Estimating SNS 149

The original intention was to consider an N balance approach for base estimates of SNS, and annual measures of SNS on each field were made on this basis. If previous N applications and N offtakes are known then it should be possible to make some allowances for N leaching and

changes to soil organic matter to predict SNS spatially. The expectation would be that areas giving highest yields and proteins with uniform N applications would be areas where N offtakes were higher and resulting SNS would be lower. However, the unexpectedly strong relationship between yield potential and harvested SNS seen in the chessboard experiments refutes the applicability of the N balance hypotheses on a spatial basis, unless full estimation of mineralisation from

differences in soil organic matter could be made. An N balance approach may however still give a worthwhile method of judging average field SNS to use as a baseline.

Crop sensing can also be used to judge differences in SNS, assuming that nitrogen has been the major limitation to growth and that other factors have not been responsible for the spatial variation in the crop. Chapter 5 shows there is feasibility of getting an SNS estimate from canopy sensing on an absolute basis by using NDVI with estimated NDVI of an N-unlimited crop from knowledge of

data suggests that large errors can be associated with SNS predictions, and experience from other work shows that poor prediction of SNS can be costly (Kindred et al., 2012).

We therefore advocate the use of the calibration developed in Chapter 5 to spatially adjust the estimate of SNS around a field average, with constraints on what the maximum and minimum SNS could be in the field.

To estimate the average field SNS we suggest using an N-balance type approach as inferred in the HGCA wheat N Management Guide. First the autumn N residue is estimated based on the balance of N applied (or fixed) and that removed in the crop. Typical OSR and bean crops are expected to leave a residue of around 120 kg N/ha, whereas cereals might leave 80kg/ha. Estimates for other crops is given in Table 9. These estimates may be adjusted based on the amount of fertiliser N applied, its efficiency recovery by the crop (eg was spring very dry so N uptake limited?) and the achieved yield and N offtake in crop & straw.

Table 9. Estimated typical autumn N residues for a range of previous crops. Values are inferred from N Management Guide. Previous crop Estimated N Residue Kg/ha High N Grass 150 High N veg 150 bare land 130 Med N veg 125 Potatoes 125 Oilseed rape 120 Beans 120 Peas 120 Grazed fodder 100 low N grass 100

uncropped land with green cover 90

Wheat 80 feed barley 80 malting barley 70 Triticale 60 Oats 60 Forage cut 60 low N vegetables 60 sugar beet 50

Table 10. Typical retentiveness of RB209 Soil groups in low, medium and high rainfall conditions.

Rainfall

Soil type low moderate wet

Deep clays 90% 80% 60%

Deep silty soils 100% 90% 80%

medium soils 80% 70% 50%

Sands 40% 20% 10%

Sandy loams 60% 40% 20%

Shallow soils not over sandstone 70% 50% 30%

shallow soils over sandstone 65% 45% 25%

Multiplying the N residue by retentiveness gives an estimate of the SNS available in spring. An estimate then also is needed for the likely mineralisation of the soil, and also N made available by deposition. The N Management Guide uses an adjustment of 20kg/ha to account for the difference between typical measured spring SNS and that at harvest. Additional mineralisation may be expected on soils with high SOM% or where organic resources (farmyard manure, composts, biosolids etc) have been regularly used in the recent past. There aren’t currently reliable & robust methods to estimate the additional N available from mineralisation from knowledge of SOM%, but an indicative relationship of 10kg/ha per 1% increase in SOM above 4% provides a sensible basis for judgement (Kindred et al., 2012). Soil measures using anaerobic incubation can be used to give indicative estimates of Additionally Available N. However, over-estimating N mineralisation can be costly. Mineralisation of N can also be inferred from past experience of fields, for example meadow land or fields which are prone to lodging are likely to have greater mineralisation. Given the

relationships seen here between yield potential and SNS, it may be that higher yielding fields could be taken to infer greater mineralisation potential.

In the absence of a robust predictive methodology for mineralisation it is most appropriate to allow a manual adjustment for expected mineralisation, with expected values between 10 and 50 kg/ha for most soils. Mineralisation from organic and peat soils can be much higher.

The residue N multiplied by retentiveness plus the mineralisation estimate gives the baseline SNS. The canopy sensing methodology comparing measured NDVI with expected N-unlimited NDVI can be used to check the ‘baseline’ SNS before any fertiliser N has been applied, by estimating the mean, minimum and maximum predicted SNS in the field. This can be a tool to compare to the estimated baseline SNS and adjustments made if the crop seems to be growing faster or slower than expected.

Spatial differences in SNS can be estimated using NDVI differences from the mean NDVI for the field to estimate the difference in SNS using the equation with thermal time developed in Chapter

5. This allows a rational basis for generating variable SNS predictions across the field but ensuring that the field average is based upon sound estimates and that extreme high or low estimates are constrained to a sensible range.

This approach also allows the estimation of variation in SNS even after N has been applied, so long as early N applications are uniform. This is important as the prediction of SNS from canopy sensing increases with time, so estimates of SNS variability can still be made up to late March before the main variable rate applications.