Planting Density and Fertilization Evidently Influence the Fiber Yield of Hemp (Cannabis sativa L.)
Abstract
:1. Introduction
2. Materials and Methods
2.1. Materials
2.2. Methods
2.3. Data Analysis
3. Results
3.1. Establishment and Verification of the Fiber Yield Model
3.2. Main-Effect Analysis of Factors
Nitrogen fertilizer: Y2 = 2907.75 + 156.17X2 − 160.15X22
Phosphorus fertilizer: Y3 = 2907.75 + 1. 08X3 − 123.65X32
Potassium fertilizer: Y4 = 2907.75 − 21.50X4− 144.15X4
3.3. Analysis of Single Factor Effects
3.4. Analysis of the Marginal Yield Effect of Single Factors
Nitrogen fertilizer: dY/dX2 = 156.17 − 320.3X2
Phosphorus fertilizer: dY/dX3 = 1.08 − 247.3X3
Potassium fertilizer: dY/dX4 =-21.50 − 288.3X4
3.5. Optimization of Agronomic Methods Plan
4. Discussion
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Agronomic Variable | Alternative Gradient | Variable Design | ||||
---|---|---|---|---|---|---|
−2 | −1 | 0 | 1 | 2 | ||
Density (plants ha−1) (X1) | 150 000 | 100,000 | 250,000 | 400,000 | 550,000 | 700,000 |
N (kg ha−1) (X2) | 75 | 75 | 150 | 225 | 300 | 375 |
P (kg ha−1) (X3) | 30 | 30 | 60 | 90 | 120 | 150 |
K (kg ha−1) (X4) | 75 | 75 | 150 | 225 | 300 | 375 |
No | Density (X1) | N (X2) | P2O5 (X3) | K2O (X4) | Mean Fiber Yield (kg ha−1) |
---|---|---|---|---|---|
1 | 1 | 1 | 1 | 1 | 2251 |
2 | 1 | 1 | 1 | −1 | 2479 |
3 | 1 | 1 | −1 | 1 | 2193 |
4 | 1 | 1 | −1 | −1 | 2821 |
5 | 1 | −1 | 1 | 1 | 2051 |
6 | 1 | −1 | 1 | −1 | 2131 |
7 | 1 | −1 | −1 | 1 | 1708 |
8 | 1 | −1 | −1 | −1 | 2239 |
9 | −1 | 1 | 1 | 1 | 2387 |
10 | −1 | 1 | 1 | −1 | 2523 |
11 | −1 | 1 | −1 | 1 | 2824 |
12 | −1 | 1 | −1 | −1 | 2399 |
13 | −1 | −1 | 1 | 1 | 2035 |
14 | −1 | −1 | 1 | −1 | 2603 |
15 | v | −1 | −1 | 1 | 2102 |
16 | −1 | −1 | −1 | −1 | 2236 |
17 | −2 | 0 | 0 | 0 | 2436 |
18 | 2 | 0 | 0 | 0 | 1701 |
19 | 0 | −2 | 0 | 0 | 1968 |
20 | 0 | 2 | 0 | 0 | 2456 |
21 | 0 | 0 | −2 | 0 | 2336 |
22 | 0 | 0 | 2 | 0 | 2380 |
23 | 0 | 0 | 0 | −2 | 1935 |
24 | 0 | 0 | 0 | 2 | 2617 |
25 | 0 | 0 | 0 | 0 | 2833 |
26 | 0 | 0 | 0 | 0 | 3175 |
27 | 0 | 0 | 0 | 0 | 3048 |
28 | 0 | 0 | 0 | 0 | 2976 |
29 | 0 | 0 | 0 | 0 | 3294 |
30 | 0 | 0 | 0 | 0 | 3000 |
31 | 0 | 0 | 0 | 0 | 2849 |
32 | 0 | 0 | 0 | 0 | 2809 |
33 | 0 | 0 | 0 | 0 | 2936 |
34 | 0 | 0 | 0 | 0 | 3205 |
35 | 0 | 0 | 0 | 0 | 2579 |
36 | 0 | 0 | 0 | 0 | 2189 |
Source | Sum of Squares | df | Mean Squares | Partial Correlation | F-Value | p-Value |
---|---|---|---|---|---|---|
X1 | 305,101.49 | 1 | 305,101.49 | −0.3761 | 3.4596 | 0.0770 |
X2 | 585,312.64 | 1 | 585,312.64 | 0.4900 | 6.6369 | 0.0176 * |
X3 | 28.17 | 1 | 28.17 | 0.0039 | 0.0003 | 0.9859 |
X4 | 11,094.00 | 1 | 11,094.00 | −0.0772 | 0.1258 | 0.7264 |
X12 | 122,9573.30 | 1 | 1,229,573.30 | −0.6317 | 13.9422 | 0.0012 ** |
X2 | 820,693.98 | 1 | 820,693.98 | −0.5541 | 9.3059 | 0.0061 ** |
X32 | 489,225.33 | 1 | 489,225.33 | −0.4571 | 5.5474 | 0.0283 * |
X42 | 664,896.66 | 1 | 664,896.66 | −0.5140 | 7.5393 | 0.0121 * |
X1X2 | 13,110.25 | 1 | 13,110.25 | 0.0838 | 0.1487 | 0.7037 |
X1X3 | 81.00 | 1 | 81.00 | −0.0066 | 0.0009 | 0.9761 |
X1X4 | 69,432.25 | 1 | 69,432.25 | −0.1901 | 0.7873 | 0.3850 |
X2X3 | 80,089.00 | 1 | 80,089.00 | −0.2036 | 0.9081 | 0.3515 |
X2X4 | 34,782.25 | 1 | 34,782.25 | 0.1358 | 0.3944 | 0.5368 |
X3X4 | 1296.00 | 1 | 1296.00 | −0.0264 | 0.0147 | 0.9047 |
Regression | 4,304,716.47 | 14 | 307,479.75 | F2 = 3.48654 | 0.0100 ** | |
Residual | 1,851,999.75 | 21 | 88,190.46 | |||
Lack of fit | 865,925.50 | 10 | 86,592.55 | F1 = 0.96597 | 0.4991 | |
Pure error | 986,074.250 | 11 | 89,643.11 | |||
Total error | 6,156,716.2222 | 35 |
Factor | Density (X1) | N (X2) | P (X3) | K (X4) | |||||
---|---|---|---|---|---|---|---|---|---|
Degree | Frequency (%) | Degree | Frequency (%) | Degree | Frequency (%) | Degree | Frequency (%) | ||
Variable design | −2 | 10 | 7.46 | 0 | 0 | 8 | 5.97 | 8 | 5.97 |
−1 | 44 | 32.84 | 19 | 14.18 | 36 | 26.87 | 36 | 26.87 | |
0 | 60 | 44.78 | 48 | 35.82 | 46 | 34.33 | 46 | 34.33 | |
1 | 20 | 14.93 | 48 | 35.82 | 36 | 26.87 | 36 | 26.87 | |
2 | 0 | 0 | 19 | 14.18 | 8 | 5.97 | 8 | 5.97 | |
Weighted mean | −0.328 | 0.500 | 0 | 0 | |||||
Standard error | 0.0710 | 0.0780 | 0.0870 | 0.0870 | |||||
95% Confidence interval | −0.467~−0.190 | 0.347~0.653 | −0.171~0.171 | −0.171~0.171 | |||||
Optimal range | 329,950~371,500 plant ha−1 | 251.03~273.98 kg ha−1 | 84.87~95.13 kg ha−1 | 212.18~237.83 kg ha−1 |
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Deng, G.; Du, G.; Yang, Y.; Bao, Y.; Liu, F. Planting Density and Fertilization Evidently Influence the Fiber Yield of Hemp (Cannabis sativa L.). Agronomy 2019, 9, 368. https://doi.org/10.3390/agronomy9070368
Deng G, Du G, Yang Y, Bao Y, Liu F. Planting Density and Fertilization Evidently Influence the Fiber Yield of Hemp (Cannabis sativa L.). Agronomy. 2019; 9(7):368. https://doi.org/10.3390/agronomy9070368
Chicago/Turabian StyleDeng, Gang, Guanghui Du, Yang Yang, Yaning Bao, and Feihu Liu. 2019. "Planting Density and Fertilization Evidently Influence the Fiber Yield of Hemp (Cannabis sativa L.)" Agronomy 9, no. 7: 368. https://doi.org/10.3390/agronomy9070368
APA StyleDeng, G., Du, G., Yang, Y., Bao, Y., & Liu, F. (2019). Planting Density and Fertilization Evidently Influence the Fiber Yield of Hemp (Cannabis sativa L.). Agronomy, 9(7), 368. https://doi.org/10.3390/agronomy9070368