Shorten Paul R, Leath Shane R, Schmidt Jana, Ghamkhar Kioumars
1AgResearch, Ruakura Research Centre, Private Bag 3123, Hamilton, 3240 New Zealand.
2AgResearch, Grasslands Research Centre, Private Bag 11008, Palmerston North, 4442 New Zealand.
Plant Methods. 2019 Jun 6;15:63. doi: 10.1186/s13007-019-0448-2. eCollection 2019.
The quality of forage plants is a crucial component of animal performance and a limiting factor in pasture based production systems. Key forage attributes that may require improvement include the sugar, lipid, protein and energy contents of the vegetative parts of these plants. The aim of this study was to evaluate the potential capacity of hyperspectral imaging (HSI) for non-invasive assessment of forage chemical composition. Hyperspectral image data within the visible near-infrared range into the extended near-infrared covering 550-1700 nm wavelengths were obtained from 185 accessions of ryegrass (), which were also analysed for 13 forage quality attributes.
Medium to high predictive power was observed for the HSI models of total sugars (R validation of 0.58), high molecular weight sugars (R validation of 0.63), %Ash (R validation of 0.50) and %Nitrogen (R validation of 0.70). Significant HSI models with low R validation of 0.1-0.5 were also obtained for low molecular weight sugars, NDF (%), ADF (%), DOMD (% DM), ME (MJ/kg DM), DM (%), Ca (mg/g) and OM (%). We also observed significant differences in the chemical composition between the pseudostems and leaves of the plants for each accession. The power of HSI for prediction of these differences within plants was also demonstrated.
This study paves the way for the HSI technology to be used for in-field estimation of forage composition attributes in perennial ryegrass. This will allow more rapid genetic-based selection and breeding for a trait that is normally expensive to measure providing a cheaper, non-destructive and high throughput screening tool.
饲用植物的质量是动物生产性能的关键组成部分，也是基于牧场的生产系统中的一个限制因素。可能需要改进的关键饲用属性包括这些植物营养部分的糖、脂质、蛋白质和能量含量。本研究的目的是评估高光谱成像（HSI）对饲用化学成分进行非侵入性评估的潜在能力。从185份黑麦草种质中获取了550 - 1700纳米波长范围内的可见近红外到扩展近红外的高光谱图像数据，并对其13种饲用质量属性进行了分析。
总糖（验证集R为0.58）、高分子量糖（验证集R为0.63）、灰分百分比（验证集R为0.50）和氮百分比（验证集R为0.70）的HSI模型具有中到高的预测能力。对于低分子量糖、中性洗涤纤维（%）、酸性洗涤纤维（%）、可消化有机物（%干物质）、代谢能（兆焦/千克干物质）、干物质（%）、钙（毫克/克）和有机物（%），也获得了验证集R为0.1 - 0.5的显著HSI模型。我们还观察到每个种质的植物假茎和叶片之间的化学成分存在显著差异。HSI预测植物内部这些差异的能力也得到了证明。
本研究为HSI技术用于多年生黑麦草饲用成分属性的田间估计铺平了道路。这将允许对通常测量成本高昂的性状进行更快速的基于遗传的选择和育种，提供一种更便宜、非破坏性且高通量的筛选工具。
