Soil Moisture Levels Research Articles

Impact of Crop Residue, Nutrients, and Soil Moisture on Methane Emissions from Soil under Long-Term Conservation Tillage

Greenhouse gas emissions from agricultural production systems are a major area of concern in mitigating climate change. Therefore, a study was conducted to investigate the effects of crop residue, nutrient management, and soil moisture on methane (CH4) emissions from maize, rice, soybean, and wheat production systems. In this study, incubation experiments were conducted with four residue types (maize, rice, soybean, wheat), seven nutrient management treatments {N0P0K0 (no nutrients), N0PK, N100PK, N150PK, N100PK + manure@ 5 Mg ha−1, N100PK + biochar@ 5 Mg ha−1, N150PK+ biochar@ 5 Mg ha−1}, and two soil moisture levels (80% FC, and 60% FC). The results of this study indicated that interactive effects of residue type, nutrient management, and soil moisture significantly affected methane (CH4) fluxes. After 87 days of incubation, the treatment receiving rice residue with N100PK at 60% FC had the highest cumulative CH4 mitigation of −19.4 µg C kg−1 soil, and the highest emission of CH4 was observed in wheat residue application with N0PK at 80% FC (+12.93 µg C kg−1 soil). Nutrient management had mixed effects on CH4 emissions across residue and soil moisture levels in the following order: N150PK > N0PK > N150PK + biochar > N0P0K0 > N100PK + manure > N100PK + biochar > N100PK. Decreasing soil moisture from 80% FC to 60% FC reduced methane emissions across all residue types and nutrient treatments. Wheat and maize residues exhibited the highest carbon mineralization rates, followed by rice and soybean residues. Nutrient inputs generally decreased residue carbon mineralization. The regression analysis indicated that soil moisture and residue C mineralization were the two dominant predictor variables that estimated 31% of soil methane fluxes in Vertisols. The results of this study show the complexity of methane dynamics and emphasize the importance of integrated crop, nutrient, and soil moisture (irrigation) management strategies that need to be developed to minimize methane emissions from agricultural production systems to mitigate climate change.

Cloud-based system for monitoring event-based hydrological processes based on dense sensor network and NB-IoT connectivity

Hydrologists claim high-quality experimental data are required to improve the understanding of hydrological processes. Though accurate devices for measuring hydrological processes are available, the on-site deployment and operation of effective monitoring networks face many relevant issues caused by the peculiar characteristics of hydrological systems. In this manuscript, we present a self-developed system for monitoring events-based hydrological processes comprising a dense network with both soil moisture and water level gauges connected by NB-IoT technology integrated into a cloud system for near real-time gathering of information. We designed, built and calibrated the sensors and integrated them into a cloud system. We deployed them in two monitoring networks and gathered the data from several experimental runs (battery lifecycles). Results showed the suitability of the sensors and the network to properly monitor the processes solving the initial relevant issues mainly derived from connectivity issues and battery duration.

Enhancing Soil Conditions and Maize Yield Efficiency through Rational Conservation Tillage in Aeolian Semi-Arid Regions: A TOPSIS Analysis

Conservation tillage technology possesses substantial potential to enhance agricultural production efficiency and tackle issues such as wind erosion and land degradation in semi-arid regions. The integration of no-tillage and straw mulching technologies in the conventional aeolian semi-arid agricultural zones of western Liaoning, China, has led to notable improvements in crop yield and soil quality. However, a comprehensive assessment of the mechanisms and kinetics involved in soil nutrient variations is yet to be conducted. During a two-year study period, we assessed four tillage systems in the aeolian semi-arid regions of Northern China: no-tillage with full straw mulching (NTFS), no-tillage with half straw mulching (NTHS), no-tillage without straw mulching (NT), and conventional tillage (CT). The investigation focused on examining nutrient conditions, enhancing photosynthetic activity, and increasing maize yield while improving water use efficiency (WUE). Our findings emphasize the beneficial impact of combining no-tillage and straw mulching on enhancing soil water retention, resulting in a notable rise in soil moisture levels during the crucial growth phases of maize. This approach also positively influenced soil nutrient levels, particularly in the 0–20 cm layer, fostering an environment conducive to maize cultivation. In terms of ecological benefits, no-tillage with straw mulching curtailed soil sediment transport and wind erosion, notably at 30–40 cm heights, aiding in the ecological protection of the region. The yield and WUE were substantially higher under NTFS and NTHS than under CT, with NTHS demonstrating the most significant gains in yield (14.5% to 16.6%) and WUE (18.3% to 21.7%) throughout the study period. A TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) analysis confirmed NTHS as the optimal treatment, achieving the highest scores for soil water, nutrient availability, wind erosion control, maize photosynthesis, yield, and WUE, thus emerging as the most effective conservation tillage strategy for sustainable agriculture in aeolian semi-arid regions.

A Smart Farm Monitoring and Control System using Wireless Sensor Network

Agriculture is one of the most important sector that accounts for economic growth in the developing countries of the world. Many developing countries are now focusing on agricultural development, yet the sector is not without challenges including climate changes, drought, flooding to mention but a few. These result in poor yield of agricultural products. In this work, we l developed a robust smart system to enhance both irrigation and flooding monitoring and control, leveraging on Wireless Sensor Networks (WSNs) to boost agricultural production. In the system’s implementation, Arduino based instrumentation, integrated with temperature, soil moisture and water level sensor shall be adopted to monitor the agricultural environment, while reporting the status wirelessly through the Radio Frequency (RF) modules to the base station. The base station will evaluate the received data and either activates or deactivates the irrigation or drainage pump using specified threshold values. The remote reporting the state of farm shall be done by deploying a 900Mhz transmitter interfaced with the system’s controller. This work shall be validated using an experimental test bed, which shall be used for field experiments, data collection and evaluation. The result of the work shall highlight the potentials of WSN technology in monitoring and control technology of both irrigation and flooding within the farm and in turn boost productivity

Investigating FWI Moisture Codes in Relation to Satellite-Derived Soil Moisture Data across Varied Resolutions

In the Mediterranean region, particularly in Antalya, southern Türkiye, rising forest fire risks due to climate change threaten ecosystems, property, and lives. Reduced soil moisture during the growing season is a key factor increasing fire risk by stressing plants and lowering fuel moisture content. This study assessed soil moisture and fuel moisture content (FMC) in ten fires (2019–2021) affecting over 50 hectares. The Fire Weather Index (FWI) and its components (FFMC, DMC, DC) were calculated using data from the General Directorate of Meteorology, EFFIS (8 km), and ERA5 (≈28 km) satellite sources. Relationships between FMCs, satellite-based soil moisture datasets (SMAP, SMOS), and land surface temperature (LST) data (MODIS, Landsat 8) were analyzed. Strong correlations were found between FWI codes and satellite soil moisture, particularly with SMAP. Positive correlations were observed between LST and FWIs, while negative correlations were evident with soil moisture. Statistical models integrating in situ soil moisture and EFFIS FWI (R: −0.86, −0.84, −0.83 for FFMC, DMC, DC) predicted soil moisture levels during extended fire events effectively, with model accuracy assessed through RMSE (0.60–3.64%). The SMAP surface (0–5 cm) dataset yielded a lower RMSE of 0.60–2.08%, aligning with its higher correlation. This study underlines the critical role of soil moisture in comprehensive fire risk assessments and highlights the necessity of incorporating modeled soil moisture data in fire management strategies, particularly in regions lacking comprehensive in situ monitoring.

Soil moisture prediction based on integrating the CEEMDAN decomposition technique with LSTM in the proximity of Prosopis Juliflora

Human-made adaptations in plant diversity, particularly the introduction of alien invasive plant species, can result in problems such as depletion of groundwater, extraction of natural water resources, and modifications in the local biodiversity. In addition to regulating ecosystems and accurate farming, precise forecasting of soil moisture in the vicinity of these exotic plant species can greatly assist in various other agricultural applications. In this study, we present an improved approach for predicting soil moisture levels at different depths in the vicinity of Prosopis Juliflora (IAPS) by integrating long short-term memory (LSTM) and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). CEEMDAN is employed as a preprocessing and denoising technique to decompose the initial soil moisture data into several intrinsic mode functions (IMF). After that, LSTM is used to predict all the IMF (Intrinsic Mode Function) parts from the CEEMDAN process. The individual prediction outcomes of each IMF are combined to obtain the final prediction results. Soil moisture measurements were collected continuously in the Coimbatore area of Tamil Nadu at multi-layer soil depths of 10 cm, 30 cm, 60 cm, and 100 cm. The provided data was utilized for training models and achieving optimal accuracy in forecasting soil moisture levels one month prior. The proposed CEEMDAN-LSTM model achieved the highest R2, ranging from 0.985 to 0.995 at various depths when compared to RNN, LSTM, EMD-LSTM, EEMD-LSTM models, and existing literature models. The results show that the proposed method works well at predicting soil moisture at different depths, which can help improve the planting pattern and schedule of native species to increase the area’s green cover.

Modeling of Automatic Plant Watering System using ATMega328 MCU Arduino Microcontroller Board: Mobile Application Result Display

Some gardeners still struggle to manually water their plants, preventing them from drying out and dying. Due to the inadequate water supply, plants do not grow and die as a result of soil drying. Watering plants is a labor-intensive job in day-to- day greenhouses. Knowing when and how-to water is an important aspect of watering the soil. The gardener needs an automatic plant watering system. This study aims to design and develop an automatic plant watering system using an Arduino device to provide an efficient and effective plant watering system that will help farmers increase plant production. To ease some problems for gardeners and farmers in growing their plants, the Arduino device ATmega328 Microcontroller detects and senses the soil conditions and moisture level and supplies the water plant if required. The microcontroller coded the water of plants when detecting the dryness of the soil. A qualitative and quantitative data analysis using a Chi-squared test to determine the deviation in one or more categories resulted in a 0.05% mean of 3.09 as perceived by 100 gardeners and farmers as a “significant” hypothesis. The V-model method iterates the agility process of the system designed in terms of phases. Five (5) IT experts validated and evaluated the system test capability and reliability result the mean of 2.96, interpreted as “Average”. The findings of series hardware system calibration test during the incubation and evaluation, the coefficient of stability (0-1) for the qualitative data resulted 85%, interpret as “Good Reliability” in functionalities, IPO, and detection. It concludes that research studies on the technology, particularly in Automatic Plant Watering System using Arduino Detection: Web Application meets the standard and significantly perceived the efficiency and effectiveness acquired an overall mean of 5.0, interpreted as “Very Satisfactory.” Recommending for the implementation

Development Of Smart Greenhouse Farming Based On Internet Of Things

This project aim is to develop smart greenhouse farming based on IoT system for monitoring the quality for the plant which use sensor and Node-RED as a website for monitoring. The monitoring system using microcontroller such as humidity sensor,soil moisture to control the condition of soil moisture level. The phenomenon of greenhouse plants is particularly significant since it has the potential to help improve the economy and cut manufacturing costs. Given the agricultural industry's lack of human resources, one of the cultivation techniques that have to be improved is hydroponic plants, also known as greenhouse plants. Hydroponic plant technology can be used in conjunction with hydroponic plants. Tropical greenhouses have various advantages in plant cultivation as well as practical manufacturing potential.This technology can be utilized to apply according to plant demands to maximize yield in agriculture. The project employed is a tool for monitoring soil moisture that is based on a prototype using an online platform for monitoring humidity and plant quality, and it can assist farmers in decreasing the budget for production.

AN AUTOMATED CROP MONITORING AND IRRIGATION SYSTEM WITH PREDICTIVE ANALYSIS

Background: The rising global population and decreasing agricultural land challenge the food supply, worsened by land degradation and water scarcity. Using IoT solutions, precision farming offers promise for addressing these issues by enhancing productivity, profitability, and environmental sustainability. The paper proposes an IoT-based automated monitoring and irrigation system with predictive analysis as a solution. Aims: To develop an IoT-based irrigation model with cloud computing, a mobile app for remote monitoring and irrigation control, and a predictive analysis method for forecasting weather conditions. Methods: IoT sensors are connected to a node MCU, and they monitor real-time temperature, humidity, and soil moisture. A water pump adjusts irrigation accordingly. Data is sent to ThingSpeak for visualization and Firebase for storage and is accessible via a mobile app. Predictive analysis combines sensor and historical weather data using the Random Forest algorithm to optimize irrigation. This determines the ideal irrigation frequency, duration, and timing based on predicted temperature and soil moisture levels, ensuring optimal soil moisture for plant growth. Results: The system exhibited encouraging outcomes. It displayed transformative potential by harnessing IoT, cloud computing, and predictive analytics. Offering precise soil moisture monitoring, instant data access, and tailored advice, the system can elevate crop management, streamline water consumption, and boost productivity among Indian farmers. Discussion: The automated crop monitoring and irrigation system has transformative potential in farming. Some future scope and enhancements may include broadening crop compatibility, integrating satellite data, enhancing pest monitoring, improving connectivity, enriching the mobile app, scaling up through partnerships, and refining the predictive models. Conclusions: The IoT-driven crop monitoring system revolutionizes Indian agriculture by optimizing irrigation and integrating weather forecasts. With mobile app enhancements and future improvements, it promises sustainable farming and empowerment for farmers.

Rhizosphere hydraulic regulation in maize: tailoring rhizosphere properties to varying soil textures and moistures

Abstract Aims Motivated by recent studies highlighting the active role of plants in influencing the hydraulic properties of the rhizosphere through mucilage exudation, this study explored the effect of plant rhizosphere regulation on the emergent hydraulic properties of the rhizosphere under varying soil texture and moisture levels. Methods Maize (Zea mays) plants were cultivated in sand and loam soils exposed to varying moisture levels. Neutron radiography was employed to quantify the profile of water content ($${\theta }_{r})$$ θ r ) around lateral and crown roots. The $${\theta }_{r}$$ θ r were used to derive rhizosphere hydraulic indices such as rhizosphere extension ($${d}_{rh}$$ d rh ), water content at the root-soil interface ($${\theta }_{rs}$$ θ rs ), and water content in the rhizosphere ($${\theta }_{rh}$$ θ rh ). The latter two attributes were normalized by the respective bulk soil water contents ($${\theta }_{rs}^{*}$$ θ rs ∗ , $${\theta }_{rh}^{*}$$ θ rh ∗ ). Results Results showed a higher $${\theta }_{rs}^{*}$$ θ rs ∗ , $${\theta }_{rh}^{*}$$ θ rh ∗ and $${d}_{rh}$$ d rh in coarse-textured soil versus fine-textured soil. This indicates a more pronounced rhizosphere development in sand compared to loam, possibly via mucilage exudation to maintain better root-soil contact. In contrast, soil water content did not impact the rhizosphere properties of crown roots but impacted the rhizosphere properties of lateral roots. The derived rhizosphere water retention curves revealed a higher water-holding capacity of the rhizosphere in both soils compared to bulk soil. Conclusions Our study underscores that soil-grown maize plants dynamically adjust the hydraulic properties of their rhizosphere in response to external factors, primarily aiming to optimize root-soil contact. That leads to a more pronounced rhizosphere in coarser textures, interacting with the soil moisture.

Experimental study to understand the effects of deficit irrigation in maize

ABSTRACT Given the challenges posed by climate change and the scarcity of water, it is essential to adopt sustainable irrigation practices that do not compromise crop yields. Research studies are crucial to determine the optimal deficit soil moisture levels to be maintained for cultivation in different soil types. This study examines the response of maize grown on loamy sand soil under different water deficit moisture contents by monitoring the variation of the crop growth in terms of the leaf area index, biomass weight, root depth and yield. The daily soil moisture is measured to understand the actual evapotranspiration from the study plots. From the experiments, the optimal moisture content identified is 13%, and the plot maintained at this moisture content has shown the highest evapotranspiration, yield and biomass. The yield response factor of the maize grown in water deficit conditions is also observed to be very close to the value reported by FAO. As expected, the yield response factor is found to be sensitive to water stress. The deficit irrigation at the optimal moisture content of 13% could be recommended for maize cultivation in loamy sand soil in North Indian climatic conditions. Such considerations will be vital for achieving sustainable irrigation goals.

Peculiarities of water exchange of Quercus robur and Acer campestre in an oak-field maple forest

We studied the physiological and biochemical parameters of water exchange of two broadleaf forest species, Quercus robur L. and Acer campestre L., which grow under different levels of water supply. The study was conducted in the lower third of the northern slope and the middle third of the southern exposure slope in the “Viyskovyi” ravine. It was established that the content of total water in the leaves of Q. robur is higher than that of A. campestre under both mesophilic and xerophilic conditions. In A. campestre, the gradual dehydration of leaves during the growing season is more pronounced. The water-holding capacity of the leaves increases in both species, especially in July and August on the southern exposure slope, which is consistent with changes in the content of hydrophilic colloids. This can be considered as an adaptation of plants against rigorous hydrothermal conditions. The leaves of A. campestre retain water better and are characterized by a greater number of hydrophilic colloids compared to the leaves of Q. robur at different levels of water supply. Under xerophilic growth conditions, the suction power of the cellular junctions of leaves is more significant than under mesophilic conditions. At both experimental sites, this indicator is always higher in case of A. campestre, while the difference is greater only under xerophilic conditions. The increase in suction force in leaf cells occurs in parallel with the increase in soil dryness. The leaves of A. campestre have a greater water deficit and suction power, better water-holding capacity due to a greater content of hydrophilic colloids, and a lower intensity of transpiration. The leaves of Q. robur have a lower water deficit and a lower water-holding capacity, which is based on the ability to resist the lack of moisture by the development of a deep root system that allows water to be absorbed from its deep horizons. The obtained data make it possible to clarify the peculiarities of the water regime of tree species during their simultaneous growth in forest phytocoenoses and adaptation to different levels of soil moisture.

Sex-specific phosphorus (P)-use and -acquisition in dioecious Populus euphratica as affected by soil moisture levels

Abstract Dioecious plants show sexual dimorphism in their phosphorus (P) availability responses. However, the understanding of sex-specific strategies for P utilization and acquisition under varying soil moisture levels remains unclear. Here, we assessed a range of root functional traits, soil P properties, total foliar P concentration ([P]), and leaf chemical P fractions – inorganic P ([Pi]), metabolite P ([PM]), lipid P ([PL]), nucleic acid P ([PN]), and residual P ([PR]) – as well as other leaf functional traits in female and male trees under different soil moisture levels (25% for high and 7% for low). Our results showed that females had larger specific root length (SRL) under well-watered conditions, resulting in greater root foraging capacity. This led to a 36.33% decrease in soil active inorganic P in the rhizosphere and a 66.86% increase in total foliar [P], along with all five foliar chemical P fractions ([Pi], [PM], [PL], [PN], and [PR]) compared to males. However, males exhibited significantly higher photosynthetic P utilization efficiency (PPUE) than females. Especially under low soil moisture levels, males exhibited a significant reduction in soil active organic P, coupled with a large increase in the exudation of soil phosphatases and carboxylates. Furthermore, the proportion of metabolite P in total foliar [P] was 41.96% higher in males than in females. Mantel and Spearman correlation analyses revealed distinct coordination and trade-offs between foliar P fraction allocation and below-ground P acquisition strategies between the two sexes. Leveraging these sex-specific strategies could enhance the resilience of dioecious populations in forest plantations facing climate-induced variability.

Growth, yield, and fiber quality of cotton plants under drought stress are positively affected by seed priming with potassium nitrate

Growth, productivity, and fiber quality of cotton (Gossypium hirsutum L.), a vital fiber-producing cash crop, are severely affected under drought conditions. Seed priming has a proven role in enhancing crop tolerance to abiotic stress, including drought. The objective of this study was to evaluate the effects of seed priming with KNO3 on cotton productivity and fiber quality under drought stress. A germination experiment was established under laboratory conditions with five treatments of seed priming (non-primed or control treatment, hydropriming, and priming with 2.5, 5.0, and 7.5 g KNO3 L−1). Another experiment under polyhouse conditions based on the same seed priming treatments was conducted under three levels of soil water contents (field capacity [FC] 100%: FC100, 75%: FC75, and 50%: FC50). The results obtained showed that there was a clear reduction in the different parameters tested at FC50 in comparison to FC100 (39–54%, 32–44%, 6–12%, and 7–12% reduction for boll number per plant, seed cotton yield, fiber strength, and leaf relative water content, respectively, across all priming treatments). Seed priming with KNO3 at 5 g L−1 effectively alleviated the detrimental effects originated from drought stress and caused 61–73%, 13–16%, and 16–23% increase in seed cotton yield, fiber length, and fiber strength, respectively, across soil moisture levels when compared with the control treatment. The same KNO3 dose caused an increase of 78% in water productivity in comparison to the control plants at FC50. Priming cotton seeds with 5 g KNO3 L−1 holds promise to obtain synchronized germination, enhance fiber quality, and increase yield, especially under water-limited conditions, thereby promoting economic viability and environmental sustainability. Additionally, this practice enhances water productivity, leading to significant water savings and reduced irrigation costs for cotton farmers. This method could be used as potential technology in advancing sustainable agriculture in water-constraint conditions.

Impact of alternate partial root-zone irrigation on the rhizosphere microbiota of alfalfa plants inoculated with rhizobia.

Water is an important constraint on alfalfa (Medicago sativa) production in arid and semiarid areas, and alternate irrigation in root areas has water-saving potential for alfalfa production. To investigate the impact of alternate partial root-zone irrigation (APRI) on the rhizosphere soil microorganisms of alfalfa, this study subjected alfalfa plants to different irrigation methods and irrigation levels. The growth status and rhizosphere soil microbial community diversity of alfalfa plants under alternate root-zone watering treatment were analyzed through laboratory experiments and high-throughput sequencing. The results showed that at soil moisture levels of 80% field moisture capacity (FMC) and 60% FMC, APRI had no significant impact on the biomass or nodule number of alfalfa. However, 40% FMC significantly reduced the individual plant dry weight, chlorophyll content, and nodule number of the alfalfa plants. APRI increased the relative abundance of Actinomycetes in the alfalfa rhizosphere soil. Moreover, at 60% FMC, the MBC and MBN of rhizosphere, relative abundance of Actinobacteria and unclassified K fungi and Chao 1 index of bacteria significantly increased under APRI treatment. While relative abundance of Ascomycetes and Proteobacteria in the alfalfa rhizosphere significantly reduced under 60% FMC + APRI treatment. In summary, under the same irrigation conditions, APRI did not significantly affect the growth of alfalfa in the short term. And 60%FMC + APRI treatment did significantly affect the groups, structure and diversity of the rhizosphere soil microbial communities.

Analysis of Flash Drought and Its Impact on Forest Normalized Difference Vegetation Index (NDVI) in Northeast China from 2000 to 2020

Flash drought is characterized by rapid onset and short-duration drought conditions caused by a combination of factors, including high evaporation, high temperature, and prolonged periods of little to no precipitation, leading to a sudden and severe decrease in soil moisture levels. In comparison to conventional drought, it is more susceptible to the effects of global warming and has the potential to become a common drought phenomenon in the coming years, necessitating further research. In this paper, we focused on flash drought events, specifically in forest parts of northeastern China that are included within the Greater Khingan Mountains (GKM), Lesser Khingan Mountains (LKM), and Changbai Mountains (CM), using daily soil moisture data as well as SPOT- VEGETATION NDVI satellite data from 2000 to 2020 and determined their impact on the forest NDVI. Our major findings are as follows. (1) The forest within GKM had the maximum area being affected by flash drought events. (2) The frequency ranged from 1 to 2 times, whereas the total duration varied between 20 and 55 days over the study area in a 21-year period. (3) Flash drought was most common in the plant-growing seasons. (4) The flash drought events had a negative influence on the forest NDVI. Our study contributes to a deeper understanding of the flash drought dynamics in forest areas of northeast China for flash drought monitoring, prediction, and management strategies in this region.

Anthropogenic shrub encroachment has accelerated the degradation of desert steppe soil over the past four decades

Anthropogenic and natural shrub encroachment have similar ecological consequences on native grassland ecosystems. In fact, there is an accelerating trend toward anthropogenic shrub encroachment, as opposed to the century-long process of natural shrub encroachment. However, the soil quality during the transition of anthropogenic shrub encroachment into grasslands remains insufficiently understood. Here, we used a soil quality assessment method that utilized three datasets and two scoring methods to evaluate changes in soil quality during the anthropogenic transition from temperate desert grassland to shrubland. Our findings demonstrated that the soil quality index decreased with increasing shrub cover, from 0.49 in the desert grassland to 0.31 in the shrubland. Our final results revealed a gradual and significant decline of 36.73 % in soil quality during the transition from desert grassland to shrubland. Reduced soil moisture levels, nutrient availability, and microbial activity characterized this decline. Nearly four decades of anthropogenic shrub encroachment have exacerbated soil drought conditions while leading to a decrease in perennial herbaceous plants and an increase in bare ground cover; these factors can explain the observed decline in soil quality. These findings emphasize the importance of considering soil moisture availability and potential thresholds when implementing revegetation strategies in arid and semiarid regions.

Exploring the variability of root system architecture under drought stress in heat-tolerant spring-wheat lines

Background and aimsGlobal wheat production is under threat due to climate change, specifically heat and drought and their combination. This study aims to address the root trait responses of heat-tolerant wheat genotypes to drought.MethodsThe variability in root traits of CIMMYT wheat lines, which were previously developed for heat stress tolerance (HTWL), was evaluated alongside 10 Pakistani-approved varieties under three cultivation conditions and soil moisture levels.ResultsOur findings revealed that the plasticity of the wheat root system is highly pronounced, with rhizosphere conditions exerting a more substantial influence (5–49%) than the genotypic response (1–14%). Furthermore, in the hydroponic and pot system, we noted higher maximum-root length (1.5–1.8 fold) and root-to-shoot ratio (3.4–10.6 fold) as compared to field condition, while the root biomass was substantially higher in the field trial (3-57 fold). Nonetheless, persistent drought conditions exerted contrasting impact with reduction in most of the traits except specific root length and harvest index which were increased under drought.ConclusionsThe variation in root traits against drought indicates the potential for the development of improved genotypes that can withstand multiple stresses. Furthermore, it is crucial to consider rhizosphere conditions when selecting genotypes, as the plasticity of wheat roots may lead to misinterpretations if rhizosphere conditions are disregarded. Root dry weight and root-to-shoot ratio are more stable traits as compared to maximum root length and specific root length. It is recommended to evaluate a broader range of rhizosphere conditions to select tolerant genotypes.

Effects of soil moisture on soil viral reproductive strategies in an agricultural soil

AbstractGenomic evidence suggests that lysogenic viruses significantly influence the evolution of their host communities and soil microbial ecology and functionality. However, the response of soil viral reproductive strategies (VRS) to environmental factors, in particular soil water stress, remains poorly understood. We investigated this by employing a laboratory microcosm incubation system with different soil moisture levels (30%, 60% and 90% field capacity). Our study focused on soil biochemical properties, bacterial and viral populations, lysogenic fractions and virus/bacteria ratio (VBR). The results showed that soil moisture significantly affected bacterial and viral counts, lysogenic fractions and VBR (p < 0.01), with bacterial counts increasing and viral counts decreasing with increasing soil moisture. The lysogenic fraction peaked at low moisture, suggesting a shift in viral strategy under hydration stress, which may affect virus‐bacteria interactions and nutrient dynamics, enhancing host adaptability. Analyses using correlation, random forest and structural equation modelling identified soil moisture as the dominant factor shaping VRS by altering nutrient availability and host population. These findings provide a new insight into microbial regulation of feedback to environmental change from the life history strategies of soil viruses.

Smart irrigation system using node microcontroller unit ESP8266 and Ubidots cloud platform

The agricultural irrigation system is extremely important. For optimal harvest yields, farmers must manage rice plant quality by monitoring water, soil, and temperature on agricultural fields. If market demand rises, traditional rice field irrigation in Indonesia will make things harder for farmers. This modern era requires a system that lets farmers monitor and regulate agricultural fields anywhere, anytime. We need a solution that can control the irrigation system remotely using an internet of things (IoT) device and a smartphone. This study employed the Ubidots IoT cloud platform. In addition, the study uses soil moisture and temperature sensors to monitor conditions in agricultural regions, while pumps function as irrigation systems. The test results indicate the proper design of the system. Each trial collected data. The pump will turn on and off automatically based on soil moisture criteria, with the pump active while the soil moisture is less than 20% and deactivated when the soil moisture exceeds 20%. In simulation mode, the pump operates for an average of 0–5 seconds of watering. The monitoring system shows the current soil temperature and moisture levels. Temperature sensors respond in 1-3 seconds, whereas soil moisture sensors respond in 0–4 seconds.