Breure T S, Milne A E, Webster R, Haefele S M, Hannam J A, Moreno-Rojas S, Corstanje R
Rothamsted Research, Harpenden, AL5 2JQ UK.
Cranfield University, Cranfield, MK43 0AL Bedfordshire UK.
Precis Agric. 2021;22(1):226-248. doi: 10.1007/s11119-020-09739-x. Epub 2020 Aug 10.
How well could one predict the growth of a leafy crop from reflectance spectra from the soil and how might a grower manage the crop in the light of those predictions? Topsoil from two fields was sampled and analysed for various nutrients, particle-size distribution and organic carbon concentration. Crop measurements (lettuce diameter) were derived from aerial-imagery. Reflectance spectra were obtained in the laboratory from the soil in the near- and mid-infrared ranges, and these were used to predict crop performance by partial least squares regression (PLSR). Individual soil properties were also predicted from the spectra by PLSR. These estimated soil properties were used to predict lettuce diameter with a linear model (LM) and a linear mixed model (LMM): considering differences between lettuce varieties and the spatial correlation between data points. The PLSR predictions of the soil properties and lettuce diameter were close to observed values. Prediction of lettuce diameter from the estimated soil properties with the LMs gave somewhat poorer results than PLSR that used the soil spectra as predictor variables. Predictions from LMMs were more precise than those from the PLSR using soil spectra. All model predictions improved when the effects of variety were considered. Predictions from the reflectance spectra, via the estimation of soil properties, can enable growers to decide what treatments to apply to grow lettuce and how to vary their treatments within their fields to maximize the net profit from the crop.
从土壤的反射光谱中预测叶菜类作物的生长情况有多准确？种植者又如何根据这些预测来管理作物呢？采集了两块田地的表土样本，分析了各种养分、粒度分布和有机碳浓度。作物测量数据（生菜直径）来自航空影像。在实验室中获取了土壤在近红外和中红外范围内的反射光谱，并通过偏最小二乘回归（PLSR）用于预测作物表现。还通过PLSR从光谱中预测了各个土壤属性。这些估计的土壤属性用于通过线性模型（LM）和线性混合模型（LMM）预测生菜直径：考虑生菜品种之间的差异以及数据点之间的空间相关性。土壤属性和生菜直径的PLSR预测值与观测值接近。用线性模型根据估计的土壤属性预测生菜直径，结果比使用土壤光谱作为预测变量的PLSR稍差。线性混合模型的预测比使用土壤光谱的PLSR更精确。当考虑品种的影响时，所有模型预测都有所改进。通过估计土壤属性从反射光谱进行预测，可以使种植者决定对生菜采用何种处理方法，以及如何在田间调整处理方式以实现作物净利润最大化。
