Development of Management Zones for Site-Specific Fertilization in Mustard Fields

Haroon, Zainab; Cheema, Muhammad Jehanzeb Masud; Saleem, Shoaib Rashid; Anjum, Muhammad Naveed; Amin, Muhammad; Tahir, Muhammad Naveed; Iqbal, Tahir; Khan, Faiza

doi:10.3390/environsciproc2022023001

Cited by 3 publications

(4 citation statements)

References 8 publications

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“…This shows that the characteristics of data are the factors determining the accuracy of interpolated values of soil properties. This agrees with previous studies [19], [20] claiming that data characteristics determine accuracy of interpolated soil property values, with smaller sill resulting in smaller RMSE, as observed in pH. As Table 4 shows, the values of RMSE also show a similar pattern, in that SAP is having much higher RMSE compared to those in pH and EC.…”

Section: The Results Of Interpolationsupporting

confidence: 90%

“…Although different interpolation methods do exist, there is no universal one that can be applied to any conditions, and the choice of interpolation depends mainly on the nature of the phenomena and the characteristics of the data [18]. Different spatial interpolation methods resulted in different soil properties being accurately interpolated [19]. Geostatistical techniques have a substantial role in examining how soil fertility qualities vary in other areas [20].…”

Section: Introductionmentioning

confidence: 99%

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Characterizing, Predicting, and Mapping Soil Available Phosphor, pH, and Electrical Conductivity in Rubber Plantation Based on Three Different Methods of Ordinary Kriging in GIS Environment

Wijayanto,

Nareswari,

Saputra

et al. 2024

Int. J. Adv. Sci. Eng. Inf. Technol.

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Soils in rubber plantations have unique characteristics due to their prolonged and excessive uses. These practices have changed soil qualities, production, and the geographical distribution of soil conditions. For this reason, research on the spatial analysis of soil attributes in rubber plantations is essential. Although it is acknowledged that there are proven methods for accounting for geographical variability, their use in rubber plantations is still somewhat restricted. Evidence also demonstrates ongoing debate and variation in the findings on the capacity of methods to predict spatial variability. Therefore, the primary goal of this study is to examine how well various approaches perform when using ordinary kriging to analyze spatial variability of three soil properties: Soil Available Potassium (SAP), pH, and Electrical Conductivity. The methodology employed in this work includes (a) grid sampling for data collection, (b) interpolation using Ordinary kriging with three methods (exponential, spherical, and Gaussian), (c) mapping, and (d) evaluation. The findings of this study demonstrate that semi-variogram analysis using three distinct methods yields somewhat varied outcomes with accuracy in higher order from SAP, soil EC, and soil pH. The results of this study also show that the different methods have unique characteristics when representing spatial structure. These findings suggested that the number of samples and the selection of interpolation techniques are essential factors in studying these three soil properties and determining the accuracy of the results.

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Section: The Results Of Interpolationsupporting

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Characterizing, Predicting, and Mapping Soil Available Phosphor, pH, and Electrical Conductivity in Rubber Plantation Based on Three Different Methods of Ordinary Kriging in GIS Environment

Wijayanto,

Nareswari,

Saputra

et al. 2024

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…For this paradigm shift, the agricultural data should be managed in combination with longitudinal data and geospatial data for implementing agricultural practices at the right time and location. In particular, geospatial data enable farmers to apply input materials based on crop needs on a precise site-specific basis 12 . Given big data and systematic data management, AI such as machine learning and deep learning algorithms can be used for predictions and data-driven decisions.…”

Section: Introductionmentioning

confidence: 99%

An agricultural digital twin for mandarins demonstrates the potential for individualized agriculture

Kim,

Heo

2024

Nat Commun

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A digital twin is a digital representation that closely resembles or replicates a real world object by combining interdisciplinary knowledge and advanced technologies. Digital twins have been applied to various fields, including to the agricultural field. Given big data and systematic data management, digital twins can be used for predicting future outcomes. In this study, we endeavor to create an agricultural digital twin using mandarins as a model crop. We employ an Open API to aggregate data from various sources across Jeju Island, covering an area of approximately 185,000 hectares. The collected data are visualized and analyzed at regional, inter-orchard, and intra-orchard scales. We observe that the intra-orchard analysis explains the variation of fruit quality substantially more than the inter-orchard analysis. Our data visualization and analysis, incorporating statistical models and machine learning algorithms, demonstrate the potential use of agricultural digital twins in the future, particularly in the context of micro-precision and individualized agriculture. This concept extends the current management practices based on data-driven decisions, and it offers a glimpse into the future of individualized agriculture by enabling customized treatment for plants, akin to personalized medicine for humans.

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“…Geospatial data enable farmers to apply input materials based on crop needs on a precise site-speci c basis. 5 In turn, machine learning and deep learning (AI) are used to analyze these data, providing farmers with insights for improving decision making, predicting crop yields, and supplying the required input resources (water, fertilizer, pesticides, etc.) for every square meter in a crop eld as needed at every plant growth stage.…”

mentioning

confidence: 99%

Can digital twin technology make individualized-agriculture a reality?

Heo

Chung

2023

Preprint

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An agricultural Digital Twin is a sufficiently realistic digital representation of agriculture in an interdisciplinary context that utilizes various advanced technologies to enable the prediction of future results by creating virtual twins that are identical to the real objects on a computer, which can be used to simulate real-life situations to synchronize the simulated virtual world with the realworld. To develop agricultural digital twins, we targeted mandarin as model plant and gathered various datasets using the Open API. This study aimed to integrate four datasets from various sources, visualize and analyze the data, and establish digital twins for Citrus orchard management for the first time. The resulting digital twins showed potential not only to support integrated data-driven decision-making but to advance individualized agriculture as well. In the final stage of precision agriculture, individualized agriculture will open new horizons for the current agricultural system by enabling individual farming tailored to suit every plant requirement and condition as plants grow and develop, such as personalized medicine for humans.

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Development of Management Zones for Site-Specific Fertilization in Mustard Fields

Cited by 3 publications

References 8 publications

Characterizing, Predicting, and Mapping Soil Available Phosphor, pH, and Electrical Conductivity in Rubber Plantation Based on Three Different Methods of Ordinary Kriging in GIS Environment

Characterizing, Predicting, and Mapping Soil Available Phosphor, pH, and Electrical Conductivity in Rubber Plantation Based on Three Different Methods of Ordinary Kriging in GIS Environment

An agricultural digital twin for mandarins demonstrates the potential for individualized agriculture

Can digital twin technology make individualized-agriculture a reality?

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