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您的检索式：作者名="P.SCHACHTMAN"
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| 1 | Seasonal water use characteristics of tall wheatgrass [Agropyron elongatum (Host) Beauv.] in a saline environment显示文摘 | T. M.BLEBY M.AUCOTE A. K.KENNETT‐SMITH G. R.WALKER D. P.SCHACHTMAN | 2008 | Plant Cell & Environment2008，，11： | 1 |
| 2 | Evaluation of UAV-derived multimodal remote sensing data for biomass prediction and drought tolerance assessment in bioenergy sorghum显示文摘Screening for drought tolerance is critical to ensure high biomass production of bioenergy sorghum in arid or semi-arid environments. The bottleneck in drought tolerance selection is the challenge of accurately predicting biomass for a large number of genotypes. Although biomass prediction by lowaltitude remote sensing has been widely investigated on various crops, the performance of the predictions are not consistent, especially when applied in a breeding context with hundreds of genotypes. In some cases, biomass prediction of a large group of genotypes benefited from multimodal remote sensing data;while in other cases, the benefits were not obvious. In this study, we evaluated the performance of single and multimodal data(thermal, RGB, and multispectral) derived from an unmanned aerial vehicle(UAV) for biomass prediction for drought tolerance assessments within a context of bioenergy sorghum breeding. The biomass of 360 sorghum genotypes grown under well-watered and water-stressed regimes was predicted with a series of UAV-derived canopy features, including canopy structure, spectral reflectance, and thermal radiation features. Biomass predictions using canopy features derived from the multimodal data showed comparable performance with the best results obtained with the single modal data with coefficients of determination(R2) ranging from 0.40 to 0.53 under water-stressed environment and0.11 to 0.35 under well-watered environment. The significance in biomass prediction was highest with multispectral followed by RGB and lowest with the thermal sensor. Finally, two well-recognized yieldbased drought tolerance indices were calculated from ground truth biomass data and UAV predicted biomass, respectively. Results showed that the geometric mean productivity index outperformed the yield stability index in terms of the potential for reliable predictions by the remotely sensed data.Collectively, this study demonstrated a promising strategy for the use of different UAV-based imaging sensors to quantify yield-based drought tolerance. | Jiating Li Daniel P.Schachtman Cody F.Creech Lin Wang Yufeng Ge Yeyin Shi | 2022 | The Crop Journal2022，10，5： | 1 |
| 3 | Recent Advances in Nutrient Sensing and Signaling显示文摘 | Daniel P.Schachtman | 2012 | Molecular Plant2012，5，6： | 0 |
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