Weather conditions in the southwestern U.S. are variable and influence crop growing periods with late spring and early fall frosts which significantly impact cropping seasons. With the development of new maize hybrids, grain yield, evapotranspiration, and water use efficiency can be substantially impacted by planting density and planting date. Thus, it is critical that the optimum plant density and planting date for maximum grain yield be determined for local conditions. Field experiments were conducted to evaluate six plant densities (54700, 64600, 74600, 88000, 101700 and 120100 pph) under seven planting dates (from April 23 to June 5 in 2019 and from April 21 to June 10 in 2020) to determine the planting window and the optimum density. Plots were sprinkler irrigated and crop management was similar across all planting dates during the two growing seasons. The results showed that crop height and leaf area index varied with plant density and planting date. Grain yield also varied with plant density and planting date. The highest grain yield (16.8 Mg ha^-1) was observed under the density 101,700 pph and the first planting trended to provide the best grain yield in 2019. In 2020, the highest grain yield (17.77 Mg ha^-1) was obtained under the density 88,000 pph and the May 18 planting provided the best grain yield and the greatest WUE. Plant density 88,000 plant /ha was revealed the optimum density that maximized grain yield and WUE and maize planting after May 25 is not recommended.

Planting dates can have a significant impact on corn yields. In some cases, a higher seeding rate could help negate some of the negative impacts of a later planting date on yield. To compare the interrelationship of seeding rate to planting date to corn yield, this one-year study in Tifton, GA included four seeding rates of 24,000, 32,000, 40,000 and 48,000 seeds per acre in a Randomized Complete Block Design across three spring planting dates of March 15, March 31, and April 12 (n=48). Final grain yield and yield components were determined, including kernels per row, kernel rows per ear, ear length, and 100-seed weight. The greatest grain yield was found in the March 31 planting date, while across the planting dates 24,000 seeds per acre consistently yielded less than the 32,000, 40,000, and 48,000 seeds per acre. Results of this one-year study indicate that as planting date is delayed from mid-March to mid-April, an increase in seeding rate from 32,000 to 48,000 seeds per acre is necessary to stabilize yield. Complete results will be presented at the 2024 ASA Southern Branch Meeting.

Low forage yields from old alfalfa stands are often a result of poor management practices. Appropriate agronomic amendments such as potassium (K) fertilization and harvest time can help revive the yielding potentials of old alfalfa stands. A study was conducted on a 13-yr old alfalfa stand at UW SAREC, Lingle in 2019 to determine K × harvest time interaction effect on yield response of old alfalfa stands. Six K rates (0, 56, 112, 168, 224, and 280 kg K₂O ha^-1) were applied to alfalfa. Alfalfa was harvested at two harvest times (early harvest, late bud to early [10%] bloom; late harvest, 7 days after early harvest). Treatments were organized in a randomized complete block design with four replications. The 224 kg K₂O ha^-1produced the highest total forage yield (8.0 Mg ha^-1) at early harvest, while 168 kg K₂O ha^-1produced the highest total forage yield (8.6 Mg ha^-1) at late harvest. Differences in plant growth at both harvest times might have influenced the amount of K required to improve yield. There was a quadratic relationship (P= 0.01, R²= 0.39) between forage yield and relative water content of alfalfa. This suggests that the moisture content in alfalfa in conjunction with K and harvest management plays an important role in alfalfa for yield potential. Overall, preliminary results of the study indicate that applying K to old stands of alfalfa based on when to harvest could be a viable option to improve forage production of declining old alfalfa stands.

Smart agriculture (SA) is an innovative concept gaining popularity with an expected market value of $33 billion by 2027. Machine learning (ML) and artificial intelligence (AI) have significant potential to transform agriculture by analyzing Big Data to provide valuable insights and data-driven decision support systems (DSSs). Large scale production systems capitalize on SA technologies to improve production efficiency while preserving valuable fossil waters and minimizing fertilizer, chemical, and labor inputs. Small and mid-scale production systems (SMSPSs) account for 42.6% of the US agriculture production value compared to 43.8% for large scale systems. Implementing SA technologies in SMSPSs can be inefficient and cost prohibitive. This presentation addresses challenges and solutions for establishing “smart co-ops” to harness the full potential of SA to optimize production efficiency, sustainability, and profitability in SMSPSs. Producers need low-cost DSSs that integrate local climate data with soil moisture sensors and multispectral imaging to identify and proactively manage crop stresses. Rural and economically distressed communities often lack the resources and infrastructure to support internet of things (IoT) connectivity. Therefore, establishing a network of smart ag co-ops capable of data collection and management for secure, anonymous data sharing is paramount for enhancing the contribution of S-MPSSs to global food security. Smart co-ops could better distribute equipment costs and ML computational requirements among its members. To train the future workforce, bridging the communication gap among current and future producers, agronomists, and computer scientists is vital for maintaining sustainable production systems and food security into the future.

Corn (Zea mays L.) is a versatile crop, ranked third in acerage within Mississippi (MS). Nonetheless, current stagnant yield, inefficient nitrogen (N) recovery, and fertilizer cost fluctuations are inflicting economic losses and contributing to environmental degradation. Failure to seek a new strategy poses a risk to meeting the global food demand, creating uncertainty in food supply. The utilization of biostimulants in agricultural practices has garnered significant interest due to their potential to enhance crop productivity and reduce environmental impact. Thus, a field study was carried out in 2022 and 2023 at two locations in MS. A split plot design was implemented, with four N rates as main plot including 0 (control), 90, 180, 224 kg N ha^-1 at Starkville and an additional 270 kg N ha^-1 at Stoneville. Subplot factor was six microbial biostimulants (Source^®, Envita^®, iNvigorate^®, Blue N^®, Micro AZ^TM, and Bio level phosN^®) applied as both foliar and in-furrow at V4-V5 growth stages, alongwith a no biostimulant check. R statistical software was used to analyze the data. Nitrogen rates significantly influenced grain yield at all site years, whereas biostimulants showed no effect on yield. Specifically, in Starkville 2023 the yield varied from 3.77 Mg ha^-1 in control to 12.15 Mg ha^-1 at 180 kg N ha^-1. In Stoneville (2022 & 2023) the yield ranged from 6.55 to 14.20 Mg ha^-1. Overall, microbial biostimulants had no impact on corn yield and further research on diverse biostimulant categories is warranted to reveal their potential benefits for productivity and environmental sustainability.

Urea based nitrogen (N) fertilizers commonly used in TN hay production systems are susceptible to ammonia volatilization during urea hydrolysis. Under hot, humid conditions the potential for volatilization increases. To minimize volatilization losses, producers will apply N stabilizer products, such as Agrotain or Nutrisphere. Enhanced efficiency fertilizers, such as Environmentally Smart Nitrogen (ESN), may offer better protection against N loss and extend N availability throughout the growing season. This research aims to evaluate the influence of stabilized and enhanced efficiency fertilizers on dry matter yield (DMY) in TN hay production systems. In 2023, 2 m x 3 m plots were established in a mixed species hay field at Cookeville, TN. The experiment was conducted as a randomized complete block design with three replications. Treatments were a control (zero N), untreated urea, urea treated with either Agrotain, Altera, or Nutrisphere, and ESN. Treatments were applied at 56 kg ha^-1. After 30 d, plots were harvested and analyzed for DMY using PROC GLM (α=0.05) (SAS v9.4). Yields ranged between 800 kg ha^-1 (Nutrisphere) and 1020 kg ha^-1 (ESN). Compared to the control (859 kg ha^-1), there were no differences among treatments. Rainfall was received 48 h after fertilizer application and was well distributed throughout the growing season likely minimizing the potential for N loss. Producers could apply ESN to maximize DMY. However, research under more controlled conditions is necessary.

Roots and mycorrhizal associates have the potential to accelerate soil organic matter (SOM) decomposition via priming effects, but also to slow SOM decomposition by stabilizing microbial residues and lowering resource availability for free-living microbes. As such, the net effects of these dynamics on soil C and nutrient cycling are difficult to predict, particularly in ecosystems dominated by plants from different mycorrhizal guilds. We combined observations and experiments to test the hypothesis that root and fungal trait variation between trees associating with arbuscular mycorrhizal (AM) fungi vs. ectomycorrhizal (ECM) fungi would lead to predictable variation in SOM dynamics in six temperate forests in the eastern US. Specifically, we compared the quantity and quality of root-derived C inputs to soil, as well the consequences of root-derived inputs for SOM dynamics and nitrogen (N) cycling, across mycorrhizal gradients (forest plots varying in the relative abundance of AM vs. ECM trees; n = 9 plots per site). We found that while ECM trees produced more roots (P < 0.05), AM trees released more root-derived C to soil (P < 0.05), with much of the C ending up in the mineral-associated organic matter (MAOM) fraction. Isotope labeling experiments revealed that fast leaf litter decay also promoted MAOM formation (especially AM litters), but also induced priming effects. Collectively we find that AM-dominated soils have the potential to store more C and N than ECM-dominated soils owing to differences in root-derived inputs. As such, shifts in the relative abundance of AM and ECM trees may have profound implications for SOM dynamics and forest sensitivity to global change.

Denitrification, the anaerobic microbial conversion of nitrate and nitrite to the gases nitric oxide, nitrous oxide, and dinitrogen, is important to soil fertility, water quality, greenhouse gas emissions. The process is notoriously difficult to quantify and therefore difficult to manage. The "Managing Denitrification" community of the Environmental Quality Section of the ASA is a great example of how scientists can come together, grapple with uncertainty, and produce useful conclusions.

Here, I highlight recent work including: 1) New measurements of dinitrogen and nitrous oxide flux. 2) Analysis of differential controls of these fluxes. 3) The occurrence of "hotspots" and "hot moments" of denitrification in soil profiles. 4) The emergence of nitrogen oligotrophication as a controller of denitrification at larger scales.

New measurements show that dinitrogen fluxes are much larger than nitrous oxide fluxes and are controlled by different factors. These measurements also show the importance of hotspots of activity associated with confining layers at depth (up to 50 cm) in the soil profile, and hot moments of activity associated with drying and rewetting events. At broader spatial and temporal scales, declines in nitrogen deposition, lengthening of the growing season, increases in atmospheric carbon dioxide levels, and other changes are reducing nitrogen availability in the environment. While this oligotrophication has led to reductions in nitrous oxide flux and denitrification potential in forest ecosystems, its importance in agronomic systems has yet to be analyzed.

Continued work on denitrification will likely lead to further increases in our ability to model and manage nitrogen pollution problems at field, landscape and regional scales. Still we must always recognize that the world is changing in complex and surprising ways. A community of researchers will be needed to address the challenges of denitrification and its importance to nitrogen dynamics in a changing world.