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미국
Data from: Effect of cutting management on the forage production and quality of tepary bean
,Data of the biomass production and forage quality (nutritive value) of 13 different tepary bean genotypes managed at four different cutting heights and three different cutting dates. The data was collected during 2020 and 2021 to compare cutting management of tepary bean with forage soybean. The study occurred at the Oklahoma and Central Plains Agricultural Research Center, El Reno, OK (35◦ 34’ N; 98◦ 2’ W, 414 m a.s.l.). Total rainfall was 340 mm in 2019 and 271 mm in 2020. No supplemental irrigation was applied. Weedy grasses were controlled with Clethodim 2EC throughout the growing season at a rate of 231.5 g a.i. ha-1). The study began June 10th 2020 and lasted 90 days. The study was replicated June 10th 2021 and lasted 90 days. Biomass was harvested from 0.5 m row lengths at 30-, 45-, or 90-day (end of season) intervals and at heights of 5, 10, or 15 cm above ground level to determine biomass production and regrowth ability. The 90-day interval was cut at 5 cm only and served as a control. Subsampling occurred 3 times for the 30 day, 2 times for the 45 day, and once for the 90 day. The 3–30-day samplings were summed to compare to the 90-day cutting. Likewise, the 2–45-day samplings were summed to compare to the 90-day cutting. Fresh weight of biomass was determined for clipped biomass, samples were dried at 60 ◦C for 72 hr, re-weighed to define dry matter, and subsequently ground to a 2.0 mm particle size for laboratory analysis using a Thomas Scientific Wiley Mill (Swedesboro, NJ, USA). The ground particles were thoroughly mixed and ~50g were scanned with a benchtop NIR (Unity Scientific Spectra Star XT with UCal calibration software, Westborough, MA, USA), and measures of forage quality [acid detergent fiber (ADF), in vitro True Digestibility (IVTD), neutral detergent fiber (NDF), total nitrogen content (N), total digestible nutrients (TDN), and a TDN:CP ratio] were evaluated. The benchtop NIR was validated with wet chemistry each year using approximately 10% of the samples. Occasionally, due to reduced plant growth, replications were combined to determine the forage quality of a genotype. Collected biomass (gm-2) was converted to Mgha-1). Data were analyzed with the Proc GLIMMIX procedure in SAS Studio 3.8. Genotype, cutting management (cutting height and cutting interval), and their interactions were considered fixed effects while the intercept of the linear predictor was considered a random effect with year as the subject (level). During the 90-day period from June to September, some genotypes of tepary bean provided greater amounts of biomass and forage quality than Laredo (forage soybean). The optimal management regime for tepary bean for forage was noted for one end of season (90-day) harvest as this was the best combination of biomass accumulation and forage nutritive value (forage quality). Breeding efforts to improve the forage characteristics and agronomic performance of tepary bean are required. Studies to determine the optimal planting rate/density are needed to refine and expand the use of tepary bean as an alternative forage.,
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연관 데이터
농림축산식품부 시설채소 온실현황
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⚪ 시설채소 온실현황 및 채소류 생산실적 조사를 통하여 농산물 생산 및 수급대책 수립및 학술연구 및 농업정책 등에 필요한 기초자료로 활용코자 함⚪ 시설채소 온실현황 및 채소류 생산실적 조사는 국가승인통계로 「통계법」 제18조 및「농업통계조사규칙」 기획재정부령 제509호(2015.11.16)에 따라 실시하고 있음
렛츠팜 - 대상 작물의 재배 시나리오별 재배환경 매칭 데이터
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작물/재배시나리오/재배환경정보 매칭 데이터
렛츠팜 - 대상 작물의 재배 시나리오별 시설 외기 매칭 데이터
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재배지+시설의 외기 환경정보
Time series of expected livestock forage biomass in the semi-arid grasslands of the western U.S. (2000-2018)
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Management and disturbances have significant effects on grassland forage production. When using satellite remote sensing to monitor climate impacts such as drought stress on annual forage production, minimizing these effects provides a clearer climate signal in the productivity data. The use of an ecosystem performance approach for assessment of seasonal and interannual climate impacts on forage production in semi-arid grasslands proved to be a successful method in a case study covering the Nebraska Sandhills. In this study we developed a time series (2000-2018) of the Expected Ecosystem Performance (EEP), which serves as a proxy for annual forage production after accounting for non-climatic influences, while minimizing effects of management and disturbances. The EEP was an output of a piecewise regression tree model that establishes relationships between seasonal climate variables, site specific growth potential, and long-term variability in growth to capture changes in Actual Ecosystem Performance measured by time-integrated Normalized Difference Vegetation Index (derived from eMODIS). We converted the unitless EEP to biomass using the SSURGO range production data. These results were then compared to ground-observed biomass in various locations of the study area and revealed strong positive relationships (R-squared = 0.67). An analysis of data from years beyond the model training period suggests that the model can be used for creating historical and future estimates of forage production in the western U.S. The newly-established relationships between seasonal climate and site-specific characteristics can be used for creating timely post-season forage production assessments and, when combined with seasonal climate forecasts or scenarios, they can serve for producing within-season estimates of annual forage production. These could lead to more informed decision making by livestock producers and land managers. This approach is transferable to other areas where climate and remotely-sensed NDVI data exist and therefore could be used to monitor climate impacts on annual forage production across the global grasslands.
렛츠팜 - (대상 작물)비파괴 생육 측정용 초분광 데이터
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대상작물/시설(지역)별 초분광 전부 혹은 일부 이미지 데이터
농림축산식품부 가축분뇨 작물별 액비살포현황
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축산농가 가축분뇨처리 및 자원화에 관한 자료로 축산농가, 액비저장조 관리 현황
Greg Rebetzke - Wallumbilla 2024 wheat trials: Impact of Sowing Depth, Coleoptile Traits, and Soil Strength on Emergence and Biomass Across Multiple Field Trials
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This dataset comprises detailed agronomic measurements from a series of wheat field trials conducted in Roma, Queensland, designed to investigate the effects of sowing depth, coleoptile type, soil strength, and other factors on plant emergence, growth, and yield. The collection includes two primary Excel (.xlsx) files: a master data sheet containing raw and processed measurements from individual plots across multiple trials (MET, Pressure, Seed Size), and an analysis workbook summarizing statistical outputs and model selections. These main files are complemented by MET Deep Tiny Tag and MET Shallow Tiny Tag .csv files. The master sheet documents plot-level data for each trial, including sowing conditions (depth, date, soil strength at multiple depths), plant traits (coleoptile length and diameter), emergence counts at multiple intervals (7, 14, 21 days after sowing), and final emergence. It also includes biomass and grain yield metrics, harvest index, grain quality parameters (protein, moisture, test weight, screenings), and maturity dates. Each plot is identified by location, replicate, treatment, and variety, with coleoptile type (long or conventional) and seed size (standard or large) noted where relevant. The analysis workbook provides statistical summaries from ANOVA and regression models, highlighting significant effects and interactions among depth, variety, coleoptile type, and soil strength. It includes model selection outputs for emergence and coleoptile traits, with R² values and p-values for various combinations of predictors. Environmental conditions such as soil strength was measured at sowing and at multiple intervals post-sowing using gravimetric and pressure-based methods. Drone imagery, EM38 surveys, and weather station data were also collected to support spatial and temporal analysis. Data was processed using GenStat with fixed and random effects models, and transformations were applied where necessary to meet distributional assumptions. The dataset includes over 70 variables, with definitions embedded in column headers and trial documentation. Codes such as LCW (long coleoptile wheat) and conventional types are used to distinguish genetic traits. The dataset is structured to support multivariate analysis and is suitable for evaluating genotype by environment interactions, emergence dynamics, and yield formation under varying agronomic conditions.
농촌진흥청 국립농업과학원 작물별 비료 표준사용량 처방 정보
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비료 표준사용량 처방(질소, 인산, 칼리) 정보를 제공하며, 비료표준 작물코드 값으로 조회