Soil Moisture Research Articles

Evaluating the performances of SVR and XGBoost for short-range forecasting of heatwaves across different temperature zones of India

This research aims to forecast maximum temperatures and the frequency of heatwave days across four different temperature zones (Zone 1, 2, 3 and 4) in India. These four zones are categorized based on the 30-year average maximum temperatures (T30AMT) during the summer months of April, May, and June (AMJ). Two Machine Learning (ML) algorithms eXtreme Gradient Boosting (XGBoost) and Support Vector Regression (SVR) are employed to achieve this goal. The study utilizes nine key atmospheric variables namely air temperature, geopotential height, relative humidity, U-wind, V-wind, soil moisture, solar radiation, sea surface temperature, and mean sea level pressure at a daily scale spanning from 1991 to 2020 for the months of March, April, May, and June as predictors. The India Meteorological Department daily maximum temperature data spanning from 1991 to 2020 for the months of AMJ serves as the predictands. ML models are developed using spatially averaged atmospheric variables and daily maximum temperature across the grids falling within each temperature zone. Results indicate that for a 7-day lead time, SVR outperforms XGBoost in Zone-1 (T30AMT > 38 °C) and Zone-2 (T30AMT: 35.01 °C–38 °C) by more accurately capturing peak temperatures during training and testing. Conversely, for a 15-day lead time in Zone-1, XGBoost better predicts temperature peaks in both phases. In Zone-3 (T30AMT: 30 °C–35 °C) and Zone-4 (T30AMT < 30 °C) for both lead times, the performance of both models decline, indicating models and input variables are more effective in predicting higher temperatures typical of Zone-1 and 2 but less so in Zone-3 and 4. In a nutshell, the study attempts to highlight the capability of advanced ML techniques combined with spatial climate data to enhance the prediction of extreme heatwave events. These insights can aid in heatwave preparedness, climate management, and adaptation strategies for different Indian temperature zones.

Investigation of in-field heterogeneities in the development of soybean according to remote sensing data and properties of the plow horizon (on the example of the south of the Far East)

Assessment of crop heterogeneities is one of the key conditions for predicting crop yield and increasing the economic efficiency of farming. The aim of the study is to develop methods for assessing in-field heterogeneities of soybean based on remote sensing data and to determine the relationship between soybean productivity indicators and soil characteristics. The work was carried out on meadow-brown heavy loamy soil, 10 plots of soybean field with a total area of 36.9 ha (Khabarovsk Krai) were selected for sampling in May and August 2023. The research was based on the results of remote sensing data processing (Sentinel-2 satellite, DJI Mavic3M quadrocopter), assessment of soybean productivity indicators, agrochemical, physical and chemical characteristics of soils and micro- and macroelement composition. The spatial distribution of NDVI modeled from Sentinel-2 data (early August) corresponded to the distribution of NDVI from DJI Mavic3M data. NDVI index values in August 2023 were found to be significantly correlated with soybean height (R = 0.64) and number of beans (R = 0.64). Soil moisture has a positive correlation with NDVI (R = 0.87) and soybean height (R = 0.68) for the entire growing season of the crop. NDVI was positively correlated with Hg (R = 0.79) and negatively correlated with pH (R = –0.79). The content of N-NO3 ranged from 2.51 to 6.84 mg/kg (V = 35.12 %), mobile forms of Р2О5 – from 2.47 to 6.07 mg/100 g (V = 33.13 %), К2О – from 4.98 to 9.37 mg/100 g (V = 20.07 %). The variability of N-NO3 and Р2О5 content decreased to 11.61 % and 21.99 % by August. No significant changes in soil bulk composition were observed between the first and second sampling. Variation of content of rare-earth elements (Sc, Y, lanthanides) in the selected sites did not exceed 5 %. By the date of the second sampling a significant decrease by 4–10.5 % (p 0.05) in the content of rare earth elements in soil was noted.

Applying Systems Thinking to Sustainable Beef Production Management: Modeling-Based Evidence for Enhancing Ecosystem Services

We used systems thinking (ST) to identify the critical components of beef cattle production through the lens of ecosystem services (ES), offering a holistic approach to address its adverse externalities. We identified eight critical feedback loops in beef production systems: (i) grazing and soil health, (ii) manure management and soil fertility, (iii) feed efficiency and meat production, (iv) water use and soil moisture, (v) cultural services and community engagement, (vi) energy use, (vii) carbon sequestration and climate regulation, and (viii) environmental impact. Our analysis reveals how these interconnected loops influence each other, demonstrating the complex nature of beef production systems. The dynamic hypothesis identified through the loops indicated that improved grazing and manure management practices enhance soil health, leading to better vegetation growth and cattle nutrition, which, in turn, have a positive impact on economic returns to producers and society, all of which encourage the continuation of interlinked beef and ecosystem stewardship practices. The management of beef production ES using ST might help cattle systems across the globe to contribute to 9 of the 17 different United Nations’ Sustainable Development Goals, including the “zero hunger” and “climate action” goals. We discussed the evaluation framework for agrifood systems developed by the economics of ecosystems and biodiversity to illustrate how ST in beef cattle systems could be harnessed to simultaneously achieve the intended environmental, economic, social, and health impacts of beef cattle systems. Our analysis of the literature for modeling and empirical case studies indicates that ST can reveal hidden feedback loops and interactions overlooked by traditional practices, leading to more sustainable beef cattle production outcomes. ST offers a robust framework for enhancing ES in beef cattle production by recognizing the interconnectedness of ecological and agricultural systems, enabling policymakers and managers to develop more effective and sustainable strategies that ensure the long-term health and resilience of humans and ES.

Robinia pseudoacacia Quickly Adjusts Its Water Uptake after Rainfall in Seasonally Dry Regions

Precipitation is a key factor affecting plant growth and development in seasonally arid regions. However, most of the traditional hydrological methods mainly select typically sunny days for sampling, and the immediate water absorption strategy of plants during and after rainfall is still unclear. This study used stable hydrogen and oxygen isotope technology to study the soil moisture absorption rates of Robinia pseudoacacia and the soil moisture content at different soil layers at different sampling times (0, 6, 12, 18 and 24 h) after rainfall. The results showed that the moisture content of the shallow soil layer decreased, while that of the deep soil layer increased over time after rainfall. R. pseudoacacia mainly utilized water from the 0–20 and 20–40 cm soil layers at 6 h after rainfall, which accounted for 36.52% and 22.25% of the rainfall, respectively. At 24 h, the 40–60, 60–80 and 80–100 cm soil layers contributed 25.25%, 18.44% and 24.45% of the water content, respectively. The shallow soil layer retained more rainfall within 6 h after rain fell, and the water retention ratio of the medium–shallow soil layer (0–60 cm) increased to 48.4%, retaining more water at 14–20 h. At 12 h, the medium–shallow soil layer (0–60 cm), runoff and groundwater constituted 37.1%, 14.4% and 15.7% of the precipitation, respectively, and rainfall retained in the deep soil layer (60–100 cm) accounted for 32.8%. In summary, R. pseudoacacia tends to use a large amount of shallow soil water in seasonally arid regions when precipitation supplements the surface soil moisture content and it utilizes deep soil water when the rainfall infiltrates and recharges the deep soil layer. Since R. pseudoacacia is sensitive to precipitation, it can quickly adjust its water absorption depth range during the short-term rainfall period to absorb as much precipitation as possible.

Mulching Improves the Soil Hydrothermal Environment, Soil Aggregate Content, and Potato Yield in Dry Farmland

Mulching could effectively improve the soil hydrothermal environment, improve changes in the soil structure, increase entropy, and conserve soil moisture to solve the problem of grain reduction caused by perennial drought in Northwest China. Thus, a two-growing-season field experiment (2020–2021) with five treatments (PM1, biodegradable plastic film mulching; PM2, plastic film mulching; SM1, straw strip mulching; SM2, crushed corn straw full mulching; and CK, no mulching as the control) was conducted to investigate the effects of different mulching materials on the soil hydrothermal environment, soil aggregate distribution, stability, and tuber yield of rainfed potato farmland in Northwest China. Over two growing seasons, mulching planting, on average, increased (p &lt; 0.05) the soil moisture at the 0–200 cm depth by 9.0% relative to CK (SM2 (11.6%) &gt; SM1 (10.3%) &gt; PM2 (8.6%) &gt; PM1 (7.0%)). The mulching treatments significantly regulated the soil temperature during the whole growth period, in which plastic mulching significantly increased the soil temperature of the 0–25 cm soil depth during the whole growth period by 2.1 °C (PM2 (2.1 °C) &gt; PM1 (2.0 °C)); meanwhile, straw mulching significantly reduced the soil temperature by 1.4 °C (SM2 (0.9 °C) &gt; SM1 (0.6 °C)). All mulching treatments improved the soil macroaggregate content and soil aggregate stability in all soil depths from 0 to 40 cm, with increases of 31.4% and 27.1% in the mean weight diameter (MWD) and 22.6% and 21.2% in the geometric mean diameter (GWD) compared with CK, respectively. Straw and plastic mulching significantly increased the fresh tuber yield by 12.5% and 12.6% compared with CK, respectively. The increases were greatest in SM2 and PM2. Crushed corn straw full mulching is difficult to sow and harvest; therefore, straw strip mulching could improve the soil hydrothermal environment, increase production, and provide an environmentally friendly technology for dryland potato production.

Soil Physical Properties of Oil Palm Plantations in Tidal Areas of Peatland

Oil palm (Elaeis guineensis Jacq) is a plant with a higher vegetable oil content than other oil-producing plants, so palm oil is widely used as the main raw material for processed vegetable oil. The increasingly limited land area and the larger land area in Indonesia so that the space for plantation companies to expand the land is increasingly limited, so that the expansion of oil palm plantations began to change from optimal land to suboptimal land. Soil physical properties are properties related to the shape or original condition of the soil. This study aims to determine the physical properties of soil (texture, porosity, moisture content, soil color, particle density, and bulk density) on peatlands in the tidal area of PT Sinar Gunung Sawit Raya. This research used survey method with descriptive analysis. The soil samples taken were peat soil in the tidal area with purposive random sampling method at a depth of 30 cm. This research was conducted in November 2023 and continued in the laboratory for testing each soil physical properties. The results of the research on soil physical properties at PT Sinar Gunung Sawit Raya on peatlands in tidal areas show that the soil texture is loamy sand, soil porosity is good, moisture content is relatively normal, soil color looks relatively dark, particle density is still low and bulk density value shows low. Keywords: Loamy sand, Oil palm, Peat soil, Soil color, Soil texture.

Optimized Soil Moisture Mapping Strategies on the Tibetan Plateau Using Downscaled and Interpolated Maps as Mutual Covariates

Accurate high-resolution soil moisture maps are crucial for a better understanding of hydrological processes and energy cycles. Mapping strategies such as downscaling and interpolation have been developed to obtain high-resolution soil moisture maps from multi-source inputs. However, research on the optimization performance of integrating downscaling and interpolation, especially through the use of mutual covariates, remains unclear. In this study, we compared four methods—two standalone methods based on downscaling and interpolation strategies and two combined methods that utilize soil moisture maps as mutual covariates within each strategy—in a case study of daily soil moisture mapping at a 1 km resolution in the Tibetan Plateau. We assessed mapping performance in terms of prediction accuracy and differences in spatial coverage. The results indicated that introducing interpolated soil moisture maps into the downscaling strategy significantly improved prediction accuracy (RMSE: −5.94%, correlation coefficient: +14.02%) but was limited to localized spatial coverage (6.9% of grid cells) near in situ sites. Conversely, integrating downscaled soil moisture maps into the interpolation strategy resulted in only modest gains in prediction accuracy (RMSE: −1.07%, correlation coefficient: +1.04%), yet facilitated broader spatial coverage (40.4% of grid cells). This study highlights the critical differences between downscaling and interpolation strategies in terms of accuracy improvement and spatial coverage, providing a reference for optimizing soil moisture mapping over large areas.

Study on Shear Strength of Soil–Root Systems of Different Vegetation Types

The root systems of vegetation significantly contribute to enhancing slope stability. The shear strength of soil–root systems is a crucial parameter for assessing slope stability. This study focuses on six types of vegetation in the Yellow River Basin of China (woodland: Populus przewalskii and Broussonetia papyrifera; shrubland: Periploca sepium and Ziziphus jujuba; grassland: Artemisia hedinii and Setaria viridis), employing in situ shear tests and the Wu–Waldron model (Wu model) to investigate the shear strength of soil–root systems. The results show that the shear stress–displacement curves for P. przewalskii, B. papyrifera, and Z. jujuba are higher and steeper, with clear inflection points. The tensile strength of the roots from the six vegetation types decreases as the root diameter increases. According to the Wu model, the additional root cohesion is ranked as follows: A. hedinii &gt; B. papyrifera &gt; P. przewalskii &gt; Z. jujuba &gt; P. sepium &gt; S. viridis. Based on the in situ shear tests, the shear strength increments are ranked as follows: Z. jujuba &gt; B. papyrifera &gt; P. przewalskii &gt; A. hedinii &gt; P. sepium &gt; S. viridis. Overall, the additional root cohesion obtained by the Wu model in each soil layer is greater than the shear strength increment measured from the in situ shear tests. In the 0–30 cm soil layers, the soil–root systems of Z. jujuba, B. papyrifera, P. przewalskii, and A. hedinii exhibit a better shear strength, whereas P. sepium and S. viridis perform poorly. A principal component analysis reveals that the shear strength of the soil–root systems of different vegetation types is primarily influenced by the soil moisture content and root mass density. Z. jujuba, B. papyrifera, P. przewalskii, and A. hedinii are recommended for ecological restoration projects in the Yellow River Basin of China.

Mini Tractor Operated Groundnut Digger Cum Inverter: A Concept

Groundnut holds a pivotal role in Indian agriculture, serving as a critical crop contributing to both the economic and nutritional sectors of the country. Timely harvesting of groundnut is a crucial phase in its cultivation, particularly during peak seasons, when labor shortages can lead to delays, resulting in significant yield losses. To mitigate labor constraints during peak harvesting periods and ensure timely operations, a conceptual model of a groundnut digger has been proposed. This digger is designed to uproot, collect, and transport groundnut plants, inverting them as it moves through the field. The groundnut digger consists of two primary units: a digging unit and a conveying unit. The digging unit is engineered to penetrate the soil to a precise depth, extracting the groundnut plants along with the attached pods to the surface. The conveying unit is responsible for lifting the plants and transporting them to a pod-inverting mechanism, which inverts the plant to reduce the risk of aflatoxin contamination in the pods due to excessive soil moisture.

Spatiotemporal desynchronization in the propagation from meteorological to soil moisture drought in the Loess Plateau, China

Study RegionThe Loess Plateau (LP) of China Study FocusMeteorological drought (MD) would propagate to soil moisture drought (SMD) with spatiotemporal desynchronization. The spatial desynchronization between them has frequently been ignored in previous studies due to limitation of identified droughts, which did not consider their 3-dimensional (3D, i.e. longitude, latitude and time) properties. This study presents a 3D perspective on the spatiotemporal desynchronization in the propagation from meteorological to soil moisture drought in the the Loess Plateau (LP) of China, using an improved drought matching method. Event Synchronization (ES) is extended to determine temporal linkage of the two types of droughts and spatial connection is tested using overlapping area. New Hydrological Insights for the RegionThe results showed that: (1) the improved method is reasonable for identifying MDs that trigger SMDs, down to specific clusters; (2) 8 SMDs preceded MDs 1 month, while approximately 79 % of SMDs did not recover after the determination of MDs; (3) severity of MD is an impact factor on recovery lag, while antecedent soil moisture dominates onset lag with the relative importance of approximately 50 %; and (4) incompletely overlap in migration trajectory between the two types of droughts was mainly caused by temperature, followed by antecedent soil moisture and potential evapotranspiration, with relative importance of 55 %, 14 % and 12 %, respectively.

When are you measuring soil β‐glucosidase activities in cropping systems?

AbstractIn situ soil respiration is driven by annual patterns of temperature and soil moisture, but what about extracellular enzyme activities responsible for depolymerizing soil organic matter? We conducted biweekly measurements of potential soil β‐glucosidase activities during a 4‐month period from March soil thawing through July in annually cropped field plots in eastern South Dakota. Our objective was to determine the best sampling time to resolve the effects of crop rotational diversity on soil microbial activities. Potential β‐glucosidase activities were elevated immediately following soil thaw, peaked in May, and declined to their lowest value in mid‐summer. Temperature and precipitation had no value in predicting enzyme activities; however, enzyme activities were affected by crop rotational diversity and responded to current crop and previous crop. These findings are pertinent to the use of soil extracellular enzymes in soil health assessments and as indicators of microbial substrate preference with implications for soil carbon stabilization.

Soil Organic Carbon Stocks Depend Differently on Physicochemical Features in Subtropical Seasonally Flooded Wetland and Non‐flooded Shoreland Forest

ABSTRACTIn recent years, an increasing number of ecosystems are threatened by seasonal flooding, changing non‐flooded shoreland (NF) into seasonally flooded wetland (SF), but the consequences of this hydrological change for soil organic carbon (SOC) dynamics remain unknown. In this study, we investigated how the SOC content was determined by flooding duration and soil physicochemical variables in adjacent SF and NF at six depths (0–10 cm, 10–20 cm, 20–30 cm, 30–50 cm, 50–70 cm, and 70–100 cm) at Shengjin Lake in subtropical China. Soil physicochemistry and SOC composition were analyzed, and Fourier‐transformed infrared spectroscopy (FTIR) was used to resolve the SOC composition. Neither SOC content nor the vertical distribution of SOC was distinguishable between the sites. However, FTIR data revealed that plant‐originated aliphatics and amides were higher at NF than SF sites, with the opposite pattern for aromatics. At SF sites, SOC content was positively affected by soil moisture and flooding duration and was negatively impacted by soil particle size at most soil layers. At NF sites, SOC content was mainly affected by silt and total Fe at the top 20 cm soil, while a higher fraction of plant‐derived labile C was positively correlated to SOC contents at 30–100 cm depth. The results hence indicated a strong effect of seasonal flooding on SOC dynamics in terrestrial ecosystems. SOC stabilization induced by low mineralization and high adsorption played a central role at SF sites, while SOC formation through plant input was more important at NF sites. Our findings suggest that management strategies designed to conserve SOC will need to be site‐specific.

Correlation change analysis and NDVI prediction in the Yellow River Basin of China using complex networks and GRNN-PSRLSTM.

The Normalized Difference Vegetation Index (NDVI) is affected by various environmental factors, and its relationship with these factors is complex. In order to explore the complex relationship between NDVI and environmental factors, this paper adopts the complex network method to construct a correlation fluctuation network and analyze the interaction between them. It is found that temperature, precipitation, soil moisture, sunshine duration, and PM2.5 are all correlated with NDVI to varying degrees, and their combined correlation with NDVI varies over time. The correlation typically takes 3 to 6months to change, and it tends to persist to some extent. Moreover, we fuse a generalized regression neural network (GRNN) with a long-short-term memory (LSTM) network combining phase space reconstruction (PSR) to propose a GRNN-PSRLSTM prediction model. The model achieves the prediction of monthly NDVI using the five environmental factors of the fluctuation network. The results indicate that the averages of root mean squared error (RMSE) and mean absolute percentage error (MAPE) predicted by the GRNN-PSRLSTM model in the nine provinces are 0.0232 and 0.0564 respectively. This model performs better in the assessment metrics for monthly NDVI forecasts. These findings are significant for evaluating vegetation changes and have some theoretical value for the ecological protection of the Yellow River Basin.

Improving seedling recruitment and dryland restoration using a targeted fungicide seed coating

The success of seeding projects in dryland systems is sporadic, with failure primarily driven by low seedling emergence and survival. This sporadic nature is influenced by a litany of biotic and abiotic factors that act in synergy to reduce recruitment success. These factors include erratic seasonal weather patterns, competition, predation, plant material selection, and restoration strategies. While some events are unpredictable and uncontrollable, others may be mitigated through thoughtful deliberation. Pathogenesis from soil‐ and seed‐borne fungi is one form of predation that can decrease restoration success. Fungicide seed coatings are technologies used in the agriculture industry to mitigate fungal pathogens, yet these technologies are novel to wildland seeding. We evaluated the ability of a fungicide seed coating to improve bluebunch wheatgrass (Pseudoroegneria spicata) recruitment across a broad range of sites and determined how soil and climate affected seed treatments across six sites over 650 linear kilometers of sagebrush‐steppe habitat in the Great Basin, United States. Treatment effects varied by site; however, on average, the fungicide treatment increased germination by 16%, emergence by 42%, and juvenile plant establishment by 60%. Fungicide seed treatments had a greater effect on seedling emergence in soils with lower pH, reduced wet‐thermal accumulation during the winter, lower bulk density, and higher organic matter content. Most notably, the fungicide seed treatment resulted in higher plant establishment when emerged seedlings were exposed to longer periods of available soil moisture. Fungicide seed treatments can improve the establishment of native bunchgrasses, when soil and climate regimes are considered.

Optimizing wheat supplementary irrigation: Integrating soil stress and crop water stress index for smart scheduling

A two-year field experiment was conducted to integrate soil moisture stress with the Crop Water Stress Index (CWSI) for optimizing irrigation in winter wheat (Triticum aestivum L.) under varying irrigation regimes. The study took place at the Water Technology Centre (WTC-02) of ICAR-IARI, New Delhi, where the climate shows a blend of monsoon-influenced humid subtropical and semi-arid conditions. Using a randomized block design (RBD), five irrigation treatments were applied: full irrigation and deficit irrigation (DI) at 15 %, 30 %, 45 %, and 60 % levels. Canopy and ambient air temperature data, along with vapor pressure deficit (VPD), were recorded using a developed integrated sensing device to empirically determine the lower baseline equations and upper threshold for CWSI computation at pre-heading and post-heading stages. The slope (m), intercept (c) of the lower baseline equation, and upper threshold (UL) for pre-heading and post-heading were found: m: −1.94, c: −1.33, UL: 1.92°C and m: −1.30, c: −2.37, UL: 2.0°C, respectively. Results showed that increasing water deficit levels led to significant reductions in grain yield, biomass production, and harvest index. A strong negative correlation (R² = 0.95 and 0.93) between mean seasonal CWSI and yield attributes highlighted the utility of CWSI in yield prediction under varying irrigation regimes. It is recommended to schedule irrigation based on the CWSI approach when CWSI ≥0.35 for optimum wheat yields. Integrating CWSI with soil moisture stress provides valuable real-time insights into crop water status, enabling more precise and smart irrigation scheduling.

Rice Fallow Pulses for Agricultural Potential for South Asian Region

Rice fallows refer to lowland areas where rice is cultivated during the monsoon (kharif season) is left unplanted during the winter season (rabi) due to various factors. These include the early cessation of monsoon rains which resulted in soil moisture stress during winter season; waterlogging and excessive moisture in November/December, led to unsuitable for winter crops; the absence of suitable varieties for late planting at winter season and socio-economic issues such as damage by stray cattle or blue bulls. India accounts for a significant majority of about 79% of South Asia's total rice fallows which amount to 11.65 million hectares out of the region's total of 15.0 million hectares. The review assess the scenario of rice fallow pulses, production technologies and its constraints in the present trend.

Elevation, Soil and Environmental Factors Determine the Spatial and Quantitative Distribution of Qinghai Spruce Recruitment Biomass in Mountainous (Alpine) Watersheds

Soil heterogeneity observed in the alpine environment plays a very important role in the growth of forest recruitment. However, the mechanisms by which the biomass accumulation and allocation patterns of forest recruitment respond to such environmental differences are unclear, which hinders a thorough understanding of climate change’s impact on forest biomass. We hypothesized that soil heterogeneity influences the distribution of Qinghai spruce recruitment biomass along with elevation. In the frame of this study, carried out in the northern Tibetan Plateau, forest Qinghai spruce recruitment data were combined with soil data derived from 24 sample plots, while permutation multifactor ANOVA and multiple linear regression were utilized to reveal the characteristics of forest recruits’ above- and below-ground biomass and their allocation patterns in response to soil heterogeneity. According to the results, the soil heterogeneity mainly affected the distribution characteristics of recruits’ above- and below-ground biomass at different elevations, while the recruits’ root–shoot ratio variability was influenced by a combination of soil and other environmental factors. Soil organic carbon (SOC) had the greatest effect on the variability of the above- and below-ground biomass of spruce recruits, with R2 of 0.280 and 0.257, respectively. Soil organic carbon and soil moisture content (SMC) had a significant effect on the variability of the root–shoot ratio, with R2 of 0.168 and 0.165, respectively. Soil total nitrogen (TN) and soil organic carbon were the main influencing factors of the above-ground biomass of forest recruits, with contribution rates of 43.15% and 35.28%, respectively. Soil total nitrogen and soil organic carbon were also the main factors influencing the below-ground biomass of forest recruits, with contribution rates of 42.52% and 37.24%, respectively, and both of them had a positive effect on biomass accumulation, and the magnitude of the influence varied with the elevation gradient. Soil moisture content was the main influence factor of spruce recruits’ root–shoot ratio, with a contribution rate of 54.12%. Decreasing soil moisture content would significantly increase the root–shoot ratio of spruce recruits and promote plants to allocate more biomass to root growth. Changes in elevation not only affected the intensity of the effect of soil factors on spruce recruitment biomass and its allocation pattern but even led to a change in the positive and negative effects.

Revealing the Feedback Loop: Comparing Satellite and Sensor-derived Meteorological Parameters in Groundnut Cultivation

Background: Studying soil moisture’s impact on groundnut, particularly under rainfed conditions, is crucial for improving quality and yield. This research explores soil moisture dynamics using satellite and in-situ sensor data to enhance water management and crop predictions, focusing on the feedback mechanisms between soil moisture and weather variables. Methods: The study was conducted at the Oil Seed Research Station (TNAU) in Tindivanam, Tamil Nadu, using the groundnut variety TMV 14. Soil moisture and weather parameters were monitored at critical crop growth stages, using an in-situ soil moisture sensor (Cr 300) and compared with satellite data from SMAP, ERA5 and Sentinel 1A with environmental factors. Statistical analysis was carried out using SAS JMP Pro and network analysis via Cytoscape 3.8.2 software. Result: Pre-monsoon soil moisture SMAP, SMAP-RZSM and sensor (VWC at 10cm) were found to increase in post-monsoon. Strong positive correlations were attained between SMAP-SM and SMAP-RZSM (r=0.94), indicating a reinforcing loop. Whereas, the negative feedback was observed between SMAP-SM and deeper soil temperatures (-0.91). It underscores the dynamic interplay between soil moisture and atmospheric conditions, essential for developing efficient water management strategies and enhancing crop resilience to climate variability.

Migration of Plutonium, Micro- and Macroelements in the “Soil–Plant” System at Different Soil Moisture

In the vegetation experiment, the plutonium, micro- and macroelements migration in the “soil–agricultural plant” system depending on soil moisture in the range from 15 to 40٪ of absolute soil moisture were studied. The content of 239Pu was analyzed by α-spectrometry with preliminary radiochemical isolation. The elemental composition was analyzed by the ICP-MS and ICP-AES methods. Beans (Fabaceae) variety “Amber” were used as a test culture. The plutonium transfer factor obtained in the vegetation experiments are in the range of of 5.3×10–4–1.5×10–2, with an average value of 5.4×10–3 for the aboveground part of bean and range of 4.5×10–2–2.7×10–1, with an average of 1.6×10–1 for bean roots. It was determined that the distribution of plutonium, micro and macro elements in the vegetative organs of plants is not equally, the transfer factor of plutonium for the aboveground part of plants is lower than for the root part. It has been established that the accumulation of plutonium, micro- and macroelements, depending on soil moisture, is different for the organs of beans. The dependence of plutonium accumulation by plants on soil moisture is significantly higher than for other considered elements. A decrease in the coefficient of accumulation of plutonium in the aerial part of the beans is recorded with an increase in soil moisture up to two orders of magnitude. There is a trend towards a slight decrease in the accumulation coefficients of Fe, Mg, Mn, Cr, Mo, Ni, Co, Cu. For the root system of beans, a clear dependence of the accumulation of the considered elements on soil moisture is not observed.

Smart Irrigation System Using Soil Moisture Prediction with Deep CNN for Various Soil Types

Soil moisture sensing plays a crucial role in agriculture as it directly impacts plant growth and can significantly enhance crop productivity. With the advent of technology, agriculture applications have undergone a revolution, enabling more advanced and efficient practices. One such advancement is the use of soil moisture sensors, which provide valuable information about the current water level of the soil, including whether it is dry, wet, or excessively saturated. These sensors have become indispensable tools for farmers and growers, empowering them to make informed decisions regarding irrigation schedules, water management strategies, and overall crop health. By accurately assessing soil moisture levels, farmers can optimize water usage, prevent water stress or overwatering, and promote healthier plant development, ultimately leading to improved yields and sustainability in agriculture. The objective of the proposed study is to investigate the effective soil moisture sensors by considering three sensors and an automated system for watering the soil for agriculture. A comparative analysis is performed for different commercial off-the-shelf soil moisture sensors in cost, accuracy, durability, and corrosion resistance. Secondly, this study further gives soil moisture reading as data input to the Convolutional Neural Network (CNN) to classify whether water is required or not for the soil at a particular temperature which would help to conserve water and develop agriculture. An automated system which can help the farmer for irrigation and cultivation of the soil obtained accuracy as 95.23%.