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Published in Agron J 100:376-380 (2008)
DOI: 10.2134/agrojnl2006.0352
© 2008 American Society of Agronomy
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PEANUT

Peanut Response to Planting Date and Potential of Canopy Reflectance as an Indicator of Pod Maturation

Danésha S. Carleya, David L. Jordana,*, L. Cecil Dharmasrib, Turner B. Suttonc, Rick L. Brandenburgd and Michael G. Burtona

a Dep. of Crop Science, Box 7620, North Carolina State Univ., Raleigh, NC 27695
b Syngenta Crop Protection, P.O. Box 18300, Greensboro, NC 27419
c Dep. of Plant Pathology, Box 7404, North Carolina State Univ., Raleigh, NC 27695
d Dep. of Entomology, Box 7613, North Carolina State Univ., Raleigh, NC 27695

* Corresponding author (david_jordan{at}ncsu.edu).


   ABSTRACT
TOP
 NOTES
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 REFERENCES
 
Determining when to dig peanut (Arachis hypogea L.) is complicated because of its indeterminate growth habit. Pod mesocarp color is often used an indicator of pod maturation. However, this process is time consuming and is usually based on a relatively small subsample of pods from peanut fields. Research was conducted during 2003–2005 to determine if reflectance of the peanut canopy, using multispectral imaging (350–2500 nm), could be used as an indicator of pod maturation. The cultivars VA 98R and NC-V 11 were planted beginning in early May through early June during each year with reflectance and the percentage of pods at optimum maturity (percentage of pods with brown or black mesocarp color) determined in mid-September. The highest yield observed for VA 98R across the 3 yr of the experiment was noted when peanut was planted in mid-May rather than early or late May or when planted in early June when peanut was dug based on optimum pod maturity using pod mesocarp color. Pod yield for the cultivar NC-V 11 did not differ when comparing planting dates. For cultivar VA 98R, Pearson's correlations were significant for all bandwidth categories except the normalized difference vegetation index (NDVI) when reflectance was compared with percentage of mature pods. Reflectance for NC-V 11 was not significant for any of the correlations even though significant differences in the percentage of mature pods were noted in mid September when comparing planting dates. These data suggest that canopy reflectance could potentially aid in predicting pod maturation, but more research is needed to determine feasibility of this approach.

Abbreviations: DAE, number of days between peanut emergence and digging • HUA, heat unit accumulations • MIR, mid-infrared • NIR, near-infrared • NDVI, normalized difference vegetation index • UMIR, upper mid-infrared • %ELK, percentage of extra large kernels • %FP, percentage of fancy pods • %MP, mature pods • %TSMK, percentage of total sound mature kernels


   NOTES
TOP
 NOTES
ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 REFERENCES
 
All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher.

Received for publication December 11, 2006.
   INTRODUCTION
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 REFERENCES
 
PEANUT GROWN IN NORTH CAROLINA generally require as many as 160 d to reach optimum maturity, and peanut is generally seeded in early or mid-May to allow time for pods to reach maximum maturity (Jordan et al., 2005; Jordan, 2006a). Stresses from biotic and abiotic sources can delay development of peanut, and in some cases these stresses can lower yield (Aquino et al., 1992; Funderburk et al., 1998). Digging too early can reduce yield by 15% and economic value by 21% (Wright and Porter, 1991), while cultivar selection can influence yield equally or more than digging date alone (Mozingo et al., 1991). Pattee et al. (1980) reported that with some cultivars yield increased with later harvest dates while yield of other cultivars reached a peak and then began to decline.

Peanut growth is indeterminate, and this can lead to challenges when defining when to dig and invert vines. Determining the mesocarp color of pods is used by most growers and their advisors in North Carolina and throughout the southeastern United States to define pod maturity and initiate digging (Jordan et al., 2005; Jordan, 2006a; Williams and Drexler, 1981). This approach involves removing approximately 150 harvestable pods from each field and exposing the mesocarp (Williams and Drexler, 1981). A darker mesocarp is correlated with increased kernel and pod development. This approach is time consuming, is based on a relatively small sample, and often requires multiple samplings over time (Jordan et al., 2005). An easier and less time consuming approach would be attractive to peanut growers and their advisors.

Heat unit accumulation, often expressed in growing-degree days, has been used to help predict maturity, monitor crop progress, and predict phenology of peanut and other agronomic crops (Baskervi and Emin, 1969; Bell and Wright, 1998; Lawlor et al., 1990; Leon et al., 2001; Schwarte et al., 2005; Viator et al., 2005). Heat unit accumulations are poorly correlated with optimal yield when soil moisture is limited (Idso et al., 1978). While this approach can be a good indicator of when to begin careful assessment of peanut pod maturity, it is not an adequate indicator alone.

Nutter and Littrell (1996) suggested that reflectance measurements of healthy peanut canopies have a better relationship with pod yield than assessments based on percentage defoliation caused by leaf spot diseases. Nutter et al. (1990) used a handheld multispectral radiometer to compare visual and spectral assessments of foliar diseases as a means of measuring fungicide efficacy in peanut. In all but one case, they were able to determine that percentage reflectance increased linearly as disease incidence and severity decreased. Reflectance measurements were correlated more closely with pod yield than the traditional visual assessments leading the researchers to suggest that reflectance measurements from peanut canopies can provide more accurate means to evaluate fungicide efficacy in peanut.

In other studies using reflectance to study agricultural crops (Osborne et al., 2004; Zhao et al., 2003), different wavelengths were found to have high correlation with N content. Osborne et al. (2002) found that reflectance was higher in certain blue and near-infrared wavelengths when corn (Zea mays L.) foliage was N deficient and that reflectance is significantly influenced by the presence of weeds, N rate, and their interaction. Reflectance studies on grain sorghum [Sorghum bicolor (L.) Moench] followed changes in red-edge characteristics found that N deficit stress generally increased leaf reflectance at 555 and 715 nm wavelengths and caused shift in the red-edge to shorter wavelengths (Zhao et al., 2005). Newton et al. (2004) reported that reflectance measurements could be used to aid in accurate estimation of disease and yield response to fungicide in barley (Hordeum vulgare L.). However, cultivar and developmental stage had a large influence on measurements. Nutter (1989) used one single bandwidth at 800 nm wavelength band to assess peanut leaves affected by late leaf spot [Cercosporidium personatum (Berk. & M. A. Curtis) Deighton]. This research provided support for the hypothesis that reflectance from canopies could provide a quick and nondestructive method of measuring disease intensity and the amount of green leaf area contributing to pod yield. The possibility of using hyperspectral and multispectral imaging to determine reflectance and correlate reflectance with pod maturity has not been conclusively determined in peanut.

Since optimum harvest date for peanut is crucial for maintaining high yield and quality potential of the crop, the objective of this research was to determine if canopy reflectance can be used as an indicator of pod maturity and to determine the influence of planting date on pod yield and market grade characteristics of peanut.


   MATERIALS AND METHODS
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS AND DISCUSSION
 REFERENCES
 
The experiment was conducted in North Carolina from 2003–2005 at the Peanut Belt Research Station located near Lewiston-Woodville on a Norfolk loamy sand (fine-loamy, siliceous, thermic, Typic Paleudults). Peanut was planted in conventional raised beds to establish a final in-row plant population of 13 plants m–1 and was irrigated occasionally to supplement rainfall. Seeds were placed 5 to 8 cm deep depending on soil moisture. Aldicarb {O-[(methylamino)carbonyl]oxime} (Temik, Bayer Crop Science, Research Triangle Park, NC) was applied at 1.1 kg ai ha–1 in the seed furrow at planting as a granular formulation before seed drop. Production and pest management practices were held constant across the experiment and were based on Cooperative Extension Service recommendations for the region (Brandenburg, 2006; Jordan, 2006a, 2006b; Shew, 2006).

Treatments consisted of planting the Virginia market type cultivar ‘VA 98R’ on four dates beginning in early May through early June. Planting dates during 2003 and 2004 were 5 May, 15 May, and 25 May. Planting dates during 2005 were 2 May, 18 May, and 27 May. The early June planting dates during 2003, 2004, and 2005 were 4 June, 6 June, and 6 June, respectively. In a separate experiment, the cultivar ‘NC-V 11’ was planted 5 May and 4 June during 2004, and 5 May and 6 June during 2005. The experimental design was a randomized complete block with planting dates replicated four times.

The number of days between peanut emergence and digging (DAE) for each planting date were recorded to determine heat unit accumulations (HUA) using a 13°C minimum and 35°C maximum (Table 1 ). Growing degree units were calculated by determining the average of the daily high and low temperatures, subtracting 13, and adding these values through the period between emergence and digging. Peanut was combined 4 to 7 d after digging and final yield converted to 8% moisture. A 1-kg sample from each plot was removed to determine percentages of extra large kernels (%ELK), total sound mature kernels (%TSMK), and fancy pods (%FP) using market grading criteria (USDA, 2005).


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  		Table 1. Heat unit accumulation and days from peanut emergence to digging for experiments conducted from 2003 through 2005 when the cultivars VA 98R and NC-V 11 were planted on four dates from early May through early June.{dagger}

 
In mid-September, 100 pods were removed from each plot and were subjected to mesocarp color determination as described previously (Jordan et al., 2005; Williams and Drexler, 1981). The percentage of pods in the brown and black color categories was referred to as mature pods (%MP) ready for digging. Peanut for each planting date was dug based on prediction of when approximately 65% of pods would be in the combination of brown and black mesocarp color categories for Virginia market type peanut (Jordan et al., 2005). Pod mesocarp color was determined two or three times during late September and early October to determine optimum digging date for the latter planting dates. Pod yield, %ELK, %TSMK, and %FP were determined as described previously.

Canopy reflectance in the form of radiant energy was determined as close to solar noon as possible using a portable spectroradiometer (ASD FieldSpec Pro, Analytical Spectral Devices, Boulder, CO) in mid-September within 3 d after determining pod mesocarp color. The spectroradiometer was optimized using a standardized white reference panel (Analytical Spectral Devices, Boulder, CO) before measurements were taken and every 15 to 20 min during data collection. Hyperspectral measurements were collected using a 23° field-of-view optic, and the sensor was held 80 cm above the canopy. Twenty hyperspectral reflectance measurements were taken per plot and combined to get an average reading for each bandwidth from 350 to 2500 nm (nm) for each plot. Bandwidths were combined into nine bandwidth groupings (Table 2 ) (Avery and Berlin, 1992). Because broad bands (from 10 to 70 nm in width) in the visible (400–760) and near-infrared (NIR) (800–1049) regions of the spectrum have been demonstrated to be optimum for estimating crop biophysical information (Thenkabail et al., 2000), bands within this region were used for this analysis, as were bands in the mid-infrared (MIR) (1550–1750) and upper mid-infrared (UMIR) (2000–2400). Portions of the spectrum coincide with water absorption bands [mid-infrared (1350–1450 nm) and far-infrared (1800–1950 nm)], which obscure reflectance measurements, and were not used for this analysis (Hatfield and Pinter, 1993). However, the normalized difference vegetation index (NDVI) was calculated using NDVI = near infrared – red/near infrared + red. The NDVI, a number between –1 and +1, quantifies the relative difference between the near infrared reflectance "peak" and red reflectance "dip" in the spectral signature (Nilsson, 1995).


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  		Table 2. Corresponding wavelength characterization for bandwidth categories used to compare canopy reflectance on a single date in September regardless of planting date.{dagger}

 
Data for %MP, pod yield, %ELK, %TSMK, and %FP were subjected to analyses of variance each location and each year using appropriate error terms for fixed and random effects (Carmer et al., 1989). Analyses of agronomic data were conducted using PROC GLM and reflectance data was analyzed using PROC CORR (SAS 8.1, SAS Institute, Cary, NC). Means of significant main effects and interactions were separated using Fisher's Protected LSD test at P ≤ 0.05. Reflectance data were pooled over years for both data sets (VA 98R in 2003–2005, and NC-V 11 in 2004–2005) based on a strong F-test statistic in each case. Pearson correlation was used to determine relationships between reflectance and %MP.


   RESULTS AND DISCUSSION
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
REFERENCES
 
The interaction of year x planting date was significant for %MP (P = 0.0087), pod yield (P = 0.0001), and %ELK (P = 0.0099) in the experiment with the cultivar VA 98R. In contrast, the interaction of year x planting date was not significant for %TSMK (P = 0.3437) and %FP (P = 0.0627).

Differences in %MP were noted during all years for the cultivar VA 98R when %MP was determined for all planting dates in mid-September (Table 3 ). The highest %MP was observed when peanut was planted 5 May during 2003 compared with %MP for plantings on 25 May or 4 June. No differences in %MP were noted for the 5 and 15 May plantings during each year. The %MP was lower when peanut was planted in June compared with planting in early or mid-May during 2004 and 2005.


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  		Table 3. Interaction of year and planting date for percentage of mature pods and pod yield for the cultivar VA 98R for experiments conducted during 2003, 2004, and 2005.{dagger}

 
When digging was based on mesocarp color, pod yield of VA 98R was highest during 2003 when peanut was planted 15 May with the lowest yield noted for the 5 May or 25 May planting date (Table 3). During 2004, the highest yield was obtained when peanut was planted 15 May. The lowest yield was obtained when peanut was planted in early June. No difference in pod yield was found during 2005 when peanut was planted 5, 15, or 25 May. Yield did not differ when comparing the last three planting dates. Mozingo et al. (1991) determined that Virginia type cultivars planted in favorable conditions yielded higher when planted 1 May rather than in late April. Conversely, Linker and Coble (1990) suggested that the ideal time to plant peanut in North Carolina was 20 April to 10 May. Hurt et al. (2005) reported that planting peanut in mid-May was more effective in minimizing tomato spotted wilt (caused by the Tospovirus tomato spotted wilt virus) of peanut than planting in early May. In these experiments, visual symptoms characteristic of tomato spotted wilt were not observed regardless of planting date or cultivar. When planting dates were compared, no differences in %ELK or %TSMK were found during 2003, 2004, and 2005 (data not shown). Differences in %FP were found in 2005, but not during 2003 and 2004 (data not shown). The %FP was lower when peanut was planted 6 June 2005 compared with earlier plantings (data not shown).

The number of days between emergence and digging for peanut planted at different dates varied among years, especially when 2003 and 2004 were compared with 2005 (Table 1). These results reflect warmer temperatures, especially in May and June, during 2004 compared with those during 2003 and 2005 (data not shown). Heat unit accumulation over the four planting dates varied by 62, 304, and 104 growing degree units (13°C base and 35°C ceiling) during 2003, 2004, and 2005, respectively (Table 1). Although some differences in DAE to digging for planting dates were noted, a consistent trend was not observed when comparing planting dates across years.

The %MP in mid-September was higher for the cultivar NC-V 11 when peanut was planted 5 May compared with planting in early June (Table 4 ). No differences in pod yield, %ELK, %TSMK, and %FP were noted when comparing planting dates (data not shown). Heat unit accumulations differed by 441 and 197 growing degree units during 2004 and 2005, respectively (Table 1).


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  		Table 4. Interaction of year and planting date for percentage of mature pods and pod yield for the cultivar NC-V 11 for experiments conducted during 2004 and 2005.{dagger}

 
Collectively, these data indicate that while pod yield can differ among planting dates, especially for the cultivar VA 98R, there were few differences in %ELK, %TSMK, and %FP. These data also indicate that digging peanut based on pod mesocarp color continues to be effective in optimizing market grade characteristics.

Canopy reflectance based on Pearson's correlations was significantly different for all bandwidth categories except the NDVI when reflectance was compared to percentage of mature pods for VA 98R during 2003–2005 (Table 5 ). Although some variation was noted when comparing planting dates for the cultivar VA 98R, reflectance values suggest that there is a negative correlation between canopy reflectance and %MP. In contrast, no differences in reflectance were found for the cultivar NC-V 11 when reflectance was compared to percentage of mature pods during 2004–2005 (Table 5). The %MP differed in all years when comparing planting dates for both cultivars (Tables 3 and 4). These data suggest that canopy reflectance could potentially aid in predicting pod maturation.


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  		Table 5. Significance of correlations among percentage of mature pods and canopy reflectance when the culitvars VA 98R or NC-V 11 were planted on different dates ranging from early May through early June when canopy reflectance was recorded on a single day in September.{dagger}

 
While our data suggests that most of the bandwidths chosen in this research provided a means to compare reflectance with the aboveground canopy growth, at least in the case of VA 98R, the NDVI was not correlated with pod maturity in either the VA 98R or the NC-V11. Earlier research with runner and Valencia market type peanut assessed the potential of NDVI to evaluate crop maturity (Sanders et al., 2002) using polynomial regression determined a slight degree of correlation with NDVI and DAP. However, the authors reported that neither market type of peanut could be clearly related using a regression curve when DAP and NDVI were compared. In our research we used a Virginia market-type peanut, and it is possible that the growth habit for VA 98R and NC-V 11 was different enough to cause reflectance measurements to be more variable than runner or Valencia market type peanut.

A number of other factors could be contributing to the negative correlations found in this research. It is possible that the crop stage was negatively affected by low-water conditions. Under water stress, the internal structure of the leaf is altered; therefore, there is a shift in the shape of the spectral peak and a change in the contrast in vegetation canopy reflectance (Filella and Penuelas, 1994). Other research has also shown that water availability impacts spectral measurements. Aparicio et al. (2000) found that spectral correlations between NDVI and wheat maturity were positively correlated only in the second half of the grain filling stage under field irrigation. In this case, the authors suggest that during grain filling, the effect of drought (or a reduction in water) and the natural senescence of the leaves of the crop both simultaneously affect the spectral characteristics. In our research, fields were rainfed with occasional irrigation. It is possible that they were under slight drought conditions, making the canopy more susceptible to water stress, resulting in a negative relationship between spectral characteristics and canopy reflectance. Research in wheat yield assessment using reflectance (Ferrio et al., 2005) reported negative coefficients for the 700 to 750 nm wavelengths, and the wavelengths between 950 and 1000 nm. Filella and Penuelas (1994) reported that these regions are closely related to chlorophyll content, biomass, and water status. Due to the nature of this experiment, some compounding factors could not be controlled, or were not foreseen. In future experiments it may be helpful to take multiple measurements throughout the season, along with pod maturity assessments. Also, canopy width measurements were not taken at the same time as the reflectance measurements; however, in each case the canopy had fully closed. It may be beneficial to follow canopy closure, and the effect it has on canopy reflectance in Virginia-market type peanut. Additionally, despite effective fungicidal treatments, some leaf spot diseases were visible at the time of data collection, possibly confounding reflectance results from so-called "healthy" plants. More research is needed with completely nondiseased and nonstressed plants compared with plants that are stressed to establish discriminating reflectance signatures.

Furthermore, while it may be possible to use this technology to study pod maturity and canopy reflectance we only found significant correlations in VA 98R but not in NC-V11. Other research supports genotypic variation in spectral measurements for some crops. Babar et al. (2006) reported a significant interaction between wheat genotypes and spectral values when 30 lines of wheat were compared under different irrigation levels. Due to this variation, these researchers suggest that spectral reflectance can be used by breeders to aid differentiate genotypes and aid in selection of superior breeding lines. To this end, with the right experimental design in place, it is conceivable for peanut breeders to use this technology to aid in cultivar selection.

Planting date affected peanut yield and market grade characteristics for the cultivar VA 98R but not NC-V 11. Planting VA 98R in mid-May provided yield that was as high or higher than planting this cultivar in early May, late May, or early June during the 3 yr of study. Results from these experiments demonstrate limitations to using DAE and to a lesser degree HUA for predicting the optimum digging date. While HUA does appear to have potential as a tool to aid in predicting pod maturation, monitoring pod development several times before anticipated optimum maturity, of which HUA can be an initial indicator, continues to be the most effective approach to determining when to dig peanut.


   ACKNOWLEDGMENTS
 
Appreciation is expressed to Dewayne Johnson, Brenda Penny, Carl Murphey, and the staff at the Peanut Belt Research Station for assistance with these experiments and to Dan Reynolds and David Shaw at Mississippi State University for discussions concerning use of hyperspectral and multispectral imagery to evaluate canopy reflectance. We also thank Cavelle Brownie for her help with the statistical analysis. This research was supported by funds administered through the North Carolina Biotechnology Center Grant 2002-CFG-8012, the North Carolina Peanut Growers Association, the National Peanut Board, and USAID Peanut CRSP (LAG-G-00-96-90013-00).

All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher.


   REFERENCES
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 REFERENCES
 




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