Unraveling spatial heterogeneity of soil legacy phosphorus in subtropical grasslands.

Humans have profoundly altered phosphorus (P) cycling across scales. Agriculturally driven changes (e.g., excessive P-fertilization and manure addition), in particular, have resulted in pronounced P accumulations in soils, often known as "soil legacy P." These legacy P reserves serve as persistent and long-term nonpoint sources, inducing downstream eutrophication and ecosystem services degradation. While there is considerable scientific and policy interest in legacy P, its fine-scale spatial heterogeneity, underlying drivers, and scales of variance remain unclear. Here we present an extensive field sampling (150-m interval grid) and analysis of 1438 surface soils (0-15 cm) in 2020 for two typical subtropical grassland types managed for livestock production: Intensively managed (IM) and Semi-natural (SN) pastures. We ask the following questions: (1) What is the spatial variability, and are there hotspots of soil legacy P? (2) Does soil legacy P vary primarily within pastures, among pastures, or between pasture types? (3) How does soil legacy P relate to pasture management intensity, soil and geographic characteristics? and (4) What is the relationship between soil legacy P and aboveground plant tissue P concentration? Our results showed that three measurements of soil legacy P (total P, Mehlich-1, and Mehlich-3 extractable P representing labile P pools) varied substantially across the landscape. Spatial autoregressive models revealed that soil organic matter, pH, available Fe and Al, elevation, and pasture management intensity were crucial predictors for spatial patterns of soil P, although models were more reliable for predicting total P (68.9%) than labile P. Our analysis further demonstrated that total variance in soil legacy P was greater in IM than SN pastures, and intensified pasture management rescaled spatial patterns of soil legacy P. In particular, after controlling for sample size, soil P was extremely variable at small scales, with variance diminished as spatial scale increased. Our results suggest that broad pasture- or farm-level best management practices may be limited and less efficient, especially for more IM pastures. Rather, management to curtail soil legacy P and mitigate P loading and losses should be implemented at fine scales designed to target spatially distinct P hotspots across the landscape.

Soil legacy phosphorus and loss risk in subtropical grasslands.

The accumulation of soil legacy phosphorus (P) due to past fertilization practices poses a persistent challenge for agroecosystem management and water quality conservation. This study investigates the spatial distribution and risk assessment of soil legacy P in subtropical grasslands managed for cow-calf operations in Florida, with two pasture types along the intensity gradient: improved vs semi-native pastures. Soil samples from 1438 locations revealed substantial spatial variation in soil legacy P, with total P concentrations ranging from 11.46 to 619.54 mg/kg and Mehlich-1 P concentrations spanning 0.2-187.27 mg/kg. Our analyses revealed that most of the sites in semi-native pastures may function as P sinks by exhibiting positive Soil P Storage Capacity (SPSC) values, despite having high levels of soil total P. These locales of higher SPSC values were associated with high levels of aluminum, iron, and organic matter that can adsorb P. In addition, our results from spatial random forest modelling demonstrated that factors including elevation, soil organic matter, available water storage, pasture type, soil pH, and soil order are important to explain and predict spatial variations in SPSC. Incorporating SPSC into the Phosphorus Index (PI) spatial assessment, we further determined that only 3% of the study area was considered as high or very high PI categories indicative of a significant risk for P loss. Our evaluation of SPSC and PI underscores the complexity inherent in P dynamics, emphasizing the need for a holistic approach to assessing P loss risk. Insights from this work not only help optimize agronomic practices but also promote sustainable land management, thus ensuring the long-term health and sustainability of grass-dominated agroecosystems.

The LTAR Grazing Land Common Experiment at Archbold Biological Station-University of Florida.

The Archbold Biological Station-University of Florida (ABS-UF) Long-term Agroecosystem Research (LTAR) site lies in the heart of south-central Florida, representing subtropical humid grazing lands in North America and globally. Beef producers in this region face challenges due to climate variability, limited nutritive value of forages, poor soils, public concerns about water quality and greenhouse gas emissions, management trade-offs, economic uncertainty, and increasing urban encroachment. The ABS-UF Common Experiment, co-designed with stakeholders, will assess innovative management systems in comparison to prevailing management systems on key indicators of sustainability. Innovative management systems being tested are alternative fire (frequency and spatial extent) and grazing practices (stocking rate and system). The common experiment framework was implemented across a management intensity gradient spanning from native rangeland to cultivated pastures, including embedded wetlands. Issues that have arisen to date include difficulties in implementing prescribed fire and reduced productivity in cultivated pastures associated with innovative management, which led to an adjustment of the experimental treatment. A stakeholder advisory council will codesign future alternative treatments and guide experimental changes in this long-term experiment. Stakeholder engagement efforts revealed research priorities centered on financial strength, carbon (C) and greenhouse gas emissions, and water quality. Stakeholders are also interested in testing emerging technology such as the utility of virtual fencing. Results from ABS-UF provide a unique perspective from subtropical humid grazing lands for continental-scale cross-site synthesis on sustainable agroecosystems across LTAR.

Soil temperature forecasting using a hybrid artificial neural network in Florida subtropical grazinglands agro-ecosystems.

Soil temperature is a key meteorological parameter that plays an important role in determining rates of physical, chemical and biological reactions in the soil. Ground temperature can vary substantially under different land cover types and climatic conditions. Proper prediction of soil temperature is thus essential for the accurate simulation of land surface processes. In this study, two intelligent neural models-artificial neural networks (ANNs) and Sperm Swarm Optimization (SSO) were used for estimating of soil temperatures at four depths (5, 10, 20, 50 cm) using seven-year meteorological data acquired from Archbold Biological Station in South Florida. The results of this study in subtropical grazinglands of Florida showed that the integrated artificial neural network and SSO models (MLP-SSO) were more accurate tools than the original structure of artificial neural network methods for soil temperature forecasting. In conclusion, this study recommends the hybrid MLP-SSO model as a suitable tool for soil temperature prediction at different soil depths.

Indicators of water use efficiency across diverse agroecosystems and spatiotemporal scales.

Understanding the relationship between water and production within and across agroecosystems is essential for addressing several agricultural challenges of the 21st century: providing food, fuel, and fiber to a growing human population, reducing the environmental impacts of agricultural production, and adapting food systems to climate change. Of all human activities, agriculture has the highest demand for water globally. Therefore, increasing water use efficiency (WUE), or producing 'more crop per drop', has been a long-term goal of agricultural management, engineering, and crop breeding. WUE is a widely used term applied across a diverse array of spatial scales, spanning from the leaf to the globe, and over temporal scales ranging from seconds to months to years. The measurement, interpretation, and complexity of WUE varies enormously across these spatial and temporal scales, challenging comparisons within and across diverse agroecosystems. The goals of this review are to evaluate common indicators of WUE in agricultural production and assess tradeoffs when applying these indicators within and across agroecosystems amidst a changing climate. We examine three questions: (1) what are the uses and limitations of common WUE indicators, (2) how can WUE indicators be applied within and across agroecosystems, and (3) how can WUE indicators help adapt agriculture to climate change? Addressing these agricultural challenges will require land managers, producers, policy makers, researchers, and consumers to evaluate costs and benefits of practices and innovations of water use in agricultural production. Clearly defining and interpreting WUE in the most scale-appropriate way is crucial for advancing agroecosystem sustainability.

Changing forest water yields in response to climate warming: results from long-term experimental watershed sites across North America.

Climate warming is projected to affect forest water yields but the effects are expected to vary. We investigated how forest type and age affect water yield resilience to climate warming. To answer this question, we examined the variability in historical water yields at long-term experimental catchments across Canada and the United States over 5-year cool and warm periods. Using the theoretical framework of the Budyko curve, we calculated the effects of climate warming on the annual partitioning of precipitation (P) into evapotranspiration (ET) and water yield. Deviation (d) was defined as a catchment's change in actual ET divided by P [AET/P; evaporative index (EI)] coincident with a shift from a cool to a warm period - a positive d indicates an upward shift in EI and smaller than expected water yields, and a negative d indicates a downward shift in EI and larger than expected water yields. Elasticity was defined as the ratio of interannual variation in potential ET divided by P (PET/P; dryness index) to interannual variation in the EI - high elasticity indicates low d despite large range in drying index (i.e., resilient water yields), low elasticity indicates high d despite small range in drying index (i.e., nonresilient water yields). Although the data needed to fully evaluate ecosystems based on these metrics are limited, we were able to identify some characteristics of response among forest types. Alpine sites showed the greatest sensitivity to climate warming with any warming leading to increased water yields. Conifer forests included catchments with lowest elasticity and stable to larger water yields. Deciduous forests included catchments with intermediate elasticity and stable to smaller water yields. Mixed coniferous/deciduous forests included catchments with highest elasticity and stable water yields. Forest type appeared to influence the resilience of catchment water yields to climate warming, with conifer and deciduous catchments more susceptible to climate warming than the more diverse mixed forest catchments.