Range Of Soil Moisture Research Articles

Ecosystem water limitation shifts driven by soil moisture in the Loess Plateau, China

In the context of climate change, the functionality of ecosystems is primarily influenced by the availability of water and energy supply. However, there is limited research that comprehensively uses energy indicators to explore how climate change affects the water and energy limiting states of ecosystems. Here we evaluated the historical and future water and energy limitations using the Ecosystem Limitation Index (ELI) derived from evapotranspiration (ET), soil moisture (SM), net radiation (Rn), and air temperature (Ta), and conducted an in-depth analysis of the dominant factors. The results indicate that: (1) The degree of water limitation deepened initially and then weakened. Over 68 % of the region became drier initially, while over 83 % became wetter later. (2) In terms of area, soil moisture emerged as a critical factor influencing the variations in water and energy constraints within the Loess Plateau. Further research revealed the range of critical soil moisture (CSM) for the transition of water-energy limitation state is 0.286 mm3mm−3, and it varies with changes in temperature, soil texture, vegetation cover, and season. (3) Future projections suggest a transition towards heightened water limitations across the Loess Plateau. These findings underscore the efficacy of ELI in assessing and predicting dynamic ecosystem changes, offering valuable insights into the impacts of climate change on water and energy cycles within semi-arid ecosystems.

INDICATION OF ENVIRONMENTAL CONDITIONS ACCORDING TO THE DENDROFLORA COMPOSITION IN DIFFERENT ZONES OF THE PRYRICHKOVYI PARK IN POLTAVA

The dendroflora of the Pryrichkovyi Park in Poltava and the correspondence of woody plant species composition to environmental conditions were studied by analyzing the amplitude of the leading environmental factors.Twelve species of deciduous trees have been identified, the representatives of seven genera of six families of the order Angiospermae, or Magnoliophyta. The highest Margalef index was calculated in zone 1, and the highest dominance index in zone 3. In all three park zones, 6 species of trees are represented – 2 Acer sp., 2 Populus sp., Salix alba, and Tilia cordata. The Sørensen-Czekanovsky index indicates the greatest similarity in the species composition of trees between zones 1–2 and 2–3 and the least similarity between zones 1 and 3.The lowest light index (Lc) was calculated for zone 2, where the tree density is the highest. The high-light-tolerant species – Robinia pseudoacacia and Betula pendula – grow in zone 1 (with a high recreational load). The soil moisture range (Hd) tolerance is the greatest for Populus tremulae, which is common in all three park zones. The lowest soil aeration index (Ae) in zone 1 is explained by its greatest compaction due to recreational load. The soil acidity index (Rc) increases in zone 3, where willows and poplars grow.Analysis of the ecological amplitudes of certain indicators confirms that environmental conditions in all park zones are favorable for the majority of presented tree species. Therefore, the analysis of the environmental amplitudes is recommended to use when assessing new species for planting. The data obtained make it possible to continue the study of the condition of the most common tree species and resistance to natural and anthropogenic factors of their damage and injury, in particular, recreation, vehicle emissions, phytophagous insects, and pathogens.

Re-evaluating soil moisture-based drought criteria for rainfed crops in peninsular India

Background: Peninsular India, being completely under the influence of monsoonal climate, suffers crop yield variability due to rainfall distribution-induced soil moisture constraints. Timely and appropriate assessment of this rainfall and soil moisture-induced crop yield variability serves as a key for exemplary relief assistance. Per cent available soil moisture (PASM) is one among several drought declaration indices followed by stakeholders in India for declaration of drought, needs re-evaluation as the existing criteria in unable to capture the yield loss due to ineffective classification of PASM categories. This study attempts to revise the agricultural drought classes by PASM based on relationships established between yield of major rainfed crops of the study region and PASM.Methods: Analysis of yield variability due to PASM was carried out based on long term observations in experiments conducted at five dry farming locations (Akola, Parbhani, Kovilpatti, Ananthapuramu and Bengaluru) of peninsular India. The average yield for each category of PASM was calculated and tabulated for regression analysis. The PASM versus yield in each group was correlated and regression equations were developed if significant positive correlations were established.Results: The range of available soil moisture to obtain at least 50 percent of optimum yield in cereals (maize: 26 and finger millet: 52.9 PASM), pulses (pigeon pea: 37.2 PASM), oilseeds (soybean: 26.8 to 30.5, groundnut: 53.8 to 61.7 PASM) and commercial crops (cotton: 26.3 PASM) was 26–61 percent.Conclusion: The revised PASM-based drought classes (0–50 severe; 51–75 mild and 76–100 no drought) would help in drought declaration and precise identification of drought-hit areas for meaningful relief assistance. However, there is further investigation is needed to include a soil component for further fine-tuning of the criteria.

CNN-BiLSTM: A Novel Deep Learning Model for Near-Real-Time Daily Wildfire Spread Prediction

Wildfires significantly threaten ecosystems and human lives, necessitating effective prediction models for the management of this destructive phenomenon. This study integrates Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) modules to develop a novel deep learning model called CNN-BiLSTM for near-real-time wildfire spread prediction to capture spatial and temporal patterns. This study uses the Visible Infrared Imaging Radiometer Suite (VIIRS) active fire product and a wide range of environmental variables, including topography, land cover, temperature, NDVI, wind informaiton, precipitation, soil moisture, and runoff to train the CNN-BiLSTM model. A comprehensive exploration of parameter configurations and settings was conducted to optimize the model’s performance. The evaluation results and their comparison with benchmark models, such as a Long Short-Term Memory (LSTM) and CNN-LSTM models, demonstrate the effectiveness of the CNN-BiLSTM model with IoU of F1 Score of 0.58 and 0.73 for validation and training sets, respectively. This innovative approach offers a promising avenue for enhancing wildfire management efforts through its capacity for near-real-time prediction, marking a significant step forward in mitigating the impact of wildfires.

Survival, growth, and functional traits of tropical wet forest tree seedlings across an experimental soil moisture gradient in Puerto Rico.

Droughts are predicted to become more frequent and intense in many tropical regions, which may cause shifts in plant community composition. Especially in diverse tropical communities, understanding how traits mediate demographic responses to drought can help provide insight into the effects of climate change on these ecosystems. To understand tropical tree responses to reduced soil moisture, we grew seedlings of eight species across an experimental soil moisture gradient at the Luquillo Experimental Forest, Puerto Rico. We quantified survival and growth over an 8-month period and characterized demographic responses in terms of tolerance to low soil moisture-defined as survival and growth rates under low soil moisture conditions-and sensitivity to variation in soil moisture-defined as more pronounced changes in demographic rates across the observed range of soil moisture. We then compared demographic responses with interspecific variation in a suite of 11 (root, stem, and leaf) functional traits, measured on individuals that survived the experiment. Lower soil moisture was associated with reduced survival and growth but traits mediated species-specific responses. Species with relatively conservative traits (e.g., high leaf mass per area), had higher survival at low soil moisture whereas species with more extensive root systems were more sensitive to soil moisture, in that they exhibited more pronounced changes in growth across the experimental soil moisture gradient. Our results suggest that increasing drought will favor species with more conservative traits that confer greater survival in low soil moisture conditions.

Adhesion theory and model for air humidity impact on dust emission

It has been suggested that air humidity influences dust emission under very dry conditions and adhesion might be the responsible process which changes the binding between soil particles. The process of adhesion is so far poorly understood and difficult to quantify. Here, a critical examination of the relevant studies is provided, and an adhesion model is proposed. Both isothermal-kinematic and diffusion processes can limit the soil liquid–water and water–vapor exchange in soil, but for the particle size range concerned in aeolian studies, diffusion appears to be the limiting process. The model shows that soil moisture in the topsoil layers is positively correlated with air humidity, but with delays of several hours. The model performance is influenced by several parameters but is particularly sensitive to the equilibrium soil water–vapor content. This implies that soil microscopic properties can strongly influence adhesion. A new formulation of soil water retention function covering the entire soil moisture range is also proposed, which links soil water retention function and pore size distribution. Using a relationship between soil particle-size and pore-size distributions, an adhesion-affected grain size can be estimated, which defines the upper size limit of soil particles influenced by soil moisture. This study explains how air humidity influences soil moisture through adhesion and dust emission and why low air humidity promotes the emission of finer dust particles.

Soil-Powered Computing

Human-caused climate degradation and the explosion of electronic waste have pushed the computing community to explore fundamental alternatives to the current battery-powered, over-provisioned ubiquitous computing devices that need constant replacement and recharging. Soil Microbial Fuel Cells (SMFCs) offer promise as a renewable energy source that is biocompatible and viable in difficult environments where traditional batteries and solar panels fall short. However, SMFC development is in its infancy, and challenges like robustness to environmental factors and low power output stymie efforts to implement real-world applications in terrestrial environments. This work details a 2-year iterative process that uncovers barriers to practical SMFC design for powering electronics, which we address through a mechanistic understanding of SMFC theory from the literature. We present nine months of deployment data gathered from four SMFC experiments exploring cell geometries, resulting in an improved SMFC that generates power across a wider soil moisture range. From these experiments, we extracted key lessons and a testing framework, assessed SMFC's field performance, contextualized improvements with emerging and existing computing systems, and demonstrated the improved SMFC powering a wireless sensor for soil moisture and touch sensing. We contribute our data, methodology, and designs to establish the foundation for a sustainable, soil-powered future.

Surface soil moisture estimation in bare agricultural soil using modified Dubois model for Sentinel-1 C-band SAR data

Surface soil moisture has vital role in water energy balance, climate change and agriculture mainly for crop water requirements and irrigation scheduling. Microwave remote sensing with its unique characteristics of high penetration and sensitivity towards dielectric constant, has enabled the researchers to explore various techniques for soil moisture estimation. With the launch of Sentinel-1 (A&B) Synthetic Aperture Radar (SAR) satellites, the hindrance in accessing high spatial and temporal resolution data is eliminated. The current study focuses on surface soil moisture estimation for bare agricultural fields in the semi-arid region. Field soil moisture up to 5 cm depth using HydraGo Probe sensor and surface roughness synchronizing with satellite pass dates were collected from total 102 locations spanning four dates. Volumetric and sensor-based soil moisture are well correlated with R2 = 0.85. The Modified Dubois Model (MDM) was applied to obtain the relative permittivity of the soil for the backscattering coefficient (σ◦) for VV polarization, which is used as one of the inputs in universal Topp’s model for soil moisture calculation. Model derived soil moisture is well correlated with ground-based soil moisture for the entire range of the soil moisture (0.02-0.18 m3m-3) with R2 = 0.85 and RMSE=0.005. The entire soil moisture was categorized in three soil moisture ranges to evaluate the sensitivity. The highest correlation was observed for 0.06-0.1 m3m-3 with R2 = 0.73 and RMSE=0.003 followed by 0.015-0.6 m3m-3 with R2 = 0.81 and RMSE=0.001 and 0.11-0.18 m3m-3 with R2 = 0.48 and RMSE=0.019 which is significantly low. Performance accuracy of MDM is encouraging for bare soil moisture estimation for even the lower range of surface soil moisture.

Geospatial Analysis For Determination Of Preferential Soil Conditions For The Desert Locust Oviposition

Understanding the thresholds of influencing parameters that favor the habitability of dangerous pests like desert locusts (Schistocerca gregaria) can aid in early detection and eradication using control operations. The life cycle of the desert locust and its behavioral changes are associated with the weather patterns and the region’s ecosystem settings. This study attempts to retrieve the preferential soil conditions like texture and moisture at the surface and subsurface levels for egg-laying by desert locusts. Towards this, Locust Hub, a comprehensive database of desert locusts maintained and disseminated by the Food and Agriculture Organisation under the Locust Watch program, was used to identify breeding site locations for 2017-2021. In this research, we extracted sand-silt-clay percentage at these breeding sites using SoilGrids ver. 2.0 from the World Soil Information Service database facilitated by International Soil Reference and Information Centre. Similarly, soil moisture conditions extracted from Level-4 data products of the Soil Moisture Active Passive mission for all these breeding sites aided in essaying the optimal soil conditions for the desert locust’s oviposition. This study’s results confirm the sand percentage in a broad range of 55-70% (for 90% of samples), followed by a narrow range of silt and clay with 19-24% and ~14-20%, respectively. Our study reveals that female desert locusts prefer sandy loam-textured soils for oviposition. The preferred range of soil moisture at the surface and subsurface resulted in 5-10% and 10-20%, respectively. These results confirm that dampness is required at the surface soil for initiating the oviposition by female desert locusts. Results from this research can aid in the early identification of breeding grounds during desert locusts’ invasion period.

Screening of two native isolates of entomopathogenic nematodes, Heterorhabditis indica and Heterorhabditis bacteriophora, for temperature and moisture stress tolerance

Entomopathogenic nematodes (EPNs) are obligate parasites infecting a wide range ofinsect species and are oneof promising biocontrol agents with potential in insect pest management. Temperature and soil moisture play an important role for their sustenance in soil and their efficiency varies greatly under different climatic conditions. The present studywas aimed to study the effect oftemperature and soil moisture on survival, infectivity and reproduction of two EPNs viz., Heterorhabditis indica and H. bacteriophora. The IJs of both the EPNs were exposed to different temperatures and soil moisture levels. Results revealed that survival of both EPNs effected at 5ºC. Survival of H. indica decreased from 54% to 29% after exposure to 5ºC for 48 h. None of the EPNs survived at 35ºC and 40ºC. Infectivity and reproduction potential of both the EPNs tested was highest at 30ºC. No decrease in infectivity was observed with increase in exposure time to different temperatures in both the EPN species. Slight decrease in reproductive potential was observed in H. bacteriophora when exposed to 30ºC for 48h. None of the tested EPNs infected Galleria mellonella larva at 5ºC, 35ºC and 40ºC. Both the EPNs tested showed high infectivity at soil moistures more than 10%. Minimum infection was observed at soil moistures below 10%. Our study revealed that temperature range of 20ºC-30ºC and soil moisture of >10% was found optimum for survival, infectivity and reproduction ofthe testedEPNs. Both the EPNs in this study showed similar temperature and moisture requirements.

Modeling of soil moisture and water fluxes in a maize field for the optimization of irrigation

Precision irrigation is becoming more and more important in agricultural crop production due to the limited water resources resulting from the negative effects of climate change. Modeling of irrigation can support decision makers in the maintenance of the quantitative and qualitative parameters of the cultivated crops, while improving the efficiency of water consumption. In the present paper, a 3D hydrodynamic model was created in the HYDRUS environment to support the modeling of the temporal changes and spatial variations in the water balance (WB) of a maize cultivated Hungarian study site, thereby providing inputs for irrigation scheduling and variable rate irrigation for stakeholders. Beside soil physical parameters, crop evapotranspiration (ETc) values were also considered as model inputs for the phenological crop development stages of the maize from the sowing to the harvesting. This period was between the 3rd of May and the 10th of September in 2020 and the 20th of May and 14th of September for the year 2021. The performance of the model for soil moisture conditions were validated by on-field soil moisture measurements. The overall performance (full vegetation period) of the model was good (r2 = 0.88 for 2020 and r2 = 0.91 for 2021), but slightly varied among different phenological stages. Based on the model, the water balance of the investigated area was determined without any irrigation, alongside the cumulative water fluxes (CWF) through the boundaries including the root water uptake (RWU) of the maize as well. The results revealed that the incoming precipitation was not sufficient to supplement the water content in the soil to the optimal soil moisture range, except when the precipitation level is high enough to balance it. It was concluded that the water balance was negative for the investigated time periods without any irrigation. The specific water deficit (WD) values were calculated considering the area of the study site, which is 1439 m3/ha for 2020 and 2068 m3/ha for 2021. From the obtained results, optimal irrigation schedules were presented to keep the soil moisture content in the optimal range (20.92–14.41 V/V%) in the modeled area for two different vegetation periods. It was found that due to the irrigation the crop water productivity (CWP) increased by 6% and 4% in 2020 and 2021. Overall, the model is able to cope with changing circumstances that could help to mitigate the negative effects of climate change with the reasonable use of limited water resources in the future.

Skill and lead time of vegetation drought impact forecasts based on soil moisture observations

Timely and skilful forecasts of vegetation drought impacts should enable more proactive drought preparedness, management, and mitigation. Numerous previous studies found a temporal correlation between soil moisture and vegetation condition. However, a correlation across the full range of soil moisture and vegetation condition does not automatically translate into skill in forecasting infrequent events such as agricultural droughts. Here, we develop a threshold- or impact-based forecasting framework to assess early warning capability (EWC). We analysed the skill and lead time achieved using soil moisture observations at multiple depths as predictors of subsequent vegetation drought impacts inferred from MODIS satellite observations at 93 sites across the United States. Forecast thresholds were expressed in terms of seasonally-adjusted standardised anomalies (z-scores) to distinguish climate-related impacts from any seasonal vegetation cycle. Different combinations of soil moisture integration depth, satellite vegetation indices and threshold levels were tested. Near-Infrared Reflectance of vegetation (NIRv) yielded a marginally better EWC than other indicators of vegetation drought impact. The optimal soil moisture integration depth varied between land cover types, from 0 to 10 cm for cropping systems to 0–100 cm for grassland and savanna. The greatest skill improvements were achieved using similar z-score threshold values for the soil moisture trigger and vegetation impact, producing typical lead times of two to four weeks. Further research is recommended to combine the framework developed here with spatially continuous soil moisture analyses or forecasts available from remote sensing and land surface models.

Soil microbes support Janzen's mountain passes hypothesis: The role of local-scale climate variability along a tropical montane gradient.

Tropical montane ecosystems are the centers of biodiversity, and Janzen proposed that mountain climate variability plays a key role in sustaining this biodiversity. We test this hypothesis for soil bacteria and fungi along a 265-1,400 m elevational gradient on Hainan Island of tropical China, representing diverse vegetation types from deciduous monsoon forest to cloud forest. We found that bacterial and fungal diversity declined as elevation increased, and the dissimilarity of both groups increased with increasing separation in elevation, although changes in bacteria were larger than in fungi. Seasonal alterations and the range of soil moisture in the growing season were found to be the dominant drivers of fungal richness and Shannon diversity, whereas soil pH was the major driver of bacterial diversity. Dissimilarities of bacterial and fungal communities were best predicted by climate, particularly seasonal changes in soil temperature, with weaker influences of soil physicochemistry and vegetation. The dominant effect of seasonality in soil temperature was further detected in cloud forests, which harbored a higher proportion of unique bacterial species and dissimilarity of bacterial and fungal communities. Our findings suggest that local-climate variability plays a crucial role in structuring the distribution of soil microbial communities along a tropical montane gradient, which generally supports Janzen's hypothesis. Such a sensitivity to climatic variability suggests that soil microbial communities along tropical montane gradients may shift in response to future climate scenarios.

Evaluating the effect of soil moisture during sowing on the depth of cereal crops seeding

Evaluation of the depth of seeding cereal crops after sowing by units equipped with tyne coulters and tine cultivators under different soil moisture conditions in the steppe zone of Siberia is given. The studies were conducted from 2018 to 2021 in the steppe zone of the Altai Territory. Soil moisture was determined using an HH-2 electronic moisture meter from Delta-T Devices, the principle of operation of which is based on measuring the electrical conductivity of the soil. The depth of seed placement was determined by the etiolated (clarified) part of the plants. When comparing the results, the value of standard deviations from the average value of seed placement depth was taken, because according to the principle of evaluation it corresponds to the agrotechnical requirements for sowing. The standard deviations from the seed placement depth after sowing by the machines equipped with tyne coulters and tine cultivators found in the soil moisture range from 12 to 40% were analyzed. Standard deviations from the average defined seed placement depth are summarized in scatter diagrams. All deviations from the depth of placement after sowing with these coulters were within the acceptable limits of changes in agrotechnical requirements, which are ± 10 mm. The relationship between deviations from the average depth of seeding and soil moisture at the time of sowing was established. Relationship equations of these indicators are highly significant, as evidenced by high coefficients of determination. These equations will allow agricultural producers of the steppe zone of the Altai Territory with knowledge of soil moisture at the time of sowing to predict its quality component after sprouting, since the uniformity of seed distribution on the depth of seeding affects the crop yield.

Time series soil moisture retrieval from SAR data: Multi-temporal constraints and a global validation

Time series algorithms for soil moisture retrieval from synthetic aperture radar (SAR) data have steadily increased in popularity over the past decade due to the feasibility of decoupling the effect of other surface variables, and the increasing availability of dense time series SAR data. While soil moisture inversion from time series data can utilize more independent observations, the value of further constraints on the inversion process are widely acknowledged. However, how to constrain a time series retrieval for global soil moisture mapping is still unresolved. In this study, three kinds of time series constraints were further developed and evaluated, including the use of 1) temporal behavior of soil moisture and soil moisture bounds; 2) temporal behavior of vegetation or time-invariant vegetation; and 3) time series ensemble skill. The effect of these constraints was investigated using 4 years (2016–2019) C-band Sentinel-1 data collected over 547 worldwide stations from 17 networks available on the international soil moisture network (ISMN) and intensive ground samples collected during the Fifth Soil Moisture Active and Passive Experiment (SMAPEx-5). While the effect of these temporal retrieval skills varies in time and space, the global validation yielded four general suggestions: 1) the assumption of time-invariant vegetation contributed negatively even for a short retrieval period of ≤12 days; 2) reliable soil moisture bounds of each retrieval period can substantially improve the retrieval statistics at the cost of an underestimated soil moisture range; 3) the temporal constraints of soil moisture and vegetation need to be used together with the soil moisture bounds for reliable estimation; 4) the use of an ensemble retrieval could partly remove the retrieval uncertainties at the expense of underestimating soil moisture variation. The use of these constraints resulted in a competitive correlation coefficient (R: 0.64), root mean square error (RMSE: 0.072 m3/m3) and unbiased RMSE (ubRMSE: 0.052 m3/m3) at a spatial grid of 100 m, with similar performance achieved across a retrieval window up to 132 days.

Assessing the Potential of Combined SMAP and In-Situ Soil Moisture for Improving Streamflow Forecast

Soil moisture is an essential hydrological variable for a suite of hydrological applications. Its spatio-temporal variability can be estimated using satellite remote sensing (e.g., SMOS and SMAP) and in-situ measurements. However, both have their own strengths and limitations. For example, remote sensing has the strength of maintaining the spatial variability of near-surface soil moisture, while in-situ measurements are accurate and preserve the dynamics range of soil moisture at both surface and larger depths. Hence, this study is aimed at (1) merging the strength of SMAP with in-situ measurements and (2) exploring the effectiveness of merged SMAP/in-situ soil moisture in improving ensemble streamflow forecasts. The conditional merging technique was adopted to merge the SMAP-enhanced soil moisture (9 km) and its downscaled version (1 km) separately with the in-situ soil moisture collected over the au Saumon watershed, a 1025 km2 watershed located in Eastern Canada. The random forest machine learning technique was used for downscaling of the near-surface SMAP-enhanced soil moisture to 1 km resolution, whereas the exponential filter was used for vertical extrapolation of the SMAP near-surface soil moisture. A simple data assimilation technique known as direct insertion was used to update the topsoil layer of a physically-based distributed hydrological model with four soil moisture products: (1) the merged SMAP/in-situ soil moisture at 9 and 1 km resolutions; (2) the original SMAP-enhanced (9 km), (3) downscaled SMAP-enhanced (1 km), and (4) interpolated in-situ surface soil moisture. In addition, the vertically extrapolated merged SMAP/in-situ soil moisture and subsurface (rootzone) in-situ soil moisture were used to update the intermediate layer of the model. Results indicate that downscaling of the SMAP-enhanced soil moisture to 1 km resolution improved the spatial variability of soil moisture while maintaining the spatial pattern of its original counterpart. Similarly, merging of the SMAP with in- situ soil moisture preserved the dynamic range of in-situ soil moisture and maintained the spatial heterogeneity of SMAP soil moisture. Updating of the top layer of the model with the 1 km merged SMAP/in-situ soil moisture improved the ensemble streamflow forecast compared to the model updated with either the SMAP-enhanced or in-situ soil moisture alone. On the other hand, updating the top and intermediate layers of the model with surface and vertically extrapolated SMAP/in-situ soil moisture, respectively, did not further improve the accuracy of the ensemble streamflow forecast. Overall, this study demonstrated the potential of merging the SMAP and in-situ soil moisture for streamflow forecast.

Studies on Controlling Variables of the Conveying Unit of a Power Tiller Operated Groundnut Digger

A power tiller operated groundnut digger was designed and developed in the Dept. of Farm Machinery and Power Engineering, CAET, OUAT, Bhubaneswar during 2021–22. The objective of this project was to optimize the operational parameters of the digger's conveying unit using the RSM technique. The study analysed the effects of soil moisture content and conveyor speed, on the conveying efficiency and soil separation efficiency with two different types of peg geometries. The central composite rotatable design (CCRD) method with a quadratic model was utilized to correlate the independent parameters with the conveying and soil separation efficiencies. The results indicated that variations in soil moisture content and conveyor speed had a significant impact on the conveying and soil separation efficiencies. The optimum parameters for the conveying unit of a power tiller operated groundnut digger were found to be a soil moisture range of 9.0-12.0%, a conveyor speed of 1.0 m/s and straight shank with bending tip pegs, resulting in a conveying efficiency and soil separation efficiency of 89.45% and 71.45%, respectively.

GNSS-R monitoring of soil moisture dynamics in areas of severe drought: example of Dahra in the Sahelian climatic zone (Senegal)

ABSTRACT With population growth, water will increase in the following decades tremendously. The optimization of water allocation for agriculture requires accurate soil moisture (SM) monitoring. Recent Global Navigation Satellite System Reflectometry (GNSS-R) studies take advantage of continuously emitted navigation signals by the Global Navigation Satellite System (GNSS) constellations to retrieve spatiotemporal soil moisture changes for soil with high clay content. It presents the advantage of sensing a whole surface around a reference GNSS antenna. This article focuses on sandy SM monitoring in the driest condition observed in the study field of Dahra, (Senegal). The area consists of 95% sand and in situ volumetric soil moisture (VSM) range from ~3% to ~5% durinf the dry to the rainy season. Unfortunately, the GNSS signals’ waves penetrated deep into the soil during the dry period and strongly reduced the accuracy of GNSS reflectometry (GNSS-R) surface moisture measurements. However, we obtain VSM estimate at low/medium penetration depth. The correlation reaches 0.9 with VSM error lower than 0.16% for the 5–10-cm-depth probes and achieves excellent temporal monitoring to benefit from the antenna heights directly correlated to spatial resolution. The SM measurement models in our research are potentially valuable tools that contribute to the planning of sustainable agriculture, especially in countries often affected by drought.

Assessing the sensitive spectral bands for soybean water status monitoring and soil moisture prediction using leaf-based hyperspectral reflectance

The stability of soybean yields in Brazil is regularly affected by drought periods, and soil management practices are crucial to expanding the water holding capacity of the soil and providing higher levels of moisture during critical periods, which contribute to increasing crop yields, relieving the need for non-agricultural areas to be converted into croplands. The research reported herein aimed to quantitatively monitor the soil moisture of a soybean crop through leaf-based hyperspectral reflectance and suggest a remote sensing-based approach that might assist in identifying soil management zones. A field experiment at the Brazilian Agricultural Research Corporation during 2016/2017, 2017/2018, and 2018/2019 cropping seasons had ten soybean genotypes subjected to four water conditions: irrigated, non-irrigated, and water deficit induced at the vegetative or reproductive stages. The soil of the experimental site is characterized as Udox Oxisol. Leaf reflectance (400–2500 nm) was collected by the spectroradiometer FieldSpec 3 Jr simultaneously with soil moisture (0–20 and 20–40 cm depths) at eleven dates. Data submitted to Principal Component Analysis (PCA) evaluated the clustering of water conditions and which are the most critical spectral wavelengths to characterize the plant water status. The Partial Least Squares Regression (PLSR) was applied to develop a quantitative spectral model to predict soil moisture. The PCA explained over 93% of the spectral variance within each assessment day, and shortwave infrared wavelengths presented a higher contribution to water status clustering. At the cross-validation step, the PLSR presented R2 up to 0.860 and 0.906 (0–20 and 20–40 cm) underperforming when soil moisture showed no significant differences between water conditions. Using samples from all assessment days, PLSR presented R2 = 0.609 and 0.722 (0–20 and 20–40 cm) at the external validation step (RMSE = 2.7 and 1.9, respectively), with a soil moisture range equal to 16–35% and 20–35% at both depths, remarkably outperforming the traditional univariate spectral models. Our results contribute to soil moisture assessment in extensive soybean areas regardless of the stage of crop development and provide a significant contribution since the Brazilian soybean crop calendar might present differences of over 30 days within the same production region. Due to that, soybean plants are rarely at the same phenological stage on a given date in the season.

Effects of Soil Type, Temperature, and Moisture on Development of Fusarium Root Rot of Soybean by Fusarium solani (FSSC 11) and Fusarium tricinctum.

Fusarium solani FSSC 11 and F. tricinctum are important root rot pathogens of soybean in North Dakota. The roles of soil type, temperature, and moisture in disease development by both species are poorly documented. To assess the effect of soil type on disease, three types of soil (Glyndon sandy loam, La Prairie silt loam, and Fargo clay) that represent soils of the soybean production region in the Red River Valley were examined in greenhouse, microplot, and growth chamber studies. Disease incidence and lesion length on roots were evaluated at growth stages V3 and R6. Soil type significantly affected disease development, with higher severity in the lighter soils of Glyndon sandy loam and La Prairie silt loam compared with Fargo clay. Soil type also interacted with Fusarium species, in which the maximum severity was observed in Glyndon sandy loam for F. solani, and in La Prairie silt loam for F. tricinctum. In addition, the cumulative effects of soil type, temperature, and soil moisture were tested in a growth chamber. Emergence and disease on seedlings were evaluated at growth stage V3. Significant reductions in emergence occurred at 10°C in treatments with F. solani and F. tricinctum, but there was no significant difference among the three soils. Infection was visible at temperatures of 10 to 20°C for F. solani and 15 to 20°C for F. tricinctum. F. solani caused the greatest infection at 20°C in Glyndon sandy loam, while it was at 15°C in La Prairie silt loam for F. tricinctum. The isolates of the two Fusarium species caused root rot in soil moisture ranging from 20 to 100% water holding capacity (WHC). The greatest reduction in emergence caused by the Fusarium spp. was observed at 80% WHC in silt loam and clay soils and 40% WHC in sandy loam soil, when compared with the same WHC in noninfested soils. Ranges of soil moisture causing infection were negatively correlated with temperature. At the lower temperature there was a broader range of soil moistures resulting in infection compared with higher temperatures. At 18°C, most infection occurred at soil moistures of 20 to 80% WHC, while it was 40 to 80% WHC at 28°C. Disease caused by F. solani was favored by a temperature of 18°C with high soil moisture (60 to 80% WHC) or 28°C with low soil moisture (20 to 40% WHC), while F. tricinctum was favored by cooler temperature and lower soil moisture.