Using Canopy Measurements to Predict Soybean Seed Yield

Exploration and genetic analyses of canopy leaf pigmentation changes in soybean (Glycine max L.): unveiling a novel phenotype

KEY MESSAGE

Pigmentation changes in canopy leaves were first reported, and subsequent genetic analyses identified a major QTL associated with levels of pigmentation changes, suggesting Glyma.06G202300 as a candidate gene. An unexpected reddish-purple pigmentation in upper canopy leaves was discovered during the late reproductive stages in soybean (Glycine max L.) genotypes. Two sensitive genotypes, 'Uram' and PI 96983, exhibited anomalous canopy leaf pigmentation changes (CLPC), while 'Daepung' did not. The objectives of this study were to: (i) characterize the physiological features of pigmented canopy leaves compared with non-pigmented leaves, (ii) evaluate phenotypic variation in a combined recombinant inbred line (RIL) population (N = 169 RILs) under field conditions, and (iii) genetically identify quantitative trait loci (QTL) for CLPC via joint population linkage analysis. Comparison between pigmented and normal leaves revealed different Fv/Fm of photosystem II, hyperspectral reflectance, and cellular properties, suggesting the pigmentation changes occur in response to an undefined abiotic stress. A highly significant QTL was identified on chromosome 6, explaining ~ 62.8% of phenotypic variance. Based on the QTL result, Glyma.06G202300 encoding flavonoid 3'-hydroxylase (F3'H) was identified as a candidate gene. In both Uram and PI 96983, a 1-bp deletion was confirmed in the third exon of Glyma.06G202300 that results in a premature stop codon in both Uram and PI 96983 and a truncated F3'H protein lacking important domains. Additionally, gene expression analyses uncovered significant differences between pigmented and non-pigmented leaves. This is the first report of a novel symptom and an associated major QTL. These results will provide soybean geneticists and breeders with valuable knowledge regarding physiological changes that may affect soybean production. Further studies are required to elucidate the causal environmental stress and the underlying molecular mechanisms.

Quantitative Monitoring of Leaf Area Index in Rice Based on Hyperspectral Feature Bands and Ridge Regression Algorithm

Leaf area index (LAI) is one of the indicators measuring the growth of rice in the field. LAI monitoring plays an important role in ensuring the stable increase of grain yield. In this study, the canopy reflectance spectrum of rice was obtained by ASD at the elongation, booting, heading and post-flowering stages of rice, and the correlations between the original reflectance (OR), first-derivative transformation (FD), reciprocal transformation (1/R), and logarithmic transformation (LOG) with LAI were analyzed. Characteristic bands of spectral data were then selected based on the successive projections algorithm (SPA) and Pearson correlation. Moreover, ridge regression (RR), partial least squares (PLS), and multivariate stepwise regression (MSR) were conducted to establish estimation models based on characteristic bands and vegetation indices. The research results showed that the correlation between canopy spectrum and LAI was significantly improved after FD transformation. Modeling using SPA to select FD characteristic bands performed better than using Pearson correlation. The optimal modeling combination was FD-SPA-VI-RR, with the coefficient of determination (R2) of 0.807 and the root-mean-square error (RMSE) of 0.794 for the training set, R2 of 0.878 and RMSE of 0.773 for the validation set 1, and R2 of 0.705 and RMSE of 1.026 for the validation set 2. The results indicated that the present model may predict the rice LAI accurately, meeting the requirements of large-scale statistical monitoring of rice growth indicators in the field.

The Study on the Relationship between Normalized Difference Vegetation Index and Fractional Green Canopy Cover in Five Selected Crops

Crop models are of great use and importance in modern agriculture. Most models imply spatial vegetation indices, such as NDVI, or canopy cover characteristics, such as FGCC, to provide estimation of crops conditions and forecast productivity. The purpose of the study was to (1) determine the possibility of mutual conversion between spatial NDVI and Canopeo-derived FGCC in five crops (grain corn, sunflower, tomato, millet, and winter wheat) and (2) estimate the precision of such a conversion. The data set of the study was formed by the OneSoil AI derived satellite imagery on NDVI for the studied crops in different stages of their growing season combined with Canopeo-processed photographs of vegetating crops in the field with FGCC percentage calculation. The sets of NDVI and FGCC values were paired up and then statistically processed to obtain polynomial equations of NDVI into FGCC and inverse conversion for each crop. The results of the study revealed that mutual conversion between spatial NDVI and Canopeo-derived FGCC is possible. There is a strong direct correlation (R 2 within 0.6779-0.9000 depending on the crop) between the studied indices for all crops. Close-growing crops, especially winter wheat, showed the highest correlation, while row crops and especially tomatoes had a less strong relationship between vegetation indices. The models for mutual conversion between FGCC and NDVI could be incorporated into the yield simulation models to improve the forecasting capacities.