RSM and ANN Modeling of Camelina (Camelina sativa L. Crantz) Seed Yield as Affected by Nitrogen, Sulfur, Cow Manure, and Row Spacing

• Published in: Horticulturae 2024-01, Vol.10 (1), p.66
• Main Authors: Yari, Mohsen, Rokhzadi, Asad, Shamsi, Keyvan, Pasari, Babak, Rahimi, Abdol Rahman
• Format: Article
• Language: English
• Subjects: Agricultural production; Artificial intelligence; artificial neural network; Artificial neural networks; Camelina; Camelina sativa; Cattle manure; Comparative analysis; Crop yield; Crop yields; Crops; Cruciferae; Design factors; Economic justification; Environmental aspects; Experiments; Fertilization; Fertilizers; Fruits; Growth; Management planning; Manures; Measurement; Modelling; Neural networks; Nitrogen; Oils & fats; Oilseed crops; Oilseeds; Plant spacing; Planting; Planting density; Potassium; Response surface methodology; Root-mean-square errors; Row spacing; seed yield prediction; Seeds; Standard error; Statistical analysis; Sulfur; Variables
• ISSN: 2311-7524; 2311-7524
• DOI: 10.3390/horticulturae10010066

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.