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«International 17 Workshop th Nitrogen The was jointly organised by Teagasc and AFBI Printed by Print Depot Suggested citation Authors, 2012. Title ...»

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3. Results & Discussion Petiole N and N-NO3 increase enhance must contents of YAN, sugars (or alcohol) and pH, and decreases volatile acids, tartaric acid and polyphenols (Table 1). High nitrogen concentration in petiole was related with decreasing of other nutrients, except for Ca. Plant water status on veraison was negatively related to N in petiole and YAN must contents. On the contrary, water availability on maturation period was related positively with N petiole and YAN must contents. Canopy management treatments exhibit a strong effect on must organic nitrogen composition (Table 2). In fact, leaf removal affects significantly to amino acids concentration, while shoot growth control by topping was similar to control. Moreover, amino acids balance was significantly affected by canopy treatments.

4. Conclusions Grapevine nitrogen uptake was dependent on soil water availability and this was affected by canopy management practices. Both water status as canopy management were interacted affecting must characteristics and amino acids profile and, so, affecting wine vintage effect.

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Table 2. Results of berry juice amino acid analysis by canopy management treatments.

Aspartic Ac., Glutamic Ac., Asparagine, Serine, Glutamine, Histidine, Glycine, Threonine, Arginine, Alanine, GABA, Tyrosine, Valine, Methionine, Tryptophan, Phenylalanine, Isoleucine, Leucine, Lysine, Proline ( mg l-1)

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References Guitart, A., Hernandez-Orte, P. and J.F. Cacho. 1997. Effects of maceration on the amino acid content of Chardonnay musts and wines. Vitis 36, 43-47.

Choné, X.; C. Van Leeuwen; Dubourdieu, D. and Gaudillère, J.P. 2001. Stem Water Potential is a Sensitive Indicator of Grapevine Water Status. Ann Bot (2001) 87 (4), 477-483.

Van Leeuwen, C., Trégoat, O., Choné, X., Gaudillère, J. and Pernet, D. 2004. Different environmental conditions, different results: the role of controlled environmental stress on grape quality potential and the way to monitor it.

Proceedings thirteenth australian wine industry technical conference, 1-8

Nitrogen Workshop 2012

Effect of nitrogen fertilizer rate on wheat flour extensibility González-Torralba, J.a, Ezquerra, A.a, Goñi, J.b, Arregui, L.M.a a Agricultural Production Department, Public University of Navarre, Pamplona, Spain b INTIA, S.A. (Instituto Navarro de Tecnologías e Infraestructuras Agroalimentarias (Área Agrícola)), Villava, Spain

1. Background & Objectives Nitrogen (N) fertilization in wheat is essential to obtain the desired grain yield and quality. One of the characteristics of wheat flour that determine its performance during baking is the extensibility.

Increases in N fertilizer rate promote increments of dough extensibility (Garrido-Lestache et al., 2004). The objective of this work was to know how this increment is affected by year and genotype.

2. Materials & Methods Field trials were conducted during five years at three sites in Navarra (northern Spain) under a humid Mediterranean climate with a randomized complete block design with four replications. Five to six N fertilization treatments (urea, 46%) were used, including a control without N fertilization.

Plots were rain-fed, except in 2009/10, when they received irrigation. One to four bread wheat cultivars (Triticum aestivum L.) were sown. Yield response to N rate (Y) (t ha-1 on a 120 g kg-1 water basis) was fitted to a quadratic-plus-plateau model, defined by equation 1 and 2.

Y = a + bN + cN2 if N Nop [1] Y = M if N ≥ Nop [2] Where N is the fertilizer rate (kg N ha-1), a is grain yield predicted for the unfertilized control treatment, b and c are linear and quadratic coefficients, respectively, and Nop is the intersection of the two functions (the smallest N rate required to reach M, the plateau yield). Grain samples were milled to white flour. Dough extensibility (L) (mm) was assessed by Chopin Alveograph.

Extensibility response to N (L) was fitted to a linear model, defined by equation 3.

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Where N is the fertilizer rate (kg N ha-1), d is the extensibility predicted for the unfertilized control treatment and e is the linear coefficient. Extensibility for Nop treatment (LNop) was calculated for each year introducing Nop values in equation 3.

3. Results & Discussion Yield response to N followed a quadratic-plus-plateau model. In contrast, extensibility response to N followed a linear model, indicating that flour quality can be improved by application of N rates higher than Nop. Maximum yield, Nop and LNop varied among cultivars and years, showing the effects of genotype, site, particularly soil N mineral content before fertilization, weather and management practices like irrigation (Table 1). Despite L models were different for each cultivar and year, linear coefficients were very similar, indicating that in most cases L increase per N fertilizer unit had a value close to 0.3 mm. A different trend observed for Berdún in 2006/07 might be explained by a lower efficiency of N fertilizer. Calculated values that relate N rate and L to Nop and LNop allowed to establish a single correlation that included all cultivars and years (Figure 1).

Extensibility response to N fertilizer was the same within the fertilizer rate range in which N was

–  –  –





Figure 1. Correlation between N fertilizer rate (kg ha-1) and dough extensibility (mm) (A) and between N rate minus N optimum for yield (Nop), and extensibility minus extensibility calculated for Nop (B) in Berdún 2005/06 (▲), 2006/07 (Δ), 2007/08 (x), 2008/09 (●), 2009/10 (○), Osado 2009/10 (◊), Badiel 2009/10 (■) and Nogal 2009/10 (♦).

4. Conclusion Extensibility for each N rate varied among cultivars and years, due to genotype, environmental and soil conditions. Within each cultivar and year, extensibility increased linearly with N rate independent of whether N rate was under or above Nop. A single regression could be made for all years and cultivars, indicating an increase of extensibility of 0.3 mm per kg N, approximately.

Acknowledgments This research was funded by INIA projects RTA2005-00219-C03-03 and RTA2009-00028-C03-02. Jon GonzálezTorralba was predoctoral scholarship holder of the Public University of Navarre (Spain). The authors thank Guria S.A.

(Vilafranquina Group) for excellent technical assistance.

References Garrido-Lestache E., López-Bellido R.J. and López-Bellido L. 2004. Effect of N rate, timing and splitting and N type on bread-making quality in hard red spring wheat under rainfed Mediterranean conditions. Field Crops Research 85, 213-236.

Johansson E., Prieto-Linde M.L. and Jönsson J.Ö. 2001. Effects of wheat cultivar and nitrogen application on storage protein composition and breadmaking quality. Cereal Chemistry 78, 19-25.

Nitrogen Workshop 2012

Effect of pretreatment on estimation of slurry composition by NIR spectroscopy with different probes Finzi, A.a, Oberti, R. a, Negri, A.S. b, Perazzolo, F. a, Cocolo G. a, Tambone, F. b, Cabassi, G. c, Provolo, G. a a Università degli Studi di Milano, Department of Agricultural Engineering, Milano, Italy b Università degli Studi di Milano, Department of Plant Production, Milano, Italy c CRA-Agricultural Research Centre-Research Centre for Fodder Crops and Dairy Production (CRA-FLC), Lodi, Italy

1. Background & Objectives In order to apply the correct rate of manure to minimize environmental risk and optimized crop production, farmers would benefit of a method to quickly determine in-situ manure nutrient concentrations (Millmier et al., 2000). NIR spectroscopy could satisfy this necessity as demonstrated by positive laboratory experiences in the analysis of livestock effluents. Studies showed the good predictive capability of models for parameters such as dry matter, total nitrogen and ammonia. Ye et al. (2005) report for different slurries R2 between 0.80-0.97 and RPD (ratio of standard error of performance to standard deviation) higher than 3 for total solids (TS), volatile solid (VS), total nitrogen (Ntot), ammonia nitrogen (NH3-N). Reeves and Van Kessel (2000) affirmed that the NIR spectroscopy is able to predict, for dairy manures, the contents of moisture, total C, total N and NH3-N with R2 greater than 0.9.

The aim of this study is to evaluate the possible field use of this type of analysis, trying to identify the factors to be controlled in order to obtain accurate and reproducible values of Ntot and NH4 contained in slurry, while seeking to develop a user-friendly technology. Therefore, we were interested in figuring out how different pre-treatments and probes are related with the capability of prediction on Ntot and NH4 amount, determined with NIR technology, in the dairy and swine slurry in order to individuate reliable experimental conditions.

2. Materials & Methods We used 23 samples of livestock slurries taken from dairy and swine farms. After collection, each sample was divided into three subsamples. The first subsample was not treated (raw slurry), the second was homogenized (50-100 μm) with a homogenizer (Ultra Turrax IKA ® T18 ™) and the third was filtered with 1 mm mesh filter. The NIR spectroscope (NIR Buchi Flex-N-500) adopts the technology of interferometer and Fourier transform. This instrument makes lectures in the spectral range between 1000-2500 nm with a resolution of 2 nm and 32 scans per spectrum The NIR analysis was performed on the same day of lab analysis on the samples stored at +4°C. Each analysis was carried out with two different settings of NIR spectroscope, and that is with the petri and the optical fiber probe. For each experimental condition 3 spectra were acquired. A total of 18 spectra for each sample had thus been obtained (3 subsample x 2 probes x 3 replicates). The spectra obtained were processed with CAMO Unscrambler 9.7. The Partial Least Squares (PLS) regression was performed by setting categorical variables (treatment, probe, slurry) to correlate the spectra with the contents of Ntot and NH4. The PLS was assessed both in calibration and cross-validation using the uncertainty test and considering 20 Latent Variables. The PLS was performed by dividing the spectra into subgroups according to different treatments (raw, homogenized, filtered) and, alternatively, to the probes by which the reading was carried out (optical fiber and petri). In the experimental plan defined, to identify the best experimental condition to predict the content of Ntot and NH4, R2, RMSECV (Root Mean Square Error in Cross validation) and RPD were considered in order to evaluate the PLS models in cross-validation of NIR spectra (Jacobi et al., 2011).

Nitrogen Workshop 2012

3. Results & Discussion In table 1 the main results of PLS analysis are reported. In general, RPD and R2 values belonging to dairy slurry are lower and RMSECV are higher than those relating to swine slurry: we can assume that the analysis conducted on cattles referred on a more complex substrate. Prediction of Ntot in dairy slurry is better with petri than fiber, while filtered is the worst pre-treatments when petri is used. In swine slurry the better pre-treatment is filtration but the differences with homogenized and raw are limited. The results for the probes are similar.

Prediction of NH4 in dairy slurry achieves good results with all pre-treatment and the fiber-filtered seems to be the best combination. In swine slurry R2 and RPD of the probes for homogenized and filtered are all similar and achieve high values.

–  –  –

4. Conclusion From the results emerged that the samples may benefit of a pre-treatment prior NIR analysis, because both filtered and homogenized slurry generally showed higher RPD and R2 values. It is possible that the presence of high-size particles of raw slurry hinders the optical path. Results do not show great differences between R2 and RPD for petri and fiber measurements.

Therefore, for a practical use of NIR for the prediction of nutrient content of slurry, it might be advisable to use a filtration in order to remove coarse particles while homogenisation does not seem useful. Optical fiber use can be a more practical solution for field measurement without affecting significantly the accuracy of prediction.

Acknowledgement:

Activity carried out in the framework of the Project Biogesteca (“Piattaforma di biotecnologie verdi e di tecniche gestionali per un sistema agricolo ad elevata sostenibilità ambientale” di cui all'accordo istituzionale sottoscritto il 15/3/2011 e repertoriato il 21/3/2011 al n. 15083/RCC), granted by Lombardy Region References Ye, W., Lorimor, J.C., Hurburgh, C., Zhang, H. and Hattey, J. 2005. Application of near-infrared reflectance spectroscopy for determination of nutrient contents in liquid and solid manures. Transaction of the ASAE 48(5), 1911Jacobi, H.F., Moschner, C.R. and Hartung, E. 2011. Use of near infrared spectroscopy in online-monitoring of feeding substrate quality in anaerobic digestion. Bioresource Tecnology 102, 4688-4696.

Millmier, A., Lorimor, J., Hurburgh, C., Fulhage, C., Hattey, J. and Zhang, H. 2000. Near-infrared sensing of manure nutrients. Transaction of the ASAE 43(4), 903-908.

Reeves, J.B. and Van Kessel, J.S. 2000. Near-infrared spectroscopic determination of carbon, total nitrogen, and ammonium-N in dairy manures. 83, 1829-1836.

Nitrogen Workshop 2012

Enhanced biological nitrogen fixation in grassland swards for soil Nitrogen management G. McColluma, D. Nelsona, and J.R Raoa,b a Agri Food and Biosciences Institute Northern Ireland (AFBINI), Newforge Lane, Belfast BT9 5PX.

b University of Ulster, Coleraine, Co. L’derry, BT52 1SA UK.

1. Background & Objectives EU/UK/NI Nutrient Directives (e.g. Anon, 2007) outline stringent control on nitrogen fertiliser usage and supports the worldwide research seeking alternative ‘biological solutions’ (using microbial inoculants plant growth promoting rhizobacteria or PGPR) for soil and crop nutrient management. The contribution of Biological Nitrogen Fixation (BNF) from legumes to primary agricultural production is desirable (e.g.



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