Study On Correlation Of Data From Yield Monitoring System And Hand Samples

Ingeli, Martin; Galambošová, Jana; Macák, M.; Rataj, V.

doi:10.1515/ata-2015-0003

Cited by 4 publications

(4 citation statements)

References 2 publications

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“…The correlations between spatial data are strongly scale-dependent [52] and of course depend on the coincidence of location between the sampled and monitored data, that in our study is not possible since the hand yield sampling was destructive (before yield mapping). The correlation coefficients found for all three crop seasons (ranging from 0.40 to 0.50) were in general lower but similar to the findings of Ingeli et al [16]. The latter is the only published paper to have compared two sources of yield data, the hand sampled data as independent variable and yield monitoring data as dependent variable.…”

Section: Yield Map and Yield Samplesupporting

confidence: 84%

“…Over the last 25 years, several studies have analyzed the sources of errors that cause this non-normality and proposed different processing techniques to reduce their effect [11,14,15]. However, among the studies highlighting accuracy issues associated with the use of the yield monitoring systems, only one compared yield monitoring with hand sampled data [16].…”

Section: Introductionmentioning

confidence: 99%

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A Precision Agriculture Approach for Durum Wheat Yield Assessment Using Remote Sensing Data and Yield Mapping

et al. 2019

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The availability of big data in agriculture, enhanced by free remote sensing data and on-board sensor-based data, provides an opportunity to understand within-field and year-to-year variability and promote precision farming practices for site-specific management. This paper explores the performance in durum wheat yield estimation using different technologies and data processing methods. A state-of-the-art data cleaning technique has been applied to data from a yield monitoring system, giving a good agreement between yield monitoring data and hand sampled data. The potential use of Sentinel-2 and Landsat-8 images in precision agriculture for within-field production variability is then assessed, and the optimal time for remote sensing to relate to durum wheat yield is also explored. Comparison of the Normalized Difference Vegetation Index(NDVI) with yield monitoring data reveals significant and highly positive linear relationships (r ranging from 0.54 to 0.74) explaining most within-field variability for all the images acquired between March and April. Remote sensing data analyzed with these methods could be used to assess durum wheat yield and above all to depict spatial variability in order to adopt site-specific management and improve productivity, save time and provide a potential alternative to traditional farming practices.

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Section: Yield Map and Yield Samplesupporting

confidence: 84%

Section: Introductionmentioning

confidence: 99%

A Precision Agriculture Approach for Durum Wheat Yield Assessment Using Remote Sensing Data and Yield Mapping

et al. 2019

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“…Construction of greenhouse is carried out in order to have a controlled environment in agriculture. This is achieved by installation of equipment to monitor and control air temperature, relative humidity, vapour pressure deficit and light levels (Ingeli et al, 2015). Greenhouses are intensively used for protection of tender or out-of-season plants from excessive heat or cold conditions in countries such as Israel, United States of America, Australia, India and Turkey.…”

Section: Introductionmentioning

confidence: 99%

Effects of planting date and growth environment on production of seed tubers from true potato seed (TPS)

Deshi,

Daniel

2024

njbot

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An experiment was carried out to evaluate the effects of planting date and growth environment on the production of potato (Solanum tuberosum L.) seed tubers from true potato seed (TPS) in Jos, Plateau State, Nigeria in the year 2019. A completely randomised design was used, consisting of three planting dates (May 10, May 25, June 9, 2019) and three growth environments (shaded field, open field and greenhouse), giving a total of nine treatment combinations in three replicates. Data collected were subjected to analysis of variance using the statistical software SAS, 9.0 version. The means were separated using the least significant difference. The results showed that planting date had significant (p<0.05) effect on mean establishment count, number of leaves at all sampling dates except at 104, 118 and 146 days after planting (DAP); plant height at all sampling dates except at 62, 118 and 132 DAP; mean number and weight of tubers at harvest. Growth environment had significant (p<0.05) effect on mean number of leaves produced at 132 to 174 DAP; plant height at all sampling dates except at 62 and 132 DAP, mean number and weight of tubers at harvest. Interaction of planting dates and growth environment was significant on mean plant height at 132 to 174 DAP. It is concluded that early planting, shading and open growth environment enhanced seed tuber production from true potato seed.

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“…As shown by Ingeli et al (2015), yield maps are a reliable source of information when the trend in yield across the field needs to be defined.…”

mentioning

confidence: 99%

Application of Clustering Method to Determine Production Zones of Field

Ingeli

Galambošová

Prokeinová

et al. 2015

Acta Technologica Agriculturae

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Determining the production zones of field is an important analysis in the precision farming technology as these may be used to control field operations in site-specific application. The aim of this paper was to evaluate the potential to identify the yield potential zones based on historical yield maps and to evaluate the procedure over the growing extent of input data. Standardized yield values from six growing seasons were considered. Suitable datasets were created, and hierarchical and non-hierarchical clustering methods were applied to create clusters. Results showed that using the data from commercial combine monitoring systems enables determining the zones. Multiple yield data are recommended as the values of analyses increase with the increased number of input datasets. However, commercial data have limitations in terms of complexity.

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Study On Correlation Of Data From Yield Monitoring System And Hand Samples

Cited by 4 publications

References 2 publications

A Precision Agriculture Approach for Durum Wheat Yield Assessment Using Remote Sensing Data and Yield Mapping

A Precision Agriculture Approach for Durum Wheat Yield Assessment Using Remote Sensing Data and Yield Mapping

Effects of planting date and growth environment on production of seed tubers from true potato seed (TPS)

Application of Clustering Method to Determine Production Zones of Field

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