Spatial validation of crop models for precision agriculture

Basso, Bruno; Ritchie, J. T.; Pierce, Francis J.; Braga, Ricardo P.; Jones, J. W.

doi:10.1016/s0308-521x(00)00063-9

Cited by 179 publications

(97 citation statements)

References 15 publications

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“…Estimating agricultural production is necessary for a wide variety of applications: early warning of food security, famine and drought [1][2][3][4]; supporting validation of biophysical crop models [5,6]; a substitute for yield maps to analyse yield consistency [7], and to optimise spatially explicit fertiliser application [8][9][10]. The collection of reliable and timely information of crop performance can also influence pricing policies, marketing and trading decisions [13,14] and is important for government, growers, insurance and agricultural companies [11,12] so that logistical issues can be anticipated [12].…”

Section: Introductionmentioning

confidence: 99%

Testing the Temporal Ability of Landsat Imagery and Precision Agriculture Technology to Provide High Resolution Historical Estimates of Wheat Yield at the Farm Scale

2013

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Abstract:The long term archiving of both Landsat imagery and wheat yield mapping datasets sensed by precision agriculture technology has the potential through the development of statistical relationships to predict high resolution estimates of wheat yield over large areas for multiple seasons. Quantifying past yield performance over different growing seasons can inform agricultural management decisions ranging from fertilizer applications at the sub-paddock scale to changes in land use at a landscape scale. However, an understanding of the magnitude of prediction errors is needed. In this study, we examine the predictive wheat yield relationships developed from Normalised Difference Vegetation Index (NDVI) acquired Landsat imagery and combine-mounted yield monitors for three Western Australian farms over different growing seasons. We further analysed their predictive capability when these relationships are used to extrapolate yield from one farm to another. Over all seasons, the best predictions were achieved with imagery acquired in September. Of the five seasons reviewed, three showed very reasonable prediction accuracies, with the low and high rainfall years providing good predictions. Medium rainfall years showed the greatest variation in prediction accuracy with marginal to poor predictions resulting from narrow ranges of measured wheat yield and NDVI values. These results demonstrate the potential benefit of fusing together two high resolution datasets to create robust wheat yield prediction models over different growing seasons, the outputs of which can be used to inform agricultural decision making.

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Section: Introductionmentioning

confidence: 99%

Testing the Temporal Ability of Landsat Imagery and Precision Agriculture Technology to Provide High Resolution Historical Estimates of Wheat Yield at the Farm Scale

2013

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“…A média correspondência do NDVI com a produtividade de grãos também era esperada. Em trabalhos baseados em sensores hiperspectrais, a relação de produtividade de grãos com o NDVI tende a ser significativa em estádios mais avançados das culturas, como no florescimento (Basso et al, 2001). Em estádios mais precoces, Casa & Castrignanò (2007) observaram que a variabilidade temporal da produtividade é mais forte que a espacial e, consequentemente, a sua predição, baseada apenas no NDVI, é instável ao longo do ciclo da cultura.…”

Section: Validation Of Model For Wheat Yield Prediction Potential Usiunclassified

Validação de modelo para predição do potencial produtivo de trigo com sensor óptico ativo

Grohs

Bredemeier

Poletto

et al. 2011

Pesq. agropec. bras.

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Resumo -O objetivo deste trabalho foi validar um modelo baseado no índice de vegetação por diferença normalizada (NDVI), para a predição da variabilidade espacial da biomassa acumulada na parte aérea e da produtividade de grãos de trigo. Foram realizadas leituras georreferenciadas de NDVI e biomassa, no final do afilhamento, e produtividade. O modelo proposto permitiu relacionar classes de variabilidade espacial do NDVI e da biomassa, com 81% de correspondência entre elas. Entretanto, a relação entre as classes de variabilidade espacial da produtividade e do NDVI foi menor, com 48% de correspondência.Termos para indexação: Triticum aestivum, agricultura de precisão, NDVI, variabilidade espacial. Validation of model for wheat yield prediction potential using active optical sensorAbstract -The objective of this work was to validate a model based on the normalized difference vegetation index (NDVI) for the prediction of spatial variability of accumulated biomass in shoots and wheat grain yield. Georeferenced readings of NDVI and biomass, at the end of tillering, and of grain yield were carried out. The proposed method allowed relating classes of spatial variability of NDVI and biomass, with 81% of correspondence between them. However, the relation between spatial variability of yield and NDVI was lower, with 48% of correspondence.Index terms: Triticum aestivum, precision agriculture, NDVI, spatial variability.Atualmente, é crescente a demanda por informações referentes à variabilidade espacial de indicadores dinâmicos que estimem o potencial produtivo de culturas (McBratney et al., 2005). A estimativa deste potencial, em um determinado estádio fenológico das plantas, viabiliza o manejo de sitio específico em lavouras, como adubações em taxa variável (Milani et al., 2006). O desenvolvimento de novos instrumentos, baseados em sensoriamento remoto, tem levado à expansão de práticas agrícolas aplicadas em tempo real. Alguns desses sensores são gerados com base em variáveis espectrais, como o índice de vegetação por diferença normalizada (NDVI). Um exemplo é o GreenSeeker (Trimble, Sunnyvale, Califórnia, EUA), equipamento portátil, utilizado para adequar adubações nitrogenadas em taxa variável e tempo real (Raun et al., 2005). O NDVI foi proposto por Rouse et al. (1973) para a quantificação do crescimento da vegetação e da biomassa acumulada, e varia de -1 a +1. Quanto maior o seu valor, maior o vigor de desenvolvimento da cultura (Rouse et al., 1973). A partir desse princípio, Raun et al. (2005) desenvolveram algoritmos para aplicações de doses nitrogenadas em trigo e milho, com esse sensor. No Brasil, Povh et al. (2008) adaptaram essa metodologia, para ajuste de doses nitrogenadas em trigo, centeio e triticale. Grohs et al. (2009), no Rio Grande do Sul, propuseram um modelo para a estimativa da variabilidade espacial do potencial produtivo em cereais de inverno, no final do período de afilhamento, entre a emissão da quinta e da sétima folha. Essa fase é considerada crítica para o manejo da adubação nitrogenada desses ...

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“…Within the "from farm to fork" chain, various heterogeneous data including genetic-trait information are to be considered as part of the computational modelling for prevision and forecast; most of them have a geo-location or spatial component or would be required to have one to be used from a plethora of model applications of various complexities. These are either biophysical, agro-economically based and more mechanistic or deterministic orientated [6,13,14,19,30,36] or more stochastic orientated [2,5,12,23,42,48,52], more rule-based including agent-based orientated [32,45,49] but all contain a combination of those types. Therefore, a cross-disciplinary expertise driven from geospatial sciences methodologies appeared to be needed to develop an integrating framework for relevant data sources, in order to allow knowledge gathering across all subjects relevant to Food Security.…”

Section: Introductionmentioning

confidence: 99%

Geospatial binding for transdisciplinary research in crop science: the GRASPgfs initiative

Leibovici

Anand

Santos

et al. 2017

Open geospatial data, softw. stand.

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The paper retraces the GRASPgfs endeavor (Geospatial Resource for Agricultural Species and Pests with integrated workflow modelling to support Global Food Security) between multiple disciplines around a common objective of facilitating research and model simulations for sustainable food security. Within this endeavor, the geospatial media has been the enabler for multidisciplinary research in crop modelling. Geospatial genetic-trait variations and associations with environmental forecasting were the main focus of the GRASPgfs. Designing the platform achieving this objective generated a transdisciplinary vision of modelling and forecasting for sustainable agriculture. Based on interoperability principles, seamless access as well as sharing for data, metadata and processing models, the design is described in this paper. This geospatial binding facilitates and supports new types of hypotheses and analysis as illustrated in the paper with a landscape genetic case study (bambara groundnut) and a crop disease modelling (eyespot disease). The approach and the eGRASP platform are generic enough to accommodate further complexity into the integrated modelling that this geospatial binding enables.

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Spatial validation of crop models for precision agriculture

Cited by 179 publications

References 15 publications

Testing the Temporal Ability of Landsat Imagery and Precision Agriculture Technology to Provide High Resolution Historical Estimates of Wheat Yield at the Farm Scale

Testing the Temporal Ability of Landsat Imagery and Precision Agriculture Technology to Provide High Resolution Historical Estimates of Wheat Yield at the Farm Scale

Validação de modelo para predição do potencial produtivo de trigo com sensor óptico ativo

Geospatial binding for transdisciplinary research in crop science: the GRASPgfs initiative

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