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Determination of a plant population density threshold for optimizing cotton lint yield: A synthesis
Field Crops Research 230 (2019) 11–16
Contents lists available at ScienceDirect
Field Crops Research journal homepage: www.elsevier.com/locate/fcr
Determination of a plant population density threshold for optimizing cotton lint yield: A synthesis
T
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Curtis Adamsa, , Santanu Thapaa, Emi Kimurab a b
Texas A&M AgriLife Research, 11708 Highway 70 South, Vernon, TX, 76384, USA Texas A&M AgriLife Extension, 11708 Highway 70 South, Vernon, TX, 76384, USA
A R T I C LE I N FO
A B S T R A C T
Keywords: Cotton lint Population density Threshold Yield optimization
Technology fees associated with modern cotton cultivars have increased seed costs considerably, giving producers the impetus to reduce plant population density, where possible. Most recent studies on cotton population density, conducted across diverse environments, report similar patterns of crop response: decreases in lint yield only at very low densities, with generally consistent yield across all higher densities. But no work had been done to bring the literature together, quantitively synthesizing the yield data collectively, to better pinpoint a population density threshold. And, notably, little had been reported on the effects of population density in loweryielding dryland environments. Quantitatively synthesizing population density datasets from the literature, including our own dryland data, was the objective of this research. The dryland data showed that lint yield and biomass partitioning were not affected by population density, similar to higher-yielding environments over the same range in density. Following normalization of all lint yield data (literature and dryland datasets), a breakpoint in population density at 35,000 plants ha−1 was identified, which can be interpreted as the minimum plant density at which yield may be optimized. This rate is lower than the common density recommendation of 81,000 plants ha−1 and substantially lower than the risk-averse rates at which many producers plant (129,000 seeds ha−1 or greater). The analysis showed that yield will decline precipitously below 35,000 plants ha−1, exposing the enormous risk to cotton producers in approaching this low density, particularly if significant seed or plant loss is expected. However, the analysis suggests that excessive over-seeding may be occurring in many cases, resulting in economic losses to producers. The analysis also provides guidance on a threshold for producers facing replanting decisions.
1. Introduction In recent decades, breeding and genetic modification developments in cotton (Gossypium hirsutum L.) have made substantial progress in improving the crop, in terms of yield, quality, and ease of management (USDA-ERS, 2017). These developments have also increased the cost of seed substantially (Shurley et al., 2010). The National Cotton Council of America estimated in their 2015–2016 cotton production budgets that seed costs account for about 16% of the total operating costs for U.S. cotton producers (NCCA, 2017). High seed costs create an important impetus for producers to reduce planting population densities, where possible. Population density plays a central role in optimizing productivity, management, and economic return of any field crop, including cotton. Plant population density in cotton has a substantial impact on plant architecture and other canopy dynamics, which influences disease and
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pest incidence, harvest efficiency and timing, water use, and other factors (Kaggwa-Asiimwe et al., 2013; Maddonni et al., 2001; Stewart, 2005). Population densities that are too high or too low can have negative agronomic consequences. Agronomists and Extension specialists at the University of Florida and North Carolina State University recommend achieving final population densities of about 81,000 plants ha−1 (2.5 plants ft−1 on 40 in. rows) to optimize yield and associated agronomic factors (Collins, 2015; Donahoe, 2010). A major cotton seed supplier, DeltaPine, recommended achieving the same rate (DeltaPine, 2017). Donahoe (2010) stated that optimal yields can be achieved at about 50,000 plants ha−1 (1.5 plants ft−1 on 40 in. rows), but mentioned that planting as high as 129,000 plants ha−1 (4.0 plants ft−1 on 40 in. rows) may be necessary if soil or seed conditions are poor (e.g. soil crusting is likely or the percent viable seed is low). Collins (2015) stated that over-seeding by about 20% is needed to achieve a desired density in ideal conditions and that even greater seeding excesses are
Corresponding author. E-mail address: [email protected] (C. Adams).
https://doi.org/10.1016/j.fcr.2018.10.005 Received 16 August 2018; Received in revised form 9 October 2018; Accepted 9 October 2018 0378-4290/ © 2018 Elsevier B.V. All rights reserved.
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to 33,000, 51,000, 69,000, 87,000, and 105,000 plants ha−1 in Linqing, China. In Georgia, USA, Bednarz et al. (2005) found that lint yield was reduced at 36,000 plants ha−1 relative to yield at 90,000 and 126,000 plants ha−1. In Henan, China, yield was reduced at 15,000 plants ha−1 relative to peak yields obtained at 51,000 to 87,000 plants ha−1 (Zhi et al., 2016). A two-year study in Texas, USA (Feng et al., 2014) showed no difference in lint yield from 75,300 to 226,000 plants ha−1 (a single year’s data from another location showed differences, but the crop was damaged by hail). In a relative outlier, Pettigrew and Johnson (2005) showed that lint yield peaked with plant population densities between 90,000 to 130,000 plants ha−1, while five percent lower yield was observed at 70,000 plants ha−1, in Mississippi, USA. In summary, most recent studies on cotton population density report decreases in lint yield only at very low population densities, with peak yields across broad population ranges, with no decreases in yield evident even at extremely high densities. A qualitative assessment of the literature suggests that an optimal population density for yield is low, likely far lower than current population density recommendations. No work has been done to bring this literature together, quantitatively synthesizing the yield data collectively. And, notably, most recent studies on population density were conducted in relatively highyielding rainfed or irrigated conditions and results in lower-yielding dryland conditions are missing. In this study, our first objective was to field test effects of cotton population density in lower-yielding dryland conditions, with a focus on lint yield and plant biomass partitioning. Our second objective was to combine our lint yield data with comparable data from other published studies, synthesizing the collective database to better understand how yield is affected by population density in cotton.
Table 1 A summary of lint yield in cotton as affected by population density in studies reported in the literature since year 2000. To be included, the studies conformed to the criteria given in the methods section. Source
Location
Year
Population Density (plants ha−1)
Lint Yield (kg ha−1)
Bednarz et al., 2005
Georgia, USA
2001– 2002
Dai et al., 2015
Linqing, China
2010–2013
Feng et al., 2014
Texas, USA
2007
Feng et al., 2014
Texas, USA
2008
Gwathmey et al., 2011 Gwathmey et al., 2011 Gwathmey et al., 2011 Gwathmey et al., 2011 Pettigrew and Johnson, 2005
Tennessee, USA
2006–2008
Tennessee, USA
2006–2008
Tennessee, USA
2006–2008
Tennessee, USA
2006–2008
Mississippi, USA
2001–2004
Mississippi, USA
2001
36,000 90,000 126,000 215,000 15,000 33,000 51,000 69,000 87,000 105,000 75,300 150,600 226,000 75,300 150,600 226,000 61,000 114,000 30,000 63,000 61,000 114,000 30,000 63,000 70,000 90,000 110,000 130,000 23,782 33,976 67,952 135,904 23,782 33,976 67,952 135,904 23,782 33,976 67,952 135,904 23,782 33,976 67,952 135,904 15,000 51,000 87,000 15,000 51,000 87,000
1246 b 1345 a 1376 a 1363 a 1771 b 2204 a 2231 a 2273 a 2292 a 2277 a 1278 a 1294 a 1235 a 1633 a 1776 a 1616 a 1752 a 1780 a 1459 b 1756 a 1189 a 1176 a 1087 b 1234 a 1393 b 1441 a 1471 a 1465 a 1360 a 1360 a 1310 a 1250 a 1570 a 1650 a 1630 a 1630 a 850 b 1060 a 1190 a 1140 a 1340 b 1660 a 1760 a 1650 a 950 b 1533 a 1570 a 1313 b 1546 a 1516 a
Wrather et al., 2008
Wrather et al., 2008
Mississippi, USA
2002
Wrather et al., 2008
Mississippi, USA
2003
Wrather et al., 2008
Mississippi, USA
2004
Zhi et al., 2016
Henan, China
2012
Zhi et al., 2016
Henan, China
2013
2. Materials and methods 2.1. Field study design, management, and analysis A two-year field study was conducted at the Texas A&M AgriLife Research Station at Chillicothe, TX during the 2016 and 2017 summer growing seasons on an Abilene clay loam soil in dryland conditions. The area is characterized by a semi-arid climate, with annual average precipitation of about 635 mm, mostly occurring as spring and fall precipitation with minimal summer rainfall, and high rates of evapotranspiration. ‘Phytogen 333 WRF’ cotton was planted in four rows with a row width of 102 cm (40 in.) on 9 June 2016 and 8 June 2017. Plot lengths were 59 m and 40 m in 2016 and 2017, respectively. The study was arranged as a randomized complete block design with four replications of four cotton population density rate treatments. Population densities, as determined by stand counts taken two weeks after planting, were 67,300, 115,300, and 145,400 plants ha−1. All plots received in-season inputs of herbicide, fertilizer, insecticide, and defoliation chemicals according to current recommendations for the region. Measurements of plant biomass partitioning were taken from 2-m sections of crop row on three occasions in each year: 48, 70, and 102 DAP in 2016 and 47, 77, and 104 DAP in 2017. In analysis and presentation, corresponding sampling times (days) were averaged between years. After cutting the stem just above the soil level, biomass samples were transported to the lab and partitioned into leaf, stem, and reproductive biomass components and placed in paper bags. Reproductive biomass components included flowers, squares, and bolls. Samples were dried at 60 °C in an air-forced oven. Stand counts were used to convert aerial biomass measurements into biomass per plant. Plots were harvested by a stripper on 28 November 2016 and 17 November 2017. Seed cotton samples were ginned to determine lint yield. In statistical analysis of the field data, population density treatments were considered a fixed effect in the statistical model, while years and replications (blocks) were considered random effects. The data was analyzed with SAS 9.3 (SAS Institute, Cary, NC). Differences between
needed in poor planting conditions. In the semi-arid environments of central to west Texas, for example, where planting conditions are commonly challenging, regional producers often plant at seeding rates at or in excess of 129,000 seeds ha−1. To put these figures into perspective, recent studies provide needed research-based information on how lint yield responds to population density in advanced modern cotton cultivars (Table 1). Wrather et al. (2008) reported that cotton lint yield was reduced at 24,000 plants ha−1 relative to yield at 34,000, 68,000, and 136,000 plants ha−1 in just two years out of four in a study (no differences reported in the other two years) conducted in Mississippi, USA. Gwathmey et al. (2011) found that lint yield was reduced at 30,000 plants ha−1 relative to yield from 61,000 plants ha−1 to 114,000 plants ha−1 in Tennessee, USA. In a report with relatively high yields, Dai et al. (2015) found lint to be reduced at 15,000 plants ha−1 compared 12
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treatment yields were determined by ANOVA using Proc Mixed, including post-hoc means comparisons adjusted using the Tukey procedure, with a statistical threshold of 0.05. The assumption of normality was checked for all response variables. Statistical analysis of the biomass partitioning data was done with repeated measures ANOVA in SAS with the Proc MIXED procedure. 2.2. Literature and field trial population density analysis Data was mined from the literature (Table 1) for a broad-scale analysis of the effect of population density on lint yield in modern cotton varieties. Data were included from studies conducted in a variety of environments that fit the following predetermined criteria: reports from year 2000 or later; reported lint yield (i.e. seed cotton was not used); used fixed row width and intra-row spacing (e.g. no skip row or hill drop); and presentation of numeric data that was not integrated across other treatments factors with significant interactions or an unclear interaction status. The lint yield data from the currently reported experiment was also included. Literature yields and plant densities were reported in many units (e.g. lbs ac−1) and measures (e.g. seeds ft−1), but were converted to consistent units of kg ha−1 and plants ha−1, respectively. The yield data was graphed as a function of plant population density and we observed that most datasets exhibited similar trends: yield decline only at very low population densities and a plateau in lint yield across all higher densities, whether the environment was relatively higher or lower yielding. This was corroborated by the statistical analysis presented with each dataset (Table 1). As a result, we elected to apply a normalization procedure to the data, so quantitative analysis could be performed on the combined dataset. This was done by summarizing the statistical analysis for all studies and calculating an overall average of datapoints along the yield plateaus (data values statistically considered peak yield within a given dataset) of all studies. This universal average was used in making a conversion factor for all datasets, which was individually multiplied by each dataset value, as follows: (universal plateau average / average of datapoints within dataset yield plateau) × dataset value = normalized value. Once normalized, a twosegment piecewise linear regression model was fitted to the data using SigmaPlot 13.0 (Systat Software, Inc., San Jose, CA). This procedure is commonly used to estimate “breakpoints” or trend transitions in datasets; it is often applied to determine the point or threshold when a response variable, such as yield, experiences a shift in response to treatment factors, similar to plateau models (Adams et al., 2015; Shaver et al., 2017; Toms and Lesperance, 2003).
Fig. 1. (A) Lint yield response data from the current report (marked by an asterisk) and literature reports on tests of population density in cotton; (B) the same yield response data, normalized to account for differences in yield among datasets; (C) and analysis of the normalized data, using a piecewise model, to identify a breakpoint (Xo) in the data, or the minimum population density at which yield is expected to be optimized.
3. Results precipitously at population densities below the 35,000 plants ha−1 threshold and that yield does not increase above this density threshold. A lack of data, especially at extremely low population densities (below about 15,000 plants ha−1), resulted in a lack of detail in the precise pattern of yield decline below 35,000 plants ha−1. Above 35,000 plants ha−1, the regression analysis shows that lint yield remains quite stable even to excessively high population densities.
3.1. Literature and field trial population density analysis Across the studies mined from the literature, population density ranged from 15,000 to about 226,000 plants ha−1 and lint yield ranged from about 1200 kg ha−1 to about 2300 kg ha−1 (Fig. 1A). Most literature datasets exhibited a rise in yield from very low plant population densities—as long as low enough densities were tested—and a yield plateau across all higher density treatments tested (see statistics in Table 1). The current study added data representative of dryland cotton cropping systems in semi-arid environments that are lower-yielding, which was missing from the collective dataset. In our study, population density ranged from 67,000 to 145,000 plants ha−1 and lint yield averaged 800 kg ha−1, with no differences among treatments in lint yield (Fig. 1A). In Fig. 1A, the dryland data is marked with an asterisk. A normalization procedure was applied to the datasets to enable a collective, quantitative analysis of the lint yield response to population density (Fig. 1B). In the normalized data, a breakpoint was identified at 35,336 plants ha−1 (1.1 plants ft−1 on 40 in. rows) by piecewise linear regression (Fig. 1C). The normalized data showed that yield will decline
3.2. Field trial biomass partitioning and production per plant When considered across the growing season (48, 74 and 103 days after planting), no differences were found among population density treatments in the partitioning of biomass to leaves, stems, and reproductive structures on an aerial basis (Fig. 2). More separation of treatments, without significance, was evident in leaf biomass (P = 0.2854) and stem biomass (P = 0.3374) than in reproductive (P = 0.9895) biomass. Total biomass productivity (P = 0.8921), as well as the ratio of reproductive to total biomass productivities (P = 0.8000), were the same across all population densities tested. 13
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Fig. 2. In-season measurements of crop biomass development and partitioning, including leaf, stem, reproductive, and total biomass, and the ratio of reproductive to total biomass, on aerial and per-plant bases. The error bars represent the standard error of the mean.
tested—and a yield plateau across all other treatments tested, no matter how high those treatments reached. At high population densities, some crops exhibit a yield decline associated with a full sigmodal yield response curve, while others do not (Edwards and Purcell, 2005; TetioKagho and Gardner, 1988). The lack of yield decline at high densities in cotton does not seem to be due to a lack of testing at high-enough plant population densities: at the highest density tested (226,000 plants ha−1) among the datasets, there would be a seed every 4.4 cm on rows spaced at 1 m, an excessive density in practical and physiological senses. A surprising trend in the data was the similarity in yield response to population density among datasets, despite the wide variation in yield among datasets. Wide variation in yield among the datasets indicates that non-treatment factors, such as moisture, nutrition, and genetic yield potential, often limited yield. But to the extent that the cotton crops reported in literature differed in yield potential and had other (non-population density) yield-limiting factors, lint yield responses to population density seem to have occurred independent of these factors. Pettigrew et al. (2013) reported no population density interactions with lint yield in cotton genotypes widely differing in yield potential. The breakpoint in lint yield with population density identified in
Differences in population density resulted in dramatic differences in biomass production per plant, however, as the plants adapted physiologically to their systems and available resources (Fig. 2). Percent differences in total biomass production per plant among population density treatments were minor by 48 days after planting, but quickly diverged. By 100 days after planting, total biomass per plant was 94% greater at a population density of 67,300 plants ha−1 than at 145,400 plants ha−1. For the same comparison, leaf biomass was 81% greater, stem biomass was 76% greater, and reproductive biomass was 106% greater. 4. Discussion 4.1. Literature and field trial population density analysis When viewed together, there were interesting trends in lint yield with population density across literature datasets (Fig. 1A). The tested population densities ranged about 12-fold and the reported yields, including the data from the current dryland study, ranged nearly threefold. Most literature datasets exhibited a rise in yield from very low plant population densities—as long as sufficiently low densities were 14
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population density. Boll size is inversely related to population density (Bednarz et al., 2000), which can also affect harvest efficiency, as lint recovery is generally lower from smaller bolls. Planting at higher densities can delay the initiation of fruiting and increase water use (Bednarz et al., 2005; Zhi et al., 2016). Planting at high densities has been shown to improve cotton competitiveness with weeds (Gwathmey et al., 2011; Street et al., 1981), which may be most important in modern organic cotton production. There are examples of diseases that can be controlled, within limits, by population density, such as Verticillium wilt incidence being negatively related to increased seeding rate (Wheeler et al., 2010). These factors, and others not discussed here, need to be weighed, along with yield and expected seed and plant losses, in making planting population decisions in the field.
this analysis (Fig. 1C), which can be interpreted as the minimum population density at which yield may be optimized, was about 35,000 plants ha−1 (1.1 plant ft−1 on 40 in. rows). This rate is substantially lower than common density recommendations for cotton. The most common population density recommendation we found, across University Extension agencies and industry, was 81,000 plants ha−1 (2.5 plants ft−1 on 40 in. rows) (Collins, 2015; Donahoe, 2010; DeltaPine, 2017). This is a recommendation for final population density, which can be achieved only by over-seeding to account for inevitable, and often unpredictable, seed and plant loss. Collins (2015) stated that overseeding by about 20% is needed to achieve a desire density in ideal conditions. Donahoe (2010) recommended planting as high as 129,000 plants ha−1 (4.0 plants ft−1 on 40 in. rows) when soil or seed conditions are poor. These conditions would include soil crusting, excessively hot or cool temperatures, poor seed quality or germination percentage, and other situations. Over-seeding would also be needed if plant loss, following emergence, was expected due to pests, disease, high winds/ blowing sand, or other factors. In central and west Texas, where planting and growing conditions are often challenging, regional producers commonly plant at rates at or even in excess of 129,000 seeds ha−1. In our analysis, the precipitous decline in yield that was evident at population densities below 35,000 plants ha−1 shows the enormous risk to cotton producers in approaching a density this low, particularly if substantial seed or plant loss is expected. Even in ideal environmental conditions, seed spacing is usually not perfect in practice and, at this low population density, any seed gaps would result in yield loss (Pettigrew et al., 2013). However, the analysis showed that, given that yield can be optimized at 35,000 plants ha−1 across diverse environments, excessive over-seeding may be occurring in many cases, resulting in economic losses to producers due to high seed costs. The analysis also provides guidance or a threshold for producers making replanting decisions. Following stand loss, if a producer measures a plant population density at or above 35,000 plants ha−1 without frequent, large gaps between plants, replanting may not be necessary. While this analysis is focused on the effects of population density on lint yield, there are additional factors that producers must consider when making seeding rate decision for cotton, and many of these are discussed in the following section.
4.3. Field trial biomass partitioning and production per plant Dramatic differences in biomass production per plant across population densities in our field test reflect the ability of the cotton plant to adapt physiologically to its system and available resources (Fig. 2). It is notable that while leaf (P = 0.2854) and stem (P = 0.3374) biomass trended numerically lower on an aerial basis in lower population densities than in higher densities, reproductive biomass definitively did not (P = 0.9895). The ratio of reproductive biomass to total biomass was stable among population density treatments over time (P = 0.8000) and was quite high by 102 days after planting. These observations suggest that canopy size or photosynthetic leaf area was not the limiting factor in reproductive potential or lint yield, even at the lowest population density tested. In a comparison of high- and low-density cotton, Pettigrew et al. (2013) found that increased light interception by a higher-density cotton crop was offset by the ability of the leaves of a lower-density crop to utilize sunlight more efficiently, creating a counteracting effect that equalized productivity and yield across density treatments. In a comparison of older and newer cotton cultivars, Pettigrew et al. (2013) found that newer cultivars have higher lint percentage and lint index than older cultivars, but with similar amounts of vegetative biomass.
4.2. Non-yield impacts of population density
4.4. Summary
In addition to lint yield, there are a variety of crop responses, both positive and negative for cotton production, depending on the production system and environment, that result from population density and may create tradeoffs for cotton producers. The cotton plant has an extensive ability to adapt its anatomical structure and physiological functions to accommodate changes in plant population density. For example, higher population density favors more fruit production along the primary plant axis, while lower population density results in increased growth of monopodial and sympodial branches and fruit set on those branches (Bednarz et al., 2000). This has implications on crop maturity and harvest efficiency, as secondary and tertiary bolls that arise with low population density may require additional time for full development (Gwathmey et al., 2011; Siebert et al., 2006). Wide variation in maturity among bolls with low population density can diminish fiber quality (Bednarz et al., 2005, 2006; Jones and Wells, 1998), though fruit retention has been observed to be higher at lower population densities (Bednarz et al., 2000; Jones and Wells, 1998; Siebert and Stewart, 2006; Siebert et al., 2006). Increased lateral branching with low population density can result in shorter cotton with less vegetative growth, and less vegetative plants require less plant growth regulator application to control growth, though more extensive branching and thicker stems on each plant can reduce harvest efficiency (Larson et al., 2004). In addition to shifts in the position of fruit-set and fruit maturity, boll size is affected by changes in
Studies on the effects of population density in cotton have been carried out around the world in many environments, though no work had been done to quantitively synthesize the yield data collectively, to better pinpoint a population density threshold for lint yield. And, notably, little had been reported on the effects of population density in lower-yielding dryland environments. The dryland data collected in this study, in a semi-arid environment in Texas, showed that yield was not affected by population density, similar to higher-yielding environments over the same range in density. Following normalization of all data, including literature and dryland datasets, a breakpoint at 35,000 plants ha−1 was identified, which can be interpreted as the minimum population density at which yield may be optimized. This rate is lower than the common population density recommendation for cotton of about 81,000 plants ha−1 and substantially lower than the extremely high densities at which many producers plant (129,000 seeds ha−1 or greater) in challenging growing conditions. The analysis showed that yield will decline precipitously below 35,000 plants ha−1, exposing the enormous risk to cotton producers in approaching this low density, particularly if significant seed or plant loss is expected. However, the analysis suggests that excessive over-seeding may be occurring in many cases, resulting in economic losses to producers. The analysis also provides guidance on a threshold for producers facing replanting decisions.
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Acknowledgements
Kaggwa-Asiimwe, R., Andrade-Sanchez, P., Wang, G., 2013. Plant architecture influences growth and yield response of upland cotton to population density. Field Crops Res. 145, 52–59. Larson, J.A., Gwathmey, C.O., Roberts, R.K., Hayes, R.M., 2004. Effects of plant population density on net revenues from ultra-narrow-row cotton. J. Cotton Sci. 8, 69–82. Maddonni, G.A., Otegui, M.E., Cirilo, A.G., 2001. Plant population density, row spacing and hybrid effects on maize canopy architecture and light attenuation. Field Crops Res. 71, 183–193. NCCA, 2017. National Cotton council of America. Cotton Production Costs and Returns: United States. Published online. http://www.cotton.org/econ/cropinfo/ costsreturns/usa.cfm. Pettigrew, W.T., Johnson, J.T., 2005. Effects of different seeding rates and plant growth regulators on early-planted cotton. J. Cotton Sci. 9, 189–198. Pettigrew, W.T., Meredith, W.R., Zeng, L., 2013. Response of obsolete and modern cotton genotypes to varying plant densities. J. Cotton Sci. 17, 253–262. Shaver, B.R., Agudelo, P., Martin, S.B., 2017. Damage functions for sting nematode (Belonolaimus longicaudatus) on bermudagrass turf. Int. Turfgrass Soc. Res. J. 13, 517–523. Shurley, W.D., Smith, A.R., Culpepper, A.S., Roberts, P.M., et al., 2010. A budget analysis of glyphosate-resistant palmer amaranth control and seed technology choices in Georgia cotton. P. 467-472 Boyd, S. (Ed.), Proceedings of the 2010 Beltwide Cotton Conference. Siebert, J.D., Stewart, A.M., 2006. Influence of plant density on cotton response to mepiquat chloride application. Agron. J. 98, 1634–1639. Siebert, J.D., Stewart, A.M., Leonard, B.R., 2006. Comparative growth and yield of cotton planted at various densities and configurations. Agron. J. 98, 562–568. Stewart, S., 2005. Suggested guidelines for plant growth regulator use on Louisiana cotton. Louisiana Cooperative Extension Service. Publication Number 2919. Street, J.E., Buchanan, G.A., Crowley, R.H., McGuire, J.A., 1981. Influence of cotton (Gossypium hirsutum) densities on competitiveness of pigweed (Amaranthus spp.) and sicklepod (Cassia obtusifolia). Weed Sci. 29, 253–256. Tetio-Kagho, F., Gardner, F.P., 1988. Responses of maize to plant population density. II. Reproductive development, yield, and yield adjustments. Agron. J. 80, 935–940. Toms, J.D., Lesperance, M.L., 2003. Piecewise regression: a tool for identifying ecological thresholds. Ecology 84, 2034–2041. USDA-ERS, 2017. Recent Trends in GE Adoption. Updated in July 12, 2017. Published online. https://www.ers.usda.gov/data-products/adoption-of-geneticallyengineered-crops-in-the-us/recent-trends-in-ge-adoption/. Wheeler, T.A., Woodward, J.E., Mullinix, B.G., 2010. Effects of seeding rate on verticillium wilt incidence, yield, and value for three cotton cultivars. J. Cotton Sci. 14, 173–180. Wrather, J.A., Phipps, B.J., Stevens, W.E., Phillips, A.S., Vories, E.D., 2008. Cotton planting date and plant population effects on yield and fiber quality in the Mississippi Delta. J. Cotton Sci. 12, 1–7. Zhi, X., Han, Y., Li, Y., Wang, G., Du, W., Li, X., Mao, S., Feng, L., 2016. Effects of plant density on cotton yield components and quality. J. Integr. Agric. 15, 1469–1479.
We appreciate the agronomic expertise of Jonathan Ramirez, Tamara Royer, and Joseph Ramirez and acknowledge their contributions in carrying out the field operations of this project. This work was supported byCotton Inc. and the Texas State Support Committee (grant number 08-293TX). This work was also supported by Texas A&M AgriLife Research and the USDA National Institute of Food and Agriculture, Hatch project1011694. References Adams, C., Erickson, J., Singh, M., 2015. Investigation and synthesis of sweet sorghum crop responses to nitrogen and potassium fertilization. Field Crops Res. 178, 1–7. Bednarz, C.W., Bridges, D.C., Brown, S.M., 2000. Analysis of cotton yield stability across population densities. Agron. J. 92, 128–135. Bednarz, C.W., Shurley, W.D., Anthony, W.S., Nichols, R.L., 2005. Yield, quality, and profitability of cotton produced at varying plant densities. Agron. J. 97, 235–240. Bednarz, C.W., Nichols, R.L., Brown, S.M., 2006. Plant density modified within-canopy cotton fiber quality. Crop Sci. 46, 950–956. Collins, G., 2015. Seeding Rates and Plant Populations. NC State Extension. Published online:. (Accessed 26 April 2018). https://cotton.ces.ncsu.edu/2015/04/seedingrates-and-plant-populations-collins-edmisten/. Dai, J., Li, W., Tang, W., Zhang, D., Li, Z., Lu, H., Eneji, A.E., Dong, H., 2015. Manipulation of dry matter accumulation and partitioning with plant density in relation to yield stability of cotton under intensive management. Field Crops Res. 180, 207–215. DeltaPine, 2017. AgKnowledge Spotlight: Cotton Planting Populations and Row Spacing. Published online. (Accessed 26 April 2018). http://www.aganytime.com/Pages/ Article.aspx?article=1288. Donahoe, M., 2010. Calculating Cotton Seeding Rates. UF/IFAS Northwest District Office. Published online. (Accessed: 26 April 2018). http://lyra.ifas.ufl.edu/LyraServlet? command=getNewsletter&oid=634479&path=0.0&countyID=district1.ifas.ufl. edu. Edwards, J.T., Purcell, L.C., 2005. Soybean yield and biomass responses to increasing plant population among diverse maturity groups: I. Agronomic characteristics. Crop Sci. 45, 1770–1777. Feng, L., Mathis, G., Ritchie, G., Han, Y., Li, Y., Wang, G., Zhi, X., Bednarz, C.W., 2014. Optimizing irrigation and plant density for improved cotton yield and fiber quality. Agron. J. 106, 1111–1118. Gwathmey, C.O., Steckel, L.E., Larson, J.A., Mooney, D.F., 2011. Lower limits of cotton seeding rates in alternative row widths and patterns. Agron. J. 103, 584–592. Jones, M.A., Wells, R., 1998. Fiber yield and quality of cotton grown at two divergent population densities. Crop Sci. 38, 1190–1195.
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Determination of a plant population density threshold for optimizing cotton lint yield: A synthesis
T
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Curtis Adamsa, , Santanu Thapaa, Emi Kimurab a b
Texas A&M AgriLife Research, 11708 Highway 70 South, Vernon, TX, 76384, USA Texas A&M AgriLife Extension, 11708 Highway 70 South, Vernon, TX, 76384, USA
A R T I C LE I N FO
A B S T R A C T
Keywords: Cotton lint Population density Threshold Yield optimization
Technology fees associated with modern cotton cultivars have increased seed costs considerably, giving producers the impetus to reduce plant population density, where possible. Most recent studies on cotton population density, conducted across diverse environments, report similar patterns of crop response: decreases in lint yield only at very low densities, with generally consistent yield across all higher densities. But no work had been done to bring the literature together, quantitively synthesizing the yield data collectively, to better pinpoint a population density threshold. And, notably, little had been reported on the effects of population density in loweryielding dryland environments. Quantitatively synthesizing population density datasets from the literature, including our own dryland data, was the objective of this research. The dryland data showed that lint yield and biomass partitioning were not affected by population density, similar to higher-yielding environments over the same range in density. Following normalization of all lint yield data (literature and dryland datasets), a breakpoint in population density at 35,000 plants ha−1 was identified, which can be interpreted as the minimum plant density at which yield may be optimized. This rate is lower than the common density recommendation of 81,000 plants ha−1 and substantially lower than the risk-averse rates at which many producers plant (129,000 seeds ha−1 or greater). The analysis showed that yield will decline precipitously below 35,000 plants ha−1, exposing the enormous risk to cotton producers in approaching this low density, particularly if significant seed or plant loss is expected. However, the analysis suggests that excessive over-seeding may be occurring in many cases, resulting in economic losses to producers. The analysis also provides guidance on a threshold for producers facing replanting decisions.
1. Introduction In recent decades, breeding and genetic modification developments in cotton (Gossypium hirsutum L.) have made substantial progress in improving the crop, in terms of yield, quality, and ease of management (USDA-ERS, 2017). These developments have also increased the cost of seed substantially (Shurley et al., 2010). The National Cotton Council of America estimated in their 2015–2016 cotton production budgets that seed costs account for about 16% of the total operating costs for U.S. cotton producers (NCCA, 2017). High seed costs create an important impetus for producers to reduce planting population densities, where possible. Population density plays a central role in optimizing productivity, management, and economic return of any field crop, including cotton. Plant population density in cotton has a substantial impact on plant architecture and other canopy dynamics, which influences disease and
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pest incidence, harvest efficiency and timing, water use, and other factors (Kaggwa-Asiimwe et al., 2013; Maddonni et al., 2001; Stewart, 2005). Population densities that are too high or too low can have negative agronomic consequences. Agronomists and Extension specialists at the University of Florida and North Carolina State University recommend achieving final population densities of about 81,000 plants ha−1 (2.5 plants ft−1 on 40 in. rows) to optimize yield and associated agronomic factors (Collins, 2015; Donahoe, 2010). A major cotton seed supplier, DeltaPine, recommended achieving the same rate (DeltaPine, 2017). Donahoe (2010) stated that optimal yields can be achieved at about 50,000 plants ha−1 (1.5 plants ft−1 on 40 in. rows), but mentioned that planting as high as 129,000 plants ha−1 (4.0 plants ft−1 on 40 in. rows) may be necessary if soil or seed conditions are poor (e.g. soil crusting is likely or the percent viable seed is low). Collins (2015) stated that over-seeding by about 20% is needed to achieve a desired density in ideal conditions and that even greater seeding excesses are
Corresponding author. E-mail address: [email protected] (C. Adams).
https://doi.org/10.1016/j.fcr.2018.10.005 Received 16 August 2018; Received in revised form 9 October 2018; Accepted 9 October 2018 0378-4290/ © 2018 Elsevier B.V. All rights reserved.
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to 33,000, 51,000, 69,000, 87,000, and 105,000 plants ha−1 in Linqing, China. In Georgia, USA, Bednarz et al. (2005) found that lint yield was reduced at 36,000 plants ha−1 relative to yield at 90,000 and 126,000 plants ha−1. In Henan, China, yield was reduced at 15,000 plants ha−1 relative to peak yields obtained at 51,000 to 87,000 plants ha−1 (Zhi et al., 2016). A two-year study in Texas, USA (Feng et al., 2014) showed no difference in lint yield from 75,300 to 226,000 plants ha−1 (a single year’s data from another location showed differences, but the crop was damaged by hail). In a relative outlier, Pettigrew and Johnson (2005) showed that lint yield peaked with plant population densities between 90,000 to 130,000 plants ha−1, while five percent lower yield was observed at 70,000 plants ha−1, in Mississippi, USA. In summary, most recent studies on cotton population density report decreases in lint yield only at very low population densities, with peak yields across broad population ranges, with no decreases in yield evident even at extremely high densities. A qualitative assessment of the literature suggests that an optimal population density for yield is low, likely far lower than current population density recommendations. No work has been done to bring this literature together, quantitatively synthesizing the yield data collectively. And, notably, most recent studies on population density were conducted in relatively highyielding rainfed or irrigated conditions and results in lower-yielding dryland conditions are missing. In this study, our first objective was to field test effects of cotton population density in lower-yielding dryland conditions, with a focus on lint yield and plant biomass partitioning. Our second objective was to combine our lint yield data with comparable data from other published studies, synthesizing the collective database to better understand how yield is affected by population density in cotton.
Table 1 A summary of lint yield in cotton as affected by population density in studies reported in the literature since year 2000. To be included, the studies conformed to the criteria given in the methods section. Source
Location
Year
Population Density (plants ha−1)
Lint Yield (kg ha−1)
Bednarz et al., 2005
Georgia, USA
2001– 2002
Dai et al., 2015
Linqing, China
2010–2013
Feng et al., 2014
Texas, USA
2007
Feng et al., 2014
Texas, USA
2008
Gwathmey et al., 2011 Gwathmey et al., 2011 Gwathmey et al., 2011 Gwathmey et al., 2011 Pettigrew and Johnson, 2005
Tennessee, USA
2006–2008
Tennessee, USA
2006–2008
Tennessee, USA
2006–2008
Tennessee, USA
2006–2008
Mississippi, USA
2001–2004
Mississippi, USA
2001
36,000 90,000 126,000 215,000 15,000 33,000 51,000 69,000 87,000 105,000 75,300 150,600 226,000 75,300 150,600 226,000 61,000 114,000 30,000 63,000 61,000 114,000 30,000 63,000 70,000 90,000 110,000 130,000 23,782 33,976 67,952 135,904 23,782 33,976 67,952 135,904 23,782 33,976 67,952 135,904 23,782 33,976 67,952 135,904 15,000 51,000 87,000 15,000 51,000 87,000
1246 b 1345 a 1376 a 1363 a 1771 b 2204 a 2231 a 2273 a 2292 a 2277 a 1278 a 1294 a 1235 a 1633 a 1776 a 1616 a 1752 a 1780 a 1459 b 1756 a 1189 a 1176 a 1087 b 1234 a 1393 b 1441 a 1471 a 1465 a 1360 a 1360 a 1310 a 1250 a 1570 a 1650 a 1630 a 1630 a 850 b 1060 a 1190 a 1140 a 1340 b 1660 a 1760 a 1650 a 950 b 1533 a 1570 a 1313 b 1546 a 1516 a
Wrather et al., 2008
Wrather et al., 2008
Mississippi, USA
2002
Wrather et al., 2008
Mississippi, USA
2003
Wrather et al., 2008
Mississippi, USA
2004
Zhi et al., 2016
Henan, China
2012
Zhi et al., 2016
Henan, China
2013
2. Materials and methods 2.1. Field study design, management, and analysis A two-year field study was conducted at the Texas A&M AgriLife Research Station at Chillicothe, TX during the 2016 and 2017 summer growing seasons on an Abilene clay loam soil in dryland conditions. The area is characterized by a semi-arid climate, with annual average precipitation of about 635 mm, mostly occurring as spring and fall precipitation with minimal summer rainfall, and high rates of evapotranspiration. ‘Phytogen 333 WRF’ cotton was planted in four rows with a row width of 102 cm (40 in.) on 9 June 2016 and 8 June 2017. Plot lengths were 59 m and 40 m in 2016 and 2017, respectively. The study was arranged as a randomized complete block design with four replications of four cotton population density rate treatments. Population densities, as determined by stand counts taken two weeks after planting, were 67,300, 115,300, and 145,400 plants ha−1. All plots received in-season inputs of herbicide, fertilizer, insecticide, and defoliation chemicals according to current recommendations for the region. Measurements of plant biomass partitioning were taken from 2-m sections of crop row on three occasions in each year: 48, 70, and 102 DAP in 2016 and 47, 77, and 104 DAP in 2017. In analysis and presentation, corresponding sampling times (days) were averaged between years. After cutting the stem just above the soil level, biomass samples were transported to the lab and partitioned into leaf, stem, and reproductive biomass components and placed in paper bags. Reproductive biomass components included flowers, squares, and bolls. Samples were dried at 60 °C in an air-forced oven. Stand counts were used to convert aerial biomass measurements into biomass per plant. Plots were harvested by a stripper on 28 November 2016 and 17 November 2017. Seed cotton samples were ginned to determine lint yield. In statistical analysis of the field data, population density treatments were considered a fixed effect in the statistical model, while years and replications (blocks) were considered random effects. The data was analyzed with SAS 9.3 (SAS Institute, Cary, NC). Differences between
needed in poor planting conditions. In the semi-arid environments of central to west Texas, for example, where planting conditions are commonly challenging, regional producers often plant at seeding rates at or in excess of 129,000 seeds ha−1. To put these figures into perspective, recent studies provide needed research-based information on how lint yield responds to population density in advanced modern cotton cultivars (Table 1). Wrather et al. (2008) reported that cotton lint yield was reduced at 24,000 plants ha−1 relative to yield at 34,000, 68,000, and 136,000 plants ha−1 in just two years out of four in a study (no differences reported in the other two years) conducted in Mississippi, USA. Gwathmey et al. (2011) found that lint yield was reduced at 30,000 plants ha−1 relative to yield from 61,000 plants ha−1 to 114,000 plants ha−1 in Tennessee, USA. In a report with relatively high yields, Dai et al. (2015) found lint to be reduced at 15,000 plants ha−1 compared 12
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treatment yields were determined by ANOVA using Proc Mixed, including post-hoc means comparisons adjusted using the Tukey procedure, with a statistical threshold of 0.05. The assumption of normality was checked for all response variables. Statistical analysis of the biomass partitioning data was done with repeated measures ANOVA in SAS with the Proc MIXED procedure. 2.2. Literature and field trial population density analysis Data was mined from the literature (Table 1) for a broad-scale analysis of the effect of population density on lint yield in modern cotton varieties. Data were included from studies conducted in a variety of environments that fit the following predetermined criteria: reports from year 2000 or later; reported lint yield (i.e. seed cotton was not used); used fixed row width and intra-row spacing (e.g. no skip row or hill drop); and presentation of numeric data that was not integrated across other treatments factors with significant interactions or an unclear interaction status. The lint yield data from the currently reported experiment was also included. Literature yields and plant densities were reported in many units (e.g. lbs ac−1) and measures (e.g. seeds ft−1), but were converted to consistent units of kg ha−1 and plants ha−1, respectively. The yield data was graphed as a function of plant population density and we observed that most datasets exhibited similar trends: yield decline only at very low population densities and a plateau in lint yield across all higher densities, whether the environment was relatively higher or lower yielding. This was corroborated by the statistical analysis presented with each dataset (Table 1). As a result, we elected to apply a normalization procedure to the data, so quantitative analysis could be performed on the combined dataset. This was done by summarizing the statistical analysis for all studies and calculating an overall average of datapoints along the yield plateaus (data values statistically considered peak yield within a given dataset) of all studies. This universal average was used in making a conversion factor for all datasets, which was individually multiplied by each dataset value, as follows: (universal plateau average / average of datapoints within dataset yield plateau) × dataset value = normalized value. Once normalized, a twosegment piecewise linear regression model was fitted to the data using SigmaPlot 13.0 (Systat Software, Inc., San Jose, CA). This procedure is commonly used to estimate “breakpoints” or trend transitions in datasets; it is often applied to determine the point or threshold when a response variable, such as yield, experiences a shift in response to treatment factors, similar to plateau models (Adams et al., 2015; Shaver et al., 2017; Toms and Lesperance, 2003).
Fig. 1. (A) Lint yield response data from the current report (marked by an asterisk) and literature reports on tests of population density in cotton; (B) the same yield response data, normalized to account for differences in yield among datasets; (C) and analysis of the normalized data, using a piecewise model, to identify a breakpoint (Xo) in the data, or the minimum population density at which yield is expected to be optimized.
3. Results precipitously at population densities below the 35,000 plants ha−1 threshold and that yield does not increase above this density threshold. A lack of data, especially at extremely low population densities (below about 15,000 plants ha−1), resulted in a lack of detail in the precise pattern of yield decline below 35,000 plants ha−1. Above 35,000 plants ha−1, the regression analysis shows that lint yield remains quite stable even to excessively high population densities.
3.1. Literature and field trial population density analysis Across the studies mined from the literature, population density ranged from 15,000 to about 226,000 plants ha−1 and lint yield ranged from about 1200 kg ha−1 to about 2300 kg ha−1 (Fig. 1A). Most literature datasets exhibited a rise in yield from very low plant population densities—as long as low enough densities were tested—and a yield plateau across all higher density treatments tested (see statistics in Table 1). The current study added data representative of dryland cotton cropping systems in semi-arid environments that are lower-yielding, which was missing from the collective dataset. In our study, population density ranged from 67,000 to 145,000 plants ha−1 and lint yield averaged 800 kg ha−1, with no differences among treatments in lint yield (Fig. 1A). In Fig. 1A, the dryland data is marked with an asterisk. A normalization procedure was applied to the datasets to enable a collective, quantitative analysis of the lint yield response to population density (Fig. 1B). In the normalized data, a breakpoint was identified at 35,336 plants ha−1 (1.1 plants ft−1 on 40 in. rows) by piecewise linear regression (Fig. 1C). The normalized data showed that yield will decline
3.2. Field trial biomass partitioning and production per plant When considered across the growing season (48, 74 and 103 days after planting), no differences were found among population density treatments in the partitioning of biomass to leaves, stems, and reproductive structures on an aerial basis (Fig. 2). More separation of treatments, without significance, was evident in leaf biomass (P = 0.2854) and stem biomass (P = 0.3374) than in reproductive (P = 0.9895) biomass. Total biomass productivity (P = 0.8921), as well as the ratio of reproductive to total biomass productivities (P = 0.8000), were the same across all population densities tested. 13
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Fig. 2. In-season measurements of crop biomass development and partitioning, including leaf, stem, reproductive, and total biomass, and the ratio of reproductive to total biomass, on aerial and per-plant bases. The error bars represent the standard error of the mean.
tested—and a yield plateau across all other treatments tested, no matter how high those treatments reached. At high population densities, some crops exhibit a yield decline associated with a full sigmodal yield response curve, while others do not (Edwards and Purcell, 2005; TetioKagho and Gardner, 1988). The lack of yield decline at high densities in cotton does not seem to be due to a lack of testing at high-enough plant population densities: at the highest density tested (226,000 plants ha−1) among the datasets, there would be a seed every 4.4 cm on rows spaced at 1 m, an excessive density in practical and physiological senses. A surprising trend in the data was the similarity in yield response to population density among datasets, despite the wide variation in yield among datasets. Wide variation in yield among the datasets indicates that non-treatment factors, such as moisture, nutrition, and genetic yield potential, often limited yield. But to the extent that the cotton crops reported in literature differed in yield potential and had other (non-population density) yield-limiting factors, lint yield responses to population density seem to have occurred independent of these factors. Pettigrew et al. (2013) reported no population density interactions with lint yield in cotton genotypes widely differing in yield potential. The breakpoint in lint yield with population density identified in
Differences in population density resulted in dramatic differences in biomass production per plant, however, as the plants adapted physiologically to their systems and available resources (Fig. 2). Percent differences in total biomass production per plant among population density treatments were minor by 48 days after planting, but quickly diverged. By 100 days after planting, total biomass per plant was 94% greater at a population density of 67,300 plants ha−1 than at 145,400 plants ha−1. For the same comparison, leaf biomass was 81% greater, stem biomass was 76% greater, and reproductive biomass was 106% greater. 4. Discussion 4.1. Literature and field trial population density analysis When viewed together, there were interesting trends in lint yield with population density across literature datasets (Fig. 1A). The tested population densities ranged about 12-fold and the reported yields, including the data from the current dryland study, ranged nearly threefold. Most literature datasets exhibited a rise in yield from very low plant population densities—as long as sufficiently low densities were 14
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population density. Boll size is inversely related to population density (Bednarz et al., 2000), which can also affect harvest efficiency, as lint recovery is generally lower from smaller bolls. Planting at higher densities can delay the initiation of fruiting and increase water use (Bednarz et al., 2005; Zhi et al., 2016). Planting at high densities has been shown to improve cotton competitiveness with weeds (Gwathmey et al., 2011; Street et al., 1981), which may be most important in modern organic cotton production. There are examples of diseases that can be controlled, within limits, by population density, such as Verticillium wilt incidence being negatively related to increased seeding rate (Wheeler et al., 2010). These factors, and others not discussed here, need to be weighed, along with yield and expected seed and plant losses, in making planting population decisions in the field.
this analysis (Fig. 1C), which can be interpreted as the minimum population density at which yield may be optimized, was about 35,000 plants ha−1 (1.1 plant ft−1 on 40 in. rows). This rate is substantially lower than common density recommendations for cotton. The most common population density recommendation we found, across University Extension agencies and industry, was 81,000 plants ha−1 (2.5 plants ft−1 on 40 in. rows) (Collins, 2015; Donahoe, 2010; DeltaPine, 2017). This is a recommendation for final population density, which can be achieved only by over-seeding to account for inevitable, and often unpredictable, seed and plant loss. Collins (2015) stated that overseeding by about 20% is needed to achieve a desire density in ideal conditions. Donahoe (2010) recommended planting as high as 129,000 plants ha−1 (4.0 plants ft−1 on 40 in. rows) when soil or seed conditions are poor. These conditions would include soil crusting, excessively hot or cool temperatures, poor seed quality or germination percentage, and other situations. Over-seeding would also be needed if plant loss, following emergence, was expected due to pests, disease, high winds/ blowing sand, or other factors. In central and west Texas, where planting and growing conditions are often challenging, regional producers commonly plant at rates at or even in excess of 129,000 seeds ha−1. In our analysis, the precipitous decline in yield that was evident at population densities below 35,000 plants ha−1 shows the enormous risk to cotton producers in approaching a density this low, particularly if substantial seed or plant loss is expected. Even in ideal environmental conditions, seed spacing is usually not perfect in practice and, at this low population density, any seed gaps would result in yield loss (Pettigrew et al., 2013). However, the analysis showed that, given that yield can be optimized at 35,000 plants ha−1 across diverse environments, excessive over-seeding may be occurring in many cases, resulting in economic losses to producers due to high seed costs. The analysis also provides guidance or a threshold for producers making replanting decisions. Following stand loss, if a producer measures a plant population density at or above 35,000 plants ha−1 without frequent, large gaps between plants, replanting may not be necessary. While this analysis is focused on the effects of population density on lint yield, there are additional factors that producers must consider when making seeding rate decision for cotton, and many of these are discussed in the following section.
4.3. Field trial biomass partitioning and production per plant Dramatic differences in biomass production per plant across population densities in our field test reflect the ability of the cotton plant to adapt physiologically to its system and available resources (Fig. 2). It is notable that while leaf (P = 0.2854) and stem (P = 0.3374) biomass trended numerically lower on an aerial basis in lower population densities than in higher densities, reproductive biomass definitively did not (P = 0.9895). The ratio of reproductive biomass to total biomass was stable among population density treatments over time (P = 0.8000) and was quite high by 102 days after planting. These observations suggest that canopy size or photosynthetic leaf area was not the limiting factor in reproductive potential or lint yield, even at the lowest population density tested. In a comparison of high- and low-density cotton, Pettigrew et al. (2013) found that increased light interception by a higher-density cotton crop was offset by the ability of the leaves of a lower-density crop to utilize sunlight more efficiently, creating a counteracting effect that equalized productivity and yield across density treatments. In a comparison of older and newer cotton cultivars, Pettigrew et al. (2013) found that newer cultivars have higher lint percentage and lint index than older cultivars, but with similar amounts of vegetative biomass.
4.2. Non-yield impacts of population density
4.4. Summary
In addition to lint yield, there are a variety of crop responses, both positive and negative for cotton production, depending on the production system and environment, that result from population density and may create tradeoffs for cotton producers. The cotton plant has an extensive ability to adapt its anatomical structure and physiological functions to accommodate changes in plant population density. For example, higher population density favors more fruit production along the primary plant axis, while lower population density results in increased growth of monopodial and sympodial branches and fruit set on those branches (Bednarz et al., 2000). This has implications on crop maturity and harvest efficiency, as secondary and tertiary bolls that arise with low population density may require additional time for full development (Gwathmey et al., 2011; Siebert et al., 2006). Wide variation in maturity among bolls with low population density can diminish fiber quality (Bednarz et al., 2005, 2006; Jones and Wells, 1998), though fruit retention has been observed to be higher at lower population densities (Bednarz et al., 2000; Jones and Wells, 1998; Siebert and Stewart, 2006; Siebert et al., 2006). Increased lateral branching with low population density can result in shorter cotton with less vegetative growth, and less vegetative plants require less plant growth regulator application to control growth, though more extensive branching and thicker stems on each plant can reduce harvest efficiency (Larson et al., 2004). In addition to shifts in the position of fruit-set and fruit maturity, boll size is affected by changes in
Studies on the effects of population density in cotton have been carried out around the world in many environments, though no work had been done to quantitively synthesize the yield data collectively, to better pinpoint a population density threshold for lint yield. And, notably, little had been reported on the effects of population density in lower-yielding dryland environments. The dryland data collected in this study, in a semi-arid environment in Texas, showed that yield was not affected by population density, similar to higher-yielding environments over the same range in density. Following normalization of all data, including literature and dryland datasets, a breakpoint at 35,000 plants ha−1 was identified, which can be interpreted as the minimum population density at which yield may be optimized. This rate is lower than the common population density recommendation for cotton of about 81,000 plants ha−1 and substantially lower than the extremely high densities at which many producers plant (129,000 seeds ha−1 or greater) in challenging growing conditions. The analysis showed that yield will decline precipitously below 35,000 plants ha−1, exposing the enormous risk to cotton producers in approaching this low density, particularly if significant seed or plant loss is expected. However, the analysis suggests that excessive over-seeding may be occurring in many cases, resulting in economic losses to producers. The analysis also provides guidance on a threshold for producers facing replanting decisions.
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Acknowledgements
Kaggwa-Asiimwe, R., Andrade-Sanchez, P., Wang, G., 2013. Plant architecture influences growth and yield response of upland cotton to population density. Field Crops Res. 145, 52–59. Larson, J.A., Gwathmey, C.O., Roberts, R.K., Hayes, R.M., 2004. Effects of plant population density on net revenues from ultra-narrow-row cotton. J. Cotton Sci. 8, 69–82. Maddonni, G.A., Otegui, M.E., Cirilo, A.G., 2001. Plant population density, row spacing and hybrid effects on maize canopy architecture and light attenuation. Field Crops Res. 71, 183–193. NCCA, 2017. National Cotton council of America. Cotton Production Costs and Returns: United States. Published online. http://www.cotton.org/econ/cropinfo/ costsreturns/usa.cfm. Pettigrew, W.T., Johnson, J.T., 2005. Effects of different seeding rates and plant growth regulators on early-planted cotton. J. Cotton Sci. 9, 189–198. Pettigrew, W.T., Meredith, W.R., Zeng, L., 2013. Response of obsolete and modern cotton genotypes to varying plant densities. J. Cotton Sci. 17, 253–262. Shaver, B.R., Agudelo, P., Martin, S.B., 2017. Damage functions for sting nematode (Belonolaimus longicaudatus) on bermudagrass turf. Int. Turfgrass Soc. Res. J. 13, 517–523. Shurley, W.D., Smith, A.R., Culpepper, A.S., Roberts, P.M., et al., 2010. A budget analysis of glyphosate-resistant palmer amaranth control and seed technology choices in Georgia cotton. P. 467-472 Boyd, S. (Ed.), Proceedings of the 2010 Beltwide Cotton Conference. Siebert, J.D., Stewart, A.M., 2006. Influence of plant density on cotton response to mepiquat chloride application. Agron. J. 98, 1634–1639. Siebert, J.D., Stewart, A.M., Leonard, B.R., 2006. Comparative growth and yield of cotton planted at various densities and configurations. Agron. J. 98, 562–568. Stewart, S., 2005. Suggested guidelines for plant growth regulator use on Louisiana cotton. Louisiana Cooperative Extension Service. Publication Number 2919. Street, J.E., Buchanan, G.A., Crowley, R.H., McGuire, J.A., 1981. Influence of cotton (Gossypium hirsutum) densities on competitiveness of pigweed (Amaranthus spp.) and sicklepod (Cassia obtusifolia). Weed Sci. 29, 253–256. Tetio-Kagho, F., Gardner, F.P., 1988. Responses of maize to plant population density. II. Reproductive development, yield, and yield adjustments. Agron. J. 80, 935–940. Toms, J.D., Lesperance, M.L., 2003. Piecewise regression: a tool for identifying ecological thresholds. Ecology 84, 2034–2041. USDA-ERS, 2017. Recent Trends in GE Adoption. Updated in July 12, 2017. Published online. https://www.ers.usda.gov/data-products/adoption-of-geneticallyengineered-crops-in-the-us/recent-trends-in-ge-adoption/. Wheeler, T.A., Woodward, J.E., Mullinix, B.G., 2010. Effects of seeding rate on verticillium wilt incidence, yield, and value for three cotton cultivars. J. Cotton Sci. 14, 173–180. Wrather, J.A., Phipps, B.J., Stevens, W.E., Phillips, A.S., Vories, E.D., 2008. Cotton planting date and plant population effects on yield and fiber quality in the Mississippi Delta. J. Cotton Sci. 12, 1–7. Zhi, X., Han, Y., Li, Y., Wang, G., Du, W., Li, X., Mao, S., Feng, L., 2016. Effects of plant density on cotton yield components and quality. J. Integr. Agric. 15, 1469–1479.
We appreciate the agronomic expertise of Jonathan Ramirez, Tamara Royer, and Joseph Ramirez and acknowledge their contributions in carrying out the field operations of this project. This work was supported byCotton Inc. and the Texas State Support Committee (grant number 08-293TX). This work was also supported by Texas A&M AgriLife Research and the USDA National Institute of Food and Agriculture, Hatch project1011694. References Adams, C., Erickson, J., Singh, M., 2015. Investigation and synthesis of sweet sorghum crop responses to nitrogen and potassium fertilization. Field Crops Res. 178, 1–7. Bednarz, C.W., Bridges, D.C., Brown, S.M., 2000. Analysis of cotton yield stability across population densities. Agron. J. 92, 128–135. Bednarz, C.W., Shurley, W.D., Anthony, W.S., Nichols, R.L., 2005. Yield, quality, and profitability of cotton produced at varying plant densities. Agron. J. 97, 235–240. Bednarz, C.W., Nichols, R.L., Brown, S.M., 2006. Plant density modified within-canopy cotton fiber quality. Crop Sci. 46, 950–956. Collins, G., 2015. Seeding Rates and Plant Populations. NC State Extension. Published online:. (Accessed 26 April 2018). https://cotton.ces.ncsu.edu/2015/04/seedingrates-and-plant-populations-collins-edmisten/. Dai, J., Li, W., Tang, W., Zhang, D., Li, Z., Lu, H., Eneji, A.E., Dong, H., 2015. Manipulation of dry matter accumulation and partitioning with plant density in relation to yield stability of cotton under intensive management. Field Crops Res. 180, 207–215. DeltaPine, 2017. AgKnowledge Spotlight: Cotton Planting Populations and Row Spacing. Published online. (Accessed 26 April 2018). http://www.aganytime.com/Pages/ Article.aspx?article=1288. Donahoe, M., 2010. Calculating Cotton Seeding Rates. UF/IFAS Northwest District Office. Published online. (Accessed: 26 April 2018). http://lyra.ifas.ufl.edu/LyraServlet? command=getNewsletter&oid=634479&path=0.0&countyID=district1.ifas.ufl. edu. Edwards, J.T., Purcell, L.C., 2005. Soybean yield and biomass responses to increasing plant population among diverse maturity groups: I. Agronomic characteristics. Crop Sci. 45, 1770–1777. Feng, L., Mathis, G., Ritchie, G., Han, Y., Li, Y., Wang, G., Zhi, X., Bednarz, C.W., 2014. Optimizing irrigation and plant density for improved cotton yield and fiber quality. Agron. J. 106, 1111–1118. Gwathmey, C.O., Steckel, L.E., Larson, J.A., Mooney, D.F., 2011. Lower limits of cotton seeding rates in alternative row widths and patterns. Agron. J. 103, 584–592. Jones, M.A., Wells, R., 1998. Fiber yield and quality of cotton grown at two divergent population densities. Crop Sci. 38, 1190–1195.
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