Subjects: Agriculture, Forestry,Livestock & Aquatic Products Science >> Other Disciplines of Agriculture, Forestry,Livestock & Aquatic Products Science submitted time 2023-02-17 Cooperative journals: 《智慧农业（中英文）》
Abstract： Intercropping creates a heterogeneous canopy and triggers plastic responses in plant growth and structural development. In order to quantify the effect of planting pattern, strip width and row position on the structural development and light capture of maize and soybean in simultaneous intercropping, both experimental and modelling approaches were used. Field experiments were conducted in 2017－2018 with two sole crops (maize and soybean) and two intercrops: Two rows of maize alternating with two rows of soybeans (2:2 MS) and three rows of maize alternating with six rows of soybean (3:6 MS). The morphological traits of maize and soybean e.g., leaf length and width, internode length and diameter, leaf and petiole declination angle in different rows and different planting patterns, and photosynthetically active radiation (PAR) above and below the canopy of 2:2 MS were measured throughout the growing season. A functional-structural plant model of maize-soybean intercropping was developed in the GroIMP platform. The model was parameterized based on the morphological data set of 2017, and was validated with the leaf area index (LAI), plant height and PAR data set of 2018. The model simulated the morphological development of individual organs based on growing degree days (thermal time) and calculated the light capture at leaf level. The model well reproduced the observed dynamics of leaf area index and plant height (RMSE: 0.24－0.70 m2/m2 for LAI and 0.06－0.17 m for plant height), and the fraction of light capture in the 2:2 MS intercropping (RMSE: 0.06－0.10). Maize internode diameter in intercrops increased, but the internode length did not change. Soybean internodes in intercrops became longer and thinner compared to sole soybean probably caused by the shading imposed by maize, and the 2:2 MS had longer internodes than the 3:6 MS, indicating the effects of strip width. Simulated light capture of maize in 2:2 MS intercropping was 35.6% higher than sole maize. For maize in 3:6 MS intercropping, the light capture of the border rows and inner row were 27.8% and 20.3% higher than sole maize, respectively. Compared to sole soybean, the simulated light capture of soybean in border rows was 36.0% lower in 2:2 MS intercropping, and was 28.8% lower in 3:6 MS intercropping. For 3:6 MS intercropping, light capture of soybean in inner rows I and inner rows II were 4.1% and 1.8% lower than sole soybean, respectively. In the future, the model could be further developed and used to explore and optimize the planting patterns of maize soybean intercropping under different environmental conditions using light capture as an indicator.
Subjects: Agriculture, Forestry,Livestock & Aquatic Products Science >> Other Disciplines of Agriculture, Forestry,Livestock & Aquatic Products Science submitted time 2023-02-17 Cooperative journals: 《智慧农业（中英文）》
Abstract： In order to assess maize growth status accurately and quickly for improving maize precise management, field experiment was conducted in Gongzhuling research station, Jilin Academy of Agricultural Sciences, Jilin province. Experimental design included 3 planting densities and 5 maize materials. The near-ground hyperspectral data and the unmanned aerial vehicle (UAV) hyperspectral images were obtained when maize were during V11－V12 stage. The application abilities of the hyperspectral data obtained from the two phenotyping platforms were compared and analyzed in the estimation of maize leaf area index (LAI) and aboveground biomass. In this study, 21 commonly used spectral vegetation indices were constructed based on ground hyperspectral data, and then the estimation models of maize LAI and aboveground biomass were established based on ground hyperspectral full-bands, UAV hyperspectral full-bands and vegetation indices and partial least square regression method, respectively. According to the variance estimation of regression coefficients, the important bands of LAI and aboveground biomass were selected, and the partial least square method was also used to establish the estimation model of maize LAI and aboveground biomass based on important bands. The results showed that the canopy spectral reflectance of the same maize material increased with the increase of planting density in the near infrared bands. Among the 5 maize materials under the same planting density, the canopy spectral reflectance of wild type material was the lowest in the visible and near infrared bands. For LAI, the model constructed based on vegetation indices had the best estimation result, with R2, RMSE and rRMSE values of 0.70, 0.92 and 15.94%. For aboveground biomass, the model constructed based on the sensitive spectral bands (839－893 nm and 1336－1348 nm) had the best estimation results, with R2, RMSE and rRMSE values of 0.71, 12.31 g and 15.89%, which showed that there was information redundancy in hyperspectral bands in the estimation of aboveground biomass, and the estimation accuracy could be improved by reducing the number of spectral bands and selecting sensitive spectral bands. In summary, the UAV hyperspectral images have a good application ability in the estimation of maize LAI and aboveground biomass, and can quickly and effectively extract the parameters information of maize growth. For specific parameters, sensitive spectral bands selected can provide reliable basis for the development and practical application of multi-spectrum in the future. The study can provide a reference for the use of hyperspectral technology in the management of precision agriculture at the community scale.
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