Soil Moisture Conditions Research Articles

Identifying the Mechanism of Interaction Between Soil Moisture State and Summertime MCS Initiations in Weakly Forced Synoptic Environments Using Convective-Permitting Simulations.

This work aims to identify a mechanism of interaction between soil moisture (SM) state and the incidence of weakly forced synoptic scale MCS events during boreal summer by performing a sensitivity study using the Weather Research and Forecasting (WRF) model over the US Great Plains. A uniformly dry SM patch at a 5°×5° scale is centered at the point of a documented MCS initiation to observe spatiotemporal changes of the simulated MCS events, totaling 97 cases between 2004 and 2017. A storm-centered composite analysis of SM at the location of simulated MCS events depicted SM heterogeneity [O(100) km] structured as significantly drier soils to the southwest (SW) transitioning to wetter soils northeast (NE) of the mean simulated initiation. Further analyses showed that this SM configuration influenced near-surface fluxes, which created a gradient of 2m-temperature and 2m-humidity, also aligned SW-to-NE, which affected the growth of the planetary boundary layer to trigger MCS initiations earlier in time (∼1-2hr on average) compared to Control simulations. The implementation of the dry SM perturbation introduced drier-to-wetter SM gradients along the edges of the perturbed area, and MCS initiations were subsequently preferred on the drier side of those transition zones, with the most common orientation of simulated MCS events embedded within southwesterly flow. These results emphasize the importance of the low-level wind field alignment to organized SM gradients, which suggests that SM heterogeneity can drive MCS initiation related to near-surface atmospheric variable fluctuations as the main mechanism of interaction in weakly forced synoptic environments.

Incorporating changes in land surface temperature improves BESS evapotranspiration estimates under water-deficit conditions: A case study for US Midwest and Great Plains grasslands

Evapotranspiration (ET) is a critical climate and ecosystem variable that interconnects water, energy, and carbon cycles. Breathing Earth System Simulator (BESS) is one of the state-of-the-art biophysical models capable of producing spatio-temporal continuous ET results. However, we found that since the BESS model does not use an explicit constraint on soil moisture (SM), it has a relatively lower performance under drier conditions. Given that changes in land surface temperature (LST) are closely associated with surface water status and sensible heat energy, we hypothesize that integrating LST changes could explicitly add the soil moisture constraints and thus enhance BESS’s ability to estimate ET. Here we used the morning rise rate of LST (Trate) as a proxy of LST change because of the low noise level in Trate as well as Trate’s close relationship with daily mean sensible heat. To test the hypothesis, this study first assessed whether the performance of BESS ET can be explained by the LST change, targeting grassland sites of the AmeriFlux network in the US Midwest and Great Plains. Specifically, the ET deviation (i.e., the difference between BESS-modeled ET and field-measured ET) and Trate deviation, as well as their relationships, were investigated under different conditions of precipitation, SM, and vapor pressure deficit (VPD) at the AmeriFlux sites. Results indicated that BESS ET exhibited consistently higher performance under well-watered conditions than water-deficit conditions. Also, the deviations of ET and Trate became more negatively correlated under water-deficit conditions. Leveraging the empirical relationship between ET and Trate deviations, this study developed a new way to calibrate BESS ET based on Trate calculated from LST diurnal observations, particularly under soil or atmospheric water-deficit conditions. After calibrating BESS ET, the statistical indicators between the calibrated ET and the ground measurements showed meaningful improvements relative to those before calibration. Specifically, in the Midwest (Great Plains), R2 increased from 0.42 to 0.51 (from 0.45 to 0.46), and RMSE and absolute bias decreased by 12% and 42% (11% and 45%), respectively. This study highlights that the morning rise rate of LST can effectively constrain the ET models that have no SM constraints under water-deficit conditions.

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.

Mechanism of Irrigation Before Low-Temperature Exposure on Mitigating the Reduction in Yield Loss and Spikelet Abortion at the Jointing Stage of Wheat

The increasing frequency of low-temperature events in spring, driven by climate change, poses a serious threat to wheat production in Northern China. Understanding how low-temperature stress affects wheat yield and its components under varying moisture conditions, and exploring the role of irrigation before exposure to low temperatures, is crucial for food security and mitigating agricultural losses. In this study, four wheat cultivars—semi-spring (YZ4110, LK198) and semi-winter (ZM366, FDC21)—were tested across two years under different conditions of soil moisture (irrigation before low-temperature exposure (IBLT) and non-irrigation (NI)) and low temperatures (−2 °C, −4 °C, −6 °C, −8 °C, and −10 °C). The IBLT treatment effectively reduced leaf wilt, stem breakage, and spikelet desiccation. Low-temperature stress adversely impacted the yield per plant—including both original and regenerated yields—and yield components across all wheat varieties. Furthermore, a negative correlation was found between regenerated and original yields. Semi-spring varieties showed greater yield reduction than semi-winter varieties, with a more pronounced impact under NI compared to IBLT. This suggests that the compensatory regenerative yield is more significant in semi-spring varieties and under NI conditions. As low-temperature stress intensified, the primary determinant of yield loss shifted from grain number per spike (GNPS) to spike number per plant (SNPP) beyond a specific temperature threshold. Under NI, this threshold was −6 °C, while it was −8 °C under IBLT. Low-temperature stress led to variability in fruiting rate across different spike positions, with semi-spring varieties and NI conditions showing the most substantial reductions. Sensitivity to low temperatures varied across spikelet positions: Apical spikelets were the most sensitive, followed by basal, while central spikelets showed the largest reduction in grain number as stress levels increased, significantly contributing to reduced overall grain yield. Irrigation, variety, and low temperature had variable impacts on physiological indices in wheat. Structural equation modeling (SEM) analysis revealed that irrigation significantly enhanced wheat’s response to cold tolerance indicators—such as superoxide dismutase (SOD), proline (Pro), and peroxidase (POD)—while reducing malondialdehyde (MDA) levels. Irrigation also improved photosynthesis (Pn), chlorophyll fluorescence (Fv/Fm), and leaf water content (LWC), thereby mitigating the adverse effects of low-temperature stress and supporting grain development in the central spike positions. In summary, IBLT effectively mitigates yield losses due to low-temperature freeze injuries, with distinct yield component contributions under varying stress conditions. Furthermore, this study clarifies the spatial distribution of grain responses across different spike positions under low temperatures, providing insights into the physiological mechanisms by which irrigation mitigates grain loss. These findings provide a theoretical and scientific basis for effective agricultural practices to counter spring freeze damage and predict wheat yield under low-temperature stress.

Dry soil moisture on the Tibetan plateau drives synchronous extreme heatwaves in Europe and East Asia

Recently, extreme heatwaves have frequently concurrently swept across Europe and East Asia, causing severe cascading socioeconomic consequences. However, the nonlinear synchronization relationship between these heatwaves and their underlying physical mechanisms remains poorly understood. Utilizing the event synchronization climate network method, atmospheric dynamic diagnostics, and numerical experiments, we revealed robust synchronization between heatwaves over Europe and East Asia, strongly associated with dry soil moisture conditions over the Tibetan Plateau from the preceding winter to summer. Dry soil moisture triggers an equivalent barotropic anticyclone north of the Tibetan Plateau, coinciding with the subtropical westerly jet waveguide and initiating circumglobal atmospheric Rossby waves propagating westward and eastward. Consequently, an equivalent barotropic anticyclone develops over Europe. These anticyclones induce simultaneous heatwaves across Europe and East Asia by increasing downward solar radiation and adiabatic sinking, amplified by positive land-atmosphere feedback. Our findings significantly enhance the understanding and predictive capabilities of these synchronous heatwaves across Eurasia.

A novel conceptual model coupling crop growth and soil water-heat-salt processes in arid area

In arid farmland, the soil water, heat, and salt movement tightly couple with crop growth. Accurately expressing the coupling relationships is essential to understanding the complex agricultural hydrological system and enhancing agricultural water productivity. A novel conceptual model coupling crop growth, soil water-heat-salt movement, and energy balance in arid area with shallow groundwater was developed which has potential for use in large heterogeneous irrigation districts. The model can calculate the crop-soil system through the effect of crop canopy and leaf area index (LAI) on potential evapotranspiration and the stress of soil moisture, temperature, and salinity status to root water uptake. The energy balance equation was used for calculating the upper boundary condition for soil temperature. The model was calibrated and validated using four years of field monitoring data for two crops located in a typical arid agricultural area in China. The model performed well in simulated field hydrology and crop growth processes. The model can capture soil water-heat-salt dynamics and model field evapotranspiration with RMSE below 1.14 mm/day, and crop transpiration with R2 of above 0.61. Furthermore, the model can accurately describe crop growth processes with R2 of 0.95 and RMSE of 0.49 for LAI, and R2 of 0.99, RMSE of 12.95 cm for crop height. With few parameters, our work supplies an alternative method for quantifying agricultural hydrological processes. In particular, the model has potential to simulate the regional crop growth and soil water-heat-salt processes for heterogeneous agricultural area. This method is helpful in determining the scientific irrigation management schedule.

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.

Dialogue between algorithms and soil: Machine learning unravels the mystery of phthalates pollution in soil

Soil is a major environmental sink for the emerging organic pollutants phthalates (PAEs), and the determination of key factors influencing PAEs accumulation in soil is crucial for agricultural sustainability and food security. Aiming at the time-consuming and inefficient characteristics of traditional batch experiments and statistical prediction models in comprehensively capturing PAEs dynamics in soil, an intelligent analysis framework based on machine learning was proposed and developed. In this study, thirty features were incorporated, including soil PAEs-concentrations, pollutant emissions, agricultural inputs, soil physicochemical properties, and climatic parameters. Six data-driven machine learning models were established: Random Forest Regression (RFR), Gradient Boosting Regression Tree (GBRT), Extreme Gradient Boosting (XGBoost), Multilayer Perceptron (MLP), Support Vector Regression (SVR), and k-Nearest Neighbors (KNN). Results showed that the MLP model exhibited optimal performance in predicting soil PAEs concentrations (R²=0.8637), followed by SVR (R²=0.8132) and XGBoost (R²=0.8096). Through feature importance analysis, it was determined that hydrometeorological factors, soil moisture conditions, and nutritional characteristics were the key factors controlling PAEs spatial distribution. Furthermore, non-linear effect analysis elucidated significant synergistic interactions among these environmental covariates. The spatiotemporal prediction model revealed continuous declining trends in PAEs pollution levels in eastern coastal regions over the next 5–10 years, while accumulation tendencies were observed in inland provinces particularly in Guizhou. This study demonstrates the effectiveness and advantages of machine learning in predicting soil PAEs-pollution, providing a new perspective for pollutant risk assessment and management in the era of environmental big data.

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.

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.