Delineation of management zones in a peach orchard using multivariate and geostatistical analyses

Oldoni, Henrique; Terra, Viviane Santos Silva; Timm, Luís Carlos; Júnior, Carlos Reisser; Monteiro, Alex Becker

doi:10.1016/j.still.2019.03.008

Cited by 38 publications

(28 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Thus, in each zone, different management practices, such as nutrient application, can be carried out to increase crop production while decreasing the costs. Several studies stated that the analysis of variance is an effective method to assess the differences between the delineated zones [9,14,15,17,42]. Therefore, the one-way ANOVA was carried out to evaluate the effectiveness of PCA and fuzzy c-means cluster algorithm combination to delineate MZs and also its spatial variability.…”

Section: Clustering Analysismentioning

confidence: 99%

“…Considerable advances in statistical techniques and modeling approaches have allowed different agricultural zones to be accurately delineated with new methods like machine learning, GIS, remote sensing, geostatistics, and kriging at a regional and farm scale [5,9]. Several methods and approaches, such as delineating by an active canopy sensor [10], using soil properties by fuzzy k-means cluster analysis [11,12], fuzzy c-means clustering algorithm, principal component analysis (PCA) [12][13][14][15][16][17], and soil fertility analysis [13] have been used to delineate different MZs.…”

Section: Introductionmentioning

confidence: 99%

“…Different types of environmental and soil data could be used for delineating the MZs [10]. For instance, pedo-geomorphological factors [18], soil nutrients [19,20], intrinsic soil properties (e.g., electrical conductivity (EC), pH, particle size distribution, soil organic matter, cation exchange capacity, nitrogen, phosphorous, and potassium) [9,10,12,15,21] can be used for this purpose. Different types of information can be integrated to define MZs by using a clustering algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…In most countries, such as Iran, the fertilizer and nutrient recommendations for agricultural lands are usually uniform on a regional scale [15] or farm scale [10,26], with the spatial heterogeneity of nutrient content in soils being often ignored [9]. However, the source of chemical fertilizers in Iran commonly include nitrogen (urea, 46%), phosphorus (triple superphosphate, 46%), potassium (potassium sulfate, 46%), and also cow manure [27].…”

Section: Introductionmentioning

confidence: 99%

“…However, the source of chemical fertilizers in Iran commonly include nitrogen (urea, 46%), phosphorus (triple superphosphate, 46%), potassium (potassium sulfate, 46%), and also cow manure [27]. Therefore, the similar fertilizer recommendations in the farm or regional scales could lead to over-application of fertilizers in areas with high nutrient levels and vice versa [9,15]. Herein, Peralta et al [26] concluded that MZ delineation could increase the fertilizer efficiency of nitrogen (or other nutrients) in the commercial fields, and the risk of pollution will decrease along with a decrease in the application of resources, resulting in an increase of economic benefits.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Integration of PCA and Fuzzy Clustering for Delineation of Soil Management Zones and Cost-Efficiency Analysis in a Citrus Plantation

et al. 2020

View full text Add to dashboard Cite

Citrus spp. are one of the most important commercial crops with global marketing potential in the world, as in Iran. A soil management zone (MZ) as an appropriate approach is necessary to achieve sustainable production, along with improving soil management and increasing economic benefits in the commercial citrus plantations of northern Iran. As the first report, the biological and terrain attributes along with the physicochemical properties (57 soil samples, 0–30 cm) were used for MZ delineation using the integration of principal component analysis (PCA) and the fuzzy c-means clustering methods. An economic analysis based on the MZ results was also performed to determine the changes in each MZ using a relative cost (RC) value. The high correlation between soil properties and terrain attributes and the considerable spatial variation of these factors in the study area call for site-specific nutrient management. The optimal number of MZs was six and there was a significant heterogeneity variation among different MZs. The ranking of the MZs were MZ5 > MZ2 > MZ6 > MZ1 > MZ3 > MZ4 based on higher soil quality and lower costs per tree. The MZ4, MZ3, MZ1, MZ6, and MZ2 required 34.4, 30.6, 29.4, 9.77, and 9.44% more costs than MZ5 (as reference MZ) for achieving similar productivity, respectively. Therefore, this simple and cost-effective approach could be an initial step to utilize fertilizers site-specifically for data-scarce areas and reduce the soil property variability within the delineated MZs, which is fundamental for precision agriculture management.

show abstract

Section: Clustering Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Integration of PCA and Fuzzy Clustering for Delineation of Soil Management Zones and Cost-Efficiency Analysis in a Citrus Plantation

et al. 2020

View full text Add to dashboard Cite

show abstract

Assessment of land levelling effects on lowland soil quality indicators and water retention evaluated by multivariate and geostatistical analyses

et al. 2020

View full text Add to dashboard Cite

Studies focusing on the impact of levelling on the management of available water for rice growth and on soil hydro‐physical quality indicators cultivated in lowland soils under aerobic conditions are still scarce. The objective of this study was to evaluate the levelling impact on soil water retention and soil quality indicators by multivariate and geostatistical analyses. A 10 × 10 m grid was used to sample the 0‐ to 20‐cm layer in a 1.0‐ha experimental field, before and after levelling. Sand and clay contents, macroporosity, bulk density (Bd), organic matter (OM), weighted average diameter of aggregates, and the water retention curve were measured. Available water capacity, structural stability, and S indexes and relative field capacity were calculated. Descriptive statistics, Kolmogorov–Smirnov test, and geostatistical analysis were used. Maps of all data sets significantly correlated with Bd and OM after levelling were constructed using ordinary and indicator kriging. Kaiser–Meyer–Olkin and Bartlett tests were performed for factorial and principal component analyses which were used to reduce the dimensionality of data and of main principal component maps. Levelling negatively affected the majority of soil quality indicators, caused an increase of their spatial range, and an improved the goodness of fit of the semivariogram models used. The majority of variables were best fitted by the Gaussian model after levelling. Bd and OM probability maps were found to be useful tools for farmers wanting to utilize different strategies for topsoil management, with the goal of improving soil quality of levelled areas for future land recuperation operations.

show abstract

Spatial variability and delineation of management zones based on soil micronutrient status in apple orchard soils of Kashmir valley, India

Dad

Shafiq

2021

Environ Monit Assess

View full text Add to dashboard Cite

Delineation of management zones in a peach orchard using multivariate and geostatistical analyses

Cited by 38 publications

References 38 publications

Integration of PCA and Fuzzy Clustering for Delineation of Soil Management Zones and Cost-Efficiency Analysis in a Citrus Plantation

Integration of PCA and Fuzzy Clustering for Delineation of Soil Management Zones and Cost-Efficiency Analysis in a Citrus Plantation

Assessment of land levelling effects on lowland soil quality indicators and water retention evaluated by multivariate and geostatistical analyses

Spatial variability and delineation of management zones based on soil micronutrient status in apple orchard soils of Kashmir valley, India

Contact Info

Product

Resources

About