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.
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.