Precision Agriculture and Sustainability

Bongiovanni, Rodolfo; Lowenberg‐DeBoer, James

doi:10.1023/b:prag.0000040806.39604.aa

Cited by 551 publications

(307 citation statements)

References 35 publications

Supporting

Mentioning

240

Contrasting

Unclassified

Order By: Relevance

“…O resultado é a possibilidade de aplicar a quantidade de insumos necessária em porções do campo, permitindo uma melhor gestão do processo de produção através da variabilidade observada (EMBRAPA, 2005;WHELAN e McBRATNEY, 2000;COELHO e SILVA, 2009). Desta forma, esta técnica tem como objetivo maior aumentar a produtividade e diminuir os impactos ambientais (COELHO e SILVA, 2009;BONGIOVANNI, 2004). Impactos ambientais causados por um processo ou uma cadeia de produção podem ser avaliados, seguindo a ISO 14040, pela Avaliação do Ciclo de Vida (ACV).…”

Section: Introductionunclassified

Inventário e potencial impacto ambiental da produção de soja com agricultura de precisão na perspectiva da Avaliação do Ciclo de Vida

Júnior¹,

Ugaia²,

Gimenes³

2017

ANAP

View full text Add to dashboard Cite

Section: Introductionunclassified

Inventário e potencial impacto ambiental da produção de soja com agricultura de precisão na perspectiva da Avaliação do Ciclo de Vida

Júnior¹,

Ugaia²,

Gimenes³

2017

ANAP

View full text Add to dashboard Cite

“…In PA, with knowledge about the soil and crop requirements, fertilizers and pesticides can be applied in different amounts, at various land zones when needed. Towards these lines, Bongiovanni and Lowenberg-Deboer [5] showed that, even by cutting N rate to half of the recommended uniform rate, farm profitability is not compromised. Some other studies estimated savings of 20-60% in pesticide use, and up to 30% in fertilizer application depending on yield homogeneity [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Exploring Precision Farming Scenarios Using Fuzzy Cognitive Maps

et al. 2017

View full text Add to dashboard Cite

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%.

show abstract

“…Precision agriculture, using site-specific knowledge, can assist farmers and stakeholders in better management of their limited resources through new technologies [Bongiovanni and Lowenberg-Deboer, 2004]. Under conventional management fields receive uniform applications of fertilizers, irrigation, seed, etc., whereas a precision agriculture approach adopts a variable rate applications of inputs based on spatial variability (e.g.…”

Section: Introductionmentioning

confidence: 99%

Proximal sensing and vegetation indices for site-specific evaluation on an irrigated crop tomato

Marino

Alvino

2014

European Journal of Remote Sensing

View full text Add to dashboard Cite

The present paper deals with proximal sensing technique applied to a drip irrigated tomato field. The aim of this "on farm" research was the evaluation of three Vegetation indices, WI (Water index) WI/NDVI and TSAVI (Transformed Soil Adjusted Vegetation Index), to analyze the correlation among VIs and tomato yield, to assess the spatial variability of a tomato crop, and finally to identify homogeneous crop area. Until 90 days after transplanting, tomato field was almost uniform, either at a visual assessment or according to spectroradiometric readings. At the end of the vegetative growth stage (from 90 days after transplanting), a spot crop area showed increasing soil moisture conditions due to soil topography. In this spot area, plants first yellowed and after started dying. Among indices, TSAVI appeared the most effective to detect excess soil water conditions, infect at 105 DAT TSAVI index was highly significant correlated to tomato yield, demonstrating to be a good index for early detecting excess crop water status. This study reinforce the possibility of detecting plant water stress by spectroradiometric measurements at field scale (ground-based measurements) and at territorial level.

show abstract

Precision Agriculture and Sustainability

Cited by 551 publications

References 35 publications

Inventário e potencial impacto ambiental da produção de soja com agricultura de precisão na perspectiva da Avaliação do Ciclo de Vida

Inventário e potencial impacto ambiental da produção de soja com agricultura de precisão na perspectiva da Avaliação do Ciclo de Vida

Exploring Precision Farming Scenarios Using Fuzzy Cognitive Maps

Proximal sensing and vegetation indices for site-specific evaluation on an irrigated crop tomato

Contact Info

Product

Resources

About