Methods of producing haploid and doubled haploid oil palms

The present invention relates to haploid oil palm plants and homozygous doubled haploid oil palm plants. The invention also relates to methods for producing and selecting haploid and doubled haploid plants. More particularly, but not exclusively, the method may be used for selecting haploid and doubled haploid oil palm plants. Haploid and doubled haploid plants are selected by a large-scale screening based on a combination of the phenotype with the use of molecular methods combined with flow cytometry techniques to identify haploid and doubled haploid plants. More particularly, a method for selecting haploid and doubled haploid plants is described comprising: (a) germinating seeds; (b) selecting seedlings with atypical phenotype; (c) assessing heterozygosity using markers; (d) isolating cells from the seedlings and determining the DNA content of the cells; and (e) isolating and purifying the DNA and using defined molecular markers to characterise the genotype of the plant. The haploid oil palm plants may be used for producing homozygous doubled haploid oil palms: doubled haploids may be intercrossed to produce uniform F.sub.1 hybrids of superior properties.

Production of haploids and doubled haploids in oil palm

Background: Oil palm is the world’s most productive oil-food crop despite yielding well below its theoretical maximum. This maximum could be approached with the introduction of elite F1 varieties. The development of such elite lines has thus far been prevented by difficulties in generating homozygous parental types for F1 generation. Results: Here we present the first high-throughput screen to identify spontaneously-formed haploid (H) and doubled haploid (DH) palms. We secured over 1,000 Hs and one DH from genetically diverse material and derived further DH/mixoploid palms from Hs using colchicine. We demonstrated viability of pollen from H plants and expect to generate 100% homogeneous F1 seed from intercrosses between DH/mixoploids once they develop female inflorescences. Conclusions: This study has generated genetically diverse H/DH palms from which parental clones can be selected in sufficient numbers to enable the commercial-scale breeding of F1 varieties. The anticipated step increase in productivity may help to relieve pressure to extend palm cultivation, and limit further expansion into biodiverse rainforest.

Effects of vitamin E and fish oil inclusion in broiler diets on meat fatty acid composition and on the flavour of a composite sample of breast meat

BACKGROUND: Enriching poultry meat with long-chain n-3 polyunsaturated fatty acids (LC n-3 PUFA) can increase low population intakes of LC n-3 PUFA, but fishy taints can spoil reheated meat. This experiment determined the effect of different amounts of LC n-3 PUFA and vitamin E in the broiler diet on the fatty acid composition and sensory characteristics of the breast meat. Ross 308 broilers (120) were randomly allocated to one of five treatments from 21 to 42 days of age. Diets contained (g kg−1) 0, 9 or 18 LC n-3 PUFA (0LC, 9LC, 18LC), and 100, 150 or 200 mg LD--tocopherol acetate kg−1 (E). The five diets were 0LC100E, 9LC100E, 18LC100E, 18LC150E, 18LC200E, with four pens per diet, except 18LC100E (eight pens). Breast meat was analysed for fatty acids (uncooked) and sensory analysis by R-index (reheated). RESULTS: LC n-3 PUFA content (mg kg−1 meat) was 514 (0LC100E) and 2236 (9LC and 18LC). Compared with 0LC100E, meat from 18LC100E and 18LC150E tasted significantly different, while 23% of panellists detected fishy taints in 9LC100E and 18LC200E. CONCLUSION: Chicken meat can be enriched with nutritionally meaningful amounts of LC n-3 PUFA, but > 100 mg dl--tocopherol acetate kg−1 broiler diet is needed to protect reheated meat from oxidative deterioration. Copyright © 2010 Society of Chemical Industry

Effect of incremental levels of fish oil in the diet on ruminal lipid metabolism in growing steers

Based on the potential benefits to human health, there is interest in developing sustainable nutritional strategies to enhance the concentration of long-chain n-3 fatty acids in ruminant-derived foods. Four Aberdeen Angus steers fitted with rumen and duodenal cannulae were used in a 4 × 4 Latin square experiment with 21 d experimental periods to examine the potential of fish oil (FO) in the diet to enhance the supply of 20 : 5n-3 and 22 : 6n-3 available for absorption in growing cattle. Treatments consisted of total mixed rations based on maize silage fed at a rate of 85 g DM/kg live weight0·75/d containing 0, 8, 16 and 24 g FO/kg diet DM. Supplements of FO reduced linearly (P < 0·01) DM intake and shifted (P < 0·01) rumen fermentation towards propionate at the expense of acetate and butyrate. FO in the diet enhanced linearly (P < 0·05) the flow of trans-16 : 1, trans-18 : 1, trans-18 : 2, 20 : 5n-3 and 22 : 6n-3, and decreased linearly (P < 0·05) 18 : 0 and 18 : 3n-3 at the duodenum. Increases in the flow of trans-18 : 1 were isomer dependent and were determined primarily by higher amounts of trans-11 reaching the duodenum. In conclusion, FO alters ruminal lipid metabolism of growing cattle in a dose-dependent manner consistent with an inhibition of ruminal biohydrogenation, and enhances the amount of long-chain n-3 fatty acids at the duodenum, but the increases are marginal due to extensive biohydrogenation in the rumen.

Plasma ghrelin and oxytomodulin concentrations in lactating dairy cows receiving abomasal soybean oil, corn starch, and casein infusions

The effects of increased postruminal supply of casein, corn starch, and soybean oil on plasma concentrations of the gastrointestinal hormones ghrelin and oxyntomodulin (OXM) were investigated. Four mid-lactation Holstein cows were used in a 4×4 Latin square. Treatments were continuous abomasal infusions (23h/d) for 7 d of water, soybean oil (500g/d), corn starch (1100g/d), or casein (800g/d). Jugular vein plasma was obtained every 30min for 7h on days 1 and 7. Soybean oil and casein infusion decreased preprandial plasma ghrelin concentration by approximately 20% on both d (time-by-treatment P<0.10); however, dry matter intake (DMI) was depressed only after 7 d of oil infusion. Infusion of soybean oil, corn starch, or casein did not change the plasma OXM concentration (P>0.20). The present data indicate that plasma ghrelin concentration is depressed immediately before feeding by the postruminal infusion of soybean oil and casein, but it is not affected during the postprandial period. Plasma ghrelin concentration was not altered (P>0.20), pre- or postfeeding, by increased postruminal supply of corn starch. In addition, plasma OXM concentration did not respond (P>0.20) to postruminal nutrient infusion. In conclusion, a decrease in DMI when fat is infused could be partially explained by the decrease in prefeeding plasma ghrelin concentration, but a decrease in prefeeding plasma ghrelin concentration is not always associated with a decrease in DMI, as observed for the infusion of casein. Plasma OXM concentration was not affected by postruminal infusion of macronutrients.

Classifying monetary economics: fields and methods from past to future

We propose a simple, yet sufficiently encompassing, classification scheme of monetary economics. It comprises three fundamental fields and six recent areas that expand within and across these fields. The elements of our scheme are not found together and in their mutual relationships in earlier studies of the relevant literature; neither does this attempt aim to produce a relatively complete systematization. Our intention in taking stock is not finality or exhaustiveness. We rather suggest a viewpoint and a possible ordering of the accumulating knowledge. Our purpose is to promote discussion on the evolving nature and internal consistency of monetary economics at large.

The effect of feeding modified soyabean oil enriched with C18 : 4 n-3 to broilers on the deposition of n-3 fatty acids in chicken meat

Supplementing broiler diets with conventional vegetable oils has little effect on the long-chain n-3 PUFA (LC n-3 PUFA) content of the meat. The present study investigated the effect on fatty acid composition and sensory characteristics of chicken meat when broilers were fed oil extracted from soyabeans (SDASOY) that had been genetically engineered to produce C18 : 4n-3 (stearidonic acid (SDA), 240 mg/g oil). Three diets were fed to 120 birds (eight replicate pens of five birds) from 15 d to slaughter (41–50 d). Diets were identical apart from the oil added to them (45 and 50 g/kg as fed in the grower and finisher phases, respectively), which was either SDASOY, near-isogenic soya (CON) or fish oil (FISH). The LC n-3 PUFA content of the meat increased in the order CON, SDASOY and FISH. In breast meat with skin, the SDA concentration was 522, 13 and 37 (sem 14·4) mg/100 g meat for SDASOY, CON and FISH, respectively. Equivalent values for C20 : 5n-3 (EPA) were 53, 13 and 140 (sem 8·4); for C22 : 5n-3 (docosapentaenoic acid (DPA)) 65, 15 and 101 (sem 3·5); for C22 : 6n-3 (DHA) 19, 9 and 181 (sem 4·4). Leg meat (with skin) values for SDA were 861, 23 and 68 (sem 30·1); for EPA 87, 9 and 258 (sem 7·5); for DPA 95, 20 and 165 (sem 5·0); for DHA 29, 10 and 278 (sem 8·4). Aroma, taste and aftertaste of freshly cooked breast meat were not affected. Fishy aromas, tastes and aftertastes were associated with LC n-3 PUFA content of the meat, being most noticeable in the FISH leg meat (both freshly cooked and reheated) and in the reheated SDASOY leg meat.

Proof of concept of automated mapping of weeds in arable fields

The project investigated whether it would be possible to remove the main technical hindrance to precision application of herbicides to arable crops in the UK, namely creating geo-referenced weed maps for each field. The ultimate goal is an information system so that agronomists and farmers can plan precision weed control and create spraying maps. The project focussed on black-grass in wheat, but research was also carried out on barley and beans and on wild-oats, barren brome, rye-grass, cleavers and thistles which form stable patches in arable fields. Farmers may also make special efforts to control them. Using cameras mounted on farm machinery, the project explored the feasibility of automating the process of mapping black-grass in fields. Geo-referenced images were captured from June to December 2009, using sprayers, a tractor, combine harvesters and on foot. Cameras were mounted on the sprayer boom, on windows or on top of tractor and combine cabs and images were captured with a range of vibration levels and at speeds up to 20 km h-1. For acceptability to farmers, it was important that every image containing black-grass was classified as containing black-grass; false negatives are highly undesirable. The software algorithms recorded no false negatives in sample images analysed to date, although some black-grass heads were unclassified and there were also false positives. The density of black-grass heads per unit area estimated by machine vision increased as a linear function of the actual density with a mean detection rate of 47% of black-grass heads in sample images at T3 within a density range of 13 to 1230 heads m-2. A final part of the project was to create geo-referenced weed maps using software written in previous HGCA-funded projects and two examples show that geo-location by machine vision compares well with manually-mapped weed patches. The consortium therefore demonstrated for the first time the feasibility of using a GPS-linked computer-controlled camera system mounted on farm machinery (tractor, sprayer or combine) to geo-reference black-grass in winter wheat between black-grass head emergence and seed shedding.

Final report on research project PS2010, proof of concept of automated mapping of weeds in arable fields

Many weeds occur in patches but farmers frequently spray whole fields to control the weeds in these patches. Given a geo-referenced weed map, technology exists to confine spraying to these patches. Adoption of patch spraying by arable farmers has, however, been negligible partly due to the difficulty of constructing weed maps. Building on previous DEFRA and HGCA projects, this proposal aims to develop and evaluate a machine vision system to automate the weed mapping process. The project thereby addresses the principal technical stumbling block to widespread adoption of site specific weed management (SSWM). The accuracy of weed identification by machine vision based on a single field survey may be inadequate to create herbicide application maps. We therefore propose to test the hypothesis that sufficiently accurate weed maps can be constructed by integrating information from geo-referenced images captured automatically at different times of the year during normal field activities. Accuracy of identification will also be increased by utilising a priori knowledge of weeds present in fields. To prove this concept, images will be captured from arable fields on two farms and processed offline to identify and map the weeds, focussing especially on black-grass, wild oats, barren brome, couch grass and cleavers. As advocated by Lutman et al. (2002), the approach uncouples the weed mapping and treatment processes and builds on the observation that patches of these weeds are quite stable in arable fields. There are three main aspects to the project. 1) Machine vision hardware. Hardware component parts of the system are one or more cameras connected to a single board computer (Concurrent Solutions LLC) and interfaced with an accurate Global Positioning System (GPS) supplied by Patchwork Technology. The camera(s) will take separate measurements for each of the three primary colours of visible light (red, green and blue) in each pixel. The basic proof of concept can be achieved in principle using a single camera system, but in practice systems with more than one camera may need to be installed so that larger fractions of each field can be photographed. Hardware will be reviewed regularly during the project in response to feedback from other work packages and updated as required. 2) Image capture and weed identification software. The machine vision system will be attached to toolbars of farm machinery so that images can be collected during different field operations. Images will be captured at different ground speeds, in different directions and at different crop growth stages as well as in different crop backgrounds. Having captured geo-referenced images in the field, image analysis software will be developed to identify weed species by Murray State and Reading Universities with advice from The Arable Group. A wide range of pattern recognition and in particular Bayesian Networks will be used to advance the state of the art in machine vision-based weed identification and mapping. Weed identification algorithms used by others are inadequate for this project as we intend to collect and correlate images collected at different growth stages. Plants grown for this purpose by Herbiseed will be used in the first instance. In addition, our image capture and analysis system will include plant characteristics such as leaf shape, size, vein structure, colour and textural pattern, some of which are not detectable by other machine vision systems or are omitted by their algorithms. Using such a list of features observable using our machine vision system, we will determine those that can be used to distinguish weed species of interest. 3) Weed mapping. Geo-referenced maps of weeds in arable fields (Reading University and Syngenta) will be produced with advice from The Arable Group and Patchwork Technology. Natural infestations will be mapped in the fields but we will also introduce specimen plants in pots to facilitate more rigorous system evaluation and testing. Manual weed maps of the same fields will be generated by Reading University, Syngenta and Peter Lutman so that the accuracy of automated mapping can be assessed. The principal hypothesis and concept to be tested is that by combining maps from several surveys, a weed map with acceptable accuracy for endusers can be produced. If the concept is proved and can be commercialised, systems could be retrofitted at low cost onto existing farm machinery. The outputs of the weed mapping software would then link with the precision farming options already built into many commercial sprayers, allowing their use for targeted, site-specific herbicide applications. Immediate economic benefits would, therefore, arise directly from reducing herbicide costs. SSWM will also reduce the overall pesticide load on the crop and so may reduce pesticide residues in food and drinking water, and reduce adverse impacts of pesticides on non-target species and beneficials. Farmers may even choose to leave unsprayed some non-injurious, environmentally-beneficial, low density weed infestations. These benefits fit very well with the anticipated legislation emerging in the new EU Thematic Strategy for Pesticides which will encourage more targeted use of pesticides and greater uptake of Integrated Crop (Pest) Management approaches, and also with the requirements of the Water Framework Directive to reduce levels of pesticides in water bodies. The greater precision of weed management offered by SSWM is therefore a key element in preparing arable farming systems for the future, where policy makers and consumers want to minimise pesticide use and the carbon footprint of farming while maintaining food production and security. The mapping technology could also be used on organic farms to identify areas of fields needing mechanical weed control thereby reducing both carbon footprints and also damage to crops by, for example, spring tines. Objective i. To develop a prototype machine vision system for automated image capture during agricultural field operations; ii. To prove the concept that images captured by the machine vision system over a series of field operations can be processed to identify and geo-reference specific weeds in the field; iii. To generate weed maps from the geo-referenced, weed plants/patches identified in objective (ii).