Zone mapping application for precision-farming: a decision support tool for variable rate application

Zhang, Xiaodong; Shi, Lijian; Jia, Xinhua; Seielstad, G. A.; Helgason, Craig

doi:10.1007/s11119-009-9130-4

Cited by 72 publications

(43 citation statements)

References 26 publications

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“…The use is justified by the significant reduction in the required number of soil samples, which depending on the sampling grid used, involves intensive use of labor for sampling and laboratory analysis, thus increasing the production cost (Zhang et al, 2010). The cost of sampling and analysis may cause the application of precision agriculture techniques to be unfeasible (McCormick et al, 2009).…”

Section: Soybean Yield Spatial Variabilitymentioning

confidence: 99%

Precision agriculture applied to soybean: Part III - Spatial and temporal variability of yield

Bottega¹,

Queiroz²,

Pinto³

et al. 2017

Aust J Crop Sci

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The Brazilian Savannah is an important production center of grains despite the presence of low fertility soils. In this agricultural scenario, the delineation of management zones for the application of lime and fertilizer seems to be promising for a high potential for success and may result in the reduction in production cost with an increase in the yield. The objective of this work was to evaluate the spatial and temporal variations in soybean yield in the Brazilian Savannah involving the delineation of management zones based on the apparent soil electrical conductivity (ECa). In an agricultural field in the Brazilian Savannah, 160 sampling points were taken as the references for the mapping of soybean yield in the crop seasons of 2011

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Section: Soybean Yield Spatial Variabilitymentioning

confidence: 99%

Precision agriculture applied to soybean: Part III - Spatial and temporal variability of yield

Bottega¹,

Queiroz²,

Pinto³

et al. 2017

Aust J Crop Sci

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“…One of the techniques used to reduce the number of required soil samples involves defining management zones based on information that can be collected at a low cost (Moral et al, 2010;Xin-Zhong et al, 2009;Zhang et al, 2010). Generally, the field information used to define management zones is based on maps of apparent soil electrical conductivity, digital elevation models, maps created using remote sensing and yield maps obtained from more than one crop (Fleming et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

Definition of management zones in coffee production fields based on apparent soil electrical conductivity

Valente¹,

Queiroz²,

Pinto³

et al. 2012

Sci. agric. (Piracicaba, Braz.)

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Fertilizer application at variable rates requires dense sampling to determine the resulting field spatial variability. Defining management zones is a technique that facilitates the variable-rate application of agricultural inputs. The apparent electrical conductivity of the soil is an important factor in explaining the variability of soil physical-chemical properties. Thus, the objective of this study was to define management zones for coffee (Coffea Arabica L.) production fields based on spatial variability of the apparent electrical conductivity of the soil. The resistivity method was used to measure the apparent soil electrical conductivity. Soil samples were collected to measure the chemical and physical soil properties. The maps of spatial variability were generated using ordinary kriging method. The fuzzy k-means algorithm was used to delimit the management zones. To analyze the agreement between the management zones and the soil properties, the kappa coefficients were calculated. The best results were obtained for the management zones defined using the apparent electrical conductivity of the soil and the digital elevation model. In this case, the kappa coefficient was 0.45 for potassium, which is an element that is associated with quality coffee. The other variable that had a high kappa coefficient was remaining phosphorous; the coefficient obtained was 0.49. The remaining phosphorus is an important parameter for determining which fertilizers and soil types to study.

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“…The reason is because UMAC released a new extension to DNGP in 2008, Zone Mapping Application for Precision-farming (ZoneMAP) [16]. ZoneMAP further expanded the service by DNGP in two aspects: (1) the source of data was extended to include field measurements by end users; and (2) instead of raw data, management zones can be delineated automatically based on the selection of data.…”

Section: Digital Northern Great Plainsmentioning

confidence: 99%

Digital Northern Great Plains: A Web-Based System Delivering Near Real Time Remote Sensing Data for Precision Agriculture

2010

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Abstract:The US Northern Great Plains is one of the world's most agriculturally productive areas. Growers in the region are eager to adopt modern technology to improve productivity and income. Use of information derived from remote sensing satellites to better manage farms and rangelands while reducing environmental impacts has gained popularity in recent years. However, prohibitive costs and non-availability of near real time remote sensing imagery has slowed the adoption of this technology for in-field decision making. Digital Northern Great Plains (DNGP), a web based remote sensing data dissemination system, was developed to address these drawbacks. It provides end users easy and free access to a variety of imagery and products in near real time. With delivery of archived and current data, DNGP has helped farmers and ranchers reduce operational costs and increase productivity through a variety of innovative applications. Moreover, negative environmental impacts were lessened.

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Zone mapping application for precision-farming: a decision support tool for variable rate application

Cited by 72 publications

References 26 publications

Precision agriculture applied to soybean: Part III - Spatial and temporal variability of yield

Precision agriculture applied to soybean: Part III - Spatial and temporal variability of yield

Definition of management zones in coffee production fields based on apparent soil electrical conductivity

Digital Northern Great Plains: A Web-Based System Delivering Near Real Time Remote Sensing Data for Precision Agriculture

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