Quantification of root dynamics by destructive methods is confounded by high coefficients of variation and loss of fine roots. The minirhizotron technique is non-destructive and allows for sequential root observations to be made at the same depth in situ. Observations can be stored on video tape which facilitates data handling and computer-aided image processing. A color composite technique using digital image analyses was adapted in this study to detect barley root dynamics from sequential minirhizotron images. Plants were grown in the greenhouse in boxes (80 × 80 × 75 cm) containing soil from a surface horizon of a Typic Cryoboroll. A minirhizotron was installed at a 45 °C angle in each box. Roots intersecting the minirhizotron were observed and video-recorded at tillering, stem extension, heading, dough and ripening growth stages. The images from a particular depth were digitized from the analog video then registered to each other. Discrimination of roots from the soil matrix gave quantitative estimates of root appearance and disappearance. Changes in root appearance and disappearance were detected by assigning a separate primary color (red, green, blue) to selected growth stages, then overlaying the images to create red-green and red-green-blue color composites. The resulting composites allowed for a visual interpretation and quantification of barley root dynamics in situ.
Abstract. The MACROS crop model was evaluated for its utility to generate information on land suitability for dry season peanut cropping based on water availability at the regional scale in Khon Kaen Province, Northeast Thailand. The model was specific for the condition where crop growth is limited by water stress, and evaluated using both calibration and validation phases in sequence. In the model calibration, data sets from one peanut field experiment were used to calibrate some parameters to obtain the best agreement between experimental and simulated results. The model validation, in this study, consisted of a ‘validation A’, with emphasis on the accuracy and a ‘validation B’, with emphasis on the usefulness and relevance of the model. In the model validation A, data sets from peanut field experiments were used to validate the model under different conditions. Satisfactory agreements were found between the dynamics of observed and corresponding simulated values of shoot dry weight in every condition involved in this validation study. Also the simulated pod yields agree well with the field data. For the validation R, the model was further validated using data from 36 farm trials conducted at 5 different test sites. A high positive correlation (r= 0.91) existed between observed and simulated pod yields. Because of these satisfactory agreements between observed and corresponding simulated values, it was concluded that the model is valid and can be applied to Khon Kaen Province.
RESUMEDans une proportion de soixante-quinze pour cent, Ie bit! d'hiver canadien est cultive en Alberta et cette production est realisee iJ 45 % dans la region de WarnerForemost. L 'objectif du projet consistait iJmettre sur pied et iJ l'epreuve un modele qui permettrait de determiner l'etendue totale de la zone et la localisation du ble d'hiver dans Ie sud de l'Alberta en utilisant des donnees numeriques de satellites de teledetection des ressources. Deux segments d'essai ont ete etablis, l'un pres de Warner et l'autre iJ Foremost. Deux methodes d'evaluation de I'exactitude de la classification ont ete utilisees: I'analyse de l'etendue totale de la zone et I'analyse de localisation. L 'etendue totale de la zone recouverte de ble d'hiver a ete surestimee de 62 % et de 8 % pour Warner et Foremost, respectivement, iJpartir des donnees recueillies au printemps. L 'utilisation des donnees recueillies au printemps et iJ l'automne, disponibles uniquement pour Warner, a entraine une surestimation de 8 %. L 'evaluation de I'exactitude de la classification du bte d'hiver en fonction du lieu est importante si I'on veut evaluer l'etat des cultures et les degiits de I'hiver. Quatre-vingt-sept et quatre-vingt-dix pour cent des pixels connus representant Ie ble d'hiver dans les segments d'essai de Warner et de Foremost, respectivement, ont ete correctement classes iJ I'aide des donnees recueillies au printemps. Les illusions connexes etaient respectivement de 37 % et de 8 %. L 'utilisation des donnees recueillies au printemps et iJ l'automne, dans Ie cas de Warner, a ratnene Ie taux d'illusions iJ 12 % avec une exactitude de 83 % pour les pixels representant de ble d'hiver correctement classes. Les erreurs d'omission resultaient de pixels de frontiere non classes, probletne accentue par l'etroitesse relative des champs.
SUMMARY
Seventy-five percent of Canada's winter what production is grown in Alberta with 45% of this in the Warner-Foremost region. The objective of this project was to develop and test a model by which the total areal extent and location of winter wheat in Southern Alberta may be determined using resource satellite digital data. Two test segments were established, one near Warner and the other at Foremost.Two methods of classification accuracy assessment were employed: total areal extent and location analysis. The total areal extent of winter wheat, using spring data only, was overestimated by 62% and 8% of Warner and Foremost, respectively. The use of spring and fall data, available only for Warner, resulted in an overestimation of 8 percent. Accuracy assessment of winter wheat classification on a location basis is important if assessments of crop condition and winter kill are to be made. Within the Warnerand Foremost test segments, 87 and 90% of the known winter wheat pixels, respectively, were correctly classified using spring data. Accompanying commission errors were 37% and 8%, respectively. The use of spring and fall data for Warner reduced the commission error to 12% with an 83% accuracy for correctly c...
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