Search Results (381)

Sustainable Development Reserves are organized units in the Amazon that are essential for the proper use and sustainable management of the region’s natural resources and for the livelihoods and economy of the local communities. This study aims to provide a climatic characterization of the Madeira River Sustainable Development Reserve (MSDR), offering scientific support to efforts to assess the feasibility of implementing adaptation measures to increase the resilience of isolated Amazon communities in the face of extreme climate events. Significant statistical analyses based on time series of observational and reanalysis climate data were employed to obtain a detailed diagnosis of local climate variability. The results show that monthly mean two-meter temperatures vary from 26.5 °C in February, the coolest month, to 28 °C in August, the warmest month. Monthly precipitation averages approximately 250 mm during the rainy season, from December until May. July and August are the driest months, August and September are the warmest months, and September and October are the months with the lowest river level. Cold spells were identified in July, and warm spells were identified between July and September, making this period critical for public health. Heavy precipitation events detected by the R80, Rx1day, and Rx5days indices show an increasing trend in frequency and intensity in recent years. The analyses indicated that the MSDR has no potential for wind-energy generation; however, photovoltaic energy production is viable throughout the year. Regarding the two major commercial crops and their resilience to thermal stress, the region presents suitable conditions for açaí palm cultivation, but Brazil nut production may be adversely affected by extreme drought and heat events. The results of this study may support research on adaptation strategies that includethe preservation of local traditions and natural resources to ensure sustainable development. Full article

Soybean production in Thailand faces significant challenges from malignant weed competition, potentially reducing yields by up to 37% and incurring annual economic losses of approximately USD 3.8 billion. Pre-emergence herbicides are critical for integrated weed management, but their efficacy varies depending on local conditions and soybean varieties. This study evaluates the performance of three pre-emergence herbicides, pendimethalin (1875 g a.i. ha⁻¹), s-metolachlor (900 g a.i. ha⁻¹), and flumioxazin (125 g a.i. ha⁻¹), on weed control efficiency (WCE), soybean growth, phytotoxicity, and yield in Northeast Thailand using a randomised complete block design with two varieties (CM60 and Morkhor60) across rainy (2023) and dry (2024/2025) seasons. Herbicide performance varied seasonally: s-metolachlor showed optimal rainy season results (61.54% weed control efficiency at 63 days after herbicide application (DAA), with a yield of 1036 kg ha⁻¹), while flumioxazin excelled in dry conditions (64.32% WCE, <4% phytotoxicity, and 1243 kg ha⁻¹ yield). Pendimethalin performed poorly under wet conditions but improved in drier weather. Among five dominant weed species, Cyperus rotundus proved the most resilient. CM60 demonstrated superior herbicide tolerance and yield stability, particularly under rainy conditions. These results emphasise that season-specific herbicide selection and variety matching are crucial for herbicide resistance management and effective weed control in Thailand’s rainfed soybean systems. Full article

Decentralized sanitation in rural areas urgently requires accessible and nature-based solutions to achieve Sustainable Development Goal 6 (clean water and sanitation for all). However, monitoring studies of such ecotechnologies in disperse communities remain limited. This study evaluated the performance of an evapotranspiration tank (TEvap), designed with community participation, for the treatment of domestic sewage in a rural Quilombola household in the Brazilian Cerrado. The system (total area of 8.1 m², with about 1.0 m² per inhabitant) was monitored for 218 days, covering the rainy season and the plants’ establishment phase. After 51 days, the TEvap reached operational equilibrium, maintaining a zero-discharge regime, and after 218 days, 92.3% of the total system inlet volumes (i.e., 37.47 in 40.58 m³) were removed through evapotranspiration and uptake by cultivated plants (Musa spp.). Statistical analyses revealed correlations that were moderate to strong, and weak between the blackwater level and relative humidity (Pearson correlation coefficient, r = 0.75), temperature (r = −0.66), and per capita blackwater contribution (r = 0.28), highlighting the influence of climatic conditions on system efficiency. These results confirm the TEvap as a promising, low-maintenance, and climate-resilient technology for decentralized domestic sewage treatment in vulnerable rural communities, with the potential to support sanitation policy goals and promote public health. Full article

The performance of monthly prediction of extreme precipitation from the BCC-CPSv3-S2Sv2 model over the Yellow River Basin (YRB) using historical hindcast data from 2008 to 2022 was evaluated in this study, mainly from three aspects: overall performance in predicting daily precipitation rates, systematic biases, and monthly prediction of extreme precipitation metrics. The results showed that the BCC-CPSv3-S2Sv2 model demonstrates approximately 10-day predictive skill for summer daily precipitation over the YRB. Relatively higher skill regions concentrate in the central basin, while skill degradation proves more pronounced in downstream areas compared to the upper basin. After correcting model systematic biases, prediction skills for total precipitation-related metrics significantly surpass those of extreme precipitation indices, and metrics related to precipitation amounts demonstrate relatively higher skill compared to those associated with precipitation days. Total precipitation (TP) and rainy days (RD) exhibit comparable skills in June and July, with August showing weaker performance. Nevertheless, basin-wide predictions within 10-day lead times remain practically valuable for most regions. Prediction skills for extreme precipitation amounts and extreme precipitation days share similar spatiotemporal patterns, with high-skill regions shifting progressively south-to-north from June to August. Significant skills for June–July are constrained within 10-day leads, while August skills rarely exceed 1 week. Further analysis reveals that the predictive capability of the model predominantly originates from normal or below-normal precipitation years, whereas the accurate forecasting of extremely wet years remains a critical challenge, which highlights limitations in capturing mechanisms governing exceptional precipitation events. Full article

The yield of common bean (Phaseolus vulgaris L.) is limited by abiotic stresses such as drought and high temperatures, which frequently occur simultaneously under field conditions. This study examined 100 bean genotypes under three environmental conditions, namely, the rainy season (optimal conditions), full irrigation in the dry season (high-temperature stress), and terminal drought in the dry season (combined stress), via a 10 × 10 triple-lattice design. Agronomic parameters evaluated included days to flowering (DF), days to physiological maturity (DM), plant height (PH), aerial biomass (BIO), grain yield (YLD), and 100-seed weight (100SW). The natural temperature exceeded 35 °C during the reproductive stage of the dry season. Combined stress revealed differential adaptive mechanisms in the tested germplasms, indicating that the response to multiple stresses is more complex than the sum of individual stress responses. The average yield under optimal conditions was 1344 kg/ha, decreasing to 889 kg/ha (66.1%) under irrigation with high temperatures and to 317 kg/ha (23.6%) under terminal drought with high temperatures. Under terminal drought with high temperatures, the number of days to maturity decreased by 5%, and the seed weight decreased by 20%. The G69-33-PT and G-19158 genotypes presented high yields under high-temperature stress, with yields above 1800 kg/ha, suggesting specific physiological mechanisms for tolerance to elevated temperatures. Under combined stress, genotypes G69-Sel25, Pinto Mestizo, and Dalia presented yields above 680 kg/ha, indicating adaptations in terms of water use efficiency and tolerance to high temperature. The identification of genotypes with differential stress tolerance provides valuable genetic resources for breeding programs. The diverse origins of superior germplasms (bred lines, landraces, and commercial cultivars) highlight the importance of exploring various germplasms in the search for sources of abiotic stress tolerance for breeding projects aimed at developing cultivars adapted to climate change scenarios where drought and high temperatures occur simultaneously. Full article

Rainfall variability in the sensitive Kalahari semi-desert in Southern Africa, a region of strong climatic gradients, has not been much studied and is poorly understood. Here, anomalies in rainfall totals and moderate and heavy rain day frequencies are examined for both the summer half of the year and three bi-monthly seasons using CHIRPS rainfall data and ERA5 reanalysis. Peak rainfall occurs in January–February, with anomalously wet summers marked by a significant increase in the number of rainy days rather than rainfall intensity. Wet summers are linked to La Niña events, cyclonic anomalies over Angola, and a weakened Botswana High, which enhances low-level moisture transport and convergence over the region as well as mid-level uplift. Roughly the reverse patterns are found during anomalously dry summers. On sub-seasonal scales, ENSO and the Botswana High (the Southern Annular Mode) are negatively (positively) significantly correlated with early summer rainfall, while in mid-summer, and for the entire November–April season, only ENSO and the Botswana High are correlated with rainfall amounts. In the late summer, weak negative correlations remain with the Botswana High, but they do not achieve 95% significance. Full article

From the urban sustainability perspective and from the steps essential for regulating/balancing the microclimate features, the creation and maintenance of urban green spaces (UGS) are vital. The UGS include vegetation of any kind in urban areas such as parks, gardens, vertical gardens, trees, hedge plants, and roadside plants. This “urban green infrastructure” is a cost-effective and energy-saving means for ensuring sustainable development. The relationship between urban landscape patterns and microclimate needs to be sufficiently understood to make urban living ecologically, economically, and ergonomically justifiable. In this regard, information on diverse patterns of land use intensity or spatial growth is essential to delineate both beneficial and adverse impacts on the urban environment. With this background, the present study aimed to address water requirements of UGS plants and trees during the non-rainy months from Panaji city (Koppen classification: Am) situated on the west coast of India, which receives over 2750 mm of rainfall, almost exclusively during June–September. During the remaining eight months, irrigating the plants in the UGS becomes a serious necessity. In this regard, the daily water requirements (DWR) of 34 tree species, several species of hedge plants, and lawn areas were estimated using standard methods that included primary (field survey-based) and secondary (inputs from key-informant survey questionnaires) data collection to address water requirement of the UGS vegetation. Monthly evapotranspiration rates (ET_o) were derived in this study and were used for calculating the water requirement of the UGS. The day–night average ET_o was over 8 mm, which means that there appears to be an imminent water stress in most UGS of the city in particular during the January–May period. The DWR in seven gardens of Panaji city were ~25 L/tree, 6.77 L/m² hedge plants, and 4.57 L/m² groundcover (=lawns). The water requirements for the entire UGS in Panaji city were calculated. Using this information, the estimated total daily volume of water required for the entire UGS of 1.86 km² in Panaji city is 7.10 million liters. The current supply from borewells of 64,200 L vis a vis means that the ET_o-based DWR of 184,086 L is at a shortage of over 2.88 times and is far inadequate for meeting the daily demand of hedge plants and lawn/groundcover. Full article

Deep learning has become a widely used approach in photovoltaic (PV) power generation forecasting due to its strong self-learning and parameter optimization capabilities. In this study, we apply a deep learning algorithm, known as the time-series dense encoder (TiDE), which is an MLP-based encoder–decoder model, to forecast PV power generation. TiDE compresses historical time series and covariates into latent representations via residual connections and reconstructs future values through a temporal decoder, capturing both long- and short-term dependencies. We trained the model using data from 2020 to 2022 from Australia’s Desert Knowledge Australia Solar Centre (DKASC), with 2023 data used for testing. Forecast accuracy was evaluated using the $R^2$ coefficient of determination, mean absolute error (MAE), and root mean square error (RMSE). In the 5 min ahead forecasting test, TiDE demonstrated high short-term accuracy with an $R^2$ of 0.952, MAE of 0.150, and RMSE of 0.349, though performance declines for longer horizons, such as the 1 h ahead forecast, compared to other algorithms. For one-day-ahead forecasts, it achieved an $R^2$ of 0.712, an MAE of 0.507, and an RMSE of 0.856, effectively capturing medium-term weather trends but showing limited responsiveness to sudden weather changes. Further analysis indicated improved performance in cloudy and rainy weather, and seasonal analysis reveals higher accuracy in spring and autumn, with reduced accuracy in summer and winter due to extreme conditions. Additionally, we explore the TiDE model’s sensitivity to input environmental variables, algorithmic versatility, and the implications of forecasting errors on PV grid integration. These findings highlight TiDE’s superior forecasting accuracy and robust adaptability across weather conditions, while also revealing its limitations under abrupt changes. Full article

Background: Accurate ground segmentation in 3D point clouds is critical for robotic perception, enabling robust navigation, object detection, and environmental mapping. However, existing methods struggle with over-segmentation, under-segmentation, and computational inefficiency, particularly in dynamic or complex environments. Methods: This study proposes FASTSeg3D, a novel two-stage algorithm for real-time ground filtering. First, Range Elevation Estimation (REE) organizes point clouds efficiently while filtering outliers. Second, adaptive Window-Based Model Fitting (WBMF) addresses over-segmentation by dynamically adjusting to local geometric features. The method was rigorously evaluated in four challenging scenarios: large objects (vehicles), pedestrians, small debris/vegetation, and rainy conditions across day/night cycles. Results: FASTSeg3D achieved state-of-the-art performance, with a mean error of <7%, error sensitivity < 10%, and IoU scores > 90% in all scenarios except extreme cases (rainy/night small-object conditions). It maintained a processing speed 10× faster than comparable methods, enabling real-time operation. The algorithm also outperformed benchmarks in F1 score (avg. 94.2%) and kappa coefficient (avg. 0.91), demonstrating superior robustness. Conclusions: FASTSeg3D addresses critical limitations in ground segmentation by balancing speed and accuracy, making it ideal for real-time robotic applications in diverse environments. Its computational efficiency and adaptability to edge cases represent a significant advancement for autonomous systems. Full article

The trematodes Fasciola hepatica and F. gigantica are transmitted by lymnaeid snails and cause fascioliasis in livestock and humans. Human infection is emerging in southern and southeastern Asia. In India, the number of case reports has increased since 1993. This multidisciplinary study analyzes the epidemiological scenario of human infection. The study reviews the total of 55 fascioliasis patients, their characteristics, and geographical distribution. Causes underlying this emergence are assessed by analyzing (i) the climate change suffered by India based on 40-year-data from meteorological stations, and (ii) the geographical fascioliasis hotspots according to archeological–historical records about thousands of years of pack animal movements. The review suggests frequent misdiagnosis of the wide lowland-distributed F. gigantica with F. hepatica and emphasizes the need to obtain anamnesic information about the locality of residence and the infection source. Prevalence appears to be higher in females and in the 30–40-year age group. The time elapsed between symptom onset and diagnosis varied from 10 days to 5 years (mean 9.2 months). Infection was diagnosed by egg finding (in 12 cases), adult finding (28), serology (3), and clinics and image techniques (12). Climate diagrams and the Wb-bs forecast index show higher temperatures favoring the warm condition-preferring main snail vector Radix luteola and a precipitation increase due to fewer rainy days but more days of extreme rainfall, leading to increasing surface water availability and favoring fascioliasis transmission. Climate trends indicate a risk of future increasing fascioliasis emergence, including a seasonal infection risk from June–July to October–November. Geographical zones of high human infection risk defined by archeological–historical analyses concern: (i) the Indo-Gangetic Plains and corridors used by the old Grand Trunk Road and Daksinapatha Road, (ii) northern mountainous areas by connections with the Silk Road and Tea-Horse Road, and (iii) the hinterlands of western and eastern seaport cities involved in the past Maritime Silk Road. Routes and nodes are illustrated, all transhumant–nomadic–pastoralist groups are detailed, and livestock prevalences per state are given. A baseline defining areas and seasons of high infection risk is established for the first time in India. This is henceforth expected to be helpful for physicians, prevention measures, control initiatives, and recommendations for health administration officers. Full article

The objective of this study to evaluate the effects of two levels of concentrate supplementation (1 g/kg or 2 g/kg of body weight, BW) on the intake, apparent digestibility and performance of Girolando steers on tropical pastures during the post-weaning phase in the dry to rainy season transition. We used 20 Girolando steers (half Holstein x half Zebu), with an average initial BW of 151.15 ± 50 kg and 12 months of age. The steers grazed on Urochloa brizantha cv. Marandu pasture. The animals were randomly assigned supplementation with 1 g/kg of BW (SC1) or supplementation with 2 g/kg of BW (SC2) of a concentrate supplement. The forage dry matter intake (%BW), neutral detergent fiber intake (NDF) and NDF digestibility were higher (p < 0.05) for steers supplemented with a level of 1 g/kg of BW. Supplement intake (kg/day), non-fibrous carbohydrate (NFC) intake and NFC digestibility were higher (p < 0.05) for steers consuming 2 g/kg BW of the concentrated supplement. The body weight at slaughter (297 kg) and average daily gain (0.57 kg/day) were not influenced by the level of supplementation. The use of 1 g/kg BW of a concentrated supplement is recommended for post-weaning steers on tropical pastures during the dry to rainy season transition. Full article

Tartary buckwheat is an important characteristic multigrain crop, mainly planted in Sichuan, Guizhou, Yunnan and Tibet, and other alpine and remote ethnic mountainous areas. In order to clarify the effect of sowing date on the yield and quality of Tartary buckwheat and its relationship with meteorological factors The variety Jinqiao No. 2 was used for a two-year trial at Dingxiang Test Base in Shanxi Province on four sowing dates (15 June, 26 June, 6 July and 17 July 2022 and 19 June, 30 June, 10 July and 21 July 2023) starting from the bud stage. Responses to sowing date were investigated by examining the growth period structure, yield, yield component, quality, and their relationship to climatic factors. The results showed that meteorological factors during the grain grain-filling stage were different when the sowing date was different. Compared with other sowing times, the treatment with the sowing of early and mid-July had less than 13.5~27.9 h of sunshine, less than 28.8~48.5 mm of rainfall, more than 10.5~19 days of ≤15 °C days, but the most serious low-temperature stress (≤15 °C days up to 27 days). The yield of sowing in July was 69.8~77.0% and 69.9~79.1% lower than that of sowing in June in 2022 and 2023 respectively, and the later sowing had a lower yield. Delayed sowing is beneficial to the accumulation of flavonoids and protein in Tartary buckwheat grains, and the average value in 2022 and 2023 is 11.55% and 14.64% higher than that in the first sowing, but the content of fat and starch is significantly reduced. The result of path analysis showed that the low temperature (≤15 °C days up to 27 days) and less solar radiation duration were the key points for attaining high yield and quality, due to the mean daily temperature and ≤15 °C days from flowering to maturity had negative effect on 1000-seed weight, seed setting rate, starch and crude lipid content of Tartary buckwheat, and the direct effect of sunshine duration on the content of protein and flavonoid in Tartary buckwheat was the greatest. The yield of Tartary buckwheat sown in June was higher than that of other treatments, because of avoiding low-temperature stress and long rainy and sunless weather during the grain filling stage, which enabled the blossoming and grain filling normally and finally attained higher yield. Full article

The Quilombola community of Abacatal, located in Ananindeua, in the state of Pará, has characteristics that favor the proliferation of mosquitoes. Faunal surveys in environmental preservation areas are essential for understanding the dynamics of these vectors, whose epidemiological implications are significant. Uncontrolled human activities have an impact on temperature, humidity, and rainfall. The aim of this study was to survey the diversity of mosquito species in the Quilombola community of Abacatal. Field collections were carried out over 10 days, during the rainy and dry seasons, using the following methods: protected human attraction (PHA) and CDC traps. The results of the taxonomic identification of the samples collected revealed that the species Coquillettidia (Rhynchotaenia) venezuelensis and Culex (Melanoconion) portesi were eudominant in the area studied. The identification of species of epidemiological importance, which act as vectors for various arboviruses, highlights the relevance of monitoring in the area, especially considering that it will undergo a process of anthropization. Full article

Elaeis oleifera and Elaeis guineensis, two oil palm species capable of intercrossing to produce interspecific Elaeis oleifera × Elaeis guineensis (O × G) hybrids, exhibit genetic variability in key agronomic traits such as fruit development, oil accumulation, and bunch composition. This variability influences the productivity and oil quality of the resulting hybrids. Harvesting, a critical practice in oil palm production, significantly impacts oil yield and quality. Therefore, this study aimed to ascertain the optimum harvest point (OHP) in widely cultivated O × G hybrids and its correlation with genetic backgrounds. The O × G cultivars, “Coari × La Mé” (C × LM), “Manaos × Compacta” (M × C), and “Brazil × Djongo” (B × DJ), were examined to identify notable changes during various phenological stages of bunch ripening using the O × G BBCH scale, a standardized system for describing plant growth stages based on phenological development. The research was conducted in the Southwest Colombian oil palm zone during dry and rainy seasons. Observations revealed distinctive fruit coloration patterns and increased bunch weights throughout the maturation process. However, final fruit coloration did not consistently align with maximum oil rates, indicating it as an unsuitable descriptor for OHP. The C × LM cultivar exhibited the shortest ripening period (173 days after anthesis, DAA), while M × C showed the longest at 207 DAA, followed by B × DJ at 187 DAA. Pollination efficiency varied among cultivars, with C × LM and M × C displaying higher proportions of parthenocarpic fruits. Findings suggest harvesting can occur for all cultivars between phenological stages 807 and 809—corresponding to late maturity stages in fruit development—regardless of the time of year, when maximum oil per bunch is attained. Fruit opacity, fruit cracking, and fruit detachment at stages 807 and 809 were identified as pivotal descriptors for determining the right OHP, albeit unique to each cultivar. Implementing two of these three descriptors by field workers will likely result in the highest oil yields for O × G cultivars. In conclusion, this research provides valuable insights into optimizing oil palm harvest practices, emphasizing the importance of considering genetic variability and phenological indicators for determining the optimum harvest point in interspecific O × G hybrids. Full article

In spaced crop systems, understanding the interactions between different types of vegetation in the agroecosystem and solar radiation is essential for understanding surface radiation dynamics. This study aimed to both seasonally and spatially quantify and characterize the components of the solar radiation balance in the cultivation of green dwarf coconut. The experiment was conducted in Santa Izabel do Pará, Brazil, and monitored the following meteorological parameters: rainfall, incident global radiation (R_g), and net radiation (R_n). Landsat 8 satellite images were obtained between 2021 and 2023, and the estimates for global and net radiation were subsequently calculated. The resulting data were subjected to mean tests and performance index analysis. The dry season showed higher values of R_g and R_n due to reduced cloud cover. In contrast, the rainy season exhibited lower R_g and R_n totals, with reductions of 21% and 23%, respectively. In the irrigated area, a higher R_n/R_g fraction was observed compared to the non-irrigated area, with no significant differences between the row and inter-row zones. In the non-irrigated system, there were no seasonal differences, but a spatial difference between row and inter-row was noted, with the row having higher net radiation (9.95 MJ m⁻² day⁻¹) than the inter-row (8.36 MJ m⁻² day⁻¹), which could result in distinct energy balances at a micrometeorological scale. Spatially, the eastern portion of the study area showed higher global radiation totals, with the radiation balance predominantly ranging between 400 and 700 W m⁻². Based on the performance indices obtained, satellite-based estimates proved to be a viable alternative for characterizing the components of the radiation balance in the region, provided that the images have low cloud cover. Full article