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Effect of growing degree days, day of the year, and cropping systems on reproductive development of Kernza in Kansas

Effect of growing degree days, day of the year, and cropping systems on reproductive development... AbbreviationsDOYday of the yearferttreatment with fertilizerGDDgrowing degree daysHFhead biomass fractionHMhead meristemHMHhead meristem heightIWGintermediate wheatgrassIWGnintermediate wheatgrass seeded in narrow rowsIWGwintermediate wheatgrass seeded in wide rowsLFleaf biomass fractionMSCmean stage countRSEresidual standard errorSFstem biomass fractionTLIThe Land InstituteINTRODUCTIONIntermediate wheatgrass [Thinopyrum intermedium (Host) Barkworth & D.R. Dewey] (IWG) is an introduced, cool‐season perennial grass from Europe and Asia widely adapted to the Great Plains of North America. As a forage grass, IWG is highly palatable and drought tolerant, making it ideal for dryland agriculture. Recognizing the perennial crop ecosystem service advantages compared with annuals, efforts to domesticate IWG as a perennial grain crop were initiated in the 1980s, and continuous breeding has been underway at The Land Institute (TLI) since 2003 (DeHaan et al., 2016; Wagoner, 1990). Although IWG seed has been produced for the forage industry for decades (Kruger, 2015), significant barriers to adoption exist for IWG grown for Kernza perennial grain production, including low and inconsistent grain yields, lack of agronomic knowledge for producers, and no approved chemistries for weed and pest management (Culman et al., 2013; Jungers et al., 2017; Lanker et al., 2013; Tautges et al., 2018). To increase productivity, profitability, and early adoption of Kernza perennial grain production, in lieu of low grain yields, dual‐purpose management as both a grain and forage have been suggested (Dick et al., 2018; Favre et al., 2019; Pugliese et al., 2019; Ryan et al., 2018).Harvesting IWG biomass in the summer after grain harvest and in the fall after regrowth could be a viable option for dual‐purpose production systems in the upper Midwest (Jungers et al., 2017; Pugliese et al., 2019). Leaf and stem biomass remaining after IWG grain harvest can exceed 10 Mg ha−1, but the low quality of the straw (crude protein < 60 g kg−1 and relative feed value < 70 g kg−1) limits its utility and value as forage (Favre et al., 2019; Hunter, Sheaffer, Culman, Lazarus, & Jungers, 2020). Planting IWG in mixtures with a perennial legume, like alfalfa (Medicago sativa L.), could improve the seasonal forage quality and yield (Barnett & Posler, 1983; Sleugh et al., 2000). Dual‐purpose IWG–alfalfa management systems may (a) help maintain stable IWG grain yields under dual‐purpose management system (Tautges et al., 2018); (b) require less synthetic N fertilizer by gradually increasing the soil organic N pool over time through biological N fixation and N transfer, thus potentially reducing the risks of nitrate leaching and N2O emissions associated with N fertilizer applications (Crews et al., 2016); and (c) reduce annual weed pressure through multiple haying events and reduce pesticide applications compared with intensively managed grass or cereal monocultures (Frame & Newbould, 1986).Dual‐purpose management of winter wheat (Triticum aestivum L.) is common during late fall, winter, and spring in Kansas and the High Plains (Lollato et al., 2017). Grazing has little effect on wheat yield if fertility is adequate and livestock are removed before tillers reach the hollow stem stage in the spring (Holman et al., 2009). Grazing IWG grown for grain in winter and early spring could increase net returns and extend the spring grazing season for grazers waiting to move livestock to warm‐season native pasture. Simulated grazing of IWG before late boot stage improved stand persistence and tiller recruitment ratios (Hendrickson et al., 2005), though it is uncertain how spring grazing or haying before stem elongation (early vegetation) will affect grain yields. Like in wheat, IWG apical head meristem (HM) development begins after the induction conditions are met, and HM are visible by dissection of tillers before stem elongation (Canode et al., 1972). The optimum number of tillers with HM at or below spring grazing height is unknown for maximum forage and grain production, but spring grazing or haying IWG after HM are above the soil surface will reduce grain yield (Hopkins et al., 2003).Understanding the reproductive growth and development of IWG is critical to developing best management practices and promoting adoption of dual‐purpose forage and perennial grain production. Growth and development of agronomically major crops such as wheat, corn (Zea mays L.), and alfalfa are well established (Black et al., 1992; Lee, 2007; Nelson & Smith, 1968), and crop calendars have been developed over many years and cropping seasons. Similar multiyear and multilocation information is lacking for IWG. Crop calendars focus on growth progression over time as a response to accumulated heat units expressed as growing degree days (GDD) or day of the year (DOY) and provide insight for management decisions such as timing of fertilization and herbicide and pesticide applications. Likewise, knowledge of when and when not to graze or mechanically harvest IWG in spring will depend on accurately assessing HM height. The long‐term potential for grazing and haying multiple times may depend upon IWG growth and development responses length of growing season and precipitation.Core IdeasDay of the year and growing degree days can be used to estimate intermediate wheatgrass reproductive development.Intermediate wheatgrass reproductive growth and development respond similarly in monoculture and bicultures.Intermediate wheatgrass biomass fractions were similar across years.Perennial grass stage indices were previously defined by Moore et al. (1991) for important development stages: stem elongation, head emergence, anthesis, and hard dough (harvest). These stages are influenced by photoperiod and temperature (i.e., vernalization) for some grass species (Canode et al., 1972; Heide, 1994; Mitchell et al., 1997). Determining the mean stage count (MSC) of an IWG field is complex because IWG tiller demographics are complex. Wide ranges of developmental stages (spanning both vegetative and reproductive stages) are common in daily observations of tiller populations, and annual and regional variability in tiller demographics further complicates the issue (Duchene et al., 2021; Mitchell et al., 1998). However, growth and development of IWG grown for forage (Mitchell et al., 1998) and perennial grain (Jungers et al., 2018) progress predictably with accumulated GDD. Questions remain about whether environmental variability and modifications to the monoculture grain and forage cropping system (e.g., legume intercropping, dual‐purpose management, planting density, and fertilization) alter IWG growth and development through interspecific competition, tiller persistence dynamics, and complex genotype × environment × management interactions.Crop growth and development models have not been developed for IWG perennial grain fields in Kansas. The objectives of this study were to model IWG growth and development in Kansas in response to accumulated GDD and DOY, explore whether cropping system modifications affect IWG growth and development, and generate useful datasets for future modeling efforts necessary to use GDD or DOY as predictors for IWG perennial grain crop development across many locations. The intent was to contribute GDD and DOY models and preliminary knowledge about IWG development in IWG dual‐purpose cropping systems that assists producers in making practical management decisions and guides researchers in developing the management practices needed to achieve consistent, high IWG grain and forage yields without reducing stand persistence under multiple haying or grazing events.MATERIALS AND METHODSSite and establishmentThis study was conducted near Salina, KS (38°046′16.00″ N, 97°034′07.06″ W) during 2018 and 2019 on a Hord silt loam soil (fine‐silty, mixed, superactive, mesic Cumulic Haplustolls) (Soil Survey Staff, 2020) with a soil pH of 7.6 (1:1 water/soil; Watson & Brown, 1998), 20 g kg−1organic matter (loss on ignition; Combs & Nathan, 1998), 4.5 kg NO3–N ha−1 (Cadmium reduction, Horneck & Miller, 2005), 20 mg P kg−1 (Mehlich 3; Frank et al., 1998), and 490 mg K kg−1 (Mehlich 3 inductively coupled plasma [ICP]; Frank et al., 1998). The site was fallowed in the spring and summer of 2017. Seedbed preparation included field cultivation (Ford 24‐ft [7.3 m] field cultivator) and cultipacking (Brillion, Landoll) to improve seed‐to‐soil contact. Field plots (13.3 m2) were established 26 Sept. 2017 using a six‐row (30.5 cm) SRES Cone Drill (Seed Research Equipment Solutions) equipped with a six‐cell tray indexer that allowed for planting alternate IWG and alfalfa rows at 0.5‐cm depth. The IWG seed used in this study was from a synthetic population generated using elite parents from the third, fourth, and fifth cycles of selection from the TLI IWG breeding program. Alfalfa seed used in this study was from four different varieties: ‘Agate’(Barnes & Frosheiser, 1973), ‘Ineffective Agate’ (Barnes et al., 1990), ‘AFXexp’, and ‘HybriForce3400’ (Dairyland). Agate is a public alfalfa cultivar released jointly by the Agriculture Research Station, USDA, and the University of Minnesota. Ineffective Agate is an experimental cultivar that was selected out of Agate and is unable to fix atmospheric N2. AFXexp is a spreader type experimental cultivar. HybriForce3400 is an older hybrid cultivar.The experimental design was a split‐split plot randomized complete block with four replications where the main plot was cropping system, split plot was year (stand age), and split‐split plot was weekly clippings. There were eight total main‐plot cropping systems (Table 1): four monoculture IWG and four IWG+alfalfa intercropping systems. The four monoculture IWG cropping systems included fertilized (IWGn+fert) and unfertilized (IWGn) IWG seeded in narrow rows (30.5 cm), and fertilized (IWGw+fert) and unfertilized (IWGw) IWG seeded in wide rows (61 cm). The four intercropping systems were IWG seeded in alternating rows (30.5 cm) with each of the four alfalfa varieties previously described: Agate, Ineffective Agate, AFXexp, and Hybriforce3400 (IWG+Agate; IWG+AgateInef; IWG+AFXexp; IWG+Hybriforce3400). The IWGw+fert and IWGn+fert plots received 174 kg ha−1 broadcasted urea (46–0–0) during the second year in March 2019. The IWG+alfalfa binary plots were not fertilized.1TABLEIntermediate wheatgrass (IWG) cropping systems planted in monoculture and intercropped with alfalfa and their respective fall dormancy and winter survival ratingsCropping systemDescriptionFall dormancyaWinter survivalbSeed sourceIWG monoculturesIWGn30.5‐cm row spacingTLIIWGw61‐cm row spacingTLIIWGn+IWGn + urea (174 kg ha−1)TLIIWGw+IWGw + urea (174 kg ha−1)TLIIWG + alfalfaIWG+Hybriforce3400Hybrid4.31.8Dairyland SeedsIWG+AFXexpSpreading1.51.3Alforex SeedsIWG+AgateInefNon‐nitrogen fixing2.0NAUMNIWG+AgateNitrogen fixing2.0NANote. IWGn, IWG seeded in narrow rows; IWGw, IWG seeded in wide rows.aFall dormancy ratings are from 0 to 11, with 0 being most fall dormant and 11 being least fall dormant.bWinter survival ratings are from 1 to 6, with 1 being extremely winter hardy and 6 non‐winter hardy. NA, not applicable.Data collectionA 15‐cm clipping was taken from a single IWG row per plot at a height of 3.8 cm above the soil surface each week from the first week of April until anthesis in mid‐July. Clippings were taken sequentially within the rows to avoid collecting regrowth from previously clipped areas. Five IWG tillers from each clipping were randomly selected during the early spring vegetative stages while five random reproductive tillers were selected during and after elongation stages (Jungers et al., 2018). For each tiller, (a) the growth index stage was recorded until anthesis according to the growth index staging criteria of Moore et al. (1991) (Table 2) and used to calculate the MSC of the plot, and (b) the HM height (HMH) was measured from the cutting point above the soil to the apical HM. Prior to inflorescence emergence, tillers were dissected to measure HMH (Figure 1).2TABLEPrimary and secondary growth stages and their numerical indices and descriptions for staging growth and development of perennial grassesStageIndexDescriptionVegetative stageV01.0First leaf emergenceVn(n/N) + 0.9aNth leaf collaredElongation stageE02.0Onset of stem elongationEn(n/N) + 1.9aNth node palpableReproductive stageR03.0Boot stageR13.1Inflorescence emergenceR23.3Spikelets fully emerged; peduncle not emergedR33.5Inflorescence emerged; peduncle fully elongatedR43.7Anther emergence; anthesisR53.9Post‐anthesis; fertilizationSeed ripening stageS54.9Endosperm dry, seed ripeaWhere n equals the event number (number of leaves or nodes) and N equals the number of events within the primary stage (total number of leaves or nodes developed). General formula is P + (n/N) − 0.1, where P equals primary stage number (1 or 2 for vegetative and elongation, respectively) and n equals the event number. When N > 9, the formula P + 0.9(n/N) should be used. Adapted from Moore et al. (1991).1FIGUREDeveloping head meristems in dissected intermediate wheatgrass tillersMean stage count for the plot per week was estimated by averaging the growth index stage value for the five tillers. Maximum observed N values of six and five (i.e., observed maximum number of collared leaves) were used to calculate index scores for vegetative stages in 2018 and 2019, respectively. Maximum observed N values of five and six (i.e., observed maximum number of nodes palpable) were used to calculate index scores for elongation stages in 2018 and 2019, respectively. The five tillers were separated into leaf, stem, and head tissues and dried for 1 wk at 17.7 °C and 37% relative humidity. Dry weights of each tissue type for the five tillers were divided by the total dry weight of the five tillers to obtain the leaf (LF), stem (SF), and head (HF) biomass fractions.Daily temperature and precipitation were recorded using a Davis Weatherlink Vantage Pro2 (Davis Instruments) approximately 2.7 km from the field site. The accumulation of heat units was monitored as the accumulated GDD (Equation 1) using a base temperature of 0 °C (Frank, 1996).1GDD=dailymax.+dailymin.∘Cdailymax.+dailymin.∘C22−basetemperature∘C\begin{equation} \def\eqcellsep{&}\begin{array}{c} {\rm{GDD = }}\,{{\left[ {{\rm{daily max}}{\rm{. + daily min}}{\rm{.}}\left( {{\rm{^\circ C}}} \right)} \right]} \mathord{\left/ {\vphantom {{\left[ {{\rm{daily\, max}}{\rm{. + daily\, min}}{\rm{.}}\left( {{\rm{^\circ C}}} \right)} \right]} 2}} \right. \kern-\nulldelimiterspace} 2}\\ -\, {\rm{ base\, temperature }}\left( {{\rm{^\circ C}}} \right) \end{array} \end{equation}Daily maximum was the observed maximum temperature and daily minimum was the minimum observed temperature. When daily minimum temperature was below the base temperature, the daily minimum was set to 0 °C. Accumulation of GDD began after five consecutive days of daily air temperature above 0 °C after 1 January (Jungers et al., 2018; Mitchell et al., 1998) and ended at grain harvest.Statistical analysisStatistical analysis was performed in R (R Core Team, 2020) using split‐plot mixed‐effect linear models using lmer function (lme4 package; Bates et al., 2015). The response variables of MSC, HMH, LF, SF, and HF were initially modeled with cropping type, clipping, and year as fixed effects and block as a random effect. Response variables were modeled separately with each fixed effect and with their interactions and then compared for best fit based on a chi square test and α level of .05 using the ANOVA function (stats package; R Core Team, 2020). Years were analyzed separately to avoid confounding effects of stand age and differences in clipping dates (DOY) and accumulated GDD. The IWGn+fert and IWGw+fert treatments in Year 1 were pooled with IWGn and IWGw plots due to no fertilizer application. When fixed effects were significant, post‐hoc means and trend comparisons were made using the emmeans and emtrends functions (emmeans package; Lenth et al., 2018) with a Tukey adjustment. Trends were further compared by modeling the response variables with continuous predictor variables of accumulated GDD and DOY using logistic growth (Equation 2) or quadratic functions (Equation 3) used previously in IWG growth and development models (Jungers et al., 2018). The logistic growth equation used to model the responses of MSC and HMH to GDD and DOY had the form2y=β1β11+expβ2−x/β31+expβ2−x/β3\begin{equation}y\,{\rm{ = }}\,{{{{{\beta}}}_{\rm{1}}} \mathord{\left/ {\vphantom {{{{{\beta}}}_{\rm{1}}} {\left\{ {{\rm{1 + exp}}\left[ {\left( {{{{\beta}}}_{\rm{2}} - x} \right){\rm{/ }}{{{\beta}}}_{\rm{3}}} \right]} \right\}}}} \right. \kern-\nulldelimiterspace} {\left\{ {{\rm{1 + exp}}\left[ {\left( {{{{\beta}}}_{\rm{2}} - x} \right){\rm{/ }}{{{\beta}}}_{\rm{3}}} \right]} \right\}}}\end{equation}The coefficients β1, β2, and β3 for Equation 2 can be interpreted as the asymptote, the x axis value at the midpoint, and a scale parameter. Models for each year were fit using the nls and SSlogis functions (nlme package; Pinheiro et al., 2020) for each year and crop type combination. Quadratic equations were used to model the LF, SF, and HF to GDD and DOY using the form3y=β1+β2x+β3x2\begin{equation}y\,{\rm{ = }}\,{{{\beta}}}_{\rm{1}}{\rm{ \,+\, }}{{{\beta}}}_{\rm{2}}x{\rm{ \,+\, }}{{{\beta}}}_{\rm{3}}{x}^{\rm{2}}\end{equation}Growing degree day and DOY models for these response variables were compared using residual standard error (RSE). Models with lower RSE were considered a better fit within years. Nonsignificant fixed effects were removed from final models, and values were pooled across cropping types.RESULTSWeatherAccumulated GDD after planting totaled 1,580 until the end of 2017. Monthly average daily air temperatures and monthly accumulated GDD were greater in 2018 than in 2019 for all months except January. Monthly precipitation was lower in 2018 and higher in 2019 than the 30‐yr average (Figure 2).2FIGUREAccumulation of growing degree days (°C) and precipitation (mm) for site years and 30‐yr average near Salina, KSSubsurface irrigation totaling 90 mm in 2018 was provided 15 May to 15 June, whereas no irrigation was used in 2019. Total precipitation was 713 and 896 mm in 2018 and 2019, respectively.Growth stage and reproductive tiller developmentDifferences of MSC and HMH between cropping systems within years and clippings were found for several clippings but did not reveal a significant trend across all clippings within a year (Supplemental Figures S1–S4). Comparisons of trends for each cropping system within years did not reveal any significant differences, and values were then pooled across cropping systems. Intermediate wheatgrass MSC differed between clippings within years (Table 3) while progressing positively in a logistic growth pattern (Figure 3; Table 4) for both years.3TABLEType III tests of fixed effects of linear mixed‐effects models for mean stage count (MSC), head meristem height (HMH), and leaf, stem, and head biomass fractionsFixed effectMSCHMHLeafStemHeadFull modelYear (Y)***************Clipping (C)***************Cropping system (CS)NS************Y × C***************Y × CS*******NS†C × CS***********NSY × C × CS*************2018C***************CSNS**********C × CS*************2019C***************CS********NS***C × CS********NSNS*Significant at the .05 probability level.**Significant at the .01 probability level.***Significant at the .001 probability level.†NS, not significant.3FIGUREResponse of mean stage count (MSC) as affected by (a) accumulated growing degree days (GDD) and (b) day of the year (DOY) for intermediate wheatgrass. Values are means across all cropping systems. Regression coefficients and residual standard errors are reported in Table 44TABLECoefficient estimates and residual standard error for growth responses as affected by growing degree day (GDD) or day of the year (DOY) for intermediate wheatgrassGrowth responseFunctionYearIndependent variableβ1β2β3RSEaMSCLogisticb2018GDD4.141,0425.82 × 1020.1820194.069474.36 × 1020.192018DOY5.061443.50 × 1010.2220194.721403.04 × 1010.20HMH2018GDD1,212.61,365.4171.6134.520191,480.91,306.6144.0150.72018DOY1,217.2144.06.916134.220191,489.1151.26.887149.7LFQuadraticc2018GDD1.761−1.26 × 10−32.51 × 10−70.0720191.613−1.32 × 10−32.88 × 10−70.142018DOY4.061−3.67 × 10−28.53 × 10−50.1020193.126−2.58 × 10−25.13 × 10−50.14SF2018GDD−0.7271.20 × 10−3−2.70 × 10−70.0820190.3512.77 × 10−4−6.23 × 10−80.052018DOY−5.3596.73 × 10−2−1.90 × 10−40.082019−0.5311.25 × 10−2−3.30 × 10−00.05HF2018GDD−0.3274.74 × 10−4−9.99 × 10−80.032019−0.3554.73 × 10−4−9.49 × 10−80.032018DOY−2.3612.84 × 10−2−7.77 × 10−50.032019−1.5561.72 × 10−2−4.09 × 10−50.03Note. Values were pooled across cropping systems due to no significant differences after Tukey adjustment. MSC, mean stage count; HMH, head meristem height; LF, leaf fraction; SF, stem fraction; HF, head fraction.aRSE, residual standard error.by = β1/(1 + exp((β2 − x)/ β3)).cy = β1 + β2x + β3x2.Few differences in MSC were seen within clippings, and these coincided with stages related to number of nodes and head emergence. Models with GDD and DOY had good overall fit (low RSE) in both years, with GDD providing the best overall fit (Table 4).Intermediate wheatgrass HMH differed between clippings within years (Table 3) while progressing in a positive logistic growth pattern (Figure 4, Table 4).4FIGUREResponse of head meristem height (HMH) as affected by (a) accumulated growing degree days (GDD) and (b) day of the year (DOY) for intermediate wheatgrass with average growth index stage to describe the development morphology. Values are means across all cropping systems. Regression coefficients are reported in Table 4Models with GDD and DOY both resulted in similar fit in both years (Table 4). Rates of HMH growth were similar up until stem elongation for both GDD and DOY models in 2019. Head meristems above cutting height before first node appearance in 2018 were not observed (Table 5). In 2019, the percentage of dissected tillers with HMs before first node appearance was 16% and increased 150% in 7 d, and another 41% in the next 7 d.5TABLEGrowth stage, index, and head meristem height (HMH) based on observed growing degree days (GDD) and day of the year (DOY) and the predicted GDD and DOY to reach the same growth stage and index using logistic equations from Table 2HMHaObservedEstimatedEstimatedGrowth stageGDDDOYGDDcDOYcObservedGDDDOYTillers with head meristemsbmm%2018V4812122714114–46.336.89–Stem elongationd1,0031301st node palpable9641291,06013280.510796.497.5Bootd1,607158Infl. emerged1,6171561,679160918985986100Anther emerged2,4261852,2841791,1981,2101,2131002019V276112061911117.732.715.816.0V486312784812339.36542.957.5Stem elongationd9341311st node palpable95813497713395.3120.8112.081.5Bootd1,401157Infl. emerged1,3851551,459160963937943.4100Anther emerged2,0561831,9641791,4891,4731,474100Note. HMH was estimated with observed GDD and DOY using logistic equations from Table 2. Infl., inflorescence.aHMH observed is the mean height on the observed date that the plots were at the growth stage in the corresponding row. Estimated HMH is based on the GDD and DOY models using the observed GDD and DOY.bPercentage of tillers across all treatments with heads observed on actual DOY observed.cLogistic growth model.dStem elongation and boot stages are not measured in the field and no observed values are presented.Biomass partitioningDifferences between cropping systems within years and clippings were found for several clippings but did not reveal a significant trend across all clippings within a year (Supplemental Figures S5–S10). Comparisons of trends for each cropping system within years did not reveal any significant differences, and values were pooled across cropping systems. The total LF began declining after stem elongation on similar DOY and GDD across years and continued to decline quadratically until final clipping (grain harvest) (Figure 5).5FIGUREResponse of leaf, stem, and head biomass fractions as affected by (a) growing degree days and (b) day of the year for intermediate wheatgrass. Values are means across all cropping systems. Regression coefficients and residual standard errors are reported in Table 4In 2018, LF was lowest after 2,055 GDD (DOY 171), about 1 mo before final clipping, but continued to decline in 2019 until final clipping. Final LF was higher in 2018 than in 2019. Stem biomass fractions increased quadratically in response to increasing GDD and DOY beginning just before stem elongation. The greatest SF occurred after 1,468 and 1,255 GDD (DOY 150 and 149) in 2018 and 2019, respectively. Stem biomass fraction was higher at final clipping in 2019 than in 2018. Total head biomass was observed at earlier clippings in 2019 but increased quadratically until final clipping in both years. The greatest HF occurred after 2,196 and 2,242 GDD (DOY 177 and 190) in 2018 and 2019, respectively. Head biomass fraction was higher at final clipping in 2019 than in 2018.Modeling of leaf, stem, and head biomass with GDD and DOY had strong overall fit (low RSE) but differed slightly between years (Table 4). Leaf biomass fraction was best fit with GDD in 2018, but similar in 2019 with GDD and DOY. The SF and HF had similar fit for GDD and DOY within years.DISCUSSIONDeveloping dual‐purpose, interspecies cropping systems with IWG and alfalfa requires improved understanding of the possible interactions of spring biomass removal on summer grain production, as well as the interaction of IWG at various row spacings (Canode, 1965; Hunter, Sheaffer, Culman, Lazarus, & Jungers, 2020), species × row combinations (Dick et al., 2018), and water and nutrient availability. Using the growth index staging methods of Moore et al. (1991), this study found that reproductive tiller growth and development was not affected by the cropping system but driven similarly by GDD and DOY, although a small number of cropping system effects within clippings were observed. Both GDD and DOY were accurate predictors of vegetative and early reproductive development across years, but DOY more accurately predicted late reproductive stages than GDD across years (Table 5).Day of the year could be considered a better predictor of IWG morphological stage across dissimilar environments. When temperatures are dissimilar across sites or years, the daily accumulated GDD will differ but may not increase or decrease the rate of morphological development. The DOY estimates for onset of stem elongation, boot stage, and anthesis in this study (Table 5) were similar to those estimated for IWG in Minnesota, but GDD estimates differed between sites and years (Jungers et al., 2018). Latitude, photosynthetically active radiation, and length of vernalization induction periods more accurately predict floral emergence in some cool season forage grasses (Canode et al., 1972; Hall et al., 2009) indicating that photoperiod, which is more closely related with DOY, is crucial for determining floral emergence, whereas GDD requirements may vary across locations.Previous studies of morphological development in cool‐season grasses for grazing or rangeland management and IWG for Kernza grain production (Jungers et al., 2018; Hunter, Sheaffer, Culman, & Jungers, 2020) have not set limits on the daily maximum temperature in GDD calculations (Frank, 1996; Mitchell et al., 1998), but those studies were mostly conducted in cooler climates. Seasonal variation in daily average temperatures affected predictions that were based on GDD in this Kansas study. Winter wheat GDD calculations use cardinal minimum, maximum, and optimum temperatures for each morphological stage to account for the changes in growth rates with changes in air temperature (Wang & Engel, 1998; Xue et al., 2004), whereas information is lacking for cool‐season forage grasses. This study used the recorded daily maximum temperature, rather than setting a maximum, when calculating the daily GDD, which may account for the observed GDD × year interaction for MSC and HMH. Further IWG growth and development modeling based on GDD should focus on optimal GDD requirements between each morphological stage.Critical dual‐purpose management decisions will require accurate predictions of HM development in IWG tiller populations. This study observed developing HMs inside of IWG tillers during vegetative stages prior to stem elongation and onset of node development. In 2019, removal of spring IWG biomass would have removed or damaged heads in tillers that were above the cutting height as early as DOY 120 (6 May). This study produced some estimates of the total number of HMs above cutting height at early stages of stem elongation, but high RSE for these models revealed the difficulty in accurately modeling HMH across years. Accurate models are needed for early HM development to complement in‐field dissection of tillers to observe HMH in the field, which is difficult and time consuming.Dual‐purpose winter wheat is produced on over 3 million ha (8 million acres) across Kansas, Oklahoma, and Texas (Lollato et al., 2017) and breeding for improved dual‐purpose cropping of wheat can be achieved simultaneously while breeding for improved grain production (Carver et al., 2001). Improved varieties of IWG bred for grain production can also be used as a dual‐purpose crop. Leaf biomass of IWG has excellent forage quality (Moore et al., 1995) but declines quickly after stem elongation. Harvest of the biomass prior to stem elongation has been suggested to improve total crop profitability (Favre et al., 2019; Hunter, Sheaffer, Culman, & Jungers, 2020; Jungers et al., 2018; Ryan et al., 2018) while preserving grain production. Total biomass production and the relative amount of leaf/stem/head ratios will differ among locations and years. Jungers et al. (2018) reported leaf and stem biomass fractions as nearly equal at ∼40% each by the end of the season in Minnesota, whereas in this study, stem fractions accounted for over 50% of the biomass while leaf and head fractions were nearly equal. Further investigations are needed to determine how ratios of LF, SF, and HF might be altered by a spring biomass harvest.Tiller demographics (proportion of vegetative to reproductive tillers) of IWG forage varieties are known to vary between years, likely due to drought stress, stand age (Moore & Moser, 1995; Mitchell et al., 1998), or other environmental factors and may make predicting the number of tillers with heads above cutting height difficult on a yearly basis. Breeding for increased grain production of IWG may also alter the proportions of vegetative to reproductive tillers. Furthermore, bud initiation for new tillers is influenced by summer and fall field conditions and may affect new spring tiller growth and recruitment (Waller et al., 1985). Additional studies are needed to validate DOY and GDD models developed herein across other locations, weather conditions, and management practices, like delayed grazing, which will likely affect IWG growth, development, and tiller initiation in dual‐purpose management systems.CONCLUSIONIn this study, we provide equations for predicting growth and development of IWG using both DOY and GDD. Overall, GDD and DOY models were similar, with a few notable exceptions later in the season. Limited differences in IWG growth and development were observed among monoculture, fertilized and unfertilized, and IWG–alfalfa intercropping systems. This study looked at the first two seasons of IWG grown for Kernza perennial grain production and provides necessary parameters for improving IWG cropping system models, which will require datasets from many locations and environments to calibrate the variation across sites and years.ACKNOWLEDGMENTSWe acknowledge funding support from the Perennial Agriculture Project, a joint project between The Land Institute and the Malone Family Land Preservation Foundation and the National Institute of Food and Agriculture, USDA, through the Northeast Sustainable Agriculture Research and Education program under Subaward no. GNC18‐253. The authors would also like to thank the staff and interns at The Land Institute for their hard work and dedication. S.B. would like to thank the professors Dr. Mary Wiedenhoeft and Dr. Andrew Lenssen at Iowa State University for their excellent guidance and input in the formation of this manuscript.AUTHOR CONTRIBUTIONSSpencer Barriball: Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Project administration; Resources; Software; Visualization; Writing – original draft; Writing – review & editing. Abigail Han: Investigation, Writing‐review & editing. Brandon Schlautman: Methodology; Supervision; Writing – review & editing. Abigail Han: Investigation; Writing – review & editing.CONFLICT OF INTERESTThe authors declare there is no conflict of interest in reporting these results.REFERENCESBarnes, D. K., & Frosheiser, F. I. (1973). Registration of agate alfalfa (Reg. No. 62). Crop Science, 13(6), 768–769. https://doi.org/10.2135/cropsci1973.0011183x001300060059xBarnes, D. K., Heichel, G. H., Vance, C. P., & Peaden, R. N. (1990). 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Missouri Agricultural Experiment Station. https://www.canr.msu.edu/uploads/234/68557/rec_chem_soil_test_proce55c.pdfXue, Q., Weiss, A., & Baenziger, S. P. (2004). Predicting leaf appearance in field‐grown winter wheat: Evaluating linear and non‐linear models. Ecological Modelling, 175(3), 261–270. https://doi.org/10.1016/j.ecolmode003.10.018 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png "Agrosystems, Geosciences & Environment" Wiley

Effect of growing degree days, day of the year, and cropping systems on reproductive development of Kernza in Kansas

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AbbreviationsDOYday of the yearferttreatment with fertilizerGDDgrowing degree daysHFhead biomass fractionHMhead meristemHMHhead meristem heightIWGintermediate wheatgrassIWGnintermediate wheatgrass seeded in narrow rowsIWGwintermediate wheatgrass seeded in wide rowsLFleaf biomass fractionMSCmean stage countRSEresidual standard errorSFstem biomass fractionTLIThe Land InstituteINTRODUCTIONIntermediate wheatgrass [Thinopyrum intermedium (Host) Barkworth & D.R. Dewey] (IWG) is an introduced, cool‐season perennial grass from Europe and Asia widely adapted to the Great Plains of North America. As a forage grass, IWG is highly palatable and drought tolerant, making it ideal for dryland agriculture. Recognizing the perennial crop ecosystem service advantages compared with annuals, efforts to domesticate IWG as a perennial grain crop were initiated in the 1980s, and continuous breeding has been underway at The Land Institute (TLI) since 2003 (DeHaan et al., 2016; Wagoner, 1990). Although IWG seed has been produced for the forage industry for decades (Kruger, 2015), significant barriers to adoption exist for IWG grown for Kernza perennial grain production, including low and inconsistent grain yields, lack of agronomic knowledge for producers, and no approved chemistries for weed and pest management (Culman et al., 2013; Jungers et al., 2017; Lanker et al., 2013; Tautges et al., 2018). To increase productivity, profitability, and early adoption of Kernza perennial grain production, in lieu of low grain yields, dual‐purpose management as both a grain and forage have been suggested (Dick et al., 2018; Favre et al., 2019; Pugliese et al., 2019; Ryan et al., 2018).Harvesting IWG biomass in the summer after grain harvest and in the fall after regrowth could be a viable option for dual‐purpose production systems in the upper Midwest (Jungers et al., 2017; Pugliese et al., 2019). Leaf and stem biomass remaining after IWG grain harvest can exceed 10 Mg ha−1, but the low quality of the straw (crude protein < 60 g kg−1 and relative feed value < 70 g kg−1) limits its utility and value as forage (Favre et al., 2019; Hunter, Sheaffer, Culman, Lazarus, & Jungers, 2020). Planting IWG in mixtures with a perennial legume, like alfalfa (Medicago sativa L.), could improve the seasonal forage quality and yield (Barnett & Posler, 1983; Sleugh et al., 2000). Dual‐purpose IWG–alfalfa management systems may (a) help maintain stable IWG grain yields under dual‐purpose management system (Tautges et al., 2018); (b) require less synthetic N fertilizer by gradually increasing the soil organic N pool over time through biological N fixation and N transfer, thus potentially reducing the risks of nitrate leaching and N2O emissions associated with N fertilizer applications (Crews et al., 2016); and (c) reduce annual weed pressure through multiple haying events and reduce pesticide applications compared with intensively managed grass or cereal monocultures (Frame & Newbould, 1986).Dual‐purpose management of winter wheat (Triticum aestivum L.) is common during late fall, winter, and spring in Kansas and the High Plains (Lollato et al., 2017). Grazing has little effect on wheat yield if fertility is adequate and livestock are removed before tillers reach the hollow stem stage in the spring (Holman et al., 2009). Grazing IWG grown for grain in winter and early spring could increase net returns and extend the spring grazing season for grazers waiting to move livestock to warm‐season native pasture. Simulated grazing of IWG before late boot stage improved stand persistence and tiller recruitment ratios (Hendrickson et al., 2005), though it is uncertain how spring grazing or haying before stem elongation (early vegetation) will affect grain yields. Like in wheat, IWG apical head meristem (HM) development begins after the induction conditions are met, and HM are visible by dissection of tillers before stem elongation (Canode et al., 1972). The optimum number of tillers with HM at or below spring grazing height is unknown for maximum forage and grain production, but spring grazing or haying IWG after HM are above the soil surface will reduce grain yield (Hopkins et al., 2003).Understanding the reproductive growth and development of IWG is critical to developing best management practices and promoting adoption of dual‐purpose forage and perennial grain production. Growth and development of agronomically major crops such as wheat, corn (Zea mays L.), and alfalfa are well established (Black et al., 1992; Lee, 2007; Nelson & Smith, 1968), and crop calendars have been developed over many years and cropping seasons. Similar multiyear and multilocation information is lacking for IWG. Crop calendars focus on growth progression over time as a response to accumulated heat units expressed as growing degree days (GDD) or day of the year (DOY) and provide insight for management decisions such as timing of fertilization and herbicide and pesticide applications. Likewise, knowledge of when and when not to graze or mechanically harvest IWG in spring will depend on accurately assessing HM height. The long‐term potential for grazing and haying multiple times may depend upon IWG growth and development responses length of growing season and precipitation.Core IdeasDay of the year and growing degree days can be used to estimate intermediate wheatgrass reproductive development.Intermediate wheatgrass reproductive growth and development respond similarly in monoculture and bicultures.Intermediate wheatgrass biomass fractions were similar across years.Perennial grass stage indices were previously defined by Moore et al. (1991) for important development stages: stem elongation, head emergence, anthesis, and hard dough (harvest). These stages are influenced by photoperiod and temperature (i.e., vernalization) for some grass species (Canode et al., 1972; Heide, 1994; Mitchell et al., 1997). Determining the mean stage count (MSC) of an IWG field is complex because IWG tiller demographics are complex. Wide ranges of developmental stages (spanning both vegetative and reproductive stages) are common in daily observations of tiller populations, and annual and regional variability in tiller demographics further complicates the issue (Duchene et al., 2021; Mitchell et al., 1998). However, growth and development of IWG grown for forage (Mitchell et al., 1998) and perennial grain (Jungers et al., 2018) progress predictably with accumulated GDD. Questions remain about whether environmental variability and modifications to the monoculture grain and forage cropping system (e.g., legume intercropping, dual‐purpose management, planting density, and fertilization) alter IWG growth and development through interspecific competition, tiller persistence dynamics, and complex genotype × environment × management interactions.Crop growth and development models have not been developed for IWG perennial grain fields in Kansas. The objectives of this study were to model IWG growth and development in Kansas in response to accumulated GDD and DOY, explore whether cropping system modifications affect IWG growth and development, and generate useful datasets for future modeling efforts necessary to use GDD or DOY as predictors for IWG perennial grain crop development across many locations. The intent was to contribute GDD and DOY models and preliminary knowledge about IWG development in IWG dual‐purpose cropping systems that assists producers in making practical management decisions and guides researchers in developing the management practices needed to achieve consistent, high IWG grain and forage yields without reducing stand persistence under multiple haying or grazing events.MATERIALS AND METHODSSite and establishmentThis study was conducted near Salina, KS (38°046′16.00″ N, 97°034′07.06″ W) during 2018 and 2019 on a Hord silt loam soil (fine‐silty, mixed, superactive, mesic Cumulic Haplustolls) (Soil Survey Staff, 2020) with a soil pH of 7.6 (1:1 water/soil; Watson & Brown, 1998), 20 g kg−1organic matter (loss on ignition; Combs & Nathan, 1998), 4.5 kg NO3–N ha−1 (Cadmium reduction, Horneck & Miller, 2005), 20 mg P kg−1 (Mehlich 3; Frank et al., 1998), and 490 mg K kg−1 (Mehlich 3 inductively coupled plasma [ICP]; Frank et al., 1998). The site was fallowed in the spring and summer of 2017. Seedbed preparation included field cultivation (Ford 24‐ft [7.3 m] field cultivator) and cultipacking (Brillion, Landoll) to improve seed‐to‐soil contact. Field plots (13.3 m2) were established 26 Sept. 2017 using a six‐row (30.5 cm) SRES Cone Drill (Seed Research Equipment Solutions) equipped with a six‐cell tray indexer that allowed for planting alternate IWG and alfalfa rows at 0.5‐cm depth. The IWG seed used in this study was from a synthetic population generated using elite parents from the third, fourth, and fifth cycles of selection from the TLI IWG breeding program. Alfalfa seed used in this study was from four different varieties: ‘Agate’(Barnes & Frosheiser, 1973), ‘Ineffective Agate’ (Barnes et al., 1990), ‘AFXexp’, and ‘HybriForce3400’ (Dairyland). Agate is a public alfalfa cultivar released jointly by the Agriculture Research Station, USDA, and the University of Minnesota. Ineffective Agate is an experimental cultivar that was selected out of Agate and is unable to fix atmospheric N2. AFXexp is a spreader type experimental cultivar. HybriForce3400 is an older hybrid cultivar.The experimental design was a split‐split plot randomized complete block with four replications where the main plot was cropping system, split plot was year (stand age), and split‐split plot was weekly clippings. There were eight total main‐plot cropping systems (Table 1): four monoculture IWG and four IWG+alfalfa intercropping systems. The four monoculture IWG cropping systems included fertilized (IWGn+fert) and unfertilized (IWGn) IWG seeded in narrow rows (30.5 cm), and fertilized (IWGw+fert) and unfertilized (IWGw) IWG seeded in wide rows (61 cm). The four intercropping systems were IWG seeded in alternating rows (30.5 cm) with each of the four alfalfa varieties previously described: Agate, Ineffective Agate, AFXexp, and Hybriforce3400 (IWG+Agate; IWG+AgateInef; IWG+AFXexp; IWG+Hybriforce3400). The IWGw+fert and IWGn+fert plots received 174 kg ha−1 broadcasted urea (46–0–0) during the second year in March 2019. The IWG+alfalfa binary plots were not fertilized.1TABLEIntermediate wheatgrass (IWG) cropping systems planted in monoculture and intercropped with alfalfa and their respective fall dormancy and winter survival ratingsCropping systemDescriptionFall dormancyaWinter survivalbSeed sourceIWG monoculturesIWGn30.5‐cm row spacingTLIIWGw61‐cm row spacingTLIIWGn+IWGn + urea (174 kg ha−1)TLIIWGw+IWGw + urea (174 kg ha−1)TLIIWG + alfalfaIWG+Hybriforce3400Hybrid4.31.8Dairyland SeedsIWG+AFXexpSpreading1.51.3Alforex SeedsIWG+AgateInefNon‐nitrogen fixing2.0NAUMNIWG+AgateNitrogen fixing2.0NANote. IWGn, IWG seeded in narrow rows; IWGw, IWG seeded in wide rows.aFall dormancy ratings are from 0 to 11, with 0 being most fall dormant and 11 being least fall dormant.bWinter survival ratings are from 1 to 6, with 1 being extremely winter hardy and 6 non‐winter hardy. NA, not applicable.Data collectionA 15‐cm clipping was taken from a single IWG row per plot at a height of 3.8 cm above the soil surface each week from the first week of April until anthesis in mid‐July. Clippings were taken sequentially within the rows to avoid collecting regrowth from previously clipped areas. Five IWG tillers from each clipping were randomly selected during the early spring vegetative stages while five random reproductive tillers were selected during and after elongation stages (Jungers et al., 2018). For each tiller, (a) the growth index stage was recorded until anthesis according to the growth index staging criteria of Moore et al. (1991) (Table 2) and used to calculate the MSC of the plot, and (b) the HM height (HMH) was measured from the cutting point above the soil to the apical HM. Prior to inflorescence emergence, tillers were dissected to measure HMH (Figure 1).2TABLEPrimary and secondary growth stages and their numerical indices and descriptions for staging growth and development of perennial grassesStageIndexDescriptionVegetative stageV01.0First leaf emergenceVn(n/N) + 0.9aNth leaf collaredElongation stageE02.0Onset of stem elongationEn(n/N) + 1.9aNth node palpableReproductive stageR03.0Boot stageR13.1Inflorescence emergenceR23.3Spikelets fully emerged; peduncle not emergedR33.5Inflorescence emerged; peduncle fully elongatedR43.7Anther emergence; anthesisR53.9Post‐anthesis; fertilizationSeed ripening stageS54.9Endosperm dry, seed ripeaWhere n equals the event number (number of leaves or nodes) and N equals the number of events within the primary stage (total number of leaves or nodes developed). General formula is P + (n/N) − 0.1, where P equals primary stage number (1 or 2 for vegetative and elongation, respectively) and n equals the event number. When N > 9, the formula P + 0.9(n/N) should be used. Adapted from Moore et al. (1991).1FIGUREDeveloping head meristems in dissected intermediate wheatgrass tillersMean stage count for the plot per week was estimated by averaging the growth index stage value for the five tillers. Maximum observed N values of six and five (i.e., observed maximum number of collared leaves) were used to calculate index scores for vegetative stages in 2018 and 2019, respectively. Maximum observed N values of five and six (i.e., observed maximum number of nodes palpable) were used to calculate index scores for elongation stages in 2018 and 2019, respectively. The five tillers were separated into leaf, stem, and head tissues and dried for 1 wk at 17.7 °C and 37% relative humidity. Dry weights of each tissue type for the five tillers were divided by the total dry weight of the five tillers to obtain the leaf (LF), stem (SF), and head (HF) biomass fractions.Daily temperature and precipitation were recorded using a Davis Weatherlink Vantage Pro2 (Davis Instruments) approximately 2.7 km from the field site. The accumulation of heat units was monitored as the accumulated GDD (Equation 1) using a base temperature of 0 °C (Frank, 1996).1GDD=dailymax.+dailymin.∘Cdailymax.+dailymin.∘C22−basetemperature∘C\begin{equation} \def\eqcellsep{&}\begin{array}{c} {\rm{GDD = }}\,{{\left[ {{\rm{daily max}}{\rm{. + daily min}}{\rm{.}}\left( {{\rm{^\circ C}}} \right)} \right]} \mathord{\left/ {\vphantom {{\left[ {{\rm{daily\, max}}{\rm{. + daily\, min}}{\rm{.}}\left( {{\rm{^\circ C}}} \right)} \right]} 2}} \right. \kern-\nulldelimiterspace} 2}\\ -\, {\rm{ base\, temperature }}\left( {{\rm{^\circ C}}} \right) \end{array} \end{equation}Daily maximum was the observed maximum temperature and daily minimum was the minimum observed temperature. When daily minimum temperature was below the base temperature, the daily minimum was set to 0 °C. Accumulation of GDD began after five consecutive days of daily air temperature above 0 °C after 1 January (Jungers et al., 2018; Mitchell et al., 1998) and ended at grain harvest.Statistical analysisStatistical analysis was performed in R (R Core Team, 2020) using split‐plot mixed‐effect linear models using lmer function (lme4 package; Bates et al., 2015). The response variables of MSC, HMH, LF, SF, and HF were initially modeled with cropping type, clipping, and year as fixed effects and block as a random effect. Response variables were modeled separately with each fixed effect and with their interactions and then compared for best fit based on a chi square test and α level of .05 using the ANOVA function (stats package; R Core Team, 2020). Years were analyzed separately to avoid confounding effects of stand age and differences in clipping dates (DOY) and accumulated GDD. The IWGn+fert and IWGw+fert treatments in Year 1 were pooled with IWGn and IWGw plots due to no fertilizer application. When fixed effects were significant, post‐hoc means and trend comparisons were made using the emmeans and emtrends functions (emmeans package; Lenth et al., 2018) with a Tukey adjustment. Trends were further compared by modeling the response variables with continuous predictor variables of accumulated GDD and DOY using logistic growth (Equation 2) or quadratic functions (Equation 3) used previously in IWG growth and development models (Jungers et al., 2018). The logistic growth equation used to model the responses of MSC and HMH to GDD and DOY had the form2y=β1β11+expβ2−x/β31+expβ2−x/β3\begin{equation}y\,{\rm{ = }}\,{{{{{\beta}}}_{\rm{1}}} \mathord{\left/ {\vphantom {{{{{\beta}}}_{\rm{1}}} {\left\{ {{\rm{1 + exp}}\left[ {\left( {{{{\beta}}}_{\rm{2}} - x} \right){\rm{/ }}{{{\beta}}}_{\rm{3}}} \right]} \right\}}}} \right. \kern-\nulldelimiterspace} {\left\{ {{\rm{1 + exp}}\left[ {\left( {{{{\beta}}}_{\rm{2}} - x} \right){\rm{/ }}{{{\beta}}}_{\rm{3}}} \right]} \right\}}}\end{equation}The coefficients β1, β2, and β3 for Equation 2 can be interpreted as the asymptote, the x axis value at the midpoint, and a scale parameter. Models for each year were fit using the nls and SSlogis functions (nlme package; Pinheiro et al., 2020) for each year and crop type combination. Quadratic equations were used to model the LF, SF, and HF to GDD and DOY using the form3y=β1+β2x+β3x2\begin{equation}y\,{\rm{ = }}\,{{{\beta}}}_{\rm{1}}{\rm{ \,+\, }}{{{\beta}}}_{\rm{2}}x{\rm{ \,+\, }}{{{\beta}}}_{\rm{3}}{x}^{\rm{2}}\end{equation}Growing degree day and DOY models for these response variables were compared using residual standard error (RSE). Models with lower RSE were considered a better fit within years. Nonsignificant fixed effects were removed from final models, and values were pooled across cropping types.RESULTSWeatherAccumulated GDD after planting totaled 1,580 until the end of 2017. Monthly average daily air temperatures and monthly accumulated GDD were greater in 2018 than in 2019 for all months except January. Monthly precipitation was lower in 2018 and higher in 2019 than the 30‐yr average (Figure 2).2FIGUREAccumulation of growing degree days (°C) and precipitation (mm) for site years and 30‐yr average near Salina, KSSubsurface irrigation totaling 90 mm in 2018 was provided 15 May to 15 June, whereas no irrigation was used in 2019. Total precipitation was 713 and 896 mm in 2018 and 2019, respectively.Growth stage and reproductive tiller developmentDifferences of MSC and HMH between cropping systems within years and clippings were found for several clippings but did not reveal a significant trend across all clippings within a year (Supplemental Figures S1–S4). Comparisons of trends for each cropping system within years did not reveal any significant differences, and values were then pooled across cropping systems. Intermediate wheatgrass MSC differed between clippings within years (Table 3) while progressing positively in a logistic growth pattern (Figure 3; Table 4) for both years.3TABLEType III tests of fixed effects of linear mixed‐effects models for mean stage count (MSC), head meristem height (HMH), and leaf, stem, and head biomass fractionsFixed effectMSCHMHLeafStemHeadFull modelYear (Y)***************Clipping (C)***************Cropping system (CS)NS************Y × C***************Y × CS*******NS†C × CS***********NSY × C × CS*************2018C***************CSNS**********C × CS*************2019C***************CS********NS***C × CS********NSNS*Significant at the .05 probability level.**Significant at the .01 probability level.***Significant at the .001 probability level.†NS, not significant.3FIGUREResponse of mean stage count (MSC) as affected by (a) accumulated growing degree days (GDD) and (b) day of the year (DOY) for intermediate wheatgrass. Values are means across all cropping systems. Regression coefficients and residual standard errors are reported in Table 44TABLECoefficient estimates and residual standard error for growth responses as affected by growing degree day (GDD) or day of the year (DOY) for intermediate wheatgrassGrowth responseFunctionYearIndependent variableβ1β2β3RSEaMSCLogisticb2018GDD4.141,0425.82 × 1020.1820194.069474.36 × 1020.192018DOY5.061443.50 × 1010.2220194.721403.04 × 1010.20HMH2018GDD1,212.61,365.4171.6134.520191,480.91,306.6144.0150.72018DOY1,217.2144.06.916134.220191,489.1151.26.887149.7LFQuadraticc2018GDD1.761−1.26 × 10−32.51 × 10−70.0720191.613−1.32 × 10−32.88 × 10−70.142018DOY4.061−3.67 × 10−28.53 × 10−50.1020193.126−2.58 × 10−25.13 × 10−50.14SF2018GDD−0.7271.20 × 10−3−2.70 × 10−70.0820190.3512.77 × 10−4−6.23 × 10−80.052018DOY−5.3596.73 × 10−2−1.90 × 10−40.082019−0.5311.25 × 10−2−3.30 × 10−00.05HF2018GDD−0.3274.74 × 10−4−9.99 × 10−80.032019−0.3554.73 × 10−4−9.49 × 10−80.032018DOY−2.3612.84 × 10−2−7.77 × 10−50.032019−1.5561.72 × 10−2−4.09 × 10−50.03Note. Values were pooled across cropping systems due to no significant differences after Tukey adjustment. MSC, mean stage count; HMH, head meristem height; LF, leaf fraction; SF, stem fraction; HF, head fraction.aRSE, residual standard error.by = β1/(1 + exp((β2 − x)/ β3)).cy = β1 + β2x + β3x2.Few differences in MSC were seen within clippings, and these coincided with stages related to number of nodes and head emergence. Models with GDD and DOY had good overall fit (low RSE) in both years, with GDD providing the best overall fit (Table 4).Intermediate wheatgrass HMH differed between clippings within years (Table 3) while progressing in a positive logistic growth pattern (Figure 4, Table 4).4FIGUREResponse of head meristem height (HMH) as affected by (a) accumulated growing degree days (GDD) and (b) day of the year (DOY) for intermediate wheatgrass with average growth index stage to describe the development morphology. Values are means across all cropping systems. Regression coefficients are reported in Table 4Models with GDD and DOY both resulted in similar fit in both years (Table 4). Rates of HMH growth were similar up until stem elongation for both GDD and DOY models in 2019. Head meristems above cutting height before first node appearance in 2018 were not observed (Table 5). In 2019, the percentage of dissected tillers with HMs before first node appearance was 16% and increased 150% in 7 d, and another 41% in the next 7 d.5TABLEGrowth stage, index, and head meristem height (HMH) based on observed growing degree days (GDD) and day of the year (DOY) and the predicted GDD and DOY to reach the same growth stage and index using logistic equations from Table 2HMHaObservedEstimatedEstimatedGrowth stageGDDDOYGDDcDOYcObservedGDDDOYTillers with head meristemsbmm%2018V4812122714114–46.336.89–Stem elongationd1,0031301st node palpable9641291,06013280.510796.497.5Bootd1,607158Infl. emerged1,6171561,679160918985986100Anther emerged2,4261852,2841791,1981,2101,2131002019V276112061911117.732.715.816.0V486312784812339.36542.957.5Stem elongationd9341311st node palpable95813497713395.3120.8112.081.5Bootd1,401157Infl. emerged1,3851551,459160963937943.4100Anther emerged2,0561831,9641791,4891,4731,474100Note. HMH was estimated with observed GDD and DOY using logistic equations from Table 2. Infl., inflorescence.aHMH observed is the mean height on the observed date that the plots were at the growth stage in the corresponding row. Estimated HMH is based on the GDD and DOY models using the observed GDD and DOY.bPercentage of tillers across all treatments with heads observed on actual DOY observed.cLogistic growth model.dStem elongation and boot stages are not measured in the field and no observed values are presented.Biomass partitioningDifferences between cropping systems within years and clippings were found for several clippings but did not reveal a significant trend across all clippings within a year (Supplemental Figures S5–S10). Comparisons of trends for each cropping system within years did not reveal any significant differences, and values were pooled across cropping systems. The total LF began declining after stem elongation on similar DOY and GDD across years and continued to decline quadratically until final clipping (grain harvest) (Figure 5).5FIGUREResponse of leaf, stem, and head biomass fractions as affected by (a) growing degree days and (b) day of the year for intermediate wheatgrass. Values are means across all cropping systems. Regression coefficients and residual standard errors are reported in Table 4In 2018, LF was lowest after 2,055 GDD (DOY 171), about 1 mo before final clipping, but continued to decline in 2019 until final clipping. Final LF was higher in 2018 than in 2019. Stem biomass fractions increased quadratically in response to increasing GDD and DOY beginning just before stem elongation. The greatest SF occurred after 1,468 and 1,255 GDD (DOY 150 and 149) in 2018 and 2019, respectively. Stem biomass fraction was higher at final clipping in 2019 than in 2018. Total head biomass was observed at earlier clippings in 2019 but increased quadratically until final clipping in both years. The greatest HF occurred after 2,196 and 2,242 GDD (DOY 177 and 190) in 2018 and 2019, respectively. Head biomass fraction was higher at final clipping in 2019 than in 2018.Modeling of leaf, stem, and head biomass with GDD and DOY had strong overall fit (low RSE) but differed slightly between years (Table 4). Leaf biomass fraction was best fit with GDD in 2018, but similar in 2019 with GDD and DOY. The SF and HF had similar fit for GDD and DOY within years.DISCUSSIONDeveloping dual‐purpose, interspecies cropping systems with IWG and alfalfa requires improved understanding of the possible interactions of spring biomass removal on summer grain production, as well as the interaction of IWG at various row spacings (Canode, 1965; Hunter, Sheaffer, Culman, Lazarus, & Jungers, 2020), species × row combinations (Dick et al., 2018), and water and nutrient availability. Using the growth index staging methods of Moore et al. (1991), this study found that reproductive tiller growth and development was not affected by the cropping system but driven similarly by GDD and DOY, although a small number of cropping system effects within clippings were observed. Both GDD and DOY were accurate predictors of vegetative and early reproductive development across years, but DOY more accurately predicted late reproductive stages than GDD across years (Table 5).Day of the year could be considered a better predictor of IWG morphological stage across dissimilar environments. When temperatures are dissimilar across sites or years, the daily accumulated GDD will differ but may not increase or decrease the rate of morphological development. The DOY estimates for onset of stem elongation, boot stage, and anthesis in this study (Table 5) were similar to those estimated for IWG in Minnesota, but GDD estimates differed between sites and years (Jungers et al., 2018). Latitude, photosynthetically active radiation, and length of vernalization induction periods more accurately predict floral emergence in some cool season forage grasses (Canode et al., 1972; Hall et al., 2009) indicating that photoperiod, which is more closely related with DOY, is crucial for determining floral emergence, whereas GDD requirements may vary across locations.Previous studies of morphological development in cool‐season grasses for grazing or rangeland management and IWG for Kernza grain production (Jungers et al., 2018; Hunter, Sheaffer, Culman, & Jungers, 2020) have not set limits on the daily maximum temperature in GDD calculations (Frank, 1996; Mitchell et al., 1998), but those studies were mostly conducted in cooler climates. Seasonal variation in daily average temperatures affected predictions that were based on GDD in this Kansas study. Winter wheat GDD calculations use cardinal minimum, maximum, and optimum temperatures for each morphological stage to account for the changes in growth rates with changes in air temperature (Wang & Engel, 1998; Xue et al., 2004), whereas information is lacking for cool‐season forage grasses. This study used the recorded daily maximum temperature, rather than setting a maximum, when calculating the daily GDD, which may account for the observed GDD × year interaction for MSC and HMH. Further IWG growth and development modeling based on GDD should focus on optimal GDD requirements between each morphological stage.Critical dual‐purpose management decisions will require accurate predictions of HM development in IWG tiller populations. This study observed developing HMs inside of IWG tillers during vegetative stages prior to stem elongation and onset of node development. In 2019, removal of spring IWG biomass would have removed or damaged heads in tillers that were above the cutting height as early as DOY 120 (6 May). This study produced some estimates of the total number of HMs above cutting height at early stages of stem elongation, but high RSE for these models revealed the difficulty in accurately modeling HMH across years. Accurate models are needed for early HM development to complement in‐field dissection of tillers to observe HMH in the field, which is difficult and time consuming.Dual‐purpose winter wheat is produced on over 3 million ha (8 million acres) across Kansas, Oklahoma, and Texas (Lollato et al., 2017) and breeding for improved dual‐purpose cropping of wheat can be achieved simultaneously while breeding for improved grain production (Carver et al., 2001). Improved varieties of IWG bred for grain production can also be used as a dual‐purpose crop. Leaf biomass of IWG has excellent forage quality (Moore et al., 1995) but declines quickly after stem elongation. Harvest of the biomass prior to stem elongation has been suggested to improve total crop profitability (Favre et al., 2019; Hunter, Sheaffer, Culman, & Jungers, 2020; Jungers et al., 2018; Ryan et al., 2018) while preserving grain production. Total biomass production and the relative amount of leaf/stem/head ratios will differ among locations and years. Jungers et al. (2018) reported leaf and stem biomass fractions as nearly equal at ∼40% each by the end of the season in Minnesota, whereas in this study, stem fractions accounted for over 50% of the biomass while leaf and head fractions were nearly equal. Further investigations are needed to determine how ratios of LF, SF, and HF might be altered by a spring biomass harvest.Tiller demographics (proportion of vegetative to reproductive tillers) of IWG forage varieties are known to vary between years, likely due to drought stress, stand age (Moore & Moser, 1995; Mitchell et al., 1998), or other environmental factors and may make predicting the number of tillers with heads above cutting height difficult on a yearly basis. Breeding for increased grain production of IWG may also alter the proportions of vegetative to reproductive tillers. Furthermore, bud initiation for new tillers is influenced by summer and fall field conditions and may affect new spring tiller growth and recruitment (Waller et al., 1985). Additional studies are needed to validate DOY and GDD models developed herein across other locations, weather conditions, and management practices, like delayed grazing, which will likely affect IWG growth, development, and tiller initiation in dual‐purpose management systems.CONCLUSIONIn this study, we provide equations for predicting growth and development of IWG using both DOY and GDD. Overall, GDD and DOY models were similar, with a few notable exceptions later in the season. Limited differences in IWG growth and development were observed among monoculture, fertilized and unfertilized, and IWG–alfalfa intercropping systems. This study looked at the first two seasons of IWG grown for Kernza perennial grain production and provides necessary parameters for improving IWG cropping system models, which will require datasets from many locations and environments to calibrate the variation across sites and years.ACKNOWLEDGMENTSWe acknowledge funding support from the Perennial Agriculture Project, a joint project between The Land Institute and the Malone Family Land Preservation Foundation and the National Institute of Food and Agriculture, USDA, through the Northeast Sustainable Agriculture Research and Education program under Subaward no. GNC18‐253. The authors would also like to thank the staff and interns at The Land Institute for their hard work and dedication. 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"Agrosystems, Geosciences & Environment"Wiley

Published: Jan 1, 2022

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