Field evaluation of micropropagated and conventionally propagated ginger in subtropical Queensland

The growth and performance of micropropagated ginger (Zingiber officinale Roscoe) was compared with 'seed'-derived plants in field trials conducted in south-eastern Queensland. In the first generation ex vitro, micropropagated plants had significantly (P<0.01) reduced rhizome yield with smaller knobs and more roots. Micropropagated plants had a greater (P<0.01) shoot: root (rhizome) ratio compared with seed-derived plants. Shoots from micropropagated plants were also significantly (P<0.01) smaller with a greater number of shoots per plant. The unusual shoot morphology of the micropropagated plants did not appear to be related to the presence of benzylaminopurine, a plant growth hormone added to the multiplication medium, as plants subcultured for 3 cycles on a hormone-free medium also exhibited similar characteristics. Seed collected from the micropropagated plants and seed-derived plants was harvested and, despite the micropropagated seed being significantly (P<0.01) smaller, by the second generation ex vitro there were no significant differences between the treatments. Factors that can improve rhizome size, while reducing production costs, need to be identified before micropropagated plants can be recommended for routine use in the ginger industry as a source of disease and pest-free planting material.

Ginger (Zingiber officinale) autotetraploids with improved processing quality produced by an in vitro colchicine treatment

Ginger autotetraploids were produced by immersing shoot tips in a 0.5% w/v colchicine, 2% v/v dimethyl sulfoxide solution for 2 h. Stomatal measurements were used as an early indicator of ploidy differences in culture with mean stomata length of tetraploids (49.2 μm) being significantly larger than the diploid (38.8 µm). Of the 500 shoot tips treated, 2% were characterised as stable autotetraploid lines following field evaluation over several seasons. Results were confirmed with flow cytometry and, of the 7 lines evaluated for distinctness and uniformity, 6 were solid tetraploid mutants and 1 was a periclinal chimera. Significant differences were noted between individual tetraploid lines in terms of shoot length, leaf length, leaf width, size of rhizome sections (knob weight) and fibre content. The solid autotetraploid lines had significantly wider, greener leaves than the diploids, they had significantly fewer but thicker shoots and, although ‘Queensland’ (the diploid parent from which the tetraploids were derived) had a greater total rhizome mass at harvest, its knob size was significantly smaller. From the autotetraploid lines, one line was selected for commercial release as ‘Buderim Gold’. It compared the most favourably with ‘Queensland’ in terms of the aroma/flavour profile and fibre content at early harvest, and had consistently good rhizome yield. More importantly it produced large rhizome sections, resulting in a higher recovery of premium grade confectionery ginger and a more attractive fresh market product.

Peronospora lamii on Lamiaceae in Australia

A detailed description and illustration of the Australian specimens of Peronospora lamii on Salvia spp. and on Lamium amplexicaule (Lamiaceae) are given.

Differing mechanisms of simple nitrile formation on glucosinolate degradation in Lepidium sativum and Nasturtium officinale seeds.

Glucosinolates are sulphur-containing glycosides found in brassicaceous plants that can be hydrolysed enzymatically by plant myrosinase or non-enzymatically to form primarily isothiocyanates and/or simple nitriles. From a human health perspective, isothiocyanates are quite important because they are major inducers of carcinogen-detoxifying enzymes. Two of the most potent inducers are benzyl isothiocyanate (BITC) present in garden cress (Lepidium sativum), and phenylethyl isothiocyanate (PEITC) present in watercress (Nasturtium officinale). Previous studies on these salad crops have indicated that significant amounts of simple nitriles are produced at the expense of the isothiocyanates. These studies also suggested that nitrile formation may occur by different pathways: (1) under the control of specifier protein in garden cress and (2) by an unspecified, non-enzymatic path in watercress. In an effort to understand more about the mechanisms involved in simple nitrile formation in these species, we analysed their seeds for specifier protein and myrosinase activities, endogenous iron content and glucosinolate degradation products after addition of different iron species, specific chelators and various heat treatments. We confirmed that simple nitrile formation was predominantly under specifier protein control (thiocyanate-forming protein) in garden cress seeds. Limited thermal degradation of the major glucosinolate, glucotropaeolin (benzyl glucosinolate), occurred when seed material was heated to >120 degrees C. In the watercress seeds, however, we show for the first time that gluconasturtiin (phenylethyl glucosinolate) undergoes a non-enzymatic, iron-dependent degradation to a simple nitrile. On heating the seeds to 120 degrees C or greater, thermal degradation of this heat-labile glucosinolate increased simple nitrile levels many fold.

Pre-cropping with canola decreased Pratylenchus thornei populations, arbuscular mycorrhizal fungi, and yield of wheat.

Root-lesion nematode (Pratylenchus thornei) significantly reduces wheat yields in the northern Australian grain region. Canola is thought to have a 'biofumigation' potential to control nematodes; therefore, a field experiment was designed to compare canola with other winter crops or clean-fallow for reducing P. thornei population densities and improving growth of P. thornei-intolerant wheat (cv. Batavia) in the following year. Immediately after harvest of the first-year crops, populations of P. thornei were lowest following various canola cultivars or clean-fallow (1957-5200 P. thornei/kg dry soil) and were highest following susceptible wheat cultivars (31 033-41 294/kg dry soil). Unexpectedly, at planting of the second-year wheat crop, nematode populations were at more uniform lower levels (<5000/kg dry soil), irrespective of the previous season's treatment, and remained that way during the growing season, which was quite dry. Growth and grain yield of the second-year wheat crop were poorest on plots previously planted with canola or left fallow due to poor colonisation with arbuscular mycorrhizal (AM) fungi, with the exception of canola cv. Karoo, which had high AM fungal colonisation and low wheat yields. There were significant regressions between growth and yield parameters of the second-year wheat and levels of AMF following the pre-crop treatments. Thus, canola appears to be a good crop for reducing P. thornei populations, but AM fungal-dependence of subsequent crops should be considered, particularly in the northern Australian grain region.

ACIAR Productivity and profitability enhancing tropical pulses in Indonesia and Australia

The project aims at improving the productivity and profitability of mung beans, soy beans and peanuts.

Genome-wide association analysis and differential expression analysis of resistance to Sclerotinia stem rot in Brassica napus

Brassica napus is one of the most important oil crops in the world, and stem rot caused by the fungus Sclerotinia sclerotiorum results in major losses in yield and quality. To elucidate resistance genes and pathogenesis-related genes, genome-wide association analysis of 347 accessions was performed using the Illumina 60K Brassica SNP (single nucleotide polymorphism) array. In addition, the detached stem inoculation assay was used to select five highly resistant (R) and susceptible (S) B. napus lines, 48 h postinoculation with S. sclerotiorum for transcriptome sequencing. We identified 17 significant associations for stem resistance on chromosomes A8 and C6, five of which were on A8 and 12 on C6. The SNPs identified on A8 were located in a 409-kb haplotype block, and those on C6 were consistent with previous QTL mapping efforts. Transcriptome analysis suggested that S. sclerotiorum infection activates the immune system, sulphur metabolism, especially glutathione (GSH) and glucosinolates in both R and S genotypes. Genes found to be specific to the R genotype related to the jasmonic acid pathway, lignin biosynthesis, defence response, signal transduction and encoding transcription factors. Twenty-four genes were identified in both the SNP-trait association and transcriptome sequencing analyses, including a tau class glutathione S-transferase (GSTU) gene cluster. This study provides useful insight into the molecular mechanisms underlying the plant's response to S. sclerotiorum.

Anise Myrtle (Syzygium anisatum) Oils

Syzygium anisatum (formerly Backhousia anisata and Anetholea anisata) is an Australian rainforest tree with leaves that produce an essential oil (EO) that has the characteristic aroma of aniseed. It is referred to as aniseed myrtle or anise myrtle in the trade and the fresh and dried leaves of this plant are used as a herb in culinary applications. The EO is extracted by steam distillation of the leaves and the major aromatic volatile compound is anethole. The EO has broad spectrum antimicrobial activity but is more effective against bacteria than fungi. Indigenous Australians have used anise myrtle for its medicinal values and in recent times it has been used as a flavoring agent by the food and beverage industry. This chapter covers the use of anise myrtle EO in food and agricultural applications, botanical aspects, and chemical composition.

Lemon Myrtle (Backhousia citriodora) Oils

Lemon myrtle has been traditionally used by indigenous Australians for cooking and healing. More recently, lemon myrtle leaves are used as a dry or fresh herb in food applications and the essential oil (EO) used as a flavoring agent in food and beverages. The leaf of the lemon myrtle (Backhousia citriodora) is steam distilled to produce the EO. Lemon myrtle EO is known for its characteristic lemon flavor and the major chemical component contributing to the aroma is citral. The EO has broad spectrum antimicrobial activity and is very effective against fungi and has increased the potential of using the EO in food preservation and treatment of postharvest diseases in fruits. This chapter covers the use of lemon myrtle EO in food and agriculture applications, general usage, botanical aspects, and chemical composition.

Tasmanian Pepper Leaf (Tasmannia lanceolata) Oils

Tasmannia lanceolata, commonly known as Tasmanian pepper leaf or mountain pepper, is an Australian native plant that produces an essential oil with a characteristic pungent flavor attributed to the sesquiterpene polygodial. The dried and fresh leaves are used in culinary applications. The essential oil is produced by a solvent extraction process, and the resultant concrete is a rich source of the principal pungent molecule polygodial and other volatiles. The Tasmanian pepper leaf extract has broad-spectrum antimicrobial activity and is very effective against fungi, especially yeasts. This demonstrates its potential to be used in the food industry as a natural preservative. Indigenous Australians have used Tasmanian pepper leaves for therapeutic purposes; in recent times, it is been used as a flavoring agent and enhancer of pungency in food products. This chapter covers the use of Tasmanian pepper leaf essential oil in food applications, its botanical aspects, and its chemical composition.

Cereal grains, oilseeds and other field crops; handbook and digest

Includes bibliography.

Climate variability effects on field crops in Iberian Peninsula. Predictability

La presente Tesis constituye un avance en el conocimiento de los efectos de la variabilidad climática en los cultivos en la Península Ibérica (PI). Es bien conocido que la temperatura del océano, particularmente de la región tropical, es una de las variables más convenientes para ser utilizado como predictor climático. Los océanos son considerados como la principal fuente de almacenamiento de calor del planeta debido a la alta capacidad calorífica del agua. Cuando se libera esta energía, altera los regímenes globales de circulación atmosférica por mecanismos de teleconexión. Estos cambios en la circulación general de la atmósfera afectan a la temperatura, precipitación, humedad, viento, etc., a escala regional, los cuales afectan al crecimiento, desarrollo y rendimiento de los cultivos. Para el caso de Europa, esto implica que la variabilidad atmosférica en una región específica se asocia con la variabilidad de otras regiones adyacentes y/o remotas, como consecuencia Europa está siendo afectada por los patrones de circulaciones globales, que a su vez, se ven afectados por patrones oceánicos. El objetivo general de esta tesis es analizar la variabilidad del rendimiento de los cultivos y su relación con la variabilidad climática y teleconexiones, así como evaluar su predictibilidad. Además, esta Tesis tiene como objetivo establecer una metodología para estudiar la predictibilidad de las anomalías del rendimiento de los cultivos. El análisis se centra en trigo y maíz como referencia para otros cultivos de la PI, cultivos de invierno en secano y cultivos de verano en regadío respectivamente. Experimentos de simulación de cultivos utilizando una metodología en cadena de modelos (clima + cultivos) son diseñados para evaluar los impactos de los patrones de variabilidad climática en el rendimiento y su predictibilidad. La presente Tesis se estructura en dos partes: La primera se centra en el análisis de la variabilidad del clima y la segunda es una aplicación de predicción cuantitativa de cosechas. La primera parte está dividida en 3 capítulos y la segundo en un capitulo cubriendo los objetivos específicos del presente trabajo de investigación. Parte I. Análisis de variabilidad climática El primer capítulo muestra un análisis de la variabilidad del rendimiento potencial en una localidad como indicador bioclimático de las teleconexiones de El Niño con Europa, mostrando su importancia en la mejora de predictibilidad tanto en clima como en agricultura. Además, se presenta la metodología elegida para relacionar el rendimiento con las variables atmosféricas y oceánicas. El rendimiento de los cultivos es parcialmente determinado por la variabilidad climática atmosférica, que a su vez depende de los cambios en la temperatura de la superficie del mar (TSM). El Niño es el principal modo de variabilidad interanual de la TSM, y sus efectos se extienden en todo el mundo. Sin embargo, la predictibilidad de estos impactos es controversial, especialmente aquellos asociados con la variabilidad climática Europea, que se ha encontrado que es no estacionaria y no lineal. Este estudio mostró cómo el rendimiento potencial de los cultivos obtenidos a partir de datos de reanálisis y modelos de cultivos sirve como un índice alternativo y más eficaz de las teleconexiones de El Niño, ya que integra las no linealidades entre las variables climáticas en una única serie temporal. Las relaciones entre El Niño y las anomalías de rendimiento de los cultivos son más significativas que las contribuciones individuales de cada una de las variables atmosféricas utilizadas como entrada en el modelo de cultivo. Además, la no estacionariedad entre El Niño y la variabilidad climática europea se detectan con mayor claridad cuando se analiza la variabilidad de los rendimiento de los cultivos. La comprensión de esta relación permite una cierta predictibilidad hasta un año antes de la cosecha del cultivo. Esta predictibilidad no es constante, sino que depende tanto la modulación de la alta y baja frecuencia. En el segundo capítulo se identifica los patrones oceánicos y atmosféricos de variabilidad climática que afectan a los cultivos de verano en la PI. Además, se presentan hipótesis acerca del mecanismo eco-fisiológico a través del cual el cultivo responde. Este estudio se centra en el análisis de la variabilidad del rendimiento de maíz en la PI para todo el siglo veinte, usando un modelo de cultivo calibrado en 5 localidades españolas y datos climáticos de reanálisis para obtener series temporales largas de rendimiento potencial. Este estudio evalúa el uso de datos de reanálisis para obtener series de rendimiento de cultivos que dependen solo del clima, y utilizar estos rendimientos para analizar la influencia de los patrones oceánicos y atmosféricos. Los resultados muestran una gran fiabilidad de los datos de reanálisis. La distribución espacial asociada a la primera componente principal de la variabilidad del rendimiento muestra un comportamiento similar en todos los lugares estudiados de la PI. Se observa una alta correlación lineal entre el índice de El Niño y el rendimiento, pero no es estacionaria en el tiempo. Sin embargo, la relación entre la temperatura del aire y el rendimiento se mantiene constante a lo largo del tiempo, siendo los meses de mayor influencia durante el período de llenado del grano. En cuanto a los patrones atmosféricos, el patrón Escandinavia presentó una influencia significativa en el rendimiento en PI. En el tercer capítulo se identifica los patrones oceánicos y atmosféricos de variabilidad climática que afectan a los cultivos de invierno en la PI. Además, se presentan hipótesis acerca del mecanismo eco-fisiológico a través del cual el cultivo responde. Este estudio se centra en el análisis de la variabilidad del rendimiento de trigo en secano del Noreste (NE) de la PI. La variabilidad climática es el principal motor de los cambios en el crecimiento, desarrollo y rendimiento de los cultivos, especialmente en los sistemas de producción en secano. En la PI, los rendimientos de trigo son fuertemente dependientes de la cantidad de precipitación estacional y la distribución temporal de las mismas durante el periodo de crecimiento del cultivo. La principal fuente de variabilidad interanual de la precipitación en la PI es la Oscilación del Atlántico Norte (NAO), que se ha relacionado, en parte, con los cambios en la temperatura de la superficie del mar en el Pacífico Tropical (El Niño) y el Atlántico Tropical (TNA). La existencia de cierta predictibilidad nos ha animado a analizar la posible predicción de los rendimientos de trigo en la PI utilizando anomalías de TSM como predictor. Para ello, se ha utilizado un modelo de cultivo (calibrado en dos localidades del NE de la PI) y datos climáticos de reanálisis para obtener series temporales largas de rendimiento de trigo alcanzable y relacionar su variabilidad con anomalías de la TSM. Los resultados muestran que El Niño y la TNA influyen en el desarrollo y rendimiento del trigo en el NE de la PI, y estos impactos depende del estado concurrente de la NAO. Aunque la relación cultivo-TSM no es igual durante todo el periodo analizado, se puede explicar por un mecanismo eco-fisiológico estacionario. Durante la segunda mitad del siglo veinte, el calentamiento (enfriamiento) en la superficie del Atlántico tropical se asocia a una fase negativa (positiva) de la NAO, que ejerce una influencia positiva (negativa) en la temperatura mínima y precipitación durante el invierno y, por lo tanto, aumenta (disminuye) el rendimiento de trigo en la PI. En relación con El Niño, la correlación más alta se observó en el período 1981 -2001. En estas décadas, los altos (bajos) rendimientos se asocian con una transición El Niño - La Niña (La Niña - El Niño) o con eventos de El Niño (La Niña) que están finalizando. Para estos eventos, el patrón atmosférica asociada se asemeja a la NAO, que también influye directamente en la temperatura máxima y precipitación experimentadas por el cultivo durante la floración y llenado de grano. Los co- efectos de los dos patrones de teleconexión oceánicos ayudan a aumentar (disminuir) la precipitación y a disminuir (aumentar) la temperatura máxima en PI, por lo tanto el rendimiento de trigo aumenta (disminuye). Parte II. Predicción de cultivos. En el último capítulo se analiza los beneficios potenciales del uso de predicciones climáticas estacionales (por ejemplo de precipitación) en las predicciones de rendimientos de trigo y maíz, y explora métodos para aplicar dichos pronósticos climáticos en modelos de cultivo. Las predicciones climáticas estacionales tienen un gran potencial en las predicciones de cultivos, contribuyendo de esta manera a una mayor eficiencia de la gestión agrícola, seguridad alimentaria y de subsistencia. Los pronósticos climáticos se expresan en diferentes formas, sin embargo todos ellos son probabilísticos. Para ello, se evalúan y aplican dos métodos para desagregar las predicciones climáticas estacionales en datos diarios: 1) un generador climático estocástico condicionado (predictWTD) y 2) un simple re-muestreador basado en las probabilidades del pronóstico (FResampler1). Los dos métodos se evaluaron en un caso de estudio en el que se analizaron los impactos de tres escenarios de predicciones de precipitación estacional (predicción seco, medio y lluvioso) en el rendimiento de trigo en secano, sobre las necesidades de riego y rendimiento de maíz en la PI. Además, se estimó el margen bruto y los riesgos de la producción asociada con las predicciones de precipitación estacional extremas (seca y lluviosa). Los métodos predWTD y FResampler1 usados para desagregar los pronósticos de precipitación estacional en datos diarios, que serán usados como inputs en los modelos de cultivos, proporcionan una predicción comparable. Por lo tanto, ambos métodos parecen opciones factibles/viables para la vinculación de los pronósticos estacionales con modelos de simulación de cultivos para establecer predicciones de rendimiento o las necesidades de riego en el caso de maíz. El análisis del impacto en el margen bruto de los precios del grano de los dos cultivos (trigo y maíz) y el coste de riego (maíz) sugieren que la combinación de los precios de mercado previstos y la predicción climática estacional pueden ser una buena herramienta en la toma de decisiones de los agricultores, especialmente en predicciones secas y/o localidades con baja precipitación anual. Estos métodos permiten cuantificar los beneficios y riesgos de los agricultores ante una predicción climática estacional en la PI. Por lo tanto, seríamos capaces de establecer sistemas de alerta temprana y diseñar estrategias de adaptación del manejo del cultivo para aprovechar las condiciones favorables o reducir los efectos de condiciones adversas. La utilidad potencial de esta Tesis es la aplicación de las relaciones encontradas para predicción de cosechas de la próxima campaña agrícola. Una correcta predicción de los rendimientos podría ayudar a los agricultores a planear con antelación sus prácticas agronómicas y todos los demás aspectos relacionados con el manejo de los cultivos. Esta metodología se puede utilizar también para la predicción de las tendencias futuras de la variabilidad del rendimiento en la PI. Tanto los sectores públicos (mejora de la planificación agrícola) como privados (agricultores, compañías de seguros agrarios) pueden beneficiarse de esta mejora en la predicción de cosechas. ABSTRACT The present thesis constitutes a step forward in advancing of knowledge of the effects of climate variability on crops in the Iberian Peninsula (IP). It is well known that ocean temperature, particularly the tropical ocean, is one of the most convenient variables to be used as climate predictor. Oceans are considered as the principal heat storage of the planet due to the high heat capacity of water. When this energy is released, it alters the global atmospheric circulation regimes by teleconnection1 mechanisms. These changes in the general circulation of the atmosphere affect the regional temperature, precipitation, moisture, wind, etc., and those influence crop growth, development and yield. For the case of Europe, this implies that the atmospheric variability in a specific region is associated with the variability of others adjacent and/or remote regions as a consequence of Europe being affected by global circulations patterns which, in turn, are affected by oceanic patterns. The general objective of this Thesis is to analyze the variability of crop yields at climate time scales and its relation to the climate variability and teleconnections, as well as to evaluate their predictability. Moreover, this Thesis aims to establish a methodology to study the predictability of crop yield anomalies. The analysis focuses on wheat and maize as a reference crops for other field crops in the IP, for winter rainfed crops and summer irrigated crops respectively. Crop simulation experiments using a model chain methodology (climate + crop) are designed to evaluate the impacts of climate variability patterns on yield and its predictability. The present Thesis is structured in two parts. The first part is focused on the climate variability analyses, and the second part is an application of the quantitative crop forecasting for years that fulfill specific conditions identified in the first part. This Thesis is divided into 4 chapters, covering the specific objectives of the present research work. Part I. Climate variability analyses The first chapter shows an analysis of potential yield variability in one location, as a bioclimatic indicator of the El Niño teleconnections with Europe, putting forward its importance for improving predictability in both climate and agriculture. It also presents the chosen methodology to relate yield with atmospheric and oceanic variables. Crop yield is partially determined by atmospheric climate variability, which in turn depends on changes in the sea surface temperature (SST). El Niño is the leading mode of SST interannual variability, and its impacts extend worldwide. Nevertheless, the predictability of these impacts is controversial, especially those associated with European climate variability, which have been found to be non-stationary and non-linear. The study showed how potential2 crop yield obtained from reanalysis data and crop models serves as an alternative and more effective index of El Niño teleconnections because it integrates the nonlinearities between the climate variables in a unique time series. The relationships between El Niño and crop yield anomalies are more significant than the individual contributions of each of the atmospheric variables used as input in the crop model. Additionally, the non-stationarities between El Niño and European climate variability are more clearly detected when analyzing crop-yield variability. The understanding of this relationship allows for some predictability up to one year before the crop is harvested. This predictability is not constant, but depends on both high and low frequency modulation. The second chapter identifies the oceanic and atmospheric patterns of climate variability affecting summer cropping systems in the IP. Moreover, hypotheses about the eco-physiological mechanism behind crop response are presented. It is focused on an analysis of maize yield variability in IP for the whole twenty century, using a calibrated crop model at five contrasting Spanish locations and reanalyses climate datasets to obtain long time series of potential yield. The study tests the use of reanalysis data for obtaining only climate dependent time series of simulated crop yield for the whole region, and to use these yield to analyze the influences of oceanic and atmospheric patterns. The results show a good reliability of reanalysis data. The spatial distribution of the leading principal component of yield variability shows a similar behaviour over all the studied locations in the IP. The strong linear correlation between El Niño index and yield is remarkable, being this relation non-stationary on time, although the air temperature-yield relationship remains on time, being the highest influences during grain filling period. Regarding atmospheric patterns, the summer Scandinavian pattern has significant influence on yield in IP. The third chapter identifies the oceanic and atmospheric patterns of climate variability affecting winter cropping systems in the IP. Also, hypotheses about the eco-physiological mechanism behind crop response are presented. It is focused on an analysis of rainfed wheat yield variability in IP. Climate variability is the main driver of changes in crop growth, development and yield, especially for rainfed production systems. In IP, wheat yields are strongly dependent on seasonal rainfall amount and temporal distribution of rainfall during the growing season. The major source of precipitation interannual variability in IP is the North Atlantic Oscillation (NAO) which has been related in part with changes in the Tropical Pacific (El Niño) and Atlantic (TNA) sea surface temperature (SST). The existence of some predictability has encouraged us to analyze the possible predictability of the wheat yield in the IP using SSTs anomalies as predictor. For this purpose, a crop model with a site specific calibration for the Northeast of IP and reanalysis climate datasets have been used to obtain long time series of attainable wheat yield and relate their variability with SST anomalies. The results show that El Niño and TNA influence rainfed wheat development and yield in IP and these impacts depend on the concurrent state of the NAO. Although crop-SST relationships do not equally hold on during the whole analyzed period, they can be explained by an understood and stationary ecophysiological mechanism. During the second half of the twenty century, the positive (negative) TNA index is associated to a negative (positive) phase of NAO, which exerts a positive (negative) influence on minimum temperatures (Tmin) and precipitation (Prec) during winter and, thus, yield increases (decreases) in IP. In relation to El Niño, the highest correlation takes place in the period 1981-2001. For these decades, high (low) yields are associated with an El Niño to La Niña (La Niña to El Niño) transitions or to El Niño events finishing. For these events, the regional associated atmospheric pattern resembles the NAO, which also influences directly on the maximum temperatures (Tmax) and precipitation experienced by the crop during flowering and grain filling. The co-effects of the two teleconnection patterns help to increase (decrease) the rainfall and decrease (increase) Tmax in IP, thus on increase (decrease) wheat yield. Part II. Crop forecasting The last chapter analyses the potential benefits for wheat and maize yields prediction from using seasonal climate forecasts (precipitation), and explores methods to apply such a climate forecast to crop models. Seasonal climate prediction has significant potential to contribute to the efficiency of agricultural management, and to food and livelihood security. Climate forecasts come in different forms, but probabilistic. For this purpose, two methods were evaluated and applied for disaggregating seasonal climate forecast into daily weather realizations: 1) a conditioned stochastic weather generator (predictWTD) and 2) a simple forecast probability resampler (FResampler1). The two methods were evaluated in a case study where the impacts of three scenarios of seasonal rainfall forecasts on rainfed wheat yield, on irrigation requirements and yields of maize in IP were analyzed. In addition, we estimated the economic margins and production risks associated with extreme scenarios of seasonal rainfall forecasts (dry and wet). The predWTD and FResampler1 methods used for disaggregating seasonal rainfall forecast into daily data needed by the crop simulation models provided comparable predictability. Therefore both methods seem feasible options for linking seasonal forecasts with crop simulation models for establishing yield forecasts or irrigation water requirements. The analysis of the impact on gross margin of grain prices for both crops and maize irrigation costs suggests the combination of market prices expected and the seasonal climate forecast can be a good tool in farmer’s decision-making, especially on dry forecast and/or in locations with low annual precipitation. These methodologies would allow quantifying the benefits and risks of a seasonal weather forecast to farmers in IP. Therefore, we would be able to establish early warning systems and to design crop management adaptation strategies that take advantage of favorable conditions or reduce the effect of adverse conditions. The potential usefulness of this Thesis is to apply the relationships found to crop forecasting on the next cropping season, suggesting opportunity time windows for the prediction. The methodology can be used as well for the prediction of future trends of IP yield variability. Both public (improvement of agricultural planning) and private (decision support to farmers, insurance companies) sectors may benefit from such an improvement of crop forecasting.

Review of Nezara viridula (L.) management strategies and potential for IPM in field crops with emphasis on Australia

Nezara viridula (L.) is a cosmopolitan, polyphagous heteropteran that causes economic damage to many crop species. At present, control of N. viridula in Australia and other countries relies heavily upon insecticides, most of which are disruptive to beneficial insects, constituting a constraint on integrated pest management (IPM). Much research has been conducted into non-chemical control methods for N. viridula. This paper reviews the potential for and limitations of sterile insect technique, classical, inundative and conservation biological control, and trap cropping. None of these techniques appear to be adequate for control of N. viridula when used alone but there is scope for these non-chemical approaches to be adopted for use in integrated management of this pest. A proposal is given for one such integrated approach for future development. It includes biopesticides, trap crops and carefully targeted habitat manipulation to enhance arthropod natural enemies as well as area-wide management and grower education.

Tobacco Streak Virus in cotton-scoping study

In 2006, Tobacco streak virus (TSV) was identified as the causal agent of the devastating sunflower necrosis disease in central Queensland (CQ), and subsequently in 2007 as the cause of major losses in mungbeans in the same area. It has been a major factor in the recent downturn in the sunflower industry in CQ. Surveys in 2007/2008 as part of a one year scoping study (project 03DAQ005) found TSV in cotton in CQ. The symptoms were mostly confined to the feeding sites of the thrips and appeared as reddish spots and rings, but only occasionally the plants were systemically infected and showed a chlorotic mosaic and leaf deformation. The major objectives of this project (DAQ0002) were to determine: the incidence and distribution of TSV in cotton and its likely effect on yield; the thrips vector species associated with TSV infections in cotton; and the factors that may lead to systemic infections. In contrast to the extensive damage observed in sunflower and mungbean crops from the same region, TSV has caused no measurable damage in commercial cotton crops surveyed in CQ over the seasons 2008/9 to 2010/11. No TSV infected cotton was found in regions outside of CQ and the geographical distribution of TSV disease in cotton (and other susceptible hosts) appears to be closely related to the distribution of the major alternative host, parthenium weed. The most likely thrips species responsible for transmission of TSV into cotton is the tomato thrips (Frankliniella schultzei) and onion thrips (Thrips tabaci). Systemically infected plants are rarely seen in commercial crops and have also been rarely produced in controlled tests. It appears that systemic infection may be transient with only mild symptoms being produced intermittently. With current cultivars and conditions, it appears likely that TSV will continue to cause only minor levels of mild local lesions with no impact on yield in cotton crops. It appears that no specific control strategies are required to limit the impact of TSV in cotton. However, general farm hygiene to minimise the presence of the major alternative host of TSV, parthenium weed, is advised and may be of vital importance if TSV susceptible rotational crops such as mung beans are grown.

Weed management strategies for farming systems with herbicide tolerant cotton.

The introduction of glyphosate tolerant cotton has significantly improved the flexibility and management of a number of problem weeds in cotton systems. However, reliance on glyphosate poses risks to the industry in term of glyphosate resistance and species shift. The aims of this project were to identify these risks, and determine strategies to prevent and mitigate the potential for resistance evolution. Field surveys identified fleabane as the most common weed now in both irrigated and dryland system. Sowthistle has also increased in prevalence, and bladder ketmia and peachvine remained common. The continued reliance on glyphosate has favoured small seeded, and glyphosate tolerant species. Fleabane is both of these, with populations confirmed resistant in grains systems in Queensland and NSW. When species were assessed for their resistance risk, fleabane, liverseed grass, feathertop Rhodes grass, sowthistle and barnyard grass were determined to have high risk ratings. Management practices were also determined to rely heavily on glyphosate and therefore be high risk in summer fallows, and dryland glyphosate tolerant and conventional cotton. Situations were these high risk species are present in high risk cropping phases need particular attention. The confirmation of a glyphosate resistance barnyard grass population in a dryland glyphosate tolerant cotton system means resistance is now a reality for the cotton industry. However, experiments have shown that resistant populations can be managed with other herbicide options currently available. However, the options for fleabane management in cotton are still limited. Although some selective residual herbicides are showing promise, the majority of fleabane control tactics can only be used in other phases of the cotton rotation. An online glyphosate resistance tool has been developed. This tool allows growers to assess their individual glyphosate resistance risks, and how they can adjust their practices to reduce their risks. It also provides researchers with current information on weed species present and practices used across the industry. This tool will be extremely useful in tailoring future research and extension efforts. Simulations from the expanded glyphosate resistance model have shown that glyphosate resistance can be prevented and managed in glyphosate-tolerant cotton farming systems. However, for strategies to be successful, some effort is required. Simulations have shown the importance of controlling survivors of glyphosate applications, using effective glyphosate alternatives in fallows, and combining several effective glyphosate alternatives in crop, and these are the key to the prevention and management of glyphosate resistance.