| 1. |
- Gao, Xiang, et al.
(author)
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Planting Age Identification and Yield Prediction of Apple Orchard Using Time-Series Spectral Endmember and Logistic Growth Model
- 2023
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In: Remote Sensing. - : MDPI AG. - 2072-4292. ; 15:3, s. 642-642
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Journal article (peer-reviewed)abstract
- In response to significant shifts in dietary and lifestyle preferences, the global demand for fruits has increased dramatically, especially for apples, which are consumed worldwide. Growing apple orchards of more productive and higher quality with limited land resources is the way forward. Precise planting age identification and yield prediction are indispensable for the apple market in terms of sustainable supply, price regulation, and planting management. The planting age of apple trees significantly determines productivity, quality, and yield. Therefore, we integrated the time-series spectral endmember and logistic growth model (LGM) to accurately identify the planting age of apple orchard, and we conducted planting age-driven yield prediction using a neural network model. Firstly, we fitted the time-series spectral endmember of green photosynthetic vegetation (GV) with the LGM. By using the four-points method, the environmental carrying capacity (ECC) in the LGM was available, which serves as a crucial parameter to determine the planting age. Secondly, we combined annual planting age with historical apple yield to train the back propagation (BP) neural network model and obtained the predicted apple yields for 12 counties. The results show that the LGM method can accurately estimate the orchard planting age, with Mean Absolute Error (MAE) being 1.76 and the Root Mean Square Error (RMSE) being 2.24. The strong correlation between orchard planting age and apple yield was proved. The results of planting age-driven yield prediction have high accuracy, with the MAE up to 2.95% and the RMSE up to 3.71%. This study provides a novel method to accurately estimate apple orchard planting age and yields, which can support policy formulation and orchard planning in the future.
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| 2. |
- Yang, Xiaomin, et al.
(author)
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Phytolith-rich straw application and groundwater table management over 36 years affect the soil-plant silicon cycle of a paddy field
- 2020
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In: Plant and Soil. - : Springer. - 0032-079X .- 1573-5036. ; 454, s. 343-358
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Journal article (peer-reviewed)abstract
- Background and aims Silicon (Si) deficiency is a major constraint on rice production. The objective of this study was to evaluate the long-term influence of phytolith-rich straw return and groundwater table management on labile Si fractions in paddy soil and subsequent plant Si uptake. Methods A field experiment was conducted over 36 years in subtropical China with different application doses of phytolith-rich straw and a groundwater table of either 20 or 80 cm. An optimized sequential chemical extraction procedure allowed us to determine labile Si fractions, represented by CaCl2-Si, Acetic-Si, H2O2-Si, Oxalate-Si, and Na2CO3-Si. Additional analyses included the determination of amorphous silica particles in soil, phytoliths in supplied straw, Si in planted rice straw, and the dissolution rate of phytoliths extracted from supplied straw. Results Long-term application of phytolith-rich straw significantly increased the H2O2-Si and Na2CO3-Si contents. The CaCl2-Si (5.21-7.91 mg kg(- 1)), H2O2-Si (50.0-72.4 mg kg(- 1)) and Na2CO3-Si (3.33-4.60 g kg(- 1)) contents were positively correlated with soil organic carbon. The Si content (13.6-28.9 g kg(-& x200d;1)) in planted rice straw significantly (p < 0.05) increased with the application dose of phytolith-rich straw under both groundwater tables. This effect was significantly (p < 0.05) greater under 80 cm groundwater table than under 20 cm groundwater table for matching straw amendments. Conclusions This study indicates that long-term application of phytolith-rich straw and groundwater management significantly increase soil Si bioavailability by promoting accumulation of organic matter and phytoliths, and enhancing the soil-plant Si cycle.
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| 3. |
- Guo, Zhiming, et al.
(author)
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Nondestructive monitoring storage quality of apples at different temperatures by near-infrared transmittance spectroscopy
- 2020
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In: Food Science & Nutrition. - : WILEY. - 2048-7177. ; 8:7, s. 3793-3805
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Journal article (peer-reviewed)abstract
- Apple is the most widely planted fruit in the world and is popular in consumers because of its rich nutritional value. In this study, the portable near-infrared (NIR) transmittance spectroscopy coupled with temperature compensation and chemometric algorithms was applied to detect the storage quality of apples. The postharvest quality of apples including soluble solids content (SSC), vitamin C (VC), titratable acid (TA), and firmness was evaluated, and the portable spectrometer was used to obtain near-infrared transmittance spectra of apples in the wavelength range of 590-1,200 nm. Mixed temperature compensation method (MTC) was used to reduce the influence of temperature on the models and to improve the adaptability of the models. Then, variable selection methods, such as uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS), and successive projections algorithm (SPA), were developed to improve the performance of the models by determining characteristic variables and reducing redundancy. Comparing the full spectral models with the models established on variables selected by different variable selection methods, the CARS combined with partial least squares (PLS) showed the best performance with prediction correlation coefficient (R-p) and residual predictive deviation (RPD) values of 0.9236, 2.604 for SSC; 0.8684, 2.002 for TA; 0.8922, 2.087 for VC; and 0.8207, 1.992 for firmness, respectively. Results showed that NIR transmittance spectroscopy was feasible to detect postharvest quality of apples during storage.
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| 4. |
- Guo, Zhiming, et al.
(author)
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Quantitative detection of apple watercore and soluble solids content by near infrared transmittance spectroscopy
- 2020
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In: Journal of Food Engineering. - : ELSEVIER SCI LTD. - 0260-8774 .- 1873-5770. ; 279
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Journal article (peer-reviewed)abstract
- Near-infrared (NIR) spectroscopy as an emerging analytical technique was used for the first time to quantitatively detect the watercore degree and soluble solids content (SSC) in apple. To reduce the data processing time and meet the needs of practical application, the variable selection methods including synergy interval (SI), successive projections algorithm (SPA), genetic algorithm (GA) and competitive adaptive reweighted sampling (CARS) were used to identify the characteristic variables and simplify the models. The spectral variables closely related to the apple bioactive components were used for the establishment of the partial least squares (PLS) models. The predictive correlation coefficient (R-p), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD) were used to estimate the performance of the models. The CARS-PLS models displayed the best prediction performance using 600-1000 nm spectra with R-p, RMSEP, and RPD values of 0.9562, 1.340% and 3.720 for apple watercore degree; 0.9808, 0.327 (o)Bx and 4.845 for apple SSC, respectively. These results demonstrate the potential of the NIR transmittance spectroscopy technology for quantitative detection of SSC and watercore degree in apple fruit.
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| 5. |
- Qin, Zhilian, et al.
(author)
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Vertical distributions of organic carbon fractions under paddy and forest soils derived from black shales : Implications for potential of long-term carbon storage
- 2021
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In: Catena (Cremlingen. Print). - : Elsevier. - 0341-8162 .- 1872-6887. ; 198, s. 1-8
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Journal article (peer-reviewed)abstract
- Black shales are characterized by a high content of organic carbon (C). Few studies have focused on the influence of land use on soil organic C (SOC) fractions from soils derived from black shale (black shale soils). The objective of this study was to elucidate the influence of land use on SOC fractions in black shale soils combining chemical determination and stable C isotope analysis techniques. Herein, we determined labile organic C (LOC), semilabile organic C (Semi-LOC), and recalcitrant organic C (ROC) fractions in various depths of soils in paddy fields (0-70 cm) and forests (0-120 cm) from black shale distribution region in Hunan province, China, and then investigated delta C-13 values of these soils. Results showed that the contents of LOC, Semi-LOC, and ROC in paddy soils (1.63-7.35 g kg(-1), 0.35-1.21 g kg(-1), and 3.75-14.8 g kg(-1), respectively) and forest soils (0.73-4.94 g kg(-1), 0.12-0.89 g kg(-1), and 1.44-8.96 g kg(-1), respectively) are significantly decreased with increasing depth. The contribution made by LOC to SOC in paddy soils was significantly lower than that in forest soils, while the contribution made by ROC to SOC was significantly higher in paddy soils than that in forest soils. In these two land uses, the delta C-13 values were higher in SOC compared to the ROC fraction, while the delta C-13 values were close in the ROC fraction below 20 cm soil depth. Our study indicated that i) new C is mainly limited to the surface soil layer (0-10 cm) in forests, while it can be leached along the soil profiles in paddy fields; ii) the estimated ROC pool is similar to 900 Pg within the 0-100 cm soil layer in terrestrial ecosystems, which should better represent the ability of soil C sequestration.
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