1624 lines
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Executable File
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<dcterms:abstract>TOMSIM(l.O) and TOMGRO(I.O) are two dynamic models for tomato growth and development. Their sub-models for dry-matter distribution between leaves, stem and fruits were compared and discussed. In both models the simulated dry-matter distribution is regulated by the relative sink strengths of the plant organs. These sink strengths are quantified by the potential growth rates of individual organs, i.e. the growth rates under conditions of non-limiting assimilate supply. This approach is general and not limited to the tomato crop. In TOMGRO(J.O), fruits, leaves and internodes stay within age classes and move from class to class during development, whereas in TOMSIM (1.0), record is kept of every fruit truss separately but leaves and internodes are lumped together (i.e. no record of weight or leaf area per age class as in TOMGRO(l.O)). In TOMSIM(1.0), vegetative sink strength is a constant, whereas in TOMGRO(l.0) it is calculated from potential area expansion rate of leaves and specific leaf area. In both models, the ratio between leaf growth and stem growth is constant. In TOMGRO(l.O) there is a feed-back mechanism which controls the vegetative/generative balance: a higher demand/supply ratio for assimilates induces higher fruit abortion rates. In TOMSIM(l.O) the number of fruits set per truss is not simulated, but is an input to the model. TOM SIM (1.0) functions representing flowering rate, fruit growth period, vegetative sink strength and fruit sink strength were compared with similar TOMGRO(l.O) functions, in their dependence on temperature and physiological plant age. A sensitivity analysis was made for the effects of temperature, flowering rate, and fruit and vegetative sink strengths on dry-matter distribution for both models. A validation of both models was based upon periodic destructive harvests in: 1) a greenhouse experiment in Wageningen, using a round tomato cultivar, consisting of a control treatment and a treatment where every second truss was removed at anthesis, and 2) two greenhouse experiments conducted in Montfavet, using a beefsteak tomato cultivar. Daily shoot dry-weight increase, average 24 h greenhouse temperatures and numbers of fruits set per truss (in TOMGRO(l.O) numbers of flowers per truss) were inputs to the models. In general dry-matter distribution was simulated well by both models for the cultivar and conditions where they were developed. TOMGRO(1.0)'s poor performance in one of the validations resulted from the absence of an assimilate storage pool. To achieve reasonable agreement between measurements and simulations for situations other than where the models were developed, parameter adjustments had to be made, most likely reflecting cultivar differences. Strong and weak points of both models are discussed.</dcterms:abstract>
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<dc:title>Dry-matter partitioning in a tomato crop: Comparison of two simulation models</dc:title>
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<dcterms:abstract>A deterministic model of the growth of greenhouse tomatoes (Lycopersicon esculentum Mill), written in Pascal computer language, was developed based on a leaf assimilation model, a respiration theory, and a theory that the photosynthesis rate is controlled by both environmental conditions and leaf carbohydrate level. The model was applied to estimate the effect of carbon dioxide enrichment on tomato fruit yield, and tested with data obtained from two experiments conducted in College Station, Texas, U. S. A. in 1983 through 1984. Tomatoes were grown at 340, 700, and 1000 ppm (on a volume basis) of carbon dioxide (C02) in three 2 x 2 x 10 m translucent chambers inside a double-layered, polyethylene greenhouse. The measured values of the C02 assimilation rate and the dry mass accumulation rate of tomatoes were consistently higher than the calculated ones, but the prediction of the fruit growth and yield were rather accurate. The potential use of the model is in predicting the effects of environmental factors, or in estimating the effect of a new climate control method under a variety of environmental conditions.</dcterms:abstract>
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<dc:title>Design and Test of a Simulation Model of Tomato Growth and Yield in a Greenhouse</dc:title>
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<dc:title>Journal of the Japanese Society for Horticultural Science</dc:title>
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<dc:identifier>ISSN 0013-7626, 1880-358X</dc:identifier>
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<dcterms:alternative>Engei Gakkai zasshi</dcterms:alternative>
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<dcterms:abstract>Los modelos de simulación constituyen una herramienta fundamental para entender la complejidad que caracteriza los sistemas ecológicos y ambientales. Esto se debe a que son la única herramienta disponible para traducir una colección de hipótesis acerca de procesos ecológicos en una representación de cómo el ecosistema funciona en su totalidad. Estos permiten realizar análisis de impactos tecnológicos, económicos y ambientales, la evaluación de estrategias productivas y los pronósticos del rendimiento de los cultivos. Su empleo se enfoca generalmente a comprender mejor los problemas y anticipar la realidad que se investiga. Un buen modelo es capaz de revelar interacciones entre los diferentes componentes que no eran evidentes al estudiar cada uno de los procesos separadamente y permitirá ensayar experimentos que no se podrían realizar en el sistema real. En este trabajo se abordan las características y utilización de los modelos de simulación, algunas de las principales clasificaciones y ejemplos de trabajos realizados en Cuba con estos modelos, así como las peculiaridades del modelo DSSAT (sistema de apoyo para las decisiones de transferencia agrotecnológica). Por tanto, esta revisión pretende dar a conocer las características e
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importancia de los modelos de simulación de cultivos, como herramienta principal en los procesos de toma de decisiones, para poder lograr posteriores aplicaciones como primera aproximación de la capacidad productiva en distintas condiciones edafoclimáticas de Cuba, así como también lograr la investigación de ciertos procesos en plantas, que necesitarán una me-
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jor interpretación en su interacción frente a otros factores productivos, de insumos y ambientales.</dcterms:abstract>
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<dc:title>Modelos de simulación de cultivos: Características y usos</dc:title>
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<dcterms:abstract>Un modelo de simulación del crecimiento de un cultivo es cualquier colección de algoritmos que describe matemáticamente la respuesta de un sistema de cultivo a su entorno. La idea inicial de los modelos de crecimiento de los cultivos parte de la necesidad de integrar los conocimientos de suelo, clima, cultivos y prácticas agrícolas en una herramienta que facilite la toma decisiones. Existe un gran número de modelos matemáticos de simulación del maíz, pero el más empleado es CERES-Maize. Se trata de un modelo desarrollado en 1986 que ha sido mejorado en versiones sucesivas para hacer su empleo más fiable en entornos y condiciones de manejo más diversos. Durante el ciclo, CERES-Maize calcula cada día, el avance de la fenología, la tasa de crecimiento y el reparto de la biomasa entre los órganos en fase de crecimiento. Cuando el agua y el nitrógeno no limitan el crecimiento (crecimiento potencial), los procesos simulados se ven afectados por los valores que toman las principales variables ambientales (radiación solar, temperatura máxima y mínima diaria, y concentración de CO2 atmosférico), de factores específicos del cultivar, y de las prácticas de cultivo. Cuando el agua y el nitrógeno pueden limitar el crecimiento potencial del cultivo, CERES-Maize efectúa balances de agua y de nitrógeno diarios para estimar el impacto del déficit en el crecimiento del cultivo. Finalmente, se presentan algunos problemas que tiene CERES-Maize y, por tanto, aspectos susceptibles de mejora. También se mencionan algunos trabajos realizados en España apoyándose en diferentes modelos de simulación del cultivo del maíz</dcterms:abstract>
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<dc:title>MODELOS DE SIMULACIÓN DEL CULTIVO DE MAÍZ: FUNDAMENTOS Y APLICACIONES EN ESPAÑA</dc:title>
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<foaf:surname>de la Casa</foaf:surname>
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<foaf:givenName>Antonio</foaf:givenName>
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<dc:rights>Copyright 2007 - Instituto de Investigaciones Agropecuarias, INIA (Chile).</dc:rights>
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<dc:date>2013-10-08</dc:date>
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<dc:description>Accepted: 2013-10-08T15:33:30Z
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Publisher: Instituto de Investigaciones Agropecuarias, INIA</dc:description>
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<dcterms:abstract>Integration of satellite data with agroclimatic information can result
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in better procedures to evaluate the state and evolution of grain
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crops. In this work, phenologic calendar of corn ( Zea mays L.) crop
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based on growing-degree days procedures was integrated with Normalized
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Difference Vegetation Index (NDVI) estimations from global coverage of
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National Oceanic and Atmospheric Administration-Advanced Very High
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Resolution Radiometer (NOAA-AVHRR) system. The main objectives were: i)
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to evaluate the relationship between NDVI and corn yield in different
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stages of crop cycle; ii) to analyze the influence of sowing date, and
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iii) to develop a predictive model of county (departmental) corn yield
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using satellite and ground data. The NDVI values accumulated in
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different corn phenologic stages showed a positive association with
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yield, and this relationship was modified in function of sowing date.
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The NDVI value during the reproductive stage, for any sowing date,
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always expressed a high association with corn yield, reaching
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significant correlation values (P < 0.05) in all cases, and even
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higher (P < 0.01) for some evaluated dates. The higher sensitivity
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showed by the reproductive stage confirms that it is a critical period.
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Starting from this information, a prediction model was obtained that
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explains around 80% of corn yield variability of Marcos Juárez
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Department in Córdoba Province, Argentina.</dcterms:abstract>
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<dc:title>Normalized Difference Vegetation Index (NDVI) and phenological data integration to estimate county yield of corn in Córdoba, Argentina</dc:title>
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