Management Zones based on Correlation between Soil Compaction, Yield and Crop Data

Vrindts, Els; Mouazen, Abdul Mounem; Reyniers, M.; Maertens, Koen; Maleki, Mohammad Reza; Ramón, Herman; Baerdemaeker, Josse De

doi:10.1016/j.biosystemseng.2005.08.010

Cited by 83 publications

(36 citation statements)

References 18 publications

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“…At present, the use of site-specific management zones, rather than the traditional whole field approach, is a popular approach for farm managers to manage field variability on a site-specific basis. Management zones (MZ) are defined as sub-regions of a field that has a relatively homogeneous combination of yield-limiting factors, for which a single rate of a specific crop input is appropriate to attain maximum efficiency of farm inputs (Doerge, 1999;Vrindts et al, 2005). Besides representing areas of equal production potential, within-field management zones have many other uses.…”

Section: Introductionmentioning

confidence: 99%

“…Vrindts et al(2005) compared the management zones defined based on soil data only with the ones on soil information and crop, and found that the latter provided a better description of the yield variation. In addition, current information on crop status, for example by remote sensing, can be considered as a valuable tool which enables the management zones to be adjusted to the current growing season (Godwin et al, 2003;Vrindts et al, 2005). Long et al(1994) concluded that aerial photographs of growing crops were the most accurate for classifying a field into management units to predict grain yield.…”

Section: Introductionmentioning

confidence: 99%

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Determination of potential management zones from soil electrical conductivity, yield and crop data

Shi

et al. 2008

J. Zhejiang Univ. Sci. B

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Abstract:One approach to apply precision agriculture to optimize crop production and environmental quality is identifying management zones. In this paper, the variables of soil electrical conductivity (EC) data, cotton yield data and normalized difference vegetation index (NDVI) data in an about 15 ha field in a coastal saline land were selected as data resources, and their spatial variabilities were firstly analyzed and spatial distribution maps constructed with geostatistics technique. Then fuzzy c-means clustering algorithm was used to define management zones, fuzzy performance index (FPI) and normalized classification entropy (NCE) were used to determine the optimal cluster numbers. Finally one-way variance analysis was performed on 224 georeferenced soil and yield sampling points to assess how well the defined management zones reflected the soil properties and productivity level. The results reveal that the optimal number of management zones for the present study area was 3 and the defined management zones provided a better description of soil properties and yield variation. Statistical analyses indicate significant differences between the chemical properties of soil samples and crop yield in each management zone, and management zone 3 presented the highest nutrient level and potential crop productivity, whereas management zone 1 the lowest. Based on these findings, we conclude that fuzzy c-means clustering approach can be used to delineate management zones by using the given three variables in the coastal saline soils, and the defined management zones form an objective basis for targeting soil samples for nutrient analysis and development of site-specific application strategies.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Determination of potential management zones from soil electrical conductivity, yield and crop data

Shi

et al. 2008

J. Zhejiang Univ. Sci. B

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“…Presentation of the watershed segmentation algorithm SSMZ delineation methods are mostly based on classification tools (Vrindts et al, 2005). In this paper, a segmentation method for SSMZ definition is proposed.…”

Section: Theorymentioning

confidence: 99%

“…A majority of the existing methods are based on unsupervised clustering algorithms (Vrindts et al, 2005). Fraisse et al (1999) used an ISODATA clustering algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…Several authors proposed to use the k-means algorithm Whelan and McBratney, 2003;Hornung et al, 2006). Finally, the fuzzy c-means algorithm is now widely applied (Lark and Stafford, 1997;Fridgen et al, 2000;Vrindts et al, 2005;Li et al, 2007). The expansion of the latter approach has been made easier for researchers with the release of user-friendly software like Management Zone Analyst (Fridgen et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

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Management zone delineation using a modified watershed algorithm

Roudier¹,

Tisseyre

Poilvé³

et al. 2008

Precision Agric

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Site-specific management (SSM) is a common way to manage within-field variability. This concept divides fields into site-specific management zones (SSMZ) according to one or several soil or crop characteristics. This paper proposes an original methodology for SSMZ delineation which is able to manage different kinds of crop and/or soil images using a powerful segmentation tool: the watershed algorithm. This image analysis algorithm was adapted to the specific constraints of precision agriculture. The algorithm was tested on high-resolution bio-physical images of a set of fields in France.

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Soil mapping and delineation of management zones in the Western Ghats of coastal India

et al. 2018

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The pace of land degradation has almost doubled across the Western Ghats of India, one of the world's ‘hotspots’ for biodiversity, owing to soil erosion, mining, and rampant urbanization. The arising nutrient depletion leading to land degradation is thus a serious threat to agricultural sustainability in this region. Delineation of spatial soil property maps for nutrient management is an effective strategy for precision agriculture. The present investigation was carried out in North Goa District in Western Ghats, a seldom researched domain in the Indian coastal region. The study involved spatial array analysis of soil properties of 383 georeferenced soil samples and used fuzzy k‐means clustering for delineation of management zones (MZs). Soils were found highly acidic with low available N, P, and medium exchangeable K with a widespread Zn, Cu, and Fe deficiency and sporadic salinity. Soil properties exhibited low to high levels of skewness except for soil pH and Mn. Correlations between soil pH and K, Zn, and Mn, between EC and Fe, between soil organic carbon (SOC) and N, P, K, Cu, and Mn was positive and significant (P = 0.01). Geostatistical analysis revealed varied distribution pattern for soil properties with Gaussian (pH, P, and Cu), spherical (EC), stable (SOC), hole effect (N), K‐Bessel (K and Fe), exponential (Zn), and circular (Mn) as best fit semivariogram models with weak and strong spatial dependence. The spatial variability was mapped, and two MZs were delineated. The developed maps will be crucial in site‐specific nutrient management for agricultural and ecological sustainability in the Western Ghats of India.

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Management Zones based on Correlation between Soil Compaction, Yield and Crop Data

Cited by 83 publications

References 18 publications

Determination of potential management zones from soil electrical conductivity, yield and crop data

Determination of potential management zones from soil electrical conductivity, yield and crop data

Management zone delineation using a modified watershed algorithm

Soil mapping and delineation of management zones in the Western Ghats of coastal India

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