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

• TILLAGE & CULTIVATION · PHYSIOLOGY & BIOCHEMISTRY • Previous Articles     Next Articles

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

Abstract:

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

[1]Wilcox J R. World distribution and trade of soybean. In: soybeans: Improvement, Production and Uses, 3rd Edn. Madison: American Society of Agronomy Press, 2004. pp 1–13



[2]Clake E J, Wiseman J. Developments in plant breeding for improved nutritional quality of soybean: I. protein and amin acids content. Agric Sci, 2000, 134: 111–124



[3]Frideman M, Brandon D L. Nutritional and health benefits of soybean proteins. Agric Food Chem, 2001, 49: 1069–1086



[4]Tennakoon S B, Murty V V N, Eiumnoh A. Estimation of cropped area and grain yield of rice using remote sensing data. Int J Remote Sens, 1992, 13: 426–439



[5]Trishchenko A P. Effects of spectral response function on surface reflectance and NDVI measured with moderate resolution satellite sensors: extension to AVHRR NOAA-17, 18 and METOP-A. Remote Sens Environ, 2009, 113: 335–341



[6]Hansen P M, Schjoerring J K. Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression. Remote Sens Environ, 2003, 86: 542–553



[7]Aparicio N, Villegas D, Casadesus J, Araus J L, Royo C. Spectral vegetation indices as nondestructive tools for determining durum wheat yield. Agron J, 2000, 92: 83–91



[8]Soenen S A, Peddle D R, Hall R J, Coburn C A, Hall F G. Estimating aboveground forest biomass from canopy reflectance model inversion in mountainous terrain. Remote Sens Environ, 2010, 114: 1325–1337



[9]Gianquinto G, Orsini F, Fecondini M, Mezzetti M, Sanbo P, Bona S. A methodological approach for defining spectral indices for assessing tomato nitrogen status and yield. Eur J Agron, 2011, 35: 135–143



[10]吴琼, 齐波, 赵团结, 姚鑫锋, 朱艳, 盖钧镒. 高光谱遥感估测大豆冠层生长和籽粒产量的探讨. 作物学报, 2013, 39: 309–318



Wu Q, Qi B, Zhao T J, Yao X F, Zhu Y, Gai J Y. A tentative study on utilization of canopy hyperspectral reflectance to estimate canopy growth and seed yield in soybean. Acta Agron Sin, 2013, 39: 309–318 (in Chinese with English abstract)



[11]Mkhabela M S, Bullock P, Raj S, Wang S, Yang Y. Crop yield forecasting on the Canadian Prairies using MODIS NDVI data. Agric For Meteorol, 2011, 115: 385–393



[12]Hasegawa K, Matsuyama H, Tsuzuki H, Sweda T. Improving the estimation of leaf area index by using remotely sensed NDVI with BRDF signatures. Remote Sens Environ, 2010, 114: 514–519



[13]William R R, Gordon V J. Improving nitrogen use efficiency forcereal production. Agron J, 1999, 91: 357–363



[14]吴军华, 岳善超, 侯鹏, 孟庆峰, 崔振领, 李雯, 陈新平. 基于主动遥感的冬小麦群体动态监测. 光谱学与光谱分析, 2011, 31: 535–538



Wu J H, Yue S C, Hou P, Meng Q F, Cui Z L, Li W, Chen X P. Monitoring winter wheat population dynamics using an activecrop sensor. Spectrosc Spectr Anal, 2011, 31: 535–538 (in Chinese with English abstract)



[15]谭昌伟, 王纪华, 朱新开, 王妍, 王君婵, 童璐, 郭文善. 基于Landsat TM影像的冬小麦拔节期主要长势参数遥感监测. 中国农业科学, 2011, 44: 1358–1366



Tan C W, Wang J H, Zhu X K, Wang Y, Wang J C, Tong L, Guo W S. Monitoring main growth status parameters at jointing stage in winter wheat based on Landsat TM images. Sci Agric Sin, 2011, 44: 1358–1366 (in Chinese with English abstract)



[16]冯美臣, 肖璐洁, 杨武德, 丁光伟. 基于遥感数据和气象数据的水旱地冬小麦产量估测. 农业工程学报, 2010, 26(11): 183–188



Feng M C, Xiao L J, Yang W D, Ding G W. Predicting grain yield of irrigation-land and dry-land winter wheat based on remotesensing data and meteorological data. Trans CSAE, 2010, 26(11): 183–188 (in Chinese with English abstract)



[17]Reyniers M, Vrindts E, Baerdemaeker J D. Comparison of an aerialbased system and an on the ground continuous measuring device to predict yield of winter wheat. Eur J Agron, 2006, 24: 87–94



[18]Erdle K, Mistele B, Schmidhalter U. Comparison of active and passive spectral sensors in discriminating biomass parameters and nitrogen status in wheat cultivars. Field Crops Res, 2011, 124: 74–84



[19]Raun W R, Solie J B, Stone M L, Martin K L, Freeman K W, Mullen R W, Zhang H, Schepers J S, Johnson G V. Optical sensor-based algorithm for crop nitrogen fertilization. Commun Soil Sci Plant Anal, 2005, 36, 2759–2781



[20]Thomason W E, Phillips S B, Raymond F D. Defining useful limits for spectral reflectance measures in corn. J Plant Nutr, 2007, 30, 1263–1277



[21]Inman D, Khosla R, Reich R M, Westfall, D G. Active remote sensing and grain yield in irrigated maize. Precision Agric, 2007, 8: 241–252



[22]Phillips S B, Keahey D A, Warren J G, Mullins G L. Estimating winter wheat tiller density using spectral reflectance sensors for early-spring, Variable-Rate Nitrogen Applications. Agron J, 2004, 96: 591–600



[23]Aboelghar M, Arafat S, Saleh A, Naeemb S, Shirbeny M, Belal A. Retrieving leaf area index from SPOT4 satellite data. Egypt J Remote Sens Space Sci, 2010, 13: 121–127



[24]王磊, 白由路, 卢艳丽, 王贺, 杨俐苹. 基于GreenSeeker的冬小麦NDVI分析与产量估算. 作物学报, 2012, 38: 747–753



Wang L, Bai Y L, Lu Y L, Wang H, Yang L P. NDVI analysis and yield estimation in winter wheat based on GreenSeeker. Acta Agron Sin, 2012, 38: 747–753 (in Chinese with English abstract)



[25]Ferrio J P, Villegas D, Zarco J, Aparicio N, Araus J L, Royo C. Assessment of durum wheat yield using visible and near-infrared reflectance spectra of canopies. Field Crops Res, 2005, 94: 126–148



[26]Pinter P J, Jackson R D, Idso S B, Reginato R J. Multidate spectral reflectance as predictors of yield in water stressed wheat and barley. Int J Remote Sens, 1981, 2: 43–48



[27]Mahey R K, Singh R, Sidhu S S, Narang R S. The use of remote sensing to assess the effects of water stress on wheat. Exp Agric, 1991, 27: 423–429



[28]Lukina E V, Freeman K W, Wynn K J, Thomason W E, Mullen R W, Stone M L, Solie J B, Klatt A R, Johnson G V, Elliott R L, Raun W R. Nitrogen fertilization optimization algorithm based on in-season estimates of yield and plant nitrogen uptake. J Plant Nutr, 2001, 24: 885–898



[29]Raun W R, Johnson G V, Stone M L, Solie J B, Lukina E V, Thomason W E, Schepers J S. In-season prediction of potential grain yield in winter wheat using canopy reflectance. Agron J, 2001, 93: 131–138



[30]Hansen P M, Jorgensen J R, Thomsen A. Predicting grain yield and protein content in winter wheat and spring barley using repeated canopy reflectance measurements and partial least squares regression. J Agric Sci, 2002, 139: 307–318

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