Abstract
Purpose
With the continuous advancement of modern agriculture and urbanization, soil quality assessment has been considered an important guarantee for sustainable agricultural development. Despite the availability of numerous methods for assessing soil quality, little emphasis has been paid to comprehensive studies on soil quality in greenhouse agriculture. This study aims to construct a comprehensive evaluation model of greenhouse agricultural soil quality, including soil nutrition and heavy metal pollution, to better assess greenhouse soil quality.
Materials and methods
In this study, the concentrations of eight heavy metals, five soil nutrients, and nine soil-available microelements in 300 greenhouses were measured. Genetic algorithm–backpropagation (GA-BP) neural networks and backpropagation (BP) neural networks were used to construct a comprehensive soil quality evaluation model, and the soil quality of the greenhouse in the study area was evaluated based on soil nutrients and heavy metal pollution.
Results
The results showed that the prediction accuracy of both models exceeded 85%. However, constructed utilizing the genetic algorithm–backpropagation (GA-BP), the evaluation model can be more effective in assessing soil quality, with an accuracy of 96.1%. In this study, the soil quality was categorized into eight levels: IA, IB, IC, IIA, IIB, IIC, IIIA, and IIIB. 80.6% of the samples were IIA and IIB, suggesting that the soil quality of greenhouse planting sheds in this research area was poor, with severe heavy metal pollution, although soil nutrients were relatively sufficient.
Conclusions
This study holds significance for assessing soil quality in greenhouse agriculture and improving agricultural scientific management.
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Data availability
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
References
Ayoubi S, Sahrawat KL (2011) Comparing multivariate regression and artificial neural network to predict barley production from soil characteristics in Northern Iran. Arch Agron Soil Sci 57:549–565. https://doi.org/10.1080/03650341003631400
Briat JF, Gojon A, Plassard C, Rouached H, Lemaire G (2020) Reappraisal of the central role of soil nutrient availability in nutrient management in light of recent advances in plant nutrition at crop and molecular levels. Eur J Agron 116:126069. https://doi.org/10.1016/j.eja.2020.126069
Cai J, Xu K, Zhu Y, Hu F, Li L (2020) Prediction and analysis of net ecosystem carbon exchange based on gradient boosting regression and random forest. Appl Energy 262:114566. https://doi.org/10.1016/j.apenergy.2020.114566
Chen F, Jiang X, Tang F, Bian Y (2012) Application of AHP and GIS in evaluation of agricultural soil heavy metals pollution. Environ Pollut Cont 34(7):6–8
Chen S, Lin B, Li Y, Zhou S (2020) Spatial and temporal changes of soil properties and soil fertility evaluation in a large grain-production area of subtropical plain, China. Geoderma 357:113937. https://doi.org/10.1016/j.geoderma.2019.113937
Chen T, Chang Q, Clevers JGPW, Kooistra L (2015) Rapid identification of soil cadmium pollution risk at regional scale based on visible and near-infrared spectroscopy. Environ Pollut 206(2015):217–226. https://doi.org/10.1016/j.envpol.2015.07.009
Crnković DM, Antanasijević DZ, Pocajt VV, Perić-Grujić AA, Antonović D, Ristić MĐ (2016) Unsupervised classification and multi-criteria decision analysis as chemometric tools for the assessment of sediment quality: a case study of the Danube and Sava River. CATENA 144:11–22. https://doi.org/10.1016/j.catena.2016.04.025
Cui Z, Wang Y, Zhao N, Yu R, Xu G, Yu Y (2018) Spatial distribution and risk assessment of heavy metals in paddy soils of Yongshuyu irrigation area from Songhua River Basin. Northeast China Chin Geogr Sci 28(5):797–809. https://doi.org/10.1007/s11769-018-0991-1
Ding S, Su C, Yu J (2011) An optimizing BP neural network algorithm based on genetic algorithm. Artif Intell Rev 36(2):153–162. https://doi.org/10.1007/s10462-011-9208-z
Fan Y, Zhang Y, Chen Z, Wang X, Huang B (2021) Comprehensive assessments of soil fertility and environmental quality in Plastic Greenhouse Production Systems. Geoderma 385:114899. https://doi.org/10.1016/j.geoderma.2020.114899
Fan Y, Zhang Y, Hess F, Huang B, Chen Z (2020) Nutrient balance and soil changes in plastic greenhouse vegetable production. Nutr Cycl Agroecosys 117(1):77–92. https://doi.org/10.1007/s10705-020-10057-x
Fei X, **ao R, Christakos G, Langousis A, Ren Z, Tian Y, Lv X (2019) Comprehensive assessment and source apportionment of heavy metals in Shanghai agricultural soils with different fertility levels. Ecol Indic 106(Nov.):105508.1–105508. 106:105508. https://doi.org/10.1016/j.ecolind.2019.105508
Fernandes MMH, Coelho AP, Fernandes C, da SilvaMF MCCD (2019) Estimation of soil organic matter content by modeling with artificial neural networks. Geoderma 350:46–51. https://doi.org/10.1016/j.geoderma.2019.04.044
Gąsiorek M, Kowalska J, Mazurek R, Pająk M (2017) Comprehensive assessment of heavy metal pollution in topsoil of historical urban park on an example of the Planty Park in Krakow (Poland). Chemosphere 179:148–158. https://doi.org/10.1016/j.chemosphere.2017.03.1064
Guo CX, Shen GX, Huang LH, Qian XY, Xu XH, Massimo P (2009) Control of soil salinization and reduction of N & P loss with drip fertigation in greenhouse. J Agro-Environ Sci 2:287–291
Haque M, Sudhakar KV (2002) ANN back-propagation prediction model for fracture toughness in microalloy steel. Int J Fatigue 24(9):1003–1010. https://doi.org/10.1016/S0142-1123(01)00207-9
Holland JH (1975) Adaptation in natural and artificial systems. The University of Michigan Press, Ann Arbor, MI
Hou Q, Yang Z, Ji J, Yu T, Chen G, Li J, **a X, Zhang M, Yuan X (2014) Annual net input fluxes of heavy metals of the agro-ecosystem in the Yangtze River delta, China. J Geochem Explor 139:68–84. https://doi.org/10.1016/j.gexplo.2013.08.007
Hou YX, Zhao HF, Zhang Z, Wu KN (2018) A novel method for predicting cadmium concentration in rice grain using genetic algorithm and back-propagation neural network based on soil properties. Environ Sci Pollut R 25(35):35682–35692. https://doi.org/10.1007/s11356-018-3458-0
Hu X, Tang S, Cao W, Meng L, Bai J, Gao S, Zeng N, Chang D, Wang X (2015) Effects of plantation and utilization of green manures during the summer fallow season on soil dissolved organic carbon and nitrogen, and inorganic nitrogen in greenhouse. Soil Fertil Sci China 3:21–28
Jia Z, Zhou S, Su Q, Yi H, Wang J (2017) Comparison study on the estimation of the spatial distribution of regional soil metal(loid)s pollution based on Kriging interpolation and BP Neural Network. Int J Env Res Pub He 15(1):34. https://doi.org/10.3390/ijerph15010034
Jiang X, Lu W, Yang Q, Zhao H (2014) Application of support vector machine in soil environmental quality assessment. China Environ Sci 34(5):1229–1235
Karkaj ES, Sepehry A, Barani H, Motamedi J, Shahbzi F (2019) Establishing a suitable soil quality index for semi-arid rangeland ecosystems in northwest of Iran. Soil Sci Plant Nutr 19(3):648–658. https://doi.org/10.1007/s42729-019-00065-47
Li J, Wan X, Liu X, Chen Y, Slaughter LC, Weindorf DC, Dong Y (2019) Changes in soil physical and chemical characteristics in intensively cultivated greenhouse vegetable fields in North China. Soil Tillage Res 195:104366. https://doi.org/10.1016/j.still.2019.104366
Li H, Leng W, Zhou Y, Chen F, **u Z, Yang D (2014) Evaluation models for soil nutrient based on support vector machine and artificial neural networks. Sci World J 2014. https://doi.org/10.1155/2014/478569
Li P, Man X, Zhu D, Jiang T, Tang J, **e H (2020) Comparison of principal component analysis and grey correlation in soil fertility evaluation model
Li P, Zhang TL, Wang XX, Yu DS (2013) Development of biological soil quality indicator system for subtropical China. Soil Tillage Res 126:112–118. https://doi.org/10.1016/j.still.2012.07.011
Liu GS (1996) Soil physical and chemical analysis and description of soil profiles. China Standard Methods Press, Bei**g
Liu Y, Wang H, Zhang H, Liber K (2016) A comprehensive support vector machine-based classification model for soil quality assessment. Soil Tillage Res 155:19–26. https://doi.org/10.1016/j.still.2015.07.006
Liu Z, Zhou W, Shen J, Li S, He P, Liang G (2014) Soil quality assessment of albic soils with different productivities for Eastern China. Soil Tillage Res 140:74–81. https://doi.org/10.1016/j.still.2014.02.010
Lu RK (1999) Analysis methods of soil science and agricultural chemistry. Agriculture Science and Technology Press, Bei**g
Lu W, Luo H, He L, Duan W, Tao Y, Wang X, Li S (2022) Detection of heavy metals in vegetable soil based on THz spectroscopy. Comput Electron Agr 197:106923. https://doi.org/10.1016/j.compag.2022.106923
Ma J, Chen Y, Zhou J, Wang K, Wu J (2020) Soil quality should be accurate evaluated at the beginning of lifecycle after land consolidation for eco-sustainable development on the Loess Plateau. J Cleaner Prod 267:122244. https://doi.org/10.1016/j.jclepro.2020.122244
Ma L, Cheng W, Qi J (2018) Coordinated evaluation and development model of oasis urbanization from the perspective of new urbanization: a case study in Shandan County of Hexi Corridor. China Sustain Cities Soc 39:78–92. https://doi.org/10.1016/j.scs.2018.02.00
Martínez LL, Poleto C (2014) Assessment of diffuse pollution associated with metals in urban sediments using the geoaccumulation index (Igeo). J Soil Sediment 14(7):12511257. https://doi.org/10.1007/s11368-014-0871-y
McCall J (2005) Genetic algorithms for modelling and optimisation. Comput Appl Math 184(1):205–222. https://doi.org/10.1016/j.cam.2004.07.034
Nelson DW, Sommers LE (1982) Total carbon, organic carbon, and organic matter 1. In: A.L. Page (Ed.), Methods of soil analysis. Part 2. Chemical and Microbiological. 9:539–579
Nourzadeh M, Hashemy SM, Rodriguez Martin JA, Bahrami HA, Moshashaei S (2013) Using fuzzy clustering algorithms to describe the distribution of trace elements in arable calcareous soils in Northwest Iran. Arch Agron Soil Sci 59(3):435448. https://doi.org/10.1080/03650340.2011.636356
Page AL, Miller RH, Keeney DR (1982) Methods of soil analysis. Part 2: chemical and microbiological properties. ASA, Madison
Pang X, Dai J, Yu C, Ren T, Liu H, Zhang H, Cao H, Zeng X, Ren W, Wang Z, Zhao X (2019) Soil geochemical reference value of 17 cities in Shandong Province. Shandong Land and Resources 35(1):36–45
Qi H, Wu Z, Zhang L, Li J, Zhou J, Jun Z, Zhu B (2021) Monitoring of peanut leaves chlorophyll content based on drone-based multispectral image feature extraction. Comput Electron Agr 187:106292. https://doi.org/10.1016/j.compag.2021.106292
Qiao YN, Liu HB (2019) Spatial prediction of soil available microelement contents and quantitative analysis of influential factors in farmland. Soils 51(2):399–405
Rumelhart DE, Hinton GE, McClelland JL (1986) A general framework for parallel distributed processing. Cambridge: MIT Press. Chapter 2, Parallel distributed. P46-P76
Sergeev AP, Buevich AG, Baglaeva EM, Shichkin AV (2019) Combining spatial autocorrelation with machine learning increases prediction accuracy of soil heavy metals. CATENA 174:425–435. https://doi.org/10.1016/j.catena.2018.11.037
Shao W, Guan Q, Tan Z, Luo H, Li H, Sun Y, Ma Y (2021) Application of BP - ANN model in evaluation of soil quality in the arid area. Northwest China Soil Tillage Res 208:104907. https://doi.org/10.1016/j.still.2020.104907
Sun J, Pan L, Zhan Y, Lu H, Tsang DCW, Liu W, Wang X, Li X, Zhu L (2016) Contamination of phthalate esters, organochlorine pesticides and polybrominated diphenyl ethers in agricultural soils from the Yangtze River Delta of China. Sci Total Environ 544(2016):670–676. https://doi.org/10.1016/j.scitotenv.2015.12.012
Wang M, Chen H, Zhang W, Wang K (2018) Soil nutrients and stoichiometric ratios as affected by land use and lithology at county scale in a karst area, Southwest China. Sci Total Environ 619:1299–1307. https://doi.org/10.1016/j.scitotenv.2017.11.175
Wang XG, Lü XT, Zhang HY, Dijkstra FA, Jiang YG, Wang XB, Lu JY, Wang ZW, Han XG (2020) Changes in soil C:N: P stoichiometry along an aridity gradient in drylands of Northern China. Geoderma 361:114087. https://doi.org/10.1016/j.geoderma.2019.114087
Wang S, Zhang N, Wu L, Wang Y (2016) Wind speed forecasting based on the hybrid ensemble empirical mode decomposition and GA-BP Neural Network method. Renew Energ 94:629–636. https://doi.org/10.1016/j.renene.2016.03.103
Winter G, Périaux J, Galán M, Cuesta P (1996) Genetic algorithms in engineering and computer science. John Wiley & Sons, Inc. https://doi.org/10.1021/ed042p191
Wu C, Chen Y, Hong X, Liu Z, Peng C (2020) Evaluating soil nutrients of Dacrydium pectinatum in China using machine learning techniques. For Ecosyst 7(1):30. https://doi.org/10.1186/s40663-020-00232-5
Xu Z, Mi W, Mi N, Fan X, Zhou Y, Tian Y (2021) Comprehensive evaluation of soil quality in a desert steppe influenced by industrial activities in Northern China. Sci Rep 11(1):17493. https://doi.org/10.1038/s41598-021-96948-7
Yu X, Efe MO, Kaynak O (2002) A general backpropagation algorithm for feedforward neural networks learning. IEEE Transactions on Neural Networks. Processing: Explorations in the microstructure of cognition. p45–76. 76. https://doi.org/10.1109/72.977323
Yu H, Ni SJ, He ZW, Zhang CJ, Nan X, Kong B, Weng ZY (2014) Analysis of the spatial relationship between heavy metals in soil and human activities based on landscape geochemical interpretation. J Geochem Explor 146:136–148. https://doi.org/10.1016/j.gexplo.2014.08.010
Zhang X, Zhong T, Liu L, Ouyan X (2015) Impact of soil heavy metal pollution on food safety in China. PLoS ONE 10(8):e0135182. https://doi.org/10.1371/journal.pone.0135182
Zhao DM, Wang JJ, Jiang XP, Zhen JN, Miao J, Wang JZ, Wu GF (2022) Reflectance spectroscopy for assessing heavy metal pollution indices in mangrove sediments using XGBOOST method and physicochemical properties. CATENA 211:105967. https://doi.org/10.1016/j.catena.2021.105967
Zhao Q, Wang Z, Jiang Q (2007) Applying attribute recognition theoretical model to evaluate soil fertility. AGR SYST 23(3):265–267
Zhao Y, Zhang Z, Li B, Zhao X, Lu J, Tang X (2021) Accurate determination and comprehensive evaluation of heavy metals in different soils from Jilin province in Northeast China. Anal Lett 54(12):1901–1928. https://doi.org/10.1080/00032719.2020.1828908
Zuber SM, Behnke GD, Nafziger ED, Villamil MB (2017) Multivariate assessment of soil quality indicators for crop rotation and tillage in Illinois. Soil Tillage Res 174:147–155. https://doi.org/10.1016/j.still.2017.07.007
Funding
This research was funded by Top Talents Program for One Case One Discussion of Shandong Province, Academy of Ecological Unmanned Farm (2019ZBXC200), and Zibo School-City Integration Development Project (2019ZBXC053, 2019ZBXC143).
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Sun, Y., Zhang, J., Bai, J. et al. Comprehensive assessment of soil quality in greenhouse agriculture based on genetic algorithm and neural network. J Soils Sediments 24, 1302–1315 (2024). https://doi.org/10.1007/s11368-023-03706-5
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DOI: https://doi.org/10.1007/s11368-023-03706-5