Management Zone Analyst (MZA)

Fridgen, Jon J.; Kitchen, Newell R.; Sudduth, Kenneth A.; Drummond, S. T.; Wiebold, William J.; Fraisse, Clyde W.

doi:10.2134/agronj2004.0100

Cited by 238 publications

(121 citation statements)

References 19 publications

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“…Thereafter, fuzzy c-means clustering that represents an unsupervised continuous classification procedure, was performed on data of the PCs retained from the analysis (Li et al, 2013). The Management Zone Analyst software (MZA 1.01; University of Missouri-Columbia, USA) (Fridgen et al, 2004) was used, adopting Euclidean distances of data points from cluster centres.…”

Section: Clusteringmentioning

confidence: 99%

“…SSM allows a field to be split into areas that express a relatively homogeneous combination of yield limiting factors, for which a single rate of a specific crop input is appropriate (Fridgen et al, 2004;Chang et al, 2014;Damian et al, 2016). The choice of the algorithm for delineating such areas is another point of debate, although data clustering is a widely accepted method, and fuzzy c-means algorithms are one of the most common techniques for data clustering (Guastaferro et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

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Assessing wheat spatial variation based on proximal and remote spectral vegetation indices and soil properties

et al. 2017

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Assessing the spatial variation of soil and crop properties is the basis for site specific management of crop practices in precision agriculture applications. To this aim, proximal and remote spectral vegetation indices are increasingly replacing soil analysis. In this study the spatial variation of soil properties, proximal and remote spectral vegetation indices were compared in a winter wheat (Triticum aestivum L.) crop grown in a 4.15 ha field in northern Italy. Soil analysis (particle size distribution, pH, carbonates, C, total N, available P, exchangeable cations and electrical conductivity) was geo-referentially carried out; the proximal indices chlorophyll content by N-Tester and normalised difference vegetation index through GreenSeeker were determined in three dates during stem elongation; the remote indices PurePixel TM chlorophyll index and PurePixel TM vegetation index were determined through the Landsat 8 satellite in three dates during the same wheat stage. Dry biomass yield (DBY), grain yield (GY) and yield components were determined at harvest. Soil, proximal and remote data were submitted to principal component analysis (PCA), and the retained PCs were clustered to delineate areas at low, intermediate and high yield potential, based on soil parameters (CLUsp), proximal (CLUpi), and remote vegetation indices (CLUri). DBY and GY were significantly correlated with several soil parameters and vegetation indices. Spatial distribution of soil and crop data consistently depicted a low performing area (GY<3 Mg ha -1 ) and a high performing one (GY>8 Mg ha -1 ). CLUsp determined a lower GY difference between low and high performing area (+60%), compared to CLUpi and CLUri (almost +100%). In CLUsp and CLUpi the low and high performing area were of similar size (25 and 29% for the two respective areas in CLUsp; 25 and 33% in CLUpi), whereas in CLUri they were quite different (16 and 46%). Lastly, yield potential levels determined by vegetation indices (CLUpi and CLUri) exhibited a better degree of agreement with DBY and GY levels, than soil parameters (CLUsp). In exchange for this, the above referred soil parameters are quite consistent in time, allowing soil data to be used for more years. On concluding, PCA followed by clustering resulted in a robust delineation of field areas at different yield potential. This is the premise for developing research driven strategies of practical use.

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Section: Clusteringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessing wheat spatial variation based on proximal and remote spectral vegetation indices and soil properties

et al. 2017

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“…Only the ER maps for the frequencies 15 kHz and 10 kHz were related to soil depths of agronomical interest and they were considered to delineate the SSMUs (Ortuani et al, 2016), by using the MZA software (Fridgen et al, 2004) implementing a fuzzy c-means unsupervised clustering method (Odeh et al, 1992). Three zones (i.e., a, b, c) were delineated for both cases related to the EMI data acquired in October 2014 and April 2015 (Facchi et al, 2015).…”

Section: Classification Resultsmentioning

confidence: 99%

Uav Multispectral Survey to Map Soil and Crop for Precision Farming Applications

Sona

Passoni

Pinto

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission I, ICWG I/VbKEY WORDS: UAV, multispectral images, DEM, orthophoto, NDVI, precision farming, soil and crop characterization ABSTRACT New sensors mounted on UAV and optimal procedures for survey, data acquisition and analysis are continuously developed and tested for applications in precision farming. Procedures to integrate multispectral aerial data about soil and crop and ground-based proximal geophysical data are a recent research topic aimed to delineate homogeneous zones for the management of agricultural inputs (i.e., water, nutrients). Multispectral and multitemporal orthomosaics were produced over a test field (a 100 m x 200 m plot within a maize field), to map vegetation and soil indices, as well as crop heights, with suitable ground resolution. UAV flights were performed in two moments during the crop season, before sowing on bare soil, and just before flowering when maize was nearly at the maximum height. Two cameras, for color (RGB) and false color (NIR-RG) images, were used. The images were processed in Agisoft Photoscan to produce Digital Surface Model (DSM) of bare soil and crop, and multispectral orthophotos. To overcome some difficulties in the automatic searching of matching points for the block adjustment of the crop image, also the scientific software developed by Politecnico of Milan was used to enhance images orientation. Surveys and image processing are described, as well as results about classification of multispectral-multitemporal orthophotos and soil indices.

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“…To scale yield equally across sites and years, it was standardized to zero mean and unit variance (observed yield was subtracted from the annual mean, and then divided by the standard deviation) (Ferguson et al 2003). These data were analyzed by the Management Zone Analyst software (MZA 1.0.1, University of Missouri-Columbia, Columbia, Missouri, USA) (Fridgen et al 2004). The measure of similarity (Euclidean distance) and the fuzziness exponent (1.30) were left at the default values.…”

Section: Management Zone Classificationmentioning

confidence: 99%

“…The NCE determines the amount of disorganization created by dividing the data into classes (Lark and Stafford 1997) and FPI is a measure of membership sharing (fuzziness) among classes (Odeh et al 1992). The best classification was determined when NCE and or FPI were at a minimum, representing the least membership sharing (FPI) or greatest amount of organization (NCE) as a result of the clustering process (Fridgen et al 2004). …”

Section: Management Zone Classificationmentioning

confidence: 99%

Delineating site-specific management zones for pH-induced iron chlorosis

et al. 2008

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Iron chlorosis can limit crop yield, especially on calcareous soil. Typical management for iron chlorosis includes the use of iron fertilizers or chlorosis tolerant cultivars. Calcareous and non-calcareous soil can be interspersed within fields. If chlorosisprone areas within fields can be predicted accurately, site-specific use of iron fertilizers and chlorosis-tolerant cultivars might be more profitable than uniform management. In this study, the use of vegetation indices (VI) derived from aerial imagery, on-the-go measurement of soil pH and apparent soil electrical conductivity (EC a ) were evaluated for their potential to delineate chlorosis management zones. The study was conducted at six sites in 2004 and 2005. There was a significant statistical relationship between grain yield and selected properties at two sites (sites 1 (2005) and 3), moderate relationships at sites 2 and 4, and weak relationships at site 5. For sites 1 (2005) and 3, and generally across all sites, yield was predicted best with the combination of NDVI and deep EC a . These two properties were used to delineate chlorosis management zones for all sites. Sites 1 and 3 showed a good relationship between delineated zones and the selected properties, and would be good candidates for site-specific chlorosis management. For site 5, differences in the properties between mapped zones were small, and the zones had weak relationships to yield. This site would be a poor candidate for site-specific chlorosis management. Based on this study, the

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Management Zone Analyst (MZA)

Cited by 238 publications

References 19 publications

Assessing wheat spatial variation based on proximal and remote spectral vegetation indices and soil properties

Assessing wheat spatial variation based on proximal and remote spectral vegetation indices and soil properties

Uav Multispectral Survey to Map Soil and Crop for Precision Farming Applications

Delineating site-specific management zones for pH-induced iron chlorosis

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