Crop Residue Decomposition in No-Tillage Small-Grain Fields

wind erosion. Soil microenvironments are different in fields that have surface-crop residues than in tilled fields Conservation tillage fields provide different environments for biowith incorporated residues. Natural-resource simulation logical and chemical processes than tilled fields. Our understanding of decomposition does not adequately account for post-harvest residue models that address soil erosion, water quality, intedistributions or field environment variability. We hypothesized that grated pest management, nutrient management, and temperature and moisture could be used to normalize field environother issues need to consider the impacts of residues ments to optimal conditions that produce maximum decomposition on the agroecosystem and ecosystem impacts on residue rates; and biomass density could be normalized based on the fraction decomposition (Steiner, 1994). of initial biomass remaining over time. Four small grains were grown Much of our understanding of decomposition is based at Bushland, TX, to produce high-, medium-, and low-biomass dension controlled-environment studies, or field studies using ties in 36 field subplots using different seeding rate, fertilizer, and bagged residues or labeled isotopes. Mass loss over time irrigation on Pullman clay loam (fine, mixed, thermic Torrertic Paleushas been reported for few studies in natural field distritoll). During decomposition, differential irrigation increased environbutions because of inherently high variability of such mental variability (13, 5, and 0 applications to sub-subplots). Ashfree crop residue biomass was measured seven times during 14 mo. data and the high labor requirement to collect and proClimate indices related field to optimal conditions, based on the daily cess residue samples. However, it is important that we minimum of air temperature and precipitation coefficients. First-order gain a better understanding of decomposition in realistic decomposition coefficients, k, were determined by plot, using the field environments. For example, Stott et al. (1990) recumulative climate index to represent time. Irrigation did not affect k ported that about 16 to 18% of total residue biomass was (P , 0.45), indicating that the moisture index accounted for irrigation in standing stubble, following harvest in decomposition effects; but crops had different coefficients (P , 0.062). Initial biomass studies at Pullman, WA; but the fraction of remaining density was inversely related to k (P , 0.008), indicating that climatebiomass increased to 85% after 49 wk during 1 yr, conbased indices inadequately normalized environments across density trasted to 0% by 32 wk in the next year. In the second treatments. The k was correlated to initial biomass (r 5 20.49), fracyear, the stems fell during winter snows; but in the first tion-standing initial biomass (r 5 20.37), and initial N concentration in standing biomass (r 5 0.32). Climate indices may allow normalizayear, when the stubble didn’t fall during winter, the tion of field environments important to decomposition and other mass loss was extremely low from stubble, compared agroecosystem processes if density effects on atmosphere–soil-residue with the residues that were on the soil surface. Most interactions can be better quantified. decomposition models cannot account for this type of interaction between the plant material and the environment. M of grain crops in the USA has changed Organisms that drive decomposition experience cysubstantially from traditional practices involving cles of population growth and activity due to variable intensive tillage following harvest to practices such as field environments, and these cycles may impact decomno-tillage, ridge tillage, and other conservation tillage position rates in ways that are not accounted for in systems that leave residues undisturbed following harcurrent agroecosystem models. Taylor and Parkinson vest. These management systems have been adopted to (1988a) conducted decomposition studies of leaf litter increase production efficiency and reduce water and Abbreviations: A, constant in temperature coefficient equation; Biomass Density Treatments: H, high; M, medium; L, low. Crops: B, J.L. Steiner and H.H. Schomberg, USDA–ARS, 1420 Experiment barley; O, oat; S, spring wheat; W, winter wheat. DAH, days after Station Rd., Watkinsville, GA 30677; P.W. Unger and J. Cresap, harvest; DD, decomposition day; k, decomposition coefficient; Mo, USDA–ARS, P.O. Drawer 10, Bushland, TX 79012. Received 17 Aug. initial biomass; Mt, total biomass at time t; P, precipitation (or irriga1998. *Corresponding author ([email protected]). tion); PC, precipitation coefficient; T, temperature; TC, temperature coefficient; Topt, optimum temperature for decomposition. Published in Soil Sci. Soc. Am. J. 63:1817–1824 (1999). 1818 SOIL SCI. SOC. AM. J., VOL. 63, NOVEMBER–DECEMBER 1999 in microcosms and reported that litter absorbed water tive minimum environmental factor (data not shown). The treatments fell on a single decomposition line, exand decomposed faster following 14 freeze–thaw cycles than after a single freeze–thaw cycle. Differences in cept for (i) the highest temperature, which was above the model optimum temperature, and (ii) the wettest water absorption and decomposition did not persist past the first 2 to 3 mo and a single freeze–thaw cycle was moisture treatment, which perhaps was oxygen-limited. In an analysis of persistence of standing stems (Steiner more important than the number of cycles in causing physical changes in the plant litter that affects decompoet al., 1994), precipitation and air temperature served as reasonable parameters to normalize climatic effects sition. However, Taylor and Parkinson (1988a) concluded (based on a series of studies) that freezing before over time, similar to the model of Stroo et al. (1989). For surface-placed bagged residue decomposition studies, a permanent snow cover and decomposition beneath snow remained important parts of the litter mass loss precipitation-based index performed as well as a soil water content-based index to normalize environmental that are not well understood. Microcosm studies of wetting and drying cycles (Taylor and Parkinson, 1988b) conditions across irrigation treatments (Schomberg et al., 1996). indicated that pine (Pinus contorta Loud. 3 P. banksiana Lamb.) needles that went through 14 wet–dry cycles Small grains are predominant crops in the Great Plains and the Pacific Northwest regions of the USA, absorbed water and decomposed faster than needles that went through a single cycle, but the reverse was and managing small-grain residue is critical to controlling wind erosion in these regions. Small grains are also true for aspen (Populus tremuloides Michx.) leaves. For both species, the effect was only important during the important in Upper Midwest and southeastern cropping systems, contributing to the control of water erosion first 2 to 3 mo of decomposition. In forest sites in western Canada, litter layers exhibited trends of increasing moisbecause of the good groundcover and relatively slow decomposition rates of the residues, compared with ture content with depth, and only the top 1 cm went through wetting and drying cycles (about 10–15 yr21), those of other crops grown in these regions. Smith and Peckenpaugh (1985) measured the decomposition rate indicating to the authors that wet–dry cycles are probably not a significant factor for their environment. of 23 small grain straws in bags buried 15 cm at Kimberly, Idaho. They reported 54 to 75% decomposition Many researchers have developed temperature and moisture factors to quantify climatic limitations to deduring a 384-d period, with hard red winter wheat and triticale (Triticale hexaploides Lart.) straw decomposing composition. An advantage of normalizing weather data to optimal temperature and moisture conditions is that faster than soft white wheat or barley straw. Decomposition rate for these varieties was not consistently related it allows the use of decomposition coefficients developed in controlled environments to predict decomposito the C/N ratio (33:199) or N concentration (2.2–12.5 g kg21) of the initial residues. tion in field environments. Hunt (1977) calculated decomposition in grasslands as an empirical function of Clark (1968) reviewed literature regarding the rate of addition effect (varying amounts of fine plant material soil water tension, a quadratic effect of temperature from 0 to 388C and as an empirical function of N in the mixed with a constant volume of soil) on soil organic matter decomposition that indicated an inverse relationsoil. He calculated a maximum potential decomposition rate and then multiplied it by the moisture, temperature, ship of the rate of addition to the decomposition rate. However, studies on the related soil volume effect (a and N factors to determine an actual rate. In developing and testing a crop-residue decomposition model, Gregconstant amount of plant material mixed with varying amounts of soil) indicated a decrease in percent-C ory et al. (1985) and Ghidey et al. (1985) multiplied temperature and moisture factors and then divided by evolved as CO2 as soil increased, an apparent contradiction to the rate of addition studies. Jenkinson (1971) the initial C/N ratio of the residue and accumulated this factor over time to calculate residue decomposition. reviewed C-14 studies and found mixed results, but concluded that when the rate of addition does not exceed Andrn and Paustian (1987) found that a one-compartment model using a Q10 temperature factor and a log– about 1.5% of the dry weight of the soil, the decomposition rate can be assumed to be independent of the quanlinear function of soil water potential described mass loss of barley (Hordeum vulgare L.) straw decomposed tity of plant material added. In subsequent work, Jenkinson (1977) reported that the percent labeled-C evolved in bags at 10 to 15 cm below the soil surface better than other decomposition models (e.g., multi-compartment) as CO2 tended to increase slightly as the rate of addition increased, but concluded that except for short-term inand climate factors. In an analysis of environmental limitations on wheat (Triticum aestivum L.) residue decubations of N-poor material, percentage decomposition of organic matter in the soil would be substantially composition, Stroo et al. (1989) applied the law of the minimum to temperature and moisture factors and independent of loading rate. Brown and Dickey (1970) reported that percentage found that accumulating the daily minimum of temperature and moisture factors was better related to observed mass loss was inversely related to the initial amount (1121–6726 kg ha21 equivalent) of bagged wheat straw mass loss than a cumulative factor based on multiplication of the daily factors. Data reported by Summerell in above-soil, soil-surface, and buried-field exposures. Stott et al. (1990) also reported an inverse relationship and Burgess (1989) on decomposition of wheat straw across a range of controlled temperature and moisture of initial wheat straw biomass (1680–6000 kg ha21) to percentage mass loss using grab samples from no-tillage conditions fit the Stroo decomposition model well when the environment data were used to calculate the cumulafields. Stroo et al. (1989) reported that surface-placed STEINER ET AL.: CROP RESIDUE DECOMPOSITION IN NO-TILLAGE FIELDS 1819 Table 1. Growing season treatments used to produce three residue biomass densities at harvest for small grain decomposition plots. Seeding Fertilizer† Growing Biomass Non-grain Head Density rate (N, P) season irrigation at maturity biomass number kg ha21 mm g m22 m22 Barley (winter) High 112 135, 168 435 750 (50)‡ 480 (31) 360 (44) Medium 84 55, 168 335 480 (23) 330 (33) 300 (23) Low 67 0, 168 95 270 (52) 207 (28) 180 (71) Oat (spring) High 112 135, 168 320 450 (75) 290 (40) 300 (25) Medium 84 55, 168 235 420 (23) 260 (12) 370 (7) Low 67 0, 168 95 250 (66) 140 (39) 200 (57) Spring Wheat High 112 135, 168 320 700 (4) 460 (15) 340 (15) Medium 84 55, 168 235 510 (81) 320 (54) 230 (7) Low 67 0, 168 95 310 (102) 190 (63) 150 (20) Winter Wheat High 112 135, 168 435 840 (174) 500 (97) 560 (152) Medium 84 55, 168 335 600 (19) 360 (25) 500 (27) Low 67 0, 168 95 350 (82) 250 (42) 330 (76) † Fertilizer N was applied as anhydrous ammonia, and P as triple super phosphate (0-46-0). ‡ Mean (standard deviation). et al. (1994). In summary, twelve 12by 70-m main plots were wheat straw (1to 2-cm segments) decomposed faster arranged in three randomized complete blocks of the four for 1500 and 3000 kg ha21 rates than for 6000 kg ha21, crops. Before this study, all plots had been uniformly cropped based on percent-C evolved as CO2 in laboratory studto dryland sorghum [Sorghum bicolor (L.) Moench]. Each ies. In contrast, Wagner-Riddle et al. (1996) reported a main plot was split into three subplots before planting, to linear relationship between time and fraction of initial establish density treatments with minimal irrigation pipe and mass remaining for rye (Secale cereale) cover crops (1–8 labor required to flood-irrigate level-border plots. High (H), Mg ha21, across years, sites, and treatments), which immedium (M), and low (L) initial crop-residue biomass densiplies no impact of initial residue mass on decomposition ties were obtained for each crop by differentially managing rate. Parr and Papendick (1978) summarized literature seeding rate, fertilization, and growing-season irrigation (Table 1). The L-treatment plots received an establishment irrigafrom laboratory and field studies that indicated that tion (13 December for fall-sown crops and 2 April for springresidue decomposition is inversely related to the amount sown crops). The H-treatment plots were irrigated when about of residue, but concluded that mechanisms, processes, 50% of plant-available water was depleted, as determined by and relationships to explain such a relationship were neutron probe readings from access tubes centered in each lacking, which is still the case today. subplot (10 December for fall-sown crops and 18 March, 12 Because of the importance of small grains for conserApril, and 2 May for all crops). The M-density plots received vation cropping systems, we established a study to imirrigations on 12 December (fall-sown crops), 5 April, and 14 prove our understanding of residue decomposition of May. Grain was harvested from all crops during June 1991. four small grains in varying field environments. Our To provide a range of environments during the decomposioverall goal is to develop simple decomposition models tion phase of the study, each crop-density subplot was divided into thirds with berms for treatments consisting of no-irrigathat can be applied across a wide range of climates and tion, full-irrigation, and alternate-date irrigation treatments, management systems. Specific objectives of this paper randomly assigned to sub-subplots. Full-irrigation sub-subare to determine residue-density effects and temperaplots were irrigated to maintain a moist surface (as often as ture and moisture limitations on small grain residue weekly) with the minimum amount of water (about 50 mm) decomposition, and to normalize field environments to required to flow across a 12by 22-m sub-subplot. Full-irrigaenvironmental conditions that produce maximum detion plots were irrigated on 49, 59, 77, 82, 160, 114, 168, 269, composition rates. 281, 292, 346, 382, and 388 days after harvest (DAH), while alternate-date irrigation plots were irrigated 58, 107, 169, 282, and 387 DAH. We did not irrigate when the daily mean air MATERIALS AND METHODS temperature was at or near freezing. Field Experiments Ten 1.0by 1.0-m sites were established in controlled traffic areas of each sub-subplot. Sample sites were from rows cenCrop residue biomass was monitored for 14 mo for winter tered during planting and harvest operations, to minimize wheat (Triticum aestivum L.) ‘TAM–107’, spring wheat ‘Oslo’, variability among samples. At approximately 60-d intervals, winter barley (Hordeum vulgare L.) ‘Post’, and spring oat residue biomass was measured from one site per sub-subplot. (Avena sativa L.) ‘Lew’ at the USDA–ARS, Conservation and Initial biomass was measured in July 1991 (24 DAH) and Production Research Laboratory, Bushland, TX (358N, 1028 additional samples were collected about 92, 156, 224, 301, 365, W, elevation of 1170 m, mean annual precipitation of 476 mm, and 401 DAH (biomass sampling required more than one day, mean annual temperature of 13.38C). Crops were grown on depending on the age of the residue and labor availability). a Pullman clay loam (fine, mixed thermic Torrertic Paleustoll) We used the following procedure for a biomass sample in 0.25-m rows, oriented north–south as described by Steiner collection. Intact stems that were fallen or leaning near the ground at an angle of 108 or less were collected. The remaining intact standing stems were counted and collected by cutting 1 Reference to a trade or company name is for specific information or lifting them from the soil. Remaining surface biomass was only and does not imply approval or recommendation by the USDA to the exclusion of others that may be suitable. collected with as little soil as possible. Any remaining dirty 1820 SOIL SCI. SOC. AM. J., VOL. 63, NOVEMBER–DECEMBER 1999 residue was raked and picked up by hand. This fraction conand accumulated as DDs to normalize the time scale to environmental conditions. tained a high percentage of soil and a small proportion of the total residue mass. The fallen, standing, and surface components were sieved Determining Decomposition Coefficients on a 1-mm fiberglass screen to remove soil. Soil and residue First-order exponential decomposition rates were deterthat passed the screen was added to the dirty fraction from mined using Eq. [2]: that plot. The dirty fraction was washed on 0.5-mm screens under an array of spray nozzles (6-mm nozzles mounted 0.25 m Mt/M0 5 exp2k(DD) [2] above the sample trays produced a 0.5-m diam. conical pattern, where Mt is total biomass at time t, M0 is the initial biomass, with an average flow rate of 0.1 L s2, at 330 kPa). This and k is the decomposition coefficient (g g2 DD2), and was the lowest force that maintained an even pattern without DD is the decomposition days. The initial biomass was that producing splash. Wash time was about 2 to 5 min, using the collected on 24 DAH. Because decomposition period irrigaleast water possible to minimize leaching. All components tion treatments had not been initiated at that time, only subwere dried at 608C and weighed. Samples were ground to pass subplots to be fully irrigated were sampled and that value was a 0.635-mm screen and subsamples were weighed, ashed in a used for the other two irrigation sub-subplots. The 24 DAH muffle furnace at 5008C for 4 h, and weighed to determine biomass data for spring wheat plots were anomalously low, the soil fraction of the sample. Residue mass for the fallen, based on higher subsequent residue biomass samples and on surface, and dirty fractions were corrected to ash-free mass higher values obtained from preharvest yield samples. Using and summed with standing-stem mass to obtain the total cropa linear relationship developed from barley, oat, and winter residue mass (Mt). wheat data for the preharvest and 24 DAH data, initial residue Rainfall (P, in mm) was measured in a standard weather for spring wheat (M24) was estimated for each plot based on service rain gauge about 50 m east of the experimental area. the preharvest mass (Mh) as M24 5 1.43 Mh, r 2 5 0.97. The Daily mean, maximum, and minimum air temperatures (T, in slope .1 indicates that biomass passing through the combine 8C) at 2 m were measured either at the experimental area was concentrated in the sampling rows. Residue distribution or at a Class A weather station located 1 km east of the can be strongly affected by wind direction and speed, as deexperimental site. scribed by Allmaras et al. (1985), and in our experiment, different crops were harvested on different days as they Calculating Decomposition Days reached harvest maturity. We used the concept of a decomposition day (DD) to norStatistical Analysis malize time based on climatic conditions, similar to the environmental coefficients developed by Stroo et al. (1989). AsAnalysis of variance of preharvest plant data and the initial suming that the most important environmental factors for biomass data (24 DAH) were conducted using the General decomposition are temperature and moisture, we calculated Linear Models (GLM) Procedure of SAS (1989) with crop daily temperature and moisture coefficients. Each coefficient treatments as whole plots, and density treatments as strip plots is constrained from 0 to 1, with 1 indicating conditions for with three replications. Analysis of variance of residue data maximum decomposition and 0 indicating no decomposition. on 224 and 404 DAH were also conducted using the GLM Based on the principle of most limiting factor, the lower of procedure in SAS (1989) for crop, density, and irrigation treattwo coefficients was used to represent the fractional decompoments and interactions as a strip-split plot design with three sition for a given day, relative to a day at optimum conditions. replications. The decomposition coefficient, k, was determined As described by Steiner et al. (1994), the precipitation coeffor each sub-subplot using the MODEL procedure in SAS ficient is triggered by precipitation (or irrigation) and declines until the next event. Based on Schomberg et al. (1996), we (1988). Crop, initial biomass density, and decomposition-pedecreased the coefficient to 40% of the previous day’s value riod irrigation treatment effects on k were analyzed using the (giving the equivalent of about 1.66 optimum moisture days GLM procedure of SAS (1989). The Correlation (CORR) for each precipitation event that exceeded 4 mm, assuming Procedure (SAS, 1989) was used to determine correlation moisture decreased to a negligible level after 7 d without coefficients between k and initial residue properties. The hetrewetting). The value of 4 mm used as a threshold is adequate erogeneity of slopes was tested using the procedure described to fully wet even dense layers of surface residues (Schreiber, by Freund et al. (1986) solving linear models within the GLM 1985; Savabi and Stott, 1995) and moisten the underlying soil procedure of SAS. (precipitation coefficient [PC] 5 1, when precipitation $4 mm). Smaller amounts of precipitation are assumed to be RESULTS AND DISCUSSION intercepted by the residue layer where they dry relatively quickly and the initial coefficient is calculated as PC 5 precipiThe growing-season treatments (Table 1) provided a tation/4. If another precipitation or irrigation event occurs reasonable range to represent high to low small-grain during the decay of the PC coefficient over time, PC is reset Table 2. Significance level of crop and density treatment effects based on precipitation amount. on small grain residue samples collected on 24 d after harvest. The temperature coefficient (TC) was calculated (Eq. [1]) after Stroo et al. (1989): Crop 3 Parameter Crop Density density TC 5 2(T 1 A)2 (Topt 1 A)2 2 (T 1 A)4 (Topt 1 A)4 [1] P , Total mass (g m21) 0.0001 0.0008 0.0001 Standing mass (g m21) 0.0001 0.0001 0.0018 where T is the daily average air temperature; Topt 5 328C, and Surface mass (g m21) 0.0003 0.0144 0.0044 A 5 0. The equation must be constrained to remain at 0 when Fraction standing (g g21) 0.0024 0.0140 0.0044 T , A; otherwise, it increases with decreasing low temperStem number (m22) 0.0001 0.0067 0.0033 Stem weight (g stem21) 0.0256 0.1357 0.5528 atures. Standing mass N concentration (mg g21) 0.0099 0.0347 0.0178 The daily fractional decomposition day was set equal to the Surface mass N concentration (mg g21) 0.4336 0.4226 0.0009 minimum of temperature or moisture coefficient for that day, STEINER ET AL.: CROP RESIDUE DECOMPOSITION IN NO-TILLAGE FIELDS 1821 Fig. 1. Decomposition of barley and winter wheat residue, shown as mass vs. days after harvest (a and d); normalized mass (M/M0) vs. days after harvest (b and e); and normalized mass vs. decomposition days (c and f ). Symbols are means and lines represent plus or minus one SE. For a, b, d, and e there are nine observations per mean (three irrigation 3 three replications), and for c and f there are three observations per mean (replications). biomass at maturity (840–250 g m22), nongrain biomass other crops. Another significant interaction was a very low proportion of surface biomass in the spring wheat (500 to ,150 g m22), and head number (560–150 m22). The range of plant biomass achieved by density treatcrop, compared with the total biomass for those plots, relative to other crops. Across treatments, the trend was ments was least for the oat crop, with highand mediummanagement strategies producing similar biomass and for the highest N concentration in standing biomass in the L-density treatment, but the L-density spring wheat head numbers. Growing-season crop and density treatments affected several properties of the residue at the had one of the lowest standing biomass N concentration levels of the experiment. initial biomass sampling on 24 DAH (Table 2) but significant interactions occurred between main treatment efDecomposition of barley and winter wheat are shown in Fig. 1 (spring wheat and oat show similar trends, data fects. A primary interaction was similar H and M biomass for oat, compared with a range of values for the not shown). Total biomass vs. days after harvest (Fig. Table 3. Significance level of Crop (C), Density (D), and Irrigation (I) treatment effects on small gain residue samples collected on 224 and 404 days after harvest. Parameter Crop Density C 3 D Irrigation C 3 I D 3 I C 3 D 3 I