A zone-based approach for processing and interpreting variability in multi-temporal yield data sets

Leroux, C.; Jones, Hazaël; Taylor, James A.; Clenet, Anthony; Tisseyre, Bruno

doi:10.1016/j.compag.2018.03.029

Cited by 15 publications

(12 citation statements)

References 34 publications

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“…Noisy data are often difficult to interpret when mapped, and while many approaches for data-filtering and mapping have been proposed (e.g. Lyle et al 2013;Robinson and Metternicht 2005;Sudduth and Drummond 2007;Sun et al 2013 among others), these have not been well translated into industry services for growers to date (Leroux et al 2018b). (ii) Spatial yield patterns are also often affected by managerial as well as environmental effects.…”

Section: Introductionmentioning

confidence: 99%

“…This limitation no longer exists and time series of yield data > 10 years and across multiple rotations are commonly available (e.g. Leroux et al 2018b) when working with growers who were early adopters of grain yield monitoring in the 1990s and early 2000s. These longer time series form a more coherent and robust data set, which should facilitate a better understanding of temporal and spatio-temporal variation in production.…”

Section: Introductionmentioning

confidence: 99%

“…It also permits temporal analyses to be targeted to specific conditions e.g. a specific crop type or specific climatic pattern (Leroux et al 2018b. However, as noted above, longer time series pose a problem for visual interpretation.…”

Section: Introductionmentioning

confidence: 99%

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Multi-temporal yield pattern analysis method for deriving yield zones in crop production systems

Blasch

Taylor

2020

Precision Agric

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Easy-to-use tools using modern data analysis techniques are needed to handle spatio-temporal agri-data. This research proposes a novel pattern recognition-based method, Multi-temporal Yield Pattern Analysis (MYPA), to reveal long-term (> 10 years) spatio-temporal variations in multi-temporal yield data. The specific objectives are: i) synthesis of information within multiple yield maps into a single understandable and interpretable layer that is indicative of the variability and stability in yield over a 10 + years period, and ii) evaluation of the hypothesis that the MYPA enhances multi-temporal yield interpretation compared to commonly-used statistical approaches. The MYPA method automatically identifies potential erroneous yield maps; detects yield patterns using principal component analysis; evaluates temporal yield pattern stability using a per-pixel analysis; and generates productivity-stability units based on k-means clustering and zonal statistics. The MYPA method was applied to two commercial cereal fields in Australian dryland systems and two commercial fields in a UK cool-climate system. To evaluate the MYPA, its output was compared to results from a classic, statistical yield analysis on the same data sets. The MYPA explained more of the variance in the yield data and generated larger and more coherent yield zones that are more amenable to site-specific management. Detected yield patterns were associated with varying production conditions, such as soil properties, precipitation patterns and management decisions. The MYPA was demonstrated as a robust approach that can be encoded into an easy-to-use tool to produce information layers from a time-series of yield data to support management.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi-temporal yield pattern analysis method for deriving yield zones in crop production systems

Blasch

Taylor

2020

Precision Agric

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“…2005 and 2007. Spatial yield patterns are also relatively stable from year to year, especially in 2003, 2005, 2009, 2011 and 2015, with a lower productive area on the northern section of the field and a higher-yielding one on the centre and southern portions of the field (Leroux et al, 2018). The yield total production potential and multi-annual overall production gap were computed from this historical database of yield mapping.…”

Section: Resultsmentioning

confidence: 99%

“…Nine years of yield mapping were available spanning the 2003-2015 period but only the wheat crop was considered in this study (six years) to minimize the need to standardize between different crop types in this exploratory work. More descriptive, spatial and temporal characteristics can be found in Leroux et al (2018).…”

Section: Description Of the Multi-temporal Within-field Yield Datamentioning

confidence: 99%

Production gap analysis - an operational approach to yield gap analysis using historical high-resolution yield data sets

Leroux

Taylor

Tisseyre

2019

Precision Agriculture ’19

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Yield potential has major interests in agronomical modelling such as for the computation of fertilizer recommendations. Most precise estimates of this potential can be generated with crop modelling approaches. However, such potential is often theoretical because it has never been obtained in the field and requires lots of input data to be computed, e.g. soil characteristics, management practices. In this study, a decomposition of this theoretical yield potential into a series of intermediate and more realistic potentials, denoted here production potentials, is proposed. This work comes along with a simple method relying on historical high-resolution yield datasets to compute site-specific production potentials and production gaps, i.e. the differences between actual yield and production potentials. Application of the methodology to a real database of yield mapping has proved of interest to identify areas with similar production potentials but different production gaps. Advantages and concerns regarding the use of such production potentials are discussed at the end of the study.

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Moran eigenvector filtering of multi‐year yield data with application to zone development

Long¹,

Griffith

Kvien

et al. 2021

Agrosystems Geosci & Env

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A time‐series of yield monitor data may be used to identify field areas of consistently low or high yield to serve as productivity zones for site‐specific crop management. However, transient factors that affect yield in 1 yr, but not every year, detract from this approach. The objective of this study was to illustrate Moran eigenvector spatial filtering (MESF) with results from analysis of multi‐year crop yield data from two farm fields in the United States. The MESF method accounts for temporal autocorrelation within a common factor map representing the correlation across years and partitions stochastic geographic variation into spatially structured and unstructured components. Crop rotation data were utilized from a dryland field in east‐central South Dakota and an irrigated field in southwestern Georgia. A random effects (RE) model was estimated that utilized eigenfunctions of a geographic connectivity matrix to account for spatially structured random effects (SSRE) and unstructured random effects (SURE) in standardized z scores of multi‐year crop yield. The MESF method was evaluated with conventional averaging of unfiltered yield data as a reference for comparison. In South Dakota, the SSRE accounted for 26% of the yield variance shared across years. Distinct patterns appeared to be related to changes in soil type and landscape position. The Georgia field yielded similar results. The MESF is effective for revealing structured variation in a time series of yield monitor data and may be useful for defining productivity zones within fields.

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A zone-based approach for processing and interpreting variability in multi-temporal yield data sets

Cited by 15 publications

References 34 publications

Multi-temporal yield pattern analysis method for deriving yield zones in crop production systems

Multi-temporal yield pattern analysis method for deriving yield zones in crop production systems

Production gap analysis - an operational approach to yield gap analysis using historical high-resolution yield data sets

Moran eigenvector filtering of multi‐year yield data with application to zone development

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