Analyzing and Predicting the Agronomic Effectiveness of Fertilizers Derived from Food Waste Using Data-Driven Models
Source abstract
Abstract
This study evaluates and estimates the agronomic effectiveness of food waste-derived fertilizers by analyzing plant yield and the internal efficiency of nitrogen utilization (IENU) via statistical and machine learning models. A dataset of 448 cases from various food waste treatments gathered from our experiments and the existing literature was analyzed. Plant yield and IENU exhibited substantial variability, averaging 2268 ± 3099 kg/ha and 32.3 ± 92.5 kg N/ha, respectively. Ryegrass dominated (73.77%), followed by unspecified grass (10.76%), oats (4.87%), and lettuce (2.02%). Correlation analysis revealed that decomposition duration positively influenced plant yield and IENU (r = 0.42 and 0.44), while temperature and volatile solids had negative correlations. Machine learning models outperformed linear regression in predicting plant yield and IENU, especially after preprocessing to remove missing values and outliers. Random Forest and Cubist models showed strong generalization with high R2 (0.79–0.83) for plant yield, while Cubist predicted IENU well in testing, with RMSE = 3.83 and R2 = 0.78. These findings highlight machine learning’s ability to analyze complex datasets, improve agricultural decision-making, and optimize food waste utilization.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Food Waste Reduction and Sustainability · Agriculture Sustainability and Environmental Impact
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Air and Water Purification Department, The Institute of Fluid-Flow Machinery Polish Academy of Sciences, Fiszera 14 St., 80-231 Gdansk, Poland
Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
Faculty of Environmental and Chemical Engineering, Duy Tan University, Da Nang 550000, Vietnam
Thermodynamics Department, The Institute of Fluid-Flow Machinery Polish Academy of Sciences, Fiszera 14 St., 80-231 Gdansk, Poland
Funding
Polish National Agency for Academic Exchange
References (76)
Show source-provided references
1. Álvarez Salas, M., Sica, P., Rydgård, M., Sitzmann, T.J., Nyang’au, J.O., El Mahdi, J., Moshkin, E., de Castro e Silva, H.L., Chrysanthopoulos, S., and Kopp, C. (2024). Current challenges on the widespread adoption of new bio-based fertilizers: Insights to move forward toward more circular food systems. Front. Sustain. Food Syst., 8.10.3389/fsufs.2024.1386680 ↗
2. Steffens, M., and Bünemann, E.K. (2025). Quality of bio-based fertilizers is decisive for improving soil quality in Europe—A meta-analysis. Soil Use Manag., 41.10.1111/sum.70012 ↗
3. O’Connor, J., Hoang, S.A., Bradney, L., Rinklebe, J., Kirkham, M.B., and Bolan, N.S. (2022). Value of dehydrated food waste fertiliser products in increasing soil health and crop productivity. Environ. Res., 204.10.1016/j.envres.2021.111927 ↗
4. Cheong, J.C., Lee, J.T.E., Lim, J.W., Song, S., Tan, J.K.N., Chiam, Z.Y., Yap, K.Y., Lim, E.Y., Zhang, J., and Tan, H.T.W. (2020). Closing the food waste loop: Food waste anaerobic digestate as fertilizer for the cultivation of the leafy vegetable, xiao bai cai (Brassica rapa). Sci. Total Environ., 715.10.1016/j.scitotenv.2020.136789 ↗
5. Gao, S., Lu, D., Qian, T., and Zhou, Y. (2021). Thermal hydrolyzed food waste liquor as liquid organic fertilizer. Sci. Total Environ., 775.10.1016/j.scitotenv.2021.145786 ↗
6. The World Counts (2024, December 15). Global Challenges. Available online: https://www.theworldcounts.com/challenges/people-and-poverty/hunger-and-obesity/food-waste-statistics.
7. FAO (2011). “Energy-Smart” Food for People and Climate: Issue Paper, Food and Agriculture Organization of the United Nations.
8. Elgarahy, A.M., Eloffy, M.G., Alengebawy, A., El-Sherif, D.M., Gaballah, M.S., Elwakeel, K.Z., and El-Qelish, M. (2023). Sustainable management of food waste; pre-treatment strategies, techno-economic assessment, bibliometric analysis, and potential utilizations: A systematic review. Environ. Res., 225.10.1016/j.envres.2023.115558 ↗
9. Razouk, A., Tiganescu, E., von Glahn, A.J., Abdin, A.Y., Nasim, M.J., and Jacob, C. (2024). The future in the litter bin—Bioconversion of food waste as driver of a circular bioeconomy. Front. Nutr., 11.10.3389/fnut.2024.1325190 ↗
10. Food waste anaerobic biogas slurry as fertilizer: Potential salinization on different soil layer and effect on rhizobacteria community10.1016/j.wasman.2022.04.003 ↗
11. Food-waste anaerobic digestate as a fertilizer: The agronomic properties of untreated digestate and biochar-filtered digestate residue10.1016/j.wasman.2021.10.011 ↗
12. Recycling of food waste to produce chicken feed and liquid fertiliser10.1016/j.wasman.2021.06.016 ↗
13. Kuligowski, K., Konkol, I., Świerczek, L., Chojnacka, K., Cenian, A., and Szufa, S. (2023). Evaluation of Kitchen Waste Recycling as Organic N-Fertiliser for Sustainable Agriculture under Cool and Warm Seasons. Sustainability, 15.10.3390/su15107997 ↗
14. Yang, X., Nguyen, X.C., Tran, Q.B., Huyen Nguyen, T.T., Ge, S., Nguyen, D.D., Nguyen, V.T., Le, P.C., Rene, E.R., and Singh, P. (2022). Machine learning-assisted evaluation of potential biochars for pharmaceutical removal from water. Environ. Res., 214.10.1016/j.envres.2022.113953 ↗
15. Practical application of machine learning for organic matter and harmful algal blooms in freshwater systems: A review10.1080/10643389.2023.2285691 ↗
16. Predictive Capability of Phosphate Recovery from Wastewater Using a Rough Set Machine Learning Model10.1021/acsestengg.4c00255 ↗
17. Nguyen, X.C., Ly, Q.V., Peng, W., Nguyen, V.H., Nguyen, D.D., Tran, Q.B., Huyen Nguyen, T.T., Sonne, C., Lam, S.S., and Ngo, H.H. (2021). Vertical flow constructed wetlands using expanded clay and biochar for wastewater remediation: A comparative study and prediction of effluents using machine learning. J. Hazard. Mater., 413.10.1016/j.jhazmat.2021.125426 ↗
18. Correlation between Grain Yield and Fertilizer Use Based on Back Propagation Neural Network
19. Optimization of maize planting density and fertilizer application rate based on BP neural network
20. Meng, L., Liu, H., Ustin, S.L., and Zhang, X. (2021). Predicting maize yield at the plot scale of different fertilizer systems by multi-source data and machine learning methods. Remote Sens., 13.10.3390/rs13183760 ↗
21. Thai, T.H., Omari, R.A., Barkusky, D., and Bellingrath-Kimura, S.D. (2020). Statistical analysis versus the m5p machine learning algorithm to analyze the yield of winter wheat in a long-term fertilizer experiment. Agronomy, 10.10.3390/agronomy10111779 ↗
22. Internal efficiency of nutrient utilization: What is it and how to measure it during vegetative plant growth?10.1093/jxb/erv162 ↗
23. Assessing internal crop nitrogen use efficiency in high-yielding irrigated cotton10.1007/s10705-010-9418-9 ↗
24. Kuligowski, K., Konkol, I., Świerczek, L., Woźniak, A., and Cenian, A. (2025). Conversion of Kitchen Waste into Sustainable Fertilizers: Comparative Effectiveness of Biological, Microbial, and Thermal Treatments in a Ryegrass Growth Trial. Appl. Sci., 15.10.3390/app15105281 ↗
25. Effect of food waste compost on microbial population, soil enzyme activity and lettuce growth10.1016/j.biortech.2003.10.009 ↗
26. Agronomic characteristics of five different urban waste digestates10.1016/j.jenvman.2016.01.001 ↗
27. Accumulation of microelements in oat biomass and their availability in soil fertilised with compost of plant wastes
28. Effects of compost stability on plant growth, microbiological parameters and nitrogen availability in media containing mixed garden-waste compost10.1016/0960-8524(95)00138-7 ↗
29. Potential mineralization of nitrogen from organic wastes to ryegrass and wheat crops10.1016/j.biortech.2006.07.014 ↗
30. Fertilization Value of Green Waste Compost from Krakow
31. The Effect of Fertilization on Yielding and Nitrogen Content in Tall Oat Grass
32. The Sequent Impact of Organic Wastes on Yielding and Chemical Composition of Winter Whea
33. Martyniak, D., Prokopiuk, K., Żurek, G., and Rybka, K. (2022). Measuring Fluorescence as a Means to Evaluate the Physiological Reaction to Growth Retardant Applied to Manage Turf. Agronomy, 12.10.3390/agronomy12081776 ↗
34. Jasiewicz, C., and Antonkiewicz, J. The Effect of a Dose and Kind of Fertilizer on Maize Nitrogen Concentration. Proceedings of the Environmental Pollution by Nitrogen, Olecko, Poland. publication in Polish.
35. Jamison, J., Khanal, S.K., Nguyen, N.H., and Deenik, J.L. (2021). Assessing the Effects of Digestates and Combinations of Digestates and Fertilizer on Yield and Nutrient Use of Brassica juncea (Kai Choy). Agronomy, 11.10.3390/agronomy11030509 ↗
36. Food Waste Compost Effects on Fertilizer Nitrogen Efficiency, Available Nitrogen, and Tall Fescue Yield10.2136/sssaj2002.1540a ↗
37. Effects of food waste digested materials on Lactuva sativa growth and soil composition10.1007/s13762-022-04604-y ↗
38. Combinations of cover crop mixtures and bio-waste composts enhance biomass production and nutrients accumulation: A greenhouse study10.1017/S1742170515000423 ↗
39. Sung, J., Kim, W., Oh, T.-K., and So, Y.-S. (2023). Nitrogen (N) use efficiency and yield in rice under varying types and rates of N source: Chemical fertilizer, livestock manure compost and food waste-livestock manure compost. Appl. Biol. Chem., 66.10.1186/s13765-022-00766-y ↗
40. Dragicevic, I., Sogn, T.A., and Eich-Greatorex, S. (2018). Recycling of Biogas Digestates in Crop Production—Soil and Plant Trace Metal Content and Variability. Front. Sustain. Food Syst., 2.10.3389/fsufs.2018.00045 ↗
41. Acidulocompost, a food waste compost with thermophilic lactic acid fermentation: Its effects on potato production and weed growth10.1080/1343943X.2015.1128092 ↗
42. Agro-chemical assessment of fertilizer value of composts of various origin
43. Greedy function approximation: A gradient boosting machine10.1214/aos/1013203451 ↗
44. Du, Z., Yang, L., Zhang, D., Cui, T., He, X., Xiao, T., Xie, C., and Li, H. (2022). Corn variable-rate seeding decision based on gradient boosting decision tree model. Comput. Electron. Agric., 198.10.1016/j.compag.2022.107025 ↗
45. Quinlan, J.R. (1993, January 13–19). Combining instance-based and model-based learning. Proceedings of the Tenth International Conference on International Conference on Machine Learning, Amherst, MA, USA.10.1016/B978-1-55860-307-3.50037-X ↗
46. Quinlan, J.R. (1992, January 16–18). Learning with continuous classes. Proceedings of the 5th Australian Joint Conference on Artificial Intelligence, Hobart, Tasmania.
47. Kuhn, M., and Johnson, K. (2013). Applied Predictive Modeling, Springer.10.1007/978-1-4614-6849-3 ↗
48. Nguyen, X.C., Nguyen, T.T.H., La, D.D., Kumar, G., Rene, E.R., Nguyen, D.D., Chang, S.W., Chung, W.J., Nguyen, X.H., and Nguyen, V.K. (2021). Development of machine learning—Based models to forecast solid waste generation in residential areas: A case study from Vietnam. Resour. Conserv. Recycl., 167.10.1016/j.resconrec.2020.105381 ↗
49. Chen, T., and Guestrin, C. (2016, January 13–17). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.10.1145/2939672.2939785 ↗
50. Naghdyzadegan Jahromi, M., Zand-Parsa, S., Razzaghi, F., Jamshidi, S., Didari, S., Doosthosseini, A., and Pourghasemi, H. (2023). Developing machine learning models for wheat yield prediction using ground-based data, satellite-based actual evapotranspiration and vegetation indices. Eur. J. Agron., 146.10.1016/j.eja.2023.126820 ↗
51. Huber, F., Yushchenko, A., Stratmann, B., and Steinhage, V. (2022). Extreme Gradient Boosting for yield estimation compared with Deep Learning approaches. Comput. Electron. Agric., 202.10.1016/j.compag.2022.107346 ↗
52. Islam, A., Khair, I., Ifty, R.A., Hossain, S., Arefin, M.N., and Abid, F.B.A. (2022, January 26–27). Bangladeshi Agricultural Product Forecasting Approach Using Permutation Driven Machine Learning Algorithm. Proceedings of the 2022 International Conference on Recent Progresses in Science, Engineering and Technology (ICRPSET), Rajshahi, Bangladesh.10.1109/ICRPSET57982.2022.10188538 ↗
53. Random Forests10.1023/A:1010933404324 ↗
54. Random forest in remote sensing: A review of applications and future directions10.1016/j.isprsjprs.2016.01.011 ↗
55. Parzen, E., Tanabe, K., and Kitagawa, G. (1998). A New Look at the Statistical Model Identification. Selected Papers of Hirotugu Akaike, Springer.10.1007/978-1-4612-1694-0 ↗
56. Montgomery, D.C., Peck, E.A., and Vining, G.G. (2021). Introduction to Linear Regression Analysis, John Wiley & Sons.
57. Nguyen, X.C., Nguyen, T.P., Lam, V.S., Le, P.C., Vo, T.D.H., Hoang, T.H.T., Chung, W.J., Chang, S.W., and Nguyen, D.D. (2024). Estimating ammonium changes in pilot and full-scale constructed wetlands using kinetic model, linear regression, and machine learning. Sci. Total Environ., 907.10.1016/j.scitotenv.2023.168142 ↗
58. Composting of animal manures and chemical criteria for compost maturity assessment. A review10.1016/j.biortech.2008.11.027 ↗
59. Gianico, A., Gallipoli, A., Montecchio, D., and Mininni, G. (2021). Land Application of Biosolids in Europe: Possibilities, Con-Straints and Future Perspectives. Water, 13.10.3390/w13010103 ↗
60. Nutrient transformations during composting of pig manure with bentonite10.1016/j.biortech.2012.06.065 ↗
61. Wheat yield prediction using machine learning and advanced sensing techniques10.1016/j.compag.2015.11.018 ↗
62. A Comprehensive Review of Crop Yield Prediction Using Machine Learning Approaches with Special Emphasis on Palm Oil Yield Prediction10.1109/ACCESS.2021.3075159 ↗
63. Seed yield prediction of sesame using artificial neural network10.1016/j.eja.2015.04.010 ↗
64. Nonlinear parametric modelling to study how soil properties affect crop yields and NDVI10.1016/j.compag.2017.04.016 ↗
65. Santos, L.B., Gentry, D., Tryforos, A., Fultz, L., Beasley, J., and Gentimis, T. (2024). Soybean yield prediction using machine learning algorithms under a cover crop management system. Smart Agric. Technol., 8.10.1016/j.atech.2024.100442 ↗
66. van Klompenburg, T., Kassahun, A., and Catal, C. (2020). Crop yield prediction using machine learning: A systematic literature review. Comput. Electron. Agric., 177.10.1016/j.compag.2020.105709 ↗
67. Lischeid, G., Webber, H., Sommer, M., Nendel, C., and Ewert, F. (2022). Machine learning in crop yield modelling: A powerful tool, but no surrogate for science. Agric. For. Meteorol., 312.10.1016/j.agrformet.2021.108698 ↗
68. Cao, J., Zhang, Z., Luo, Y., Zhang, L., Zhang, J., Li, Z., and Tao, F. (2021). Wheat yield predictions at a county and field scale with deep learning, machine learning, and google earth engine. Eur. J. Agron., 123.10.1016/j.eja.2020.126204 ↗
69. Sharma, V., Honkavaara, E., Hayden, M., and Kant, S. (2024). UAV Remote Sensing Phenotyping of Wheat Collection for Response to Water Stress and Yield Prediction Using Machine Learning. Plant Stress, 12.10.1016/j.stress.2024.100464 ↗
70. Liu, Y., Heuvelink, G.B.M., Bai, Z., He, P., Xu, X., Ma, J., and Masiliūnas, D. (2020). Space-time statistical analysis and modelling of nitrogen use efficiency indicators at provincial scale in China. Eur. J. Agron., 115.10.1016/j.eja.2020.126032 ↗
71. Liu, J., Zhu, Y., Tao, X., Chen, X., and Li, X. (2022). Rapid prediction of winter wheat yield and nitrogen use efficiency using consumer-grade unmanned aerial vehicles multispectral imagery. Front. Plant Sci., 13.10.3389/fpls.2022.1032170 ↗
72. Li, Y., Cui, S., Zhang, Z., Zhuang, K., Wang, Z., and Zhang, Q. (2020). Determining effects of water and nitrogen input on maize (Zea mays) yield, water- and nitrogen-use efficiency: A global synthesis. Sci. Rep., 10.10.1038/s41598-020-66613-6 ↗
73. Correndo, A.A., Rotundo, J.L., Tremblay, N., Archontoulis, S., Coulter, J.A., Ruiz-Diaz, D., Franzen, D., Franzluebbers, A.J., Nafziger, E., and Schwalbert, R. (2021). Assessing the uncertainty of maize yield without nitrogen fertilization. Field Crops Res., 260.10.1016/j.fcr.2020.107985 ↗
74. Chen, L., He, P., Zhang, H., Peng, W., Qiu, J., and Lü, F. (2024). Applications of machine learning tools for biological treatment of organic wastes: Perspectives and challenges. Circ. Econ., 3.10.1016/j.cec.2024.100088 ↗
75. Durai, S.K.S., and Shamili, M.D. (2022). Smart farming using Machine Learning and Deep Learning techniques. Decis. Anal. J., 3.10.1016/j.dajour.2022.100041 ↗
76. Machine learning for sustainable organic waste treatment: A critical review10.1038/s44296-024-00009-9 ↗