荠菜(Camelina sativa L. Crantz)种子产量受氮、硫、牛粪和行距影响的 RSM 和 ANN 模型

IF 3.1 3区 农林科学 Q1 HORTICULTURE Horticulturae Pub Date : 2024-01-09 DOI:10.3390/horticulturae10010066
Mohsen Yari, A. Rokhzadi, Keyvan Shamsi, B. Pasari, A. Rahimi
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引用次数: 0

摘要

荠菜[Camelina sativa (L.) Crantz]是十字花科一年生多用途油料作物,种植面积越来越大。预测荠菜种子产量对施肥和种植密度的响应对了解生产潜力和管理规划具有重要意义。因此,本研究旨在通过响应面法(RSM)和人工神经网络(ANN)估算荠菜种子产量受不同种植行距、氮肥(N)、硫肥(S)和牛粪(CM)施肥水平的影响。试验在 2019-2020 和 2020-2021 两个生长年进行,采用中心复合设计,四个因子包括行距(15-35 厘米)、氮(0-200 千克/公顷-1)、硫(0-100 千克/公顷-1)和牛粪(0-40 吨/公顷-1)。这两年种子产量与施肥和行距因素的 RSM 模型均具有显著的统计学意义和可接受的预测能力。荠菜种子产量随着行距的增加而减少,但对氮肥、钾肥和钙肥用量的增加呈正反应。比较各模型的性能表明,虽然 RSM 模型在预测荠菜种子产量方面具有显著性和必要的效率,但 ANN 模型更为准确。RSM 模型两年的平均判定系数 (R2)、均方根误差 (RMSE)、预测标准误差 (SEP)、平均绝对误差 (MAE) 和 Akaike 信息准则 (AICc) 分别为 0.924、51.60、5.51、41.14 和 394.05,而 ANN 模型分别为 0.968、32.62、3.54、19.55 和 351.33。根据这些结果,与 RSM 技术相比,ANN 模型可用于预测田间条件下的荠菜种子产量,可信度更高。
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RSM and ANN Modeling of Camelina (Camelina sativa L. Crantz) Seed Yield as Affected by Nitrogen, Sulfur, Cow Manure, and Row Spacing
Camelina [Camelina sativa (L.) Crantz] is an annual versatile oilseed crop of the Brassicaceae family, with an increasingly cultivated area. Predicting camelina seed yield response to fertilization and planting density is of great importance in understanding production potential and management planning. Therefore, the current study aimed to estimate the seed yield of camelina by response surface methodology (RSM) and artificial neural network (ANN) as affected by different levels of planting row spacing and nitrogen (N), sulfur (S), and cow manure (CM) fertilization. The experiment was conducted in two growing years of 2019–2020 and 2020–2021, based on a central composite design with four factors including row spacing (15–35 cm), N (0–200 kg ha−1), S (0–100 kg ha−1), and CM (0–40 t ha−1). The RSM models for seed yield versus fertilization and row spacing factors in both years were statistically significant and had an acceptable predictive ability. Camelina seed yield decreased with increasing row spacing but showed a positive response to increasing the amount of N, S, and CM fertilizers. Comparing the performance of the models showed that, although the RSM models were significant and had the necessary efficiency in predicting camelina seed yield, the ANN models were more accurate. The performance criteria of coefficient of determination (R2), root mean square error (RMSE), standard error of prediction (SEP), mean absolute error (MAE), and Akaike information criterion (AICc) averaged over the two years for the RSM model were 0.924, 51.60, 5.51, 41.14, and 394.05, respectively, and for the ANN model were 0.968, 32.62, 3.54, 19.55, and 351.33, respectively. Based on the results, the ANN modeling can be used in predicting camelina seed yield in field conditions with more confidence than the RSM technique.
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来源期刊
Horticulturae
Horticulturae HORTICULTURE-
CiteScore
3.50
自引率
19.40%
发文量
998
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