Field-based remote sensing models predict radiation use efficiency in wheat.
Field-based remote sensing models predict radiation use efficiency in wheat.
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DOI：
10.1093/jxb/erab115
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发表时间：
2021-05-04
影响因子：
6.9
通讯作者：
Murchie EH
中科院分区：
文献类型：
--
作者：
Robles-Zazueta CA;Molero G;Pinto F;Foulkes MJ;Reynolds MP;Murchie EH
辐射利用效率的预测准确率约为70%。冠层含水量、绿度和气体交换光谱指数是芸香、生物量积累和光截获的最佳预测因子。许多地区的小麦产量停滞不前或下降，需要努力提高光转换效率，即辐射利用效率（芸香）。芸香是植物生理学中的一个关键性状，因为它将光捕获和初级代谢与生物量积累和产量联系起来，但其测量耗时，这限制了其在基础研究和大规模生理育种中的应用。在这项研究中，高通量植物表型（HTPP）的方法被用于在田间生长的小麦群体与芸香和光合性状的变化，建立预测模型的芸香，生物量和截获的光合有效辐射（IPAR）。三种方法：最佳组合的传感器，冠层植被指数和偏最小二乘回归。与地面实况数据相比，使用遥感模型预测的芸香准确率高达70%。水分指数和冠层绿度指数[归一化差异植被指数（NDVI）、增强植被指数（EVI）]是预测芸香、生物量和IPAR的较好选择，与气体交换、非光化学猝灭[光化学反射指数（PRI）]和衰老[结构不敏感色素指数（SIPI）]相关的指数是营养期和灌浆期这些性状的较好预测因子。分别这些模型将有助于解释冠层过程，改善作物生长和产量建模，并可能用于预测不同作物或生态系统的芸香。
Radiation use efficiency can be predicted with ~70% accuracy. Canopy water content, greenness, and gas exchange spectral indices are the best predictors for RUE, biomass accumulation, and light interception. Wheat yields are stagnating or declining in many regions, requiring efforts to improve the light conversion efficiency, known as radiation use efficiency (RUE). RUE is a key trait in plant physiology because it links light capture and primary metabolism with biomass accumulation and yield, but its measurement is time consuming and this has limited its use in fundamental research and large-scale physiological breeding. In this study, high-throughput plant phenotyping (HTPP) approaches were used among a population of field-grown wheat with variation in RUE and photosynthetic traits to build predictive models of RUE, biomass, and intercepted photosynthetically active radiation (IPAR). Three approaches were used: best combination of sensors; canopy vegetation indices; and partial least squares regression. The use of remote sensing models predicted RUE with up to 70% accuracy compared with ground truth data. Water indices and canopy greenness indices [normalized difference vegetation index (NDVI), enhanced vegetation index (EVI)] are the better option to predict RUE, biomass, and IPAR, and indices related to gas exchange, non-photochemical quenching [photochemical reflectance index (PRI)] and senescence [structural-insensitive pigment index (SIPI)] are better predictors for these traits at the vegetative and grain-filling stages, respectively. These models will be instrumental to explain canopy processes, improve crop growth and yield modelling, and potentially be used to predict RUE in different crops or ecosystems.
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影响因子：
1.6
作者：
Cabrera-Bosquet, L.;Molero, G.;Araus, J. L.
通讯作者：
Araus, J. L.
影响因子：
6.6
作者：
Alvarado, Gregorio;Rodriguez, Francisco M.;Lopez-Cruz, Marco A.
通讯作者：
Lopez-Cruz, Marco A.
影响因子：
3.6
作者：
Galan, Rodrigo Jose;Bernal-Vasquez, Angela-Maria;Miedaner, Thomas
通讯作者：
Miedaner, Thomas
影响因子：
2.3
作者：
Babar, MA;Reynolds, MP;Stone, ML
通讯作者：
Stone, ML
影响因子：
7.3
作者：
Fu, Peng;Meacham-Hensold, Katherine;Bernacchi, Carl
通讯作者：
Bernacchi, Carl