IT as enabler of sustainable farming: An empirical analysis of farmers' adoption decision of precision agriculture technology

Aubert, Benoît A.; Schröeder, Andreas; Grimaudo, Jonathan

doi:10.1016/j.dss.2012.07.002

Cited by 392 publications

(299 citation statements)

References 61 publications

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“…There is increasing recognition of the value of local information (Lawrence, Christodoulou & Whish, 2007) and farmers' own expertise (Aubert, Schroeder & Grimaudo, 2012) as sources of information for learning and decision making.…”

Section: Information and Information Technology In Supporting Farmersmentioning

confidence: 99%

‘Unearthing farmers' information seeking contexts and challenges in digital, local and industry environments’

Starasts

2015

Library & Information Science Research

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This study describes the contexts surrounding the information seeking of Australian grain and cotton growers. Information seeking activities of 16 growers over an 18 month period provided insights into what guided their information seeking and what they considered to be 'information' that supported their self-directed learning. Contexts were highly situated, experiential and social. They found most online information to be devoid of these critical contexts, and it therefore did not contribute to their learning. Future agricultural information systems could better facilitate growers' information seeking if they incorporated local and environmental parameters, and enabled information sharing and connecting on local issues.

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Section: Information and Information Technology In Supporting Farmersmentioning

confidence: 99%

‘Unearthing farmers' information seeking contexts and challenges in digital, local and industry environments’

Starasts

2015

Library & Information Science Research

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“…where C i (k+1) is the activation value of concept C i at iteration k+1, C i (k) is the value of node C i at iteration k, w ji is the weight of the cause-effect link between C j and C i , and F is a threshold function such as sigmoid, used for squashing the results of the sum between 0 and +1, or between −1 and +1 [45], as shown by Equation (2).…”

Section: Traditional Fuzzy Cognitive Mapsmentioning

confidence: 99%

“…This approach may induce economic implications to the farmer due to investment costs and crop yield loss. The environmental cost comes from the intensification of inorganic fertilizers and pesticides that create diffuse or non-point sources of pollution, through surface and ground waters, and span to adjacent land areas where fertilizers are not needed [1,2]. The main fertilizers in use are Nitrogen (N), Potassium (K) and Phosphorus (P), where the first two are considered to have the greatest effect on soil degradation.…”

Section: Introductionmentioning

confidence: 99%

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Exploring Precision Farming Scenarios Using Fuzzy Cognitive Maps

et al. 2017

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Abstract:One of the major problems confronted in precision agriculture is uncertainty about how exactly would yield in a certain area respond to decreased application of certain nutrients. One way to deal with this type of uncertainty is the use of scenarios as a method to explore future projections from current objectives and constraints. In the absence of data, soft computing techniques can be used as effective semi-quantitative methods to produce scenario simulations, based on a consistent set of conditions. In this work, we propose a dynamic rule-based Fuzzy Cognitive Map variant to perform simulations, where the novelty resides in an enhanced forward inference algorithm with reasoning that is characterized by magnitudes of change and effects. The proposed method leverages expert knowledge to provide an estimation of crop yield, and hence it can enable farmers to gain insights about how yield varies across a field, so they can determine how to adapt fertilizer application accordingly. It allows also producing simulations that can be used by managers to identify effects of increasing or decreasing fertilizers on yield, and hence it can facilitate the adoption of precision agriculture regulations by farmers. We present an illustrative example to predict cotton yield change, as a response to stimulated management options using proactive scenarios, based on decreasing Phosphorus, Potassium and Nitrogen. The results of the case study revealed that decreasing the three nutrients by half does not decrease yield by more than 10%.

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“…The technology that applies agricultural inputs in a localized form and at the correct moment is called precision agriculture (PA). PA makes use of thematic maps built with results of a laboratory analysis of the soil's physical and chemical attributes and also of the yield in which it is considered the sample position (AUBERT et al, 2012). These maps show the spatial variability of an attribute in a particular area of analysis and are generated according to the interpolated data.…”

Section: Introductionmentioning

confidence: 99%

Interpolation methods for thematic maps of soybean yield and soil chemical attributes

Betzek¹,

Souza²,

Bazzi³

et al. 2017

SCA

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The application of precision agriculture considers the values of non-sampled places by the interpolation of sample data. The accuracy with which the maps of spatial distribution of yield and the soil attributes are produced in the interpolation process influences their application and utilization. This paper aimed to compare three interpolation methods (inverse of the distance, inverse of the square distance, and ordinary kriging) in the construction of thematic maps of soybean yield and soil chemical attributes. A set of data referred to 55 sampling units for the construction maps of soybean yield and of eight soil chemical attributes, by different interpolation methods. The comparison was made based on the error matrix, by calculating the Kappa and Tau indices, beyond the relative deviation coefficient (RDC). It was noticed that the inverse of the square distance was the interpolator that less influenced the data behavior, and the best interpolation method dependent of the variability of the studied attribute. The kriging and the inverse of the square distance were considered the methods that presented the best results in the interpolation of data. Key words: Geostatistics. Precision agriculture. Spatial variability. ResumoA aplicação da agricultura de precisão considera os valores mensurados para lugares não amostrados obtidos por meio da interpolação dos dados amostrais. A precisão com que os mapas de distribuição espacial da produtividade e atributos do solo são produzidos no processo de interpolação influencia a sua aplicação e utilização. Assim, este trabalho teve como objetivo comparar três métodos de interpolação (inverso da distância, inverso do quadrado da distância e krigagem ordinária) na construção de mapas temáticos de produtividade de soja e atributos químicos do solo. Um conjunto de dados referentes a 55 unidades de amostragem foi utilizado para a construção dos mapas de oito atributos químicos do solo e da produtividade de soja, por diferentes métodos de interpolação. A comparação foi feita com base na matriz de erro, por meio do cálculo dos índices Kappa e Tau, além do coeficiente de desvio relativo

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IT as enabler of sustainable farming: An empirical analysis of farmers' adoption decision of precision agriculture technology

Cited by 392 publications

References 61 publications

‘Unearthing farmers' information seeking contexts and challenges in digital, local and industry environments’

‘Unearthing farmers' information seeking contexts and challenges in digital, local and industry environments’

Exploring Precision Farming Scenarios Using Fuzzy Cognitive Maps

Interpolation methods for thematic maps of soybean yield and soil chemical attributes

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