Adaptation of a crop-growth model and its extension by a tuber size function for use in a seed potato forecasting system

Nemecek, Thomas; Derron, J.; Roth, Olivier; Fischlin, Andreas

doi:10.1016/s0308-521x(96)00034-0

Cited by 15 publications

(22 citation statements)

References 23 publications

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“…The resultant maximum distance would be around 200 m (without wind assistance). Individual appetitive flights will be much shorter than this, just a few metres [ 70 ] where aphids fly for only a few seconds up to around 5 minutes at a time, depending on the suitability of the habitat and aphid exhaustion [ 15 ]. Aphid flight capacity for self-propelled flight has also been shown to be strongly influenced by the age of the aphid [ 61 ] and also to be influenced by whether the aphid is viruliferous ( R. padi with BYDV): nonviruliferous aphids over 40 hours old were found to have a greater flight capacity than their viruliferous equivalent [ 62 ].…”

Section: Reviewmentioning

confidence: 99%

Cereal aphid movement: general principles and simulation modelling

Parry

2013

Mov Ecol

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Cereal aphids continue to be an important agricultural pest, with complex lifecycle and dispersal behaviours. Spatially-explicit models that are able to simulate flight initiation, movement direction, distance and timing of arrival of key aphid species can be highly valuable to area-wide pest management programmes. Here I present an overview of how knowledge about cereal aphid flight and migration can be utilized by mechanistic simulation models. This article identifies specific gaps in knowledge for researchers who may wish to further scientific understanding of aphid flight behaviour, whilst at the same time provides a synopsis of the knowledge requirements for a mechanistic approach applicable to the simulation of a wide range of insect species.Although they are one of the most comprehensively studied insect groups in entomology, it is only recently that our understanding of cereal aphid flight and migration has been translated effectively into spatially-explicit simulation models. There are now a multitude of examples available in the literature for modelling methods that address each of the four phases of the aerial transportation process (uplift, transport in the atmosphere, initial distribution, and subsequent movement). I believe it should now be possible to draw together this knowledgebase and the range of modelling methods available to simulate the entire process: integrating mechanistic simulations that estimate the initiation of migration events, with the large scale migration modelling of cereal aphids and their subsequent local movement.Electronic supplementary materialThe online version of this article (doi:10.1186/2051-3933-1-14) contains supplementary material, which is available to authorized users.

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Section: Reviewmentioning

confidence: 99%

Cereal aphid movement: general principles and simulation modelling

Parry

2013

Mov Ecol

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“…For the Gamma distribution, the probability density is as described in Aliche et al (2019), with respect to TSD in potatoes. The Weibull distribution density function was described by Nemecek et al (1996). The Gamma and Weibull distributions are considered to be flexible to right-skewed data, common in…”

Section: Discussionmentioning

confidence: 99%

“…Both Nemecek et al (1996) and Bussan et al (2007) chose to model the scale parameter from empirical data, however, they did so for different reasons. Nemecek et al (1996) suggested a fixed Weibull shape parameter, as they empirically observed that distribution fit was relatively invariant in their dataset. However, Bussan et al (2007) explicitly modelled the scale parameter because it had a high correlation to measured stem and tuber density.…”

Section: Tsd Modelmentioning

confidence: 99%

Comparison of potato (Solanum tuberosum L.) tuber size distribution fitting methods and evaluation of the relationship between soil properties and estimated distribution parameters

2021

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Accurate estimation of tuber size distribution (TSD) parameters in discretely categorized potato (Solanum tuberosum L) yield samples is desirable for estimating modal tuber sizes, which is fundamental to yield prediction. In the current work, systematic yield digs were conducted on five commercial fields (N = 119) to compare the Weibull, Gamma and Gaussian distribution functions for relative-likelihood-based goodness-of-fit to the observed discrete distributions. Parameters were estimated using maximum likelihood estimation (MLE) for the three distributions but were also derived using the percentiles approach for the Weibull distribution to compare accuracy against the MLE approaches. The relationship between TSD and soil nutrient variability was examined using the best-fitting model's parameters. The percentiles approach had lower overall relative likelihood than the MLE approaches across five locations, but had consistently lower Root Mean Square Error in the marketable tuber size range. Negative relationships were observed between the percentile-based shape parameter and the concentrations of Phosphorus and Nitrogen, with significant (non-zero-overlapping 95% confidence interval) regression coefficients for P (−0.74 ± 0.33 for distribution of proportional tuber numbers and −1.3 ± 0.62 for tuber weights). Stem density was negatively associated with the scale and mode of tuber number (regression coefficients −0.98 ± 0.63 and −1.08 ± 0.78 respectively) and tuber weight (regression coefficients −0.99 ± 0.78 and −1.04 ± 0.69 respectively) distributions. Phosphorus is negatively related to the scale of the tuber-number-based distribution while positively associating with the tuber weight distribution. The results suggest that excess P application was associated with the increase in small tubers that did not contribute significant weight to the final yield.

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“…Thus, three periods (1995-2001, 1996-2002and 1997-2003 can be used to analyse the relationship between simulation results and crop yield statistics. The advantage of such approach is that the stability of the regression results over the three windows can be determined.…”

Section: Regression Analyses With Yield Statisticsmentioning

confidence: 99%

“…Agrometeorological crop simulation models are used operationally in many parts of the world for monitoring the effect of weather conditions on crop growth and for predicting crop yields from regional to continental scales (Challinor et al, 2004;Hansen et al, 2004;Nemecek et al, 1996;Thornton et al, 1997;Vossen and Rijks, 1995;Yun, 2003). The success of the crop yield forecasting system strongly depends on the crop simulation model's ability to quantify the influence of weather, soil and management conditions on crop yield and on the systems ability to properly integrate model simulation results over a range of spatial scales (Hansen and Jones, 2000).…”

Section: Introductionmentioning

confidence: 99%