Agronomy Journal最新文献

Organic matter and soil nutrient accumulation within putting green root zones affects surface quality and performance. Research was conducted to explore differences in organic matter and nutrient content within creeping bentgrass (CBG; Agrostis stolonifera L.) and ultradwarf bermudagrass [UDBG; C. dactylon (L.) Pers. × C. transvaalensis Burtt-Davy] putting greens. Sampling was conducted from January 30 to February 23, 2023, on 22 putting greens (11 CBG and 11 UDBG) in Tennessee. Forty cores (1.91-cm diameter by 10-cm depth) were randomly collected from each putting green and analyzed for total organic material (TOM; undisturbed cores) by profile depth (0–2 cm, 2–4 cm, and 4–6 cm), TOM throughout a 10-cm core, soil organic matter (SOM; passed through sieve with 2 mm openings) throughout a 10-cm core, and nutrient content. All organic matter measurements were determined via loss-on-ignition (LOI) testing, whereas nutrient content was determined by Mehlich-3 extraction. LOI testing at 440°C resulted in greater TOM values than 360°C; however, values from both temperatures were closely related (R² = 0.99). TOM values from UDBG greens were greater than those recorded on CBG at all profile depths. Greens established via no-till conversion contained more TOM than those planted in a constructed sand-matrix root zone. While TOM and SOM were associated (R² = 0.62), removing verdure for SOM assessments reduced the amount of organic material in each sample by 38% and increased variability. Nutrient contents were lower than sufficiency level of available nutrients benchmarks but exceeded minimum levels for sustainable nutrition.

Preharvest yield estimates can be used for harvest planning, marketing, and prescribing in-season fertilizer and pesticide applications. One approach that is being widely tested is the use of machine learning (ML) or artificial intelligence (AI) algorithms to estimate yields. However, one barrier to the adoption of this approach is that ML/AI algorithms behave as a black block. An alternative approach is to create an algorithm using Bayesian statistics. In Bayesian statistics, prior information is used to help create the algorithm. However, algorithms based on Bayesian statistics are not often computationally efficient. The objective of the current study was to compare the accuracy and computational efficiency of four Bayesian models that used different assumptions to reduce the execution time. In this paper, the Bayesian multiple linear regression (BLR), Bayesian spatial, Bayesian skewed spatial regression, and the Bayesian nearest neighbor Gaussian process (NNGP) models were compared with ML non-Bayesian random forest model. In this analysis, soybean (Glycine max) yields were the response variable (y), and spaced-based blue, green, red, and near-infrared reflectance that was measured with the PlanetScope satellite were the predictor (x). Among the models tested, the Bayesian (NNGP; R²-testing = 0.485) model, which captures the short-range correlation, outperformed the (BLR; R²-testing = 0.02), Bayesian spatial regression (SRM; R²-testing = 0.087), and Bayesian skewed spatial regression (sSRM; R²-testing = 0.236) models. However, associated with improved accuracy was an increase in run time from 534 s for the BLR model to 2047 s for the NNGP model. These data show that relatively accurate within-field yield estimates can be obtained without sacrificing computational efficiency and that the coefficients have biological meaning. However, all Bayesian models had lower R² values and higher execution times than the random forest model.

State-level cooperative extension services provide fertilizer recommendations for row crops in the United States. Of these, nitrogen (N) recommendations are arguably the most important because N is the most common yield-limiting nutrient in nonlegume crop production systems. Throughout the peanut (Arachis hypogaea L.) growing region of the United States, Cooperative Extension Services generally recommends 22–67 kg N/ha credit to crops following peanut, likely due to the assumption that peanut, being a legume, contributes N to the following crop. The body of peer-reviewed literature indicates that N credits from peanut to the subsequent crop are negligible. Recent literature indicates that apparent differences in yield following peanut compared to a nonlegume are a result of nonlegume crop residue favoring N immobilization rather than N mineralization from peanut residue. Taken together, recent research corroborates the few previous scientific publications addressing the issue, namely, that cooperative extension service recommendations to reduce N fertilization to crops after peanut are not supported by the peer-reviewed literature. Future field research should include summer fallows to determine if yield differences between legumes and nonlegumes are due to N credits by the legume or N immobilization by nonlegumes. Data on N loss pathways following peanut are needed to identify management strategies that can mitigate N losses after peanut harvest. In conclusion, the preponderance of peer-reviewed science does not support current Extension recommendations regarding peanut N credits to the following crop.

Finding effective phosphorus (P) recommendation strategies to optimize corn (Zea mays L.) yield under varying yield levels and environmental conditions is continuously sought after. A 16-year study was conducted in Concord, NE, on Nora silt loam soil initially measuring 16 ± 3 mg kg⁻¹ Bray-1 P. The study evaluated the impact of different P fertilization strategies on corn yield across various growing conditions (dry, normal, and wet years). Treatments included no P or N (NPNN), no P (NP), P applied at crop removal phosphorus (CRP), and maintaining soil P at 15 (B15), 30 (B30), and 45 (B45) mg kg⁻¹ Bray-1 P, with similar nitrogen (N) rates except for NPNN. Results showed a 25% and 33% reduction in soil test phosphorus (STP) for NPNN and NP treatments, respectively. The total P required by B30 and B45 treatments was 1.8 times more than that for CRP and B15. Although B30 and B45 treatments increased corn grain P concentration by 6%–12% compared to B15 and CRP, they did not increase yields in normal and dry years. The NP led to yield reductions of 9% and 12% in normal and wet years, respectively. In contrast, CRP achieved an 8% higher yield than B15 during wet years. Economic analysis revealed that B45 yielded a 56% higher net return in normal years, while CRP offered the highest return on investment (ROI) at 4.9. This study highlights the complexity of managing soil P under varying environmental conditions, emphasizing that while maintaining higher STP levels (B30 and B45) can enhance grain P concentration, it does not significantly boost yield and ROI compared to CRP and B15.

Mississippi soybean [Glycine max (L.) Mer.] producers are under pressure to plant as much land as possible within narrow planting windows. The 5-year average planting progress is 45% of the total at the end of the optimal soybean planting window. New metering and seed delivery technology claims faster planting without sacrificing singulation, stand, or yield, but these tools need to be validated before recommendation. This study aimed to quantify soybean response to planting speeds using a precision planter (John Deere MaxEmerge 2 row units retrofitted with Ag Leader SureSpeed and SureForce) and a mechanical planter (John Deere 1700 ground-driven mechanical planter equipped with eSet meters) for a total of 7 site-years across Mississippi. In 2022, both planters were evaluated at four actual ground speeds of 7.9, 10.8, 13.5, and 16.4 km h⁻¹ in a 2 × 4 factorial design. The experimental design was modified in 2023, where the mechanical planter served as the current farmer practice check at 9.7 km h⁻¹and the precision planter speeds were 9.7, 14.5, and 17.7 km h⁻¹ at research station sites and 9.7 and 14.5 km h⁻¹ at an on-farm site. Across sites, increased planting speed generally increased plant spacing, in-row spacing variability, and decreased plant population. However, increased speed did not affect soybean yield. The precision planter at 17.7 km h⁻¹ was no different from the mechanical planter in terms of soybean plant population, spacing, and yield in 2023. Results suggest soybean producers can plant soybean at 17.7 km h⁻¹ without compromising yield.

Interest in pollinator gardens is increasing to address pollinator decline. The establishment of perennial native plants often takes a few years and establishment projects are often lost to competition from unwanted weedy vegetation. Mature sod of pollinator-serving, native plants that is free of weeds would be highly desirable to conventional turfgrass sod consumers, thus offering a new revenue stream for traditional sod producers. The objective of this study is to evaluate the influence of four foundational species treatments on 3-year floral biodiversity of pollinator-serving plants, sod tensile strength, and transplant rooting strength. Results suggest that polycultures of pollinator-serving forbs can produce a marketable sod with sufficient tensile strength for lifting and translocating and sufficient ability to establish once relocated. Sod tensile strength, however, is insufficient for lifting when the plant community is not grown over plastic or does not contain a high-shoot density or rhizomatous foundation species such as hard fescue (Festuca longifolia Thuill.) or common yarrow (Achillea millefolium L.). Unfortunately, adding hard fescue or common yarrow markedly reduces the Shannon diversity index and species richness. The force needed to lift transplanted sod after 3 months was 5553 to 6969 N m⁻² regardless of foundation species treatment and was numerically higher than the force reported by previous researchers to lift sod of various turfgrass species. Collectively, the data suggest that the best balance between preserving floral biodiversity and maximizing sod handling integrity would be approached by establishing pollinator-serving forbs alone or with a blend of native grasses over plastic.

Spatial and temporal variability in plant emergence may cause differences in nitrogen (N) uptake by crops leading to a mismatch between plant nitrogen requirements and nitrogen supply, with negative environmental and economic impacts. We aimed to understand N uptake and concentration (%) in unevenly emerged plants by conducting experiments in pre-determined yield stability zones (YSZs) in three farmers’ fields planted to maize (Zea mays L.). We found that maize emergence ranged from 64 to 124.1°C day, with significant variability between YSZ in two of three fields. Maize biomass plant-to-plant variation decreased from maize six leaves stage (V6) to maize silking stage (R1). At maize physiological maturity (R6), biomass ranged from 54 to 736 g plant⁻¹ and was significantly affected by YSZ (p < 0.001). In the cases where late-emerging plants accumulated less N than early emerging plants, this led to altered N partitioning within the plant (i.e., nitrogen harvest index decrease). Although N concentration in the grains remained unaffected by late emergence, the N concentration in the biomass increased. This was likely due to a reduced total biomass and the lack of a N sink (i.e., less yield per plant due to less grain per plant). The absence of variations in N utilization across emergence classes, coupled with the significant impact observed in the YSZ, reinforces the advantages of focusing on N management fitted to YSZ. Understanding the impact that the spatial and temporal variation of plant emergence has on maize N uptake is important in helping to improve N input prescription maps, N-use efficiency, and reduce N losses to the environment.

Cellulosic biomass-to-bioenergy systems provide fuel, reduce emissions, and offer economic benefits. Corn (Zea mays L.) and wheat (Triticum aestivum L.) residues could be used as feedstocks for biofuel production. However, the impact of residue removal on crop productivity in the Mid-Atlantic region has not been thoroughly assessed. A trial was conducted to assess crop yield and quality response to different biomass retention rates in grain cropping systems during 2015–2017. Various combinations of corn stover (0–10 Mg ha⁻¹) and wheat straw (0–3 Mg ha⁻¹) were applied in a corn–wheat/soybean [Glycine max (L.) Merr.] rotation in New Kent, VA. In Blacksburg, VA, corn stover (0–20 Mg ha⁻¹) was applied in the continuous corn system. Residues were applied after grain harvest over two production cycles for each system. Residue retention showed no significant impact on grain or crop residue yields or nutrient uptake in either system. Treatment minimally impacted feedstock quality, except wheat straw's sulfur (S) concentration, optimized at around 70% retention in New Kent. Theoretical ethanol potential (TEP) and yield remained unaffected by total residue rates in New Kent. In Blacksburg, over 2 years, a minimum TEP for corn stover corresponded to a retention rate of approximately 30%. A retention rate of more than 30% increased TEP, likely due to improved feedstock quality. Nutrient replacement costs for primary macronutrients and S uptake ranged from $18.3 to $36.9 ha⁻¹ for corn stover and $6.1 to $11.8 ha⁻¹ for wheat straw. Residue harvest or addition did not harm short-term biomass yield in Virginia's grain-based cropping systems.

Potassium (K) deficiency reduces cotton (Gossypium hirsutum L.) growth, development, lint yield, and fiber quality. The study's objective was to compare the effects of K fertilizer rate on cotton plant height (CPH), yield, and fiber quality in three cotton cultivars. Three cotton cultivars studied were NG 5711 B3XF (C1), PHY 480 W3FE (C2), and FM 1953GLTP (C3). Granular K fertilizer was surface broadcast and incorporated 1 week before planting at 34, 50, and 67 kg ha⁻¹. The CPH and canopy width were measured from 30 to 105 days after planting. The cotton CPH increased by 13%, 17%, and 12% in 2020 and by 6%, 4%, and 8% in 2021 with 34, 50, and 67 kg ha⁻¹ K fertilizer rates compared to control. The K application increased cotton canopy width by 39% in 2021 compared to 2020. The K application at 50 kg ha⁻¹ yielded significantly more cotton yield than the control in 2020. The cotton lint yield increased by 25%, 34%, and 9% in 2020 and by 4%, 17%, and 11% in 2021 with 34, 50, and 67 kg ha⁻¹ K fertilizer application rates than control. The cotton fiber staple length and color grade increased significantly with the 50 kg ha⁻¹ K application rate than the control in 2020. Cultivars significantly impacted Col-Rd and Col-b in 2020 and 2021. This study shows that fertilizer-K application improves not only yield, but also staple length and color grade in rainfed cotton crops.

The rising prices of N fertilizer led to exploring cost-saving efforts, such as intercropping cool- or warm-season legumes serving as alternative sources of N for managing fall-stockpiled tall fescue [Schedonorus arundinaceus (Schreb.) Dumort.; TF]. We aimed to evaluate red clover (Trifolium pratense L.; RC) and sunn hemp (Crotalaria juncea L.; SH) mixed with TF as alternative sources of N for stockpiling TF to increase productivity and animal performance. The experiment was conducted in Crossville, TN, in 2020 and 2021 and consisted of TF pastures mixed with RC (TRC) or SH (TSH), and TF fertilized with urea (TU). The experiment was divided into two periods: the pre-grazing period (stockpiling) (April–October) and the grazing period (October–December). After the stockpiling period, Black Angus beef (Bos taurus) steers were used for the grazing period. The study evaluated the botanical composition, herbage mass (HM), nutritive value, steer average daily gain (ADG), and net returns (NR). The TRC pastures had a greater proportion of legumes compared to TSH plots in May, October, November, and December of both years. There were no differences among treatments for the total HM and nutritive value in 2020; however, in 2021, TU had greater HM at the beginning of the grazing period and greater average crude protein values compared to the other treatments. In both years, there were no differences among treatments for ADG or NR. Therefore, producers can make the same profit considering the beef steer price and the cost of conventional and alternative N sources.