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Mineralization of humified organic matter is assumed to be confined to the upper layers. The mineralization rate of these layers is a function of a potential rate depending on the humified organic nitrogen pool, soil texture, and soil temperature and moisture (Mary et al., 1999). In addition it is also influenced by cultural technique. The decay of crop residues and organic amendments in the soil results in net mineralization or net immobilization of soil nitrogen. Each crop residue and organic product is characterized by a specific kinetic curve of N and C decomposition. The decay rate of these products depends on the nature of organic residues and soil temperature and moisture conditions (Nicolardot et al., 2001).
Nitrate transport is simulated using the approach proposed by Burns (1976). The transfer of nitrate is described by the complete mixing of the nitrate flowing into the layers with the resident nitrate of each layer, followed by the drainage of the excess water and the leaching of associated nitrate. The soil profile is divided into consecutive layers of 1 cm of depth.
To take into account the presence of winter cereals and/or catch crops, a module that simulates the growth and N absorption by these crops was also introduced.
3. Results “Reliquat Virtuel” was tested on an experiment carried out in 1991-92 at Estrées-Mons in northern France on a deep loamy soil. The experiment started in August 1991 on a field after the harvest of a winter wheat crop and the wheat straw had been completely removed. Soil cores were taken every 3-4 weeks, from three layers (0-30, 30-60 and 60-90 cm). Soil samples were analyzed to measure water content, NH4+ and NO3- concentrations.
The relationship between observed and simulated NO3--N contents in each of the three layers was relatively good (Figure 1). At the opening of the balance sheet (mid of February) the simulated inorganic N pool at 90 cm depth (87 kg N.ha-1) is close to that measured (102 kg N.ha-1).
Figure 1. Evolution of observed and simulated (with “Reliquat Virtuel”) nitrate contents in different soil layers Several tests are in progress for different agro-pedo-climatic conditions encountered in northern France.
Our first results are promising for the more common case such as deep loamy soils with or without a catch crop.
4. Conclusion “Reliquat Virtuel” is a new decision support tool system developed by INRA, LDAR and ITB to predict the N inorganic pool in the soil at the opening of the balance sheet. The first results are promising and for this type of decision tool used at field scale simulation is satisfactory. The next step is to improve the accuracy of the tool and to adapt it to diverse agricultural conditions.
This software should improve the nitrogen fertilization by (1) helping farmers to choose the fields where the N inorganic pool measure is the most useful and (2) providing a valuable solution for all fields of the farm where there is no measure.
References Machet J.M. et al. 2007. Azofert : a new decision support tool for fertilizer N advice based on a dynamic version of the predictive balance sheet method. In "16th International Symposium of the International Scientific Centre of Fertilizers", pp. 6, Gand.
Mary, B., Beaudoin, N., Justes, E. and Machet, J.M. 1999. Calculation of nitrogen mineralization and leaching in fallow soil using a simple dynamic model. European Journal of Soil Science 50, 549-566.
Nicolardot B., Recous S. and Mary B. 2001. Simulation of C and N mineralization during crop residue decomposition :
A simple dynamic model based on the C:N ratio of the residues. Plant and Soil 228, 83-103.
Burns I.G. 1976. Equations to predict the leaching of nitrate uniformly incorporated to know depth or uniformly distributed throughout a soil profile. Journal of Agricultural Science 86, 305-313.
Nitrogen Workshop 2012
Ammonium nutrition affects the accumulation of winter wheat glutenins Fuertes-Mendizábal, T.a, González-Torralba, J.b, Arregui, L.M.b, González Murua, C.a, Estavillo, J.M.a, González-Moro, M.B.a a Dpto Biología Vegetal y Ecología. Universidad del País Vasco (UPV-EHU). Apdo. 644, 48080. Bilbao.
b Dpto Producción Agraria. Universidad Pública de Navarra (UPNA)
1. Background & Objectives Bread wheat quality is a highly complex feature which is mainly determined by the amount of grain protein and the qualitative composition of that protein. Nitrogen fertilization is the agronomic practice that most widely affects the quality, since the accumulation of reserve protein is influenced not only by the amount of N fertilizer, but also by the type and timing of N source applied.
Nitrogen fertilization improves grain quality due to a rise in grain protein content (FuertesMendizábal et al., 2011). However, the N source or splitting N application has a more variable effect on grain quality. The main objective of this study was to assess the effect of applying exclusively ammonium as the N source split into two or three applications during the crop lifecycle on the composition of the reserve protein fraction responsible for bread dough strength.
2. Materials & Methods Wheat plants var. Cezanne were sown under greenhouse conditions in 1.5L pots (vermiculite:perlite 1:1). At pre-seeding, 18 mg N were supplied per plant as nitrate (KNO3) or ammonium ((NH4)2SO4) to simulate the initial soil N availability under field conditions. P, K and S (188, 188 and 129 mg plant-1) were also supplied. Micronutrients and Mg were supplied with the irrigation water (Fetrilon Combi, BASF). The N fertilizer treatments, in Table 1, comprised the same total N application but divided into 2 or 3 applications at different stages along the crop lifecycle according to the Zadoks scale. At harvest, the grain was separated from the straw and milled. Grain N content was determined by combustion with an elemental analyzer (Thermo Finingan). Glutenins were extracted from the flour and separated by RP-HPLC (Figure 1) according to Triboi et al. (2000).
Table 1. N source and rate of the different N-fertilization treatments applied at stages GS20, GS30 and GS37 according to the Zadoks scale.
3. Results & Discussion Nitrogen fertilizer applied as ammonium affected positively the grain protein concentration (GP), increasing it by 17.5% compared to the nitrate treatment (Table 2). Thus, despite receiving the same N rate, plants under NH4+ nutrition produced grains with a higher breadmaking value. Splitting the dose led to an increase in the GP when NH4+ was applied. So, the third application of NH4+ at GS37 significantly improved the grain quality compared to those that received only two, applications and
Ammonium nutrition increased the glutenin content by 15% (Table 2) but the ratio glutenin/protein was not modified. Splitting the NH4+ dose also raised glutenin content by 13%, while NO3- was applied. The increase in total glutenin content due to NH4+ nutrition was related to a rise of 16% in the low molecular weight glutenin (LMW-GS) content. The high molecular weight glutenin (HMW-GS) content showed a similar trend, although not significantly so (p0.09 instead of p0.05). Splitting application of NO3- or NH4+ into 3 doses led to an increase in LMW and HMWGS content by 10% and 18% respectively. Glutenins are, mainly, responsible for the elasticity of the bread dough, so an increment in their content due to NH4+ nutrition led to an improvement in dough strength and breadmaking quality. The polymorphism of HMW-GS is essentially genetically controlled, but the relationship between the different subunits can be influenced by the environment. In this experiment, the application of NH4+ changed the quantitative and also the qualitative composition of HMW-GS subunits, favouring the accumulation of subunits 12 and 7*.
However, splitting the dose did not change the qualitative composition of the HMW-GS subunits.
Therefore, source of N is more important than splitting of the N dose, i.e. the increase in glutenin content due to splitting is not accompanied by a change in glutenin subunit composition.
4. Conclusion The application of an exclusively ammonium N-source, specially when it is split into 3 doses, increases grain glutenin content and produces flours with increased breadmaking strength.
Acknowledgements Projects Etortek K-Egokitzen, RTA2009-00028-C03-03 and IT526-10. Authors appreciate the human and technical support of Dr. Azucena González, Phytotron Service Sgiker (UPV/EHU) References Triboi, E., Abad, A., Michelena, A., Lloveras, J., Ollier, J.L. and Daniel, C. 2000. Environmental effects on the quality of two wheat genotypes:1.Quantitative and qualitative variation of storage proteins. European Journal of Agronomy 13, 47-64.
Fuertes-Mendizábal, T., Aizpurua, A., González-Moro, M.B. and Estavillo, J.M. 2011. Improving wheat breadmaking quality by splitting the N fertilizer rate. European Journal of Agronomy 33, 52-61.
Zadoks, J.C., Chang, T. and Konzak, C.F. 1974. A decimal code for the growth stages of cereals. Weed Research 14, 415-421.
Nitrogen Workshop 2012 Automating fertiliser N management Kindred, D.a, Sylvester-Bradley, R.a a ADAS UK Ltd., Battlegate Road, Boxworth, Cambridge
1. Background & Objectives Current decision support systems for fertiliser N management are disturbingly ineffective (Kindred et al., 2012; Sylvester-Bradley et al., 2008). Precision Agriculture has long held the promise of improving fertiliser management, and there has been considerable commercial success using canopy sensing techniques to variably apply N across a field (e.g. Heege et al., 2008). However, these generally make arbitrary adjustments to N rate, rather than estimating the absolute N requirement of the crop. It is proposed here that precision farming technologies (e.g. on-combine yield and protein sensors, canopy sensing, soil electro-magnetic induction) might be integrated for two purposes: (a) to provide a novel approach to experimentation, whereby soil effects can be tested with common varieties, husbandry and weather, hence understanding of N requirements can improve, and (b) to provide an automated system for commercial N management both within and between fields, based on the best N management principles, without involving arbitrary adjustments. The variability in N requirements within fields has been assessed rarely (Lark and Wheeler, 2003) and to our knowledge the variability in components of N requirements has never been dissected. A chequerboard design (Pringle et al., 2004) has been adopted here to quantify variation in N requirements and better understand the relative importance of its components.
2. Matreials & Methods A clay loam field was selected in 2010 containing three similar soil series (Evesham. Worcester & Fladbury) and showing previous variability in grain yield fairly typical of yield-mapped fields in England. Fertiliser was applied with 10 m farm spreader booms set separately and perpendicular passes were used to give 528 10 m x 10 m plots fertilised with 0, 120, 240 or 360 kg N ha-1. Yield was measured by plot combine and samples taken for protein analysis. Whole plant grab samples taken from each plot gave N harvest index and total N uptake. Yield and grain protein data were kriged to estimate yield and protein in each plot, then exponential response curves were fitted for each plot allowing N optima to be determined, assuming a price ratio of 5 kg grain to 1 kg fertiliser N.
3. Results & Discussion An aerial view and mapped results (Figure 1) show a number of distinct zones running across the trial area, differing in their greenness in the zero-N plots and in their yield both with and without fertiliser. Grain yield varied from 4 to 8 t ha-1 without N fertiliser and from 8 to 12 t ha-1 where N was applied. N optima varied from 110 to 210 kg N ha-1. Differences in N optima were mostly associated with differences in SNS, which ranged from 60 to 180 kg N ha-1. There were differences in N demand (yield) and N recovery, but these did not usefully explain the differences in N optima;
where yields were high, SNS tended to also be high so optima were low. N recovery was similarly confounded.
This trial showed substantial variation in N requirement in a field considered to have variability representative of English cereal fields; in this case the main surprise was large variation in SNS.
However, canopy sensing measurements in March could distinguish the regions that ultimately supplied different amounts of soil N (data not shown). The variation in crop N demand in this trial
Nitrogen Workshop 2012
also proved predictable using cluster analysis from previous yield maps (Milne et al., 2011), although in this case the information did not directly explain differences in N requirement. This is the first of six chequerboard experiments to be conducted before 2013; the challenge then will be to calibrate and inter-relate canopy reflectance and other potential predictors of N requirements (e.g.
soil type, previous crop N offtake) so that they become integrated, predictive and automated.
Figure 1. Aerial view of the chequerboard trial at Flawborough, Nottinghamshire in 2010, with similarly scaled maps of optimum applied N and its components (crop N demand, soil N supply and fertiliser N recovery).
Colours are graded in equal intervals between the extremes of each variable; these being indicated on each map.
Acknowledgements This research is sponsored by the UK Department for Environment, Food and Rural Affairs through Sustainable Arable LINK Project LK09134, with contributions from ADAS, Rothamsted Research, NIAB-TAG, AgLeader, BASF, Farmade, FOSS, HGCA, Hill Court Farm Research, Masstock, Precision Decisions, Soil Essentials, SOYL, Yara, & Zeltex Inc.
References Heege, H.J., Reusch, S. and Thiessen, E. 2008. Prospects and results for optical systems for site-specific on-the-go control of nitrogen-top-dressing in Germany. Precision Agriculture 9, 115.
Kindred, D., Knight, S., Berry, P., Sylvester-Bradley, R., Hatley, D., Morris, N., Hoad, S. and White, W. 2012.
Establishing Best Practice for Estimation of Soil N Supply. HGCA Research Report on Project 3245 (in press).