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.

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.

This research was conducted to determine if nitrogen applications can be manipulated to reduce the potential for N losses. The effects of N application rate within a fertilizer source and application method on runoff N loss under simulated rainfall were investigated at Brooksville, MS, on a Brooksville silty clay (fine, smectite, thermic Aquic Hapludults). As the application rate of UAN increased, the proportion of applied N lost in surface runoff decreased. Conversely, there was no clear trend between urea application and runoff N load. Total nitrogen losses were similar between UAN and urea applications; however, greater nitrate and ammoniacal N transport was observed under UAN application compared to urea. Fertilizer application methods appear to affect runoff N loss and should be further investigated to determine effective mitigation strategies.

Mississippi (MS) soybean producers are under pressure to plant as much acreage as possible within narrow planting windows. The five-year average planting progress of MS soybean acreage is 44% during the optimal soybean planting window (April 10-May 1), after which yield declines at 26.9 kg/ha/day (0.4 bu/ac/day) or $13.3/ha/day (at $13.5/bu). New metering and seed delivery technology claims to allow faster planting (up to 19 kph) without sacrificing seed singulation, stand, or yield, but these tools need to be validated under MS conditions before recommending them to producers. This study aimed to quantify soybean response to different planting speeds using precision planting technology. A precision planter (John Deere® bar and MaxEmerge 2 row units retrofitted with Ag Leader® sure speed and sure force) was tested at 9.7, 14.5, and 17.7 kph actual ground speeds. A mechanical planter (John Deere® 1700 ground-driven mechanical planter equipped with eSet meter) was used as check at 9.7 kph. The trial was conducted during the 2023 cropping season in three locations (two Mississippi State University research stations at Brooksville and Stoneville and one on-farm in Marks, MS) in a randomized complete block design with four replications. Plots were four rows × 41 m long in Stoneville and eight rows × ≥400 m long in Brooksville and on-farm. Soybean variety 4526XFS and farmer-cultivated variety (NK44J4) were planted at 345,800 seeds ha^-1. Increased plan speed in the precision planter, increased plant spacing and in-row variability, lowered plant populations but did not affect yield. The precision planter at 17.7 kph exhibited the same level of performance as the mechanical planter at 9.7 kph. The result suggests MS soybean producers can plant more acreage within the critical planting window.

Keywords: Glycine max, planting speed, plant stand variability, precision planting technology, seed singulation, soybean.

Soybean [Glycine max (L.) Merr.] grown in midsouthern USA production systems are experiencing increased potassium deficiency even when granular potassium fertilizer is applied according to soil test recommendations. This study was conducted to determine soybean response to foliar potassium fertilizer applications at varying rates of soil applied potassium fertilizer. The effects of foliar potassium fertilizer application at various soybean growth stages (V5, R3, R5) in combination with three levels of soil applied granular potassium fertilizer (0, 101, 135 kg/ha K₂O) at planting were investigated at Mississippi State University’s Delta Research and Extension Center on a commerce very fine sandy loam (fine-silty, mixed, superactive, nonacid, thermic Fluvaquentic Endoaquept). Results to include soybean physiological traits (plant height, number of nodes, number of pods) and soybean grain yield will be discussed at length.

Farmers apply organic manure in conventionally tilled fields to enhance the soil's organic matter and plant-available nutrients. Manure differs in nutrient composition between animal species. Nutrients and sediments in runoff water are also influenced by the soil management practices and timing of rainfall. We hypothesize that runoff volume, sediment, and nutrient concentrations of nitrogen (N) and phosphorus (P) in runoff water will differ with application rates on conventionally tilled soil during a rainfall event. Hence, an artificial rainfall simulation was conducted in a conventionally tilled soil to evaluate how application rates (poultry litter: 1-, 2-, 3-, and 4-ton acre^-1: swine manure: 5k -, 10k -, 15k -, and 20k - gallons acre^-1) and rainfall timing (1, 2, and 3 weeks past manure application) affect runoff volume, and sediments and various species of N and P in runoff water during a one-inch rainfall event. The soil was collected in pans (21×12×2.5, in³) from a conventionally tilled field in Town Creek, Alabama. These pans were placed under a rainfall simulator to simulate one acre-inch of rainfall. Runoff water samples were collected and analyzed for total suspended solids and IN, DRP, TP, PP, TDP, and TOP using the standard protocols. Results will be presented.

SM (Swine manure), PL (Poultry litter), dissolved reactive phosphorus (DRP), inorganic nitrogen (NO₃-N + NH₄-N), TP (Total Phosphorous), PP (Particulate Phosphorous), TDP ( Total Dissolved Phosphorous), TOP ( Total Organic Phosphorous)