Abstract
Diminished water quality in the mid-southern United States is often resultant from improper poultry litter management in pastures. Grassland best management practices (BMPs) need to be identified that increase soil-water infiltration. Therefore, the objective of this research was to evaluate infiltration rates based on pasture BMPs (aeration timing, subsurface banded litter, and gypsum) in hay and grazed systems from 2018 to 2019. Twenty minute falling-head infiltration measurements were conducted at zero, one, and three months following management implementation in hay (Experiment 1) and grazed (Experiment 2) systems. Management effects on forage quality and yield were also measured in the hay system. In Experiment 1, infiltration differed across treatments and by month following BMP implementation. Averaged across months (Experiment 1), the greatest infiltration rate occurred for June aeration, which was the only month when aeration increased infiltration compared to the control under hay conditions. In Experiment 1, averaged across treatments, greatest infiltration occurred three months following treatment implementation. Gypsum had no effect (P > 0.05) on infiltration rates. Under grazed conditions (Experiment 2), infiltration rates differed by treatment and month and were greatest one- and three-months following June aeration, three months following April aeration, and three months following subsurface banded litter treatment. February aeration and subsurface litter applications resulted in lower (P < 0.05) infiltration rates than the control in grazed systems. Across months for the grazed experiment, June aeration had the greatest (P > 0.05) infiltration rates. Among all BMPs temporally, June aeration improved soil-water infiltration under both hay and grazed systems. Therefore, pasture aeration implemented in June following broiler litter applications can likely reduce nutrients in surface runoff in grassland systems, regardless of management (hay or grazing) compared to traditional surface-broadcast only practices.
Arkansas ranks third in the country for broiler production (USDA NASS 2018), with production primarily concentrated in the western portion of the state. With the extent of poultry production, large quantities of litter are created, which is an abundant source of fertilizer (Edwards and Daniel 1992; Ashworth et al. 2019). However, surface land applications of poultry litter at recommended nitrogen (N) rates for forage crops often result in excess phosphorus (P) applications and are associated with non-point source pollution (Edwards and Daniel 1992; Sharpley 1997; Patterson et al. 1998; de Koff et al. 2011). As P moves downstream, eutrophication may occur due to nutrient excess (Schindler 1977; DeLaune et al. 2004; Franklin et al. 2007). This leads to a greater demand for microbial oxygen (O), thus reducing aquatic dissolved O and causing hypoxia and fish kills. Therefore, best management practice (BMP) evaluations are needed to identify strategies and BMP timing that increase infiltration and reduce adverse water quality. Each potential BMP is described in detail below.
Pasture aeration, also known as renovation, is the use of a tractor-drawn implement with tines for cutting into the soil surface as a mechanized disturbance process. Examples of aerator types include spiked-drum, short-shanked, and double-roller (Hancock and Wynne 2009; Vendramini and Silveria 2009). Aeration use as a BMP has been extensively studied (Burgess et al. 2000; Moore Jr. et al. 2005; Franklin et al. 2006, 2007, 2011; Butler et al. 2008; Ingram et al. 2009; Vendramini and Silveria 2009; Curran Cournane et al. 2011; de Koff et al. 2011). Aerating pastures may reduce nutrient loss and increase infiltration (de Koff et al. 2011; Franklin et al. 2011), increase yield (Davies et al. 1989; Moore Jr. et al. 2005), incorporate seed or a soil amendment (Franklin et al. 2006; Maguire et al. 2011), break up thatch (Hancock and Wynne 2009), and reduce soil compaction (Kissel and Burnett 1979; Burgess et al. 2000; Hancock and Wynne 2009). However, long-term benefits of aeration, as well as timing or intra-annual effects, are not well studied. However, Curran Cournane et al. (2011) observed no differences between aerated and control treatments six months after aeration, with the lack of differences attributed to poor soil structure. de Koff et al. (2011) also observed no differences three months after aeration. Burgess et al. (2000) suggested that atmospheric and soil conditions should be ideal (i.e., aeration should occur when drought and/or high rainfall events are less frequent). Therefore, research is needed to evaluate aeration timing effects on soil surface water infiltration throughout the year to develop BMP recommendations for optimum timing and quantifying the attenuating effects of aeration.
Applying poultry litter in bands beneath the surface as a potential BMP has also been studied (Pote et al. 2003, 2009, 2011; Liu et al. 2016). A tractor-drawn implement that injects dry poultry litter (<20% moisture content) beneath the soil surface with minimal disturbance has been developed by USDA Agricultural Research Service (ARS) (Pote et al. 2011) and is known as the “Subsurfer.” This implement has reportedly comparable or improved fertility benefits to that of fertilizer and surface applications of litter (Pote et al. 2003, 2009; Liu et al. 2016; Ashworth et al. 2020). Additionally, subsurface applications have shown to reduce nutrient losses relative to surface poultry litter applications (Pote et al. 2009; Liu et al. 2016). Nutrient reductions are achieved by the no-tillage bands created via the Subsurfer’s incorporation technique, thus decreasing surface flow and increasing infiltration (Pote et al. 2003, 2009; Liu et al. 2016) and decreasing losses due to ammonia (NH3) volatilization (Moore Jr. et al. 2011). This in turn leads to reductions in loads of major nutrients including N and P (Liu et al. 2016). Furthermore, NH3 losses are reduced upwards of 84% compared to surface applications (Pote and Meisinger 2014; Liu et al. 2016). Additionally, previous research has shown that average annual loads of nitrate (NO3-N), total N (TN), soluble reactive P (SRP), and total P (TP) loads were reduced 49%, 42%, 28%, and 35% following pasture aeration and by 78%, 72%, 55%, and 59% from subsurface applying poultry litter, relative to surface applications, respectively (Ashworth et al. 2021).
Additionally, surface applications of gypsum (calcium sulfate dihydrate [CaSO4·2H2O]) are a noted BMP for benefitting pasture hydrology (Tirado-Corbala et al. 2013). Adding gypsum to soils may decrease dispersion and increase flocculation, which is necessary for soil structure formation and stabilization (Chen and Dick 2011). Other agricultural uses of gypsum have been summarized by Dick et al. (2006), and include yield increases, decreased sediment runoff, and crop quality improvements. Furthermore, gypsum has been shown to increase water infiltration rates (Keren and Shainberg 1981; Miller 1987; Miller and Scifres 1988). Yu et al. (2003) evaluated surface-applied gypsum (at rates of 2 and 4 Mg ha–1) and observed infiltration rates nearly doubled in a soil column rainfall simulation study. Recommended rates of gypsum application for improving water quality (i.e., reduction of P concentrations) and physical properties (i.e., increased infiltration and percolation) range from 1.1 Mg ha–1 to 10.1 Mg ha–1 (Norton and Rhoton 2007; Sumner 2007), with recommended rates varying based on soil texture, production system, and soil fertility. Therefore, more research is needed on the ability of gypsum to increase infiltration rates and improve forage yields.
Studies evaluating differences in infiltration rates between hay and grazed systems have been limited. Frequency and intensity of livestock grazing affects plant cover, soil compaction, and bulk density, thus potentially decreasing infiltration rates (Greene et al. 1994; Gilley et al. 1996; Haghnazari et al. 2015). In Georgia, Franzluebbers et al. (2011) found no differences in measured infiltration between hay and low intensity and high intensity grazed systems, while in Alberta, Burk et al. (1999) showed no differences in infiltration rates due to grazing. More knowledge is necessary to quantifying infiltration rates for BMPs under grazed and hay management in the mid-South.
Arkansas producers need alternative management strategies for ensuring poultry litter, an abundant source of valuable N and P, is utilized economically and ecologically on hay and grazed pastures. Thus, the objective of this study was to identify factors (i.e., BMP treatment, aeration timing, and pasture management [hay versus grazed]) affecting rate of infiltration, forage yield, and quality. It is hypothesized that April aeration will have the greatest benefit to infiltration, as without aeration, precipitation can crust the soil from cattle and farm vehicle traffic, and April averages more precipitation than February or June. Furthermore, it is hypothesized that utilization of the Subsurfer will mimic the beneficial effects of the aerator (increase infiltration) owing to the slits created during subsurface application. It is also hypothesized that April aeration will produce the greatest forage yields in the hay system, with forage productivity increasing based on increased infiltration and thus nutrient availability.
Materials and Methods
Site Description and Experimental Design. Two experiments were conducted at the USDA ARS Dale Bumpers Small Farms Research Center in Booneville, Arkansas (35°6′4″ N, 93°56′12″ W) in 2018 to 2019. The soil is mapped as Leadvale silt loam on a 3% to 8% slope (fine-silty, siliceous, semi-active, thermic Typic Fragiudults) (USDA NRCS 2019), and the site is approximately 1.18 km from the Petit Jean River. Pastures were dominated by bermudagrass (Cynodon dactylon L.), and the site had not received poultry litter application for at least four years. The area receives 1,265 mm of precipitation annually, while the average annual temperature is 15.6°C (figure 1) (NOAA 2019). The study area was divided by electric fencing for the two experiments, or Experiment 1 (hay) and Experiment 2 (grazed). Replicates were blocked based on slope. Plots were 3.05 × 3.05 m and located in an undisturbed area in Year-2 to avoid confounding results from Year-1. The grazed system was employed by allowing steers (1.94 animal units ha–1) to graze from January to September to a 15 cm stubble height during both years.
Daily mean and normal temperatures and precipitation (vertical bars) at the Dale Bumpers Small Farms Research Center in Booneville, Arkansas. Data obtained from the US National Oceanic and Atmospheric Administration (NOAA 2019).
Main plots included BMPs, i.e., subsurface litter applications, aeration timing, and gypsum application (Experiment 1 only). For the first treatment, an XL7 aerator (AGRITECH, Benton, Arkansas), rented from the Beaver Watershed Alliance of Springdale, Arkansas, was used to renovate in February, April, or June. Subsurfer applications of 5.6 Mg ha–1 of litter occurred on April 12, 2018, and April 16, 2019. For this treatment, subsurface poultry litter applications (internal auger system) were banded 5 cm wide and to an 8 cm depth in four bands using a 76 cm band spacing. The equipment used was a tractor-drawn prototype and is further described by Pote et al. (2011). Poultry litter application dates coincided with sourced litter availability from poultry house clean-outs. Gypsum treatments were only represented in Experiment 1, due to potential threat of cattle toxicity (Wagnon 1960; Bourke and Ottaway 1998; Gould et al. 2002). Gypsum rates included a high (4 Mg ha–1) and low (0.5 Mg ha–1) rate on non-aerated plots. All plots (except the Subsurfer treatment) received annual surface poultry litter applications of 5.6 Mg ha–1 on April 5, 2018, and April 16, 2019. Gypsum and litter applications occurred prior to the April aeration and subsequent measurements. The three treatments were replicated thrice, and in each experimental (1 and 2) controls were represented by a surface application of poultry litter.
Measurements. Measurements details can be found in table 1. Briefly, two infiltration measurements per plot occurred immediately after treatment implementation (month zero) on all plots and occurred one and three months thereafter. Control plots were measured on all infiltration measurement dates.
Timeline of aeration and subsequent infiltration measurements at the USDA Agricultural Research Service Dale Bumpers Small Farms Research Center in Booneville, Arkansas, in 2018 and 2019.
Double-ringed, falling-head, 30 cm inner ring infiltrometers (Turf-Tec International, Tallahassee, Florida) were slowly inserted approximately 15 cm into the soil using the bucket of a standard backhoe loader tractor with a large steel plate that covered the circumference of the outer ring. After installation, volumetric water content was measured in the 0 to 6 cm depth interval in triplicate in the inner ring of the infiltrometer using a TH2O theta probe (Dynamax, Fresno, California). The procedure for measurements followed a standard method for infiltration into soils (ASTM 1994). Tap water was used to fill the outer ring to approximately halfway up the height of the ring. Subsequently, the inner ring was filled to a height greater than the outer ring. This sequence occurred so that water in the inner ring would not flow in any direction but vertically downward. The stopwatch was started and the height of water was recorded using a fixed-position ruler immediately when water flow into the inner ring ceased. This represented time zero, and for the next 20 minutes (or until all the water within the inner ring had infiltrated) the height of the column was recorded at 1, 2, 3, 4, 5, 8, 10, 12, 15, 18, and 20 minutes thereafter. From these measurements, average infiltration rate over the 20 minutes time period (or the time duration that it took for the water to completely infiltrate if the time was less than 20 minutes) was calculated.
Soil Samples. Concurrent with day 0 measurements, outside of the ring installation locations but within the plot, five 0 to 15 cm soil samples were collected using a 2 cm push probe and composited in a bucket prior to BMP implementation. Plant material was manually removed and samples were dried in a forced-air oven at 70°C for 48 hours. Samples were subsequently ground and sieved to pass a 2 mm mesh for a later modified hydrometer method to determine soil texture (Gee and Or 2002). Additionally, total carbon (C) and N were determined via combustion using a VarioMax CN analyzer (Elementar Americas, Mt. Laurel, New Jersey). Soil pH was determined using a 1:10 soil mass:water volume mixture. Mehlich-III and water extractable nutrient concentrations (i.e., aluminum [Al], arsenic [As], boron [B], calcium [Ca], cadmium [Cd], cobalt [Co], chromium [Cr], copper [Cu], iron [Fe], potassium [K], magnesium [Mg], manganese [Mn], molybdenum [Mo], sodium [Na], nickel [Ni], phosphorus [P], lead [Pb], sulfur [S], selenium [Se], titanium [Ti], and zinc [Zn]) were determined using a 1:10 soil mass:extractant solution volume ratio (Tucker 1992) and analyzed by inductively coupled argon-plasma spectrometry (ICP, Agilent Technologies, Santa Clara, California).
Plant Samples. Yields for plots in Experiment 1 (hay) were measured using a Carter 3-chute forage harvester (Carter Manufacturing, Brookston, Indiana) on May 9 and July 26, 2018, and May 6 and July 8, 2019 (Harvest 1 and 2, respectively). Collected forage samples (6 cm stubble) were dried in a forced-air oven at 70°C for 48 hours to determine moisture content and dry matter plot yield. Subsequently, subsamples were ground using a Wiley mill (Thomas Scientific, Swedesboro, New Jersey) to pass a 1 mm mesh. Total C and N were measured by high-temperature combustion utilizing a VarioMax CN analyzer (Elementar Americas, Mt. Laurel, New Jersey). For elemental analysis, samples were digested with concentrated nitric acid (HNO3) and hydrogen peroxide (H2O2) and analyzed by inductively coupled argon-plasma spectrometry (Agilent Technologies, Santa Clara, California). Lignin, ash, neutral digestible fiber (NDF), and acid digestible fiber (ADF) were determined via an ANKOM 2000 Fiber Analyzer (ANKOM Technologies, Macedon, New York) (Van Soest and Robertson 1980; Ankom 2020).
Analysis of Data and Model Development. Analysis of variance (ANOVA) tests of the explanatory variable (infiltration) was performed using the MIXED procedure of SAS (SAS V9.3; SAS Institute, Cary, North Carolina) to determine water infiltration differences from BMPs under both experiments (hay and grazed systems). Because of treatment differences across management systems (e.g., gypsum treatments in Experiment 1 only), each data set was analyzed as separate experiments. In each model, BMPs (pasture aeration, subsurface litter incorporation, etc.) and a surface application of poultry litter (control) were considered fixed effects in a two-factor factorially arranged design, with month after BMP implementation (e.g., zero, one, or three) considered a repeated measure. For the repeated measure, an autoregressive covariance was used and the denominator degrees of freedom for the Type III F-test were adjusted with the Kenward-Roger method (Gomez et al. 2005). However, the –2 Loglikelihood did not change under the repeated-measure analysis (did not drop by at least five per covariance parameter), and the autoregressive correlation value (0.24) indicated a weak correlation among observations, so autoregressive covariance was dropped. Thereafter, an additional model, which analyzed BMPs per management system (Experiment 1 versus 2) was run using a mixed model analysis of variance (MMAOV). In all models, treatments and month after BMP implementation (i.e., zero, one, and three) were the fixed effects, and year and replication were entered as random effects using PROC MIXED (SAS V9.3; SAS Institute, Cary, North Carolina). When effects or interaction confluences were found, mean separations were performed using the SAS macro “pdmix800” (Saxton 1998) with Fisher’s least significant difference (LSD) at a Type I error rate of 5% (SAS 2007).
An additional model was run on yield in Experiment 1 to evaluate BMP influence on forage yield and quality. For this evaluation, BMP effects were analyzed per management system, with treatments and harvest timing considered fixed effects and year and replication considered random. Mean separation and significance levels were handled the same as the ANOVA model described above.
Results and Discussion
Generally, soil properties (table 2) were consistent across years. Soil texture and structure, water temperature and salinity, and initial volumetric water content were similar for all plots throughout the study (table 2). Poultry litter, however (table 2), had somewhat varying composition between years, especially for Ca, Mg, and moisture content. Given lack of soil variation, it is not likely that any treatment-induced changes occurred owing to inherent soil property differences.
Soil (0 to 15 cm) and poultry litter analysis for infiltration rate experiments at the Dale Bumpers Small Farms Research Center in Booneville, Arkansas, in 2018 and 2019. Carbon (C) and nitrogen (N) were analyzed by combustion; phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), and sulfur (S) by Mehlich-III in soils and total acid digestion in litter; and sand, silt, and clay by hydrometer.
Infiltration-Experiment 1: Hay System. When averaged over the two study years, infiltration rates differed (P < 0.05) (table 3, figure 2) among treatments and months for Experiment 1, although there was no interaction for these two main effects (P > 0.05). Overall, June aeration had the greatest (P < 0.05; 0.68 cm min–1) (figure 2) infiltration rate. All other treatments had slower (P < 0.05) infiltration and did not differ across 2018 and 2019. The hypothesis that April aeration would have the greatest infiltration was therefore rejected, perhaps owing to April receiving below average precipitation, which would diminish aeration effects. When averaging across treatments, three months after aeration had the greatest (P < 0.05; 0.56 cm min–1) infiltration rate, followed by the initial measurement (0.42 cm min–1), which was faster (P < 0.05) than one month after aeration (0.28 cm min–1). The lack of difference in rates between the aeration and control treatments agrees with Franklin et al. (2006, 2007) and Vendramini and Silveria (2009) who showed aeration did not affect runoff volume (i.e., infiltration). Franklin et al. (2007, 2011) suggested aeration effectiveness depends on soil hydrological properties and using soil properties such as redoximorphic features as an indicator of the most optimal time for aeration. Gypsum and litter incorporation had no effect on infiltration compared to the untreated control. These results were also in conflict with Pote et al. (2009), where use of the Subsurfer increased infiltration. Yu et al. (2003) showed that 2 and 4 Mg ha–1 gypsum increased infiltration rates over the control; however, this result was not observed in our field study.
Analysis of variance of infiltration rates measured at the Dale Bumpers Small Farms Research Center in Booneville, Arkansas, in 2018 and 2019.
Infiltration rates measured for a hay experiment by best management practice at the Dale Bumpers Small Farms Research Center in Booneville, Arkansas, in 2018 to 2019. Litter was applied to all plots in April of both years. Means with the same letter are not different at P < 0.05.
Infiltration-Experiment 2: Grazed System. Similar to Experiment 1, averaged across years (as there was no year effect), infiltration rate differed (P < 0.05) (table 3, figure 3a) among treatments and months for the grazed experiment. The fastest rate (1.00 cm min–1) (figure 3a) was measured three months following the June aeration, which did not differ (P > 0.05) from the rates measured three months after the April aeration and the Subsurfer treatment (0.99 and 0.72 cm min–1, respectively) and one month after the June aeration (0.94 cm min–1). Our hypothesis that April aeration would result in the greatest infiltration rate was again rejected. The Subsurfer and April aeration treatments behaved similarly, as subsurface litter applications create slits in the soil surface (Pote et al. 2009), thus mimicking aeration. For the June aeration, pastures within the study area undergo hot and dry summers common to western Arkansas; thus, soils are dry and aeration may create large cracks within the soil. If soil is dry at the start of infiltration, macropores that are initially open to the surface that serve as an infiltration pathway start to fill, and infiltration rates decline sharply (Brady and Weil 2002).
Infiltration rates measured for a grazed experiment (a) 0, 1, and 3 months and (b) averaged over time after best management practice implementation at the Dale Bumpers Small Farms Research Center in Booneville, Arkansas, in 2018 and 2019. Litter was applied to all plots in April of both years. Means with the same letter are not different at P < 0.05.
The slowest rate was measured immediately after Subsurfer application (0.08 cm min–1) (figure 3a), which did not differ (P > 0.05) from the rates measured immediately after February and April aeration (0.34 and 0.10 cm min–1, respectively), one month after February and April aeration (0.17 and 0.14 cm min–1, respectively), and three months after February aeration (0.14 cm min–1). As previously mentioned, the aerator breaks through the soil surface; however, in February, the effect of aeration was minimal and compaction more than likely occurred from the weight of the aerator, outweighing the positive effects of the pores created.
Averaged over the two years of the study, June aeration had the greatest (P < 0.05; 0.84 cm min–1) (figure 3b) infiltration rate averaged across months. The control plots measured the second greatest infiltration rate, which did not differ (P > 0.05) from the April aeration. The February aeration had the slowest infiltration rate (figure 3b) (0.22 cm min–1) averaged over the two years, which did not differ (P > 0.05) from the Subsurfer or April aeration treatment infiltration rates, averaged across time points.
Infiltration rates were not analyzed between experiments due to main effect differences (gypsum applications in Experiment 1). However, rates measured (figures 2 and 3) were similar between experiments across treatments, suggesting cattle grazing did not adversely affect infiltration in this study.
Because precipitation amounts were greater in unexpected periods of the year (232% and 87% increases for February in 2018 and 2019, respectively, and 119% increase for June in 2019), our infiltration results were somewhat mixed. However, February aeration did not increase infiltration rates in this study. Therefore, these results suggest that in hay and grazed systems, effects of aeration can still be beneficial up to three months after June aeration.
Yield and Forage Quality in Experiment 1. Averaged across years, yield did not differ (P > 0.05) (table 4) by treatment. However, across all treatments, yield was greater (P < 0.05) for Harvest 2 (5.92 Mg ha–1) compared to Harvest 1 (3.84 Mg ha–1). It was expected that the second harvest would have greater yields due to the increases in seasonal precipitation and bermudagrass maturation and peak growth during the second harvest period (May to July). Overall, authors rejected the hypothesis that BMPs would increase forage yields and forage nutrient concentrations (as only two nutrients varied per treatment).
Analysis of variance of forage yield and quality for the hay experiment at the Dale Bumpers Small Farms Research Center in Booneville, Arkansas, in 2018 and 2019.
Even though all plots were fertilized at the same rate, previous research (Maguire et al. 2011; Pote et al. 2009, 2011; Ashworth et al. 2020, 2021) has shown yield increases of subsurface applied litter compared to surface applied litter. This is mainly due to the decreases in nutrient losses after applications. However, Liu et al. (2016) found no differences between surface applied and subsurface applied litter in corn (Zea mays L.). Similarly, gypsum has been shown to increase yields, especially at our higher rate. Nevertheless, the high rate of gypsum had the greatest yield numerically (data not shown). Multiple studies (Burgess et al. 2000; de Koff et al. 2011; Lemus 2011), however, found no differences in yield due to aeration. Davies et al. (1989) reported yield increases, but this was after 26 years of compaction. Conversely, according to other studies, aeration may decrease yields due to mechanical damage to roots (Maguire et al. 2011), counteracting the benefit of the increased soil porosity for increased nutrient retention. Ingram et al. (2011) also found no increases in forage production regardless of time of aeration on silty soils; furthermore, when aeration occurred in moist conditions, aerating led to decreased production. Given that three months after June aeration infiltration was increased compared to the control, it would be expected that a greater yield would occur with June aeration. Overall, the null hypothesis for yield was rejected, as yield benefits were not observed following BMP implementation.
Multiple forage quality parameters differed by harvest across 2018 and 2019 (P < 0.05) (table 4). Ash, P, and K concentrations varied (P < 0.05) by treatment and harvest (across years), with ash concentrations being greatest (P < 0.05) for the first harvest for the April aeration treatment, and P concentration being greatest (P < 0.05) for the high rate of gypsum and control during Harvest 1. Forage K concentration was greatest (P < 0.05) for Harvest 2 when using the Subsurfer, and lowest (P < 0.05) for Harvest 2 in the control across years.
Forage Ca concentration varied (P < 0.05) (table 4) among treatments across years, but did not differ (P > 0.05) by harvest. Greatest Ca concentration occurred in the high gypsum application, which was expected as gypsum contains high amounts of Ca. Neutral-digestible fiber, ADF, lignin, and Mg and S concentrations all increased (P < 0.05) from Harvest 1 to Harvest 2 across years but did not differ (P > 0.05) (table 4) by treatment, while forage K concentration decreased (P < 0.05) from Harvest 1 to Harvest 2 but did not differ (P > 0.05) by treatment. There was also no treatment by harvest interactions for NDF ADF, lignin, K, Mg, or S.
Summary and Conclusions
Management practices can drive infiltration rates (Haghnazari et al. 2015). Producers need BMPs that conserve nutrients while protecting downstream water quality. This study aimed to evaluate potential BMPs for ensuring poultry litter applications are applied with minimal losses to the air, soil, and water.
Because soil properties at the site were similar, treatments were likely the main driver for observed infiltration rate differences. As is evidenced by these experiments, June aeration provided the greatest water infiltration. Water infiltration rates were similar between hay and grazed systems, thus indicating cattle grazing pressure did not adversely affect overall trends observed (Burk et al. 1999; Franzluebbers et al. 2011).
Study limitations can be related to the aerator used in this study. The tines on the aerator were relatively small and shallow compared to other models (de Koff et al. 2011). Deeper, wider tines would alter the efficacy of pasture aeration. Potential future studies should evaluate differences in aerator effectiveness by dimensions and specifications. Future studies on infiltration should also measure effects longer after the initial aeration than was measured in this study, especially since we expected April aeration to have the greatest impact on infiltration and yields. It could be expected that, depending on the aerator model, aeration impacts on infiltration and yield may still be effective beyond three months (de Koff et al. 2011; Curran Cournane et al. 2011). Furthermore, our results may not be applicable to all areas, especially with different soils and vegetation and in different moisture regimes. Also, future work is needed to identify the economic feasibility of these pasture BMPs.
Acknowledgements
This research was made possible by the USDA Natural Resources Conservation Service, Arkansas Conservation Innovation Grant (58-6022-8-004). Authors are also grateful for Becky Roark and Melissa Welch (Beaver Watershed Alliance, Fayetteville, Arkansas) for providing the aerator, Michael Schmidt (USDA Agricultural Research Service [ARS], Booneville, Arkansas) for assistance with measurements, and Steve Haller and Brent Woolley (USDA ARS, Booneville, Arkansas) for assistance with harvest and pasture management. Mention of tradenames or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the USDA.
- Received November 3, 2020.
- Revision received March 19, 2021.
- Accepted May 22, 2021.
- © 2022 by the Soil and Water Conservation Society
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