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AGROCLIMATOLOGY

In-Season Prediction of Potential Grain Yield in Winter Wheat Using Canopy Reflectance

^a Dep. of Plant and Soil Sci., Oklahoma State Univ., Stillwater, OK 74078
^b Dep. of Biosystems and Agric. Eng., Oklahoma State Univ., Stillwater, OK 74078
^c USDA-ARS, Lincoln, NE 68583

Corresponding author (wrr{at}mail.pss.okstate.edu)

	ABSTRACT

TOP NOTES ABSTRACT INTRODUCTION Yield Goals In-Season Soil Testing Indirect Measures for Grain... Definitions of Measured,... Prediction of Biomass and... Materials and methods Results Discussion Conclusions REFERENCES

 
Nitrogen fertilization rates in cereal production systems are generally determined by subtracting soil test N from a specified N requirement based on the grain yield goal, which represents the best achievable grain yield in the last 4 to 5 yr. If grain yield could be predicted in season, topdress N rates could be adjusted based on projected N removal. Our study was conducted to determine if the potential grain yield of winter wheat (Triticum aestivum L.) could be predicted using in-season spectral measurements collected between January and March. The normalized difference vegetation index (NDVI) was determined from reflectance measurements under daytime lighting in the red and near-infrared (NIR) regions of the spectra. In-season estimated yield (EY) was computed using the sum of two postdormancy NDVI measurements (Jan. and Mar.) divided by the cumulative growing degree days (GDD) from the first to second reading. A significant relationship between grain yield and EY was observed when combining all nine locations across a 2-yr period. Our estimates of potential grain yield (made in early Mar.) differed from measured grain yield (mid-July) at three sites where yield-altering factors (e.g., late summer rains delayed harvest and increased grain yield loss due to lodging and shattering) were encountered after the final sensing. Evaluating data from six of the nine locations across a 2-yr period, EY values explained 83% of the variability in measured grain yield. Use of EY may assist in refining in-season application of fertilizer N based on predicted potential grain yield.

Abbreviations: EY, in-season estimated yield • GDD, growing degree days • NDVI, normalized difference vegetation index • NIR, near infrared • PPNT, preplant NO₃ test • PSNT, presidedress NO₃ test

	INTRODUCTION

TOP NOTES ABSTRACT INTRODUCTION Yield Goals In-Season Soil Testing Indirect Measures for Grain... Definitions of Measured,... Prediction of Biomass and... Materials and methods Results Discussion Conclusions REFERENCES

 
HISTORICALLY, grain yield goals have been the most reliable method available for estimating preplant fertilizer N rates. Recent advancements in weather forecasting and crop modeling have enabled the development of technologies for predicting potential grain yields, and thus have allowed for in-season nutrient adjustments to reflect early crop development and growing conditions.

	Yield Goals

TOP NOTES ABSTRACT INTRODUCTION Yield Goals In-Season Soil Testing Indirect Measures for Grain... Definitions of Measured,... Prediction of Biomass and... Materials and methods Results Discussion Conclusions REFERENCES

 
Crop grain yield may be expressed simply as a function of all conditions of the growing environment, or growth factors, and any preconceived yield goal or limit set by management. In dryland agriculture, it is usually advantageous to set the grain yield goal above that of average yields to fully take advantage of above-average growing conditions (Johnson, 1991). A yield goal was defined by Dahnke et al. (1988) as the "yield per acre you hope to grow." They further noted that what you hope to grow and what you end up with are two different things. Yield goals can vary all the way from past average yield to potential yield (Dahnke et al., 1988). The authors defined potential grain yield as the highest possible yield obtainable with ideal management, soil, and weather. In our work, what they define as potential grain yield would be maximum grain yield because potential yield is associated with specific soil and weather conditions that can change annually. For most farmers, North Dakota State University recommends that the grain yield goal is the highest yield attained in the last 4 to 5 yr and is usually 30 to 33% higher than the average yield (R.J. Goos, personal communication, 1998).

Rehm and Schmitt (1989) noted that with favorable soil moisture at planting, it would be smart to aim for a 10 to 20% increase over the recent average when selecting a grain yield goal. They also indicated that if soil moisture is limiting, the use of history and past maximums (used to generate avg.) may not be the best method for setting a grain yield goal for the upcoming crop. The use of farm or county averages was not suggested for progressive farmers concerned with high farm profitability (Rehm and Schmitt, 1989).

Black and Bauer (1988) reported that the grain yield goal should be based on how much water is available to the winter wheat crop from stored soil water to a depth of 1.5 m in the spring plus the anticipated amount of growing-season precipitation. An estimation the N fertilization requirements can be achieved by combining the grain yield goal, soil test NO₃–N, and a simple estimate of the N use efficiency. Several states recommend that farmers apply 33 kg N ha^-1 for every 1 Mg of wheat (2 lb N acre^-1 for every bushel of wheat) they hope to produce, minus the amount of NO₃–N in the surface (0–15 cm) soil profile (Johnson et al., 1997). Therefore, when grain yield goals are applied, it explicitly places the risk of predicting the environment (good or bad year) on the producer. University extension (e.g., soil testing), fertilizer dealers, and private consulting organizations have historically used grain yield goals due to the lack of a better alternative.

	In-Season Soil Testing

TOP NOTES ABSTRACT INTRODUCTION Yield Goals In-Season Soil Testing Indirect Measures for Grain... Definitions of Measured,... Prediction of Biomass and... Materials and methods Results Discussion Conclusions REFERENCES

 
Initial work by Magdoff et al. (1984) evaluated the use of an in-season NO₃–N soil test for corn (Zea mays L.) by sampling soils to 30 cm when plants are 15 to 30 cm tall. This test, which was later referred to as the presidedress NO₃ test (PSNT), was useful for predicting N needs in the northeastern portions of the USA. The benefits of PSNT over yield goals to recommend N for corn was shown by Durieux et al. (1995) where less N was applied with no reduction in grain yield. Sims et al. (1995) indicated that the leaf chlorophyll meter could be an alternative to the PSNT for refining in-season fertilizer N requirements in corn. Spellman et al. (1996) showed that the critical PSNT ranged between 13 and 15 mg N kg^-1 for the 0- to 30-cm soil sampling depth for irrigated corn grown in a semiarid environment in Colorado—well below the 21 mg N kg^-1 suggested for humid regions of the USA. Bundy and Andraski (1995) indicated that separating NO₃ test data according to the potential yield of soils (medium and high based on depth of root zone, water holding capacity, and length of growing season) may improve the utility of the preplant NO₃ test (PPNT) and PSNT for making N recommendations for corn when soil test values are in the N responsive region.

Fox et al. (1993) evaluated the PSNT, NIR spectrophotometer reflectance from soil samples taken at planting, and an at-planting soil NO₃ test for use in predicting the grain yield and soil N-supplying capability. These methods did not predict the relative grain yield or the potential to supply N. However, they noted that NIR preplant soil testing did predict whether or not humid-region corn fields would respond to N fertilizer.

	Indirect Measures for Grain Yield Prediction

TOP NOTES ABSTRACT INTRODUCTION Yield Goals In-Season Soil Testing Indirect Measures for Grain... Definitions of Measured,... Prediction of Biomass and... Materials and methods Results Discussion Conclusions REFERENCES

 
Estimating crop yields is an important application of remote sensing (Lillesand and Kiefer, 1994; Moran et al., 1997). The NDVI, calculated with measurements of reflected light from the red and NIR bands, has long been used as an indirect measure of crop yield, including that of wheat (Tucker et al., 1980; Pinter et al., 1981). Aase and Siddoway (1981) confirmed the relationship of NDVI to wheat grain yield but noted that the relationship deteriorated rapidly as wheat ripened. Soil background, view and solar angles, atmospheric conditions, and crop canopy architecture are also important factors affecting NDVI (Huete, 1987; Jackson and Huete, 1991). Pinter et al. (1981) reported that summing NDVI values from late-season (Feekes 10.5, flowering to grain fill) spectral measurements was useful in predicting the grain yield of wheat. Bartholome (1988) reported that accumulated NDVI was a more stable predictor of millet (Panicum miliaceum L.) and sorghum [Sorghum bicolor (L.) Moench] grain yields than a single spectral measurement. Rasmussen (1992) calculated a sampling-interval weighted average NDVI by integrating multitemporal spectral measurements with time, which improved the grain yield estimates of millet from a single spectral measurement. Smith et al. (1995) reported that sensing twice and combining NDVI using a linear model improved correlation with wheat grain yield compared with sensing once. Rasmussen (1998) failed to improve the correlation of the NDVI to grain yield by integrating the product of multitemporal NDVI measurements and photosynthetically active radiation.

	Definitions of Measured, Potential, and Maximum Grain Yields

TOP NOTES ABSTRACT INTRODUCTION Yield Goals In-Season Soil Testing Indirect Measures for Grain... Definitions of Measured,... Prediction of Biomass and... Materials and methods Results Discussion Conclusions REFERENCES

 
Measured grain yield is that which is actually harvested in a given year at a given site (independent of scale). Potential grain yield is that which is predicted for a given year and site, based on the assumption that the level of growth factors that are responsible for the early development of the crop will be maintained (limitations that existed at early stages of growth will continue to similarly influence development to maturity, e.g., N deficiency). Maximum grain yield is that which is achievable when all manageable growth factors (e.g., nutrients, insects, disease, and weeds) are nonlimiting, and the environment is ideal. Depending on the environment, potential grain yield would always be maximum grain yield.

	Prediction of Biomass and Percent Coverage

TOP NOTES ABSTRACT INTRODUCTION Yield Goals In-Season Soil Testing Indirect Measures for Grain... Definitions of Measured,... Prediction of Biomass and... Materials and methods Results Discussion Conclusions REFERENCES

 
Recent work has shown that NDVI measurements in winter wheat between Feekes Physiological Growth Stages 4 and 5 can provide a reliable prediction of both N uptake and biomass (Stone et al., 1996; Solie et al., 1996). The percentage of soil covered by wheat was highly correlated with NDVI at Feekes Growth Stages 4 and 5, and both NDVI and coverage were correlated with vegetative biomass (Lukina et al., 1999). In these trials, plant coverage was generally >50% at Feekes 4 and 60% at Feekes 5. Similar work by Reeves et al. (1993) used direct in-season measurements of total N uptake in winter wheat at Feekes Growth Stage 5 to predict grain yield.

Much of the work associated with making fertilizer recommendations has not considered the potential for using in-season prediction of potential grain yield. Therefore, the objective of this work was to evaluate the use of early season red and NIR spectral reflectance field measurements of wheat tissue combined with GDD to predict potential grain yield.

	Materials and methods

TOP NOTES ABSTRACT INTRODUCTION Yield Goals In-Season Soil Testing Indirect Measures for Grain... Definitions of Measured,... Prediction of Biomass and... Materials and methods Results Discussion Conclusions REFERENCES

 
During the winter months of 1998 and 1999, spectral reflectance readings were taken from nine winter wheat experiments. Each experiment was either an ongoing long-term experiment (no. assigned in the 1960s and 1970s as 222, 301, 502, and 801) or a short-term (1–3 yr) field experiment that included the evaluation of preplant N rates. Each of these locations is further defined in Table 1. The soils at each of these locations follow: Perkins, Teller sandy loam (fine-loamy, mixed, thermic Udic Argiustolls); Tipton, Tipton silt loam (fine-loamy, mixed, thermic Pachic Argiustolls); Stillwater, Kirkland silt loam (fine, mixed, thermic Udertic Paleustolls); Stillwater-Efaw, Norge silt loam (fine-silty, mixed, thermic Udic Paleustolls); Lahoma, Grant silt loam (fine-silty, mixed, thermic Udic Argiustolls); and Haskell, Taloka silt loam (fine, mixed, thermic Mollic Albaqualfs). The row spacing x N rate (S x N) and Efaw anhydrous ammonia (AA) experiments were each 1-yr trials. The N rate experiment at Perkins was initiated in 1996. Experiments 222, 301, and 502 were initiated in 1969, 1993, and 1971, respectively, and all three evaluated the annual rates of applied N at constant levels of P and K (Table 1). Winter wheat was planted at a 78 kg ha^-1 seeding rate using a 0.19-m row spacing, excluding the S N experiment at Perkins (spacing ranged from 0.15–0.30 m).

All of the experiments included in this study are described in Table 1. Varietal differences were not targeted in this work because the findings of Sembiring et al. (2000) showed limited differences in postdormancy NDVI readings for common wheat varieties grown in this region. Reflectance readings from all experiments were collected at two postdormancy dates. The two dates (Time-1 and Time-2, respectively) where readings were collected generally corresponded to Feekes Growth Stage 4 (leaf sheaths beginning to lengthen) and 5 (pseudo-stem formed by sheaths of leaves that are strongly erect) (Large, 1954). Due to differences in planting times and growing conditions, spectral reflectance readings were collected between January and March (Table 1). All of the reflectance readings from wheat were taken from a 4.0-m² area between 1000 and 1600 h under natural lighting.

Reflectance values (the ratio of incident and reflected values) were used in the NDVI calculation to minimize the error associated with cloud cover, shadows, and sun angle. The modified equation used was

where NIR_ref and Red_ref are the magnitude of reflected light and NIR_inc and Red_inc are the magnitude of incident light.

In both years, grain yield was determined using a self-propelled combine from the same 4.0-m² area where spectral reflectance data were collected. We assumed that growth from planting in October to the mid winter months of January and February would provide an excellent indicator of wheat health in each 4.0-m² area, and thus the early season growth-limiting conditions for small areas as well. The sum of NDVI at Time-1 and Time-2 divided by GDD between the two dates (Tmin and Tmax recorded from daily data) was computed and evaluated as an index for the in-season prediction of potential grain yield (in-season estimated yield, or EY). Minimum and maximum air temperatures and rainfall data were collected within 1.7 km of the actual experiment at all locations.

The EY index was one of many indices evaluated that included mathematical combinations of the following: the NDVI at Time-1 and Time-2, GDD and total days from Time-1 to Time-2, GDD and total days from planting to Time-1, and GDD and total days from planting to Time-2. The EY value was expected to reflect a point on the potential growth curve for that season, and thus provide an estimate of potential grain yield based on local growing conditions between planting and the dates of sensing. This index was found to include all sites and had a high combined r² when compared with the many other indices tested.

Measured grain yield was considered to be the best available measure of potential grain yield, especially where limited stress occurred after sensor readings in late February and early March. The use of GDD in the computation of EY allowed us to integrate early season growing conditions and growth rate. This approach is consistent with work by Rickman et al. (1996) showing the relationship between aboveground dry mass and cumulative GDD (Fig. 1) . Dividing the sum of NDVI at Time-1 and Time-2 by GDD results in a unit of predicted biomass (using NDVI) per GDD.

View larger version (18K): [in this window] [in a new window]   Fig. 1 Schematic relationship (Rickman et al., 1996) between aboveground dry mass (estimated using NDVI) obtained at two stages of growth (T1 and T2) and the cumulative growing degree days (GDD). Calculation of in-season estimated yield (EY) is also shown

	Results

TOP NOTES ABSTRACT INTRODUCTION Yield Goals In-Season Soil Testing Indirect Measures for Grain... Definitions of Measured,... Prediction of Biomass and... Materials and methods Results Discussion Conclusions REFERENCES

 
It is important to note that grain yield limiting factors associated with post Feekes 5 environments can cause measured grain yields to differ from predicted potential grain yields. Therefore, it was critical to identify those sites where obvious yield-limiting or yield-enhancing factors were present following the final sensor measurement. In this regard, we recognized that it would be extremely difficult to identify an index that would reliably predict measured grain yield across nine locations where planting date, harvest date, sensor dates, rain, and GDD differed.

Although many indices were evaluated that included NDVI at Times 1 and 2 (e.g., GDD from planting to Time-1 and Time-2, days from Time-1 and Time-2, and days from planting), the EY index proved to account for more of the variability in measured grain yield, especially when sites where postsensing grain yield limiting factors were considered. It was not until GDD was used as a divisor (combined with mid-winter sensor data) that we found models that included the majority of the nine sites studied. Therefore, regression analysis reported in this paper focuses on results from EY that included GDD in its calculation.

Estimated Yield vs. Grain Yield (All Nine Locations)
The relationship between measured grain yield and EY for all nine locations is illustrated in Fig. 2 . Although definite differences were noted between the nine experiments included in this work, quadratic and exponential models for the entire data set resulted in coefficients of determination (r²) > 0.50 and were highly significant (P > 0.0001).

View larger version (27K): [in this window] [in a new window]   Fig. 2 Relationship between in-season estimated grain yield (EY) computed from two postdormancy NDVI readings divided by the cumulative growing degree days (GDD) (from Time-1 to Time-2) and measured grain yield in nine winter wheat experiments, 1998 and 1999 (confidence limits for point estimates about the exponential model)

Grain yields were much lower than predicted by EY for the N x P Perkins experiment in 1999. Although EY values reflected a much higher potential grain yield, yields were characteristically lower at this site. Forage growth was excellent early in the season as was the plant development up to flowering. However, without timely rain, the sandy loam soil at this site dries out quickly, and the lower moisture storage becomes more yield-limiting than the silt loam and clay loam soils at other sites. As a result, measured grain yields were lower than what would have been predicted using EY even though plant stands and growth up to late February were indicative of a higher yielding crop.

Measured grain yields at the Efaw AA experiment in 1999 were higher than what would have been expected using EY. This experiment was located on an alluvial portion of the landscape and received added moisture via runoff from adjoining slopes. Forage growth was abnormally low at this site due to the late (9 Nov.) planting date (Table 1). As a result, the potential grain yields that were estimated using EY were low because wheat plants were small when sensed in February and March. However, forage growth improved significantly later in the season. Late-season wheat growth benefitted from the added moisture received via runoff and a soil profile that provided ample water during grain fill. We believe these factors caused potential grain yields to be underestimated using EY.

Estimated Yield vs. Grain Yield (Six Locations)
When data were removed for the three sites where grain yield was strongly influenced by abnormal postsensing conditions (N x P Perkins, 1999; Efaw AA, 1999; and Experiment 502, 1999), the relationship between measured grain yield and EY improved (Fig. 3) . Including data obtained from these six locations across a 2-yr period, EY values explained 83% of the variability in grain yield (P > 0.0001). Two of the six experiments included in this data set were 450 km apart, and rain from planting to harvest ranged from 645 (S x N Tipton, 1998) to 1016 mm (Experiment 801, 1999). Considering the range of factors that affect the final grain yield, and the influence of environment from Time-2 to maturity, we considered it important to find an index that closely predicted potential grain yield.

View larger version (22K): [in this window] [in a new window]   Fig. 3 Relationship between in-season estimated grain yield (EY) computed from two postdormancy NDVI readings divided by the cumulative growing degree days (GDD) (from Time-1 to Time-2) and measured grain yield in six of nine winter wheat experiments, 1998 and 1999 (confidence limits for point estimates about the exponential model)

	Discussion

TOP NOTES ABSTRACT INTRODUCTION Yield Goals In-Season Soil Testing Indirect Measures for Grain... Definitions of Measured,... Prediction of Biomass and... Materials and methods Results Discussion Conclusions REFERENCES

Data collected in the 1998 and 1999 growing seasons were unique because adequate moisture was present at planting and continued throughout each growing season. Only limited moisture stress was present, and all sites received timely rain near flowering. For this reason, measured grain yield and potential grain yield were expected to be similar for 1998 and 1999, and we believe this contributed strongly to our finding a high correlation of EY with measured grain yield at six locations. We would not expect EY to be highly correlated with measured grain yield in all growing seasons because so many things can happen to the wheat crop from postdormancy to maturity (e.g., frost, disease, and drought). However, our interest was in developing a yield parameter that was seasonal sensitive, intrinsic, and would reflect the potential grain yield likely to be realized in that season more than traditional yield goal estimates. If growth was poor from planting to Time-2, it is unlikely that a high potential grain yield would be realized. Similarly, if growth was excellent from planting to postdormancy, but declined from the first to second reading (e.g., Time-1 to Time-2, drought, and frost damage), potential grain yield would be expected to be lower.

The 10- to 40-d period immediately following dormancy is critical in terms of the resulting grain yield. Obtaining two sensor readings during this period provides a measure of crop development and growing conditions. Unlike growth models that rely on various inputs to predict plant growth, optical sensing uses the plant as the indicator. The first reading establishes a base measurement of crop condition, and the second reading assesses postdormancy change across a short, measured time period. Combined, these two readings and the adjustment for GDD should provide a reasonable indication of the potential grain yield. Typically, information on the early season growing conditions is accessed by the first reading in late February. The period from planting (mid-Oct.) to the end of dormancy (late February) represents more than half of the growing season and provides information on the potential grain yield. Once potential yield is determined, topdress N rates could be adjusted based on projected grain N removal. Sensing beyond Feekes Physiological Stage 5 (Time-2) is not practical for winter wheat grown in the United States because significant stand damage is encountered when topdress equipment is used for N applications following this time.

When spatially precise estimates of potential grain yield are made, these estimates will be determined at the finest resolutions (1 m²) where differences in soil test parameters are found (Solie et al., 1996; Solie et al., 1999). At coarser resolutions (>30 m), the variation in the potential grain yield will be masked by averaging, and benefits that may have been realized in treating the variability can be lost. Thus, one of the reasons why we found such good correlation between EY and measured grain yield (or potential grain yield) was because we were operating at a resolution of 4 m². In this regard, topdress N rates based on an in-season prediction of potential grain yield must take place at the same resolution where spatial variability is encountered.

	Conclusions

TOP NOTES ABSTRACT INTRODUCTION Yield Goals In-Season Soil Testing Indirect Measures for Grain... Definitions of Measured,... Prediction of Biomass and... Materials and methods Results Discussion Conclusions REFERENCES

 
We propose the use of two postdormancy spectral reflectance readings (NDVI collected at or near Feekes Growth Stage 4 and again at Feekes Growth Stage 5) to assess the initial status of plant growth from planting to the end of dormancy and to assess postdormancy growth across a short time period (Jan. to Mar.). Adding these two NDVI readings and dividing by the GDD between readings provides an indication of what potential grain yield should be for a wide range of growing conditions, planting times, and sensing dates. For the 2 yr evaluated, measured grain yield and estimated potential grain yield, or EY, were expected to be similar because the dryland growing conditions were near ideal throughout the season. Including data obtained from six locations for a 2-yr period, EY values explained 83% of the variability in the grain yield, and thus were an early season indication of potential grain yield (measured grain yield used as the indicator variable) across a range of growing environments.

	ACKNOWLEDGMENTS

 
The authors wish to thank J.M. LaRuffa, S.B. Phillips, J.L. Dennis, D.A. Cossey, M.J. DeLeon, C.W. Woolfolk, R.W. Mullen, B.M. Howell, and Jing Wang for their assistance with field and lab work.

	NOTES

TOP NOTES ABSTRACT INTRODUCTION Yield Goals In-Season Soil Testing Indirect Measures for Grain... Definitions of Measured,... Prediction of Biomass and... Materials and methods Results Discussion Conclusions REFERENCES

 
Contribution of the Oklahoma Agric. Exp. Stn.

Received for publication January 19, 2000.

	REFERENCES

TOP NOTES ABSTRACT INTRODUCTION Yield Goals In-Season Soil Testing Indirect Measures for Grain... Definitions of Measured,... Prediction of Biomass and... Materials and methods Results Discussion Conclusions REFERENCES

 

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