An analysis of the factors that influence sugarcane yield in Northern Argentina using classification and regression trees

Ferraro, Diego; Rivero, Dario E.; Ghersa, Claudio M.

doi:10.1016/j.fcr.2009.02.014

Cited by 64 publications

(54 citation statements)

References 34 publications

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“…This highlights the importance for the method used to evaluate blocks of outstanding performance to consider the distribution of the data at the moment of classification (in the case of k-means) instead of a fixed arbitrary value. Using clusters of outstanding blocks that are more homogeneous is expected to facilitate the search for beneficial patterns (HAN et al, 2012;FERRARO et al, 2009). …”

Section: Resultsmentioning

confidence: 99%

Identification of commercial blocks of outstanding performance of sugarcane using data mining

Peloia

Rodrigues

2016

Eng. Agríc.

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ABSTRACT:In order to achieve more efficient agricultural production systems, studies relating to the patterns of influence factors on commercial blocks of outstanding performance can be performed to assist management practices. The performance is considered to be the difference between the yield of a given block and the average yield of the homogeneous group that it belongs to. The methods available to identify these outstanding blocks are usually subjective. The aim of this study was to propose an objective and repeatable approach to identify outstanding performance blocks. The proposed approach consisted of performance determination, using regression trees, and the classification of these blocks by k-means clustering. This approach was illustrated using a sugarcane model. The main factors influencing the tonnes of cane per hectare (TCH) and total recoverable sugar (TRS) yields were found to be crop age and water availability during ripening, respectively. These were used to create potential yield groups, and blocks with high and low performance were identified. The proposed approach was found to be valid in the identification of outstanding sugarcane blocks, and it can be applied to different crops or in the context of precision agriculture.

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Section: Resultsmentioning

confidence: 99%

Identification of commercial blocks of outstanding performance of sugarcane using data mining

Peloia

Rodrigues

2016

Eng. Agríc.

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“…Markers to this axis and points at which they intersect this axis define the stability of genotypes. Accordingly Genotypes 12,35,10,5,46,17,28,19,8,2,20,14 and 4 located to the right side of the ordinate and accumulated in order better brix% in cane while genotypes 12, 35, 10, 17, 28, 19, 8 and 2 had short projections and are both high yielder and stable genotypes in brix accumulation over crop ages and across locations. The result is consistent with the results of maturity classification displayed using PCA bi-plots for using (Figure 2a and 2b).…”

Section: Ammi2 Analysismentioning

confidence: 93%

“…Multivariate techniques are suitable for analyzing many variables simultaneously and have been widely used to measure the diversity in germplasm collections assess the relative contributions that various traits [12] and classify variables based on a specific factor [13]. These multivariate techniques have also been used in the classification of germplasm collections, multi-environment trials and analysis of factors contributing to yield [14,15].…”

Section: Advances In Crop Science and Technologymentioning

confidence: 99%

“…Meteorological and soil data: Minimum and maximum temperature and relative humidity of sampling day were recorded over crop ages (10,12,14,16,17 and 18 months) and across Wonji, Finchaa, Tendaho and Metahara Sugar Estates was included (Table 3). Moreover, monthly mean temperature, relative humidity and pan evaporation was collected during the sampling month.…”

Section: Data Collectionmentioning

confidence: 99%

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Maturity Classification of Sugarcane (Saccharum officinarum L) Genotypes Grown Under Different Production Environments of Ethiopia

Ftwi¹,

Mekibib²,

Tesfa³

2017

Adv Crop Sci Tech

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Sugarcane (Saccharum officinarum L.) yield productivity is potentially affected by maturity which is genotypic and environment dependent. The limited information about the effects of genotypes, maturity and climate on sugarcane yield has been the main concern in sugarcane production of Ethiopia. The objectives of this study were to characterize sugarcane production environments of Ethiopia using meteorological and maturity data, classify genotypes using maturity data collected over growing seasons and across locations (Sugar Estates) and identify the major climatic conditions affecting the variability in sugarcane maturity. The design of the experiment was simple lattice design replicated across five locations and three seasons. Recoverable sucrose percentage and Brix Data of 49 genotypes evaluated across environments were collected over crop ages and subjected to ANOVA, Principal Component Analyses (PCA) and AMMI2 analyses. Results from ANOVA revealed brix percentage measurements were highly significantly (P<0.01) affected by environment, genotype and their interaction effects. Both principal component and additive main and multiplicative interaction (AMMI2) bi-plots generated similar genotype adaptations to maturity and successfully classify the genotypes in to early, medium and late under specific environments. Most of the CIRAD advanced lines were early maturing with wide range of maturing periods while those genotypes that were introduced at mid-way selection state were medium maturity. The correlation analysis made between environmental covariates and environmental IPCA scores demonstrated mean, maximum and minimum temperature and relative humidity regimes were the major climatic conditions affecting genotype × location interactions for brix percentage within 14-16 months of crop ages while minimum relative humidity was the major environmental factor that affect maturity across locations within 10-18 months crop ages. Further applications of these techniques will involve the inclusion of covariates derived from soil, climatic and management factors over diverse regions.

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“…Nesse trabalho, os autores concluem que uma maior ênfase deveria ser dada ao manejo da cultura do que à escolha das variedades, de modo a garantir maior longevidade dos canaviais. Já Ferraro et al (2009), em seu trabalho de análise de bancos de dados de produtividade da cana de açúcar utilizando técnica de mineração de dados, classificaram diversas variáveis, tanto de manejo quanto ambientais, de acordo com sua importância hierárquica na variabilidade das produtividades (TCH), e concluíram que as chuvas tiveram um peso relativamente pequeno para explicar a variabilidade das produtividades quando comparada com outros fatores não associados ao clima, como número de cortes e variedade.…”

Section: Introductionunclassified

Modelagem da produtividade da cultura da cana de açúcar por meio do uso de técnicas de mineração de dados

Hammer¹

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An analysis of the factors that influence sugarcane yield in Northern Argentina using classification and regression trees

Cited by 64 publications

References 34 publications

Identification of commercial blocks of outstanding performance of sugarcane using data mining

Identification of commercial blocks of outstanding performance of sugarcane using data mining

Maturity Classification of Sugarcane (Saccharum officinarum L) Genotypes Grown Under Different Production Environments of Ethiopia

Modelagem da produtividade da cultura da cana de açúcar por meio do uso de técnicas de mineração de dados

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