Scientia Agricultura Sinica ›› 2022, Vol. 55 ›› Issue (6): 1127-1138.doi: 10.3864/j.issn.0578-1752.2022.06.006


Prediction of Maize Yield in Relay Strip Intercropping Under Different Water and Nitrogen Conditions Based on PLS

TAN XianMing(),ZHANG JiaWei,WANG ZhongLin,CHEN JunXu,YANG Feng(),YANG WenYu   

  1. College of Agronomy, Sichuan Agricultural University/Key Laboratory of Crop Ecophysiology and Farming System in Southwest, Ministry of Agriculture/Sichuan Engineering Research Center for Crop Strip Intercropping System, Chengdu 611130
  • Received:2021-05-17 Accepted:2021-10-08 Online:2022-03-16 Published:2022-03-25
  • Contact: Feng YANG;


【Objective】 This study was designed mainly to provide technical means for non-destructive prediction of intercropped maize yield. The prime objective of our study was to construct a “hyperspectral parameter-photosynthetic pigment-yield” model from the hyperspectral data. 【Method】 Based on field trials of different years, locations, varieties, and treatments (nitrogen fertilizer, moisture), the relationships among photosynthetic pigment parameters, canopy hyperspectral parameters, and maize yield at each growth period and the entire growth period of intercropping maize were comprehensively analyzed. In addition, the optimal growth period and photosynthetic pigment parameters for maize yield prediction were also clarified. Then, the prediction model of yield was constructed based on linear function, quadratic function and partial least squares regression (PLS). 【Result】 Among the photosynthetic pigment-yield prediction models, the PLS prediction model of yield based on canopy carotenoid density had the best effect (R 2=0.882, RMSE=0.669 t·hm -2). In the spectral parameter-photosynthetic pigment analysis, the chlorophyll content during the tasseling stage had the best correlation with the band free combination index rRVI (534, 546) (r=0.927). The correlation between the other photosynthetic pigment parameters and the corresponding spectral index was above 0.797. In the hyperspectral parameter- photosynthetic pigment-yield prediction model, the chlorophyll content, carotenoid content, canopy chlorophyll density, and canopy carotenoid density were used as connection points, and using the spectral indices of rNDVI (534, 546), rRVI (531, 555), rNDVI (532, 546), and rNDVI (531, 555) as independent variables, the PLS output prediction model had better effect (R2=0.509, RMSE=1.352 t·hm -2). 【Conclusion】 In intercropping maize, the pigment parameters were used as a bridge between spectral data and yield. A prediction model was established through PLS regression, which could achieve a better estimation of maize yield and provide the theoretical and technical reference for field management and growth monitoring of maize in intercropping.

Key words: relay intercropping, maize, hyperspectral, pigment parameters, yield, partial least squares regression

Table 1

Vegetation indexes and spectral indexes quoted in this paper"

Vegetation index and spectral index
叶绿素反演植被指数 Transformed chlorophyll absorption in reflectance index, TCARI 3×[(R700-R670)-0.2×(R700-R550)×(R700/R670)] [20]
绿度植被指数 Green normalized difference vegetation index, GNDVI (R800-R550)/(R800+R550) [20]
光化学植被指数 photochemical reflectance index, PRI (R531-R570)/(R531+R570) [21]
rDVI(Ri, Rj) Random difference vegetation index Ri-Rj
rRVI(Ri, Rj) Random ratio vegetation index Ri/Rj
rNDVI(Ri, Rj) Random normalized difference vegetation index (Ri-Rj)/(Ri+Rj)

Table 2

Yield difference in maturity period of each treatment (t·hm-2)"

Jointing stage
Filling stage
Jointing stage
Tasseling stage
Filling stage
CK(N1) 7.113±0.825a 5.112±0.549a 4.483±0.124a 5.573±0.206a 4.296±0.466b 8.829±0.332a 7.021±0.075a
L(N2) 6.473±0.495a 4.739±0.657a 3.780±0.125bc 4.639±0.079a 5.343±0.679ab 7.409±0.505b 8.978±0.316a 7.119±0.518a
M(N3) 5.914±0.230ab 4.494±0.676a 4.102±0.222b 5.250±0.483a 5.907±0.148a 9.700±0.863a 9.854±0.477a 8.237±0.662a
S(N4) 4.355±0.232b 4.493±0.582a 3.496±0.138c 3.627±0.258b 5.919±0.146a 9.956±0.171a 8.181±0.476a

Table 3

Photosynthetic pigment parameters in each growth period"

生育时期 Growth period 试验组成 Test composition Ct (mg·cm-2) CCD (g·m-2) Car (mg·cm-2) CCarD (mg·m-2)
拔节期 Jointing stage Exp.(1-5) n=54 0.056±0.026 0.544±0.426 0.008±0.007 84.778±98.435
抽雄期 Tasseling stage Exp(3-5) n=33 0.107±0.068 4.615±3.588 0.012±0.005 502.782±299.183
灌浆期 Filling stage Exp(1-2,4-5) n=40 0.077±0.064 3.255±3.334 0.009±0.005 349.077±279.695
总样本 Total sample Exp.(1-5) n=127 0.076±0.056 2.457±3.127 0.009±0.006 276.637±286.108

Table 4

Correlation analysis of photosynthetic pigment parameters and yield"

生育时期 Growth period 试验组成 Test composition Ct CCD Car CCarD
拔节期 Jointing stage Exp.(1-5) n=54 -0.111 -0.240 -0.321* -0.358**
抽雄期 Tasseling stage Exp(3-5) n=33 -0.729** -0.663** -0.616** -0.565**
灌浆期 Filling stage Exp(1-2,4-5) n=40 -0.275 0.283 -0.342* -0.288
总样本 Total sample Exp.(1-5) n=127 -0.330** -0.295** -0.373** -0.287**

Table 5

Regression analysis between pigment parameters and yield in maize"

Pigment parameter
R2 RMSE (t·hm-2) R2 RMSE (t·hm-2)
Linear and non-
linear model
Ct (mg·cm-2) y = -22.183x + 9.450 0.560 1.349 0.472 1.401
y = -280.49x2+ 47.191x + 6.5568 0.669 1.170 0.545 1.338
CCD (g·m-2) y = -0.383x + 8.8655 0.467 1.485 0.387 1.517
y = 0.006x2 - 0.4563x + 8.9976 0.468 1.484 0.392 1.510
Car (mg·cm-2) y = -238.06x + 9.9226 0.399 1.577 0.298 1.616
y = -45375x2 + 887.63x + 4.2694 0.646 1.209 0.477 1.437
CCarD (mg·m-2) y = -0.0039x + 9.079 0.345 1.646 0.269 1.657
y = -3E-06x2 + 0.0002x + 8.1775 0.364 1.636 0.264 1.691
Ct (mg·cm-2), CCD (g·m-2)
Car (mg·cm-2), CCarD (mg·m-2)
(x1:Ct, x2:CCD, x3:Car, x4:CCarD)
0.859 0.768 0.882 0.669

Fig. 1

Correlation analysis of photosynthetic pigment parameters with the original and first derivative spectra"

Table 6

Correlation analysis of photosynthetic pigment parameters with vegetation indexes"

Photosynthetic pigment
Ct -0.138 0.090 0.185
CCD -0.184 0.095 0.212
Car -0.189 0.260* 0.118
CCarD -0.239* 0.128 0.164

Fig. 2

Correlation analysis of photosynthetic pigment parameters and rRVI"

Table 7

Correlation analysis of photosynthetic pigment parameters and spectral indexes"

Pigment parameter
Original spectrum
Ct 1009,972 0.882** 534,546 0.927** 534,546 0.926**
CCD 1009,972 0.831** 531,555 0.912** 533,539 0.904**
Car 1011,962 0.853** 532,546 0.908** 532,546 0.908**
CCarD 1009,975 0.797** 531,555 0.895** 531,555 0.894**
First derivative spectrum
Ct 1491,769 0.898** 544,449 0.865** 1895,1451 0.861**
CCD 1491,768 0.854** 544,448 0.868** 544,448 0.853**
Car 1491,769 0.892** 650,688 0.846** 1491,741 0.828**
CCarD 1491,763 0.857** 544,448 0.840** 544,448 0.826**

Table 8

Linear and non-linear regression analysis between photosynthetic pigment parameters and spectral indexes"

Pigment parameter
Spectral index
Calibration set (n=90)
Test set (n=40)
rDVI (1009,972) 线性 Linear y=0.0753x-0.0694 0.779 0.033 0.792 0.033
二次Quadratic y = -0.0043x2 + 0.0976x -0.0952 0.780 0.032 0.790 0.031
rRVI (534,546) 线性 Linear y=5.181x-4.612 0.859 0.026 0.820 0.028
二次Quadratic y = 39.363x2 - 66.684x + 28.183 0.866 0.025 0.812 0.029
rNDVI (534,546) 线性 Linear y=9.4714x+0.5494 0.857 0.026 0.820 0.028
二次Quadratic y = 145.48x2 + 22.759x +0.8462 0.867 0.025 0.813 0.029
rDVI (1009,972) 线性 Linear y=3.7883x-4.2894 0.690 2.058 0.695 1.930
二次Quadratic y = -0.0206x2 + 3.8945x -4.4126 0.690 2.191 0.538 2.476
rRVI (531,555) 线性 Linear y=168.25x-138.36 0.831 1.520 0.813 1.556
二次Quadratic y = 1211.3x2 - 1899.1x + 743.25 0.847 1.445 0.859 1.377
rNDVI (533,539) 线性 Linear y=697.07x+25.798 0.829 1.529 0.820 1.459
二次Quadratic y = 14598x2 + 1548.5x + 37.863 0.837 1.493 0.839 1.391
Car (mg·cm-2) rDVI (1011,962) 线性 Linear y=0.0046x+0.0008 0.727 0.003 0.667 0.003
二次Quadratic y = -0.0008x2 + 0.0088x -0.0041 0.749 0.003 0.677 0.003
rRVI (532,546) 线性 Linear y=0.3379x-0.2864 0.824 0.002 0.775 0.003
二次Quadratic y = -0.0011x2 + 0.3399x - 0.2873 0.824 0.002 0.789 0.003
rNDVI (532,546) 线性 Linear y=0.5999x+0.0493 0.824 0.002 0.776 0.003
二次Quadratic y = 0.8472x2 + 0.704x + 0.0524 0.824 0.002 0.790 0.003
CCarD (mg·m-2) rDVI (1009,975) 线性 Linear y=328.64x-259.68 0.635 186.720 0.641 175.510
二次Quadratic y = 17.113x2 + 243.8x - 164.28 0.637 186.426 0.671 174.162
rRVI (531,555) 线性 Linear y=13824x-11244 0.801 137.950 0.797 136.940
二次Quadratic y = 43623x2 - 60627x + 20505 0.804 136.888 0.824 130.002
rNDVI (531,555) 线性 Linear y=23718x+2432.8 0.800 138.340 0.792 151.290
二次Quadratic y = 156703x2 + 48613x + 3400.1 0.804 136.771 0.819 129.656

Fig. 3

Comparison of estimated and predicted photosynthetic pigment parameters in maize based on hyperspectral remote sensing"

Table 9

Yield estimation models of maize"

Pigment parameter
Spectral index
函数 Function 建模集 Calibration set 验证集 Test set
R2 RMSE (t·hm-2) R2 RMSE (t·hm-2)
Car rNDVI (532,546) y=0.5999x+0.0493 y = -45375x2 + 887.63x + 4.2694 0.526 1.217 0.485 0.965
Ct rNDVI (534,546) y = 145.48x2 + 22.759x +0.8462 y=-79.7789x1-0.6549x2+529.075x3+0.0123x4+6.002
(x1:Ct, x2:CCD, x3:Car, x4:CCarD)
0.596 1.304 0.509 1.352
CCD rRVI (531,555) y = 1211.3x2 - 1899.1x + 743.25
Car rNDVI (532,546) y = 0.8472x2 + 0.704x + 0.0524
CCarD rNDVI (531,555) y = 156703x2 + 48613x + 3400.1
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