Global attention to climate change issues, especially air temperature changes, has drastically increased over the last half-century. Along with population growth, greater surface temperature, and higher greenhouse gas (GHG) emissions, there are growing concerns for ecosystem sustainability and other human existence on earth. The contribution of agriculture to GHG emissions indicates a level of 18% of total GHGs, mainly from carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N₂O). Thus, minimizing the effects of climate change by reducing GHG emissions is crucial and can be accomplished by truly understanding the carbon footprint (CF) phenomenon. Therefore, the purposes of this study were to improve understanding of CF alteration due to agricultural management and fertility practices. CF is a popular concept in agro-environmental sciences due to its role in the environmental impact assessments related to alternative solutions and global climate change. Soil moisture content, soil temperature, porosity, and water-filled pore space are some of the soil properties directly related to GHG emissions. These properties raise the role of soil structure and soil health in the CF approach. These properties and GHG emissions are also affected by different land-use changes, soil types, and agricultural management practices. Soil management practices globally have the potential to alter atmospheric GHG emissions. Therefore, the relations between photosynthesis and GHG emissions as impacted by agricultural management practices, especially focusing on soil and related systems, must be considered. We conclude that environmental factors, land use, and agricultural practices should be considered in the management of CF when maximizing crop productivity.

Estimating corn (Zea mays L.) grain and stover yield during the growing season is an appealing idea. An accurate yield estimation could benefit farmers, as well as corn‐related industries. The objective of this study was to develop regression equations to estimate corn grain and stover yield using easily accessible information (N fertilization rate and cumulative precipitation), and simple plant morphological measurements such as height, stem diameter at various heights, height of the first ear, and plants per hectare. Measurements made at silking (R1) were used since the maximum explainable variability would not exceed 58% at early stages of plant development (V2–V10; between 2‐leaf and 10‐leaf stages). The experiment was conducted from 2009 until 2011 in two locations, in north and central Alabama, under no‐tillage and non‐irrigated conditions. Treatments were assigned to a 3 × 4 × 2 complete factorial design arranged in a split‐split‐plot with three replications. Factors were; winter rye (Secale cereale L.) cover crop (main plot), N fertilization rates (subplot), and stover residue harvest (sub‐subplot). All measurements from this study, across years and locations, were used in the final regression equations to create robust prediction models. Equations, with and without intercept, were developed and compared according to several statistical criteria. The final grain yield equation at R1 growth stage included an intercept with a R² of 0.7705. The final stover equation also included an intercept (R² = 0.8473). This study suggests that N rate, total precipitation amount from planting until silking, and simple plant morphological measurements can be used to predict corn yield.

Snowmelt nutrient loss is an important but poorly understood process in cold climates. We measured nutrient losses at three sites after dairy manure was applied on top of an established snowpack. Treatments included no manure controls and three manure solids levels (12–19.4% solids = High; 7.5–8.0% = Medium; 2.9–5.5% = Low) applied at 26,670 L ha⁻¹ to all treatments. Snowmelt runoff was monitored and analyzed for dissolved reactive P (DRP), total P (TP), total N (TN), ammonium-N, organic-N, and total solids (TS) concentrations. Results showed that manure application dramatically increased N and P loading compared to controls. Across site-years, manure application increased average runoff TP, DRP, and TN concentrations by 1.3- to 13.3-fold, 1.5- to 21-fold, and 1.4- to 14.2-fold, respectively, relative to controls. While cumulative N, P, and TS losses generally increased with manure solids, Medium/Low showed equal or greater nutrient transfer to runoff for some events. TN and TP lost in runoff were linearly related to manure solids concentration; however, N and P loss as a percent of applied showed the opposite trend. The results indicate that applying manure on top of snow resulted in high nutrient losses when runoff occurred regardless of manure solids content.