Quantitatively estimating main soil water-soluble salt ions content based on Visible-near infrared wavelength selected using GC, SR and VIP

Soil salinization is the primary obstacle to the sustainable development of agriculture and eco-environment in arid regions. The accurate inversion of the major water-soluble salt ions in the soil using visible-near infrared (VIS-NIR) spectroscopy technique can enhance the effectiveness of saline soil management. However, the accuracy of spectral models of soil salt ions turns out to be affected by high dimensionality and noise information of spectral data. This study aims to improve the model accuracy by optimizing the spectral models based on the exploration of the sensitive spectral intervals of different salt ions. To this end, 120 soil samples were collected from Shahaoqu Irrigation Area in Inner Mongolia, China. After determining the raw reflectance spectrum and content of salt ions in the lab, the spectral data were pre-treated by standard normal variable (SNV). Subsequently the sensitive spectral intervals of each ion were selected using methods of gray correlation (GC), stepwise regression (SR) and variable importance in projection (VIP). Finally, the performance of both models of partial least squares regression (PLSR) and support vector regression (SVR) was investigated on the basis of the sensitive spectral intervals. The results indicated that the model accuracy based on the sensitive spectral intervals selected using different analytical methods turned out to be different: VIP was the highest, SR came next and GC was the lowest. The optimal inversion models of different ions were different. In general, both PLSR and SVR had achieved satisfactory model accuracy, but PLSR outperformed SVR in the forecasting effects. Great difference existed among the optimal inversion accuracy of different ions: the predicative accuracy of Ca2+, Na+, Cl−, Mg2+ and SO42− was very high, that of CO32− was high and K+ was relatively lower, but HCO3− failed to have any predicative power. These findings provide a new approach for the optimization of the spectral model of water-soluble salt ions and improvement of its predicative precision

Simulation and Prediction of Ion Transport in the Reclamation of Sodic Soils with Gypsum Based on the Support Vector Machine

The effect of gypsum on the physical and chemical characteristics of sodic soils is nonlinear and controlled by multiple factors. The support vector machine (SVM) is able to solve practical problems such as small samples, nonlinearity, high dimensions, and local minima points. This paper reports the use of the SVM regression method to predict changes in the chemical properties of sodic soils under different gypsum application rates in a soil column experiment and to evaluate the effect of gypsum reclamation on sodic soils. The research results show that (1) the SVM soil solute transport model using the Matlab toolbox represents the change in Ca2+ and Na+ in the soil solution and leachate well, with a high prediction accuracy. (2) Using the SVM model to predict the spatial and temporal variations in the soil solute content is feasible and does not require a specific mathematical model. The SVM model can take full advantage of the distribution characteristics of the training sample. (3) The workload of the soil solute transport prediction model based on the SVM is greatly reduced by not having to determine the hydrodynamic dispersion coefficient and retardation coefficient, and the model is thus highly practical

Assessing the relationship between the soil dielectric constant and electrical conductivity using 5TE sensors in the field conditions

Postprint (published version

Multi-scale targeting of land degradation in northern Uzbekistan using satellite remote sensing

Advancing land degradation (LD) in the irrigated agro-ecosystems of Uzbekistan hinders sustainable development of this predominantly agricultural country. Until now, only sparse and out-of-date information on current land conditions of the irrigated cropland has been available. An improved understanding of this phenomenon as well as operational tools for LD monitoring is therefore a pre-requisite for multi-scale targeting of land rehabilitation practices and sustainable land management. This research aimed to enhance spatial knowledge on the cropland degradation in the irrigated agro-ecosystems in northern Uzbekistan to support policy interventions on land rehabilitation measures. At the regional level, the study combines linear trend analysis, spatial relational analysis, and logistic regression modeling to expose the LD trend and to analyze the causes. Time series of 250-m Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI), summed over the growing seasons of 2000-2010, were used to determine areas with an apparent negative vegetation trend; this was interpreted as an indicator of LD. The assessment revealed a significant decline in cropland productivity across 23% (94,835 ha) of the arable area. The results of the logistic modeling indicate that the spatial pattern of the observed trend is mainly associated with the level of the groundwater table, land-use intensity, low soil quality, slope, and salinity of the groundwater. To quantify the extent of the cropland degradation at the local level, this research combines object-based change detection and spectral mixture analysis for vegetation cover decline mapping based on multitemporal Landsat TM images from 1998 and 2009. Spatial distribution of fields with decreased vegetation cover is mainly associated with abandoned cropland and land with inherently low-fertility soils located on the outreaches of the irrigation system and bordering natural sandy deserts. The comparison of the Landsat-based map with the LD trend map yielded an overall agreement of 93%. The proposed methodological approach is a useful supplement to the commonly applied trend analysis for detecting LD in cases when plot-specific data are needed but satellite time series of high spatial resolution are not available. To contribute to land rehabilitation options, a GIS-based multi-criteria decision-making approach is elaborated for assessing suitability of degraded irrigated cropland for establishing Elaeagnus angustifolia L. plantations while considering the specific environmental setting of the irrigated agro-ecosystems. The approach utilizes expert knowledge, fuzzy logic, and weighted linear combination to produce a suitability map for the degraded irrigated land. The results reveal that degraded cropland has higher than average suitability potential for afforestation with E. angustifolia. The assessment allows improved understanding of the spatial variability of suitability of degraded irrigated cropland for E. angustifolia and, subsequently, for better-informed spatial planning decisions on land restoration. The results of this research can serve as decision-making support for agricultural planners and policy makers, and can also be used for operational monitoring of cropland degradation in irrigated lowlands in northern Uzbekistan. The elaborated approach can also serve as a basis for LD assessments in similar irrigated agro-ecosystems in Central Asia and elsewhere.Multisclare Bewertung der Landdegradation in Nord-Uzbekistan unter der Verwendung von Satellitenfernerkundung Die zunehmende Landdegradation (LD) in den bewässerten Agrarökosystemen in Usbekistan behindert die nachhaltige Entwicklung dieses vorwiegend landwirtschaftlich geprägten Landes. Bis heute sind nur wenige und veraltete Informationen über die aktuellen Bodenbedingungen der bewässerten Anbauflächen verfügbar. Ein besseres Verständnis dieses Phänomens sowie operationelle Werkzeuge für LD-Monitoring sind daher Voraussetzung für ein nachhaltiges Landmanagement sowie für Landrehabilitationsmaßnahmen. Ziel dieser Studie war es, das räumliche Verständnis der Degradierung von Anbaugebieten in den bewässerten Agrarökosystemsn des nördlichen Usbekistans zu verbessern, um staatliche Interventionen in Bezug auf Landrehabilitationsmaßnahmen zu unterstützen Auf der regionalen Ebene kombiniert die Studie lineare Trendanalyse, räumliche relationale Analyse sowie logistischer Regressionsmodellierung, um den LD-Trend darzustellen und Gründe zu analysieren. Zeitreihen von 250-m Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) Bildern wurden für den Zeitraum der Anbauperioden zwischen 2000-2010 untersucht, um Bereiche mit einem offensichtlich negativen Vegetationstrend zu ermitteln. Dieser negative Trend kann als Indikator für LD interpretiert werden. Die Untersuchung ergab eine signifikante Abnahme der Bodenproduktivität auf 23% (94,835 ha) der Anbaufläche. Zudem deuten die Ergebnisse der logistischen Modellierung darauf hin, dass das räumliche Muster des beobachteten Trends überwiegend mit der Höhe des Grundwasserspiegels, der Landnutzungsintensität, der geringen Bodenqualität, der Hangneigung sowie der Grundwasserversalzung zusammenhängt. Um das Ausmaß der Degradation der Anbauflächen auf der lokalen Ebene zu quantifizieren, kombiniert diese Studie objektbasierte Erkennung von Veränderungen und spektrale Mischungsanalyse für die Abnahme der Vegetationsbedeckung auf der Grundlage von multitemporalen Landsat-TM-Bildern im Zeitraum von 1998 bis 2009. Die räumliche Verteilung der Felder mit abnehmender Vegetationsbedeckung hängt überwiegend mit verlassenen Anbauflächen sowie mit nährstoffarmen Böden in den Randbereichen des Bewässerungssystems und an den Grenzen zu natürlichen Sandwüsten zusammen. Ein Vergleich mit der Karte des LD-Trends ergab insgesamt eine Übereinstimmung von 93%. Der vorgeschlagene Ansatz ist eine nützliche Ergänzung zu der häufig angewendeten Trendanalyse für die Ermittlung von LD in Regionen, für die keine Satellitenbildzeitreihen mit hoher Auflösung verfügbar sind. Als Beitrag zu Landrehabilitationsmöglichkeiten, wird ein GIS-basierter Multi-Kriterien-Ansatz zur Einschätzung der Eignung von degradierten bewässerten Anbauflächen für Elaeagnus angustifolia L. Plantagen beschrieben, der gleichzeitig die spezifischen Umweltbedingungen der bewässerten Agrarökosysteme berücksichtigt. Dieser Ansatz beinhaltet Expertenwissen, Fuzzy-Logik und gewichtete lineare Kombination, um eine Eignungskarte für die bewässerten degradierten Anbauflächen herzustellen. Die Ergebnisse zeigen, dass diese Flächen ein überdurchschnittliches Eignungspotenzial für die Aufforstung mit E. angustifolia aufweisen. Diese Studie trägt zu einem verbesserten Verständnis der räumlichen Variabilität der Eignung von solchen Flächen für E. angustifolia bei. Die Ergebnisse dieser Studie können als Entscheidungshilfe für landwirtschaftliche Planer und politische Entscheidungsträger sowie für verbesserte Landrehabilitationsmaßnahmen und operationelles Monitoring der Degradation von Anbauflächen im nördlichen Usbekistan eingesetzt werden. Zudem kann der beschriebene Ansatz als Grundlage für LD-Untersuchungen in ähnlichen bewässerten Agrarökosystemen in Zentralasien und anderswo dienen

Evaluation and prediction of groundwater quality for irrigation using an integrated water quality indices, machine learning models and GIS approaches: a representative case study

Agriculture has significantly aided in meeting the food needs of growing population. In addition, it has boosted economic development in irrigated regions. In this study, an assessment of the groundwater (GW) quality for agricultural land was carried out in El Kharga Oasis, Western Desert of Egypt. Several irrigation water quality indices (IWQIs) and geographic information systems (GIS) were used for the modeling development. Two machine learning (ML) models (i.e., adaptive neuro-fuzzy inference system (ANFIS) and support vector machine (SVM)) were developed for the prediction of eight IWQIs, including the irrigation water quality index (IWQI), sodium adsorption ratio (SAR), soluble sodium percentage (SSP), potential salinity (PS), residual sodium carbonate index (RSC), and Kelley index (KI). The physicochemical parameters included T°, pH, EC, TDS, K+, Na+, Mg2+, Ca2+, Cl−, SO42−, HCO3−, CO32−, and NO3−, and they were measured in 140 GW wells. The hydrochemical facies of the GW resources were of Ca-Mg-SO4, mixed Ca-Mg-Cl-SO4, Na-Cl, Ca-Mg-HCO3, and mixed Na-Ca-HCO3 types, which revealed silicate weathering, dissolution of gypsum/calcite/dolomite/ halite, rock–water interactions, and reverse ion exchange processes. The IWQI, SAR, KI, and PS showed that the majority of the GW samples were categorized for irrigation purposes into no restriction (67.85%), excellent (100%), good (57.85%), and excellent to good (65.71%), respectively. Moreover, the majority of the selected samples were categorized as excellent to good and safe for irrigation according to the SSP and RSC. The performance of the simulation models was evaluated based on several prediction skills criteria, which revealed that the ANFIS model and SVM model were capable of simulating the IWQIs with reasonable accuracy for both training “determination coefficient (R2)” (R2 = 0.99 and 0.97) and testing (R2 = 0.97 and 0.76). The presented models’ promising accuracy illustrates their potential for use in IWQI prediction. The findings indicate the potential for ML methods of geographically dispersed hydrogeochemical data, such as ANFIS and SVM, to be used for assessing the GW quality for irrigation. The proposed methodological approach offers a useful tool for identifying the crucial hydrogeochemical components for GW evolution assessment and mitigation measures related to GW management in arid and semi-arid environments

Simulation of site-specific irrigation control strategies with sparse input data

Crop and irrigation water use efficiencies may be improved by managing irrigation application timing and volumes using physical and agronomic principles. However, the crop water requirement may be spatially variable due to different soil properties and genetic variations in the crop across the field. Adaptive control strategies can be used to locally control water applications in response to in-field temporal and spatial variability with the aim of maximising both crop development and water use efficiency. A simulation framework ‘VARIwise’ has been created to aid the development, evaluation and management of spatially and temporally varied adaptive irrigation control strategies (McCarthy et al., 2010). VARIwise enables alternative control strategies to be simulated with different crop and environmental conditions and at a range of spatial resolutions. An iterative learning controller and model predictive controller have been implemented in VARIwise to improve the irrigation of cotton. The iterative learning control strategy involves using the soil moisture response to the previous irrigation volume to adjust the applied irrigation volume applied at the next irrigation event. For field implementation this controller has low data requirements as only soil moisture data is required after each irrigation event. In contrast, a model predictive controller has high data requirements as measured soil and plant data are required at a high spatial resolution in a field implementation. Model predictive control involves using a calibrated model to determine the irrigation application and/or timing which results in the highest predicted yield or water use efficiency. The implementation of these strategies is described and a case study is presented to demonstrate the operation of the strategies with various levels of data availability. It is concluded that in situations of sparse data, the iterative learning controller performs significantly better than a model predictive controller

Air pollution and livestock production

The air in a livestock farming environment contains high concentrations of dust particles and gaseous pollutants. The total inhalable dust can enter the nose and mouth during normal breathing and the thoracic dust can reach into the lungs. However, it is the respirable dust particles that can penetrate further into the gas-exchange region, making it the most hazardous dust component. Prolonged exposure to high concentrations of dust particles can lead to respiratory health issues for both livestock and farming staff. Ammonia, an example of a gaseous pollutant, is derived from the decomposition of nitrous compounds. Increased exposure to ammonia may also have an effect on the health of humans and livestock. There are a number of technologies available to ensure exposure to these pollutants is minimised. Through proactive means, (the optimal design and management of livestock buildings) air quality can be improved to reduce the likelihood of risks associated with sub-optimal air quality. Once air problems have taken hold, other reduction methods need to be applied utilising a more reactive approach. A key requirement for the control of concentration and exposure of airborne pollutants to an acceptable level is to be able to conduct real-time measurements of these pollutants. This paper provides a review of airborne pollution including methods to both measure and control the concentration of pollutants in livestock buildings

Geo-Environmental Approaches for the Analysis and Assessment of Groundwater Resources at Catchment-Scale

This book focuses on the tools and methods used for tackling the complexity of the different hydrological and hydrogeological set-ups, the hydrodynamic patterns, the site specifications, and the wide variability of internal and external factors and/or processes on the catchment-scale level that impose the need for combined integrated approaches of robust methods. This Special Issue aims to provide successful applications or new insights on the stand-alone or joint considerations of groundwater resources assessment and characterization methods and explore new state-of-the-art methodological concepts in light of a rapidly changing environment

Groundwater Management Optimization and Saltwater Intrusion Mitigation under Uncertainty

Groundwater is valuable to supply fresh water to the public, industries, agriculture, etc. However, excessive pumping has caused groundwater storage degradation, water quality deterioration and saltwater intrusion problems. Reliable groundwater flow and solute transport modeling is needed for sustainable groundwater management and aquifer remediation design. However, challenges exist because of highly complex subsurface environments, computationally intensive groundwater models as well as inevitable uncertainties. The first research goal is to explore conjunctive use of feasible hydraulic control approaches for groundwater management and aquifer remediation. Water budget analysis is conducted to understand how groundwater withdrawals affect water levels. A mixed integer multi-objective optimization model is constructed to derive optimal freshwater pumping strategies and investigate how to promote the optimality through regulating pumping locations. A solute transport model for the Baton Rouge multi-aquifer system is developed to assess saltwater encroachment under current condition. Potential saltwater scavenging approach is proposed to mitigate the salinization issue in the Baton Rouge area. The second research goal aims to develop robust surrogate-assisted simulation-optimization modeling methods for saltwater intrusion mitigation. Machine learning based surrogate models (response surface regression model, artificial neural network and support vector machine) were developed to replace a complex high-fidelity solute transport model for predicting saltwater intrusion. Two different methods including Bayesian model averaging and Bayesian set pair analysis are used to construct ensemble surrogates and quantify model prediction uncertainties. Besides. different optimization models that incorporate multiple ensemble surrogates are formulated to obtain optimal saltwater scavenging strategies. Chance-constrained programming is used to account for model selection uncertainty in probabilistic nonlinear concentration constraints. The results show that conjunctive use of hydraulic control approaches would be effective to mitigate saltwater intrusion but needs decades. Machine learning based ensemble surrogates can build accurate models with high computing efficiency, and hence save great efforts in groundwater remediation design. Including model selection uncertainty through multimodel inference and model averaging provides more reliable remediation strategies compared with the single-surrogate assisted approach

Groundwater research and management: integrating science into management decisions. Proceedings of IWMI-ITP-NIH International Workshop on "Creating Synergy Between Groundwater Research and Management in South and Southeast Asia," Roorkee, India, 8-9 February 2005

Groundwater management / Governance / Groundwater development / Artificial recharge / Water quality / Aquifers / Groundwater irrigation / Water balance / Simulation models / Watershed management / Water harvesting / Decision making / South East Asia / Bangladesh / China / India / Nepal / Pakistan / Syria