Financial Analysis of the Investment in Precision Agriculture Techniques on Cotton Crop

Baio, Fábio Henrique Rojo; Silva, Simone P. da; Camolese, Herivelton da Silva; Neves, Danilo Carvalho

doi:10.1590/1809-4430-eng.agric.v37n4p838-847/2017

Cited by 12 publications

(10 citation statements)

References 6 publications

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“…Furthermore, due to shared costs between project partners, ROI becomes a collective performance measure that can be estimated only when each member has acquired the according technological benefits. A complex profitability study is required to specify the precision service offerings, as presented in [55,56]. Each partner in the DIAS project owns and offers different assets in this endeavor, thus calculating the overall ROI of UAV usage from the side of farmers is not yet possible.…”

Section: Benefits and Costsmentioning

confidence: 99%

Precision Agriculture: A Remote Sensing Monitoring System Architecture

2019

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Smart Farming is a development that emphasizes on the use of modern technologies in the cyber-physical field management cycle. Technologies such as the Internet of Things (IoT) and Cloud Computing have accelerated the digital transformation of the conventional agricultural practices promising increased production rate and product quality. The adoption of smart farming though is hampered because of the lack of models providing guidance to practitioners regarding the necessary components that constitute IoT-based monitoring systems. To guide the process of designing and implementing Smart farming monitoring systems, in this paper we propose a generic reference architecture model, taking also into consideration a very important non-functional requirement, the energy consumption restriction. Moreover, we present and discuss the technologies that incorporate the seven layers of the architecture model that are the Sensor Layer, the Link Layer, the Encapsulation Layer, the Middleware Layer, the Configuration Layer, the Management Layer and the Application Layer. Furthermore, the proposed Reference Architecture model is exemplified in a real-world application for surveying Saffron agriculture in Kozani, Greece.

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Section: Benefits and Costsmentioning

confidence: 99%

Precision Agriculture: A Remote Sensing Monitoring System Architecture

2019

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“…One of the tools that can be used to maximize cotton farmer profitability is precision agriculture. It consists of a series of management techniques performed accurately on smaller land units, unlike conventional management which considers an entire field as uniform area (Baio et al 2017). In economic terms, the use of this technology makes it possible to prioritize investments in areas where production potential is more effective, ensuring greater economic returns (Amaral et al 2015).…”

Section: Introductionmentioning

confidence: 99%

Correlations and Principal Components Analysis for Defining Management Zones in Cotton

Baio

Faraun

Teodoro

et al. 2020

BJD

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One approach for using variable rate fertilizer applications in precision agriculture is to divide an area into management zones. The objectives were: (i) to identify the chemical, physical and phenological properties that have the highest correlation with the yield; (ii) to use principal component analysis (PCA) to identify what physical, chemical, and phenological properties contribute to greater spatial variability; (iii) and to use these variables in the establishing management zones (MZ) for cotton through fuzzy k-means clustering analysis, associated with the geostatistics technique by the ordinary kriging method. The experiment was carried out in a cotton field in the Chapadões region in 2015. Phenological variables of cotton (plant height, number of bolls, number of capsules, opening percentage and Red Edge vegetation index) and chemical (pH, Ca, Mg, H+Al, V%, Ca/Mg, CEC, K, Al3+ and P) and physical (total soil porosity, soil density, soil moisture, soil mechanical resistance to penetration, clay content, and macro and micro-porosity) attributes of the soil were evaluated to define management zones. The variables that showed the highest correlation with cotton yield were pH, phosphorus, soil moisture measured at 39 and 70 days after cotton emergence (DAE), number of bolls at 107 DAE and red edge vegetation index at 53 DAE. The map with four MZ has a better

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“…However, the modernization of agriculture and implementation of precision agriculture (PA) tools have shown that soil nutrient levels, nutrient amounts removed by plants, and nutrient losses are not uniformly distributed in the field (Molin, 2002, Mallarino & Wittry, 2004, Santi et al, 2012. Thus, geo-referenced soil sampling for recognizing the spatial variability of soil attributes and application of variable rates of fertilizers and correctives has been widely adopted in Brazil (Corá & Beraldo, 2006, Soares Filho & Cunha, 2015, Baio et al, 2017. Soil fertility mapping can optimize the use of agricultural inputs, increase crop yield, promote higher profitability for farmers and mitigate environmental impacts derived from agriculture (Mallarino & Wittry, 2004, Baio et al, 2017.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, geo-referenced soil sampling for recognizing the spatial variability of soil attributes and application of variable rates of fertilizers and correctives has been widely adopted in Brazil (Corá & Beraldo, 2006, Soares Filho & Cunha, 2015, Baio et al, 2017. Soil fertility mapping can optimize the use of agricultural inputs, increase crop yield, promote higher profitability for farmers and mitigate environmental impacts derived from agriculture (Mallarino & Wittry, 2004, Baio et al, 2017.…”

Section: Introductionmentioning

confidence: 99%

Transition From Systematic to Directed Soil Sampling Designs in an Area Managed With Precision Agriculture

Pias

Cherubin²,

Santi

et al. 2019

Eng. Agríc.

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In agricultural areas with a historical of systematic soil sampling, alternative methodologies such as directed sampling design based on management zones (MZ) have been proposed to reduce sampling costs. The aim of this study was to evaluate the technical and economic impacts of replacing a dense systematic soil sampling design (cell size of 0.5 ha) by a systematic sampling with a smaller number of samples (cell size ranging from 1 to 4.5 ha), directed or conventional sampling design on the mapping of soil plant-available phosphorus (P), exchangeable potassium (K), and pHwater. The study was carried out in an agricultural area of 120 ha with soil classified as an Oxisol. The directed sampling designs were based on MZ delimited from data of elevation and overlapping of crop yield maps. Our finding revealed that systematic samplings with grids larger than 2 ha were not efficient to detect the spatial variability of soil P, K and pHwater. Larger systematic grid sizes, directed and conventional sampling design resulted in more generalist thematic maps, losing information about spatial variability of the soil attributes. Thus, from a technical point of view, soil sampling designs with a low density were little efficients, particularly for mapping P and K, due to their higher spatial variability. However, because soil P and K contents were close to or above critical levels and soil acidity was low (average pH close to 5.5), the different sampling designs presented little influence on fertilizer and liming recommendations. Therefore, we concluded that systematic soil sampling design may be replaced by soil sampling directed based on MZ or even by conventional sampling in soils with high fertility to reduce sampling costs. Nevertheless, crop responses must be monitored to validate fertilization management based on these simplifications on soil sampling procedure.

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Financial Analysis of the Investment in Precision Agriculture Techniques on Cotton Crop

Cited by 12 publications

References 6 publications

Precision Agriculture: A Remote Sensing Monitoring System Architecture

Precision Agriculture: A Remote Sensing Monitoring System Architecture

Correlations and Principal Components Analysis for Defining Management Zones in Cotton

Transition From Systematic to Directed Soil Sampling Designs in an Area Managed With Precision Agriculture

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