Understanding and predicting grain nitrogen concentration in malting barley
Nolan, Eamon David
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Grain nitrogen (N) concentration is a major quality criterion of malting barley for which there is a narrow range that producers must meet to satisfy market requirements (1.52 – 1.84 %). In recent years growers in Ireland have had difficulty producing grain with a high enough N concentration to meet these requirements using standard recommended agronomic regimes. The reasons for the lower than expected grain N concentrations are not known. There is interest from growers and maltsters in the development of a system to forecast likely grain N concentration from crop measurements made at or before flowering. A forecasting system would allow growers to identify crops at risk of falling below specification and to apply late N fertiliser if needed. It would also enable maltsters to plan grain intake and malting operations in advance of harvest. The aim of this project was to investigate the potential for predicting grain N concentrations of spring barley from crop and soil measurements made at ear emergence. The main objectives were to 1) investigate the relationships between measurements made at ear emergence and grain N concentration at harvest in order to identify which characteristics should be included as variables in multiple regression models to explain variation in grain N concentration, 2) to use the models with independent data sets to predict grain N concentration and test the accuracy of the predictions, 3) to quantify the recovery by the crop of fertiliser N applied at anthesis and its effects on grain N concentration and 4) to determine whether non-destructive techniques can provide estimates of crop growth and N content for use in the prediction models. Field experiments were established with plots of spring barley (Hordeum vulgare cv. SY Taberna) at one site in 2013 and two sites in 2014 representative of those employed in malting barley production in Ireland. Fertiliser N applications were varied over the range 0 – 210 kg N/ha (with dressings split between sowing and mid-tillering) to provide a range of crop growth and grain N concentrations. In some experiments additional applications of N were made at anthesis to quantify effects on grain N concentration and seed rate treatments (150, 300 and 600 seeds per m-2) imposed to test the accuracy of predictions of grain N concentration in crops of varying canopy structure. Destructive samples were taken to determine total crop N content and canopy N distribution at ear emergence and harvest. Measurements of soil mineral N availability, ear numbers per m-2 and the number of spikelets per ear were made at ear emergence. Final grain yield and quality were also determined at harvest. Grain N concentration is the quotient of grain N content and grain yield. Both grain N content and yield explained a significant amount of the variation in grain N concentration observed across sites and fertiliser N treatments indicating that estimates of both must be included in models to predict N concentration. Grain N content was strongly related to total crop N content at harvest (P<0.001; R2 = 0.96), which in turn was related to canopy N content at ear emergence (P<0.001; R2 = 0.94). Similarly, grain yield was strongly related to total crop biomass at harvest (P<0.001; R2 = 0.83), which in turn was related to crop biomass at ear emergence (P<0.001; R2 = 0.88). These results indicated that predictions of grain N concentration might be possible from measurements of crop N content and biomass at ear emergence and that the effects of variation in harvest index, nitrogen harvest index and post-anthesis N uptake on grain N concentration are likely to be negligible under normal agronomic conditions in Ireland. Weather conditions in 2013 were unusually dry and estimates of soil moisture deficit and available water capacity indicated that the crop was water stressed. In 2014 weather conditions were close to the long term averages for the sites. Multiple regression models using canopy N content and biomass at ear emergence as explanatory variables accounted for 91% of the variation in grain N concentration when data from 2014 were used and 80% when data from both 2013 and 2014 were combined. The models developed using data from plots sown at 300 seed per m-2 in 2014 were tested against independent data from plots sown at 150 seeds per m-2 in the same year and at the same sites to test the accuracy of predictions across plant populations and canopy structures. The models were also tested using data from experimental plots and commercial fields collected in 2015 to test the accuracy of predictions in a different year across a range of sites and varieties. Values of grain N concentration predicted from measurements at ear emergence were compared with actual grain N concentrations measured at harvest. The accuracy of predictions was good with an R2 of 0.80 and RMSE of 0.114 %N for the test across seed rates and R2 of 0.80 and RMSE 0.220 %N for the validation in 2015 across sites and varieties. In 2014, grain N concentrations were increased significantly by applications of additional N fertiliser at anthesis with apparent recoveries (increase in N content (kg) /kg fertiliser N applied) in grain averaging 50% over the range of application rates indicating scope for increasing grain N concentration in crops predicted to be at risk of not meeting malting specifications Non-destructive measurements displayed significant relationships with N content and biomass at GS 59 across a combination of sites and seasons. However, issues in performance relating to instrument saturation were obvious and estimates never produced more accurate predictions of grain N concentration than destructive sampling. The results show that grain N concentration of spring barley can be predicted with good accuracy from measurements of canopy N and crop biomass made at ear emergence when the weather conditions are comparable to the long term average for the region. As conditions of drought are rare in Ireland, the prediction models are a potentially valuable tool to aid crop management and post-harvest operations by growers and maltsters. Further testing will be needed before users can be confident in the reliability of predictions over years and a larger set of varieties.