Soil Moisture Research Articles

The 2020 Heatwave Led to a Larger Enhancement in Annual Gross Primary Production in West Siberia Than in East Siberia

AbstractSpring and summer vegetation productivity in Siberia shows opposing responses to warmer spring. Spring warming causes excessive vegetation growth and earlier start of photosynthesis, enhancing productivity in spring. However, this leads to reduced productivity in the following season (i.e., summer) through soil moisture depletion. To understand how an exceptional spring heatwave (HW) affected ecosystem carbon uptake, we investigated the spatiotemporal cascade of gross primary production (GPP) and multiple climate variables over Siberia in 2020, using a satellite‐retrieved GPP product (GOSIF‐GPP) and the ERA5‐Land reanalysis data set for 2001–2020. Results showed a positive impact of anomalous spring warming on annual GPP (GPPann). GPPann from GOSIF‐GPP in West Siberia (55°–70°N, 50°–90°E) was enhanced by up to 10% above the 2001–2019 average despite continued dry conditions from May to August. In East Siberia (55–70°N, 90–130°E), the GPP increases for May and June were sufficient to compensate for marked reduction of GPP in July due to negative anomaly in radiation. In addition, the higher sensitivity of GPPann to spring temperature in West Siberia than in East Siberia suggests that GPP increase coupled with strong warming and respective excessive vegetation growth might be more pronounced in the western region, as observed in 2020. Our results indicate that the warming trend in spring, combined with possible extreme heat events, could elevate annual carbon uptake in Siberia, particularly in West Siberia. Further, this case study for the extreme HW event that occurred in 2020 can provide useful insight for understanding future change in carbon uptake over Siberia.

Precision Soil Moisture Monitoring Through Drone-Based Hyperspectral Imaging and PCA-Driven Machine Learning

Accurately estimating soil moisture at multiple depths is essential for sustainable farming practices, as it supports efficient irrigation management, optimizes crop yields, and conserves water resources. This study integrates a drone-mounted hyperspectral sensor with machine learning techniques to enhance soil moisture estimation at 10 cm and 30 cm depths in a cornfield. The primary aim was to understand the relationship between root zone water content and canopy reflectance, pinpoint the depths where this relationship is most significant, identify the most informative wavelengths, and train a machine learning model using those wavelengths to estimate soil moisture. Our results demonstrate that PCA effectively detected critical variables for soil moisture estimation, with the ANN model outperforming other machine learning algorithms, including Random Forest (RF), Support Vector Regression (SVR), and Gradient Boosting (XGBoost). Model comparisons between irrigated and non-irrigated treatments showed that soil moisture in non-irrigated plots could be estimated with greater accuracy across various dates. This finding indicates that plants experiencing high water stress exhibit more significant spectral variability in their canopy, enhancing the correlation with soil moisture in the root zone. Moreover, over the growing season, when corn exhibits high chlorophyll content and increased resilience to environmental stressors, the correlation between canopy spectrum and root zone soil moisture weakens. Error analysis revealed the lowest relative estimation errors in non-irrigated plots at a 30 cm depth, aligning with periods of elevated water stress at shallower levels, which drove deeper root growth and strengthened the canopy reflectance relationship. This correlation corresponded to lower RMSE values, highlighting improved model accuracy.

Spatial and temporal variability of soil moisture active and passive (SMAP) droughts and their impacts on vegetation in the Central Highlands of Vietnam.

Drought is a reoccurring natural phenomenon that presents significant challenges to agricultural production, ecosystem stability, and water resource management. The Central Highlands of Vietnam, a major region of industrial crops and vegetation ecosystems, has become increasingly vulnerable to drought impacts. Despite this vulnerability, limited research has explored the specific characteristics of drought and its seasonal effects on vegetation ecosystems in the region. This study addressed these gaps by providing a detailed analysis of recent soil moisture drought characteristics and their seasonal impacts on vegetation from 2015 to 2023 using weekly soil moisture active passive (SMAP) and moderate resolution imaging spectroradiometer (MODIS) satellite time series observations. This analysis derived the soil moisture anomaly index as a proxy to assess drought characteristics and used correlation analysis to quantify their impacts on seasonal vegetation dynamics. Our spatial analysis identified the most significant drought years in 2015 and 2019 in the study region, while the wettest conditions were detected in 2017 and 2022 over the study period. Notably, significant soil moisture deficits were observed in August and October throughout the study period, even though these months typically fall within the rainy season. On average, nearly 25 drought events were detected in the region from 2015 to 2023 due to soil moisture deficits, each lasting approximately 6 weeks. The impact of drought events on the vegetation ecosystem was seasonally pronounced in spring and winter, where droughts were notably higher. Our results provide valuable insights into informed decision-making and sustainable agricultural practices in the region. Understanding the spatial and temporal patterns of drought and its seasonal effects on vegetation can help policymakers and farmers develop targeted strategies to mitigate the adverse impacts, enhance water management practices, and promote drought-resistant crop varieties, thereby maintaining agricultural productivity and ecosystem health amidst increasing climate variability.

PERFORMANCE OF A LOCALLY FABRICATED ONE-ROW POTATO HARVESTER FOR WALK-TRACTOR

This study evaluates the performance of a locally fabricated one-row mechanized potato harvester designed for small and medium-scale farms in Kenya, focusing on its efficiency across different soil conditions. Mechanized harvesting addresses inefficiencies and post-harvest losses in potato farming, particularly in Kenya's diverse agricultural environments. The harvester was tested under dry soil (moisture content below 15%) and moderately wet soil (moisture content between 20% and 25%) conditions. Key performance metrics assessed included harvesting efficiency, tuber damage rate, fuel consumption, and field capacity. The harvester achieved an average efficiency of 93.4%, with higher performance observed in moderately wet soil (94.7%) compared to dry soil (92.1%). The average tuber damage rate was 4.2%, with more significant damage occurring in dry soil (4.8%) than in wet soil (3.6%). Fuel consumption remained consistent at 2.5 liters/hectare, and field capacity was measured at 0.4 hectares/hour. These results demonstrate the harvester's reliability and adaptability to local farming conditions, providing a mechanical potato harvesting solution for small-scale farmers in Kenya.

Subsurface Irrigation With Ceramic Emitters Improved the Water Productivity and Nitrogen Use Efficiency of Greenhouse Melons.

ABSTRACTIncreasing nitrogen use efficiency in agricultural fields is crucial for sustainable agricultural development. In a 2‐year greenhouse study, we compared two methods, subsurface irrigation with ceramic emitters (SICE) and subsurface drip irrigation (SDI), to evaluate their effects on soil moisture and nitrogen distribution, photosynthetic efficiency and plant stoichiometry. The results showed that SICE can reduce the soil nitrate nitrogen content by approximately 20% while maintaining the soil moisture content at 70%–80%, improving the photosynthetic efficiency and melon yield. Under the optimal yield treatment (applying 60.67 kg of nitrogen fertilizer per hectare), compared with SDI, SICE increased yield by 8%, water productivity by 13%, nitrogen absorption efficiency by 4% and nitrogen use efficiency by 3%. In addition, by analysing the stoichiometric characteristics of melon at different nitrogen fertilizer application rates and growth stages under the two irrigation methods, we found that the main reason for the increase in SICE yield was the coupled effect of the increase in plant photosynthetic efficiency and the need for plants to maintain their internal stoichiometric balance.

A Dynamic Monitoring Framework for Spring Low-Temperature Disasters Affecting Winter Wheat: Exploring Environmental Coercion and Mitigation Mechanisms

Abstract: The implementation of real-time dynamic monitoring of disaster formation and severity is essential for the timely adoption of disaster prevention and mitigation measures, which in turn minimizes disaster-related losses and safeguards agricultural production safety. This study establishes a low-temperature disaster (LTD) monitoring system based on machine learning algorithms, which primarily consists of a module for identifying types of disasters and a module for simulating the evolution of LTDs. This study firstly employed the KNN model combined with a piecewise function to determine the daily dynamic minimum critical temperature for low-temperature stress (LTS) experienced by winter wheat in the Huang-Huai-Hai (HHH) region after regreening, with the fitting model’s R2, RMSE, MAE, NRMSE, and MBE values being 0.95, 0.79, 0.53, 0.13, and 1.716 × 10−11, respectively. This model serves as the foundation for determining the process by which winter wheat is subjected to LTS. Subsequently, using the XGBoost algorithm to analyze the differences between spring frost and cold damage patterns, a model for identifying types of spring LTDs was developed. The validation accuracy of the model reached 86.67%. In the development of the module simulating the evolution of LTDs, the XGBoost algorithm was initially employed to construct the Low-Temperature Disaster Index (LTDI), facilitating the daily identification of LTD occurrences. Subsequently, the Low-Temperature Disaster Process Accumulation Index (LDPI) is utilized to quantify the severity of the disaster. Validation results indicate that 79.81% of the test set samples exhibit a severity level consistent with historical records. An analysis of the environmental stress-mitigation mechanisms of LTDs reveals that cooling induced by cold air passage and ground radiation are the primary stress mechanisms in the formation of LTDs. In contrast, the release of latent heat from water vapor upon cooling and the transfer of sensible heat from soil moisture serve as the principal mitigation mechanisms. In summary, the developed monitoring framework for LTDs, based on environmental patterns of LTD formation, demonstrates strong generalization capabilities in the HHH region, enabling daily dynamic assessments of the evolution and severity of LTDs.

Nitrogen and Water Additions Affect N2O Dynamics in Temperate Steppe by Regulating Soil Matrix and Microbial Abundance

Elucidating the effects of nitrogen and water addition on N2O dynamics is critical, as N2O is a key driver of climate change (including nitrogen deposition and shifting precipitation patterns) and stratospheric ozone depletion. The temperate steppe is a notable natural source of this potent greenhouse gas. This study uses field observations and soil sampling to investigate the seasonal pattern of N2O emissions in the temperate steppe of Inner Mongolia and the mechanism by which nitrogen and water additions, as two different types of factors, alter this seasonal pattern. It explores the regulatory roles of environmental factors, soil physicochemical properties, microbial community structure, and abundance of functional genes in influencing N2O emissions. These results indicate that the effects of nitrogen and water addition on N2O emission mechanisms vary throughout the growing season. Nitrogen application consistently increase N2O emissions. In contrast, water addition suppresses N2O emissions during the early growing season but promotes emissions during the peak and late growing seasons. In the early growing season, nitrogen addition primarily increased the dissolved organic nitrogen (DON) levels, which provided a matrix for nitrification and promoted N2O emissions. Meanwhile, water addition increased soil moisture, enhancing the abundance of the nosZ (nitrous oxide reductase) gene while reducing nitrate nitrogen (NO3−-N) levels, as well as AOA (ammonia-oxidizing archaea) amoA and AOB (ammonia-oxidizing bacteria) amoA gene expression, thereby lowering N2O emissions. During the peak growing season, nitrogen’s role in adjusting pH and ammonium nitrogen (NH4+-N), along with amplifying AOB amoA, spiked N2O emissions. Water addition affects the balance between nitrification and denitrification by altering aerobic and anaerobic soil conditions, ultimately increasing N2O emissions by inhibiting nosZ. As the growing season waned and precipitation decreased, temperature also became a driver of N2O emissions. Structural equation modeling reveals that the impacts of nitrogen and water on N2O flux variations through nitrification and denitrification are more significant during the peak growing season. This research uncovers innovative insights into how nitrogen and water additions differently impact N2O dynamics across various stages of the growing season in the temperate steppe, providing a scientific basis for predicting and managing N2O emissions within these ecosystems.

Relationship between Topographic Wetness Index and Soil Physical Properties in Cikapundung Sub-watershed Using Geographic Information Systems

Purpose: This study aimed to analyze the relationship between Topographic Wetness Index (TWI) and soil physical properties (permeability, porosity, bulk density, and soil moisture content) and their spatial distribution in the Cikapundung Sub-watershed. Methods: A survey method was conducted through descriptive, correlation, and interpolation approaches. Thirty soil samples were collected based on a two-way stratification method using TWI and slope gradient. TWI was derived from Digital Elevation Model (DEM) data using GIS. Soil physical properties were analyzed in laboratory and their spatial distribution was mapped using interpolation techniques. Results: TWI showed varying degrees of correlation with soil physical properties: significant positive correlation with soil permeability (r = 0.37), weak negative correlation with soil moisture content (r = -0.30), and very weak correlations with bulk density and soil porosity (r = -0.03 and r = 0.03). Spatial analysis revealed heterogeneous distribution patterns of soil physical properties across the sub-watershed. Conclusion: TWI can only be effectively used as a single predictor for soil permeability, but not for bulk density, porosity, and soil moisture content in the Cikapundung Sub-watershed. This suggests the need for additional environmental parameters when predicting these soil physical properties.

Ecological filters shape arbuscular mycorrhizal fungal communities in the rhizosphere of secondary vegetation species in a temperate forest.

The community assembly of arbuscular mycorrhizal fungi (AMF) in the rhizosphere results from the recruitment and selection of different AMF species with different functional traits. The aim of this study was to analyze the relationship between biotic and abiotic factors and the AMF community assembly in the rhizosphere of four secondary vegetation (SV) plant species in a temperate forest. We selected four sites at two altitudes, and we marked five individuals per plant species at each site. Soil rhizosphere samples were collected from each SV plant species, during the rainy and dry seasons. Soil samples from the rhizosphere of each plant species were analyzed for AMF spores, organic matter (OM), pH, soil moisture, and available phosphorus, and nitrogen. Three ecological filters influenced the AMF community assembly: host plant identity, abiotic factors, and AMF species co-occurrence. This assembly consisted of 61 AMF species, with different β-diversity values among plant species across seasons and altitudes. Canonical correspondence analysis revealed that AMF community composition is linked to OM and available P and N, with only a few AMF species co-occurring, while most do not. Our study highlights how ecological filters shape AMF structure, which is essential for understanding how soil and environmental factors affect AMF in SV plant species across seasons and altitudes.

Rainfed agriculture in Ethiopia: a systematic review of green water management pathways to improve water and food security

It is widely acknowledged that the world is currently experiencing an unprecedented water shortage, with agriculture being a crucial contributor. This paper presents a synthesis of available evidence, identify knowledge gaps, and make a state-of-the-art synthesis on green water management in Ethiopia. A systematic review methodology was implemented, encompassing the compilation and analysis of peer-reviewed and gray literature. The paper demonstrates that rainfed agriculture, which relies on “green water” (soil moisture from rainfall), accounts for 80% of cultivated land and 60-70% of global crop production. However, green water management has not received adequate attention in water policy and land rehabilitation programs in Ethiopia, where irrigation is limited. The analysis reveals a large yield gap and water productivity gap for major crops like maize, sorghum, and wheat in Ethiopia’s rainfed agriculture. Increasing crop yields through better soil, water, and crop management practices can significantly improve water productivity, offering “windows of opportunity” to enhance food and water security. Thus, a paradigm shift from the traditional narrow focus on soil erosion control towards an integrated green-blue water management approach in water and agricultural policies and programs is urgently required. Increased investments and expertise in green water management at the government level are crucial. Optimizing the use of green water resources in rainfed farming can also unlock Ethiopia’s export potential while improving domestic water and food security through strategic virtual water trade. In conclusion, the review highlights unlocking the potential of green water resources through targeted investments and policy support for rainfed agriculture can significantly contribute to Ethiopia’s water and food security objectives in a cost-effective and environmentally sustainable manner.

Soil Organic Carbon Dynamics Across Various Land Use Systems Along Elevational Gradients in the North-eastern Himalayas

Aims: To study soil organic carbon content across different land use systems along four elevational gradients in the Himalayan Region of West Bengal. Place and Duration of Study: Kalimpong and Darjeeling districts of West Bengal, India, between April to June 2019. Methodology: Six major land use systems were selected spanning an altitudinal gradient of 400-500 m, 900-1000 m, 1400-1500 m, and 1900-2000 m, ensuring that each land use system was represented at every elevation. The land use systems included: 1. open cropland with rice, maize, winter vegetables, and pulses; 2. mandarin orchard; 3. large cardamom-based agroforestry under alder and Albizia species; 4. ginger-based agroforestry with mixed shade trees; 5. tea plantation under Albizia species; and 6. undisturbed forest. Results: Undisturbed forests had the highest OC content (21.74 g kg-1), followed by tea plantations (19.28 g kg-1), large cardamom (17.86 g kg-1), ginger-based agroforestry (15.98 g kg-1) and mandarin orchard (13.94 g kg-1). While, open crop fields had the lowest OC content (10.92 g kg-1) across various elevational gradients. Soil organic carbon showed a positive correlation with elevation and a decrease with soil depth. At higher elevations, high soil moisture and relative humidity enhance the production of above- and below-ground biomass, increasing soil organic carbon content, while lower temperatures reduce microbial decomposition, further promoting organic carbon sequestration. Conclusion: These findings provide crucial insights for researchers and policymakers, providing valuable information to assist in decision-making for sustainable land use management.

MECANIZAREA LUCRĂRILOR AGRICOLE PE PAJIȘTI PRIN VARIANTE TEHNOLOGICE CU INPUTURI MINIME. REVIEW

Grassland farming plays a vital role in sustainable agricultural systems, providing forage resources for livestock production and contributing to environmental conservation. However, the labor-intensive nature of grassland management requires significant challenges for farmers. The adoption of appropriate mechanization technologies can improve efficiency, reduce labor requirements, and enhance overall productivity. This paper investigates the mechanization of grassland farming through technological variants with minimal inputs. The incorporation of sensor technologies and data analytics facilitates real-time monitoring of grass growth, enabling farmers to make decisions regarding grazing rotations and forage quality. Additionally, the utilization of smart sensors for soil moisture and nutrient content allows for targeted application of inputs, reducing waste and optimizing resource utilization. Overall, this article highlights the potential of mechanization and technological variants with minimal inputs to make efficient the grassland farming, improving productivity, sustainability and the livelihoods of farmers.

Cotton Irrigation Scheduling: Which Approach is the Best Fit for Georgia?

Cotton (Gossypium hirsutum) is one of the most difficult crops to manage irrigation effectively due to the crop’s perennial physiology. In recent years, many new technologies have been developed to help improve irrigation management. The main objective of this study was to evaluate various irrigation management tools and to assist farmers in determining which method is best for their operation. Other objectives included monitoring soil moisture and determining the optimal irrigation application point of each method by logging total rainfall and irrigation distribution throughout the growing season. A three-year study was conducted at the University of Georgia (UGA) Stripling Irrigation Research Park near Camilla, GA where cotton was grown on loamy sand soil. A lateral movement, overhead sprinkler system equipped with a variable rate system allowed plots to be irrigated independently based on treatment. Irrigation treatments included 20- and 45-kPa weighted average soil water tension (SWT) measurements made using three Watermark SWT sensors placed in two of the three replicates. The UGA SmartIrrigation Cotton app (SI app), UGA Checkbook method, and a rainfed check were included in the trial. Each irrigation method was evaluated based on crop yield, irrigation water-use efficiency, and profitability. The analysis revealed significant variations in several metrics between treatments and validates the 45-kPa SWT threshold and SI app are top-performing advanced irrigation scheduling tools and showed the importance of advanced irrigation scheduling and the strengths and weaknesses of each method.

Association between hydroclimatic factors and vegetation health: Impact of climate change in the past and future.

This study investigates the potential impact of future climate scenarios designated by different shared socioeconomic pathways (SSPs) on vegetation health. Considering the entire Indian mainland as the study region, which exhibits a diverse range of climate and vegetation regimes, we analysed long-term past (1981-2020) and future (2021-2100) changes in vegetation greenness across seven vegetation types and four seasons. In order to gain insight into the intricate interrelationships between vegetation and hydroclimatic factors (soil moisture, precipitation, solar radiation, and temperature), a Standardized Vegetation Index (SVI) is used as a proxy for vegetation health, and a bivariate copula-based probabilistic model is developed incorporating a Combined Climate Index (CCI) derived through Supervised Principal Component Analysis (SPCA) and the SVI. Our results indicate that the water-limited areas are more sensitive to precipitation and soil moisture, whereas energy-limited areas are primarily influenced by temperature and solar radiation. Consequently, an overall increase in the vegetation greenness is observed over the past decades in most of water-limited regions, and almost no change or slight decline in greenness over the northeastern regions, where precipitation is abundant but it is an energy-limited region due to high convective activity. Future projections (2021-2100) indicate an overall increase in greenness during monsoons. However, browning (loss of greenness) is anticipated to intensify over time, especially in the northeast. This study demonstrates the model's efficacy in capturing the complex vegetation-climate relationship, highlighting its potential for application across diverse geographical regions and providing insights into the implications of climate change.

Deep eutectic solvent-mediated extraction of lignin: A novel strategy for producing high-quality biopolymers in controlled-release mulching applications.

Microplastic contamination of low-density polyethylene mulch and nutrient loss from fertilizers present significant challenges in the crop-growing. In this study, the focus was on creating a biodegradable film that combines the advantages of plastic film, thermal insulation and water retention, as well as the controlled release of fertilizer. A key innovation was the efficient introduction of low molecular weight and low dispersibility of poplar lignin into chitosan and polyvinyl alcohol matrices. The lignin was extracted using deep eutectic solvents of binary carboxylic acids (choline chloride and maleic acid). The refined lignin was used as a superhydrophobic additive to improve the mechanical properties, hydrophobicity, and controlled nutrient release properties of the films through cross-linking. The mulch attained a tensile strength of 37.6 MPa, an elongation of 644.1 %, and a precise release of 53.1 % urea over 30 d at the ideal lignin content ratio (10 %). Furthermore, the film proficiently regulated soil temperature and moisture content. Successful enhancement of cabbage growth was achieved by actual measurements. This discovery provides innovative ideas for the development of nutrient slow-release high-strength integrated agricultural mulching films to promote sustainable, high-quality green agriculture.

Effects of land consolidation on moisture and surface temperature regime of agricultural soil blocks using remote sensing data in the Czech Republic

Abstract The Czech Republic has experienced collectivization, transforming individual land ownership into state ownership through agricultural land consolidation after World War II. Land consolidation has led to the expansion of agricultural soil blocks as well as the disappearance of vital landscape features such as shrubs, hedges, lone trees, and wetlands, all of which serve a variety of ecological functions. The disappearance of the landscape elements causes some important problems, such as soil degradation and erosion. The Czech Ministry of Agriculture has enforced a regulation that was developed by the European Union to improve agriculture by enforcing cross-compliance requirements, which encompass the observance of Good Agricultural and Environmental Condition (GAEC) standards. The regulation disincentivizes single-crop cultivation on areas larger than 30 hectares since 2020 to reduce the negative effects of land consolidations on agriculture and soil quality. In this context, the study examines the relationship between land surface temperature (LST) and moisture of agricultural plants and soil blocks in the South Bohemia, Czech Republic, using Landsat-8 remote sensing data from 2018 to 2021. The mono-window algorithm (MWA) was used to obtain LST, and the precision of Landsat LST data was evaluated using MODIS daily LST data. The NRMSE ≤ 0.15 and RMSE ≤ 3.5 C were found with a strong positive relationship r ≥ 0.65 between datasets. Additionally, the density of vegetation and the moisture index of soil and plants were calculated using spectral remote sensing indices such as the normalized difference vegetation index (NDVI), normalized difference moisture index (NDMI), and soil moisture index (SMI). The statistical relationships among those spectral indices with LST were examined. The analyzes showed that the implementation of GAEC 7d improved the relationship between NDVI and NDMI, with the correlation increasing from r = 0.80 in 2019 to 0.92 in 2021. Meanwhile, the inverse relationship between NDMI and LST changed from − 0.71 to -0.67. These changes in correlation suggest that plant water retention in the region increased following the regulation, and the soil structure may have also improved.

Characteristics of Soil Microbial Community Structure in Different Land Use Types of the Huanghe Alluvial Plain

The Huanghe alluvial plain plays a crucial role in biodiversity conservation. However, its ecosystem has become sensitive and fragile due to long-term human disturbances. Enhancing the resilience of this ecosystem and promoting the sustainable use of land resources are key to addressing its ecological challenges. Soil microbial communities are vital to ecosystem functioning, and land use is a major human factor influencing their structure and diversity. Existing research on the Huanghe alluvial plain primarily focuses on soil physicochemical properties and moisture content, with relatively limited attention given to soil microorganisms. Therefore, this study, using the Wudi Tanyang Forest Farm in the Huanghe alluvial plain as a case study, employs high-throughput metagenomic sequencing to analyze the composition and diversity of soil bacteria, eukaryota, archaea, and virus communities in five different land use types (Tamarix chinensis forest, Fraxinus chinensis forest, farmland, wetland, and grassland). The results indicate that: (1) At the phylum level, the top three bacteria communities were Pseudomonadota, Acidobacteriota, and Actinomycetota; the top three in the eukaryota communities were Ascomycota, Mucoromycota, and Basidiomycotina; the top three in the archaea communities were Nitrososphaerota, Euryarchaeota, and Candidatus Thermoplasmatota; and the virus communities were dominated by Uroviricota; (2) The microbial community structure of the Tamarix chinensis forest and the Fraxinus chinensis forest was similar, and was significantly different from the other three land use types; (3) The land use type had a significant effect on the diversity of the soil microbial communities, with a higher diversity in the wetland and grassland soils; (4) The dominant species of the soil microbial communities under different land use types showed significant differences. This study provides theoretical support for land use optimization and sustainable soil management in the Huanghe alluvial plain region.

The Role of Sugarcane Trash in Soil Fertility and Plant Growth: A Review

Sugarcane needs a hot and humid climate and can be grown on a variety of soils that can retain moisture. Owing to its long life cycle it heavily depletes soil nutrients during its growth period. Sugarcane, being a heavy feeder requires nearly 208 kg N, 53 kg P2O5, and 280 kg K2O from the soil to produce 100 t ha-1 sugarcane. Sugarcane, being a C4 plant, produces a large quantity of biomass. Approximately 10-15 t of trash, constituting 10-12% weight of cane harvested, is produced by the sugarcane crop. Generally, cane trash contains 68% organic matter, 0.42% N, 0.15% P, 0.57% K, 0.48% Ca, and 0.12% Mg, besides 25.7, 20.45, 236.4, and 16.8 ppm Zn, Fe, Mn and Ca, respectively. Trash conserves soil moisture, C, and N in the soil and provides energy to increase the yield. The trash application conserves the nutrients in the soil, also aids their availability to the plant, and reduces the fertilizer requirements through recycling nutrients in the soil from residue. Organic mulches also create a better physical, chemical, and biological environment of soils and in turn, improve crop productivity. Burning of trash deteriorate the fertility as well as soil health. It also reduces the soil microbial activity and destabilize of soil aggregates. Significant N losses from residues can occur during burning by high temperature volatilization. In the burnt system, >70% of the organic matter and nutrients in the trash are lost to the atmosphere.

Dynamics of Annual Сone Crops of Siberian Fir (Abies sibirica Ledeb.) in Conifer Forests of Pre-Ural Region (Russia) Based on 47 Years of Observations

Because seed reproduction is the sole means of reproduction available for coniferous tree species, it plays a crucial role in determining the species’ ecological adaptability and the competitiveness of species under specific biocoenosis conditions. Therefore, the primary goal of studying the periodicity and cyclical production of cones (seeds) is to forecast the peaks and recessions of natural renewal in various forest ecosystems. The crop dynamics of Siberian fir (Abies sibirica Ledeb.) cones in three mature natural conifer forests in the broad-leaf coniferous forest subzone of the pre-Ural region (Russia) was analysed based on long-term observations spanning 47 years. The conifer forests investigated had a considerable deficit of cones (seeds) for natural renewal under the forest canopy. High cone crops occur every 10 years or twice a decade under most favourable conditions. However, cone production has no distinct periodicity, and it is impossible to forecast a high crop of cones based only on long-term data. The levels of fir crop cones were clearly correlated with climate factors. Late winter climate in previous (weak and moderate positive correlation) and current years (weak and moderate negative correlation) affected the fir cone crop. High and even average fir cone crops occur spontaneously with no discernible pattern. In coniferous forests, cone crops are highest on slopes with high insolation levels and on sustainably wet soils.

Sustainable and Low-Cost Greenhouse Soil Moisture Monitoring Using Battery-Free RFID Sensors

Intelligent irrigation based on measurements of soil moisture levels in every pot in a greenhouse can not only improve plant productivity and quality but also save water. However, existing soil moisture sensors are too expensive to deploy in every pot. We therefore introduce GreenTag, a low-cost RFID-based soil moisture sensing system whose accuracy is comparable to that of an expensive soil moisture sensor. Our key idea is to attach two RFID tags to a plant’s container so that changes in soil moisture content are reflected in their Differential Minimum Response Threshold (DMRT) metric at the reader. We show that a low-pass filtered DMRT metric is robust to changes both in the RF environment (e.g., from human movement) and in pot locations. In addition, we propose a fast DMRT acquisition algorithm and a time-efficient tag query protocol, which can reduce the sensing latency by 90%. In a realistic setting, GreenTag achieves a 90-percentile moisture estimation errors of 5%, which is comparable to the 4% errors using expensive soil moisture sensors. Moreover, this accuracy is maintained despite changes in the RF environment and container locations. We also show the effectiveness of GreenTag in a real greenhouse.