Analysis of Andean blackberry (Rubus glaucus) production models obtained by means of artificial neural networks exploiting information collected by small-scale growers in Colombia and publicly available meteorological data

Jiménez, Daniel; Cock, James H.; Satizábal, Héctor F.; S, Miguel A. Barreto; Pérez-Uribe, Andrés; Jarvis, Andy; Damme, Patrick Van

doi:10.1016/j.compag.2009.08.008

Cited by 27 publications

(17 citation statements)

References 31 publications

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“…Expert opinion on its own is appropriate for defining HEs in those crops which have been well researched, but in the case of less well researched crops their performance over a range of conditions can guide the delimitation of HEs [ 3 , 27 , 28 ]. In sugarcane, ( Saccharum officinarum ) and coffee ( Coffea arabica) , a wealth of knowledge about the crops, generated through formal experiments and informal experiences, equipped researchers and growers with information to define the NCFs (e.g., climate), CFs (e.g., specific management approaches), likely to influence production [ 3 , 42 , 70 ].…”

Section: Discussionmentioning

confidence: 99%

“…Thus, in Andean blackberry the productivity of two geographically distinct regions differed markedly even though their HEs were similar. Assuming that the differences were not due to distinct growing conditions or HEs, the differences were attributed to management [ 27 ]. Thus, strategies that obtain datasets from production units with similar NCFs, but from distinct locations, or datasets with ample variation in the same HEs even though geographically concentrated, are advantageous.…”

Section: Discussionmentioning

confidence: 99%

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From Observation to Information: Data-Driven Understanding of on Farm Yield Variation

et al. 2016

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Agriculture research uses “recommendation domains” to develop and transfer crop management practices adapted to specific contexts. The scale of recommendation domains is large when compared to individual production sites and often encompasses less environmental variation than farmers manage. Farmers constantly observe crop response to management practices at a field scale. These observations are of little use for other farms if the site and the weather are not described. The value of information obtained from farmers’ experiences and controlled experiments is enhanced when the circumstances under which it was generated are characterized within the conceptual framework of a recommendation domain, this latter defined by Non-Controllable Factors (NCFs). Controllable Factors (CFs) refer to those which farmers manage. Using a combination of expert guidance and a multi-stage analytic process, we evaluated the interplay of CFs and NCFs on plantain productivity in farmers’ fields. Data were obtained from multiple sources, including farmers. Experts identified candidate variables likely to influence yields. The influence of the candidate variables on yields was tested through conditional forests analysis. Factor analysis then clustered harvests produced under similar NCFs, into Homologous Events (HEs). The relationship between NCFs, CFs and productivity in intercropped plantain were analyzed with mixed models. Inclusion of HEs increased the explanatory power of models. Low median yields in monocropping coupled with the occasional high yields within most HEs indicated that most of these farmers were not using practices that exploited the yield potential of those HEs. Varieties grown by farmers were associated with particular HEs. This indicates that farmers do adapt their management to the particular conditions of their HEs. Our observations confirm that the definition of HEs as recommendation domains at a small-scale is valid, and that the effectiveness of distinct management practices for specific micro-recommendation domains can be identified with the methodologies developed.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

From Observation to Information: Data-Driven Understanding of on Farm Yield Variation

et al. 2016

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“…Multilayer perceptrons predicted with a reasonable degree of accuracy the production response of the crop. The information obtained from ANN modeling can be used to determine sites that are suitable for Andean blackberry production, and to transfer of management practices from sites of high productivity to sites with similar environmental conditions, which currently have lower levels of productivity [14].…”

Section: F Blackberrymentioning

confidence: 99%

Artificial Neural Networks in Fruits: A Comprehensive Review

Goyal¹

2014

IJIGSP

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Abstract-This review discusses the application of artificial neural networks (ANN) modeling in fruits. It covers all fruits in which ANN modeling has been applied. ANN is quite a new and easy computational modeling approach used for prediction, which has become popular and accepted by food industry, researchers, scientists and students. ANNs have been applied in almost every field of science and technology, viz., speech synthesis & recognition, pattern classification, adaptive interfaces between humans & complex physical systems, clustering, function approximation, image data compression, non-linear system modeling, associative memory, combinatorial optimization, control and several others, as they have proved valuable tools for obtaining the required output. ANN provides an exciting alternative method for solving a variety of problems in different areas of science and engineering. The aim of this communication is to discover the recent advances of ANN technology implemented in fruits, and discuss the critical role that ANN plays in predictive modelling.

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“…The Andean blackberries are produced from Mexico to Ecuador, being widely cultivated in Colombia, Costa-Rica and Ecuador where they grow all year round [30][31][32]. This is the only native species of Rubus grown commercially in the countries of Central and South America, where it is prized for its attractive dark-red color (Fig.…”

Section: Agronomic Aspectsmentioning

confidence: 99%

Andean berries from Ecuador: A review on Botany, Agronomy, Chemistry and Health Potential

Carrillo‐Perdomo

Aller

Cruz-Quintana

et al. 2015

JBR

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Abstract. Interest in exploring new and exotic types of berries has grown in recent years. Highly valued for its unique flavor, texture and color, recent researches have shown that Andean berries are an important source of bioactive compounds. This article provides botanical and agronomic descriptions and reviews the chemical and biological activities of two types of berries (Physalis peruviana L. and Solanum betaceum C.) and one commonly known as a berry, Rubus glaucus B. All highly consumed in Ecuador and enjoying great popularity in Andean traditional medicine. Although both traditional folk medicine and composition of these berries suggest significant health benefits, few studies to date have investigated these potentials.

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Analysis of Andean blackberry (Rubus glaucus) production models obtained by means of artificial neural networks exploiting information collected by small-scale growers in Colombia and publicly available meteorological data

Cited by 27 publications

References 31 publications

From Observation to Information: Data-Driven Understanding of on Farm Yield Variation

From Observation to Information: Data-Driven Understanding of on Farm Yield Variation

Artificial Neural Networks in Fruits: A Comprehensive Review

Andean berries from Ecuador: A review on Botany, Agronomy, Chemistry and Health Potential

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