Identifying potential soybean management zones from multi-year yield data

2005 ◽  
Vol 46 (1-3) ◽  
pp. 309-327 ◽  
Author(s):  
Dan B. Jaynes ◽  
Tom S. Colvin ◽  
Tom C. Kaspar
Keyword(s):  
Yield Data ◽  
Management Zones

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

Agronomy ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 183
Author(s):  
Michele Denora ◽  
Marco Fiorentini ◽  
Stefano Zenobi ◽  
Paola A. Deligios ◽  
Roberto Orsini ◽  
...  
Keyword(s):  
Low Cost ◽  
Optimal Number ◽  
Machine Learning Algorithms ◽  
Yield Data ◽  
Management Zones ◽  
Soil Sensors ◽  
Zone Management ◽  
Gap Statistic ◽  
Vegetation Indexes ◽  
Significant Difference

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.

scholarly journals Delineation of management zones with spatial data fusion and belief theory

2019 ◽  
Vol 21 (4) ◽  
pp. 802-830
Author(s):  
Claudia Vallentin ◽  
Eike Stefan Dobers ◽  
Sibylle Itzerott ◽  
Birgit Kleinschmit ◽  
Daniel Spengler
Keyword(s):  
Data Fusion ◽  
Spatial Data ◽  
Precision Agriculture ◽  
Agricultural Land ◽  
Expert Knowledge ◽  
Relevant Information ◽  
High Yield ◽  
Model Parameters ◽  
Yield Data ◽  
Management Zones

AbstractPrecision agriculture, as part of modern agriculture, thrives on an enormously growing amount of information and data for processing and application. The spatial data used for yield forecasting or the delimitation of management zones are very diverse, often of different quality and in different units to each other. For various reasons, approaches to combining geodata are complex, but necessary if all relevant information is to be taken into account. Data fusion with belief structures offers the possibility to link geodata with expert knowledge, to include experiences and beliefs in the process and to maintain the comprehensibility of the framework in contrast to other “black box” models. This study shows the possibility of dividing agricultural land into management zones by combining soil information, relief structures and multi-temporal satellite data using the transferable belief model. It is able to bring in the knowledge and experience of farmers with their fields and can thus offer practical assistance in management measures without taking decisions out of hand. At the same time, the method provides a solution to combine all the valuable spatial data that correlate with crop vitality and yield. For the development of the method, eleven data sets in each possible combination and different model parameters were fused. The most relevant results for the practice and the comprehensibility of the model are presented in this study. The aim of the method is a zoned field map with three classes: “low yield”, “medium yield” and “high yield”. It is shown that not all data are equally relevant for the modelling of yield classes and that the phenology of the plant is of particular importance for the selection of satellite images. The results were validated with yield data and show promising potential for use in precision agriculture.

scholarly journals Determining the management zones with hierarchic and non-hierarchic clustering methods

2014 ◽  
Vol 60 (Special Issue) ◽  
pp. S44-S51 ◽  
Author(s):  
J. Galambošová ◽  
V. Rataj ◽  
R. Prokeinová ◽  
J. Prešinská
Keyword(s):  
Precision Agriculture ◽  
Clustering Methods ◽  
Clustering Method ◽  
Yield Data ◽  
Number Of Clusters ◽  
Management Zones ◽  
Electromagnetic Data ◽  
Clustering Criterion

Delineation of the management zones of a field is commonly used in precision agriculture technology. There are many techniques used to identify management zones. The most used technique is k-means clustering, where the number of clusters is managed by the user. The paper deals with clustering the yield data and electromagnetic data of a 17 ha field using the Ward’s method followed by the k-means clustering method. The cubic clustering criterion was used to determine the number of clusters. Based on results, it can be concluded that it is beneficial to combine the k-means clustering method with the hierarchic method (Ward’s method).

EFFECT OF SOWING DATE ON GRAIN YIELD OF CRAB GRASS

2001 ◽  
Vol 49 (3) ◽  
pp. 293-297
Author(s):  
S. O. Bakare ◽  
M. G. M. Kolo ◽  
J. A. Oladiran
Keyword(s):  
Grain Yield ◽  
Interaction Effect ◽  
Significant Interaction ◽  
Sowing Date ◽  
Yield Data ◽  
Significant Interaction Effect ◽  
Sowing Dates

There was a significant interaction effect between the variety and the sowing date for the number of productive tillers, indicating that the response to sowing date varied with the variety. A significant reduction in the number of productive tillers became evident when sowing was delayed till 26 June in the straggling variety as compared to sowing dates in May. Lower numbers of productive tillers were also recorded when the sowing of the erect variety was further delayed till 10 July. The grain yield data showed that it is not advisable to sow the straggling variety later than 12 June, while sowing may continue till about 26 June for the erect variety in the study area.

Utilizing Grain Yield Data from Wheat Demonstration Plots

1990 ◽  
Vol 19 (2) ◽  
pp. 164-166 ◽  
Author(s):  
Liu Ben-Hui ◽  
J. P. Shroyer ◽  
T. S. Cox
Keyword(s):  
Grain Yield ◽  
Yield Data

Interference of Three Annual Grasses with Grain Sorghum (Sorghum bicolor)

1990 ◽  
Vol 4 (2) ◽  
pp. 245-249 ◽  
Author(s):  
Brenda S. Smith ◽  
Don S. Murray ◽  
J. D. Green ◽  
Wan M. Wanyahaya ◽  
David L. Weeks
Keyword(s):  
Linear Regression ◽  
Grain Yield ◽  
Field Experiments ◽  
Grain Sorghum ◽  
Grass Species ◽  
Row Spacing ◽  
Yield Data ◽  
Weed Density ◽  
Annual Grasses ◽  
Wide Row Spacing

Barnyardgrass, large crabgrass, and Texas panicum were evaluated in field experiments over 3 yr to measure their duration of interference and density on grain sorghum yield. When grain yield data were converted to a percentage of the weed-free control, linear regression predicted a 3.6% yield loss for each week of weed interference regardless of year or grass species. Grain sorghum grown in a narrow (61-cm) row spacing was affected little by full-season interference; however, in wide (91-cm) rows, interference increased as grass density increased. Data from the wide-row spacing were described by linear regression following conversion of grain yield to percentages and weed density to log10. A separate nonlinear model also was derived which could predict the effect of weed density on grain sorghum yield.

scholarly journals The Use of Crop Yield Autocorrelation Data as a Sustainable Approach to Adjust Agronomic Inputs

2021 ◽  
Vol 13 (4) ◽  
pp. 2362
Author(s):  
Thomas M. Koutsos ◽  
Georgios C. Menexes ◽  
Andreas P. Mamolos
Keyword(s):  
Decision Making ◽  
Spatial Analysis ◽  
Crop Yield ◽  
Spatial Autocorrelation Analysis ◽  
Autocorrelation Analysis ◽  
Yield Data ◽  
Site Specific ◽  
Production Areas ◽  
Short Time ◽  
Monitor Data

Agricultural fields have natural within-field soil variations that can be extensive, are usually contiguous, and are not always traceable. As a result, in many cases, site-specific attention is required to adjust inputs and optimize crop performance. Researchers, such as agronomists, agricultural engineers, or economists and other scientists, have shown increased interest in performing yield monitor data analysis to improve farmers’ decision-making concerning the better management of the agronomic inputs in the fields, while following a much more sustainable approach. In this case, spatial analysis of crop yield data with the form of spatial autocorrelation analysis can be used as a practical sustainable approach to locate statistically significant low-production areas. The resulted insights can be used as prescription maps on the tractors to reduce overall inputs and farming costs. This aim of this work is to present the benefits of conducting spatial analysis of yield crop data as a sustainable approach. Current work proves that the implementation of this process is costless, easy to perform and provides a better understanding of the current agronomic needs for better decision-making within a short time, adopting a sustainable approach.

scholarly journals The Relationship between Soil Electrical Parameters and Compaction of Sandy Clay Loam Soil

Agriculture ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 114
Author(s):  
Katarzyna Pentoś ◽  
Krzysztof Pieczarka ◽  
Kamil Serwata
Keyword(s):  
Electrical Conductivity ◽  
Magnetic Susceptibility ◽  
Precision Agriculture ◽  
Soil Compaction ◽  
Soil Layer ◽  
Soil Parameters ◽  
Soil Electrical Conductivity ◽  
Electrical Parameters ◽  
Management Zones ◽  
Additional Information

Soil spatial variability mapping allows the delimitation of the number of soil samples investigated to describe agricultural areas; it is crucial in precision agriculture. Electrical soil parameters are promising factors for the delimitation of management zones. One of the soil parameters that affects yield is soil compaction. The objective of this work was to indicate electrical parameters useful for the delimitation of management zones connected with soil compaction. For this purpose, the measurement of apparent soil electrical conductivity and magnetic susceptibility was conducted at two depths: 0.5 and 1 m. Soil compaction was measured for a soil layer at 0–0.5 m. Relationships between electrical soil parameters and soil compaction were modelled with the use of two types of neural networks—multilayer perceptron (MLP) and radial basis function (RBF). Better prediction quality was observed for RBF models. It can be stated that in the mathematical model, the apparent soil electrical conductivity affects soil compaction significantly more than magnetic susceptibility. However, magnetic susceptibility gives additional information about soil properties, and therefore, both electrical parameters should be used simultaneously for the delimitation of management zones.

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