Detecting interactive effects of N fertilization and heat stress on maize productivity by remote sensing techniques |
| |
| Institution: | 1. Unit of Plant Physiology, Faculty of Biology, University of Barcelona, Av. Diagonal, 643, 08028 Barcelona, Spain;2. Department of Crop and Forest Sciences and AGROTECNIO (Centre for Research in Agrotechnology), University of Lleida, Av. Rovira Roure 191, E-25198 Lleida, Spain;3. ICREA (Catalonian Institution for Research and Advanced Studies), Spain;1. CEIGRAM-Universidad Politécnica de Madrid, ETSIAAB, 28040, Madrid, Spain;2. IFAPA-Centro Alameda del Obispo, Junta de Andalucía, P.O. Box 3092, 14080, Córdoba, Spain;3. Área de Producción Vegetal, Universidad de Oviedo, EPM, 33600, Mieres, Spain;4. University of Castilla-La Mancha, Department of Economic Analysis, Toledo, Spain;5. IFEVA-CONICET, Facultad de Agronomía, Universidad de Buenos Aires, Argentina;6. CONICET-INTA, Facultad de Agronomía, Universidad de Buenos Aires, Argentina;1. Department of Crop and Forest Sciences and AGROTECNIO (Center for Research in Agrotechnology), University of Lleida, Av. Rovira Roure 191, 25198 Lleida, Spain;2. ICREA, Catalonian Institution for Research and Advanced Studies, Spain;1. College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling, Shaanxi Province, 712100, P.R. China;2. Department of Watershed Sciences, Utah State University, Logan, UT, USA;1. College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100193, China;2. Chongqing Academy of Agricultural Sciences, Chongqing, China;1. Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Ctra de La Coruña Km 7.5, 28040 Madrid, Spain;2. Instituto de Agricultura Sostenible (IAS), Consejo Superior de Investigaciones Científicas (CSIC), Alameda Del Obispo S/n, 14004 Córdoba, Spain;3. School of Agricultural Engineering, Universidad Politécnica de Madrid, Avda. Complutense S/n, 28040 Madrid, Spain;1. College of Agronomy and Biotechnology, China Agricultural University, Beijing 100193, P.R.China;2. Institute of Crop Science, Chinese Academy of Agricultural Sciences/Key Laboratory of Crop Physiology and Ecology, Ministry of Agriculture, Beijing 100081, P.R.China;3. Wuqiao Experimental Station, China Agricultural University, Cangzhou 061800, P.R.China |
| |
| Abstract: | The objective of this study was to compare the performance of two different remotely sensed techniques in detecting the effects of terminal heat stress and N fertilization on final maize aerial biomass (AB) and grain yield (GY). The study was conducted under field conditions for two consecutive growing seasons. Six N treatments combining three doses 0, 100, 200 Kg N ha?1] and two timings at V4 and at 15 days before silking] were applied. Within each N treatment three heat treatments were applied (pre-flowering, post-flowering and the control treatment at ambient air temperature). Remote sensing measurements were taken with a multispectral band camera to measure the normalized difference vegetation index (NDVI) and a digital Red/Green/Blue (RGB) camera to measure the normalized green red difference index (NGRDI). Both indices failed to predict the GY of pre-flowering heat-treated plants due to grain set establishment problems that could not be detected by vegetation indices which are designed to capture differences in green canopy area. In contrast, both the NGRDI and the NDVI correlated positively with GY and AB in the control heat treatment and to a lesser extent in the post-flowering heat treatment. Under the control heat treatment, the NGRDI exhibited higher correlations with AB and GY than the NDVI across the N fertilization treatments. Since the NGRDI is formulated based only on the reflectance in the visible regions (VIS) of the spectrum (Green and Red) without dependence on the near infrared regions (NIR), it performs better than the NDVI. This is because it overcame the reported saturation patterns at high leaf area index and was more efficient at capturing even small differences in leaf colour (chlorophyll content) due to the different applied N treatments. Also, the NGRDI seemed to be a more seasonally independent parameter than the NDVI, which is more affected by temporal variability within the field, and thus the NGRDI predicted AB and GY better than the NDVI when combining the data of the two growing seasons. |
| |
| Keywords: | Abiotic stresses Digital RGB images Multispectral images Vegetation indices |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
