Prediction of cereal feed value by near infrared spectroscopy

Feed value in form of FEsv (Feed unit / kg dry matter, for piglets) and FEso (Feed unit / kg dry matter, for sows), EDOM (Enzyme Degradable Organic Matter) and EDOMi (Enzyme Degradable Organic Matter, Ileum) is used in the feed evaluation system for pigs. Analysis of feed value have highlighted that there is a significant variation between varieties as well as due to an environmental variation between regions and the harvest year. The chemical analysis is, however, time-consuming and costly, and it is therefore desirable to have a rapid and less expensive method, which makes it possible to carry out more analyses in-situ

Advances in Nutrient Management of Grass Seed Crops

Nutrient management of herbage grass seed crops is just as complex as for other agricultural crops when the aim is to optimise economic net return and minimise environmental impact. The use of economic optimum nitrogen (N) application rate (ECO-N) defined as the N application rate that maximise net return for the seed grower is an easy and applicable method for seed growers. Besides the economic advantages of implementing ECO-N there is an additional positive environmental effect as ECO-N is lower than the N rate that maximises seed yield and a lower N application rate will concomitantly lower the potential risk of N leaching. Another interesting but not yet implemented method for N management in grass seed production is the use of canopy reflectance and calculation of crop index or using data for multivariate data analysis to measure plant N status and predict seed yield. The use of canopy reflectance in combination with critical N dilution curves is very interesting and promising. The practical way of using these methods would be to measure plant N status, compare estimated plant N status with critical N status and intervene by applying more N if necessary. Grass seed production is a biological complex process and focusing on N will only succeed if other nutrients, water and the seed yield potential are not a limiting factor for the final seed yield

Nitrogen Uptake and Utilisation in Red Fescue (\u3cem\u3eFestuca rubra\u3c/em\u3e var. \u3cem\u3erubra\u3c/em\u3e) for Seed Production

Application and utilisation of nitrogen (N) is important from both an agronomic and environmental point of view. Increased utilisation of applied N will increase seed yield and the amount of N removed in the harvested straw and seed and concomitantly lower the amount of N than can potentially leach to ground and surface water. From an agronomic viewpoint it is important to understand the relationship between higher N uptake and seed yield. We know from several field experiments that the current year has a large impact on the uptake and utilisation of applied N due to soil available water, temperature and precipitation. On the other hand we have only limited knowledge in the area of utilisation of seed yield potential in combination with utilisation of N. Aims of the current study were therefore to explain seed yield increase by nitrogen (N) use efficiency (NUE), N uptake efficiency (NUpE), N utilization efficiency (NUtE) and number of fertile tillers in two red fescue field experiments in 2004 and 2005

Prediction of Enzyme Digestibility of Organic Matter (EDOM) using Spectroscopy and Chemometrics

Wheat and triticale are a primary energy source in the feedstuffs in swine diets. Enzyme digestibility of organic matter (EDOM) is an index used in the feed evaluation system for pigs. This is based on the close linear relationship between the in vitro enzyme digestibility of organic matter (EDOM) and in vivo total tract digestibility of energy (Boisen & Fernández, 1997). EDOM is a complex parameter calculated from the protein, carbohydrate, starch and fibre fractions undergoing a three step enzymatic incubation. It is therefore a challenge to relate EDOM to specific chemical bonds

Prediction of cereal feed value using spectroscopy and chemometrics

Feed value in form of FEsv (Feed unit / kg dry matter, for piglets) and FEso (Feed unit / kg dry matter, for sows), EDOM (Enzyme Degradable Organic Matter) and EDOMi (Enzyme Degradable Organic Matter, Ileum) is used in the feed evaluation system for pigs. Analysis of feed value have highlighted that there is a significant variation between varieties as well as due to an environmental variation between regions and the harvest year. The chemical analysis is, however, time-consuming and costly, and it is therefore desirable to have a rapid and less expensive method, which makes it possible to carry out more analyses in-situ. Near infra-red reflection spectroscopy (NIRS) is appropriate as a standard analysis of dry matter, total N and starch in grains, since it is rapid (approximately 1 minute per measurement of a ground test) and cheap. NIRS is therefore appropriate as a quick method for the determination of EDOM, EDOMi, FEso and FEsv. The outcome of a successful NIRS calibration will be a relatively cheap tool to monitor, diversify and evaluate the quality of cereals for animal feed, a possible tool to assess the feed value of new varieties in the variety testing and a useful, cheap and rapid tool for cereal breeders. A collection of 1213 grain samples of wheat, triticale, barley and rye, and related chemical reference analyses to describe the feed value have been established. The samples originate from available field trials over a three-year period. The chemical reference analyses are dry matter, crude protein, crude ash, crude oils and fats, EDOM, EDOMi, FEso and FEsv. All samples were ground on a laboratory mill and scans were obtained using a QFA-Flex 400 FT-NIR instrument (Q-interline, Roskilde, Denmark). The samples were packed in glass vials with a height of 6 cm and a diameter of 2.6 cm and measured using a rotating sample device. The sample was rotated with a speed of three rounds per minute, with a measuring sample window at the rotating sample device has a diameter of 6 mm and the analysis surface is ~ 510 mm2. NIR measurements in the range from 800 to 2500 nm with data collection at every 2 nm were performed on grounded and dried aliquots. The development of the NIRS method to determine feed value has been struggled by the fact that the chemical reference analysis has been subject to considerable error. Despite this, it has been possible to develop a wide-ranging calibration model predicting the feed value FEsv and FEso for wheat, barley and triticale. Status of the developed model is a SEP (standard error of performance) of 1.7% for EDOM, 1.7% for EDOMi, 2.2% for FEsv and of 1.8% for FEso. For the assessment of method repeatability in relation to the chemical uncertainty of feed value, the prediction error has to be compared with the error in the chemical analysis. Prediction error by NIRS prediction of feed value is above the error of the chemical measurement. The conclusion is that it is possible to predict the feed value in cereals with NIRS quickly and cheaply, but prediction error with this method is relatively high in relation to a chemical determination of the feed value. A further improvement of the NIRS method will probably be possible with the addition of further references (several years, varieties and sites), which is therefore recommended. Likewise a classification of the feed value into 4 or more groups may be a solution. The current model for prediction of grain feed value with NIRS is yet only suitable as a guiding rule

Path and Ridge Regression Analysis of Seed Yield and Seed Yield Components of Russian Wildrye (Psathyrostachys juncea Nevski) under Field Conditions

The correlations among seed yield components, and their direct and indirect effects on the seed yield (Z) of Russina wildrye (Psathyrostachys juncea Nevski) were investigated. The seed yield components: fertile tillers m-2 (Y1), spikelets per fertile tillers (Y2), florets per spikelet- (Y3), seed numbers per spikelet (Y4) and seed weight (Y5) were counted and the Z were determined in field experiments from 2003 to 2006 via big sample size. Y1 was the most important seed yield component describing the Z and Y2 was the least. The total direct effects of the Y1, Y3 and Y5 to the Z were positive while Y4 and Y2 were weakly negative. The total effects (directs plus indirects) of the components were positively contributed to the Z by path analyses. The seed yield components Y1, Y2, Y4 and Y5 were significantly (P<0.001) correlated with the Z for 4 years totally, while in the individual years, Y2 were not significant correlated with Y3, Y4 and Y5 by Peason correlation analyses in the five components in the plant seed production. Therefore, selection for high seed yield through direct selection for large Y1, Y2 and Y3 would be effective for breeding programs in grasses. Furthermore, it is the most important that, via ridge regression, a steady algorithm model between Z and the five yield components was founded, which can be closely estimated the seed yield via the components

What are the limiting factors to seed quality in organic production of grass and clover seed and how to improve yield

In conventional seed production of grasses and clover it is recognised that a low plant density in the seed crop stimulates the reproductive development and hence increase seed yield. Most temperate forage species of grasses and white clover establish slowly and have a relatively poor ground cover, which limits the competition against weeds. In order to optimise quality and yield in organic grass and clover seed production alternative establishment methods have been identified. These methods enhance crop competitiveness against weeds and allow for mechanical weed control. Establishing the under sown seed crop right between the cereal cover crop row provides a higher ground cover, and the establishment rate of the seed crop is enhanced. This method of establishment is recommended when weed density is low or where the prevailing weeds produce seeds that can be separated from the harvested grass or clover seed. Establishing the under sown seed crop in the cereal row allows for different strategies of mechanical weed control and our results show that perennial ryegrass tolerates a range of strategies of mechanical weed control including up to three harrowings. However, grass and clover species with a low seed weight and/or a slow establishment rate might not establish successfully when sown in the cereal row. Identifying the optimal establishment technique must take the occurrence of weed species and the weed density into account