Electromagnetic Induction Measurements for Investigating Soil Salinization Caused by Saline Reclaimed Water

This paper focused on the use of electromagnetic induction measurements in order to investigate soil salinization caused by irrigation with saline reclaimed water. An experimental activity was carried out during the growing season of tomato crop in order to evaluate expected soil salinization effects caused by different saline agro-industrial wastewaters used as irrigation sources. Soil electrical conductivity, strictly related to the soil salinity, has been monitored for three months by means of Electromagnetic Induction (EMI) measurements, and evident differences in the soil response have been observed. The study highlighted two aspects that can improve soil investigation due to the utilization of geophysical tools. First, EMI data can map large areas in a short period of time with an unprecedented level of detail by overcoming practical difficulties in order to massively sample soil. At the same time, repeated measurements over time allow updating real-time soil salinity maps by using accurate correlations with soil electrical conductivity. This application points out how integrated agro-geophysical research approaches can play a strategic role in agricultural saline water management in order to prevent soil salinization risks in medium to long-term periods.

Prediction Model of Soil Electrical Conductivity Based on ELM Optimized by Bald Eagle Search Algorithm

Soil electrical conductivity is one of the indispensable and important parameters in fine agriculture management, and a suitable soil electrical conductivity can promote good plant growth. Prediction model of soil electrical conductivity is constructed to effectively obtain the conductivity values of soil, which can provide a reference basis for irrigation and fertilization management and prediction evaluation in fine agriculture. Prediction model of soil electrical conductivity based on extreme learning machine (ELM) optimized by bald eagle search (BES) algorithm is proposed in this paper. In the prediction model, the input weights and bias values of the ELM network were optimized using the BES algorithm, and the performance of the model was evaluated with parameters such as mean square error (MSE), coefficient of determination (R^2). Also, the correlations of parameters such as soil temperature, moisture content, pH, and water potential in the soil conductivity prediction model were determined using the exploratory data analysis (EDA) and HeatMap heat map tools. Finally, the proposed model was compared with back propagation neural network (BP), radial basis function networks (RBF), support vector machine (SVM), gated recurrent neural network (GRNN), long short-term memory (LSTM), particle swarm algorithm (PSO) optimization ELM, genetic algorithm (GA) optimized ELM prediction model. The experimental results showed that MSE, R^2 of the proposed model are 4.09 and 0.941, which are better than the other models. Also the results verified the effectiveness of the proposed method, which is a feasible prediction method to guide the irrigation and fertilization management in fine agriculture, because of its good prediction effect on soil conductivity.

Mungbean germplasms tolerance to salinity stress correlated with age character and potential yield

Mungbean is of the important legume commodity in Indonesia, however the production is still encountering the abiotic salinity stress. Fifty mungbean accessions were evaluated for the salinity tolerance at Indonesian Legumes and Tuber Crops Research Institute (ILETRI). The experimental was arranged in Split Plot Design consisted of two factors and three replications. The main plot was two environments, normal and salinity stress that was applied using diluted seawater, while the subplot was mungbean accessions. The parameters including soil electrical conductivity, time to first flowering and seed maturation, plant height, leaf chlorophyll index, salinity toxicity score, and seed yield per plant were observed. Salinity stress decreased mungbean seed yield with ranged from 61.33 - 100%. The highest stress tolerance index (STI) were found in Vima 4-MLGV 1118 (STI=0.58) and MLGV 1065 (STI=0.53). Five accessions did not able to produce any seed. Mungbean accessions tolerance to salinity stress was negatively correlated with the time of first flowering and seed maturation, while it was positively correlated with potential yield both in normal and salinity stress. It is suggested that selection and evaluation of mungbean to salinity stress in the future should be categorized both by the plant age and potential yield.

Crop Response to Combined Availability of Soil Water and Its Salinity Level: Theory, Experiments and Validation on Golf Courses

The phenomenological expression showing crop yield to be directly dependent on water deficiency, under saline conditions, has encouraged a continued focus on salinity as a viable approach to increase crop yields. This work reassesses crop response to availability of saline soil water ASW in two stages (A) Develop a simple approach suggesting that permanent wilting point (WP) increases under high saline soil water tension and relative yield of Lettuce (Lactuca sativa L., var longifolia Lam., cv. Nevada) and maize (Zea Mays L., cv. Jubilee sweet) decrease. (B) Using a deterministic numerical soil water model to validate the theory on Bermuda grass of golf courses. The experimental plots were established in the North Negev, Israel (Sweet corn) and the Algarve, Portugal (Lettuce and Bermuda grass covering the golf courses). Sprinkler irrigation and line source techniques were used for water application, creating a saline gradient under a precise irrigation water distribution. Two salinity empirical models were tested (Mass and Hoffman MH and van Genuchten–Gupta vGG). Their empirical models were modified and instead of soil electrical conductivity of irrigation water (ECe) we used wilting point (WP) and RASW to follow the changes in relative yield. The validation was conducted with theoretical soil plant atmosphere water (SPAW) to predict the results on golf courses. It is concluded that an alternative S-shaped response model provides better fit to our experimental data sets. Modified MH model (Yr = Y/Ymax = a* (ASW–threshold’s constant) revealed that a single dimensionless curve could be used to express yield—salinity interference when represented by varying ASW. The vGG model: vGG can represent salt tolerance of most crops, by using varying wilting point of average root zone salinity, at which the yield has declined by 50%. The abscissa of both models was based on WP rather than the standard soil electrical conductivity (ECw). The correlation between the experimental data and WP or relative available soil water (RASW) was acceptable and, therefore, their usefulness for prediction of relative yield is acceptable as well. The objectives of this study were: 1. To develop a simple model describing the effect of salinity through soil water availability on crop production; 2. To replace the standard varying soil electrical conductivity ECe used by MH and vGG models by two soil parameters (at wilting point- θwp and at field capacity ϴfc) in order to describe the relationship between them and relative yield. 3. Validate the new model with respect to independent salinity on Golf courses and a mathematical deterministic model.

Pilot Study of Eco-Physiological Pepper Responses to Starfish-Based Organic Soil Amendments in Open-Field and Greenhouse Cultivations

This pilot study was conducted to compare eco-physiological responses to starfish (SF)-treated red peppers (Capsicum annuum L.) in organic open-field (OF) and greenhouse (GH) cultivations in commercial farmhouses, South Korea in 2019. Treatments included starfish-liquid fertilizer (LF) applied in OF (SF-OF) and GH (SF-GH) plots, and SF + seaweed in OF (SFS-OF) and GH (SFS-GH). Weekly pH levels in SFS-LF were fluctuated for a 16-week storage period at room temperature due to having higher soluble salt levels than those of SF-LF. All experimental plots were ranged on soil pH between 7.1 and 7.4. SF- and SFS-GH plots resulted in increased soil electrical conductivity, organic matter, and increased concentrations of total nitrogen, phosphorous pentoxide, potassium oxide, and magnesium oxide, as well as exhibiting a richer and more diverse bacterial community. Leaf width and length and plant height increased in plants cultivated in GH, with low canopy width and stem diameter also observed to have increased. Total fruit yields were approximately two times higher for peppers cultivated in GH compared to OF cultivated peppers.

Identifying Within-Field Spatial and Temporal Crop Water Stress to Conserve Irrigation Resources with Variable-Rate Irrigation

Addressing within-field and within-season variability of crop water stress is critical for spatially variable irrigation. This study measures interactions between spatially variable soil properties and temporally variable crop water dynamics; and whether modelling soil water depletion is an effective approach to guide variable-rate irrigation (VRI). Energy and water balance equations were used to model crop water stress at 85 locations within a 22 ha field of winter wheat (Triticum aestivum L.) under uniform and spatially variable irrigation. Significant within-field variability of soil water holding capacity (SWHC; 145–360 mm 1.2 m−1), soil electrical conductivity (0.22–49 mS m−1), spring soil water (314–471 mm 1.2 m−1), and the onset of crop water stress were observed. Topographic features and modelled onset of crop water stress were significant predictors of crop yield while soil moisture at spring green-up, elevation, and soil electrical conductivity were significant predictors of the onset of crop water stress. These results show that modelling soil water depletion can be an effective scheduling tool in VRI. Irrigation zones and scheduling efforts should consider expanding to include temporally dynamic factors, including spring soil water content and the onset of crop water stress.

Spatial and Temporal Variability of Soil Salinity in the Yangtze River Estuary Using Electromagnetic Induction

Soil salt-water dynamics in the Yangtze River Estuary (YRE) is complex and soil salinity is an obstacle to regional agricultural production and the ecological environment in the YRE. Runoff into the sea is reduced during the impoundment period as the result of the water-storing process of the Three Gorges Reservoir (TGR) in the upper reaches of the Yangtze River, which causes serious seawater intrusion. Soil salinity is a problem due to shallow and saline groundwater under serious seawater intrusion in the YRE. In this research, we focused on the temporal variation and spatial distribution characteristics of soil salinity in the YRE using geostatistics combined with proximally sensed information obtained by an electromagnetic induction (EM) survey method in typical years under the impoundment of the TGR. The EM survey with proximal sensing method was applied to perform soil salinity survey in field in the Yangtze River Estuary, allowing quick determination and quantitative assessment of spatial and temporal variation of soil salinity from 2006 to 2017. We developed regional soil salinity survey and mapping by coupling limited laboratory data with proximal sensed data obtained from EM. We interpreted the soil electrical conductivity by constructing a linear model between the apparent electrical conductivity data measured by an EM 38 device and the soil electrical conductivity (EC) of soil samples measured in laboratory. Then, soil electrical conductivity was converted to soil salt content (soil salinity g kg−1) through established linear regression model based on the laboratory data of soil salinity and soil EC. Semivariograms of regional soil salinity in the survey years were fitted and ordinary kriging interpolation was applied in interpolation and mapping of regional soil salinity. The cross-validation results showed that the prediction results were acceptable. The soil salinity distribution under different survey years was presented and the area of salt affected soil was calculated using geostatistics method. The results of spatial distribution of soil salinity showed that soil salinity near the riverbanks and coastlines was higher than that of inland. The spatial distribution of groundwater depth and salinity revealed that shallow groundwater and high groundwater salinity influenced the spatial distribution characteristics of soil salinity. Under long-term impoundment of the Three Gorges Reservoir, the variation of soil salinity in different hydrological years was analyzed. Results showed that the area affected by soil salinity gradually increased in different hydrological year types under the impoundment of the TGR.