes is limited. This study used an optimized MaxEnt ecological niche model and Geographic Information System (GIS) to predict the potential suitable habitats of L. japonicum under current and future climate conditions (2041-2060 and 2061-2080) across three greenhouse gas emission scenarios (SSP126, SSP245, SSP585). Results show that under current climatic conditions, the potential habitat of L. japonicum spans approximately 216.31 × 104 km², with high suitability areas concentrated in southern and eastern China. In future climate scenarios, While the total suitable habitat area remains stable, the area of high suitability is significantly reduced. Specifically, under the SSP126 scenario, high suitability areas are projected to decrease by 44.1% during 2041-2060. The centroid of high suitability areas is expected to shift northward, though a localized southward shift is observed under the SSP126 scenario. Key environmental factors influencing the species’ distribution include temperature seasonality (bio4), May precipitation (prec5), and mean diurnal temperature range (bio2). These findings highlight the potential impacts of climate change on L. japonicum’s distribution and are crucial for the conservation and sustainable utilization of the species in China, particularly under changing climatic conditions.

ABSTRACTThis study assesses the impact of climate change on the potential distribution of the traditional Chinese medicinal plant Wikstroemia indica, employing an optimized maximum entropy (MaxEnt) model for the first time for this species under multiple climate scenarios. Our analysis, based on 902 occurrence records and key environmental variables, provides clear evidence that climate change will significantly alter its distribution pattern. The results demonstrated that annual mean temperature (69.4% contribution) and mean diurnal temperature range (12.6% contribution) were the principal climatic factors affecting the distribution of W. indica. Under current climatic conditions, the total potential suitable habitat area for W. indica in China was calculated to be 153.31 × 104 km2, accounting for 15.97% of China's land area. Projections indicate significant habitat expansion under future climate scenarios: under the SSP1‐2.6 scenario, the total suitable habitat area would increase by 32.0% to 202.42 × 104 km2 by the 2090s; under the SSP5‐8.5 scenario, suitable habitat was anticipated to expand by 49.6% to 229.39 × 104 km2. Furthermore, the distribution centroid of W. indica was predicted to shift 76.68–119.91 km northwestward by the 2050s. The key message is that W. indica demonstrates considerable resilience to climate change, with its suitable habitat expected to expand and shift northwestward. This quantitative prediction, based on robust modeling evidence, provides critical insights for future conservation planning, sustainable management, and utilization strategies for this important medicinal resource in the context of global environmental change.

Dendrobium shixingense Z. L. Chen, S. J. Zeng & J. Duan, a National Class II Protected wild plant species in China, is renowned for its rich polysaccharide content and remarkable medicinal value. Delineating priority conservation areas for this species is critically important for its sustainable conservation and management. In this study, the MaxEnt model was applied to predict its potential distribution patterns under multiple climate scenarios, while the Marxan and InVEST models were utilized to identify priority conservation zones. Results demonstrate that the primary distribution of D. shixingense is concentrated in southeastern China, particularly within Guangdong, Fujian, Guangxi, and Jiangxi provinces, with a total suitable habitat area of 79.41 × 104km2. Future projections indicate an expansion of suitable habitats, with key environmental drivers identified as precipitation of the coldest quarter (Bio19), mean diurnal temperature range (Bio2), among others. Priority conservation areas are predominantly located in Shixing County and Ruyuan Yao Autonomous County of Shaoguan City; Xing’an County of Guilin City and other specified regions. These findings indicate that climate change will substantially impact the distribution of D. shixingense, potentially altering both the extent and quality of suitable habitats. priority conservation areas are concentrated in ecologically stable regions, necessitating enhanced protection efforts in these zones. Collectively, this research provides a robust scientific foundation for formulating effective conservation strategies and advancing the sustainable development of D. shixingense.