A Heritage Agronomic Study as a Database for Monitoring the Soil Salinity of an Irrigated District in NE Spain

This article presents and reviews the soil salinity data provided by a rescued vintage agronomic report on an irrigated area of 35,875 ha located in the center of the Ebro River basin, in the NE of mainland Spain. These data come from a soil sampling campaign conducted from May to the first half of July 1975 for the purpose of delineating saline and non-saline soils. The agronomic report was produced in response to demands from farmers to combat soil salinity, and represents the state of the art in those years for salinity studies. Our paper presents the scrubbed soil salinity data for this year, checking their consistency and locating the study sites. The main finding is the unearthing of this heritage report and the discussion of its soil salinity data. We show that the report supplies an assessment and a baseline for further soil salinity tracking by conducting new measurements either by direct soil sampling or by nondestructive techniques, providing an estimate of soil salinity at different locations. This task is feasible, as shown in our previously published articles involving nearby areas. A comparison of the salt amount in the soil over the years would provide a means to evaluate irrigation methods for sustainable land management. This comparison can be conducted simultaneously with analysis of other agricultural features described in the report for the irrigation district in 1975.

Using Sentinel-2 Images for Soil Organic Carbon Content Mapping in Croplands of Southwestern France. The Usefulness of Sentinel-1/2 Derived Moisture Maps and Mismatches between Sentinel Images and Sampling Dates

In agronomy, soil organic carbon (SOC) content is important for the development and growth of crops. From an environmental monitoring viewpoint, SOC sequestration is essential for mitigating the emission of greenhouse gases into the atmosphere. SOC dynamics in cropland soils should be further studied through various approaches including remote sensing. In order to predict SOC content over croplands in southwestern France (area of 22,177 km²), this study addresses (i) the influence of the dates on which Sentinel-2 (S2) images were acquired in the springs of 2017–2018 as well as the influence of the soil sampling period of a set of samples collected between 2005 and 2018, (ii) the use of soil moisture products (SMPs) derived from Sentinel-1/2 satellites to analyze the influence of surface soil moisture on model performance when included as a covariate, and (iii) whether the spatial distribution of SOC as mapped using S2 is related to terrain-derived attributes. The influences of S2 image dates and soil sampling periods were analyzed for bare topsoil. The dates of the S2 images with the best performance (RPD ≥ 1.7) were 6 April and 26 May 2017, using soil samples collected between 2016 and 2018. The soil sampling dates were also analyzed using SMP values. Soil moisture values were extracted for each sample and integrated into partial least squares regression (PLSR) models. The use of soil moisture as a covariate had no effect on the prediction performance of the models; however, SMP values were used to select the driest dates, effectively mapping topsoil organic carbon. S2 was able to predict high SOC contents in the specific soil types located on the old terraces (mesas) shaped by rivers flowing from the southwestern Pyrénées.

Quantification of Root Biomass in Post-Mining Areas in the Municipality of Capitão Poço – PA, Brazil

The present work aimed to quantify the concentrations and biomass stock of fine andthick roots, in three areas in the municipality of Capitão Poço-PA, Brazil. The areas used were degraded area, recovery area and native forest. For soil sampling, 24 trenches were opened, measuring 70 x 70 x 100 cm. In these trenches, soil samples were taken at depths 0-10, 10-20, 20-30, 30-40, 40-50, 50-60, 60-80 and 80-100 cm and sieving was carried out.All roots and other underground plant structures that remained in the sieve were collected by manual collection. The roots were separated into two diameter classes: fine roots ≤ 5 mm and thick roots > 5 mm, kiln dried and weighed.In the analysis, higherconcentrationsofthickand fine roots were observed in an area of native forest at depths of 0-10 and 10-20 cm. In the areas analyzed in this study, the root density in the topsoil of 0-10 cm was mainly composed of fine roots.In the three areas analyzed in this study, it was observed that from a depth of 10-20 cm there were decreases in theconcentrationsofthick roots. The area under recovery approached the area of native forest in the concentration of fine roots, demonstrating possible improvements in soil quality and recovery is probably actually taking place.

The Effect of Soil Sampling Density and Spatial Autocorrelation on Interpolation Accuracy of Chemical Soil Properties in Arable Cropland

Knowledge of the relationship between soil sampling density and spatial autocorrelation with interpolation accuracy allows more time- and cost-efficient spatial analysis. Previous studies produced contradictory observations regarding this relationship, and this study aims to determine and explore under which conditions the interpolation accuracy of chemical soil properties is affected. The study area covered 823.4 ha of agricultural land with 160 soil samples containing phosphorus pentoxide (P2O5) and potassium oxide (K2O) values. The original set was split into eight subsets using a geographically stratified random split method, interpolated using the ordinary kriging (OK) and inverse distance weighted (IDW) methods. OK and IDW achieved similar interpolation accuracy regardless of the soil chemical property and sampling density, contrary to the majority of previous studies which observed the superiority of kriging as a deterministic interpolation method. The primary dependence of interpolation accuracy to soil sampling density was observed, having R2 in the range of 56.5–83.4% for the interpolation accuracy assessment. While this study enables farmers to perform efficient soil sampling according to the desired level of detail, it could also prove useful to professions dependent on field sampling, such as biology, geology, and mining.

A Mobile Semi-Autonomous Robot For Soil Sampling

For agrarian economies such as India, the quality of the soil is critical for maximized yield sustainable cultivation. When a large area is utilized, sample testing of soil is essential. The use of a robotic system for sampling is vital for saving time and replacing manual laborious work. This work represents a robotic system that was deployed for soil sample collection. A mechanical soil sampling and storage system based on augers and turntable storage is used in the system. Using a GPS-driven algorithm, the robot navigates autonomously to desired sampling locations. It collects data from the sampling area using sensors connected to an Arduino board. A proof-of-concept demonstrator proved that such a solution can be successfully scaled and deployed, which will aid in more efficient cultivation and sustainable agriculture.

A Mobile Semi-Autonomous Robot For Soil Sampling

For agrarian economies such as India, the quality of the soil is critical for maximized yield sustainable cultivation. When a large area is utilized, sample testing of soil is essential. The use of a robotic system for sampling is vital for saving time and replacing manual laborious work. This work represents a robotic system that was deployed for soil sample collection. A mechanical soil sampling and storage system based on augers and turntable storage is used in the system. Using a GPS-driven algorithm, the robot navigates autonomously to desired sampling locations. It collects data from the sampling area using sensors connected to an Arduino board. A proof-of-concept demonstrator proved that such a solution can be successfully scaled and deployed, which will aid in more efficient cultivation and sustainable agriculture.

Novel Tools for Adjusting Spatial Variability in the Early Sugarcane Breeding Stage

The detection of spatial variability in field trials has great potential for accelerating plant breeding progress due to the possibility of better controlling non-genetic variation. Therefore, we aimed to evaluate a digital soil mapping approach and a high-density soil sampling procedure for identifying and adjusting spatial dependence in the early sugarcane breeding stage. Two experiments were conducted in regions with different soil classifications. High-density sampling of soil physical and chemical properties was performed in a regular grid to investigate the structure of spatial variability. Soil apparent electrical conductivity (ECa) was measured in both experimental areas with an EM38-MK2® sensor. In addition, principal component analysis (PCA) was employed to reduce the dimensionality of the physical and chemical soil data sets. After conducting the PCA and obtaining different thematic maps, we determined each experimental plot’s exact position within the field. Tons of cane per hectare (TCH) data for each experiment were obtained and analyzed using mixed linear models. When environmental covariates were considered, a previous forward model selection step was applied to incorporate the variables. The PCA based on high-density soil sampling data captured part of the total variability in the data for Experimental Area 1 and was suggested to be an efficient index to be incorporated as a covariate in the statistical model, reducing the experimental error (residual variation coefficient, CVe). When incorporated into the different statistical models, the ECa information increased the selection accuracy of the experimental genotypes. Therefore, we demonstrate that the genetic parameter increased when both approaches (spatial analysis and environmental covariates) were employed.

Soil contamination in the area of magnesite mining in urban and agrarian land

Contamination of soil with potential risk elements is one of the most pressing environmental problems in the world and causes serious environmental damage, but also threatens human health. This paper presents the results of research that was focused on analyzing soil contamination in the field of magnesite mining in urban and agrarian land nearby the former factory in Košice (Slovakia). Field and laboratory research were performed. Soil sampling was performed in 10 localities of the studied area. The content of risk elements (Cd, Hg, Pb, Cr, Zn, Cu, As, Ni, Mn, Mg) in soils was analyzed under laboratory conditions. The obtained data expressed as average concentrations of metals in sampled soils, as well as background values of the contents of monitored elements for the soils of the Slovak Republic, were used to assess soil pollution and identify the environmental risk. The acquired knowledge about the contamination of the soil with risk elements in the area around the former magnesite factory in Košice could help in the planning of remediation measures and improve the state of the environment in the studied area.

Diversity and abundance of arbuscular fungi mycorrhizal (AMF) in rhizosphere Zea mays in tidal swamp

Sefrila M, Ghulamahdi M, Purwono Melati M, Mansur I. 2021. Diversity and abundance of arbuscular fungi mycorrhizal (AMF) in rhizosphere Zea mays in tidal swamp. Biodiversitas 22: 5071-5076. This study aims to find out the diversity and dominance of AMF spores and look at the morphology of fungi mycorrhizal arbuscular fungi that exist in the rooting area of corn (Zea mays L.) crops in the tidal swamp. The study was conducted in September 2020. Soil sampling at the tidal swamp village of Mulyasari Tanjung Lago District, Banyuasin, South Sumatra, Indonesia randomly sampling the corn root zone method. The research stages are soil sampling, soil chemistry analysis, AMF isolation and trapping, and morphological identification of AMF spores. The results showed the number of spores found in soil samples in the corn crop rhizosphere before trapping was less when compared to after trapping. The spores' shape is round, oblong, and oval, with colors ranging from clear, yellow, to brown. AMF spores found come from 2 genera namely (Acaulospora scrobiculata, A. bireticulata, A. mellea, A. laevis) and Glomus (Glomus monosporum, G. constrictum, G. manihotis).