,Data were collected on the Central Plains Experimental Range (CPER) from 2014-2018, near Nunn, Colorado as part of the common experiments in grazinglands for the Long-Term Agroecosystem Research network. LTAR scientists seek to create new knowledge regarding sustainable management of grazinglands. This dataset on cattle foraging behavior and distribution provides new information towards understanding how management practices influence grazing livestock movements in space and time. The common experiment at CPER is called Collaborative Adaptive Rangeland Management (CARM) and is a ten-year ranch-scale (2,600-ha) social-ecological experiment designed to examine how adaptive rotations of a single large cattle herd among paddocks within a heterogeneous landscape during the growing season (collaborative, adaptive rangeland management; CARM) contrasts with continuous, season-long grazing of paddocks by small non-rotational herds (traditional rangeland management; TRM). Differences in movement patterns between the two treatments were examined with data collected from global positioning system tracking collars (Lotek 3300LR GPS) combined with activity sensors. These data were used to determine daily metrics of foraging behavior by steers in both treatments at five-minute intervals and include (1) location, (2) distance moved within 5 minutes, and (3) and grazing activity. These data are from the first half of the CARM experiment to support the publication, "Adaptive, multi-paddock, rotational grazing management alters foraging behavior and spatial grazing distribution of free-ranging cattle.",Resources in this dataset:,,

국립축산과학원에서 제공하는 축산 경영 및 최신 동향 분석 에 대한 공공데이터로, 제공, 등록일자, 조회수, 파일 다운로드 경로 등을 제공합니다.

충청북도 옥천군 축산 및 가금류 농가 현황(사업장명, 축종구분,행정동명, 사육두수, 소재지) 등의 데이터를 제공 합니다.

농림축산식품부_축산 교반관리 현황 사업 정보 데이터로 보조사업에 참여한 월별 축사 수, 가축 두수 합, 미생물살포량 총합 정보를 제공합니다(2021년)

전년도 기준 재배 최적 모델 및 복합환경제어 데이터

,A compilation of experimental forage data from 108 unique locations across the United States, with harvest dates ranging from 1958 to 2022. This dataset contains a subset of the data compiled in the initial stages of development of the Forage Data Hub. In particular, these are the 37,970 data entries used for the forage system resiliency analysis presented in the primary article.,Resources in this dataset:,

,Grass-Cast: Experimental Grassland Productivity Forecast for the Great Plains,Grass-Cast uses almost 40 years of historical data on weather and vegetation growth in order to project grassland productivity in the Western U.S. More details on the projection model and method can be found at https://esajournals.onlinelibrary.wiley.com/doi/full/10.1002/ecs2.3280.,Every spring, ranchers in the drought‐prone U.S. Great Plains face the same difficult challenge—trying to estimate how much forage will be available for livestock to graze during the upcoming summer grazing season. To reduce this uncertainty in predicting forage availability, we developed an innovative new grassland productivity forecast system, named Grass‐Cast, to provide science‐informed estimates of growing season aboveground net primary production (ANPP). Grass‐Cast uses over 30 yr of historical data including weather and the satellite‐derived normalized vegetation difference index (NDVI)—combined with ecosystem modeling and seasonal precipitation forecasts—to predict if rangelands in individual counties are likely to produce below‐normal, near‐normal, or above‐normal amounts of grass biomass (lbs/ac). Grass‐Cast also provides a view of rangeland productivity in the broader region, to assist in larger‐scale decision‐making—such as where forage resources for grazing might be more plentiful if a rancher’s own region is at risk of drought. Grass‐Cast is updated approximately every two weeks from April through July. Each Grass‐Cast forecast provides three scenarios of ANPP for the upcoming growing season based on different precipitation outlooks. Near real‐time 8‐d NDVI can be used to supplement Grass‐Cast in predicting cumulative growing season NDVI and ANPP starting in mid‐April for the Southern Great Plains and mid‐May to early June for the Central and Northern Great Plains. Here, we present the scientific basis and methods for Grass‐Cast along with the county‐level production forecasts from 2017 and 2018 for ten states in the U.S. Great Plains. The correlation between early growing season forecasts and the end‐of‐growing season ANPP estimate is >50% by late May or early June. In a retrospective evaluation, we compared Grass‐Cast end‐of‐growing season ANPP results to an independent dataset and found that the two agreed 69% of the time over a 20‐yr period. Although some predictive tools exist for forecasting upcoming growing season conditions, none predict actual productivity for the entire Great Plains. The Grass‐Cast system could be adapted to predict grassland ANPP outside of the Great Plains or to predict perennial biofuel grass production.,This new experimental grassland forecast is the result of a collaboration between Colorado State University, U.S. Department of Agriculture (USDA), National Drought Mitigation Center, and the University of Arizona. Funding for this project was provided by the USDA Natural Resources Conservation Service (NRCS), USDA Agricultural Research Service (ARS), and the National Drought Mitigation Center.,Watch for updates on the Grass-Cast website or on Twitter (@PeckAgEc). Project Contact: Dannele Peck, Director of the USDA Northern Plains Climate Hub, at dannele.peck@ars.usda.gov or 970-744-9043.,,

,Dr. Kevin Bronson provides a unique nitrogen and water management in cotton agricultural research dataset for compute, including notation of field events and operations, an intermediate analysis mega-table of correlated and calculated parameters, and laboratory analysis results generated during the experimentation, plus high-resolution plot level intermediate data analysis tables of SAS process output, as well as the complete raw data sensor recorded logger outputs.,This data was collected using a Hamby rig as a high-throughput proximal plant phenotyping platform.,The Hamby 6000 rig Ellis W. Chenault, & Allen F. Wiese. (1989). Construction of a High-Clearance Plot Sprayer. Weed Technology, 3(4), 659–662. http://www.jstor.org/stable/3987560,Dr. Bronson modified an old high-clearance Hamby 6000 rig, adding a tank and pump with a rear boom, to perform precision liquid N applications. A Raven control unit with GPS supplied variable rate delivery options.,The 12 volt Holland Scientific GeoScoutX data recorder and associated CropCircle ACS-470 sensors with GPS signal, was easy to mount and run on the vehicle as an attached rugged data acquisition module, and allowed the measuring of plants using custom proximal active optical reflectance sensing. The HS data logger was positioned near the operator, and sensors were positioned in front of the rig, on forward protruding armature attached to a hydraulic front boom assembly, facing downward in nadir view 1 m above the average canopy height. A 34-size class AGM battery sat under the operator and provided the data system electrical power supply.,Data suffered reduced input from Conley. Although every effort was afforded to capture adequate quality across all metrics, experiment exterior considerations were such that canopy temperature data is absent, and canopy height is weak due to technical underperformance. Thankfully, reflectance data quality was maintained or improved through the implementation of new hardware by Bronson.,See included README file for operational details and further description of the measured data signals.,Summary: Active optical proximal cotton canopy sensing spatial data and including few additional related metrics and weak low-frequency ultrasonic derived height are presented. Agronomic nitrogen and irrigation management related field operations are listed. Unique research experimentation intermediate analysis table is made available, along with raw data. The raw data recordings, and annotated table outputs with calculated VIs are made available. Plot polygon coordinate designations allow a re-intersection spatial analysis. Data was collected in the 2014 season at Maricopa Agricultural Center, Arizona, USA. High throughput proximal plant phenotyping via electronic sampling and data processing method approach is exampled using a modified high-clearance Hamby spray-rig. Acquired data conforms to location standard methodologies of the plant phenotyping. SAS and GIS compute processing output tables, including Excel formatted examples are presented, where data tabulation and analysis is available. Additional ultrasonic data signal explanation is offered as annotated time-series charts. The weekly proximal sensing data collected include the primary canopy reflectance at six wavelengths. Lint and seed yields, first open boll biomass, and nitrogen uptake were also determined. Soil profile nitrate to 1.8 m depth was determined in 30-cm increments, before planting and after harvest. Nitrous oxide emissions were determined with 1-L vented chambers (samples taken at 0, 12, and 24 minutes). Nitrous oxide was determined by gas chromatography (electron detection detector).,