Use of Reflectance Sensors to Optimise Nutrient Management.

Transcription

1 Use of Reflectance Sensors to Optimise Nutrient Management. Ian Yule and Reddy Pullanagari. NZ Centre for Precision Agriculture, Massey University, Palmerston North. Abstract. There is considerable interest in using sensors which measure the light reflectance from crops in order to work out their fertiliser requirements and therefore optimise fertiliser use. These sensors operate in the (VIS) visible spectrum and the (NIR) near infrared. The reflectance properties give an indication of crop biomass which can be calibrated against tiller number or (GAI) green area index, in the case of cereals or simply biomass in maize. This paper explains some of the principles behind these sensors and the types of vegetative indices used to describe the crop in order that fertiliser optimisation can be achieved. Introduction. Present best practise does not take either spatial or temporal variation into account when deciding on the level of nutrient to apply to a crop, but it is difficult to see how this could be achieved without the benefit of additional information for the crop grower. The sensors described in this paper are a vehicle to provide such information in allowing spatially referenced measurement of the crop canopy to be made. In order to improve Nitrogen Use Efficiency (NUE), we must identify the causes of low NUE with standard practice. This was partially identified by Solari et al (2008) but can be extended to include: 1) Poor synchrony between soil N management practices and crop demand. 2) Uniform N application on spatially variable landscape producing spatially variable N requirements. 3) Failure to apply fertiliser accurately so that target applications are not met leading to sub-optimal use of fertiliser. 4) Failure to account for temporal variability and the influence of differences in between season and within season weather patterns on soil N availability and crop demands. At the present time there are a number of crop growth models available to farmers but these do not include a spatial element and so spatial differences are not taken into account and the farmer is back to farming by averages. At the same time there is mounting pressure for farmers to improve their environmental performance and reduce leaching from crop growing situations. The focus of this paper is the strategic management of nitrogen fertiliser, in particular the application of remote sensing techniques used to assess the crop s growth at the time of fertiliser application. This can be completed in real-time when a ground based remote (proximal) sensor is used. This can be used to re-calculate a whole field nitrogen application or when linked to a spreader capable of using variable rate application technology (VRAT), the application of nitrogen can be altered on the go. This allows the nitrogen application to be matched to that required at any point in the field in order

2 to achieve the full yield potential of any area of the paddock. If VRAT is not to be employed then the information can be gathered before-hand using a hand held device to take samples over the field. In order for optimised nitrogen application to operate correctly the following elements must be in place: A reliable and real time method of determining the crop growth. A plan of action to maximise the utilisation of fertiliser based on the above information and crop environment, i.e. a growth model for the crop being monitored. A reliable method to accurately deliver the correct amount of fertiliser to any point in the paddock. The system must be economically viable. A number of economic studies have been performed to assess the financial performance of precision N management. Two main objectives have been tested, firstly to increase yield and second to improve nitrogen use efficiency. General consensus is that yield can be improved by 3 to 8% and nitrogen input can be reduced by up to 30% with no loss of yield. Trial results vary considerably and are clearly site and time specific. A study by Hyrien (2007) showed that a gain in margin was achieved through increased yield rather than savings in fertiliser. Hyrien quoted trials from around the UK which varied from an improved gross margin of 18 Euro to a maximum of 98 Euro. Most of these trials have been completed when commodity prices were much lower than their current levels and so it would appear that there is significant financial gain to be made from the successful adoption of this technology. In crops such as maize the total spend on fertiliser is around $1,000 per ha., Corsons (2008), of that 300kgha -1 of Urea is used, current cost of Urea is $1,100 per tonne. A 15% reduction in nitrogen fertiliser would give a $50 per ha. saving. A 5% yield increase could bring about a further improvement in margin of $200 per ha. In winter wheat the urea use is similar and one could reasonably expect significant savings to be made. For a mixed farmer typical of the Canterbury region there may be significant opportunities to reduce the use of expensive growth regulators on crops such as ryegrass, monitoring crop development through the use of reflectance sensors to estimate biomass could also resolve issues around the nitrogen fertilisation of crops such as ryegrass for seed production. A three year winter wheat trial is detailed on Greenseeker website: quotes improvement in performance where, yields were generally low at around 3 tonnes per ha but a 30% saving in fertiliser costs was claimed along with an 8.3 % increase in yield. It is not clear at this stage if similar improvement could be achieved in the high yielding but high input farming of the Canterbury Plains, it may simply be that in the American trial quoted they were using fertiliser inputs that were significantly than their low yield levels warranted. In maize Khoshla (2007) reported a US$17 to US$52 per ac., (NZ$73 NZ$223 per ha.) improvement in net return from zonal management as opposed to land that is uniformly managed. Godwin (2003) reported a number of studies which again had variable results, although they were all positive and

3 demonstrated a range of potential benefits. Earl et al (1996) estimated a possible benefit of ha -1 ( NZ$88.63 per ha.) when wheat price was 125 t -1. Barnhisel et al (1996) reported increased returns of 57 ha -1 (NZ$150 per ha.) when maize seed rates were varied according to soil depth. Other projects were reported with a similar rate of return. More recently Havrankova et al (2008) reported only very minor improvement in economic performance as a result of using these techniques. Remote Sensing Systems There are a range of instruments available to non-destructively measure crop bio-mass. These use the reflectance properties of the crop to estimate this. These instruments vary from those which utilise light at the visible and near infrared to those capable of measuring reflectance at many wavelengths from the visible ( nm) through near infrared NIR( nm) and onto short wave infrared radiation SWIR( nm). Some units are described as passive, these utilise the ambient light, while active units have their own light source. There are a number of advantages to these, such as the reduced influence on results of changing ambient light conditions and extended work hours. This increased usability has moved this type of instrument from the realms of research tool only, to useable sensing systems that can be utilised in a commercial setting and these are now being employed throughout the world. Figure 2 Reflectance Spectra of Crop from Reusch(2008) describing the Yara sensor A full description of available systems is given in MAF SFF project no: L08/042 which was jointly sponsored by the Foundation for Arable Research. The basic principles of operation are similar

4 between instruments. Although the results you get from one instrument compared to another will be similar and show similar trends they will not be identical. There are a number of factors that are different in terms of the way the instruments operate. The simpler instruments use either two or three bands to measure reflectance, one or two bands in the visible range and one in the near infrared. Crops with a greater amount of chlorophyll will tend to absorb more light in the visible range, (hence their darker appearance) while in the near infrared they tend to reflect more. Figure 1 illustrates the use of ratios that are then used to describe and compare the crop. These sensors are usually linked to GPS and the position of the reading can be mapped. In the case of VRAT they are linked to the controller on the fertiliser spreader in order to adjust the application rate of the fertiliser as it is being spread. Numerous mathematical vegetative indices have been developed which reduce complex spectra to a single value; these indices, which are based on knowledge of the reflectance properties of the biochemical components in leaves, such as chlorophyll (Curran et al., 1990; Gitelson & Merzlyak, 1994, 1996; Gitelson et al., 1996; Blackburn, 1998; Datt, 1998, 1999; Adams et al., 1999; Gamon & Surfus, 1999), nitrogen(sanches et. al.,2008), protein, and biophysical properties like leaf area, leaf area index, and biomass (Haboudane et al., 2004). In addition, the range of spectral reflectance associated with soil type and moisture. Curran et al. (1990), Adams et al. (1999), Datt (1999), and Gamon & Surfus (1999) give more complete reviews of some of the practical and theoretical considerations of reflectance spectroscopy. Tremendous efforts have been devoted to improve indices and render them insensitive to illumination, viewing geometry (In the case of remote sensing) (Jackson & Pinter, 1986 and Gausman, 1985), canopy architecture, leaf water content and composition, atmospheric conditions and soil background. Even though vegetation indices still have intrinsic limitations, they are not a single measure of a specific variable like LAI, pigment content, biomass, and plant geometry. Many researchers have been developing various indices for desired features, which are listed in table1. Table 1 Indices used in remote sensing of crops. Atmosphereic Indices: Atmospheric Resistant Vegetation Index ARVI (NIR-(Red-Blue))/(NIR+(Red-Blue)) Kaufman & Tanre (1996) Soil and Atmospherically Resistant Vegetative Indice SARVI (1+L) (R 800-R 670)/ (R 800+R 670+L) kaufman & Tanre (1992) General and Structural indices: Normalized difference vegetation index NDVI (NIR VIS)/ (NIR + VIS) Rouse et al. (1974) Simple Ratio Index SR NIR/Red Jordan (1974) Wide Dynamic Range Vegetation Index WDRI [(α+1) NDVI+ (α-1)]/ [(α-1) NDVI+ (α+1)] Gitelson (2004) Perpendicular Vegetation Index PVI 1/ a 2 +1 (NIR-a Red-b) Richardson & Weigand (1977) a= slope of the soil line, b= soil line intercept Difference Vegetative Index DVI NIR Red Jordan (1969) Renormalized Difference Vegetation Index RDVI (R 800-R 670)/ ( R 800+R 670) Rougean and Breon (1995) Modified Simple Ratio MSR (R 800/R 670-1)/ (R 800/R 670+1) Chen (1996) Zarco-Tejada & Miller ZTM R 750/R 710 Zarco-Tejada et al. (2001)

5 Soil-Line Indices: The following indices developed to account the changes in soil optical properties such as soil background and to minimize the background influence. Soil-Adjusted vegetative indice SAVI (1+L) (R₈₀₀-R₆₇₀) (R₈₀₀-R₆₇₀ +L) Huete (1988) L = A correction factor, ranges from 0 for very high vegetation cover to 1 for very low vegetation cover. Transformed Soil Adjusted Vegetation Index TSAVI a(nir-a red-blue)/[anir+red-ab+x(1+a 2 )] Baret and Guyot (1991) a= Slope of the soil line; b= Soil line intercept; X= Adjustment factor to minimize soil noise Adjusted Transformed Soil Adjusted Vegetative Index ATSAVI Baret and Guyot (1991) Soil Adjusted vegetative Index SAVI 2 NIR/Red+b a Major, baret & Guyot (1990) Soil and Atmospherically Resistant Vegetative Indice SARVI (1+L) (R800-R670)/ (R800+R670+L) kaufman & Tanre (1992) Modified Soil Adjusted Vegetative indice MSAVI ½[2R (2R 800+1) 2-8(R 800-R 670) Qi et al. (1994) Modified second soil-adjusted vegetation index MSAVI2 ½[2 NIR+1- (2 NIR+1) 2-8 (NIR-R) Qi et al. (1994) Optimized Soil-Adjusted Vegetation index OSAVI (1+0.16) (R 800-R 670)/ (R 800+R ) Rondeaux et al. (1996) Chlorophyll and Leaf Area Index Indices: Greenness Index GI R 554/R 677 Normalized Pigment Chlorophyll Index NPCI (R 680-R 430)/ (R 680-R 430) Penuelas et al. (1994) Chlorophyll Absorbtion Ratio Index CARI R 700/R 670 Kim et al. (1994) Modified Chlorophyll Absorption Reflectance Index MCARI [(R 700-R 670)-0.2 (R 700-R 550)] R 700/R 670 Daughtry et al. (2000) Transformed CAR TCARI 3[(R 700-R 670)-0.2 (R 700-R 550) (R 700/R 670)] Haboudane et al. (2002) Modified Chlorophyll Absorption in Reflectance Index MCARI [2.5(R 800-R 670)-1.3 (R 800-R 550)] Haboudane et al. (2004) Modified Chlorophyll Absorption in Reflectance Index MCARI 2 1.5[2.5(R 800-R 670)-1.3 (R 800-R 550)] Haboudane et al. (2004) Triangular Vegetation Index TVI 0.5 [120 (R 750-R 550)-200 (R 670-R 550)] Broge and Leblanc (2000) Modified Triangular Vegetation Index MTVI 1 1.2[1.2(R 800-R 500)-2.5 (R 670-R 550)] Haboudane et al. (2004) Modified second triangular vegetation index MTVI 2 1.5[1.2(NIR-Green)-2.5(Red-Green)]/ (2 NIR+1)2-(6 NIR-5 R 670)-0.5 Haboudane et al., (2004) Water Indices: Normalized Difference Water Index NDWI (R860-R1240)/ (R 860+R 1240) Gao (1996) Simple ratio water Index SRWI R 858/R 1240 Zarco-Tejada et al. (2003) Plant water Index PWI R 970/R 900 Peñuelas et al. (1997) Clearly this is a major area of research for the scientific community and one which is growing and increasing in terms of the practical application of the results as the research matures. The indices which are most widely used are the Simple Ratio Index SRI and the NDVI or normalised difference vegetation index. Figure 2 illustrates the broad principle of how the decision regarding the amount of fertiliser to be applied is made once a crop has been sensed and the NDVI established. Research such as Schepers et al (2005) developed the idea of a sufficiency index, if a crop is at a point where the observed had

6 an NDVI within 5% of the N rich strip then it is deemed to have sufficient nitrogen and no further would added. The N-rich strip is discussed more fully in Maff SFF Report: L08/042, but it is basically a proportion of the field that has had the maximum desirable N application. Max Fert App Lower Limit of NDVI Nitrogen Application Average App Min Fert App Bare Soil Average NDVI Max Limit For NDVI Normalised Difference Vegetation Index, (NDVI) Figure 2 Nitrogen Application applied according to NDVI, adapted from Reusch(2008) In maize where a reference strip is used the decision around fertiliser application is taken around comparing the inverse of the simple ratio for both the target and the reference strip. A Nitrogen recommendation could take the following form for maize as described by Sudduth et al (2005). N ρvis ρ recommendation = ρ ρ NIR TARGET VIS TARGET REF NIR REF Where TARGET refers to the crop in the paddock and REF refers to the high fertiliser input strip or patch in the paddock. There are a number of these algorithms in use at the present time and they vary with yield expectation, geographical position and soil types for example. These equations give the slope to the graph in Figure 2. If we use Figures 1 and 2 to illustrate and consider maize requirements for different parts of a paddock, consider the 200 kgha -1 application is the reference strip and the target readings are produced from the crop that has had 60kgha -1 of applied N. Using the equation above we can calculate the fertiliser requirement as being: Assume we are measuring reflectance at 550nm and 800nm. The reading for target is 0.08 in the 550 nm waveband and 0.34 at the 800nm. The reference area or N rich strip has the value 0.06 at 550nm and 0.48 at the 800nm band.

7 By substituting the values into the equation above it is possible to calculate that the N application would be kgha -1. If we now consider a different target (the one equivalent to the 120kgha -1 application of N, then reflectance at 550nm is 0.07 and 0.41 at 800nm. The fertiliser requirement using the same equation would be 141.5kgha -1. These spectral readings are used purely to illustrate the principle. Conclusions: There is clear evidence from around the world that these sensors can assist in achieving an improved NUE. The fact that the results are so variable across the world supports the view that research needs to be completed in New Zealand to assess their potential here. A lot of the work on cereals for example, has been completed on non-irrigated crops. Work in maize has taken place where the yield potential was lower than many areas of New Zealand. A more comprehensive review of these sensors is presented by the Foundation for Arable Research in the report of MAF SFF project no: L08/042. References: Blackburn, G.A. and Pitman, J.I., Biophysical controls on the directional spectral reflectance properties of bracken (Pteridium aquilinum) canopies: results of a field experiment. International Journal of Remote Sensing, 20(11): Blackburn, G.A., Quantifying chlorophylls and caroteniods at leaf and canopy scales: An evaluation of some hyperspectral approaches. Remote Sensing of Environment 66 (3), Baret and Guyot, 1991 F. Baret and G. Guyot, Potentials and limits of vegetation indices for LAI and APAR assessment, Remote Sens. Environ. 35 (1991), pp Broge, B.H., and Leblanc, E. (2000). Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll ensity. Remote Sens. Environ. 81: Chen, J. (1996). Evaluation of vegetation indices and modified simple ratio for boreal applications. Can. J. Remote Sens., 22, Corsons Maize Silage Guide, 17th Oct Visited Curran, P. J., Windham, W. R., and Gholz, H. L. (1995). Exploring the relationship between reflectance red edge and chlorophyll content in slash pine leaves. Tree Physiology 15, pp Curran, P.J., Dungan. And Gholtz, H.L Exploring the relationship between reflectance red edge and chlorophyll content of slash pine. Tree physisology. 7: Daughtry, C.S.T., C.L. Walthall, M.S. Kim, E. Brown de Colstoun, and J.E. McMurtrey III Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sens. Environ.74: Earl, R, Wheeler, P. N., Blackmore, B.S., Godwin, R.J., 1996 Precision Farming- the management of variability. Landwards, 51(4),

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