Simulating soil salinity dynamics, cotton yield and evapotranspiration under drip irrigation by ensemble machine learning

We thank the China Scholarship Council (CSC) for providing a scholarship (202206710073) to Zewei Jiang. This work was supported by the Fundamental Research Funds for the Central Universities (B220203009), the Postgraduate Research & Practice Program of Jiangsu Province (KYCX22_0669), the Water Conservancy Science and Technology Project of Jiangxi Province (201921ZDKT06, 202124ZDKT09), the National Natural Science Foundation of China (51879076), the Fundamental Research Funds for the Central Universities (B210204016), Science & Technology Specific Projects in Agricultural High-tech Industrial Demonstration Area of the Yellow River Delta, Grant No: 2022SZX01.Peer reviewedPublisher PD

Sustainability of irrigated agriculture under salinity pressure – A study in semiarid Tunisia

In semiarid and arid Tunisia, water quality and agricultural practices are the major contributing factors to the degradation of soil resources threatening the sustainability of irrigation systems and agricultural productivity. Nowadays, about 50% of the total irrigated areas in Tunisia are considered at high risk for salinization. The aim of this thesis was to study soil management and salinity relationships in order to assure sustainable irrigated agriculture in areas under salinity pressure. To prevent further soil degradation, farmers and rural development officers need guidance and better tools for the measurement, prediction, and monitoring of soil salinity at different observation scales, and associated agronomical strategy. Field experiments were performed in semi-arid Nabeul (sandy soil), semi-arid Kalâat Landalous (clay soil), and the desertic Fatnassa oasis (gypsiferous soil). The longest observation period represented 17 years. Besides field studies, laboratory experiments were used to develop accurate soil salinity measurements and prediction techniques. In saline gypsiferous soil, the WET sensor can give similar accuracy of soil salinity as the TDR if calibrated values of the soil parameters are used instead of standard values. At the Fatnassa oasis scale, the predicted values of ECe and depth of shallow groundwater Dgw using electromagnetic induction EM-38 were found to be in agreement with observed values with acceptable accuracy. At Kalâat Landalous (1400 ha), the applicability of artificial neural network (ANN) models for predicting the spatial soil salinity (ECe) was found to be better than multivariate linear regression (MLR) models. In semi-arid and desertic Tunisia, irrigation and drainage reduce soil salinity and dilute the shallow groundwater. However, the ECgw has a larger impact than soil salinity variation on salt balance. Based on the findings related to variation in the spatial and temporal soil and groundwater properties, soil salinization factors were identified and the level of soil “salinization risk unit” (SRU) was developed. The groundwater properties, especially the Dgw, could be considered as the main cause of soil salinization risk in arid Tunisia. However, under an efficient drainage network and water management, the soil salinization could be considered as a reversible process. The SRU mapping can be used by both land planners and farmers to make appropriate decisions related to crop production and soil and water management

Simulating soil salinity dynamics, cotton yield and evapotranspiration under drip irrigation by ensemble machine learning

Cotton is widely used in textile, decoration, and industry, but it is also threatened by soil salinization. Drip irrigation plays an important role in improving water and fertilization utilization efficiency and ensuring crop production in arid areas. Accurate prediction of soil salinity and crop evapotranspiration under drip irrigation is essential to guide water management practices in arid and saline areas. However, traditional hydrological models such as Hydrus require more variety of input parameters and user expertise, which limits its application in practice, and machine learning (ML) provides a potential alternative. Based on a global dataset collected from 134 pieces of literature, we proposed a method to comprehensively simulate soil salinity, evapotranspiration (ET) and cotton yield. Results showed that it was recommended to predict soil salinity, crop evapotranspiration and cotton yield based on soil data (bulk density), meteorological factors, irrigation data and other data. Among them, meteorological factors include annual average temperature, total precipitation, year. Irrigation data include salinity in irrigation water, soil matric potential and irrigation water volume, while other data include soil depth, distance from dripper, days after sowing (for EC and soil salinity), fertilization rate (for yield and ET). The accuracy of the model has reached a satisfactory level, R2 in 0.78-0.99. The performance of stacking ensemble ML was better than that of a single model, i.e., gradient boosting decision tree (GBDT); random forest (RF); extreme gradient boosting regression (XGBR), with R2 increased by 0.02%-19.31%. In all input combinations, other data have a greater impact on the model accuracy, while the RMSE of the S1 scenario (input without meteorological factors) without meteorological data has little difference, which is -34.22%~19.20% higher than that of full input. Given the wide application of drip irrigation in cotton, we recommend the application of ensemble ML to predict soil salinity and crop evapotranspiration, thus serving as the basis for adjusting the irrigation schedule

Study of the Soil Water Movement in Irrigated Agriculture

In irrigated agriculture, the study of the various ways water infiltrates into the soils is necessary. In this respect, soil hydraulic properties, such as soil moisture retention curve, diffusivity, and hydraulic conductivity functions, play a crucial role, as they control the infiltration process and the soil water and solute movement. This Special Issue presents the recent developments in the various aspects of soil water movement in irrigated agriculture through a number of research topics that tackle one or more of the following challenges: irrigation systems and one-, two-, and three-dimensional soil water movement; one-, two-, and three-dimensional infiltration analysis from a disc infiltrometer; dielectric devices for monitoring soil water content and methods for assessment of soil water pressure head; soil hydraulic properties and their temporal and spatial variability under the irrigation situations; saturated–unsaturated flow model in irrigated soils; soil water redistribution and the role of hysteresis; soil water movement and drainage in irrigated agriculture; salt accumulation, soil salinization, and soil salinity assessment; effect of salts on hydraulic conductivity; and soil conditioners and mulches that change the upper soil hydraulic properties and their effect on soil water movement

Calibrating a flow model in an irrigation network: Case study in Alicante, Spain

[EN] The usefulness of models depends on their validation in a calibration process, ensuring that simulated flows and pressure values in any line are really occurring and, therefore, becoming a powerful decision tool for many aspects in the network management (i.e., selection of hydraulic machines in pumped systems, reduction of the installed power in operation, analysis of theoretical energy recovery). A new proposed method to assign consumptions patterns and to determine flows over time in irrigation networks is calibrated in the present research. As novelty, the present paper proposes a robust calibration strategy for flow assignment in lines, based on some key performance indicators (KPIF) coming from traditional hydrological models (Nash-Sutcliffe coefficient (non-dimensional index), root relative square error (error index) and percent bias (tendency index)). The proposed strategy for calibration was applied to a real case in Alicante (Spain), with a goodness of fit considered as very good in many indicators. KPIF parameters observed present a satisfactory goodness of fit of the series, considering their repeatability. Average Nash-Sutcliffe coefficient value oscillated between 0.30 and 0.63, average percent bias values were below 10% in all the range, and average root relative square error values varied between 0.65 and 0.80.Pérez-Sánchez, M.; Sánchez-Romero, F.; Ramos, HM.; López Jiménez, PA. (2017). Calibrating a flow model in an irrigation network: Case study in Alicante, Spain. Spanish Journal of Agricultural Research (Online). 15(1):1-13. doi:10.5424/sjar/2017151-10144S113151Abbasi, F., Feyen, J., & van Genuchten, M. T. (2004). Two-dimensional simulation of water flow and solute transport below furrows: model calibration and validation. 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Adjusting Nodal Demands in SCADA Constrained Real-Time Water Distribution Network Models. Journal of Hydraulic Engineering, 132(1), 102-110. doi:10.1061/(asce)0733-9429(2006)132:1(102)Delgoda, D., Malano, H., Saleem, S. K., & Halgamuge, M. N. (2016). Irrigation control based on model predictive control (MPC): Formulation of theory and validation using weather forecast data and AQUACROP model. Environmental Modelling & Software, 78, 40-53. doi:10.1016/j.envsoft.2015.12.012Emec, S., Bilge, P., & Seliger, G. (2015). Design of production systems with hybrid energy and water generation for sustainable value creation. Clean Technologies and Environmental Policy, 17(7), 1807-1829. doi:10.1007/s10098-015-0947-4Ghiassi, M., Zimbra, D. K., & Saidane, H. (2008). Urban Water Demand Forecasting with a Dynamic Artificial Neural Network Model. Journal of Water Resources Planning and Management, 134(2), 138-146. doi:10.1061/(asce)0733-9496(2008)134:2(138)Granados A, 1986. Redes colectivas del riego a presión. Escuela de Ingenieros de Caminos y Puertos, Univ. Politécnica, Madrid.Granados, A., Martín-Carrasco, F. J., García de Jalón, S., & Iglesias, A. (2015). Adaptation of irrigation networks to climate change: Linking robust design and stakeholder contribution. Spanish Journal of Agricultural Research, 13(4), e1205. doi:10.5424/sjar/2015134-7549Gupta, H. V., Sorooshian, S., & Yapo, P. O. (1999). Status of Automatic Calibration for Hydrologic Models: Comparison with Multilevel Expert Calibration. Journal of Hydrologic Engineering, 4(2), 135-143. doi:10.1061/(asce)1084-0699(1999)4:2(135)Lamaddalena N, Sagardoy JA, 2000. Performance analysis of on-demand pressurized irrigation systems. FAO, Roma, Italy. 132 pp.Legates, D. R., & McCabe, G. J. (1999). Evaluating the use of «goodness-of-fit» Measures in hydrologic and hydroclimatic model validation. Water Resources Research, 35(1), 233-241. doi:10.1029/1998wr900018Martínez-Solano, J., Iglesias-Rey, P. 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Comparison of two different artificial neural network models for prediction of soil penetration resistance

A time-varying, nonlinear soil-plant system contains many unknown elements that can be quantified based on analytical methodologies. Artificial Neural Networks (ANNs) are a widely used mathematical computing, modelling, and predicting method that estimates unknown values of variables from known values of others. This paper aims to simulate relationship between soil moisture, bulk density, porosity ratio, depth, and penetration resistance and to estimate soil penetration resistance with the help of ANNs. For this aim, the Generalized Regression Neural network (GRNN) and Radial Basis Function (RBF) models were developed and compared for the estimation of soil penetration resistance values in MATLAB. A dataset of 153 samples was collected from experimental field. From the 153 data, 102 data (33%) were selected for training and the remaining 51 data (67%) were used for testing. The estimation process was implemented 10 replications using randomly selected testing and training data. Mean Squared Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) were used to evaluate estimation accuracy on the developed ANN methods. Based on MSE, RMSE, MAE and Standard Deviation (SD), statistical results showed that the GRNN modelling presented better results than the RBF model in predicting soil penetration resistance success

An artificial neural network approach to the estimation of stem water potential from frequency domain reflectometry soil moisture measurements and meteorological data

Stem water potential seems to be a sensitive measure of plant water status. Nonetheless, it is a labour-intensive measurement and is not suited for automatic irrigation scheduling or control. This study describes the application of artificial neural networks to estimate stem water potential from soil moisture at different depths and standard meteorological variables, considering a limited data set. The experiment was carried out with `Navelina¿ citrus trees grafted on `Cleopatra¿ mandarin. Principal components analysis and multiple linear regression were used preliminarily to assess the relationships among observations and to propose other models to allow a comparative analysis, respectively. Two principal components account for the systematic data variation. The optimum regression equation of stem water potential considered temperature, relative humidity, solar radiation and soil moisture at 50 cm as input variables, with a determination coefficient of 0.852. When compared with their corresponding regression models, ANNs presented considerably higher performance accuracy (with an optimum determination coefficient of 0.926) due to a higher input-output mapping ability.The authors are grateful to TECVASA, which obtained a subsidy from the Conselleria de Agricultura, Pesca y Alimentacion de la Generalitat Valenciana (DOCV 5493, 19 April 2007, no. exp.: 2007TAHAVAL00018), and to the Valencian Institute for Agricultural Research (IVIA) for providing the meteorological data for this study.Martí Pérez, PC.; Gasque Albalate, M.; González Altozano, P. (2013). An artificial neural network approach to the estimation of stem water potential from frequency domain reflectometry soil moisture measurements and meteorological data. Computers and Electronics in Agriculture. 91:75-86. doi:10.1016/j.compag.2012.12.001S75869

Forecasting Root-Zone Electrical Conductivity of Nutrient Solutions in Closed-Loop Soilless Cultures via a Recurrent Neural Network Using Environmental and Cultivation Information

In existing closed-loop soilless cultures, nutrient solutions are controlled by the electrical conductivity (EC) of the solution. However, the EC of nutrient solutions is affected by both growth environments and crop growth, so it is hard to predict the EC of nutrient solution. The objective of this study was to predict the EC of root-zone nutrient solutions in closed-loop soilless cultures using recurrent neural network (RNN). In a test greenhouse with sweet peppers (Capsicum annuum L.), data were measured every 10 s from October 15 to December 31, 2014. Mean values for every hour were analyzed. Validation accuracy (R2) of a single-layer long short-term memory (LSTM) was 0.92 and root-mean-square error (RMSE) was 0.07, which were the best results among the different RNNs. The trained LSTM predicted the substrate EC accurately at all ranges. Test accuracy (R2) was 0.72 and RMSE was 0.08, which were lower than values for the validation. Deep learning algorithms were more accurate when more data were added for training. The addition of other environmental factors or plant growth data would improve model robustness. A trained LSTM can control the nutrient solutions in closed-loop soilless cultures based on predicted future EC. Therefore, the algorithm can make a planned management of nutrient solutions possible, reducing resource waste

Development of a Low-Cost Open-Source Platform for Smart Irrigation Systems

Nowadays, smart irrigation is becoming an essential goal in agriculture, where water and energy are increasingly limited resources. Its importance will grow in the coming years in the agricultural sector where the optimal use of resources and environmental sustainability are becoming more important every day. However, implementing smart irrigation is not an easy task for most farmers since it is based on knowledge of the different processes and factors that determine the crop water requirements. Thanks to technological developments, it is possible to design new tools such as sensors or platforms that can be connected to soil-water-plant-atmosphere models to assist in the optimization and automation of irrigation. In this work, a low-cost, open-source IoT system for smart irrigation has been developed that can be easily integrated with other platforms and supports a large number of sensors. The platform uses the FIWARE framework together with customized components and can be deployed using edge computing and/or cloud computing systems. To improve decision-making, the platform integrates an irrigation model that calculates soil water balance and wet bulb dimensions to determine the best irrigation strategy for drip irrigation systems. In addition, an energy efficient open-source datalogger has been designed. The datalogger supports a wide range of communications and is compatible with analog sensors, SDI-12 and RS-485 protocols. The IoT system has been deployed on an olive farm and has been in operation for one irrigation season. Based on the results obtained, advantages of using these technologies over traditional methods are discussed

Investigation into the ability of pervious pavement to treat stormwater for aquifer storage and recovery

Pervious pavement is investigated as a tool to treat stormwater for Aquifer Storage and Recovery (ASR). Adding a layer of Granular Activated Carbon (GAC) and adding a sand layer to the base were investigated separately. Both arrangements were shown to be capable of reducing ammonium to meet ASR requirements, with the sand layer setup also meeting phosphate requirements. Suspended solids and oxidised nitrogen were more problematic and should be the focus of further research