Moisture Levels Research Articles

Application of hydrogel polymers and fertilizers for increasing winter wheat grain yield in conditions of rain-fed agriculture

This study aims to address the critical issue of water scarcity, which is being intensified by climate change and presents significant challenges to rain-fed agriculture, especially in arid regions. The research focuses on enhancing winter wheat grain yield productivity under rain-fed conditions through the utilization of locally produced, environmentally friendly hydrogel polymers. The methodology involved a field experiment conducted in the Spitak community, Armenia, from 2022 to 2023, covering an area of 630 m². Along with control groups, the study had 21 experimental treatments that used Aquasource and Van polymers in different ways, with and without nitrogen, phosphorus, and potassium (NPK) fertilizer. The findings indicated that the application of Aquasource and Van polymers at doses of 50-100 kg ha⁻¹ and 750-1000 kg ha⁻¹, respectively, without fertilizers significantly increased soil moisture absorption during the tillering and heading stages by 11.5-12.5% and 5.5-7.5%, compared to the controls. On the other hand, using these polymers with NPK fertilizer (N50P50K50 and N100P100K100) together made it harder for plants to absorb water, especially when more polymers were used. Soil moisture levels had a substantial impact on winter wheat grain yield metrics during these growth stages, demonstrating a strong correlation (R = 0.71-0.91). Enhanced moisture absorption resulting from polymer application led to a 22-24% increase in winter wheat grain yield. The practical implications of this study lie in its identification of key factors that influence winter wheat yield in rain-fed agriculture, particularly regarding locally sourced polymer dosage and application methods, both independently and in conjunction with fertilizers.

Determining Fermentation Conditions to Enhance Antioxidant Properties and Nutritional Value of Basil Seeds Using Lactobacillus plantarum

Fermented seeds and their bioactive compounds have captivated global interest due to their functional properties. Basil seeds are used worldwide in the food, cosmetic, and pharmaceutical industries, but their functional and nutritional properties after fermentation are not known. The aim of this study was to optimize the effect of fermentation on the improvement in the functional properties of basil seeds by Lactobacillus plantarum. Basil seed samples were categorized into seven water volumes (named A–G) and fermented for 24, 48, 72, and 96 h with L. Plantarum. The results show that the pH and total microbial content (TMC) significantly changed after 24 h of fermentation (p < 0.05). Fermentation significantly increased the antioxidant properties and niacin content of basil seeds compared with non-fermented control samples (p < 0.05). Fermented samples G-24, G-48, and G-72 (samples with a ratio of 1 g (basil):10 mL (water) fermented for 24,48 and 72 h), exhibited the highest DPPH and ABTS• scavenging activity. The total polyphenol content (TPC) was most prominent in the samples G-72, G-24, and G-48, respectively. Sample G-48 showed the highest antioxidant activity. Notably, sample G-24 showed a significant increase in niacin content (64 µg/mL). These results underscore that varying moisture levels and fermentation durations have a significant impact on the nutritional/functional value of basil seeds. Overall, fermenting basil seeds with L. plantarum increased their functional properties with greater antioxidant and TPC activities as well as increased nutritional value.

The Potential of Building Information Modeling in Agricultural Operations

Building Information Modeling (BIM) has been widely adopted in the construction industry, providing a virtual representation of physical objects and systems. In the context of agriculture, BIM can be applied to monitor and manage the physical and operational characteristics of farms and agricultural facilities using BIM digital twins. A BIM digital twin is a virtual representation of a physical object or system that is based on data from BIM models and other sources. The integration of sensors is essential in creating a BIM digital twin as they provide real-time data that can be used to update and maintain the virtual representation. The use of BIM digital twins in agriculture has the potential to improve the management of agricultural facilities, reduce environmental pollution, and promote the sustainability of agricultural land. Sensors can be used in agriculture to monitor various parameters such as soil moisture, temperature, and nutrient levels. BIM digital twins can also be used to monitor and manage sources of pollution, such as the use of pesticides and fertilizers, and to develop strategies to minimize their impact on the environment. BIM digital twins provide a comprehensive view of the condition of agricultural land and the factors that influence it, such as weather patterns, industry activities, and human activities. 4D BIM is an extension of traditional 3D BIM that adds a time dimension to the model. In the context of agriculture, 4D BIM can be used to visualize and simulate various processes related to time and soil management, such as planting and harvesting cycles, and to evaluate the impact of different planting strategies on crop yields. 4D BIM can also be used to model the impact of weather patterns on crop growth and to develop contingency plans for weather- related events. The use of BIM in agriculture has the potential to transform the industry by providing a new level of insight and control over agricultural operations. BIM models can be used to simulate and visualize different scenarios for agricultural facilities, such as the use of water, fertilizer, and energy, leading to improved efficiency and reduced waste. By using BIM to optimize operations, agricultural companies can increase their productivity and profitability. The integration of BIM digital twins and 4D BIM in agriculture has the potential to revolutionize the industry by providing new tools for monitoring and managing agricultural operations. The use of BIM digital twins and 4D BIM can help reduce environmental pollution, promote sustainability, and improve the efficiency and productivity of agricultural operations. Testing is needed to fully understand the potential benefits of BIM and BIM digital twins in agriculture and to develop best practices for their implementation.

Use of aqueous liquor from digested sludge pyrolysis for biogas production: characterization, toxicity assessment, and rate-limiting step determination

This study assessed the characteristics and toxicity of aqueous pyrolytic liquid (APL) derived from digested sewage sludge on anaerobic digestion (AD) and determined its rate-limiting step. Digested sewage sludge was pyrolyzed at multiple temperatures (350–650 °C) and moisture levels (0–40.4 %), resulting in APLs with varying AD toxicities. APL 350 °C-0 % showed the least toxicity, whereas APL 650 °C-40.4 % exhibited the greatest toxicity. Glucose (GL) and sodium acetate (SA) were introduced to elucidate the rate-limiting steps. SA, but not GL, enhanced APL conversion to CH4. And volatile fatty acid lack was observed in treatments without SA addition. This suggested that acidification was the primary rate-limiting step. This finding was confirmed using the modified Gompertz model: SA considerably improved the maximum methane production rate, whereas GL did not. Insights gained from this research clarified the feasibility and potential of AD for APL utilization and conversion.

Selection and evaluation of optimal medium for Eisenia fetida in sustainable waste recycling

Present study explored the optimal medium for survival, growth, and reproductive performance of Eisenia fetida using kitchen waste, cow dung mixtures, and rice straw in a volume of 3834. Experimental setups involved various combinations of different experimental set such as set I:kitchen waste alone (3 kg); set II kitchen waste:dung (2 kg:1 kg); set III kitchen waste:dung (1 kg:2 kg) set IV Kitchen waste: Dung: Rice straw (1 kg:1 kg:1 kg) and set V:dung alone (3 kg) to evaluate earthworm survival rates. Eisenia fetida were introduced three days after setup initiation, with temperatures maintained between 20 °C–28 °C and moisture levels at 50–60%. Jute cloth covers prevented external intrusion and earthworm escape. The study monitored earthworm weight, cocoon production, hatchlings, and waste in a 90 day experiment, collecting adult earthworms, cocoons, and juveniles for observation. Results revealed that specific ratios of kitchen waste and cow dung significantly influenced earthworm survival, reproduction and vermicompost quality. The study highlights the significance of optimizing waste recycling processes using earthworms and emphasizes the need for suitable mediums to improve waste conversion efficiency and vermicompost quality.

Comprehensive analysis of nutritional activity in Garcinia kola stem bark (Clusiaceae), Stone Breaker (Phyllanthus niruri) leaves and their blends: Implications for therapeutic applications

This study investigates the nutritional and mineral composition of Garcinia kola Stem Bark (Clusiaceae) and Stone Breaker (Phyllanthus niruri) leaves, processed through fermentation, blanching, and drying, to evaluate their potential health benefits. The samples were divided into three groups: Garcinia kola Stem Bark, Stone Breaker, and a 50 %:50 % mixture of both. The proximate analysis revealed significant variations. Moisture content ranged from 6.03 % to 7.38 %, with blanched Stone Breaker having the lowest and blanched Garcinia kola the highest, indicating differences in storage stability. Fermented samples showed higher ash content (up to 8.66 %), suggesting increased mineral availability due to microbial activity. Protein content was highest in fermented Stone Breaker (19.73 %), highlighting the benefits of fermentation in enhancing protein levels. Mineral analysis showed that blanched Stone Breaker had the highest potassium content (1872.8 mg/100 g), beneficial for cardiovascular health, while fermented samples had notable levels of calcium (402.2 mg/100 g) and magnesium (426.8 mg/100 g), supporting bone health and energy metabolism. Fermented Stone Breaker also emerged as the best overall sample due to its high protein content, higher ash content, and balanced moisture and fat levels, making it nutritionally rich and stable for storage. These findings emphasize the impact of processing methods on the nutritional and mineral profiles of herbal blends. Fermented Stone Breaker, in particular, offers a promising balance of essential nutrients for dietary enhancement and therapeutic health promotion.

Spatio-Temporal Distribution of Water Availability in Marshyangdi Basin: Hydrological Model Development

This Study has developed a hydrological model for the Marshyangdi river basin in Western Nepal, showing considerable potential for developing water resources and overall national prosperity. The model is also used to characterise hydrology and assess the availability of water resources across different spatial and temporal dimensions, hence improving our comprehension of water availability and its possible applications. The newly developed hydrological model in the Soil and Water Assessment Tool (SWAT) replicates the mean flows, hydrological pattern, and flow duration curve at the basin and 54 sub-basin outputs. Additionally, the model undergoes calibration and validation, employing Sufi-2 to fine-tune hydrological parameters. Based on the data generated by the model, the annual average precipitation (P) in the Marshyangdi basin is projected to be 1843 mm. Approximately 17.88% of the average yearly rainfall is lost via evapotranspiration; however, this varies significantly across different locations and time periods. Contrasting the two, the mountains have a higher moisture level than the Himalayan regions. The predicted average annual flow volume at the basin outflow is 9467.423 million cubic metres (MCM). Examining water shortages, finding regions with excess water, and developing solutions to address the competing needs of agriculture, industry, urban areas, and ecosystems are advantageous. Therefore, this method may improve the effectiveness of strategic planning and the long-term management of water resources.

Yield and Yield Attributes of Wheat (Triticum aestivum L.) Crop under Different Thermal and Moisture Regimes

A field experiment was carried out at Agrometeorological Research Farm of ANDU and T., Kumarganj, Ayodhya (UP) during rabi 2022-23 and 2023-24 to study the impact of different thermal regimes/sowing environments and moisture levels on wheat yield and yield attributes. Treatment consisted of three thermal regimes viz.15th November, 25th November and 5th December in main plot with four moisture levels viz, I1 at CRI, I2-CRI+tillering, I3-CRI+ jointing and milking, I4-CRI+ jointing+anthesis and dough stage in sub plot. Results reveal that 15th November thermal regime and I4 moisture level obtained higher values of effective tillers m-2, length of the spike, no. of spikes/plant, no. of grains/ear, no. of spikelet/spike, test weight followed by 25th November and minimum values were observed for crops sown on 5th December thermal regime during both year of experiment. There was significant variation observed in the grain yield, straw yield, biological yield, and harvest index among the three distinct thermal regimes. The highest yield 47.25 and 47.73 q ha-1 were recorded under the 15th November thermal regime, followed by 25th November (44.75 and 45.2 q ha-1) and minimum at 5th December 36.75 and 37.1 q ha-1 during both year of experiment respectively. Among the moisture level I4 has obtained the maximum values of yield 46.67 and 47.1 q ha-1during 2022-23 and 2023-24, respectively.

Understanding Excipient-Induced Crystallization of Spray-Dried Amorphous Solid Dispersion

This study investigates the compatibility of excipients with the model system SDI-X and their role in the induced crystallization of the amorphous compound-X in tablet formulations. We aimed to establish a straightforward and practical screening approach for evaluating excipient-induced crystallization of SDI in tablet matrices. Three methodologies—binary powder mixture, binary compact, and bilayer tablets—were employed to qualitatively and quantitatively evaluate the recrystallization of SDI-X with various excipients under accelerated storage conditions. The results demonstrated that binary compacts, providing direct physical contact between SDI-X and excipients, are superior in reflecting realistic drug-excipient contact within pharmaceutical tablets, enabling a more accurate assessment of excipient-induced crystallization for SDI-X. In contrast, the broadly used conventional binary blends can significantly underestimate this risk due to insufficient proximity. In addition, the bilayer tablets further confirmed that crystallization initiates at the contact surface between SDI-X and the excipients. The study highlighted that not only hygroscopicity but also the type of excipient and its physical contact with SDI-X significantly influence the recrystallization extent and rate of SDI-X. Interestingly, less hygroscopic diluents such as mannitol and lactose induced much higher levels of crystallization of SDIs, contrary to expectations based on moisture content alone. This suggests that the excipient type and contact surface are more critical in inducing recrystallization than just the level of moisture. The findings emphasize the need for careful excipient selection, study design, and sample preparation to enable appropriate assessments of SDI-excipient compatibility.

Closely related species differ in their traits, but competition induces high intra-specific variability.

Theories explaining community assembly assume that biotic and abiotic filters sort species into communities based on the values of their traits and are thus based on between-species trait variability (BTV). Nevertheless, these filters act on individuals rather than on species. Consequently, the selection is also influenced by intraspecific trait variability (ITV) and its drivers. These drivers may be abiotic (e.g., water availability) or biotic (e.g., competition). Although closely related species should have similar traits, many of them coexist. We investigated the relative magnitudes of BTV and ITV in coexisting closely related species and how their individual traits differ under different drivers of ITV. We manipulated conditions in a greenhouse pot experiment with four common Carex species, where individuals of each species originated from four source localities. Individuals were grown in factorial combinations of two moisture levels, with and without a competitor (grass species Holcus lanatus, a frequent competitor). We analyzed the variability of six morphological traits on individuals in the greenhouse and three morphological traits in the source localities. Species identity was the main determinant of differences in most traits. Competition exerted a greater effect than water availability. For leaf dry matter content (LDMC) and vegetative height, competition's effect even exceeded the variability among species. On the contrary, for specific leaf area (SLA) and clonal spread, the interspecific differences exceeded ITV induced by experimental treatments. SLA measured in the greenhouse closely correlated with values measured in field populations, while LDMC did not. The variability caused by source locality of ramets in the greenhouse was small, although sometimes significant. Closely related species differ in their traits, but for some traits, ITV can exceed BTV. We can expect that ITV can modify the processes of community assembly, particularly among coexisting closely related species.

Artificial Intelligence Using FFNN Models for Computing Soil Complex Permittivity and Diesel Pollution Content

Soil pollution caused by hydrocarbons, such as diesel, poses significant risks to both human health and the ecosystem. The evaluation of soil pollution and various soil engineering applications often relies on the analysis of complex permittivity, encompassing parameters such as dielectric constant and dielectric loss. Various computational models, including theoretical physics-based models, mixture theory models, statistical empirical models, and artificial neural network (ANN) models, have been explored for computing soil complex permittivity and predicting water and pollutant content. Theoretical models require detailed data that is often unavailable, and thus have limited applicability. Mixture models tend to underestimate soil characteristics due to inaccuracies in permittivity estimation of soil phases. While empirical models are widely used, their applicability is restricted to specific soil types, datasets, and locations. ANN models offer promising predictions, accommodating nonlinear phenomena and allowing for missing information and variables. In this study, capacitive electromagnetic electrode sensors were utilized to determine the complex permittivity of soil contaminated with varying levels of diesel at different moisture levels. Theoretical mixture, empirical, and Feed Forward Neural Network (FFNN) models were employed to compute the permittivity of polluted soil based on its phases and to predict the level of diesel pollution. A comparison of these modeling approaches revealed that the FFNN model exhibited the best performance. The ANN model demonstrated superior performance metrics, including a high correlation coefficient and lower mean square error. Specifically, the correlation coefficients for the FFNN model were 0.9942 for training samples, 0.9967 for validation samples, and 0.9977 for test samples. Additionally, the ANN model yielded the lowest mean square error compared to the other three models. Doi: 10.28991/CEJ-2024-010-09-018 Full Text: PDF

Combined inoculation of arbuscular mycorrhiza fungi with Meso‐rhizobium improves nutrient uptake, growth performance, and moisture stress tolerance of chickpea (Cicer arietinum L.)

AbstractBiofertilizers can be better alternatives to chemical fertilizers to enhance plant nutrition and productivity as they improve the soil fertility and crop productivity in an eco‐friendly and cost‐effective manner. A pot experiment was conducted between December 2018 and March 2019 in southern Ethiopia to evaluate the combined inoculation of arbuscular mycorrhizal fungi (AMF) and Meso‐rhizobium (MR) on biomass yield, nutrient uptake, and moisture stress tolerance of chickpea (Cicer arietinum L.) (variety: Habru). The experiment was executed as a factorial arrangement using a completely randomized design with three replications. The treatments were control (non‐fertilized), sole AM fungi inoculation, AM fungi inoculation with phosphorus fertilizer (20 kg P ha−1) and MR, and sole inorganic fertilizers (20 kg P;10 kg N ha−1) at four different moisture levels (optimum throughout the growing season, stressed at vegetative, flowering, and seed filling stages). The results demonstrated that biomass yields were limited by moisture stress, especially at vegetative and flowering stages of chickpea. Sole and co‐application of AMF with MR and inorganic P increased biomass yields on average by 19%, 39%, and 33% under water stress conditions, respectively, compared to the non‐inoculated control. The application of AMF with MR and inorganic P also significantly increased nodulation, AMF colonization, and nutrient uptake, but these effects were dependent on soil moisture status. In conclusion, there are potential advantages to be gained from sole and combined AMF application with rhizobium to improve growth and productivity of chickpea through enhanced nutrient and water uptake, though the results of this pot experiment should be validated through field trials.

Novel Cosmetic Ingredient CS-AA Polyion Complex and Skin Moisturizing Effect.

The study explored the enhanced skin moisturizing capabilities and moisture retention effects achieved by forming a polyion complex using sulfated glycosaminoglycan (GAG), specifically chondroitin sulfate (CS), and amino acids (AA) such as glutamine (Q) and arginine (R). The overall hydration effect of this CS-AA complex was examined. After analyzing the CS-AA polyion complex structure using spectroscopic methods, the ex vivo moisture retention ability was assessed under dry conditions using porcine skin samples. Additionally, the efficacy of the CS-AA polyion complex in reducing transepidermal water loss (TEWL) and improving skin hydration was evaluated on human subjects using a digital evaporimeter and a corneometer, respectively. Validating a systematic reduction in particle size, the following order was observed: CS>CS/AA simple mixture>CS-AA complex based on dynamic light scattering (DLS) and transmission electron microscopy (TEM) analysis. Furthermore, observations revealed that the CS-AA complex exhibits negligible surface charge. Additionally, Fourier-transform infrared spectroscopy (FT-IR) analysis demonstrated a distinct peak shift in the complex, confirming the successful formation of the CS-AA complex. Subsequently, the water-holding effect through porcine skin was assessed, revealing a notable improvement in moisture retention (weight loss) for the CS-Q complex: 40.6% (1h), 20.5% (2h), and 18.7% (4h) compared to glycerin. Similarly, the CS-R complex demonstrated enhancements of 50.2% (1h), 37.5% (2h), and 33% (4h) compared to glycerin. Furthermore, TEWL improvement efficacy on human skin demonstrated approximately 25% improvement for both the CS-Q complex and CS-R complex, surpassing the modest 12.5% and 18% improvements witnessed with water and glycerin applications, respectively. Finally, employing a corneometer, hydration changes in the skin were monitored over 4 weeks. Although CS alone exhibited nominal alterations, the CS-Q complex and CS-R complex showed a significant increase in moisture levels after 4weeks of application. In this study, polyion complexes were successfully formed between CS, a sulfated GAG, and AA. Comparisons with glycerin, a well-known moisturizing agent, confirmed that the CS-AA complex exhibits superior moisturizing effects in various aspects. These findings suggest that the CS-AA complex is a more effective ingredient than CS or AA alone in terms of efficacy.

Invasive plant species support each other's growth in low-nutrient conditions but compete when nutrients are abundant.

Globally, numerous ecosystems have been co-invaded by multiple exotic plant species that can have competitive or facilitative interactions with each other and with native plants. Invaded ecosystems often exhibit spatial heterogeneity in soil moisture and nutrient levels, with some habitats having more nutrient-rich and moist soils than others. The stress-gradient hypothesis predicts that plants are likely to engage in facilitative interactions when growing in stressful environments, such as nutrient-deficient or water-deficient soils. In contrast, when resources are abundant, competitive interactions between plants should prevail. The invasional meltdown hypothesis proposes that facilitative interactions between invasive species can enhance their establishment and amplify their ecological impact. Considering both hypotheses can offer insights into the complex interactions among invasive and native plants across environmental gradients. However, experimental tests of the effects of soil moisture and nutrient co-limitation on interactions between invasive and native plants at both interspecific and intraspecific levels in light of these hypotheses are lacking. We performed a greenhouse pot experiment in which we cultivated individual focal plants from five congeneric pairs of invasive and native species. Each focal plant was subjected to one of three levels of plant-plant interactions: (1) intraspecific, in which the focal plant was grown with another individual of the same species; (2) interspecific, involving a native and an invasive plant; and (3) interspecific, involving two native or invasive individuals. These plant-plant interaction treatments were fully crossed with two levels of water availability (drought vs. well-watered) and two levels of nutrient supply (low vs. high). Consistent with the stress-gradient and invasional meltdown hypotheses, our findings show that under low-nutrient conditions, the biomass production of invasive focal plants was facilitated by invasive interspecific neighbors. However, under high-nutrient conditions, the biomass production of invasive focal plants was suppressed by invasive interspecific neighbors. When competing with native interspecific neighbors, high-nutrient conditions similarly enhanced the biomass production of both invasive and native focal plants. Invasive and native focal plants were neither competitively suppressed nor facilitated by conspecific neighbors. Taken together, these results suggest that co-occurring invasive exotic plant species may facilitate each other in low-nutrient habitats but compete in high-nutrient habitats.

IoT-based smart irrigation management system to enhance agricultural water security using embedded systems, telemetry data, and cloud computing

Agriculture, the key sector for food production, faces challenges exacerbated by the growing global demand for food and the scarcity of water resources. Traditional irrigation methods often lead to inefficient use of water, resulting in wastage. Moreover, unpredictable weather conditions and the need to adopt sustainable farming practices are driving us to develop advanced irrigation systems. This paper proposed a smart irrigation management system using new technology like 1) embedded systems, 2) Internet of Things (IoT), 3) telemetry data, 4) cloud computing, communication protocol, and 5) sensors to collect and process real-time data of the smart agriculture. This new technology and intelligent algorithm are used in this paper to improve agricultural practices. The architecture of the proposed scheme comprises three layers: 1) IoT devices, 2) ThingsBoard cloud, and 3) the dashboard, each playing a pivotal role in ensuring seamless data flow, secure communication, and enhanced user interaction. The algorithm orchestrates system operations, incorporating sensor data reading, JavaScript Object Syntax (JSON) payload creation, and secure telemetry data transmission via Hypertext Transfer Protocol (HTTP) protocol to the ThingsBoard cloud. Pump activation decisions are governed by predefined thresholds, preventing an over-irrigation system. The evaluation of system performance involves deploying temperature, humidity, moisture, and water level sensors in a testing field. Further, before data is sent to the cloud, sensor values are calibrated using a functional map. Tests carried out with temperature, humidity, and water level sensors demonstrate the system's dynamic efficiency. By visualizing and analyzing environmental information via ThingsBoard, the results provide real-time data on environmental parameters of the system proposed, improving the efficiency of water use and the sustainability of farming practices. In addition, the system features e-mail notifications for alerts. The integration of e-mail notifications reinforces monitoring and management practices by alerting farm owners and users. This study showcases the efficacy of the proposed paper in enhancing sustainability, water usage, and supporting smart agriculture practices. Further, this paper integrates embedded systems, IoT, cloud computing, and advanced algorithms to optimize and enhance crop productivity and contribute to global food and water security.

Soil moisture inversion based on multiple drought indices and RBFNN: A case study of northern Hebei Province

Drought has a significant impact on crop growth and productivity, highlighting the critical need for precise and timely soil moisture estimation to mitigate agricultural losses. This study focuses on soil moisture retrieval in northern Hebei Province during July 2012, utilizing eight widely employed remote sensing drought indices derived from MODIS satellite data. These indices were cross-referenced with measured soil moisture levels for analysis. Based on their correlation coefficients, a composite remote sensing drought index set comprising six indices was identified. Furthermore, a radial basis function neural network (RBFNN) was employed to estimate soil relative humidity. The accuracy evaluation of the soil moisture estimation model, which integrates multiple remote sensing drought indices and the RBFNN, demonstrated clear superiority over models relying on single drought indices. The model achieved an average estimation accuracy of 87.54 % for soil relative humidity at a depth of 10 cm (SM10) and 87.36 % for a 20 cm depth (SM20). The root mean square errors (RMSE) for the test sets were 0.093 and 0.092, respectively. Validation results for July 2013 indicated that the inversion accurately reflected the actual soil moisture conditions, effectively capturing dynamic moisture changes. These results fully verify the reliability and practicability of the model. These findings introduce a novel approach to local agricultural soil moisture estimation, with significant implications for enhancing agricultural water resource management and decision-making processes.

Modeling the impact of long-term land use changes on deep soil hydrological processes in the Loess Plateau, China

Land use change can significantly affect soil hydrology in arid and semi-arid regions, making it crucial to understand the relationship between vegetation roots and soil moisture. Current models often fail to predict root growth and its impacts on water dynamics accurately. Our work presents a novel model that seamlessly integrates the Community Land Model (CLM) with the Soil & Water Assessment Tool (SWAT). Furthermore, it enhances the root module within the CLM, enabling more accurate simulations of dynamic root depth and distribution across varying tree ages. This improvement particularly considers the crucial processes of dormancy and plant maturity. Soil moisture and root patterns under apple trees of varying ages and in wheat fields on the Loess Plateau was analyzed. Our findings indicate that our dynamic root depth model outperforms traditional static models, and can accurately reflect soil moisture levels with high precision (R2 = 0.80–0.81; Nash-Sutcliffe efficiency (NSE) = 0.65–0.75). In contrast to methods that utilize fixed root depths, dynamic root simulation can provide new insights. As apple orchards mature, the roots of 22-year-old apple trees have been found to reach a depth of 21 m in the soil. Conversely, the maximum root depth of wheat is limited to 1.9 m. This latter finding aligns more closely with the measured root depths, highlighting the accuracy of dynamic simulations. This model reveals that older apple orchards show decreased soil moisture at greater depths (>20 m), contrasting with wheat fields that affect moisture mostly within the top 2 m. Our results underscore the crucial role of dynamic modeling in comprehending root-soil water interactions. Furthermore, they imply that extended orchard cultivation practices can lead to a substantial depletion of deep soil moisture. Specifically, over a period of 1 to 22 years, a water deficit of up to 85 mm yr−1 has been observed. For a 22-year-old forest, the D-D (dynamic distributions of coarse and fine roots) method calculates a significant cumulative deep Soil Water Storage loss. Over the course of 22 years, this loss amounts to 1664 mm, which is almost three times compare to the annual rainfall recorded. Such a large loss has the potential to significant impact on groundwater recharge. This highlights the need for careful consideration in future afforestation efforts to prevent increased soil aridity.

Production of Amylase by Solid State Fermentation Using Agricultural Waste

This study presents a comprehensive investigation into the production of amylase, a crucial enzyme with wide-ranging industrial applications, using locally sourced substrates from Kachchh, Gujarat. The research employed the Bacillus licheniformis strain and substrates such as coconut, rice husk, wheat bran, paddy straw, and maize straw. The study found paddy straw to be the most promising substrate for amylase production. The research also systematically optimized various process parameters for amylase production in Solid-State Fermentation (SSF) using the One Variable at a Time (OVAT) method. These parameters included incubation period, temperature, inoculum level, additional carbon sources, starch concentrations, additional nitrogen sources, initial pH, different mineral salt ions, initial moisture level, and surfactants. The results showed that the optimal conditions for maximum amylase yield were an incubation period of 48 hours, an incubation temperature of 35°C, an inoculum level of 10%, starch as the additional carbon source, a starch concentration of 2.5%, yeast extract as the additional nitrogen source, an initial pH of 7, NaCl as the mineral salt, an initial moisture level of 75%, and Tween 80 as the surfactant. This research provides a reliable and sustainable approach to enzyme production, offering valuable insights for the optimization of the solid-state fermentation process for maximum amylase production.

Study of moisture sorption thermodynamic in canola oilseed and drying energy requirement considerations

AbstractThe objective of this study is to derive the thermodynamic characteristics from sorption isotherm data for canola. The semi‐gravimetric method was utilized at three different temperatures (25, 40, and 55°C) and seven air relative humidity levels within the range of 11%–90%. The observed data indicated that the equilibrium moisture content of the sample decreased as the temperature increased. The “GAB and BET” models were applied to fit the empirical data, which demonstrated a Type III isotherm, and the monolayer water content was subsequently determined using these models. Thermodynamic properties such as “isosteric heat,” “net isosteric heat,” “differential entropy,” “net integral entropy,” and “net integral enthalpy” were determined from isothermal sorption curves. The results show that as moisture content increases, both the sorption isosteric heat and the differential entropy of sorption decrease. This indicates that at higher moisture levels, the energy required for additional moisture adsorption and the changes in entropy are reduced. Similarly, the net isosteric heat of sorption and the net integral enthalpy of sorption also decrease with increasing moisture content, consistent with the observed reductions in isosteric heat and differential entropy. The specific absorption surface area for each temperature was determined by calculating the monolayer moisture content using both the “GAB and BET models.” The net integral entropy had an increasing trend in the range of 4%–4.5% (db%), while it decreased in the range of 4.5%–6.8% of moisture content. In addition, the spreading pressure at three levels of temperature was reported. Finally, an empirical relation was employed to illustrate the cumulative energy requirement for drying versus moisture content. The results indicated that at low moisture content levels, the drying process required significantly higher energy.Practical applicationsMoisture sorption isotherms are essential for understanding the interaction between water and food ingredients. This knowledge is vital for improving food processing methods such as drying, mixing, cooling, and storage. In industry, isotherms can help determine the best drying method to maintain food quality, identify the optimal mixing conditions to ensure consistency, establish cooling protocols to prevent spoilage, and set storage guidelines to extend shelf life. In addition, understanding thermodynamic properties is crucial for regulating moisture absorption and release, achieving the desired food texture, managing surface characteristics, and calculating the energy needed for effective dehydration processes.

Mechanisms and advancements in microwave-enhanced CO2 mineralization of lightweight porous concrete

Lightweight porous concrete (LPC) typically exhibits low early strength, which can be addressed by enhancing early strength through carbon dioxide (CO2) mineralization curing while achieving permanent CO2 sequestration. The efficacy of this process relies on maintaining optimal moisture levels. This study investigates the influence of moisture content on the performance of solid waste-based lightweight porous concrete blocks (RSFAC) subjected to microwave heating pretreatment. Moisture levels were adjusted to 10%, 20%, 30%, and 40% (initial water-to-cement ratio of 45.4%) before undergoing mineralization curing within an environment set at a temperature of 20 °C, relative humidity 75%, and a CO2 concentration of 40% for a duration of 0.5 h. The impact of moisture content on RSFAC's compressive strength, carbon sequestration rate, mineralization products, and microstructure was evaluated. Results indicate that compared to conventional oven pretreatment methods requiring nearly 10 h, microwave pretreatment technology achieves equivalent water content within just 50 min, significantly reducing the time needed for mineralization pretreatment. RSFAC, when pretreated with microwave heating, outperforms untreated samples (Ref-0 and Ref-1) in terms of compressive strength and carbon sequestration efficiency. Optimal performance was observed at a moisture content level of 30%, with the highest recorded compressive strength achieved at 1, 3, and 7 d respectively. At the curing age of 7 d, the compressive strength reached up to 2.32 MPa while the carbon sequestration rate reached approximately 11.61%. Furthermore, aragonite and calcite (tetrahedral and hexahedral), without poorly crystalline spherulites, were identified as the main crystalline CaCO3 generated by RSFAC under this specific moisture content condition; pores larger than 50 nm served as primary reaction sites for RSFAC mineralization curing, whereas there was a significant increase in pore proportion with sizes less than 20 nm after filling with mineralization products.