Crop, Forage and Turfgrass Management最新文献

Accurate forage dry matter (DM) concentration estimation is essential for maximizing animal performance and minimizing feed costs. One possible method of estimating DM for rebalancing rations daily involves the use of hand-held near infrared reflectance spectrometer instruments. The SCiO Cup is one of the hand-held instruments that could be used to estimate forage DM, but a thorough evaluation of its effectiveness has not been conducted. Haylage samples (n = 600) from 143 bunker silos were collected across New York State over three years, and vacuum packed for eventual analysis using a SCiO Cup. Samples ranged from pure alfalfa (Medicago L.) to pure grass but were mostly from mixed species. All but one sample received a DM value estimated from several available calibrations pre-loaded in the device. Sixty samples (representing 10% of the sample population) were too wet or dry to generate a result using the mixed silage calibration. For the remaining 90% of samples, SCiO Cup DM estimates were within 3.22%units of oven DM 80% of the time. Precision of the instrument evaluated with multiple scanning of samples using the mixed silage calibration was very good, with the average standard deviation of three values of 0.40 (n = 200). The mixed silage calibration was more effective for predicting DM of this set of haylages than either legume or grass silage calibrations.

Little bluestem [Schizachyrium scoparium (Michx.) Nash.] is a warm-season perennial grass that is sometimes planted with fine fescues (Festuca spp.) in infrequently mowed rough areas on golf courses, commonly referred to as naturalized or native grass areas and minimal-mow rough. This native species is often used for its aesthetically pleasing reddish-gold culms and inflorescences during late summer and fall.

In Pennsylvania, perennial and annual grass weeds invade fine fescue/little bluestem rough, and golf course managers occasionally use postemergence herbicides as control options (Landschoot, 2018). ACCase-inhibiting herbicides control a variety of grass weeds, but relatively few studies have examined the effects of these herbicides on little bluestem (Patton et al., 2021).

The objective of this study was to evaluate the tolerance of little bluestem to four ACCase-inhibiting herbicides: fenoxaprop, fluazifop, quizalofop, and sethoxydim. Experiments were conducted in 2021 and 2022 in adjacent areas at the Landscape Management Research Center in University Park, PA. Both experiments were performed in an eight-year-old non-irrigated and non-fertilized stand of strong creeping red fescue (Festuca rubra ssp. rubra Gaudin) ‘Garnet’ and little bluestem ‘Ft. Indiantown Gap-PA Ecotype’ (Ernst Conservation Seed). Little bluestem visual cover in the experiment areas at the time of treatment applications was approximately 50-60%. The stand was mowed once per year in October at 5 inches. Soil at the experiment site is a Hagerstown silt loam (fine, mixed, mesic, Typic Hapludalf), with a pH of 6.4, 38 mg/kg Mehlich-3 P, and 186 mg/kg Mehlich-3 K.

Herbicide treatments included fenoxaprop (Acclaim Extra, 0.57 lb fenoxaprop/gal; Bayer Environmental Science) at 28 fl oz product/acre with 0.25% v/v non-ionic surfactant (Lesco 90/10 Nonionic Surfactant; Lesco Inc.); sethoxydim (Segment II, 1.5 lb sethoxydim/gal; BASF) at 16 fl oz product/acre with methylated seed oil at 1.5 pints/acre (Lesco Methylated Seed Oil; Lesco Inc.); fluazifop (Fusilade II T/O, 2 lb fluazifop/gal; Syngenta Crop Protection LLC) at 16 fl oz product/acre with 0.25% v/v non-ionic surfactant; and quizalofop (Assure II, 0.88 lb quizalofop/gal; Amvac Chemical Corp.) at 12 fl oz product/acre with 0.25% v/v non-ionic surfactant. A non-treated control was included in each experiment. Herbicide treatment rates were based on maximum product label rates for control of grass weeds in fine fescue. All treatments were applied once on June 17, 2021, and June 8, 2022. Application dates coincide with preferred timing for control of grass weeds in central Pennsylvania. Total precipitation during the 2021 and 2022 evaluation periods was 19.7 and 9.7 inches, respectively.

All herbicide treatments were applied using a backpack sprayer equipped with a boom fitted with a 9504E flat fan nozzle (TeeJet Technologies) at 40 psi with a wa

The ancient wheats einkorn (Triticum monococcum L.), emmer (Triticum turgidum L.), and spelt (Triticum spelta L.) are currently attracting renewed consumer interest due to their unique flavor profiles and high nutritional quality compared with modern bread (Triticum aestivum L.) and durum (Triticum durum L.) wheat. Ancient wheats are well suited for production in marginal lands and may be well adapted to Wyoming growing conditions. A 2-year study was conducted in three locations in Wyoming (Powell, Sheridan, and Lingle, WY) under irrigated and rainfed conditions to identify the agronomic potential of spring planted spelt, emmer, and einkorn in Wyoming. Across locations, grain yields averaged 832 lbs acre⁻¹ for einkorn, 1,492 lbs acre⁻¹ for emmer, and 1064 lbs acre⁻¹ for spelt with 14.7–15.9% protein. In 2017, irrigated spring wheat yield in Wyoming averaged 3642 lbs acre⁻¹ and dryland yield averaged 1020 lbs acre⁻¹. The Powell irrigated location was the highest yielding and perhaps the best suited for ancient wheat production. Continued research on variety selection and management is needed to further improve the yield and profitability of ancient wheats in Wyoming.

Frequent monitoring and accurate estimation of corn (Zea mays L.) kernel moisture are necessary for timing harvests and maximizing profits. Harvesting grain above the U.S. market threshold (15.5%) increases the risk of grain shrinkage and cost of artificial drying, and leads to a loss in profitability (Martinez-Feria et al., 2019) as well as grain quality concerns (Chai et al., 2017). Among corn growers, the standard ways to estimate kernel moisture involve tabletop and portable grain analysis computers (GAC; Sadaka & Rosentrater, 2019). However, GACs require destructive ear sampling and regular calibrations which can be time and labor intensive, so growers might only collect ears from small areas of large fields. Due to spatial variation in grain moisture in large fields (Miao et al., 2006), moisture estimated by sampling ears from a small area will often be unrepresentative of field-level kernel moisture content. Therefore, there is an urgent need for handheld sensors that can accurately and rapidly estimate kernel moisture to conduct sampling over larger spaces in a timely and cost-effective manner.

Commercially available pin and ring-type handheld sensors that can conveniently and rapidly estimate kernel moisture content non-destructively have been evaluated (Fan et al., 2020; Fan et al., 2021). Although these sensors have shown high accuracy (R² = .80), there are some significant concerns. For example, pin-type sensors poke holes into kernels, resulting in severe kernel damage (Fan et al., 2020). Ring-type sensors, on the other hand, operate only within a narrow kernel moisture range (15–32%) and specific ear diameter (2 inches) (Fan et al., 2020). It is essential to evaluate new handheld sensors that can potentially estimate a wide range of kernel moisture content across ear sizes without damaging the kernels. Therefore, the objective of this study was to evaluate the accuracy of a handheld sensor that operates using near-infrared spectroscopy (NIRS) technology to estimate kernel moisture content in-field.

The NIRS sensor evaluated, commercially known as SCiO kernel moisture analyzer (Consumer Physics, Israel), has three major components: the SCiO sensor, the mobile app to operate the system, and the cloud-based telemetry system (Figure 1). The wavelength and ambient temperature range for sensor operation is 750–1050 nm and 32–100 ^οF, respectively. To measure kernel moisture, SCiO is fixed to an oval-shaped corn sensor accessory (2.7 inches long and 1.9 inches wide), attached to the dehusked ear (Figure 2a), and scanned five times. For each scan, the kernels are illuminated with near-infrared light. The reflected spectrum is measured and sent to the cloud, where chemometric models and machine learning algorithms are used to estimate moisture based on past observations and developmenta

Agricultural ditches constructed to provide an outlet for improved subsurface drainage systems (i.e., tile drains) are ubiquitous in the poorly drained but highly productive soils of the midwestern United States. Many ditches require regular maintenance to remove deposited sediments due to reduced hydraulic capacity and blockage of subsurface tile outlets. Dredge material is often spoiled along the edge-of-field as an economical means of disposal. Placement of dredge materials at the top of a ditch can impede surface drainage, creating depressional areas in fields that can negatively impact crop growth.

An alternative approach to spoil management would be to place dredge materials further into fields to level the ground or as a soil amendment. Studies have shown the potential beneficial effects of lake and river dredge soil amendments on crop yields ( Brigham et al., 2021; Darmody & Diaz, 2017). However, farmers may be reluctant to spread dredge materials in fields due to concerns over redistribution of weed seed banks accumulated in deposited ditch sediments. In this study, we collected sediments from ditches in three regions of Ohio and germinated the weed seed banks in a greenhouse mesocosm study. We tested for regional differences in number of weed species and dry biomass per mesocosm. Additionally, we assessed potential controllability of the weeds using common chemical herbicides.

We collected sediment samples from ditches at three sites from each of three regions (NE, northeast; NW, northwest; and W, west) in Ohio (Figure 1). Sediment samples were collected in December 2020 following a series of freeze–thaw events. Sediments were allowed to dry, then packed into mesocosms (13.0 inch × 8.0 inch plastic containers) to a depth of 4.0 inches and arranged on a table in the greenhouse using a randomized complete block design with four replications (Figure 2). Sediment mesocosms were watered using a mist irrigation system and soil moisture levels adequate for germination were maintained throughout the duration of the study. During the first 45 days, greenhouse temperatures were maintained between 50–60°F for germination of winter annuals. Ambient temperatures were then raised to 78–90°F for an additional 45 days for germination of summer annual weeds. At the termination of the study, aboveground biomass was collected, identified, and dried for the determination of biomass for each species by mesocosm. Differences in means were tested with one-way analysis of variance and pairwise comparison of means using Student's t and least significant differences (α = 0.05) in JMP Pro 15.2.0 software (SAS Institute, Inc.). Dry biomass and number of weed species were response variables, region was the factor, and data were blocked by replication. To determine potential controllability of germinated weed species, we consulted the labels of several common herbicides used on agronomic crops in Ohio, including gl

Palmer amaranth (Amaranthus palmeri Watson) is one of the most difficult-to-control weeds in several economically important crops in the United States. Growth characteristics of Palmer amaranth can be affected by the cropping system. Research was conducted in North Carolina in 2019 to determine height and seed production of Palmer amaranth grown season long in the presence of corn (Zea mays L.), cotton (Gossypium hirsutum L.), and peanut (Arachis hypogaea L.). Research was also conducted to determine transgenerational effects due to interference from these crops. Palmer amaranth produced more seed when grown with cotton (17 times greater) and peanut (12 times greater) compared with corn; no difference was noted between cotton and peanut. Palmer amaranth height in the field at physiological maturity was similar in corn (80 inches) and cotton (77 inches) and taller in height than peanut (63 inches). When progeny from plants in the field were grown in the greenhouse in the absence of crop interference, differences in the height of progeny and height of the mother plant in the presence of crop interference were ranked similarly with respect to crop. Palmer amaranth height in the presence of corn and cotton was similar (57 and 58 inches, respectively) and it exceeded height when the weed was grown with peanut (51 inches). These results demonstrate transgenerational effects due to previous crop (e.g., corn, cotton, and peanut) for Palmer amaranth.

A well-designed crop rotation can create an unstable environment that disrupts weed population growth rates. In combination with effective herbicide programs, growers may maintain weed populations at levels below competitive and economic thresholds. The objectives of the present study were to evaluate how the preceding rotational crop determines the response of weed populations to in-season postemergence herbicide programs and the weed population density of the following crop season. The first-year crop treatments were corn (Zea mays L.), cotton (Gossypium hirsutum L.), peanut (Arachis hypogaea L.), grain sorghum [Sorghum bicolor (L.) Moench.], and soybean [Glycine max (L.) Merr.]. In the second year, all plots were planted with cotton, and herbicide treatments were single applications 2 or 6 weeks after planting (WAP), two sequential applications 2 and 4 or 4 and 6 WAP, three sequential applications 2, 4, and 6 WAP, and a weedy control without herbicides was included. In the absence of herbicides, corn had the lowest population growth rates for broadleaf weeds (λ = 0.8) while peanut and grain sorghum had the highest (λ = 1.7 and 1.3, respectively). The results indicated that herbicide applications focused exclusively on preventing yield loss may not be sufficient to ensure weed population reductions. Thus, the observed population growth rates (λ = 2 for grassy weeds and λ = 1.26 for broadleaved weeds) indicated that weed issues would continue increasing, despite meeting yield goals. Considering population growth rates when assessing weed management strategies is key to determining the sustainability of the crop production operation.