Tamegrass Forage Options for Growing Heifers During the Summer

Developing methods to provide high quality forage through a majority of the year is important for cattle operations. The purpose of this study was to determine forage management options to off-set the summer “slump” with fescue. Four grass pasture treatments (10 pastures total; 1.67 ha each) were used in a completely randomized design and stocked with growing heifers (initial wt 190 ± 9.1 kg). Pasture treatments consisted of fescue (FES; n = 2 pastures with 4 head/pasture), crabgrass (CRAB; n = 3 pastures with 4 head/pasture), bermudagrass (BERM; n = 3 pastures with 5 head/pasture), and sorghum-sudan interseeded into fescue (SS-FES; n = 2 pastures with 6 head/pasture from April to July, then 4 head/pasture from July to November). Heifers were weighed in April, May, July, September, and November. Heifers assigned to FES/SS-FES pastures grazed from April to November (213 d) and for CRAB and BERM treatments, May through September (131 d). Heifers on FES grazed continuously, while other grass treatments were rotationally grazed. Heifers remained in treatment pasture through the entire grazing period. Data were analyzed using mixed procedures of SAS with pasture as experimental unit. Average daily gain for the entire grazing period was greater for heifers on SS-FES as compared to all other grass treatments (P = 0.001). Between April and May, FES heifers had greater ADG than SS-FES (P = 0.001); yet, heavier stocking rate in SS-FES resulted in similar BW gain/ha (P = 0.16). May to July ADG and BW gain/ha was greatest for BERM, then CRAB, with FES and SS-FES having the lowest gains (P < 0.001). From July through September, ADG was greater for SS-FES and CRAB as compared to FES, with BERM intermediate (P = 0.03); and BW gain/ha tended to be lower for FES compared to other treatments (P = 0.10). Average daily gain and BW gain/ha were greater for SS-FES than FES (P = 0.001) from late September to November. As a summer grazing option, warm season grass alternatives, either as the sole source of pasture or interseeded into fescue, are better options for gain as compared to fescue alone.

Estimation of Botanical Composition in Mixed Clover–Grass Fields Using Machine Learning-Based Image Analysis

This study aims to provide an effective image analysis method for clover detection and botanical composition (BC) estimation in clover–grass mixture fields. Three transfer learning methods, namely, fine-tuned DeepLab V3+, SegNet, and fully convolutional network-8s (FCN-8s), were utilized to detect clover fractions (on an area basis). The detected clover fraction (CFdetected), together with auxiliary variables, viz., measured clover height (Hclover) and grass height (Hgrass), were used to build multiple linear regression (MLR) and back propagation neural network (BPNN) models for BC estimation. A total of 347 clover–grass images were used to build the estimation model on clover fraction and BC. Of the 347 samples, 226 images were augmented to 904 images for training, 25 were selected for validation, and the remaining 96 samples were used as an independent dataset for testing. Testing results showed that the intersection-over-union (IoU) values based on the DeepLab V3+, SegNet, and FCN-8s were 0.73, 0.57, and 0.60, respectively. The root mean square error (RMSE) values for the three transfer learning methods were 8.5, 10.6, and 10.0%. Subsequently, models based on BPNN and MLR were built to estimate BC, by using either CFdetected only or CFdetected, grass height, and clover height all together. Results showed that BPNN was generally superior to MLR in terms of estimating BC. The BPNN model only using CFdetected had a RMSE of 8.7%. In contrast, the BPNN model using all three variables (CFdetected, Hclover, and Hgrass) as inputs had an RMSE of 6.6%, implying that DeepLab V3+ together with BPNN can provide good estimation of BC and can offer a promising method for improving forage management.

Commoning the bloom? Rethinking bee forage management in industrial agriculture

Managed and wild bee populations are declining around the world, in part due to lost access to bee forage (i.e., nectar and pollen). As bee forage diminishes, the remaining acres become sites of contestation between beekeepers, land managers, ecologists, and regulatory agencies. This article applies a commons framing to contextualize these conflicts and attempts to resolve them. Drawing from the concepts of commons and commoning, I argue that nectar and pollen are common-pool resources for pollinators, beekeepers, and land managers, currently managed through varied access arrangements such as informal usufruct rights and pseudo-commoning practices. Like commoning, pseudo-commoning aims to collectively manage a resource through a set of protocols that involve equitable resource sharing and communication. However, because pseudo-commons are implemented from the top down, for example, from institutional actors driven in part by economic interests, they often do not result in widespread adoption on the ground. Through a case in California almond orchards, I make two additional arguments. First, because beekeepers are largely migratory and do not own the land they need for production, their subordinate position to landowners can challenge equitable bee forage management. Second, while floral pseudo-commons may aim to counter the negative effects of industrialized agricultural production (e.g., by limiting pesticide exposure to honey bees), they also provide a “fix” that supports and expands industrial agriculture by stabilizing managed bee pollination services. Increasing reliance on managed bee pollination services can thus disincentivize transitions to sustainable food production, such as adopting diversified practices that would support native bee populations and reduce the need for managed honey bees on farms.

Site-Specific Forage Management of Sericea Lespedeza: Geospatial Technology-Based Forage Quality and Yield Enhancement Model Development

Site-specific forage management (SSFM), comprising growth observation, impact assessment, and timely strategic response to small variations in sericea lespedeza (SL; Lespedeza cuneata (Dum-Cours.) G. Don) production, has been envisioned as a life-changing approach for resource-poor (R-P) farmers in developing countries, assisting in the effective rearing of their small ruminants. The application of geospatial technologies, including geographic information systems (GIS), remote sensing, global navigation satellite system, and information technology, can support SSFM but has not been widely used for site-specific forage management. From our previous studies, it appears that the entire range of condensed tannins of lespedeza, namely extractable condensed tannin (ECT), fiber-bound condensed tannin (FBCT), and protein-bound condensed tannin (PBCT), as well as crude protein (CP), are excellent for promoting small ruminant digestion and overall health. The goal of this study was to develop an SSFM strategy for SL to enhance animal production in areas of drought-prone, low pH, marginally infertile soils. To achieve this goal, study objectives were to: (i) develop statistical and artificial neural networks-based (ANN) models to identify if a sound correlation exists among forage growth environmental features and SL-ECT content; (ii) determine suitability criteria, including climate, soil, and land use/land cover (LULC), for mass scale production of SL and collect supporting environmental geospatial data; and (iii) develop an automated geospatial model for SL growth suitability analysis in relation to optimal areas for its production in a case-study location. Telemetric data and individual climatologic parameters (including minimum, maximum, and average temperature, humidity, dewpoint, soil temperatures at three depths, soil moisture, evapotranspiration, total solar radiation, and precipitation) were found to correlate well (>75%) with the forage production parameters, including values of SL-ECT from the Fort Valley State University (FVSU) research station in Georgia in the southern United States. A backpropagation neural network (BPNN) model was developed using similar climatic input parameters, along with elevation (topography) and a normalized difference vegetation index (NDVI) to estimate the forage’s ECT with a testing root mean square error (RMSE) of 1.18%. With good correlation obtained between the climatic, soil, slope, and land cover input parameters, and SL-ECT as the output parameter, an SSFM model was developed with potential application to R-P farmers in areas suitable for SL establishment and growth. Eswatini (previously Swaziland), a landlocked country in southern Africa, in which numerous R-P small ruminant (sheep and goat) farmers reside, was used as the case study location to develop the SL production suitability model. Geospatial data were used for automated model development in an ArcGIS Pro ModelBuilder platform to provide information on where to grow SL efficiently to economically feed small ruminants. Land use/land cover, soil, topography, and climate based geospatial data of the region helped in the development of the automated SSFM geospatial model for spatial growth suitability location determination to assist farmers of Eswatini with their SL production decision making. This automated model can easily be replicated for farmers in other countries in Africa, as well as in other parts of the world having similar climatic conditions.

The role of forage management in addressing challenges facing Australasian dairy farming

Forage management underpins the viability of pastoral dairy systems. This review investigated recent developments in forage research and their potential to enable pastoral dairy systems to meet the challenges that will be faced over the next 10 years. Grazing management, complementary forages, pasture diversity, fertiliser use, chemical restriction, irrigation management and pasture breeding are considered. None of these areas of research are looking to increase production directly through increased inputs, but, rather, they aim to lift maximum potential production, defend against production decline or improve the efficiency of the resource base and inputs. Technology approaches consistently focus on improving efficiency, while genetic improvement or the use of complementary forages and species diversity aim to lift production. These approaches do not require additional labour to implement, but many will require an increase in skill level. Only a few areas will help address animal welfare (e.g. the use of selected complementary forages and novel endophytes) and only complementary forages will help address increased competition from non-dairy alternatives, by positively influencing the properties of milk. Overall, the diversity of activity and potential effects will provide managers of pastoral dairy systems with the best tools to respond to the production and environmental challenges they face over the next 10 years.

Applied research on dairy cattle feeding systems in Colombian high tropics

Sustainable intensification of dairy production is a strategy to consolidate a competitive sector. However, technologies adoption is limited due to the difficulty to accessing to research results. The objective of this document was to review a compilation of research works carried out mainly by the Colombian Agricultural Research Corporation (Agrosavia) in specialized dairy feeding systems of Colombian high tropics. In soils issue, research has been carried out on soil recovery and grassland renewal. Recently, the growth of grasses has been evaluated under different environmental conditions to generate efficient forage management schemes. In 2018 was registered Altoandina oat to forage use, but, in Colombian high tropic, there is a limited offer to forages due to absence of breeding program and evaluation of germplasm. Integrated management of Collaria scenica has been developed. Characterization and use of tree and shrub species have been carried out to silvopastoral systems (SSPs). In feeding systems, Agrosavia has been working on determining chemical and nutritional compositional of feed resources and design of supplementation schemes to improve animal response. Methane production increases when mature forage species are used, contrary a balanced diet or grazing in SSPs reduces methane emissions. Finally, research developments must incorporate and recognize the production costs of the system to ensure the adoption of technologies.

145 Eco-efficiency of Northeast U.S. grass-fed beef systems

Grass-fed beef systems (GFB) are one approach proposed to improve the sustainability of animal-sourced foods. Our objective was to estimate carbon (CF) and reactive nitrogen (NF) footprints and cost of production (COP) for northeastern U.S. GFB. A partial life cycle assessment was conducted using the Integrated Farm System Model (IFSM). Systems were characterized using surveys and interviews. Representative operations were simulated in IFSM by grouping farms with similar environments and forage management practices. Environments were defined using USDA Plant Hardiness Zones. Farms were categorized as feed sufficient (FS: produced all supplemental forage on-farm) or feed importing (FI; purchased all supplemental forage). Differences between zones and farm types were analyzed using Dunn’s test with a Bonferroni correction and the Kruskal-Wallis test, respectively. Warmer zones supported longer grazing seasons (P < 0.01), greater pasture legume content (P < 0.05), and denser stocking rates (P < 0.01), resulting in twice the market weight production per hectare compared with farms in cooler zones (P < 0.01). Grazing season length and pasture legume content were similar between farm types (P > 0.1). FI herds were half the size of FS herds (P < 0.01), used 37% more land per animal (P < 0.01), and had 10% lighter market weights with 52% lower market weight production per hectare (P < 0.05). CF, NF, and COP/animal were 1.1, 1.3, and 2.1-fold greater for FI farms, respectively. Zone differences in climate, soils, and pasture legume content affected footprints. In warmer zones where soils had greater clay content, N2O contributed a greater portion of CF and NF, and NO3 leaching was moderate. In the coolest zone with coarser soil, N2O contributed a small portion of CF and NF, with greater NO3 losses. Analyses suggest attention to nitrogen management, efficient resource utilization, and botanical composition might reduce CF and NF.