scholarly journals Delineation of irrigation management zones in a Quartzipsamment of the Brazilian semiarid region

2016 ◽  
Vol 51 (9) ◽  
pp. 1283-1294 ◽  
Author(s):  
Henrique Oldoni ◽  
Luís Henrique Bassoi
Keyword(s):  
Soil Properties ◽  
Clustering Algorithm ◽  
Irrigation Management ◽  
Optimal Number ◽  
Semiarid Region ◽  
Management Zones ◽  
Distribution Maps ◽  
Management Zone ◽  
Brazilian Semiarid ◽  
Fuzzy C Means Clustering

Abstract The objective of this work was to delineate irrigation management zones using geostatistics and multivariate analysis in different combinations of physical and hydraulic soil properties, as well as to determine the optimal number of management zones in order to avoid overlaping. A field experiment was carried out in a Quartzipsamment, for two years, in an irrigated orchard of table grape, in the Senador Nilo Coelho Irrigation Scheme, in the municipality of Petrolina, in the state of Pernanbuco, Brazil. Soil samples were collected for the determination of soil physico-hydraulic properties. A portable meter was used to measure soil apparent electrical conductivity. Spatial distribution maps were generated using ordinary kriging. Management zones for five different combinations of soil properties were defined using the fuzzy c-means clustering algorithm, and two indexes were applied to determine the optimal number of management zones. Two combinations of soil properties can be used in the management zone planning in order to monitor soil moisture.

scholarly journals Management zones using fuzzy clustering based on spatial-temporal variability of soil and corn yield

2015 ◽  
Vol 35 (3) ◽  
pp. 470-483 ◽  
Author(s):  
Marcos S. Rodrigues ◽  
José E. Corá
Keyword(s):  
Temporal Variability ◽  
Clustering Algorithm ◽  
Spatial And Temporal Variability ◽  
Optimum Number ◽  
Corn Yield ◽  
Management Zones ◽  
Soil Attributes ◽  
Fuzzy C Means ◽  
Management Zone ◽  
Fuzzy C Means Clustering

Clustering soil and crop data can be used as a basis for the definition of management zones because the data are grouped into clusters based on the similar interaction of these variables. Therefore, the objective of this study was to identify management zones using fuzzy c-means clustering analysis based on the spatial and temporal variability of soil attributes and corn yield. The study site (18 by 250-m in size) was located in Jaboticabal, São Paulo/Brazil. Corn yield was measured in one hundred 4.5 by 10-m cells along four parallel transects (25 observations per transect) over five growing seasons between 2001 and 2010. Soil chemical and physical attributes were measured. SAS procedure MIXED was used to identify which variable(s) most influenced the spatial variability of corn yield over the five study years. Basis saturation (BS) was the variable that better related to corn yield, thus, semivariograms models were fitted for BS and corn yield and then, data values were krigged. Management Zone Analyst software was used to carry out the fuzzy c-means clustering algorithm. The optimum number of management zones can change over time, as well as the degree of agreement between the BS and corn yield management zone maps. Thus, it is very important take into account the temporal variability of crop yield and soil attributes to delineate management zones accurately.

scholarly journals Irrigation Management Scale and Water Application Method to Improve Yield and Water Productivity of Field-Grown Strawberries

Agronomy ◽  
2019 ◽  
Vol 9 (6) ◽  
pp. 286 ◽  
Author(s):  
Guillaume Létourneau ◽  
Jean Caron
Keyword(s):  
Spatial Variability ◽  
Soil Properties ◽  
Water Productivity ◽  
Irrigation Management ◽  
Richards Equation ◽  
Water Application ◽  
Experimental Site ◽  
Management Zones ◽  
Rock Fragments ◽  
Application Method

Improvements in water productivity are of primary importance for maintaining agricultural productivity and sustainability. Water potential-based irrigation management has proven effective for this purpose with many different crops, including strawberries. However, problems related to spatial variability of soil properties and irrigation efficiency were reported when applying this management method to strawberries in soils with rock fragments. In this study, a field-scale experiment was performed to evaluate the impacts of three irrigation management scales and a pulsed water application method on strawberry yield and water productivity. An analytical solution to Richards’ equation was also used to establish critical soil water potentials for this crop and evaluate the effects of the variability in the soil properties. Results showed that spatial variability of soil properties at the experimental site was important but not enough to influence crop response to irrigation practices. The studied properties did not present any spatial structure that could allow establishing specific management zones. A four-fold reduction in the size of the irrigation management zones had no effect on yield and increased the water applications. Pulsed application led to significant yield (22%) and water productivity (36%) increases compared with the standard water application method used by the producer at the experimental site.

scholarly journals Productivity of soybean in management zones with application of different sowing densities

2018 ◽  
Vol 48 (12) ◽  
Author(s):  
Rafaela Greici da Motta Camicia ◽  
Marcio Furlan Maggi ◽  
Eduardo Godoy de Souza ◽  
Claudio Leones Bazzi ◽  
Evandro André Konopatzki ◽  
...  
Keyword(s):  
Economic Analysis ◽  
Clustering Algorithm ◽  
Descriptive Analysis ◽  
Seeding Density ◽  
Row Spacing ◽  
Seed Density ◽  
Management Zones ◽  
Fuzzy C Means ◽  
Productive Potential ◽  
Fuzzy C Means Clustering

ABSTRACT: The present study aimed to assess the efficiency of sowing at variable rates for soybean cultivation in two management zones (MZs) which were defined based on stable attributes and correlated with productivity using the Fuzzy C-means clustering algorithm and the kriging interpolation.Seeding was carried out in the 2015/2016 and 2017/2018 crops with a variation of 20% of seeds and crop row spacing of 0.70m. In each MZ, 8 plots with higher and lower seed density were established. Productivity was measured using a harvest monitor connected to a harvester. Data were filtered and submitted to descriptive analysis. Productivity maps were generated using the inverse square distance interpolation for each seeding density. In the MZ with the highest productive potential (MZ 1), the productivity was 3.39 and 3.18t ha-1, and in the MZ with the lowest productive potential (MZ 2) the productivity was 3.30 and 3.11t ha-1 for the years 2016 and 2018, respectively. Interpolation estimated higher productivity with the application of 15 plants m-1. Based on the economic analysis, it is suggested in this study the application of 214,000 plants ha-1 in both MZs.

Soil properties and microclimate of two predominant landscapes in the Brazilian semiarid region: Comparison between a seasonally dry tropical forest and a deforested area

2021 ◽  
Vol 207 ◽  
pp. 104852
Author(s):  
Thieres George Freire da Silva ◽  
Maria Gabriela de Queiroz ◽  
Sérgio Zolnier ◽  
Luciana Sandra Bastos de Souza ◽  
Carlos André Alves de Souza ◽  
...  
Keyword(s):  
Tropical Forest ◽  
Soil Properties ◽  
Semiarid Region ◽  
Seasonally Dry Tropical Forest ◽  
Dry Tropical Forest ◽  
Seasonally Dry ◽  
Brazilian Semiarid

Improved Fuzzy C-Means Based on the Optimal Number of Clusters

2013 ◽  
Vol 392 ◽  
pp. 803-807 ◽  
Author(s):  
Xue Bo Feng ◽  
Fang Yao ◽  
Zhi Gang Li ◽  
Xiao Jing Yang
Keyword(s):  
Convergence Rate ◽  
Clustering Algorithm ◽  
Optimal Number ◽  
Data Set ◽  
Number Of Clusters ◽  
Fuzzy C Means ◽  
Initial Cluster ◽  
Fuzzy C Means Clustering ◽  
Fcm Clustering ◽  
Optimal Number Of Clusters

According to the number of cluster centers, initial cluster centers, fuzzy factor, iterations and threshold, Fuzzy C-means clustering algorithm (FCM) clusters the data set. FCM will encounter the initialization problem of clustering prototype. Firstly, the article combines the maximum and minimum distance algorithm and K-means algorithm to determine the number of clusters and the initial cluster centers. Secondly, the article determines the optimal number of clusters with Silhouette indicators. Finally, the article improves the convergence rate of FCM by revising membership constantly. The improved FCM has good clustering effect, enhances the optimized capability, and improves the efficiency and effectiveness of the clustering. It has better tightness in the class, scatter among classes and cluster stability and faster convergence rate than the traditional FCM clustering method.

A Method of Feature Automatic Selection Based on Mutual Information Grouping and Clustering

2014 ◽  
Vol 543-547 ◽  
pp. 1613-1618
Author(s):  
Man Sheng Xiao ◽  
Zhe Xiao ◽  
Zhi Liu
Keyword(s):  
Mutual Information ◽  
Clustering Algorithm ◽  
Data Classification ◽  
Optimal Number ◽  
Massive Data ◽  
Data Sets ◽  
Maximum Correlation ◽  
Automatic Selection ◽  
Feature Correlation ◽  
Fuzzy C Means Clustering

For the problem about a large number of irrelevant and redundant features may reduce the performance of data classification in massive data sets, a method of feature automatic selection based on mutual information and fuzzy clustering algorithm is proposed. The method is carried out as follows: The first is to work out the feature correlation based on mutual information, and to group the data according to the feature of the maximum correlation. The second is to automatically determine the optimal number of feature and compression features dimension by fuzzy c-means clustering algorithm in the data groups. The theoretical analysis and the experiment indicate that the method can obtain higher efficiency in data classification.

Interactions between landscape defined management zones and grazing management systems

2017 ◽  
Vol 8 (2) ◽  
pp. 787-791 ◽  
Author(s):  
E. M. Pena-Yewtukhiw ◽  
D. Mata-Padrino ◽  
J. H. Grove
Keyword(s):  
Soil Properties ◽  
Bulk Density ◽  
Weather Conditions ◽  
Soil Strength ◽  
Forage Yield ◽  
Grazing Management ◽  
Weather Extremes ◽  
Near Surface ◽  
Management Zones ◽  
Management Zone

Yield and landscape are commonly used to guide management zone delineation. However, production system choice and management can interact with landscape attributes and weather. The objective of this study was to evaluate forage yield and soil properties in three landscape defined (elevation based) management zones, and under two different grazing systems. Changes in soil properties (soil strength, bulk density, moisture, bioavailable nutrients) and forage productivity (biomass), as related to grazing management and management zone, were measured. Bulk density, moisture, and forage biomass were greater at higher elevation. Soil strength decreased as elevation increased, and was greater near-surface after winter grazing ended. The response of landscape delineated management zones varied with extreme weather conditions and treatment. Lower zones were more sensitive to weather extremes than higher elevations, directly affecting biomass accumulation. In conclusion, we observed interactions between the grazing treatments and the management zones.

scholarly journals IMPACTS OF CLIMATE CHANGE SCENARIOS IN THE BRAZILIAN SEMIARID REGION ON WATERMELON CULTIVARS

2020 ◽  
Vol 33 (3) ◽  
pp. 794-802
Author(s):  
TALYANA KADJA DE MELO ◽  
JOSÉ ESPÍNOLA SOBRINHO ◽  
JOSÉ FRANSCIMAR DE MEDEIROS ◽  
VLADIMIR BATISTA FIGUEIREDO ◽  
JOSÉ SILEREUDO DA SILVA ◽  
...  
Keyword(s):  
Climate Change ◽  
Relative Humidity ◽  
Climate Changes ◽  
Irrigation Management ◽  
Future Climate ◽  
Semiarid Region ◽  
Climate Change Scenarios ◽  
Brazilian Semiarid ◽  
Ipcc Report ◽  
Impacts Of Climate Change

ABSTRACT The phenology and water demand of crops can be altered by climate change. The objective of this study was to evaluate the impacts of climate change (temperature and relative humidity) on the development and evapotranspiration (ETc) of watermelon cultivare (Citrullus lanatus Schrad) in irrigated plantations in the Brazilian semiarid region. The experiments were conducted at the Rafael Fernandes Experimental Farm, in the municipality of Mossoró, RN, Brazil. The first experiment was carried out from February to April 2006, evaluating the cultivar Mickylee. In the second experiment, carried out from September to November 2009, the cultivar Quetzali was used. Two climate change scenarios were evaluated based on the Intergovernmental Panel on Climate Change (IPCC) report: an optimistic one named B2 and a pessimistic one named A2. It was found that climate change may cause reductions in crop vegetative cycle of 14.1 and 26.9% for the cultivar Mickylee and 7.9 and 11.1% for the cultivar Quetzali, for the optimistic and pessimistic scenarios, respectively, compared to the current climate scenario. Future climate changes will increase the watermelon crop coefficient (Kc), for the conditions under which the present study was carried out, increasing the daily ETc the total ETc, mainly for the cultivar Mickylee. Future climate changes in air temperature and relative humidity will reach limits above those tolerated by the crop, which will lead to changes in the cultural practices and irrigation management.

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