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The objective of this experiment was to determine the dietary inclusion rate of camelina cake (CC) that would support the growth performance of growing-finishing pigs similar to that of a corn-soybean meal-based diet. Pigs (n = 192; BW = 35.2 kg; Duroc x (Yorkshire x Landrace)), balanced for sex and initial weight, were assigned to pens (8 pigs/pen) and pens were assigned randomly to one of four dietary treatments (6 pens/treatment). Treatments consisted of a non GMO corn-soybean meal control diet (CON), or CON containing 5% (5CC), 10% (10CC), or 15% (15CC) camelina cake. Feed disappearance on a pen basis and individual body weights of pigs were recorded every other week to calculate average daily gain (ADG), average daily feed intake (ADFI), and gain to feed ratio (G:F) on a pen basis. Prior to harvest, real-time ultrasonic measurements of back fat depth and loin eye area were collected on all live pigs. Pigs were harvested as a single group at about 23 weeks of age at a commercial abattoir. Data were analyzed using Proc Glimmix with dietary treatment as a fixed effect and pen serving as the experimental unit. Growth performance data collected over time were analyzed using repeated measures within the Proc Glimmix procedure. Overall, pigs fed CON exhibited similar ADG to those consuming 5CC and higher ADG than pigs consuming 10CC and 15CC diets (1.10 kg vs. 1.05 kg for 10CC and 1.02 kg for 15CC; P < 0.05 for both mean comparisons). Pigs fed CON consumed more feed than pigs fed any of the CC diets (ADFI = 2.66 kg for CON vs. 2.46 kg for 5CC, 2.46 kg for 10CC and 2.47 kg for 15CC; P < 0.05 for all). These differences resulted in heavier (P < 0.05) CON-fed pigs at marketing than 10CC or 15CC-fed pigs. There were no differences in any carcass traits analyzed. From these data, we conclude that feeding up to 5% CC in corn-soybean meal-based diets did not negatively influence growth performance, or carcass traits of growing-finishing pigs.

Seedling emergence is one of the most important phenological processes that influence the success of weed species. Therefore, predicting weed emergence timing plays a critical role in scheduling weed management measures. Important efforts have been made in the attempt to develop models to predict seedling emergence patterns for weed species under field conditions. Empirical emergence models have been the most common tools used for this purpose. They are based mainly on the use of temperature, soil moisture and light. In this review, we present the more popular empirical models, highlight some statistical and biological limitations that could affect their predictive accuracy and, finally, we present a new generation of modelling approaches to tackle the problems of conventional empirical models, focusing mainly on soft computing techniques. We hope that this review will inspire weed modellers and that it will serve as a basis for discussion and as a frame of reference when we proceed to advance the modelling of field weed emergence. Fil: Gonzalez Andujar, J. L.. Instituto de Agricultura Sostenible. Consejo Superior de Investigaciones Científicas; España Fil: Chantre Balacca, Guillermo Ruben. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Centro de Recursos Naturales Renovables de la Zona Semiárida. Universidad Nacional del Sur. Centro de Recursos Naturales Renovables de la Zona Semiárida; Argentina Fil: Morvillo, C.. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Vegetal; Argentina Fil: Blanco, Anibal Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina Fil: Forcella, F.. United States Department of Agriculture; Estados Unidos

Weed emergence models and weed population models have shown to be important tools for decision making. However, there have been no attempts to integrate a weed emergence model with a population dynamics model to build an improved model with increased predictive capacity. In this paper, a method of integrating both types of model is presented and an application building a mathematical model based on previously reported seedling emergence and population dynamics data to simulate cohort-dependent population dynamics of wild oat is given. Three management scenarios (S1, S2, S3) were considered. In S1, farmers are not aware of the time of weed emergence make control decisions as a stochastic process. Under S2, farmers are aware of the time of weed emergence and make decisions considering the time of emergence. The effect of 100% control when 80, 90, 95 and 100% of wild oats had emerged was examined. In S3 there was "no control". In the absence of control the wild oat population grew in a sigmoid manner and reached an equilibrium density at about 16,000 seeds/square m in the soil seed bank. In S1, simulation resulted in an average population equilibrium at about 13,000 seeds/square m. This equilibrium position represented only a 19% reduction of the carrying capacity of the system. In S2, the 95% and 100% emerged weeds, produced population extinction after 16 and 6 years, respectively. In S2 with 90% and 80% of emerged weeds the carrying capacity of the system was reduced by 95% and 28%, respectively. Scenario S2 with minimum uncertainty always gave better results than S1. Integrating simple population models with emergence models would help farmers in long-term decision making for weed management., Se construyó un modelo matemático basado en datos de emergencia y demografía para simular la dinámica de poblaciones de la avena loca (Avena fatua L.). Tres escenarios de manejo fueron considerados (S1, S2 y S3). En S1, los agricultores carecen de conocimiento sobre el proceso temporal de la emergencia de la avena loca y toman sus decisiones de manera aleatoria. En S2, los agricultores tienen conocimiento del proceso temporal de la emergencia de la mala hierba. Consideramos el efecto sobre la dinámica de poblaciones de un control del 100% cuando han emergido el 80, 90, 95 y 100% de las plantas. El S3 fue el correspondiente a la no aplicación de medidas de control. En S3 la población creció sigmoidalmente, alcanzando una densidad de equilibrio de aproximadamente 16.000 semillas/m cuadrado en el suelo. La simulación correspondiente a S1 dio lugar a un equilibrio de la población de 13.000 semillas/m cuadrado. Este equilibrio supone solamente una reducción del 19% de la capacidad de carga del sistema. En S2, con las medidas de control aplicadas con el 95% y 100% de la población emergida, se produjo una extinción de la población después de 16 y 6 años, respectivamente. Sin embargo, S2 con el 90% y el 80% de la población emergida dio lugar a un banco de semillas que representó el 95% y 28%, respectivamente, de la capacidad de carga del sistema. S2 supone una reducción de la incertidumbre del agricultor y produjo siempre resultados mejores que el S1. La integración de modelos de dinámica de poblaciones con modelos de emergencia puede ser una herramienta de ayuda para los agricultores en la toma de decisiones a largo plazo.

The emergence behaviour of weed species in relation to cultural and meteorological events was studied. Dissimilarities between populations in dormancy and germination ecology, between-year maturation conditions and seed quality and burial site climate, all contribute to potentially unpredictable variability. Therefore, a weed emergence data set was produced for weed seeds of Stellaria media and Chenopodium album matured and collected from three populations (Italy, Sweden and UK). The seeds were collected in two consecutive seasons (1999 and 2000) and subsequently buried in the autumn of the same year of maturation in eight contrasting climatic locations throughout Europe and the USA. The experiment sought to explore and explain differences between the three populations in their emergence behaviour. Evidence was demonstrated of synchrony in the timing of the emergence of different populations of a species at a given burial site. The relative magnitudes of emergence from the three populations at a given burial site in a given year were generally similar across all the burial sites in the study. The resulting dataset was also used to construct a simple weed emergence model, which was tested for its application to the range of different burial environments and populations. The study demonstrated the possibility of using a simple thermal-time-based model to describe part of the emergence behaviour across different burial sites, seed populations and seasons, and a simple winter chilling relationship to adjust for the magnitude of the flush of emergence at a given burial site. This study demonstrates the possibility for developing robust generic models for simple predictions of emergence timing across populations.

To date, several crop : weed competition models have been developed. Developers of the various models were invited to compare model performance using a common data set. The data set consisted of wheat and Lolium rigidum grown in monoculture and mixtures under dryland and irrigated conditions. Results from four crop : weed competition models are presented: almanac, apsim, cropsim and intercom. For all models, deviations between observed and predicted values for monoculture wheat were only slightly lower than for wheat grown in competition with L. rigidum , even though the workshop participants had access to monoculture data while parameterizing models. Much of the error in simulating competition outcome was associated with difficulties in accurately simulating growth of individual species. Relatively simple competition algorithms were capable of accounting for the majority of the competition response. Increasing model complexity did not appear to dramatically improve model accuracy. Comparison of specific competition processes, such as radiation interception, was very difficult since the effects of these processes within each model could not be isolated. Algorithms for competition processes need to be modularised in such a way that exchange, evaluation and comparison across models is facilitated.

The emergence behaviour of weed species in relation to cultural and meteorological events was studied. Dissimilarities between populations in dormancy and germination ecology, between-year maturation conditions and seed quality and burial site climate, all contribute to potentially unpredictable variability. Therefore, a weed emergence data set was produced for weed seeds of Stellaria media and Chenopodium album matured and collected from three populations (Italy, Sweden and UK). The seeds were collected in two consecutive seasons (1999 and 2000) and subsequently buried in the autumn of the same year of maturation in eight contrasting climatic locations throughout Europe and the USA. The experiment sought to explore and explain differences between the three populations in their emergence behaviour. Evidence was demonstrated of synchrony in the timing of the emergence of different populations of a species at a given burial site. The relative magnitudes of emergence from the three populations at a given burial site in a given year were generally similar across all the burial sites in the study. The resulting dataset was also used to construct a simple weed emergence model, which was tested for its application to the range of different burial environments and populations. The study demonstrated the possibility of using a simple thermal-time-based model to describe part of the emergence behaviour across different burial sites, seed populations and seasons, and a simple winter chilling relationship to adjust for the magnitude of the flush of emergence at a given burial site. This study demonstrates the possibility for developing robust generic models for simple predictions of emergence timing across populations.

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