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Oliver Körner, Jesper Mazanti Aaslyng, Andrea Utoft Andreassen, and Niels Holst

than 60%, predictions were within a 1 °C range (60% and 66% for z 1 and z 3 , respectively) ( Fig. 8 ). The 95% confidence interval in Expt. 2 was much higher than in Expt. 1 (i.e., 3.8 °C, Fig. 9 ). Fig. 7. Simulated versus measured leaf

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Kevin M. Folta

, particularly predicted features consistent with exploitation of land. This is the kind of evolutionary prediction that N.I. Vavilov made for plant species in the early 1900s. Throughout the study of evolution and speciation, scientists have carefully analyzed

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Isabelle Grechi, Nadine Hilgert, Michel Génard, and Françoise Lescourret

also a related uncertainty in model output. Therefore, the uncertainty of the prediction must also be quantified to use the model correctly. The first approach we applied, which is largely used by crop modelers, was sensitivity analysis. This approach

Open access

Richard P. Marini, Tara Auxt Baugher, Megan Muehlbauer, Sherif Sherif, Robert Crassweller, and James R. Schupp

identify blocks of trees with high potential for bitter pit so they can sell the fruit immediately. We recently reported a bitter pit prediction model for ‘Honeycrisp’ based on the average shoot length (SL) and ratio of N to Ca in the peel of apples sampled

Open access

James A. Schrader, Paul A. Domoto, Gail R. Nonnecke, and Diana R. Cochran

season do not yet exist in the literature. The current report describes the development and evaluation of a prediction system that uses values from multiple factors to provide real-time predictions for the phenological timing of cold-climate wine grapes

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Tadahisa Higashide

reports of successful prediction of tomato yield ( Adams, 2002 ; Heuvelink, 1995 ), it is difficult to predict the weekly pattern of yield. Yield in single-truss tomato was strongly correlated with total light received from anthesis to harvesting ( McAvoy

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Steven J. McKay, James M. Bradeen, and James J. Luby

cultivars. Data from these non-progeny genotypes were retained in the analyses to assist in the prediction of panelist effects but are not discussed further. Harvest, sample preparation, and randomization. Apples were harvested weekly in 2005, 2006, and 2007

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Yuto Kitamura, Hisayo Yamane, Akira Yukimori, Hiroyoshi Shimo, Koji Numaguchi, and Ryutaro Tao

of temperatures below −3 °C are unclear, but the accuracy of predictions based on our regression curve ( Fig. 3 ; Table 2 ) would likely not be affected because of the infrequency of temperatures below −3 °C in Wakayama Prefecture, even during winter

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H.N. De Silva, D.S. Tustin, W.M. Cashmore, C.J. Stanley, G. Lupton, and S.J. McArtney

A number of mass—diameter equations were compared for their potential use in indirect measurement of fruit masses of `Royal Gala' apple (Malus ×domestica). The fruit fresh-mass—diameter relationship changed with time during the season, hence no single function fitted the data well. Smooth piecewise functions that assume different relationships for intervening segments of a curve bounded by knots on the x-axis are particularly useful for modeling such data. The curve is said to be smooth because the first derivative of the function is continuous on the interval, including the knots. Two such equations, a three-parameter piecewise power function and a five-parameter spline exponential function, provided good fits to data. For both equations, the estimated mean bias on individual fruit predictions was within 5% of predicted mass over the two validating data sets. As for the precision conditional on no bias, a sample size of 20 fruit gave standard errors within 2.5% of mean predicted mass. These precisions are adequate to meet the industry requirements for monitoring fruit mass through the growing season. There was evidence of a seasonal difference in the estimated bias, but we were unable to confirm that this variation resulted from seasonal differences in fruit shape. Application of these two equations to data from other regions suggested that divergence from the estimated functional form may in fact be greater under increasingly different climatic conditions. Hence, further investigations to identify possible sources of these differences are necessary before the proposed equations can be applied across climatically different regions.

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S. Castro Bustamante and T.K. Hartz

seasonal soil N min potential would also be useful to inform in-season N management. In recent years, several laboratory methods for soil N min prediction have been proposed that could be practical for routine agronomic use. Short-term C min following