Simulating spatially-explicit crop dynamics of agricultural landscapes: The ATLAS simulator

Thierry, Hugo; Vialatte, Aude; Choisis, Jean Philippe; Gaudou, Benoît; Parry, Hazel Ruth; Monteil, Claude

doi:10.1016/j.ecoinf.2017.05.006

Cited by 22 publications

(17 citation statements)

References 42 publications

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“…Visualisations in viticulture enable a better vineyard monitoring, reducing costs and at the same time, generating a more transparent representation of the existent variability in the vineyard, which is valuable for the optimisation • AgroDSS [31] • AquaGIS [32] • • ATLAS [33] • • Blauth et al [34] • Byishimo et al [35] • CAMDT [36] • CropGIS [37] • • CropSAT [38] • • DIDAS [39] • DyNoFlo [40] • • Galindo et al [41] • GeoVisage [42] • Geovit [43] • GramyaVikas [44] • HydroQual [45] • Li et al [46] • LMTool [47] • • Luvisi et al [48] • mDSS [49] • SmartScape [50] • • VBoxReporting [51] Vite.net [52] • • • visualizeR [8] • ViPER [53] • Ochola et al [54] • Falcao et al [55] • LandCaRe DSS [56] • ValorE [57] • Agroland [58] • Gandhi et al [59] • • CaNaSTA [60] • eFarmer [61] • FARMERS [62] • PlanteInfo [63] • CropScape [64] • SIMAGRI [65] • FDSSFIS [66] • MOTIFS [67] • CarrotAge [68] • AgriSensor [69] • • • CognitiveInputs [70] • • Ruß et al [71] Tan et al…”

Section: Viticulturementioning

confidence: 99%

“…We found four tools ( [28], [32], [33], [38]) that visually support end-users in the wheat production domain. AgMine [28] provides valuable information for Western Australian wheat growers, using diverse visualisation techniques to illustrate seasonal rainfall and yield Figure 6: The use of visualisation in wheat production a) AgMine [28], b) AquaGIS/AquaCrop [32], c) ATLAS [33], d) cropSAT [38] production ( Figure 6a). AquaCrop [32] provides simulation analysis towards the impact of climate change on wheat yield in Southern Spain by visualising yield and rainfall ( Figure 6b).…”

Section: Wheat Productionmentioning

confidence: 99%

“…AquaCrop [32] provides simulation analysis towards the impact of climate change on wheat yield in Southern Spain by visualising yield and rainfall ( Figure 6b). ATLAS [33] simulates crop availability at the landscape scale by visualising in 2D and 3D maps different crop scenarios in relation to pests, diseases and biological control ( Figure 6c). cropSAT [38] is a DSS to visually analyse nitrogen fertilisation of winter wheat using satellite images: farmers can recognise and explain visualised variation in the crop biomass ( Figure 6d).…”

Section: Wheat Productionmentioning

confidence: 99%

“…Examples include ViPER [53] ( Figure 13a) and AQUAGIS [32] (Figure 13b), where plots of physical landscapes are virtually displayed on a 2D map. Example usage of 3D maps includes AgriAG [29] (Figure 13c) and ATLAS [33] (Figure 13d). Both 2D and 3D maps are often layered with various colour representations, such as a heatmap to further visualise areas of interest in the farm.…”

Section: Mapmentioning

confidence: 99%

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A review of visualisations in agricultural decision support systems: An HCI perspective

Gutiérrez

Htun

Schlenz³

et al. 2019

Computers and Electronics in Agriculture

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Decision Support Systems (DSSs) are used in precision agriculture to provide feedback to a variety of stakeholders, including farmers, advisers, researchers and policymakers. However, increments in the amount of data might lead to data quality issues, and as these applications scale into big, real-time monitoring systems the problem gets even more challenging. Visualisation is a powerful technique used in these systems that provides an indispensable step in assisting end-users to understand and interpret the data. In this paper, we present a systematic review to synthesise literature related to the use of visualisation techniques in the domain of agriculture. The search identified 61 eligible articles, from which we established end-users, visualisation techniques and data collection methods across different application domains. We found visualisation techniques used in various areas of agriculture, including viticulture, dairy farming, wheat production and irrigation management. Our results show that the majority of DSSs utilise maps, together with satellite imagery, as the central visualisation. Also, we observed that there is an excellent opportunity for dashboards to enable end-users with better interaction support to understand the uncertainty of data. Based on this analysis, we provide design guidelines towards the implementation of more interactive and visual DSSs.

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Section: Viticulturementioning

confidence: 99%

Section: Wheat Productionmentioning

confidence: 99%

Section: Wheat Productionmentioning

confidence: 99%

Section: Mapmentioning

confidence: 99%

See 2 more Smart Citations

A review of visualisations in agricultural decision support systems: An HCI perspective

Gutiérrez

Htun

Schlenz³

et al. 2019

Computers and Electronics in Agriculture

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“…Treating a 'real' landscape as absolute truth can therefore be misleading. To account for this and to assess model sensitivity towards this, researchers would need to introduce some sort of variability into the 'real' landscapes (Thierry et al, 2017). Depending on the research question, there might also be some privacy issues around the use of real data (especially when modelling pest dynamics in a landscape).…”

Section: Introductionmentioning

confidence: 99%

An alternative method for deriving a USLE nomograph K factor equation

2017

Syme, G., Hatton MacDonald, D., Fulton, B. And Piantadosi, J. (Eds) MODSIM2017, 22nd International Congress on Modelling and Si

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Artificial landscapes are often used in landscape ecological models to investigate questions around movement of individuals, the spread of invasive plants and diseases or the stability of meta-populations. Using these artificial landscapes has some strong benefits such as simplification of landscape complexity, a means to replicate landscape scenarios or the potential to study systematic gradients. Communicating the results to non-theoreticians, however, is often difficult because there is often no quantitative comparison made to link them with reality. Other models that directly use digitized real landscapes as input do not have this barrier, but they are often lacking generalizability or the potential to forecast the effect of changes in the environment.In this study, we propose a method to generate artificial landscapes with key parameters derived a priori from real landscapes through analyses based on Geographical Information Systems (GIS) data: surface cover, spatial aggregation and patch size. Existing methods of generating artificial landscape often do only a posteriori comparisons to prove that their landscapes are 'realistic'. We show that, by estimating the parameters beforehand, we can generate artificial landscapes incorporating multiple land use types that can be directly compared to existing landscapes in a quantitative manner. This allows for more targeted landscape generation and vastly reduces the parameter space that needs to be covered. At the same time, this method does not reduce the potential of the models in terms of being reproducible and transferable.We show an application of this method with two contrasting, complex agricultural landscapes from eastern Sub-Saharan Africa and South-east Australia. We artificially generated landscapes using key parameters from an analysis of digitized real landscapes and found that the algorithm allows flexibility in single target parameters while retaining 'realism'. Realism is assessed in different way. For the Australian data, we compare ranges in land use cover and aggregation between real and generated landscapes. For the African data, we found a log-linear relationship between these two variables in the empirical data that we then used to generate the realistic artificial landscapes. By creating a measurable link between real and artificial landscapes this method will help reduce communication barriers between theoretical scientists and the general public, increasing the impact of our science.

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Simulating seasonal drivers of aphid dynamics to explore agronomic scenarios

et al. 2021

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With the regulation of pesticides in European agricultural landscapes, it is important to understand how pest populations respond to climate and landscape variables in the absence of pesticides at different spatial-temporal scales. While models have described individual biological processes, few have simulated complete life cycles at such scales. We developed a spatially explicit simulation model of the dynamics of the bird cherry-oat aphid (Rhopalosiphum padi) in a pesticide-free simulated landscape using data from an agricultural landscape located in southwest France. Using GLMMs, we ran two statistical methods, one at the crop level, focusing on aphid densities within each crop individually (wheat and its regrowth, corn, and sorghum), and another at the landscape level where aphid densities were not differentiated by crops. For each season, we analyzed how temperature, immigration, and habitat availability impacted on aphid densities. Predictors of aphid densities varied between crops and between seasons, and models for each individual crop resulted in better predictions of aphid densities than landscape-level models. Aphid immigration and temperature were important predictors of aphid densities across models but varied in the directionality of their effects. Moreover, landscape composition was a significant predictor in only four of the nine seasonal crop models. This highlights the complexity of pest-landscape interactions and the necessity of considering fine spatial-temporal scales to identify key factors that influence aphid densities, essential for developing future regulation methods. We used our model to explore the potential effects of two agronomic scenarios on aphid densities: (1) replacement of corn with sorghum, where increases in available sorghum led to the dilution of aphid populations in sorghum in spring and their concentration in summer, and (2) abandonment of pastures for wheat fields, which had no significant effect on aphid densities at the landscape scale. By simulating potential future agronomic practices, we can identify the risks of such changes and inform policy and decision-makers to better anticipate pest dynamics in the absence of pesticides. This approach can be applied to other systems where agronomic and land cover data are available, and to other pest species for which biological processes are described in the literature.

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Simulating spatially-explicit crop dynamics of agricultural landscapes: The ATLAS simulator

Cited by 22 publications

References 42 publications

A review of visualisations in agricultural decision support systems: An HCI perspective

A review of visualisations in agricultural decision support systems: An HCI perspective

An alternative method for deriving a USLE nomograph K factor equation

Simulating seasonal drivers of aphid dynamics to explore agronomic scenarios

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