Soil Moisture Conditions Research Articles

Pros and Cons of Different Irrigation Scheduling Approaches: A Review

Scheduling irrigation involves making a decision of how much water to apply and when. Three factors enter into the decision: water needs of the plants, water availability, and storing capacity of the soil around the roots. When to irrigate should be greatly influenced by water needs of the plants. Irrigation scheduling (IS) aims to give plants the right amount of water at the right times in order to promote plant growth and achieve high yield and/or quality. The four most popular ways of operating irrigation scheduling includes: evapotranspiration and water balance (ET-WB), soil moisture status, plant water status, and models based irrigation scheduling. When the four types of irrigation scheduling systems are thoroughly examined, it becomes clear that they are all centered on soil moisture, which serves as a link or bridge between crop water needed for growth and irrigation management). A few studies have been accomplished on pros and cons of different irrigation scheduling approaches. The purpose of this review was to provide some information on pros and cons of four selected irrigation scheduling methods, viz: evapotranspiration and water balance (ET-WB), soil moisture status, plant water status, and models based irrigation scheduling. When the four types of irrigation scheduling systems are thoroughly examined, it becomes clear that they are all centered on soil moisture, which serves as a link or bridge between crop water needed for growth and irrigation management. Plant-based techniques can need professional oversight since farmers may find it difficult to understand the tracked data, which reduces the dependability of irrigation. When built software or a procedure is based on a well-calibrated model, model-based methods are simpler for users. When compared to the soil moisture sample irrigation scheduling approach, the evapotranspiration losses irrigation scheduling method uses less irrigation water regardless of the minimal yield difference.

Low-Cost Sensors for the Measurement of Soil Water Content for Rainfall-Induced Shallow Landslide Early Warning Systems

Monitoring soil water content (SWC) can improve the effectiveness of early warning systems (EWSs) designed to mitigate rainfall-induced shallow landslide risk. In extensive areas, like along linear infrastructures, the adoption of cost-effective sensors is critical for the EWS implementation. The present study aims to evaluate the reliability of different low-cost SWC sensors (frequency domain reflectometry and capacitance-based) in capturing soil moisture conditions critical for EWS, without performing soil-specific calibration. The reliability of the low-cost sensors is assessed through a comparative analysis of their measurements against those from high-cost and well-established sensors (time domain reflectometry) over a two-year period in a shallow landslide-prone area of Oltrepò Pavese, Italy. Although no landslides are observed during the monitoring period, meteorological conditions are reconstructed and statistical analysis of sensor’s responses to different rainfall events is conducted. Results indicate that, despite differences in absolute readings, low-cost sensors effectively capture relative SWC variations and demonstrate sensitivity to rainfall events across both cold and warm periods. The presented low-cost sensors can serve as reliable indicators of soil infiltration and saturation levels, highlighting their potential for real-time monitoring within extensive networks for EWS.

Characterization and prediction of hydraulic properties of traffic-compacted forest soils based on soil information and traffic treatments

Key messageThe hydraulic properties of compacted and rutted soils were evaluated through in-situ infiltration experiments and predicted based on soil texture class and traffic treatments. A significant decrease in saturated soil water content and soil hydraulic conductivity at saturation was observed. The resulting soil hydraulic parameters, when integrated into a soil water transfer model, effectively simulated water dynamics in these impacted forest soils, providing a crucial first step toward developing decision support tools for real-time trafficability. This approach can assist forest managers in minimizing the extent of soil compaction.ContextTo overcome trafficability issues of forest soils induced by heavy logging machinery, planning support tools are needed to determine suitable soil moisture conditions for traffic.AimsThis study aimed to identify the soil properties that differ significantly between undisturbed and compacted soils and to provide several estimation tools to predict the hydraulic properties of compacted soils beneath the skid trails.MethodsFour hundred seventeen water infiltration tests were conducted on 19 forest sites, mostly in North-eastern France, and analysed with the BEST method to estimate the hydraulic properties of the skid trails and undisturbed soils. The hydraulic properties of the skid trails were predicted thanks to linear mixed effect models using a bulk treatment effect, a site effect, or a skid trail degradation score. The predicted hydraulic properties were tested using a water flow model to assess their relevance regarding the prediction of water dynamics in skid trails.ResultsThe compaction effect was only significant for the logarithm of the hydraulic conductivity at saturation (log10(Ksat)) and the soil water content at saturation (θsat). For the skid trails, θsat was reduced by - 0.02 and − 0.11 m3m−3 in the 0 − 10 cm and 15 − 25 cm layers respectively, compared to undisturbed topsoil (0 − 10 cm). log10(Ksat) was reduced by − 0.38 and − 0.85 for skid trails in the 0 − 10 and 15 − 25 cm soil layers respectively, compared to undisturbed topsoil. The use of a pedotransfer function, in replacement of water infiltration tests, and their combination with the same correction coefficients proved to efficiently simulate the difference in water dynamics between skid trails and undisturbed forest soils.ConclusionEstimation of soil hydraulic properties based on in situ water infiltration experiments proved efficient to simulate water dynamics in compacted and rutted forest soils. Yet, further studies are needed to identify the most adapted pedotransfer function to forest soils and to test the generalisation of our findings in different conditions, especially deeply rutted soils (rut depths > 12 cm).

Assessment of Soil Moisture through Climatic Variables Using Remote Sensing Data in Semi-Arid Sokoto State, Nigeria

Soil moisture availability is an indispensable requirement for not only the growth of crops but also other living organisms in any ecological settings This study determined soil moisture condition in Sokoto State using climate-based water index. Satellite-based estimations of mean monthly rainfall data was obtained from the USGS/FEWS database for each of the local government areas. Also, approximations of monthly values of minimum and maximum air temperatures (AIRS) were downloaded from the NASA website. The latter variables were utilised in the computation of potential evapotranspiration using Hargreaves equation. It was very clear from the results that water surplus occurred in the months of July, August and September across almost all the locations. In addition, places lying in the southern part of the State such as Kebbe, Yabo, Tambuwal and Shagari recorded higher positive values of moisture availability in contrast to their counter parts in the extreme north bordering Niger republic. Thus, this study revealed slight spatio-temporal variation in soil moisture condition across the entire study area decreasing from the southern locations towards the north. Hence, the research could help improve agricultural planning and suggest irrigation during dry periods. Consequently, the findings could offer useful insights for managing water resources and improving farming in dry areas.

Effects of water table depth variations on canopy transpiration from Mongolian pine plantations in arid ecosystems

Groundwater is a critical moisture source for the growth and survival of dryland forests. Spatiotemporal variations in water table depth (WTD) affect soil water availability in the root zone, which influences canopy transpiration (Ec). However, to date, relatively few studies have been conducted on how changes in WTD regulate Ec. We measured the meteorological variables, volumetric water content (VWC), sap flow, and WTD in 2022 and 2023 from April to October in three Mongolian pine plantations located in Mu Us Sandy Land, China. The initial WTDs were 4 m (WTD4), 9 m (WTD9), and 13 m (WTD13), respectively. The mean VWC did not differ significantly between WTD4, WTD9, and WTD13 in the 0–50 cm layer. However, there was a significant decrease in the mean VWC with increasing WTD in the 50–200 cm layer (p < 0.05). Ec decreased significantly with increasing WTD (p < 0.01), with mean daily Ec of 1.25, 0.75, and 0.28 mm day−1 for WTD4, WTD9, and WTD13, respectively. Under soil drought conditions, Ec decreased significantly at WTD9 and WTD4 (p < 0.05). Meanwhile, Ec at WTD13 did not vary significantly under the different soil moisture conditions. At WTD9 and WTD13, Ec was mainly influenced by VWC. Meanwhile, at WTD4, Ec was influenced by the vapor pressure deficit and solar radiation. The diameter at breast height and leaf area index at WTD13 were significantly lower than those at WTD9 and WTD4 (p < 0.05). Although Mongolian pine could alleviate soil drought by adjusting Ec through morphological and physiological adaptations, it still suffered from the adverse effects of reduced water availability at deep WTD sites. This study establishes a basis for predicting the response of forests in semi-arid areas to changes in WTD. It contributes to the optimization of silvicultural design in similar areas and reduces ecological risks from the over-exploitation of groundwater.

Factors that influence the migration of sorbed pesticides in polyethylene and biodegradable mesoplastics

Trifluralin, Chlorpyrifos, and Procymidone migration performance from polyethylene (PE) and biodegradable (Mater-Bi: M-B) mulching films was examined. Desorption of pesticides from PE and M-B was studied using soil-plastic microcosms, considering temperature, soil humidity, and mulching film type as experimental variables. Trifluralin and Chlorpyrifos desorption was higher for PE than for M-B under all experimental conditions. In both cases, as the temperature increased from 25º C to 40º C, pesticide migration also increased, whereas as the soil humidity raised from 30% to 60%, pesticide desorption decreased. In the case of Procymidone, migration from PE and M-B at 25º C was similar under both soil moisture conditions. Migration percentages were similar for both mulch films at 40ºC and 30% soil humidity. However, at higher soil moisture (60%), migration from M-B was greater than from PE. A linear relationship was observed between the percentage of migration and the vapor pressure of the pesticides. In all cases, migration increased with higher vapor pressure, indicating a possible migration mechanism in the vapor phase. Pesticide migration increased at high temperatures (40º C). The effect of soil humidity in reducing pesticide migration was more significant at lower levels (30%).In addition, the mesoplastic sorption of pesticides in soil columns was studied using PE and M-B films. While the recoveries for Trifluralin, Chlorpyrifos, and Procymidone in the PE films were 0.05% ± 0.01%, 0.13% ± 0.03%, and non-detectable, the recoveries for M-B were: 0.49% ± 0.07%, 0.31% ± 0.09%, and 0.17% ± 0.10%, respectively, indicating that M-B was a better adsorbent than PE in all cases. This behavior should be considered in combination with the lower migration percentages observed for this type of mulching film in the microcosm experiments. These results could indicate a potential carrier effect of pesticide on biomesoplastic in the environment.

Drivers of Widespread Floods in Indian River Basins

Abstract Widespread floods affecting multiple subbasins in a river basin have implications for infrastructure, agriculture, environment, and groundwater recharge. However, the crucial linkage between widespread floods and their drivers remains unexplored for Indian subcontinental river basins. Here, we examine the occurrence and drivers of widespread flooding in seven Indian subcontinental river basins during the observed climate (1959–2020). The peninsular river basins have a high probability of widespread flooding, compared to the transboundary basins of the Ganga and Brahmaputra. Favorable antecedent baseflow and soil moisture conditions, uniform precipitation distribution, and precipitation seasonality determine the probability of widespread floods in Indian river basins. The widespread floods are associated with large atmospheric circulations that cause precipitation in a large part of a river basin. Our findings highlight the prominent drivers and mechanisms of widespread floods with implications for flood mitigation in India.

Modelling of the trade-off between the deep soil moisture and vegetation restoration in the hilly area of the Loess Plateau, China

Excessive depletion of soil moisture by artificial forests in the vegetation restoration areas of the Loess Plateau has attracted widespread attention. To assess potential risks of soil moisture deficit, we needed on-site vegetation and soil sampling data, as well as UAV images from the Chaigou Watershed for three-dimensional analysis, combining both sampling and raster data. Three-dimensional surfaces for assessment of trade-off were established innovatively by the local regression and interpolation methods. The results indicated that soil moisture benefits at 20–40 cm depth are lower than at 0–20 cm due to infiltration and surface disturbance. In some areas of the Chaigou Watershed, grass and shrub-grass vegetation are facing risks of soil moisture deficit based on trade-off values (RSMD) and multiparameter evaluations. Analysis of deep soil water content variability revealed the moisture decreases significantly with the deepening of the soil layer in some plots. R (Richness), H (Shannon’s Diversity), Margalef, COHESION, and CONTAG were applicable in interpolation and fitted with the local regression model (R2 > 0.6) corresponding to the trade-off, but SPLIT was proven to be inapplicable in this study area. The zero trade-off inflection points were 6 %–8 %, while the trend inflection points were 7.5 %–9 % for soil moisture and vegetation indices of vegetation and landscape in typical sampling sites of the Chaigou Watershed. Three-dimensional fitting model is more comprehensive and effective in assessing deep soil moisture conditions and grass plots on shady slopes generally had the best trade-off status in this region.

Mucilage facilitates root water uptake under edaphic stress: first evidence at the plant scale.

Mucilage has been hypothesized to soften the gradients in matric potential at the root-soil interface, hereby facilitating root water uptake in dry soils and maintaining transpiration with a moderate decline in leaf water potential. So far, this hypothesis has been tested only through simplified experiments and numerical simulations. However, the impact of mucilage on the relationship between transpiration rate (E) and leaf water potential (ψleaf) at the plant scale remains speculative. We utilized an automated root pressure chamber to measure the E(ψleaf) relationship in two cowpea genotypes with contrasting mucilage production. We then leveraged a soil-plant hydraulic model to reproduce the experimental observations and inferred the matric potential at the root-soil interface for both genotypes. In wet soil, the relationship between the leaf water potential and transpiration rate (E) was linear for both genotypes. However, as the soil progressively dried, the E(ψleaf) relationship exhibited nonlinearity. Genotype with low mucilage production exhibited nonlinearity earlier during soil drying, i.e. in wetter soil conditions, (soil water content < 0.36cm3 cm-3) compared to Genotype with high mucilage production (soil water content < 0.30cm3 cm-3). The incidence of nonlinearity was concomitant with the decline in matric potential across the rhizosphere. High mucilage production attenuated water potential diminution at the root-soil interface with increased E. This shows, for the first time at the plant scale, that root mucilage softened the gradients in matric potential and maintained transpiration in drying soils. The model simulations indicate that a plausible explanation for this effect is an enhanced hydraulic conductivity of the rhizosphere in genotype with higher mucilage production. Mucilage exudation maintains the hydraulic continuity between soil and roots and decelerates the drop in matric potential near the root surface, hereby postponing the hydraulic limitations to transpiration during soil drying.

SISTEM PENYIRAMAN OTOMATIS PADA PEMBIBITAN PRE-NURSERY KELAPA SAWIT BERBASIS INTERNET OF THINGS

The application of automation technology in the agricultural sector is a highly effective solution for improving the efficiency and productivity of seedling cultivation, particularly in oil palm nurseries. CV Pangean Raya TBS is an oil palm nursery business located in Solok Selatan Regency. CV Pangean Raya TBS is currently facing challenges in the watering process of oil palm seedlings, as it is still done manually. This manual method leads to wasted time, labor, and water. Manual watering often results in uneven water distribution, which can affect the quality of the oil palm seedlings. This research aims to design and implement an efficient Internet of Things (IoT)-based automatic watering system using a soil moisture sensor, ESP32 module, and RTC. The system is designed to monitor soil moisture conditions in real time and regulate watering automatically. The automatic watering is based on the moisture values detected by the sensor. Watering can also be manually controlled via a smartphone when needed, such as during rainfall, to prevent water wastage and overwatering of the oil palm seedlings. This system can help plantation owners optimize water usage, increase seedling productivity, and reduce dependence on manual labor. The research results indicate that the watering system can operate automatically based on the moisture data received, making it effective in conserving resources, improving productivity, and providing better control over plant conditions.

Minimizing Risks of Off-target Movement of Florpyrauxifen-benzyl by Coating onto Urea

ABSTRACT Commercialization of florpyrauxifen-benzyl as Loyant® in 2018 as a synthetic auxin herbicide in rice was followed by soybean injury due to off-target movement of spray applications in the mid-southern United States. Concerns surrounding off-target movement led to the exploration of an alternative application method to help alleviate the issue. Field experiments were conducted in 2020 and 2021 to explore the likelihood of a reduction in soybean injury following applications of florpyrauxifen-benzyl coated on urea in narrow- and wide-row soybean systems and to determine the likelihood of volatilization from this novel application method. Florpyrauxifen-benzyl spray-applied at 0.18 g ai ha-1 caused greater than 60% injury, while coating the herbicide on urea at 5.63 g ai ha-1 never exceeded 30% injury in narrow-row soybean. Similarly, florpyrauxifen-benzyl spray-applied at 0.18 g ai ha-1 caused greater than 50% injury while coating the herbicide on urea at 5.63 g ai ha-1 never exceeded 30% injury in wide-row soybean. As soybean injury increased, relative yield decreased in both narrow- and wide-row soybean. Spray-applied florpyrauxifen-benzyl decreased relative soybean groundcover, yield components, and soybean survival rate as the herbicide rate increased, whereas coating the herbicide on urea resulted in little to no decrease in both narrow- and wide-row soybean assessments. No negative impacts on relative yield and yield components of soybean from florpyrauxifen-benzyl coated on urea indicates that even though visible injury may persist, there is a low likelihood of any yield losses associated with the herbicide exposure using this application method. Additionally, coating the florpyrauxifen-benzyl on urea did not increase the likelihood of volatilization under any of the evaluated soil moisture conditions. Overall, applying florpyrauxifen-benzyl coated on urea is likely to be a safer application method and can reduce soybean injury compared to spray applying the herbicide when favorable off-target movement conditions exist.

Performance of infiltration trenches in different soils in native vegetation and urban area of the Federal District of Brazil

The increase in flooding events in major cities has driven the search for new strategies that incorporate Low Impact Development (LID) units into urban drainage. This research analyzes the performance of infiltration trenches to determine flow and infiltration physical parameters in different soil types in the Federal District of Brazil (FDB), comparing urban area and preserved Cerrado biome. The results show that most soils in the Federal District of Brazil have good infiltration conditions, particularly the Red Latosol and Red-Yellow Latosol. Infiltration trenches were effective in storing significant volumes of water, especially during initial runoff events where favorable soil moisture conditions exist. The Regolithic Neosol exhibited low infiltrability, making its use for infiltration trenches not recommended. Keywords: cerrado, infiltration trench, red latosol.

Integrating multi-source remote sensing and machine learning for root-zone soil moisture and yield prediction of winter oilseed rape (Brassica napus L.): A new perspective from the temperature-vegetation index feature space

Accurately assessing root-zone soil moisture is crucial for precision irrigation, as it directly influences crop yield. The Temperature-Vegetation Index (Ts-VI) Feature Space, which combines land surface temperature (Ts) and vegetation index (VI), is widely used to evaluate root-zone soil moisture in vegetated areas. However, its effectiveness in estimating crop yield remains unclear. Therefore, the objectives of this study are: (1) to collect multispectral and thermal infrared remote sensing data from a two-year (2021–2023) field experiment on winter oilseed rape (Brassica napus L.), and to optimize and evaluate the fitting methods of the dry and wet edges of the Ts-VI feature space based on the selected vegetation indices; (2) to analyze the spatiotemporal patterns of the Temperature Vegetation Dryness Index (TVDI) derived from the optimized Ts-VI feature space and estimate root-zone soil moisture (SM) and crop yield; and (3) to precisely invert the SM and yield of winter oilseed rape in the 0–60 cm root-zone using three machine learning algorithms—Support Vector Regression (SVR), Extreme Gradient Boosting Regression (XGBR), and Random Forest Regression (RFR)—based on the optimized TVDI. Results indicate that, among the various fitting methods, the polynomial fitting method shows the best performance. The performance of the root-zone soil moisture prediction models across different growth stages follows the order of budding stage > seedling stage > flowering stage, and with the increase of soil depth, the performance of the model gradually deteriorates.In the yield inversion of winter oilseed rape, TVDI effectively predicts yield, with the coefficient of determination (R2) ranging from 0.430 to 0.480 and RMSE ranging from 213.399 to 267.212 kg ha−1 during the seedling stage, R2 ranging from 0.640 to 0.747 and RMSE ranging from 110.712 to 178.133 kg ha−1 during the budding stage, and R2 ranging from 0.680 to 0.773 and RMSE ranging from 83.815 to 147.301 kg ha−1 during the flowering stage. The flowering stage effectively reflects crop yield trends and allows for accurate yield prediction of winter oilseed rape up to two months in advance. A comparison of the modeling results from XGBR, SVR, and RFR shows that XGBR provides the best fit for both root-zone soil moisture and yield predictions. Compared to linear regression models, the three machine learning models significantly improve accuracy and fit, providing more precise evaluations of root-zone soil moisture and yield. In addition, through the comparison and verification of this method in other regions, it shows that the results also have certain reference value. The combination of the Ts-VI feature space and machine learning algorithms not only enables precise monitoring of root-zone soil moisture conditions but also predicts future crop yield trends, offering valuable insights for water resource management and irrigation decision-making in precision agriculture.

Reinforcement Learning Algorithm for Optimising Durian Irrigation Systems: Maximising Growth and Water Efficiency

This study presents a Reinforcement Learning-based algorithm designed to optimise irrigation for Durio Zibethinus (i.e., durian) trees, aiming to maximise tree growth and reduce water usage. Traditional irrigation methods, as well as current machine learning models, often focus only on soil moisture and weather data, neglecting critical factors like actual tree growth. This study proposed a reinforcement learning irrigation (RL-Irr) algorithm incorporating tree growth stages, soil moisture, and weather conditions to determine precise irrigation needs. The algorithm was developed by calibrating the AQUACROP model using data from actual durian plantations where rain-fed irrigation (rain-fed) was practised. Daily irrigation volumes were calculated based on real-time soil moisture, weather forecasts, and weekly tree growth measurements. The reinforcement learning method was used to optimise irrigation schedules, with rewards based on soil moisture, tree growth, rainfall, and weather conditions. The algorithm was tested using AQUACROP simulations and compared against soil moisture balance irrigation (SMB-Irr) and rain-fed. The results showed that the RL-Irr reduced water use by up to 75 percent while maintaining tree growth. These findings suggest the algorithm could significantly improve water efficiency in durian farming, though real-world applications should consider potential model limitations.

Modelling net primary productivity using near real-time land cover data and soil moisture information

ABSTRACT The objective of this study is to develop a methodological approach to assess ecosystem functioning in terms of carbon uptake and sequestration. To achieve this, a model was developed to forecast Net Primary Productivity (NPP) and tested in a rural area, the Thrace region (NE Greece). Monthly net photosynthesis data from July 2015 to February 2024 was estimated using the MODerate Resolution Imaging Spectroradiometer (MODIS) Aqua MYD17A2H v061 dataset for the study area. This dataset was used as the dependent variable in an Autoregressive Integrated Moving Average (ARIMA) model. The predictive ability of monthly Land Cover (LC) and Soil Moisture (SM) data at various time lags was examined, and variables found to be significant predictors were introduced into the model as external predictors. The study found that crop areas and SM conditions exert the greatest influence on NPP, with areas containing trees exerting a lesser impact. Additionally, NPP was estimated for seven different LC and SM scenarios from March 2023 to February 2024.

Physiological Behavior of Rubber Plants (&lt;i&gt;Hevea Brasilliensis&lt;/i&gt;) to Different Soil Moisture Conditions

Drought conditions can severely impact rubber (&lt;i&gt;Hevea brasiliensis&lt;/i&gt;) plantations, leading to economic loss in Malaysia. The study aimed to assess the impact of varying soil moisture levels on the physiological characteristics of five latex timber clones (LTCs) of rubber, with the goal of identifying the most suitable clone for specific soil moisture conditions. These conditions include (1) field capacity, (2) 75% available water (AW), (3) 50% AW, (4) 25% AW, and (5) wilting point, with the ultimate objective of optimizing cultivation methods and fostering sustainable rubber production in Malaysia. The five clones under investigation include RRIM3001, RRIM2025, RRIM2001, RRIM928, and PB350. Leaf chlorophyll content, stomatal conductance, and net photosynthesis were measured 4 and 8 months after treatment (MAT). The findings indicated significant effects of moisture stress on various physiological attributes, including total chlorophyll content, relative chlorophyll content, stomatal conductance, and net photosynthesis rate. At 4 and 8 MATs, the clones subjected to field capacity exhibited the highest values for these physiological characteristics, followed by those exposed to 75% available water, with the lowest values observed at the wilting point. RRIM3001 consistently exhibited the highest total chlorophyll content, stomatal conductance, and net photosynthesis among the clones at both sampling dates. The highest net photosynthesis was observed in the RRIM3001 clone under field capacity conditions. Furthermore, a significant positive correlation was identified between total chlorophyll and relative chlorophyll contents, as well as between net photosynthesis and stomatal conductance. These findings carry practical implications for water management during the initial growth phase of rubber seedlings and for replanting initiatives in rubber plantations.

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.

Spatial Aggregations of the Grey Field Slug Deroceras reticulatum Are Unstable Under Abnormally High Soil Moisture Conditions.

Deroceras reticulatum in arable fields display spatio-temporally stable slug patches that have been well documented under typical soil moisture conditions. The effect of abnormally high soil moisture on slug patch stability, however, is unknown. In this study, stepped gradient choice tests comparing soil moisture levels of 50-125% soil capacity showed slug preferences for levels in a range near to 125%. Activity became erratic, however, when given a choice of high moisture levels (125-370%), potentially because slugs searched for preferred conditions. Slug spatial aggregation was investigated in 21 commercial fields in 2023/24, a season of extreme rainfall, and then compared to years exhibiting typical rainfall (2015-2018). Slug patches occurred in 27.2% of assessment visits to fields during 2023/24 compared to 96.4% in typical years, suggesting weather conditions leading to abnormally high soil moisture are significantly associated with the breakdown of slug spatial aggregation behaviour. Random forest models identified the weather predictors (precipitation, relative humidity, temperature) with the highest impact on slug distribution and relative abundance, with the assessment date and region also related to relative abundance. However, a complex of environmental parameters affects soil moisture content, and no statistically significant effects of individual weather predictors emerged. The results are discussed in relation to slug behaviour in the context of their impact on targeted slug treatments.

Single and combined effects of metal-based fungicides on Eisenia andrei in different scenarios of climatic change.

This study evaluated the ecotoxicity of metal-based fungicides under the current scenarios of global climatic change (20°C and 25°C) and moisture content (30% and 50%) in single and binary mixtures of copper oxychloride (CuOx) [200, 500 and 1000mg/kg] and mancozeb (MnZn) [44, 850 and 1250mg/kg]. Endpoints assessed included mortality, changes in biomass, avoidance behaviour, and reproduction utilising standardised protocols (ISO and OECD). The changes in biomass and mortality tests lasted 28days, followed by a 28-day reproduction test and a two-day avoidance test. In all temperature-moisture combinations, the mortality rate in the exposure groups exceeded 10% only in the CuOx1000 and CuOx1000 + MnZn1250 mg/kg groups. However, at 20°C and 30% moisture, the mortality rate exceeded 10% only in the CuOx500 + MnZn850 mg/kg treatment. Relative growth rates in the CuOx and MnZn treatment groups decreased with increasing concentrations. In CuOx MnZn and the binary mixture treatments at 20°C 30% and 25°C 50% conditions, avoidance response behaviour was greater than 80% throughout the exposure, except in CuOx200 mg/kg, MnZn44 mg/kg and CuOx200 + MnZn44 mg/kg. The reproduction of exposed earthworms in all treatment groups was concentration-dependent and influenced by varying temperatures and soil moisture conditions. No juveniles or cocoons were produced in the CuOx1000 mg/kg treatment at 25°C 30%, indicating that copper oxychloride may be more toxic than mancozeb, especially in drought conditions. This study found that different temperatures and soil moisture levels altered the ecotoxicity of CuOx and MnZn. It can be concluded that climate change is likely to significantly impact the outcomes of metals to earthworms and their ecological activities.

Combined Drought Index Using High-Resolution Hydrological Models and Explainable Artificial Intelligence Techniques in Türkiye

We developed a combined drought index to better monitor agricultural drought events. To develop the index, different combinations of the temperature condition index, precipitation condition index, vegetation condition index, soil moisture condition index, gross primary productivity, and normalized difference water index were used to obtain a single drought severity index. To obtain more effective results, a mesoscale hydrologic model was used to obtain soil moisture values. The SHapley Additive exPlanations (SHAP) algorithm was used to calculate the weights for the combined index. To provide input to the SHAP model, crop yield was predicted using a machine learning model, with the training set yielding a correlation coefficient (R) of 0.8, while the test set values were calculated to be 0.68. The representativeness of the new index in drought situations was compared with established indices, including the Standardized Precipitation-Evapotranspiration Index (SPEI) and the Self-Calibrated Palmer Drought Severity Index (scPDSI). The index showed the highest correlation with an R-value of 0.82, followed by the SPEI with 0.7 and scPDSI with 0.48. This study contributes a different perspective for effective detection of agricultural drought events. The integration of an increased volume of data from remote sensing systems with technological advances could facilitate the development of significantly more efficient agricultural drought monitoring systems.