SoilNutri: A Passive Metasurface-based, Low-cost System for Soil Moisture and Nitrogen Monitoring

The design of a Soil moisture monitoring system with the Smart Farming Concept for IoT-based Chili Cultivation is a system design tool used to monitor Soil moisture in cayenne pepper cultivation. The monitoring system can determine Soil moisture conditions by monitoring Soil moisture information using IoT-based Microcontroller technology so that farmers can monitor the growth of cayenne pepper plants without the need to come to the Greenhouse location to monitor. The design of this monitoring systemuses a Soil moisture sensor. Where the Soil moisture sensor works is to monitor in real-time the state of Soil moisture in a Greenhouse. The classification of Soil moisture on soil quality so that the growth of cayenne pepper plants does not experience problems for dry soil and 61% to 100% for wet soil. The optimal Soil moisture content for cayenne pepper plant growth is in the range of 60% to 70% moist. The program flow to create a Soil moisture monitoring system for IoT-based chili cultivation goes through 3 main stages, namely the initialization, setup, and loop stages so that it can produce a display on the Soil moisture monitoring system application in real time. The results of the design of a Soil moisture monitoring tool based on trials show the optimal Soil moisture value at an average humidity of 63.5% and automatic watering will be carried out by the system when the Soil moisture level decreases from 60% so that it can be concluded that the IoT- based Soil moisture monitoring system can work well.

Herb layer contributes significantly to the species diversity of forest ecosystem and reacts rapidly to changes in the soil characteristics. Composition, structural parameters and productivity of the herbaceous vegetation of five forest stands in the Central Himalaya of India, situated at Nainital district of Uttarakhand, India were investigated. At each site, 1 ha plot was established and herbaceous vegetation was analysed by placing 30 random quadrats of 50x50 cm at each site. To determine the soil moisture and soil nitrogen, soil samples were collected from each site and analysed in the laboratory. The aim of the present investigation was to comprehend the dependency among herb density, diversity, biomass, soil moisture and total soil nitrogen. Results showed that chosen sites contrasted in terms of soil moisture, total soil nitrogen, herbaceous diversity, and biomass. In this study, 70 genera, 73 species from 31 families of herbs were recorded. Total number of herbaceous species recorded in the stands varied from 28 (PI and HG) to 50 (TA).Total individuals of all herb species were 110, 114,141, 164 and 188 plants m-2 in RU, HG, PI, BP and TA forest stands, respectively. Index of similarity and species turnover ranged from 34.48 to 62.50 and 0.31 to 0.54 respectively. Herb density, diversity and biomass showed positive correlation with soil moisture and soil nitrogen. These findings suggested that the soil moisture and nitrogen enhanced the herbaceous diversity by ameliorating soil conditions. This study also demonstrated a straight relationship between herbaceous diversity and biomass indicating the significance of species diversity for ample generation of biomass in forest ecosystem.

The influence of vegetation cover on the spectroscopic estimation of soil properties

Our understanding of positive and negative plant interactions is primarily based on vascular plants, as is the prediction that facilitative effects dominate in harsh environments. It remains unclear whether this understanding is also applicable to moss-vascular plant interactions, which are likely to be influential in low-temperature environments with extensive moss ground cover such as boreal forest and arctic tundra. In a field experiment in high-arctic tundra, we investigated positive and negative impacts of the moss layer on vascular plants. Ramets of the shrub Salix polaris, herb Bistorta vivipara, grass Alopecurus borealis and rush Luzula confusa were transplanted into plots manipulated to contain bare soil, shallow moss (3cm) and deep moss (6cm) and harvested after three growing seasons. The moss layer had both positive and negative impacts upon vascular plant growth, the relative extent of which varied among vascular plant species. Deep moss cover reduced soil temperature and nitrogen availability, and this was reflected in reduced graminoid productivity. Shrub and herb biomass were greatest in shallow moss, where soil moisture also appeared to be highest. The relative importance of the mechanisms by which moss may influence vascular plants, through effects on soil temperature, moisture and nitrogen availability, was investigated in a phytotron growth experiment. Soil temperature, and not nutrient availability, determined Alopecurus growth, whereas Salix only responded to increased temperature if soil nitrogen was also increased. We propose a conceptual model showing the relative importance of positive and negative influences of the moss mat on vascular plants along a gradient of moss depth and illustrate species-specific outcomes. Our findings suggest that, through their strong influence on the soil environment, mat-forming mosses structure the composition of vascular plant communities. Thus, for plant interaction theory to be widely applicable to extreme environments such as the Arctic, growth forms other than vascular plants should be considered.

Old-fields (44, aged 1–15 years, from Czech Republic and Hungary) were sorted according to their soil moisture and nitrogen content into wet, mesic or dry, and nutrient poor, moderate or nutrient rich categories, resulting in 8 combinations (dry and nutrient rich fields were not present). The vegetation of old fields was sampled using phytosociological releves. The changes in species cover data and importance of species trait categories were analysed in relation to three environmental factors, i.e., time since abandonment, soil moisture and total soil nitrogen using ordination, generalized linear models (GLM) and regression tree methods. Successional seres in the first 15 years after field abandonment were divergent. Species diversity significantly decreased with increasing site moisture and was highest in sites with moderate nitrogen content; while the relationship with time was not significant. Raunkiaer life forms and life strategies (sensu Grime) were generally the most predictive species traits considering species occurrence during the course of succession, the type of dispersal considering the different moisture status, and the ability to lateral spread considering the nutrient status of the old-fields. Most trends appeared in both parametric GLM and non-parametric regression tree analyses, several only in GLM. We consider regression trees to be a more convenient tool than GLM in cases such as ours with a rather small number of samples and robust character of data. Another advantage is that a hierarchy of species traits is taken into account. Thus, the occurrence of a species along an environmental gradient can be predicted if the species possesses a certain combination of traits.

The soil microbial community is not only driven by plant composition but is also disturbed by the soil environment. Intercropping affects the soil microenvironment through plant interaction, but the understanding of the relationship between soil microbial community and environment in intercropping is still weak. In this study, milk vetch intercropping with rapeseed was used to explore the interaction between soil microorganisms and environment. The results showed that the soil moisture content of intercropping was higher than that of monoculture during the reproductive period of rapeseed growth (flowering and podding stages). The contents of soil total nitrogen and alkali-hydrolyzable nitrogen in intercropping were higher than those in monoculture. The dominant soil microbial communities in intercropping were the same as in monoculture and included Chloroflexi, Proteobacteria, Actinobacteria, Acidobacteria, Firmicutes, Gemmatimonates and Bacteroidetes. However, intercropping increased the Shannon index and decreased the Simpson’s index of the soil microbial community. The changes in the soil microbial community were mainly related to soil temperature, moisture, pH, total nitrogen, alkali-hydrolyzable nitrogen and available potassium. Moreover, there was a negative correlation between soil moisture and microorganisms and a positive correlation between nitrogen and microorganisms. Thus, milk vetch–rapeseed intercropping could not only improve soil nitrogen content, but also change soil microbial community diversity. In dryland red soil, the effect of milk vetch–rapeseed intercropping on soil moisture and nitrogen was the key factor contributing to the changes in the soil microbial community. When planting rapeseed in the future, we could consider the application of intercropping with milk vetch, which can contribute to regulating the soil nitrogen pool and improving microbial diversity.

The relentless growth of the global population, compounded by the adversities posed by climate change, has triggered an unprecedented demand for sustenance. This burgeoning need places an imperative on the agricultural sector to adopt practices that are both sustainable and efficient. Traditional farming methods, marked by inefficiencies and environmental ramifications, prove inadequate in meeting this escalating demand. Inresponse, precision farming emerges as a transformative solution, centering on data-driven decision-making and the optimization of resources. This groundbreaking whitepaper meticulously navigates the intersection of precision farming and augmented reality (AR) technology, showcasing a paradigm shift in agriculturalpractices. AR, a revolutionary tool overlaying digital insights onto the physical world,empowers farmers with real-time information on soil conditions, crop health, and moisture Levels. Harnessing AR's capabilities, farmers gain unprecedented control over irrigation and fertilizer applications, minimizing waste while maximizing yields. The paper dives deep into the modern challenges of agriculture, articulates the substantial benefits of precision farming, and underscores AR's transformative potential in surmounting these challenges. Precision farming, elucidated as the fusion of technology to gather and analyze data on parameters like soil moisture and weather conditions, is detailed with utmost clarity. In contrast to the antiquated one-size-fits-all approach of traditional farming, precision farming, facilitated by real-time data and analytics, enables bespoke crop management. This optimization minimizes resource usage, curtails waste, and augments productivity to Unparalleled levels. The whitepaper seamlessly dissects precision farming into two indispensable components:data capture and data visualization. The Internet of Things (IoT) emerges as the linchpin for data capture, employing sensors that collect real-time data transmitted to a centralized platform for astute analysis. Concurrently, AR, the second component, emerges as the pivotal interface, empowering farmers to seamlessly visualize and interpret data for judicious decision-making. In the current corporate panorama, stalwarts like Augmenta and Grow Glide, coupled with potential contributors like Rams Creative Technologies Pvt. Ltd. and emerging entities likeInfosys Limited, stand as vanguards propelling technological advancements to bolster precision farming. However, the potential for widespread AR adoption in precision farming faces impediments. Concerns regarding the safety and health risks associated with AR hardware, coupled with formidable development costs, and a prevailing lack of awareness pose substantial challenges.

Soil moisture is fundamentally important for drought monitoring, hydrologic forecasting, weather and climate predictions, agriculture and forest management, and many other applications. Advancements in satellite observations and land data assimilation systems (LDAS) have created new opportunities for field-scale soil moisture monitoring locally and across the globe. Using satellite and LDAS data to estimate soil moisture across multiple scales would benefit many applications and scientific research, especially for locations where ground soil moisture observations are not available. In this study, we introduce a deep learning emulator, namely Scalable Deep Learning for Soil Moisture Monitoring (SDLS), for root-zone soil moisture monitoring from the field to the regional scales. The SDLS method uses LDAS forcings and simulations from sampled locations to a bidirectional long short-term memory (B-LSTM) deep learning model, and is further applied to 30-m satellite-based evapotranspiration (ET), land cover, and topographic data, and digital soil property data. Evaluation of SDLS emulations demonstrates robust performance, with a mean squared error (MSE) below 0.0004, a Pearson correlation coefficient exceeding 0.8, and a Kling-Gupta Efficiency (KGE) score above 0.75 against LDAS soil moisture. SDLS method can generate daily soil moisture at 30-m resolution and can capture field-scale variability and drought, well matching with in situ observations. With additional deep learning postprocessing, the performance of the SDLS soil moisture against in situ observations can be further improved. The strength of the SDLS method lies in its ability to leverage process-based physical knowledge in land surface models to estimate soil moisture using satellite observations in a scalable way, which can be readily applied to new locations without the need for ground observations.  

The temperature, moisture, and salt content of soil in alpine regions are sensitive to changes in climatic factors and are important indicators of ecosystem functions. In this study, we collected soil moisture, temperature and electrical conductivity data at different depths at a sampling site on Bird Island in Qinghai Lake during winter using a continuous soil temperature, moisture and salt content monitoring system and analyzed their variations and influential factors. The variation in soil moisture showed an obvious ‘V-shaped’ pattern from 00:00 to 23:00 and an upward trend with soil layer depth. From 00:00 to 23:00, the overall soil temperature data fitted a ‘unimodal’ curve and showed a clear and continuous upward trend with soil layer depth at a rate of 0.684 (p < 0.001). Soil electrical conductivity data also exhibited a distinct ‘V-shaped’ pattern from 00:00 to 23:00 and a continuous increase with increasing soil depth. The correlation between soil temperature, moisture, and conductivity and the spatial distribution of five climate factors indicated that climate factors accounted for 53.6% of the changes in soil temperature, moisture, and salinity. Climate factors showed a significant positive correlation with soil temperature, moisture, and conductivity (p < 0.001), and air temperature was the most important factor influencing soil temperature and soil moisture changes, whereas wind direction was the most important factor influencing soil conductivity. (Wind direction and wind speed affect soil evapotranspiration, and then affect soil moisture and solute transport process). The results of this preliminary study reveal the characteristics associated with soil temperature, moisture, and salinity changes in winter within the wetlands of Bird Island on Qinghai Lake in the context of climate change, and they can be used as valuable reference data in further studies investigating associated changes in ecosystem functions.

Introducción: Las escalas ecológicas incluyen especies vegetales que responden ante cambios en las condiciones del hábitat. Estas plantas actúan como fitoindicadores que integran factores ambientales. Objetivo: Identificar hábitats adecuados para las especies vegetales a partir de datos de fitoindicaciones. Materiales y métodos: Se establecieron 47 parcelas (100 m2) en un área forestal de la Reserva Natural Estatal de Mordovia. En cada parcela se registró la composición de especies y se calcularon los valores medios de cuatro factores ambientales (sombreado, humedad, nitrógeno y pH del suelo), utilizando la escala ecológica de Tsyganov. Los cambios en los valores de los factores se visualizaron con mapas de contorno para cada factor. Las diferencias entre la composición por especies se determinaron a través del índice de similitud de Jaccard. Resultados y discusión: Las parcelas se han dispuesto principalmente a lo largo de gradientes de pH, disponibilidad de nitrógeno y humedad del suelo, mientras que el sombreado del hábitat no ha sido tan influyente. Los mapas de gradiente demostraron similitud notable entre los factores ambientales y la distribución vegetativa, la cual se confirmó por la identificación de tres áreas superpuestas en estos mapas. Los métodos de fitoindicación permiten mostrar cambios en los valores de los factores ambientales en un área forestal, y en combinación con un índice de similitud de especies es posible predecir la idoneidad del hábitat para plantas amenazadas. Conclusiones: Es posible indicar y encontrar un área adecuada para el establecimiento de plantas sobre la base del enfoque de la escala ecológica de fitoindicación. Dado que las plantas amenazadas tienen requerimientos ambientales más rigurosos, este método es especialmente adecuado para tales investigaciones.

Global climate change is expected to increase the aridity of semiarid landscapes by increasing heat stress and decreasing soil moisture, negatively impacting plant survival, growth, and reproduction and forcing major shifts in plant community composition. To better understand potential vegetation shifts, we investigated how aspect-mediated differences in soil moisture and nitrogen influence tree-grass and tree species coexistence in pinyon-juniper woodlands in the southwestern United States (at the Sevilleta Long Term Ecological Research Station in New Mexico). We measured soil moisture, total mineralized soil nitrogen, grass cover, and woody cover and demography of the two dominant trees—two-needle pinyon pine (Pinus edulis) and one-seeded juniper (Juniperus monosperma)—along 32 transects on north-facing and south-facing slopes. Tree cover was greater on north-facing than on south-facing slopes, reflecting significantly higher soil moisture on northern aspects. Aspect did not affect soil nitrogen or grass cover. Population structure for juniper was influenced by aspect while reproductive output was influenced by aspect in both species, suggesting that differences in drought tolerance between these codominant tree species may be responsible for differences in the utilization of resources in our study area. Our results suggest that soil moisture can affect tree cover and tree-grass coexistence more than does nitrogen. Increasing aridity due to global climate change is likely to decrease woody cover in semiarid landscapes, resulting in systems codominated by grasses and woody species.

Mediterranean ecosystems comprise the second biodiversity hotspot area after tropical rain forests and will be most affected by global climate change. Therefore, it is important to understand community dynamics for effective conservation in this region. We investigated the relationships between soil moisture, nitrogen forms and community structuring in Quercus ilexL., Erica arborea L. and Sarcopoterium spinosum (L.) Spach communities, representing different successional stages, distributed as Mediterranean enclaves on the Sinop Peninsula (Turkey). The soil moisture, ammonium, nitrate and nitrite content were measured seasonally. Differences in these abiotic parameters within and between communities over seasons were tested. Previously collected biotic data were then used to analyze the relationship between soil parameters and community structure. Significant differences in soil parameters within and between seasons were found within and between communities. Our results show that there are different relationships between soil moisture, nitrogen forms and community structure in Mediterranean plant communities representing different successional stages. Differentiation in vegetation structure during succession cause changes especially in the water and nitrate content of the soil, and these changes in turn affect the continuity of community structure in Mediterranean plant communities.

Determining the frequency, depth and velocity of preferential flow by high frequency soil moisture monitoring

ABSTRACTHourly observations of soil moisture volumetric content (at depths of 0–0.4 m) have been conducted for a few years at 55 climate stations with diverse soil types and annual rainfall in New Zealand. Thirty two of these stations also have surface meteorological observations. In this study, these observations were used for the first time to validate a land surface model (LSM) for soil moisture simulation, the Joint UK Land Environment Simulator (JULES). At the 32 stations, the yearly mean absolute differences (MADs) of the simulated soil moisture volumetric ratio were about 5% or smaller, except at four South Island stations, where larger MADs of 8–12% were found. Further analysis of rainfall and soil moisture observations at these four stations showed that the observed soil moisture includes a contribution from lateral soil water flow, which is not considered in the one‐dimensional version of JULES used in this study. For the remaining 28 stations, the relatively small yearly MADs (∼5%) were mainly due to biases. To understand the major factors contributing to the errors/biases, three sensitivity tests were conducted. These tests showed that errors in the soil texture data were the most likely contributor to the errors in simulated soil moisture. This study demonstrates that for a better simulation of soil moisture in New Zealand, especially in the lower South Island, a LSM needs to include lateral soil moisture/water flow, and to use more accurate prescriptions of the hydraulic and thermal properties of the soil.

Surface soil moisture variation on small agricultural watersheds