AI and machine learning for soil analysis: an assessment of sustainable agricultural practices

Abstract

Sustainable agricultural practices help to manage and use natural resources efficiently. Due to global climate and geospatial land design, soil texture, soil–water content (SWC), and other parameters vary greatly; thus, real time, robust, and accurate soil analytical measurements are difficult to be developed. Conventional statistical analysis tools take longer to analyze and interpret data, which may have delayed a crucial decision. Therefore, this review paper is presented to develop the researcher’s insight toward robust, accurate, and quick soil analysis using artificial intelligence (AI), deep learning (DL), and machine learning (ML) platforms to attain robustness in SWC and soil texture analysis. Machine learning algorithms, such as random forests, support vector machines, and neural networks, can be employed to develop predictive models based on available soil data and auxiliary environmental variables. Geostatistical techniques, including kriging and co-kriging, help interpolate and extrapolate soil property values to unsampled locations, improving the spatial representation of the data set. The false positivity in SWC results and bugs in advanced detection techniques are also evaluated, which may lead to wrong agricultural practices. Moreover, the advantages of AI data processing over general statistical analysis for robust and noise-free results have also been discussed in light of smart irrigation technologies. Conclusively, the conventional statistical tools for SWCs and soil texture analysis are not enough to practice and manage ergonomic land management. The broader geospatial non-numeric data are more suitable for AI processing that may soon help soil scientists develop a global SWC database.

Keywords

Intelligent agriculture / Agronomic forecasting / Soil texture / Water content analysis / Smarter agriculture 4.0

References

[1] Adhikari S, Timms W, Mahmud MAP. Optimising water holding capacity and hydrophobicity of biochar for soil amendment—a review. Sci Total Environ, 2022, 851: 158043.

[2] Agegnehu G, Nelson PN, Bird MI. Crop yield, plant nutrient uptake and soil physicochemical properties under organic soil amendments and nitrogen fertilization on Nitisols. Soil Tillage Res, 2016, 160: 1-13.

[3] Ali N, Bilal M, Khan A, Ali F, Iqbal HMN. Effective exploitation of anionic, nonionic, and nanoparticle-stabilized surfactant foams for petroleum hydrocarbon contaminated soil remediation. Sci Total Environ, 2020, 704: 135391.

[4] Amarasekare P. Competitive coexistence in spatially structured environments: a synthesis. Ecol Lett, 2003, 6(12): 1109-1122.

[5] Barman U, Choudhury RD. Soil texture classification using multi class support vector machine. Inform Process Agric, 2020, 7(2): 318-332.

[6] Barros MT, Siljak H, Mullen P, Papadias C, Hyttinen J, Marchetti N. Objective supervised machine learning-based classification and inference of biological neuronal networks. Molecules, 2022

[7] Beheshti A, Raiesi F, Golchin A. Soil properties, C fractions and their dynamics in land use conversion from native forests to croplands in northern Iran. Agric Ecosyst Environ, 2012, 148: 121-133.

[8] Ben-Noah I, Nitsan I, Cohen B, Kaplan G, Friedman SP. Soil aeration using air injection in a citrus orchard with shallow groundwater. Agric Water Manag, 2021, 245: 106664.

[9] Bhattarai SP, Su N, Midmore DJ. Oxygation unlocks yield potentials of crops in oxygen-limited soil environments in advances in agronomy, 2005, Cambridge: Academic Press, 313-377.

[10] Bhunia RK, Sinha K, Kaur R, Kaur S, Chawla K. A holistic view of the genetic factors involved in triggering hydrolytic and oxidative rancidity of rice bran lipids. Food Rev Int, 2023, 39(1): 441-466.

[11] Bittelli M. Measuring soil water content: a review. HortTechnology, 2011, 21(3): 293-300.

[12] Blanco H, Lal R. Blanco H, Lal R. Soil fertility management. Soil conservation and management, 2023, Springer Nature Switzerland: Cham, 363-390.

[13] Bormann H. Towards a hydrologically motivated soil texture classification. Geoderma, 2010, 157(3): 142-153.

[14] Bouajila K, Hechmi S, Mechri M, Jeddi FB, Jedidi N. Short-term effects of Sulla residues and farmyard manure amendments on soil properties: cation exchange capacity (CEC), base cations (BC), and percentage base saturation (PBS). Arab J Geosci, 2023, 16(7): 410.

[15] Boyer JS. Turgor and the transport of CO2 and water across the cuticle (epidermis) of leaves. J Exp Bot, 2015, 66(9): 2625-2633.

[16] Bünemann EK, . Soil quality—a critical review. Soil Biol Biochem, 2018, 120: 105-125.

[17] Chen P, Liu J, Wei C, Xue W, Tian H. Approach to rapidly determining the water retention curves for fine-grained soils in capillary regime based on the NMR technique. J Eng Mech, 2017, 143(7): 04017032.

[18] Chen X, . Root physiological adaptations that enhance the grain yield and nutrient use efficiency of maize (Zea mays L) and their dependency on phosphorus placement depth. Field Crops Res, 2022, 276: 108378.

[19] Ciocca F, Lunati I, Van de Giesen N, Parlange MB. Heated optical fiber for distributed soil-moisture measurements: a lysimeter experiment. Vadose Zone J, 2012

[20] Clauser NM, Felissia FE, Area MC, Vallejos ME. Integrating the new age of bioeconomy and industry 4.0 into biorefinery process design. BioResources, 2022, 17(3): 5510.

[21] de Obade VP, Gaya C. Digital technology dilemma: on unlocking the soil quality index conundrum. Bioresour Bioprocess, 2021, 8(1): 6.

[22] Dotaniya ML, Meena VD. Rhizosphere effect on nutrient availability in soil and its uptake by plants: a review. Proc Natl Acad Sci India Sect B Biol Sci, 2015, 85(1): 1-12.

[23] Dragone G, Kerssemakers AAJ, Driessen JLSP, Yamakawa CK, Brumano LP, Mussatto SI. Innovation and strategic orientations for the development of advanced biorefineries. Bioresour Technol, 2020, 302: 122847.

[24] Drizo A, Johnston C, Guðmundsson J. An inventory of good management practices for nutrient reduction, recycling and recovery from agricultural runoff in Europe’s Northern Periphery and Arctic Region. Water, 2022

[25] Dwevedi A, Kumar P, Kumar P, Kumar Y, Sharma YK, Kayastha AM. Grumezescu AM. 15—Soil sensors: detailed insight into research updates, significance, and future prospects. New pesticides and soil sensors, 2017, Cambridge: Academic Press, 561-594.

[26] Emamgolizadeh S, Bateni SM, Shahsavani D, Ashrafi T, Ghorbani H. Estimation of soil cation exchange capacity using Genetic Expression Programming (GEP) and Multivariate Adaptive Regression Splines (MARS). J Hydrol, 2015, 529: 1590-1600.

[27] Friedrich S, . Is there a role for statistics in artificial intelligence. Adv Data Anal Classif, 2022, 16(4): 823-846.

[28] Fu Z-D, . Effects of maize-soybean relay intercropping on crop nutrient uptake and soil bacterial community. J Integr Agric, 2019, 18(9): 2006-2018.

[29] Fu Y, Horton R, Heitman J. Estimation of soil water retention curves from soil bulk electrical conductivity and water content measurements. Soil Tillage Res, 2021, 209: 104948.

[30] Gao L, . A deep neural network based SMAP soil moisture product. Remote Sens Environ, 2022, 277: 113059.

[31] García-Gamero V, Vanwalleghem T, Peña A, Román-Sánchez A, Finke PA. Modelling the effect of catena position and hydrology on soil chemical weathering. SOIL, 2022, 8(1): 319-335.

[32] Hao M, . Impacts of changes in vegetation on saturated hydraulic conductivity of soil in subtropical forests. Sci Rep, 2019, 9(1): 8372.

[33] Hassan-Esfahani L, Torres-Rua A, Jensen A, McKee M. Assessment of surface soil moisture using high-resolution multi-spectral imagery and artificial neural networks. Remote Sens, 2015, 7(3): 2627-2646.

[34] Heiskanen J (1997) Air-filled porosity of eight growing media based on sphagnum peat during drying from container capacity. International Society for Horticultural Science (ISHS), Leuven, Belgium, pp. 277–286

[35] Henderson TL, Baumgardner MF, Franzmeier DP, Stott DE, Coster DC. High dimensional reflectance analysis of soil organic matter. Soil Sci Soc Am J, 1992, 56(3): 865-872.

[36] Hosseini M, Bahrami H, Khormali F, Khavazi K, Mokhtassi-Bidgoli A. Artificial intelligence statistical analysis of soil respiration improves predictions compared to regression methods. J Soil Sci Plant Nutr, 2021, 21(3): 2242-2251.

[37] Huang H, . Water content quantitatively affects metabolic rates over the course of plant ontogeny. New Phytol, 2020, 228(5): 1524-1534.

[38] Huang Q, Zhang H, Zhang L, Xu B. Bacterial microbiota in different types of processed meat products: diversity, adaptation, and co-occurrence. Crit Rev Food Sci Nutr, 2023

[39] Hueso S, García C, Hernández T. Severe drought conditions modify the microbial community structure, size and activity in amended and unamended soils. Soil Biol Biochem, 2012, 50: 167-173.

[40] Jamil N, . Physical and chemical properties of soil quality indicating forests productivity: a review. American-Eurasian J Toxicol Sci, 2016, 8(2): 60-68.

[41] Jian S, Zhao C, Fang S, Yu K. Effects of different vegetation restoration on soil water storage and water balance in the Chinese Loess Plateau. Agric for Meteorol, 2015, 206: 85-96.

[42] Johnson NS, . Invited review: machine learning for materials developments in metals additive manufacturing. Addit Manuf, 2020, 36: 101641.

[43] Jong SC, Ong DEL, Oh E. State-of-the-art review of geotechnical-driven artificial intelligence techniques in underground soil-structure interaction. Tunn Undergr Space Technol, 2021, 113: 103946.

[44] Karakan E. Comparative analysis of atterberg limits, liquidity index, flow index and undrained shear strength behavior in binary clay mixtures. Appl Sci, 2022, 12(17): 8616.

[45] Karimi B, . A meta-analysis of the ecotoxicological impact of viticultural practices on soil biodiversity. Environ Chem Lett, 2020, 18(6): 1947-1966.

[46] Keller T, Håkansson I. Estimation of reference bulk density from soil particle size distribution and soil organic matter content. Geoderma, 2010, 154(3): 398-406.

[47] Khaled H, Fawy AH. Effect of different levels of humic acids on the nutrient content, plant growth, and soil properties under conditions of salinity. Soil Water Res, 2011, 6(1): 21-29.

[48] Kodali RK Sahu A (2016) An IoT based soil moisture monitoring on Losant platform. In 2016 2nd International Conference on Contemporary Computing and Informatics (IC3I), 2016, pp. 764–768.

[49] Kose U, Prasath VS, Mondal MRH, Podder P, Bharati S. Artificial intelligence and smart agriculture technology, 2022, Boca Raton: CRC Press

[50] Kouadio L, Deo RC, Byrareddy V, Adamowski JF, Mushtaq S, Phuong Nguyen V. Artificial intelligence approach for the prediction of Robusta coffee yield using soil fertility properties. Comput Electron Agric, 2018, 155: 324-338.

[51] Lal R, Shukla MK. Principles of soil physics, 2004, Boca Raton: CRC Press

[52] Lambers H, Oliveira RS. Lambers H, Oliveira RS. Plant water relations. Plant physiological ecology, 2019, Cham: Springer International Publishing, 187-263.

[53] Leucci G. Ground penetrating radar: an application to estimate volumetric water content and reinforced bar diameter in concrete structures. J Adv Concr Technol, 2012, 10(12): 411-422.

[54] Li X, Chang SX, Salifu KF. Soil texture and layering effects on water and salt dynamics in the presence of a water table: a review. Environ Rev, 2014, 22(1): 41-50.

[55] Li D, Gao G, Shao M, Fu B. Predicting available water of soil from particle-size distribution and bulk density in an oasis–desert transect in northwestern China. J Hydrol, 2016, 538: 539-550.

[56] Liu Q, Yasufuku N, Omine K, Hazarika H. Automatic soil water retention test system with volume change measurement for sandy and silty soils. Soils Found, 2012, 52(2): 368-380.

[57] Liu Z, Zhou W, Shen J, Li S, Ai C. Soil quality assessment of yellow clayey paddy soils with different productivity. Biol Fertil Soils, 2014, 50(3): 537-548.

[58] Liu X, Liang J, Gu L. Photosynthetic and environmental regulations of the dynamics of soil respiration in a forest ecosystem revealed by analyses of decadal time series. Agric for Meteorol, 2020, 282–283: 107863.

[59] Liu J-W, . Surfactant-enhanced remediation of oil-contaminated soil and groundwater: a review. Sci Total Environ, 2021, 756: 144142.

[60] Liu X, . PiLSL: pairwise interaction learning-based graph neural network for synthetic lethality prediction in human cancers. Bioinformatics, 2022, 38: ii106-ii112.

[61] Liu X-F, Zhu H-H, Wu B, Li J, Liu T-X, Shi B. Artificial intelligence-based fiber optic sensing for soil moisture measurement with different cover conditions. Measurement, 2023, 206: 112312.

[62] Ma C, . The role and mechanism of commercial macroalgae for soil conditioner and nutrient uptake catalyzer. Plant Growth Regul, 2022, 97(3): 455-476.

[63] Mahmoudi N, . Mutating fuzzy logic model with various rigorous meta-heuristic algorithms for soil moisture content estimation. Agric Water Manag, 2022, 261: 107342.

[64] Maino A, . Airborne radiometric surveys and machine learning algorithms for revealing soil texture. Remote Sens, 2022

[65] Mallah S, . Predicting soil textural classes using random forest models: learning from imbalanced dataset. Agronomy, 2022

[66] Mallet F, Marc V, Douvinet J, Rossello P, Joly D, Ruy S. Assessing soil water content variation in a small mountainous catchment over different time scales and land covers using geographical variables. J Hydrol, 2020, 591: 125593.

[67] Månsson KF, Olsson MO, Falkengren-Grerup U, Bengtsson G. Soil moisture variations affect short-term plant-microbial competition for ammonium, glycine, and glutamate. Ecol Evol, 2014, 4(7): 1061-1072.

[68] Martinelli G, Gasser M-O. Machine learning models for predicting soil particle size fractions from routine soil analyses in Quebec. Soil Sci Soc Am J, 2022, 86(6): 1509-1522.

[69] Masha M, Yirgu T, Debele M, Belayneh M. Effectiveness of community-based soil and water conservation in improving soil property in Damota Area, Southern Ethiopia. Appl Environ Soil Sci, 2021, 2021: 5510587.

[70] Mei X, . The variability in soil water storage on the loess hillslopes in China and its estimation. CATENA, 2019, 172: 807-818.

[71] Miranda-Apodaca J, Pérez-López U, Lacuesta M, Mena-Petite A, Muñoz-Rueda A. The interaction between drought and elevated CO2 in water relations in two grassland species is species-specific. J Plant Physiol, 2018, 220: 193-202.

[72] Mulla AR, Rodd SM, Sankpal SV, Yedave PM, Ghorpade YB, Nejkar MV. Assessment of water logging and its remedial measures. Int J New Media Stud Int Peer Rev Sch Index J, 2023, 10(1): 143-151.

[73] Naimi S, Ayoubi S, Demattê JAM, Zeraatpisheh M, Amorim MTA, Mello FA. Spatial prediction of soil surface properties in an arid region using synthetic soil image and machine learning. Geocarto Int, 2022, 37(25): 8230-8253.

[74] Ojeda Olivares EA, Belmonte Jiménez SI, Sandoval Torres S, Campos Enríquez JO, Tiefenbacher JP, Takaro TK. A simple method to evaluate groundwater vulnerability in urbanizing agricultural regions. J Environ Manag, 2020, 261: 110164.

[75] Owe M, de Jeu R, Holmes T. Multisensor historical climatology of satellite-derived global land surface moisture. J Geophys Res Earth Surf, 2008

[76] Pandey DK, Hunjra AI, Bhaskar R, Al-Faryan MA. Artificial intelligence, machine learning and big data in natural resources management: a comprehensive bibliometric review of literature spanning 1975–2022. Resour Policy, 2023, 86: 104250.

[77] Pandya AB. Chadha G, Pandya AB. Data usage for development, management of water resources. Water governance and management in India: issues and perspectives, 2021, Singapore: Springer Singapore, 131-163.

[78] Pastén-Zapata E, Ledesma-Ruiz R, Harter T, Ramírez AI, Mahlknecht J. Assessment of sources and fate of nitrate in shallow groundwater of an agricultural area by using a multi-tracer approach. Sci Total Environ, 2014, 470: 855-864.

[79] Penghui L, . Metaheuristic optimization algorithms hybridized with artificial intelligence model for soil temperature prediction: novel model. IEEE Access, 2020, 8: 51884-51904.

[80] Pereira LS, Paredes P, Jovanovic N. Soil water balance models for determining crop water and irrigation requirements and irrigation scheduling focusing on the FAO56 method and the dual Kc approach. Agric Water Manag, 2020, 241: 106357.

[81] Pernet CA, Ribi Forclaz A. Revisiting the Food and Agriculture Organization (FAO): international histories of agriculture, nutrition, and development. Int Hist Rev, 2019, 41(2): 345-350.

[82] Pham BT, Son LH, Hoang T-A, Nguyen D-M, Tien Bui D. Prediction of shear strength of soft soil using machine learning methods. CATENA, 2018, 166: 181-191.

[83] Phoon K-K, Santoso A, Quek S-T. Probabilistic analysis of soil-water characteristic curves. J Geotech Geoenviron Eng, 2010, 136(3): 445-455.

[84] Pregitzer KS, King JS. BassiriRad H. Effects of soil temperature on nutrient uptake. Nutrient acquisition by plants an ecological perspective, 2005, Berlin, Heidelberg: Springer, 277-310.

[85] Rahardjo H, Kim Y, Satyanaga A. Role of unsaturated soil mechanics in geotechnical engineering. Int J Geo-Eng, 2019, 10(1): 8.

[86] Raja MNA, Shukla SK. Predicting the settlement of geosynthetic-reinforced soil foundations using evolutionary artificial intelligence technique. Geotext Geomembr, 2021, 49(5): 1280-1293.

[87] Ratshiedana PE, Abd Elbasit MAM, Adam E, Chirima JG, Liu G, Economon EB. Determination of soil electrical conductivity and moisture on different soil layers using electromagnetic techniques in irrigated arid environments in South Africa. Water, 2023

[88] Rayne N, Aula L. Livestock manure and the impacts on soil health: a review. Soil Syst, 2020

[89] Richards LA. Capillary conduction of liquids through porous mediums. Physics, 2004, 1(5): 318-333.

[90] Riese FM, Keller S (2019) Soil texture classification with 1D convolutional neural networks based on hyperspectral data, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume IV-2/W5, 2019 ISPRS Geospatial Week 2019, 10–14 June 2019, Enschede, The Netherlands

[91] Rooney EC, . Soil pore network response to freeze-thaw cycles in permafrost aggregates. Geoderma, 2022, 411: 115674.

[92] Roshan SH, Kazemitabar J, Kheradmandian G. Artificial intelligence aided agricultural sensors for plant frostbite protection. Appl Artif Intell, 2022, 36(1): 2031814.

[93] Ruszczak B, Boguszewska-Mańkowska DJS. Soil moisture a posteriori measurements enhancement using ensemble learning. Sensors, 2022, 22(12): 4591.

[94] Schlüter S, . Microscale carbon distribution around pores and particulate organic matter varies with soil moisture regime. Nat Commun, 2022, 13(1): 2098.

[95] Sharma A, Sen S. Droughts risk management strategies and determinants of preparedness: insights from Madhya Pradesh, India. Nat Hazards, 2022, 114(2): 2243-2281.

[96] Sharma A, Weindorf DC, Wang D, Chakraborty S. Characterizing soils via portable X-ray fluorescence spectrometer: 4. Cation exchange capacity (CEC). Geoderma, 2015, 239–240: 130-134.

[97] Singh IR, Nair PN. Evaluation of physicochemical properties with the availability of plant nutrients in forests and crop farms in different Fijian provinces. Plant Sci Today, 2023, 10(3): 211-219.

[98] Stein A, Gerstner K, Kreft H. Environmental heterogeneity as a universal driver of species richness across taxa, biomes and spatial scales. Ecol Lett, 2014, 17(7): 866-880.

[99] Sun W, Zhu H, Guo S. Soil organic carbon as a function of land use and topography on the Loess Plateau of China. Ecol Eng, 2015, 83: 249-257.

[100] Tian K, Yang A, Nie K, Zhang H, Xu J, Wang X. Experimental study of steady seepage in unsaturated loess soil. Acta Geotech, 2020, 15(9): 2681-2689.

[101] Ulusoy Y, Tekin Y, Tümsavaş Z, Mouazen AM. Prediction of soil cation exchange capacity using visible and near infrared spectroscopy. Biosyst Eng, 2016, 152: 79-93.

[102] Vereecken H, . Soil hydrology: recent methodological advances, challenges, and perspectives. Water Resour Res, 2015, 51(4): 2616-2633.

[103] Vico G, Tang FHM, Brunsell NA, Crews TE, Katul GG. Photosynthetic capacity, canopy size and rooting depth mediate response to heat and water stress of annual and perennial grain crops. Agric for Meteorol, 2023, 341: 109666.

[104] Villalobos M, Avila-Forcada AP, Gutierrez-Ruiz ME. An improved gravimetric method to determine total petroleum hydrocarbons in contaminated soils. Water Air Soil Pollut, 2008, 194(1): 151-161.

[105] Vos M, Wolf AB, Jennings SJ, Kowalchuk GA. Micro-scale determinants of bacterial diversity in soil. FEMS Microbiol Rev, 2013, 37(6): 936-954.

[106] Wadoux AM-C, Minasny B, McBratney AB. Machine learning for digital soil mapping: applications, challenges and suggested solutions. Earth Sci Rev, 2020, 210: 103359.

[107] Wang C, Li S-Y, He X-J, Chen Q, Zhang H, Liu X-Y. Improved prediction of water retention characteristic based on soil gradation and clay fraction. Geoderma, 2021, 404: 115293.

[108] Wang Q, . Coastal soil texture controls soil organic carbon distribution and storage of mangroves in China. CATENA, 2021, 207: 105709.

[109] Wang S, . Advances in data preprocessing for biomedical data fusion: an overview of the methods, challenges, and prospects. Inform Fus, 2021, 76: 376-421.

[110] Wang F, Wang Q, Adams CA, Sun Y, Zhang S. Effects of microplastics on soil properties: current knowledge and future perspectives. J Hazard Mater, 2022, 424: 127531.

[111] Wang H, Garg A, Ping Y, Sreedeep S, Chen R. Effects of biochar derived from coconut shell on soil hydraulic properties under salt stress in roadside bioretention. Waste Biomass Valorization, 2023, 14(3): 1005-1022.

[112] Wilson DJ, Western AW, Grayson RB. Identifying and quantifying sources of variability in temporal and spatial soil moisture observations. Water Resour Res, 2004

[113] Wuddivira MN, . Estimation of soil clay content from hygroscopic water content measurements. Soil Sci Soc Am J, 2012, 76(5): 1529-1535.

[114] Xu J-S, Yang X-L. Three-dimensional stability analysis of slope in unsaturated soils considering strength nonlinearity under water drawdown. Eng Geol, 2018, 237: 102-115.

[115] Xu Y, Gao Y, Li W, Chen S, Li Y, Shi Y. Effects of compound water retention agent on soil nutrients and soil microbial diversity of winter wheat in saline-alkali land. Chem Biol Technol Agric, 2023, 10(1): 2.

[116] Yan Y, . Sulfur mass balance and speciation in the water cap during early-stage development in the first pilot pit lake in the Alberta Oil Sands. Environ Chem, 2022, 19(4): 236-253.

[117] Yu B, Kumbier K. Artificial intelligence and statistics. Front Inform Technol Electron Eng, 2018, 19(1): 6-9.

[118] Zhai Y, Thomasson JA, Boggess JE, Sui R. Soil texture classification with artificial neural networks operating on remote sensing data. Comput Electron Agric, 2006, 54(2): 53-68.

[119] Zhang M, Shi W. Systematic comparison of five machine-learning methods in classification and interpolation of soil particle size fractions using different transformed data. Hydrol Earth Syst Sci Discuss, 2019, 2019: 1-39.

[120] Zhang F, Zhao C, Lourenço SDN, Dong S, Jiang Y. Factors affecting the soil–water retention curve of Chinese loess. Bull Eng Geol Environ, 2021, 80(1): 717-729.

[121] Zhang Y, . Evaluating soil salt dynamics in a field drip-irrigated with brackish water and leached with freshwater during different crop growth stages. Agric Water Manag, 2021, 244: 106601.

[122] Zhao J, Lin L, Yang K, Liu Q, Qian G. Influences of land use on water quality in a reticular river network area: a case study in Shanghai, China. Landsc Urban Plan, 2015, 137: 20-29.

[123] Zhao C, Jia X, Zhu Y, Shao M. Long-term temporal variations of soil water content under different vegetation types in the Loess Plateau, China. CATENA, 2017, 158: 55-62.

[124] Zhao H, Xiao Q, Miao Y, Wang Z, Wang Q. Sources and transformations of nitrate constrained by nitrate isotopes and Bayesian model in karst surface water, Guilin, Southwest China. Environ Sci Pollut Res, 2020, 27(17): 21299-21310.