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Acta Agron Sin ›› 2014, Vol. 40 ›› Issue (04): 657-666.doi: 10.3724/SP.J.1006.2014.00657


Prediction for Soybean Grain Yield Using Active Sensor GreenSeeker

ZHANG Ning1,**,QI Bo1,**,ZHAO Jin-Ming1,ZHANG Xiao-Yan2,WANG Su-Ge2,ZHAO Tuan-Jie1,GAI Jun-Yi1,*   

  1. 1 Soybean Research Institute / National Center for Soybean Improvement / Key Laboratory for Biology and Genetic Improvement of Soybean (General), Minister of Agriculture / National Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing 210095, China;
    2 Shofine Academician Workstation, Jining 272000, China
  • Received:2013-10-11 Revised:2014-01-12 Online:2014-04-12 Published:2014-02-14
  • Contact: 盖钧镒, E-mail: sri@njau.edu.cn


Active remote sensing can be used to monitor soybean growth with a convenient, fast and nondestructive technology. At Shofine Academician Workstation, a total of 1272 soybean lines, including  breeding lines and recombinant inbred lines (NJRIKY), were grouped and tested in random complete block, block in replication or lattice design with three replicates in 2011 and 2012, respectively. Using active remote sensor GreenSeeker, the canopy NDVI (normalized difference vegetation index) was measured at seedling, flowering, podding and seed-filling stages, from which the yield prediction models depending on NDVI measurements were established and analyzed. The results showed that the soybean canopy NDVI presented a low-high-low changing trend from the early to the late stages. Among the single stage prediction models for yield, that for seed-filling stage was the best with higher coefficient of determination and lower standard errors. However, for a precise prediction, the regression of yield on NDVI at multiple stages was better than the others. Among which, the yield prediction model constructed from NDVI at flowering, podding and seed-filling stages of all breeding lines was the best one (y = e6.9–4.1x1+4.3x2+1.4x3) with R2 = 0.66. Using this model to predict the NJRIKY lines, the coincidence between the measured and predicted values was 0.59. This model can be used at the middle stage of breeding programs for yield prediction of the breeding lines without replicated yield test.

Key words: Soybean, Yield, NDVI, Active sensor, Remote sensing prediction

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