Analysis of spatio-temporal variation of crop yield in China using stepwise multiple linear regression

Liu, Yingxia; Heuvelink, G.B.M.; Bai, Z.G.; He, Ping; Xu, Xinpeng; Ding, Wencheng; Huang, Shaohui

doi:10.1016/j.fcr.2021.108098

Cited by 37 publications

(13 citation statements)

References 35 publications

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“…Guided by the conceptual framework, four potential macroenvironmental determinants, including the government policy, entrepreneurship and investment, market demand, and regional transportation facilities, were selected (Table 1). A multiple stepwise regression (MSR) model, an extension of linear regression models based on Pearson's correlation coefficient, was carried out to discover the empirical relationships between multiple types of dependent variables and independent variables [49]. The MSR model in this study can be derived as:…”

Section: Identifying Key Determinants Of the Spatiotemporal Patterns ...mentioning

confidence: 99%

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Characterizing the Spatiotemporal Patterns and Key Determinants of Homestay Industry Agglomeration in Rural China Using Multi Geospatial Datasets

Zheng

Huang

2022

Sustainability

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Understanding the spatiotemporal patterns and key determinants of rural homestay industry agglomeration is crucial for the well-planning and well-management of rural tourism during the process of rural revitalization in China. By employing multi geospatial datasets, this study investigated the long-term spatiotemporal patterns and their key determinants of homestay inns during the period 2004–2019 in Moganshan, a well-known rural tourism destination in Zhejiang Province, China. The kernel density estimation and spatial autocorrelation were integrated to identify the hotspots of rural homestay inns at a fine scale. The key determinants were further uncovered using multiple stepwise regression and logistic regression models. The result shows that the overall growth of homestay inns was slow at the early stage and has progressed rapidly since 2014, with 94.2% of homestay inns newly opened during the period 2014–2019. The first hotspot was located in Moganshan National Park and then spread to the surrounding villages. Three hotspot zones have emerged, including the northern hotspot zone (Sihe-Xiantan), central hotspot zone (Houwu-Park-Liaoyuan), and southern hotspot zone (Ziling-Laoling-Lanshukeng) by 2019. The modeling indicates that government policy was an essential determinant for the increase in homestay inns, followed by entrepreneurship and investment. The new homestay inns were more likely to occur in settlements close to scenic spots, river networks, and cultivated land. Abundant scenic spots and heterogeneous landscapes were also preferred when selecting sites and executing landscape design for homestay inns. Our empirical study has provided practical insights for policy makers, entrepreneurs, and planners for future sustainable homestay industry development.

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Section: Identifying Key Determinants Of the Spatiotemporal Patterns ...mentioning

confidence: 99%

“…Then, to maintain the model with the highest determination coefficient, variables with no significant parameters were gradually eliminated. Finally, the MSR model was constructed while maintaining the significance of the parameters [49].…”

Section: Identifying Key Determinants Of the Spatiotemporal Patterns ...mentioning

confidence: 99%

Characterizing the Spatiotemporal Patterns and Key Determinants of Homestay Industry Agglomeration in Rural China Using Multi Geospatial Datasets

Zheng

Huang

2022

Sustainability

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“…SRA is applied for predicting the multiple linear regression models between several dependent and explanatory variables using the observed data (Liu et al, 2021). According to the results of correlation and the other three indices were seen as independent variables (X 1 , X 2 , and X 3 ).…”

Section: Stepwise Regression Analysis (Sra)mentioning

confidence: 99%

Quality improvement of freeze‐dried carrots as affected by sugar‐osmotic and hot‐air pre‐treatments

Lyu

Chen

et al. 2022

Food Processing Preservation

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To improve the visual-sensory and nutritional quality of freeze-dried carrots (FDC), the effects of sugar-osmotic dehydration (SO, 40°Brix, 3 h) and hot-air pre-drying (HAPD, 80°C, 10 min) on the FDC quality were investigated. Overall, SO&HAPD significantly improved the redness (8.26%-11.67%), yellowness (1.24%-5.82%) and ABTS radical scavenging activity (1.40%-16.00%) of FDC. FDC pre-treated by sucrose showed the highest a* value (33.31), which was related to the β-carotene content. The influence order of independent variables on the redness was ABTS radical scavenging activity > total sugar content > β-carotene content. As for the carotenoid retention, the sample only treated by HAPD had the highest contents of β-carotene (119.97 μg/g) and α-carotene (93.73 μg/g). These results indicated that SO&HAPD and HAPD were effective pre-treatments for improving the color and carotenoid contents of FDC, respectively. This work provides an experimental-theoretical basis for the quality enhancement of carrot products. Practical applicationsFreeze drying (FD) preserves the nutrients of the fresh material to the greatest extent; however, it undesirably decreases the redness/yellowness level of yellow vegetables/ fruits. This discoloration reduces the edible quality of freeze-dried products and affects the consumers' acceptance. Sugar osmotic dehydration (SOD) is widely used in the food production of high moisture materials to prevent the color damage and to improve the redness of samples. The positive effects of the sugar-osmotic combined with hot-air pre-drying treatments (SO&HAPD) on the physio-chemical quality improvement of freeze-dried carrots (FDC) were explored. SO&HAPD was an effective way to improve the redness, yellowness and ABTS radical scavenging activity of FDC. The core indices (ABTS radical scavenging activity, total sugar content, βcarotene content) selected through the stepwise regression analysis may help control the color quality of FDC for industrial scale-up. Moreover, this study can also provide an experimental-theoretical basis for the FD industry in producing yellow vegetables/ fruits with original colors and high nutritional retention.

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“…The stepwise multiple linear regression (SMLR) model could remove variables that caused multicollinearity and determine the independent variables that have a significant effect on the target object [46]. In light of previous studies, SMLR was developed to reveal the main temporal factors (Table 1) influencing changes in urban landscape patterns of CUAG [28,47].…”

Section: Stepwise Multiple Linear Regressionmentioning

confidence: 99%

Spatial-Temporal Driving Factors of Urban Landscape Changes in the Karst Mountainous Regions of Southwest China: A Case Study in Central Urban Area of Guiyang City

et al. 2022

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Rapid urban expansion has significantly altered the regional landscape pattern, posing a serious threat to the sustainable development of natural and social ecosystems. By using landscape patterns indices and an area transfer matrix, this study analyzed the spatial-temporal changes of landscape patterns in the karst mountainous cities of southwest China from 2000 to 2020, by taking the central urban area of Guiyang City (CUAG) as the study area. This study explored the spatial and temporal driving factors of landscape pattern changes by using stepwise multiple linear regression and geographic detector methods. The results show: (1) CUAG’s landscape types altered changed drastically, with the area of forestland and construction land rapid increment and cultivated land decrement significantly. (2) The patches of construction land and forestland tended to be aggregated, the degree of fragmentation was reduced, and the shape was complex; cultivated land fragmentation was intensified. The connectivity of the landscape was improved, while the level of landscape diversity declined, the trend of landscape homogenization was obvious. (3) Socioeconomic and geographical endowment drivers have determined landscape pattern changes. The findings of this study may be used to interpret other similar landscapes worldwide and may imply the protection of urban ecosystem and sustainable development.

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Analysis of spatio-temporal variation of crop yield in China using stepwise multiple linear regression

Cited by 37 publications

References 35 publications

Characterizing the Spatiotemporal Patterns and Key Determinants of Homestay Industry Agglomeration in Rural China Using Multi Geospatial Datasets

Characterizing the Spatiotemporal Patterns and Key Determinants of Homestay Industry Agglomeration in Rural China Using Multi Geospatial Datasets

Quality improvement of freeze‐dried carrots as affected by sugar‐osmotic and hot‐air pre‐treatments

Spatial-Temporal Driving Factors of Urban Landscape Changes in the Karst Mountainous Regions of Southwest China: A Case Study in Central Urban Area of Guiyang City

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