Soil Moisture Research Articles

Moisture-responsive root-branching pathways identified in diverse maize breeding germplasm

Plants grow complex root systems to extract unevenly distributed resources from soils. Spatial differences in soil moisture are perceived by root tips, leading to the patterning of new root branches toward available water in a process called hydropatterning. Little is known about hydropatterning behavior and its genetic basis in crop plants. Here, we developed an assay to measure hydropatterning in maize and revealed substantial differences between tropical/subtropical and temperate maize breeding germplasm that likely resulted from divergent selection. Genetic analysis of hydropatterning confirmed the regulatory role of auxin and revealed that the gaseous hormone ethylene locally inhibits root branching from air-exposed tissues. Our results demonstrate how distinct signaling pathways translate spatial patterns of water availability to developmental programs that determine root architecture.

Modeling and Nonlinear Analysis of Plant–Soil Moisture Interactions for Sustainable Land Management: Insights for Desertification Mitigation

This research explores the dynamics of vegetation patterns under changing environmental conditions, considering the United Nations Sustainable Development Goal 15: “Protect, restore, and promote the sustainable use of terrestrial ecosystems; combat desertification; halt and reverse land degradation; and prevent biodiversity loss.” In this context, this study presents a modeling and nonlinear analysis framework for plant–soil-moisture interactions, including Holling-II functional response and hyperbolic mortality models. The primary goal is to explore how nonlinear soil–water interactions influence vegetation patterns in semi-arid ecosystems. Moreover, the influence of nonlinear soil–water interaction on the establishment of population patterns is investigated. The formation and evolution of these patterns are explored using theoretical analysis and numerical simulations, as well as important factors and critical thresholds. These insights are crucial for addressing desertification, a key challenge in semi-arid regions that threatens biodiversity, ecosystem services, and sustainable land management. The model, which includes environmental parameters such as rainfall, plant growth rates, and soil moisture, was tested using both theoretical analysis and numerical simulations. These characteristics are carefully adjusted to find important thresholds influencing the danger of desertification. Simulation scenarios, run under set initial conditions and varying parameters, yield useful insights into the pattern of patch development under dynamically changing environmental conditions. The findings revealed that changes in environmental conditions, such as rainfall and plant growth rates, prompted Hopf bifurcation, resulting in the production of three distinct patterns: a dotted pattern, a striped pattern, and a combination of both. The creation of these patterns provides essential information about the sustainability of environmental equilibrium. The variation curve of the average plant biomass reveals that the biomass fluctuates around a constant period, with the amplitude initially increasing, then decreasing, and gradually stabilizing. This research provides a solid foundation for addressing desertification risks, using water resources responsibly, and contributing to a better understanding of ecosystem stability.

Influence of Rice Residue Management on Sunflower Growth, Bulk Density and Moisture Content of Soil in a Rice-sunflower Cropping System

A field experiment was conducted during 2022-23 and 2023-24 to assess the impact of kharif rice residue management on sunflower growth and soil bulk density and moisture content in a rice-sunflower cropping system on Alfisols using a randomized block design. Results indicated that the highest dry matter accumulation per plant (20.59 g plant⁻¹) was observed in the treatment involving straw incorporation with a C:N:P ratio adjusted to 30:1:0.3 prior to incorporation (T7), outperforming residue removal (T2) and burning (T1) treatments. Bulk density (BD) was highest (1.38 Mg m⁻³) in residue removal and burning treatments with RDF application and lowest (1.34 Mg m⁻³) in residue retention with zero tillage sunflower and RDF (T3). Additionally, the higher gravimetric moisture (GM) content was recorded in treatments with a C:N:P ratio adjustment of 30:1:0.3 before incorporation, while the lowest GM content was observed in residue removal with RDF application.

A laboratory-scale physical model for freeze-thaw studies in soil columns under simulated climate change conditions.

Responses of soils to climate warming during winter can be studied by monitoring soil temperature variations in the vadose zone. Freeze-thaw cycles during winter can have a significant impact on soil biogeochemical and physical processes. Observing soil temperature at different depths offers important insights into heat availability, which influences biogeochemical activity and solute movement driven by temperature gradients. However, it is difficult to replicate the relevant processes in soil columns without maintaining freezing temperatures with an unfrozen soil layer below the freezing interface. This paper describes the development and experimental verification of a laboratory-scale physical model to assess the effect of freeze-thaw cycles on biogeochemical processes in unsaturated soil. The results show that the experimental design can (i) induce cyclical freezing of soil down to desirable depths, (ii) maintain vertical temperature gradients and (iii) ensure the rest of the column remains unfrozen below the freezing interface for pore water sampling. The rate of freezing is suitable for quick freeze-thaw cycles (0.19°C/min). This setup can be further developed to observe solute transport and attenuation in variably-saturated soil and soil moisture status for different freeze-thaw regimes under simulated climate change scenarios.

A Monitoring Method for Agricultural Soil Moisture Using Wireless Sensors and the Biswas Model

Efficient monitoring of soil moisture is crucial for optimizing water usage and ensuring crop health in agricultural fields, especially under rainfed conditions. This study proposes a high-throughput soil moisture monitoring method that integrates LoRa-based wireless sensor networks with region-specific statistical models. Wireless sensors were deployed in the top 0–0.2 m soil layer to gather real-time moisture data, which were then combined with the Biswas model to estimate soil moisture distribution down to a depth of 2.0 m. The model was calibrated using field capacity and crop wilting coefficients. Results demonstrated a strong correlation between model predictions and actual measured soil moisture storage, with a coefficient of determination (R2) exceeding 0.94. Additionally, 83% of sample points had relative errors below 18.5%, and for depths of 0–1.2 m, 90% of sample points had relative errors under 15%. The system effectively tracked daily soil moisture dynamics during maize growth, with predicted evapotranspiration relative errors under 10.25%. This method provides a cost-effective and scalable tool for soil moisture monitoring, supporting irrigation optimization and improving water use efficiency in dryland agriculture.

Evaluating ecosystem multifunctionality in tree-based intercropping: a case study from southern Québec, Canada

Agroforestry is increasingly recognized as an effective tool for enhancing multifunctionality in agroecosystems globally, improving land-use efficiency and delivering multiple ecosystem services (ES). This study investigates the multifunctionality of tree-based intercropping (TBI) systems, which integrate widely spaced rows of trees with agricultural crops and can be adapted to different climates. We assessed spatial gradients of 11 ES indicators based on field measurements taken at increasing distances from the tree rows within a temperate TBI system of 50 trees ha-1, aged 7 to 10 years. These indicators were compared between the TBI system and agricultural control plots, which were managed similarly to the cultivated alleys in the TBI system but without trees. We measured light availability, soil moisture and crop yields (forage and wheat) across cultivated alleys associated with three tree species compositions: 1) red oak in monoculture, 2) hybrid poplar in monoculture, and 3) a mix of red oak and hybrid poplar alternating along the row. The greatest variation in ES indicators within the cultivated alleys, compared to the agricultural controls, was frequently observed nearest to the tree rows. Specifically, yields of annual crops (wheat and corn), soil moisture, P supply, NO3- leaching rates and bulk density decreased, while potential evaporation increased in areas closest to the tree rows, in contrast to measurements taken near the alley centre and in the control plots. Other ES indicators, including forage yield, N and K supplies, and soil C stocks, remained unaffected by the TBI system. Our results suggest that trade-offs between ES may occur at fine scales and be location-specific within TBI systems. Plots containing poplar (alone or mixed with red oak) exhibited lower soil moisture and light availability compared to those with red oak only, resulting in a greater decrease in wheat yield at the tree-crop interface. Conversely, C stocks in fast-growing poplar biomass were substantially higher than those in red oak. We conclude that composition of tree species is crucial in determining trade-offs in ES delivery within TBI systems. At the system level, we found comparable levels of multifunctionality between TBI and control plots, likely due to the limited sample size of aggregated data.

Dynamic Modeling of Soil Water Dynamics and Nitrogen Species Transport with Multi-Crop Rotations Under Variable-Saturated Conditions

Excessive application of nitrogen (N) fertilizers in agriculture poses significant environmental risks, notably nitrate leaching into groundwater. This study evaluates soil water dynamics and the transport of urea, ammonium, and nitrate under variable-saturated conditions in a long-term experimental field in Croatia, Europe. Utilizing HYDRUS-1D and HYDRUS-2D models, we simulated water flow and nitrogen transformation and transport across six lysimeter-monitored locations over four years (2019–2023), incorporating diverse crop rotations and N addition. Key modeled processes included nitrification, urea hydrolysis, and nitrate leaching, integrating field-measured parameters and climatic conditions. The models achieved high reliability, with R2 values for water flow ranging from 0.58 to 0.97 and for nitrate transport from 0.13 to 0.97; however, some cases reported lower reliability. Results revealed that nitrate leaching was influenced by precipitation patterns, soil moisture, crop growth stages, and fertilization timing. Peak nitrate losses were observed during early crop growth and post-harvest periods when elevated soil moisture and reduced plant uptake coincided. The findings highlight the importance of optimizing nitrogen application strategies to balance crop productivity and environmental protection. This research demonstrates the effectiveness of numerical modeling as a tool for sustainable nitrogen management and groundwater quality preservation in agricultural systems. It also indicates the need for further development by capturing some of the processes such as identification in the N cycle.

First report of Fusarium proliferatum causing rhizome rot on Polygonatum cyrtonema in China.

Polygonatum cyrtonema Hua (Asparagaceae) is a perennial herb widely distributed in China (Chen et al. 2021). The rhizome has antioxidant, anti-inflammatory, and immunomodulatory properties and is traditionally used to treat dizziness, diabetes, and asthma (Lu et al. 2023; Pang et al. 2022). About 3000 P. cyrtonema plants were planted in five fields at the Institute of Medicinal Plant Development (40.04°N, 116.28°E), Beijing, China, where rhizome rot was observed from June to October 2023, with an incidence rate of about 10%. Most cases occurred in low-lying, waterlogged fields. Initially, infected plants had no obvious aboveground symptoms but had brown spots on their rhizomes. In severe cases, plants exhibited aboveground withering and brown rot in rhizomes. A fungus was isolated from symptomatic plants (isolation rate 73%), and single spores were used to grow pure colonies on potato-dextrose-agar (PDA) and incubated for 5 d at 25°C in the dark. Eleven isolates with the same morphology were obtained. The upper surface of the colonies was white, with cottony mycelium and a light purple center. The lower surface of the colonies was hazel in the center. Two representative isolates, C76 and C78, were cultivated on carnation leaf agar medium. Microconidia of the isolates were aseptate, oval, elliptic or clavate, and their dimensions were 4.9 to 12.1 × 1.5 to 4.5 μm (n = 50). Macroconidia were long, slender and thin, rod-shaped or slightly curved, with one to five septa, and their dimensions were 15.3 to 44.9 × 2.7 to 4.5 μm (n = 50). The isolates were classified as Fusarium based on morphology (Leslie and Summerell 2006). To determine the species identity, we sequenced the translation elongation factor (TEF-1α) and partial RNA polymerase second largest subunit (RPB2) of two isolates (i.e., C76 and C78, GenBank access numbers: TEF-1α, PQ550044, PQ285402; RBP2, PQ550045, PQ285403, respectively) (Crous et al 2009) and compared them to other Fusarium species found at Fusarium-ID and GenBank databases. Both isolates exhibited 99.84% (TEF-1α, MT305203) and 100% (RBP2, LT841252) similarity with Fusarium proliferatum. The phylogenetic tree was constructed by combining TEF-1α, and RBP2 using MEGA6, and the two isolates clustered with F. proliferatum. To demonstrate pathogenicity, 2 mL of conidial suspension (1 × 106 conidia/mL) of the isolate of F. proliferatum designed C76 were dropped on the surface of four rhizomes without wounding. For the control, sterile water was applied on two control rhizomes. All treatments were repeated three times. Seedlings were grown at 25°C in moist soil. After 7 d, inoculated roots exhibited similar symptoms to those in the field, while control roots showed no symptoms. The same isolate was reisolated from diseased roots and was identified based on morphological characteristics, fulfilling Koch's postulates. Based on morphology and molecular biology, the isolates were identified as F. proliferatum, a ubiquitous pathogen (Li, et al 2017) that was reported to cause leaf spot on P. cyrtonema (Zhou, et al. 2021). This is the first report of F. proliferatum causing rhizome rot on P. cyrtonema. This research will help develop strategies to manage rhizome rot incited by F. proliferatum.

CAMELS-IND: hydrometeorological time series and catchment attributes for 228 catchments in Peninsular India

Abstract. We introduce CAMELS-IND (Catchment Attributes and MEteorology for Large-sample Studies – India), a dataset containing hydrometeorological time series and catchment attributes for 472 catchments in Peninsular India, of which 228 catchments have observed streamflow data available for over 30 % of the period between 1980 to 2020. Peninsular India covers 15 interstate river basins defined by the Central Water Commission (CWC), where river flow and water level datasets are available for several gauge stations through the open-source India Water Resources Information System (India-WRIS). However, many of these gauge stations lack reliable metadata, and data are not in an analysis-ready format for large-sample hydrological studies. Therefore, we utilized 472 gauge stations and their catchment boundaries, characterized as stations with reliable metadata, from the Geospatial dataset for hydrologic analyses in India (GHI) (Goteti, 2023). For each of these catchments, CAMELS-IND provides a catchment mean time series of meteorological forcings for 41 years (1980–2020) and 211 catchment attributes representing hydroclimatic and land cover characteristics extracted from multiple data sources (including ground-based observations, remote sensing-based products, and reanalyses datasets). CAMELS-IND follows the same standards of the previously developed CAMELS datasets for the USA, Chile, Brazil, Great Britain, Australia, Switzerland, and Germany to facilitate comparisons with catchments of those countries and inclusion in global hydrological studies. Notably, CAMELS-IND includes available observed streamflow and catchment mean time series of 19 meteorological forcings, including precipitation, maximum, minimum, average temperature, long-wave and short-wave radiation flux, U and V components of wind, relative humidity, evaporation rates from canopy and soil surface, actual and potential evapotranspiration, and soil moisture of four layers (covering depth up to 3 m below ground) for detailed hydrometeorological studies. We also derived catchment attributes representing human influences, including the number of dams and their utilization, total volume contents of dams in catchments, population density, and increases in urban and agricultural land covers to facilitate studies to understand human influences on catchment hydrology. Furthermore, the dataset includes predicted streamflow time series from a regionally trained long short-term memory (LSTM)-based hydrological model for all 472 catchments which can fill gaps in observed streamflow data or serve as a benchmark for testing and developing new hydrological models. We envision that CAMELS-IND will provide a strong foundation for a community-led effort toward gaining new hydrological insights from hydrologically distinct Indian catchments and solving pertinent issues related to water management, quantification and risk assessment of hydrologic extremes, unraveling regional-scale hydrologic functioning, and climate change impact assessment of catchments across India. The CAMELS-IND dataset is available at https://doi.org/10.5281/zenodo.14005378 (Mangukiya et al., 2024).

Canopy structure modulates the sensitivity of subalpine forest stands to interannual snowpack and precipitation variability

Abstract. A declining spring snowpack is expected to have widespread effects on montane and subalpine forests in western North America and across the globe. The way that tree water demands respond to this change will have important impacts on forest health and downstream water subsidies. Here, we present data from a network of sap velocity sensors and xylem water isotope measurements from three common tree species (Picea engelmannii, Abies lasiocarpa and Populus tremuloides) across a hillslope transect in a subalpine watershed in the Upper Colorado River basin. We use these data to compare tree- and stand-level responses to the historically high spring snowpack but low summer rainfall of 2019 against the low spring snowpack but high summer rainfall amounts of 2021 and 2022. From the sap velocity data, we found that only 40 % of the trees showed an increase in cumulative transpiration in response to the large snowpack year (2019), illustrating the absence of a common response to interannual spring snowpack variability. The trees that increased water use during the year with the large spring snowpack were all found in dense canopy stands – irrespective of species – while trees in open-canopy stands were more reliant on summer rains and, thus, more active during the years with modest snow and higher summer rain amounts. Using the sap velocity data along with supporting measurements of soil moisture and snow depth, we propose three mechanisms that lead to stand density modulating the tree-level response to changing seasonality of precipitation: Topographically mediated convergence zones have consistent access to recharge from snowmelt which supports denser stands with high water demands that are more reliant and sensitive to changing snow. Interception of summer rain in dense stands reduces the throughfall of summer rain to surface soils, limiting the sensitivity of the dense stands to changes in summer rain. Shading in dense stands allows the snowpack to persist deeper into the growing season, providing high local reliance on snow during the fore-summer (early-summer) drought period. Combining data generated from natural gradients in stand density, like this experiment, with results from controlled forest-thinning experiments can be used to develop a better understanding of the responses of forested ecosystems to futures with reduced spring snowpack.

Changes in Soil Properties, Organic Carbon, and Nutrient Stocks After Land‐Use Change From Forests to Grasslands in Kumaun Himalaya, India

ABSTRACTLand‐use changes are anticipated to be a substantial contributor to global change climate, substantially causing significant modifications in soil characteristics. This study addressed the impact of land‐use change from native forests to grasslands on the soil physico‐chemical properties in entirely replicated grasslands of three different forest zones (Oak, Pine and Cypress) in temperate region of Kumaun Himalaya. A total of 162 soil samples (6 sites × 3 plots × 3 seasons × 3 depths = 162 samples) were randomly collected from each site in triplicates from depths. The soil texture, bulk density (bD), porosity, water holding capacity, soil moisture content, pH, organic carbon (SOC), total nitrogen (TN), available phosphorus (P) and available potassium (K) were determined at different depths in forest and grassland sites. Results showed that soil bD, pH, SOC, TN, P and K significantly (p < 0.05) decreased with increasing depth. Moreover, conversion of forests into grassland reduced nutrient concentrations, physical qualities (bD and porosity), and pH levels. The decreasing trend of nutrient along the soil depth explains that the zone of nutrient accumulation is not well established in these grasslands because of the substantial leaching effect. Our findings indicate that conversion of natural forests into grasslands resulted in significant losses of SOC and TN stocks which can be attributed to the disturbance of natural forests. Therefore, while making land‐use change plans, the impact of these alterations on soil nutrients must be considered. These findings emphasize the value of establishing natural vegetation (forests) in these areas to retain nutrients and safeguard soil against runoff and erosion. However, anticipating the physico‐chemical impacts of land‐use alteration necessitates a better comprehension of its relations with other drivers of global change, such as changing climate and nitrogen deposition.

Relationship between Drought Frequency and Desertification Progress in Australia

Purpose: The aim of the study was to analyze the relationship between drought frequency and desertification progress in Australia. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Frequent droughts in Australia accelerate desertification, particularly in arid regions like the Murray-Darling Basin. Rising temperatures, reduced rainfall, and extreme weather events degrade soil, reduce vegetation, and threaten agriculture. ENSO-driven droughts worsen water scarcity, impacting ecosystems and farming. Satellite data show declining soil moisture and groundwater levels, increasing land degradation. Without sustainable land management, reforestation, and water conservation, desertification risks will rise, threatening biodiversity and food security. Unique Contribution to Theory, Practice and Policy: Theory of desertification, aridity index and climate variability theory & threshold theory of land degradation may be used to anchor future studies on the relationship between drought frequency and desertification progress in Australia. Implementing drought-resistant crops, precision irrigation systems, and soil conservation techniques can significantly reduce the rate of desertification in agricultural regions. Governments should establish joint climate resilience frameworks, particularly in transboundary drylands and shared water basins, to prevent cross-border desertification effects.

Populus deltoides is suitable for moist and short-term flooded soil conditions on the basis of its relative growth rate and stoichiometry

To understand the effects of water stress on plant growth, nutrients distribution and their stoichiometry in different organs of poplar seedlings, and further explore the validation of growth rate hypothesis (GRH) under water stress treatments. Poplar seedlings (Populus deltoides 'Nanlin 3804') were grown under drought (D), normal water management (CK), low-level flooding (LF), high-level flooding (HF) and high-level flooding followed by flood recovery (FR) treatments in 60 days. Poplar seedling growth, nutrients contents and stoichiometry among different treatments were analyzed. The seedlings had greater relative growth rate of biomass, height and stem-basal diameter (BRGR, HRGR, and SRGR, respectively) under flooding treatments, especially in FR treatment (P < 0.05). Among different organs, stem had the highest BRGR. The N, P and K concentrations were highest in D treatment (P < 0.05). Leaves and stems had greater nutrient concentrations and stoichiometries than roots under different treatments (P < 0.05). The plant stoichiometry were positively correlated with BRGR and HRGR but negatively correlated with SRGR from whole-plant perspective. In organ level, BRGR were negatively correlated with root stoichiometry, but were positively correlated with stem and leaf stoichiometry. Poplar seedlings are suitable for cultivation in relatively moist soil or under short-term periodic flooding conditions. This study provides a scientific basis for the cultivation of high-quality seedlings and the selection of suitable afforestation sites for existing poplar clones.

Research on Architecture, Applications, and Performance Evaluation using Wireless Sensor Networks for Environmental Surveillance

Wireless Sensor Networks (WSNs) have emerged as a powerful tool for environmental monitoring, offering scalable and cost-effective solutions for tracking environmental parameters such as air quality, soil moisture, and temperature. This paper presents a comprehensive overview of WSNs, highlighting their architecture, key components, applications, and performance metrics. A case study is included, presenting experimental results that evaluate the effectiveness of WSNs in various environmental settings. The paper discusses energy efficiency, scalability, and data accuracy, while also addressing challenges and proposing future directions for WSN technology in environmental monitoring

Elevation gradient effects on grassland species diversity and phylogenetic in the two-river source forest region of the Altai Mountains, Xinjiang, China

Altitude, as a key environmental factor, shapes the spatial patterns of species diversity, phylogenetic diversity, and community phylogenetic structure. Studying grassland diversity and phylogenetic structure along altitudinal gradients helps clarify how altitude-driven environmental changes influence community assembly, and reveal vertical patterns in community formation. This study examines grasslands at 1300–2500 m elevation in the Two-River Source Forest Area, Altai Mountains, Xinjiang. Six elevation gradients (200 m intervals) were surveyed with 90 grassland quadrats, documenting community characteristics and environmental data. The study analyzes the patterns of species composition, diversity, and phylogeny across different elevation gradients and explores their relationships with key environmental factors. The results indicate that the grassland species composition is dominated by species from the Poaceae, Rosaceae, and Asteraceae families, with Poa annua (annual bluegrass) being the dominant species within Poaceae. The species diversity along the elevation gradient exhibits a bimodal trend, with an initial increase, followed by a decrease, another increase, and finally a decline as the elevation rises. In contrast, phylogenetic diversity shows a unimodal pattern, characterized by an initial increase followed by a decline with increasing elevation. Although the phylogenetic structure did not exhibit a significant trend of transitioning from divergence to clustering along the altitudinal gradient, the overall phylogenetic pattern of grassland communities tended toward clustering. Further analysis reveals significant correlations between species diversity and environmental factors such as temperature, precipitation, forest cover, and soil moisture. However, no environmental factors were found to have a significant correlation with the phylogenetic indices.

A Novel Agricultural Remote Sensing Drought Index (ARSDI) for high-resolution drought assessment in Africa using Sentinel and Landsat data.

Drought poses a significant threat to agricultural productivity in Africa due to climate variability and water scarcity. To improve drought monitoring using high-resolution remote sensing data, this paper proposes a novel Agricultural Remote Sensing Drought Index (ARSDI). Imagery from Sentinel-1(S-1), Sentinel-2 (S-2), and Landsat 8 was utilized to develop a drought monitoring system for 2017-2023, during which Egypt and Kenya experienced significant drought events. In Kenya, particularly from 2021 to 2023, recurrent droughts severely affected agricultural output, necessitating the evaluation of ARSDI's efficiency in capturing drought severity. Similarly, in Egypt, drought-like conditions linked to reduced Nile River flow and rainfall variability posed significant challenges. ARSDI exhibited strong correlations with traditional drought indicators, such as the Soil Moisture Condition Index (SMCI), ranging from 0.64 to 0.88 in Egypt and 0.74 to 0.85 in Kenya. It also correlated well with the Vegetation Health Index (VHI), with values (0.96 to 0.98) in Egypt and (0.53 to 0.98) in Kenya. Furthermore, ARSDI aligned with Standardized Precipitation Index (SPI), ranging (0.44 to 0.71) in Egypt and (0.47 to 0.60) in Kenya. By integrating Sentinel-1 radar data, ARSDI mitigated the limitations of cloud cover, providing more reliable vegetation monitoring. Compared to other indices, such as the Normalized Difference Vegetation Index (NDVI), ARSDI exhibited superior performance, particularly during the drought events 2019 in Egypt and 2020 in Kenya, demonstrating its robustness in assessing soil moisture and vegetation health.

Soil organic carbon and related properties under conservation agriculture and contrasting conventional fields in Northern Malawi

Conservation agriculture (CA) is widely promoted as an agroecology-based approach for soil conservation. Several studies have focused on effects of CA on crop yields and soil moisture dynamics in sub-Saharan Africa, with limited focus on effects of CA on soil organic carbon (SOC) and associated fractions. We collected representative soil samples from 30 paired farms under CA and conventional tillage in Mzimba district, north of Malawi to determine effects of tillage and soil depth on soil physico-chemical properties, total SOC, and organic carbon fractions. Undisturbed soil cores were collected for bulk density measurements. Different SOC pools were determined using the soil fractionation method, while soil physico-chemical analyses were conducted using standard laboratory methods on disturbed soil samples. Soil organic carbon content ranged from 0.4-1.8% in CA plots. This was significantly larger than SOC contents of 0.4-1.5% measured under conventional tilled plots. Tillage type and soil depth had significant interaction effects on SOC. For example, larger contents of SOC were measured at depths of 0-10 cm compared to 10-30 cm under CA plots. Soil depth had significant effects on most soil properties compared to tillage. Examples include Heavy Particulate Organic Matter-Carbon (POM-C) fraction, Mineral Associated Organic Matter-Carbon (MAOM-C), nitrogen in MAOM fraction and nitrogen in the Light POM fractions. These were larger in the 0-10 cm soil depth than in the 10-30 cm soil depth. In contrast, tillage type only had significant effects on the Heavy POM-C and MAOM-C fractions, which were larger under CA than conventional tilled plots. Conservation agriculture showed capacity to improve total SOC and its associated fractions, a finding relevant towards understanding effects of land management on carbon storage. However, challenges of competing residue use as feed, mulch, and fuel continue to impede mulching under CA systems. Longer term studies and use of alternative mulching options could be employed to recognise noticeable changes in other SOC beneficial pools in fields under CA.

Performance of ‘Sabiá’ Grass Irrigated with Drippers Installed in the Subsurface

ABSTRACTSubsurface irrigation, which provides greater water efficiency and reduces surface soil wetting, is an effective alternative for minimizing water losses through evaporation, especially under cultivation conditions that require greater resource conservation. The ‘Sabiá’ grass, an agronomically relevant forage species, may exhibit different responses when irrigated by subsurface systems. Thus, the objective of this study was to evaluate the performance of ‘Sabiá’ grass irrigated with drippers installed at different depths during different climatic seasons. The experiment was carried out from January to July 2022 under open‐air conditions in Viçosa, MG, Brazil, in a completely randomized design in split plots with four replicates. The plots consisted of four cycles of ‘Sabiá’ grass, and the subplots consisted of seven depths of dripper installation (superficial, 5, 10, 15, 20, 25, and 30 cm). ‘Sabiá’ grass was cultivated in pots, and the recommendations for irrigation were based on crop evapotranspiration (ETc), which was measured in two drainage lysimeters. The water consumption and morphogenetic and agronomic characteristics of ‘Sabiá’ grass were evaluated. The total water consumption of ‘Sabiá’ grass in cycles 1, 2, 3, and 4 was 42.4, 26.7, 14.9, and 11.5, respectively. The growth, development and productivity of ‘Sabiá’ grass decreased from cycle 1 (summer) to cycle 4 (winter). Morphogenic characteristics were slightly affected by the different dripper installation depths. ‘Sabiá’ grass presented lower shoot fresh mass, shoot dry mass and water use productivity at the greatest dripper depths. ‘Sabiá’ grass presented greater root system development when the dripper was installed between 10 and 15 cm deep. The normalized difference vegetation index (NDVI) proved to be a promising tool for estimating the biomass production of ‘Sabiá’ grass. In view of these results, drip tapes should be installed between depths of 10 and 20 cm for the cultivation of ‘Sabiá’ grass in clay soil.

Assessing Climate Change Resilience in Central Indian Agriculture: A Regional Indicators-based Approach and Agro-climatic Zone Mapping

Climate change has adversely hampered the agricultural economy of India, especially central India. To get insight into the present level of climate change resilience in Central India (Madhya Pradesh, Maharashtra, Chhattisgarh, Southern UP) a composite climate change resilience capacity index (CCRCI) was developed for selected 102 districts using 50 climatic, soil, crop, livestock and socio-economic indicators in agriculture. Mann- Kendall non-parametric trend analysis was employed to evaluate long-term climatic trends (kharif 1981- summer 2023) for key climatic indicators like daily average temperature, precipitation, relative humidity and root zone soil wetness. Standard methodologies of index development like normalisation and principal component analysis (PCA) for weight assignment were executed. After developing CCRCI, agro-climatic zone-wise mapping was done for all the selected districts. Results revealed that Maharashtra had the largest number of high climate-resilience districts, followed by Madhya Pradesh with a mix of resilience levels in Chhattisgarh and Southern UP showing significant gaps in climate preparedness. The findings underscore the need for targeted interventions in low-resilience districts, particularly in agro-climatic Zones VII (Eastern Plateau and Hills Region) and IX (Western Plateau and Hills Region), where climate exposure and limited adaptability pose significant risks to agriculture. This mapping highlights the diversity of challenges and opportunities in central India, offering a framework for region-specific climate change resilience planning.

Impact of Weather Parameters and Fungicides Strategies on the Management of Root & Stem Rot in Sesame Caused by Macrophomina phaseolina

Root & stem rot caused by Macrophomina phaseolina is an important disease. It spreads through seed and soil residual materials. In India root & stem rot disease can induce 5-100% yield losses under epidemic condition. The effect of various weather parameters on the development of root & stem rot in sesame was investigated during Kharif 2023 and 2024. The various weather parameters viz., temperature (max), temperature (min), relative humidity (morning), relative humidity (evening), soil temperature, soil moisture, rainfall and rainy days under natural condition. Two year data revealed that there is significant correlation between disease incidence and weather parameters. The weather parameters viz., temperature (minimum), relative humidity (evening), soil moisture, rainfall showed a significant negative correlation with disease incidence, while soil temperature showed a significant positive correlation during both years. Regression analysis revealed that all the weather parameters contributed 99.96% and 96.04% towards disease development during both years. Due to lack of resistance in sesame genotypes, disease is managed through fungicides. Management of root & stem rot disease in sesame was done under In vitro and In vivo conditions. Average pooled data analysis revealed that, seed treatment with Carbendazim 12% WP + Mancozeb 63% WP followed by two foliar application of Tebuconazole 50% WG + Trifloxystrobin 25% WG was found most effective in minimizing the disease incidence (20.49%) and per cent disease reduction over the control (59.51%) with yield 478.98 kg/ha and B:C ratio 1.69. The key benefit to use this fungicides against plant disease is that it provides both protective and curative activity, reduce risk of fungicide resistant development, it also offers broader spectrum of disease control, and it gives both quick action (trifloxystrobin) and extended protection (tebuconazole).