Peanut is an important oilseed crop and legume species, with more than 1.9 M tons produced annually in the U.S. Being indeterminate, peanut continually flowers and sets pods throughout the growing season, leading to the potential harvest of both mature and immature pods. To quantify the physiological impacts of peanut seed maturity, a two-year field study was conducted to elucidate the difference in canopy structure and reproductive characteristics, including flower production, yield, and grade between seed obtained from immature and mature seed of two commercial peanut cultivars: TUFRunner™ '727' and FloRun™ '107'. Data indicated that seed from the yellow class of pods have lower vigor and overall plant development and performance; further, plants developed from immature seed never achieved a level of performance comparable to that of the mature brown/black pod classes. There were differences between cultivars in the severity of the impact of immaturity, with larger detrimental effects on immature TUFRunner™ '727', which exhibited reduced emergence. Despite these cultivar differences, this study illustrated that mature seed performs better in a field setting than immature seed. These results are critically important to disproving the 'catch-up' assumption: seed maturity not only has an impact on emergence, but on subsequent life history and performance traits through the remainder of the season.
In the U.S., peanut (
Seed maturity has the potential to affect seed quality, albeit an imprecise term that often is assumed to include emergence and vigor but could also impact subsequent crop development and yield. Seed maturation is critically important to subsequent crop performance or vigor because it involves the processes after embryo growth inhibition that involve the accumulation of storage reserves, the acquisition of desiccation and other stress tolerance, DNA repair, the establishment of regulatory networks and signaling pathways, the establishment of dormancy in some species, and the achievement of metabolic quiescence allowing cellular processes to resume once imbibition takes place (
Peanut has relatively large seeds and cotyledons, and desiccation tolerance may be achieved relatively late in the maturation process, or even beyond mass maturity (
While seed germination is defined as the first visual appearance of the radicle (
As for other crops, the need to quantify seed vigor for peanut is essential, particularly with increasing issues with stand establishment noted by producers and other industry representatives over the past decade. Maturity may be a real key to determining seed vigor for peanut. With limited research reported about peanut seed maturity,
To address the dearth of studies in peanut that link seed maturity to subsequent life history traits and overall crop performance, a field study was conducted over a two-year period quantifying the difference in physiological responses between seed obtained from immature and mature pods. The objective was to compare critical characteristics of crop performance between plants established using both mature and immature seed for two commercial peanut cultivars. Specifically, the plants evaluated were grown from seed in the immature yellow class and the combined mature brown/black classes. Crop characteristics measured include emergence, leaf area index (LAI), normalized difference vegetation index (NDVI), flower production, yield and the resulting maturity of the harvested seed.
The location of the field trial was the Agronomy Teaching Farm (+29.63, -82.36) at the University of Florida campus in Gainesville, Florida. Genotypes tested were TUFRunner™ '727' (University of Florida, BL Tillman- personal communication) and FloRun™ '107' (
All seed were treated with azoxystrobin (Syngenta Corp., North Carolina) fungicide at the registered rate before planting as a preventative for seed borne diseases prior to emergence. The foliar fungicide pyraclostrobin (BASF Corp., New Jersey) was applied at the registered rate in 2014 but delayed due to an expectation that the trial would be terminated after full emergence; however, when treatment differences persisted, fungicide application began at 63 d after planting (DAP) and was repeated every 21 d in order to maintain the plots through optimum maturity. However, the delayed fungicide application resulted in heavy leafspot pressure and the need to harvest prior to full maturity. Fungicide applications in 2015 followed typical recommendations and commenced at 40 DAP and were repeated every 21 d. Beside azoxystrobin and pyraclostrobin fungicides, no other pesticide treatments were applied and weeds were controlled manually in both years.
In 2014 and 2015 plots received 19.05 mm of irrigation at planting. In 2014 they received two irrigation applications of 19.05 mm the month after planting, whereas 2015 had adequate rainfall and did not need additional irrigation. Weather data for the trials in both years was collected from the Gainesville Regional Airport (approximately 15 km away from the field site) and a station established on the roof of the University of Florida Physics Department (approximately 2.7 km away from the field site).
Over the growing period, data were collected to analyze the differences pertaining to emergence, growth, and development. Data impacting canopy structure included emergence, leaf area index (LAI), normalized difference vegetation index (NDVI) and other reflectance parameters were collected. With exception of sampling for the maturity ratio, all measurements taken prior to harvest were nondestructive, so as not to remove or damage plant tissue. For this study, emergence was defined as the opening of the first set of true leaves which follow the cotyledons. Once emergence had begun, daily stand emergence was quantified each afternoon until the numbers reached maximum levels and no longer changed. Once the emergence rates had peaked, the daily counts ceased and a final count was taken one week later. As the crop canopy began to expand, canopy architectural changes were documented by quantifying LAI using a Licor LAI 2200 (Licor, Nebraska, USA). Two groups of five points were taken for each LAI measurement, ten points total per reading. Each group of five readings consisted of one above canopy reading, and then four below canopy readings approximately 23 cm apart (taken in that order); the first set of five points were taken parallel to the row, and the last five were taken perpendicular to the row. The LAI measurements were taken wk throughout the season in sections of row that contained no plants missing i.e. sections that had a contiguous stand.
Multiple reflectance vegetation indices were measured using the Crop Circle model ACS-470 (Holland Scientific, Nebraska, USA) on two rows per plot. Readings were averaged over the length of the plot. The Crop Circle was configured to measure spectral characteristics between 440 to 800 nm using 12.5 mm diameter filters, resulting in the calculation of five different vegetation indices including: the normalized difference red-edge (NDRE), NDVI, red-edge band reflectance, near infrared (NIR) waveband, and red band reflectance. The sensor measurements of red band reflectance (700-750 nm) is assumed to be representative of the chlorophyll status until the canopy LAI nears 2.0; while the orange, yellow, and green bands are assumed to be more appropriate for larger canopies until the LAI approaches 4.0 or more (Holland Scientific, 2005). The sensor readings of NIR radiation (760-900 nm) are assumed to be representative of the amount of biomass present, as NIR reflectance is indicative of living vegetation (Holland Scientific, 2005). In both years, the Crop Circle was used on a wk basis. As in LAI, only contiguous sections were measured.
The reproductive characteristics measured included average number of flowers per plant, maturity assessment at harvest, yield, and grade. The plants began flowering approximately 30 DAP in both years. After flowering had commenced, the number of new flowers (not wilted from previous d) per row were recorded daily for four consecutive days, followed by wk counts conducted at 10:00 a.m. until the number of flowers no longer increased. The number of flowers per plot was divided by the number of plants in the row as determined by a final stand count to determine the average number of flowers per plant.
In 2014 and 2015, a Peanut Field Agronomic Resource Manager (PeanutFARM) account was used to gauge adjusted growing degree days (aGDDs) for each DAP as well as an accumulated value through the season. Harvest was anticipated at 135-150 DAP for the cultivars used in the study, corresponding to approximately 2,400-2,500 aGDDs. Research has shown that optimum peanut maturity in the southeastern U.S. correlates to approximately 2,500 aGDDs, with a maturity ratio (mature pods compared to total pods) of a blasted sample at 70% or higher (
The mesocarp color was determined for the MR using a pressure washer for removal of the exocarp (
All data were evaluated in JMP 10 (SAS, North Carolina USA) through univariate analysis using a mixed model ANOVA consisting of a factorial design. The model differed slightly for each data set, depending on whether the data was repeated (in time) or non repeated measures. The restricted maximum likelihood method was used with standard least squares and an emphasis on effect leverage. The following traits were analyzed using repeated measures: emergence, LAI, and NDVI. This model included the fixed factors of cultivar, maturity class, and date; the rep nested within cultivar and maturity class was treated as random. The traits of maturity ratio, yield, and grade were analyzed as non-repeated measures with fixed factors in the model of cultivar and maturity class and rep as a random factor. One standard error of the mean is displayed in all graphical representations of data from this experiment.
In 2014 and 2015, emergence was affected by cultivar, maturity class, and date with two and three-way interactions (
After emergence, LAI was measured through the season to determine if lasting differences in maturity class would be evident in later life history traits. The canopy development for the yellow classes of each variety were notably delayed when compared to the brown/black maturity classes (
The Crop Circle also determined lasting influences of seed maturity class on the resulting canopy reflectance parameters across the season (
For 2014, flower production was affected by all the factors in the model with the exception of date, with two and three-way interactions. Interactions included cultivar by maturity class, cultivar by date, maturity class by date, and the three-way interaction among cultivar by maturity class by date (
In 2014, the maturity ratio, yield, and grade were all different between maturity classes (
Peanut growers and researchers have assumed that seed maturity would likely affect crop emergence such that a delay in emergence would be exhibited for immature (yellow) as opposed to mature (brown/black) seed. However, most have assumed that this difference in emergence would be overcome quickly during the season, the assumption being that plants from yellow seed would 'catch up with those from brown/black seed and that plant performance would not vary through the majority of the season. This study has shown this assumption to be false and that it is critical to pay more attention to quantifying and optimizing seed maturity within the peanut industry. Therefore, the results of this study are important to disproving the catch-up assumption: seed maturity not only has an impact on emergence, but on subsequent life history and performance traits through the remainder of the season. This impact projects its influence even into the next crop, as the maturity and grade of the resulting seed is changed as well. However, given that the extremes measured in this study do not exist in practice, the impact of immature seed in commercial seed lots is likely less than observed in this research.
While seed vigor remains the central focus of the current study, defining this trait as derived from a single measure is complex and probably not appropriate. This research evaluated vigor in from measures of emergence, as well as subsequent evaluations of crop performance from LAI and reflectance. The first crop characteristic studied in this experiment was emergence. The cultivar and its maturity class clearly influenced emergence such that immature seed (yellow) had a delayed daily emergence and lower overall total emergence. This was expected, as the impact that maturity has on germination and emergence has been demonstrated in other crops such as soybean, common vetch, and canola (
The performance of each genotype and its maturity classes were also impacted by the climate conditions in each year and some of the variability between years may be linked to these varying weather patterns. In 2014, plots likely received an adequate amount of precipitation early in the season; while in 2015, there was little to no rain for the first three weeks after planting, leading to an extended period before maximum emergence. Planting date to final emergence encompassed 25 days in 2014, and nearly doubled in 2015 to 47 d. The total rainfall recorded for the 2014 growing season was 623 mm, whereas 666 mm was documented in 2015. Similar to a study by
The LAI patterns overall showed a normal crop development with increasing LAI values up to 5 in 2014 and 2015. However, poor emergence for the yellow class of both cultivars resulted in gaps within the rows as well as delays in canopy development. To eliminate the influence of bare soil or plant gaps in measurements of subsequent crop performance and to concentrate on quantification of delayed development alone, measures of NDVI and LAI were conducted in spans within the rows that had intact stands. Reflective of the slower emergence in the yellow maturity classes for each genotype, this maturity class had reduced LAI development throughout the season in both years. Although the brown/black classes of each genotype were similar for emergence, TUFRunner™ '727' had the highest LAI at the end of the season for both yeas. This was most likely due to its different growth style and canopy structure when compared to FloRun™ '107'. It was observed that TUFRunner™ '727' grew outwards, while FloRun™ '107' grew more upright before the canopy proceeded to expand outwards. It could be expected that LAI would impact assimilation through its direct measure of assimilative area and link back to yield. Thus, the decreased yields in 2014 may be partially explained by the reduction in LAI for that year. However, the link between LAI and yield is not always the case; for example, in soybean,
In regard to flower production in both years, the brown/black class produced more flowers than the yellow class for each cultivar during the early to mid-season but then declined; while the yellow class for each cultivar continued to climb until surpassing the brown/black classes late in the season. This shift in production occurred for each cultivar 60 to 70 DAP in both years. These patterns would indicate that the yellow class had the potential to catch up to the black class, at least in flower production. However, with maximum flower production being reached only by 76 DAP on average for the yellow classes, this would result in needing nearly 150 to 160 DAP to reach an optimal harvest maturity level for the yellow class. This would be an unlikely scenario as temperatures would be decreasing and disease pressure increasing as the season progressed into the autumn. The differences in flower production from 2014 to 2015 can be attributed to a healthier canopy in 2015 with low disease pressure (leaf spot).
This study shows that crop performance may be differentially affected by maturity level throughout the season as emergence, LAI, and flower production differed between plants established from mature vs. immature pods. From a field management standpoint, it would be critical to determine maturity level on a whole field basis to predict yield and possibly grade variability influenced by seed maturity. The most logical choice would be utilizing remote sensing technology to help in decisions about harvest timing, vulnerability to stress effects leading possibly to aflatoxin or increased disease pressure, or even segregation of parts of the field with optimal maturity for seed peanut. In the current study, vegetative reflectance was capable of separating the maturity classes for both cultivars during the season. Four out of the five variables measured (NDRE, NDVI, Red-Edge, and NIR) increased throughout the season as the canopy grew and progressed (
The maturity ratio quantified in this study reflects what was seen in the flower counts; that the maturity of the yellow class was delayed and never reached a comparable level with the black class. In general, the brown/black class typically had a higher percentage of mature pods in both years for TUFRunner™ '727' and in 2014 for FloRun™ '107'. There are critical biochemical and physiological processes that occur during the seed maturation process. For example, when peanut nears physiological maturity, the water content of seeds decreases from about 62 to 30-40%, and both the fat-carbohydrate and protein-carbohydrate ratios increase (
The reduced maturity level in the current study was certainly translated into yield in 2014 (
One of the most important findings of this study was the variability between the two cultivars in their level of tolerance to immaturity: for TUFRunner™ '727', immature seed appeared to be more negatively impacted by immaturity by differences noted in emergence, flowering pattern, and yield impact. There is evidence of this variation among genotypes for the impact of maturity is present in other crop species. For example,
The main objective of this experiment was to compare critical characteristics of crop performance between mature and immature seed throughout the season. It was hypothesized that the immature yellow class would lag behind the mature brown/black class at the beginning of the season, but eventually match the performance of the mature seed. This study confirmed that the yellow class does indeed lag behind but disproved the catch-up hypothesis because plants from immature seed in most cases were not able to match the performance parameters of plants from the mature brown/black class. The mature brown/black classes of each cultivar were found to be consistently higher than the yellow classes in respect to every performance trait measured, with the exception of two leaf level processes and yield in 2015. The detrimental effects of immaturity were also found to be inconsistent among cultivars. A genotypic difference was quantified, with the yellow class of TUFRunner™ '727' performing lower than the yellow class of FloRun™ '107' as shown by traits such as emergence and the maturity ratio; despite it performing better in regard to flower production.
The data accumulated in this study during 2014 and 2015 indicated that mature seed, regardless of cultivar, performs greater in a field setting than immature seed. The amount of immature seed planted by growers each year could possibly be minimized by maintaining an optimum harvest date when growing for seed peanut. This is because the harvest date chosen by the grower will impact the percentage of immature seed at harvest, which is later moved to the shellers and incorporated into lots of seed saved for the following year's planting. This accurate harvest determination rests quite heavily on the grower because determining seed maturity by the time the crop reaches the sheller is difficult. This was particularly evident by the finding that grade was an inaccurate representation of maturity in this study.
The authors would like to acknowledge funding and encouragement from the National Peanut Board, Florida Peanut Producers Association, and Georgia Peanut Commission to continue research as a means to promote grower profitability.
Graduate research assistant, Professor, Professor, and Professor, Agronomy Dept., University of Florida, Gainesville, FL 32611;
Professor, Crop and Soil Sciences Dept., University of Georgia, Tifton, GA 31793;
Associate Extension Scientist, Entomology and Nematology Dept., University of Florida, Gainesville, FL 32611;
Associate Professor, Soil and Water Sciences Dept., University of Florida, Gainesville, FL 32611;
Assistant Professor, Agronomy Dept., National Chiayi University, Chiayi, Taiwan 60004