Simulation and evaluation of tomato growth by AquaCrop model under different agricultural waste materials

Changquan Zhou, Wenju Zhao, Haolin Li, Feng Ma, Keqian Wu

Abstract


This study aimed to enhance the utilization of agricultural waste and identify the most suitable agricultural waste materials for tomato cultivation. It utilized a locally modified substrate labeled as CK, along with five different groups of agricultural waste materials, designated as T1 (organic fertilizer: loessial soil: straw in a ratio of 4:5:1), T2 (organic fertilizer: loessial soil: straw: grains in a ratio of 3:5:1:1), T3 (organic fertilizer: loessial soil: straw: grains in a ratio of 2:5:1:2), T4 (organic fertilizer:loessial soil:straw:grains in a ratio of 1:5:1:3), and T5 ( loessial soil:straw:grains in a ratio of 5:1:4), the AquaCrop model was employed to validate soil water content and tomato growth and yield under these treatments. Furthermore, a multi-objective genetic algorithm was employed to determine the optimal agricultural waste materials that would ensure maximum tomato yield, water use efficiency (WUE), partial factor productivity of fertilizer (PFP) and sugar-acid ratio. The results indicated that the AquaCrop model reasonably simulated volumetric soil water content, tomato canopy cover, and biomass, with root mean square error (RMSE) ranges of 20.0-69.4 mm, 15.2%-25.1%, and 1.093-3.469 t/hm2, respectively. The CK group exhibited an R-squared (R2) value of 0.63 for volumetric soil water contents, while the ratio scenarios showed R2 values exceeding 0.80. The multi-objective genetic optimization algorithm identified T5 as the optimal ratio scenario, resulting in maximum tomato yield, WUE, PFP, and quality. This study offers a theoretical foundation for the efficient utilization of agricultural wastes and the production of high-quality fruits and vegetables.
Keywords: agricultural waste materials, AquaCrop model, NSGA-II algorithm, tomato, quality
DOI: 10.25165/j.ijabe.20241705.8539

Citation: Zhou C Q, Zhao W J, Li H L, Ma F, Wu K Q. Simulation and evaluation of tomato growth by AquaCrop model under different agricultural waste material. Int J Agric & Biol Eng, 2024; 17(5): 112-119.

Keywords


agricultural waste materials, AquaCrop model, NSGA-II algorithm, tomato, quality

Full Text:

PDF

References


Lu Q M, Yan D, Wu P W, Chen L, Yagoub A E A, Ji Q H, et al. Ultrasound-NATDES/DMSO system for corn straw biomass conversion into platform compounds. Renewable Energy, 2022; 190: 675–683.

Zhan X Y, Zhang A P, Zhang Q W. Controlling agricultural non-point source pollution: Thinking and practice in the era of agricultural green high-quality development. Transactions of the CSAE, 2020; 36（20）: 1–7. （in Chinese）

Joseph S, Kammann C I, Shepherd J G, Conte P, Schmidt H-P, Hagemann N, et al. Microstructural and associated chemical changes during the composting of a high temperature biochar: mechanisms for nitrate, phosphate and other nutrient retention and release. Science of the Total Environment, 2018; 618: 1210–1223.

Dadrasnia A, de Bona Muñoz I, Yáñez E H, Lamkaddam I U, Mora M, Ponsá S, et al. Sustainable nutrient recovery from animal manure: A review of current best practice technology and the potential for freeze concentration. Journal of Cleaner Production, 2021; 315: 128106.

Jatav H S, Rajput V D, Minkina T, Singh S K, Chejara S, Gorovtsov A, et al. Sustainable approach and safe use of biochar and its possible consequences. Sustainability, 2021; 13（18）: 10362.

Gustavo Adolfo F-G, Wolf-Anno B, Martin R, Christina S. Co-composting of biochar and nitrogen-poor organic residues: Nitrogen losses and fate of polycyclic aromatic hydrocarbons. Waste Management, 2022; 143: 84–94.

Zhao W J, Ji H, Wang Z J, Fan Y W, Liu Y H. Resisting drought and promoting root ecological raising module and manufacturing method, 2021. AU 2020104391.

Steduto P, Hsiao T C, Raes D, Fereres E. AquaCrop—The FAO crop model to simulate yield response to water: I. Concepts and underlying principles. Agronomy Journal, 2009; 101（3）: 426–437.

Raes D, Steduto P, Hsiao T C, Fereres E. AquaCrop—the FAO crop model to simulate yield response to water: II. Main algorithms and software description. Agronomy Journal, 2009; 101（3）: 438–447.

Du W Y, He X C, Shamaila Z, Hu Z F, Zeng A J, Muller J. Yield and biomass prediction testing of AquaCrop model for winter wheat. Transactions of the CSAM, 2011; 42（4）: 174–174, 183.

Katerji N, Campi P, Mastrorilli M. Productivity, evapotranspiration, and water use efficiency of corn and tomato crops simulated by AquaCrop under contrasting water stress conditions in the Mediterranean region. Agricultural Water Management, 2013; 130: 14–26.

Linker R, Ioslovich I, Sylaios G, Plauborg F, Battilani A. Optimal model-based deficit irrigation scheduling using AquaCrop: A simulation study with cotton, potato and tomato. Agricultural Water Management, 2016; 163: 236–243.

Xu J, Jia K, Zhu J F, Hu M, Wang N, Gao J. Combined application of nitrogen and sulfur improving quality of substrate culture garlic bulbs. Transactions of the CSAE, 2017; 33（4）: 203–208. （in Chinese）

Guo D X, Zhao R H, Xing X G, Ma X Y. Global sensitivity and uncertainty analysis of the AquaCrop model for maize under different irrigation and fertilizer management conditions. Archives of Agronomy and Soil Science, 2020; 66（8）: 1115–1133.

Linker R, Ioslovich I. Assimilation of canopy cover and biomass measurements in the crop model AquaCrop. Biosystems Engineering, 2017; 162: 57–66.

Zhang J P, Li K J, Gao Y, Feng D, Zheng C L, Cao C Y, et al. Evaluation of saline water irrigation on cotton growth and yield using the AquaCrop crop simulation model. Agricultural Water Management, 2022; 261: 107355.

Shao D G, Le Z H, Xu B L, Hu N J, Tian Y N. Optimization of irrigation scheduling for organic rice based on AquaCrop. Transactions of the CSAE, 2018; 34（19）: 114–122. （in Chinese）

Ran H, Kang S Z, Li F S, Du T S, Tong L, Li S E, et al. Parameterization of the AquaCrop model for full and deficit irrigated maize for seed production in arid Northwest China. Agricultural Water Management, 2018; 203: 438–450.

Dubey S K, Sharma D. Assessment of climate change impact on yield of major crops in the Banas River Basin, India. Science of the Total Environment, 2018; 635: 10–19.

Song J, Li J, Yang Q H, Mao X M, Yang J, Wang K. Multi-objective optimization and its application on irrigation scheduling based on AquaCrop and NSGA-II. Journal of Hydraulic Engineering, 2018; 49（10）: 1284–1295. （in Chinese）

Nyathi M K, van Halsema G E, Annandale J G, Struik P C. Calibration and validation of the AquaCrop model for repeatedly harvested leafy vegetables grown under different irrigation regimes. Agricultural Water Management, 2018; 208: 107–119.

Adeboye O B, Schultz B, Adekalu K O, Prasad K C. Performance evaluation of AquaCrop in simulating soil water storage, yield, and water productivity of rainfed soybeans （Glycine max L. merr） in Ile-Ife, Nigeria. Agricultural Water Management, 2019; 213: 1130–1146.

Takács S, Csengeri E, Pék Z, BíróT, Szuvandzsiev P, Palotás G, et al. Performance evaluation of aquacrop model in processing tomato biomass, fruit yield and water stress indicator modelling. Water, 2021; 13（24）: 3587.

Zhao W J, Ma F, Cao W, Ma F F, Han L. Effects of water and fertilizer coupling on the yield and quality of tomatoes. Transactions of the CSAE, 2022; 38（2）: 95–101.

Amiri E. Calibration and testing of the Aquacrop model for rice under water and nitrogen management. Communications in Soil Science and Plant Analysis, 2016; 47（3）: 387–403.

Bello Z A, Walker S. Evaluating AquaCrop model for simulating production of amaranthus （Amaranthus cruentus） a leafy vegetable, under irrigation and rainfed conditions. Agricultural and Forest Meteorology, 2017; 247: 300–310.

Malik A, Shakir A S, Ajmal M, Khan M J, Khan T A. Assessment of AquaCrop model in simulating sugar beet canopy cover, biomass and root yield under different irrigation and field management practices in semi-arid regions of Pakistan. Water Resources Management, 2017; 31（13）: 4275–4292.

Lu Y, Wei C Z, McCabe M F, Sheffield J. Multi-variable assimilation into a modified AquaCrop model for improved maize simulation without management or crop phenology information. Agricultural Water Management, 2022; 266: 107576.

Zeleke K T, Luckett D, Cowley R. Calibration and testing of the FAO AquaCrop model for canola. Agronomy Journal, 2011; 103（6）: 1610–1618.

Iqbal M A, Shen Y J, Stricevic R, Pei H W, Sun H Y, Amiri E, et al. Evaluation of the FAO AquaCrop model for winter wheat on the North China Plain under deficit irrigation from field experiment to regional yield simulation. Agricultural Water Management, 2014; 135: 61–72.

Xu K, Zhu X F, Liu Y, Hou C Y. Vulnerability of drought disaster of maize in China based on AquaCrop model. Transactions of the CSAE, 2020; 36（1）: 154–161. （in Chinese）

Liu Z G, Wang J Z, Xu Y F, Li P P. Lettuce yield and root distribution in substrates under drip irrigation and micro-sprinkler irrigation. Transactions of the CSAM, 2014; 45（2）: 156–160. （in Chinese）

Li Y L, Guo W Z, Zhao Q, Li Y K, Yang Z Q, Xue X Z. Irrigation scheduling based on moisture and electric conductivity sensors in organic culture of cucumber. Transactions of the CSAM, 2017; 48（6）: 263–270. （in Chinese）

Xiong J, Chen Q, Wang J G, Liu W. Effect of culture systems on salt accumulation in substrate and nutrient use efficiency of tomato. Transactions of the CSAM, 2017; 48（2）: 224–231.

Palencia P, Bordonaba J G, Martínez F, Terry L A. Investigating the effect of different soilless substrates on strawberry productivity and fruit composition. Scientia Horticulturae, 2016; 203: 12–19.

Yang Y, Ni X Y, Zhou Z J, Yu L X, Liu B M, Yang Y, et al. Performance of matrix-based slow-release urea in reducing nitrogen loss and improving maize yields and profits. Field Crops Research, 2017; 212: 73–81.




Copyright (c) 2024 International Journal of Agricultural and Biological Engineering

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

2023-2026 Copyright IJABE Editing and Publishing Office