72 resultados para Chickpea


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In Tunisia, broomrape ( Orobanche foetida Poir.) causes major drawbacks especially in faba bean ( Vicia faba L.) Chickpea ( Cicer arietinum L.) suffers little damage compared to faba bean, but with the winter sowing chickpea cultivars, broomrape might become a serious problem for chickpea cultivation. The development of resistant cultivars remains the most efficient way to solve this problem. The behavior of six chickpea genotypes to O. foetida was studied under field natural infestation and artificial inoculation in pots and petri dishes in greenhouse conditions. During the cropping seasons 2010-2011 and 2012-2013 the level of infection was very low. The number of emerged parasites per host plant varied from 0.18 to 0.43 and the incidence from 6.5% to 23%. Among the six tested genotypes, G1, G2, and G4 showed partial resistance to O. foetida with low number and dry weight of emerged parasite and high grain yield compared to the other genotypes, although no significant differences were recorded. In pot experiments, the number and total dry weight of broomrape per plant were lower for G1 and G2 genotypes than the other genotypes. Parasitism does not affect significantly the shoot dry weight and number of pods of these genotypes. The total chlorophyll content was significantly reduced under infestation in all genotypes. In Petri dishes experiments, results showed that percent germination of O. foetida seeds varied from 49% to 65% and does not play a role in the resistance of chickpea genotypes. In contrast, broomrape attachment was lower and slower for the genotypes G1, G2, and G4 than the other genotypes. Resistance in chickpea genotypes was characterized by few parasite attachments on roots and a limited growth of established tubercles. No necrosis of attached tubercles was observed in the different experiments.

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One of the faba bean viruses found in West Asia and North Africa was identified as broad bean mottle virus (BBMV) by host reactions, particle morphology and size, serology, and granular, often vesiculated cytoplasmic inclusions. Detailed research on four isolates, one each from Morocco, Tunisia, Sudan and Syria, provided new information on the virus. The isolates, though indistinguishable in ELISA or gel-diffusion tests, differed slightly in host range and symptoms. Twenty-one species (12 legumes and 9 non-legumes) out of 27 tested were systemically infected, and 14 of these by all four isolates. Infection in several species was symptomless, but major legumes such as chickpea, lentil and especially pea, suffered severely from infection. All 23 genotypes of faba bean, 2 of chickpea, 4 of lentil, 11 out of 21 of Phaseolus bean, and 16 out of 17 of pea were systemically sensitive to the virus. Twelve plant species were found to be new potential hosts and cucumber a new local-lesion test plant of the virus. BBMV particles occurred in faba bean plants in very high concentrations and seed transmission in this species (1.37%) was confirmed. An isolate from Syria was purified and two antisera were produced, one of which was used in ELISA to detect BBMV in faba bean field samples. Two hundred and three out of the 789 samples with symptoms suggestive of virus infection collected in 1985, 1986 and 1987, were found infected with BBMV: 4 out of 70 (4/70) tested samples from Egypt, 0/44 from Lebanon, 1/15 from Morocco, 46/254 from Sudan, 72/269 from Syria and 80/137 from Tunisia. This is the first report on its occurrence in Egypt, Syria and Tunisia. The virus is a potential threat to crop improvement in the region.

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Most mastreviruses (family Geminiviridae) infect monocotyledonous hosts and are transmitted by leafhopper vectors. Only two mastrevirus species, Tobacco yellow dwarf virus from Australia and Bean yellow dwarf virus (BeYDV) from South Africa, have been identified whose members infect dicotyledonous plants. We have identified two distinct mastreviruses in chickpea stunt disease (CSD)-affected chickpea originating from Pakistan. The first is an isolate of BeYDV, previously only known to occur in South Africa. The second is a member of a new species with the BeYDV isolates as its closest relatives. A PCR-based diagnostic test was developed to differentiate these two virus species. Our results show that BeYDV plays no role in the etiology of CSD in Pakistan, while the second virus occurs widely in chickpea across Pakistan. A genomic clone of the new virus was infectious to chickpea (Cicer arietinum L.) and induced symptoms typical of CSD. We propose the use of the name Chickpea chlorotic dwarf Pakistan virus for the new species. The significance of these findings with respect to our understanding of the evolution, origin and geographic spread of dicot-infecting mastreviruses is discussed. © 2008 Springer-Verlag.

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Nucleopolyhedrovirus (NPV) has become an integral part of integrated pest management (IPM) in many Australian agricultural and horticultural crops. This is the culmination of years of work conducted by researchers at the Queensland Department of Primary Industries and Fisheries (QDPI&F) and Ag Biotech Australia Pty Ltd. In the early 1970’s researchers at QDPI&F identified and isolated a virus in Helicoverpa armigera populations in the field. This NPV was extensively studied and shown to be highly specific to Helicoverpa and Heliothis species. Further work showed that when used appropriately the virus could be used effectively to manage these insects in crops such as sorghum, cotton, chickpea and sweet corn. A similar virus was first commercially produced in the USA in the 1970’s. This product, Elcar®, was introduced into Australia in the late 1970’s by Shell Chemicals with limited success. A major factor contributing to the poor adoption of Elcar was the concurrent enormous success of the synthetic pyrethroids. The importance of integrated pest management was probably also not widely accepted at that time. Gradual development of insect resistance to synthetic pyrethroids and other synthetic insecticides in Australia and the increased awareness of the importance of IPM meant that researchers once again turned their attentions to environmentally friendly pest management tools such NPV and beneficial insects. In the 1990’s a company called Rhone-Poulenc registered an NPV for use in Australian sorghum, chickpea and cotton. This product, Gemstar®, was imported from the USA. In 2000 Ag Biotech Australia established an in-vivo production facility in Australia to produce commercial volumes of a product similar to the imported product. This product was branded, ViVUS®, and was first registered and sold commercially in Australia in 2003. The initial production of ViVUS used a virus identical to the American product but replicating it in an Australian Helicoverpa species, H. armigera. Subsequent research collaboration between QDPI&F and Ag Biotech reinvigorated interest in the local virus strain. This was purified and the production system adapted to produce it on a commercial scale. This new version of ViVUS, which was branded ViVUS Gold®, was first registered and sold commercially in 2004. Widespread insect resistance to insecticides and a greater understanding of integrated pest management is leading to increased adoption of technologies such NPV in Australian agriculture.

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Introducing nitrogen (N)-fixing legumes into cereal-based crop rotations reduces synthetic fertiliser-N use and may mitigate soil emissions of nitrous oxide (N2O). Current IPCC calculations assume 100% of legume biomass N as the anthropogenic N input and use 1% of this as an emission factor (EF)—the percentage of input N emitted as N2O. However, legumes also utilise soil inorganic N, so legume-fixed N is typically less than 100% of legume biomass N. In two field experiments, we measured soil N2O emissions from a black Vertosol in sub-tropical Australia for 12 months after sowing of chickpea (Cicer arietinum L.), canola (Brassica napus L.), faba bean (Vicia faba L.), and field pea (Pisum sativum L.). Cumulative N2O emissions from N-fertilised canola (624 g N2O-N ha−1) greatly exceeded those from chickpea (127 g N2O-N ha−1) in Experiment 1. Similarly, N2O emitted from canola (385 g N2O-N ha−1) in Experiment 2 was significantly greater than chickpea (166 g N2O-N ha−1), faba bean (166 g N2O-N ha−1) or field pea (135 g N2O-N ha−1). Highest losses from canola were recorded during the growing season, whereas 75% of the annual N2O losses from the legumes occurred post-harvest. Legume N2-fixation provided 37–43% (chickpea), 54% (field pea) and 64% (faba bean) of total plant biomass N. Using only fixed-N inputs, we calculated EFs for chickpea (0.13–0.31%), field pea (0.18%) and faba bean (0.04%) that were significantly less than N-fertilised canola (0.48–0.78%) (P < 0.05), suggesting legume-fixed N is a less emissive form of N input to the soil than fertiliser N. Inputs of legume-fixed N should be more accurately quantified to properly gauge the potential for legumes to mitigate soil N2O emissions. EF’s from legume crops need to be revised and should include a factor for the proportion of the legume’s N derived from the atmosphere.

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Salinity, sodicity, acidity, and phytotoxic levels of chloride (Cl) in subsoils are major constraints to crop production in many soils of north-eastern Australia because they reduce the ability of crop roots to extract water and nutrients from the soil. The complex interactions and correlations among soil properties result in multi-colinearity between soil properties and crop yield that makes it difficult to determine which constraint is the major limitation. We used ridge-regression analysis to overcome colinearity to evaluate the contribution of soil factors and water supply to the variation in the yields of 5 winter crops on soils with various levels and combinations of subsoil constraints in the region. Subsoil constraints measured were soil Cl, electrical conductivity of the saturation extract (ECse), and exchangeable sodium percentage (ESP). The ridge regression procedure selected several of the variables used in a descriptive model, which included in-crop rainfall, plant-available soil water at sowing in the 0.90-1.10 m soil layer, and soil Cl in the 0.90-1.10 m soil layer, and accounted for 77-85% of the variation in the grain yields of the 5 winter crops. Inclusion of ESP of the top soil (0.0-0.10 m soil layer) marginally increased the descriptive capability of the models for bread wheat, barley and durum wheat. Subsoil Cl concentration was found to be an effective substitute for subsoil water extraction. The estimates of the critical levels of subsoil Cl for a 10% reduction in the grain yield were 492 mg cl/kg for chickpea, 662 mg Cl/kg for durum wheat, 854 mg Cl/kg for bread wheat, 980 mg Cl/kg for canola, and 1012 mg Cl/kg for barley, thus suggesting that chickpea and durum wheat were more sensitive to subsoil Cl than bread wheat, barley, and canola.

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Information on the effects of growing cotton (Gossypium hirsutum L.)-based crop rotations on soil quality of dryland Vertisols is sparse. The objective of this study was to quantify the effects of growing cereal and leguminous crops in rotation with dryland cotton on physical and chemical properties of a grey Vertisol near Warra, SE Queensland, Australia. The experimental treatments, selected after consultations with local cotton growers, were continuous cotton (T1), cotton-sorghum (Sorghum bicolor (L.) Moench.) (T2), cotton-wheat (Triticum aestivum L.) double cropped (T3), cotton-chickpea (Cicer arietinum L.) double cropped followed by wheat (T4) and cotton-wheat (T5). From 1993 to 1996 land preparation was by chisel ploughing to about 0.2 m followed by two to four cultivations with a Gyral tyne cultivator. Thereafter all crops were sown with zero tillage except for cultivation with a chisel plough to about 0.07-0.1 m after cotton picking to control heliothis moth pupae. Soil was sampled from 1996 to 2004 and physical (air-filled porosity of oven-dried soil, an indicator of soil compaction; plastic limit; linear shrinkage; dispersion index) and chemical (pH in 0.01 M CaCl2, organic carbon, exchangeable Ca, Mg, K and Na contents) properties measured. Crop rotation affected soil properties only with respect to exchangeable Na content and air-filled porosity. In the surface 0.15 m during 2000 and 2001 lowest air-filled porosity occurred with T1 (average of 34.6 m3/100 m3) and the highest with T3 (average of 38.9 m3/100 m3). Air-filled porosity decreased in the same depth between 1997 and 1998 from 45.0 to 36.1 m3/100 m3, presumably due to smearing and compaction caused by shallow cultivation in wet soil. In the subsoil, T1 and T2 frequently had lower air-filled porosity values in comparison with T3, T4 and T5, particularly during the early stages of the experiment, although values under T1 increased subsequently. In general, compaction was less under rotations which included a wheat crop (T3, T4, T5). For example, average air-filled porosity (in m3/100 m3) in the 0.15-0.30 m depth from 1996 to 1999 was 19.8 with both T1 and T2, and 21.2 with T3, 21.1 with T4 and 21.5 with T5. From 2000 to 2004, average air-filled porosity (in m3/100 m3) in the same depth was 21.3 with T1, 19.0 with T2, 19.8 with T3, 20.0 with T4 and 20.5 with T5. The rotation which included chickpea (T4) resulted in the lowest exchangeable Na content, although differences among rotations were small. Where only a cereal crop with a fibrous root system was sown in rotation with cotton (T2, T3, T5) linear shrinkage in the 0.45-0.60 m depth was lower than in rotations, which included tap-rooted crops such as chickpea (T4) or continuous cotton (T1). Dispersion index and organic carbon decreased, and plastic limit increased with time. Soil organic carbon stocks decreased at a rate of 1.2 Mg/ha/year. Lowest average cotton lint yield occurred with T2 (0.54 Mg/ha) and highest wheat yield with T3 (2.8 Mg/ha). Rotations which include a wheat crop are more likely to result in better soil structure and cotton lint yield than cotton-sorghum or continuous cotton.

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Background: With the advances in DNA sequencer-based technologies, it has become possible to automate several steps of the genotyping process leading to increased throughput. To efficiently handle the large amounts of genotypic data generated and help with quality control, there is a strong need for a software system that can help with the tracking of samples and capture and management of data at different steps of the process. Such systems, while serving to manage the workflow precisely, also encourage good laboratory practice by standardizing protocols, recording and annotating data from every step of the workflow Results: A laboratory information management system (LIMS) has been designed and implemented at the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) that meets the requirements of a moderately high throughput molecular genotyping facility. The application is designed as modules and is simple to learn and use. The application leads the user through each step of the process from starting an experiment to the storing of output data from the genotype detection step with auto-binning of alleles; thus ensuring that every DNA sample is handled in an identical manner and all the necessary data are captured. The application keeps track of DNA samples and generated data. Data entry into the system is through the use of forms for file uploads. The LIMS provides functions to trace back to the electrophoresis gel files or sample source for any genotypic data and for repeating experiments. The LIMS is being presently used for the capture of high throughput SSR (simple-sequence repeat) genotyping data from the legume (chickpea, groundnut and pigeonpea) and cereal (sorghum and millets) crops of importance in the semi-arid tropics. Conclusions: A laboratory information management system is available that has been found useful in the management of microsatellite genotype data in a moderately high throughput genotyping laboratory. The application with source code is freely available for academic users and can be downloaded from http://www.icrisat.org/bt-software-d-lims.htm

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Assessing the sustainability of crop and soil management practices in wheat-based rotations requires a well-tested model with the demonstrated ability to sensibly predict crop productivity and changes in the soil resource. The Agricultural Production Systems Simulator (APSIM) suite of models was parameterised and subsequently used to predict biomass production, yield, crop water and nitrogen (N) use, as well as long-term soil water and organic matter dynamics in wheat/chickpea systems at Tel Hadya, north-western Syria. The model satisfactorily simulated the productivity and water and N use of wheat and chickpea crops grown under different N and/or water supply levels in the 1998-99 and 1999-2000 experimental seasons. Analysis of soil-water dynamics showed that the 2-stage soil evaporation model in APSIM's cascading water-balance module did not sufficiently explain the actual soil drying following crop harvest under conditions where unused water remained in the soil profile. This might have been related to evaporation from soil cracks in the montmorillonitic clay soil, a process not explicitly simulated by APSIM. Soil-water dynamics in wheat-fallow and wheat-chickpea rotations (1987-98) were nevertheless well simulated when the soil water content in 0-0.45 m soil depth was set to 'air dry' at the end of the growing season each year. The model satisfactorily simulated the amounts of NO3-N in the soil, whereas it underestimated the amounts of NH 4-N. Ammonium fixation might be part of the soil mineral-N dynamics at the study site because montmorillonite is the major clay mineral. This process is not simulated by APSIM's nitrogen module. APSIM was capable of predicting long-term trends (1985-98) in soil organic matter in wheat-fallow and wheat-chickpea rotations at Tel Hadya as reported in literature. Overall, results showed that the model is generic and mature enough to be extended to this set of environmental conditions and can therefore be applied to assess the sustainability of wheat-chickpea rotations at Tel Hadya.

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The main weeds and weed management practices undertaken in broad acre dryland cropping areas of north-eastern Australia have been identified. The information was collected in a comprehensive postal survey of both growers and agronomists from Dubbo in New South Wales (NSW) through to Clermont in central Queensland, where 237 surveys were returned. A very diverse weed flora of 105 weeds from 91 genera was identified for the three cropping zones within the region (central Queensland, southern Queensland and northern NSW). Twenty-three weeds were common to all cropping zones. The major common weeds were Sonchus oleraceus, Rapistrum rugosum, Echinochloa spp. and Urochloa panicoides. The main weeds were identified for both summer and winter fallows, and sorghum, wheat and chickpea crops for each of the zones, with some commonality as well as floral uniqueness recorded. More genera were recorded in the fallows than in crops, and those in summer fallows exceeded the number in winter. Across the region, weed management relied heavily on herbicides. In fallows, glyphosate and mixes with glyphosate were very common, although the importance of the glyphosate mix partner differed among the cropping zones. Use and importance of pre-emergence herbicides in-crop varied considerably among the zones. In wheat, more graminicides were used in northern NSW than in southern Queensland, and virtually none were used in central Queensland, reflecting the differences in winter grass weed flora across the region. Atrazine was the major herbicide used in sorghum, although metolachlor was also used predominantly in northern NSW. Fallow and inter-row cultivation were used more often in the southern areas of the region. Grazing of fallows was more prominent in northern NSW. High crop seeding rates were not commonly recorded indicating that growers are not using crop competition as a tool for weed management. Although many management practices were recorded overall, few growers were using integrated weed management, and herbicide resistance has been and continues to be an issue for the region.

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Cereal grain is one of the main export commodities of Australian agriculture. Over the past decade, crop yield forecasts for wheat and sorghum have shown appreciable utility for industry planning at shire, state, and national scales. There is now an increasing drive from industry for more accurate and cost-effective crop production forecasts. In order to generate production estimates, accurate crop area estimates are needed by the end of the cropping season. Multivariate methods for analysing remotely sensed Enhanced Vegetation Index (EVI) from 16-day Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery within the cropping period (i.e. April-November) were investigated to estimate crop area for wheat, barley, chickpea, and total winter cropped area for a case study region in NE Australia. Each pixel classification method was trained on ground truth data collected from the study region. Three approaches to pixel classification were examined: (i) cluster analysis of trajectories of EVI values from consecutive multi-date imagery during the crop growth period; (ii) harmonic analysis of the time series (HANTS) of the EVI values; and (iii) principal component analysis (PCA) of the time series of EVI values. Images classified using these three approaches were compared with each other, and with a classification based on the single MODIS image taken at peak EVI. Imagery for the 2003 and 2004 seasons was used to assess the ability of the methods to determine wheat, barley, chickpea, and total cropped area estimates. The accuracy at pixel scale was determined by the percent correct classification metric by contrasting all pixel scale samples with independent pixel observations. At a shire level, aggregated total crop area estimates were compared with surveyed estimates. All multi-temporal methods showed significant overall capability to estimate total winter crop area. There was high accuracy at pixel scale (>98% correct classification) for identifying overall winter cropping. However, discrimination among crops was less accurate. Although the use of single-date EVI data produced high accuracy for estimates of wheat area at shire scale, the result contradicted the poor pixel-scale accuracy associated with this approach, due to fortuitous compensating errors. Further studies are needed to extrapolate the multi-temporal approaches to other geographical areas and to improve the lead time for deriving cropped-area estimates before harvest.