Sugarcane breeding is resource-intensive and time-consuming, and could benefit substantially from the integration of aerial phenotyping (AP) for rapidly identifying genotypes with superior yield traits. The study aimed to assess the feasibility of using AP to enhance sugarcane breeding by rapidly identifying genotypes with superior yield traits. The specific objectives of the study were to: (1) assess the impacts of canopy cover and stomatal conductance on stalk dry mass yield (SDM); (2) assess the feasibility of estimating these traits with aerially sensed normalized difference vegetation index (NDVI) and canopy temperature (Tc); (3) evaluate the potential for predicting SDM from NDVI and Tc; (4) formulate best AP procedures. The study comprised a replicated field trial near Komatipoort, South Africa, with 54 genotypes grown under well-watered and water deficit conditions. Traits were measured on the ground (canopy cover and stomatal conductance) and remotely sensed from the air with a drone (NDVI and Tc) throughout the plant and first ratoon crops, and SDM was measured at harvest. Measurements were categorized by crop water status and extent of canopy cover, and phenotypic trait correlations were analyzed for these different categories. The study confirmed canopy cover and stomatal conductance as influential traits for determining SDM. Canopy cover could be used as a proxy for identifying high- and low-yielding genotypes early on in water stress-free crops. Findings suggest that high stomatal conductance benefits well-watered crops, while relatively low conductance could be advantageous in dry environments, though further investigation is needed. Canopy cover was predicted well from NDVI at partial canopy for well-watered crops, while the prediction of stomatal conductance from Tc lacked reliability. It was concluded that NDVI and Tc could be used to identify high- and low-yielding genotypes when measured earlier on in the growth cycle for well-watered crops. Results also showed potential for using water treatment differences in Tc and SDM to identify drought tolerant genotypes. Lastly, the study highlighted methodological challenges and insights for future agronomic trait prediction using AP techniques. The findings of this study will be used in further testing in the early stages of the breeding programme along with the breeding populations, ultimately helping to manage breeding strategies for target environments. This has the potential to enhance breeding efficiency and ultimately genetic gains towards productive sugarcane cultivars for the future.

中文翻译：

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甘蔗产量和耐旱性的空中表型分析

甘蔗育种是资源密集型且耗时的，可以从空中表型分析 (AP) 的整合中获益匪浅，以快速识别具有优异产量性状的基因型。该研究旨在评估利用 AP 通过快速鉴定具有优异产量性状的基因型来增强甘蔗育种的可行性。研究的具体目标是：（1）评估冠层覆盖度和气孔导度对茎秆干物质产量（SDM）的影响； （2）评估利用航空遥感归一化植被指数（NDVI）和冠层温度（Tc）估计这些性状的可行性； (3) 评估根据 NDVI 和 Tc 预测 SDM 的潜力； (4)制定最佳AP程序。该研究包括在南非科马蒂普特附近进行的重复田间试验，其中 54 种基因型在浇水良好和缺水的条件下生长。在地面上测量了整个植物和第一批宿根作物的性状（冠层覆盖度和气孔导度），并使用无人机从空中遥感（NDVI 和 Tc），并在收获时测量了 SDM。根据作物水分状况和冠层覆盖范围对测量结果进行分类，并分析这些不同类别的表型性状相关性。该研究证实冠层覆盖度和气孔导度是确定 SDM 的影响因素。冠层覆盖度可以作为早期识别无水胁迫作物高产和低产基因型的指标。研究结果表明，高气孔导度有利于浇水良好的作物，而相对较低的气孔导度在干燥环境中可能有利，但还需要进一步研究。对于浇水良好的作物，从部分冠层的 NDVI 可以很好地预测冠层覆盖度，而从 Tc 预测气孔导度缺乏可靠性。结论是，在浇水良好的作物生长周期的早期进行测量时，NDVI 和 Tc 可用于识别高产和低产基因型。结果还表明，利用水处理的 Tc 和 SDM 差异来识别耐旱基因型具有潜力。最后，该研究强调了使用 AP 技术预测未来农艺性状的方法挑战和见解。这项研究的结果将用于育种计划早期阶段以及育种种群的进一步测试，最终帮助管理目标环境的育种策略。这有可能提高育种效率，并最终获得未来高产甘蔗品种的遗传收益。