Indexed By
SJR Rank

SCImago Journal & Country Rank

Article Tools
Email this article (Login required)
Email the author (Login required)
About The Authors

Heru Bagus Pulunggono
Department of Soil Science and Land Resource, Faculty of Agriculture, IPB University
Indonesia

Vira Widya Kartika
Bachelor Program of Soil Science and Land Resource Department, IPB University, Bogor 16680
Indonesia

Desi Nadalia
Department of Soil Science and Land Resource, Faculty of Agriculture, IPB University
Indonesia

Lina Lathifah Nurazizah
Bachelor Program of Agronomy and Horticulture Department, IPB University, Bogor 16680
Indonesia

Moh Zulfajrin
Bachelor Program of Soil Science and Land Resource Department, IPB University, Bogor 16680

User
Author Guidelines

Visitor Statistic

Evaluating the changes of Ultisol chemical properties and fertility characteristics due to animal manure amelioration

Heru Bagus Pulunggono, Vira Widya Kartika, Desi Nadalia, Lina Lathifah Nurazizah, Moh Zulfajrin
  J. Degrade. Min. Land Manage. , pp. 3545-3560  
Viewed : 108 times

Abstract


Amending Ultisols using organic matter encourages a paramount improvement in its chemistry and fertility characteristics. This study was aimed to evaluate the changes in soil chemical properties due to the animal manure amelioration in Ultisol in the Jasinga, Bogor, West Java, using classical and advanced statistical methods. Composite soil samples were collected then incubated with three types of animal manure (cow, chicken, and goat) and four rate levels (0, 2.5, 5, and 7.5% of dry weight). The dynamics of eleven soil variables (pH, organic C, total N, cation exchange complex/CEC, base saturation/BS, and exchangeable Al, H, Ca, Mg, K, and Na) were observed four times (0, 2, 4, and 6 weeks). Basic cation saturation ratio/BCSR and sufficiency level of available nutrients/SLAN soil fertility approaches were applied. Modeling comparison was done among multiple linear regression/MLR, machine learning/ML (tree regression/TR, random forest/RF, gradient boosting machine/GBM), and deep learning/DL (multilayer perceptron/MLP). Most of the soil chemical and fertility parameters exhibited strong relation among three applied factors. Generally, their values failed to reach the BCSR’s ideal soil and national SLAN’s sufficiency criteria; oppositely, they were categorized as sufficient based on the global SLAN approach. Multivariate analysis revealed the similarity among manure type and rate, whereas incubation time showed the opposite trend. MLR usage was convenient in modeling BS, pH H2O, and Al saturation. Meanwhile, CEC modeling requires more sophisticated methods. This study highlighted the possible improvement of Ultisol chemical properties and fertility characteristics by amending it with a higher rate and low C/N ratio of animal manure, and using ML to capture non-linear relationships in soil.

Keywords


BCSR-SLAN; incubation time ;machine learning; rate; soil amendment

Full Text:

PDF

References


Adam, M., Ibrahim, I., Sulieman, M., Zeraatpisheh, M., Mishra, G. and Brevik, E.C. 2021. Predicting soil cation exchange capacity in Entisols with divergent textural classes: the case of Northern Sudan soils. Air, Soil and Water Research. 14:1-14, doi:10.1177/11786221211042381.

Aitken, R.L. and Scott, B.J. 1999. Magnesium. In: Peverill, K.I., Sparrow, L.A., Reuter, D.J. (Eds.) Soil Analysis: An Interpretation Manual. Melbourne (AU): CSIRO Publishing. pp.255–262. doi:10.1071/9780643101357.

Alade, A.A., Azeez, J.O., Ajiboye, G.A., Adewuyi, S., Olowoboko, T.B. and Hussein, S. M. 2019. Influence of animal manure mixture on soil nitrogen indices and maize growth. Russian Agricultural Sciences 45(2):175-185, doi:10.3103/s1068367419020022.

Albrecht, W.A. 1975. The Albrecht papers. Vol. 1: Foundation concepts. Acres USA, Kansas City.

Anda, M. 2012. Cation imbalance and heavy metal content of seven Indonesian soils as affected by elemental compositions of parent rocks. Geoderma 189-190:388-396, doi:10.1016/j.geoderma.2012.05.009.

Armanto, M.E. 2019. Soil variability and sugarcane (Saccharum officinarum L.) biomass along ultisol toposequences. Journal of Ecological Engineering 20(7):196-204, doi:10.12911/22998993/109856.

Beysolow II, T. 2017. Introduction to Deep Learning. In: Introduction to Deep Learning Using R. Berkeley(US):Apress, doi:10.1007/978-1-4842-2734-31.

Bojórquez-Quintal, E., Escalante-Magaña, C., Echevarría-Machado, I. and Martínez-Estévez, M. 2017 Aluminum, a friend or foe of higher plants in acid soils. Frontier in Plant Science 8:1767, doi:10.3389/fpls.2017.01767.

Bonomelli, C., Gil, P.M. and Schaffer B. 2019. Effect of soil type on calcium absorption and partitioning in young avocado (Persea americana Mill.) trees. Agronomy 9(12):1-11, doi:10.3390/agronomy9120837.

Breiman, L. 2001. Random forests. Machine Learning 45:5–32, doi:10.1023/A:1010933404324.

Breiman, L., Friedman, J.H., Olshen, R.A., and Stone. C.J. 1984. Classification And Regression Trees. Boca Raton (US):Routledge. doi:10.1201/9781315139470.

Brock, C., Jackson-Smith, D., Culman, S., Doohan, D. and Herms, C. 2020. Soil balancing within organic farming: negotiating meanings and boundaries in an alternative agricultural community of practice. Agriculture and Human Values 38:449-465, doi:10.1007/s10460-020-10165-y.

Bruce, R.C. 1999. Calcium. In: Peverill, K.I., Sparrow, L.A., Reuter, D.J. (Eds.) Soil Analysis: An Interpretation Manual. Melbourne (AU): CSIRO Publishing. pp.247-254, doi:10.1071/9780643101357.

Ch’ng, H.Y., Ahmed, O.H. and Majid, N.M.A. 2015. Improving phosphorus availability, nutrient uptake and dry matter production of Zea mays L. on a tropical acid soil using poultry manure biochar and pineapple leaves compost. Experimental Agriculture 52(03):447-465, doi:10.1017/s0014479715000204.

Chaganti, V.N. and Culman, S.W. 2017. Historical perspective of soil balancing theory and identifying knowledge gaps: a review. Crop, Forage and Turfgrass Management 3(1):1-7, doi:10.2134/cftm2016.10.0072.

Chaganti, V.N., Culman, S.W., Herms, C., Sprunger, C.D., Brock, C., Soto, A.L. and Doohan, D. 2021. Base cation saturation ratios, soil health, and yield in organic field crops. Agronomy Journal 113(5):4190-4200, doi:10.1002/agj2.20785.

Chen, T. and Guestrin, C. 2016. XGBoost: Reliable large-scale tree boosting system. In: M. Shah, A. Smola, C. Aggarwal, D. Shen, and R. Rastogi (Eds.) Proceedings of the 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA (pp. 785-794).

Culman, S.W., Brock, C., Doohan, D., Jackson-Smith, D., Herms, C., Chaganti, V.N., Kleinhenz, M., Sprunger, C.D. and Spargo, J. 2021. Base cation saturation ratios vs. sufficiency level of nutrients: a false dichotomy in practice. Agronomy Journal 113(6): 5623-5634, doi:10.1002/agj2.20787.

Dauer, J.M. and Perakis, S.S. 2014. Calcium oxalate contribution to calcium cycling in forests of contrasting nutrient status. Forest Ecology and Management 334: 64-73. doi:10.1016/j.foreco.2014.08.029.

Dong, W., Zhang, X., Wang, H., Dai, X., Sun, X., Qiu, W. and Yang, F. 2012. Effect of different fertilizer application on the soil fertility of paddy soils in red soil region of Southern China. PLoS ONE 7(9):1-9, doi:10.1371/journal.pone.0044504.

Eviati and Sulaeman. 2009. Technical Guidelines for Soil, Plant, and Fertilizer Chemical Analysis. Second Edition. Bogor (ID): Indonesian Center for Agricultural Land Resources Research and Development (in Indonesian).

Feng, S., Zhou, H. and Dong, H. 2019. Using deep neural network with small dataset to predict material defects. Materials and Design 162:300-310, doi:10.1016/j.matdes.2018.11.060.

Ferreira, G.W.D., Soares, E.M.B., Oliveira, F.C.C., Silva, I.R., Dungait, J.A.J., Souza, I.F. and Vergütz, L. 2016. Nutrient release from decomposing Eucalyptus harvest residues following simulated management practices in multiple sites in Brazil. Forest Ecology and Management 370:1-11, doi:10.1016/j.foreco.2016.03.047.

Friedman, J.H. 2001. Greedy function approximation: a gradient boosting machine. The Annals of Statistics. 29(5):1189-1232, doi:10.1214/aos/1013203451.

Fritsch, S., Guenther, F., Wright, M.N., Suling, M. and Mueller, S.M. 2019. Package ‘neuralnet’. Training of Neural Networks. Retrieved from https://cran.r-project.org/web/packages/neuralnet/index.html.

Gouley, R.J.C. 1999. Potassium. In: Peverill, K.I., Sparrow, L.A., and Reuter, D.J. (Eds.) Soil Analysis: An Interpretation Manual. Melbourne (AU): CSIRO Publishing. pp.229–245, doi:10.1071/9780643101357.

Greenwell, B., Boehmke, B. and Cunningham, J. 2020. Package ‘gbm’. Generalized Boosted Regression Models. Retrieved from https://cran.r-project.org/web/packages/gbm/index.html.

Ho, T.K. 1998. The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(8):832-844, doi:10.1109/34.709601.

Huang, J., Yu, Z., Gao, H., Yan, X., Chang, J., Wang, C., Hu, J. and Zhang, L. 2017. Chemical structures and characteristics of animal manures and composts during composting and assessment of maturity indices. PLoS ONE 12(6):e0178110, doi:10.1371/journal.pone.0178110.

Husson, F., Josse, J., Le, S. and Mazet, J. 2020. Package ‘FactoMineR’. Multivariate Exploratory Data Analysis and Data Mining. Retrieved from https://cran.r-project.org/web/packages/FactoMineR/index.html.

Jaiswal, S.K., Naamala, J. and Dakora, F.D. 2018. Nature and mechanisms of aluminium toxicity, tolerance and amelioration in symbiotic legumes and rhizobia. Biology and Fertility of Soils 54(3):309-318, doi:10.1007%2Fs00374-018-1262-0.

Kasno, A., Setyorini, D. and Widowati, L.R. 2021. Cations ratio and its relationship with other soil nutrients of Java intensified lowland rice. IOP Conference Series: Earth and Environmental Science 648:1-9, doi:10.1088/1755-1315/648/1/012015.

Kassambara, A. and Mundt, F. 2021. Package: 'factoextra'. Extract and Visualize the Results of Multivariate Data Analyses. Retrieved from https://cran.r-project.org/web/packages/factoextra/index.html.

Kopittke, P.M. and Menzies, N.W. 2007. A review of the use of the basic cation saturation ratio and the “ideal” soil. Soil Science Society of America Journal 71: 259-265, doi:10.2136/sssaj2006.0186.

Kuhn, M., Wing, J., Weston, S., Williams, A., Keefer, C., Engelhardt, A., Cooper T., Mayer Z., Kenkel, B., R Core Team, Benesty, M., Lescarbeau, R., Ziem A., Scrucca, L., Tang, Y., Candan, C. and Hunt, T. 2021. Package ‘caret’. Classification and Regression Training. Retrieved from https://cran.r-project.org/web/packages/caret/.

Liakos, K., Busato, P., Moshou, D., Pearson, S. and Bochtis, D. 2018. Machine learning in agriculture: a review. Sensors 18(8):2674. http://dx.doi.org/10.3390/s18082674.

Liaw, A. and Wiener, M. 2018. Package ‘randomForest’. Breiman and Cutler’s Random Forests for Classification and Regression. Retrieved from https://cran.r-project.org/web/packages/rpart/index.html.

Liebhardt, W.C. 1981. The basic cation saturation concept and lime and potassium recommendations on Delaware’s coastal plain soils. Soil Science Society of America Journal 45(3):544-549, doi:10.2136/sssaj1981.03615995004500030022x.

Liu, H., Xu, W., Li, J., Yu, Z., Zeng, Q., Tan, W. and Mi, W. 2021. Short‐term effect of manure and straw application on bacterial and fungal community compositions and abundances in an acidic paddy soil. Journal of Soils and Sediments 21: 3057-3071, doi:10.1007/s11368-021-03005-x.

Manikandan, M., Chun, S., Kazibwe, Z., Gopal, J., Singh, U.B. and Oh, J.-W. 2020. Phenomenal bombardment of antibiotic in poultry: Contemplating the environmental repercussions. International Journal of Environmental Research and Public Health 17(14):5053, doi:10.3390/ijerph17145053.

Manyi-Loh, C., Mamphweli, S.N., Meyer, E.L., Makaka, G., Simon, M. and Okoh, A.I. 2016. An overview of the control of bacterial pathogens in cattle manure. International Journal of Environmental Research and Public Health 13(9):843, doi:10.3390/ijerph13090843.

Masmoudi, S., Magdich, S., Rigane, H., Medhioub, K., Rebai, A. and Ammar, E. 2018. Effects of compost and manure application rate on the soil physico-chemical layers properties and plant productivity. Waste and Biomass Valorization 11:1883-1894, doi:10.1007/s12649-018-0543-z.

Meena, A. 2021. Nutrients balance approach with emphasis on base cations and ratios concepts as a decisions-support tool in optimizing fertilizers use. African Journal of Agricultural Research 17(4):629-641, doi:10.5897/AJAR2021.15463.

Montgomery, D.C., Peck, E.A. and Vining G.G. 2012. Introduction to Linear Regression Analysis, 5th Edition. Edison(US):John Wiley and Sons, Inc.

Muktamar, Z., Lifia. and Adiprasetyo, T. 2020. Phosphorus availability as affected by the application of organic amendments in Ultisols. Journal of Soil Science and Agroclimatology 17(1):16-22, doi:10.20961/stjssa.v17i1.41282.

Mutammimah, U., Minardi, S. and Suryono, S. 2020. Organic amendments effect on the soil chemical properties of marginal land and soybean yield. Journal of Degraded and Mining Lands Management 7(4):2263-2268, doi:10.15243/jdmlm.2020.074.2263.

Ngo, P.-T., Rumpel, C., Doan Thu, T., Henry-des-Tureaux, T., Dang, D.-K. and Jouquet, P. 2014. Use of organic substrates for increasing soil organic matter quality and carbon sequestration of tropical degraded soil: a 3-year mesocosms experiment. Carbon Management 5(2):155-168, doi:10.1080/17583004.2014.912868.

Nielsen, D. 2016. Tree Boosting With XGBoost - Why Does XGBoost Win “Every” Machine Learning Competition? Norwegian University of Science and Technology. Master Theses.

Olden, J.D., Joy, M.K. and Death, R.G. 2004. An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data. Ecological Modelling 178(3-4):389-397, doi:10.1016/j.ecolmodel.2004.03.013.

Padarian, J., Minasny, B. and McBratney, A. B. 2020. Machine learning and soil sciences: a review aided by machine learning tools. Soil 6(1):35-52, doi:10.5194/soil-6-35-2020.

Pandey, B., Singh, S., Roy, L.B., Shekhar, S., Singh, R.K., Prasad, B. and Singh, K.K.K. 2021. Phytostabilization of coal mine overburden waste, exploiting the phytoremedial efficacy of lemongrass under varying level of cow dung manure. Ecotoxicology and Enviromental Safety 208:111757, doi:10.1016/j.ecoenv.2020.111757.

Peng, X., Zhu, Q.H., Xie, Z.B., Darboux, F. and Holden, N.M. 2016. The impact of manure, straw and biochar amendments on aggregation and erosion in a hillslope Ultisol. Catena 138:30-37, doi:10.1016/j.catena.2015.11.008.

Puslitanak. 2000. Indonesia's Land Resources and Management. Center for Soil and Agroclimate Research. Agricultural Research and Development Agency. Department of Agriculture. Bogor. Pp 169-172 (in Indonesian).

R Core Team 2021. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.

Ranjbar, F. and Jalali, M. 2012. Calcium, magnesium, sodium, and potassium release during decomposition of some organic residues. Communications in Soil Science and Plant Analysis 43(4):645-659, doi:10.1080/00103624.2012.644005.

Rawal, A., Chakraborty, S., Li, B., Lewis, K., Godoy, M., Paulette, L. and Weindorf, D.C. 2019. Determination of base saturation percentage in agricultural soils via portable X-ray fluorescence spectrometer. Geoderma 338:375-382, doi:10.1016/j.geoderma.2018.12.032.

Ridgeway, G. 2020. Generalized Boosted Models: A guide to the gbm package. Retrieved from https://cran.r-project.org/web/gbm/vignettes.

Roobroeck, D., Palm, C.A., Nziguheba, G., Weil, R. and Vanlauwe, B. 2021. Assessing and understanding non-responsiveness of maize and soybean to fertilizer application in African smallholder farms. Agriculture, Ecosystem, and Environment 107165, doi:10.1016/j.agee.2020.107165.

Rossiter, D.G. 2018. Past, present and future of information technology in pedometrics. Geoderma 324:131-137, doi:10.1016/j.geoderma.2018.03.009.

Seyedmohammadi, J., Esmaeelnejad, L. and Ramezanpour, H. 2016. Determination of a suitable model for prediction of soil cation exchange capacity. Modeling Earth Systems and Environment 2(3):156, doi:10.1007/s40808-016-0217-4.

Sharma, A., Weindorf, D. C., Wang, D. and Chakraborty, S. 2015. Characterizing soils via portable X-ray fluorescence spectrometer: 4. Cation exchange capacity (CEC). Geoderma 239-240:130-134, doi:10.1016/j.geoderma.2014.10.001.

Sharma, A., Weindorf, D.C., Man, T., Aldabaa, A.A.A. and Chakraborty, S. 2014. Characterizing soils via portable X-ray fluorescence spectrometer: 3. Soil reaction (pH). Geoderma 232-234:141-147, doi:10.1016/j.geoderma.2014.05.005.

Shi, R., Liu, Z., Li, Y., Jiang, T., Xu, M., Li, J. and Xu, R. 2019. Mechanisms for increasing soil resistance to acidification by long-term manure application. Soil and Tillage Research 185:77-84, doi:10.1016/j.still.2018.09.004.

Singh, S., Tripathi, D.K., Singh, S., Sharma, S., Dubey, N.K., Chauhan, D. K. and Vaculík, M. 2017. Toxicity of aluminium on various levels of plant cells and organism: A review. Environmental and Experimental Botany 137:177-193, doi:10.1016/j.envexpbot.2017.01.005.

Souza, H.A., Parent, S.E., Rozane, D.E., Amorim, D.A., Modesto, V.C., Natale, W. and Parent, L.E. 2016. Guava waste to sustain guava (Psidium gujava) agroecosystem: nutrient “balance” concept. Frontiers in Plant Science 1252(7):1- 13, doi:10.3389/fpls.2016.01252.

Speybroeck, N. 2011. Classification and regression trees. International Journal of Public Health 57(1):243-246, doi:10.1007/s00038-011-0315-z.

Statistics Indonesia of West Java. 2021. West Java Province in Figures 2021. Bandung (ID):Central Bureau of Statistics, https://jabar.bps.go.id (in Indonesian).

Teixeira, A., Pelegrino, M.H.P., Faria, E.M., Silva, S.H.G., Gonçalves, M.G.M., Júnior, F.W.A., Gomide, L., de Pádua Junior, A.L., de Souza, I.A., Chakraborty, S., Weindorf, D.C., Guilherme, L.R.G. and Curi, N. 2020. Tropical soil pH and sorption complex prediction via portable X-ray fluorescence spectrometry. Geoderma 361:114132, doi:10.1016/j.geoderma.2019.114132.

Therneau, T., Atkinson, B. and Ripley, B. 2022. Package ‘rpart’. Recursive Partitioning and Regression Trees. Retrieved from https://cran.r-project.org/web/packages/rpart/index.html.

Wongsaroj, L., Chanabun, R., Tunsakul, N., Prombutara, P., Panha, S. and Somboonna, N. 2021. First reported quantitative microbiota in different livestock manures used as organic fertilizers in the Northeast of Thailand. Scientific Reports 11(1), doi:10.1038/s41598-020-80543-3.

Wright, M.N., Wager, S. and Probst, P. 2021. Package ‘ranger’. A Fast Implementation of Random Forests. Retrieved from https://cran.r-project.org/web/packages/ranger/index.html.

Ye, G., Lin, Y., Liu, D., Chen, Z., Lou, J., Bolan, N., Fan, J. and Ding, W. 2019. Long-term application of manure over plant residues mitigates acidification, builds soil organic carbon and shifts prokaryotic diversity in acidic Utisols. Applied Soil Ecology 133:24-33, doi:10.1016/j.apsoil.2018.09.008.

Zhao, X.Q. and Shen, R.F. 2018. Aluminum–nitrogen interactions in the soil–plant system. Frontier in Plant Science 9:807, doi:10.3389/fpls.2018.00807.

Zhou, H., Peng, X., Perfect, E., Xiao, T. and Peng, G. 2013. Effects of organic and inorganic fertilization on soil aggregation in an ultisol as characterized by synchrotron based X-ray micro-computed tomography. Geoderma 195-196:23-30, doi:10.1016/j.geoderma.2012.11.003.

Zimmer, G.F. 2017. The Biological Farmer: A Complete Guide to the Sustainable and Profitable Biological System of Farming. Austin, TX: Acres U.S.A.


Refbacks

  • There are currently no refbacks.




Copyright (c) 2022 Journal of Degraded and Mining Lands Management

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Indexed By