Soil Information Research Articles

Spatial Evaluation of Land and Soil Properties with Geography Information Systems (GIS): The Case Study from Meriç District of Thrace Region in Türkiye

This research was carried out within the scope of spatial evaluation of the land and soil properties of Edirne-Meriç district, located in the Thrace region (Türkiye) by using GIS. Arc GIS 10.3.1 software was used in the classification of soil and land features. Digital soil maps (1/25.000 scale) were used to determine soil and land properties. Alos Palsar (12.5m) satellite images were used to determine land elevation and aspect distributions. As a result of the research, it was seen that the soil class with the largest area in terms of large soil groups in Meriç district is lime-free brown forest soils (261.2 km2). III. It was determined that class lands (153.7 km2) cover the largest area. In general, it was observed that the soil depth was greater than 150 cm (261.9 km2). It was determined that 23.3 km2 of the Meriç district lands were exposed to severe water erosion. It was determined that the study area consists of lands with a steep slope of 12-20% (126.7 km2). It has been observed that the height distribution of Meriç district lands varies between 4.7-120.5 m. It was defined that the majority of the lands were in the southwestern direction group. It is thought that the soil and land information database created as a result of the research will make significant contributions to researchers and the public, institutions and organizations.

Are Plant Functional Types Fit for Purpose?

AbstractFor over 40 years, Plant Functional Types (PFTs) have been used to discretize the ∼400,000 species of terrestrial plants into “similar” classes. Within Earth System Models (ESMs), PFTs simplify terrestrial biosphere modeling in combination with soil information and other site characteristics. However, in flux analysis studies, PFT schemes are often implemented as the sole analytical lens to clarify complex behavior. This usage assumes that PFTs adequately enable a mapping between climate inputs and flux outputs. Here, we show that random forest models, trained using aggregated climate and flux measurements from 245 eddy‐covariance sites, cannot accurately predict PFT groupings, regardless of the nature of the PFT scheme. Similarly, PFTs provide negligible benefit when using site climate to predict site flux regimes and vice versa. While use of PFT classifications is convenient, our results suggest they do not aid analytical skill, which has important implications for future terrestrial flux studies.

Characterization of the Energy Balance of Wheat Grown under Irrigation in the Hot, Arid Environment of Sudan

An analysis of the crop microclimate is essential for assessing the climate’s appropriateness for cultivation. Here, the Bowen ratio (BR) was used to characterize the energy balance in an irrigated wheat field in a hot, arid environment in Sudan. The hourly BR was calculated using micrometeorological data, including net radiation (Rn) and soil heat flux (G), observed in the 2021–2022 and 2022–2023 growing seasons (December–March) and used to compute hourly daytime latent heat (LE) and sensible heat (H) fluxes during the days before and after irrigation. In both seasons, the observed significant evaporative cooling effect of irrigation led to a daily maximum temperature difference of 2.5–5.7 °C between the wheat field and a nearby meteorological station in a non-vegetated desert area. The energy balance calculation results showed that LE was dominant over H and G. Because BR tended to be negative, H was negative; thus, LE was larger than Rn because of sensible heat advection from the surrounding area. Further, a decrease in G after irrigation indicated an alteration in the soil’s thermal properties. These results provide insights into the micrometeorological response of irrigated wheat to a hot, arid environment and useful information for understanding soil–plant–atmosphere interactions in hot, dry environments.

Evaluating the extrapolation potential of random forest digital soil mapping

Spatial soil information is essential for informed decision-making in a wide range of fields. Digital soil mapping (DSM) using machine learning algorithms has become a popular approach for generating soil maps. DSM capitalises on the relation between environmental variables (i.e., features) and a soil property of interest. It typically needs a training dataset that covers the feature space well. Mapping in areas where there are no training data is challenging, because extrapolation in geographic space often induces extrapolation in feature space and can seriously deteriorate prediction accuracy. The objective of this study was to analyse the extrapolation effects of random forest DSM models by predicting topsoil properties (OC, clay, and pH) in four African countries using soil data from the ISRIC Africa Soil Profiles database. The study was conducted in eight experiments whereby soil data from one or three countries were used to predict in the other countries. We calculated similarities between donor and recipient areas using four measures, including soil type similarity, homosoil, dissimilarity index by area of applicability (AOA), and quantile regression forest (QRF) prediction interval width. The aim was to determine the level of agreement between these four measures and identify the method that had the strongest agreement with common validation metrics. The results indicated a positive correlation between soil type similarity, homosoil and dissimilarity index by AOA. Surprisingly, we observed a negative correlation between dissimilarity index by AOA and QRF prediction interval width. Although the cross-validation results for the trained models were acceptable, the extrapolation results were unsatisfactory, highlighting the risk of extrapolation. Using soil data from three countries instead of one increased the similarities for all measures, but it had a limited effect on improving extrapolation. Also, none of the measures had a strong correlation with the validation metrics. This was particularly disappointing for AOA and QRF, which we had expected to be strong indicators of extrapolation prediction performance. Results showed that homosoil and soil type methods had the strongest correlation with validation metrics. The results for this case study revealed limitations of using AOA and QRF as measures of extrapolation effects, highlighting the importance of not relying on these methods blindly. Further research and more case studies are needed to address the effects of extrapolation of DSM models.

Experience in creating an information system for the rational use of soil resources

The structure and functions of the “SoilGeographical Database of Russia Information System” (ISSGDBR) implemented on the platform of the Soil Science Faculty of the Lomonosov Moscow State University are considered. The current state of national soil databases and developing on that base international soil information systems are described. The created information system consists of three sections. The first section includes soil maps of different scales and maps of the natural conditions of the territory of the Russian Federation. The second section is represented by a naturalattribute database, which is based on a soil profile with associated analytical characteristics. In the third section, with the help of software tools, the collected varied information about the soil cover becomes the basis for modeling the spatial distribution of various indicators, performing calculations in applied projects for a wide range of users. The ISSGDBR is considered as an expert and analytical center, which acts as a coordinator and supports the functioning of a distributed network, as well as the development and distributing of internationally relevant standards for description, storage, presentation of soil information and data exchange through the use of specialized programs adapted to regional features. In addition, the purpose of the center is the implementation of communication between primary digital resources of natural and soil information localized in various organizations, agrochemicalservice regional centers, and federal agencies accumulating information necessary for making managerial decisions.

Autonomous soil vision scanning system for intelligent subgrade compaction

The wide application of intelligent compaction in subgrade construction is limited by the evaluation accuracy. The current practice assumes the compacted site as a homogeneous soil condition, ignoring the influence of soil property. This paper describes an advanced autonomous soil scanning system based on the computer-vision approach to recognize the soil gradation in real-time. A hybrid intelligent model is developed through a series of field compaction tests, integrating Deeplabv3+ and XGBoost algorithms. The proposed method is effective in segmenting and quantifying the large particles while the fine-grained soil is completed by the prediction model. The scanned soil information correlates well with the intelligent compaction indicators, providing essential parameters for the accurate assessment of subgrade compaction quality. The soil vision scanning system is combined with the intelligent compaction system to enable the roller autonomous learning of the whole construction area.

Degraded Peatlands and Their Utilization Opportunities in Kepulauan Meranti Regency, Riau Province, Indonesia

Degraded peatlands are caused by various factors, including land fires, water mismanagement, and mining activities. Indicators of degraded peatlands can be identified through the type of land cover observed in the field, which commonly includes shrubs and barren land (open areas of ex-mining land). Therefore, this study aimed to determine the dynamics of the existence of degraded land and its utilization opportunities using spatial, image, and time series analysis methodDepartment Forest Resources Conservation & Ecotourism, Forestry Faculty, Bogor Agricultural University Kampus IPB Darmaga PO.Box 168 Bogor-Indonesias. To conduct the analysis, various data sources were employed, including Landsat imagery, the Regional Spatial Planning (RTRW) map, hotspots data, rainfall data, landforms maps, peatlands data, and soil information. The results of the study showed that between 1999 and 2019, the average area of degraded peatlands amounted to 198,084.54 hectares. Furthermore, the optimal area of degraded peatlands utilized for agriculture amounted to 7,122.45 hectares . Inventory of degraded peatland using the land cover approach can be obtained faster, cheaper, easier than the terrestrial method. Opportunities for using degraded land for agricultural purposes were also identified through the selection of plant species that exhibited adaptability to peatlands and held economic value. These selected plants, including sago, rubber, areca nut, and liberika coffee, were developed within the Meranti Islands Regency.

LandBench 1.0: A benchmark dataset and evaluation metrics for data-driven land surface variables prediction

The advancements in deep learning methods have presented new opportunities and challenges for predicting land surface variables (LSVs) due to their similarity with computer sciences tasks. However, few researchers focus on the benchmark datasets for LSVs predictions that hampers fair comparisons of different data-driven deep learning models. Hence, we propose a LSVs benchmark dataset and prediction toolbox to boost research in data-driven LSVs modeling and improve the consistency of data-driven deep learning models for LSVs. LSVs benchmark dataset contains a large number of hydrology-related variables, such as global soil moisture, runoff, etc., which can verify the simulation of hydrological processes. Various global data from European Centre for Medium-Range Weather Forecasts reanalysis 5 (ERA5), ERA5-land, global gridded soil information (SoilGrid), soil moisture storage capacity (SMSC), and moderate-resolution imaging spectroradiometer (MODIS) datasets have been pre-processed into daily data at 0.5-, 1-, 2-, and 4-degree resolutions to facilitate their use in data-driven models. Simple statistical metrics, i.e., the root mean squared error and correlation coefficient, are chosen to evaluate the performance of different deep learning (DL) models, including convolutional neural network, long short-term memory and convolution long short-term memory models, with lead times of 1 and 5 days. A processed-based model serves as a physic baseline, soil moisture and surface sensible heat fluxes are taken as the target variables. The developed benchmark dataset and evaluation metrics for predicting LSVs using data-driven approaches, named as the LandBench toolbox, were implemented using Pytorch. This toolbox facilitates the reimplementation of existing methods, the development of novel predictive models, and the utilization of unified evaluation metrics. Additionally, the toolbox incorporates address mapping technology to enable high-resolution global predictions with constrained computing resources. We hope LandBench will not only serves as a standardized framework, fostering equitable model comparisons, but also provides indispensable data and a robust scientific foundation essential for advancing climate change research, disaster management, and sustainable development initiatives.

Inversion of soil properties with hyperspectral reflectance in construction areas of high-standard farmland

ABSTRACT High-standard farmland construction is an important process that can enhance food security and accelerate new-style modernization agriculture. Hyperspectral remote sensing can provide data and technical support for this type of construction to provide a reference when optimizing high-standard farmland construction areas. This study was performed in Xinzheng City, the primary grain-producing areas in Henan Province. Field sampling and indoor hyperspectral spectroscopy (350~2500 nm) were combined; spectral transformations such as continuum removal (CR) were performed after Savitzky‒Golay (SG) convolution smoothing; and the best hyperspectral bands were selected as the common index of the soil properties by correlation analysis and fuzzy clustering maximum tree. A hyperspectral inversion model was built for the panel data model of the fixed effect variable coefficient based on the ordinary least squares estimation method (OLS), including panel data describing pH, organic matter, nitrogen, phosphorus, potassium, iron, chromium, cadmium, zinc, copper, and lead of 116 samples in Xinzheng City. Results show that the panel data model is of good quality overall, and the goodness of fit is higher ( R 2 = 0.9991, F = 2195.67). The precision test results indicate that the models performed well at both description and prediction, including accurate quantification, with an RPD above 2.5. Thus, the proposed model provides an important basis for soil information management, resource evaluation, and a reference when optimizing high-standard farmland construction processes.

Defining the Multiscalar Index Timescale—Soil Water Depth Continuum for the Southwestern United States

AbstractThe lack of long‐term reliable in‐situ soil moisture data sets creates unique drought monitoring challenges for the semi‐arid Southwestern United States (“Southwest”). Land managers use multiscalar meteorological drought indices, like the Standardized Precipitation Index (SPI) and Standardized Precipitation‐Evapotranspiration Index (SPEI), as proxies for soil water availability; however, objectively identifying the index and timescale that best represents soil water availability in semi‐arid environments remains a significant gap for applying available climate information to land management action. Here, we couple site‐specific soil modeling with high resolution, spatially continuous meteorological data sets to define the relationship between index timescale length and soil water availability of different depths. By creating new matric potential indices (MPI) from 0 to 200 cm, we correlate MPI time series with the SPI and SPEI at different timescale lengths to identify the best index‐timescale combination for each depth. Results indicate the general relationship between highest correlating index‐timescale and MPI‐depth operates roughly on a 1‐month:5 cm step progression at shallow depths (<80 cm). Analysis by soil texture class shows that soils with higher clay content produce shallower sloped relationships (>1‐month:5 cm) than sandy soils (<1‐month:5 cm). Overall, the SPI produced higher correlations and less error with the MPI compared to the SPEI across all texture classes and depths. Thus, the SPI is a good indicator of soil water availability at shallow depths (<80 cm) and should be considered for soil drought monitoring on Southwestern drylands. However, land managers should consult local soils information, if available, given the impacts of texture class on timescale‐depth relationships.

Root water uptake model shows age-related water uptake patterns of apple trees on the Chinese Loess Plateau

Study regionChangwu Tableland, Chinese Loess Plateau Study focusSoil water status and water demand are two important factors to influence root water uptake (RWU) patterns, but the mechanisms involved in deep-rooted plants remain unknown. This study addresses this knowledge gap by investigating diurnal variations in RWU patterns for two different-aged apple orchards (7 vs. 19 years) on the Chinese Loess Plateau (CLP). We achieved this by inputting measured plant and soil information into a physical-based numerical RWU model. New hydrological insights for the regionThe results showed that the 19-year-old orchard exhibits distinct characteristics compared to the 7-year-old orchard. Specifically, the 19-year-old orchard had less deep soil water content, resulting in lower matric potential and hydraulic conductance. In contrast to cropland soil water, the cumulative deep soil water (>2 m) deficits were 17.6 and 1108.3 mm in the 7-year-old and 19-year-old orchards, respectively. Despite the 19-year-old orchard having 67% of its fine roots below 5 m soil depth, their contribution ratio to total RWU was only 30–40%, indicating less available deep soil water for the older orchard.Sap flow and leaf water potential had bell-shaped diurnal variations, leading to shifting soil water contributions to RWU in the 7-year-old and 19-year-old orchards. These changes were particularly evident when soil water potential in the root zone was heterogeneous, resulting in contrasting RWU patterns during different water demand periods. Notably, the 7-year-old orchard exhibited less water contribution from deep 2–5 m layer and enriched xylem water isotopes at midday with high water demand, while the 19-year-old orchard had more water contribution from deep 5–21.6 m layer and depleted xylem water isotopes. These contrasting RWU variations shed light on the profound impact of orchard age on deep RWU patterns, especially under the varying water demand conditions after CLP afforestation.

Assimilating Seismic Data And Soil Basic Information to Predict Near-surface Permeability And Water Content

Soil permeability plays a key role in the study of earth sciences for agriculture and forestry. Because the traditional laboratory measurement is time-consuming and laborious, and only discrete profile data can be obtained. In this paper, a simple and efficient method using soil transfer function (PTFs), is proposed to estimate permeability and other parameters indirectly by seismic wave observation. Based on Biot’s theory establishing the relationship between shear-wave velocity and permeability, and the basic soil information in the study area, including land survey, logging and drilling data, using the PTFs constraint to estimate saturated hydraulic conductivity, soil components, saturated water content and residual water content may favor the physical corrections of the PTFs. The results show that there are three distinct layers of permeability in the study area, consistent with the velocity structure. The measured basic information further verifies the accuracy of the saturated permeability coefficient estimation, which shows the reliability of the improved PTFs. The established near surface permeability estimation method by integrating seismic wave observation and soil basic information may provide further assimilation for parameter estimation combining geophysical observation and hydrogeographic statistics.

Risk assessment for cropland abandonment in mountainous area based on AHP and PCA—Take Yunnan Province in China as an example

Assessing the risk of cropland abandonment in mountainous area is essential for ensuring food security. However, the current evaluation indicator system is incomplete, and the reliability analysis of the results is lacking. This paper proposes a spatially comprehensive assessment method of cropland abandonment risk based on AHP and PCA using multi-source data of land use, abandoned cropland, administrative area, population density, GDP, cropland soil information, and climate, and applies the method to Yunnan Province in China. Firstly, 14 indicators were selected from four aspects, including social factors, farming conditions, cropland quality, and climate factors, to construct a three-layer indicator system of “target-criteria-indicator”. Fuzzy membership was used to calculate the indicator layer, and then the PCA method was applied to calculate the first principal component for the criterion layer. Finally, the AHP method was used to calculate the weighted superposition of the criterion layer. The proposed method showed good reliability in the study areas. In Yunnan Province, 33.03% of cropland is at high risk of abandonment, 37.52% is at medium risk, and 29.45% is at low risk. The high risk area is mainly distributed in the Lancang River Basin and Honghe River Basin. Social factors were found to play the greatest role in the risk of cropland abandonment in Yunnan Province. Factors such as low population density, large shape coefficient of cropland, and low annual average precipitation were identified as the main drivers of cropland abandonment in this region. The research results of this paper can provide decision-making reference for preventing and controlling cropland abandonment in mountainous areas.

Modelling soil organic carbon dynamics under extreme climate and land use and land cover changes in Western Oromia Regional state, Ethiopia

The combined effects of changes in climate and land use and land cover can lead to a decrease in soil organic carbon, potentially affecting soil fertility and agricultural output. The study aimed to evaluate the dynamics of soil organic carbon under various extreme climate and land use and land cover scenarios. The data on land use types and extreme climate indices between 2015 and 2070 were, respectively, sourced from the IPCC and the European Copernicus Climate Change Service webpages. The 2015 baseline data for soil organic carbon was obtained from the African Soil Information Service's website. Data quality control and model validation were conducted to ensure the reliability of the collected data and the predictive model. A generalized regression model was chosen for its accuracy and reliability in predicting soil organic carbon dynamics under different shared socio-economic pathways such as SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios. The study revealed that variations in extreme climate and land use patterns significantly influenced the organic carbon content of the soil. Increased dry days and the conversion of forest and grassland into farmland resulted in a drop in soil organic carbon, while increased wet days and warming temperatures significantly increase it under each scenario. The soil organic carbon content increased by 5.82 and 2.8 g/kg for the SSP1-2.6 and SSP2-4.5 scenarios, respectively, but decreased by 6.90 g/kg under the SSP5-8.5 scenario. Overall, the higher emission scenarios had a significant negative impact on soil organic carbon levels, while the low emission scenarios had a positive impact. Sustainable land management practices are crucial for preserving and managing soil organic carbon levels.

Precision Nitrogen Fertilization for Opium Poppy Using Combined Proximal and Remote Sensor Data Fusion

Proper management of within-field variability is crucial for maximizing crop yield, production outcomes and resource use efficiency and reducing environmental impacts. This study evaluated the agroeconomic and environmental feasibilities of site-specific nitrogen fertilization (SNF) in opium poppy (Papaver somniferum L.). On-line visible and near-infrared reflectance spectroscopy was used to estimate soil pH, organic carbon (OC), soil organic matter (SOM), P, K, Mg, Ca, Na, moisture content (MC), Ca:Mg and K:Mg for one field in Spain. Normalized difference vegetation indexes of the previous crop were retrieved from Sentine-2 images. Rasterization of soil and crop data layers created a spatially homogenous dataset followed by delineation of a management zone (MZ) map using a k-means cluster analysis. MZ clusters were ranked relying on the within-cluster soil fertility attributes. A strip experiment was conducted by creating parallel stripes distributed over the MZ map, over which two SNF treatments (i.e., SNF-Kings approach [KA] and SNF-Robin Hood approach [RHA]) were compared against the uniform rate N (URN) control treatment. In SNF-KA, the highest and lowest N dose was applied in the most and least fertile MZ, respectively, whereas the opposite approach was adopted in the SNF-RHA treatment. Yield and cost–benefit analyses provided both SNF treatments to produce more yield (KA = 2.72 and RHA = 2.74 t ha−1) than the URN (2.64 t ha−1) treatment, leading to increasing gross margins by EUR 91 ha−1 (SNF–KA) and EUR 88.5 ha−1 (SNF–RHA). While SNF-KA reduced N input by 66.54 kg N ha−1, SNF–RHA applied more N by 17.90 kg N ha−1 than URN. Additionally, SNF–RHA attempted to equalize yield responses to N across MZ classes, with a small increase in N input. This study, therefore, suggests adopting SNF–RHA for increasing yield and gross margin and accurate distribution of N according to per MZ N response. Future studies, however, should address the limitations of the current study by delineating MZ maps with the incorporation of additional soil information (e.g., mineral N and clay) for optimizing N doses as well as evaluating agroeconomic performance across multiple sites and years using a full-budget analysis.

Geoacoustic and geophysical data‐driven seafloor sediment classification through machine learning algorithms with property‐centered oversampling techniques

AbstractThis study aims to classify seafloor sediments using physics‐inspired and data‐driven soil models combined with machine learning algorithms and oversampling techniques. The field data used for the input variables include porosity, S‐ and P‐wave velocities and depth. The soil information reported in the original literature and the “six reference sediments” and effective stress‐versus‐depth models proposed by the previous study confirm the sediment type across all of the input variables. We use three machine learning algorithms and four oversampling methods to enhance the performance accuracy and overcome data imbalance in the minority class. The results show that the averaged accuracy of sediment classification with original data corresponds to 0.88 for porosity, 0.61 for S‐wave velocity, and 0.97 for P‐wave velocity. In particular, the enhanced accuracy with oversampled input variables becomes more pronounced when the depth data are considered in a dataset. The class‐oriented grouping method newly proposed in this study appears to be a robust approach to enhancing performance. Surprisingly, model‐based input variables lead to the best performance in all cases. The proposed analyses conducted using machine learning algorithms and oversampling techniques within the physics‐inspired models could be extended to obtain a first‐order assessment of marine sediment properties.

Modelling and prediction of major soil chemical properties with Random Forest: Machine learning as tool to understand soil-environment relationships in Antarctica

Digital Soil Mapping includes quantitative estimations of soil attributes in areas with little or no soil information, being important in remote landscapes such as the Antarctic ice-free areas. Our objective was to predict the spatial distribution of major soil chemical attributes in two important Antarctic Peninsula regions; as well as to identify the main environmental drivers of these attributes. For this, we compiled one of the largest soil databases of Antarctica and applied the machine learning algorithm Random Forest to predict seven soil chemical attributes. We also used covariates selection and partial dependence analysis to better understand the relationships of the attributes with the environmental covariates. Bases sum was the attribute which presented the highest prediction performance, whereas Na and P predictions presented the lowest. Both accuracy and uncertainty indicators showed the difficulties of Random Forest in handling natural outliers. The soil attributes distribution was related to the mean annual temperature and annual precipitation, multispectral bands and indexes related to the vegetation response, rookeries distance expressing the birds’ activity, and topographic attributes. The attributes showed a climatic gradient, with higher values of bases sum, pH, and Na in Northern Antarctic Peninsula, and higher total organic carbon, H + Al and P in Maritime Antarctic. This shows the climate covariates influence the soil variability in a macroscale, whereas terrain predictors control the soil at local scales. This study showed the Digital Soil Mapping potential to surpass the limitations of conventional mapping and indicated the feasibility in obtaining interpretable predictions which can be directly associated with the soil forming factors. Finally, the data generated can be used as reference to monitor the impacts of the climate changes on the soils of Antarctica.

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.

A low-cost portable system for on-site detection of soil pH and potassium levels using 3D printed sensors

Accurate data on the nutrient content of soil is essential for optimum fertiliser application in agriculture. Frequent and regular soil tests hold the key to mitigate difficulties that arise from lack of accurate information for soil and crop management, such as increased costs for fertilisers, and environmental degradation. Traditionally soil nutrient contents levels are measured from laborious methods which involves collecting field samples and returning to the laboratory for analysis. Laboratory analysis often requires sample pre-treatment, chemical extractants and expensive instrumentation. A low-cost on-site detection method for soil nutrients can increase data necessary for smart fertiliser application that can in turn increase farm profit and ensure environmental protection. Here we present a low-cost miniaturised multi-ion sensing system for on-site detection of pH and potassium. The sensing system consists of a PSoC4 microcontroller and novel 3D printed miniaturised ion selective electrodes for the simultaneous determination of potassium and pH. The 3D printed electrode's pH sensing capability and potassium ion sensing was validated using known pH and potassium ion concentrations, exhibiting high selectivity and excellent Nernstian sensitivities for pH (−61.05 mV/pH) and potassium (49.50 mV/decade). Soil pore water samples from various locations and depths were extracted using a MacroRhizon sampler and tested using the novel portable detection system. On-site pH and potassium determinations were acquired in extremely small volumes samples without the requirement of sample preparation.

Mapping surface soil organic carbon density by combining different soil sampling data sources and prediction models in Yangtze River Delta, China

Soil organic carbon (SOC) is a crucial soil property governing various agricultural, ecological, and environmental processes in the terrestrial ecosystem. However, acquiring accurate and high resolution SOC density (SOCD) map remained a challenge in the current researches. Based on soil samples collected from soil survey and published literatures (952 samples), this study tested different approaches for generating high accuracy dataset of 0–20 cm SOCD with the spatial resolution of 90-m in the Yangtze River Delta. Two spatial prediction methods, including artificial neural network (ANN, represented non-linear), multiscale geographically weighted regression (MGWR, represented linear) were applied to generated the SOCD maps. Then three fusion models, including multiple linear regression fusion (MLR_F), ANN fusion (ANN_F) and a self-developed model (multiscale geographically normalize weighted fusion, MGNW_F), were applied to fuse the prediction results of spatial interpolation methods. Among spatial interpolation methods, the ANN performed better in capturing the SOCD spatial pattern, while the MGWR performed better in accuracy measures of mean square error, mean absolute error and coefficient of determination. After data fusing by the three models, improved accuracy measures than the ANN and MGWR were achieved. The predictions of fusion models also showed better reliability than the soil dataset of high resolution National Soil Information Grids of China, mainly due to that more soil samples were included in the prediction. Among these fusion models, the MGNW_F performed slightly better than the other two and had the strongest adaptability to the overall environment of the study area. Findings of this study highlighted the potentials of acquiring additional soil samples from existing literatures and fusing different methods for enhancing the accuracy in mapping SOCD.