Assessing the temporal stability of spatial patterns in crop yields using combine yield monitor data

2005 ◽  
Vol 85 (3) ◽  
pp. 439-451 ◽  
Author(s):  
John D Lauzon ◽  
David J Fallow ◽  
Ivan P O’Halloran ◽  
Sharon D. L. Gregory ◽  
A. Peter von Bertoldi
Keyword(s):  
Crop Yields ◽  
Temporal Stability ◽  
Grid Size ◽  
M Cell ◽  
Site Specific ◽  
Management Zones ◽  
Crop Choice ◽  
Stability Maps ◽  
Monitor Data

Using previous years’ yield patterns may be one method of breaking a field into management zones for the purpose of site-specific management. For this method to be useful there must be temporal stability of yield patterns and there must be a sound method of assessing the spatial-temporal stability of yield in a field. To this end, a method was developed to give a non-biased estimate of the within-field spatial-temporal stability of yield. The method determined the probability that the normalized yield for all years available at a given location in the field fit within the accuracy limits of the combine. Combine accuracies of ± 2.5%, 5% and 10% of the field mean yield and gridded data of 3 m, 6 m, and 9 m cell sizes, as well as crop choice were all included in the model to assess its sensitivity to changes in these factors. The resulting spatial-temporal stability maps were well correlated with visual estimations of the spatial yield patterns. The model results were highly influenced by the inputted combine accuracy, but grid size and crop choice had little affect on the proportion of the field or the spatial pattern of temporal stability in the two field sites examined. The sensitivity of the model to changes in the input value for the combine accuracy indicated that a good estimate of this value is required for the determination of the stable patterns in a field. Key words: Spatial-temporal stability, yield

Determination of site-specific management zones using soil physico-chemical properties and crop yields in coastal reclaimed farmland

Geoderma ◽  
2014 ◽  
Vol 232-234 ◽  
pp. 381-393 ◽  
Author(s):  
Rong-Jiang Yao ◽  
Jing-Song Yang ◽  
Tong-Juan Zhang ◽  
Peng Gao ◽  
Xiang-Ping Wang ◽  
...  
Keyword(s):  
Crop Yields ◽  
Chemical Properties ◽  
Site Specific ◽  
Management Zones ◽  
Physico Chemical ◽  
Specific Management

Delineation of management zones with measurements of soil apparent electrical conductivity in the southeastern pampas

2013 ◽  
Vol 93 (2) ◽  
pp. 205-218 ◽  
Author(s):  
Nahuel Raúl Peralta ◽  
José Luis Costa ◽  
Mónica Balzarini ◽  
Hernán Angelini
Keyword(s):  
Electrical Conductivity ◽  
Soil Properties ◽  
Stepwise Regression ◽  
Clay Soil ◽  
Crop Yields ◽  
Management Strategies ◽  
Site Specific ◽  
Management Zones ◽  
Apparent Electrical Conductivity ◽  
Specific Management

Peralta, N. R., Costa, J. L., Balzarini, M. and Angelini, H. 2013. Delineation of management zones with measurements of soil apparent electrical conductivity in the southeastern pampas. Can. J. Soil Sci. 93: 205–218. Site-specific management demands the identification of subfield regions with homogeneous characteristics (management zones). However, determination of subfield areas is difficult because of complex correlations and spatial variability of soil properties responsible for variations in crop yields within the field. We evaluated whether apparent electrical conductivity (ECa) is a potential estimator of soil properties, and a tool for the delimitation of homogeneous zones. ECamapping of a total of 647 ha was performed in four sites of Argentinean pampas, with two fields per site composed of several soil series. Soil properties and ECawere analyzed using principal components (PC)–stepwise regression and ANOVA. The PC–stepwise regression showed that clay, soil organic matter (SOM), cation exchange capacity (CEC) and soil gravimetric water content (θg) are key loading factors, for explaining the ECa(R2≥0.50). In contrast, silt, sand, extract electrical conductivity (ECext), pH values and [Formula: see text]-N content were not able to explain the ECa. The ANOVA showed that ECameasurements successfully delimited three homogeneous soil zones associated with spatial distribution of clay, soil moisture, CEC, SOM content and pH. These results suggest that field-scale ECamaps have the potential to design sampling zones to implement site-specific management strategies.

scholarly journals Determination of management zones from normalized and standardized equivalent produtivity maps in the soybean culture

2011 ◽  
Vol 31 (5) ◽  
pp. 895-905 ◽  
Author(s):  
Grazieli Suszek ◽  
Eduardo G. de Souza ◽  
Miguel A. Uribe-Opazo ◽  
Lucia H. P. Nobrega
Keyword(s):  
Precision Agriculture ◽  
Agricultural Sector ◽  
Standard Score ◽  
New Techniques ◽  
Site Specific ◽  
Management Zones ◽  
Productivity Data ◽  
Productive Area ◽  
Definition Of

Through the site-specific management, the precision agriculture brings new techniques for the agricultural sector, as well as a larger detailing of the used methods and increase of the global efficiency of the system. The objective of this work was to analyze two techniques for definition of management zones using soybean yield maps, in a productive area handled with localized fertilization and other with conventional fertilization. The sampling area has 1.74 ha, with 128 plots with site-specific fertilization and 128 plots with conventional fertilization. The productivity data were normalized by two techniques (normalized and standardized equivalent productivity), being later classified in management zones. It can be concluded that the two methods of management zones definition had revealed to be efficient, presenting similarities in the data disposal. Due to the fact that the equivalent standardized productivity uses standard score, it contemplates a better statistics justification.

scholarly journals Identificación de zonas de manejo sitio-específico a partir de la combinación de variables de suelo

2012 ◽  
Vol 13 (1) ◽  
pp. 47 ◽  
Author(s):  
Mariano Córdoba ◽  
Mónica Balzarini ◽  
Cecilia Bruno ◽  
José Luis Costa
Keyword(s):  
Spatial Variability ◽  
Principal Component ◽  
Production Potential ◽  
Site Specific ◽  
Management Zones ◽  
Soil Variables ◽  
Regionalized Variables ◽  
Several Variables ◽  
Specific Management

<p>El manejo sitio-específico demanda la identificación de sub-regiones homogéneas, o zonas de manejo (ZM), dentro del espacio productivo. Sin embargo, definir ZM suele ser complejo debido a que la variabilidad espacial del suelo puede depender de varias variables. La zonificación o delimitación de ZM puede realizarse utilizando una variable de suelo a la vez o considerando varias variables simultáneamente. Entre los métodos de análisis multivariado, difundido para la zonificación, se encuentra el análisis de conglomerados fuzzy k-means (KM) y el análisis de componentes principales (PCA). No obstante, como otros métodos multivariados, éstos no han sido desarrollados específicamente para datos georreferenciados. Una nueva versión del PCA, conocido como MULTISPATI-PCA (PCAe), permite contemplar la autocorrelación espacial entre datos de variables regionalizadas. El objetivo de este estudio fue proponer una nueva estrategia de análisis para la identificación de ZM, combinando la aplicación KM y PCAe sobre datos de múltiples variables de suelo. La capacidad del método propuesto se evaluó en base a la comparación de los rendimientos promedios alcanzados en cada zona delimitada, tanto para la combinación de KM con PCA, la aplicación tradicional de KM sobre las variables originales y la nueva propuesta KM-PCAe. Los resultados mostraron que KM-PCAe fue el único método que permitió distinguir zonas estadísticamente diferentes en cuanto al potencial productivo. Se concluye que la combinación propuesta constituye una herramienta importante para el mapeo de la variabilidad espacial y la identificación de ZM a partir de datos georreferenciados. </p><p> </p><p><strong>Identification of site-specific management zones from combination of soil variables</strong></p>Site-specific management demands the identification of homogeneous subfield regions within the field or management zones (MZ). However, due to the spatial variability of soil variables, determination of MZ from several variables, is often complex. Although the zonification or delimitation of MZ may be univariate, it is more appropriate to consider all variables simultaneously. Fuzzy k-means clustering (KM) and principal component analysis (PCA) are multivariate analyses that have been used for zonification. Nevertheless, PCA and KM have not been explicitly developed for georeferenced data. Novel versions of PCA, known as MULTISPATI-PCA (PCAe), incorporate spatial autocorrelation among data of neighbor sites of regionalized variables. The objective of this study was to propose a new analytical tool to identify homogeneous zones from the combination of KM and PCAe on multiple soil variable data. The performance of proposed method was assessed through comparison of the average yields obtained in each zone delimited by combination of KM with PCA, as well as KM on the original variables and the new proposed method KM-PCAe. The results showed that KM-PCAe was the only method able to identify zones statistically different in terms of production potential. PCAe and its combination with KM are useful tools to map spatial variability and to identify MZ within fields from georeferenced data.

scholarly journals Subfield crop yields and temporal stability in thousands of US Midwest fields

2021 ◽  
Author(s):  
Bernardo Maestrini ◽  
Bruno Basso
Keyword(s):  
Standard Deviation ◽  
Temporal Variability ◽  
Crop Yields ◽  
Temporal Stability ◽  
Large Dataset ◽  
Yield Distribution ◽  
Yield Difference ◽  
The Mean ◽  
Us Midwest ◽  
Monitor Data

AbstractUnderstanding subfield crop yields and temporal stability is critical to better manage crops. Several algorithms have proposed to study within-field temporal variability but they were mostly limited to few fields. In this study, a large dataset composed of 5520 yield maps from 768 fields provided by farmers was used to investigate the influence of subfield yield distribution skewness on temporal variability. The data are used to test two intuitive algorithms for mapping stability: one based on standard deviation and the second based on pixel ranking and percentiles. The analysis of yield monitor data indicates that yield distribution is asymmetric, and it tends to be negatively skewed (p < 0.05) for all of the four crops analyzed, meaning that low yielding areas are lower in frequency but cover a larger range of low values. The mean yield difference between the pixels classified as high-and-stable and the pixels classified as low-and-stable was 1.04 Mg ha−1 for maize, 0.39 Mg ha−1 for cotton, 0.34 Mg ha−1 for soybean, and 0.59 Mg ha−1 for wheat. The yield of the unstable zones was similar to the pixels classified as low-and-stable by the standard deviation algorithm, whereas the two-way outlier algorithm did not exhibit this bias. Furthermore, the increase in the number years of yield maps available induced a modest but significant increase in the certainty of stability classifications, and the proportion of unstable pixels increased with the precipitation heterogeneity between the years comprising the yield maps.

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.

Toward Fast Determination of Protein Stability Maps: Experimental and Theoretical Analysis of Mutants of a Nocardiopsis prasina Serine Protease

Biochemistry ◽  
2012 ◽  
Vol 51 (26) ◽  
pp. 5339-5347 ◽  
Author(s):  
Damien Farrell ◽  
Helen Webb ◽  
Michael A. Johnston ◽  
Thomas A. Poulsen ◽  
Fergal O’Meara ◽  
...  
Keyword(s):  
Theoretical Analysis ◽  
Protein Stability ◽  
Serine Protease ◽  
Fast Determination ◽  
Stability Maps

scholarly journals Delineating site-specific management zones for pH-induced iron chlorosis

2008 ◽  
Vol 9 (1-2) ◽  
pp. 71-84 ◽  
Author(s):  
T. Kyaw ◽  
R. B. Ferguson ◽  
V. I. Adamchuk ◽  
D. B. Marx ◽  
D. D. Tarkalson ◽  
...  
Keyword(s):  
Iron Chlorosis ◽  
Site Specific ◽  
Management Zones ◽  
Specific Management
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