scholarly journals Optimum size in grid soil sampling for variable rate application in site-specific management

2011 ◽  
Vol 68 (3) ◽  
pp. 386-392 ◽  
Author(s):  
Marcos Rafael Nanni ◽  
Fabrício Pinheiro Povh ◽  
José Alexandre Melo Demattê ◽  
Roney Berti de Oliveira ◽  
Marcelo Luiz Chicati ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Precision Agriculture ◽  
Production Efficiency ◽  
Sampling Interval ◽  
Soil Samples ◽  
Soil Sampling ◽  
Kappa Statistics ◽  
Increase Production Efficiency ◽  
Site Specific ◽  
Specific Management

The importance of understanding spatial variability of soils is connected to crop management planning. This understanding makes it possible to treat soil not as a uniform, but a variable entity, and it enables site-specific management to increase production efficiency, which is the target of precision agriculture. Questions remain as the optimum soil sampling interval needed to make site-specific fertilizer recommendations in Brazil. The objectives of this study were: i) to evaluate the spatial variability of the main attributes that influence fertilization recommendations, using georeferenced soil samples arranged in grid patterns of different resolutions; ii) to compare the spatial maps generated with those obtained with the standard sampling of 1 sample ha-1, in order to verify the appropriateness of the spatial resolution. The attributes evaluated were phosphorus (P), potassium (K), organic matter (OM), base saturation (V%) and clay. Soil samples were collected in a 100 × 100 m georeferenced grid. Thinning was performed in order to create a grid with one sample every 2.07, 2.88, 3.75 and 7.20 ha. Geostatistical techniques, such as semivariogram and interpolation using kriging, were used to analyze the attributes at the different grid resolutions. This analysis was performed with the Vesper software package. The maps created by this method were compared using the kappa statistics. Additionally, correlation graphs were drawn by plotting the observed values against the estimated values using cross-validation. P, K and V%, a finer sampling resolution than the one using 1 sample ha-1 is required, while for OM and clay coarser resolutions of one sample every two and three hectares, respectively, may be acceptable.

Delineating site-specific management zones for precision agriculture

2015 ◽  
Vol 154 (2) ◽  
pp. 273-286 ◽  
Author(s):  
H. U. FARID ◽  
A. BAKHSH ◽  
N. AHMAD ◽  
A. AHMAD ◽  
Z. MAHMOOD-KHAN
Keyword(s):  
Spatial Variability ◽  
Grain Yield ◽  
Precision Farming ◽  
Farming Practices ◽  
Wheat Grain ◽  
Site Specific ◽  
Management Zones ◽  
Management Zone ◽  
Specific Management ◽  
Landscape Attributes

SUMMARYDelineating site-specific management zones within fields can be helpful in addressing spatial variability effects for adopting precision farming practices. A 3-year (2008/09 to 2010/11) field study was conducted at the Postgraduate Agricultural Research Station, University of Agriculture, Faisalabad, Pakistan, to identify the most important soil and landscape attributes influencing wheat grain yield, which can be used for delineating management zones. A total of 48 soil samples were collected from the top 300 mm of soil in 8-ha experimental field divided into regular grids of 24 × 67 m prior to sowing wheat. Soil and landscape attributes such as elevation, % of sand, silt and clay by volume, soil electrical conductivity (EC), pH, soil nitrogen (N) and soil phosphorus (P) were included in the analysis. Artificial neural network (ANN) analysis showed that % sand, % clay, elevation, soil N and soil EC were important variables for delineating management zones. Different management zone schemes ranging from three to six were developed and evaluated based on performance indicators using Management Zone Analyst (MZA V0·1) software. The fuzziness performance index (FPI) and normalized classification entropy NCE indices showed minimum values for a four management zone scheme, indicating its appropriateness for the experimental field. The coefficient of variation values of soil and landscape attributes decreased for each management zone within the four management zone scheme compared to the entire field, which showed improved homogeneity. The evaluation of the four management zone scheme using normalized wheat grain yield data showed distinct means for each management zone, verifying spatial variability effects and the need for its management. The results indicated that the approach based on ANN and MZA software analysis can be helpful in delineating management zones within the field, to promote precision farming practices effectively.

scholarly journals Multi-species weed spatial variability and site-specific management maps in cultivated sunflower

Weed Science ◽  
2003 ◽  
Vol 51 (3) ◽  
pp. 319-328 ◽  
Author(s):  
Montserrat Jurado-Expósito ◽  
Francisca López-Granados ◽  
Luis García-Torres ◽  
Alfonso García-Ferrer ◽  
Manuel Sánchez de la Orden ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Site Specific ◽  
Specific Management

scholarly journals 649 Summary of the Status of Precision Agriculture

HortScience ◽  
1999 ◽  
Vol 34 (3) ◽  
pp. 559E-559 ◽  
Author(s):  
Douglas C. Sanders
Keyword(s):  
Environmental Impacts ◽  
Precision Agriculture ◽  
Production Systems ◽  
Great Depth ◽  
Site Specific ◽  
Improve Production ◽  
Planning And Management ◽  
The Status ◽  
Specific Management

The diversity of site-specific management opportunities is demonstrated by the list of topics and speakers we have in the colloquium. These techniques will help use to better understand, adapt, and adjust horticultural management to the benefit of producers, researchers, and the consumer. With these technologies we will be able to reduce costs, environmental impacts, and improve production, and quality. Horticulture will use more both remote and manually operated devices that allow more intensive planning and management of our production systems. This colloquium has just scratched the surface of the potential of these techniques in horticulture. We hope that the sampling will whet your appetite for great depth of study of the opportunities that are just around the corner.

scholarly journals Spatial variability of soil pH sampled by two methodologies used in precision agriculture in farms under crop rotation

DYNA ◽  
2019 ◽  
Vol 86 (209) ◽  
pp. 289-297
Author(s):  
Gabriel Araújo e Silva Ferraz ◽  
Brenon Diennevan Souza Barbosa ◽  
Étore Francisco Reynaldo ◽  
Sthéfany Airane Dos Santos ◽  
Jose Roberto Moreira Ribeiro Gonçalves ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Spatial Variability ◽  
Soil Ph ◽  
Precision Agriculture ◽  
Traditional Method ◽  
Sampling Method ◽  
Sampling Methods ◽  
Spherical Model ◽  
Soil Samples ◽  
Determination Methods

This study aimed to characterize the spatial variability of pH in soils of two farms in the state of Paraná, Brazil, based on two different sampling methods used in precision agriculture, by means of geostatistical analyzes. The first method of sampling the pH grid consisted in the collection of soil samples by the traditional method (1 point / ha). The second method of pH determination was by on-the-go soil sensor (200 points / ha). The spherical model was better suited to most semivariograms, regardless of the sampling method. After adjusting the semivariograms for soil pH determination methods, thematic maps were made using normal kriging. The best spatial distribution of pH was obtained where the attribute was sampled by the on-the-go sensor. The number of pH samples collected and the sampling method influenced the visual representation of pH variability.

scholarly journals Influence of Soil Properties on Maize and Wheat Nitrogen Status Assessment from Sentinel-2 Data

2020 ◽  
Vol 12 (14) ◽  
pp. 2175
Author(s):  
Alberto Crema ◽  
Mirco Boschetti ◽  
Francesco Nutini ◽  
Donato Cillis ◽  
Raffaele Casa
Keyword(s):  
Soil Properties ◽  
Precision Agriculture ◽  
N Fertilization ◽  
Soil Samples ◽  
Site Specific ◽  
Plant Nitrogen ◽  
Plant Nitrogen Uptake ◽  
Status Assessment ◽  
N Status ◽  
Sentinel 2

Soil properties variability is a factor that greatly influences cereals crops production and interacts with a proper assessment of crop nutritional status, which is fundamental to support site-specific management able to guarantee a sustainable crop production. Several management strategies of precision agriculture are now available to adjust the nitrogen (N) input to the actual crop needs. Many of the methods have been developed for proximal sensors, but increasing attention is being given to satellite-based N management systems, many of which rely on the assessment of the N status of crops. In this study, the reliability of the crop nutritional status assessment through the estimation of the nitrogen nutrition index (NNI) from Sentinel-2 (S2) satellite images was examined, focusing of the impact of soil properties variability for crop nitrogen deficiency monitoring. Vegetation indices (VIs) and biophysical variables (BVs), such as the green area index (GAI_S2), leaf chlorophyll content (Cab_S2), and canopy chlorophyll content (CCC_S2), derived from S2 imagery, were used to investigate plant N status and NNI retrieval, in the perspective of its use for guiding site-specific N fertilization. Field experiments were conducted on maize and on durum wheat, manipulating 4 groups of plots, according to soil characteristics identified by a soil map and quantified by soil samples analysis, with different N treatments. Field data collection highlighted different responses of the crops to N rate and soil type in terms of NNI, biomass (W), and nitrogen concentration (Na%). For both crops, plots in one soil class (FOR1) evidenced considerably lower values of BVs and stress conditions with respect to others soil classes even for high N rates. Soil samples analyses showed for FOR1 soil class statistically significant differences for pH, compared to the other soil classes, indicating that this property could be a limiting factor for nutrient absorption, hence crop growth, regardless of the amount of N distributed to the crop. The correlation analysis between measured crop related BVs and satellite-based products (VIs and S2_BVs) shows that it is possible to: (i) directly derive NNI from CCC_S2 (R2 = 0.76) and either normalized difference red edge index (NDRE) for maize (R2 = 0.79) or transformed chlorophyll absorption ratio index (TCARI) for durum wheat (R2 = 0.61); (ii) indirectly estimate NNI as the ratio of plant nitrogen uptake (PNUa) and critical plant nitrogen uptake (PNUc) derived using CCC_S2 (R2 = 0.77) and GAI_S2 (R2 = 0.68), respectively. Results of this study confirm that NNI is a good indicator to monitor plants N status, but also highlights the importance of linking this information to soil properties to support N site-specific fertilization in the precision agriculture framework. These findings contribute to rational agro-practices devoted to avoid N fertilization excesses and consequent environmental losses, bringing out the real limiting factors for optimal crop growth.

scholarly journals Expedited generation of terrain digital classes in flat areas from UAV images for precision agriculture purposes

2017 ◽  
Vol 8 (2) ◽  
pp. 828-832
Author(s):  
M. C. Pineda ◽  
C. Perdomo ◽  
R. Caballero ◽  
A. Valera ◽  
J. A. Martínez-Casasnovas ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Topographic Wetness Index ◽  
Site Specific ◽  
Soil Variables ◽  
Digital Terrain ◽  
Digital Elevation ◽  
Elevation Model ◽  
Uav Images ◽  
Specific Management

Precision agriculture (PA) requires reasonably homogeneous areas for site-specific management. This work explores the applicability of digital terrain classes obtained from a digital elevation model derived from UAV-acquired images, to define management units in in a relative flat area of about 6 ha. Elevation, together with other terrain variables such as: slope degree, profile curvature, plan curvature, topographic wetness index, sediment transport index, were clustered using the Fuzzy Kohonen Clustering Network (FKCN). Four terrain classes were obtained. The result was compared with a map produced by a classification of soil properties previously interpolated by ordinary kriging. The results suggest that areas for site-specific management can be defined from terrain classes based on environmental covariates, saving time and cost in comparison with interpolation of soil variables.

scholarly journals Establishing management zones using soil electrical conductivity and other soil properties by the fuzzy clustering technique

2008 ◽  
Vol 65 (6) ◽  
pp. 567-573 ◽  
Author(s):  
José Paulo Molin ◽  
Cesar Nunes de Castro
Keyword(s):  
Electrical Conductivity ◽  
Soil Fertility ◽  
Soil Properties ◽  
Precision Agriculture ◽  
Principal Component ◽  
Site Specific ◽  
Management Zones ◽  
Physical Attributes ◽  
Definition Of ◽  
Specific Management

The design of site-specific management zones that can successfully define uniform regions of soil fertility attributes that are of importance to crop growth is one of the most challenging steps in precision agriculture. One important method of so proceeding is based solely on crop yield stability using information from yield maps; however, it is possible to accomplish this using soil information. In this study the soil was sampled for electrical conductivity and eleven other soil properties, aiming to define uniform site-specific management zones in relation to these variables. Principal component analysis was used to group variables and fuzzy logic classification was used for clustering the transformed variables. The importance of electrical conductivity in this process was evaluated based on its correlation with soil fertility and physical attributes. The results confirmed the utility of electrical conductivity in the definition of management zones and the feasibility of the proposed method.

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