Uncertainty in phosphorus fluxes and budgets across the US long-term agroecosystem research network.

Phosphorus (P) budgets can be useful tools for understanding nutrient cycling and quantifying the effectiveness of nutrient management planning and policies; however, uncertainties in agricultural nutrient budgets are not often quantitatively assessed. The objective of this study was to evaluate uncertainty in P fluxes (fertilizer/manure application, atmospheric deposition, irrigation, crop removal, surface runoff, and leachate) and the propagation of these uncertainties to annual P budgets. Data from 56 cropping systems in the P-FLUX database, which spans diverse rotations and landscapes across the United States and Canada, were evaluated.

Detecting Intra-Field Variation in Rice Yield With Unmanned Aerial Vehicle Imagery and Deep Learning.

Unmanned aerial vehicles (UAVs) equipped with multispectral sensors offer high spatial and temporal resolution imagery for monitoring crop stress at early stages of development. Analysis of UAV-derived data with advanced machine learning models could improve real-time management in agricultural systems, but guidance for this integration is currently limited. Here we compare two deep learning-based strategies for early warning detection of crop stress, using multitemporal imagery throughout the growing season to predict field-scale yield in irrigated rice in eastern Arkansas.

Substantial hysteresis in emergent temperature sensitivity of global wetland CH emissions.

Wetland methane (CH) emissions ([Formula: see text]) are important in global carbon budgets and climate change assessments. Currently, [Formula: see text] projections rely on prescribed static temperature sensitivity that varies among biogeochemical models. Meta-analyses have proposed a consistent [Formula: see text] temperature dependence across spatial scales for use in models; however, site-level studies demonstrate that [Formula: see text] are often controlled by factors beyond temperature.

Rice Inundation Assessment Using Polarimetric UAVSAR Data.

Irrigated rice requires intense water management under typical agronomic practices. Cost effective tools to improve the efficiency and assessment of water use is a key need for industry and resource managers to scale ecosystem services. In this research we advance model-based decomposition and machine learning to map inundated rice using time-series polarimetric, -band Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) observations.

Methane Emission Reductions from the Alternate Wetting and Drying of Rice Fields Detected Using the Eddy Covariance Method.

Rice cultivation contributes 11% of the global 308 Tg CH anthropogenic emissions. The alternate wetting and drying (AWD) irrigation practice can conserve water while reducing CH emissions through the deliberate, periodic introduction of aerobic soil conditions. This paper is the first to measure the impact of AWD on rice field CH emissions using the eddy covariance (EC) method.

Greenhouse Gas Emissions and Management Practices that Affect Emissions in US Rice Systems.

Previous reviews have quantified factors affecting greenhouse gas (GHG) emissions from Asian rice ( L.) systems, but not from rice systems typical for the United States, which often vary considerably particularly in practices (i.e.

Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds.

Pollutant concentrations and loads in watersheds vary considerably with time and space. Accurate and timely information on the magnitude of pollutants in water resources is a prerequisite for understanding the drivers of the pollutant loads and for making informed water resource management decisions. The commonly used "grab sampling" method provides the concentrations of pollutants at the time of sampling (i.

Water quality of surface runoff and lint yield in cotton under furrow irrigation in Northeast Arkansas.

Use of furrow irrigation in row crop production is a common practice through much of the Midsouth US and yet, nutrients can be transported off-site through surface runoff. A field study with cotton (Gossypium hirsutum, L.) was conducted to understand the impact of furrow tillage practices and nitrogen (N) fertilizer placement on characteristics of runoff water quality during the growing season.