Soil Moisture Variability Research Articles

Relationships Between Soil Respiration and Crop Productivity in Different Crop Fields

ABSTRACT Investigating the seasonal variations in soil respiration in different crop fields and their relationships with crop productivity is crucial to understanding the key biotic controls of soil respiration. A field experiment was performed during the 2020‒2021 winter wheat‒soybean, canola‒maize and broad bean‒sweet potato growing seasons. The seasonal variations in soil respiration, soil temperature and moisture were measured. The variables that were associated with crop productivity were also determined. The results showed that crop types significantly (p < .05) affected the mean seasonal soil respiration. Most variables that were associated with crop productivity exhibited obvious seasonal variation patterns. The soil temperature, moisture and crop productivity comprehensively influenced the soil respiration, and models based on these potential controlling factors explained 45.7%‒59.2% (R 2 = 0.457‒0.592) of the variation in soil respiration in the different crop rotation fields. A model based on the mean seasonal soil temperature, moisture, leaf area index, and root carbon content explained 68.3% (R 2 = 0.683) of the variation in the mean seasonal soil respiration across the different crop fields. We demonstrated the potential of effectively characterizing the variations in soil respiration in agroecosystems using temperature, moisture and crop productivity.

Soil Residual Activity of Tetflupyrolimet and the Influence of Soil Moisture and Flood Timeliness on Barnyardgrass Efficacy

ABSTRACT Tetflupyrolimet is the first herbicide with a novel site of action to be commercialized for use in agronomic crops in three decades. Direct-seed rice field experiments were conducted at research facilities near Stuttgart (silt loam), AR, and Keiser (clay), AR, to evaluate tetflupyrolimet as a preemergence herbicide versus commercial standards. Greenhouse experiments determined the influence of soil moisture on pre- and postemergence (POST) barnyardgrass control with tetflupyrolimet and clomazone, and the impact of a delayed flood on efficacy when POST-applied. For the field experiments, clomazone, tetflupyrolimet, and quinclorac were applied individually PRE at 336 and 560, 134 and 224, and 336 and 560 g ai ha-1, respectively, on a silt loam and clay soil, along with clomazone plus tetflupyrolimet and clomazone plus quinclorac at the same rates. The soil moisture experiment included a single PRE and POST application of clomazone at 336 g ai ha-1, tetflupyrolimet at 134 g ai ha-1, and a mixture at the respective rates, on a silt loam soil at 50, 75, and 100% of field capacity. For the flood timing experiment, tetflupyrolimet was applied to 2- to 3-leaf barnyardgrass at 134 g ai ha-1, and a flood was established at 4 hr after treatment (HAT) and 5 and 10 d after treatment (DAT). Barnyardgrass control with a tetflupyrolimet and clomazone mixture was comparable to clomazone plus quinclorac when averaged over all evaluations on silt loam and clay texture soils (≥91%). Soil moisture interacted with herbicide treatments for PRE and POST barnyardgrass efficacy when averaged over DAT, with tetflupyrolimet plus clomazone generally providing the greatest and most consistent control across regimes. Flooding barnyardgrass at 4 HAT provided superior control to later flood timings. Tetflupyrolimet is an effective residual barnyardgrass herbicide, and the addition of clomazone will aid in providing consistent control across varying soil moisture conditions.

The RADARSAT Constellation Mission for Soil Moisture Retrieval of Bare Soil by Compact Polarimetry and Random Forest Regression

The RADARSAT Constellation Mission (RCM) performance evaluation is currently in progress for core Synthetic Aperture Radar (SAR) applications. This study aims to investigate the retrieval of Soil Moisture Content (SMC) in bare soil with RCM compact polarimetry and Random Forest Regression (RFR). The focus is on RH (right circular transmit and linear horizontal receive signal) and RV (right circular transmit and linear vertical receive signal) backscattering, which are the primary RCM Compact Polarimetric (CP) products. SMC retrieval is pursued over a wide range of radar incidence angles. Then, an attempt is made to retrieve SMC at higher radar incidence angles only. Furthermore, soil moisture maps are produced and used for analyzing the captured soil moisture variability. CP SAR images acquired with the RCM SC30MCP mode over three Canadian experimental sites are considered in our study. The sites are equipped with calibrated Real-Time In-Situ Soil Monitoring for Agriculture (RISMA) stations. A RFR retrieval algorithm was able to predict SMC with a correlation of 0.75 when compared to in-situ soil moisture measurements. A Root Mean Square Error (RMSE) = 5.9%, a bias = −1.5%, and an unbiased RMSE (ubRMSE) = 5.7% are achieved. A degradation in performance is reported for SMC retrieval under higher radar incidence angles. Results of our study indicate promising performance for capturing near-surface soil moisture variability under bare soil conditions.

Predictive Modeling of Soil Moisture Variability Using Machine Learning: Insights from Dry and Wet Soil Cultures

Soil moisture prediction is crucial for optimizing irrigation practices and advancing precision agriculture. This study presents a predictive modeling approach leveraging machine learning techniques to analyze soil moisture variability in dry and wet soil cultures. Utilizing data collected through IoT-enabled sensors, this research applies regression-based algorithms to forecast moisture trends. A robust data preprocessing framework, including interpolation and augmentation, was implemented to address missing data challenges. Experimental results demonstrate high prediction accuracy, with minute-wise temporal granularity outperforming other datasets. The findings highlight the potential for integrating machine learning models into sustainable irrigation practices.

Mechanisms of N2O Emission in Drip-Irrigated Saline Soils: Unraveling the Role of Soil Moisture Variation in Nitrification and Denitrification

Drip irrigation generates structural bodies in soil, forming layered structures with moisture content gradients. There are four typical soil moisture characteristic values in this concentric structure as saturation capacity (θs), field capacity (FC), 60% field capacity (60% FC), and 30% field capacity (30% FC). In this study, we simulated these four soil water characteristic values to conduct an indoor soil incubation experiment under three different incubation conditions: aerobic (O), aerobic with 10 pa acetylene (OC), and anaerobic (AO). The results indicate that in soil with saturated water content, denitrification under aerobic conditions leads to high N2O emissions; in soil at field holding capacity, nitrification under aerobic conditions dominates low N2O emissions, which is most conducive to N2O reduction and greenhouse gas emission mitigation; in soil with 60% of field holding capacity, denitrification under anaerobic conditions leads to high N2O emissions; and in soil with 30% of field holding capacity, microbial activity decreases, inhibiting nitrification, denitrification, and N2O emissions. Our research has found that when conducting aerobic drip irrigation in soil at field capacity (FC), denitrification was reduced by 99%, nitrification was increased by 70%, and microbial activity was enhanced by 5%. Therefore, during drip irrigation, the position and flow rate of the dripper should be controlled to reduce soil water saturation areas, maintain soil aeration, control soil moisture content below field holding capacity, promote the nitrification process, reduce N2O emissions, and improve soil nitrogen use efficiency. Our study elucidates the characteristics of nitrogen transformation and N2O emissions across various soil moisture contents within the soil water structure under drip irrigation conditions, providing a scientific basis for the formulation of precise irrigation management practices and strategies for efficient soil nitrogen utilization.

Оценка объемной влажности почвы реанализа ERA5 по данным станционных наблюдений влагозапасов в регионах Российской Федерации

The study compares volumetric soil moisture based on the data from the fifth-generation global climate reanalysis (ERA5) and observations of productive soil moisture reserves in various soil layers at ten Roshydromet stations for the growing seasons from 2011 to 2023. The coordination of these series in mm of the water layer of total moisture with account of biases is carried out. It is shown that the reanalysis reproduces the main features of seasonal variations in soil moisture in the layers of 0–50 and 0–100 cm, its dynamics during the growing seasons, as well as the episodes of excessive moisture or drought conditions.

Spatial extension of soil water regime variables derived from soil moisture values using geomorphological variables in Hungary

Accurate measurement and spatial extension of soil properties are essential in geoinformatics and precision agriculture for effective resource management, particularly irrigation planning. This study addresses the challenge of extending soil moisture data and related soil water regime variables in heterogeneous agricultural landscapes by integrating geomorphological variables (GVs) derived from high-resolution digital elevation models (DEM). In digital soil mapping, machine learning and geostatistical models often struggle with validation due to data scarcity and variability across space through many geographical regions that come from the point readings of soil properties. A different approach was developed in the form of a new methodology combining two hourly Sentek soil moisture measurements from the topsoil with DEM-derived GVs to model and extend soil water regime variables. The research was conducted on an agricultural field in a hilly area with diverse geomorphological variability. The model’s performance was validated using cross-validation techniques. The monitoring and spatial extension results indicate that GVs enhance the spatial prediction of soil moisture, capturing periodic fluctuations in the upper soil layer more effectively by using in-situ, time series soil moisture sensor readings rather than traditional, on field, one time reading approaches. We observed that certain GVs, such as the slope, both type of curvatures and the convergence, were strong predictors of soil moisture variation, enabling the model to produce more accurate irrigation recommendations for agricultural areas with similar geomorphological areas. One of the soil water regime variables was validated during the preliminary validation with mixed results. The main issue was coming from the field use and spatial scarcity of the measurements. Our approach not only provides a different method for spatially extending the current soil water regime data but also offers a framework for improving irrigation decision-making with the help of other value rates and limit related soil regime variables derived from the time series readings from the soil moisture sensors. With its variables, the model allows for forecasts of soil moisture changes, which can inform better irrigation scheduling and water resource management, all based on data from the soil monitoring sensor system.

Exploring Drought Resistance Genes from the Roots of the Wheat Cultivar Yunhan1818.

The root is an important organ by which plants directly sense variation in soil moisture. The discovery of drought stress-responsive genes in roots is very important for the improvement of drought tolerance in wheat varieties via molecular approaches. In this study, transcriptome sequencing was conducted on the roots of drought-tolerant wheat cultivar YH1818 seedlings at 0, 2, and 7 days after treatment (DAT). Based on a weighted gene correlation network analysis of differentially expressed genes (DEGs), 14 coexpression modules were identified, of which five modules comprising 3107 DEGs were related to 2 or 7 DAT under drought stress conditions. A total of 223,357 single-nucleotide polymorphisms (SNPs) of these DEGs were retrieved from public databases. Using the R language package and GAPIT program, association analysis was performed between the 223,357 SNPs and the drought tolerance coefficient (DTC) values of six drought resistance-related traits in 114 wheat germplasms. The results revealed that 18 high-confidence SNPs of 10 DEGs, including TaPK, TaRFP, TaMCO, TaPOD, TaC3H-ZF, TaGRP, TaDHODH, TaPPDK, TaLectin, and TaARF7-A, were associated with drought tolerance. The RT-qPCR results confirmed that these genes were significantly upregulated by drought stress at 7 DAT. Among them, TaARF7-A contained three DTC-related SNPs, which presented two haplotypes in the tested wheat germplasms. YH1818 belongs to the Hap1 allele, which is involved in increased drought tolerance. This study revealed key modules and candidate genes for understanding the drought-stress response mechanism in wheat roots.

Satellite-based re-examination of global soil moisture variation

Satellite-based re-examination of global soil moisture variation

Forest plant indicator values for moisture reflect atmospheric vapour pressure deficit rather than soil water content.

Soil moisture shapes ecological patterns and processes, but it is difficult to continuously measure soil moisture variability across the landscape. To overcome these limitations, soil moisture is often bioindicated using community-weighted means of the Ellenberg indicator values of vascular plant species. However, the ecology and distribution of plant species reflect soil water supply as well as atmospheric water demand. Therefore, we hypothesized that Ellenberg moisture values can also reflect atmospheric water demand expressed as a vapour pressure deficit (VPD). To test this hypothesis, we disentangled the relationships among soil water content, atmospheric vapour pressure deficit, and Ellenberg moisture values in the understory plant communities of temperate broadleaved forests in central Europe. Ellenberg moisture values reflected atmospheric VPD rather than soil water content consistently across local, landscape, and regional spatial scales, regardless of vegetation plot size, depth as well as method of soil moisture measurement. Using in situ microclimate measurements, we discovered that forest plant indicator values for moisture reflect an atmospheric VPD rather than soil water content. Many ecological patterns and processes correlated with Ellenberg moisture values and previously attributed to soil water supply are thus more likely driven by atmospheric water demand.

Real-Time Soil Nutrient Monitoring Using NPK Sensors: Enhancing Precision Agriculture

Prediction of various parameters in the agriculture field using sensors is a significant topic nowadays. However, in many scenarios, the sensor data does not accurately detect the real parameter(/s) in the agriculture field. The sensor data may vary due to various external factors, whereas the real parameters don’t vary too much for a particular agriculture field. The present work introduces a modified neural network approach to predict real agricultural parameters from sensor data with accuracy caused by several external factors and demonstrates enhanced predictive accuracy and adaptability. The neural network takes the sensor data as input in various weather conditions and tries to find out the original real parameters of that sensor data. The real-time sensor data was collected from multiple agricultural sites. The results demonstrated high predictive accuracy, with the neural network outperforming traditional statistical methods in forecasting soil moisture and other vital variables. Additionally, the model’s ability to generalize across different environmental conditions enhances its applicability in various crop management scenarios. The study concludes that neural networks hold significant potential for improving the efficiency of smart agriculture systems by providing timely, data driven insights for farmers and agronomists. Further research will explore the integration of deep learning models and edge computing to enhance scalability and realtime responsiveness in field applications. The aforementioned research highlights the significance of NPK sensors in sustainable farming methods, namely in enabling accurate nutrient management via real-time data.

Deducing land–atmosphere coupling regimes from SMAP soil moisture

Abstract. In recent years, there has been a growing recognition of the significance of land–atmosphere (L–A) interactions and feedback mechanisms in understanding and predicting Earth's water and energy cycles. Soil moisture plays a critical role in mediating the strength of L–A interactions and is important for understanding the complex and governing processes across this interface. This study aims to identify the significance of soil moisture in identifying L–A coupling strength within the convective triggering potential (CTP) and humidity index (HI) framework. To address this, a consistent and reliable dataset of atmospheric profiles is created by merging CTP and HI using triple collocation (TC) with three reanalysis datasets. The merged CTP and HI product demonstrates enhanced performance globally compared to the individual datasets when validated with radiosonde and satellite observations. This merged product of CTP and HI is then used to compare the L–A coupling strength based on Soil Moisture Active Passive Level 3 (SMAPL3) and SMAP Level 4 (SMAPL4) over 2 decades (2003–2022) where L–A coupling strength is defined as the persistence probability within the dry and wet coupling regimes. Results indicate that the persistency-based coupling strength is related to the ability of soil moisture to predict future atmospheric humidity and dry vs. wet coupling state. The coupling strength in SMAPL4 is consistently stronger than in SMAPL3 and is likely due to its reliance on a land surface model and reduced susceptibility to random noise. The difference in coupling strength based on the same CTP–HI underscores the importance of soil moisture data in estimating coupling strength within the CTP–HI framework. These findings lay the groundwork for understanding the role of L–A interactions and drought evolution due to soil moisture variations by providing insight into the quantification of coupling strength and its role in drought monitoring and forecast efforts.

Effects of nitrification and urease inhibitors on nitrous oxide emissions and concentrations driven by soil moisture in sandy soils

Soil moisture and nitrogen (N) fertilizer control the balance between nitrous oxide (N₂O) production and consumption in soil. However, the impact of nitrification and urease inhibitors on soil N₂O production and consumption under varying soil moisture levels remains insufficiently understood. In this study, a soil column experiment was conducted to investigate N₂O concentrations and accumulation at different soil depths (0, 5, 15, 30, and 60 cm) in sandy soil over two months. The soil was collected from a drip-irrigated field subjected to long-term cotton cultivation in an arid region. Four fertilizer treatments were applied: urea, urea + nitrification inhibitor (Dicyandiamide, DCD), urea + nitrification and urease inhibitors [N-(n-butyl) thiophosphoric triamide, NBPT], and an unfertilized control. These treatments were tested under three soil moisture levels [23%, 46%, and 70% water-filled pore space (WFPS)]. The results showed that treatments with inhibitors significantly reduced cumulative surface N₂O emissions by 33.2%–58.2% compared to urea alone. Notably, the treatment with both DCD and NBPT achieved the greatest reductions in surface emissions and in N₂O concentrations at 5 cm and 15 cm soil depths at 70% WFPS. Additionally, across all N fertilizer sources, 70% WFPS led to increased N₂O concentrations in the soil profile compared to lower soil moisture levels. Column N₂O accumulation was positively correlated with surface cumulative N₂O emissions under all three moisture conditions. Our study highlights that under high soil moisture conditions, DCD and NBPT can significantly mitigate intense N₂O emissions in sandy soils. This emission reduction is likely attributed to enhanced nitrification within the 0–15 cm soil layer rather than reduced N₂O diffusivity and denitrification along the soil profile. Therefore, we recommend applying nitrification and urease inhibitors under high soil moisture conditions as an effective strategy to reduce N₂O emissions in sandy soils.

Fine-scale surficial soil moisture mapping using UAS-based L-band remote sensing in a mixed oak-grassland landscape

Soil moisture maps provide quantitative information that, along with climate and energy balance, is critical to integrate with hydrologic processes for characterizing landscape conditions. However, soil moisture maps are difficult to produce for natural landscapes because of vegetation cover and complex topography. Satellite-based L-band microwave sensors are commonly used to develop spatial soil moisture data products, but most existing L-band satellites provide only coarse scale (one to tens of kilometers grid size), information that is unsuitable for measuring soil moisture variation at hillslope or watershed-scales. L-band sensors are typically deployed on satellite platforms and aircraft but have been too large to deploy on small uncrewed aircraft systems (UAS). There is a need for greater spatial resolution and development of effective measures of soil moisture across a variety of natural vegetation types. To address these challenges, a novel UAS-based L-band radiometer system was evaluated that has recently been tested in agricultural settings. In this study, L-band UAS was used to map soil moisture at 3–50-m (m) resolution in a 13 square kilometer (km2) mixed grassland-forested landscape in Sonoma County, California. The results represent the first application of this technology in a natural landscape with complex topography and vegetation. The L-band inversion of the radiative transfer model produced soil moisture maps with an average unbiased root mean squared error (ubRMSE) of 0.07 m3/m3 and a bias of 0.02 m3/m3. Improved fine-scale soil moisture maps developed using UAS-based systems may be used to help inform wildfire risk, improve hydrologic models, streamflow forecasting, and early detection of landslides.

Enhancing Groundwater Recharge and Yield by Adoption of Continuous Contour Trenches in Micro Catchments

In dryland agricultural sloping fields, whenever the rainfall event occurs, runoff begins and flows down from the slopes causing erosion. In such conditions, defined special measures can be adopted, which reduce runoff enabling the water to infiltrate down to the ground. Contour trenches are made mainly with this objective. The implementation of continuous contour trenches was done under various schemes viz. EGS, SGRY, RLEGP, DLFM, etc. in Maharashtra. To have definite quantification of conservation, recharge, soil moisture variations owing to acceptance of CCTs the research work was done on small catchment basis so that the appraisal and checking of CCTs is possible. It will be useful for researchers, field officers, farmers and NGOs. The study was conducted at the research field of the AICRP for Dryland Agriculture, Dr. Panjabrao Deshmukh Krishi Vidyapeeth, Akola. The catchment of 1.0 ha area was divided into two parts. One part is having CCTs and other kept as a control. The observations of LAI, soil moisture and groundwater level were collected. The Leaf Area Index (LAI) and interception component of seasonal crop and perennial plantations (Custard apple and Hanuman Phal) in a small catchment for the years 2022-23 and 2023-24 was observed to be highest at the flowering (3.67) and (3.61) stages, respectively. For perennial plantations, the LAI was observed highest at the developed fruit stages of the Custard apple and Hanuman phal. During both these years, canopy interception was also observed more in CCT adopted field compared to control field for seasonal and perennial plantations. The soil moisture was observed more in the catchment of CCT over control catchment. It was observed that by adoption of CCTs the groundwater recharge was increased by 20.51% over control (without CCT field). The acceptance of CCT resulted in yield increase of seasonal crop by 35.30% and yielding of fruits was more in CCT field by 49.10% over control.

Climatological Evaluation of Three Assimilation and Reanalysis Datasets on Soil Moisture over the Tibetan Plateau

Soil moisture is critical in the linkage between the land and atmosphere of energy and water exchange, especially over the Tibetan Plateau (TP). However, due to the lack of in situ plateau soil moisture measurements, the reanalyzed and assimilated data are the major supplements for TP climate research. Based on observations from 1992 to 2013, this study provides a comprehensive evaluation of three sets of assimilation and reanalysis products (GLDAS, ERA5-Land, and MERRA-2) on the climatic mean and variability of soil moisture over the Tibetan Plateau (TPSM). For the climatic mean, GLDAS captures the spatial distribution and annual cycle of TPSM better than other datasets in terms of lower spatial RMSE (0.07 m3×m-3) and bias (0.06 m3×m-3). In terms of the climatic variability of TPSM, the multi-data average (MDA) highlights its advantages in reducing the bias relative to any single data product. MDA describes the TPSM anomalies more stably and accurately in terms of temporal trend and variation (r = 0.94), as well as the dipole spatial pattern in EOF1. When considering both the climatic mean and spatial variability, the performance of MDA is more accurate and balanced than that of a single data product. This study overcomes the deficiency of limited time and space in previous evaluations of TPSM and indicates that multi-data averaging may be a more effective approach in the climate investigation of TPSM.

Mapping soil moisture across the UK: assimilating cosmic-ray neutron sensors, remotely sensed indices, rainfall radar and catchment water balance data in a Bayesian hierarchical model

Abstract. Soil moisture is important in many hydrological and ecological processes. However, data sets which are currently available have issues with accuracy and resolution. To translate remotely sensed data to an absolute measure of soil moisture requires mapped estimates of soil hydrological properties and estimates of vegetation properties, and this introduces considerable uncertainty. We present an alternative methodology for producing daily maps of soil moisture over the UK at 2 km resolution (“SMUK”). The method is based on a simple linear statistical model, calibrated with 5 years of daily data from cosmic-ray neutron sensors at ∼ 40 sites across the country. The model is driven by precipitation, humidity, a remotely sensed “soil water index” satellite product and soil porosity. The spatial variation in the parameter describing the soil water retention (and thereby the response to precipitation) was estimated using daily water balance data from ∼ 1200 catchments with good coverage across the country. The model parameters were estimated by Bayesian calibration using a Markov chain–Monte Carlo method, so as to characterise the posterior uncertainty in the parameters and predictions. The approach reduces uncertainty by integrating multiple data sources, all of which have weaknesses but together act as a better constraint on the true soil moisture. The model explains around 70 % of the variance in the daily observations with a root-mean-square error of 0.05 m3 m−3, better than results from more complex process-based models. Given the high resolution of the inputs in time and space, the model can predict the very detailed variation in soil moisture which arises from the sporadic nature of precipitation events, including the small-scale and short-term variations associated with orographic and convective rainfall. Predictions over the period 2016 to 2023 demonstrated realistic patterns following the passage of weather fronts and prolonged droughts. The model has negligible computation time, and inputs and predictions are updated daily, lagging approximately 1 week behind real time.

Optimizing residue return with soil moisture and nutrient stoichiometry reduced greenhouse gas fluxes in Alfisols

Optimum soil moisture and high crop residue return (RR) can increase the active pool of soil organic carbon and nitrogen, thus modulating the magnitude of greenhouse gas (GHG) fluxes. To determine the effect of soil moisture on the threshold level of RR for the wheat production system, we analyzed the relationship between GHG fluxes and RR at four levels, namely 0, 5, 10, and 15 Mg ha−1 (R0, R5, R10, and R15) under two soil moisture content (80% FC and 100% FC) and three levels of nutrient management (NS0: no nutrient; NS1, NS2= 3x NS1). Nutrient input (N and P) in NS1 balanced the residue C/nutrient stoichiometry to achieve 30% stabilization of the residue C input in RR (R5). All RR treatments (cf. R0) were found to significantly reduce N2O emission in moderate soil moisture content (80% FC) by 22–56% across nutrient management due to enhanced soil C mineralization, microbial biomass carbon, and N immobilization. However, averaged across nutrient management, a linear increase in N2O emission was observed with increasing RR under 100% FC soil moisture. A significant decrease in CH4 emission by ca. 46% in most RR treatments was observed in 100% FC compared with the R0. The N2O emission was negatively correlated (p = &amp;lt;0.001) with nutrient stoichiometry. Partial least square (PLS) regression indicated that GHG emissions were more responsive (values &amp;gt; 0.8) to management variables (RR rate, nitrogen (N) input rate, soil moisture, and nutrient stoichiometry of C: N) and post-incubation soil properties (SMBC and NO3-N) in Alfisols. This study demonstrated that the mechanisms responsible for RR effects on soil N2O, CH4 fluxes, and carbon mineralization depend on soil moisture and nutrient management, shifting the nutrient stoichiometry of residue C: N: P.

Quantifying Residual Soil Moisture through Empirical Orthogonal Functions Analysis to Support Legume-Based Cropping Systems

AbstractThis study investigates spatiotemporal variability of residual soil moisture during the OND (October-November-December) season in Ethiopia and its implications for crop productivity. Employing advanced statistical techniques, we analyze spatial and temporal distribution of soil moisture across Ethiopia from 1981 to 2020, focusing on selected crops including legumes: chickpea, field peas, common bean, soybean and alfalfa, to assess the potential of residual moisture to support post-rainy season cropping. Results indicate pronounced east-west moisture gradients, with eastern regions of Ethiopia exhibiting lower moisture levels (&lt; 60 kg.m-2) compared to western regions (&gt; 150 kg.m-2). The central highlands, which are pivotal for agricultural activities, demonstrate significant variability in moisture (standard deviations &gt; 25 kg.m-2), with implications on agricultural sustainability. The northern and southeastern tips of the country are particularly vulnerable to prolonged drought, where climate change and frequent dry spells exacerbate moisture deficits, consequently impacting crop productivity. Despite these challenges, promising opportunities for future crop production emerge in the southeastern region, which is characterized by increasing moisture trend over time ($$\:\tau\:=0.59$$). Findings further indicate that residual moisture adequately meets and supports crop water requirements in the western, central, and southwestern Ethiopia. In these regions, residual moisture supports more than 90% of cropland water requirements of various crops during the initial and late-season growth stages, whereas water requirement coverage drops to less than 20% during the mid-season growth stage. Therefore, by utilizing residual soil moisture alongside supplemental irrigation, Ethiopian farmers can meet crop water needs for double cropping and enhance resilience to climate variability.

Synthesis and Evaluation of Starch-Grafted-Poly[(Acrylic Acid)-Co-Acrylamide] Based Nanoclay Polymer Composite Fertilizers for Slow Release of Nitrogen in Soil.

Nitrogen (N) losses from conventional N fertilizers contribute to environmental degradation and low N use efficiency. Highlighting the need for slow-release fertilizers (SRFs) to mitigate these problems, this study aims to develop slow-release N fertilizers using starch-grafted-poly[(acrylic acid)-co-acrylamide] based nanoclay polymer composites (NCPCs) and investigate their efficacy for slow N delivery in soil. Three types of NCPCs, NCPC(A) (poly [(acrylic acid)-co-acrylamide]), NCPC(W) (wheat starch-grafted-poly[(acrylic acid)-co-acrylamide), and NCPC(M) (maize starch-grafted-poly[(acrylic acid)-co-acrylamide) were prepared and characterized using FTIR spectroscopy and X-ray diffraction techniques. N-release behaviour of the products was assessed under two distinct soils, i.e., Assam (Typic Hapludults, pH 4.2) and Delhi (Typic Haplustepts, pH 7.9) soils. Additionally, the effects of varying soil moisture and temperature levels on N release were studied in the Assam soil. The N-release kinetics of the synthesized fertilizers were assessed using zero-order, first-order, Higuchi, and Korsmeyer-Peppas models. Degradability of the NCPCs was evaluated by measuring evolved CO2-C under various soil conditions as an indicator of microbial degradation. The results indicated that NCPC fertilizers significantly slowed down the release of N compared to urea. According to the R2 values obtained, it was evident that the first-order kinetic model most accurately describes the N release from both urea and NCPC-based N fertilizers in the studied soils. Among the formulations, NCPC(A) exhibited the lowest N release (42.94-53.76%), followed by NCPC(M) (51.05-61.70%), NCPC(W) (54.86-67.75%), and urea (74.33-84.27%) after 21 days of incubation. The rate of N release was lower in the Assam soil compared to the Delhi soil, with higher soil moisture and temperature levels accelerating the release. Starch addition improved the biodegradability of the NCPCs, with NCPC(W) showing the highest cumulative CO2-C evolution (18.18-22.62 mg g-1), followed by NCPC(M) (15.54-20.97 mg g-1) and NCPC(A) (10.89-19.53 mg g-1). In conclusion, NCPC-based slow-release fertilizers demonstrated a more gradual N release compared to conventional urea and the inclusion of starch enhanced their degradability in the soil, which confirms their potential for sustainable agricultural applications. However, soil properties and environmental factors influenced the N release and degradation rates of NCPCs.