S. M. Moges, W.R. Raun, K. Girma, K. W. Freeman, H. Zhang, D. B. Arnall,

B. Tubana, R. Teal, S. L. Holtz, O. Walsh and B. Chung

 

Department of Plant and Soil Sciences,

Oklahoma State University, Stillwater, Oklahoma, USA

 

ABSTRACT

 

Sensor based nitrogen (N) management technology has helped to improve fertilizer recommendations for various crops. The objective of this study was to estimate the yield potential of grain sorghum (Sorghum bicolor L. Moench) using a hand held optical sensor. This experiment was conducted with four levels of N (50,100,150,200 kg ha^-1) and three application timing (Preplant, topdress and split) arranged in a randomized complete block design in three replications at three locations, in Oklahoma in 2004 and 2005. Sensor readings were taken using red (650 ± 10 nm) and green (550 + 12.5 nm) sensors at growth stages 2, 3, 5, 6 and 7. Results from statistical analysis have shown that 68 and 71% of the variation in sorghum grain yield was explained by red and green NDVI, respectively. Similarly, grain N content was correlated to both green (r²= 0.52) and red (r²=0.49) NDVI readings at growth stage 3. In-season estimated yield (INSEY) was also found correlated with final grain yield. The results of this experiment suggest that INSEY can be used as a tool to predict mid-season sorghum grain yield potential.  

 

Introduction

According to work by Johnston (2000), N fertilizer has significantly increased yield in the past few decades as compared to any other agricultural input. Smith et al (1990) reported that corn and sorghum yield would have dropped by 41 and 19%, respectively, without N fertilizer application. Due to economic and environmental reasons (Feinerman et al., 1990), agricultural inputs have to be managed efficiently specially at periods of high production. Currently, nitrogen use efficiency (NUE) of grain production is about 33% and about 45% in forage production (Raun and Johnson 1999).      

A major factor limiting NUE in traditional N management schemes is routine application of large doses of N early in the season, before the crop can effectively utilize it.  This stored N fertilizer is at considerable risk to environmental losses as noted in a review by Raun and Johnson (1999). They pointed out that previous research has shown NUE could be greatly improved by moving away from early season application and towards a greater emphasis on mid-season applications of N fertilizer in amounts that better coincide with crop needs.

Most conventional methods of N fertilizer recommendations were developed on a state or regional scale, so it is questionable whether these methods can reasonably be used for variable-rate N management that attempts to account for within-field spatial and temporal variability (Hergert et al., 1997). Several research studies have found large differences in crop yield and crop N response within individual fields (Kitchen et al., 1995; Vetch et al., 1995), confirming the need for reliable methods to generate site-specific N recommendations (Hergert et al., 1997). Farmers often use uniform rates for N fertilization based on expected yield (yield goal) that could be inconsistent from field-to-field and year-to-year. In most instances expected yield can be higher or lower than the actual yield depending on factors that are difficult to predict prior to fertilization.  

According to Pierce and Nowak (1999) there are three basic management approaches currently being tested for variable-rate N applications. The first involves determining plant-available N levels from field grid sampling and interpreting N rates based on current recommendations (i.e., N balance equation). The second approach bases N rates on observed crop N responses using replicated strips with varying N rates across the landscape. The third approach involves determining crop N status by monitoring (i.e., light reflectance or chlorophyll content). Currently, the third approach is the most efficient technology used in site-specific nutrient management programs as it minimizes time, labor and cost of fertilizer application.

Numerous researchers (Wade et al., 1994; Ramsey et al., 1995; Roderick et al., 1996) have utilized normalized difference vegetative index (NDVI), derived from a very high resolution radiometer collected from satellite platforms, to assess the health and condition of crops and natural vegetation over large geographical regions. Alternatively, Gitelson et al. (1996) proposed the use of the green normalized difference vegetation index (GNDVI) (where the green band is substituted for the red band in the NDVI equation), which may prove to be more useful for assessing canopy variation in green crop biomass. Shanahan et al. (2001) showed that GNDVI values derived from images acquired during mid-grain filling were highly correlated with grain yield.

Estimating in-season yield potential is a key factor for success towards responsible management of N from environmental and economic perspective. As has been demonstrated by Raun et al. (2001), estimated yield (EY) was an excellent predictor of grain yield under diverse environmental conditions. This index was later modified as in-season estimate of yield (INSEY) and computed as the ratio of NDVI reading to number of growing degree days from planting to sensing  greater than zero (GDD>0) (Raun et al., 2002). This work has demonstrated 15% increase in NUE in winter wheat.  

Most grain sorghum producers usually apply N fertilizer pre-plant without considering the prevailing soil-plant nutrient status within the growing season. Therefore the objective of this experiment is to estimate sorghum grain yield potential using INSEY which subsequently be used for appropriate fertilizer prescription that accounts for N status of the crop up to the sensing date.

 

Materials and methods

In the summers of 2004 and 2005, a total of five experiments were conducted in Oklahoma at three locations: Lake Carl Blackwell (Port silt loam-fine-silty, mixed, thermic Cumulic Haplustolls), Hennessey (Shellabarger sandy loam fine-loamy, mixed, thermic Udic Argiustioll) and Efaw (Easpur loam fine-loamy, mixed, superactive, thermic Fluventic Haplustoll). Initial soil analyses results are presented in Table 1. Four N fertilizer rates (50, 100, 150, and 200 kg ha ^-1) at three application timings (100% preplant, 100% topdress and 50/50 split applications) were arranged in a randomized complete block design with three replications on plot sizes of 6 x 3 m².

In 2004, sorghum (DK-44 hybrid) was planted on May 6 at all locations at 75 cm row spacing and at a population of 111,150 plants ha^-1. A blanket rate of 24.4 kg P ha^-1 was applied pre-plant and incorporated into the soil. Pre-plant N rates were applied before planting as urea (46% N). Top dress nitrogen was applied as UAN (28% N) at sorghum growth stage 3 (growing point differentiation) between June 14 and 16. In 2005, DK-44 hybrid was planted on May 17 at Efaw and Lake Carl Blackwell and on May 18 at Hennessey and the same management practices were carried as in 2004. The two middle rows were used for physical and sensor measurements and harvested for final grain yield.

A GreenSeeker® Hand Held Optical Sensor (NTech Industries, Inc.) was used to collect normalized difference vegetative index (NDVI) measurements.  This device uses a patented technique to measure crop reflectance and to calculate NDVI.  The unit senses a 0.6 x 0.01 m area when held at a distance of approximately 0.6 to 1.0 m from the illuminated surface.  The sensed dimensions remain approximately constant over the height range of the sensor.  The sensor unit has self-contained illumination in the red (650 ± 10 nm), green (550 + 12.5) and NIR (770 ± 15 nm) bands.  The device measures the fraction of the emitted light in the sensed area that is returned to the sensor (reflectance). It samples at a rate (approximately 1000 measurements second^-1) and averages measurements between outputs.  It displays NDVI at a rate of 10 readings second^-1. The two middle sorghum rows were sensed by passing the sensor over each row with the sensor held at a height of approximately 0.7 m above the crop canopy and oriented so that the 0.6 m sensed width was perpendicular to the row (nadir) and centered over the row. Sensor readings in each year were taken at growth stage 2 (collar of 5^th leaf is visible which occurs approximately 20 days after emergence), 3 (growing point differentiation which occur 35 days after emergence), 5 (boot stage), 6 (half bloom), and 7 (soft dough).

Final plot grain yield was obtained from two middle rows which were harvested using a Massey Fergusson 8XP plot combine equipped with automatic scale and moisture meter. Total N in the grain was determined using a Carlo Erba  (Milan, Italy) NA-1500 dry combustion analyzer (Schepers et al., 1989) after grain samples were dried (70 ^oC for three days) and ground to pass a 0.125 mm (120-mesh) sieve.

In-season estimated yield (INSEY) was calculated as a ratio of average plot NDVI and the number days from planting to sensing. Relationship among NDVI, INSEY and grain yield were determined using Proc Reg procedure in SAS (SAS, 1999).

 

 

 

 

 

Results and Discussion

Crop Year 2004

Results of simple linear regression analysis showed that there was a significant relationship between NDVI and final sorghum grain yield from sensor readings collected at growth stage 3.  When evaluating this relationship over stages of growth, the best correlation between grain yield and red and green NDVI was obtained at growth stage 3 with r² (coefficient of simple determination) of 0.68 and 0.71, respectively (Figures 1 and 2). Similarly, simple regression analysis of the combined location data revealed that INSEY was significantly correlated with both red (r²=0.68) and green (r²=0.71) NDVI (Figures 3 and 4).  It should be noted that the use of INSEY did not significantly improve this relationship over that of NDVI alone for either red or green.  At the same growth stage, total N in the grain was also significantly correlated with red (r²= 0.49) and green (r²= 0.52) NDVI (Figures 5 and 6).

Crop Year 2005

            Results over sites (Efaw and Hennessey) from simple regression analysis of grain yield on red NDVI showed only limited correlation for growth stages 2 and 3 (Figures 7 and 8).  Similar results were noted for green NDVI at growth stage 3 (data not presented). Relationship between grain yield and NDVI was weak at growth stages beyond growth point differentiation. In-season estimated yield where NDVI values were divided by the number of days from planting to sensing did not improve this relationship over sites.

 

Combined years

In both 2004 and 2005 crop years, it was observed that the relationship of NDVI and grain yield was better at growth stage 3. This stage is a period of rapid growth and nutrient uptake by the sorghum plant (Vanderlip, 1993). The relationship with final grain yield was improved at growth stage 3 when compared to other stages evaluated.  This indicates the importance of collecting early season sensor measurements for projecting grain yield subsequently enable adjustment of N needs.

            Combined data over all locations and years appeared to show a trend somewhat similar to what was found from data on individual location on grain yield and NDVI relationships at growth stage 3. The scatter plot of grain yield on both green and red NDVI illustrated a detectible trend for most sites, excluding the 2005 Lake Carl Blackwell site (Figures 9 and 10). Since this site was irrigated throughout the growing period, higher grain yields were obtained compared to the other locations and the response was also noticeably different. However, it should be noted that when using INSEY, the combined data were normalized and the outer boundary for detecting yield potential was quite clear using the green NDVI sensor (Figure 12).  Scatter below the outer boundary is expected since post sensing conditions can lead to the underestimation of yield potential (drought stress, disease, insect and bird damage, etc.).  But, what is important to consider is that both rainfed and irrigated sites could be combined on one graph when using INSEY (green NDVI sensor), further suggesting that early season detection of growth rate (biomass produced per day, estimated using NDVI divided by the number of days from planting to sensing) is in fact related to final grain yield. 

            This same trend was noted when using the red NDVI at growth stage 3 over sites and years versus sorghum grain yield (Figure 9).  However, for this data, INSEY failed to normalize all sites, as was noted for INSEY when using green NDVI (Figure 11).  Excluding the Lake Carl Blackwell site, red NDVI and INSEY did provide reasonable detection of sorghum grain yield potential (outer left hand boundary of the data).  In general the combined location and year data showed that the INSEY and grain yield relationship could be explained by red and green NDVI with green having slightly better performance.

 

CONCLUSIONS

The results obtained from this experiment suggest that yield potential prediction in sorghum using spectral measurements should be carried out at a stage of critical biomass production and nutrient demand. This was shown by the relationship of INSEY and final grain yield at sorghum growth stage 3. Red and the green NDVI sensor data collected at growth stage 3 were highly correlated with final sorghum grain yield.  However, the use of INSEY as has been employed in wheat and corn trials was not as effective in normalizing sites whereby one yield prediction equation could be established. 

 

 

 

Reference

Feinerman, E., E.K. Choi, and S.R. Johnson. 1990. Uncertainty and split nitrogen

application in crop production. Amer. J. Agr. Econ. 72:975-984.

 

Johnston, A. E. 2000. Efficient use of nutrients in agricultural production systems. 

Commun Soil Sci Plant Anal 31:1599-1620.

Gitelson, A.A., Y.J. Kaufman, and M.N. Merzlyak. 1996. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sens. Environ. 58:289–298.

 

Hergert, G.W., W.L. Pan, D.R. Huggins, J.H. Grove, and T.R. Peck.      1997. Adequacies of current fertilizer recommendations for site-specific management. p. 283–300. In F.J. Pierce and E.J. Sadler (ed.) The state of         site-specific management for agriculture. ASA, CSSA, and SSSA,      Madison, WI.

Kitchen, N.R., D.F. Hughes, K.A. Sudduth, and S.J. Birrell. 1995. Comparison of variable rate to single rate nitrogen fertilizer application: Corn production and residual soil NO₃–N. p. 427–439. In P.C. Robert et al. (ed.) Site-specific management for agricultural systems. ASA, CSSA, and SSSA, Madison, WI.

 

Pierce, F.J., and P. Nowak. 1999. Aspects of precision agriculture. Adv. Agron.
67:1–85.

 

Ramsey, R.D., A. Falconer, and J.R. Jensen. 1995. The relationship between
NOAA-AVHRR NDVI and ecoregions in Utah. Remote Sens. Environ. 53:188–198.

 

Raun, W. R., and G. V. Johnson. 1999. Improving nitrogen use efficiency for 

cereal production. Agron J 91:357-363.

Raun, W. R., G. V. Johnson, M. L. Stone, J. B. Solie, E. V. Lukina, and W. E.  

Thomason. 2001. In-season prediction of potential grain yield in winter wheat 

using canopy reflectance. Agron J 93:131-178.

Raun, W. R., J. B. Solie, G. V. Johnson, M. L. Stone, R. W. Mullen, K. W. 

Freeman, W. E. Thomason, and E. V. Lukina. 2002. Improving nitrogen use 

efficiency in cereal grain production with optical sensing and variable rate 

application. Agron J 94:815-820.

 

Roderick, M., R. Smith, and G. Lodwick. 1996. Calibrating long-term AVHRR-
derived NDVI imagery. Remote Sens. Environ. 58:1–12.

SAS Institute Inc. 1998. SAS/STAT User’s guide. 6.03 ed. SAS Institute  Inc. Cary, NC.

 

Schepers, J. S, D. D. Francis, and M. T. Thompson. 1989. Simultaneous determination of total C, total N, and 15N on soil and plant material. Commun. Soil Sci. Plant Anal. 20:949-959.

Shanahan, J.F., J.S. Schepers, D.D. Francis, G.E. Varvel, W.W. Wilhelm, J.M. Tringe, M.R. Schlemmer and D.J. Major. 2001. Use of remote-sensing imagery to estimate corn grain yield. Agron. J. 93:583-589.        
                       

Smith, E.G., R.D. Knutson, C.R. Taylor, J.B. Penson. 1990. Impact of chemical use reduction on crop yields and costs. Texas A&M Univ., Dep. of Agric. Economics, Agric. and Food Policy Center, College Station.

 

Vanderlip, R.L. 1993. How a sorghum plant develops. Cooperative extension service Contribution No. 1203, Kansas Agricultural Experiment Station, Manhattan, Kansas.

 

Vetch, J.A., G.L. Malzer, P.C. Robert, and D.R. Huggins. 1995. Nitrogen specific management by soil condition: Managing fertilizer nitrogen in corn. In P.C. Robert et al. (ed.) Site-specific management for agricultural systems. ASA, CSSA, and SSSA, Madison, WI.

Wade, G., R. Mueller, P. Cook, and P. Doraiswamy. 1994. AVHRR map products
for crop condition assessment: A geographic information systems approach. Photogramm. Eng. Rem. Sens. 60:1145–1150.