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Dumont, B.; Basso, B.; Leemans, V.; Bodson, B.; Destain, J.-P.; Destain, M.-F. |
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A comparison of within-season yield prediction algorithms based on crop model behaviour analysis |
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Journal Article |
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Year |
2015 |
Publication |
Agricultural and Forest Meteorology |
Abbreviated Journal |
Agricultural and Forest Meteorology |
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204 |
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10-21 |
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Keywords |
stics crop model; climate variability; lars-wg; yield prediction; log-normal distribution; convergence in law theorem; central limit theorem; weather generator; nitrogen balances; generic model; wheat; simulation; climate; stics; variability; skewness; efficiency |
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The development of methodologies for predicting crop yield, in real-time and in response to different agro-climatic conditions, could help to improve the farm management decision process by providing an analysis of expected yields in relation to the costs of investment in particular practices. Based on the use of crop models, this paper compares the ability of two methodologies to predict wheat yield (Triticum aestivum L.), one based on stochastically generated climatic data and the other on mean climate data. It was shown that the numerical experimental yield distribution could be considered as a log-normal distribution. This function is representative of the overall model behaviour. The lack of statistical differences between the numerical realisations and the logistic curve showed in turn that the Generalised Central Limit Theorem (GCLT) was applicable to our case study. In addition, the predictions obtained using both climatic inputs were found to be similar at the inter and intra-annual time-steps, with the root mean square and normalised deviation values below an acceptable level of 10% in 90% of the climatic situations. The predictive observed lead-times were also similar for both approaches. Given (i) the mathematical formulation of crop models, (ii) the applicability of the CLT and GLTC to the climatic inputs and model outputs, respectively, and (iii) the equivalence of the predictive abilities, it could be concluded that the two methodologies were equally valid in terms of yield prediction. These observations indicated that the Convergence in Law Theorem was applicable in this case study. For purely predictive purposes, the findings favoured an algorithm based on a mean climate approach, which needed far less time (by 300-fold) to run and converge on same predictive lead time than the stochastic approach. (C) 2015 Elsevier B.V. All rights reserved. |
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English |
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0168-1923 |
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CropM |
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MA @ admin @ |
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4647 |
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Hutchings, N. |
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A comparison of farm-scale models to estimate greenhouse gas emissions from dairy farms in Europe |
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2015 |
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FACCE MACSUR Reports |
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5 |
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Sp5-26 |
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Farm-scale models quantify the cycling of nitrogen (N) and carbon (C) so are powerful tools for assessing the impact of management-related decisions on greenhouse gas (GHG) emissions, especially on dairy cattle farms, where the internal cycling is particularly important. Farm models range in focus (economic, environmental) and the detail with which they represent C and N cycling. We compared four models from this range in terms of on-farm production and emissions of GHGs, using standardized scenarios. The models compared were SFarMod, DairyWise, FarmAC and HolosNor. The scenarios compared were based on two soil types (sandy clay versus heavy clay), two roughage systems (grass only versus grass and maize), and two climate types (Eindhoven versus Santander). Standard farm characteristics were; area (50 ha), milk yield (7000 kg/head/year), fertiliser (275 kg N and 150 kg N/ha/year for grass and maize, respectively). Potential yields for grass 10t dry matter (DM)/ha/year in both areas, maize 14 t DM/ha/ year in Eindhoven and 18t DM/ha/ year in Santander. The import of animal feed and the export/import manure and forages was minimized. Similar total farm direct GHG emissions for all models disguised a variation between models in the contribution of the different on-farm sources. There were large differences between models in the predictions of indirect GHG emission from nitrate leaching. Results could be explained by differences between models in the assumptions made and detail with which underlying processes were represented. We conclude that the choice of an appropriate farm model is highly dependent upon the role it should play and the context within which it will operate, so the current diversity of farm models will continue into the future. No Label |
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MACSUR Science Conference 2015 »Integrated Climate Risk Assessment in Agriculture & Food«, 8–9+10 April 2015, Reading, UK |
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MA @ admin @ |
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2141 |
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