Spatial influence of physical and chemical parameters on management zone definition in apple orchards

Gebler, Luciano; Grego, C. R.; Vieira, Abel Lisboa; Kuse, Leonardo da Rosa

doi:10.1590/1809-4430-eng.agric.v35n6p1160-1171/2015

Cited by 7 publications

(3 citation statements)

References 13 publications

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“…OR "precision livestock production" OR "precision livestock farming" OR "precision irrigation" AND Tabela 6 -Ferramentas de agricultura de precisão na América do Sul (2008). (BOTTEGA et al, 2017;DE-MATTÊ et al, 2017;MACHADO;LANÇAS, 2016;GEBLER et al, 2015;KRAMER et al, 2014;NANNI et al, 2011).…”

Section: Definição De Adoção Tecnológicaunclassified

Adoção Da Agricultura De Precisão Na América Do Sul: O Estado Da Arte Em Argentina, Brasil E Colômbia

Pamplona

Silva

2019

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A América do Sul faz grande contribuição relativa à produção agrícola mundial. O crescimento extensivo da atividade agrícola na região aproxima-se do limite, restando à modalidade intensiva o potencial de superar desafios como sustentabilidade e competitividade, capaz ainda de produzir desenvolvimento econômico. As inovações são essenciais nesse sentido, especialmente as associadas à agricultura de precisão (AP), entendida como um conjunto de inovações organizacionais e de processo que conjuga mecanização com tecnologias de informação e comunicação (TIC), permitindo singularização da unidade de produção agrícola, ao se valer da variabilidade espacial e temporal em áreas de pequena escala. O objetivo deste artigo é analisar o “estado da arte” da adoção da AP na América do Sul, a partir do levantamento e da análise da produção acadêmica sobre o tema. Esta pesquisa de natureza exploratória enfoca a experiência de três países representativos das condições econômicas e edafoclimáticas da Região: Argentina, Brasil e Colômbia.

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Section: Definição De Adoção Tecnológicaunclassified

Adoção Da Agricultura De Precisão Na América Do Sul: O Estado Da Arte Em Argentina, Brasil E Colômbia

Pamplona

Silva

2019

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“…Advanced knowledge and proper characterization of Brazilian plantations depend on obtaining a series of information procedures that go beyond the traditional production model. Therefore, it is important that we use precision agriculture in the study of the spatial variability of factors that guide the production and can assist in the management of the crop [5].…”

Section: Introductionmentioning

confidence: 99%

Spatial Variability of Soil Resistance to Penetration in Fruit Cultivation in Eastern Amazonia

et al. 2023

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The application of precision agriculture in cocoa and papaya cultivation in Brazil is still incipient. This study aimed to evaluate the spatial variability of the physical attributes of soil cultivated with a consortium of papaya and cocoa. The study was conducted in two sampling grids of 50 points, in two areas cultivated with papaya and cocoa with different planting times (three and eleven months). The soil attributes soil resistance to penetration (RP) and soil gravimetric moisture (UG) were determined at soil depths of 0–20, 20–40 and 40–60 cm. The data were submitted to an exploratory and descriptive analysis. Subsequently, a geostatistical analysis was performed to quantify spatial dependence and then interpolation of the data through kriging. The maps showed weak spatial variability for the UG and RP. In the two areas, it was observed that the depth of 0–20 cm had a lower RP (1.7 Mpa) and a higher UG (40 g g−1), and as the depth was higher, had a higher RP (4.4 Mpa) and a lower UG (38 g g−1). Area 1 presented higher RP values in depth, showing greater susceptibility to compaction. The area characterized by the consortium of papaya and cocoa presented more susceptible to compaction. The mechanical resistance of the soil to penetration was more critical in the 40–60 cm layer for the two consortia evaluated, evidencing areas with possible restriction to plant growth.

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“…Precision Fruit Growing (PFG) is a branch of traditional Precision Agriculture that focuses on understanding and improving the production of perennial fruit species through the assessment of a cycle repeated annually -from the implantation of the trees to their eradication (Gebler et al al., 2015).…”

Section: Introductionmentioning

confidence: 99%

Computational Models in Precision Fruit Growing: Reviewing the Impact of Temporal Variability on Perennial Crop Yield Assessment

2023

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Early yield information of perennial crops is crucial for growers and the industry, which allows cost reduction and bene ts crop planning. However, the yield assessment of perennial crops by computational models can be challenging due to diverse aspects of interannual variability that act on the crops. This review aimed to investigate and analyze the literature on yield estimation and forecasting modeling of perennial cropping systems. We reviewed 49 articles and categorized them according to their yield assessment strategy, modeling class used, and input variable characteristics. The strategies of yield assessment were discussed in the context of their principal improvement challenges. According to our investigation, image processing and deep learning models are emerging techniques for yield estimation.On the other hand, machine learning algorithms, such as Arti cial Neural Networks and Decision Trees, were applied to yield forecasting with reasonable time in advance of harvest. Emphasis is placed on the lack of representative long-term datasets for developing computational models, which can lead to accurate early yield forecasting of perennial crops.

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Spatial influence of physical and chemical parameters on management zone definition in apple orchards

Cited by 7 publications

References 13 publications

Adoção Da Agricultura De Precisão Na América Do Sul: O Estado Da Arte Em Argentina, Brasil E Colômbia

Adoção Da Agricultura De Precisão Na América Do Sul: O Estado Da Arte Em Argentina, Brasil E Colômbia

Spatial Variability of Soil Resistance to Penetration in Fruit Cultivation in Eastern Amazonia

Computational Models in Precision Fruit Growing: Reviewing the Impact of Temporal Variability on Perennial Crop Yield Assessment

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