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Autumn grazing management aims to accumulate herbage for defoliation prior to a decrease in growth rates for the extension of the grazing season. The current study investigated the impact of building different target herbage masses (THMs) in autumn and imposing one of three different defoliation dates (DDs) between mid-October and late November on light transmitted to the base of the sward, free leaf lamina (FLL), leaf stage and internode elongation. Four THMs (low, medium, high and very high) and three DDs (DD1—15 October, DD2—7 November and DD3—21 November) were assigned to a 4 × 3 split plot design over two years. Light transmitted to the base of the sward was greatest in the low THM and decreased in all other THMs. Internode elongation increased in tillers in the medium to the very high THMs. Defoliation of the medium, high and, in particular, the very high THMs earlier in autumn reduced the effect of decreased light transmission on internode elongation. This study highlights that, as light transmitted to the base of the sward decreases, internode elongation increases, and this could negatively impact sward structure. [ABSTRACT FROM AUTHOR]

Aphids are major pests of cereal and pasture grasses throughout the world, vectoring disease and reducing plant production. There are few control options other than insecticides. Epichloë endophytes that produce loline alkaloids in their hosts provide a possible mechanism of control, with both meadow fescue and tall fescue naturally infected with loline-producing endophytes showing a resistance to Rhopalosiphum padi. We screened Elymus spp. naturally infected with endophytes that produced loline alkaloids at concentrations known to affect aphids on fescue but found no effect on these insects infesting Elymus. A synthetic loline-producing endophyte association with rye also had no effect on the aphids. After hypothesizing that the lolines were being translocated in the xylem in Elymus and rye rather than the phloem, we tested the rye and meadow fescue infected with loline-producing endophytes against a xylem feeding spittlebug. The endophyte in rye inhibited the feeding of the insect and reduced its survival, whereas the endophyte-infected meadow fescue had no effect on the spittlebug but reduced the number of aphids. Lolines applied to the potting medium of endophyte-free and endophyte-infected rye, ryegrass, and tall fescue resulted in a decrease in the aphid populations on the endophyte-free pasture grasses relative to the untreated controls but had no effect on aphid numbers on the rye. We tentatively conclude that lolines, produced in both natural and synthetic association with Elymus and rye, are partitioned in the xylem rather than the phloem, where they are inaccessible to aphids. [ABSTRACT FROM AUTHOR]

Simple Summary: Limited studies have been reported so far on the rumen degradability characteristic of low-quality forage. In the current study, three low-quality forages (Chinese rye grass, barley grass, and naked oat straw) were selected. Various nutritional parameters such as rumen degradation characteristics, chemical composition and feeding value of three roughages were studied in detail. Among the three roughages, the Chinese rye grass (Leymus chinensis) was superior in terms of rumen degradability characteristics, chemical composition and feeding value. The current study was designed to investigate the chemical composition, rumen degradation characteristics, and feeding value of three roughages commonly used in Asia as ruminant feed, including Chinese rye grass (CRG), barley grass (BG), and naked oat straw (NO). Four Holstein Friesian cows equipped with permanent rumen fistulas were chosen for experimental trials in the current study. The nylon bag method was carried out to measure the crude protein (CP), acid detergent fiber (ADF), ruminal degradability of dry matter (DM), and neutral detergent fiber (NDF). Our analysis revealed that the contents of CP in the CRG (9.0%) and BG (8.9%) were higher than in the NO (5.94%). The contents of NDF in the CRG (64.97%) and NO (63.83%) were lower than in the BG (67.33%), and the content of ADF in the CRG (37.03%) was lower than in the BG (37.93%) and NO (38.28%). The ED values of DM in the NO and CRG were significantly higher (p < 0.001) than in the BG. The effective degradability (ED) values of NDF were the highest in the CRG and lowest in the NO (p < 0.001). In addition, the ED values of ADF were the highest in the CRG and lowest in the BG (p < 0.001). The ED value of CP in the CRG was significantly higher than that in the BG and NO (p < 0.001). The estimated total digestible nutrients (TDN) (54.56%) and DM degradation rate (DDM) (60.06%) of the CRG were higher than those of BG and NO. In addition, the expected DM intake (DMI), estimated relative feed value (RFV), and estimated relative feed quality (RFQ) of the BG were lower than those of the CRG and NO. Altogether, based on our findings, we concluded that the nutritional quality, feeding value and effective rumen degradation rate of CRG were better than of BG and NO. [ABSTRACT FROM AUTHOR]

High-throughput field phenotyping using close remote sensing platforms and sensors for non-destructive assessment of plant traits can support the objective evaluation of yield predictions of large breeding trials. The main objective of this study was to examine the potential of unmanned aerial vehicle (UAV)-based structural and spectral features and their combination in herbage yield predictions across diploid and tetraploid varieties and breeding populations of perennial ryegrass (Lolium perenne L.). Canopy structural (i.e., canopy height) and spectral (i.e., vegetation indices) information were derived from data gathered with two sensors: a consumer-grade RGB and a 10-band multispectral (MS) camera system, which were compared in the analysis. A total of 468 field plots comprising 115 diploid and 112 tetraploid varieties and populations were considered in this study. A modelling framework established to predict dry matter yield (DMY), was used to test three machine learning algorithms, including Partial Least Squares Regression (PLSR), Random Forest (RF), and Support Vector Machines (SVM). The results of the nested cross-validation revealed: (a) the fusion of structural and spectral features achieved better DMY estimates as compared to models fitted with structural or spectral data only, irrespective of the sensor, ploidy level or machine learning algorithm applied; (b) models built with MS-based predictor variables, despite their lower spatial resolution, slightly outperformed the RGB-based models, as lower mean relative root mean square error (rRMSE) values were delivered; and (c) on average, the RF technique reported the best model performances among tested algorithms, regardless of the dataset used. The approach introduced in this study can provide accurate yield estimates (up to an RMSE = 308 kg ha−1) and useful information for breeders and practical farm-scale applications. [ABSTRACT FROM AUTHOR]