Correlation between grain quality of foxtail millet ( Setaria italica [L.] P. Beauv.) and environmental factors on multivariate statistical analysis

Abstract

Correlation between grain quality of foxtail millet (Setaria italica [L.] P. Beauv.) and environmental factors on multivariate statistical analysis

Development of a Comprehensive Quality Evaluation System for Foxtail Millet from Different Ecological Regions

“…The ADV ranged from 1.17 to 4.60, and Yuci had the highest ADV, which was significantly higher than that of Zezhou, Dingxiang, and Xingxian. Ning et al [21] found a lower ADV of millet grown in Yuci than was found in this study, likely due to differences in characteristics and interannual environments. The GC of crops determines whether they are soft or firm when cooked [36].…”

“…The alkali digestion value (ADV) was determined according to the method described by Ning et al [ 21 ]. Twenty shelled foxtail millet grains of uniform size were placed in a Petri dish, and 10 mL of 1.7% KOH was pipetted into the dish until the grains were completely submerged.…”

“…We calculated the comprehensive principal component model by taking the ratio of the four principal components to the corresponding feature values and obtained the sum of the feature values of all extracted principal components as follows: The comprehensive scores were calculated using the quality evaluation system, and the results showed that foxtail millet produced in Dingxiang (0.89) had the highest comprehensive quality, followed by Zezhou (0.10), Qinxian (0.02), and Xingxian (−0.12); the quality of foxtail millet in Yuci (−0.88) was the lowest (Table 2). The quality of foxtail millet is affected by genetic traits [3], production area [22], environmental factors [21], harvest period [4,14], cultivation methods [41], and farming systems [42]. Similarly, variations in foxtail millet quality in different production areas were the result of comprehensive factors including variations in soil texture, fertility level, cultivation system, and climatic factors such as sunshine duration, temperature, and precipitation [21,29].…”

“…The relationship between different quality indicators can be clarified using multivariate statistical analyses, such as cluster analysis, correlation analysis, and principal component analysis. After classifying and simplifying the indicators, a comprehensive evaluation may be carried out [ 21 ].…”

Application progress of grey model technology in agricultural science

“…Peng (2017) used grey incidence analysis to study the degree of incidence between meteorological disasters and grain yield per unit area and 5 specific grain crops in Henan Province from the perspective of light, moderate and severe disasters. Ning et al (2017) measured the degree of grey incidence between comprehensive quality of millet and environmental factors in 5 representative locations in Shanxi Province under different ecological conditions. Zhao et al (2017b) analyzed the spatio-temporal variation characteristics of degree of incidence between winter wheat yield at county level and climatic factors in the growth period of winter wheat.…”