Assessing the Contribution of ECa and NDVI in the Delineation of Management Zones in a Vineyard

Esteves, Catarina; Fangueiro, David; Braga, Ricardo P.; Martins, Miguel; Botelho, Manuel; Ribeiro, Henrique

doi:10.3390/agronomy12061331

Cited by 13 publications

(20 citation statements)

References 53 publications

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“…These studies often reveal a positive correlation between ECa and clay content and a negative correlation with sand content. Moreover, other soil properties are also related to ECa, such as CEC, pH, organic matter and different soil nutrients [5][6][7]31]. In this case study, the proposed zoning explained variations in some key soil properties related to soil fertility, such as clay content, CEC or OM.…”

Section: Discussionmentioning

confidence: 81%

“…There are numerous studies where relationships between ECa and soil texture have been established [6,7,9]. These studies often reveal a positive correlation between ECa and clay content and a negative correlation with sand content.…”

Section: Discussionmentioning

confidence: 99%

“…Implementing site-specific management strategies in a field is based on the MZs, but the accurate delineation of MZs in a field is difficult because many factors can affect crop yield and quality. However, the main physical and chemical properties of soil have to be considered by any approach to determine MZs because productivity is highly related to the spatial patterns of soil nutrients [5,6].…”

Section: Introductionmentioning

confidence: 99%

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Using a Non-Contact Sensor to Delineate Management Zones in Vineyards and Validation with the Rasch Model

Moral,

Rebollo,

Serrano

2023

Sensors

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The production of high-quality wines is one of the primary goals of modern oenology. In this regard, it is known that the potential quality of a wine begins to be determined in the vineyard, where the quality of the grape, initially, and later that of the wine, will be influenced by the soil properties. Given the spatial variability of the fundamental soil properties related to the potential grape production, such as texture, soil organic matter content, or cation exchange capacity, it seems that a uniform management of a vineyard is not the most optimal way to achieve higher grape quality. In this sense, the delineation of zones with similar soil characteristics to implement site-specific management is essential, reinforcing the interest in incorporating technologies and methods to determine these homogeneous zones. A case study was conducted in a 3.3 ha vineyard located near Évora, south of Portugal. A non-contact sensor (DUALEM 1S) was used to measure soil apparent electrical conductivity (ECa) in the vineyard, and later, a kriged ECa map was generated. ECa and elevation maps were utilised to delineate homogeneous zones (management zones, MZs) in the field through a clustering process. MZs were validated using some soil properties (texture; pH; organic matter—OM; phosphorous—P2O5; potassium—K2O; the sum of the exchange bases—SEB; and cation exchange capacity—CEC), which were determined from 20 soil samples taken in the different MZs. Validation was also performed using Rasch measures, which were defined based on the formulation of the objective and probabilistic Rasch model, integrating the information from the aforementioned soil properties at each sampling location. The comparison of the MZs was more evident with the use of the Rasch model, as only one value was to be employed in each MZ. Finally, an additional validation was conducted using a vegetation index to consider the plant response, which was different in each MZ. The use of a non-contact sensor to measure ECa constitutes an efficient technological tool for implementing site-specific management in viticulture, which allows for the improvement of decision-making processes by considering the inherent spatial variability of the soil.

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Section: Discussionmentioning

confidence: 81%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Using a Non-Contact Sensor to Delineate Management Zones in Vineyards and Validation with the Rasch Model

Moral,

Rebollo,

Serrano

2023

Sensors

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“…This is of utmost importance in PV. Estevez et al [ 23 ] suggested the combination of different methods because not all tools are suitable to delineate all types of soil. In their experiment, electromagnetic induction (EMI) sensor-based information and the normalized difference vegetation index together proved to be more effective and beneficial than any of them alone.…”

Section: Resultsmentioning

confidence: 99%

How can proximal sensors help decision-making in grape production?

Mizik

2023

Heliyon

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“…In this way, the unknown data can be predicted, and the generalization ability of the soil spectral correction method can be guaranteed by the regression equation. For the SOM prediction model, the soil spectral correction redistributes the observation information of the original hyperspectral remote sensing to ensure the uniformity of the soil physical properties of all pixels, which helps improve the spatiotemporal transferability of the model [45][46][47].…”

Section: Introductionmentioning

confidence: 99%

Improving The Spatiotemporal Transferability of Hyperspectral Remote Sensing for Estimating Soil Organic Matter by Minimizing The Coupling Effect of Soil Physical Properties on The Spectrum

Sui,

Jiang,

Lin

et al. 2024

Preprint

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Soil organic matter (SOM) is important for the global carbon cycle, and hyperspectral remote sensing has proven a promising method for fast SOM content estimation. However, soil physical properties significantly affect the sensitivity of satellite hyperspectral imaging to SOM, leading to poor generalization ability of the estimation model. This study aims to improve the spatiotemporal transferability of the SOM prediction model by alleviating the coupling effect of soil physical properties on the spectra. Based on satellite hyperspectral images and soil physical variables, including soil moisture (SM), soil surface roughness (root mean squared height, RMSH), and soil bulk weight (SBW), a soil spectral correction strategy was established based on the information unmixing method. Two important grain-producing areas in Northeast China were selected as study areas to verify the performance and transferability of the spectral correction model and SOM content prediction model. The results showed that soil spectral corrections based on fourth-order polynomials and the XG-Boost algorithm had excellent accuracy and generalization ability, with residual predictive deviations (RPD) exceeding 1.4 in almost all bands. In addition, when the soil spectral correction strategy was adopted, the accuracy of the SOM prediction model and the generalization ability after model migration were significantly improved. The SOM prediction accuracy based on the XG-Boost corrected spectrum was the highest, with a coefficient of determination (R2) of 0.76, root mean square error (RMSE) of 5.74 g/kg, and RPD of 1.68. The prediction accuracy, R2, RMSE, and RPD of the model after migration were 0.72, 6.71 g/kg, and 1.53, respectively. Compared with the direct migration prediction of the model, adopting the soil spectral correction strategy based on fourth-order polynomials and XG-Boost reduced the RMSE of the SOM prediction results by 57.90% and 60.27%, respectively. The performance comparison highlighted the advantages of considering soil physical properties in regional-scale SOM prediction.

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Assessing the Contribution of ECa and NDVI in the Delineation of Management Zones in a Vineyard

Cited by 13 publications

References 53 publications

Using a Non-Contact Sensor to Delineate Management Zones in Vineyards and Validation with the Rasch Model

Using a Non-Contact Sensor to Delineate Management Zones in Vineyards and Validation with the Rasch Model

How can proximal sensors help decision-making in grape production?

Improving The Spatiotemporal Transferability of Hyperspectral Remote Sensing for Estimating Soil Organic Matter by Minimizing The Coupling Effect of Soil Physical Properties on The Spectrum

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