Search Results (1,175)

Examining the changing patterns and underlying mechanisms of soil biomass carbon stocks constitutes a fundamental aspect of soil biology. Despite the potential influence of the sulfur cycle and the life strategies of organisms on community biomass, these factors have rarely been studied in tandem. Biocrusts are model systems for studying soil ecosystems. In this study, metagenomic analysis of biocrusts related to different life strategies from five batches over four consecutive years demonstrated that, in free-living communities, microbial biomass carbon (MBC) synthesis, via assimilatory sulfate reduction (ASR), is primarily coupled with the 3-hydroxypropionate/4-hydroxybutyrate and Calvin–Benson–Bassham cycles. These pathways are affected by the oxidation-reduction potential (Eh), pH, electrical conductivity, and nutrient levels. The decomposition of organic carbon (OC) via dissimilatory sulfate reduction (DSR) was accompanied by the production of dimethyl sulfide (DMS), which was influenced by the C/S ratio and moisture, whereas the synthesis of MBC by symbiotic communities was found to be affected by Eh and pH, and decomposition was affected by the C/S ratio. The MBC stock was influenced by all strategies, with resource strategies having the greatest impacts during the growing season, and the contribution of chemotrophic energy was most significant in free-living communities. In conclusion, the MBC in biocrusts is associated with both ASR and DSR and is facilitated by the A-, S-, and P-strategies under the regulation of the stoichiometric C/S ratio. The exploration of microbial life strategies and sulfur cycling in biocrusts within arid ecosystems in this study offers a new perspective on the patterns of change in soil biomass carbon stocks. Full article

Biochar application in dry regions holds promise for improving soil properties, but its impact on soil salinity remains controversial. To evaluate the short-term effect of biochar on soil salinity under dry conditions, we conducted a meta-analysis of 149 observations from 40 peer-reviewed publications conducted in Mediterranean, arid, and semi-arid climates, or under simulated dry/saline conditions. Overall, biochar addition significantly increased soil electrical conductivity (EC) by 34.63% compared to controls. However, this effect was highly dependent on pedoclimatic conditions, soil pH, biochar feedstock types, pH and EC, irrigation practices, and management factors. The most substantial increases in salinity occurred when applying biochar produced from high-ash feedstocks (e.g., seafood shell powder, peanut shell), at high application rates (>20 t ha⁻¹), to soils with low initial organic carbon content, or in the absence of a leaching fraction. In contrast, the use of biochar made from low-ash ligneous materials at rates ≤ 20 t ha⁻¹ did not significantly increase soil EC. Random forest analysis identified biochar EC, initial soil EC, and biochar pH as the most influential factors. We conclude that the risk of biochar-induced salinization in drylands can be effectively minimized by selecting appropriate lower-EC biochar, applying it at moderate application rates, and implementing irrigation with a leaching fraction. These findings provide critical guidelines for the sustainable implementation of biochar technology in water-scarce environments. Full article

This study addresses water scarcity in arid regions by developing low-temperature-sintered porous ceramics for agricultural water management, utilizing coal gangue solid waste as the primary resource. Systematic single-factor experiments first identified the optimal sintering temperature (615 °C) and polystyrene content (25%) that critically balance pore formation and structural integrity. Building on this, orthogonal experiment optimization yielded an optimal formulation exhibiting exceptional comprehensive performance (coal gangue 20 g, starch 25 g, glass powder 11 g, polystyrene 27 g): 149.70% water absorption, 57.75 h water retention, 77.28% porosity, and 0.55 MPa compressive strength. The material’s graded pore structure, achieved through composite pore-formers (polystyrene/starch) and diatomaceous earth, underlies its enhanced capillary action. The pot experiment of Chinese cabbage confirmed its effect, shortened the emergence time of seedlings to <24 h, and significantly improved the emergence rate and the growth of seedlings in the early stage (7 days). This work provides a new way for the value of coal gangue in dryland agriculture and ecological restoration. Full article

Hylocereus species are promising for enhancing fruit productivity in arid regions, but high solar radiation often leads to yield loss. This study aimed to evaluate the short-term impact of different shading levels on the physiological performance, productivity, and post-harvest quality of Hylocereus costaricensis under semi-arid conditions. Plants were grown in the field under two shade levels, i.e., 35 and 50% and their performances were compared to plants under control, i.e., 0% of shade or full sunlight. The nighttime CO₂ assimilation and productivity increased significantly by 310.5 and 114.6% and 34.3 and 50.14% for plants under 35 and 50% of shade, respectively, compared to the control. A Principal Component Analysis (PCA) revealed that shade enhanced skin betalain (BETS) and phenolic content (PETP), whereas non-shaded plants expressed traits more closely associated with plant and fruit photoprotective pigment synthesis, i.e., total carotenoids and yellow flavonoids, respectively, along with total sugar accumulation, underscoring the significant impact of shading on both metabolic activity and overall agronomic outcomes. Shading within the 35% to 50% range is effective to cope with high solar radiation by improving photosynthetic capacity, productivity, and post-harvest quality, especially regarding the accumulation of pigments such as betalains, indicating that shade as an agro-technique is a valuable approach for the cultivation of Hylocereus species in dryland regions. Full article

To address the challenges of low efficiency and high damage rates in dryland safflower harvesting, a roller-brush type harvesting device was developed. The design was developed following a detailed analysis of the spatial distribution and mechanical characteristics of safflower plants. The pose adjustment process begins with helical grooves clamping and contacting the plant stem. The propulsion action of the helix then forces the stem to undergo a predetermined deflection displacement. The optimal picking pose occurs when the plant’s longitudinal axis is perpendicular to the rotational axis of the picking roller brush. In this position, the picking roller brush shears the filaments at the necking zone through gentle contact with the fruit balls. This mechanism transforms the traditional pull-off separation into a low-damage shear-separation mode. The Box–Behnken test was designed to find the optimal combination of parameters for picking: picking roller brush speed of 282.5 r/min, roller brush spacing of 3.7 mm, and brush bristle diameter of 0.1 mm. Verification tests showed the picking, damage and fruit injury rates were 92.4%, 7.1% and 1.2%, respectively, with standard deviations of 5.42%, 0.51%, and 0.08%. The harvesting efficiency reached 0.053 hm²/h, 8.48 to 12.01 times higher than manual harvesting. Full article

In Mediterranean drylands, where year-to-year climatic variability and soil constraints (e.g., compaction or shallow profiles) often limit the feasibility of strict no-tillage (NT), strategic tillage (ST) has emerged as a pragmatic support tool within conservation agriculture. To evaluate its short-term effects, multi-country field trials were established in Morocco, Tunisia, Türkiye, and Spain across a rainfall gradient (250–580 mm). We assessed soil water content (SWC), crop biomass, and yield under ST compared with NT systems. Results were context-dependent. SWC responses varied: largely unchanged in Morocco and Tunisia, slightly increased in Morocco in 2023, and significantly reduced in Spain in 2022. Biomass generally showed no significant change, with modest decreases in Morocco and modest increases in Tunisia. Yield effects were more pronounced: pooled data from Morocco indicated a significant reduction under ST, and Tunisia showed a significant yield loss in 2021. Türkiye exhibited non-significant declines in both SWC and yield, while Spain experienced yield-neutral but SWC-reducing outcomes. Overall, ST did not have negative effects across sites. Instead, its impacts were strongly conditioned by local soils, rainfall distribution, and crop context. These findings highlight that ST can be considered as a pragmatic tool to overcome some of the agronomic difficulties in the Mediterranean region with little or no negative effects on productivity of soil moisture. Full article

Rice, as one of the world’s three major staple crops, provides a food source for nearly half of the global population. Timely and accurate acquisition of rice cultivation information is crucial for optimizing spatial distribution, guiding production practices, and safeguarding food security. Taking Bishan District of Chongqing as the study area, NDVI values were derived from Sentinel-2 satellite imagery to construct standard NDVI time-series curves for typical land-cover types, including paddy fields, dryland, water bodies, construction land, and forest and grassland. These curves were then used in the NDVI time-series characteristics method to identify paddy fields. First, the Euclidean distance between the standard NDVI time series of paddy fields and those of other land-cover types was calculated. The sum of these element-wise differences was used to determine the upper threshold for paddy field extraction. Second, the mean absolute deviation between elements of the rice sample dataset and the standard NDVI time series was calculated for each time step. The sum of these average deviations was used as the lower threshold to extract the initial paddy field data. On this basis, an extreme-value constraint was introduced to reduce the interference of mixed pixels from forest and grassland and construction land, effectively eliminating anomalous pixels and improving the accuracy of paddy field identification. Finally, the results were validated and compared with those from other extraction methods. The results indicate that: (1) Paddy fields exhibit distinct NDVI time-series characteristics throughout the entire growing season, which can serve as a reference standard. By calculating the Euclidean distance between the NDVI curves of other land-cover types and those of paddy fields, similarity can be quantified, enabling rice identification. (2) The extraction method based on NDVI time-series characteristics successfully identified paddy fields through the appropriate setting of thresholds. The overall accuracy and Kappa coefficient remained high, while the F1-score consistently exceeded 0.8, indicating a good balance between precision and recall. Furthermore, the bootstrap uncertainty analysis revealed narrow 95% confidence intervals across all metrics, confirming the robustness and statistical reliability of the results. Overall, the proposed method demonstrated excellent performance in paddy field classification and significantly outperformed traditional machine learning methods implemented on the GEE platform. (3) Mixed pixels considerably affected the accuracy of rice classification; however, the introduction of the extreme-value constraint effectively mitigated this influence and further improved classification results. Full article

In semiarid, fragmented landscapes where data scarcity challenges effective land management, accurate soil moisture monitoring is critical. This study presents a high-resolution analysis that integrates remote sensing, in situ data, and machine learning to predict soil moisture and evaluate the impact of land conservation practices. A Long Short-Term Memory (LSTM) model combined with Random Forest gap-filling achieved strong predictive performance (R² = 0.84; RMSE = 0.103 cm³ cm⁻³), outperforming SMAP satellite estimates by approximately 30% across key accuracy metrics. The model was applied to 222 field sites in northern Ghana to quantify the effects of stone bunds on soil moisture retention. The results revealed that fields with stone bunds maintained 4–6% higher moisture than non-bunded fields, particularly on steep slopes and in areas with low to moderate topographic wetness. These findings demonstrate the capability of combining remote sensing and deep learning for fine-scale soil-moisture prediction and provide quantitative evidence of how nature-based solutions enhance water retention and climate resilience in dryland agricultural systems. Full article

Taro (Colocasia esculenta) is a tropical root crop widely cultivated for its edible corms and leaves. The objective of this study was to determine the effect of taro morphometric parameters on prolificacy, yield and nutritional value under dryland production. Two sites were used to grow small, medium and large propagules generated under controlled environment conditions from a local landrace. Plant prolificacy, in terms of corms per plant, crop yield (t·ha⁻¹) and nutrient content (macro- and micronutrients) and fibre were used to determine taro quality. The size of propagule was associated with both productivity and nutritional value. There was a positive correlation between propagule size and starch content. A decline in both Acid Detergent Fibre (13%) and Neutral Detergent Fibre (25%) occurred in larger corms. The protein and macronutrient contents improved with corm size, but the micronutrient content decreased. This study revealed that there are benefits in the utilisation of a wide range of corm sizes for upland production purposes. However, there is a need to investigate and expand knowledge of taro food components to include its potential value for specific nutritional and industrial purposes. Full article

Food production is among the most environmentally intensive human activities, and its impacts are intensifying under population growth and increasingly resource-demanding consumption patterns. Agricultural practices have responded through the expansion of irrigated croplands, aiming to secure food supply but also fostering a complex agribusiness system with inherent contradictions. A central issue is the systematic overproduction of perishable crops. When supply surpasses demand, prices often fall below production costs, resulting in the routine disposal of large volumes of fresh produce—frequently before entering distribution channels. This study quantifies the environmental burden of this waste by calculating the water and carbon footprints of discarded fruits and vegetables in Spain between 2018 and 2024, based on official data. Across this period, 483,624 tons of surplus produce were discarded, equivalent to a water footprint of nearly 36 hm³ and a carbon footprint of 36,694 tCO₂-eq. In a region already facing severe water stress, widespread groundwater overexploitation, and growing dependence on inter-basin transfers and desalination, such chronic waste represents a significant inefficiency. The results highlight the urgent need to reassess current food production practices and address systemic imbalances to support a more sustainable and resource-efficient agricultural model. Full article

Millet yield per unit area in northern China’s drylands is constrained by climate, soil, and management factors, complicating forecasts under limited, nonlinear, heterogeneous data. In order to enhance the accuracy and stability of operational forecasting, this study utilised observational data from five locations in southwestern Heilongjiang Province spanning 2014 to 2023. Eight ground-based hydrothermal and meteorological factors were used as inputs to build an improved BP neural network optimised by IWOA, with enhancements to both algorithm and workflow. Adaptive inertia weight and EOBL were introduced to balance global exploration and local exploitation, enabling better hyperparameter solutions. Results show that IWOA-BP significantly outperforms baseline BP and WOA-BP on an annual scale. The RMSE was 2.74, the R² was 0.94, the MAPE was 5.9, and the RPD was 4.16. The implementation of additional seasonal rolling forecasts for the 2024 validation period entailed the construction of cumulative information flows from January to August. Cross-regional validation in Fangzheng County produced error magnitudes consistent with the primary study area, thereby demonstrating the model’s reliable generalization ability across both temporal and spatial dimensions. Full article

The Normalized Difference Vegetation Index (NDVI) is a fundamental metric for monitoring terrestrial ecosystem dynamics and assessing ecological responses to climate change. However, uncertainties persist across NDVI products, and a comprehensive assessment of their consistency is lacking. This study conducts a multi-faceted evaluation of three NDVI products, GIMMS V1.2 NDVI (NDVI_3g+), PKU GIMMS NDVI (NDVI_pku), and MODIS NDVI (NDVI_mod), to elucidate their performance across ecosystem applications. Our analysis encompasses a comparative analysis of NDVI values, trends, sensitivity to root-zone soil moisture (RSM), and performance in tracking photosynthesis benchmarked against solar-induced chlorophyll fluorescence (SIF). Our results reveal that NDVI_3g+ deviates notably from NDVI_pku and NDVI_mod over cold climates and Evergreen Broadleaf Forest (EBF). Additionally, NDVI_3g+ exhibits significant global browning, in contrast to the significant greening observed for NDVI_pku and NDVI_mod. Although their responses to RSM are generally uncertain, consistent positive responses appear in Drylands, with NDVI_mod showing the highest sensitivity. Additionally, the three NDVI products have high seasonality consistency with SIF, except over EBF, and NDVI_pku and NDVI_3g+ achieve the highest and lowest overall anomaly consistency with SIF, respectively. Furthermore, converting NDVI_3g+, NDVI_pku, and NDVI_mod to the corresponding kernel NDVIs improves seasonality consistency with SIF across 85% of the globe. Full article

Understanding land surface fluxes is essential for sustaining dryland ecosystem functioning and services. However, the scarcity of in situ measurements poses a significant challenge to dryland monitoring. Satellite optical and thermal remote sensing data can provide the instantaneous estimates of land surface fluxes, such as surface temperature (LST), net radiation (Rn), sensible heat flux (H), evapotranspiration (latent heat flux, LE), and gross primary productivity (GPP). However, satellite-based estimates are often limited by sensor revisit frequencies and cloud-cover conditions. To facilitate temporally continuous estimation, process-based land surface models are often used to integrate sparse remote sensing observations and meteorological inputs, thereby generating continuous estimates of energy, water, and carbon fluxes. However, the impact of satellite thermal data accuracy and temporal resolutions on simulating land surface fluxes is under-explored, particularly in dryland ecosystems. Therefore, this study assessed the accuracy of Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared data in a dryland tussock grassland ecosystem in southern Spain. We also assessed the incorporation and various temporal frequencies of thermal data into process-based modelling for simulating land surface fluxes. The model simulations were validated against in situ measurements from eddy covariance towers. Results show that MODIS LST has a high correlation but large bias with in situ measurements (R² = 0.81, RMSE = 4.34 °C). After a linear correction of MODIS LST with in situ measurements, we found that the adjusted MODIS LST can effectively improve the half-hourly simulation of LST, Rn, H, LE, SWC, and GPP with relative RMSEs of 7.84, 5.67, 7.81, 11.32, 6.59, and 13.09%, respectively. Such performance is close to the flux simulations driven by in situ LST. We also found that by adjusting the revisit frequency of the satellite sensor to 8 days, the model performance of simulating surface fluxes did not change significantly. This study provides insights into how satellite thermal remote sensing can be integrated with the process-based model to understand dryland ecosystem functioning, which is critical for ecological management and climate adaptation strategies. Full article

Background: The study evaluated the acute effect of dryland maximum strength (MS) training on water polo performance. Methods: Twelve female players (20.3 ± 1.4 years) underwent initial assessments, including a head-out 20 m swim and a one-repetition maximum (1RM) strength test in three exercises: bench press, seated pull row, and half squat. These exercises were used as the experimental (EXP) condition. During the main testing sessions, participants completed the EXP and a control (CON) condition. In the EXP, players completed MS training (three sets of six repetitions at 80% 1RM), followed 15 min later by in-water testing. In the CON, only the in-water tests were performed. These included a 10 s tethered swim to measure force, a 20 m head-out swim at maximum intensity to measure performance time, ten goal-targeted throws to reach the highest accuracy and throwing velocity, and three in-water vertical jumps as high as possible. Results: The performance time in the head-out 20 m swim (EXP: 14.21 ± 0.4, CON: 14.18 ± 0.5 s), tethered swimming force (EXP: 86.85 ± 14.82, CON: 89.58 ± 15.92 N), shooting velocity (EXP: 14.67 ± 1.19, CON: 14.91 ± 0.32 m·s⁻¹), shooting accuracy (EXP: 16.5 ± 5.4, CON: 19.0 ± 5.1 points), and in-water vertical jump height (EXP: 51.7 ± 5.6, CON: 52.9 ± 4.2 cm) were no different between conditions (p > 0.05). Conclusions: Dryland maximum strength training performed with high loads (80% 1RM) does not impair subsequent performance during sport-specific testing in female water polo players. These findings suggest that such MS training can be safely implemented 15 min prior to in-water training sessions. Full article

In arid environments such as the Namib Desert, non-rainfall water sources—including dew and fog—constitute indispensable yet understudied components of the regional hydrological cycle. These moisture inputs play a critical role in sustaining ecological functionality and biogeochemical processes, but remain among the least quantified facets of desert ecohydrology. The present study investigates multi-year trends in morning dew formation within the Namib Desert, utilizing observations from the Gobabeb–Namib Research Institute between 2015 and 2022. Meteorological data from the Southern African Science Service Centre for Climate and Adaptive Land Management (SASSCAL), in conjunction with direct field observations of dew, were used to develop an empirical equation to estimate dew occurrence. A sensitivity analysis verified the robustness of this formulation, and subsequent validation using field data confirmed its reliability (84.84% accuracy). During this eight-year period, the annual number of days with morning dew decreased from 170 in 2015 to 140 in 2022, representing an overall decline of approximately 18%. However, the total daily dew occurrence across 24 h remained relatively constant, indicating that the observed decline is confined primarily to morning condensation events. Dew formation was most prevalent during the wet season (December–May). Both monthly and annual analyses revealed a discernible declining trend in morning dew occurrence across this hyperarid ecosystem (p < 0.05). This decline corresponded with a gradual increase in both air and soil temperatures (approximately +0.03 °C yr⁻¹) and a slight but consistent decrease in relative humidity (approximately −0.26% yr⁻¹) between 2015 and 2022. The principal drivers of this decline include rising soil and air temperatures and decreasing atmospheric humidity. The analysis further identified an inverse relationship between air temperature and dew formation, implying that climatic warming intensifies evaporative demand and thereby suppresses dew condensation. Random forest analysis identified soil temperature, air temperature, and relative humidity as the most important predictors influencing dew occurrence, whereas wind speed and direction played lesser roles. Collectively, these findings underscore the vulnerability of dew-dependent ecosystems to anthropogenic climate change and highlight the imperative to continue investigating non-rainfall moisture dynamics in desert environments. Full article