Optimal Number Of Management Zones Research Articles

Validation of Rapid and Low-Cost Approach for the Delineation of Zone Management Based on Machine Learning Algorithms

Proximal soil sensors are receiving strong attention from several disciplinary fields, and this has led to a rise in their availability in the market in the last two decades. The aim of this work was to validate agronomically a zone management delineation procedure from electromagnetic induction (EMI) maps applied to two different rainfed durum wheat fields. The k-means algorithm was applied based on the gap statistic index for the identification of the optimal number of management zones and their positions. Traditional statistical analysis was performed to detect significant differences in soil characteristics and crop response of each management zones. The procedure showed the presence of two management zones at both two sites under analysis, and it was agronomically validated by the significant difference in soil texture (+24.17%), bulk density (+6.46%), organic matter (+39.29%), organic carbon (+39.4%), total carbonates (+25.34%), total nitrogen (+30.14%), protein (+1.50%) and yield data (+1.07 t ha−1). Moreover, six unmanned aerial vehicle (UAV) flight missions were performed to investigate the relationship between five vegetation indexes and the EMI maps. The results suggest performing the multispectral images acquisition during the flowering phenological stages to attribute the crop spatial variability to different soil proprieties.

Using multivariate analysis to design management zones

Soil chemical and physical attributes are important in any agricultural cropping system, but in precision agriculture they are more relevant due to the possibility of application using different management practices along a produc­tion field. However, the correlation between these attributes has been little explored in the delineation of man­agement zones. This work aims to maximize the use of joint spatial variability for soil attributes. Its secondary objectives were 1) reduction of spatial variability dimensionality among all attributes and 2) assessment of agree­ment between univariate and multivariate management zones. The management zones resulting from the inter­polation of attribute values, as well as from the scores of each of the three main components, were delineated using the Fuzzy c-means algorithm. The fuzzy performance and modified partition entropy indexes were used to determine the optimal number of management zones. The Kappa index was used to evaluate the agreement of management zones obtained from attributes with those obtained from principal components. By using principal component analysis, it was possible to reduce the dimensionality of the number of variables that contribute to the joint spatial variability existing in the study area. There was no complete agreement between the uni- and multi­variate management zones outlined, which is why further studies on the subject are needed.

Site-specific Nutrient Management Zones in Soybean Field Using Multivariate Analysis: An Approach Based on Variable Rate Fertilization

ABSTRACTDelineation of site-specific nutrient management zones (MZ) provides a basis for practical and cost-effective management of spatial soil fertility in precision agriculture. Therefore, the objective of this study was the delineation of MZs in a soybean field using geostatistics, principal component analysis (PCA), and the fuzzy k-means algorithm. The study was carried out in a field with 204 ha located in São Desidério, western Bahia state, Brazil (12 ° 25 ‘ 12” S, 45 ° 29ʹ 46” W). To do so, samples of soil attributes (0–20 cm), soybean yield, electrical conductivity (EC) at 0.20 m (EC02), 0.50 m (EC05), 1.00 m (EC1), 2.00 m (EC2) soil profile depth, and the Normalized Difference Vegetation Index (NDVI) were obtained in 204 points (100 x 100 m grid). After soil sampling and laboratory analyzes, the data were submitted to descriptive statistics and a Spearman correlation analysis was performed to select those attributes related to soybean yield. Then, the spatial variability of these attributes was assessed and spatial distribution maps were constructed using geostatistical tools. Next, PCA and fuzzy k-means algorithm were then performed to delineate MZs. Finally, the agreement between the MZs maps obtained from the PCA and soybean yield was assessed using the Kappa index. Results showed that the optimal number of MZs was two, which resulted in a Kappa index of 0.61 (very good). Moreover, the analysis of variance indicated heterogeneity between all attributes analyzed in the MZs. Finally, the defined MZs provide a basis of information for site-specific nutrient management.

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

The soil nutritional management in peach orchards in Southern Brazil is still based on conventional and uniform recommendations, without taking into consideration the spatial variability of the physical environment. This might result in over-application of fertilizers in areas with high nutrient levels and under-application in areas with low nutrient levels, which will cause high heterogeneity in the peach production and fruit quality resulting in a low profit of farmers. The objectives of this work were to characterize the spatial variability of soil and tree attributes, in a peach orchard using classical statistics and geostatistical tools as well as to delineate potential management zones based on the soil and tree attributes using the principal component and fuzzy c-means clustering analyses. A 1.8 ha peach orchard, in Morro Redondo, State of Rio Grande do Sul, Brazil, was selected as the study area and a grid of 101 trees was established. Close to each of these trees, soil samples were collected in the 0−0.10 m and 0.10−0.20 m layers to determine sand, silt and clay fractions, soil bulk density, volumetric water content, pH in water, organic matter, available phosphorus as well as exchangeable potassium, magnesium and calcium contents. During the years of 2010, 2011 and 2012, peach yield, number of fruits per tree, average fruit weight per individual tree, flesh firmness and total soluble solids content per tree were determined. All data sets were submitted to descriptive statistics and the Kolmogorov–Smirnov test as well as to geostatistical analysis. Principal component and fuzzy c-means clustering analyses were used to delineate the management zones based on the optimal number of clusters identified employing the fuzziness performance index and the modified participation entropy. Results showed high spatial variability of yield and quality of peaches. It was found that the optimal number of management zones for the peach orchard was two and most of the soil and tree attributes presented statistical differences in each defined management zone. The defined management zones based on soil and tree attributes open the opportunity to farmers for site-specific management in the peach orchard, aiming at precision agriculture and directing soil sampling design as well.

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

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.

Delineation of management zones using an active canopy sensor for a tobacco field

Recent precision agriculture research has focused on the utilization of management zones (MZs) as a method for variable-rate fertilizer applications. This study tries to make use of an active canopy sensor to define MZs in contrast with the ones using soil properties. To achieve this objective, a 20-m grid-sampling scheme was imposed on the field with 101 points, and we collected soil samples from the top 20cm at each point before tobacco planting in April 2013. The normalized difference vegetative index (NDVI) data at five growth stages of the tobacco growth cycle were measured by using a GreenSeeker handheld crop sensor at the location of each sample point. Yield mapping was carried out at harvest. Through stepwise multiple analysis, we identified the key yield-limiting factors of soil properties and stages of NDVI measured respectively. For soil property data, OM, AP and Fe were used for yield-based MZs while for canopy parameters, NDVI during knee-high and flowering were utilized. Fuzzy c-means clustering algorithm was performed in order to determine the optimal number of the clusters. Results showed that the optimal number of MZs with regard to the two methods were both five and variance analysis indicated the heterogeneity of soil properties and yield among them. Consequently, it is feasible to use an active canopy sensor to delineate management zones for tobacco-planting fields.

Definition of Management Zones for Enhancing Cultivated Land Conservation Using Combined Spatial Data

The loss of cultivated land has increasingly become an issue of regional and national concern in China. Definition of management zones is an important measure to protect limited cultivated land resource. In this study, combined spatial data were applied to define management zones in Fuyang city, China. The yield of cultivated land was first calculated and evaluated and the spatial distribution pattern mapped; the limiting factors affecting the yield were then explored; and their maps of the spatial variability were presented using geostatistics analysis. Data were jointly analyzed for management zone definition using a combination of principal component analysis with a fuzzy clustering method, two cluster validity functions were used to determine the optimal number of cluster. Finally one-way variance analysis was performed on 3,620 soil sampling points to assess how well the defined management zones reflected the soil properties and productivity level. It was shown that there existed great potential for increasing grain production, and the amount of cultivated land played a key role in maintaining security in grain production. Organic matter, total nitrogen, available phosphorus, elevation, thickness of the plow layer, and probability of irrigation guarantee were the main limiting factors affecting the yield. The optimal number of management zones was three, and there existed significantly statistical differences between the crop yield and field parameters in each defined management zone. Management zone I presented the highest potential crop yield, fertility level, and best agricultural production condition, whereas management zone III lowest. The study showed that the procedures used may be effective in automatically defining management zones; by the development of different management zones, different strategies of cultivated land management and practice in each zone could be determined, which is of great importance to enhance cultivated land conservation, stabilize agricultural production, promote sustainable use of cultivated land and guarantee food security.

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

A web-based decision support tool, zone mapping application for precision farming (ZoneMAP, http://zonemap.umac.org), has been developed to automatically determine the optimal number of management zones and delineate them using satellite imagery and field data provided by users. Application rates, such as of fertilizer, can be prescribed for each zone and downloaded in a variety of formats to ensure compatibility with GPS-enabled farming equipment. ZoneMAP is linked to Digital Northern Great Plains, a web-based application which hosts an archive of satellite imagery, as well as high resolution imagery from airborne sensors. Management zones created by ZoneMAP mapped natural variation of the soil organic matter and other nutrients relatively well and were consistent with zone maps created by traditional means. The results demonstrated that ZoneMAP can serve as an effective and easy-to-use tool for those who practice precision agriculture.

Advances in precision agriculture in south-eastern Australia. IV. Spatial variability in plant-available water capacity of soil and its relationship with yield in site-specific management zones

Spatial variability in grain yield can arise from variation in many different soil and terrain properties. Identification of important sources of variation that bear significant relationship with grain yield can help achieve more effective site-specific management. This study had three aims: (i) a geostatistical description/modelling of the paddock-level spatial structure in variability of plant-available water capacity (PAWC) and related soil properties, (ii) to determine optimal number of management zones in the paddock, and (iii) to assess if the variability in PAWC and related soil properties is significantly associated with the variability in grain yield across the management zones. Particle size distribution, bulk density (BD), field capacity (FC), permanent wilting point (PWP), and soil water content (SWC) at sowing were measured at 4 soil depths (to 0.60 m) at 50 representative spatial sampling locations across a paddock near Birchip (Victoria). PAWC and plant-available water at sowing (PAWs) were derived from these data. Moderate to strong spatial dependence across the paddock was observed. The magnitude of the structural variation and of range varied widely across different soil properties and depths. The south-east edge and the central areas of the paddock had higher clay content, FC, PWP, PAWC, and lower PAWs. The paddock was divided into 6 potential management zones using combined header yield and normalised difference vegetation index (NDVI). The adequacy of zoning was evaluated using relative variability (RV) of header yield and soil properties. The mean RV for 3 zones differed little from that of 6 management zones for header yield and for each measured soil property, indicating division of the paddock into 3 zones to be adequate. The results from residual maximum likelihood (ReML) analysis showed that low yield zones had significantly higher clay content, FC, PWP, SWC, and PAWC and significantly lower PAWs than both medium and high yield zones. The mean FC, PWP, and PAWC in the low yield zones were, respectively, 25%, 26%, and 28% higher, and PAWs 36% lower than their corresponding values in the high yield zones. Linear regression analysis indicated that 59–96% of the observed variation in grain yield across management zones could be explained by variation in PWP. The practical implications of these results are discussed.

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

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.

Delineation of site-specific management zones using fuzzy clustering analysis in a coastal saline land

Recent research in precision agriculture has focused on use of management zones as a method for variable application of inputs. In this paper, five soil and landscape attributes, including a NDVI image, soil electrical conductivity, total nitrogen, organic matter and cation exchange capacity acquired for a coastal saline land were selected as data sources, and their spatial variability were analyzed and spatial distribution maps constructed with geostatistics technique. Principal component analysis and fuzzy c-means clustering algorithm were then performed to delineate the management zones, fuzzy performance index (FPI) and normalized classification entropy (NCE) was used to determine the optimal cluster number. To assess whether the defined three management zones can be used to characterize spatial variability of soil chemical properties and crop productivity, 139 georeferenced soil and yield sampling points across each management zone was examined by using variance analysis. It was found that the optimal number of management zones for the study area was three and there existed significantly statistical differences between the chemical properties of soil samples and yield data in each defined management zone, management zone 3 presented highest nutrient level and potential crop productivity, whereas management zone 1 lowest. The results revealed that the given five variables could be aggregated into management zones that characterize spatial variability in soil chemical properties and crop productivity. The defined management zones not only can direct soil sampling design, but also provide valuable information for site-specific management in precision agriculture.